edegan.com - User contributions [en]

University Patents

2018-04-05T19:32:43Z

Dayton: /* Carnegie Classifications */

{{McNair Projects
|Has title=University Patents
|Has owner=Julia Wang, Meghana Pannala, Anne Dayton
|Has keywords=Patent
|Has project status=Active
}}

==Research Paper==
Since university system file for patents out of a central office, its mot possible to determine where the research was conducted. This presents problems when ranking universities by patent production.

In order to publish this paper as a Baker Institute Research Report, we will need to do more to resolve this issue beyond weighting for the University of California.

==Carnegie Classifications==
We propose using public data from the Carnegie Classifications of Institutes of Higher Education to resolve the university systems issue.

TASKS:
1) Separate schools affiliated with universities/university systems in our rankings from all others included in Carnegie data.

2)Group these by system.
Determine the number of schools in each system
3) Determine their classification.
1 if Doctoral 0 if not (In Carnegie 2015 Basic Classification [BASIC2015] 15, 16, 17)
1 if 4-year medical school 0 if not (BASIC2015 25) Check (BASIC2015 26)
1 if 4-year engineering 0 if not (BASIC2015 27) Check (BASIC2015 28)
4) Determine the total count of the categories in number 3 for each university system.

5) Use total count to weight regressions

6) Create ranking tables that rank university systems and universities by dividing number of patents by number of doctoral, med-school, 4-year engineering.

==Project Overview==
Goal: list of all universities and # of patents associated with each university and patent licensing activity

patent reassignment to startups associated with these universities

clinical trials (from Catherine) data to rank universities R&D engagement

identify list of universities: board of regents, universities in patent data (find patterns associated with university assignees) -- @Julia: Where did we get this list? I'd love to be able filter by country - Meghana

AUTM?

==Timeline to Deliverable & Documentation==

Meghana: focus on grants

Julia: focus on patent counts

Working in E:McNair/University Patents

'''3/20-3/24'''

Learn SQL, clean data

'''3/27-3/31'''

Counts of data, name matching

Patent Counts
*Ran the matcher in the server, only matched ~40,000 entries
*Working with Jeemin to develop matcher

'''4/3-4/7'''

Counts of data, name matching

Patent Counts
*Jeemin is a savior, matched and counted 128,000 entries (Jeemin_matcher_matched.txt), manually went through the 6,000 unmatched (Jeemin_matcher_unmatched.txt), Check [[University Patent Matching]]
*Ranking for all time is in Patent Counting ( >Total (All Time) )
*Need to talk with Meghana about grant data
*Need to find variables for regression, develop ranking for last decade and last year
*Also need to account for school size (faculty? students? research funding?)

'''4/10-4/14'''

Develop ranking

Patent Counts
*Finish all of above
*Start drafting
*Load data onto database -> E:McNair/University Patents/unidatajoin.sql in univ (database)

'''4/17-4/21'''

*Combined Patent Counts, R&D Expenditure, NSF Grant Data
*STATA regressions in E:McNair/University Patents/STATA
**for historical data

'''4/24'''

From Ed:
*Copied inventors table out of PatentDB database on RDP
*Script in E:McNair/PatentData, MoveInventors.sql
*Copy of data in Z:AllPatents and ran import into patent (database)
For Marcela:
*Need table of all patents associated with matches, join patent numbers with inventors
*Group by last name and first initial (=1 inventor)

'''5/1'''

*Joined patent numbers with inventors -> unipatentinventorcount.sql
**Z:univ/InventorCounts.txt
*Decade patent counts -> Z:univ/DecadePatentCounts
*Calculated Hirschman-Herfindahl index -> E:McNair/University Patents/Inventor Counts and Superstars

'''5/5'''

Artifacts:
#10-Year Ranking All
#10-Year Ranking Private
#10-Year Ranking Public
#Top 10 Movers and Shakers
#Graph of Concentration of Innovation
#Number of University Patents Over Time
#Inventor Concentration
#Regression Table

*Created Graph of Concentration of Innovation -> E:McNair/University Patents/Inventor Counts and Superstars -> GraphUPS
*Created Top Inventors Chart -> University Patents/Report Articles/Inventor Rankings

'''5/9'''

*Figuring out what's up with the university puller
*Developing new 10-Year Rankings
*Need to update above two articles
*Need to load into STATA and re-run regressions -> University Patents/STATA/univdata.do

'''5/10'''

*Figured out what was wrong with the data pull and now everything's good!
*Updated information on the draft below
*Fantastic Artifacts and Where to Find Them
**10-Year Ranking All
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Public
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Private
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**Top 10 Movers & Shakers (% change between first 5 years (2006-2010) vs next 5 years (2011-2015) within top 100
**Graph of Concentration of Innovation (numbers of patents)
***Pie graph (University Patents > Report Artifacts > Concentration of Innovation)
***(Excel sheet where this came from is University Patents > Inventor Counts and Concentrations)
**Trends Over Time
***Total vs. top 10
***Graph (University Patents > MoversandTop10)
**Inventor Concentration
***Chart (University Patents > Report Artifacts > Inventor Rankings)
**Reg Table
***+Explanation of variables
***(STATA do-file with the most statistically significant regressions is in University Patents > STATA > univdata.do)

Issue brief draft (VERY rough): https://docs.google.com/a/rice.edu/document/d/1MayXQEQ_pM0LMeiV39iaNFjtMZYSB-Ah9gCx1rcgW9M/edit?usp=sharing

==Ranking Development Notes==
Sources of University Funding R&D [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% comes from the Federal Government
*Less than 10% comes from state and local governments
*Roughly 20% is from the University itself (endowment?? tuition? not 100% sure)
*Roughly 5% is from private industry
*Less than 10% Other

Federal Government Funding Breakdown [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% of federal funding is from the NIH
*Roughly 15% is from the NSF
*Roughly 11% from Department of Defense
*Roughly 4% from NASA
*Roughly 4% from the Department of Energy
*Roughly 3% from the USDA
*Roughly 1% from the EPA (probably will go to zero with new admin)
*Roughly 1% from the Department of Education
*Marginal amounts from everywhere else (<1%)

NOTES
*overall ranking (total number of patents)
*weight by faculty (people)
*weight by research funding
*weight by endowment (maybe)
*public vs private
*individuals: surname and first initial
**ranking by inventors - who are the superstar inventors and where are they (define superstar inventor - idk 20 patents and rank universities by how many superstars are present
**Herfindahl-Hirschmann Index

==Key Words==

===Universities (Patent Assignees)===
BOARD OF REGENTS - pretty much exclusively describes universities

UNIVERSITY - also exclusively describes universities
*Can we do a close match with University? (its the only word on this list that's frequently misspelled)

SCHOOL - often used in combination with medicine or medical to describe medical schools
*also are used to describe actual schools (i.e. HISD) and other businesses
*sometimes present in addresses - can we cut off the adresses?
*also used in combination with Business (i.e. Harvard Business School)
*use school only in conjuction with medical, medicine, and business

COLLEGE
*need to cut off addresses for this to work
*need to ensure that we include the space after
*excl. College Boulevard, college blvd, etc.
*double check everything with College Park

INSTITUTE OF TECHNOLOGY - almost exclusively describes universities but sometimes describes external research institutes

POLYTECHNIC - exclusively university (i.e. RPI, Virginia Tech)

RESEARCH FOUNDATION: VAST majority are university
*Exclusions: it's really difficult to distinguish between university affiliated and not, besides just looking them up on the google
**Novartis
**Progeria
**Washington Research Foundation
**Blood Center of Wisconsins
**Mental Hygiene
**Celiac Sprue
**Fidia
**Samuel Waxman Cancer Center
***founded by a faculty member at Mt. Sinai Medical School,not affil w/a single university but researchers are also usually faculty at various universities
**lifenet
**HealthPartners
**Dr. Susan Love
**La Jolla Cancer Research Foundation
**Children's Hospital (? grants degrees but is a hospital system)
**Medforte
**International Mask
**Palo Alto Medical Foundation

====Exclusions====
*LLC
*LLP

===Technology Transfer Offices===

====Job Titles====
*Patent Portfolio Manager
*Intellectual Property Manager
*Licensing Associate/Licensing Liaison
*Biological Materials Specialist
*Industrial Contracts Officer
*Technology Licensing Officer
*Associate Officer
*Technology Licensing Associates
*Patent Coordinator/Patent Administrator
*IP Portfolio Specialist
*MTA Coordinator
*Sponsored Research Administrator
*Technology Transfer Specialist/Technology Transfer Associate
*Contracts Associate
*Portfolio Director
*Contracts Specialist
*Industry Contracts Analyst
*Patent Prosecution Analyst
*(Associate) Director of Intellectual Property
*(Associate) Director of Technology Transfer Policy
*(Associate) Director of Technology Transactions
*(Associate) Director of Agreement Administration
*Senior Associate - watch
*Patent Attorney/Patent Agent/Patent Counsel/Paralegal - watch

====TTO Names====
*Office of Technology Transfer/Technology Transfer Office
*Office of Technology Licensing/Technology Licensing Office
*Office of Technology Development
*Technology Ventures
*Innovation Services
*Intellectual Property & Industry Research Alliances
*InnovationAccess
*Invention Transfer Group
*Technology Development Group
*Office of Business Development
*Office of Technology Commercialization
*Office of Innovation and Commercialization
*Innovation, Technology & Alliances
*Technology & Industry Alliances
*Office for Management of Intellectual Property
*Innovation & Partnerships Office

===University-Affiliated Startups (Patent Assignees)===

==What to get from other people==

Avesh has clinical trial data on wiki and bulk drive (FDA Trials Data), need to build it into normal form, use clinical trials data to rank R&D engagement of universities, will be building up portfolios of different types of companies

Marcela is cleaning up patent data, has given patent assignee names -- got it! Could we filter by country and get strictly American names?

Catherine has zip codes of medical centers, use to look through patent data

==Questions==
*How innovative are universities compared to publicly-traded firms, etc.? (firms: 100 active patents at any given time)
*How do universities license?
*What does the average portfolio look like for universities? (compared to publicly traded, VC-backed, etc)
*What can explain the differences in rankings? (size, quality of universities, TTOs and quality/experience of workers - searched LinkedIns, geography, entrepreneurship programs, NIH/NSF grants)

==To Do==

*Create ranking
**Based on patent portfolio
**Licensing income
**Quality of TTO
**Publications (specifically based on research)?
**Amount received in grants/Amount of grants - we have numbers on amount of NIH grants and NSF grants
***NIH/NSF/STTR
**Control for school size/endowment
**Startups affiliated with university
***Value generated from these startups? (ask Avesh)
**Existence of entrepreneurship program
**Geography

*Describe average patent portfolio of university
**Number of patents
**Maybe categorize by research area?
*Describe how universities license
**USPTO earned revenue

Deliverable
*Ranking
*Explanation of what makes a good ranking/what factors a school needs to be more successful

Artifacts
*Grant Data
*Research Funding
*Star Scientists
*Regression with variables from Carnegie Classification (10-15 variables)
*Licensing (?)

===What We Have===

*Lit Review
*List of all post-secondary institutions (University Accreditation 3.2016_trim) - U.S. Department of Education
*Classification of universities (Carnegie Classification 2015) - Carnegie Classification
** includes all accredited degree-granting colleges and universities that are included in the National Center for Education Statistics Integrated Postsecondary Education Data System (IPEDS).
** Coverage: 4666 institutions of higher learning
** Variables: includes 94 variables
*** Enrollment data - subdivided into graduate and undergraduate
*** degrees conferred - divided into level and division
*** information on faculty (number, divided into levels i.e. assistant, assoc. and full time)
**** coverage for this variable is REALLY LOW
*** non-faculty research staff (includes post docs)
*** Science and Engineering Research and Development expenditures
*** Non-STEM research and Development expenditures
*Amount spent on R&D (Higher Ed R&D Rankings) - National Science Foundation
** includes just total R&D expenditures
*STTR full data 1995-2015 (STTRData) - Small Business Administration
*University Patent Numbers 1969-2012 - USPTO
*Patents that resulted from NIH grants
*Information about NSF grants

===What We Need===

*List of university-affiliated startups
**looks like AUTM STATT data has this because it was used in a study by Yael Hochberg [https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2418000]
**[https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-research-universities-2015/#3cb9829b3823| Rankings of Research Universities (2015)]
**FORBES ranked the country’s most entrepreneurial schools based on the numbers of alumni and students who have identified themselves as founders and business owners on LinkedIn (adjusted to total student body size). This year we rank both research universities and [https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-colleges-2015/#2b2909e04604| smaller colleges] separately.
*data about TTOs (# and quality of employees)
**LinkedIn crawler: working with Jeemin and Peter
*licensing data from AUTM [http://www.autm.net/statt] or already within the patent data?

==Name Matching==
*Jeemin has found all the correct and incorrect spellings of assignees with "University"
*Julia working on "Institute"
*We have a list of Universities that have patents from NIH grants, which could be a starting point for our ranking constituents

==Lit Reviews==

===Thursby, J. & Thursby, M.: Who Is Selling the Ivory Tower? Sources of Growth in University Licensing (2002)===

[http://pubsonline.informs.org/doi/pdf/10.1287/mnsc.48.1.90.14271]

@article{thursby2002who,
title={Who Is Selling the Ivory Tower? Sources of Growth in University Licensing},
author={Thursby, Jerry G. and Thursby, Marie C.},
journal={Management Science},
volume={48},
number={1},
pages={90--104},
year={2002},
publisher={INFORMS},
filename={Thursby Thursby (2002) - Who Is Selling the Ivory Tower}
}

*Reliance of industry on university inventions has increased
**AUTM surveys show 7.1% growth in yearly inventions disclosure from 1994-1998 for 64 universities that responded every year
*Primary reason for more disclosures may be increased propensity for faculty to disclose, rather than change in research focus
*Universities becoming more receptive to industry contracts
*Negative total TFP growth of licenses executed (-1.7% annual growth) - growth in disclosures and patent applications greater than the corresponding growth in licenses executed.
**Marginal university innovation offered to the market has declined in commercial appeal
**Universities are delving more deeply into the available pool of innovations to increase commercial activities
*No evidence on the importance of learning by doing on the part of TTOs except to note negative association between TTO growth and TFP growth in licensing
**Suggests at least the possibility of learning by doing effects

'''Other literature'''

*On the role of patents and publications in the transfer process: Adams 1990, Henderson et al. 1998, and Jaffe et al. 1993
*On consulting, sponsored research or institutional ties: Cohen et al. 1998; Mansfield 1995; Zucker et al. 1994, 1998
*On the nature of university licensing: Jensen and Thursby 2001, Mowery et al. 2001a,b, Mowery et al. 2001, Siegel et al. 1999, Thursby et al. 2001, Thursby and Kemp 2001

===Thursby, J., Jensen, Thursby, M.: Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities (2001)===

[https://pdfs.semanticscholar.org/637f/048c05ee83eb84a914f69e087f52acea1b6c.pdf]

@article{thursby2001objectives,
title={Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities},
author={Thursby, Jerry G., Jensen, Richard, and Thursby, Marie C.},
journal={The Journal of Technology Transfer},
volume={26},
number={1},
pages={59--72},
year={2001},
publisher={Springer},
abstract={This paper describes results of our survey of licensing at 62 research universities. We consider ownership, income splits, stage of development, marketing, license policies and characteristics, goals of licensing and the role of the inventor in licensing. Based on these results we analyze the relationship between licensing outcomes and both the objectives of the TTO and the characteristics of the technologies. Patent applications grow one-to-one with disclosures, while sponsored research grows similarly with licenses executed. Royalties are typically larger the higher the quality of the faculty and the higher the fraction of licenses that are executed at latter stages of development. Sponsored research is more likely to be included in a license if the new technology is at an early stage of development or if the TTO evaluates it as important. We find that additional disclosures generate smaller percentage increases in licenses, and those increases in licenses generate smaller percentage increases in royalties.},
filename={Thursby et al (2001) - Objectives, Characteristics and Outcomes of University Licensing}
}

*University licensing has increased dramatically post-Bayh-Dole (1980)
**According to AUTM 1996, licenses executed increased 75% from 1991-1996, (total: 13,087)
*Survey of TTOs of 62 major US universities
**Majority of universities retain titles to inventions
**All universities split income with inventors
**Royalties generate most of the revenue of licensing

*Open question: Is the increased propensity of faculty to disclose a response to financial incentives or an increase in the effectiveness of TTOs in inducing disclosure?

Survey
*62/135 universities responded
*63% public, and 62% of public universities that responded were land-grant
*37% private
*average industry sponsored research $16.9 mil, federally sponsored $149.6 mil (1996)
*average TTO: 26.3 licenses executed, 92.3 invention disclosures, 30.1 new patent apps, $4.2 mil income (1996)
*35% of respondents had reorganized TTO since 1990
*90% of universities allow faculty to establish and operate businesses based on technology owned by university but developed in faculty's research
*Inventions disclosed: 33% med schools, 29% engineering, 22% science, 6% agriculture, 10% other
*Majority of invention disclosures in nascent stage (proof of concept - 45% or prototype - 37%)
*Patents often applied for after knowing commercial viability, licensed technologies often not protected by patents
*60% of universities said small companies more likely to take early stage technologies and large companies more likely to take late stage - small firms may have advantage in "innovative" research (Holmstrom 1989)
*TTOs obtain smaller upfront fees the more uncertain the technology being licensed is
*Universities usually do not take equity in the license

Regression of licensing outcomes

[[File:LicensingOutcomeReg.png|500px]]

*Dependent variables: royalties, sponsored research, patents (new applications), licenses executed
*Independent variables: importance of outcome to TTO, types of inventions, measure of size of university's licensing operation/potential
*Logs of all variables except indicator variables
*Probit for frequency of sponsored research
*INVDIS: number of disclosures
*TTOSIZE: number of licensing individuals
*TTOEVAL_1 = 1: if TTOs that said licenses/patents are "not very important"
*TTOEVAL_2 = 1: if TTOs that said licenses/patents are "moderately important"
*PROOF: % of licensed disclosures that were "proof of concept but no prototype"
*PTYPE: "Prototype available but only lab scale"
*MEDSCHL: 1 if med school exists
*QUAL: academic quality of faculty (1993 NRC's survey results of academic quality of Ph.D. granting departments)
*LICENSES: number of licenses executed
*SPONRES: amount of sponsored research
*SPONFREQ: frequency that sponsored research is tied to license (according to TTO)
*patent apps grow 1-to-1 with disclosures
*sponsored research grows with licenses executed
*more licenses executed at universities with large TTOs and med schools
*higher royalties with higher quality of faculty and higher fraction of licenses executed at later stages of development
*additional disclosures generate smaller % increases in licenses, which generate smaller % increases in royalties (TTOs generally effective at tapping pool of available technologies in their universities)

===Thursby, J., Fuller, Thursby, M.: US Faculty Patenting: Inside and Outside the University (2009)===

[http://www.nber.org/papers/w13256.pdf]

@article{thursby2009us,
title = "US Faculty Patenting: Inside and Outside the University",
author = "Jerry G. Thursby, Anne W. Fuller, and Marie C. Thursby",
journal={Research Policy},
volume={38},
number={1},
pages={14--25},
year={2009},
publisher={Elsevier},
abstract = {This paper examines the empirical anomaly that in a sample of 5811 patents on which US faculty are listed as inventors, 26% of the patents are assigned solely to firms rather than to the faculty member's university as is dictated by US university employment policies or the Bayh Dole Act. In this paper we estimate a series of probability models of assignment as a function of patent characteristics, university policy, and inventor fields in order to examine the extent to which outside assignment is nefarious or comes from legitimate activities, such as consulting. Patents assigned to firms (whether established or start-ups with inventor as principal) are less basic than those assigned to universities suggesting these patents result from faculty consulting. A higher inventor share increases the likelihood of university assignment as compared with assignment to a firm in which the inventor is a principal but it has no effect on consulting with established firms versus assignment to the university. Faculty in the physical sciences and engineering are more likely to assign their patents to established firms than those in biological sciences.},
filename={Thursby et al (2009) - US Faculty Patenting},
}

*only 62.4% of patents by university faculty members of 87 universities were assigned solely to universities
*identifying US university patents by institutional assignment misses significant percentage of faculty innovation in US universities
*higher inventor share increases likelihood of university assignment compared with assignment to a firm where inventor is principal
*possibilities: faculty in low share universities may be more willing to seek outside remuneration via assignment to start-up where they are principal; revenue shares may not affect startup activity but simply reduce number of inventions disclosed to university

Sources
*Faculty names from NRC
*Compared with inventor names in NBER Patent Database
*Excluded faculty who do not patent

Patent/Inventor Pairs
*MIT: 315
*Wisconsin: 232
*Stanford: 223
*UC San Diego: 216
*UC Berkeley: 207

Out of 5811 patents:
*1513 assigned solely to firms
*241 assigned to both firms and universities
*327 unassigned
*faculty are principals in assignee firms for 32.3% of patents assigned solely to firms and 24% of patents assigned to both (lower bound)

=== Valdivia: University Start-ups: Critical for Improving Technology Transfer (2013) ===

[https://www.brookings.edu/wp-content/uploads/2016/06/Valdivia_Tech-Transfer_v29_No-Embargo.pdf]

*Current emphasis on licensing patents, but most university TTOs do not generate enough to cover operating expenses
*Asymmetry in distribution of resources across the university system, only a few universities benefit from high licensing revenues
**top 8 universities took 50% of licensing income, top 16 universities took nearly 75% of income
**only 37 universities have been in the top 20 during the last decade (listed in University Patents > LicensingGrossIncome2003-2012.txt)
*Universities face much more pressure to demonstrate the economic impact of their R&D contracts
**97.6% of total public contracts obtained by universities are for basic/applied research
**2% of university research delivers ready-to-use technologies
**academic research is much more dependent on government funding than industry research is
*Government pressure for universities to be more responsive to market forces, more entrepreneurial, more attuned to needs of industry
*TTOs are costly to universities
*1979: 30 universities with TTO -> 1999: 174 universities (AUTM)
*2010: 206 US universities have very high or high research activity, all with TTOs (but not all report to AUTM) (Carnegie Classification of Higher Education)
*Over last 20 years, 87% of universities did not break even
*Clash between aims of university (non-profit) and TTO (essentially a business unit)
*By nurturing start-ups, TTOs can add most economic value to an invention disclosure
*2003: universities initiated 330 startups, 2012: 647 startups
*2012: 3715 operating university startups, almost double the number in 2000 (AUTM 2013)
*Startups mitigate financial risk by reducing reliance on blockbuster patents, increase diversification of portfolio
*Policy proposal: the government should increase funding for Small Business Technology Transfer Program (directed to university start-ups)
**H.R.2981: reapportions STTR funds at 0.05% in the next 2 years and 0.1% henceforth for university enterprises at proof of concept stage
*Should increase portion that agencies set aside for STTR
**STTR should have Phase III like SBIR to fund commercialization efforts
*Need equitable distribution across university system

=== The Bayh-Dole Act and High-Technology Entrepreneurship in U.S. Universities: Chicken, Egg, or Something Else? (2004) ===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.484.1612&rep=rep1&type=pdf]

==== Findings ====

* University research has an unusually significant impact on industrial innovation in the biomedical sector
* " This work also suggests that academic research rarely produces “prototypes” of inventions for development
and commercialization by industry—instead, academic research informs the methods and disciplines
employed by firms in their R&D facilities."
* The U.S. higher education system is much larger and more heterogenous than other developed countries - this encourages competition
* The passage of the Bayh-Dole Act was one part of a broader shift in U.S. policy toward stronger
intellectual property rights
* "Universities increased their share of patenting from less than 0.3% in 1963 to nearly 4% by 1999, but the rate of growth in this share begins to accelerate before rather than after 1980."
* "the Act's provisions expressed Congressional support for the negotiation of exclusive licenses between universities and industrial firms for the results of federally funded research"
* licensing revenues account for only a miniscule portion of universities' overall academic budgets
* the acceleration in growth of patenting and licensing began before the passage of the Bayh-Dole Act so this acceleration cannot be wholly attributed to the Act
* "the flow of knowledge and technology between university and industrial research is a two-way flow," despite previous characterization as wholly from academia to industry
* patents seem to be "especially important channels for technology transfer" in the biomedical sector

==== Data Sources ====
* 5 different case studies
1. Cotransformation: a process to transfer genes into mammalian cells (Columbia University).

2. Gallium Nitride: a semiconductor with both military and commercial applications (University of California).

3. Xalatan: a glaucoma treatment (Columbia University).

4. Ames II Tests: a bacteria assay for testing potential carcinogenic properties of pharmaceuticals and cosmetics (University of California).

5. Soluble CD4: a prototype for a drug to fight AIDS (Columbia University).

==== Critiques ====
* focuses on case studies as source of data

===Dornbusch, Schmoch, Schulze, Bethke: Identification of University-Based Patents: A New Large-Scale Approach (2012)===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.1368&rep=rep1&type=pdf]

Specific for German case, but certain points can help us

European convergence to US model (Bayh-Dole seen as main driver behind growing patent portfolios of US universities)

*University-owned patents (assigned to universities or their TTOs) and university-invented patents (assigned to university-affiliated authors)

Matching lists: traditional matching of lists of university staff/professors with inventor data
*time-consuming, costly, possibly not updated
*typically limited to tenured professors
*does not include Ph.D. students, assistants, lecturers

Matched authors of scientific publications and inventors on patents
*beware of homonyms

Patent data from EPO Worldwide Patent Statistical Database (PATSTAT)

Publication data from Elsevier (Scopus)

Country of origin: Inventor Country/Assignee Country/Applicant Country = Germany, Location of Organization to which author is affiliated =Germany
*Restrict dataset to authors from German organizations and inventors with residence in Germany

The organization

Names: To keep precision high, leave out names with initials only

Location: Postal/zip codes (PATSTAT provides address of inventor's residence, SCOPUS provides info for organization, 96.5% of first digit of inventor and organization postal codes are the same, 85.9% first two digits)

Time window: 2-year window between application and publication date

=== Singh A. and Wong P.K: University patenting activities and their link to the quantity and quality of scientific publications (2009) ===

[https://pdfs.semanticscholar.org/bb3a/df09ca693cdcff6c1f6aaf18113cabec2655.pdf]
==== Findings ====
* patenting by 281 leading world universities has consistently grown faster than general American patenting from 1977 - 2000
* North American university patenting growth has slowed relative to universities outside North America since the mid-1990s
* Between 2003-2005, they found that university patenting output has significant correlation with the both the quality and quantity of scientific publishing in North America
* In European and Australian universities, patenting correlated only with the quantity of scientific publishing, not with the quality
* In universities Europe, Australia, and North America, patenting correlated only with the quality of scientific publishing

==== Data Sources ====
* USPTO Patenting Data
* Shanghai Jiao Tong University's Academic Ranking of World Universities (ARWU)
* Times Higher Education Supplement's World University Ranking (WUR)
* Quantity was measured by counts of publications
* Quality was measured by citations to said publications
* the relationship between research and patenting was evaluated in two ways
** At the institutional level: patents assigned to universities
** At the individual level: patents with university researchers as the inventors

==== Critiques ====
* citations are not really a perfect measure of research quality and citations have little to do with practical use of the study (i.e. how much technological innovation is generated as a result of academic research publications)

==== Other Discoveries ====

* Study by Landry R., Amara N., and Saihi, M. (2006)
* (Owen Smith and Powell 2003) found that "organizations involved in technological commercialization tend to have higher publication rates than those who are not"
* (Lach and Schankerman 2003) found that "licensing revenues at the university level are positively influenced by publication citations per faculty"

University Patents

2018-04-05T19:31:55Z

Dayton: /* Carnegie Classifications */

{{McNair Projects
|Has title=University Patents
|Has owner=Julia Wang, Meghana Pannala, Anne Dayton
|Has keywords=Patent
|Has project status=Active
}}

==Research Paper==
Since university system file for patents out of a central office, its mot possible to determine where the research was conducted. This presents problems when ranking universities by patent production.

In order to publish this paper as a Baker Institute Research Report, we will need to do more to resolve this issue beyond weighting for the University of California.

==Carnegie Classifications==
We propose using public data from the Carnegie Classifications of Institutes of Higher Education to resolve the university systems issue.

TASKS:
1) Separate schools affiliated with universities/university systems in our rankings from all others included in Carnegie data.

2)Group these by system.
Determine the number of schools in each system
3) Determine their classification.
1 if Doctoral 0 if not (In Carnegie 2015 Basic Classification [BASIC2015] 15, 16, 17)
1 if 4-year medical school 0 if not (BASIC2015 25) Check (BASIC2015 26)
1 if 4-year engineering 0 if not (BASIC2015 27) Check ((BASIC2015 28)
4) Determine the total count of the categories in number 3 for each university system.

5) Use total count to weight regressions

6) Create ranking tables that rank university systems and universities by dividing number of patents by number of doctoral, med-school, 4-year engineering.

==Project Overview==
Goal: list of all universities and # of patents associated with each university and patent licensing activity

patent reassignment to startups associated with these universities

clinical trials (from Catherine) data to rank universities R&D engagement

identify list of universities: board of regents, universities in patent data (find patterns associated with university assignees) -- @Julia: Where did we get this list? I'd love to be able filter by country - Meghana

AUTM?

==Timeline to Deliverable & Documentation==

Meghana: focus on grants

Julia: focus on patent counts

Working in E:McNair/University Patents

'''3/20-3/24'''

Learn SQL, clean data

'''3/27-3/31'''

Counts of data, name matching

Patent Counts
*Ran the matcher in the server, only matched ~40,000 entries
*Working with Jeemin to develop matcher

'''4/3-4/7'''

Counts of data, name matching

Patent Counts
*Jeemin is a savior, matched and counted 128,000 entries (Jeemin_matcher_matched.txt), manually went through the 6,000 unmatched (Jeemin_matcher_unmatched.txt), Check [[University Patent Matching]]
*Ranking for all time is in Patent Counting ( >Total (All Time) )
*Need to talk with Meghana about grant data
*Need to find variables for regression, develop ranking for last decade and last year
*Also need to account for school size (faculty? students? research funding?)

'''4/10-4/14'''

Develop ranking

Patent Counts
*Finish all of above
*Start drafting
*Load data onto database -> E:McNair/University Patents/unidatajoin.sql in univ (database)

'''4/17-4/21'''

*Combined Patent Counts, R&D Expenditure, NSF Grant Data
*STATA regressions in E:McNair/University Patents/STATA
**for historical data

'''4/24'''

From Ed:
*Copied inventors table out of PatentDB database on RDP
*Script in E:McNair/PatentData, MoveInventors.sql
*Copy of data in Z:AllPatents and ran import into patent (database)
For Marcela:
*Need table of all patents associated with matches, join patent numbers with inventors
*Group by last name and first initial (=1 inventor)

'''5/1'''

*Joined patent numbers with inventors -> unipatentinventorcount.sql
**Z:univ/InventorCounts.txt
*Decade patent counts -> Z:univ/DecadePatentCounts
*Calculated Hirschman-Herfindahl index -> E:McNair/University Patents/Inventor Counts and Superstars

'''5/5'''

Artifacts:
#10-Year Ranking All
#10-Year Ranking Private
#10-Year Ranking Public
#Top 10 Movers and Shakers
#Graph of Concentration of Innovation
#Number of University Patents Over Time
#Inventor Concentration
#Regression Table

*Created Graph of Concentration of Innovation -> E:McNair/University Patents/Inventor Counts and Superstars -> GraphUPS
*Created Top Inventors Chart -> University Patents/Report Articles/Inventor Rankings

'''5/9'''

*Figuring out what's up with the university puller
*Developing new 10-Year Rankings
*Need to update above two articles
*Need to load into STATA and re-run regressions -> University Patents/STATA/univdata.do

'''5/10'''

*Figured out what was wrong with the data pull and now everything's good!
*Updated information on the draft below
*Fantastic Artifacts and Where to Find Them
**10-Year Ranking All
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Public
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Private
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**Top 10 Movers & Shakers (% change between first 5 years (2006-2010) vs next 5 years (2011-2015) within top 100
**Graph of Concentration of Innovation (numbers of patents)
***Pie graph (University Patents > Report Artifacts > Concentration of Innovation)
***(Excel sheet where this came from is University Patents > Inventor Counts and Concentrations)
**Trends Over Time
***Total vs. top 10
***Graph (University Patents > MoversandTop10)
**Inventor Concentration
***Chart (University Patents > Report Artifacts > Inventor Rankings)
**Reg Table
***+Explanation of variables
***(STATA do-file with the most statistically significant regressions is in University Patents > STATA > univdata.do)

Issue brief draft (VERY rough): https://docs.google.com/a/rice.edu/document/d/1MayXQEQ_pM0LMeiV39iaNFjtMZYSB-Ah9gCx1rcgW9M/edit?usp=sharing

==Ranking Development Notes==
Sources of University Funding R&D [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% comes from the Federal Government
*Less than 10% comes from state and local governments
*Roughly 20% is from the University itself (endowment?? tuition? not 100% sure)
*Roughly 5% is from private industry
*Less than 10% Other

Federal Government Funding Breakdown [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% of federal funding is from the NIH
*Roughly 15% is from the NSF
*Roughly 11% from Department of Defense
*Roughly 4% from NASA
*Roughly 4% from the Department of Energy
*Roughly 3% from the USDA
*Roughly 1% from the EPA (probably will go to zero with new admin)
*Roughly 1% from the Department of Education
*Marginal amounts from everywhere else (<1%)

NOTES
*overall ranking (total number of patents)
*weight by faculty (people)
*weight by research funding
*weight by endowment (maybe)
*public vs private
*individuals: surname and first initial
**ranking by inventors - who are the superstar inventors and where are they (define superstar inventor - idk 20 patents and rank universities by how many superstars are present
**Herfindahl-Hirschmann Index

==Key Words==

===Universities (Patent Assignees)===
BOARD OF REGENTS - pretty much exclusively describes universities

UNIVERSITY - also exclusively describes universities
*Can we do a close match with University? (its the only word on this list that's frequently misspelled)

SCHOOL - often used in combination with medicine or medical to describe medical schools
*also are used to describe actual schools (i.e. HISD) and other businesses
*sometimes present in addresses - can we cut off the adresses?
*also used in combination with Business (i.e. Harvard Business School)
*use school only in conjuction with medical, medicine, and business

COLLEGE
*need to cut off addresses for this to work
*need to ensure that we include the space after
*excl. College Boulevard, college blvd, etc.
*double check everything with College Park

INSTITUTE OF TECHNOLOGY - almost exclusively describes universities but sometimes describes external research institutes

POLYTECHNIC - exclusively university (i.e. RPI, Virginia Tech)

RESEARCH FOUNDATION: VAST majority are university
*Exclusions: it's really difficult to distinguish between university affiliated and not, besides just looking them up on the google
**Novartis
**Progeria
**Washington Research Foundation
**Blood Center of Wisconsins
**Mental Hygiene
**Celiac Sprue
**Fidia
**Samuel Waxman Cancer Center
***founded by a faculty member at Mt. Sinai Medical School,not affil w/a single university but researchers are also usually faculty at various universities
**lifenet
**HealthPartners
**Dr. Susan Love
**La Jolla Cancer Research Foundation
**Children's Hospital (? grants degrees but is a hospital system)
**Medforte
**International Mask
**Palo Alto Medical Foundation

====Exclusions====
*LLC
*LLP

===Technology Transfer Offices===

====Job Titles====
*Patent Portfolio Manager
*Intellectual Property Manager
*Licensing Associate/Licensing Liaison
*Biological Materials Specialist
*Industrial Contracts Officer
*Technology Licensing Officer
*Associate Officer
*Technology Licensing Associates
*Patent Coordinator/Patent Administrator
*IP Portfolio Specialist
*MTA Coordinator
*Sponsored Research Administrator
*Technology Transfer Specialist/Technology Transfer Associate
*Contracts Associate
*Portfolio Director
*Contracts Specialist
*Industry Contracts Analyst
*Patent Prosecution Analyst
*(Associate) Director of Intellectual Property
*(Associate) Director of Technology Transfer Policy
*(Associate) Director of Technology Transactions
*(Associate) Director of Agreement Administration
*Senior Associate - watch
*Patent Attorney/Patent Agent/Patent Counsel/Paralegal - watch

====TTO Names====
*Office of Technology Transfer/Technology Transfer Office
*Office of Technology Licensing/Technology Licensing Office
*Office of Technology Development
*Technology Ventures
*Innovation Services
*Intellectual Property & Industry Research Alliances
*InnovationAccess
*Invention Transfer Group
*Technology Development Group
*Office of Business Development
*Office of Technology Commercialization
*Office of Innovation and Commercialization
*Innovation, Technology & Alliances
*Technology & Industry Alliances
*Office for Management of Intellectual Property
*Innovation & Partnerships Office

===University-Affiliated Startups (Patent Assignees)===

==What to get from other people==

Avesh has clinical trial data on wiki and bulk drive (FDA Trials Data), need to build it into normal form, use clinical trials data to rank R&D engagement of universities, will be building up portfolios of different types of companies

Marcela is cleaning up patent data, has given patent assignee names -- got it! Could we filter by country and get strictly American names?

Catherine has zip codes of medical centers, use to look through patent data

==Questions==
*How innovative are universities compared to publicly-traded firms, etc.? (firms: 100 active patents at any given time)
*How do universities license?
*What does the average portfolio look like for universities? (compared to publicly traded, VC-backed, etc)
*What can explain the differences in rankings? (size, quality of universities, TTOs and quality/experience of workers - searched LinkedIns, geography, entrepreneurship programs, NIH/NSF grants)

==To Do==

*Create ranking
**Based on patent portfolio
**Licensing income
**Quality of TTO
**Publications (specifically based on research)?
**Amount received in grants/Amount of grants - we have numbers on amount of NIH grants and NSF grants
***NIH/NSF/STTR
**Control for school size/endowment
**Startups affiliated with university
***Value generated from these startups? (ask Avesh)
**Existence of entrepreneurship program
**Geography

*Describe average patent portfolio of university
**Number of patents
**Maybe categorize by research area?
*Describe how universities license
**USPTO earned revenue

Deliverable
*Ranking
*Explanation of what makes a good ranking/what factors a school needs to be more successful

Artifacts
*Grant Data
*Research Funding
*Star Scientists
*Regression with variables from Carnegie Classification (10-15 variables)
*Licensing (?)

===What We Have===

*Lit Review
*List of all post-secondary institutions (University Accreditation 3.2016_trim) - U.S. Department of Education
*Classification of universities (Carnegie Classification 2015) - Carnegie Classification
** includes all accredited degree-granting colleges and universities that are included in the National Center for Education Statistics Integrated Postsecondary Education Data System (IPEDS).
** Coverage: 4666 institutions of higher learning
** Variables: includes 94 variables
*** Enrollment data - subdivided into graduate and undergraduate
*** degrees conferred - divided into level and division
*** information on faculty (number, divided into levels i.e. assistant, assoc. and full time)
**** coverage for this variable is REALLY LOW
*** non-faculty research staff (includes post docs)
*** Science and Engineering Research and Development expenditures
*** Non-STEM research and Development expenditures
*Amount spent on R&D (Higher Ed R&D Rankings) - National Science Foundation
** includes just total R&D expenditures
*STTR full data 1995-2015 (STTRData) - Small Business Administration
*University Patent Numbers 1969-2012 - USPTO
*Patents that resulted from NIH grants
*Information about NSF grants

===What We Need===

*List of university-affiliated startups
**looks like AUTM STATT data has this because it was used in a study by Yael Hochberg [https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2418000]
**[https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-research-universities-2015/#3cb9829b3823| Rankings of Research Universities (2015)]
**FORBES ranked the country’s most entrepreneurial schools based on the numbers of alumni and students who have identified themselves as founders and business owners on LinkedIn (adjusted to total student body size). This year we rank both research universities and [https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-colleges-2015/#2b2909e04604| smaller colleges] separately.
*data about TTOs (# and quality of employees)
**LinkedIn crawler: working with Jeemin and Peter
*licensing data from AUTM [http://www.autm.net/statt] or already within the patent data?

==Name Matching==
*Jeemin has found all the correct and incorrect spellings of assignees with "University"
*Julia working on "Institute"
*We have a list of Universities that have patents from NIH grants, which could be a starting point for our ranking constituents

==Lit Reviews==

===Thursby, J. & Thursby, M.: Who Is Selling the Ivory Tower? Sources of Growth in University Licensing (2002)===

[http://pubsonline.informs.org/doi/pdf/10.1287/mnsc.48.1.90.14271]

@article{thursby2002who,
title={Who Is Selling the Ivory Tower? Sources of Growth in University Licensing},
author={Thursby, Jerry G. and Thursby, Marie C.},
journal={Management Science},
volume={48},
number={1},
pages={90--104},
year={2002},
publisher={INFORMS},
filename={Thursby Thursby (2002) - Who Is Selling the Ivory Tower}
}

*Reliance of industry on university inventions has increased
**AUTM surveys show 7.1% growth in yearly inventions disclosure from 1994-1998 for 64 universities that responded every year
*Primary reason for more disclosures may be increased propensity for faculty to disclose, rather than change in research focus
*Universities becoming more receptive to industry contracts
*Negative total TFP growth of licenses executed (-1.7% annual growth) - growth in disclosures and patent applications greater than the corresponding growth in licenses executed.
**Marginal university innovation offered to the market has declined in commercial appeal
**Universities are delving more deeply into the available pool of innovations to increase commercial activities
*No evidence on the importance of learning by doing on the part of TTOs except to note negative association between TTO growth and TFP growth in licensing
**Suggests at least the possibility of learning by doing effects

'''Other literature'''

*On the role of patents and publications in the transfer process: Adams 1990, Henderson et al. 1998, and Jaffe et al. 1993
*On consulting, sponsored research or institutional ties: Cohen et al. 1998; Mansfield 1995; Zucker et al. 1994, 1998
*On the nature of university licensing: Jensen and Thursby 2001, Mowery et al. 2001a,b, Mowery et al. 2001, Siegel et al. 1999, Thursby et al. 2001, Thursby and Kemp 2001

===Thursby, J., Jensen, Thursby, M.: Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities (2001)===

[https://pdfs.semanticscholar.org/637f/048c05ee83eb84a914f69e087f52acea1b6c.pdf]

@article{thursby2001objectives,
title={Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities},
author={Thursby, Jerry G., Jensen, Richard, and Thursby, Marie C.},
journal={The Journal of Technology Transfer},
volume={26},
number={1},
pages={59--72},
year={2001},
publisher={Springer},
abstract={This paper describes results of our survey of licensing at 62 research universities. We consider ownership, income splits, stage of development, marketing, license policies and characteristics, goals of licensing and the role of the inventor in licensing. Based on these results we analyze the relationship between licensing outcomes and both the objectives of the TTO and the characteristics of the technologies. Patent applications grow one-to-one with disclosures, while sponsored research grows similarly with licenses executed. Royalties are typically larger the higher the quality of the faculty and the higher the fraction of licenses that are executed at latter stages of development. Sponsored research is more likely to be included in a license if the new technology is at an early stage of development or if the TTO evaluates it as important. We find that additional disclosures generate smaller percentage increases in licenses, and those increases in licenses generate smaller percentage increases in royalties.},
filename={Thursby et al (2001) - Objectives, Characteristics and Outcomes of University Licensing}
}

*University licensing has increased dramatically post-Bayh-Dole (1980)
**According to AUTM 1996, licenses executed increased 75% from 1991-1996, (total: 13,087)
*Survey of TTOs of 62 major US universities
**Majority of universities retain titles to inventions
**All universities split income with inventors
**Royalties generate most of the revenue of licensing

*Open question: Is the increased propensity of faculty to disclose a response to financial incentives or an increase in the effectiveness of TTOs in inducing disclosure?

Survey
*62/135 universities responded
*63% public, and 62% of public universities that responded were land-grant
*37% private
*average industry sponsored research $16.9 mil, federally sponsored $149.6 mil (1996)
*average TTO: 26.3 licenses executed, 92.3 invention disclosures, 30.1 new patent apps, $4.2 mil income (1996)
*35% of respondents had reorganized TTO since 1990
*90% of universities allow faculty to establish and operate businesses based on technology owned by university but developed in faculty's research
*Inventions disclosed: 33% med schools, 29% engineering, 22% science, 6% agriculture, 10% other
*Majority of invention disclosures in nascent stage (proof of concept - 45% or prototype - 37%)
*Patents often applied for after knowing commercial viability, licensed technologies often not protected by patents
*60% of universities said small companies more likely to take early stage technologies and large companies more likely to take late stage - small firms may have advantage in "innovative" research (Holmstrom 1989)
*TTOs obtain smaller upfront fees the more uncertain the technology being licensed is
*Universities usually do not take equity in the license

Regression of licensing outcomes

[[File:LicensingOutcomeReg.png|500px]]

*Dependent variables: royalties, sponsored research, patents (new applications), licenses executed
*Independent variables: importance of outcome to TTO, types of inventions, measure of size of university's licensing operation/potential
*Logs of all variables except indicator variables
*Probit for frequency of sponsored research
*INVDIS: number of disclosures
*TTOSIZE: number of licensing individuals
*TTOEVAL_1 = 1: if TTOs that said licenses/patents are "not very important"
*TTOEVAL_2 = 1: if TTOs that said licenses/patents are "moderately important"
*PROOF: % of licensed disclosures that were "proof of concept but no prototype"
*PTYPE: "Prototype available but only lab scale"
*MEDSCHL: 1 if med school exists
*QUAL: academic quality of faculty (1993 NRC's survey results of academic quality of Ph.D. granting departments)
*LICENSES: number of licenses executed
*SPONRES: amount of sponsored research
*SPONFREQ: frequency that sponsored research is tied to license (according to TTO)
*patent apps grow 1-to-1 with disclosures
*sponsored research grows with licenses executed
*more licenses executed at universities with large TTOs and med schools
*higher royalties with higher quality of faculty and higher fraction of licenses executed at later stages of development
*additional disclosures generate smaller % increases in licenses, which generate smaller % increases in royalties (TTOs generally effective at tapping pool of available technologies in their universities)

===Thursby, J., Fuller, Thursby, M.: US Faculty Patenting: Inside and Outside the University (2009)===

[http://www.nber.org/papers/w13256.pdf]

@article{thursby2009us,
title = "US Faculty Patenting: Inside and Outside the University",
author = "Jerry G. Thursby, Anne W. Fuller, and Marie C. Thursby",
journal={Research Policy},
volume={38},
number={1},
pages={14--25},
year={2009},
publisher={Elsevier},
abstract = {This paper examines the empirical anomaly that in a sample of 5811 patents on which US faculty are listed as inventors, 26% of the patents are assigned solely to firms rather than to the faculty member's university as is dictated by US university employment policies or the Bayh Dole Act. In this paper we estimate a series of probability models of assignment as a function of patent characteristics, university policy, and inventor fields in order to examine the extent to which outside assignment is nefarious or comes from legitimate activities, such as consulting. Patents assigned to firms (whether established or start-ups with inventor as principal) are less basic than those assigned to universities suggesting these patents result from faculty consulting. A higher inventor share increases the likelihood of university assignment as compared with assignment to a firm in which the inventor is a principal but it has no effect on consulting with established firms versus assignment to the university. Faculty in the physical sciences and engineering are more likely to assign their patents to established firms than those in biological sciences.},
filename={Thursby et al (2009) - US Faculty Patenting},
}

*only 62.4% of patents by university faculty members of 87 universities were assigned solely to universities
*identifying US university patents by institutional assignment misses significant percentage of faculty innovation in US universities
*higher inventor share increases likelihood of university assignment compared with assignment to a firm where inventor is principal
*possibilities: faculty in low share universities may be more willing to seek outside remuneration via assignment to start-up where they are principal; revenue shares may not affect startup activity but simply reduce number of inventions disclosed to university

Sources
*Faculty names from NRC
*Compared with inventor names in NBER Patent Database
*Excluded faculty who do not patent

Patent/Inventor Pairs
*MIT: 315
*Wisconsin: 232
*Stanford: 223
*UC San Diego: 216
*UC Berkeley: 207

Out of 5811 patents:
*1513 assigned solely to firms
*241 assigned to both firms and universities
*327 unassigned
*faculty are principals in assignee firms for 32.3% of patents assigned solely to firms and 24% of patents assigned to both (lower bound)

=== Valdivia: University Start-ups: Critical for Improving Technology Transfer (2013) ===

[https://www.brookings.edu/wp-content/uploads/2016/06/Valdivia_Tech-Transfer_v29_No-Embargo.pdf]

*Current emphasis on licensing patents, but most university TTOs do not generate enough to cover operating expenses
*Asymmetry in distribution of resources across the university system, only a few universities benefit from high licensing revenues
**top 8 universities took 50% of licensing income, top 16 universities took nearly 75% of income
**only 37 universities have been in the top 20 during the last decade (listed in University Patents > LicensingGrossIncome2003-2012.txt)
*Universities face much more pressure to demonstrate the economic impact of their R&D contracts
**97.6% of total public contracts obtained by universities are for basic/applied research
**2% of university research delivers ready-to-use technologies
**academic research is much more dependent on government funding than industry research is
*Government pressure for universities to be more responsive to market forces, more entrepreneurial, more attuned to needs of industry
*TTOs are costly to universities
*1979: 30 universities with TTO -> 1999: 174 universities (AUTM)
*2010: 206 US universities have very high or high research activity, all with TTOs (but not all report to AUTM) (Carnegie Classification of Higher Education)
*Over last 20 years, 87% of universities did not break even
*Clash between aims of university (non-profit) and TTO (essentially a business unit)
*By nurturing start-ups, TTOs can add most economic value to an invention disclosure
*2003: universities initiated 330 startups, 2012: 647 startups
*2012: 3715 operating university startups, almost double the number in 2000 (AUTM 2013)
*Startups mitigate financial risk by reducing reliance on blockbuster patents, increase diversification of portfolio
*Policy proposal: the government should increase funding for Small Business Technology Transfer Program (directed to university start-ups)
**H.R.2981: reapportions STTR funds at 0.05% in the next 2 years and 0.1% henceforth for university enterprises at proof of concept stage
*Should increase portion that agencies set aside for STTR
**STTR should have Phase III like SBIR to fund commercialization efforts
*Need equitable distribution across university system

=== The Bayh-Dole Act and High-Technology Entrepreneurship in U.S. Universities: Chicken, Egg, or Something Else? (2004) ===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.484.1612&rep=rep1&type=pdf]

==== Findings ====

* University research has an unusually significant impact on industrial innovation in the biomedical sector
* " This work also suggests that academic research rarely produces “prototypes” of inventions for development
and commercialization by industry—instead, academic research informs the methods and disciplines
employed by firms in their R&D facilities."
* The U.S. higher education system is much larger and more heterogenous than other developed countries - this encourages competition
* The passage of the Bayh-Dole Act was one part of a broader shift in U.S. policy toward stronger
intellectual property rights
* "Universities increased their share of patenting from less than 0.3% in 1963 to nearly 4% by 1999, but the rate of growth in this share begins to accelerate before rather than after 1980."
* "the Act's provisions expressed Congressional support for the negotiation of exclusive licenses between universities and industrial firms for the results of federally funded research"
* licensing revenues account for only a miniscule portion of universities' overall academic budgets
* the acceleration in growth of patenting and licensing began before the passage of the Bayh-Dole Act so this acceleration cannot be wholly attributed to the Act
* "the flow of knowledge and technology between university and industrial research is a two-way flow," despite previous characterization as wholly from academia to industry
* patents seem to be "especially important channels for technology transfer" in the biomedical sector

==== Data Sources ====
* 5 different case studies
1. Cotransformation: a process to transfer genes into mammalian cells (Columbia University).

2. Gallium Nitride: a semiconductor with both military and commercial applications (University of California).

3. Xalatan: a glaucoma treatment (Columbia University).

4. Ames II Tests: a bacteria assay for testing potential carcinogenic properties of pharmaceuticals and cosmetics (University of California).

5. Soluble CD4: a prototype for a drug to fight AIDS (Columbia University).

==== Critiques ====
* focuses on case studies as source of data

===Dornbusch, Schmoch, Schulze, Bethke: Identification of University-Based Patents: A New Large-Scale Approach (2012)===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.1368&rep=rep1&type=pdf]

Specific for German case, but certain points can help us

European convergence to US model (Bayh-Dole seen as main driver behind growing patent portfolios of US universities)

*University-owned patents (assigned to universities or their TTOs) and university-invented patents (assigned to university-affiliated authors)

Matching lists: traditional matching of lists of university staff/professors with inventor data
*time-consuming, costly, possibly not updated
*typically limited to tenured professors
*does not include Ph.D. students, assistants, lecturers

Matched authors of scientific publications and inventors on patents
*beware of homonyms

Patent data from EPO Worldwide Patent Statistical Database (PATSTAT)

Publication data from Elsevier (Scopus)

Country of origin: Inventor Country/Assignee Country/Applicant Country = Germany, Location of Organization to which author is affiliated =Germany
*Restrict dataset to authors from German organizations and inventors with residence in Germany

The organization

Names: To keep precision high, leave out names with initials only

Location: Postal/zip codes (PATSTAT provides address of inventor's residence, SCOPUS provides info for organization, 96.5% of first digit of inventor and organization postal codes are the same, 85.9% first two digits)

Time window: 2-year window between application and publication date

=== Singh A. and Wong P.K: University patenting activities and their link to the quantity and quality of scientific publications (2009) ===

[https://pdfs.semanticscholar.org/bb3a/df09ca693cdcff6c1f6aaf18113cabec2655.pdf]
==== Findings ====
* patenting by 281 leading world universities has consistently grown faster than general American patenting from 1977 - 2000
* North American university patenting growth has slowed relative to universities outside North America since the mid-1990s
* Between 2003-2005, they found that university patenting output has significant correlation with the both the quality and quantity of scientific publishing in North America
* In European and Australian universities, patenting correlated only with the quantity of scientific publishing, not with the quality
* In universities Europe, Australia, and North America, patenting correlated only with the quality of scientific publishing

==== Data Sources ====
* USPTO Patenting Data
* Shanghai Jiao Tong University's Academic Ranking of World Universities (ARWU)
* Times Higher Education Supplement's World University Ranking (WUR)
* Quantity was measured by counts of publications
* Quality was measured by citations to said publications
* the relationship between research and patenting was evaluated in two ways
** At the institutional level: patents assigned to universities
** At the individual level: patents with university researchers as the inventors

==== Critiques ====
* citations are not really a perfect measure of research quality and citations have little to do with practical use of the study (i.e. how much technological innovation is generated as a result of academic research publications)

==== Other Discoveries ====

* Study by Landry R., Amara N., and Saihi, M. (2006)
* (Owen Smith and Powell 2003) found that "organizations involved in technological commercialization tend to have higher publication rates than those who are not"
* (Lach and Schankerman 2003) found that "licensing revenues at the university level are positively influenced by publication citations per faculty"

University Patents

2018-04-05T19:16:25Z

Dayton: /* Carnegie Classifications */

{{McNair Projects
|Has title=University Patents
|Has owner=Julia Wang, Meghana Pannala, Anne Dayton
|Has keywords=Patent
|Has project status=Active
}}

==Research Paper==
Since university system file for patents out of a central office, its mot possible to determine where the research was conducted. This presents problems when ranking universities by patent production.

In order to publish this paper as a Baker Institute Research Report, we will need to do more to resolve this issue beyond weighting for the University of California.

==Carnegie Classifications==
We propose using public data from the Carnegie Classifications of Institutes of Higher Education to resolve the university systems issue.

TASKS:
1) Separate schools affiliated with universities/university systems in our rankings from all others included in Carnegie data.

2)Group these by system.
Determine the number of schools in each system
3) Determine their classification.
1 if Doctoral 0 if not
1 if 4-year medical school 0 if not
1 if 4-year engineering 0 if not
4) Determine the total count of the categories in number 3 for each university system.

5) Use total count to weight regressions

6) Create ranking tables that rank university systems and universities by dividing number of patents by number of doctoral, med-school, 4-year engineering.

==Project Overview==
Goal: list of all universities and # of patents associated with each university and patent licensing activity

patent reassignment to startups associated with these universities

clinical trials (from Catherine) data to rank universities R&D engagement

identify list of universities: board of regents, universities in patent data (find patterns associated with university assignees) -- @Julia: Where did we get this list? I'd love to be able filter by country - Meghana

AUTM?

==Timeline to Deliverable & Documentation==

Meghana: focus on grants

Julia: focus on patent counts

Working in E:McNair/University Patents

'''3/20-3/24'''

Learn SQL, clean data

'''3/27-3/31'''

Counts of data, name matching

Patent Counts
*Ran the matcher in the server, only matched ~40,000 entries
*Working with Jeemin to develop matcher

'''4/3-4/7'''

Counts of data, name matching

Patent Counts
*Jeemin is a savior, matched and counted 128,000 entries (Jeemin_matcher_matched.txt), manually went through the 6,000 unmatched (Jeemin_matcher_unmatched.txt), Check [[University Patent Matching]]
*Ranking for all time is in Patent Counting ( >Total (All Time) )
*Need to talk with Meghana about grant data
*Need to find variables for regression, develop ranking for last decade and last year
*Also need to account for school size (faculty? students? research funding?)

'''4/10-4/14'''

Develop ranking

Patent Counts
*Finish all of above
*Start drafting
*Load data onto database -> E:McNair/University Patents/unidatajoin.sql in univ (database)

'''4/17-4/21'''

*Combined Patent Counts, R&D Expenditure, NSF Grant Data
*STATA regressions in E:McNair/University Patents/STATA
**for historical data

'''4/24'''

From Ed:
*Copied inventors table out of PatentDB database on RDP
*Script in E:McNair/PatentData, MoveInventors.sql
*Copy of data in Z:AllPatents and ran import into patent (database)
For Marcela:
*Need table of all patents associated with matches, join patent numbers with inventors
*Group by last name and first initial (=1 inventor)

'''5/1'''

*Joined patent numbers with inventors -> unipatentinventorcount.sql
**Z:univ/InventorCounts.txt
*Decade patent counts -> Z:univ/DecadePatentCounts
*Calculated Hirschman-Herfindahl index -> E:McNair/University Patents/Inventor Counts and Superstars

'''5/5'''

Artifacts:
#10-Year Ranking All
#10-Year Ranking Private
#10-Year Ranking Public
#Top 10 Movers and Shakers
#Graph of Concentration of Innovation
#Number of University Patents Over Time
#Inventor Concentration
#Regression Table

*Created Graph of Concentration of Innovation -> E:McNair/University Patents/Inventor Counts and Superstars -> GraphUPS
*Created Top Inventors Chart -> University Patents/Report Articles/Inventor Rankings

'''5/9'''

*Figuring out what's up with the university puller
*Developing new 10-Year Rankings
*Need to update above two articles
*Need to load into STATA and re-run regressions -> University Patents/STATA/univdata.do

'''5/10'''

*Figured out what was wrong with the data pull and now everything's good!
*Updated information on the draft below
*Fantastic Artifacts and Where to Find Them
**10-Year Ranking All
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Public
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Private
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**Top 10 Movers & Shakers (% change between first 5 years (2006-2010) vs next 5 years (2011-2015) within top 100
**Graph of Concentration of Innovation (numbers of patents)
***Pie graph (University Patents > Report Artifacts > Concentration of Innovation)
***(Excel sheet where this came from is University Patents > Inventor Counts and Concentrations)
**Trends Over Time
***Total vs. top 10
***Graph (University Patents > MoversandTop10)
**Inventor Concentration
***Chart (University Patents > Report Artifacts > Inventor Rankings)
**Reg Table
***+Explanation of variables
***(STATA do-file with the most statistically significant regressions is in University Patents > STATA > univdata.do)

Issue brief draft (VERY rough): https://docs.google.com/a/rice.edu/document/d/1MayXQEQ_pM0LMeiV39iaNFjtMZYSB-Ah9gCx1rcgW9M/edit?usp=sharing

==Ranking Development Notes==
Sources of University Funding R&D [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% comes from the Federal Government
*Less than 10% comes from state and local governments
*Roughly 20% is from the University itself (endowment?? tuition? not 100% sure)
*Roughly 5% is from private industry
*Less than 10% Other

Federal Government Funding Breakdown [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% of federal funding is from the NIH
*Roughly 15% is from the NSF
*Roughly 11% from Department of Defense
*Roughly 4% from NASA
*Roughly 4% from the Department of Energy
*Roughly 3% from the USDA
*Roughly 1% from the EPA (probably will go to zero with new admin)
*Roughly 1% from the Department of Education
*Marginal amounts from everywhere else (<1%)

NOTES
*overall ranking (total number of patents)
*weight by faculty (people)
*weight by research funding
*weight by endowment (maybe)
*public vs private
*individuals: surname and first initial
**ranking by inventors - who are the superstar inventors and where are they (define superstar inventor - idk 20 patents and rank universities by how many superstars are present
**Herfindahl-Hirschmann Index

==Key Words==

===Universities (Patent Assignees)===
BOARD OF REGENTS - pretty much exclusively describes universities

UNIVERSITY - also exclusively describes universities
*Can we do a close match with University? (its the only word on this list that's frequently misspelled)

SCHOOL - often used in combination with medicine or medical to describe medical schools
*also are used to describe actual schools (i.e. HISD) and other businesses
*sometimes present in addresses - can we cut off the adresses?
*also used in combination with Business (i.e. Harvard Business School)
*use school only in conjuction with medical, medicine, and business

COLLEGE
*need to cut off addresses for this to work
*need to ensure that we include the space after
*excl. College Boulevard, college blvd, etc.
*double check everything with College Park

INSTITUTE OF TECHNOLOGY - almost exclusively describes universities but sometimes describes external research institutes

POLYTECHNIC - exclusively university (i.e. RPI, Virginia Tech)

RESEARCH FOUNDATION: VAST majority are university
*Exclusions: it's really difficult to distinguish between university affiliated and not, besides just looking them up on the google
**Novartis
**Progeria
**Washington Research Foundation
**Blood Center of Wisconsins
**Mental Hygiene
**Celiac Sprue
**Fidia
**Samuel Waxman Cancer Center
***founded by a faculty member at Mt. Sinai Medical School,not affil w/a single university but researchers are also usually faculty at various universities
**lifenet
**HealthPartners
**Dr. Susan Love
**La Jolla Cancer Research Foundation
**Children's Hospital (? grants degrees but is a hospital system)
**Medforte
**International Mask
**Palo Alto Medical Foundation

====Exclusions====
*LLC
*LLP

===Technology Transfer Offices===

====Job Titles====
*Patent Portfolio Manager
*Intellectual Property Manager
*Licensing Associate/Licensing Liaison
*Biological Materials Specialist
*Industrial Contracts Officer
*Technology Licensing Officer
*Associate Officer
*Technology Licensing Associates
*Patent Coordinator/Patent Administrator
*IP Portfolio Specialist
*MTA Coordinator
*Sponsored Research Administrator
*Technology Transfer Specialist/Technology Transfer Associate
*Contracts Associate
*Portfolio Director
*Contracts Specialist
*Industry Contracts Analyst
*Patent Prosecution Analyst
*(Associate) Director of Intellectual Property
*(Associate) Director of Technology Transfer Policy
*(Associate) Director of Technology Transactions
*(Associate) Director of Agreement Administration
*Senior Associate - watch
*Patent Attorney/Patent Agent/Patent Counsel/Paralegal - watch

====TTO Names====
*Office of Technology Transfer/Technology Transfer Office
*Office of Technology Licensing/Technology Licensing Office
*Office of Technology Development
*Technology Ventures
*Innovation Services
*Intellectual Property & Industry Research Alliances
*InnovationAccess
*Invention Transfer Group
*Technology Development Group
*Office of Business Development
*Office of Technology Commercialization
*Office of Innovation and Commercialization
*Innovation, Technology & Alliances
*Technology & Industry Alliances
*Office for Management of Intellectual Property
*Innovation & Partnerships Office

===University-Affiliated Startups (Patent Assignees)===

==What to get from other people==

Avesh has clinical trial data on wiki and bulk drive (FDA Trials Data), need to build it into normal form, use clinical trials data to rank R&D engagement of universities, will be building up portfolios of different types of companies

Marcela is cleaning up patent data, has given patent assignee names -- got it! Could we filter by country and get strictly American names?

Catherine has zip codes of medical centers, use to look through patent data

==Questions==
*How innovative are universities compared to publicly-traded firms, etc.? (firms: 100 active patents at any given time)
*How do universities license?
*What does the average portfolio look like for universities? (compared to publicly traded, VC-backed, etc)
*What can explain the differences in rankings? (size, quality of universities, TTOs and quality/experience of workers - searched LinkedIns, geography, entrepreneurship programs, NIH/NSF grants)

==To Do==

*Create ranking
**Based on patent portfolio
**Licensing income
**Quality of TTO
**Publications (specifically based on research)?
**Amount received in grants/Amount of grants - we have numbers on amount of NIH grants and NSF grants
***NIH/NSF/STTR
**Control for school size/endowment
**Startups affiliated with university
***Value generated from these startups? (ask Avesh)
**Existence of entrepreneurship program
**Geography

*Describe average patent portfolio of university
**Number of patents
**Maybe categorize by research area?
*Describe how universities license
**USPTO earned revenue

Deliverable
*Ranking
*Explanation of what makes a good ranking/what factors a school needs to be more successful

Artifacts
*Grant Data
*Research Funding
*Star Scientists
*Regression with variables from Carnegie Classification (10-15 variables)
*Licensing (?)

===What We Have===

*Lit Review
*List of all post-secondary institutions (University Accreditation 3.2016_trim) - U.S. Department of Education
*Classification of universities (Carnegie Classification 2015) - Carnegie Classification
** includes all accredited degree-granting colleges and universities that are included in the National Center for Education Statistics Integrated Postsecondary Education Data System (IPEDS).
** Coverage: 4666 institutions of higher learning
** Variables: includes 94 variables
*** Enrollment data - subdivided into graduate and undergraduate
*** degrees conferred - divided into level and division
*** information on faculty (number, divided into levels i.e. assistant, assoc. and full time)
**** coverage for this variable is REALLY LOW
*** non-faculty research staff (includes post docs)
*** Science and Engineering Research and Development expenditures
*** Non-STEM research and Development expenditures
*Amount spent on R&D (Higher Ed R&D Rankings) - National Science Foundation
** includes just total R&D expenditures
*STTR full data 1995-2015 (STTRData) - Small Business Administration
*University Patent Numbers 1969-2012 - USPTO
*Patents that resulted from NIH grants
*Information about NSF grants

===What We Need===

*List of university-affiliated startups
**looks like AUTM STATT data has this because it was used in a study by Yael Hochberg [https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2418000]
**[https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-research-universities-2015/#3cb9829b3823| Rankings of Research Universities (2015)]
**FORBES ranked the country’s most entrepreneurial schools based on the numbers of alumni and students who have identified themselves as founders and business owners on LinkedIn (adjusted to total student body size). This year we rank both research universities and [https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-colleges-2015/#2b2909e04604| smaller colleges] separately.
*data about TTOs (# and quality of employees)
**LinkedIn crawler: working with Jeemin and Peter
*licensing data from AUTM [http://www.autm.net/statt] or already within the patent data?

==Name Matching==
*Jeemin has found all the correct and incorrect spellings of assignees with "University"
*Julia working on "Institute"
*We have a list of Universities that have patents from NIH grants, which could be a starting point for our ranking constituents

==Lit Reviews==

===Thursby, J. & Thursby, M.: Who Is Selling the Ivory Tower? Sources of Growth in University Licensing (2002)===

[http://pubsonline.informs.org/doi/pdf/10.1287/mnsc.48.1.90.14271]

@article{thursby2002who,
title={Who Is Selling the Ivory Tower? Sources of Growth in University Licensing},
author={Thursby, Jerry G. and Thursby, Marie C.},
journal={Management Science},
volume={48},
number={1},
pages={90--104},
year={2002},
publisher={INFORMS},
filename={Thursby Thursby (2002) - Who Is Selling the Ivory Tower}
}

*Reliance of industry on university inventions has increased
**AUTM surveys show 7.1% growth in yearly inventions disclosure from 1994-1998 for 64 universities that responded every year
*Primary reason for more disclosures may be increased propensity for faculty to disclose, rather than change in research focus
*Universities becoming more receptive to industry contracts
*Negative total TFP growth of licenses executed (-1.7% annual growth) - growth in disclosures and patent applications greater than the corresponding growth in licenses executed.
**Marginal university innovation offered to the market has declined in commercial appeal
**Universities are delving more deeply into the available pool of innovations to increase commercial activities
*No evidence on the importance of learning by doing on the part of TTOs except to note negative association between TTO growth and TFP growth in licensing
**Suggests at least the possibility of learning by doing effects

'''Other literature'''

*On the role of patents and publications in the transfer process: Adams 1990, Henderson et al. 1998, and Jaffe et al. 1993
*On consulting, sponsored research or institutional ties: Cohen et al. 1998; Mansfield 1995; Zucker et al. 1994, 1998
*On the nature of university licensing: Jensen and Thursby 2001, Mowery et al. 2001a,b, Mowery et al. 2001, Siegel et al. 1999, Thursby et al. 2001, Thursby and Kemp 2001

===Thursby, J., Jensen, Thursby, M.: Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities (2001)===

[https://pdfs.semanticscholar.org/637f/048c05ee83eb84a914f69e087f52acea1b6c.pdf]

@article{thursby2001objectives,
title={Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities},
author={Thursby, Jerry G., Jensen, Richard, and Thursby, Marie C.},
journal={The Journal of Technology Transfer},
volume={26},
number={1},
pages={59--72},
year={2001},
publisher={Springer},
abstract={This paper describes results of our survey of licensing at 62 research universities. We consider ownership, income splits, stage of development, marketing, license policies and characteristics, goals of licensing and the role of the inventor in licensing. Based on these results we analyze the relationship between licensing outcomes and both the objectives of the TTO and the characteristics of the technologies. Patent applications grow one-to-one with disclosures, while sponsored research grows similarly with licenses executed. Royalties are typically larger the higher the quality of the faculty and the higher the fraction of licenses that are executed at latter stages of development. Sponsored research is more likely to be included in a license if the new technology is at an early stage of development or if the TTO evaluates it as important. We find that additional disclosures generate smaller percentage increases in licenses, and those increases in licenses generate smaller percentage increases in royalties.},
filename={Thursby et al (2001) - Objectives, Characteristics and Outcomes of University Licensing}
}

*University licensing has increased dramatically post-Bayh-Dole (1980)
**According to AUTM 1996, licenses executed increased 75% from 1991-1996, (total: 13,087)
*Survey of TTOs of 62 major US universities
**Majority of universities retain titles to inventions
**All universities split income with inventors
**Royalties generate most of the revenue of licensing

*Open question: Is the increased propensity of faculty to disclose a response to financial incentives or an increase in the effectiveness of TTOs in inducing disclosure?

Survey
*62/135 universities responded
*63% public, and 62% of public universities that responded were land-grant
*37% private
*average industry sponsored research $16.9 mil, federally sponsored $149.6 mil (1996)
*average TTO: 26.3 licenses executed, 92.3 invention disclosures, 30.1 new patent apps, $4.2 mil income (1996)
*35% of respondents had reorganized TTO since 1990
*90% of universities allow faculty to establish and operate businesses based on technology owned by university but developed in faculty's research
*Inventions disclosed: 33% med schools, 29% engineering, 22% science, 6% agriculture, 10% other
*Majority of invention disclosures in nascent stage (proof of concept - 45% or prototype - 37%)
*Patents often applied for after knowing commercial viability, licensed technologies often not protected by patents
*60% of universities said small companies more likely to take early stage technologies and large companies more likely to take late stage - small firms may have advantage in "innovative" research (Holmstrom 1989)
*TTOs obtain smaller upfront fees the more uncertain the technology being licensed is
*Universities usually do not take equity in the license

Regression of licensing outcomes

[[File:LicensingOutcomeReg.png|500px]]

*Dependent variables: royalties, sponsored research, patents (new applications), licenses executed
*Independent variables: importance of outcome to TTO, types of inventions, measure of size of university's licensing operation/potential
*Logs of all variables except indicator variables
*Probit for frequency of sponsored research
*INVDIS: number of disclosures
*TTOSIZE: number of licensing individuals
*TTOEVAL_1 = 1: if TTOs that said licenses/patents are "not very important"
*TTOEVAL_2 = 1: if TTOs that said licenses/patents are "moderately important"
*PROOF: % of licensed disclosures that were "proof of concept but no prototype"
*PTYPE: "Prototype available but only lab scale"
*MEDSCHL: 1 if med school exists
*QUAL: academic quality of faculty (1993 NRC's survey results of academic quality of Ph.D. granting departments)
*LICENSES: number of licenses executed
*SPONRES: amount of sponsored research
*SPONFREQ: frequency that sponsored research is tied to license (according to TTO)
*patent apps grow 1-to-1 with disclosures
*sponsored research grows with licenses executed
*more licenses executed at universities with large TTOs and med schools
*higher royalties with higher quality of faculty and higher fraction of licenses executed at later stages of development
*additional disclosures generate smaller % increases in licenses, which generate smaller % increases in royalties (TTOs generally effective at tapping pool of available technologies in their universities)

===Thursby, J., Fuller, Thursby, M.: US Faculty Patenting: Inside and Outside the University (2009)===

[http://www.nber.org/papers/w13256.pdf]

@article{thursby2009us,
title = "US Faculty Patenting: Inside and Outside the University",
author = "Jerry G. Thursby, Anne W. Fuller, and Marie C. Thursby",
journal={Research Policy},
volume={38},
number={1},
pages={14--25},
year={2009},
publisher={Elsevier},
abstract = {This paper examines the empirical anomaly that in a sample of 5811 patents on which US faculty are listed as inventors, 26% of the patents are assigned solely to firms rather than to the faculty member's university as is dictated by US university employment policies or the Bayh Dole Act. In this paper we estimate a series of probability models of assignment as a function of patent characteristics, university policy, and inventor fields in order to examine the extent to which outside assignment is nefarious or comes from legitimate activities, such as consulting. Patents assigned to firms (whether established or start-ups with inventor as principal) are less basic than those assigned to universities suggesting these patents result from faculty consulting. A higher inventor share increases the likelihood of university assignment as compared with assignment to a firm in which the inventor is a principal but it has no effect on consulting with established firms versus assignment to the university. Faculty in the physical sciences and engineering are more likely to assign their patents to established firms than those in biological sciences.},
filename={Thursby et al (2009) - US Faculty Patenting},
}

*only 62.4% of patents by university faculty members of 87 universities were assigned solely to universities
*identifying US university patents by institutional assignment misses significant percentage of faculty innovation in US universities
*higher inventor share increases likelihood of university assignment compared with assignment to a firm where inventor is principal
*possibilities: faculty in low share universities may be more willing to seek outside remuneration via assignment to start-up where they are principal; revenue shares may not affect startup activity but simply reduce number of inventions disclosed to university

Sources
*Faculty names from NRC
*Compared with inventor names in NBER Patent Database
*Excluded faculty who do not patent

Patent/Inventor Pairs
*MIT: 315
*Wisconsin: 232
*Stanford: 223
*UC San Diego: 216
*UC Berkeley: 207

Out of 5811 patents:
*1513 assigned solely to firms
*241 assigned to both firms and universities
*327 unassigned
*faculty are principals in assignee firms for 32.3% of patents assigned solely to firms and 24% of patents assigned to both (lower bound)

=== Valdivia: University Start-ups: Critical for Improving Technology Transfer (2013) ===

[https://www.brookings.edu/wp-content/uploads/2016/06/Valdivia_Tech-Transfer_v29_No-Embargo.pdf]

*Current emphasis on licensing patents, but most university TTOs do not generate enough to cover operating expenses
*Asymmetry in distribution of resources across the university system, only a few universities benefit from high licensing revenues
**top 8 universities took 50% of licensing income, top 16 universities took nearly 75% of income
**only 37 universities have been in the top 20 during the last decade (listed in University Patents > LicensingGrossIncome2003-2012.txt)
*Universities face much more pressure to demonstrate the economic impact of their R&D contracts
**97.6% of total public contracts obtained by universities are for basic/applied research
**2% of university research delivers ready-to-use technologies
**academic research is much more dependent on government funding than industry research is
*Government pressure for universities to be more responsive to market forces, more entrepreneurial, more attuned to needs of industry
*TTOs are costly to universities
*1979: 30 universities with TTO -> 1999: 174 universities (AUTM)
*2010: 206 US universities have very high or high research activity, all with TTOs (but not all report to AUTM) (Carnegie Classification of Higher Education)
*Over last 20 years, 87% of universities did not break even
*Clash between aims of university (non-profit) and TTO (essentially a business unit)
*By nurturing start-ups, TTOs can add most economic value to an invention disclosure
*2003: universities initiated 330 startups, 2012: 647 startups
*2012: 3715 operating university startups, almost double the number in 2000 (AUTM 2013)
*Startups mitigate financial risk by reducing reliance on blockbuster patents, increase diversification of portfolio
*Policy proposal: the government should increase funding for Small Business Technology Transfer Program (directed to university start-ups)
**H.R.2981: reapportions STTR funds at 0.05% in the next 2 years and 0.1% henceforth for university enterprises at proof of concept stage
*Should increase portion that agencies set aside for STTR
**STTR should have Phase III like SBIR to fund commercialization efforts
*Need equitable distribution across university system

=== The Bayh-Dole Act and High-Technology Entrepreneurship in U.S. Universities: Chicken, Egg, or Something Else? (2004) ===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.484.1612&rep=rep1&type=pdf]

==== Findings ====

* University research has an unusually significant impact on industrial innovation in the biomedical sector
* " This work also suggests that academic research rarely produces “prototypes” of inventions for development
and commercialization by industry—instead, academic research informs the methods and disciplines
employed by firms in their R&D facilities."
* The U.S. higher education system is much larger and more heterogenous than other developed countries - this encourages competition
* The passage of the Bayh-Dole Act was one part of a broader shift in U.S. policy toward stronger
intellectual property rights
* "Universities increased their share of patenting from less than 0.3% in 1963 to nearly 4% by 1999, but the rate of growth in this share begins to accelerate before rather than after 1980."
* "the Act's provisions expressed Congressional support for the negotiation of exclusive licenses between universities and industrial firms for the results of federally funded research"
* licensing revenues account for only a miniscule portion of universities' overall academic budgets
* the acceleration in growth of patenting and licensing began before the passage of the Bayh-Dole Act so this acceleration cannot be wholly attributed to the Act
* "the flow of knowledge and technology between university and industrial research is a two-way flow," despite previous characterization as wholly from academia to industry
* patents seem to be "especially important channels for technology transfer" in the biomedical sector

==== Data Sources ====
* 5 different case studies
1. Cotransformation: a process to transfer genes into mammalian cells (Columbia University).

2. Gallium Nitride: a semiconductor with both military and commercial applications (University of California).

3. Xalatan: a glaucoma treatment (Columbia University).

4. Ames II Tests: a bacteria assay for testing potential carcinogenic properties of pharmaceuticals and cosmetics (University of California).

5. Soluble CD4: a prototype for a drug to fight AIDS (Columbia University).

==== Critiques ====
* focuses on case studies as source of data

===Dornbusch, Schmoch, Schulze, Bethke: Identification of University-Based Patents: A New Large-Scale Approach (2012)===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.1368&rep=rep1&type=pdf]

Specific for German case, but certain points can help us

European convergence to US model (Bayh-Dole seen as main driver behind growing patent portfolios of US universities)

*University-owned patents (assigned to universities or their TTOs) and university-invented patents (assigned to university-affiliated authors)

Matching lists: traditional matching of lists of university staff/professors with inventor data
*time-consuming, costly, possibly not updated
*typically limited to tenured professors
*does not include Ph.D. students, assistants, lecturers

Matched authors of scientific publications and inventors on patents
*beware of homonyms

Patent data from EPO Worldwide Patent Statistical Database (PATSTAT)

Publication data from Elsevier (Scopus)

Country of origin: Inventor Country/Assignee Country/Applicant Country = Germany, Location of Organization to which author is affiliated =Germany
*Restrict dataset to authors from German organizations and inventors with residence in Germany

The organization

Names: To keep precision high, leave out names with initials only

Location: Postal/zip codes (PATSTAT provides address of inventor's residence, SCOPUS provides info for organization, 96.5% of first digit of inventor and organization postal codes are the same, 85.9% first two digits)

Time window: 2-year window between application and publication date

=== Singh A. and Wong P.K: University patenting activities and their link to the quantity and quality of scientific publications (2009) ===

[https://pdfs.semanticscholar.org/bb3a/df09ca693cdcff6c1f6aaf18113cabec2655.pdf]
==== Findings ====
* patenting by 281 leading world universities has consistently grown faster than general American patenting from 1977 - 2000
* North American university patenting growth has slowed relative to universities outside North America since the mid-1990s
* Between 2003-2005, they found that university patenting output has significant correlation with the both the quality and quantity of scientific publishing in North America
* In European and Australian universities, patenting correlated only with the quantity of scientific publishing, not with the quality
* In universities Europe, Australia, and North America, patenting correlated only with the quality of scientific publishing

==== Data Sources ====
* USPTO Patenting Data
* Shanghai Jiao Tong University's Academic Ranking of World Universities (ARWU)
* Times Higher Education Supplement's World University Ranking (WUR)
* Quantity was measured by counts of publications
* Quality was measured by citations to said publications
* the relationship between research and patenting was evaluated in two ways
** At the institutional level: patents assigned to universities
** At the individual level: patents with university researchers as the inventors

==== Critiques ====
* citations are not really a perfect measure of research quality and citations have little to do with practical use of the study (i.e. how much technological innovation is generated as a result of academic research publications)

==== Other Discoveries ====

* Study by Landry R., Amara N., and Saihi, M. (2006)
* (Owen Smith and Powell 2003) found that "organizations involved in technological commercialization tend to have higher publication rates than those who are not"
* (Lach and Schankerman 2003) found that "licensing revenues at the university level are positively influenced by publication citations per faculty"

University Patents

2018-04-05T19:15:58Z

Dayton: /* Carnegie Classifications */

{{McNair Projects
|Has title=University Patents
|Has owner=Julia Wang, Meghana Pannala, Anne Dayton
|Has keywords=Patent
|Has project status=Active
}}

==Research Paper==
Since university system file for patents out of a central office, its mot possible to determine where the research was conducted. This presents problems when ranking universities by patent production.

In order to publish this paper as a Baker Institute Research Report, we will need to do more to resolve this issue beyond weighting for the University of California.

==Carnegie Classifications==
We propose using public data from the Carnegie Classifications of Institutes of Higher Education to resolve the university systems issue.

TASKS:
1) Separate schools affiliated with universities/university systems in our rankings from all others included in Carnegie data.

2)Group these by system.
Determine the number of schools in each system
3) Determine their classification.
1 if Doctoral 0 if not
1 if 4-year medical school 0 if not
1 if 4-year engineering 0 if not
4) Determine the total count of the categories in number 3 for each university system.
5) Use total count to weight regressions
6) Create ranking tables that rank university systems and universities by dividing number of patents by number of doctoral, med-school, 4-year engineering.

==Project Overview==
Goal: list of all universities and # of patents associated with each university and patent licensing activity

patent reassignment to startups associated with these universities

clinical trials (from Catherine) data to rank universities R&D engagement

identify list of universities: board of regents, universities in patent data (find patterns associated with university assignees) -- @Julia: Where did we get this list? I'd love to be able filter by country - Meghana

AUTM?

==Timeline to Deliverable & Documentation==

Meghana: focus on grants

Julia: focus on patent counts

Working in E:McNair/University Patents

'''3/20-3/24'''

Learn SQL, clean data

'''3/27-3/31'''

Counts of data, name matching

Patent Counts
*Ran the matcher in the server, only matched ~40,000 entries
*Working with Jeemin to develop matcher

'''4/3-4/7'''

Counts of data, name matching

Patent Counts
*Jeemin is a savior, matched and counted 128,000 entries (Jeemin_matcher_matched.txt), manually went through the 6,000 unmatched (Jeemin_matcher_unmatched.txt), Check [[University Patent Matching]]
*Ranking for all time is in Patent Counting ( >Total (All Time) )
*Need to talk with Meghana about grant data
*Need to find variables for regression, develop ranking for last decade and last year
*Also need to account for school size (faculty? students? research funding?)

'''4/10-4/14'''

Develop ranking

Patent Counts
*Finish all of above
*Start drafting
*Load data onto database -> E:McNair/University Patents/unidatajoin.sql in univ (database)

'''4/17-4/21'''

*Combined Patent Counts, R&D Expenditure, NSF Grant Data
*STATA regressions in E:McNair/University Patents/STATA
**for historical data

'''4/24'''

From Ed:
*Copied inventors table out of PatentDB database on RDP
*Script in E:McNair/PatentData, MoveInventors.sql
*Copy of data in Z:AllPatents and ran import into patent (database)
For Marcela:
*Need table of all patents associated with matches, join patent numbers with inventors
*Group by last name and first initial (=1 inventor)

'''5/1'''

*Joined patent numbers with inventors -> unipatentinventorcount.sql
**Z:univ/InventorCounts.txt
*Decade patent counts -> Z:univ/DecadePatentCounts
*Calculated Hirschman-Herfindahl index -> E:McNair/University Patents/Inventor Counts and Superstars

'''5/5'''

Artifacts:
#10-Year Ranking All
#10-Year Ranking Private
#10-Year Ranking Public
#Top 10 Movers and Shakers
#Graph of Concentration of Innovation
#Number of University Patents Over Time
#Inventor Concentration
#Regression Table

*Created Graph of Concentration of Innovation -> E:McNair/University Patents/Inventor Counts and Superstars -> GraphUPS
*Created Top Inventors Chart -> University Patents/Report Articles/Inventor Rankings

'''5/9'''

*Figuring out what's up with the university puller
*Developing new 10-Year Rankings
*Need to update above two articles
*Need to load into STATA and re-run regressions -> University Patents/STATA/univdata.do

'''5/10'''

*Figured out what was wrong with the data pull and now everything's good!
*Updated information on the draft below
*Fantastic Artifacts and Where to Find Them
**10-Year Ranking All
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Public
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Private
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**Top 10 Movers & Shakers (% change between first 5 years (2006-2010) vs next 5 years (2011-2015) within top 100
**Graph of Concentration of Innovation (numbers of patents)
***Pie graph (University Patents > Report Artifacts > Concentration of Innovation)
***(Excel sheet where this came from is University Patents > Inventor Counts and Concentrations)
**Trends Over Time
***Total vs. top 10
***Graph (University Patents > MoversandTop10)
**Inventor Concentration
***Chart (University Patents > Report Artifacts > Inventor Rankings)
**Reg Table
***+Explanation of variables
***(STATA do-file with the most statistically significant regressions is in University Patents > STATA > univdata.do)

Issue brief draft (VERY rough): https://docs.google.com/a/rice.edu/document/d/1MayXQEQ_pM0LMeiV39iaNFjtMZYSB-Ah9gCx1rcgW9M/edit?usp=sharing

==Ranking Development Notes==
Sources of University Funding R&D [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% comes from the Federal Government
*Less than 10% comes from state and local governments
*Roughly 20% is from the University itself (endowment?? tuition? not 100% sure)
*Roughly 5% is from private industry
*Less than 10% Other

Federal Government Funding Breakdown [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% of federal funding is from the NIH
*Roughly 15% is from the NSF
*Roughly 11% from Department of Defense
*Roughly 4% from NASA
*Roughly 4% from the Department of Energy
*Roughly 3% from the USDA
*Roughly 1% from the EPA (probably will go to zero with new admin)
*Roughly 1% from the Department of Education
*Marginal amounts from everywhere else (<1%)

NOTES
*overall ranking (total number of patents)
*weight by faculty (people)
*weight by research funding
*weight by endowment (maybe)
*public vs private
*individuals: surname and first initial
**ranking by inventors - who are the superstar inventors and where are they (define superstar inventor - idk 20 patents and rank universities by how many superstars are present
**Herfindahl-Hirschmann Index

==Key Words==

===Universities (Patent Assignees)===
BOARD OF REGENTS - pretty much exclusively describes universities

UNIVERSITY - also exclusively describes universities
*Can we do a close match with University? (its the only word on this list that's frequently misspelled)

SCHOOL - often used in combination with medicine or medical to describe medical schools
*also are used to describe actual schools (i.e. HISD) and other businesses
*sometimes present in addresses - can we cut off the adresses?
*also used in combination with Business (i.e. Harvard Business School)
*use school only in conjuction with medical, medicine, and business

COLLEGE
*need to cut off addresses for this to work
*need to ensure that we include the space after
*excl. College Boulevard, college blvd, etc.
*double check everything with College Park

INSTITUTE OF TECHNOLOGY - almost exclusively describes universities but sometimes describes external research institutes

POLYTECHNIC - exclusively university (i.e. RPI, Virginia Tech)

RESEARCH FOUNDATION: VAST majority are university
*Exclusions: it's really difficult to distinguish between university affiliated and not, besides just looking them up on the google
**Novartis
**Progeria
**Washington Research Foundation
**Blood Center of Wisconsins
**Mental Hygiene
**Celiac Sprue
**Fidia
**Samuel Waxman Cancer Center
***founded by a faculty member at Mt. Sinai Medical School,not affil w/a single university but researchers are also usually faculty at various universities
**lifenet
**HealthPartners
**Dr. Susan Love
**La Jolla Cancer Research Foundation
**Children's Hospital (? grants degrees but is a hospital system)
**Medforte
**International Mask
**Palo Alto Medical Foundation

====Exclusions====
*LLC
*LLP

===Technology Transfer Offices===

====Job Titles====
*Patent Portfolio Manager
*Intellectual Property Manager
*Licensing Associate/Licensing Liaison
*Biological Materials Specialist
*Industrial Contracts Officer
*Technology Licensing Officer
*Associate Officer
*Technology Licensing Associates
*Patent Coordinator/Patent Administrator
*IP Portfolio Specialist
*MTA Coordinator
*Sponsored Research Administrator
*Technology Transfer Specialist/Technology Transfer Associate
*Contracts Associate
*Portfolio Director
*Contracts Specialist
*Industry Contracts Analyst
*Patent Prosecution Analyst
*(Associate) Director of Intellectual Property
*(Associate) Director of Technology Transfer Policy
*(Associate) Director of Technology Transactions
*(Associate) Director of Agreement Administration
*Senior Associate - watch
*Patent Attorney/Patent Agent/Patent Counsel/Paralegal - watch

====TTO Names====
*Office of Technology Transfer/Technology Transfer Office
*Office of Technology Licensing/Technology Licensing Office
*Office of Technology Development
*Technology Ventures
*Innovation Services
*Intellectual Property & Industry Research Alliances
*InnovationAccess
*Invention Transfer Group
*Technology Development Group
*Office of Business Development
*Office of Technology Commercialization
*Office of Innovation and Commercialization
*Innovation, Technology & Alliances
*Technology & Industry Alliances
*Office for Management of Intellectual Property
*Innovation & Partnerships Office

===University-Affiliated Startups (Patent Assignees)===

==What to get from other people==

Avesh has clinical trial data on wiki and bulk drive (FDA Trials Data), need to build it into normal form, use clinical trials data to rank R&D engagement of universities, will be building up portfolios of different types of companies

Marcela is cleaning up patent data, has given patent assignee names -- got it! Could we filter by country and get strictly American names?

Catherine has zip codes of medical centers, use to look through patent data

==Questions==
*How innovative are universities compared to publicly-traded firms, etc.? (firms: 100 active patents at any given time)
*How do universities license?
*What does the average portfolio look like for universities? (compared to publicly traded, VC-backed, etc)
*What can explain the differences in rankings? (size, quality of universities, TTOs and quality/experience of workers - searched LinkedIns, geography, entrepreneurship programs, NIH/NSF grants)

==To Do==

*Create ranking
**Based on patent portfolio
**Licensing income
**Quality of TTO
**Publications (specifically based on research)?
**Amount received in grants/Amount of grants - we have numbers on amount of NIH grants and NSF grants
***NIH/NSF/STTR
**Control for school size/endowment
**Startups affiliated with university
***Value generated from these startups? (ask Avesh)
**Existence of entrepreneurship program
**Geography

*Describe average patent portfolio of university
**Number of patents
**Maybe categorize by research area?
*Describe how universities license
**USPTO earned revenue

Deliverable
*Ranking
*Explanation of what makes a good ranking/what factors a school needs to be more successful

Artifacts
*Grant Data
*Research Funding
*Star Scientists
*Regression with variables from Carnegie Classification (10-15 variables)
*Licensing (?)

===What We Have===

*Lit Review
*List of all post-secondary institutions (University Accreditation 3.2016_trim) - U.S. Department of Education
*Classification of universities (Carnegie Classification 2015) - Carnegie Classification
** includes all accredited degree-granting colleges and universities that are included in the National Center for Education Statistics Integrated Postsecondary Education Data System (IPEDS).
** Coverage: 4666 institutions of higher learning
** Variables: includes 94 variables
*** Enrollment data - subdivided into graduate and undergraduate
*** degrees conferred - divided into level and division
*** information on faculty (number, divided into levels i.e. assistant, assoc. and full time)
**** coverage for this variable is REALLY LOW
*** non-faculty research staff (includes post docs)
*** Science and Engineering Research and Development expenditures
*** Non-STEM research and Development expenditures
*Amount spent on R&D (Higher Ed R&D Rankings) - National Science Foundation
** includes just total R&D expenditures
*STTR full data 1995-2015 (STTRData) - Small Business Administration
*University Patent Numbers 1969-2012 - USPTO
*Patents that resulted from NIH grants
*Information about NSF grants

===What We Need===

*List of university-affiliated startups
**looks like AUTM STATT data has this because it was used in a study by Yael Hochberg [https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2418000]
**[https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-research-universities-2015/#3cb9829b3823| Rankings of Research Universities (2015)]
**FORBES ranked the country’s most entrepreneurial schools based on the numbers of alumni and students who have identified themselves as founders and business owners on LinkedIn (adjusted to total student body size). This year we rank both research universities and [https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-colleges-2015/#2b2909e04604| smaller colleges] separately.
*data about TTOs (# and quality of employees)
**LinkedIn crawler: working with Jeemin and Peter
*licensing data from AUTM [http://www.autm.net/statt] or already within the patent data?

==Name Matching==
*Jeemin has found all the correct and incorrect spellings of assignees with "University"
*Julia working on "Institute"
*We have a list of Universities that have patents from NIH grants, which could be a starting point for our ranking constituents

==Lit Reviews==

===Thursby, J. & Thursby, M.: Who Is Selling the Ivory Tower? Sources of Growth in University Licensing (2002)===

[http://pubsonline.informs.org/doi/pdf/10.1287/mnsc.48.1.90.14271]

@article{thursby2002who,
title={Who Is Selling the Ivory Tower? Sources of Growth in University Licensing},
author={Thursby, Jerry G. and Thursby, Marie C.},
journal={Management Science},
volume={48},
number={1},
pages={90--104},
year={2002},
publisher={INFORMS},
filename={Thursby Thursby (2002) - Who Is Selling the Ivory Tower}
}

*Reliance of industry on university inventions has increased
**AUTM surveys show 7.1% growth in yearly inventions disclosure from 1994-1998 for 64 universities that responded every year
*Primary reason for more disclosures may be increased propensity for faculty to disclose, rather than change in research focus
*Universities becoming more receptive to industry contracts
*Negative total TFP growth of licenses executed (-1.7% annual growth) - growth in disclosures and patent applications greater than the corresponding growth in licenses executed.
**Marginal university innovation offered to the market has declined in commercial appeal
**Universities are delving more deeply into the available pool of innovations to increase commercial activities
*No evidence on the importance of learning by doing on the part of TTOs except to note negative association between TTO growth and TFP growth in licensing
**Suggests at least the possibility of learning by doing effects

'''Other literature'''

*On the role of patents and publications in the transfer process: Adams 1990, Henderson et al. 1998, and Jaffe et al. 1993
*On consulting, sponsored research or institutional ties: Cohen et al. 1998; Mansfield 1995; Zucker et al. 1994, 1998
*On the nature of university licensing: Jensen and Thursby 2001, Mowery et al. 2001a,b, Mowery et al. 2001, Siegel et al. 1999, Thursby et al. 2001, Thursby and Kemp 2001

===Thursby, J., Jensen, Thursby, M.: Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities (2001)===

[https://pdfs.semanticscholar.org/637f/048c05ee83eb84a914f69e087f52acea1b6c.pdf]

@article{thursby2001objectives,
title={Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities},
author={Thursby, Jerry G., Jensen, Richard, and Thursby, Marie C.},
journal={The Journal of Technology Transfer},
volume={26},
number={1},
pages={59--72},
year={2001},
publisher={Springer},
abstract={This paper describes results of our survey of licensing at 62 research universities. We consider ownership, income splits, stage of development, marketing, license policies and characteristics, goals of licensing and the role of the inventor in licensing. Based on these results we analyze the relationship between licensing outcomes and both the objectives of the TTO and the characteristics of the technologies. Patent applications grow one-to-one with disclosures, while sponsored research grows similarly with licenses executed. Royalties are typically larger the higher the quality of the faculty and the higher the fraction of licenses that are executed at latter stages of development. Sponsored research is more likely to be included in a license if the new technology is at an early stage of development or if the TTO evaluates it as important. We find that additional disclosures generate smaller percentage increases in licenses, and those increases in licenses generate smaller percentage increases in royalties.},
filename={Thursby et al (2001) - Objectives, Characteristics and Outcomes of University Licensing}
}

*University licensing has increased dramatically post-Bayh-Dole (1980)
**According to AUTM 1996, licenses executed increased 75% from 1991-1996, (total: 13,087)
*Survey of TTOs of 62 major US universities
**Majority of universities retain titles to inventions
**All universities split income with inventors
**Royalties generate most of the revenue of licensing

*Open question: Is the increased propensity of faculty to disclose a response to financial incentives or an increase in the effectiveness of TTOs in inducing disclosure?

Survey
*62/135 universities responded
*63% public, and 62% of public universities that responded were land-grant
*37% private
*average industry sponsored research $16.9 mil, federally sponsored $149.6 mil (1996)
*average TTO: 26.3 licenses executed, 92.3 invention disclosures, 30.1 new patent apps, $4.2 mil income (1996)
*35% of respondents had reorganized TTO since 1990
*90% of universities allow faculty to establish and operate businesses based on technology owned by university but developed in faculty's research
*Inventions disclosed: 33% med schools, 29% engineering, 22% science, 6% agriculture, 10% other
*Majority of invention disclosures in nascent stage (proof of concept - 45% or prototype - 37%)
*Patents often applied for after knowing commercial viability, licensed technologies often not protected by patents
*60% of universities said small companies more likely to take early stage technologies and large companies more likely to take late stage - small firms may have advantage in "innovative" research (Holmstrom 1989)
*TTOs obtain smaller upfront fees the more uncertain the technology being licensed is
*Universities usually do not take equity in the license

Regression of licensing outcomes

[[File:LicensingOutcomeReg.png|500px]]

*Dependent variables: royalties, sponsored research, patents (new applications), licenses executed
*Independent variables: importance of outcome to TTO, types of inventions, measure of size of university's licensing operation/potential
*Logs of all variables except indicator variables
*Probit for frequency of sponsored research
*INVDIS: number of disclosures
*TTOSIZE: number of licensing individuals
*TTOEVAL_1 = 1: if TTOs that said licenses/patents are "not very important"
*TTOEVAL_2 = 1: if TTOs that said licenses/patents are "moderately important"
*PROOF: % of licensed disclosures that were "proof of concept but no prototype"
*PTYPE: "Prototype available but only lab scale"
*MEDSCHL: 1 if med school exists
*QUAL: academic quality of faculty (1993 NRC's survey results of academic quality of Ph.D. granting departments)
*LICENSES: number of licenses executed
*SPONRES: amount of sponsored research
*SPONFREQ: frequency that sponsored research is tied to license (according to TTO)
*patent apps grow 1-to-1 with disclosures
*sponsored research grows with licenses executed
*more licenses executed at universities with large TTOs and med schools
*higher royalties with higher quality of faculty and higher fraction of licenses executed at later stages of development
*additional disclosures generate smaller % increases in licenses, which generate smaller % increases in royalties (TTOs generally effective at tapping pool of available technologies in their universities)

===Thursby, J., Fuller, Thursby, M.: US Faculty Patenting: Inside and Outside the University (2009)===

[http://www.nber.org/papers/w13256.pdf]

@article{thursby2009us,
title = "US Faculty Patenting: Inside and Outside the University",
author = "Jerry G. Thursby, Anne W. Fuller, and Marie C. Thursby",
journal={Research Policy},
volume={38},
number={1},
pages={14--25},
year={2009},
publisher={Elsevier},
abstract = {This paper examines the empirical anomaly that in a sample of 5811 patents on which US faculty are listed as inventors, 26% of the patents are assigned solely to firms rather than to the faculty member's university as is dictated by US university employment policies or the Bayh Dole Act. In this paper we estimate a series of probability models of assignment as a function of patent characteristics, university policy, and inventor fields in order to examine the extent to which outside assignment is nefarious or comes from legitimate activities, such as consulting. Patents assigned to firms (whether established or start-ups with inventor as principal) are less basic than those assigned to universities suggesting these patents result from faculty consulting. A higher inventor share increases the likelihood of university assignment as compared with assignment to a firm in which the inventor is a principal but it has no effect on consulting with established firms versus assignment to the university. Faculty in the physical sciences and engineering are more likely to assign their patents to established firms than those in biological sciences.},
filename={Thursby et al (2009) - US Faculty Patenting},
}

*only 62.4% of patents by university faculty members of 87 universities were assigned solely to universities
*identifying US university patents by institutional assignment misses significant percentage of faculty innovation in US universities
*higher inventor share increases likelihood of university assignment compared with assignment to a firm where inventor is principal
*possibilities: faculty in low share universities may be more willing to seek outside remuneration via assignment to start-up where they are principal; revenue shares may not affect startup activity but simply reduce number of inventions disclosed to university

Sources
*Faculty names from NRC
*Compared with inventor names in NBER Patent Database
*Excluded faculty who do not patent

Patent/Inventor Pairs
*MIT: 315
*Wisconsin: 232
*Stanford: 223
*UC San Diego: 216
*UC Berkeley: 207

Out of 5811 patents:
*1513 assigned solely to firms
*241 assigned to both firms and universities
*327 unassigned
*faculty are principals in assignee firms for 32.3% of patents assigned solely to firms and 24% of patents assigned to both (lower bound)

=== Valdivia: University Start-ups: Critical for Improving Technology Transfer (2013) ===

[https://www.brookings.edu/wp-content/uploads/2016/06/Valdivia_Tech-Transfer_v29_No-Embargo.pdf]

*Current emphasis on licensing patents, but most university TTOs do not generate enough to cover operating expenses
*Asymmetry in distribution of resources across the university system, only a few universities benefit from high licensing revenues
**top 8 universities took 50% of licensing income, top 16 universities took nearly 75% of income
**only 37 universities have been in the top 20 during the last decade (listed in University Patents > LicensingGrossIncome2003-2012.txt)
*Universities face much more pressure to demonstrate the economic impact of their R&D contracts
**97.6% of total public contracts obtained by universities are for basic/applied research
**2% of university research delivers ready-to-use technologies
**academic research is much more dependent on government funding than industry research is
*Government pressure for universities to be more responsive to market forces, more entrepreneurial, more attuned to needs of industry
*TTOs are costly to universities
*1979: 30 universities with TTO -> 1999: 174 universities (AUTM)
*2010: 206 US universities have very high or high research activity, all with TTOs (but not all report to AUTM) (Carnegie Classification of Higher Education)
*Over last 20 years, 87% of universities did not break even
*Clash between aims of university (non-profit) and TTO (essentially a business unit)
*By nurturing start-ups, TTOs can add most economic value to an invention disclosure
*2003: universities initiated 330 startups, 2012: 647 startups
*2012: 3715 operating university startups, almost double the number in 2000 (AUTM 2013)
*Startups mitigate financial risk by reducing reliance on blockbuster patents, increase diversification of portfolio
*Policy proposal: the government should increase funding for Small Business Technology Transfer Program (directed to university start-ups)
**H.R.2981: reapportions STTR funds at 0.05% in the next 2 years and 0.1% henceforth for university enterprises at proof of concept stage
*Should increase portion that agencies set aside for STTR
**STTR should have Phase III like SBIR to fund commercialization efforts
*Need equitable distribution across university system

=== The Bayh-Dole Act and High-Technology Entrepreneurship in U.S. Universities: Chicken, Egg, or Something Else? (2004) ===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.484.1612&rep=rep1&type=pdf]

==== Findings ====

* University research has an unusually significant impact on industrial innovation in the biomedical sector
* " This work also suggests that academic research rarely produces “prototypes” of inventions for development
and commercialization by industry—instead, academic research informs the methods and disciplines
employed by firms in their R&D facilities."
* The U.S. higher education system is much larger and more heterogenous than other developed countries - this encourages competition
* The passage of the Bayh-Dole Act was one part of a broader shift in U.S. policy toward stronger
intellectual property rights
* "Universities increased their share of patenting from less than 0.3% in 1963 to nearly 4% by 1999, but the rate of growth in this share begins to accelerate before rather than after 1980."
* "the Act's provisions expressed Congressional support for the negotiation of exclusive licenses between universities and industrial firms for the results of federally funded research"
* licensing revenues account for only a miniscule portion of universities' overall academic budgets
* the acceleration in growth of patenting and licensing began before the passage of the Bayh-Dole Act so this acceleration cannot be wholly attributed to the Act
* "the flow of knowledge and technology between university and industrial research is a two-way flow," despite previous characterization as wholly from academia to industry
* patents seem to be "especially important channels for technology transfer" in the biomedical sector

==== Data Sources ====
* 5 different case studies
1. Cotransformation: a process to transfer genes into mammalian cells (Columbia University).

2. Gallium Nitride: a semiconductor with both military and commercial applications (University of California).

3. Xalatan: a glaucoma treatment (Columbia University).

4. Ames II Tests: a bacteria assay for testing potential carcinogenic properties of pharmaceuticals and cosmetics (University of California).

5. Soluble CD4: a prototype for a drug to fight AIDS (Columbia University).

==== Critiques ====
* focuses on case studies as source of data

===Dornbusch, Schmoch, Schulze, Bethke: Identification of University-Based Patents: A New Large-Scale Approach (2012)===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.1368&rep=rep1&type=pdf]

Specific for German case, but certain points can help us

European convergence to US model (Bayh-Dole seen as main driver behind growing patent portfolios of US universities)

*University-owned patents (assigned to universities or their TTOs) and university-invented patents (assigned to university-affiliated authors)

Matching lists: traditional matching of lists of university staff/professors with inventor data
*time-consuming, costly, possibly not updated
*typically limited to tenured professors
*does not include Ph.D. students, assistants, lecturers

Matched authors of scientific publications and inventors on patents
*beware of homonyms

Patent data from EPO Worldwide Patent Statistical Database (PATSTAT)

Publication data from Elsevier (Scopus)

Country of origin: Inventor Country/Assignee Country/Applicant Country = Germany, Location of Organization to which author is affiliated =Germany
*Restrict dataset to authors from German organizations and inventors with residence in Germany

The organization

Names: To keep precision high, leave out names with initials only

Location: Postal/zip codes (PATSTAT provides address of inventor's residence, SCOPUS provides info for organization, 96.5% of first digit of inventor and organization postal codes are the same, 85.9% first two digits)

Time window: 2-year window between application and publication date

=== Singh A. and Wong P.K: University patenting activities and their link to the quantity and quality of scientific publications (2009) ===

[https://pdfs.semanticscholar.org/bb3a/df09ca693cdcff6c1f6aaf18113cabec2655.pdf]
==== Findings ====
* patenting by 281 leading world universities has consistently grown faster than general American patenting from 1977 - 2000
* North American university patenting growth has slowed relative to universities outside North America since the mid-1990s
* Between 2003-2005, they found that university patenting output has significant correlation with the both the quality and quantity of scientific publishing in North America
* In European and Australian universities, patenting correlated only with the quantity of scientific publishing, not with the quality
* In universities Europe, Australia, and North America, patenting correlated only with the quality of scientific publishing

==== Data Sources ====
* USPTO Patenting Data
* Shanghai Jiao Tong University's Academic Ranking of World Universities (ARWU)
* Times Higher Education Supplement's World University Ranking (WUR)
* Quantity was measured by counts of publications
* Quality was measured by citations to said publications
* the relationship between research and patenting was evaluated in two ways
** At the institutional level: patents assigned to universities
** At the individual level: patents with university researchers as the inventors

==== Critiques ====
* citations are not really a perfect measure of research quality and citations have little to do with practical use of the study (i.e. how much technological innovation is generated as a result of academic research publications)

==== Other Discoveries ====

* Study by Landry R., Amara N., and Saihi, M. (2006)
* (Owen Smith and Powell 2003) found that "organizations involved in technological commercialization tend to have higher publication rates than those who are not"
* (Lach and Schankerman 2003) found that "licensing revenues at the university level are positively influenced by publication citations per faculty"

University Patents

2018-04-05T19:15:24Z

Dayton: /* Research Paper= */

{{McNair Projects
|Has title=University Patents
|Has owner=Julia Wang, Meghana Pannala, Anne Dayton
|Has keywords=Patent
|Has project status=Active
}}

==Research Paper==
Since university system file for patents out of a central office, its mot possible to determine where the research was conducted. This presents problems when ranking universities by patent production.

In order to publish this paper as a Baker Institute Research Report, we will need to do more to resolve this issue beyond weighting for the University of California.

==Carnegie Classifications==
We propose using public data from the Carnegie Classifications of Institutes of Higher Education to resolve the university systems issue.

TASKS:
1) Separate schools affiliated with universities/university systems in our rankings from all others included in Carnegie data.
2)Group these by system.
Determine the number of schools in each system
3) Determine their classification.
1 if Doctoral 0 if not
1 if 4-year medical school 0 if not
1 if 4-year engineering 0 if not
4) Determine the total count of the categories in number 3 for each university system.
5) Use total count to weight regressions
6) Create ranking tables that rank university systems and universities by dividing number of patents by number of doctoral, med-school, 4-year engineering.

==Project Overview==
Goal: list of all universities and # of patents associated with each university and patent licensing activity

patent reassignment to startups associated with these universities

clinical trials (from Catherine) data to rank universities R&D engagement

identify list of universities: board of regents, universities in patent data (find patterns associated with university assignees) -- @Julia: Where did we get this list? I'd love to be able filter by country - Meghana

AUTM?

==Timeline to Deliverable & Documentation==

Meghana: focus on grants

Julia: focus on patent counts

Working in E:McNair/University Patents

'''3/20-3/24'''

Learn SQL, clean data

'''3/27-3/31'''

Counts of data, name matching

Patent Counts
*Ran the matcher in the server, only matched ~40,000 entries
*Working with Jeemin to develop matcher

'''4/3-4/7'''

Counts of data, name matching

Patent Counts
*Jeemin is a savior, matched and counted 128,000 entries (Jeemin_matcher_matched.txt), manually went through the 6,000 unmatched (Jeemin_matcher_unmatched.txt), Check [[University Patent Matching]]
*Ranking for all time is in Patent Counting ( >Total (All Time) )
*Need to talk with Meghana about grant data
*Need to find variables for regression, develop ranking for last decade and last year
*Also need to account for school size (faculty? students? research funding?)

'''4/10-4/14'''

Develop ranking

Patent Counts
*Finish all of above
*Start drafting
*Load data onto database -> E:McNair/University Patents/unidatajoin.sql in univ (database)

'''4/17-4/21'''

*Combined Patent Counts, R&D Expenditure, NSF Grant Data
*STATA regressions in E:McNair/University Patents/STATA
**for historical data

'''4/24'''

From Ed:
*Copied inventors table out of PatentDB database on RDP
*Script in E:McNair/PatentData, MoveInventors.sql
*Copy of data in Z:AllPatents and ran import into patent (database)
For Marcela:
*Need table of all patents associated with matches, join patent numbers with inventors
*Group by last name and first initial (=1 inventor)

'''5/1'''

*Joined patent numbers with inventors -> unipatentinventorcount.sql
**Z:univ/InventorCounts.txt
*Decade patent counts -> Z:univ/DecadePatentCounts
*Calculated Hirschman-Herfindahl index -> E:McNair/University Patents/Inventor Counts and Superstars

'''5/5'''

Artifacts:
#10-Year Ranking All
#10-Year Ranking Private
#10-Year Ranking Public
#Top 10 Movers and Shakers
#Graph of Concentration of Innovation
#Number of University Patents Over Time
#Inventor Concentration
#Regression Table

*Created Graph of Concentration of Innovation -> E:McNair/University Patents/Inventor Counts and Superstars -> GraphUPS
*Created Top Inventors Chart -> University Patents/Report Articles/Inventor Rankings

'''5/9'''

*Figuring out what's up with the university puller
*Developing new 10-Year Rankings
*Need to update above two articles
*Need to load into STATA and re-run regressions -> University Patents/STATA/univdata.do

'''5/10'''

*Figured out what was wrong with the data pull and now everything's good!
*Updated information on the draft below
*Fantastic Artifacts and Where to Find Them
**10-Year Ranking All
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Public
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Private
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**Top 10 Movers & Shakers (% change between first 5 years (2006-2010) vs next 5 years (2011-2015) within top 100
**Graph of Concentration of Innovation (numbers of patents)
***Pie graph (University Patents > Report Artifacts > Concentration of Innovation)
***(Excel sheet where this came from is University Patents > Inventor Counts and Concentrations)
**Trends Over Time
***Total vs. top 10
***Graph (University Patents > MoversandTop10)
**Inventor Concentration
***Chart (University Patents > Report Artifacts > Inventor Rankings)
**Reg Table
***+Explanation of variables
***(STATA do-file with the most statistically significant regressions is in University Patents > STATA > univdata.do)

Issue brief draft (VERY rough): https://docs.google.com/a/rice.edu/document/d/1MayXQEQ_pM0LMeiV39iaNFjtMZYSB-Ah9gCx1rcgW9M/edit?usp=sharing

==Ranking Development Notes==
Sources of University Funding R&D [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% comes from the Federal Government
*Less than 10% comes from state and local governments
*Roughly 20% is from the University itself (endowment?? tuition? not 100% sure)
*Roughly 5% is from private industry
*Less than 10% Other

Federal Government Funding Breakdown [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% of federal funding is from the NIH
*Roughly 15% is from the NSF
*Roughly 11% from Department of Defense
*Roughly 4% from NASA
*Roughly 4% from the Department of Energy
*Roughly 3% from the USDA
*Roughly 1% from the EPA (probably will go to zero with new admin)
*Roughly 1% from the Department of Education
*Marginal amounts from everywhere else (<1%)

NOTES
*overall ranking (total number of patents)
*weight by faculty (people)
*weight by research funding
*weight by endowment (maybe)
*public vs private
*individuals: surname and first initial
**ranking by inventors - who are the superstar inventors and where are they (define superstar inventor - idk 20 patents and rank universities by how many superstars are present
**Herfindahl-Hirschmann Index

==Key Words==

===Universities (Patent Assignees)===
BOARD OF REGENTS - pretty much exclusively describes universities

UNIVERSITY - also exclusively describes universities
*Can we do a close match with University? (its the only word on this list that's frequently misspelled)

SCHOOL - often used in combination with medicine or medical to describe medical schools
*also are used to describe actual schools (i.e. HISD) and other businesses
*sometimes present in addresses - can we cut off the adresses?
*also used in combination with Business (i.e. Harvard Business School)
*use school only in conjuction with medical, medicine, and business

COLLEGE
*need to cut off addresses for this to work
*need to ensure that we include the space after
*excl. College Boulevard, college blvd, etc.
*double check everything with College Park

INSTITUTE OF TECHNOLOGY - almost exclusively describes universities but sometimes describes external research institutes

POLYTECHNIC - exclusively university (i.e. RPI, Virginia Tech)

RESEARCH FOUNDATION: VAST majority are university
*Exclusions: it's really difficult to distinguish between university affiliated and not, besides just looking them up on the google
**Novartis
**Progeria
**Washington Research Foundation
**Blood Center of Wisconsins
**Mental Hygiene
**Celiac Sprue
**Fidia
**Samuel Waxman Cancer Center
***founded by a faculty member at Mt. Sinai Medical School,not affil w/a single university but researchers are also usually faculty at various universities
**lifenet
**HealthPartners
**Dr. Susan Love
**La Jolla Cancer Research Foundation
**Children's Hospital (? grants degrees but is a hospital system)
**Medforte
**International Mask
**Palo Alto Medical Foundation

====Exclusions====
*LLC
*LLP

===Technology Transfer Offices===

====Job Titles====
*Patent Portfolio Manager
*Intellectual Property Manager
*Licensing Associate/Licensing Liaison
*Biological Materials Specialist
*Industrial Contracts Officer
*Technology Licensing Officer
*Associate Officer
*Technology Licensing Associates
*Patent Coordinator/Patent Administrator
*IP Portfolio Specialist
*MTA Coordinator
*Sponsored Research Administrator
*Technology Transfer Specialist/Technology Transfer Associate
*Contracts Associate
*Portfolio Director
*Contracts Specialist
*Industry Contracts Analyst
*Patent Prosecution Analyst
*(Associate) Director of Intellectual Property
*(Associate) Director of Technology Transfer Policy
*(Associate) Director of Technology Transactions
*(Associate) Director of Agreement Administration
*Senior Associate - watch
*Patent Attorney/Patent Agent/Patent Counsel/Paralegal - watch

====TTO Names====
*Office of Technology Transfer/Technology Transfer Office
*Office of Technology Licensing/Technology Licensing Office
*Office of Technology Development
*Technology Ventures
*Innovation Services
*Intellectual Property & Industry Research Alliances
*InnovationAccess
*Invention Transfer Group
*Technology Development Group
*Office of Business Development
*Office of Technology Commercialization
*Office of Innovation and Commercialization
*Innovation, Technology & Alliances
*Technology & Industry Alliances
*Office for Management of Intellectual Property
*Innovation & Partnerships Office

===University-Affiliated Startups (Patent Assignees)===

==What to get from other people==

Avesh has clinical trial data on wiki and bulk drive (FDA Trials Data), need to build it into normal form, use clinical trials data to rank R&D engagement of universities, will be building up portfolios of different types of companies

Marcela is cleaning up patent data, has given patent assignee names -- got it! Could we filter by country and get strictly American names?

Catherine has zip codes of medical centers, use to look through patent data

==Questions==
*How innovative are universities compared to publicly-traded firms, etc.? (firms: 100 active patents at any given time)
*How do universities license?
*What does the average portfolio look like for universities? (compared to publicly traded, VC-backed, etc)
*What can explain the differences in rankings? (size, quality of universities, TTOs and quality/experience of workers - searched LinkedIns, geography, entrepreneurship programs, NIH/NSF grants)

==To Do==

*Create ranking
**Based on patent portfolio
**Licensing income
**Quality of TTO
**Publications (specifically based on research)?
**Amount received in grants/Amount of grants - we have numbers on amount of NIH grants and NSF grants
***NIH/NSF/STTR
**Control for school size/endowment
**Startups affiliated with university
***Value generated from these startups? (ask Avesh)
**Existence of entrepreneurship program
**Geography

*Describe average patent portfolio of university
**Number of patents
**Maybe categorize by research area?
*Describe how universities license
**USPTO earned revenue

Deliverable
*Ranking
*Explanation of what makes a good ranking/what factors a school needs to be more successful

Artifacts
*Grant Data
*Research Funding
*Star Scientists
*Regression with variables from Carnegie Classification (10-15 variables)
*Licensing (?)

===What We Have===

*Lit Review
*List of all post-secondary institutions (University Accreditation 3.2016_trim) - U.S. Department of Education
*Classification of universities (Carnegie Classification 2015) - Carnegie Classification
** includes all accredited degree-granting colleges and universities that are included in the National Center for Education Statistics Integrated Postsecondary Education Data System (IPEDS).
** Coverage: 4666 institutions of higher learning
** Variables: includes 94 variables
*** Enrollment data - subdivided into graduate and undergraduate
*** degrees conferred - divided into level and division
*** information on faculty (number, divided into levels i.e. assistant, assoc. and full time)
**** coverage for this variable is REALLY LOW
*** non-faculty research staff (includes post docs)
*** Science and Engineering Research and Development expenditures
*** Non-STEM research and Development expenditures
*Amount spent on R&D (Higher Ed R&D Rankings) - National Science Foundation
** includes just total R&D expenditures
*STTR full data 1995-2015 (STTRData) - Small Business Administration
*University Patent Numbers 1969-2012 - USPTO
*Patents that resulted from NIH grants
*Information about NSF grants

===What We Need===

*List of university-affiliated startups
**looks like AUTM STATT data has this because it was used in a study by Yael Hochberg [https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2418000]
**[https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-research-universities-2015/#3cb9829b3823| Rankings of Research Universities (2015)]
**FORBES ranked the country’s most entrepreneurial schools based on the numbers of alumni and students who have identified themselves as founders and business owners on LinkedIn (adjusted to total student body size). This year we rank both research universities and [https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-colleges-2015/#2b2909e04604| smaller colleges] separately.
*data about TTOs (# and quality of employees)
**LinkedIn crawler: working with Jeemin and Peter
*licensing data from AUTM [http://www.autm.net/statt] or already within the patent data?

==Name Matching==
*Jeemin has found all the correct and incorrect spellings of assignees with "University"
*Julia working on "Institute"
*We have a list of Universities that have patents from NIH grants, which could be a starting point for our ranking constituents

==Lit Reviews==

===Thursby, J. & Thursby, M.: Who Is Selling the Ivory Tower? Sources of Growth in University Licensing (2002)===

[http://pubsonline.informs.org/doi/pdf/10.1287/mnsc.48.1.90.14271]

@article{thursby2002who,
title={Who Is Selling the Ivory Tower? Sources of Growth in University Licensing},
author={Thursby, Jerry G. and Thursby, Marie C.},
journal={Management Science},
volume={48},
number={1},
pages={90--104},
year={2002},
publisher={INFORMS},
filename={Thursby Thursby (2002) - Who Is Selling the Ivory Tower}
}

*Reliance of industry on university inventions has increased
**AUTM surveys show 7.1% growth in yearly inventions disclosure from 1994-1998 for 64 universities that responded every year
*Primary reason for more disclosures may be increased propensity for faculty to disclose, rather than change in research focus
*Universities becoming more receptive to industry contracts
*Negative total TFP growth of licenses executed (-1.7% annual growth) - growth in disclosures and patent applications greater than the corresponding growth in licenses executed.
**Marginal university innovation offered to the market has declined in commercial appeal
**Universities are delving more deeply into the available pool of innovations to increase commercial activities
*No evidence on the importance of learning by doing on the part of TTOs except to note negative association between TTO growth and TFP growth in licensing
**Suggests at least the possibility of learning by doing effects

'''Other literature'''

*On the role of patents and publications in the transfer process: Adams 1990, Henderson et al. 1998, and Jaffe et al. 1993
*On consulting, sponsored research or institutional ties: Cohen et al. 1998; Mansfield 1995; Zucker et al. 1994, 1998
*On the nature of university licensing: Jensen and Thursby 2001, Mowery et al. 2001a,b, Mowery et al. 2001, Siegel et al. 1999, Thursby et al. 2001, Thursby and Kemp 2001

===Thursby, J., Jensen, Thursby, M.: Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities (2001)===

[https://pdfs.semanticscholar.org/637f/048c05ee83eb84a914f69e087f52acea1b6c.pdf]

@article{thursby2001objectives,
title={Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities},
author={Thursby, Jerry G., Jensen, Richard, and Thursby, Marie C.},
journal={The Journal of Technology Transfer},
volume={26},
number={1},
pages={59--72},
year={2001},
publisher={Springer},
abstract={This paper describes results of our survey of licensing at 62 research universities. We consider ownership, income splits, stage of development, marketing, license policies and characteristics, goals of licensing and the role of the inventor in licensing. Based on these results we analyze the relationship between licensing outcomes and both the objectives of the TTO and the characteristics of the technologies. Patent applications grow one-to-one with disclosures, while sponsored research grows similarly with licenses executed. Royalties are typically larger the higher the quality of the faculty and the higher the fraction of licenses that are executed at latter stages of development. Sponsored research is more likely to be included in a license if the new technology is at an early stage of development or if the TTO evaluates it as important. We find that additional disclosures generate smaller percentage increases in licenses, and those increases in licenses generate smaller percentage increases in royalties.},
filename={Thursby et al (2001) - Objectives, Characteristics and Outcomes of University Licensing}
}

*University licensing has increased dramatically post-Bayh-Dole (1980)
**According to AUTM 1996, licenses executed increased 75% from 1991-1996, (total: 13,087)
*Survey of TTOs of 62 major US universities
**Majority of universities retain titles to inventions
**All universities split income with inventors
**Royalties generate most of the revenue of licensing

*Open question: Is the increased propensity of faculty to disclose a response to financial incentives or an increase in the effectiveness of TTOs in inducing disclosure?

Survey
*62/135 universities responded
*63% public, and 62% of public universities that responded were land-grant
*37% private
*average industry sponsored research $16.9 mil, federally sponsored $149.6 mil (1996)
*average TTO: 26.3 licenses executed, 92.3 invention disclosures, 30.1 new patent apps, $4.2 mil income (1996)
*35% of respondents had reorganized TTO since 1990
*90% of universities allow faculty to establish and operate businesses based on technology owned by university but developed in faculty's research
*Inventions disclosed: 33% med schools, 29% engineering, 22% science, 6% agriculture, 10% other
*Majority of invention disclosures in nascent stage (proof of concept - 45% or prototype - 37%)
*Patents often applied for after knowing commercial viability, licensed technologies often not protected by patents
*60% of universities said small companies more likely to take early stage technologies and large companies more likely to take late stage - small firms may have advantage in "innovative" research (Holmstrom 1989)
*TTOs obtain smaller upfront fees the more uncertain the technology being licensed is
*Universities usually do not take equity in the license

Regression of licensing outcomes

[[File:LicensingOutcomeReg.png|500px]]

*Dependent variables: royalties, sponsored research, patents (new applications), licenses executed
*Independent variables: importance of outcome to TTO, types of inventions, measure of size of university's licensing operation/potential
*Logs of all variables except indicator variables
*Probit for frequency of sponsored research
*INVDIS: number of disclosures
*TTOSIZE: number of licensing individuals
*TTOEVAL_1 = 1: if TTOs that said licenses/patents are "not very important"
*TTOEVAL_2 = 1: if TTOs that said licenses/patents are "moderately important"
*PROOF: % of licensed disclosures that were "proof of concept but no prototype"
*PTYPE: "Prototype available but only lab scale"
*MEDSCHL: 1 if med school exists
*QUAL: academic quality of faculty (1993 NRC's survey results of academic quality of Ph.D. granting departments)
*LICENSES: number of licenses executed
*SPONRES: amount of sponsored research
*SPONFREQ: frequency that sponsored research is tied to license (according to TTO)
*patent apps grow 1-to-1 with disclosures
*sponsored research grows with licenses executed
*more licenses executed at universities with large TTOs and med schools
*higher royalties with higher quality of faculty and higher fraction of licenses executed at later stages of development
*additional disclosures generate smaller % increases in licenses, which generate smaller % increases in royalties (TTOs generally effective at tapping pool of available technologies in their universities)

===Thursby, J., Fuller, Thursby, M.: US Faculty Patenting: Inside and Outside the University (2009)===

[http://www.nber.org/papers/w13256.pdf]

@article{thursby2009us,
title = "US Faculty Patenting: Inside and Outside the University",
author = "Jerry G. Thursby, Anne W. Fuller, and Marie C. Thursby",
journal={Research Policy},
volume={38},
number={1},
pages={14--25},
year={2009},
publisher={Elsevier},
abstract = {This paper examines the empirical anomaly that in a sample of 5811 patents on which US faculty are listed as inventors, 26% of the patents are assigned solely to firms rather than to the faculty member's university as is dictated by US university employment policies or the Bayh Dole Act. In this paper we estimate a series of probability models of assignment as a function of patent characteristics, university policy, and inventor fields in order to examine the extent to which outside assignment is nefarious or comes from legitimate activities, such as consulting. Patents assigned to firms (whether established or start-ups with inventor as principal) are less basic than those assigned to universities suggesting these patents result from faculty consulting. A higher inventor share increases the likelihood of university assignment as compared with assignment to a firm in which the inventor is a principal but it has no effect on consulting with established firms versus assignment to the university. Faculty in the physical sciences and engineering are more likely to assign their patents to established firms than those in biological sciences.},
filename={Thursby et al (2009) - US Faculty Patenting},
}

*only 62.4% of patents by university faculty members of 87 universities were assigned solely to universities
*identifying US university patents by institutional assignment misses significant percentage of faculty innovation in US universities
*higher inventor share increases likelihood of university assignment compared with assignment to a firm where inventor is principal
*possibilities: faculty in low share universities may be more willing to seek outside remuneration via assignment to start-up where they are principal; revenue shares may not affect startup activity but simply reduce number of inventions disclosed to university

Sources
*Faculty names from NRC
*Compared with inventor names in NBER Patent Database
*Excluded faculty who do not patent

Patent/Inventor Pairs
*MIT: 315
*Wisconsin: 232
*Stanford: 223
*UC San Diego: 216
*UC Berkeley: 207

Out of 5811 patents:
*1513 assigned solely to firms
*241 assigned to both firms and universities
*327 unassigned
*faculty are principals in assignee firms for 32.3% of patents assigned solely to firms and 24% of patents assigned to both (lower bound)

=== Valdivia: University Start-ups: Critical for Improving Technology Transfer (2013) ===

[https://www.brookings.edu/wp-content/uploads/2016/06/Valdivia_Tech-Transfer_v29_No-Embargo.pdf]

*Current emphasis on licensing patents, but most university TTOs do not generate enough to cover operating expenses
*Asymmetry in distribution of resources across the university system, only a few universities benefit from high licensing revenues
**top 8 universities took 50% of licensing income, top 16 universities took nearly 75% of income
**only 37 universities have been in the top 20 during the last decade (listed in University Patents > LicensingGrossIncome2003-2012.txt)
*Universities face much more pressure to demonstrate the economic impact of their R&D contracts
**97.6% of total public contracts obtained by universities are for basic/applied research
**2% of university research delivers ready-to-use technologies
**academic research is much more dependent on government funding than industry research is
*Government pressure for universities to be more responsive to market forces, more entrepreneurial, more attuned to needs of industry
*TTOs are costly to universities
*1979: 30 universities with TTO -> 1999: 174 universities (AUTM)
*2010: 206 US universities have very high or high research activity, all with TTOs (but not all report to AUTM) (Carnegie Classification of Higher Education)
*Over last 20 years, 87% of universities did not break even
*Clash between aims of university (non-profit) and TTO (essentially a business unit)
*By nurturing start-ups, TTOs can add most economic value to an invention disclosure
*2003: universities initiated 330 startups, 2012: 647 startups
*2012: 3715 operating university startups, almost double the number in 2000 (AUTM 2013)
*Startups mitigate financial risk by reducing reliance on blockbuster patents, increase diversification of portfolio
*Policy proposal: the government should increase funding for Small Business Technology Transfer Program (directed to university start-ups)
**H.R.2981: reapportions STTR funds at 0.05% in the next 2 years and 0.1% henceforth for university enterprises at proof of concept stage
*Should increase portion that agencies set aside for STTR
**STTR should have Phase III like SBIR to fund commercialization efforts
*Need equitable distribution across university system

=== The Bayh-Dole Act and High-Technology Entrepreneurship in U.S. Universities: Chicken, Egg, or Something Else? (2004) ===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.484.1612&rep=rep1&type=pdf]

==== Findings ====

* University research has an unusually significant impact on industrial innovation in the biomedical sector
* " This work also suggests that academic research rarely produces “prototypes” of inventions for development
and commercialization by industry—instead, academic research informs the methods and disciplines
employed by firms in their R&D facilities."
* The U.S. higher education system is much larger and more heterogenous than other developed countries - this encourages competition
* The passage of the Bayh-Dole Act was one part of a broader shift in U.S. policy toward stronger
intellectual property rights
* "Universities increased their share of patenting from less than 0.3% in 1963 to nearly 4% by 1999, but the rate of growth in this share begins to accelerate before rather than after 1980."
* "the Act's provisions expressed Congressional support for the negotiation of exclusive licenses between universities and industrial firms for the results of federally funded research"
* licensing revenues account for only a miniscule portion of universities' overall academic budgets
* the acceleration in growth of patenting and licensing began before the passage of the Bayh-Dole Act so this acceleration cannot be wholly attributed to the Act
* "the flow of knowledge and technology between university and industrial research is a two-way flow," despite previous characterization as wholly from academia to industry
* patents seem to be "especially important channels for technology transfer" in the biomedical sector

==== Data Sources ====
* 5 different case studies
1. Cotransformation: a process to transfer genes into mammalian cells (Columbia University).

2. Gallium Nitride: a semiconductor with both military and commercial applications (University of California).

3. Xalatan: a glaucoma treatment (Columbia University).

4. Ames II Tests: a bacteria assay for testing potential carcinogenic properties of pharmaceuticals and cosmetics (University of California).

5. Soluble CD4: a prototype for a drug to fight AIDS (Columbia University).

==== Critiques ====
* focuses on case studies as source of data

===Dornbusch, Schmoch, Schulze, Bethke: Identification of University-Based Patents: A New Large-Scale Approach (2012)===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.1368&rep=rep1&type=pdf]

Specific for German case, but certain points can help us

European convergence to US model (Bayh-Dole seen as main driver behind growing patent portfolios of US universities)

*University-owned patents (assigned to universities or their TTOs) and university-invented patents (assigned to university-affiliated authors)

Matching lists: traditional matching of lists of university staff/professors with inventor data
*time-consuming, costly, possibly not updated
*typically limited to tenured professors
*does not include Ph.D. students, assistants, lecturers

Matched authors of scientific publications and inventors on patents
*beware of homonyms

Patent data from EPO Worldwide Patent Statistical Database (PATSTAT)

Publication data from Elsevier (Scopus)

Country of origin: Inventor Country/Assignee Country/Applicant Country = Germany, Location of Organization to which author is affiliated =Germany
*Restrict dataset to authors from German organizations and inventors with residence in Germany

The organization

Names: To keep precision high, leave out names with initials only

Location: Postal/zip codes (PATSTAT provides address of inventor's residence, SCOPUS provides info for organization, 96.5% of first digit of inventor and organization postal codes are the same, 85.9% first two digits)

Time window: 2-year window between application and publication date

=== Singh A. and Wong P.K: University patenting activities and their link to the quantity and quality of scientific publications (2009) ===

[https://pdfs.semanticscholar.org/bb3a/df09ca693cdcff6c1f6aaf18113cabec2655.pdf]
==== Findings ====
* patenting by 281 leading world universities has consistently grown faster than general American patenting from 1977 - 2000
* North American university patenting growth has slowed relative to universities outside North America since the mid-1990s
* Between 2003-2005, they found that university patenting output has significant correlation with the both the quality and quantity of scientific publishing in North America
* In European and Australian universities, patenting correlated only with the quantity of scientific publishing, not with the quality
* In universities Europe, Australia, and North America, patenting correlated only with the quality of scientific publishing

==== Data Sources ====
* USPTO Patenting Data
* Shanghai Jiao Tong University's Academic Ranking of World Universities (ARWU)
* Times Higher Education Supplement's World University Ranking (WUR)
* Quantity was measured by counts of publications
* Quality was measured by citations to said publications
* the relationship between research and patenting was evaluated in two ways
** At the institutional level: patents assigned to universities
** At the individual level: patents with university researchers as the inventors

==== Critiques ====
* citations are not really a perfect measure of research quality and citations have little to do with practical use of the study (i.e. how much technological innovation is generated as a result of academic research publications)

==== Other Discoveries ====

* Study by Landry R., Amara N., and Saihi, M. (2006)
* (Owen Smith and Powell 2003) found that "organizations involved in technological commercialization tend to have higher publication rates than those who are not"
* (Lach and Schankerman 2003) found that "licensing revenues at the university level are positively influenced by publication citations per faculty"

University Patents

2018-04-05T19:15:01Z

Dayton:

{{McNair Projects
|Has title=University Patents
|Has owner=Julia Wang, Meghana Pannala, Anne Dayton
|Has keywords=Patent
|Has project status=Active
}}

==Research Paper===
Since university system file for patents out of a central office, its mot possible to determine where the research was conducted. This presents problems when ranking universities by patent production.

In order to publish this paper as a Baker Institute Research Report, we will need to do more to resolve this issue beyond weighting for the University of California.

==Carnegie Classifications==
We propose using public data from the Carnegie Classifications of Institutes of Higher Education to resolve the university systems issue.

TASKS:
1) Separate schools affiliated with universities/university systems in our rankings from all others included in Carnegie data.
2)Group these by system.
Determine the number of schools in each system
3) Determine their classification.
1 if Doctoral 0 if not
1 if 4-year medical school 0 if not
1 if 4-year engineering 0 if not
4) Determine the total count of the categories in number 3 for each university system.
5) Use total count to weight regressions
6) Create ranking tables that rank university systems and universities by dividing number of patents by number of doctoral, med-school, 4-year engineering.

==Project Overview==
Goal: list of all universities and # of patents associated with each university and patent licensing activity

patent reassignment to startups associated with these universities

clinical trials (from Catherine) data to rank universities R&D engagement

identify list of universities: board of regents, universities in patent data (find patterns associated with university assignees) -- @Julia: Where did we get this list? I'd love to be able filter by country - Meghana

AUTM?

==Timeline to Deliverable & Documentation==

Meghana: focus on grants

Julia: focus on patent counts

Working in E:McNair/University Patents

'''3/20-3/24'''

Learn SQL, clean data

'''3/27-3/31'''

Counts of data, name matching

Patent Counts
*Ran the matcher in the server, only matched ~40,000 entries
*Working with Jeemin to develop matcher

'''4/3-4/7'''

Counts of data, name matching

Patent Counts
*Jeemin is a savior, matched and counted 128,000 entries (Jeemin_matcher_matched.txt), manually went through the 6,000 unmatched (Jeemin_matcher_unmatched.txt), Check [[University Patent Matching]]
*Ranking for all time is in Patent Counting ( >Total (All Time) )
*Need to talk with Meghana about grant data
*Need to find variables for regression, develop ranking for last decade and last year
*Also need to account for school size (faculty? students? research funding?)

'''4/10-4/14'''

Develop ranking

Patent Counts
*Finish all of above
*Start drafting
*Load data onto database -> E:McNair/University Patents/unidatajoin.sql in univ (database)

'''4/17-4/21'''

*Combined Patent Counts, R&D Expenditure, NSF Grant Data
*STATA regressions in E:McNair/University Patents/STATA
**for historical data

'''4/24'''

From Ed:
*Copied inventors table out of PatentDB database on RDP
*Script in E:McNair/PatentData, MoveInventors.sql
*Copy of data in Z:AllPatents and ran import into patent (database)
For Marcela:
*Need table of all patents associated with matches, join patent numbers with inventors
*Group by last name and first initial (=1 inventor)

'''5/1'''

*Joined patent numbers with inventors -> unipatentinventorcount.sql
**Z:univ/InventorCounts.txt
*Decade patent counts -> Z:univ/DecadePatentCounts
*Calculated Hirschman-Herfindahl index -> E:McNair/University Patents/Inventor Counts and Superstars

'''5/5'''

Artifacts:
#10-Year Ranking All
#10-Year Ranking Private
#10-Year Ranking Public
#Top 10 Movers and Shakers
#Graph of Concentration of Innovation
#Number of University Patents Over Time
#Inventor Concentration
#Regression Table

*Created Graph of Concentration of Innovation -> E:McNair/University Patents/Inventor Counts and Superstars -> GraphUPS
*Created Top Inventors Chart -> University Patents/Report Articles/Inventor Rankings

'''5/9'''

*Figuring out what's up with the university puller
*Developing new 10-Year Rankings
*Need to update above two articles
*Need to load into STATA and re-run regressions -> University Patents/STATA/univdata.do

'''5/10'''

*Figured out what was wrong with the data pull and now everything's good!
*Updated information on the draft below
*Fantastic Artifacts and Where to Find Them
**10-Year Ranking All
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Public
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**10-Year Ranking Private
***Chart (University Patents > Report Artifacts > 10-Year University Patent Rankings)
**Top 10 Movers & Shakers (% change between first 5 years (2006-2010) vs next 5 years (2011-2015) within top 100
**Graph of Concentration of Innovation (numbers of patents)
***Pie graph (University Patents > Report Artifacts > Concentration of Innovation)
***(Excel sheet where this came from is University Patents > Inventor Counts and Concentrations)
**Trends Over Time
***Total vs. top 10
***Graph (University Patents > MoversandTop10)
**Inventor Concentration
***Chart (University Patents > Report Artifacts > Inventor Rankings)
**Reg Table
***+Explanation of variables
***(STATA do-file with the most statistically significant regressions is in University Patents > STATA > univdata.do)

Issue brief draft (VERY rough): https://docs.google.com/a/rice.edu/document/d/1MayXQEQ_pM0LMeiV39iaNFjtMZYSB-Ah9gCx1rcgW9M/edit?usp=sharing

==Ranking Development Notes==
Sources of University Funding R&D [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% comes from the Federal Government
*Less than 10% comes from state and local governments
*Roughly 20% is from the University itself (endowment?? tuition? not 100% sure)
*Roughly 5% is from private industry
*Less than 10% Other

Federal Government Funding Breakdown [https://www.aaas.org/page/rd-colleges-and-universities]
*Roughly 60% of federal funding is from the NIH
*Roughly 15% is from the NSF
*Roughly 11% from Department of Defense
*Roughly 4% from NASA
*Roughly 4% from the Department of Energy
*Roughly 3% from the USDA
*Roughly 1% from the EPA (probably will go to zero with new admin)
*Roughly 1% from the Department of Education
*Marginal amounts from everywhere else (<1%)

NOTES
*overall ranking (total number of patents)
*weight by faculty (people)
*weight by research funding
*weight by endowment (maybe)
*public vs private
*individuals: surname and first initial
**ranking by inventors - who are the superstar inventors and where are they (define superstar inventor - idk 20 patents and rank universities by how many superstars are present
**Herfindahl-Hirschmann Index

==Key Words==

===Universities (Patent Assignees)===
BOARD OF REGENTS - pretty much exclusively describes universities

UNIVERSITY - also exclusively describes universities
*Can we do a close match with University? (its the only word on this list that's frequently misspelled)

SCHOOL - often used in combination with medicine or medical to describe medical schools
*also are used to describe actual schools (i.e. HISD) and other businesses
*sometimes present in addresses - can we cut off the adresses?
*also used in combination with Business (i.e. Harvard Business School)
*use school only in conjuction with medical, medicine, and business

COLLEGE
*need to cut off addresses for this to work
*need to ensure that we include the space after
*excl. College Boulevard, college blvd, etc.
*double check everything with College Park

INSTITUTE OF TECHNOLOGY - almost exclusively describes universities but sometimes describes external research institutes

POLYTECHNIC - exclusively university (i.e. RPI, Virginia Tech)

RESEARCH FOUNDATION: VAST majority are university
*Exclusions: it's really difficult to distinguish between university affiliated and not, besides just looking them up on the google
**Novartis
**Progeria
**Washington Research Foundation
**Blood Center of Wisconsins
**Mental Hygiene
**Celiac Sprue
**Fidia
**Samuel Waxman Cancer Center
***founded by a faculty member at Mt. Sinai Medical School,not affil w/a single university but researchers are also usually faculty at various universities
**lifenet
**HealthPartners
**Dr. Susan Love
**La Jolla Cancer Research Foundation
**Children's Hospital (? grants degrees but is a hospital system)
**Medforte
**International Mask
**Palo Alto Medical Foundation

====Exclusions====
*LLC
*LLP

===Technology Transfer Offices===

====Job Titles====
*Patent Portfolio Manager
*Intellectual Property Manager
*Licensing Associate/Licensing Liaison
*Biological Materials Specialist
*Industrial Contracts Officer
*Technology Licensing Officer
*Associate Officer
*Technology Licensing Associates
*Patent Coordinator/Patent Administrator
*IP Portfolio Specialist
*MTA Coordinator
*Sponsored Research Administrator
*Technology Transfer Specialist/Technology Transfer Associate
*Contracts Associate
*Portfolio Director
*Contracts Specialist
*Industry Contracts Analyst
*Patent Prosecution Analyst
*(Associate) Director of Intellectual Property
*(Associate) Director of Technology Transfer Policy
*(Associate) Director of Technology Transactions
*(Associate) Director of Agreement Administration
*Senior Associate - watch
*Patent Attorney/Patent Agent/Patent Counsel/Paralegal - watch

====TTO Names====
*Office of Technology Transfer/Technology Transfer Office
*Office of Technology Licensing/Technology Licensing Office
*Office of Technology Development
*Technology Ventures
*Innovation Services
*Intellectual Property & Industry Research Alliances
*InnovationAccess
*Invention Transfer Group
*Technology Development Group
*Office of Business Development
*Office of Technology Commercialization
*Office of Innovation and Commercialization
*Innovation, Technology & Alliances
*Technology & Industry Alliances
*Office for Management of Intellectual Property
*Innovation & Partnerships Office

===University-Affiliated Startups (Patent Assignees)===

==What to get from other people==

Avesh has clinical trial data on wiki and bulk drive (FDA Trials Data), need to build it into normal form, use clinical trials data to rank R&D engagement of universities, will be building up portfolios of different types of companies

Marcela is cleaning up patent data, has given patent assignee names -- got it! Could we filter by country and get strictly American names?

Catherine has zip codes of medical centers, use to look through patent data

==Questions==
*How innovative are universities compared to publicly-traded firms, etc.? (firms: 100 active patents at any given time)
*How do universities license?
*What does the average portfolio look like for universities? (compared to publicly traded, VC-backed, etc)
*What can explain the differences in rankings? (size, quality of universities, TTOs and quality/experience of workers - searched LinkedIns, geography, entrepreneurship programs, NIH/NSF grants)

==To Do==

*Create ranking
**Based on patent portfolio
**Licensing income
**Quality of TTO
**Publications (specifically based on research)?
**Amount received in grants/Amount of grants - we have numbers on amount of NIH grants and NSF grants
***NIH/NSF/STTR
**Control for school size/endowment
**Startups affiliated with university
***Value generated from these startups? (ask Avesh)
**Existence of entrepreneurship program
**Geography

*Describe average patent portfolio of university
**Number of patents
**Maybe categorize by research area?
*Describe how universities license
**USPTO earned revenue

Deliverable
*Ranking
*Explanation of what makes a good ranking/what factors a school needs to be more successful

Artifacts
*Grant Data
*Research Funding
*Star Scientists
*Regression with variables from Carnegie Classification (10-15 variables)
*Licensing (?)

===What We Have===

*Lit Review
*List of all post-secondary institutions (University Accreditation 3.2016_trim) - U.S. Department of Education
*Classification of universities (Carnegie Classification 2015) - Carnegie Classification
** includes all accredited degree-granting colleges and universities that are included in the National Center for Education Statistics Integrated Postsecondary Education Data System (IPEDS).
** Coverage: 4666 institutions of higher learning
** Variables: includes 94 variables
*** Enrollment data - subdivided into graduate and undergraduate
*** degrees conferred - divided into level and division
*** information on faculty (number, divided into levels i.e. assistant, assoc. and full time)
**** coverage for this variable is REALLY LOW
*** non-faculty research staff (includes post docs)
*** Science and Engineering Research and Development expenditures
*** Non-STEM research and Development expenditures
*Amount spent on R&D (Higher Ed R&D Rankings) - National Science Foundation
** includes just total R&D expenditures
*STTR full data 1995-2015 (STTRData) - Small Business Administration
*University Patent Numbers 1969-2012 - USPTO
*Patents that resulted from NIH grants
*Information about NSF grants

===What We Need===

*List of university-affiliated startups
**looks like AUTM STATT data has this because it was used in a study by Yael Hochberg [https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2418000]
**[https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-research-universities-2015/#3cb9829b3823| Rankings of Research Universities (2015)]
**FORBES ranked the country’s most entrepreneurial schools based on the numbers of alumni and students who have identified themselves as founders and business owners on LinkedIn (adjusted to total student body size). This year we rank both research universities and [https://www.forbes.com/sites/liyanchen/2015/07/29/americas-most-entrepreneurial-colleges-2015/#2b2909e04604| smaller colleges] separately.
*data about TTOs (# and quality of employees)
**LinkedIn crawler: working with Jeemin and Peter
*licensing data from AUTM [http://www.autm.net/statt] or already within the patent data?

==Name Matching==
*Jeemin has found all the correct and incorrect spellings of assignees with "University"
*Julia working on "Institute"
*We have a list of Universities that have patents from NIH grants, which could be a starting point for our ranking constituents

==Lit Reviews==

===Thursby, J. & Thursby, M.: Who Is Selling the Ivory Tower? Sources of Growth in University Licensing (2002)===

[http://pubsonline.informs.org/doi/pdf/10.1287/mnsc.48.1.90.14271]

@article{thursby2002who,
title={Who Is Selling the Ivory Tower? Sources of Growth in University Licensing},
author={Thursby, Jerry G. and Thursby, Marie C.},
journal={Management Science},
volume={48},
number={1},
pages={90--104},
year={2002},
publisher={INFORMS},
filename={Thursby Thursby (2002) - Who Is Selling the Ivory Tower}
}

*Reliance of industry on university inventions has increased
**AUTM surveys show 7.1% growth in yearly inventions disclosure from 1994-1998 for 64 universities that responded every year
*Primary reason for more disclosures may be increased propensity for faculty to disclose, rather than change in research focus
*Universities becoming more receptive to industry contracts
*Negative total TFP growth of licenses executed (-1.7% annual growth) - growth in disclosures and patent applications greater than the corresponding growth in licenses executed.
**Marginal university innovation offered to the market has declined in commercial appeal
**Universities are delving more deeply into the available pool of innovations to increase commercial activities
*No evidence on the importance of learning by doing on the part of TTOs except to note negative association between TTO growth and TFP growth in licensing
**Suggests at least the possibility of learning by doing effects

'''Other literature'''

*On the role of patents and publications in the transfer process: Adams 1990, Henderson et al. 1998, and Jaffe et al. 1993
*On consulting, sponsored research or institutional ties: Cohen et al. 1998; Mansfield 1995; Zucker et al. 1994, 1998
*On the nature of university licensing: Jensen and Thursby 2001, Mowery et al. 2001a,b, Mowery et al. 2001, Siegel et al. 1999, Thursby et al. 2001, Thursby and Kemp 2001

===Thursby, J., Jensen, Thursby, M.: Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities (2001)===

[https://pdfs.semanticscholar.org/637f/048c05ee83eb84a914f69e087f52acea1b6c.pdf]

@article{thursby2001objectives,
title={Objectives, Characteristics and Outcomes of University Licensing: A Survey of Major U.S. Universities},
author={Thursby, Jerry G., Jensen, Richard, and Thursby, Marie C.},
journal={The Journal of Technology Transfer},
volume={26},
number={1},
pages={59--72},
year={2001},
publisher={Springer},
abstract={This paper describes results of our survey of licensing at 62 research universities. We consider ownership, income splits, stage of development, marketing, license policies and characteristics, goals of licensing and the role of the inventor in licensing. Based on these results we analyze the relationship between licensing outcomes and both the objectives of the TTO and the characteristics of the technologies. Patent applications grow one-to-one with disclosures, while sponsored research grows similarly with licenses executed. Royalties are typically larger the higher the quality of the faculty and the higher the fraction of licenses that are executed at latter stages of development. Sponsored research is more likely to be included in a license if the new technology is at an early stage of development or if the TTO evaluates it as important. We find that additional disclosures generate smaller percentage increases in licenses, and those increases in licenses generate smaller percentage increases in royalties.},
filename={Thursby et al (2001) - Objectives, Characteristics and Outcomes of University Licensing}
}

*University licensing has increased dramatically post-Bayh-Dole (1980)
**According to AUTM 1996, licenses executed increased 75% from 1991-1996, (total: 13,087)
*Survey of TTOs of 62 major US universities
**Majority of universities retain titles to inventions
**All universities split income with inventors
**Royalties generate most of the revenue of licensing

*Open question: Is the increased propensity of faculty to disclose a response to financial incentives or an increase in the effectiveness of TTOs in inducing disclosure?

Survey
*62/135 universities responded
*63% public, and 62% of public universities that responded were land-grant
*37% private
*average industry sponsored research $16.9 mil, federally sponsored $149.6 mil (1996)
*average TTO: 26.3 licenses executed, 92.3 invention disclosures, 30.1 new patent apps, $4.2 mil income (1996)
*35% of respondents had reorganized TTO since 1990
*90% of universities allow faculty to establish and operate businesses based on technology owned by university but developed in faculty's research
*Inventions disclosed: 33% med schools, 29% engineering, 22% science, 6% agriculture, 10% other
*Majority of invention disclosures in nascent stage (proof of concept - 45% or prototype - 37%)
*Patents often applied for after knowing commercial viability, licensed technologies often not protected by patents
*60% of universities said small companies more likely to take early stage technologies and large companies more likely to take late stage - small firms may have advantage in "innovative" research (Holmstrom 1989)
*TTOs obtain smaller upfront fees the more uncertain the technology being licensed is
*Universities usually do not take equity in the license

Regression of licensing outcomes

[[File:LicensingOutcomeReg.png|500px]]

*Dependent variables: royalties, sponsored research, patents (new applications), licenses executed
*Independent variables: importance of outcome to TTO, types of inventions, measure of size of university's licensing operation/potential
*Logs of all variables except indicator variables
*Probit for frequency of sponsored research
*INVDIS: number of disclosures
*TTOSIZE: number of licensing individuals
*TTOEVAL_1 = 1: if TTOs that said licenses/patents are "not very important"
*TTOEVAL_2 = 1: if TTOs that said licenses/patents are "moderately important"
*PROOF: % of licensed disclosures that were "proof of concept but no prototype"
*PTYPE: "Prototype available but only lab scale"
*MEDSCHL: 1 if med school exists
*QUAL: academic quality of faculty (1993 NRC's survey results of academic quality of Ph.D. granting departments)
*LICENSES: number of licenses executed
*SPONRES: amount of sponsored research
*SPONFREQ: frequency that sponsored research is tied to license (according to TTO)
*patent apps grow 1-to-1 with disclosures
*sponsored research grows with licenses executed
*more licenses executed at universities with large TTOs and med schools
*higher royalties with higher quality of faculty and higher fraction of licenses executed at later stages of development
*additional disclosures generate smaller % increases in licenses, which generate smaller % increases in royalties (TTOs generally effective at tapping pool of available technologies in their universities)

===Thursby, J., Fuller, Thursby, M.: US Faculty Patenting: Inside and Outside the University (2009)===

[http://www.nber.org/papers/w13256.pdf]

@article{thursby2009us,
title = "US Faculty Patenting: Inside and Outside the University",
author = "Jerry G. Thursby, Anne W. Fuller, and Marie C. Thursby",
journal={Research Policy},
volume={38},
number={1},
pages={14--25},
year={2009},
publisher={Elsevier},
abstract = {This paper examines the empirical anomaly that in a sample of 5811 patents on which US faculty are listed as inventors, 26% of the patents are assigned solely to firms rather than to the faculty member's university as is dictated by US university employment policies or the Bayh Dole Act. In this paper we estimate a series of probability models of assignment as a function of patent characteristics, university policy, and inventor fields in order to examine the extent to which outside assignment is nefarious or comes from legitimate activities, such as consulting. Patents assigned to firms (whether established or start-ups with inventor as principal) are less basic than those assigned to universities suggesting these patents result from faculty consulting. A higher inventor share increases the likelihood of university assignment as compared with assignment to a firm in which the inventor is a principal but it has no effect on consulting with established firms versus assignment to the university. Faculty in the physical sciences and engineering are more likely to assign their patents to established firms than those in biological sciences.},
filename={Thursby et al (2009) - US Faculty Patenting},
}

*only 62.4% of patents by university faculty members of 87 universities were assigned solely to universities
*identifying US university patents by institutional assignment misses significant percentage of faculty innovation in US universities
*higher inventor share increases likelihood of university assignment compared with assignment to a firm where inventor is principal
*possibilities: faculty in low share universities may be more willing to seek outside remuneration via assignment to start-up where they are principal; revenue shares may not affect startup activity but simply reduce number of inventions disclosed to university

Sources
*Faculty names from NRC
*Compared with inventor names in NBER Patent Database
*Excluded faculty who do not patent

Patent/Inventor Pairs
*MIT: 315
*Wisconsin: 232
*Stanford: 223
*UC San Diego: 216
*UC Berkeley: 207

Out of 5811 patents:
*1513 assigned solely to firms
*241 assigned to both firms and universities
*327 unassigned
*faculty are principals in assignee firms for 32.3% of patents assigned solely to firms and 24% of patents assigned to both (lower bound)

=== Valdivia: University Start-ups: Critical for Improving Technology Transfer (2013) ===

[https://www.brookings.edu/wp-content/uploads/2016/06/Valdivia_Tech-Transfer_v29_No-Embargo.pdf]

*Current emphasis on licensing patents, but most university TTOs do not generate enough to cover operating expenses
*Asymmetry in distribution of resources across the university system, only a few universities benefit from high licensing revenues
**top 8 universities took 50% of licensing income, top 16 universities took nearly 75% of income
**only 37 universities have been in the top 20 during the last decade (listed in University Patents > LicensingGrossIncome2003-2012.txt)
*Universities face much more pressure to demonstrate the economic impact of their R&D contracts
**97.6% of total public contracts obtained by universities are for basic/applied research
**2% of university research delivers ready-to-use technologies
**academic research is much more dependent on government funding than industry research is
*Government pressure for universities to be more responsive to market forces, more entrepreneurial, more attuned to needs of industry
*TTOs are costly to universities
*1979: 30 universities with TTO -> 1999: 174 universities (AUTM)
*2010: 206 US universities have very high or high research activity, all with TTOs (but not all report to AUTM) (Carnegie Classification of Higher Education)
*Over last 20 years, 87% of universities did not break even
*Clash between aims of university (non-profit) and TTO (essentially a business unit)
*By nurturing start-ups, TTOs can add most economic value to an invention disclosure
*2003: universities initiated 330 startups, 2012: 647 startups
*2012: 3715 operating university startups, almost double the number in 2000 (AUTM 2013)
*Startups mitigate financial risk by reducing reliance on blockbuster patents, increase diversification of portfolio
*Policy proposal: the government should increase funding for Small Business Technology Transfer Program (directed to university start-ups)
**H.R.2981: reapportions STTR funds at 0.05% in the next 2 years and 0.1% henceforth for university enterprises at proof of concept stage
*Should increase portion that agencies set aside for STTR
**STTR should have Phase III like SBIR to fund commercialization efforts
*Need equitable distribution across university system

=== The Bayh-Dole Act and High-Technology Entrepreneurship in U.S. Universities: Chicken, Egg, or Something Else? (2004) ===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.484.1612&rep=rep1&type=pdf]

==== Findings ====

* University research has an unusually significant impact on industrial innovation in the biomedical sector
* " This work also suggests that academic research rarely produces “prototypes” of inventions for development
and commercialization by industry—instead, academic research informs the methods and disciplines
employed by firms in their R&D facilities."
* The U.S. higher education system is much larger and more heterogenous than other developed countries - this encourages competition
* The passage of the Bayh-Dole Act was one part of a broader shift in U.S. policy toward stronger
intellectual property rights
* "Universities increased their share of patenting from less than 0.3% in 1963 to nearly 4% by 1999, but the rate of growth in this share begins to accelerate before rather than after 1980."
* "the Act's provisions expressed Congressional support for the negotiation of exclusive licenses between universities and industrial firms for the results of federally funded research"
* licensing revenues account for only a miniscule portion of universities' overall academic budgets
* the acceleration in growth of patenting and licensing began before the passage of the Bayh-Dole Act so this acceleration cannot be wholly attributed to the Act
* "the flow of knowledge and technology between university and industrial research is a two-way flow," despite previous characterization as wholly from academia to industry
* patents seem to be "especially important channels for technology transfer" in the biomedical sector

==== Data Sources ====
* 5 different case studies
1. Cotransformation: a process to transfer genes into mammalian cells (Columbia University).

2. Gallium Nitride: a semiconductor with both military and commercial applications (University of California).

3. Xalatan: a glaucoma treatment (Columbia University).

4. Ames II Tests: a bacteria assay for testing potential carcinogenic properties of pharmaceuticals and cosmetics (University of California).

5. Soluble CD4: a prototype for a drug to fight AIDS (Columbia University).

==== Critiques ====
* focuses on case studies as source of data

===Dornbusch, Schmoch, Schulze, Bethke: Identification of University-Based Patents: A New Large-Scale Approach (2012)===

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.1368&rep=rep1&type=pdf]

Specific for German case, but certain points can help us

European convergence to US model (Bayh-Dole seen as main driver behind growing patent portfolios of US universities)

*University-owned patents (assigned to universities or their TTOs) and university-invented patents (assigned to university-affiliated authors)

Matching lists: traditional matching of lists of university staff/professors with inventor data
*time-consuming, costly, possibly not updated
*typically limited to tenured professors
*does not include Ph.D. students, assistants, lecturers

Matched authors of scientific publications and inventors on patents
*beware of homonyms

Patent data from EPO Worldwide Patent Statistical Database (PATSTAT)

Publication data from Elsevier (Scopus)

Country of origin: Inventor Country/Assignee Country/Applicant Country = Germany, Location of Organization to which author is affiliated =Germany
*Restrict dataset to authors from German organizations and inventors with residence in Germany

The organization

Names: To keep precision high, leave out names with initials only

Location: Postal/zip codes (PATSTAT provides address of inventor's residence, SCOPUS provides info for organization, 96.5% of first digit of inventor and organization postal codes are the same, 85.9% first two digits)

Time window: 2-year window between application and publication date

=== Singh A. and Wong P.K: University patenting activities and their link to the quantity and quality of scientific publications (2009) ===

[https://pdfs.semanticscholar.org/bb3a/df09ca693cdcff6c1f6aaf18113cabec2655.pdf]
==== Findings ====
* patenting by 281 leading world universities has consistently grown faster than general American patenting from 1977 - 2000
* North American university patenting growth has slowed relative to universities outside North America since the mid-1990s
* Between 2003-2005, they found that university patenting output has significant correlation with the both the quality and quantity of scientific publishing in North America
* In European and Australian universities, patenting correlated only with the quantity of scientific publishing, not with the quality
* In universities Europe, Australia, and North America, patenting correlated only with the quality of scientific publishing

==== Data Sources ====
* USPTO Patenting Data
* Shanghai Jiao Tong University's Academic Ranking of World Universities (ARWU)
* Times Higher Education Supplement's World University Ranking (WUR)
* Quantity was measured by counts of publications
* Quality was measured by citations to said publications
* the relationship between research and patenting was evaluated in two ways
** At the institutional level: patents assigned to universities
** At the individual level: patents with university researchers as the inventors

==== Critiques ====
* citations are not really a perfect measure of research quality and citations have little to do with practical use of the study (i.e. how much technological innovation is generated as a result of academic research publications)

==== Other Discoveries ====

* Study by Landry R., Amara N., and Saihi, M. (2006)
* (Owen Smith and Powell 2003) found that "organizations involved in technological commercialization tend to have higher publication rates than those who are not"
* (Lach and Schankerman 2003) found that "licensing revenues at the university level are positively influenced by publication citations per faculty"

McNair Center Branding

2017-11-15T21:06:39Z

Dayton:

[[Category: McNair Admin]]

==Palette==

[[File:Websitepalette.jpg]]

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Hex Color codes
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[[admin_classification::IT Build| ]]

==Logo Font==
Roboto regular

Predicting Venture Capital (Blog Post)

2017-11-15T15:56:11Z

Dayton:

{{BlogPost
|Has title=Predicting Venture Capital (Blog Post)
|Has author=Dylan Dickens
|Part of series=Entrepreneurship 101
|Has content status=Tabled
|Has Graphics status=None
}}
==Abstract==
Short and corny blog post on what to read into on venture capital.

==Text==
From Snapchat to DoorDash, and Uber to Lyft, 2016 has already been a bustling year for venture capital as we reach the start of Q3(1). With a total of $33.3 billion raised over 190 deals, 2016 looks to continue breaking records. According to statics from Pitchbook News and Analysis, Q2 2016 raised more in VC funding than any quarter over the past three years.

Laid to rest, now it seems, are the rumors from last quarter(3) that venture capital was entering a "pullback" in which a "continued slowdown for investment and deals into venture capital" was causing macroeconomic concern. The worry surrounding the lack of "unicorns," firms which are valued at over $1 billion, in Q1 2016 seems to have been abated as well. The "trend of well-known firms securing $1 billion-plus in new funds" rose in Q2 of 2016(2).

This dramatic, less than three month, reversal of trends and news demonstrates an excellent point: the incredible volatility of venture capital. This is a trend long-recognized by academics and professional researchers into the field, outlined as early as 1999 from the National Bureau of Economic Research(4). This volatility of raising funds for high-risk investments is further compounded by the volatility of the IPO and acquisition process.

What trends then, can one even attempt to discuss among such a speculative and ever changing industry? One aspect of venture capital which has always held true beyond its volatility is its value. While oft debated exactly how valuable any one investment fund may be, extensive research has shown that venture capital is threefold valuable(5). Value is extracted financially, for both the startup in the immediate, and the investors in the long run, and economically, with major economic growth being driven by the rise of successful VC-backed enterprises(6).

Value is also extracted in terms of human capital and guidance, with new businesses or businesses about to take steps in innovation receiving disproportionate benefits from VC(5). This is a component of venture capitalists lending their expertise and knowledge in addition to their funds to nascent businesses.

Individual investment values have been shown to be surprisingly positive, with research from the NBER(7) showing that only one in ten investments not returning any money. One in four investments on the other hand, were shown to have a rate of return above 50%. While these statistics may be positively biased, other estimates point to 2/10 investments making substantial amounts, and only a small amount of investments not at least breaking even.

The take away from this summary, starting in popular news and "instant analysis" and ending with academia and "professional analysis" is that venture capital, like many other aspects of our world, is difficult to predict in the moment. It is a powerful and volatile industry, with enormous economic potential and high risk. It is an industry that, like we saw in 2016, may seem to be on its death bed one quarter, and breaking records the next. For the policymaker and student of entrepreneurship, heed these words; about the only things you can predict with venture capital are that she's a strong and ever changing mistress, and she's here to stay.

Google Doc Link: https://docs.google.com/a/rice.edu/document/d/1-18KkY4y1_xV_kjidVToQM0mp16ViCeViAX1UYC5dbE/edit?usp=sharing

==References==

(1)http://venturebeat.com/2016/04/13/vcs-raised-billions-in-q1-even-as-they-made-fewest-investments-in-3-years/
(2)http://pitchbook.com/news/articles/why-last-years-valuation-bubble-led-to-more-record-vc-fundraising-in-2q
(3)https://home.kpmg.com/xx/en/home/media/press-releases/2016/04/venture-capital-pullback-deepens-in-q1-2016.html
(4)http://www.nber.org/papers/w6906.pdf
(5)http://www.sciencedirect.com/science/article/pii/S0883902696000523
(6)http://www.legis.state.tx.us/tlodocs/84R/handouts/C2702016041213001/be8b25df-8858-4430-b508-ec4109449d38.PDF outlined
(7)http://papers.ssrn.com/sol3/papers.cfm?abstract_id=136388

Regulatory Challenges for Innovation Policy (Blog Post)

2017-11-15T15:55:13Z

Dayton:

{{BlogPost
|Has title=Regulatory Challenges for Innovation Policy (Blog Post)
|Has author=Avesh Krishna
|Has content status=Tabled
|Has graphics status=None
|Has processing notes=Avesh to fix and find peer editor
}}
==Abstract==

==Blog Post==

The Founding Fathers had the foresight to include patents among the functions of the federal government in the constitution (Article I, Section 8). George Washington urged Congress to implement protections for innovation in his first State of the Union address resulting the third act of the first Congress: the Patent Act of 1790. The Framers’ recognition of the crucial importance of innovation to economic growth has served as the solid foundation of the free enterprise system.

This strong system of intellectual property protection needs to be updated to fulfill the Framers’ intentions. The balancing act of protecting intellectual property and preventing exploitation of those protections has proved difficult to accomplish. At either extreme, the value of patents and, in tandem, incentive to innovate, is diminished.

Due to lobbying from special interest groups and large corporations, the current patent system shortsightedly addresses scapegoats rather than solving chronic issues. Patent enforcement must simultaneously support innovation and protect intellectual property. Too much regulation hinders innovation by prolonging the time needed to move an idea into the market. No protection of intellectual property reduces the incentive to innovate and hinders economic growth. Poorly designed, intrusive government regulation leads to frivolous legal challenges.

The 2011 America Invents Act (AIA) was geared to assuage concerns about patent trolls -- entities that raise revenue by enforcing patents they acquire without implementing them -- while benefiting large corporations at the expense of small businesses and individual inventors. Small companies and individual inventors were forced to file earlier and are unable to patent ideas if they are brought to market before patenting. Additionally, smaller entities that lack capital may be unable to patent ideas or enforce their patents against large corporations due to higher costs under the provisions in the AIA. Large corporations and lobbyists have hoodwinked policy makers, convincing them NPEs are the largest threat to the patent system. In reality, they are but a minor threat, even for large corporations.
Pending legislation, chiefly the Innovation Act and PATENT (Promoting American Talent and Entrepreneurship) Act, continues this unnecessary focus on fighting trolls, while doing little to resolve other issues regarding the patent system. Both the Innovation Act, introduced by Rep. Goodlatte of Virginia, and the PATENT act, introduced by Sen. Grassley of Iowa, fail to distinguish between Patent Assertion Entities (trolls) and NPEs, of which PAE’s are a subset. While many PAE’s are bad actors who only enforce patents to financial gain without innovating themselves, NPEs include small businesses and individual inventors who want to protect and license their intellectual property. Rather than reducing the threat that PAEs pose to small businesses, these bills will unfairly benefit large established corporations while harming small businesses. While this legislation may move forward in the next Congress, neither bill has made progress since moving out of committee.

Congress has failed to fulfill its constitutional mandate to protect intellectual property. In the resulting vacuum, many of the major reforms to patent policy has come from court decisions. The next major decision expected will likely involve the constitutionality of inter partes review (IPR) that was introduced with the American Invents Act of 2011. IPR enables third parties to challenge the validity of patents based on prior art evidence to an internal patent office board. Prior art is vaguely defined but can encompass any and all knowledge, products, previous patents, or publications that demonstrate use of the claims in the challenged patent before that patent was filed. IPR has led to an increase of challenges to the validity of patents. Opponents of IPR claim it harms small businesses and individual inventors by decreasing intellectual property rights. IPR has made it cheaper and easier to challenge patent claims without increasing the number of patents being invalidated.

MCM Portfolio LLC vs Hewlett-Packard, is pending petition from the Supreme Court and can be expected to be heard sometime before the end of 2017. This case calls into question whether IPR violates Article III of the constitution and if it violates the 7th amendment. The Article III issue arises since IPR is conducted before the Patent Trial and Appeal Board, an administrative division of the USPTO, rather than before federal judges in court. Following from this, the 7th Amendment, which guarantees a right to trial by jury, is claimed to be violated during the IPR process as there is no trial nor jury.
We can expect a steady trickle of cases attacking provisions of the American Invents Act though the success of such cases cannot yet be predicted. To fulfill their constitutional obligation to protect intellectual property, Congress must act to reform the patent system. Inaction will harm small business and lead to jurists deciding matters that the Framers intended to leave to the legislature.

==References==
Original text by Avesh Krishna

==Link==
https://docs.google.com/a/rice.edu/document/d/1Qyp7dnBoGlomGfCa12BnauNF-h_vWvRvDPU2l43XonM/edit?usp=sharing

[[Category:Innovation Policy]]

Theories of Entrepreneurship (Blog Post)

2017-11-15T15:54:22Z

Dayton:

{{BlogPost
|Has title=Theories of Entrepreneurship (Blog Post)
|Has author=Dylan Dickens
|Part of series=Entrepreneurship 101
|Has content status=Tabled
|Has Graphics status=None
}}
==Abstract==
Low-quality blog post on theories in relation to entrepreneurship from Nash to Schumpeter to Engelhardt

==Text==
As defined by the father of the theory himself, John Nash, a game theory bargaining situation occurs in many forms, generally involving two actors who have the opportunity to collaborate for mutual benefit in more than one way(1). Game theory, the overarching economic postulation concerning how rational actors interact in a given scenario has been used for years to both analyze and predict various elements of the United States economy. From business culture to stakeholder investments, international finance to domestic tax laws(2), game theory has been lauded in its usage throughout economics. One area where it may falter is in the area of entrepreneurship.
Dr. Lucas Engelhardt makes the argument that game theory sometimes fails to predict entrepreneurial actions because entrepreneurs don't play the game, they change it to better suit their needs(3). His argument, as delivered orally in a lecture at the Mises Institute for Austrian Economics, Freedom, and Peace starts with the quintessential prisoner's dilemma. As formalized in academia, The prisoner's dilemma consists of two members of a criminal gang who are arrested and imprisoned. Each prisoner is in solitary confinement with no means of communicating with the other. The prosecutors lack sufficient evidence to convict the pair on the principal charge. They hope to get both sentenced to a year in prison on a lesser charge. Simultaneously, the prosecutors offer each prisoner a bargain. Each prisoner is given the opportunity either to: betray the other by testifying that the other committed the crime, or to cooperate with the other by remaining silent.
The outcomes then read as: If A and B each betray the other, each of them serves 2 years in prison, if A betrays B but B remains silent, A will be set free and B will serve 3 years in prison and vice versa, and if A and B both remain silent, both of them will only serve 1 year in prison on the lesser charge. Classical game theory suggests that the prisoners, each acting in rational self-preservation, will individually chose betray the other, leading to the collectively worst outcome. Engelhardt suggests that an "entrepreneurial" criminal would approach this game in a different way, by changing the rules through foresight. An entrepreneurial criminal, in Englehardt's words, would have foreseen the possibility of getting caught, and, before the crime had hired a third gang member to stand outside the police station and break the knees of any individual who left free. This third gang member, and the understanding of additional consequences among the two original gang members changes the game and provides a stronger force to work towards the best outcome.
While this definition of an entrepreneur through game theory may be dubious at best, it reflects a trend in entrepreneurship that has been around since the early 20th century and Harvard economist Joseph Shumpeter. Schumpeterians define entrepreneurs as innovators: people who come up with ideas and embody those ideas in high-growth companies. This is essentially the same definition in non-game terms, entrepreneurs are individuals who bring something new to the table. Schumpeterians further distinguish between “replicative” entrepreneurs who set up small businesses which resemble already existing small businesses, and “innovative” entrepreneurs who upset and disorganize the existing way of doing things. They also distinguish between “small businesses” and “high-growth businesses”. Both sorts have an important role in a successful economy. But they are nevertheless very different sorts of organisations(5).
These formal definitions of an entrepreneur are key to keep in mind whether one is crafting policies related to entrepreneurs, attempting to start their own business, or merely wants to know more. Form the early theories of John Nash and Joseph Shumpeter to the modern lectures of Lucas Engelhardt show some of the evolution and range of broad economic theory towards entrepreneurs. Once common theme runs throughout however, entrepreneurs bring something new to the stage. They innovate, create, and shake up the status quo, and are a driving force in the economy of the future.

Google Doc Link: https://docs.google.com/a/rice.edu/document/d/1jzcvJCG8k-5jRZm9NBuaGNL7ecykOJXEBRy-QkcyCN0/edit?usp=sharing

==References==

The Future of Healthcare and Small Businesses

2017-11-15T15:54:00Z

Dayton:

{{BlogPost
|Has title=The Future of Healthcare and Small Businesses
|Has author=Meghana Gaur
|Has content status=Tabled
|Has processing notes=From Tay: should we table this piece or look into rewriting it with Trump's plans in mind?
|Has project status=Repurpose
}}
When analyzing the effects of the Affordable Care Act (ACA) on small businesses in light of the status quo and general trends of the health insurance market, it seems unlikely that the bill will turn out to be the job-killer or enemy of small business it was predicted to be. Although insurance premiums and deductibles are likely to rise, they have not had detrimental effects on economic growth in recent years. Ex-Congressional Budget Office (CBO) Head Douglas Elmendorf, who chaired the office until 2015, said in an interview with the Bloomberg Bureau of National Affairs (BNA) that the “ACA has not seen the kinds of increased costs and disruptions in the insurance market that critics had feared.” [http://www.bna.com/excbo-chief-elmendorf-n17179927007/]

==What is the outlook of Obamacare in 2016?==

Still even more uncertain than the effects of the ACA on small business is the future of the act itself. While 2016 Democratic Presidential Nominee Hillary Clinton vows to defend the ACA, strengthen its benefits, and minimize any detrimental effect on small business, [https://www.hillaryclinton.com/issues/health-care/] Republican Candidate Donald Trump vehemently opposes the act, stating that he will request a congressional repeal of the Obamacare on his first day in office. [https://www.donaldjtrump.com/positions/healthcare-reform]

The CBO and JCT forecasted that repealing the ACA would increase the number of uninsured non-elderly people by about 19 million in 2016 and by roughly 24 million in “all subsequent years through 2025, compared with the number who are projected to be uninsured under the ACA.” This amounts to an average annual increase of approximately 8 million, with coverage purchased individually or obtained through Medicaid falling by between 30 and 32 million. [https://www.cbo.gov/publication/50252]

In an interview with Bloomberg BNA, ex-CBO chief Douglas Elmendorf ,when asked about repealing the ACA, claimed that “there are no alternatives to the Affordable Care Act that are on the table with independent, reputable estimates of their effects on insurance coverage and the federal budget.” [http://www.bna.com/excbo-chief-elmendorf-n17179927007/]

==If the answer is not to repeal, then how can we reform?==

One alternative to push back against the trend of rising premiums is to increase competition and choice in the insurance provider market. Premium increases vary between states, but some Americans are witnessing “insurers exit the health exchange marketplace, limiting choice, and as a result, boosting premiums” even more than others, depending on their state of residence. [http://health.usnews.com/health-news/health-insurance/articles/2016-01-06/aca-changes-more-small-business-owners-to-offer-coverage-face-increased-compliance]
The CBO has found that premiums tend to be lower in markets with more insurers, hospitals, and physicians. This might seem obvious as insurers would be forced to lower rates to win over enrollees and remain competitive in a market with more suppliers; coupled with an increase in the supply of physicians and hospitals, insurers could negotiate lower payment rates, and as a result, offer lower premiums! However, while it may seem like obvious economics to increase competition among insurers, the CBO warns that “efforts to reduce increase competition among insurers, like other effort to reduce insurance premiums” might produce “complex effects”, as most insurance, hospital care, and physicians’ services markets are already quite concentrated. [https://www.cbo.gov/publication/51130]

==The Election and Healthcare==
According to the Centers for Disease Control and Prevention’s National Center for Health Statistics, the proportion of uninsured Americans dropped below ten percent in 2015 for the first time in history. [http://www.wsj.com/articles/number-of-uninsured-in-u-s-drops-below-10-for-first-time-in-2015-1463501948] While proponents of the Affordable Care Act could argue that this statistic serves as a defense of the act in of itself, its opponents argue that the law explain rising costs in healthcare.

Since its inception, the ACA has taken on a decisive, divisive, and defining role in American politics, markets, and everyday life. Critics of the mandate have complained about its negative effects on small businesses and pointed to the bill as the sole source of increases in premiums. Future executive and congressional leadership will determine the trajectory of healthcare reform in America, and it’s no bewilderment that the controversy surrounding “Obamacare” in American politics has thrust healthcare to the center stage of the 2016 U.S. Presidential Election.

American small business owners and employees need leaders that are willing and prepared to reap the potential benefits of the ACA, while reforming to protect against the detriments of rising premiums. In order to ensure that access to affordable and comprehensive health insurance coverage remains accessible to all citizens, a preemptive, "one-size fits all" policy to replace or eliminate the ACA should be avoided. [http://www.creative-healthcare.com/pdf/The-role-of-Emotional-Intelligence.pdf]

==Link to Google Doc==

https://docs.google.com/document/d/1tdmarlvaY9zrWiqQB7dVEqnukS0rmiV38VO-1-WFK4o/edit

==References==

1. http://www.bna.com/excbo-chief-elmendorf-n17179927007/

2. https://www.hillaryclinton.com/issues/health-care/

3. https://www.donaldjtrump.com/positions/healthcare-reform

4. https://www.cbo.gov/publication/50252

5. http://www.bna.com/excbo-chief-elmendorf-n17179927007/

6. http://health.usnews.com/health-news/health-insurance/articles/2016-01-06/aca-changes-more-small-business-owners-to-offer-coverage-face-increased-compliance

7. https://www.cbo.gov/publication/51130

8. http://www.wsj.com/articles/number-of-uninsured-in-u-s-drops-below-10-for-first-time-in-2015-1463501948

9. Delmatoff, J., & Lazarus, I. R., F.A.C.H.E. (2014). The most effective leadership style for the new landscape of healthcare. Journal of Healthcare Management, 59(4), 245-9

The Brexit and Entrepreneurship (Report)

2017-11-15T15:53:38Z

Dayton:

{{BlogPost
|Has title=The Brexit and Entrepreneurship (Report)
|Has author=Dylan Dickens
|Has content status=Tabled
|Has graphics status=None
|Has project status=Repurpose
}}
==Abstract==
800 words on the Brexit and its affect on entrepreneurship.

==Text==
High-tech, high-growth small businesses represent both the moidern sterotype financial powerhouse of entrepreneurship. In order to best promote the development and growth of these nascent firms, a healthy "entrepreneurship ecosystem" must be maintained. This ecosystem, a unique combination of various institutions, cultures, and trends largely relies on four major factors to impower and drive entrepreneurship. Cutting edge research, large pools of venture capital, an established post-entreprenuer tech industry, and entreprenuerial institutions such as accelerators and incubators round off the four pillars of a healthy entreprenuership ecosysetm(1).

With such a diverse range of sectors from finance to start-ups, and academia to high tech, rarely do all four pillars fall at once. Usually, if one component of the ecosystem falters, the environment will stutter rather than collapsing. On June 23rd just past 10:00 p.m., this all may have changed. In a vote from 16,141,241 to 17,410,742, the United Kingdom voted to leave the European Union in a historic referendum(2). While the future for the U.K., and the world, seems to be cloudy, the immidiately measurable effects seem to spell out doom for a healthy british entreprenuerhsip ecosystem.

The opinions of English entreprenuers appear vary dramatically. From Husayn Kassai, CEO and co-founder of Onfido, stating, “there is a lot of uncertainty around but one thing for sure is that this is bad news for the tech industry," to Nena Chaletzos, founder of Luxtripper, who claims “The leave vote gives us more flexibility in a global economy, and I think that we will get better deals and be more competitive globally," the almost 50/50 split in the U.K. constitutency seems tio have carried over to its entreprenuers(3). Retuning to the four pillars of entrepreneurship ecosystems however, how will the Brexit likely effect the future of British ingenuity?

Starting with the key factor of acadmeic research, the "leave decision" appears to be a loss. Stephen Hawking and around 150 other members of the Royal Society at Cambridge University have called to decision a "disaster for UK science and universities." The disaster largely stems from research funding. While overall the U.K. experienced a net loss in funds contributed to the E.U. versus funds recieved, it has actually experienced a net gain of about 3.4 billion pounds in research, development and innovation funds contributed to the E.U. versus funds recieved. In the area of scientific research, the U.K. waas a major benificiary of E.U. redistribution. Further detriments of a Brexit arrise from a potential falling out with the European Space Agency and drop in collaboration with the larger European scientific community(4).

The second pillar of availible venture capital appears to also be suffering in light of the leave decision. The IMF used its annual report on the British economy to say Brexit would plunge the UK into recession next year and that it could see no economic advantage in leaving the EU(5). This is a critical assment not only in its ominous nature, but also in its potential implications for venture capital. Regression-based analysis from the finanical journal ''Venture Capital'' conducted on the 2008 recession shows that "financial crisis is associated with a 20% decrease in the average amount of funds raised per funding round" in a destructive blow to venture capital(6).

The bad news doesn't seem to stop there. Chugging right along to the third aspect of entreprneurship ecosystems, an established post-entreprenuer tech industry, this too seems to be in danger due to Brexit fallout. As the Guardian reports, the well established FinTech, or financial technology industry in London is heavily considering relocation after the decision(7). "Headquartering elsewhere is now a possibility", says Taavet Hinrikus, the chief executive and co-founder of TransferWise, "as loss of talent and regulatory uncertainty rear their heads, we will probably not grow the team based here [in London] much more." With the Brexit causing immediate damages in scientific research, venture capital, and the tech industry, the final pillar of entreprneurship crumbles as well.

Entreprenuership institutions such as accelerators, incubators and hubs cannot exist, let alone thirve without well, entreprenuers. With research stagnating, venture capital dropping, and tech firms searching for new grounds, the decision to leave has seemingly brought all four pillars of entreprenuership to a crumble. This fallout is one of the many effects which compelled 10 Nobel prize in economics winners to declare "We believe that the UK would be better off economically inside the EU." in a pleading open letter published the Sunday before the vote. Perhaps Toby Coppel, the co-founder of British venture capital firm Mosaic, expresses the concerns for the future best; "The next entrepreneur who’s 22 years old, graduating from a technical university may, instead of moving to London to do their tech startup, decide to go elsewhere instead. I think that’s one of the biggest concerns I have about the trajectory of the London ecosystem."

==References==

(1)http://www.legis.state.tx.us/tlodocs/84R/handouts/C2702016041213001/be8b25df-8858-4430-b508-ec4109449d38.PDF

(2)http://www.wsj.com/livecoverage/eureferendum

(3)http://venturebeat.com/2016/06/24/u-k-entrepreneurs-react-to-brexit-vote/

(4)http://www.theverge.com/2016/6/24/12023230/brexit-impact-science-research-uk-eu-funding-collaboration

(5)https://www.theguardian.com/business/2016/jun/18/imf-says-brexit-would-trigger-uk-recession-eu-referendum

(6)What is the effect of the financial crisis on venture capital financing?
Empirical evidence from US Internet start-ups

(7)https://www.theguardian.com/technology/2016/jun/24/british-tech-firms-eye-relocation-after-brexit-vote

(8)http://www.theguardian.com/politics/2016/jun/19/we-nobel-prize-winning-economists-believe-the-uk-is-better-off-in-the-eu

[[Category:Entrepreneurship Policy]]

Tackling Stagnation of Texas Entrepreneurship (Blog Post)

2017-11-15T15:53:06Z

Dayton:

{{BlogPost
|Has title=Tackling Stagnation of Texas Entrepreneurship (Blog Post)
|Has author=Dylan Dickens
|Part of series=Texas Entrepreneurship
|Has content status=Tabled
|Has Graphics status=None
}}
==Abstract==
A blog post focusing on the status of Texas entrepreneurship and a potential solution.

==Blog Text==

"If current trends continue, Texas, the second-largest state in the U.S. in terms of gross domestic product (GDP), will be
struggling to remain in the top 10 for venture capital investment within the next decade. The reason for Texas’ relative decline is simple: while other high-ranking states are growing their venture capital investment at extremely fast rates, Texas’ venture capital investment has decreased 19% over the past 10 years in real terms." In this damning [http://bakerinstitute.org/files/10556/ analysis] backed by inflation [www.bls.gov/data/inflation_calculator.htm data] from the U.S. Bureau of Labor Statistics, a serious concern for the future of the Texan economy is laid out.

Venture capital investment is critical to maintaining a healthy entrepreneurship ecosystem. As [http://www.legis.state.tx.us/tlodocs/84R/handouts/C2702016041213001/be8b25df-8858-4430-b508-ec4109449d38.PDF outlined] by Larry Peterson, the Executive Director for the Texas Foundation for Innovative Communities, high-growth companies developed from healthy entrepreneurship ecosystems account for only 2 – 7% of all companies in a given year, but account for all net jobs and GDP growth. He elaborates that in order to preserve the Texan economy in light of dropping oil prices, rather than reducing services or raising taxes, Texas should rather try stimulating growth.

Innovation and entrepreneurship, which are "calculated to drive about 60% of this economic growth," require strong sources of capital, labor and technology, a healthy entrepreneurship ecosystem, to thrive. Texas has low costs, relatively low taxes, moderate
regulatory burden, and tort restraint, which add up to a pretty solid economic formula to promote standard business, but unfortunately its not enough to promote entrepreneurship. "Mighty research universities, large pools of venture capital, experienced serial entrepreneurs, large technology companies, and vibrant entrepreneurial resources all combine to provide robust entrepreneurship growth," Peterson continues, outlining the necessities for a vibrant Texan future.

The largest identified lag in these necessities within Texas is capital. While there is "no shortage of capital" in Texas, there are seemingly unnecessary barriers to access, a lack of information, and little government support. In order to avoid appropriation, there are two suggested routes of government supported entrepreneurship without having government funded entrepreneurship. Taking a play out of Michigan's book, a one-time, government-funded State Small Business Credit Initiative, or SSBCI, could be a solution. No direct appropriation is made, but future revenues are used as collateral to raise funds from the private sector.
Managed prudently, the returns cover return of capital and program expenses, and can even yield a small surplus for the state. In the SSBCI model, privately managed funds are making investment decisions, alongside a multiple of private capital, usually 4x-20x, invested alongside state funds.

The second apparent option lies in an Economically Targeted Initiative or ETI. This provides a means to leverage state trust funds such as retirement funds and endowments, for the secondary purpose of building a state venture industry. Texas currently maintains more than $200B in such funds, in which which tens of billions are already invested in venture funds but primarily in other states. All of these trust funds are currently empowered to make co-investments with leading venture funds into Texas companies, or give tie-breaker preference to Texas-based fund managers, but there is no systematic disclosure or promotion of this information. By spreading a notion of "Texas-first" to encourage ETI investment into Texas-based entrepreneurship, this overwhelming source of capital could be used to kick-start the Texan entrepreneurship ecosystem.

Google Doc Link: https://docs.google.com/a/rice.edu/document/d/1kZwbgOVFUJSmpA3pFruXJRBeAd9sMa3FhBouTZz6rkQ/edit?usp=sharing

==References==
In line. See above.

[[Category:Innovation Policy]]

Swedish Entrepreneurship (Blog Post)

2017-11-15T15:52:45Z

Dayton:

{{BlogPost
|Has title=Swedish Entrepreneurship (Blog Post)
|Has author=Dylan Dickens
|Part of series=Entrepreneurship Abroad
|Has content status=Tabled
|Has Graphics status=None
}}
"Det är en bra uppfattning", or "that is a good idea" in English, is a Swedish phrase likely used in the early stages of many familiar start-ups. In a seemingly confusing and not oft reported trend the cold Nordic nation of less than 10 million people is responsible for churning out a slew of major tech firms from a seemingly unstoppable entrepreneur base. QlikTech, MySQL, TradeDoubler, Skype, SoundCloud, Klarna, Spotify, Rebtel, King.com, the Pirate Bay, Kazaa, and Mojang, some of the most recognizable names in tech, are all firms [https://pando.com/2012/11/20/why-tiny-stockholm-has-the-most-stunning-startup-ecosystem-since-tel-aviv/ started] by Stockholmers over the past 25 years. Sweden, when combined with the other Nordic countries of Denmark, Norway, Iceland and Finland, make up for 6.5% of the world’s billion-dollar exits from 2005 to 2012, according to statistics provided by Stockholm-based venture capital firm Creandum. What factors allows such a small environment to be so effective at producing entrepreneurs?

There appear to be a few answers to this question. Sweden has a GDP per capita of $47,900, 26th in the [https://www.cia.gov/library/publications/resources/the-world-factbook/geos/sw.html world], and a highly educated population, ranking among the top 20 nations in terms of education expenditure. The nation also [http://worldhappiness.report/wp-content/uploads/sites/2/2016/03/HR-V1_web.pdf ranks] as the 10th happiest on earth. While these factors certainly seem to give a Swedish entrepreneur a head start, a 2013 [http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2289134 study] from the Stockholm School of Economics uncovers other interesting traits. The study identifies Sweden's social democratic political system with low income inequality and Swede's cultural aversion to "showing off" as two large factors in promoting entrepreneurial spirit. These factors helped produce the identified crowding-in spirit of "if they can do it, I can do it," in the small, largely homogeneous country.

Other cited factors include the government-sponsored, widely developed broadband network pushed out in the 1990s, and the government-instigated PC-lending program that put computers in the hands of even the lower classes from an early stage, which helped cultivate a high-tech, early-adopter society. Swedish winters were also mentioned, as they are long, dark, and cold, which supposedly encourage people to stay inside and "noodle away" at creative endeavors, such as programming or gaming. These factors combine into an impressive history of both well established tech firms and second generation start ups to draw expertise and inspiration from.

Finally, a model of looking inward for talent and outward for business allows Swedish start ups to take advantage of highly educated, low cost Swedish technicians for labor and the relatively homogeneous greater Nordic market as a jumping-off pad. Plus, after viewing Swedish simplicity like IKEA, you have to admit they have pretty desirable design skills as well. Not all is cheery in the entrepreneur atmosphere of Stockholm however, the study also cites a lack of a "critical mass" of investors, a cultural aversion to crowdfunding, and overall deficiency in starting capital as barriers to startups. All of this in mind however, it is clear to see the many traits and factors which help Stockholm rule the tech startup world.

Google Doc Link: https://docs.google.com/a/rice.edu/document/d/1w-XLEh0u5xgp0mu2ia6ey9v9lqbrlfnWnuXUX_eq-uE/edit?usp=sharing

Spotlight on Innovation: Space

2017-11-15T15:52:19Z

Dayton:

{{BlogPost
|Has title=Spotlight on Innovation: Space
|Has author=Carlin Cherry
|Has content status=Tabled
|Has project status=Repurpose
}}
==Abstract==
There's a lot on this issue, so i might subdivide it into more bite-sized blog posts as i continue to research.

==Public Sector==
-NASA on mars by 2020
http://www.space.com/26134-innovation-getting-us-to-mars-in-2020.html

-things needed to go to mars/for further deep space exploration
-http://www.prnewswire.com/news-releases/nasa-itech-fosters-technology-needed-for-journey-to-mars-300331916.html

==Private Sector==
-Seattle new innovation center for space flight
http://www.nytimes.com/2016/08/02/science/seattle-space-flight-innovation-center.html?_r=0

-boom in small satellites
https://www.fastcompany.com/3058795/the-next-frontier-for-ambitious-entrepreneurs-space

-3d space printer
http://www.thespacereview.com/article/2348/1

Social Media Usage in Entrepreneurship (Blog Post)

2017-11-15T15:51:49Z

Dayton:

{{BlogPost
|Has title=Social Media Usage in Entrepreneurship (Blog Post)
|Has author=Dylan Dickens
|Part of series=Social Factors in Entrepreneurship
|Has content status=Tabled
|Has Graphics status=None
}}
==Abstract==
This blog post aims to reveal the powerful effects of social media on SME's and point out the significant gap in usage.

==Blog Post==
Social media is an incredible tool. From the obviously marketable Pinterest, LinkedIn and Twitter to the seemingly frivolous Facebook, reddit and Instagram, social media's diversity of audiences is only rivaled by its efficiency in reaching them. While social media tends to permeate into every aspect of a person's life these days, [http://docsdrive.com/pdfs/medwelljournals/sscience/2016/639-644.pd research] from ''The Social Sciences'' seems to suggest that "not many" entrepreneurs of small to medium sized firms know about the benefits of social media in entrepreneurship. Thankfully, statistics from the ''Heartland Monitor'' [http://heartlandmonitor.com/ poll] conducted by Allstate and NationalJournal reveal some substantial benefits.

59% of social media users say that a company’s social media activities make the company appear “accessible and responsive," and 64% of social media users want to see companies use social media for customer service. Furthermore, the poll's statistics show that just being available on social media can be beneficial to sales. These benefits are further reinforced by [http://www.ijhssnet.com/journals/Vol_3_No_10_Special_Issue_May_2013/24.pdf research] on small to medium sized enterprises, or SME's, from the ''International Journal of Humanities and Social Science''. The study claims that social media tools offer greater market accessibility and customer relations which in turn have a significant impact on the growth of SMEs. The study further postulates that geographical barriers can be broken down by the use of social media and that social media allows businesses to communicate speedily and cheaply with customers.

One of the more novel findings of the study is the vale of social media for creating a customer database based off of digitally recorded preferences, locations, and trends. While the study found little effect on SMEs from social media in the areas of pricing and innovation, the study still strongly recommends that policies which encourage best practices in social media be adopted in order to promote the growth of SMEs. Despite all of this evidence, the ''Heartland Monitor'' [http://heartlandmonitor.com/ poll] reveals that only 50% of small businesses are using social media.

If the benefits are so apparent, why aren't more small businesses using social media? When asking this question, the researchers [http://docsdrive.com/pdfs/medwelljournals/sscience/2016/639-644.pd from] ''The Social Sciences'' found that the biggest barriers were a "lack of computer knowledge" and a "failure to see an effect of social media on small business. An independent [https://www.linkedin.com/pulse/3-reasons-small-businesses-dont-use-social-media-why-zena study] published in the LinkedIn Pulse claims that a lack of perceived return on investment, a lack of time, and a perception that their consumer base does not use social media are the largest dampeners of SME social media performance.

Keeping in mind the seemingly well-established links between social media presence and small business performance, a potentially convincing set of strategies may emerge. Through subsidized educational programs, computer skills among entrepreneurs could increase and the initial investment and time costs of a social media presence could be reduced. A spreading of the empirical data linking social media use with business returns could also potentially be beneficial allowing entrepreneurs to envision a return on investment and feel more comfortable taking the first critical steps into the social media arena. In the "dog-eat-dog" world of small business today, it is critical to maintain the advantage, and a seemingly clear way to do that is through social media use.

Google Doc Link: https://docs.google.com/a/rice.edu/document/d/1GDZIxgwUAQI71asiVeSQx6ft1h7x8nTM04ACw9M9FJY/edit?usp=sharing

==References==
In line. See above.

Poaching and Entrepreneurship (Blog Post)

2017-11-15T15:51:21Z

Dayton:

{{BlogPost
|Has title=Poaching and Entrepreneurship (Blog Post)
|Has author=Dylan Dickens
|Part of series=Social Factors in Entrepreneurship
|Has content status=Tabled
|Has Graphics status=None
}}
==Abstract==
A blog post focusing on some trends in business poaching.

==Blog Text==
"StIllannoyed? No wonder." read billboards [http://www.usatoday.com/story/money/business/2014/06/28/gop-governors-poach-jobs-mike-pence-rick-perry/11125529/ funded] by the Indiana government along the major thoroughfares of the greater Chicago area. Victor Smith, Indiana's Secretary of Commerce, in a practice known as business-poaching attempts to draw Illinois based businesses and startups to Indiana instead, a low-tax state that "works." Indiana is not alone in this practice, USA Today reports that former Texas Governor Rick Perry also actively pushed his state towards poaching. This practice brings about a larger conundrum though, in the highly competitive and often close-packed entrepreneur ecosystem, how are issues such as talent poaching, information sharing, and trade security maintained?

Take for example a co-working space shared by a variety of tech start-ups, at which point do the benefits of sharing know-how, resources, and mentorship in a hub or incubator environment become more of a liability than an asset? [http://isb.sagepub.com/content/25/4/361.short Research] from Queen's University Belfast suggests that there is a natural transition between nascent firms and more developed firms from requiring the support of proximity to being hampered by it. Incubators were found top be effective support mechanisms for fledgling enterprises in that they provide basic facilities, office space, administrative staff and expert managers during the volatile start-up and growth process.

The research further found however that as the firms grew and sought to enhance credibility and protect market share, disadvantages emerged regarding incubator placement. Firm proximity created tensions concerning privacy, the protection of intellectual property and competitive strategies. It was also evident that as the firms became more mature, the need to develop independent, secure internal systems could be impeded by the ready availability of support and advice from the incubator management team. Similar results were seemingly replicated through an [http://www.sciencedirect.com/science/article/pii/S0014292106000183 article] in ''European Economic Review'' concerning information sharing among banking entities.

It was suggested that information sharing reduces relationship benefits, and, therefore relaxes competition for initial market shares. Information sharing introduces a welfare trade off by promoting equilibrium profits at the expense of talented entrepreneurs whenever market power persists in credit market, whereas it is a matter of indifference without market power. Thus information sharing may induce exclusion of creditworthy borrowers from credit markets. So how can information sharing and business poaching be combated?

Cliff Oxford, founder of the Oxford Center for Entrepreneurs, [http://boss.blogs.nytimes.com/2014/09/11/heres-how-to-keep-competitors-from-poaching-your-employees/?_r=0 writing] for the New York Times suggests a solution. In order to avoid the "huge and demoralizing loss for the culture and profits of a company" that is talent and information poaching, Oxford claims a new hire “break-up fee” worked for him and his company. He claims the plan worked as follows: employees started at about 50% less than market rate, but by month 12 they were 20% ahead of the market. By the end of the second year, they were 40% ahead of the market, and by year five they could be as much as 100% ahead of the market, and "worth every penny." Keeping in mind the potentially disastrous effects of business poaching and information dissemination, plans such as Oxfords may be a solution to reap the many benefits of co-working in entrepreneurship while avoiding the detriments.

Google Doc Link: https://docs.google.com/a/rice.edu/document/d/1IHrXdtEwh5BlIms76-bG0OHZN3m16R8nAubRTp6e_6M/edit?usp=sharing

==Resources==
Inline. See above.

Muslim Entrepreneurs (Issue Brief)

2017-11-15T15:50:52Z

Dayton:

{{BlogPost
|Has title=Muslim Entrepreneurs (Issue Brief)
|Has author=Dylan Dickens
|Has content status=Tabled
|Has processing notes=Decrease focus on Trump; differentiate between high-tech, high-growth and small businesses
|Has project status=Repurpose
}}
==Abstract==
1,400 words on the effects of Muslim entrepreneurs on the ecosystem, and some of the dangers of anti-Muslim demagogy in terms of entrepreneurship.

==Text==
As Aristophanes wrote in the 5th Century B.C., "A demagogue must be neither an educated nor an honest man; he has to be an ignoramus and a rogue." The United States 2016 presidential race seems to be showing signs of demagogy absent since the time of Ross Perot in the late 90's. Due to a variety of factors, ranging from a marked increase in violent Islamist fundamentalist and extremist actions in the 21st Century(1) to a disproportionately negative portrayal of the culture and actions of their religion as a whole in American media(2), Muslim Americans have disproportionately bore the load of populist pressure in this cycle. As the Bookings Institute's Center for Middle East Policy(3) purports, Presumptive Republican presidential nominee Donald Trump's proposal to ban Muslims from entering the United States “until we figure out what’s going on,” is dangerous, bigoted and "genuinely frightening." As it so happens, its a terrible idea economically as well.

Entrepreneurship is an incredible boon for the economy. The mere less than 1% of entrepreneurial firms which receive venture capital(4) alone are responsible for 5.3%-7.3% of job growth (5) and 35% of companies which reach an initial public offering(6). In his testimony for the Texas House Investments & Financial Services, Larry Peterson the Executive Director for the Texas Foundation for Innovative Communities, claimed that these high-growth companies will account for nearly all net jobs and GDP growth in the state of Texas, and will be responsible for driving about 60% of necessary economic growth in our state(7). This comes at little surprise, as while Texas under performs in some aspects of its entrepreneurship ecosystem, such as available capital(8), it vastly over performs in one key area: immigration. Immigration has been highlighted by the Small Business & Entrepreneurship Council(9) as a key issue in small business and entrepreneurship. The council cites a 2012 report from the Small Business Administration that reveals higher rates in business ownership, business formation, and business exportation among the U.S. immigrant population as compared to the non-immigrant population(10). The council further quotes a 2014 Kauffman Foundation report(11) in saying that "Immigrants were nearly twice as likely to start businesses each month as were the native-born in 2013," as well as Karen Gordon Mills, the former Administrator of the U.S. Small Business Administration, as saying: “In 2011 alone, immigrants started 28 percent of all new U.S. businesses, despite accounting for only 13 percent of the U.S. population(12)."

Muslim Americans are not just disproportionately affected by 2016's revival of demagogy, as it turns out, Muslim Americans are also disproportionately likely to start their own business as well. As Forbes reports,(13) the percentage of Americans involved in start-ups hit a record 13% in 2012. Gallup reports(14) a much higher, more than double, rate of start-up involvement among Muslims, with around 27% of Muslim Americans reporting to own their own business. Iran, a nation with 99.7% of its population following the faith of Islam(15) has an incredible rate of immigrant entrepreneurship, with nearly a quarter, 24.4% of all Iranian immigrants in the United States owning their own business(16). Other nations with above average rates of Islamic devotees such as India and Canada, also posses higher than average rates of business ownership among their American diaspora. There are plenty of less-than-empirical tales of Muslim entrepreneurship as well such as the rise of the Somali-entrepreneur powered Karmel Square Mall in Minneapolis(17), to the multi-billion dollar yogurt emperor, Hamdi Ulukaya, founder of Chobani(18).

Perhaps this should be unsurprising. As the journal ''Asian Social Science'' explains, "entrepreneurship is in fact a part of Islamic culture and Prophet Muhammad, peace be upon him, and his companions are examples of this. Islam is not opposite with entrepreneurship, and always invites all Muslims to be innovative, entrepreneurial and active(19)." Islam does not just represent a seemingly entrepreneurial faith system, but also a massive, and ever growing market for entrepreneurs to take advantage. Islam, with around 1.7 billion adherents is now the worlds second largest religion and expected to surpass Christianity, the worlds largest religion, by the year 2050(20). As Forbes calculates, these 1.7 billion consumers make up twice the number of consumers in Africa and account for nearly 30% of the emerging market total(21). In their words, "it’s hard for any multinational to claim they are truly international if they don’t have a strategy for selling to the Muslim world." According to the Saudi Gazette, the overall value contributed by Muslim consumers worldwide to the global digital economy stands at $107 billion, representing 5.8% of the total global digital economy. Moreover, the growth in total value of Muslim consumers’ contribution to the global digital economy, 17% by 2020, is expected to outweigh the growth of the total global digital economy(22).

The Guardian summarizes this incredible opportunity best in its coverage of the 2016 Muslim Lifestyle Expo(23), "The halal market is worth $2.1 trillion annually worldwide, and is increasing by $500 billion a year. This huge opportunity is being missed by corporate brands, but is being taken by storm by young Muslim startups." These are startups that the United States, a nation with a long history of religious tolerance, immigration, and entrepreneurship could be fully harnessing. Dangerous, bigoted, and Islamophobic rhetoric from demagogues is preventing this from happening. Imam Khalid Latif, chaplain of New York University’s Islamic Center described Muslim Americans as "a population that has been marginalized in every sense of the word." He further claimed that many Muslims have to choose between practicing their faith and “just being able to live day to day(24).” This attitude has permeated to the business world as well. “We don’t do halal” is a common response when asked whether a company might target the Muslim consumer. The idea of ‘halal’ has branding challenges in the non-Muslim world. It’s become an emotional topic and many brand owners would be keen to promote their products in another way which avoids the negative light sometimes misappropriated to Islam through Western rhetoric(21).

The misinformation and rhetoric is directly stymieing Muslim entrepreneurs in America. Venture capitalists, normally eager to back firms with the potential to reach more than billion users, are standoffish when it comes to startups with Muslim founders, or ones that are targeted at a Muslim audience. Discrimination, media coverage, and election rhetoric were cited as reasons for this discrepancy. The lack of venture capital forces these entrepreneurs to resort to meager crowdfunding instead(25). “In talking with investors and in the entrepreneurship landscape, it feels like there is a lot of heartburn associated with Islamic-related startups,” says Amin Aaser, the co-founded of Noor Kids, a line of children’s books designed to empower Muslim children. "When you say that you’re doing a product for Muslims, many people say it’s a niche product," says Navid Akhtar, the CEO of Alchemiya, a Netflix-like video service with content focused on Muslim life. "But it’s not," he continues referencing the current 1.7 billion Muslims on Earth, a number expected to rise to 2.8 billion over the next 35 years. This testimony from Muslim entrepreneurs shows the direct effects of anti-Muslim discussion and misinformation on the entrepreneurship ecosystem. Further marginalization and failure to support arrises from the uniqueness of Islamic finance, wherein some Muslims chose to to follow the Quran and Sunnah in economic matters. These Sharia principles include the prohibition of interest, riba, transactions based on uncertainty, gharar, gambling, maysir, and investing in prohibited industries such as alcohol, weapons, pork and so on(26).

The discussion and misinformation on Islam is in no short supply this election cycle. From the aforementioned call by Donald Trump to ban Muslims from entering the United States(3) and his proposed policy for a database of Muslims and requiring Muslims to carry special identification cards to former Arkansas Governor Mike Huckabee's infamous quote "It's time to wake up and smell the falafel. We are importing terrorism,” in regards to Muslim Syrian refugees, 2016 has contained plenty of damaging rhetoric(27). Not only is this talk dangerous fuel for the fire of anti-western terrorist groups, and embarrassing on a geopolitical scale, it is marginalizing the largest emerging market on Earth and effectively cutting the United States off from billions of potential earnings and millions of potential jobs. Perhaps a candidate who claims he will "be the greatest job-producing president in all of American history,(28)" may want to change their tune.

==References==
@article{ferrero2005radicalization,
title={Radicalization as a reaction to failure: an economic model of Islamic extremism},
author={Ferrero, Mario},
journal={Public Choice},
volume={122},
number={1-2},
pages={199--220},
year={2005},
publisher={Springer}
citation={1}
}
@article{mishra2008islam,
title={Islam and Democracy Comparing Post-9/11 Representations in the US Prestige Press in the Turkish, Iraqi, and Iranian Contexts},
author={Mishra, Smeeta},
journal={Journal of Communication Inquiry},
volume={32},
number={2},
pages={155--178},
year={2008},
publisher={Sage Publications}
citation={2}
}
@article{hamid2016trump's,
title={Trump's proposed ban on Muslims},
author={Hamid, Shadi},
journal={Bookings Institute's Center for Middle East Policy},
year={2016},
publisher={Brookngs}
citation={3}
}

(4)Hellmann, Thomas F., Manju Puri, and Marco Da Rin. 2011. "A Survey of Venture Capital Research." NBER Working Paper No. 17523. doi:10.3386/w17523

@article{manju2012on,
title={On the Life Cycle Dynamics of Venture-Capital- and Non-Venture-Capital-Financed Firms},
author={Puri, Manju, and Rebecca, Zarutskie},
journal={The Journal of Finance},
volume={67},
number={6},
pages={2247-2293},
year={2012},
publisher={Sage Publications}
citation={5}
}
(6)Hellmann, Thomas F., Manju Puri, and Marco Da Rin. 2011. "A Survey of Venture Capital Research." NBER Working Paper No. 17523. doi:10.3386/w17523

(7)http://www.legis.state.tx.us/tlodocs/84R/handouts/C2702016041213001/be8b25df-8858-4430-b508-ec4109449d38.PDF

(8)http://bakerinstitute.org/research/?research_focus=44&research_type=27

(9)http://sbecouncil.org/2014/07/21/entrepreneurship-immigration-reform-and-the-economy/

(10)https://www.sba.gov/sites/default/files/rs396tot.pdf

(11)http://www.kauffman.org/~/media/kauffman_org/research%20reports%20and%20covers/2014/04/kiea_2014_report.pdf

(12)https://www.sba.gov/blogs/nation-immigrants-and-entrepreneurs

(13)http://www.forbes.com/sites/elainepofeldt/2013/05/27/u-s-entrepreneurship-hits-record-high/#184dd6c673aa

(14)http://www.huffingtonpost.com/michael-wolfe/muslim-biz-entrepreneurs-_b_9548540.html

(15)https://en.wikipedia.org/wiki/Islam_by_country

(16)http://poseidon01.ssrn.com/delivery.php?ID=408124104112066029093070082121066107123017009086086028121012017064005100014065119111030053120027118109011117070022085082121031123038039076068066114001088071122118014032071101030105068069116114000109094031068069114109119102091120081098068064100001017

(17)http://www.startribune.com/women-drive-success-of-somali-mall-in-minneapolis/323386601/

(18)http://www.wnd.com/2016/01/yogurt-billionaire-hire-more-muslim-refugees/#

(19)file:///C:/Users/DylanDickens/Downloads/5988-18811-1-PB.pdf

(20)http://www.pewforum.org/2009/10/07/mapping-the-global-muslim-population/

(21)http://www.forbes.com/sites/bensimpfendorfer/2015/04/17/muslim-consumers-and-digital-gummies/#40968f2732d8

(22)http://www.mostresource.org/storybank/muslims-influence-on-global-digital-economy-climbs/

(23)https://www.theguardian.com/world/2016/apr/07/muslim-lifestyle-expo-london-global-brands-spending-power?CMP=Share_iOSApp_Other

(24)http://www.huffingtonpost.com/entry/muslim-americans-open-up-about-how-islamophobia-has-affected-their-lives_us_56f997e9e4b0143a9b48f6fd

(25)http://mashable.com/2016/05/22/muslim-entrepreneurs/#.lPTedkqc8qS

(26)http://zilzarlife.com/has-islamic-finance-failed-our-muslim-entrepreneurs/

(27)http://www.islamophobia.org/15-reports/164-islamophobia-in-the-2016-presidential-election.html

(28)https://www.donaldjtrump.com/issues/

Measuring Entrepreneurship (Blog Post)

2017-11-15T15:50:13Z

Dayton:

{{BlogPost
|Has title=Measuring Entrepreneurship (Blog Post)
|Has author=Dylan Dickens
|Part of series=Entrepreneurship 101
|Has content status=Tabled
|Has Graphics status=None
}}
==Abstract==
Short blog post examining the current measurable determinants, and some of the potential issues in measuring, entrepreneurship.

==Text==
Starting with the formal definitions of early 18th century Irish economist Richard Cantillon, the word "entrepreneur" has existed in the economic lexicon for literal centuries now(1) . Regardless of its long-standing theoretical history with economics, only in more recent years has a more quantitative and empirical definition of the term been sought(2). Due to its wide reaching consequences convoluted internal mechanisms, and array of sources, the very nature of entrepreneurship defies detailed empirical definition. While not necessarily a huge problem in strictly economic terms, this poses a massive problem in a large and ever-increasing field related to entrepreneurship: policy.

Researchers from the Organisation for Economic Co-operation and Development outline the issue as follows, "Where there are policy references to entrepreneurship, most simply equate it with small and medium sized enterprises in general, or even numbers of self-employed. Neither of which fully captures the totality of entrepreneurship(3)," They further elaborate that the lack of predefined empirical entrepreneurship indicators compounds the general ambiguity of entrepreneurship when discussed in policy arenas. This ambiguity was the spark for an array of research on a pressing question, what is entrepreneurship in a measurable policy-friendly sense?

The OECD aims to define entrepreneurship through a wide array of indicators, wisely demonstrating that no one single factor can accurately measure and compare entrepreneurship across various nations. The six core indicators used are employer enterprise births, rate of high-growth firms based on employment growth, rate of high-growth firms based on turnover growth, Gazelle rates based on employment, Gazelle rates based on turnover, and employer enterprise deaths. The logic behind these indicators revolves around the nature of entrepreneurship to birth firms, employ many individuals, lead to the turnover of many individuals in the sector, generate high-growth firms, or Gazelles, which hire and fire many individuals, and the nature of entrepreneurship to fail.
While these indicators are in no means exhaustive, as admitted by the Organisation for Economic Co-operation and Development itself, there are other interesting prospects for measurement which have been suggested. Academics from the Research Institute of Industrial Economics in Stockholm, Sweden, purport that by measuring self-made billionaires who became wealthy by founding new firms(4). Their results, in a study of 996 billionaires across 50 nations over 14 years yielded some interesting results. Predictably, countries with higher income, higher trust, lower taxes, more venture capital investment and lower regulatory burdens had higher entrepreneurship rates as measured by self-made billionaires. This high entrepreneurship high billionaire nations scored lower however on seemingly related measures such as self-employment, small business ownership and firm startup rates.

The above research shows that while much work has been done on the empirical measures of entrepreneurship, there still remains an ambiguity that could be problematic in policy crafting. Perhaps the lesson to be drawn is one of understanding, that while it is understandable for policy makers to desire succinct, empirical, indicators to generate entrepreneurship goals around, the data is often times more convoluted and ambiguous than any stakeholder would like. Perhaps with this understanding, and further support into the research on entrepreneurship, a more empirically backed and beneficial future can be generated for all.

Google Doc Link: https://docs.google.com/a/rice.edu/document/d/1ufcpCWNAzVyN0ZxhGkuSU96a0OIRNfNe0HMBDPfa8kQ/edit?usp=sharing

==References==

(1)https://en.wikipedia.org/wiki/Richard_Cantillon

(2)https://ideas.repec.org/h/elg/eechap/3856_8.html

(3)https://www1.oecd.org/std/business-stats/39629644.pdf

(4)http://www.ifn.se/wfiles/wp/wp959.pdf

Innovation and the U.S. Military (Blog Post)

2017-11-15T15:49:46Z

Dayton:

{{BlogPost
|Has title=Innovation and the U.S. Military (Blog Post)
|Has author=Dylan Dickens
|Part of series=Applied Innovation
|Has content status=Tabled
|Has processing notes=Eliza to peer review 11/7/16
|Has Graphics status=None
}}
==Blog Material==

The F-35 Lightning II program between the United States and Lockheed Martin has been described as the perfect example of a modern military R&D disaster. The fighter jet, with its plans to wield literal laser weapons(1), and fulfill multiple roles from stealth to vertical takeoff(2) sounds like something out of science fiction. The program's lofty goals and many setbacks however have caused a political and financial uproar with Politico calling the project "$163 billion over budget [and] seven years behind schedule(3)." From the GAO warnings as far back as 2006(4) that the project would run over budget, to a report in 2015(5) that the F-35 would be "unable to enter realistic combat scenarios unsupported," its safe to declare the project less than a success.

The greatest disappointment from the F-35 program is not the billions of dollars lost in development and back-alley deals, but the harsh wake-up call that the worlds "greatest military," the engine of innovation which help churn out GPS, digital cameras, and microwaves, is no longer working. In classic entrepreneurial fashion however, there is opportunity in this failure. The major failures in the F-35 program reflect the major failures in the system at large including a lack of budgeting, a faulty contracting system, and a discrepancy in innovation.

A 2010 internal pentagon report stated that "affordability is no longer embraced as a core pillar" when asked to comment on the exponential increase in cost for the program. This mentality, spurned by an exorbitant defense budget and limited financial oversight represents one of the core problems with the Department of Defense system of innovation. There is little accountability for an idea being cost effective. This is further exacerbated by the second issue of a faulty contracting system.

When the United States awards multi-billion dollar defense contracts to companies before they have a system of accountability and solid development plan it inevitably leads to disasters such as the F-35 program. Lockheed Martin and other contractors in fact, have very little incentive to do any kind of cost control as the longer they can prolong a contract, the more funding they will receive. This combined with a powerful congressional lobby have allowed the contract system of innovation to fail.

Finally, the very nature of defense innovation has fallen into a discrepancy, where companies are innovating for the sake of innovating, rather than attempting to meet specific goals or fulfill current gaps. Many features of the F-35 have been called out as redundant and unnecessary in our modern world, and many of its "innovations" are mere upgrades and reshuffles of already existing technologies. With all of the above, what is the solution for military innovation?

The RAND Corporation(7) has put forth an interesting idea to handle military R&D in a similar way to many major corporations, by having a strategic investments arm which follows a venture capital model. By investing in smaller companies and lending military resources and expertise to their development teams, a strategic investment wing of the military could help start-ups develop cutting edge technologies at a fraction of the current price. This would solve the key issues, providing a return-on-investment rather than a contract style arrangement with developers, and would focus on true, cost-effective innovation rather than the current overblown targets such as the F-35.

Google Doc Link: https://docs.google.com/a/rice.edu/document/d/1DXcQzkBHOOtPjKiPIHgYYf4kdUTV5tAShYnu6NG9HZg/edit?usp=sharing

(1)
http://web.archive.org/web/20040626050316/http://www.aviationnow.com/content/publication/awst/20020708/aw32.htm
(2)https://en.wikipedia.org/wiki/Lockheed_Martin_F-35_Lightning_II#Armament
(3)http://www.politico.com/story/2014/02/f-35-fighter-plane-costs-103579
(4)http://www.gao.gov/new.items/d06356.pdf
(5)http://aviationweek.com/defense/test-report-points-f-35-s-combat-limits-0?NL=AW-05&Issue=AW-05_20160201_AW-05_373&sfvc4enews=42&cl=article_1
(6)http://www.reuters.com/article/lockheed-fighter-idUSN1123180820100312
(7)http://www.rand.org/natsec_area/products/vc.html

==From the Old Policy Report==

===Abstract===
1,700 words proposing a strategic venture capital investment arm for the DoD, similar to In-Q-Tel, the strategic investment wing of the CIA.

===Current Issue===
The F-35 Lightning II program between the United States Department of Defense and Lockheed Martin has manifested into a research and development nightmare. The program's intent is to generate a 5th generation fighter jet capable of replacing the currently utilized F-16 Fighting Falcon's developed in the 1970's. Reports on the program have not bore good news however, with **Politico** declaring the project "$163 billion over budget [and] seven years behind schedule(1)," and **Breaking Defense** pointing out the fact that the antiquated F-16 has beaten the novel F-35 in test dogfights(2).

The program was lofty from the get go. The F-35 was designed to fulfill multiple roles including stealth reconnaissance, ground attack, air defense, vertical takeoff, precision bombing and air support(3). There were plans for the fighter jet to wield literal laser weapons(4) and dominate the skies, yet internal Defense Department reports in 2015 announced that the F-35 would be "unable to enter realistic combat scenarios unsupported(5)." The lofty goals and eventual technological disappointment of the F-35 program have been further compounded by the programs seeming inability to follow budget. As far back as 2006 the Government Accountability Office warned that the project would run over budget with its current model, and recommended delays in appropriation till further test flights and prototypes were completed(6). Now, $163 billion dollars later with little revision to the contracting model, an internal Pentagon report stated that "affordability is no longer embraced as a core pillar" when asked to comment on the exponential increase in cost for the program(7).

The F-35 program, in all of its disaster, should not have come at a total surprise, as academic literature for many years has failed to draw meaningful economic benefits from research on military R&D. As early as 1967 literature review from the **Institute of Electrical and Electronics Engineers** showed a trend of needing increased "outside assistance," in military R&D(8). Further contemporary research from the IEEE shows that military R&D spending has "no statistically visible evidence of direct effect" on the U.S. economy(9) and **Economic Development Quarterly** reports that extensive military R&D generally detracts from private sector R&D, which in long run hurts U.S. competitiveness(10). This amass of evidence against military R&D is not to say its unnecessary, it is the same engine of innovation which helped to produce GPS, digital cameras, and microwaves after all, and is necessary to keep the nation safe.

The United States spends over $76 billion annually on Defense R&D, which is more than the total defense expenditures, not just for R&D, but for all defense purposes, of every other country in the world except China(11). As the **Center for American Progress** notes, "we no longer have the luxury of throwing countless billions of dollars at Pentagon R&D and hoping they land somewhere(11)." While military R&D may be necessary and have a great track record of innovation, modern analysis, academic literature, and the F-35 Lightning II program provide convincing evidence that policy change is in order.

===Potential Solution===
Innovation is a key component of the private sector, and as reported by **Science Progress**, nearly 2/3 of all U.S. research and development investments are currently in industry(12). That is a nearly $230 billion sector which can be harnessed by the Department of Defense. Two different studies published in **Research Policy** indicate the successes of private sector led and public sector funded high-tech R&D(13)(14), demonstrating the past and future feasibility of public-private research partnership. While current contract partnerships do exist in the defense sector, they are notoriously complicated(15), politically motivated(16), and lead to "buying before flying" fiasco such as the F-35 program where the already-purchased product does not live up to expectations.

While the contract model has seemingly failed the United States, another option may be inherited from the private sector, strategic venture capital investment. Venture capital, defined as "financing that investors provide to startup companies and small businesses that are believed to have long-term growth potential(17)," is an essential component of the entrepreneurship ecosystem. Critical for startups to grow, venture capital is generally considered a high risk investment for its backers but can have major payoffs. Extensive research has shown that venture capital is valuable for the investor, investee, and overall economy(18). Venture capitalists almost always join the companies which they invest in, lending sector expertise, guidance and knowledge to the nascent business.

In order for a venture capital firm to generate profit, the businesses they invest in must succeed, so a strong desire for return on investment is instilled into the young company. This desire has been shown to be overwhelmingly positive, with research from the National Bureau of Economic Research showing that only one in ten investments does not return any money(19). One in four investments on the other hand, were shown to have a rate of return above 50%. While these statistics may be positively biased, other estimates point to two in ten investments making substantial amounts, and only a small amount of investments not at least breaking even. Venture capital does not only generate profits for both the investor and investee, but also the greater economy with major growth being driven by the rise of successful VC-backed enterprises(20).

Strategic venture capital investment firms, the subject of this proposal, are usually affiliates of corporations that invest on behalf of their parent company. Some examples include the Peacock Equity Fund of GE/NBC Universal, Time Warner Investments of Time Warner Cable, and Steamboat Ventures of The Walt Disney Company(21). While traditional venture capital firms provide funds to young companies with the desire to see these companies grow, strategic investors, due to their corporate backers desires, have an additional strategic goal beyond just this financial growth. Sometimes strategic's invest to fill in gaps where the corporate parent might see an opportunity to strengthen itself, and sometimes they invest in complementary companies where growth could help both the corporate sponsor and nascent company. In other cases, strategic investment firms make a sector bet and invest in an area that isn't directly related today but is of interest for the future. Generally these strategic investments are preparations for an eventual acquisition of the new company by the corporate backer.

Strategic venture capital investment, that is venture capital with the goal to produce strategic innovations rather than solely turn a profit, has been successfully demonstrated in the private sector and has even made an appearance in the public. Noticing the growing gap between defense and civilian R&D in 1999 the Central Intelligence Agency founded In-Q-Tel, a private, non-profit entity with the mission to "“to exploit and develop new and emerging information technologies and pursue R&D that produce innovative solutions to the most difficult problems facing the CIA and the Intelligence Community(22).” In-Q-Tel is run just like a strategic venture capital firm. It has tech-savvy entrepreneurs who know what it takes to get a small business off the ground at the helm, a highly knowledgeable in-house technology team for evaluation and mentoring, an innovative and creative corporate culture and flexible capital at its disposal. In the decade and a half since its inception, In-Q-Tel has been declared by the CIA as an evolving, useful, and effective tool.

Despite the inherent risks of venture capital, the Business Executives for National Security in their testimony to congress stated that "In-Q-Tel’s potential advantage to the CIA outweighs the risk," and internal memos suggest that In-Q-Tel's return on investment has been larger than anticipated(23). Major tenets of its success cited include the technical
review performed by In-Q-Tel and the prestige of having the CIA as a customer(21). These are tenets which can be replicated by the Department of Defense. Strategic venture capital investment, from the private sector to the public, has demonstrated an effective model of research and development which is innovation and profits focused, feasible, and has a proven track record.

===Specific Proposal===
Drawing on venture capital research from the expert Kauffman Foundation(25), and the In-Q-Tel model, a strategic venture capital investment arm for the Department of Defense is recommended to first complement, and eventually subsume its existing R&D capabilities. This proposed new arm of the DoD would accomplish many of the goals highlighted in a less specific but similar proposal from the RAND Corporation(26). The DoD strategic would allow for the U.S. military to hold its position as a major innovator and technological giant while closing the existing gap between public and private R&D by blurring the lines further between the two. It would allow minimum taxpayer resources to be invested, and after a startup period, should run self-sufficient with the potential for a return on investment such as the In-Q-Tel program.

Following the In-Q-Tel format, the DoD strategic is recommended to be founded as a private, non-profit entity with strong ties to the Department of Defense. As a recommended physical location for operations, Boston's Route 128, long considered the high-tech industry's response to IT's Silicon Valley, would work well. Venture capital firms which are physically closer to the companies they invest in are often more successful, and much importance is attributed to micro-geography in the world of entrepreneurship. An executive unit consisting of successful high-tech entrepreneurs turned venture capitalists is required, as individuals with know how in both running a high-tech startup and investing through venture capital will be required. Other members of this should include military officials who can lend their expertise and connections to the strategic. Once again following the In-Q-Tel model, an in-house team of technology experts is required for both prospective investment evaluation and eventual mentoring of startup technologists.

The culture of this strategic will have to be different from the Department of Defense as a whole, with focuses on creativity, flexibility, and innovation. Without this corporate "startup" culture the strategic will struggle to promote innovation and growth within its investments. Emphasis must be placed on return on investment, as a constant stream of funding promotes lazy investment review and does not incentivize either the strategic or the startup to act cost effective and meet goals. Bureaucratic "red tape" must be minimized, and while oversight and security are necessary, alternate paradigms may be considered for the strategic in contrast to the DoD as a whole.

Understanding the past successes of strategic venture capital investment, and recognizing the current failures in U.S. military innovation, it is imperative that policymakers seek another solution. The mounting deficit and out of control defense budget demand a change in action, a change whose need is only further demonstrated through contractual R&D disasters such as the F-35 Lightning program. A promising solution exists in harnessing the private sector's demonstrated advances in R&D over the public sector. One of the most successful models in private sector R&D, strategic venture capital investment, has shown great promise in both the private and public sectors. Following a similar model to the strategic investments wing of the CIA, In-Q-Tel, the Department of Defense could revolutionize military innovation and bring the U.S. to a more stable sense financially without sacrificing technological superiority.

[[Category:Innovation Policy]]

Houston SBA Loans (Blog Post)

2017-11-15T15:49:21Z

Dayton:

{{BlogPost
|Has title=Houston SBA Loans (Blog Post)
|Has author=Dylan Dickens
|Part of series=Texas Entrepreneurship
|Has content status=Tabled
|Has graphics status=None
}}
==Abstract==
A blog post investigating the lending habits of the Houston SBA office.

==Blog Post==
The Small Business Administration, founded in 1953, [https://www.sba.gov/about-sba/what-we-do/mission states] its mission as "to aid, counsel, assist and protect the interests of small business concerns, to preserve free competitive enterprise and to maintain and strengthen the overall economy of our nation." One of the key ways it does this is through start-up loans. The biggest program is the 7(a) Loan Guarantee, which guarantees as much as 85% of loans up to $150,000 and 75% of loans of more than $150,000 up to $5,000,000.

7(a) loan applications are made to and funded by SBA accredited partners. When a business applies for an SBA loan, it is actually applying for a commercial loan, structured according to SBA requirements. The lender is largely protected by the SBA guarantee. A small business pays between 7.5%-9.5% interest on their loan. This interest is split between: banks, which can charge no more than 2.75% on top of the prime rate, and a SBA guarantee fee ranging between 2%-3.75%.

While the 7(a) loan program is highly utilized and important, finding data on the program is difficult. Take for example the local Houston SBA office, which [https://www.sba.gov/offices/district/tx/houston provides] data by loan amount and lender for the 7(a) program but does not specify if the amount is the loan amount approved or the loan amount disbursed. Considering many of their national office records specify that loans are recorded by the amount approved, one may assume that these numbers at the local level reflect that practice. Within that assumption however, where are the records for the amounts actually disbursed? This would not be a major concern except that a Congressional Research Service [http://nationalaglawcenter.org/wp-content/uploads/assets/crs/R41146.pdf report] found that the amount of 7(a) loans approved annually is typically about 10% to 15% higher than
the amount disbursed.

Beyond just a clear record of dispersal, there are no records for the number of loans issued by lender, just merely the total amount. This makes tracking the number of actual businesses receiving loans very difficult, and make the small business environment seemingly less transparent. When examining the available records, some further interesting businesses practices may be noted. Across the first 6 months of fiscal year 2016, ranging from October 2015 to March 2016, the total approved loan amounts have increased 370% from $7,448,000 to $27,581,100. Two banks have ranked as the single biggest lenders, Texas Advantage Community Bank in October and November of 2015 and BBVA Compass across the other four months. Texas Advantage Community Bank, as according to [http://banks.credio.com/l/4542/Texas-Advantage-Community-Bank Credio.com] ranks at a 73/100, below average for a community level bank. BBVA Compass ranks average for a multinational bank.

While it is difficult to draw any conclusions from this information, what is important to keep in mind is the difficulty in accessibility and the apparent lack of local-level oversight. Further information on the disbursal rates and selection process for lender banks would be ideal, and could help reduce the perceived opacity surrounding the SBA. Perhaps future investigation will reveal more hardened information concerning the practices of the local Houston SBA office and its 7(a) loan practices.

Google Doc Link: https://docs.google.com/a/rice.edu/document/d/1iogyY6WtlxyMFiMrGxkKl7pzecqWlCDHC5sp8o4l57k/edit?usp=sharing

==References==
https://www.sba.gov/offices/district/tx/houston

Entrepreneurship as Diplomacy

2017-11-15T15:48:41Z

Dayton:

{{BlogPost
|Has title=Entrepreneurship as Diplomacy
|Has author=Catherine Kirby; Dylan Dickens
|Has content status=Tabled
|Has processing notes=tabled post until more info on South America context and more knowledge of how new administration will handle Obama's programs.
|Has Graphics status=None
}}
Entrepreneurship as Diplomacy
As the largest driver of economic growth, the innovation market and entrepreneurship ecosystem are key component of any nation’s financial engine. Intaking a fuel of novel ideas and motivated business people to churn out almost all new businesses and the vast majority of new jobs, micro and macro economists agree that entrepreneurship has resounding positive effects. Entrepreneurship has also been linked to noneconomic benefits such as community development, social change and increases in egalitarianism and democracy. Through promoting entrepreneurship, the United States has a unique opportunity to utilize diplomacy to improve the perception of America abroad while also improving the lives of the nations the U.S. targets.
Historic Cases

Chile
The “Miracle of Chile,” is an example of how entrepreneurship-focused economic policy can uplift a nation through economic and democratic reform to a place on the global stage.
When army chief Augusto Pinochet led a successful coup d’etat in 1973 against democratically elected socialist President Salvador Allende, he inherited a yet unimplemented economic policy known as El ladrillo, translated literally to “the brick.” Co-created by right-wing Chilean economists, University of Chicago taught U.S. economists and the CIA, El ladrillo recommended deregulation and privatization. Specifically it made the central bank independent, cut tariffs, privatized the state-controlled pension system, state industries and banks, and reduced taxes. Pinochet’s stated aim was to “make Chile not a nation of proletarians, but a nation of entrepreneurs.”
Chile’s GDP rate of growth went from -5% in 1973 to 5% in 2004, but that was not even considered its most powerful effect. As American economist Milton Friedman, one of the architects of the plan stated, “the Chilean economy did very well, but more importantly, in the end the central government, the military junta, was replaced by a democratic society. So the really important thing about the Chilean business is that free markets did work their way in bringing about a free society.”

Brazil
Nelson A. Rockefeller believed that South America needed a strong middle class similar to the United States to ensure success and help solve economic problems. Additionally, this initiative aimed at strengthening U.S.-Latin American relations with entrepreneurship as diplomacy. He intended to spark this success founding a mutual fund called Fundo Crescinco, hoping it would motivate participation from the middle class in Brazil. The fund also included many Brazilian-owned enterprises, contributing to their development.
While his fund cannot be credited with being the single founder of a middle class in Brazil, Fundo Crescinco did aid in the development of the Brazilian stock market. Additionally, Fundo Crescinco did achieve an amount of middle-class participation while producing a profitable mutual fund. The press coverage of the mutual fund involved 64 news mentions: 36 favorable mentions, 21 neutral, and 7 unfavorable references. Overall, IBEC (The International Basic Economy Corporation) through Fundo Crescinco made a significant profit and Brazil acquired a valuable corporation.

Current U.S. Efforts: Spark Initiative
The Spark Initiative furthers former President Obama’s goal of advancing entrepreneurship globally. The programs listed below are not a complete list of Obama administration initiatives, but they showcase the variety of implemented organizations. These programs differ greatly from the huge initiatives in Chile and Brazil especially on size. However, their subtlety and grassroots background is their strength. The changes seen would be bottom up instead of top down, perhaps a more valuable shift to a nation than forced top down change.

Global Entrepreneur Summit
The Global Entrepreneur Summit links emerging entrepreneurs from around the world with leaders from business, government and innovation organizations. These summits, previously held in Turkey, Malaysia, and Morocco, have led to many initiatives, collaborations between nations, and increased economic opportunities for participants.

MEPI
The U.S.-Middle East Partnership Initiative focuses on expanding entrepreneurship in the Middle East and Northern Africa (MENA) region. Regionwide, it has invested $4 million in youth entrepreneurship through national and region-wide start-up competitions for student-run businesses. This region-wide effort involves 22,000 young people.
In Saudi Arabia, the focus is on female social entrepreneurship. The program involves taking 100 female college students and empowering them to take leadership roles in their communities through social entrepreneurship. For example, after horrific floods in Jeddah, participants organized online campaigns to clean up the city and hold public officials accountable
MEPI started Project Liftoff, based in a variety of countries in MENA, to support high-impact entrepreneurs. In Yemen, the project will involve promoting entrepreneurship to 30,000 secondary students and rural youth to help them become active citizens.

WECREATE
Implemented in Africa, Women’s Entrepreneurial Centers of Resources, Education, Access, and Training for Economic Empowerment (WECREATE) serve as center community locations containing resources for women. Partnering with Caterpillar, the Japanese government, and the State Department, these centers aim to launch 563 new startups, create 7,206 new jobs, certify 915 female mentors and coaches, train 97 female entrepreneurial champions, mentor and train 6,075 women entrepreneurs. The program will additionally train men and boys to be “agents of change” and learn about the benefits of women empowerment.

MUSEIC
The Mexico-U.S. Entrepreneurship and Innovation Council (MUSEIC) works to encourage innovative entrepreneurship and cross border relations between nations. This program has sponsored a variety of activities including seminars, conferences, and startup boot camps. The seminars, one hosted in Mexico City and the other hosted in Guadalajara, set the basis for the La Idea Business Pitch Competition. This event had over 850 participants; of the 16 finalists, two were mexican.
Looking Toward the Future
Despite all of the potential benefits of entrepreneurial foreign policy, there seems to be a disconnect between these proposed successes and actual implementation. Inc.com reports that U.S. venture capital, both private and public, largely avoid foreign companies. Foreign Affairs calls for more developing nations to follow venture capital strategies in their near future. While past precedent such as the “Miracle of Chile,” and current programs at the State Department lend credence to the policy of “developmental entrepreneurship,” implementation still appears to be falling short. If more smart policies focused on non-competition, entrepreneurial institutions, available capital, and ecosystem growth can be implemented in viable nations abroad, perhaps future socio economic miracles can spring up all throughout the world.

[[Category:Entrepreneurship Policy]]

Education Innovation Clusters

2017-11-15T15:48:17Z

Dayton:

{{BlogPost
|Has title=Education Innovation Clusters
|Has author=Meghana Gaur
|Has content status=Tabled
|Has processing notes=Needs more context and focus
|Has project status=Repurpose
}}
By accelerating the pace of innovation in learning sciences and technologies, the United States has the opportunity to close the achievement gap, improve national competitiveness, and drive economic growth. Accelerating the pace of innovation requires a fresh approach to research and development and the infrastructure that supports it. Creating a new education innovation ecosystem requires new types of partnerships that cross traditional domain silos. This has been demonstrated in other industries that have been successful at accelerating innovation. Across the country education innovation clusters are forming, where forward-thinking partners focus on breakthroughs in learning technologies, streamlining research in learning science and accelerating the development of new tools and approaches for learning. In combination with a supportive regulatory and funding context, these innovation centers have the potential to lead the nation in the creation of new knowledge, tools and outcomes.

==Education Innovation Clusters Overview==

*'''Educational partners''' provide an environment where emerging learning technologies can be piloted and new solutions can be developed with input from students and teachers. Educational partners might be early learning providers, public and/or private schools, libraries, community centers, after school programs, institutions of higher education, or virtual learning environments. These innovative schools and learning centers provide the flexibility and capability to rapidly develop, test, and collect data on new approaches and products.

*'''Research partners''' conduct basic and applied research related to advancing the field of learning science. They can be integrally connected to the entrepreneurial partners to ensure effective design of learning technologies. They would also have deep relationships with the educational partners to streamline the collection of data and outcomes to conduct ongoing evaluations of the products and approaches developed in the cluster.

*'''Commercial partners''' infuse new technologies to address problems and help scale and market successful implementations. They can also provide investment capital to accelerate the commercialization of intellectual property and increase the probability of success of new enterprises built on the knowledge and solutions generated in the cluster. The quality and relevance of products will greatly improve with consistent access to the educational and research partners in the cluster.

Source: Office of Educational Technology

==Abstract==

==Google Link==

https://docs.google.com/document/d/1EJEHLsIEBQWPaG-8Byxu6TKEJ3xlpz8tFD21A16d7P8/edit

==References==

http://tech.ed.gov/innovationclusters/

http://digitalpromise.org/initiative/education-innovation-clusters/

Business Dynamism in High Tech (Blog Post)

2017-11-15T15:47:44Z

Dayton:

{{BlogPost
|Has title=Business Dynamism in High Tech (Blog Post)
|Has author=Carlin Cherry and Dylan Dickens
|Has content status=Tabled
|Has processing notes=Anne sent Dylan comments 11/1/16; After revising, Dylan to send to peer reviewer before returning to Anne.
}}
==Abstract==
This blog post identifies a potential cause behind the decline of business dynamism in high technology sectors.

==Blog Post==

The American perception of entrepreneurship and innovation is an overwhelmingly positive one; Americans believe not only that there are good opportunities for starting a business, but also, as according to Forbes, that they have the [http://www.forbes.com/sites/elainepofeldt/2013/05/27/u-s-entrepreneurship-hits-record-high/#7144d97d73aa capabilities] to start one themselves. The Global Entrepreneurship Monitor U.S. [http://www.babson.edu/news-events/babson-news/PublishingImages/babson-gem-info-graphic-692.png report] found that 43% of Americans believe there are good opportunities for entrepreneurship, and that 56% of American adults believe they have the capability to start a business. Recent trends in business birth and death rates however tell a more sinister story.

A [http://www.kauffman.org/~/media/kauffman_org/research%20reports%20and%20covers/2014/02/declining_business_dynamism_in_us_high_tech_sector.pdf%20Kauffman study] from the Kauffman Foundation reported sustained declines in entrepreneurship and business dynamism across the U.S. economy. Though the Wall Street Journal and many Americans [http://blogs.wsj.com/washwire/2014/05/06/snowden-effect-americans-still-like-tech-industry-but-not-telecom/ still view] the high-tech sector as the pinnacle of entrepreneurship and innovation, the Kauffman Foundation [http://www.kauffman.org/~/media/kauffman_org/research%20reports%20and%20covers/2014/02/declining_business_dynamism_in_us_high_tech_sector.pdf%20Kauffman found] the decline in business dynamism that occurred broadly across the U.S. economy over the past two decades also dragged on the high-tech sector in the post-2000 period. The high-tech sector, or the group of industries with very high shares of workers in the STEM occupations of science, technology, engineering, and math has been found to be in decline, which begs the question: what happened in the American economy to cause the decline of business dynamism in high technology sectors?

During the period of aggregate productivity and job growth in the 1990's, the high tech sector and newly listed public companies exhibited increases in indicators in dynamism and entrepreneurship. However, since 2000, the National Bureau of Economic Research has [http://econweb.umd.edu/~haltiwan/Haltiwanger_Kauffman_Conference_August_1_2015.pdf observed] the high tech sector and publicly traded firms exhibiting a decline in dynamism. The number of IPOs has fallen in the post-2000 period and those that have entered have not exhibited the same rapid growth as earlier cohorts.

Why the decline? The primary challenge start-ups in general face is access to capital. According to [http://newsroom.bankofamerica.com/sites/bankofamerica.newshq.businesswire.com/files/press_kit/additional/Fall_2014_Bank_of_America_Small_Business_Owner_Report.pdf Bank of America], only 29 percent of small business owners said they’ve applied for a business loan over the last two years. Millennials report turning to friends and family for loans, rather than taking out a traditional loan from a bank. Millennials also have less access to personal capital in that they often take on significant student debt when attending college. A [http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2417676 study] from Penn State University found "a significant and economically meaningful negative correlation between changes in student debt and net new businesses employing one to four employees, the firms most dependent on personal debt for financing." Personal debt and lack of access to capital are particularly problematic in the high tech sector where millennials compose a majority of the workforce and are more comfortable making advances in technology, as [https://www.pwc.com/gx/en/managing-tomorrows-people/future-of-work/assets/reshaping-the-workplace.pdf noted] by PricewaterhouseCoopers.

The overwhelmingly large presence of millennial entrepreneurs in the high-tech sector combined with the overwhelmingly large burden of college debt in millennial's lives may be causing the "one-two knockout punch" which has contributed to the decline in business dynamism. While student debt is used to fund increases in human capital, the utilization of student debt reduces an individual's ability to access other forms of credit. As a result, the Penn State study's findings may suggest a debt trade-off in which larger amounts of student debt lower an individual's ability to start a new business. The study further found that an increase of one standard deviation in student debt use results in a decrease of 70 new small businesses per county, a decline of approximately 14.4%.

Millennials and recent college graduates comprise a large majority of those with interest in the high technology workforce. Because their access to capital appears to be limited both institutionally and due to high levels of student debt, many of these workforce participants may be either unable or unwilling to take on more debt to start their own businesses. This is perhaps the cause of the decline in business dynamism in the high technology sector. If this connection proves true, a decrease of 70 small businesses per increase in standard deviation in student debt could be expected. Perhaps increasing access to capital through easily attainable bank loans or implementing policies aimed towards lowering student debt could help to reverse the trend.

==References==
In line. See above.
Original text by Carlin Cherry? Edited and formatted by Dylan Dickens

==Link==
https://docs.google.com/document/d/1cHWPSHRkYKfGPwtPlgBKLn70Xt7BPfv3sEi-yfMemBY/edit

Yunnie Huang

2017-10-30T15:59:02Z

Dayton: /* Progress at McNair */

{{McNair Staff
|position=Research Team
|name=Yunjing (Yunnie) Huang
|user_image=Yunnie.jpg
|degree=BA
|major=CAAM; Mathematical Economic Analysis
|class=2020
|join_date=10/23/2017
|skills=Matlab, Excel
|interests=dance, pingpong, travel
|fun_fact=I went to the same high school as Michelle Obama.
|email=yh49@rice.edu
|status=Active
}}
==Early Life==
Yunnie was born in Guangzhou, China, in 1998. Guangzhou is the third biggest city in China. Yunnie has an older sister and she was born "illegally" during a time when China's "One-child policy" was enforced. Her favorite childhood memory is spending Lunar New Year in her grandparents' house and setting off fireworks with her cousins. In 2008, She moved to Chicago with her family. She received her high school degree from Whitney M. Young Magnet High School. She loves everything about the city, except for the cold winters. That helped her make her decision to study at Rice University where she can enjoy the sunny Houston weather.

==Future Goals==
Yunnie is planning to study abroad in London in the second semester of her junior year, because she enjoys being exposed to different cultures and experiences.
When she graduates from Rice with her bachelor's degrees in Math Economics Analysis and Computational and Applied Math, she wants to work as either a consultant or data analyst. She is planning to go back to graduate school after working for a few years to study either industrial engineering, finance, or business.

==Progress at McNair==
Yunnie's work log with daily activities-
[[Yunnie Huang (Work Log)]]

[[Startup Density Literature Review]]

Yunnie Huang

2017-10-30T15:58:32Z

Dayton: /* Progress at McNair */

{{McNair Staff
|position=Research Team
|name=Yunjing (Yunnie) Huang
|user_image=Yunnie.jpg
|degree=BA
|major=CAAM; Mathematical Economic Analysis
|class=2020
|join_date=10/23/2017
|skills=Matlab, Excel
|interests=dance, pingpong, travel
|fun_fact=I went to the same high school as Michelle Obama.
|email=yh49@rice.edu
|status=Active
}}
==Early Life==
Yunnie was born in Guangzhou, China, in 1998. Guangzhou is the third biggest city in China. Yunnie has an older sister and she was born "illegally" during a time when China's "One-child policy" was enforced. Her favorite childhood memory is spending Lunar New Year in her grandparents' house and setting off fireworks with her cousins. In 2008, She moved to Chicago with her family. She received her high school degree from Whitney M. Young Magnet High School. She loves everything about the city, except for the cold winters. That helped her make her decision to study at Rice University where she can enjoy the sunny Houston weather.

==Future Goals==
Yunnie is planning to study abroad in London in the second semester of her junior year, because she enjoys being exposed to different cultures and experiences.
When she graduates from Rice with her bachelor's degrees in Math Economics Analysis and Computational and Applied Math, she wants to work as either a consultant or data analyst. She is planning to go back to graduate school after working for a few years to study either industrial engineering, finance, or business.

==Progress at McNair==
Yunnie's work log with daily activities- [[Yunnie Huang (Work Log)]]
[[Startup Density Literature Review]]

Startup Density Literature Review

2017-10-30T15:55:01Z

Dayton:

{{McNair Projects
|Has title=Startup Density Literature Review
|Has owner=Yunnie Huang
|Has start date=10/23/2017
|Is dependent on=Urban Start-up Agglomeration,
}}
Below is a list of citations I have gathered looking up key words related to startup.

==startup density==

Path-Dependent Startup Hubs - Comparing Metropolitan Performance: High-Tech and ICT Startup Density by Dane Stangler
@techreport{stangler_path-dependent_2013,
address = {Rochester, NY},
title = {Path-{Dependent} {Startup} {Hubs} - {Comparing} {Metropolitan} {Performance}: {High}-{Tech} and {ICT} {Startup} {Density}},
shorttitle = {Path-{Dependent} {Startup} {Hubs} - {Comparing} {Metropolitan} {Performance}},
url = {https://papers.ssrn.com/abstract=2321145},
abstract = {Kansas City and other areas viewed as "new" startup hubs actually have been fostering a culture of entrepreneurship for some time. Many of these cities have a history of strong technology sectors or experienced strong growth among technology startups over the past two decades. A strong regional or local culture of technology entrepreneurship is not a recent phenomenon, contrary to the opinions of many. The top 10 cities in 2010 also ranked among the top 20 cities two decades earlier.This analysis shows that many cities' recent adoption of new entrepreneurship programs is more an indication of the underlying strength of the region and its base of talent on which those programs can build than it is a cause of startup activity. Cities such as Kansas City, Seattle, Portland and Boise all owe their emerging entrepreneurial ecosystems to many years of spinoffs and entrepreneurial spawning.Research universities and other postsecondary institutions are important for metropolitan entrepreneurship, but are not the sole cause in spurring such activity. Instead, the most fertile source of entrepreneurial spawning is the population of existing companies, which has implications for economic policymaking and economic development strategies.Entrepreneurs come from somewhere - this seems obvious, but that observation runs against the prevailing stereotype that entrepreneurs are, or should be, recent college grads or college dropouts. That 'somewhere' usually is a previous job in a big company or at an institution, such as a university, which helps explain the age distribution of entrepreneurs.However, regions should be careful in turning these observations into policy. While spinoffs are important for tech startup growth, such a strategy could be wrongly interpreted as supporting traditional economic development strategies of tax incentives for big companies. More work must be done to understand the local and regional dynamics of entrepreneurship, barriers that may exist to catalyzing a self-fulfilling dynamic of entrepreneurial spinoffs and what the proper role of supporting institutions should be.},
number = {ID 2321145},
urldate = {2017-10-24},
institution = {Social Science Research Network},
author = {Stangler, Dane},
month = sep,
year = {2013},
keywords = {entrepreneur, local entrepreneurship, regional entrepreneurship, startup hub}

Startup and Quadidtationary Drive of Plasma Current by Lower Hybrid Waves in a Tokamaki by K. Toi
@article{toi_startup_1984,
title = {Startup and {Quasistationary} {Drive} of {Plasma} {Current} by {Lower} {Hybrid} {Waves} in a {Tokamak}},
volume = {52},
url = {https://link.aps.org/doi/10.1103/PhysRevLett.52.2144},
doi = {10.1103/PhysRevLett.52.2144},
abstract = {A plasma current is initiated and raised to a quasistationary level of about 20 kA by injection of the lower hybrid wave into a cold and low-density plasma produced by electron cyclotron resonance. The plasma current rises more slowly than the experimentally obtained LpRp magnetic diffusion time of the bulk plasma. The current rise time is inversely proportional to the bulk electron density, and agrees well with the collision time of the current-carrying high-energy electrons with the bulk plasma.},
number = {24},
urldate = {2017-10-24},
journal = {Physical Review Letters},
author = {Toi, K. and Ohkubo, K. and Kawahata, K. and Kawasumi, Y. and Matsuoka, K. and Noda, N. and Ogawa, Y. and Sato, K. and Tanahashi, S. and Tetsuka, T. and Kako, E. and Hirokura, S. and Taniguchi, Y. and Kitagawa, S. and Hamada, Y. and Fujita, J. and Matsuura, K.},
month = jun,
year = {1984},
pages = {2144--2147}

Tech Starts: High-Technology Business Formation and Job Creation in the United States by Ian Hathaway
@techreport{hathaway_tech_2013,
address = {Rochester, NY},
title = {Tech {Starts}: {High}-{Technology} {Business} {Formation} and {Job} {Creation} in the {United} {States}},
shorttitle = {Tech {Starts}},
url = {https://papers.ssrn.com/abstract=2310617},
abstract = {New and young businesses — as opposed to small businesses generally — play an outsized role in net job creation in the United States. But not all new businesses are the same — the substantial majority of nascent entrepreneurs do not intend to grow their businesses significantly or innovate, and many more never do. Differentiating growth-oriented “start-ups” from the rest of young businesses is an important distinction that has been underrepresented in research on business dynamics and in small business policy.To advance the conversation, we contrast business and job creation dynamics in the entire U.S. private sector with the innovative high-tech sector — defined here as the group of industries with very high shares of employees in the STEM fields of science, technology, engineering, and math. We highlight these differences at the national level, as well as detailing regions throughout the country where high-tech start-ups are being formed each year. The major findings include:• The high-tech sector and the information and communications technology (ICT) segment of high-tech are important contributors to entrepreneurship in the U.S. economy. During the last three decades, the high-tech sector was 23 percent more likely and ICT 48 percent more likely than the private sector as a whole to witness a new business formation.• High-tech firm births were 69 percent higher in 2011 compared with 1980; they were 210 percent higher for ICT and 9 percent lower for the private sector as a whole during the same period. This is important because the productivity growth and job creation unleashed by these new and young firms — aged less than five years — require a continual flow of births each year.• Of new and young firms, high-tech companies play an outsized role in job creation. High tech businesses start lean but grow rapidly in the early years, and their job creation is so robust that it offsets job losses from early-stage business failures. This is a key distinction from young firms across the entire private sector, where net job losses resulting from the high rate of early-stage failures are substantial.• Young firms exhibit an “up-or-out” dynamic, where they tend to either fail or grow rapidly in the early years. The job-creating strength of surviving young firms, while strong for young businesses across the private sector as a whole, is especially distinct for high-tech start-ups: the net job creation rate of these surviving young firms is twice as robust. • High-tech and ICT firm formations are becoming increasingly geographically dispersed. As technological advancement allows for the production of high-tech goods and services in a wider set of areas, many regions are catching up. The opposite has been true for the private sector as a whole, where new business growth has been occurring most in regions with already higher rates of new business formation.},
number = {ID 2310617},
urldate = {2017-10-24},
institution = {Social Science Research Network},
author = {Hathaway, Ian},
month = aug,
year = {2013},
keywords = {entrepreneur, entrepreneurship, high-tech, job creation, startup, technology}

Demonstration of Plasma Startup by Coaxial Helicity Injection by R. Raman
@article{raman_demonstration_2003,
title = {Demonstration of {Plasma} {Startup} by {Coaxial} {Helicity} {Injection}},
volume = {90},
url = {https://link.aps.org/doi/10.1103/PhysRevLett.90.075005},
doi = {10.1103/PhysRevLett.90.075005},
abstract = {The first successful results on the transfer of a coaxial helicity injection (CHI) produced discharge to inductive operation are reported. CHI-assisted plasma startup is more robust than inductive only operation. After hand off for inductive operation, the initial 90 kA of CHI-produced current drops to 40 kA, then ramps up to 170 kA, using only 30 mV s, more than 30\% higher than that produced by induction alone. These significant performance enhancing results were obtained on the HIT-II spherical torus experiment (major/minor radius of 0.3/0.2 m).},
number = {7},
urldate = {2017-10-24},
journal = {Physical Review Letters},
author = {Raman, R. and Jarboe, T. R. and Nelson, B. A. and Izzo, V. A. and O’Neill, R. G. and Redd, A. J. and Smith, R. J.},
month = feb,
year = {2003},
pages = {075005}

==start-up density==

The roles of calcium in sludge granulation during uasb reactor start-up by H. Q. Yu
@article{yu_roles_2001,
title = {The roles of calcium in sludge granulation during uasb reactor start-up},
volume = {35},
issn = {0043-1354},
url = {http://www.sciencedirect.com/science/article/pii/S0043135400003456},
doi = {10.1016/S0043-1354(00)00345-6},
abstract = {Six upflow anaerobic sludge blanket (UASB) reactors were concurrently operated for 146d to examine the effects of calcium on the sludge granulation process during start-up. Introduction of Ca2+ at concentrations from 150 to 300mg/l enhanced the biomass accumulation and granulation process. The calcium concentration in the granules was nearly proportional to the calcium concentration in the feed, and calcium carbonate was the main calcium precipitate in the granules. The specific activity of granules decreased with increasing influent calcium concentration. The optimum calcium concentration for the granulation was from 150 to 300mg/l. The addition of low-concentration calcium to the UASB reactors appeared to enhance the three steps of sludge granulation: adsorption, adhesion and multiplication, but it did not lead to a different proliferation of predominant microorganisms in the granules.},
number = {4},
urldate = {2017-10-27},
journal = {Water Research},
author = {Yu, H. Q and Tay, J. H and Fang, Herbert H. P},
month = mar,
year = {2001},
keywords = {calcium, granulation, start-up, upflow anaerobic sludge blanket (UASB) reactor},
pages = {1052--1060}

Field Studies of Pedestrian Walking Speed and Start-Up Time by Richard Knoblauch
@article{knoblauch_field_1996,
title = {Field {Studies} of {Pedestrian} {Walking} {Speed} and {Start}-{Up} {Time}},
volume = {1538},
issn = {0361-1981},
url = {http://trrjournalonline.trb.org/doi/abs/10.3141/1538-04},
doi = {10.3141/1538-04},
abstract = {Today's traffic environment is not well adapted to the needs of the older pedestrian. Unfortunately, except in the case of children, little is known about the characteristics and behavior of pedestrians. Although the simple fact that older pedestrians walk more slowly than younger ones is easily supported by field data, existing data on walking speeds and start-up time (i.e., the time from the onset of a Walk signal until the pedestrian steps off the curb) have many shortcomings. A series of field studies was conducted to quantify the walking speed and start-up time of pedestrians of various ages under different conditions. Sixteen crosswalks in four urban areas were studied. Data were collected on walking speeds and start-up times relative to site and environmental factors, including street width, posted speed, curb height, grade, number of vehicle travel lanes, signal cycle length, pedestrian-signal type, street functional classification, crosswalk type, and channelization. Data on a subject group of pedestrians who appeared to be 65 years of age or older and a control group of pedestrians under age 65 were collected. Results indicate a broad range of walking speeds among pedestrians. The 15th-percentile walking speed for younger pedestrians (ages 14 to 64) was 1.25 m/sec (4.09 ft/sec); for older pedestrians (ages 65 and over) it was 0.97 m/sec (3.19 ft/sec). For design purposes values of 1.22 m/sec (4 ft/sec) for younger pedestrians and 0.91 m/sec (3 ft/sec) for older pedestrians are appropriate. Results also indicated that walking rates are influenced by a variety of factors, including the functional classification and vehicle volumes on the street being crossed, the street width, weather conditions, the number of pedestrians crossing in a group, the signal cycle length, the timing of the various pedestrian-signal phases, whether right turn on red is allowed, pedestrian signals, medians, curb cuts, crosswalk markings, stop lines, and on-street parking. However, for each of these factors, the effect on crossing speeds, although statistically significant, is not meaningful for design.},
urldate = {2017-10-27},
journal = {Transportation Research Record: Journal of the Transportation Research Board},
author = {Knoblauch, Richard and Pietrucha, Martin and Nitzburg, Marsha},
month = jan,
year = {1996},
pages = {27--38}

Environments and Strategies of Organization Start-Up: Effects on Early Survival Elaine Romanlli
@article{romanelli_environments_1989,
title = {Environments and {Strategies} of {Organization} {Start}-{Up}: {Effects} on {Early} {Survival}},
volume = {34},
issn = {0001-8392},
shorttitle = {Environments and {Strategies} of {Organization} {Start}-{Up}},
url = {http://www.jstor.org/stable/2393149},
doi = {10.2307/2393149},
abstract = {This paper explores the effects of two factors that influence the likelihood of an organization's surviving its startup years: (1) environmental resource and competitive conditions at the time of founding, and (2) strategies that an organization uses during its early years to exploit environmental conditions. The principal hypotheses link changes in industry sales and changes in concentration ratios (resource and competitive conditions) and early organizational strategies (market breadth and market aggressiveness) to the likelihood of early survival. Results from a longitudinal study of start-ups in the minicomputer industry indicate that, for most environmental conditions, specialist and aggressive strategies increase chances for early survival. When industry sales are increasing, generalists fare better than specialists. When industry sales are declining, efficient organizations have higher likelihoods of early survival than aggressive organizations. The findings suggest that founders can overcome hazards of start-up by tailoring strategies to environmental conditions.},
number = {3},
urldate = {2017-10-27},
journal = {Administrative Science Quarterly},
author = {Romanelli, Elaine},
year = {1989},
pages = {369--387}

Venture Capitalists and Cooperative Start-up Commercialization Strategy by David H. Hsu
@article{hsu_venture_2006,
title = {Venture {Capitalists} and {Cooperative} {Start}-up {Commercialization} {Strategy}},
volume = {52},
issn = {0025-1909},
url = {http://pubsonline.informs.org/doi/abs/10.1287/mnsc.1050.0480},
doi = {10.1287/mnsc.1050.0480},
abstract = {This paper examines the possible impact of venture capital (VC) backing on the commercialization direction of technology-based start-ups by asking: To what extent (if at all) do VC-funded start-ups engage in cooperative commercialization strategies (strategic alliances or technology licensing, or both) relative to a comparable set of start-ups, and with what consequences? To address these questions, I assemble a novel data set that matches firms receiving a federal research and development subsidy through the U.S. Small Business Innovative Research program to VC-funded firms by observable characteristics in five technology-intensive industries. These data allow decoupling of cooperative activity resulting from start-up development via the passage of calendar time from that due to association with VCs. An analysis of the 696 start-ups in the sample (split by an external funding source) suggests substantial boosts in both cooperative activity associated with VC-backed firms and in the likelihood of an initial public offering.},
number = {2},
urldate = {2017-10-27},
journal = {Management Science},
author = {Hsu, David H.},
month = feb,
year = {2006},
pages = {204--219}

==startup clustering==
Super-peer-based Routing and Clustering Strategies for RDF-based Peer-to-peer Networks by Wolfgang Nejdi
@inproceedings{nejdl_super-peer-based_2003,
address = {New York, NY, USA},
series = {{WWW} '03},
title = {Super-peer-based {Routing} and {Clustering} {Strategies} for {RDF}-based {Peer}-to-peer {Networks}},
isbn = {9781581136807},
url = {http://doi.acm.org/10.1145/775152.775229},
doi = {10.1145/775152.775229},
abstract = {RDF-based P2P networks have a number of advantages compared with simpler P2P networks such as Napster, Gnutella or with approaches based on distributed indices such as CAN and CHORD. RDF-based P2P networks allow complex and extendable descriptions of resources instead of fixed and limited ones, and they provide complex query facilities against these metadata instead of simple keyword-based searches.In previous papers, we have described the Edutella infrastructure and different kinds of Edutella peers implementing such an RDF-based P2P network. In this paper we will discuss these RDF-based P2P networks as a specific example of a new type of P2P networks, schema-based P2P networks and describe the use of super-peer based topologies for these networks. Super-peer based networks can provide better scalability than broadcast based networks, and do provide perfect support for inhomogeneous schema-based networks, which support different metadata schemas and ontologies (crucial for the Semantic Web). Furthermore, as we will show in this paper, they are able to support sophisticated routing and clustering strategies based on the metadata schemas, attributes and ontologies used. Especially helpful in this context is the RDF functionality to uniquely identify schemas, attributes and ontologies. The resulting routing indices can be built using dynamic frequency counting algorithms and support local mediation and transformation rules, and we will sketch some first ideas for implementing these advanced functionalities as well.},
urldate = {2017-10-27},
publisher = {ACM},
author = {Nejdl, Wolfgang and Wolpers, Martin and Siberski, Wolf and Schmitz, Christoph and Schlosser, Mario and Brunkhorst, Ingo and LÃ¶ser, Alexander},
year = {2003},
keywords = {distributed RDF repositories, peer-to-peer, schema-based routing, semantic web},
pages = {536--543}

Lowest-ID with adaptive ID reassignment by D. Gavalas
@inproceedings{gavalas_lowest-id_2006,
title = {Lowest-{ID} with adaptive {ID} reassignment: a novel mobile ad-hoc networks clustering algorithm},
shorttitle = {Lowest-{ID} with adaptive {ID} reassignment},
doi = {10.1109/ISWPC.2006.1613559},
abstract = {Clustering is a promising approach for building hierarchies and simplifying the routing process in mobile ad-hoc network environments. The main objective of clustering is to identify suitable node representatives, i.e. cluster heads (CHs), to store routing and topology information and maximize clusters stability. Traditional clustering algorithms suggest CH election exclusively based on node IDs or location information and involve frequent broadcasting of control packets, even when network topology remains unchanged. More recent works take into account additional metrics (such as energy and mobility) and optimize initial clustering. However, in many situations (e.g. in relatively static topologies) re-clustering procedure is hardly ever invoked; hence initially elected CHs soon reach battery exhaustion. Herein, we introduce an efficient distributed clustering algorithm that uses both mobility and energy metrics to provide stable cluster formations. CHs are initially elected based on the time and cost-efficient lowest-ID method. During clustering maintenance phase though, node IDs are re-assigned according to nodes mobility and energy status, ensuring that nodes with low-mobility and sufficient energy supply are assigned low IDs and, hence, are elected as CHs. Our algorithm also reduces control traffic volume since broadcast period is adjusted according to the nodes mobility pattern: we employ infrequent broadcasting for relative static network topologies, and increase broadcast frequency for highly mobile network configurations. Simulation results verify that energy consumption is uniformly distributed among network nodes and that signaling overhead is significantly decreased.},
booktitle = {2006 1st {International} {Symposium} on {Wireless} {Pervasive} {Computing}},
author = {Gavalas, D. and Pantziou, G. and Konstantopoulos, C. and Mamalis, B.},
month = jan,
year = {2006},
keywords = {Ad hoc networks, Batteries, Broadcasting, Clustering algorithms, Communication system traffic control, Intrusion detection, Network topology, Nominations and elections, Routing, Stability, ad hoc networks, adaptive ID reassignment, battery exhaustion, broadcast period, broadcasting, cluster heads, clustering maintenance, control packets, control traffic volume, cost-efficient lowest-ID method, distributed clustering algorithm, energy metrics, highly mobile network configurations, infrequent broadcasting, mobile ad-hoc networks clustering algorithm, mobile radio, nodes mobility, nodes mobility pattern, relative static network topologies, routing process, stable cluster formations, telecommunication network routing, telecommunication network topology, topology information},
pages = {5 pp.--}

A P2P hierarchical clustering live video streaming system by De-Kai Liu and Ren-Hung Hwang
@inproceedings{liu_p2p_2003,
title = {A {P}2P hierarchical clustering live video streaming system},
doi = {10.1109/ICCCN.2003.1284158},
abstract = {This paper describes P2broadcast, a novel live video streaming system for P2P networks which organizes peers into hierarchical clusters to reduce startup latency and the service interruption probability. P2broadcast has two key features: highly available and efficient join, and low service interruption probability. The highly available and efficient join algorithm uses RTT of two peers as a hint of available bandwidth between them. As a consequence, the startup latency can be shortened and overhead can be reduced. In addition, P2broadcast constructs a "short and wide" overlay tree which reduces the probability of service interruption due to the leave or failure of a peer. Our simulation results show that P2broadcast outperforms in startup latency and service interrupt probability over existing approaches in the literature.},
booktitle = {Proceedings. 12th {International} {Conference} on {Computer} {Communications} and {Networks} ({IEEE} {Cat}. {No}.03EX712)},
author = {Liu, De-Kai and Hwang, Ren-Hung},
month = oct,
year = {2003},
keywords = {Availability, Bandwidth, Clustering algorithms, Computer science, Delay estimation, IP networks, Information retrieval, Internet, Network servers, Scalability, Streaming media, application level multicasting, hierarchical clustering, live video streaming system, multicast communication, overlay tree network, peer broadcast, peer-to-peer network, round trip time, service interruption probability, startup latency},
pages = {115--120}

==start-up clustering==

University start-up formation and technology licensing with firms that go public by Joshua Powers
@article{powers_university_2005,
title = {University start-up formation and technology licensing with firms that go public: a resource-based view of academic entrepreneurship},
volume = {20},
issn = {0883-9026},
shorttitle = {University start-up formation and technology licensing with firms that go public},
url = {http://www.sciencedirect.com/science/article/pii/S0883902604000291},
doi = {10.1016/j.jbusvent.2003.12.008},
abstract = {Although academic entrepreneurship is a topic receiving some attention in the literature, higher education's appetite for expanding technology transfer activities suggests that more research is needed to inform practice. This study investigates the effects of particular resource sets on two university commercialization activities: the number of start-up companies formed and the number of initial public offering (IPO) firms to which a university had previously licensed a technology. Utilizing multisource data on 120 universities and a resource-based view of the firm framework, a set of university financial, human capital, and organizational resources were found to be significant predictors of one or both outcomes.},
number = {3},
urldate = {2017-10-27},
journal = {Journal of Business Venturing},
author = {Powers, Joshua B. and McDougall, Patricia P.},
month = may,
year = {2005},
keywords = {Entrepreneurship, Industry, Start-up formation, University},
pages = {291--311}

A taxonomy of business start-up reasons and their impact on firm growth and size by Sue Birley and Paul Westhead
@article{birley_taxonomy_1994,
title = {A taxonomy of business start-up reasons and their impact on firm growth and size},
volume = {9},
issn = {0883-9026},
url = {http://www.sciencedirect.com/science/article/pii/0883902694900248},
doi = {10.1016/0883-9026(94)90024-8},
abstract = {Based on a survey of 405 principal owner-managers of new independent business in Great Britain this paper explores two research questions— are there any differences in the reasons that owner-managers articulate for starting their businesses, and, if there are, do they appear to affect the subsequent growth and size of the businesses? The results of the study indicate an affirmative answer to the first question. From the 23 diverse reasons leading to start-up that were identified in the literature, an underlying pattern emerged via the Principal Components Analysis. Moreover, these were similar to those found in earlier studies. Thus, five of the seven components identified by the model correspond to those identified by Scheinberg and MacMillan (1988) in their eleven-country study of motivations to start a business: “Need for Approval,” “Need for Independence,” “Need for Personal Development,” “Welfare Considerations,” and “Perceived Instrumentality of Wealth.” Two further components were identified by this current study. The first vindicates the decision to add a question not included in the previous study that related to “Tax Reduction and Indirect Benefits,” and the second, the desire to “Follow Role Models” was identified by Dubini (1988) in her study in Italy. In order to take account of possible multiple motivations in the start-up period, cluster analysis was used to provide a classification of founder “types.” The seven generalized “types” of owner-managers were named as follows—the insecure (104 founders), the followers (49 founders), the status avoiders (169 founders), the confused (15 founders), the tax avoiders (18 founders), the community (49 founders), and the unfocused (1 founder). Further, evidence from the final discriminant analysis model suggested that the seven-cluster classification of owner-managers was appropriate and optimal. However, despite these clear differences between clusters, this was not found to be an indicator of subsequent size or growth, as measured by sales and employment levels. The answer to the second research question would be in the negative. Therefore, we conclude that, whereas new businesses are founded by individuals with significantly different reasons leading to start-up, once the new ventures are established these reasons have a minimal influence on the growth of new ventures and upon the subsequent wealth creation and job generation potential. This result is important for investors and policy-makers. It suggests that strategies for “picking winners” solely based upon the characteristics of owner-managers and their stated reasons for wanting to go into business are not supported. Thus, for example, targeting scarce resources to those with high opportunistic and materialistic reasons for venture initiation would miss those with a wider sense of community or those with personal needs for independence who establish similarly sized businesses with comparable levels of wealth creation.},
number = {1},
urldate = {2017-10-27},
journal = {Journal of Business Venturing},
author = {Birley, Sue and Westhead, Paul},
month = jan,
year = {1994},
pages = {7--31}

==startup agglomeration==

The New Economics Off Innovation, Spillovers And Agglomeration: Areview Of Empirical Studies by Maryann P. Feldman
@article{feldman_new_1999,
title = {The {New} {Economics} {Of} {Innovation}, {Spillovers} {And} {Agglomeration}: {Areview} {Of} {Empirical} {Studies}},
volume = {8},
issn = {1043-8599},
shorttitle = {The {New} {Economics} {Of} {Innovation}, {Spillovers} {And} {Agglomeration}},
url = {http://dx.doi.org/10.1080/10438599900000002},
doi = {10.1080/10438599900000002},
abstract = {This paper reviews recent empirical studies of location and innovation. The objective is to highlight the questions addressed, approaches adopted, and further issues that remain. The review is organized around the traditions of measuring geographically mediated spillovers and productivity studies that introduce a geographic dimension. The first part identilies four separate strains in thc empirical spillover literature: innovation production functions; the linkages between patent citations. defined as paper trails: the rnobility of skilled labor based on the notion that knowledge spillovers are transmitted through people; and, last, knowledge spillovers embodied in traded goods. The second part considers the composition of agglomeration economies, the attributes of knowlcdge, and the characteristics of firms.},
number = {1-2},
urldate = {2017-10-28},
journal = {Economics of Innovation and New Technology},
author = {Feldman, Maryann P.},
month = jan,
year = {1999},
keywords = {Geography, Innovation, L2, Location JEL Classification: 03, Spillovers},
pages = {5--25}

Geography, Industrial Organization, and Agglomeration by Stuart S. Rosenthal and William C. Strange
@article{rosenthal_geography_2003,
title = {Geography, {Industrial} {Organization}, and {Agglomeration}},
volume = {85},
issn = {0034-6535},
url = {https://doi.org/10.1162/003465303765299882},
doi = {10.1162/003465303765299882},
number = {2},
urldate = {2017-10-28},
journal = {The Review of Economics and Statistics},
author = {Rosenthal, Stuart S. and Strange, William C.},
month = may,
year = {2003},
pages = {377--393}

Chapter 49 - Evidence on the Nature and Sources of Agglomeration Economies by Stuart S. Rosenthal and William C. Strange
@incollection{rosenthal_chapter_2004,
series = {Cities and {Geography}},
title = {Chapter 49 - {Evidence} on the {Nature} and {Sources} of {Agglomeration} {Economies}},
volume = {4},
url = {http://www.sciencedirect.com/science/article/pii/S1574008004800063},
abstract = {This paper considers the empirical literature on the nature and sources of urban increasing returns, also known as agglomeration economies. An important aspect of these externalities that has not been previously emphasized is that the effects of agglomeration extend over at least three different dimensions. These are the industrial, geographic, and temporal scope of economic agglomeration economies. In each case, the literature suggests that agglomeration economies attenuate with distance. Recently, the literature has also begun to provide evidence on the microfoundations of external economies of scale. The best known of these sources are those attributed to Marshall (1920): labor market pooling, input sharing, and knowledge spillovers. Evidence to date supports the presence of all three of these forces. In addition, there is also evidence that natural advantage, home market effects, consumption opportunities, and rent-seeking all contribute to agglomeration.},
urldate = {2017-10-28},
booktitle = {Handbook of {Regional} and {Urban} {Economics}},
publisher = {Elsevier},
author = {Rosenthal, Stuart S. and Strange, William C.},
editor = {Henderson, J. Vernon and Thisse, Jacques-FranÃ§ois},
month = jan,
year = {2004},
note = {DOI: 10.1016/S1574-0080(04)80006-3},
keywords = {agglomeration economies, external economies, microfoundations, productivity, urban growth},
pages = {2119--2171}

==start-up agglomeration==

Intellectual capital and business start‐up success by Iñaki Peña
@article{pena_intellectual_2002,
title = {Intellectual capital and business start‐up success},
volume = {3},
issn = {1469-1930},
url = {http://www.emeraldinsight.com/doi/abs/10.1108/14691930210424761},
doi = {10.1108/14691930210424761},
number = {2},
urldate = {2017-10-28},
journal = {Journal of Intellectual Capital},
author = {Peña, Iñaki},
month = jun,
year = {2002},
pages = {180--198}

Aspiring, nascent and fledgling entrepreneurs: an investigation of the business start-up process by Beate Rotefoss and Lars Kolvereid
@article{rotefoss_aspiring_2005,
title = {Aspiring, nascent and fledgling entrepreneurs: an investigation of the business start-up process},
volume = {17},
issn = {0898-5626},
shorttitle = {Aspiring, nascent and fledgling entrepreneurs},
url = {http://rsa.tandfonline.com/doi/abs/10.1080/08985620500074049}
doi = {10.1080/08985620500074049},
abstract = {This study focuses on three different milestones in the business gestation process, i.e. becoming an aspiring entrepreneur, a nascent entrepreneur, and a founder of a fledgling new business. Moreover, this study uses a combination of both individual and regional (or environmental) factors in predicting individuals’ success or failure to reach each of these three milestones. Hypotheses are developed to test the effect that human and environmental resources have on the odds of reaching the different milestones in the business start-up process. The study is based on interviews of a representative sample of 9533 Norwegians aged 18 years or older. From this group, 197 respondents qualified as nascent entrepreneurs. These were subsequently interviewed in follow-up interviews conducted in 1996, 1997 and 1999. In addition, regional data at the municipality level is included to measure the available pool of environmental resources. The results indicate that entrepreneurial experience is the single most important factor for predicting the outcome of the business start-up process. Even though environmental resources play a role, human resources are generally found to be better predictors of the outcome of the business start-up process. Several important implications for policy-makers are presented.},
number = {2},
urldate = {2017-10-28},
journal = {Entrepreneurship \& Regional Development},
author = {Rotefoss, Beate and Kolvereid, Lars},
month = mar,
year = {2005},
pages = {109--127}

Juliette Richert

2017-10-25T15:14:11Z

Dayton:

{{McNair Staff
|position=Admin
|name=Juliette Richert
|user_image=11822729 10206900027716534 3655536168922150825 n.jpg
|degree=BA
|major=Mathematical Analysis of Economics; Policy Studies
|class=2020
|join_date=02/07/2020
|email=jar23@rice.edu
|status=Inactive,
}}
Juliette Richert is an administrative assistant at the Baker Institute's McNair Center.

=Early Life=
Juliette was born in New Hampshire on May 4, 1998 to Jan Richardson and Allen Richert, Jr. She and her family moved to Ridgeland, Mississippi shortly after her birth. Claire and Allen Richert, Juliette's younger sibilings, were born in Mississippi. After Juliette graduated from Ridgeland High School in May 2016, she moved to Houston to study at Rice University.

=Education=
Juliette is a freshman at Rice University majoring in Mathematical Analysis of Economics and Policy Studies with an emphasis in Social and Urban Change. She is a member of Will Rice College.
==Time at McNair==
[[Juliette Richert (Work Log)]]
[[Juliette Richert (Research Plan)]]
[[Category:McNair Staff]]

[[category:McNair Staff]]
[[position::Admin]]
[[status::Active]]

Adrian Smart

2017-10-25T15:12:12Z

Dayton:

{{McNair Staff
|position=Research Team
|name=Adrian Smart
|degree=Master of Computer Science
|class=2017
|join_date=4 May 2017
|interests=Financial Markets, Software Engineering, Turbomachinery
|email=as157@rice.edu
|status=Inactive,
}}
==Time at McNair==
[[Adrian Smart (Work Log)]]

Help:Helppage

2017-10-19T15:00:41Z

Dayton: /* Workday Norms */

'''Welcome to the Baker Institute's McNair Center for Entrepreneurship and Innovation!'''

This page will get you - our newest team member - started on setting up your own account on the Wiki and navigating its content management prowess.

''If you are part of our technical staff, please follow the instructions for [[New Tech Staff]] instead of this page.''

===User Account===
*[[Special:CreateAccount | Create a new acccount]]
*[[Special:UserLogin | Login with existing account]]

===User Page===

Every McNair staff maintains his own personal wikipage, detailing administrative information and professional/academic background, alongside a short autobiography.

Refer to [[Dylan_Dickens | Dylan's Wikipage]] for inspiration. Or refer to the comprehensive [[:Category:McNair_Staff | staff list]].

Create your own Wikipage by going to [[Form:McNair_Staff]] and filing out the form. On the first page of the form '''use your Full Name with spaces''' (like Ed Egan), not your username. Fill out the fields. For the image, upload one (see "Upload file" in the left Wiki sidebar) and just put the name of the file in the box (e.g. YourPic.jpg). Finally go to your user page (click your username at the top of the page), edit that page and include the following line:
<nowiki>#REDIRECT [[Full Name]]</nowiki>

Faculty Members to the McNair Center should also fill out a page. Please use this form [[Form:McNair_Faculty]].

===Working with the Wiki===

At McNair, the Wiki is both home to developed content in the form of an [[Current_Entrepreneurship_and_Innovation_Policies_(Wiki_Page) | independent public Wikipage]], and developing content that will serve a academic paper, issue brief or [[Business_Dynamism_in_High_Tech_(Blog_Post) | blog post]] in the future. It is the research team's main tool for knwoledge sharing, pooling resources, writing and peer-editing.

To get started,
*[[Help:Editing Content | Basic Wiki Markup Guide]]
*[[Forms and Templates]] - Working with pre-existing ones and making new ones
* When in doubt, [[Ed Egan| contact Ed]] or ask a friend!

Useful resources from wikipedia:
*[https://en.wikipedia.org/wiki/Help:Wiki_markup Complete Wiki Markup Documentation]
*[https://en.wikipedia.org/wiki/Wikipedia:Manual_of_Style Text Layout Philosophy]
*[https://en.wikipedia.org/wiki/Wikipedia:Writing_better_articles Wiki-appropriate Writing Style]

You should also be creating pages for identified topic areas and to keep track of your projects. You must secure your pages by adding them to [[:Category:Internal]] or some other appropriate category, so that they are available only to McNair staff and faculty. If you try to create a page that already exists the form will redirect you to edit that page.

===Working with the infrastructure===

====The RDP, Storing Data, and the Dbase Server====

At McNair, key software and processing power is made accessible to your personal laptop through the [[Help:Access_RDP_Sever | Remote Desktop Connection]]. Follow the link for instructions (Good luck!).

That page also contains details about [[Help:Access_RDP_Sever#Mapping_the_Network_Drive|How to Map the network drive]] and [[Help:Access_RDP_Sever#Connecting_the_Database_Server|Connecting to the database server]].

All of your day-to-day data should be stored on the E: drive of the RDP. Generally, you should create a folder for each project that you work on and store your data in:
E:\McNair\Projects\YOURPROJECT

====Working with our PostgreSQL Server====

Only tab-delimited text files that contain data that is being moved on or off the database server should be copied to the database servers bulk drive. Again create folders there to organize your data. Your folder's name should be the same as your database's name (case sensitive to avoid confusion). Note that some legacy folders and databases do not follow this schema.

To make your life easy, map the database's bulk drive on your RDP account. [[Help:Access_RDP_Sever#Connecting_the_Database_Server|Follow the instructions]] to do this. We refer to the database server's bulk drive as either 181/bulk (where 181 is the last segment of its address) or as Z:, as this is the drive letter most commonly mapped to.

There are detailed instructions on [[Working with PostgreSQL]], that include how to connect to the server, make dbases, and write SQL.

For first timers, you should also read [[How to do a Simple Database Project]].

====Using our Git Server====

Our Git Server is hosted on the RDP Server. Connect as follows:
*From the RDP: https://localhost/codebase/
*From the anywhere: https://rdp.mcnaircenter.org/codebase/ or https://128.42.44.182/codebase/

See the [[Software Repository]] page for full details of how to use the Git server, and the [[Software Repository Listing]] to see a list of (some of) the software stored there.

===Publishing through McNair Accounts===

====Twitter====

*Find one article per week and send it to the "twitter" Slack channel.
*[[Ramee Saleh]] is our twitter point person. She has access to Hootsuite as well. Contact her with any publishing questions.

====Blog====

{{:Writing Blog Posts}}

===Workday Norms===
#Upon arrival at work, please sign in to the McNair RDP. This is how we track your physical attendance at McNair.
#After signing into the RDP, please fill out your work log. This should include the hours you work that day as well as your intended accomplishments for the day (preferably with links to pages you worked on!)

<small>"Everything that you see and hear and live is made by other people; feel free to change it." - Steve Jobs</small>

===Setting up Someone Else's Account===
'''NOTE:''' In order to do this part, you need to have administrator access on the RDP and the Wiki.

====Setting Up an RDP Account====
1. Open the Server Manager. (Second button from the left on the bottom toolbar.)

2. In the Upper Right hand corner, select Tools -> Active Directory Users and Computers. A box will pop up.

3. On the left hand side of this box, there is a folder titled "Users". Right click on that folder, and select New -> User.

4. Fill in the fields, and select Next.

5. On the next screen, enter your password. Unselect the box "user must change password on next login", and check the box "password never expires".

6. Select Next, review your entries, and hit "Finish".

7. There now should be a little person icon in the "Users" folder with your name on it.

8. Remember this Username and Password. This is how you will log onto the RDP.

====Adding Someone to a Privileged Group====
Certain groups have more administrative access on the RDP. If someone needs to be added to a group, you can do this in the same window as the previous section.

1. As before, Open the Server Manager, go to Tools -> Active Directory Users and Computers, and double click on the "Users" folder.

2. Find your User in the Users folder. Right click on the User, and select the option "Add to a group...".

3. Here, you need to know what group your User needs to be added to. Type it in the box, and if it is a valid group, the name will be underlined. Then hit OK.

====Getting onto the RDP====

1. Go to the App Store (Mac) or whatever Windows uses, and search for the application, "Microsoft Remote Desktop." It should be the first application in the results. The logo is an orange box with a computer in it. Download it.

2. Open it.

3. Select the "New" button (the big +). Enter the following information:

{| class="wikitable"
|Connection Name|
| McNairRDP
|-
|PcName
|rdp.mcnaircenter.org
|-
|Username
|ad0\yourUsernameFromEarlier
|-
|Password
|yourPasswordFromEarlier
|}

Leave other values at their default value.

[[category:McNair Admin]]

Abhijit Brahme

2017-10-19T14:47:49Z

Dayton:

{{McNair Staff
|position=Tech Team
|name=Abhijit Brahme,
|user_image=abhiheadshot.jpg
|degree=BA
|major=CAAM
|class=2018,
|join_date=06/05/2017,
|skills=MATLAB, Python, R, Julia, Writing,
|interests=Basketball, Ultimate Frisbee, Dogs, NBA 2K,
|fun_fact=I once saw Myles Turner at a Hopdoddy in Dallas,
|email=mailto:aub1@rice.edu,
|skype_name=abhibrah,
|status=Inactive,
}}
==Early Life==
Born and raised in Fremont, California, Abhijit spent most of his youth dribbling a basketball during the glorious California summer and wishing he had a dog. Abhijit graduated from American High School in 2014. He has a younger sister.
== Professional Life ==
As an undergraduate at Rice University (Wiess College), Abhijit doesn't have much of a professional life. After changing his major more than a model changes clothes, Abhijit finally decided on studying Applied Math in his Junior year. Abhijit is currently the co-captain of Rice University's one and only Men's Ultimate Team, Cloud 9. In his spare time, Abhijit analyzes [https://medium.com/@brahmeabhijit/exploring-the-psychology-of-nba-stars-28b9670a2743 basketball data] for Rice University, and for fun.

== Future Goals ==
One day, Abhijit hopes to help a professional NBA team analyze their basketball data. A boy can dream.

== Time at McNair ==
[[Abhijit Brahme (Work Log) ]]

Augusta Startup Ecosystem

2017-10-03T14:34:26Z

Dayton: /* VC Data Summary */

{{McNair Projects
|Has owner=Ben Baldazo, Dylan Dickens, Joe Reilly, Taylor Jacobe,
|Has start date=9/20/2017
|Has deadline=10/6/2017
|Has notes=End product is slide deck
|Has project status=Active
}}
Link to DOC: https://docs.google.com/a/rice.edu/document/d/15EtmjxWLLta4SZEW4PlsqxXgA-yEuuZ3OE1Ciin2yHA/edit?usp=sharing

Augusta, Georgia- Want to build a startup ecosystem around the headquarters
*University of Augusta is in the local area
*US CyberCommand is going to locate their headquarters there, and in response, they want to ensure an ecosystem exists there to pull from (QUESTION: WHO IS "THEY" IN THIS SENTENCE?)
**Needs recruits, programmers
*Want to build a startup ecosystem around the headquarters
*Find all of the important inno people in Georgia
**How can we piece an ecosystem together?

=Potential Corporate Headquarters / Branches=
Potential way to see: look at donors to Augusta based Arts or Museums
*Augusta Ballet support (scroll to bottom): http://www.augustaballet.org/support/

=VC Data Summary=

Find Data here:
There is a company level VC data file, with exits, deal type flags, dead/alive years, and round summary info in
E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem

This file is '''PortCoMaster.txt'''.

Excel File with data filtered by Tay is '''Relevant VC Data'''

Augusta, Georgia: 7 Results-
*NRF Distributors Inc, Keymarket Partners, Lexicor Medical Technology Inc, Keymarket III, Solo Cup Operating Corp, Joseph Kirschner Company Inc, and Sagamore Hill Broadcasting LLC. Only Sagamore Hill Broadcasting has received funding in the last 20 years, all the rest were earlier. '''Conclusion: Augusta's VC scene is not good at all. If only one company has been VC backed in the last 20 years, this is worrisome'''
**Sagamore Hill Broadcasting owns and operates television-broadcasting stations in the Great Lakes and Southern US. It is a joint venture of the investment firm Duff Ackerman & Goodrich of San Francisco, California and former Benedek Broadcasting and Spartan Communications executive Louis Wall (who is also CEO of the group).
*None are in the categories listed below

Atlanta, Georgia: ~550 results-
*200 of these are in the last decade; Atlanta is clearly attracting VC funding. How can we expand that success to Augusta?
*Of those in the last decade, small proportion could be cybersecurity-related:
**3 Internet Security and Transaction Services
**10 Security/Firewalls/Encryption software
**None of the other categories listed below

Charleston, South Carolina- ~30 results-
*In general, these investments are far more recent than those in Augusta, with half of these results taking place within the last decade.
*Only one Security/Firewalls/Encryption- PhishLabs
*None of the other categories listed below

Other local areas
*Athens, Georgia: 15 VC funded, 7 within last decade
*Savannah, Georgia: 5 VC funded, 1 within last decade

Silicon Valley:
Accelerators
*Included these filters on '''Relevant VC Data''': CA for state and these cities: Campbell; Cupertino; Los Altos; Los Altos Hills; Los Gatos; Milpitas; Monte Sereno; Morgan Hill; Mountain View; Palo Alto; San Fransisco; San Jose; San Mateo; Santa Clara; Saratoga; Sunnyvale
**this is the list of cities Wikipedia's page on Silicon Valley includes plus San Mateo and San Francisco ADD OTHER SAN FRANCISCO BAY AREA CITIES FROM DIANA CARRANZA'S RANKING REPORT IN ECOSYSTEM>RANKING.
*7520 VC funded, 4220 over last decade
Incubators:
*Searched for incubators on AngelList (saved to "AngelList Silicon Valley Incubators (unfiltered).txt") but the results were definitely more than just incubators so filtered to everything that had "incub" in its name or description and added back ycombinator (Saved to "AngelList Silicon Valley Incubators (filtered).txt")

Relevant industries (at the indsubgroup level) for cyber security might be:
indsubgroup
-------------------------------------------------
Internet Security and Transaction Services
Other Electronics Related (incl. alarm systems)
Military Electronics (excluding communications)
Security/Alarm/Sensors
Security/FirewallsEncryption software
Computer Security Services
Defense Communications

Note that the original source data is in '''vcdb2''' and the script to make this data is '''augustavcdata.sql'''

==Recent Business News for Augusta==

*Building a new arena in Augusta, but unknown where the location will be: http://chronicle.augusta.com/news/2017-09-26/regency-supporters-unmoved-arena-site-selection-presentation, http://www.wrdw.com/content/news/Coliseum-Authority-Meeting-a-Full-House-448078573.html
* New Internship program to promote sticking in Augusta to work: "The Students2Work initiative aims to place 250 Richmond County high school students in internships throughout the Augusta area – to both improve the skills they have, and to develop new skills to better prepare them for the working world." http://chronicle.augusta.com/news/business/2017-09-06/business-education-leaders-announce-job-internship-initiative-richmond

The demand for skilled workers takes on added gravity as the region braces for an expected economic spike spurred by cyber-related businesses.

=Tasks for Ben Baldazo, Joe Reilly and Dylan Dickens on September 26=
#MOVE THIS ZIP CODE INFO TO NEW SECTION ON WIKI PAGE Determine which zip codes are associated with Augusta, Augusta University & Cyber command facility.
#*Augusta: http://zipcode.org/city/GA/AUGUSTA
#*Augusta U (zip: 30912) website: http://www.augusta.edu/
#*Fort Gordon (houses the cyber command) Zip: 30905 | Website: http://www.gordon.army.mil/
#Use Compustat to pull data on publicly traded firms in Augusta, Atlanta and Georgia.
#*See E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem\Compustat (publicly traded firms)
#**"Firms on NYSE NASDAQ AMEX yr 2016" restricted pull to those exchanges of publicly traded companies over the past year
#**"Public Firms" pull from all of compustat that goes from Jan 2010 to Sept 2017
#**Excel for both of these is likely filtered to Georgia or even Augusta
#Data on Accelerators for Augusta, Atlanta, Georgia, and Silicon Valley:
#*"E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem\Accelerators & Cohorts"
##*Data includes list of accelerators in Georgia & the available information on cohort companies from Georgia
##*Pulled from Accelerator_Data in E:\McNair\Projects\Accelerators\Fall 2017 : ATDC, Atlanta Ventures, CyberLaunch, NeuroLaunch, Points of Light Accelerator.
##*From http://www.acceleratorinfo.com/georgia.html: Startup Runway, TechStars Atlanta, Village Capital - HUB Grant Park, Georgia Tech - Flashpoint, Georgia Tech - VentureLab (Tech Transfer). This second list was not checked thoroughly yet.
#*To find number in Silicon valley included provinces: Mountain View, Palo Alto, San Francisco, San Jose, San Mateo, Santa Clara, Sunnyvale
#**Didn’t have data on number of incubators in SV but found angellist search: https://angel.co/companies?company_types[]=Incubator&locations[]=1681-Silicon+Valley,+CA
#***The problem is that most of these aren’t incubators

#Grants Data:
#*"E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem\NIH NSF Clinical Trials for Georgia by Zip"
#*Some notes in the CKnotes.txt file
#*Notes on sourcing of this data in
#**http://mcnair.bakerinstitute.org/wiki/NSF_Data
#**http://mcnair.bakerinstitute.org/wiki/NIH_Data
#**http://mcnair.bakerinstitute.org/wiki/FDA_Trials_Data
#Get data on all VC-backed firms in Augusta, Atlanta, and Georgia.
#What data on cybersecurity startups is available from VenturExpert.
# See Anne for further assignments.

=List of publicly traded firms in Georgia=

*Firms on NYSE NASDAQ AMEX yr 2016
**An excel file list of publicly traded firms (on NYSE, NASDAQ, or American Stock Exchange), with their data for Jan 1 2016-Jan 1 2017, pulled from compustat (https://wrds-web.wharton.upenn.edu/wrds/ds/compm/funda/index.cfm?navId=84), can be found in E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem. It's filtered to show only Georgia companies. It's called "Firms on...". The list files also appear in E:\McNair\Projects\Publicly Traded Firms Data.

*PublicFirms
**excel and txt file of public traded firms with records since Jan 2010 across the entire compustat database. The excel file is filtered to augusta companies

* The files show (in order on the excel sheet)
**at (total assets)
**intan (intangible assets)
**intano (other intangibles)
**revt (total revenue in millions)
**xrd (research and development expense)
**exchg (stock exchange code)
**address1
**address2
**city
**zipcode
**location (country)
**naics (North American industry classification code)
**state

*Directions for pulling general data from https://wrds-web.wharton.upenn.edu/wrds//ds/compm/funda/index.cfm?queryID=807962 with filters for Fundamentals Annual:
**Best to do this in Internet explorer or Firefox so that once the file is completed, it can be downloaded directly (as opposed to Chrome)
*Step 1: Choose a date range
*Step 2: choose GVKEY.
*Step 3: choose data items. In the search bar below, search for information you want. You can search either the abbreviation or the description itself. Once you choose a piece of information (i.e., Stock Exchange Code), a window pops up above Step 3 indicating that you can filter by these pieces of information, in addition to having them in your output file. To filter by Stock exchange, for example, click (in the Conditional Statements window) the drop down arrow, click "Stock Exchange Code". In the far right box, input the code that corresponds to each Stock Exchange (can only be found before Stock Exchange code is selected as a variable type in Step 3. If it's selected, deselect it, find it in the search box, and click on the question mark). To add more, click either "OR" or "AND" and add another code. Select all desired variables and all desired filters on those variables.
*Step 4:
**Output format: select tab-delimited text.
**Compression type: Select none
**Date format: choose either the first option or the last one.

=Documentation=
'''E Drive Folder:'''
"E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem"
*Files located in above folder with data pull description:
**"Firms on NYSE NASDAQ AMEX yr 2016" (both Excel and Text file): Fundamentals Annual for Jan 1 2017-Jan 1 2017, for firms traded on any of the following stock exchanges: NYSE, NASDAQ, American Stock Exchange. The variables pulled are listed in the above section
'''Z Drive Folder:'''
"Z:\Innovation Districts"
*Code document: augusta.sql
*table notes: augustaSQL_notes.txt
'''Database:'''
augusta

=Tentative Schedule=

Week of 9/25: Gather, compile, and synthesize data

Week of 10/1: Make recommendations, create slides and review with Ed

==Anticipated Paper Breakdown==
#Needs/Wants for an Startup Ecosystem
##Economic Factors
##Social Factors
##Environmental Factors
##Government aid
#Diagnosis of Augusta’s Current State
##How does it fulfill and not fulfill the requirements of an innovation district?
##What resources are available in Georgia as a whole that can be used in Augusta?
#Recommendations for how to achieve a Startup Ecosystem in Augusta
##What do we need in order to make a successful startup ecosystem in Augusta? (What is missing?)
##What would it look like?
##How do we make it happen?
###Specific suggestions

=Zip Codes=
*30901
*30903
*30907
*30909
*30999
*30904
*30905
*30906
*30912
*30914
*30916
*30917
*30919

=Georgia Accelerator Programs=

##*From Accelerator Data in E:\McNair\Projects\Accelerators\Summer 2017\Veeral : ATDC, Atlanta Ventures, CyberLaunch, NeuroLaunch, Points of Light Accelerator.
##*From http://www.acceleratorinfo.com/georgia.html: Startup Runway, TechStars Atlanta, Village Capital - HUB Grant Park, Georgia Tech - VentureLab (Tech Transfer).

=Georgia VC Firms=

=Resources Needed=
[[File: Startup Ecosystems.png]]
Elements of a Startup Ecosystem
''Technical Skills''
*'''Education rates of local population'''
*Research on University of Augusta
*How to attract human capital?
''Risk Tolerance''
*'''Local Economic Stability'''
*'''City's Risk Preparedness'''
''Availability of Capital''
*'''VC, Equity'''
''Visible Successes''
*What has already happened there? How can we market the city to best attract human capital and create a city brand?
''Collaborative Culture''
*'''Proximity between innovators'''
*'''Transportation availability'''
*'''Accessibility to travelers'''
''Regulatory Environment''
*Potential for tax credits, types of incentives local government can offer
''Experienced Mentors''
*Who is already there who can act as a mentor?
*Who can we attract there to act as a mentor?
**'''Patent and R&D data for the area'''
''Entrepreneurial Culture''
*What creates a strong entrepreneurial culture and how can we bring that to Augusta?

=Educational Attainment=

*For Richmond County, Georgia, which includes Augusta, the US Census Bureau projects that:
**High school graduate or higher, percent of persons age 25 years+, 2011-2015 83.3%
**Bachelor's degree or higher, percent of persons age 25 years+, 2011-2015 20.4%

Compared to the rest of the US:
**High school graduate or higher, percent of persons age 25 years+, 2011-2015 83.3% 86.7%
*Bachelor's degree or higher, percent of persons age 25 years+, 2011-2015 20.4% 29.8%

*source: https://www.census.gov/quickfacts/fact/table/richmondcountygeorgia,US/PST045216

Augusta Startup Ecosystem

2017-10-03T14:34:04Z

Dayton: /* VC Data Summary */

{{McNair Projects
|Has owner=Ben Baldazo, Dylan Dickens, Joe Reilly, Taylor Jacobe,
|Has start date=9/20/2017
|Has deadline=10/6/2017
|Has notes=End product is slide deck
|Has project status=Active
}}
Link to DOC: https://docs.google.com/a/rice.edu/document/d/15EtmjxWLLta4SZEW4PlsqxXgA-yEuuZ3OE1Ciin2yHA/edit?usp=sharing

Augusta, Georgia- Want to build a startup ecosystem around the headquarters
*University of Augusta is in the local area
*US CyberCommand is going to locate their headquarters there, and in response, they want to ensure an ecosystem exists there to pull from (QUESTION: WHO IS "THEY" IN THIS SENTENCE?)
**Needs recruits, programmers
*Want to build a startup ecosystem around the headquarters
*Find all of the important inno people in Georgia
**How can we piece an ecosystem together?

=Potential Corporate Headquarters / Branches=
Potential way to see: look at donors to Augusta based Arts or Museums
*Augusta Ballet support (scroll to bottom): http://www.augustaballet.org/support/

=VC Data Summary=

Find Data here:
There is a company level VC data file, with exits, deal type flags, dead/alive years, and round summary info in
E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem

This file is '''PortCoMaster.txt'''.

Excel File with data filtered by Tay is '''Relevant VC Data'''

Augusta, Georgia: 7 Results-
*NRF Distributors Inc, Keymarket Partners, Lexicor Medical Technology Inc, Keymarket III, Solo Cup Operating Corp, Joseph Kirschner Company Inc, and Sagamore Hill Broadcasting LLC. Only Sagamore Hill Broadcasting has received funding in the last 20 years, all the rest were earlier. '''Conclusion: Augusta's VC scene is not good at all. If only one company has been VC backed in the last 20 years, this is worrisome'''
**Sagamore Hill Broadcasting owns and operates television-broadcasting stations in the Great Lakes and Southern US. It is a joint venture of the investment firm Duff Ackerman & Goodrich of San Francisco, California and former Benedek Broadcasting and Spartan Communications executive Louis Wall (who is also CEO of the group).
*None are in the categories listed below

Atlanta, Georgia: ~550 results-
*200 of these are in the last decade; Atlanta is clearly attracting VC funding. How can we expand that success to Augusta?
*Of those in the last decade, small proportion could be cybersecurity-related:
**3 Internet Security and Transaction Services
**10 Security/Firewalls/Encryption software
**None of the other categories listed below

Charleston, South Carolina- ~30 results-
*In general, these investments are far more recent than those in Augusta, with half of these results taking place within the last decade.
*Only one Security/Firewalls/Encryption- PhishLabs
*None of the other categories listed below

Other local areas
*Athens, Georgia: 15 VC funded, 7 within last decade
*Savannah, Georgia: 5 VC funded, 1 within last decade

Silicon Valley:
Accelerators
*Included these filters on '''Relevant VC Data''': CA for state and these cities: Campbell; Cupertino; Los Altos; Los Altos Hills; Los Gatos; Milpitas; Monte Sereno; Morgan Hill; Mountain View; Palo Alto; San Fransisco; San Jose; San Mateo; Santa Clara; Saratoga; Sunnyvale
**this is the list of cities Wikipedia's page on Silicon Valley includes plus San Mateo and San Francisco ADD OTHER SAN FRANCISCO BAY AREA CITIES FROM DIANA CARRANZA"S RANKING REPORT IN ECOSYSTEM>RANKING.
*7520 VC funded, 4220 over last decade
Incubators:
*Searched for incubators on AngelList (saved to "AngelList Silicon Valley Incubators (unfiltered).txt") but the results were definitely more than just incubators so filtered to everything that had "incub" in its name or description and added back ycombinator (Saved to "AngelList Silicon Valley Incubators (filtered).txt")

Relevant industries (at the indsubgroup level) for cyber security might be:
indsubgroup
-------------------------------------------------
Internet Security and Transaction Services
Other Electronics Related (incl. alarm systems)
Military Electronics (excluding communications)
Security/Alarm/Sensors
Security/FirewallsEncryption software
Computer Security Services
Defense Communications

Note that the original source data is in '''vcdb2''' and the script to make this data is '''augustavcdata.sql'''

==Recent Business News for Augusta==

*Building a new arena in Augusta, but unknown where the location will be: http://chronicle.augusta.com/news/2017-09-26/regency-supporters-unmoved-arena-site-selection-presentation, http://www.wrdw.com/content/news/Coliseum-Authority-Meeting-a-Full-House-448078573.html
* New Internship program to promote sticking in Augusta to work: "The Students2Work initiative aims to place 250 Richmond County high school students in internships throughout the Augusta area – to both improve the skills they have, and to develop new skills to better prepare them for the working world." http://chronicle.augusta.com/news/business/2017-09-06/business-education-leaders-announce-job-internship-initiative-richmond

The demand for skilled workers takes on added gravity as the region braces for an expected economic spike spurred by cyber-related businesses.

=Tasks for Ben Baldazo, Joe Reilly and Dylan Dickens on September 26=
#MOVE THIS ZIP CODE INFO TO NEW SECTION ON WIKI PAGE Determine which zip codes are associated with Augusta, Augusta University & Cyber command facility.
#*Augusta: http://zipcode.org/city/GA/AUGUSTA
#*Augusta U (zip: 30912) website: http://www.augusta.edu/
#*Fort Gordon (houses the cyber command) Zip: 30905 | Website: http://www.gordon.army.mil/
#Use Compustat to pull data on publicly traded firms in Augusta, Atlanta and Georgia.
#*See E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem\Compustat (publicly traded firms)
#**"Firms on NYSE NASDAQ AMEX yr 2016" restricted pull to those exchanges of publicly traded companies over the past year
#**"Public Firms" pull from all of compustat that goes from Jan 2010 to Sept 2017
#**Excel for both of these is likely filtered to Georgia or even Augusta
#Data on Accelerators for Augusta, Atlanta, Georgia, and Silicon Valley:
#*"E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem\Accelerators & Cohorts"
##*Data includes list of accelerators in Georgia & the available information on cohort companies from Georgia
##*Pulled from Accelerator_Data in E:\McNair\Projects\Accelerators\Fall 2017 : ATDC, Atlanta Ventures, CyberLaunch, NeuroLaunch, Points of Light Accelerator.
##*From http://www.acceleratorinfo.com/georgia.html: Startup Runway, TechStars Atlanta, Village Capital - HUB Grant Park, Georgia Tech - Flashpoint, Georgia Tech - VentureLab (Tech Transfer). This second list was not checked thoroughly yet.
#*To find number in Silicon valley included provinces: Mountain View, Palo Alto, San Francisco, San Jose, San Mateo, Santa Clara, Sunnyvale
#**Didn’t have data on number of incubators in SV but found angellist search: https://angel.co/companies?company_types[]=Incubator&locations[]=1681-Silicon+Valley,+CA
#***The problem is that most of these aren’t incubators

#Grants Data:
#*"E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem\NIH NSF Clinical Trials for Georgia by Zip"
#*Some notes in the CKnotes.txt file
#*Notes on sourcing of this data in
#**http://mcnair.bakerinstitute.org/wiki/NSF_Data
#**http://mcnair.bakerinstitute.org/wiki/NIH_Data
#**http://mcnair.bakerinstitute.org/wiki/FDA_Trials_Data
#Get data on all VC-backed firms in Augusta, Atlanta, and Georgia.
#What data on cybersecurity startups is available from VenturExpert.
# See Anne for further assignments.

=List of publicly traded firms in Georgia=

*Firms on NYSE NASDAQ AMEX yr 2016
**An excel file list of publicly traded firms (on NYSE, NASDAQ, or American Stock Exchange), with their data for Jan 1 2016-Jan 1 2017, pulled from compustat (https://wrds-web.wharton.upenn.edu/wrds/ds/compm/funda/index.cfm?navId=84), can be found in E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem. It's filtered to show only Georgia companies. It's called "Firms on...". The list files also appear in E:\McNair\Projects\Publicly Traded Firms Data.

*PublicFirms
**excel and txt file of public traded firms with records since Jan 2010 across the entire compustat database. The excel file is filtered to augusta companies

* The files show (in order on the excel sheet)
**at (total assets)
**intan (intangible assets)
**intano (other intangibles)
**revt (total revenue in millions)
**xrd (research and development expense)
**exchg (stock exchange code)
**address1
**address2
**city
**zipcode
**location (country)
**naics (North American industry classification code)
**state

*Directions for pulling general data from https://wrds-web.wharton.upenn.edu/wrds//ds/compm/funda/index.cfm?queryID=807962 with filters for Fundamentals Annual:
**Best to do this in Internet explorer or Firefox so that once the file is completed, it can be downloaded directly (as opposed to Chrome)
*Step 1: Choose a date range
*Step 2: choose GVKEY.
*Step 3: choose data items. In the search bar below, search for information you want. You can search either the abbreviation or the description itself. Once you choose a piece of information (i.e., Stock Exchange Code), a window pops up above Step 3 indicating that you can filter by these pieces of information, in addition to having them in your output file. To filter by Stock exchange, for example, click (in the Conditional Statements window) the drop down arrow, click "Stock Exchange Code". In the far right box, input the code that corresponds to each Stock Exchange (can only be found before Stock Exchange code is selected as a variable type in Step 3. If it's selected, deselect it, find it in the search box, and click on the question mark). To add more, click either "OR" or "AND" and add another code. Select all desired variables and all desired filters on those variables.
*Step 4:
**Output format: select tab-delimited text.
**Compression type: Select none
**Date format: choose either the first option or the last one.

=Documentation=
'''E Drive Folder:'''
"E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem"
*Files located in above folder with data pull description:
**"Firms on NYSE NASDAQ AMEX yr 2016" (both Excel and Text file): Fundamentals Annual for Jan 1 2017-Jan 1 2017, for firms traded on any of the following stock exchanges: NYSE, NASDAQ, American Stock Exchange. The variables pulled are listed in the above section
'''Z Drive Folder:'''
"Z:\Innovation Districts"
*Code document: augusta.sql
*table notes: augustaSQL_notes.txt
'''Database:'''
augusta

=Tentative Schedule=

Week of 9/25: Gather, compile, and synthesize data

Week of 10/1: Make recommendations, create slides and review with Ed

==Anticipated Paper Breakdown==
#Needs/Wants for an Startup Ecosystem
##Economic Factors
##Social Factors
##Environmental Factors
##Government aid
#Diagnosis of Augusta’s Current State
##How does it fulfill and not fulfill the requirements of an innovation district?
##What resources are available in Georgia as a whole that can be used in Augusta?
#Recommendations for how to achieve a Startup Ecosystem in Augusta
##What do we need in order to make a successful startup ecosystem in Augusta? (What is missing?)
##What would it look like?
##How do we make it happen?
###Specific suggestions

=Zip Codes=
*30901
*30903
*30907
*30909
*30999
*30904
*30905
*30906
*30912
*30914
*30916
*30917
*30919

=Georgia Accelerator Programs=

##*From Accelerator Data in E:\McNair\Projects\Accelerators\Summer 2017\Veeral : ATDC, Atlanta Ventures, CyberLaunch, NeuroLaunch, Points of Light Accelerator.
##*From http://www.acceleratorinfo.com/georgia.html: Startup Runway, TechStars Atlanta, Village Capital - HUB Grant Park, Georgia Tech - VentureLab (Tech Transfer).

=Georgia VC Firms=

=Resources Needed=
[[File: Startup Ecosystems.png]]
Elements of a Startup Ecosystem
''Technical Skills''
*'''Education rates of local population'''
*Research on University of Augusta
*How to attract human capital?
''Risk Tolerance''
*'''Local Economic Stability'''
*'''City's Risk Preparedness'''
''Availability of Capital''
*'''VC, Equity'''
''Visible Successes''
*What has already happened there? How can we market the city to best attract human capital and create a city brand?
''Collaborative Culture''
*'''Proximity between innovators'''
*'''Transportation availability'''
*'''Accessibility to travelers'''
''Regulatory Environment''
*Potential for tax credits, types of incentives local government can offer
''Experienced Mentors''
*Who is already there who can act as a mentor?
*Who can we attract there to act as a mentor?
**'''Patent and R&D data for the area'''
''Entrepreneurial Culture''
*What creates a strong entrepreneurial culture and how can we bring that to Augusta?

=Educational Attainment=

*For Richmond County, Georgia, which includes Augusta, the US Census Bureau projects that:
**High school graduate or higher, percent of persons age 25 years+, 2011-2015 83.3%
**Bachelor's degree or higher, percent of persons age 25 years+, 2011-2015 20.4%

Compared to the rest of the US:
**High school graduate or higher, percent of persons age 25 years+, 2011-2015 83.3% 86.7%
*Bachelor's degree or higher, percent of persons age 25 years+, 2011-2015 20.4% 29.8%

*source: https://www.census.gov/quickfacts/fact/table/richmondcountygeorgia,US/PST045216

Augusta Startup Ecosystem

2017-10-03T14:30:32Z

Dayton: /* Active Projects */

{{McNair Projects
|Has owner=Ben Baldazo, Dylan Dickens, Joe Reilly, Taylor Jacobe,
|Has start date=9/20/2017
|Has deadline=10/6/2017
|Has notes=End product is slide deck
|Has project status=Active
}}
Link to DOC: https://docs.google.com/a/rice.edu/document/d/15EtmjxWLLta4SZEW4PlsqxXgA-yEuuZ3OE1Ciin2yHA/edit?usp=sharing

Augusta, Georgia- Want to build a startup ecosystem around the headquarters
*University of Augusta is in the local area
*US CyberCommand is going to locate their headquarters there, and in response, they want to ensure an ecosystem exists there to pull from (QUESTION: WHO IS "THEY" IN THIS SENTENCE?)
**Needs recruits, programmers
*Want to build a startup ecosystem around the headquarters
*Find all of the important inno people in Georgia
**How can we piece an ecosystem together?

=Potential Corporate Headquarters / Branches=
Potential way to see: look at donors to Augusta based Arts or Museums
*Augusta Ballet support (scroll to bottom): http://www.augustaballet.org/support/

=VC Data Summary=

Find Data here:
There is a company level VC data file, with exits, deal type flags, dead/alive years, and round summary info in
E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem

This file is '''PortCoMaster.txt'''.

Excel File with data filtered by Tay is '''Relevant VC Data'''

Augusta, Georgia: 7 Results-
*NRF Distributors Inc, Keymarket Partners, Lexicor Medical Technology Inc, Keymarket III, Solo Cup Operating Corp, Joseph Kirschner Company Inc, and Sagamore Hill Broadcasting LLC. Only Sagamore Hill Broadcasting has received funding in the last 20 years, all the rest were earlier. '''Conclusion: Augusta's VC scene is not good at all. If only one company has been VC backed in the last 20 years, this is worrisome'''
**Sagamore Hill Broadcasting owns and operates television-broadcasting stations in the Great Lakes and Southern US. It is a joint venture of the investment firm Duff Ackerman & Goodrich of San Francisco, California and former Benedek Broadcasting and Spartan Communications executive Louis Wall (who is also CEO of the group).
*None are in the categories listed below

Atlanta, Georgia: ~550 results-
*200 of these are in the last decade; Atlanta is clearly attracting VC funding. How can we expand that success to Augusta?
*Of those in the last decade, small proportion could be cybersecurity-related:
**3 Internet Security and Transaction Services
**10 Security/Firewalls/Encryption software
**None of the other categories listed below

Charleston, South Carolina- ~30 results-
*In general, these investments are far more recent than those in Augusta, with half of these results taking place within the last decade.
*Only one Security/Firewalls/Encryption- PhishLabs
*None of the other categories listed below

Other local areas
*Athens, Georgia: 15 VC funded, 7 within last decade
*Savannah, Georgia: 5 VC funded, 1 within last decade

Silicon Valley:
Accelerators
*Included these filters on '''Relevant VC Data''': CA for state and these cities: Campbell; Cupertino; Los Altos; Los Altos Hills; Los Gatos; Milpitas; Monte Sereno; Morgan Hill; Mountain View; Palo Alto; San Fransisco; San Jose; San Mateo; Santa Clara; Saratoga; Sunnyvale
**this is the list of cities Wikipedia's page on Silicon Valley includes plus San Mateo and San Francisco
*7520 VC funded, 4220 over last decade
Incubators:
*Searched for incubators on AngelList (saved to "AngelList Silicon Valley Incubators (unfiltered).txt") but the results were definitely more than just incubators so filtered to everything that had "incub" in its name or description and added back ycombinator (Saved to "AngelList Silicon Valley Incubators (filtered).txt")

Relevant industries (at the indsubgroup level) for cyber security might be:
indsubgroup
-------------------------------------------------
Internet Security and Transaction Services
Other Electronics Related (incl. alarm systems)
Military Electronics (excluding communications)
Security/Alarm/Sensors
Security/FirewallsEncryption software
Computer Security Services
Defense Communications

Note that the original source data is in '''vcdb2''' and the script to make this data is '''augustavcdata.sql'''

==Recent Business News for Augusta==

*Building a new arena in Augusta, but unknown where the location will be: http://chronicle.augusta.com/news/2017-09-26/regency-supporters-unmoved-arena-site-selection-presentation, http://www.wrdw.com/content/news/Coliseum-Authority-Meeting-a-Full-House-448078573.html
* New Internship program to promote sticking in Augusta to work: "The Students2Work initiative aims to place 250 Richmond County high school students in internships throughout the Augusta area – to both improve the skills they have, and to develop new skills to better prepare them for the working world." http://chronicle.augusta.com/news/business/2017-09-06/business-education-leaders-announce-job-internship-initiative-richmond

The demand for skilled workers takes on added gravity as the region braces for an expected economic spike spurred by cyber-related businesses.

=Tasks for Ben Baldazo, Joe Reilly and Dylan Dickens on September 26=
#MOVE THIS ZIP CODE INFO TO NEW SECTION ON WIKI PAGE Determine which zip codes are associated with Augusta, Augusta University & Cyber command facility.
#*Augusta: http://zipcode.org/city/GA/AUGUSTA
#*Augusta U (zip: 30912) website: http://www.augusta.edu/
#*Fort Gordon (houses the cyber command) Zip: 30905 | Website: http://www.gordon.army.mil/
#Use Compustat to pull data on publicly traded firms in Augusta, Atlanta and Georgia.
#*See E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem\Compustat (publicly traded firms)
#**"Firms on NYSE NASDAQ AMEX yr 2016" restricted pull to those exchanges of publicly traded companies over the past year
#**"Public Firms" pull from all of compustat that goes from Jan 2010 to Sept 2017
#**Excel for both of these is likely filtered to Georgia or even Augusta
#Data on Accelerators for Augusta, Atlanta, Georgia, and Silicon Valley:
#*"E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem\Accelerators & Cohorts"
##*Data includes list of accelerators in Georgia & the available information on cohort companies from Georgia
##*Pulled from Accelerator_Data in E:\McNair\Projects\Accelerators\Fall 2017 : ATDC, Atlanta Ventures, CyberLaunch, NeuroLaunch, Points of Light Accelerator.
##*From http://www.acceleratorinfo.com/georgia.html: Startup Runway, TechStars Atlanta, Village Capital - HUB Grant Park, Georgia Tech - Flashpoint, Georgia Tech - VentureLab (Tech Transfer). This second list was not checked thoroughly yet.
#*To find number in Silicon valley included provinces: Mountain View, Palo Alto, San Francisco, San Jose, San Mateo, Santa Clara, Sunnyvale
#**Didn’t have data on number of incubators in SV but found angellist search: https://angel.co/companies?company_types[]=Incubator&locations[]=1681-Silicon+Valley,+CA
#***The problem is that most of these aren’t incubators

#Grants Data:
#*"E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem\NIH NSF Clinical Trials for Georgia by Zip"
#*Some notes in the CKnotes.txt file
#*Notes on sourcing of this data in
#**http://mcnair.bakerinstitute.org/wiki/NSF_Data
#**http://mcnair.bakerinstitute.org/wiki/NIH_Data
#**http://mcnair.bakerinstitute.org/wiki/FDA_Trials_Data
#Get data on all VC-backed firms in Augusta, Atlanta, and Georgia.
#What data on cybersecurity startups is available from VenturExpert.
# See Anne for further assignments.

=List of publicly traded firms in Georgia=

*Firms on NYSE NASDAQ AMEX yr 2016
**An excel file list of publicly traded firms (on NYSE, NASDAQ, or American Stock Exchange), with their data for Jan 1 2016-Jan 1 2017, pulled from compustat (https://wrds-web.wharton.upenn.edu/wrds/ds/compm/funda/index.cfm?navId=84), can be found in E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem. It's filtered to show only Georgia companies. It's called "Firms on...". The list files also appear in E:\McNair\Projects\Publicly Traded Firms Data.

*PublicFirms
**excel and txt file of public traded firms with records since Jan 2010 across the entire compustat database. The excel file is filtered to augusta companies

* The files show (in order on the excel sheet)
**at (total assets)
**intan (intangible assets)
**intano (other intangibles)
**revt (total revenue in millions)
**xrd (research and development expense)
**exchg (stock exchange code)
**address1
**address2
**city
**zipcode
**location (country)
**naics (North American industry classification code)
**state

*Directions for pulling general data from https://wrds-web.wharton.upenn.edu/wrds//ds/compm/funda/index.cfm?queryID=807962 with filters for Fundamentals Annual:
**Best to do this in Internet explorer or Firefox so that once the file is completed, it can be downloaded directly (as opposed to Chrome)
*Step 1: Choose a date range
*Step 2: choose GVKEY.
*Step 3: choose data items. In the search bar below, search for information you want. You can search either the abbreviation or the description itself. Once you choose a piece of information (i.e., Stock Exchange Code), a window pops up above Step 3 indicating that you can filter by these pieces of information, in addition to having them in your output file. To filter by Stock exchange, for example, click (in the Conditional Statements window) the drop down arrow, click "Stock Exchange Code". In the far right box, input the code that corresponds to each Stock Exchange (can only be found before Stock Exchange code is selected as a variable type in Step 3. If it's selected, deselect it, find it in the search box, and click on the question mark). To add more, click either "OR" or "AND" and add another code. Select all desired variables and all desired filters on those variables.
*Step 4:
**Output format: select tab-delimited text.
**Compression type: Select none
**Date format: choose either the first option or the last one.

=Documentation=
'''E Drive Folder:'''
"E:\McNair\Projects\Innovation Districts\Augusta Startup Ecosystem"
*Files located in above folder with data pull description:
**"Firms on NYSE NASDAQ AMEX yr 2016" (both Excel and Text file): Fundamentals Annual for Jan 1 2017-Jan 1 2017, for firms traded on any of the following stock exchanges: NYSE, NASDAQ, American Stock Exchange. The variables pulled are listed in the above section
'''Z Drive Folder:'''
"Z:\Innovation Districts"
*Code document: augusta.sql
*table notes: augustaSQL_notes.txt
'''Database:'''
augusta

=Tentative Schedule=

Week of 9/25: Gather, compile, and synthesize data

Week of 10/1: Make recommendations, create slides and review with Ed

==Anticipated Paper Breakdown==
#Needs/Wants for an Startup Ecosystem
##Economic Factors
##Social Factors
##Environmental Factors
##Government aid
#Diagnosis of Augusta’s Current State
##How does it fulfill and not fulfill the requirements of an innovation district?
##What resources are available in Georgia as a whole that can be used in Augusta?
#Recommendations for how to achieve a Startup Ecosystem in Augusta
##What do we need in order to make a successful startup ecosystem in Augusta? (What is missing?)
##What would it look like?
##How do we make it happen?
###Specific suggestions

=Zip Codes=
*30901
*30903
*30907
*30909
*30999
*30904
*30905
*30906
*30912
*30914
*30916
*30917
*30919

=Georgia Accelerator Programs=

##*From Accelerator Data in E:\McNair\Projects\Accelerators\Summer 2017\Veeral : ATDC, Atlanta Ventures, CyberLaunch, NeuroLaunch, Points of Light Accelerator.
##*From http://www.acceleratorinfo.com/georgia.html: Startup Runway, TechStars Atlanta, Village Capital - HUB Grant Park, Georgia Tech - VentureLab (Tech Transfer).

=Georgia VC Firms=

=Resources Needed=
[[File: Startup Ecosystems.png]]
Elements of a Startup Ecosystem
''Technical Skills''
*'''Education rates of local population'''
*Research on University of Augusta
*How to attract human capital?
''Risk Tolerance''
*'''Local Economic Stability'''
*'''City's Risk Preparedness'''
''Availability of Capital''
*'''VC, Equity'''
''Visible Successes''
*What has already happened there? How can we market the city to best attract human capital and create a city brand?
''Collaborative Culture''
*'''Proximity between innovators'''
*'''Transportation availability'''
*'''Accessibility to travelers'''
''Regulatory Environment''
*Potential for tax credits, types of incentives local government can offer
''Experienced Mentors''
*Who is already there who can act as a mentor?
*Who can we attract there to act as a mentor?
**'''Patent and R&D data for the area'''
''Entrepreneurial Culture''
*What creates a strong entrepreneurial culture and how can we bring that to Augusta?

=Educational Attainment=

*For Richmond County, Georgia, which includes Augusta, the US Census Bureau projects that:
**High school graduate or higher, percent of persons age 25 years+, 2011-2015 83.3%
**Bachelor's degree or higher, percent of persons age 25 years+, 2011-2015 20.4%

Compared to the rest of the US:
**High school graduate or higher, percent of persons age 25 years+, 2011-2015 83.3% 86.7%
*Bachelor's degree or higher, percent of persons age 25 years+, 2011-2015 20.4% 29.8%

*source: https://www.census.gov/quickfacts/fact/table/richmondcountygeorgia,US/PST045216

Augusta Startup Ecosystem

2017-09-26T19:51:21Z

Dayton: /* Tasks for Ben Baldazo, Joe Reilly and Dylan Dickens on September 26 */

Augusta Startup Ecosystem

2017-09-26T19:50:41Z

Dayton: /* Tasks for Joe Reilly and Dylan Dickens on September 26 */

Augusta Startup Ecosystem

2017-09-26T14:19:49Z

Dayton: /* Tasks for Ben Baldazo on September 25 */

Augusta Startup Ecosystem

2017-09-25T19:02:43Z

Dayton:

Augusta Startup Ecosystem

2017-09-25T17:31:29Z

Dayton:

Augusta Startup Ecosystem

2017-09-25T17:20:04Z

Dayton: /* Tentative Schedule */

{{McNair Projects
|Has owner=Ben Baldazo, Dylan Dickens, Joe Reilly, Taylor Jacobe,
|Has start date=9/20/2017
|Has deadline=10/6/2017
|Has project status=Active
}}
Link to DOC: https://docs.google.com/a/rice.edu/document/d/15EtmjxWLLta4SZEW4PlsqxXgA-yEuuZ3OE1Ciin2yHA/edit?usp=sharing

Augusta, Georgia- Want to build a startup ecosystem around the headquarters
*University of Augusta is in the local area
*US CyberCommand is going to locate their headquarters there, and in response, they want to ensure an ecosystem exists there to pull from
**Needs recruits, programmers
*Want to build a startup ecosystem around the headquarters
*Find all of the important inno people in Georgia
**How can we piece an ecosystem together?

=Tentative Schedule=

THIS NEEDS TO BE REVISED TO REFLECT NEW SHORTER TIME FRAME. The end product is slides not a paper.
Week 1: Prep and understand need; research Augusta and what it already has

Week 2: Analyze data on Augusta and Georgia as a whole. Diagnose need. Begin writing final document's section about current state of Augusta

Week 3: Identify ways to fill need. Strategies for drawing in startup ecosystem. Begin writing final document's section about possible solutions

Week 4: Synthesize data and research to form coherent suggestions

Week 5: Final analysis of data. Finish paper.

==Anticipated Paper Breakdown==
#Needs/Wants for an Startup Ecosystem
##Economic Factors
##Social Factors
##Environmental Factors
##Government aid
#Diagnosis of Augusta’s Current State
##How does it fulfill and not fulfill the requirements of an innovation district?
##What resources are available in Georgia as a whole that can be used in Augusta?
#Recommendations for how to achieve a Startup Ecosystem in Augusta
##What do we need in order to make a successful startup ecosystem in Augusta? (What is missing?)
##What would it look like?
##How do we make it happen?
###Specific suggestions

=Resources Needed=
[[File: Startup Ecosystems.png]]
Elements of a Startup Ecosystem
''Technical Skills''
*'''Education rates of local population'''
*Research on University of Augusta
*How to attract human capital?
''Risk Tolerance''
*'''Local Economic Stability'''
*'''City's Risk Preparedness'''
''Availability of Capital''
*'''VC, Equity'''
''Visible Successes''
*What has already happened there? How can we market the city to best attract human capital and create a city brand?
''Collaborative Culture''
*'''Proximity between innovators'''
*'''Transportation availability'''
*'''Accessibility to travelers'''
''Regulatory Environment''
*Potential for tax credits, types of incentives local government can offer
''Experienced Mentors''
*Who is already there who can act as a mentor?
*Who can we attract there to act as a mentor?
**'''Patent and R&D data for the area'''
''Entrepreneurial Culture''
*What creates a strong entrepreneurial culture and how can we bring that to Augusta?

Augusta Startup Ecosystem

2017-09-25T17:18:21Z

Dayton:

{{McNair Projects
|Has owner=Ben Baldazo, Dylan Dickens, Joe Reilly, Taylor Jacobe,
|Has start date=9/20/2017
|Has deadline=10/6/2017
|Has project status=Active
}}
Link to DOC: https://docs.google.com/a/rice.edu/document/d/15EtmjxWLLta4SZEW4PlsqxXgA-yEuuZ3OE1Ciin2yHA/edit?usp=sharing

Augusta, Georgia- Want to build a startup ecosystem around the headquarters
*University of Augusta is in the local area
*US CyberCommand is going to locate their headquarters there, and in response, they want to ensure an ecosystem exists there to pull from
**Needs recruits, programmers
*Want to build a startup ecosystem around the headquarters
*Find all of the important inno people in Georgia
**How can we piece an ecosystem together?

=Tentative Schedule=
Week 1: Prep and understand need; research Augusta and what it already has

Week 2: Analyze data on Augusta and Georgia as a whole. Diagnose need. Begin writing final document's section about current state of Augusta

Week 3: Identify ways to fill need. Strategies for drawing in startup ecosystem. Begin writing final document's section about possible solutions

Week 4: Synthesize data and research to form coherent suggestions

Week 5: Final analysis of data. Finish paper.

==Anticipated Paper Breakdown==
#Needs/Wants for an Startup Ecosystem
##Economic Factors
##Social Factors
##Environmental Factors
##Government aid
#Diagnosis of Augusta’s Current State
##How does it fulfill and not fulfill the requirements of an innovation district?
##What resources are available in Georgia as a whole that can be used in Augusta?
#Recommendations for how to achieve a Startup Ecosystem in Augusta
##What do we need in order to make a successful startup ecosystem in Augusta? (What is missing?)
##What would it look like?
##How do we make it happen?
###Specific suggestions

=Resources Needed=
[[File: Startup Ecosystems.png]]
Elements of a Startup Ecosystem
''Technical Skills''
*'''Education rates of local population'''
*Research on University of Augusta
*How to attract human capital?
''Risk Tolerance''
*'''Local Economic Stability'''
*'''City's Risk Preparedness'''
''Availability of Capital''
*'''VC, Equity'''
''Visible Successes''
*What has already happened there? How can we market the city to best attract human capital and create a city brand?
''Collaborative Culture''
*'''Proximity between innovators'''
*'''Transportation availability'''
*'''Accessibility to travelers'''
''Regulatory Environment''
*Potential for tax credits, types of incentives local government can offer
''Experienced Mentors''
*Who is already there who can act as a mentor?
*Who can we attract there to act as a mentor?
**'''Patent and R&D data for the area'''
''Entrepreneurial Culture''
*What creates a strong entrepreneurial culture and how can we bring that to Augusta?

User talk:Hbrown512

2017-09-07T19:15:50Z

Dayton: Welcome!

'''Welcome to ''McNair Center''!'''
We hope you will contribute much and well.
You will probably want to read the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents help pages].
Again, welcome and have fun! [[User:Dayton|Dayton]] ([[User talk:Dayton|talk]]) 14:15, 7 September 2017 (CDT)

User:Hbrown512

2017-09-07T19:15:49Z

Dayton: Creating user page for new user.

I am a junior computer science major at Rice University. I plan to graduate in May of 2019. I was a walk-on for the Rice Men's Basketball team. Currently a Teaching Assistant for Introduction to Program Design at Rice University. I know Java, Python, and C. I have worked previously at Unitedhealthcare as a software development intern.

Project Planning for Fall 2017

2017-08-25T16:05:42Z

Dayton: /* Potential new projects */

The following projects are being considered for Fall 2017

==Continuation of pre-existing projects==

Existing projects:
*University patents - Julia Wang
*Medical Centers - Catherine Kirby
*Accelerators
**The jockey, the horse and the race track - Peter Jalbert
**Ranking seed accelerators
**Cohorts
*E&I governance research paper - Albert/Avesh
*Hubs - Hira
*VC Agglomeration - Oliver
*Patent data - Oliver
*Lex Machina!!!
*Women in VC-backed Entrep - Carlin
*VC Cities ranking - Diana

==Potential new projects==

Candidate projects:
*ITC Section 337 (and other ITC data)
*PTAB data
*Internship with Station Houston
*Issue brief on Houston VC investment - Dylan & Diana
*Issue brief on Houston VC funds - Dylan & Diana
*Matching project support - Meghana Guar
*SBIR project (needs specification)
*Crunchbase news
*Automate Weekly Roundup
*SBA project? Lending?
*Google Scholar crawler
*GIS Work
*Houston Innovation District work -- data for Amanda Edwards

[[Category:McNair Admin]]

Project Planning for Fall 2017

2017-08-25T16:04:37Z

Dayton: /* Potential new projects */

Iris Huang

2017-08-09T16:11:16Z

Dayton:

{{McNair Staff
|position=Research Team
|name=Iris Huang
|degree=BA
|major=Economics; Business minor
|class=2016
|email=xh12@rice.edu
|status=Inactive
}}

Marcela Interiano

2017-08-09T16:10:22Z

Dayton:

{{McNair Staff
|position=Research Team
|name=Marcela Interiano
|user_image=Marcela_Headshot.jpg
|degree=BSChE
|major=CHBE
|class=2017
|join_date=1 June 2016
|skills=Trilingual/Translating, Matlab, Excel, Writing
|interests=Tudor Era, Reading, Fashion and Design, Collecting Quotes, Traveling
|fun_fact=Was stung by a stingray on her foot and has the scar to prove it!
|email=emi2@rice.edu
|skype_name=minteriano14
|status=Inactive,
|college=Lovett
}}
= Bio =

Marcela Interiano was born and raised in Houston, Texas. Her parents, Leslie and Armando, emigrated from El Salvador to Houston following her father's military service in the Salvadoran civil war. She has two younger sisters, Lucia and Nicole. She attended Duchesne Academy of the Sacred Heart, a French, all-girls Catholic School, from first grade to her senior year of high school. She is currently studying Chemical Engineering at Rice University and is interested in pursuing a career in patent law. Prior to law school, she hopes to gain experience in the oil and gas industry or in consulting work. In addition to Chemical Engineering classes, Marcela is the Chair of University Court, a student judicial body at Rice, an O-Week Coordinator for her residential college, Lovett, and co-captain of the Lovett Powderpuff Team.

= Fun Facts =

# Marcela is trilingual in French, English, and Spanish.
# She loves the Tudor Era in England and finally got to visit Hampton Court in the summer of 2015.
# The Harry Potter series, both books and movies, is one of her favorite things in the entire world.

= McNair Work=

[[Marcela Interiano (Work Log)]]

[[Category:McNair Staff]]

Ravali Kruthiventi

2017-08-09T16:08:17Z

Dayton:

{{McNair Staff
|position=Tech Team
|name=Ravali Kruthiventi
|user_image=2016-05-20_11.26.46.jpg
|degree=Master of Computer Science
|major=Computer Science
|class=2017
|join_date=1 June 2016
|skills=Java, C++, C, SQL, User Interface Design, Business Analytics (IT), Project Management
|interests=Reading, Writing, Mythology
|fun_fact=Has read the entire Wheel of Time series. Really.
|email=sk99@rice.edu
|skype_name=ravali.kruthiventi@gmail.com
|status=Inactive,
|college=George R Brown
}}
==Bio==
Ravali is a Computer Science Masters student at Rice, and is currently a Research Assistant with the McNair Center. Prior to coming to Rice for her Masters program, Ravali has worked as a Business/Systems Analyst with what is now S&P Global Market Intelligence.

She is interested in data analytics, design and likes to work on projects with complex data sets.
[[Category:McNair Staff]]

Free Enterprise

2017-08-04T17:40:21Z

Dayton: /* Literature Review */

{{McNair Projects
|Has title=Free Enterprise Lit Review
|Has owner=Anne Dayton,
}}
==Literature Review==

The promotion of Free Enterprise as an American ideal originates in libertarian and conservative responses to the New Deal. Rippa (1958, 1959) shows how free enterprise was promoted in school textbooks. St. John (2010) examines its promotion in newspapers. Fines-Wolf (1994) examines the promotion of free enterprise ideals by National Association of Manufacturers (NAM) in the post-war period.

The ethics of the free enterprise system has also been a subject of discussion.

Erteszek (1982) asserts that "the moral basis of a free enterprise system faces both new and persistent challenges... these challenges cannot be met without a new, ethical vision of private enterprise, and a reconfiguration of the moral purpose of corporate life... The experience of history indicates that man will not act nobly, with compassion and fidelity, simply out of enlightened self-interest... the new vision needed by modern man and woman is to be found not in self-interest but in Judeo-Christian virtues... the Judeo-Christian system has the power to transform modern man and to stimulate him to a life of service, stewardship and compassion."

Wishloff (2003) argues that "an economic system of responsible free enterprise.. [would] be accompanied by a sense of social and moral responsibility which might have to be encouraged and enforced by the government... Our enterprises, taken as a whole, are not fulfilling their social and moral responsibilities... the root cause of [this problem is] adherence to the metaphysics of material scientism... Common sense realism is proposed as a more suitable alternative."

==Free Enterprise as Response to New Deal==

'''St. John III, Burton. 2010. “A VIEW THAT’S FIT TO PRINT: The National Association of Manufacturers’ Free Enterprise Rhetoric as Integration Propaganda in The New York Times, 1937–1939.” Journalism Studies 11 (3): 377–392.
'''

@article{st._john_iii_view_2010,
title = {A {VIEW} {THAT}'{S} {FIT} {TO} {PRINT}: {The} {National} {Association} of {Manufacturers}' free enterprise rhetoric as integration propaganda in {The} {New} {York} {Times}, 1937–1939},
volume = {11},
shorttitle = {A {VIEW} {THAT}'{S} {FIT} {TO} {PRINT}},
url = {http://www.tandfonline.com/doi/abs/10.1080/14616700903290585 },
abstract = {This study examines the appearance of National Association of Manufacturers'(NAM)
propaganda, from 1937 to 1939, in articles within The New York Times. NAM's ability to
place such rhetoric in The New York Times reveals both the presence of integration
propaganda and the beginning of a press acclimation to propaganda as news. This
examination reveals a crystallizing of professional journalism's reliance on authoritative, yet
propagandistic sources, a dynamic that persists to this day.},
number = {3},
urldate = {2017-07-19},
journal = {Journalism Studies},
author = {St. John III, Burton},
year = {2010},
pages = {377--392},
file = {Snapshot:files/39/14616700903290585.html:text/html}
}

'''Rippa, S. Alexander. 1958. “The Textbook Controversy and the Free Enterprise Campaign, 1940-1941.” History of Education Journal, 49–58.'''
'''———. 1959. “Dissemination of the Free-Enterprise Creed to American Schools.” The School Review 67 (4): 409–21. doi:10.1086/442511.'''

@article{rippa_textbook_1958,
title = {The textbook controversy and the free enterprise campaign, 1940-1941},
url = {http://www.jstor.org/stable/3692590 },
abstract = {50 HISTORY OF EDUCATION JOURNAL attack upon the" anti-advertising" material which, it
charged, had been" planted" in the social science textbooks. The editors as-serted that
Rugg's textbooks implied that" advertising is an economic waste, that a high proportion of
advertising is dishonest and that advertised products are pretty likely to be untrustworthy.'" 4
The following year the Nation's Business stated editorially that parents were still finding"
chunks of unamericanism [sic]" in Rugg's textbooks. 5 While the textbook controversy ...},
urldate = {2017-07-19},
journal = {History of Education Journal},
author = {Rippa, S. Alexander},
year = {1958},
pages = {49--58}
}

@article{rippa_dissemination_1959,
title = {Dissemination of the {Free}-{Enterprise} {Creed} to {American} {Schools}},
volume = {67},
issn = {0036-6773},
url = {http://www.journals.uchicago.edu/doi/abs/10.1086/442511 },
doi = {10.1086/442511},
abstract = {The free-enterprise campaign launched during the late thirties marked a significant turning
point in relations between business and education in the United States. Beset by adverse
publicity during the depression and deeply disturbed by New Deal victories, business
leaders turned to the nation's schools in an effort to perpetuate the free-enterprise creed.
The distribution of pamphlets to American youth and the preparation of audio-visual devices
for use in classrooms were early signs of the sudden interest of business leaders in the ...},
number = {4},
urldate = {2017-07-20},
journal = {The School Review},
author = {Rippa, S. Alexander},
month = dec,
year = {1959},
pages = {409--421},
file = {Snapshot:files/116/442511.html:text/html}
}

'''Fones-Wolf, Elizabeth A. 1994. Selling Free Enterprise: The Business Assault on Labor and Liberalism, 1945-60. University of Illinois Press.'''

@book{fones-wolf_selling_1994,
title = {Selling free enterprise: {The} business assault on labor and liberalism, 1945-60},
shorttitle = {Selling free enterprise},
url = {http://books.google.com/books?hl=en&lr=&id=W4p8-BAs0dcC&oi=fnd&pg=PA1&dq=info:uIh_mJdu3vMJ:scholar.google.com&ots=VDTieSeQme&sig=L75z9k6kOw2MJeZrE285jsHMWEM },
abstract = {During December 1951, half of the adult population of the industrial town of Latrobe,
Pennsylvania, took regular breaks from work to study economics on company time.
Employees from nineteen firms gathered in small groups to watch a series of films and to
participate in discussions that focused on the values and symbols associated with the
American way of life, including patriotism, freedom, individualism, competition, and
abundance through increasing productivity. 1 That these firms halted production and},
urldate = {2017-07-20},
publisher = {University of Illinois Press},
author = {Fones-Wolf, Elizabeth A.},
year = {1994},
file = {Snapshot:files/97/books.html:text/html}
}

==Free Enterprise & Ethics==

'''Erteszek, Jan J. 1982. “Corporate Enterprise and Christian Ethics.” Review of Social Economy 40 (3): 323–329.'''

@article{erteszek_corporate_1982,
title = {Corporate enterprise and {Christian} ethics},
volume = {40},
url = {http://www.tandfonline.com/doi/pdf/10.1080/00346768200000034 },
abstract = {Throughout the world, the moral basis of the free enterprise system faces both new and
persistent challenges. It is convincing to a Christian and a man of business that these
challenges cannot be met without a new, ethical vision of private enterprise, and a
reconfiguration of the moral purpose of corporate life. By a new vision and a moral purpose, I
mean a perception of the future that would develop the logic, stimuli, incentives, convictions
and commitments which would lead to a more serving and more caring society, one that ...},
number = {3},
urldate = {2017-07-19},
journal = {Review of Social Economy},
author = {Erteszek, Jan J.},
year = {1982},
pages = {323--329},
file = {Snapshot:files/45/00346768200000034.html:text/html }
}

'''Wishloff, Jim. 2003. “Responsible Free Enterprise: What It Is and Why We Don’t Have It.” Teaching Business Ethics 7 (3): 229–263.
'''

@article{wishloff_responsible_2003,
title = {Responsible free enterprise: {What} it is and why we don't have it},
volume = {7},
shorttitle = {Responsible free enterprise},
url = {http://www.springerlink.com/index/V2T828023263V870.pdf },
abstract = {An economic system of responsible freeenterprise would (i) give individuals and groupsthe
freedom to initiate, own and managebusiness undertakings and (ii) insist that
suchundertakings be accompanied by a sense ofsocial and moral responsibility which},
number = {3},
urldate = {2017-07-19},
journal = {Teaching Business Ethics},
author = {Wishloff, Jim},
year = {2003},
pages = {229--263},
file = {Snapshot:files/50/10.html:text/html}
}

'''Chewning, Richard C. 1984. “Can Free Enterprise Survive Ethical Schizophrenia?” Business Horizons 27 (2): 5–11.'''

@article{chewning_can_1984,
title = {Can free enterprise survive ethical schizophrenia?},
volume = {27},
url = {http://www.sciencedirect.com/science/article/pii/0007681384900028 },
abstract = {This century is seeing changes in our underlying philosophy—in how we view
existence itself and our part in it, in how we know and accept facts, in what we consider right
and wrong. Given these very basic changes in our culture, where will business as we know it
go in the future?},
number = {2},
urldate = {2017-07-25},
journal = {Business Horizons},
author = {Chewning, Richard C.},
year = {1984},
pages = {5--11},
file = {[PDF] from cbfa-cbar.org:files/164/Chewning - 1984 - Can free enterprise survive ethical schizophrenia.pdf:application/pdf;Snapshot:files/165/0007681384900028.html:text/html }
}

'''Solomon, Robert C. 2006. “Free Enterprise, Sympathy, and Virtue.” Moral Markets: The Critical Role of Values in the Economy, 16–41.'''

@article{solomon_free_2006,
title = {Free enterprise, sympathy, and virtue},
url = {http://books.google.com/books?hl=en&lr=&id=6QrvmNo2qD4C&oi=fnd&pg=PA16&dq=info:mD7mFEgIOJAJ:scholar.google.com&ots=CTmHyJIYLQ&sig=KMa2iPPxq0dUsQCX1iwLVJu0dG0 },
abstract = {How selfish soever man may be supposed, there are evidently some principles in his nature,
which interest him in the fortune of others, and render their happiness necessary to him,
though he derives nothing from it except the pleasure of seeing it. Of this kind is pity or
compassion, the emotion which we feel for the misery of others.... The greatest ruffian, the
most hardened violator of the laws of society, is not altogether without it.},
urldate = {2017-07-25},
journal = {Moral markets: The critical role of values in the economy},
author = {Solomon, Robert C.},
year = {2006},
pages = {16--41},
file = {[PDF] from researchgate.net:files/161/Solomon - 2006 - Free enterprise, sympathy, and virtue.pdf:application/pdf;Snapshot:files/162/books.html:text/html }
}

'''Demsetz, Harold. 1978. “Social Responsibility in the Enterprise Economy.” Southwestern University Law Review 10: 1.'''

@article{demsetz_social_1978,
title = {Social {Responsibility} in the {Enterprise} {Economy}},
volume = {10},
url = {http://heinonline.org/HOL/Page?handle=hein.journals/swulr10&id=19&div=&collection= },
abstract = {I wish to emphasize at the outset that this article does not present a general ethical system or
a specific ethical code to guide business behavior. Too many philosophers and economists,
good men and mediocre, have failed in their attempts to-define even" the fair price,"" the just
wage," or" fair competition." My purpose is to describe the manner in which a free enterprise
economy evaluates behavior, whatever the substantive content of such behavior may be},
journal = {Southwestern University Law Review},
author = {Demsetz, Harold},
year = {1978},
pages = {1},
file = {Social Responsibility in the Enterprise Economy 10 Southwestern University Law Review 1978:files/106/LandingPage.html:text/html }
}

'''Barach, Jeffrey A., and John B. Elstrott. 1988. “The Transactional Ethic: The Ethical Foundations of Free Enterprise Reconsidered.” Journal of Business Ethics 7 (7): 545–551.'''

@article{barach_transactional_1988,
title = {The transactional ethic: {The} ethical foundations of free enterprise reconsidered},
volume = {7},
shorttitle = {The transactional ethic},
url = {http://www.springerlink.com/index/T56T3T746576P160.pdf },
abstract = {A review of the evolution of the ethical foundations of free enterprise reveals the essentially
utilitarian ethical foundation prevailing today. To enrich those foundations the article
attempts to establish the ethical validity of free transactions by relating them to the basic
principle of interpersonal ethics: the Golden Rule. The validity of the transactional ethic is
presented as an articulation of freedom in a valid social and economic context.},
number = {7},
urldate = {2017-07-20},
journal = {Journal of Business Ethics},
author = {Barach, Jeffrey A. and Elstrott, John B.},
year = {1988},
pages = {545--551},
file = {Snapshot:files/86/10.html:text/html }
}

'''Macfie, A. L. 1967. “The Moral Justification of Free Enterprise.” Scottish Journal of Political Economy 14 (1): 1–11.'''

@article{macfie_moral_1967,
title = {The moral justification of free enterprise},
volume = {14},
url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9485.1967.tb00753.x/full },
abstract = {ADAM SMITH'S theory has been chosen as a text simply because I know no better start from
which to examine the issue before us. I have yet to find a more careful, thorough, detailed,
realistic ethical argument for freedom in economic (among other) affairs than that he gives,
in his Theory of Moral Sentiments, in his treatment of the virtues, prudence and self-
command. True, we deal here only with the individual aspect; and this does justice neither to
the facts nor to Adam Smith's full theory. But We have grossly inadequate time even for},
number = {1},
urldate = {2017-07-20},
journal = {Scottish Journal of Political Economy},
author = {Macfie, A. L.},
year = {1967},
pages = {1--11},
file = {Snapshot:files/90/full.html:text/html }
}

==William Baumol==

'''Baumol, William J. 1968. “Entrepreneurship in Economic Theory.” The American Economic Review, 64–71.'''

'''———. 1996. “Entrepreneurship: Productive, Unproductive, and Destructive.” Journal of Business Venturing 11 (1): 3–22.'''

'''———. 2002. The Free-Market Innovation Machine: Analyzing the Growth Miracle of Capitalism. Princeton University Press.
'''

@article{baumol_entrepreneurship_1968,
title = {Entrepreneurship in economic theory},
url = {http://www.jstor.org/stable/1831798 },
abstract = {The entrepreneur is at the same time one of the most intriguing and one of the most elusive
characters in the cast that constitutes the sub-ject of economic analysis. He has long been
recognized as the apex of the hierarchy that determinies the behavior of the firm and thereby
bears a heavy responsibility for the vitality of the free enterprise society. In the writings of the
classical economist his appearance was frequent, though he remained a shadowy entity
without clearly defined form and function. Only Schumpeter and, to some degree},
urldate = {2017-07-19},
journal = {The American economic review},
author = {Baumol, William J.},
year = {1968},
pages = {64--71}
}

@article{baumol_entrepreneurship:_1996,
title = {Entrepreneurship: {Productive}, unproductive, and destructive},
volume = {11},
shorttitle = {Entrepreneurship},
url = {http://www.sciencedirect.com/science/article/pii/088390269400014X },
abstract = {The basic hypothesis is that, while the total supply of entrepreneurs varies among societies,
the productive contribution of the society's entrepreneurial activities varies much more
because of their allocation between productive activities such as innovation and largely
unproductive activities such as rent seeking or organized crime. This allocation is heavily
influenced by the relative payoffs society offers to such activities. This implies that policy can
influence the allocation of entrepreneurship more effectively than it can influence its ...},
number = {1},
urldate = {2017-07-19},
journal = {Journal of Business Venturing},
author = {Baumol, William J.},
year = {1996},
pages = {3--22},
file = {[PDF] from econintel.org:files/47/Baumol - 1996 - Entrepreneurship Productive, unproductive, and de.pdf:application/pdf;Snapshot:files/48/088390269400014X.html:text/html}
}

@book{baumol_free-market_2002,
title = {The {Free}-market {Innovation} {Machine}: {Analyzing} the {Growth} {Miracle} of {Capitalism}},
isbn = {978-0-691-09615-5},
shorttitle = {The {Free}-market {Innovation} {Machine}},
abstract = {Why has capitalism produced economic growth that so vastly dwarfs the growth record of other economic systems, past and present? Why have living standards in countries from America to Germany to Japan risen exponentially over the past century? William Baumol rejects the conventional view that capitalism benefits society through price competition--that is, products and services become less costly as firms vie for consumers. Where most others have seen this as the driving force behind growth, he sees something different--a compound of systematic innovation activity within the firm, an arms race in which no firm in an innovating industry dares to fall behind the others in new products and processes, and inter-firm collaboration in the creation and use of innovations. While giving price competition due credit, Baumol stresses that large firms use innovation as a prime competitive weapon. However, as he explains it, firms do not wish to risk too much innovation, because it is costly, and can be made obsolete by rival innovation. So firms have split the difference through the sale of technology licenses and participation in technology-sharing compacts that pay huge dividends to the economy as a whole--and thereby made innovation a routine feature of economic life. This process, in Baumol's view, accounts for the unparalleled growth of modern capitalist economies. Drawing on extensive research and years of consulting work for many large global firms, Baumol shows in this original work that the capitalist growth process, at least in societies where the rule of law prevails, comes far closer to the requirements of economic efficiency than is typically understood. Resounding with rare intellectual force, this book marks a milestone in the comprehension of the accomplishments of our free-market economic system--a new understanding that, suggests the author, promises to benefit many countries that lack the advantages of this immense innovation machine.},
language = {en},
publisher = {Princeton University Press},
author = {Baumol, William J.},
year = {2002},
note = {Google-Books-ID: mYW5B4vnuUUC},
keywords = {Business \& Economics / Economics / General}
}

==Corporate Social Responsibility==

'''Drucker, Peter F. 1984. “Converting Social Problems into Business Opportunities: The New Meaning of Corporate Social Responsibility.” California Management Review 26 (2): 53–63.
'''

@article{drucker_converting_1984,
title = {Converting social problems into business opportunities: {The} new meaning of corporate social responsibility},
volume = {26},
shorttitle = {Converting social problems into business opportunities},
url = {http://scholar.google.com/scholar?cluster=4810669361408720838&hl=en&inst=2735627273918615248&inst=569367360547434339&oi=scholarr },
abstract = {Increasingly, "Social Responsibility of Business" in the years to come will no longer mean "Doing Good" or "Not Doing Harm." It will have to come to mean converting social problems into opportunities for profitable business},
number = {2},
urldate = {2017-07-19},
journal = {California management review},
author = {Drucker, Peter F.},
year = {1984},
pages = {53--63},
file = {Snapshot:files/55/scholar.html:text/html}
}

'''Davis, Keith. 1960. “Can Business Afford to Ignore Social Responsibilities?” California Management Review 2 (3): 70–76. doi:10.2307/41166246.'''

@article{davis_can_1960,
title = {Can {Business} {Afford} to {Ignore} {Social} {Responsibilities}?},
volume = {2},
issn = {0008-1256},
url = {http://dx.doi.org/10.2307/41166246 },
doi = {10.2307/41166246},
abstract = {Few persons would deny that there are significant changes taking place in social, political,
economic, and other aspects of modern culture. Some of these changes businessmen may
want and others they may dislike, but in either instance the changes do exist and must be
faced. As our culture changes, it is appropriate-even mandatory-that businessmen re-
examine their role and the functions of business in society. One area undergoing ...},
language = {en},
number = {3},
urldate = {2017-07-20},
journal = {California Management Review},
author = {Davis, Keith},
month = apr,
year = {1960},
pages = {70--76}
}

==Milton Friedman==

'''Friedman, Milton, and Rose D. Friedman,. n.d. Free to Choose: A Personal Statement.'''

@book{friedman_free_nodate,
title = {Free to {Choose}: {A} {Personal} {Statement}},
url = {http://14.139.206.50:8080/jspui/bitstream/1/2166/1/Friedman%26Friedman%20-%20Free%20To%20Choose.pdf },
author = {Friedman, Milton and Friedman,, Rose D.}
}

'''Friedman, Milton. 2007. “The Social Responsibility of Business Is to Increase Its Profits.” '''

''Bibliographic entry is incomplete & incorrect. Date is not date of original publication.''

@article{friedman_social_2007,
title = {The social responsibility of business is to increase its profits},
url = {http://link.springer.com/content/pdf/10.1007/978-3-540-70818-6.pdf#page=172 },
urldate = {2017-07-20},
journal = {Corporate ethics and corporate governance},
author = {Friedman, Milton},
year = {2007},
pages = {173--178},
file = {[PDF] from umich.edu:files/99/Friedman - 2007 - The social responsibility of business is to increa.pdf:application/pdf;Snapshot:files/100/978-3-540-70818-6.html:text/html }
}

==Students' Hostility to Free Enterprise (from the late 1970s)==

'''Kilpatrick, James L. 1975. “Why Students Are Hostile to Free Enterprise.” College Store Journal 42 (6): 92–3.'''

@article{kilpatrick_why_1975,
title = {Why {Students} {Are} {Hostile} to {Free} {Enterprise}.},
volume = {42},
url = {https://eric.ed.gov/?id=EJ126144 },
abstract = {A Gallup study is described, which was commissioned by Oklahoma State Christian College
regarding the philosophical orientation of college students generally (liberal or
conservative). The author presents selected findings and concludes that students in the
national random sample are ignorant of industrial life and generally hostile toward the
incentive system.},
number = {6},
urldate = {2017-07-19},
journal = {College Store Journal},
author = {Kilpatrick, James L.},
year = {1975},
pages = {92--3}
}

'''Metzner, Henry E., and Edwin C. Sims. 1978. “Student Attitudes toward the Free Enterprise System.” The Journal of Economic Education 10 (1): 46–50. doi:10.2307/1182166.'''

@article{metzner_student_1978,
title = {Student {Attitudes} toward the {Free} {Enterprise} {System}},
volume = {10},
issn = {0022-0485},
url = {http://www.jstor.org/stable/1182166 },
doi = {10.2307/1182166},
abstract = {Over 56 percent of the respondents to the survey felt large companies engaged in
price fixing. An even larger proportion (72 percent) believed that American businesses sell
consumers products the purchasers don't want or need. Over 85 percent of the students
believed it was wrong for business to use corporate funds to influence government
decisions. A majority (over 64 percent) of the respondents felt American business did not},
number = {1},
urldate = {2017-07-20},
journal = {The Journal of Economic Education},
author = {Metzner, Henry E. and Sims, Edwin C.},
year = {1978},
pages = {46--50}
}

==Reagan Administration==

'''Cavanach, Gerald E. 1982. “Free Enterprise Values: Delayed Gratification or Immediate Fulfillment.” Review of Social Economy 40 (3): 330–339.
'''

@article{cavanach_free_1982,
title = {Free {Enterprise} {Values}: {Delayed} {Gratification} or {Immediate} {Fulfillment}},
volume = {40},
shorttitle = {Free {Enterprise} {Values}},
url = {http://www.tandfonline.com/doi/pdf/10.1080/00346768200000035 },
abstract = {Lessened productivity along with inflation and unemployment are the principal problems
currently facing the US economy. These problems are largely a result of a short-term,
expedient time perspective. It is precisely with these issues in mind that President Ronald
Reagan set in place an extensive program to deregulate. Government regulations and
interference are expensive, intrusive and have been a principal reason for our slower
economic growth, so the argument goes. Many of these regulations are unnecessary and .},
number = {3},
urldate = {2017-07-19},
journal = {Review of Social Economy},
author = {Cavanach, Gerald E.},
year = {1982},
pages = {330--339},
file = {Snapshot:files/60/00346768200000035.html:text/html}
}

==Adam Smith ==

'''Winch, Donald. 1991. “Adam Smith: The Prophet of Free Enterprise.” History of Economics Review 16 (1): 102–106.'''

@article{winch_adam_1991,
title = {Adam {Smith}: the prophet of free enterprise},
volume = {16},
shorttitle = {Adam {Smith}},
url = {http://www.tandfonline.com/doi/pdf/10.1080/10370196.1991.11733094 },
abstract = {When Adam Smith died 200 years ago today, the event was greeted in the Times by a
supercilious obituary which alleged that he had courted local opinion in a commercial town
by converting his chair of moral philosophy at Glasgow into" a professorship of trade and},
number = {1},
urldate = {2017-07-19},
journal = {History of Economics Review},
author = {Winch, Donald},
year = {1991},
pages = {102--106},
file = {[PDF] from hetsa.org.au:files/64/Winch - 1991 - Adam Smith the prophet of free enterprise.pdf:application/pdf;Snapshot:files/73/10370196.1991.html:text/html}
}

==Critics of Free Enterprise==

'''Arnold, N. Scott. 1990. “Economists and Philosophers as Critics of the Free Enterprise System.” The Monist 73 (4): 621–641.'''

@article{arnold_economists_1990,
title = {Economists and {Philosophers} as {Critics} of the {Free} {Enterprise} {System}},
volume = {73},
url = {http://www.jstor.org/stable/27903213 },
abstract = {A favorite topic in academic political philosophy in the last third of the twentieth century has
been the shortcomings of the economic system under which most of us live. Though in
recent years there has been some recogni tion of the advantages of the market and the
difficulties with alternative economic systems, this has not slowed the steady stream of
criticism of this or that aspect of the free enterprise system. The purpose of this essay is to
give an account of the geography of various criticisms of this system and to argue that},
number = {4},
urldate = {2017-07-19},
journal = {The Monist},
author = {Arnold, N. Scott},
year = {1990},
pages = {621--641}
}

==Arthur C. Brooks==

'''Brooks, Arthur C. 2011. The Battle: How the Fight between Free Enterprise and Big Government Will Shape America’s Future. Basic books. '''

@book{brooks_battle:_2011,
title = {The battle: {How} the fight between free enterprise and big government will shape {America}'s future},
shorttitle = {The battle},
url = {http://books.google.com/books?hl=en&lr=&id=pUdfy7GqciUC&oi=fnd&pg=PR7&dq=info:RQVcSGmPUM8J:scholar.google.com&ots=4BZWvQQ4F7&sig=Fa0dBYBysgOne6NYz-eICAZUR7M },
abstract = {America faces a new culture war. It is not a war about guns, abortions, or gays—rather it is a
war against the creeping changes to our entrepreneurial culture, the true bedrock of who we
are as a people. The new culture war is a battle between free enterprise and social
democracy. Many Americans have forgotten the evils of socialism and the predations of the
American Great Society's welfare state programs. But, as American Enterprise Institute's
president Arthur C. Brooks reveals in The Battle, the forces for social democracy have},
urldate = {2017-07-20},
publisher = {Basic books},
author = {Brooks, Arthur C.},
year = {2011},
file = {Snapshot:files/92/books.html:text/html}
}

'''———. 2012. The Road to Freedom: How to Win the Fight for Free Enterprise. Basic Books'''

@book{brooks_road_2012,
title = {The road to freedom: how to win the fight for free enterprise},
shorttitle = {The road to freedom},
url = {http://books.google.com/books?hl=en&lr=&id=5tNvv4I4Z4IC&oi=fnd&pg=PA3&dq=info:9XgI-cmWyN8J:scholar.google.com&ots=izIJ1ScO7I&sig=uJPX-iEWTQlejSZ5WckAuQB_sIA },
urldate = {2017-07-19},
publisher = {Basic Books (AZ)},
author = {Brooks, Arthur C.},
year = {2012},
file = {Snapshot:files/70/books.html:text/html}
}

==Powell Memorandum==

'''Powell Jr, Lewis F. 1971. “Attack on American Free Enterprise System.” '''

@article{powell_jr_attack_1971,
title = {Attack on {American} free enterprise system},
url = {http://lawdigitalcommons.bc.edu/darter_materials/79/?utm_source=lawdigitalcommons.bc.edu%2Fdarter_materials%2F79&utm_medium=PDF&utm_campaign=PDFCoverPages },
abstract = {Abstract Memorandum from future Supreme Court Justice Lewis F. Powell to the US
Chamber of Commerce. This document came to be widely known as the Powell
Memorandum. It is seen by many as an influential document in various conservative and pro-
corporate political movements.},
urldate = {2017-07-20},
author = {Powell Jr, Lewis F.},
year = {1971},
file = {[PDF] from bc.edu:files/94/Powell Jr - 1971 - Attack on American free enterprise system.pdf:application/pdf;Snapshot:files/95/79.html:text/html}
}

==Evolutionary Psychology==

'''Richerson, Peter, and Rob Boyd. 2008. “The Evolution of Free Enterprise Values.” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=929169.'''

@article{richerson_evolution_2008,
title = {The evolution of free enterprise values},
url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=929169 },
abstract = {Free enterprise economic systems evolved in the modern period as culturally
transmitted values related to honesty, hard work, and education achievement emerged. One
evolutionary puzzle is why most economies for the past 5,000 years have had a limited role
for free enterprise given the spectacular success of modern free economies. Another is why
if humans became biologically modern 50,000 years ago did it take until 11,000 years ago
for agriculture, the economic foundation of states, to begin. Why didn't free enterprise},
urldate = {2017-07-25},
author = {Richerson, Peter and Boyd, Rob},
year = {2008},
file = {[PDF] from researchgate.net:files/155/Richerson and Boyd - 2008 - The evolution of free enterprise values.pdf:application/pdf;Snapshot:files/156/papers.html:text/html}
}

==Evolution of Capitalism==

'''Halal, William E. 1988. “The New Capitalism: Democratic Free Enterprise.” The Futurist 22 (1): 26.'''

@article{halal_new_1988,
title = {The {New} {Capitalism}: {Democratic} {Free} {Enterprise}},
volume = {22},
shorttitle = {The {New} {Capitalism}},
url = {http://search.proquest.com/openview/c1a7173397fa15385f29a523b6ce18af/1?pq-origsite=gscholar&cbl=47758},
abstract = {Abstract In the US, the" old capitalism" of the industrial past is giving way to the" new
capitalism" of the information age. The new capitalism necessitates a more balanced type of
growth that weighs benefits against costs to improve quality of life for all. To provide more
personal services, a client-driven form of marketing is emerging that shifts the emphasis from
selling to genuinely serving the customer. New capitalism also is restructuring institutions.
Leading-edge firms are developing decentralized forms of control, encouraging smal},
number = {1},
urldate = {2017-07-19},
journal = {The Futurist},
author = {Halal, William E.},
year = {1988},
pages = {26},
file = {Snapshot:files/72/218554271.html:text/html}
}

==Essays from 1956==

'''Sennholz, Mary. 1956. On Freedom and Free Enterprise: Essays in Honor of Ludwig von Mises. Ludwig von Mises Institute. '''

@book{sennholz_freedom_1956,
title = {On {Freedom} and {Free} {Enterprise}: {Essays} in {Honor} of {Ludwig} von {Mises}},
shorttitle = {On {Freedom} and {Free} {Enterprise}},
url = {http://books.google.com/books?hl=en&lr=&id=j_Z4226EKrYC&oi=fnd&pg=PR9&dq=info:CV4wpYUuyOAJ:scholar.google.com&ots=Ar5Fehu3xI&sig=N-B7Nk-8jS-ShfM7F0dbmVoGCqw},
abstract = {INDIVIDUAL valuation is the keystone of economic theory. For, fundamentally, economics
does not deal with things or material objects. Economics analyzes the logical attributes and
consequences of the existence of individual valuations." Things" enter into the picture, of
course, since there can be no valuation without things to be valued. But the essence and the
driving force of human action, and therefore of the human market economy, are the
valuations of individuals. Action is the result of choice among alternatives, and choice ...},
urldate = {2017-07-20},
publisher = {Ludwig von Mises Institute},
author = {Sennholz, Mary},
year = {1956},
file = {[HTML] from google.com:files/81/books.html:text/html}
}

Free Enterprise

2017-08-04T15:45:00Z

Dayton: /* Literature Review */

{{McNair Projects
|Has title=Free Enterprise Lit Review
|Has owner=Anne Dayton,
}}
==Literature Review==

The promotion of Free Enterprise as an American ideal originates in libertarian and conservative responses to the New Deal. Rippa (1958, 1959) shows how free enterprise was promoted in school textbooks. St. John (2010) examines its promotion in newspapers. Fones-Wolf is a monograph on mid century corporate promotion of the free enterprise ideals.

==Free Enterprise as Response to New Deal==

'''St. John III, Burton. 2010. “A VIEW THAT’S FIT TO PRINT: The National Association of Manufacturers’ Free Enterprise Rhetoric as Integration Propaganda in The New York Times, 1937–1939.” Journalism Studies 11 (3): 377–392.
'''

@article{st._john_iii_view_2010,
title = {A {VIEW} {THAT}'{S} {FIT} {TO} {PRINT}: {The} {National} {Association} of {Manufacturers}' free enterprise rhetoric as integration propaganda in {The} {New} {York} {Times}, 1937–1939},
volume = {11},
shorttitle = {A {VIEW} {THAT}'{S} {FIT} {TO} {PRINT}},
url = {http://www.tandfonline.com/doi/abs/10.1080/14616700903290585 },
abstract = {This study examines the appearance of National Association of Manufacturers'(NAM)
propaganda, from 1937 to 1939, in articles within The New York Times. NAM's ability to
place such rhetoric in The New York Times reveals both the presence of integration
propaganda and the beginning of a press acclimation to propaganda as news. This
examination reveals a crystallizing of professional journalism's reliance on authoritative, yet
propagandistic sources, a dynamic that persists to this day.},
number = {3},
urldate = {2017-07-19},
journal = {Journalism Studies},
author = {St. John III, Burton},
year = {2010},
pages = {377--392},
file = {Snapshot:files/39/14616700903290585.html:text/html}
}

'''Rippa, S. Alexander. 1958. “The Textbook Controversy and the Free Enterprise Campaign, 1940-1941.” History of Education Journal, 49–58.'''
'''———. 1959. “Dissemination of the Free-Enterprise Creed to American Schools.” The School Review 67 (4): 409–21. doi:10.1086/442511.'''

@article{rippa_textbook_1958,
title = {The textbook controversy and the free enterprise campaign, 1940-1941},
url = {http://www.jstor.org/stable/3692590 },
abstract = {50 HISTORY OF EDUCATION JOURNAL attack upon the" anti-advertising" material which, it
charged, had been" planted" in the social science textbooks. The editors as-serted that
Rugg's textbooks implied that" advertising is an economic waste, that a high proportion of
advertising is dishonest and that advertised products are pretty likely to be untrustworthy.'" 4
The following year the Nation's Business stated editorially that parents were still finding"
chunks of unamericanism [sic]" in Rugg's textbooks. 5 While the textbook controversy ...},
urldate = {2017-07-19},
journal = {History of Education Journal},
author = {Rippa, S. Alexander},
year = {1958},
pages = {49--58}
}

@article{rippa_dissemination_1959,
title = {Dissemination of the {Free}-{Enterprise} {Creed} to {American} {Schools}},
volume = {67},
issn = {0036-6773},
url = {http://www.journals.uchicago.edu/doi/abs/10.1086/442511 },
doi = {10.1086/442511},
abstract = {The free-enterprise campaign launched during the late thirties marked a significant turning
point in relations between business and education in the United States. Beset by adverse
publicity during the depression and deeply disturbed by New Deal victories, business
leaders turned to the nation's schools in an effort to perpetuate the free-enterprise creed.
The distribution of pamphlets to American youth and the preparation of audio-visual devices
for use in classrooms were early signs of the sudden interest of business leaders in the ...},
number = {4},
urldate = {2017-07-20},
journal = {The School Review},
author = {Rippa, S. Alexander},
month = dec,
year = {1959},
pages = {409--421},
file = {Snapshot:files/116/442511.html:text/html}
}

'''Fones-Wolf, Elizabeth A. 1994. Selling Free Enterprise: The Business Assault on Labor and Liberalism, 1945-60. University of Illinois Press.'''

@book{fones-wolf_selling_1994,
title = {Selling free enterprise: {The} business assault on labor and liberalism, 1945-60},
shorttitle = {Selling free enterprise},
url = {http://books.google.com/books?hl=en&lr=&id=W4p8-BAs0dcC&oi=fnd&pg=PA1&dq=info:uIh_mJdu3vMJ:scholar.google.com&ots=VDTieSeQme&sig=L75z9k6kOw2MJeZrE285jsHMWEM },
abstract = {During December 1951, half of the adult population of the industrial town of Latrobe,
Pennsylvania, took regular breaks from work to study economics on company time.
Employees from nineteen firms gathered in small groups to watch a series of films and to
participate in discussions that focused on the values and symbols associated with the
American way of life, including patriotism, freedom, individualism, competition, and
abundance through increasing productivity. 1 That these firms halted production and},
urldate = {2017-07-20},
publisher = {University of Illinois Press},
author = {Fones-Wolf, Elizabeth A.},
year = {1994},
file = {Snapshot:files/97/books.html:text/html}
}

==Free Enterprise & Ethics==

'''Erteszek, Jan J. 1982. “Corporate Enterprise and Christian Ethics.” Review of Social Economy 40 (3): 323–329.'''

@article{erteszek_corporate_1982,
title = {Corporate enterprise and {Christian} ethics},
volume = {40},
url = {http://www.tandfonline.com/doi/pdf/10.1080/00346768200000034 },
abstract = {Throughout the world, the moral basis of the free enterprise system faces both new and
persistent challenges. It is convincing to a Christian and a man of business that these
challenges cannot be met without a new, ethical vision of private enterprise, and a
reconfiguration of the moral purpose of corporate life. By a new vision and a moral purpose, I
mean a perception of the future that would develop the logic, stimuli, incentives, convictions
and commitments which would lead to a more serving and more caring society, one that ...},
number = {3},
urldate = {2017-07-19},
journal = {Review of Social Economy},
author = {Erteszek, Jan J.},
year = {1982},
pages = {323--329},
file = {Snapshot:files/45/00346768200000034.html:text/html }
}

'''Wishloff, Jim. 2003. “Responsible Free Enterprise: What It Is and Why We Don’t Have It.” Teaching Business Ethics 7 (3): 229–263.
'''

@article{wishloff_responsible_2003,
title = {Responsible free enterprise: {What} it is and why we don't have it},
volume = {7},
shorttitle = {Responsible free enterprise},
url = {http://www.springerlink.com/index/V2T828023263V870.pdf },
abstract = {An economic system of responsible freeenterprise would (i) give individuals and groupsthe
freedom to initiate, own and managebusiness undertakings and (ii) insist that
suchundertakings be accompanied by a sense ofsocial and moral responsibility which},
number = {3},
urldate = {2017-07-19},
journal = {Teaching Business Ethics},
author = {Wishloff, Jim},
year = {2003},
pages = {229--263},
file = {Snapshot:files/50/10.html:text/html}
}

'''Chewning, Richard C. 1984. “Can Free Enterprise Survive Ethical Schizophrenia?” Business Horizons 27 (2): 5–11.'''

@article{chewning_can_1984,
title = {Can free enterprise survive ethical schizophrenia?},
volume = {27},
url = {http://www.sciencedirect.com/science/article/pii/0007681384900028 },
abstract = {This century is seeing changes in our underlying philosophy—in how we view
existence itself and our part in it, in how we know and accept facts, in what we consider right
and wrong. Given these very basic changes in our culture, where will business as we know it
go in the future?},
number = {2},
urldate = {2017-07-25},
journal = {Business Horizons},
author = {Chewning, Richard C.},
year = {1984},
pages = {5--11},
file = {[PDF] from cbfa-cbar.org:files/164/Chewning - 1984 - Can free enterprise survive ethical schizophrenia.pdf:application/pdf;Snapshot:files/165/0007681384900028.html:text/html }
}

'''Solomon, Robert C. 2006. “Free Enterprise, Sympathy, and Virtue.” Moral Markets: The Critical Role of Values in the Economy, 16–41.'''

@article{solomon_free_2006,
title = {Free enterprise, sympathy, and virtue},
url = {http://books.google.com/books?hl=en&lr=&id=6QrvmNo2qD4C&oi=fnd&pg=PA16&dq=info:mD7mFEgIOJAJ:scholar.google.com&ots=CTmHyJIYLQ&sig=KMa2iPPxq0dUsQCX1iwLVJu0dG0 },
abstract = {How selfish soever man may be supposed, there are evidently some principles in his nature,
which interest him in the fortune of others, and render their happiness necessary to him,
though he derives nothing from it except the pleasure of seeing it. Of this kind is pity or
compassion, the emotion which we feel for the misery of others.... The greatest ruffian, the
most hardened violator of the laws of society, is not altogether without it.},
urldate = {2017-07-25},
journal = {Moral markets: The critical role of values in the economy},
author = {Solomon, Robert C.},
year = {2006},
pages = {16--41},
file = {[PDF] from researchgate.net:files/161/Solomon - 2006 - Free enterprise, sympathy, and virtue.pdf:application/pdf;Snapshot:files/162/books.html:text/html }
}

'''Demsetz, Harold. 1978. “Social Responsibility in the Enterprise Economy.” Southwestern University Law Review 10: 1.'''

@article{demsetz_social_1978,
title = {Social {Responsibility} in the {Enterprise} {Economy}},
volume = {10},
url = {http://heinonline.org/HOL/Page?handle=hein.journals/swulr10&id=19&div=&collection= },
abstract = {I wish to emphasize at the outset that this article does not present a general ethical system or
a specific ethical code to guide business behavior. Too many philosophers and economists,
good men and mediocre, have failed in their attempts to-define even" the fair price,"" the just
wage," or" fair competition." My purpose is to describe the manner in which a free enterprise
economy evaluates behavior, whatever the substantive content of such behavior may be},
journal = {Southwestern University Law Review},
author = {Demsetz, Harold},
year = {1978},
pages = {1},
file = {Social Responsibility in the Enterprise Economy 10 Southwestern University Law Review 1978:files/106/LandingPage.html:text/html }
}

'''Barach, Jeffrey A., and John B. Elstrott. 1988. “The Transactional Ethic: The Ethical Foundations of Free Enterprise Reconsidered.” Journal of Business Ethics 7 (7): 545–551.'''

@article{barach_transactional_1988,
title = {The transactional ethic: {The} ethical foundations of free enterprise reconsidered},
volume = {7},
shorttitle = {The transactional ethic},
url = {http://www.springerlink.com/index/T56T3T746576P160.pdf },
abstract = {A review of the evolution of the ethical foundations of free enterprise reveals the essentially
utilitarian ethical foundation prevailing today. To enrich those foundations the article
attempts to establish the ethical validity of free transactions by relating them to the basic
principle of interpersonal ethics: the Golden Rule. The validity of the transactional ethic is
presented as an articulation of freedom in a valid social and economic context.},
number = {7},
urldate = {2017-07-20},
journal = {Journal of Business Ethics},
author = {Barach, Jeffrey A. and Elstrott, John B.},
year = {1988},
pages = {545--551},
file = {Snapshot:files/86/10.html:text/html }
}

'''Macfie, A. L. 1967. “The Moral Justification of Free Enterprise.” Scottish Journal of Political Economy 14 (1): 1–11.'''

@article{macfie_moral_1967,
title = {The moral justification of free enterprise},
volume = {14},
url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9485.1967.tb00753.x/full },
abstract = {ADAM SMITH'S theory has been chosen as a text simply because I know no better start from
which to examine the issue before us. I have yet to find a more careful, thorough, detailed,
realistic ethical argument for freedom in economic (among other) affairs than that he gives,
in his Theory of Moral Sentiments, in his treatment of the virtues, prudence and self-
command. True, we deal here only with the individual aspect; and this does justice neither to
the facts nor to Adam Smith's full theory. But We have grossly inadequate time even for},
number = {1},
urldate = {2017-07-20},
journal = {Scottish Journal of Political Economy},
author = {Macfie, A. L.},
year = {1967},
pages = {1--11},
file = {Snapshot:files/90/full.html:text/html }
}

==William Baumol==

'''Baumol, William J. 1968. “Entrepreneurship in Economic Theory.” The American Economic Review, 64–71.'''

'''———. 1996. “Entrepreneurship: Productive, Unproductive, and Destructive.” Journal of Business Venturing 11 (1): 3–22.'''

'''———. 2002. The Free-Market Innovation Machine: Analyzing the Growth Miracle of Capitalism. Princeton University Press.
'''

@article{baumol_entrepreneurship_1968,
title = {Entrepreneurship in economic theory},
url = {http://www.jstor.org/stable/1831798 },
abstract = {The entrepreneur is at the same time one of the most intriguing and one of the most elusive
characters in the cast that constitutes the sub-ject of economic analysis. He has long been
recognized as the apex of the hierarchy that determinies the behavior of the firm and thereby
bears a heavy responsibility for the vitality of the free enterprise society. In the writings of the
classical economist his appearance was frequent, though he remained a shadowy entity
without clearly defined form and function. Only Schumpeter and, to some degree},
urldate = {2017-07-19},
journal = {The American economic review},
author = {Baumol, William J.},
year = {1968},
pages = {64--71}
}

@article{baumol_entrepreneurship:_1996,
title = {Entrepreneurship: {Productive}, unproductive, and destructive},
volume = {11},
shorttitle = {Entrepreneurship},
url = {http://www.sciencedirect.com/science/article/pii/088390269400014X },
abstract = {The basic hypothesis is that, while the total supply of entrepreneurs varies among societies,
the productive contribution of the society's entrepreneurial activities varies much more
because of their allocation between productive activities such as innovation and largely
unproductive activities such as rent seeking or organized crime. This allocation is heavily
influenced by the relative payoffs society offers to such activities. This implies that policy can
influence the allocation of entrepreneurship more effectively than it can influence its ...},
number = {1},
urldate = {2017-07-19},
journal = {Journal of Business Venturing},
author = {Baumol, William J.},
year = {1996},
pages = {3--22},
file = {[PDF] from econintel.org:files/47/Baumol - 1996 - Entrepreneurship Productive, unproductive, and de.pdf:application/pdf;Snapshot:files/48/088390269400014X.html:text/html}
}

@book{baumol_free-market_2002,
title = {The {Free}-market {Innovation} {Machine}: {Analyzing} the {Growth} {Miracle} of {Capitalism}},
isbn = {978-0-691-09615-5},
shorttitle = {The {Free}-market {Innovation} {Machine}},
abstract = {Why has capitalism produced economic growth that so vastly dwarfs the growth record of other economic systems, past and present? Why have living standards in countries from America to Germany to Japan risen exponentially over the past century? William Baumol rejects the conventional view that capitalism benefits society through price competition--that is, products and services become less costly as firms vie for consumers. Where most others have seen this as the driving force behind growth, he sees something different--a compound of systematic innovation activity within the firm, an arms race in which no firm in an innovating industry dares to fall behind the others in new products and processes, and inter-firm collaboration in the creation and use of innovations. While giving price competition due credit, Baumol stresses that large firms use innovation as a prime competitive weapon. However, as he explains it, firms do not wish to risk too much innovation, because it is costly, and can be made obsolete by rival innovation. So firms have split the difference through the sale of technology licenses and participation in technology-sharing compacts that pay huge dividends to the economy as a whole--and thereby made innovation a routine feature of economic life. This process, in Baumol's view, accounts for the unparalleled growth of modern capitalist economies. Drawing on extensive research and years of consulting work for many large global firms, Baumol shows in this original work that the capitalist growth process, at least in societies where the rule of law prevails, comes far closer to the requirements of economic efficiency than is typically understood. Resounding with rare intellectual force, this book marks a milestone in the comprehension of the accomplishments of our free-market economic system--a new understanding that, suggests the author, promises to benefit many countries that lack the advantages of this immense innovation machine.},
language = {en},
publisher = {Princeton University Press},
author = {Baumol, William J.},
year = {2002},
note = {Google-Books-ID: mYW5B4vnuUUC},
keywords = {Business \& Economics / Economics / General}
}

==Corporate Social Responsibility==

'''Drucker, Peter F. 1984. “Converting Social Problems into Business Opportunities: The New Meaning of Corporate Social Responsibility.” California Management Review 26 (2): 53–63.
'''

@article{drucker_converting_1984,
title = {Converting social problems into business opportunities: {The} new meaning of corporate social responsibility},
volume = {26},
shorttitle = {Converting social problems into business opportunities},
url = {http://scholar.google.com/scholar?cluster=4810669361408720838&hl=en&inst=2735627273918615248&inst=569367360547434339&oi=scholarr },
abstract = {Increasingly, "Social Responsibility of Business" in the years to come will no longer mean "Doing Good" or "Not Doing Harm." It will have to come to mean converting social problems into opportunities for profitable business},
number = {2},
urldate = {2017-07-19},
journal = {California management review},
author = {Drucker, Peter F.},
year = {1984},
pages = {53--63},
file = {Snapshot:files/55/scholar.html:text/html}
}

'''Davis, Keith. 1960. “Can Business Afford to Ignore Social Responsibilities?” California Management Review 2 (3): 70–76. doi:10.2307/41166246.'''

@article{davis_can_1960,
title = {Can {Business} {Afford} to {Ignore} {Social} {Responsibilities}?},
volume = {2},
issn = {0008-1256},
url = {http://dx.doi.org/10.2307/41166246 },
doi = {10.2307/41166246},
abstract = {Few persons would deny that there are significant changes taking place in social, political,
economic, and other aspects of modern culture. Some of these changes businessmen may
want and others they may dislike, but in either instance the changes do exist and must be
faced. As our culture changes, it is appropriate-even mandatory-that businessmen re-
examine their role and the functions of business in society. One area undergoing ...},
language = {en},
number = {3},
urldate = {2017-07-20},
journal = {California Management Review},
author = {Davis, Keith},
month = apr,
year = {1960},
pages = {70--76}
}

==Milton Friedman==

'''Friedman, Milton, and Rose D. Friedman,. n.d. Free to Choose: A Personal Statement.'''

@book{friedman_free_nodate,
title = {Free to {Choose}: {A} {Personal} {Statement}},
url = {http://14.139.206.50:8080/jspui/bitstream/1/2166/1/Friedman%26Friedman%20-%20Free%20To%20Choose.pdf },
author = {Friedman, Milton and Friedman,, Rose D.}
}

'''Friedman, Milton. 2007. “The Social Responsibility of Business Is to Increase Its Profits.” '''

''Bibliographic entry is incomplete & incorrect. Date is not date of original publication.''

@article{friedman_social_2007,
title = {The social responsibility of business is to increase its profits},
url = {http://link.springer.com/content/pdf/10.1007/978-3-540-70818-6.pdf#page=172 },
urldate = {2017-07-20},
journal = {Corporate ethics and corporate governance},
author = {Friedman, Milton},
year = {2007},
pages = {173--178},
file = {[PDF] from umich.edu:files/99/Friedman - 2007 - The social responsibility of business is to increa.pdf:application/pdf;Snapshot:files/100/978-3-540-70818-6.html:text/html }
}

==Students' Hostility to Free Enterprise (from the late 1970s)==

'''Kilpatrick, James L. 1975. “Why Students Are Hostile to Free Enterprise.” College Store Journal 42 (6): 92–3.'''

@article{kilpatrick_why_1975,
title = {Why {Students} {Are} {Hostile} to {Free} {Enterprise}.},
volume = {42},
url = {https://eric.ed.gov/?id=EJ126144 },
abstract = {A Gallup study is described, which was commissioned by Oklahoma State Christian College
regarding the philosophical orientation of college students generally (liberal or
conservative). The author presents selected findings and concludes that students in the
national random sample are ignorant of industrial life and generally hostile toward the
incentive system.},
number = {6},
urldate = {2017-07-19},
journal = {College Store Journal},
author = {Kilpatrick, James L.},
year = {1975},
pages = {92--3}
}

'''Metzner, Henry E., and Edwin C. Sims. 1978. “Student Attitudes toward the Free Enterprise System.” The Journal of Economic Education 10 (1): 46–50. doi:10.2307/1182166.'''

@article{metzner_student_1978,
title = {Student {Attitudes} toward the {Free} {Enterprise} {System}},
volume = {10},
issn = {0022-0485},
url = {http://www.jstor.org/stable/1182166 },
doi = {10.2307/1182166},
abstract = {Over 56 percent of the respondents to the survey felt large companies engaged in
price fixing. An even larger proportion (72 percent) believed that American businesses sell
consumers products the purchasers don't want or need. Over 85 percent of the students
believed it was wrong for business to use corporate funds to influence government
decisions. A majority (over 64 percent) of the respondents felt American business did not},
number = {1},
urldate = {2017-07-20},
journal = {The Journal of Economic Education},
author = {Metzner, Henry E. and Sims, Edwin C.},
year = {1978},
pages = {46--50}
}

==Reagan Administration==

'''Cavanach, Gerald E. 1982. “Free Enterprise Values: Delayed Gratification or Immediate Fulfillment.” Review of Social Economy 40 (3): 330–339.
'''

@article{cavanach_free_1982,
title = {Free {Enterprise} {Values}: {Delayed} {Gratification} or {Immediate} {Fulfillment}},
volume = {40},
shorttitle = {Free {Enterprise} {Values}},
url = {http://www.tandfonline.com/doi/pdf/10.1080/00346768200000035 },
abstract = {Lessened productivity along with inflation and unemployment are the principal problems
currently facing the US economy. These problems are largely a result of a short-term,
expedient time perspective. It is precisely with these issues in mind that President Ronald
Reagan set in place an extensive program to deregulate. Government regulations and
interference are expensive, intrusive and have been a principal reason for our slower
economic growth, so the argument goes. Many of these regulations are unnecessary and .},
number = {3},
urldate = {2017-07-19},
journal = {Review of Social Economy},
author = {Cavanach, Gerald E.},
year = {1982},
pages = {330--339},
file = {Snapshot:files/60/00346768200000035.html:text/html}
}

==Adam Smith ==

'''Winch, Donald. 1991. “Adam Smith: The Prophet of Free Enterprise.” History of Economics Review 16 (1): 102–106.'''

@article{winch_adam_1991,
title = {Adam {Smith}: the prophet of free enterprise},
volume = {16},
shorttitle = {Adam {Smith}},
url = {http://www.tandfonline.com/doi/pdf/10.1080/10370196.1991.11733094 },
abstract = {When Adam Smith died 200 years ago today, the event was greeted in the Times by a
supercilious obituary which alleged that he had courted local opinion in a commercial town
by converting his chair of moral philosophy at Glasgow into" a professorship of trade and},
number = {1},
urldate = {2017-07-19},
journal = {History of Economics Review},
author = {Winch, Donald},
year = {1991},
pages = {102--106},
file = {[PDF] from hetsa.org.au:files/64/Winch - 1991 - Adam Smith the prophet of free enterprise.pdf:application/pdf;Snapshot:files/73/10370196.1991.html:text/html}
}

==Critics of Free Enterprise==

'''Arnold, N. Scott. 1990. “Economists and Philosophers as Critics of the Free Enterprise System.” The Monist 73 (4): 621–641.'''

@article{arnold_economists_1990,
title = {Economists and {Philosophers} as {Critics} of the {Free} {Enterprise} {System}},
volume = {73},
url = {http://www.jstor.org/stable/27903213 },
abstract = {A favorite topic in academic political philosophy in the last third of the twentieth century has
been the shortcomings of the economic system under which most of us live. Though in
recent years there has been some recogni tion of the advantages of the market and the
difficulties with alternative economic systems, this has not slowed the steady stream of
criticism of this or that aspect of the free enterprise system. The purpose of this essay is to
give an account of the geography of various criticisms of this system and to argue that},
number = {4},
urldate = {2017-07-19},
journal = {The Monist},
author = {Arnold, N. Scott},
year = {1990},
pages = {621--641}
}

==Arthur C. Brooks==

'''Brooks, Arthur C. 2011. The Battle: How the Fight between Free Enterprise and Big Government Will Shape America’s Future. Basic books. '''

@book{brooks_battle:_2011,
title = {The battle: {How} the fight between free enterprise and big government will shape {America}'s future},
shorttitle = {The battle},
url = {http://books.google.com/books?hl=en&lr=&id=pUdfy7GqciUC&oi=fnd&pg=PR7&dq=info:RQVcSGmPUM8J:scholar.google.com&ots=4BZWvQQ4F7&sig=Fa0dBYBysgOne6NYz-eICAZUR7M },
abstract = {America faces a new culture war. It is not a war about guns, abortions, or gays—rather it is a
war against the creeping changes to our entrepreneurial culture, the true bedrock of who we
are as a people. The new culture war is a battle between free enterprise and social
democracy. Many Americans have forgotten the evils of socialism and the predations of the
American Great Society's welfare state programs. But, as American Enterprise Institute's
president Arthur C. Brooks reveals in The Battle, the forces for social democracy have},
urldate = {2017-07-20},
publisher = {Basic books},
author = {Brooks, Arthur C.},
year = {2011},
file = {Snapshot:files/92/books.html:text/html}
}

'''———. 2012. The Road to Freedom: How to Win the Fight for Free Enterprise. Basic Books'''

@book{brooks_road_2012,
title = {The road to freedom: how to win the fight for free enterprise},
shorttitle = {The road to freedom},
url = {http://books.google.com/books?hl=en&lr=&id=5tNvv4I4Z4IC&oi=fnd&pg=PA3&dq=info:9XgI-cmWyN8J:scholar.google.com&ots=izIJ1ScO7I&sig=uJPX-iEWTQlejSZ5WckAuQB_sIA },
urldate = {2017-07-19},
publisher = {Basic Books (AZ)},
author = {Brooks, Arthur C.},
year = {2012},
file = {Snapshot:files/70/books.html:text/html}
}

==Powell Memorandum==

'''Powell Jr, Lewis F. 1971. “Attack on American Free Enterprise System.” '''

@article{powell_jr_attack_1971,
title = {Attack on {American} free enterprise system},
url = {http://lawdigitalcommons.bc.edu/darter_materials/79/?utm_source=lawdigitalcommons.bc.edu%2Fdarter_materials%2F79&utm_medium=PDF&utm_campaign=PDFCoverPages },
abstract = {Abstract Memorandum from future Supreme Court Justice Lewis F. Powell to the US
Chamber of Commerce. This document came to be widely known as the Powell
Memorandum. It is seen by many as an influential document in various conservative and pro-
corporate political movements.},
urldate = {2017-07-20},
author = {Powell Jr, Lewis F.},
year = {1971},
file = {[PDF] from bc.edu:files/94/Powell Jr - 1971 - Attack on American free enterprise system.pdf:application/pdf;Snapshot:files/95/79.html:text/html}
}

==Evolutionary Psychology==

'''Richerson, Peter, and Rob Boyd. 2008. “The Evolution of Free Enterprise Values.” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=929169.'''

@article{richerson_evolution_2008,
title = {The evolution of free enterprise values},
url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=929169 },
abstract = {Free enterprise economic systems evolved in the modern period as culturally
transmitted values related to honesty, hard work, and education achievement emerged. One
evolutionary puzzle is why most economies for the past 5,000 years have had a limited role
for free enterprise given the spectacular success of modern free economies. Another is why
if humans became biologically modern 50,000 years ago did it take until 11,000 years ago
for agriculture, the economic foundation of states, to begin. Why didn't free enterprise},
urldate = {2017-07-25},
author = {Richerson, Peter and Boyd, Rob},
year = {2008},
file = {[PDF] from researchgate.net:files/155/Richerson and Boyd - 2008 - The evolution of free enterprise values.pdf:application/pdf;Snapshot:files/156/papers.html:text/html}
}

==Evolution of Capitalism==

'''Halal, William E. 1988. “The New Capitalism: Democratic Free Enterprise.” The Futurist 22 (1): 26.'''

@article{halal_new_1988,
title = {The {New} {Capitalism}: {Democratic} {Free} {Enterprise}},
volume = {22},
shorttitle = {The {New} {Capitalism}},
url = {http://search.proquest.com/openview/c1a7173397fa15385f29a523b6ce18af/1?pq-origsite=gscholar&cbl=47758},
abstract = {Abstract In the US, the" old capitalism" of the industrial past is giving way to the" new
capitalism" of the information age. The new capitalism necessitates a more balanced type of
growth that weighs benefits against costs to improve quality of life for all. To provide more
personal services, a client-driven form of marketing is emerging that shifts the emphasis from
selling to genuinely serving the customer. New capitalism also is restructuring institutions.
Leading-edge firms are developing decentralized forms of control, encouraging smal},
number = {1},
urldate = {2017-07-19},
journal = {The Futurist},
author = {Halal, William E.},
year = {1988},
pages = {26},
file = {Snapshot:files/72/218554271.html:text/html}
}

==Essays from 1956==

'''Sennholz, Mary. 1956. On Freedom and Free Enterprise: Essays in Honor of Ludwig von Mises. Ludwig von Mises Institute. '''

@book{sennholz_freedom_1956,
title = {On {Freedom} and {Free} {Enterprise}: {Essays} in {Honor} of {Ludwig} von {Mises}},
shorttitle = {On {Freedom} and {Free} {Enterprise}},
url = {http://books.google.com/books?hl=en&lr=&id=j_Z4226EKrYC&oi=fnd&pg=PR9&dq=info:CV4wpYUuyOAJ:scholar.google.com&ots=Ar5Fehu3xI&sig=N-B7Nk-8jS-ShfM7F0dbmVoGCqw},
abstract = {INDIVIDUAL valuation is the keystone of economic theory. For, fundamentally, economics
does not deal with things or material objects. Economics analyzes the logical attributes and
consequences of the existence of individual valuations." Things" enter into the picture, of
course, since there can be no valuation without things to be valued. But the essence and the
driving force of human action, and therefore of the human market economy, are the
valuations of individuals. Action is the result of choice among alternatives, and choice ...},
urldate = {2017-07-20},
publisher = {Ludwig von Mises Institute},
author = {Sennholz, Mary},
year = {1956},
file = {[HTML] from google.com:files/81/books.html:text/html}
}