Difference between revisions of "Determinants of Future Investment in U.S. Startup Cities"

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(Created page with "{{AcademicPaper |Has title=Determinants of Future Investment in U.S. Startup Cities |Has author=Ed Egan, |Has paper status=In development }} ==Notice== This paper was origi...")
 
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==Notice==
 
  
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This paper was originally the empirical component of [[Measuring High-Growth High-Technology Entrepreneurship Ecosystems]]
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==Notice(s)==
 +
 
 +
#This paper was originally the empirical component of [[Measuring High-Growth High-Technology Entrepreneurship Ecosystems]].
 +
#This is a working title only. The Measuring paper references it under this name for now.
 +
 
 +
===Instrument for Determinants===
 +
 
 +
I am going to try shocking the number of ESOs using the political party of the incumbent mayor. Data is available from:
 +
*https://libguides.princeton.edu/elections#s-lg-box-10082744
 +
**https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SJBWC3
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*Ballotopia has current mayors for top 100 cities: https://ballotpedia.org/List_of_current_mayors_of_the_top_100_cities_in_the_United_States
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*https://doi.org/10.7910/DVN/KFIKH8 -- downloaded mayoralelections_final.tab but it seems a subset of the above
 +
*Could scrape and process https://www.usmayors.org/elections/election-results/ but it doesn't have parties and would need to be geomatched
 +
 
 +
By far the best data is:
 +
 
 +
@data{DVN/SJBWC3_2017,
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  author = {de Benedictis-Kessner, Justin},
 +
  publisher = {Harvard Dataverse},
 +
  title = {{Replication Data for: ``Off-Cycle and Out of Office: Election Timing and the Incumbency Advantage''}},
 +
  UNF = {UNF:6:4fmCzYs43mFR+VunIFHyOg==},
 +
  year = {2017},
 +
  version = {V1},
 +
  doi = {10.7910/DVN/SJBWC3},
 +
  url = {https://doi.org/10.7910/DVN/SJBWC3}
 +
}
 +
 
 +
I got it from: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SJBWC3 . It covers mayoral elections for 1945 to 2014. The description is as follows:
 +
 
 +
''Data on 9,131 mayoral elections in which approx. 10,000 unique candidates ran in 1,016 cities of all sizes, 1950-2014. Replication data for Justin de Benedictis-Kessner, "Off-Cycle and Out of Office: Election Timing and the Incumbency Advantage," The Journal of Politics 80, no. 1 (January 2018): 119-132''
 +
 
 +
For me the useful variables are:
 +
*FIPS: 6 or 7dg (107000 BIRMINGHAM CITY, AL to 5613900 CHEYENNE CITY, WY)
 +
*year (non-continuous -- election years)
 +
*mayor_party_final (D,R,NA,NP,0 [missing?])
 +
*month
 +
*FIPS_Place_ID: FIPS_Place_ID is 4 to 5dg (7000 to 13900)
 +
 
 +
If we prepend leading zeros for 6dg, FIPS matches GEOID (used in vcdb4) 0107000 Birmingham, AL and 5613900 Cheyenne, WY. Alternatively, again prepending leading zeros, FIPS_Place_ID matches placefp (5dg) in the TigerPlaces table 07000 Burmingham, AL and 13900 Cheyenne, WY.
 +
 
 +
The code to handle the load and processing into vcdb4 is in E:\projects\MeasuringHGHTEcosystems\MayoralElections.sql
 +
 
 +
The main steps are:
 +
# Load the data
 +
# prepend zeros to FIPS
 +
# Code mayor_party_final
 +
# Blowout years
 +
# Join by GEOID, year

Revision as of 15:32, 29 May 2020

Academic Paper
Title Determinants of Future Investment in U.S. Startup Cities
Author Ed Egan
Status In development
© edegan.com, 2016


Notice(s)

  1. This paper was originally the empirical component of Measuring High-Growth High-Technology Entrepreneurship Ecosystems.
  2. This is a working title only. The Measuring paper references it under this name for now.

Instrument for Determinants

I am going to try shocking the number of ESOs using the political party of the incumbent mayor. Data is available from:

By far the best data is: 

@data{DVN/SJBWC3_2017,
 author = {de Benedictis-Kessner, Justin},
 publisher = {Harvard Dataverse},
 title = Template:Replication Data for: ``Off-Cycle and Out of Office: Election Timing and the Incumbency Advantage'',
 UNF = {UNF:6:4fmCzYs43mFR+VunIFHyOg==},
 year = {2017},
 version = {V1},
 doi = {10.7910/DVN/SJBWC3},
 url = {https://doi.org/10.7910/DVN/SJBWC3}
}

I got it from: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SJBWC3 . It covers mayoral elections for 1945 to 2014. The description is as follows:

Data on 9,131 mayoral elections in which approx. 10,000 unique candidates ran in 1,016 cities of all sizes, 1950-2014. Replication data for Justin de Benedictis-Kessner, "Off-Cycle and Out of Office: Election Timing and the Incumbency Advantage," The Journal of Politics 80, no. 1 (January 2018): 119-132

For me the useful variables are:

  • FIPS: 6 or 7dg (107000 BIRMINGHAM CITY, AL to 5613900 CHEYENNE CITY, WY)
  • year (non-continuous -- election years)
  • mayor_party_final (D,R,NA,NP,0 [missing?])
  • month
  • FIPS_Place_ID: FIPS_Place_ID is 4 to 5dg (7000 to 13900)

If we prepend leading zeros for 6dg, FIPS matches GEOID (used in vcdb4) 0107000 Birmingham, AL and 5613900 Cheyenne, WY. Alternatively, again prepending leading zeros, FIPS_Place_ID matches placefp (5dg) in the TigerPlaces table 07000 Burmingham, AL and 13900 Cheyenne, WY.

The code to handle the load and processing into vcdb4 is in E:\projects\MeasuringHGHTEcosystems\MayoralElections.sql

The main steps are:

  1. Load the data
  2. prepend zeros to FIPS
  3. Code mayor_party_final
  4. Blowout years
  5. Join by GEOID, year
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