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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== 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:# Pre-process the data. ## Keep only fips, yeardata, govid_14, name, STATE, mayor_party_final, runnerup_party_final, mayor_votes_final, runnerup_votes_final, mayor_name_final, elecdate, month, ID, FIPS_PLACE_ID, term_length, term_limit, demshare, JURIS## Remove double-quotes, remove NA, force to UTF8## Remove duplicate entries (see below)# Load the data# prepend zeros to FIPS# Code mayor_party_final: Variable is DorR, which takes 1 if D, 0 if R and NULL otherwise.# Blowout years# Join by GEOID, year There were a small number of erroneous records, which caused duplication at the FIPS-Yeardata level. I manually removed these. For future reference they are (note that geoid is fips with pre-pended zeros where applicable): SELECT geoid, yeardata, count(*) FROM mayoralelectionsclean GROUP BY geoid, yeardata HAVING count(*) >1; 3956882 2005 2 3916000 2013 2 1882862 2003 2 5323515 2010 4 1842246 2003 2 4824000 2013 2 0973770 2003 2 2567000 1973 2 1710487 2003 2 1255775 2004 2 1840788 2003 2 0952980 2003 2 3539380 2003 2 0943370 2003 2 2622000 2009 2 Using this approach I have data for some years for 109 cities in my data, and 83% of the city-year records have democrats or republicans as the winners. It's good enough to try!