{{Project|Has project output=Content|Has sponsor=McNair ProjectsCenter|Project TitleHas title=Women in Entrepreneurship (Issue Brief)|Topic Area=Social Factors in Entrepreneurship|OwnerHas owner=Carlin Cherry|Start TermHas start date=Spring 2016|End TermHas keywords=Women, Entrepreneurship|StatusHas project status=Tabled|Deliverable}}=Timeline and plan for summer=*note: I am using "hopethisworks8.txt" to make my graphs*4-5 artifacts, 4-5 specifications, last artifact a regression table (tables, charts)**something about data itself. pie chart (how many companies do we have, how many do we have ceo information for, how many do we have founder) and then relatedly many can we classify as men or women***this will influence all remaining graphs - use this info to guide the rest of your graphs***classified drs using their first name matched to common first name list, that's why we were able to classify some drs as women, some as men, and some drs but gender unknown**graph with time on x axis, % on y axis, women founders, women ceos, women management positions (management position being anything that is vp and above) over time***will write up these results-ie most of the women in "womens management positions" **conditional on having (ceo identified) how many women, how many men, drs, dr men, dr women, dr unknown. **bar graph with women in various industries, y axis will have percentage***should also make note of total percentage of VC dollars in each industry**regression table***variable names, with n and r^2, industry/year fixed effects Y/N***each one of these will correspond to a regression***variables: IPO, acquisition, exit all correspond to 0,1***rounds, dollars invested =Spring 2017===Current plan to complete project==#Lit Review (which will be compiled here [[Women in Entrepreneurship Lit Review]]). Keep track of the things that have already been done in other research reports, so that we can do something different/original or so we can prove their data wrong. Done 3/28.#Brainstorm ways to fix Dr. problem in data (assigning gender to those who are doctors), recode them. reran new tables with new coded doctors. done 4/4.#Think of new variables to add depending on the guidance from the lit review (might include adding different variables than solely womenceo/womenfounder but will depend on litreview). ##add control variable for the people in data who have no gender#I think from there I can do analysis and start to write report! ==For analysis== Note that:*finaldatasetcode.sql (in 181/Women) has been updated*hopethisworks5.txt (in both 181/Women and E/Women) has been created**Use this as your dataset now**It contains IPO, IPOyear, IPOAmount, MA, MAyear, MAAmount, and Exit**IPO, MA, and Exit are 0/1 variables. Year is an int, and Amount is a real.**Many MAs will not have amounts. Build graphs of:*Over time (last ten yrs)**percent of co's with woman ceo (binary)**percent of co's with woman founder (binary)**percent of co's with woman clevel (binary)**percent of co's with women in top management (vp and above)**average fraction of women founders**average fraction of women clevel**average fraction of women in top management (vp and above)*Some of the above by industry*Some of the above by state Regresions:*rounds womanvar w/controls*inv womanvar w/controls*exit womanvar w/controls*ipo womanvar w/controls*ma womanvar w/controls*ipoamount womanvar w/controls*maamount womanvar w/controls ==Call with SDC Platinum to determine information about the data==#How does Thompson get their data?##Why is coverage better for some firms than others?##Is the data self-reported by the companies?#How does Thompson upload executive data?##How often – after last round of funding?##After it’s uploaded, is the data updated continuously?#Better coverage for companies that get more rounds of financing/IPO/M&A? #VC data sourced from government filings, public press releases, and quarterly surveys of private equity firms. If company does not participate in survey, then SDC does not have the data, which is why coverage for some firms is better than others. #Deals team is the one that uploads all the data. As soon as SDC gets an update, or has a source that is updated, they automatically upload that to system as well. It typically takes 24-48 hours for new info to be reflected in database. #Yes, there is better coverage for companies that have IPO/M&A/get more rounds of financing. ==TO DO after 2/28==#Check cleancos and verify that it is actually clean (yes)#Make Dr. a control variable (yes)#Sum all c-level, etc.#aggregate to company level We need to build:*IPO 1/0*M&A 1/0*Number of rounds*Total invested*Is the CEO a woman 1/0*Are any of the founders women 1/0*Is CEO doctor 1/0*Number of founders*Number of founders that are doctors*Number of women founders*State - coded*Industry -code them!*Year of First investment -extracted Ed is going to add IPO and Acq to rc1andcp3 Here's the code to add zeros to cleanpeople: DROP TABLE cleanpeople2; CREATE TABLE cleanpeople2 AS SELECT prefix, firstname, lastname, jobtitle, fulltitle, cname, CASE WHEN genval IS NOT NULL THEN genval ELSE 0::int END AS women, CASE WHEN doctor IS NOT NULL THEN doctor ELSE 0::int END AS doctor, CASE WHEN charman IS NOT NULL THEN charman ELSE 0::int END AS charman, CASE WHEN ceo IS NOT NULL THEN ceo ELSE 0::int END AS ceo, CASE WHEN cfo IS NOT NULL THEN cfo ELSE 0::int END AS cfo, CASE WHEN coo IS NOT NULL THEN coo ELSE 0::int END AS coo, CASE WHEN cio IS NOT NULL THEN cio ELSE 0::int END AS cio, CASE WHEN cto IS NOT NULL THEN cto ELSE 0::int END AS cto, CASE WHEN otherclvl IS NOT NULL THEN otherclvl ELSE 0::int END AS otherclvl, CASE WHEN boardmember IS NOT NULL THEN boardmember ELSE 0::int END AS boardmember, CASE WHEN president IS NOT NULL THEN president ELSE 0::int END AS president, CASE WHEN vp IS NOT NULL THEN vp ELSE 0::int END AS vp, CASE WHEN founder IS NOT NULL THEN founder ELSE 0::int END AS founder, CASE WHEN director IS NOT NULL THEN director ELSE 0::int END AS director FROM cleanpeople WHERE jobtitle IS NOT NULL; ==TO DO up to 2/28== #Load##company##people##title lookup##state lookup#Refine people##gender 0/1##Join title lookup##Dr.'s?#Aggregate to company level (left join)##Agg. People##Join state lookup#Export! Done! Here is the code for this part of project: DROP TABLE titlelookup;CREATE TABLE titlelookup( fulltitle varchar(150), charman int, ceo int, cfo int, coo int, cio int, cto int, otherclvl int, boardmember int, president int, vp int, founder int, director int);\COPY titlelookup FROM 'Important Titles in Women2017 dataset.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV--628 DROP TABLE statelookup;CREATE TABLE statelookup( statename varchar(100), uniquecode int);\COPY statelookup FROM 'uniquestateval.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV--50 DROP TABLE people;CREATE TABLE people( prefix varchar(5), firstname varchar(50), lastname varchar(50), jobtitle varchar(150), cname varchar(150) );\COPY people FROM 'pull5-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV--186181 DROP TABLE cleancos;CREATE TABLE cleancos ASSELECT pull3info.*FROM pull3infoJOIN Cleancosbase ON pull3info.companyname=cleancosbase.companyname AND minfirstdate=firstinvestdate;--43534 DROP TABLE genvalpeople;CREATE TABLE genvalpeople ASSELECT *,CASE WHEN prefix='Ms' THEN 1::intWHEN prefix='Mrs' THEN 1::intWHEN prefix='Mr' THEN 0::intELSE Null::int END AS genval FROM people;--186181 DROP TABLE cleanpeople;CREATE TABLE cleanpeople AS SELECT genvalpeople.*, titlelookup.*FROM genvalpeople LEFT JOIN titlelookup ON genvalpeople.jobtitle=Issue Brieffulltitle;--186181 DROP TABLE uniquevalstate;CREATE TABLE uniquevalstate ASSELECT cleancos.*, statelookup.*|AudienceFROM cleancos LEFT JOIN statelookup ON cleancos.companystate=General Publicstatelookup.statename;--43534 DROP TABLE dataset;CREATE TABLE dataset ASSELECT cleanpeople.*, Policymakersuniquevalstate.*FROM cleanpeople LEFT JOIN uniquevalstate ON cleanpeople.cname=uniquevalstate.companyname;--186181
|Keywords=Women, Entrepreneurship\COPY people TO 'finaldataset.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV|Primary Billing=AccMcNair01}}--186181
[[Women in Entrepreneurship Lit Review]]==Paper==https://docs.google.com/document/d/1RHC0Namt9VpmUxINmRvHw4U34dDmgYPcUMbwhhthcCA/edit
=Spring 2017===To-doTimeline==
*Prior
**review/update lit review
**state fix-effects
*Policy
**Trump's proposed policy changes that will impact vc-backed companies, entrepreneurship/innovation in general, women in the workplace: research can be found at [[Trump and Women in Entrepreneurship]]***crowd-funding impact
*1/25 - SDC pull, rerun through tables w new added variables (i.e. industry, etc)*1/27 - read over new data and refine into something presentable, find appropriate parameters for issue brief, etc. maybe start making charts and graphs if time. @ this point check in w ed
*1/30 - finish rerun through tables w new added variables (i.e. industry, etc)*1/31 - read over new data and refine into something presentable, find appropriate parameters for issue brief, etc. maybe start making charts and graphs and chartsif time. @ this point check in w ed*2/1/31 - finish making graphs and charts, research trump policy and write it up*2/1 3 - start finalizing paper, putting all the pieces of the puzzle together, any additions or time to fix things that went off schedule*2/3 - , last edits, submit paper to anne for review
==SDC Search: Women2017=====pull 1===
*Database Selection
**VentureXpert
*Search Items:
**Date that received company received first investment: 01/01/1980 to 01/01/2017
**Moneytree Deals: Select All Moneytree Dealsventure-related deals
**Company Nation: U.S.
*Custom Report
**Company Founding Date
**Company IPO Date
**Date Received Last Investment
**Date Received First Investment
**Company Name
**Company State
**Total amt invested in company
**No. Financing Rounds Company Received
**Company Stage Level 1 at Round Date
**Company Stage Level 2 at Round Date
**Company Stage Level 3 at Round Date
**Executive's First Name
**Executive's Last Name
**Executive's Job Title
**Executive's Prefix
**VE Primary Industry Sub-Group 3
**VE Primary Industry Minor Group
**VE Primary Industry Sub-Group 1
**Company IPO
**Company Website
*Saved in 182 in mcnair/projects/women2017 as "pull1"
===pull 3===
*Database Selection
**VentureXpert
**Portfolio Companies
*Search Items:
**Date that received company received first investment: 01/01/1980 to 01/01/2017
**Venture related deals - select all venture related deals
**Company Nation: U.S.
*custom report
*company founding date
*company ipo date
*date company received last investment
*date company received first investment
*company staet
*company name
*company industry class
*company industry major group
*company industry subgroup 1
*company industry subgroup 3
*Company industry minor group
*company cusip
*total known amt invested in company
*no rouds company received investment
*company ipo status
*company website
*saved in 182 in mcnair/projects/women2017 as "pull3"
===pull 4===
*Database Selection
**VentureXpert
**Portfolio Companies
*Search Items:
**Date that received company received first investment: 01/01/1980 to 01/01/2017
**Venture-related deals
**Company Nation: U.S.
*custom report
*company name
*executive's name prefix
*executive's first name
*executive's last name
*executive's job title
*saved in 182 in mcnair/projects/women2017 as "pull4"
===pull 5===
*Database Selection
**VentureXpert
**Portfolio Companies
*Search Items:
**Date that received company received first investment: 01/01/1980 to 01/01/2017
**Venture-related deals
**Company Nation: U.S.
*custom report
*executive's name prefix
*executive's first name
*executive's last name
*executive's job title
*company name
*saved in 182 in mcnair/projects/women2017 as "pull5"
===pull 6===
*Database Selection
**VentureXpert
**Portfolio Companies
*Search Items:
**Date that received company received first investment: 01/01/1980 to 01/01/2017
**venture-related deals
**Company Nation: U.S.
*Custom Report
*executive's name prefix
*executive's first name
*executive's last name
*executive's job title
*company name
*company city
*company state
===pull 7===
*Database Selection
**VentureXpert
**Portfolio Companies
*Search Items:
**Date that received company received first investment: 01/01/1980 to 01/01/2017
**venture-related deals
**Company Nation: U.S.
*Custom Report
*executive's name prefix
*executive's first name
*executive's last name
*executive's job title
*company name
*Company ve primary industry major group
*Company ve primary industry minor group
*Company ve primary industry sub group 1
*Company ve primary industry sub group 2
*Company ve primary industry sub group 3
==What I have done up to 2/7/17 (in english, not sql)==
I have done many SDC pulls with many different variables. The most important tables are nowomenstate, nowomencity, nowomenminorind, and nowomensub2. nowomenstate shows the number of women in "power positions" in each state as a percentage of all power position workers. nowomencity does the same thing at a city level. nowomenminorind shows the number of women in power positions grouped by their company's minor industry group. nowomensub2 does the same except grouped by the company's industry sub group 2.
==Tables built up to 02/07/17==
SDC pull #1
company founding date
company ipo date
date company received last investment
date company received first investment
company staet
company name
company industry class
company industry major group
company industry subgroup 1
company industry subgroup 3
Company industry minor group
company cusip
ttoal known amt invested in company
no rouds company received investment
company ipo status
company website
SDC pull #2
company name
executive's name prefix
executive's first name
executive's last name
executive's job title
logging on:
researcher@128.42.44.181
password: 9million
cd /bulk
cd \Women2017
psql Women2017
DROP TABLE pull3info;
CREATE TABLE pull3info(
/*taken from pull3.txt*/
ipodate date,
lastinvestdate date,
firstinvestdate date,
companyfoundingdate date,
ipostatus varchar(10),
companystate varchar(50),
companyname varchar(200),
industryclass varchar(200),
majorgroup varchar(200),
minorgroup varchar(200),
subgroup1 varchar(200),
subgroup3 varchar(200),
cusip varchar(6),
totalinvested float,
numrounds int,
website varchar(200)
);
\COPY pull3info FROM 'pull3-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV
--COPY 43606
DROP TABLE cleancosbase;
CREATE TABLE cleancosbase AS
SELECT companyname, min(firstinvestdate) as minfirstdate FROM pull3info WHERE companyname !='Undisclosed Company' OR companyname !='New York Digital Health LLC' GROUP BY companyname;
--43534
DROP TABLE cleancos;
CREATE TABLE cleancos AS
SELECT pull3info.*
FROM pull3info
JOIN Cleancosbase ON pull3info.companyname=cleancosbase.companyname AND minfirstdate=firstinvestdate;
--43534
DROP TABLE pull4info;
CREATE TABLE pull4info(
/*taken from pull4.txt*/
execfirstname varchar(50),
jobtitle varchar(100),
execlastname varchar(50),
execprefix varchar(5),
companyname varchar(200)
);
\COPY pull4info FROM 'pull4-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV
/*the job title column of the exec is for some reason combined with the first name
even after normalizing the data. I will repull the data from SDC to see if it was
simply an error in the pull or something. If that doesn't work, will go to ed.
*/
SDC pull #5
executive's name prefix
executive's first name
executive's last name
executive's job title
company name
DROP TABLE pull5info;
CREATE TABLE pull5info(
/*taken from pull5.txt*/
execprefix varchar(5),
execfirstname varchar(50),
execlastname varchar(50),
jobtitle varchar(100),
companyname varchar(200)
);
\COPY pull5info FROM 'pull5-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV
--COPY 186181
DROP TABLE distinctnocompanies;
CREATE TABLE distinctcompanies AS
SELECT DISTINCT companyname FROM pull3info;
/*created this per ed's advice to see how many companies are distinct from each other
so we dont get duplicates in the dataset - only really creating a table to get the
specific number*/
--COPY 43536
/*this means there are 70 duplicates in the data. could mean that some companies in diff
states have the same name, or it could be a for real duplicate. can go back and check
on this later but rn seems like a relatively nominal amt.*/
DROP TABLE totaltable;
CREATE TABLE totaltable AS
SELECT * FROM pull5info LEFT JOIN pull3info ON pull5info.CompanyName = pull3info.companyname;
/*represents the table combining pull3info and pull5info, aka all of the info about
the companies and the executives' info matched on company name*/
/*WHERE IM AT:
START GROUPING BY WOMEN IN pull3info
ed says to rename pull3info but idc like i know what it is
*/
DROP TABLE commonnames;
CREATE TABLE commonnames(
/*common male and female names with corresponding 0 or 1*/
name varchar(100),
mf int
);
DROP TABLE commonnamesclean;
CREATE TABLE commonnamesclean AS
SELECT name, sum(mf) AS mf FROM commonnames GROUP BY name HAVING count(mf) = 1;
\COPY commonnames FROM 'realcommonnames.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV
--4654
DROP TABLE impoppl;
CREATE TABLE impoppl AS
/*selecting the people with important job titles*/
SELECT * FROM pull5info WHERE jobtitle IN ('Board Member', 'CEO', 'Founder', 'CFO',