Context Jobs, incomes and trade are hotly-debated topics each election cycle, and 2016 was certainly no exception. Coal miners, Carrier and even... Canada (TPP, NAFTA)... had their moments in the 2016 campaign spotlight. The enduring phrase coined by James Carville to explain all electoral outcomes seems accurate: "*It's the economy, stupid.*" Although the election has already received plenty of attention from data analysts over the past 18 months, I thought I would match electoral results with some standard economic data -- and try to identify any *stupidly* obvious patterns in the vote. If nothing else, I thought it would be good practice for some recently acquired Python skills and (hopefully) a worthy first contribution to the Kaggle platform. Content This is county-level electoral and economic data for select years, pieced together from a few different sources. Visit [Tony McGovern's GitHub][1] for election result data, and please read this [post by Simon Rogers][2] for more information on the data collection process. - **fips_code** - the county's Federal Information Processing Standards (FIPS) code - **county** - county name, state - **st** - two-letter state abbreviation (District of Columbia treated as a "state") - **total_ , dem_ , gop_ , oth_***YYYY* * *Votes received by all, democratic, republican, and third party (other) presidential candidates in YYYY - 2008, 2012, and 2016; Alaska data not included for 2016. The Bureau of Labor Statistics publishes labor force and unemployment statistics by county and by year in their [Local Area Unemployment Statistics (LAUS)][3]. Wages by year by all geographic areas and industries can be found [here][4] (with a helpful explanation of sectors [here][5]). - **labor_force_***YY* - average annual jobs reported ('13, '16 - first and last year of President Obama's 2nd term) - **u_rate_***YY* - average annual unemployment rate ("") - **avg_total_wages_***YY* - average annual wage - all industries ("") - **avg_pm_wages_***YY* - average annual wage - private manufacturing ("") The International Trade Administration's [Metropolitan Export Series][6] contains merchandise trade exports to the world for U.S. Metropolitan Statistical Areas (MSAs). These are the counties comprising top 50 exporting metropolitan areas (2016). - **exports_*YYYY*** - total annual exports (2012 - 2016) - **pop_*YYYY*** - population ("") Population estimates by county came from the [U.S. Census Bureau][7]. Note: I only collected population data for counties in ITA's MSAs. This means roughly 2700 of the 3000+ counties in the data set do not have population data. In hindsight, this wasn't ideal, but it doesn't preclude analysis. [1]: https://github.com/tonmcg/County_Level_Election_Results_12-16 [2]: https://simonrogers.net/2016/11/16/us-election-2016-how-to-download-county-level-results-data/ [3]: https://www.bls.gov/lau/#tables [4]: https://data.bls.gov/cew/apps/data_views/data_views.htm#tab=Tables [5]: https://www.bls.gov/iag/tgs/iag_index_naics.htm [6]: https://www.trade.gov/mas/ian/metroreport/index.asp [7]: https://www.census.gov/data/datasets/2016/demo/popest/counties-detail.html#ds