Difference between revisions of "Determinants of Future Investment in U.S. Startup Cities"
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− | This paper was originally the empirical component of [[Measuring High-Growth High-Technology Entrepreneurship Ecosystems]] | + | ==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 | ||
+ | *Ballotopia has current mayors for top 100 cities: https://ballotpedia.org/List_of_current_mayors_of_the_top_100_cities_in_the_United_States | ||
+ | *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, | ||
+ | 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)
- 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
- Ballotopia has current mayors for top 100 cities: https://ballotpedia.org/List_of_current_mayors_of_the_top_100_cities_in_the_United_States
- 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, 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:
- Load the data
- prepend zeros to FIPS
- Code mayor_party_final
- Blowout years
- Join by GEOID, year