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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*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