Determinants of Precinct-Level Voting in the 2008–2016 American Presidential Elections∗ Ryne Rohla‡ September 20, 2018

Abstract This paper uses the first national, multi-year, geocoded precinct-level dataset to measure changes in turnout and partisan support by race and education level in three recent presidential elections. After dasymmetrically matching precinct geographies to demographic data, ecological inference techniques demonstrate widening racial and education-based polarization. Race estimates vary with assumed spatial heterogeneity level, but may suggest less initial racial sorting than commonly believed, especially for black voters. Counterfactuals reveal changing subgroup partisanship drove the 2016 outcome more than differential turnout. Regression analyses decompose changes in turnout and partisan support between cycles to suggest possible motivations, finding declining importance of economic characteristics in favor of identity-related measures. Last, an instrumental variables analysis explores causal effects of the fracking boom on local voting, finding support for retrospective voting. Groups benefiting from expanded resource extraction increased turnout and Republican support while opposing groups—Native Americans and graduate degrees holders—may have become more Democratic when exposed to local fracking utilization.

Keywords: Elections, precinct data, ecological inference, retrospective voting

‡Washington State University, School of Economic Sciences, 101 Hulbert Hall, Pullman, WA 99164. Email: [email protected]. ∗Acknowledgments: I would like to thank Gregmar Galinato, Benjamin Cowan, M. Keith Chen, Matthew Birch, Anthony Delmond, Eric Dunaway, Christopher Clarke, and Casey Bolt for their com- ments and suggestions with regard to the writing of this paper. I would also like to thank John Miles Coleman, Brandon Finnigan and Decision Desk HQ, Derek Norris and OpenElections, David Bradlee, Michael McDonald, Aaron Bycoffe and FiveThirtyEight, Patrick Ruffini, Phillip Bump, Nate Cohn, Tom Giratikanon, Benjamin Anderstone, Kevin Rancik, the Harvard Election Data Archive, the University of California Berkeley’s Statewide Database, Dave Leip’s U.S. Election Atlas, and countless election and GIS officials for their assistance in collecting the election data anchoring this paper. An additional thank you to Mark Nibbelink and DrillingInfo for access to their proprietary well data. 1 Introduction

The 2016 presidential election outcome came as a shock to most political scientists due to its deviation from pre-election state polls and projections. Subsequent analyses have relied upon aggregated result data or post-election surveys, each with methodological shortcomings. A comprehensive analysis based on higher quality data and within the context of prior elections may provide a clearer picture of an event many fail to fully understand. American election research often focuses on county-level outcomes due to data availability. County-level data aggregates away variation in dense urban environments while over-representing sparse rural regions, leading to aggregation bias in estimates of individual beliefs and behavior. Sub-county “precinct” data opens more precise research opportunities by utilizing the most exact, encompassing, and representative data possible. This paper uses the first ever nationwide multi-election precinct dataset to answer how turnout and partisan support changed by race and education between the 2008, 2012, and 2016 presidential elections and probe possible reasonings for these changes. This novel dataset permits stronger statistical techniques than other studies and helps pinpoints changes with spatial granularity and statistical power previously impossible. In doing so, this paper will contribute to a wide spectrum of literature on American politics. The paper’s analysis be accomplished in three ways. First, a battery of ecological inference techniques will be employed to isolate effects of differential turnout and partisan support patterns. Second, plausible motivations for these changes will be inferred through regression analyses using economic and social covariates. Finally, causal estimates of effects from geography-specific exogenous shocks will be explored through a case study of the “fracking” boom. These analyses were chosen to exploit the unique structure and richness of precinct data and illustrate how further research could benefit from this dataset. The preponderance of voting determinant literature relies on either county-level data or post-election individual-level surveys. Numerous county-level studies document correlations with demographics and socioeconomic factors in nationwide voting behavior

1 (Mas and Moretti, 2009; Hawley, 2011; Warf, 2011; Bor, 2017; Scala and Johnson, 2017). More unique county-level analyses investigate impacts on voting from right-to- work laws (Feigenbaum, Hertel-Fernandez, and Williamson, 2018), newspaper entry and exit (Gentzkow, Shapiro, and Sinkinson, 2011), Chinese trade exposure (Autor et al., 2016), historical lynchings (Williams, 2018), and election day weather (Gomez, Hanford, and Krause, 2007). These studies could be made more powerful by the use of precinct- level data given the increasing availability of large, geocoded datasets. Literature which does access precinct-level data utilizes limited geographies such as sets of municipalities or counties (Ferreira and Gyourko, 2009; Rutchick, 2010; Augenblick and Nicholson, 2016), a single state or select groups of states (DellaVigna and Kaplan, 2007; Brunner, Ross, and Washington, 2011; Gerber, Kessler, and Meredith, 2011) or a single time period (Hersh and Nall, 2016; Martin and Yurukoglu, 2017). This limitation arises from substantial barriers to precinct data access. Given that voting is habit-forming in both turnout (Gerber et al., 2003; Fujiwara et al., 2016) and partisanship (Shachar, 2003), changes in voting behavior between elections best identify electoral responses to external stimuli. Analyzing levels rather than changes cannot discern relationships idiosyncratic to any given election and may distort individual preferences for candidate attributes. Voting exhibits two core responses to external shocks: changes in turnout and a “swing” toward one party or another, both of which may aﬀect the level of two-party polarization. Changes in polarization level are especially important to individual social and economic behavior. Political polarization aﬀects where individuals work and shop more strongly than race or religion (McConnell et al., 2018), how long we spend with relatives at family gatherings (Chen and Rohla, 2018), who individuals date and marry (Alford, Hatemi, and Hibbing 2011), and how willing we are to express our true preferences (Perez-Truglia and Cruces, 2017) including donating to campaigns (Perez-Truglia, 2018). Shocks in highly polarized environments may radically change our self-image, in- group and out-group dynamics, and how individuals treat others on a daily basis (Oc, Moore, and Bashshur, 2018).

2 Election results aggregate individual preferences and gauge reactions to the events and policies which precede them, but imperfectly communicate crucial individual- and subgroup-level information. Ecological inference techniques have attempted to recover lost information—such as the overall partisan preferences of each racial group—beginning with Goodman (1953). A flurry of statistical techniques were developed from the late- 1980s through the early-2000s, but stalled partially due to a lack of high quality data to which the techniques could be applied. While each technique relies on varying but strong assumptions (Gelman et al., 2001) and no universally agreed upon method of model validation exists, the identification of subgroup voting behavior remains vital to legal issues.1 This paper applies ecological inference to the two key demographic cleavages most responsible for recent electoral changes: race and education level. Changes in ecological estimates of subgroup voting patterns can be decomposed by regression analysis to explore potential reasons why groups may have changed voting behavior between cycles. This is frequently done in a descriptive and non-causal way due to extensive endogeneity concerns. Causal estimation of changes in voting behavior tend to be possible only for plausibly exogenous shocks or discrete policy changes. The fracking boom is one such shock. Evaluating localized, geography-specific shocks to voting behavior sees applications in several fields. Urban economics has the “homevoter hypothesis” wherein proposed local projects affecting property values directly impact voting behavior.2 An analogous literature spanning both political science and economics studies “retrospective voting” based on micro-level factors. While general economic conditions have long been known to correlate with voting behavior (Fiorina, 1978), retrospective voting literature has recently delved into impacts from localized policies changes such as wind turbine installation (Stokes, 2016), interstate highway expansion (Nall, 2013), and local infrastructure degra-

1Legal issues such as legislative district pre-clearance under the Voting Rights Act of 1965 and whether redistricting schema constitute undue bias toward minority groups. This paper’s precinct-level dataset has wide applicability to a range of redistricting-related topics and projects. 2Papers utilizing local election data have analyzed sports venues (Coates and Humphreys, 2006; Dehring, Depken, and Ward, 2008), airports (Ahlfeldt and Maennig, 2015), new housing development (Kahn, 2011), and school voucher programs (Brunner, Sonstelie, and Thayer, 2001), generally ﬁnding support for the hypothesis.

3 dation (Burnett and Kogan, 2015), along with with effects from purely exogenous shocks such as property damage from extreme weather events (Healy and Malhotra, 2010; Cole, Healy, and Werker, 2012; Chen, 2013), and spatially-clustered lottery winnings (Bagues and Esteve-Volart, 2016). This paper’s analysis of precinct results first reveals widening variance between each election and increasing gaps between median and mean partisan outcomes. Ecologi- cal inference techniques show wide variation in estimates of racial voting by the spatial heterogeneity level assumed, while education-based estimates show less variance. The benchmark spatial King-Rosen model with geographically-weighted regression covariate suggests turnout rates for minorities peaked in 2008 before declining in 2012 and 2016, with the exception of Hispanics, while white voters have increased turnout rates. This model also implies racial voting patterns were more polarized in 2016, but that voters may not be as racially polarized as commonly believed and found in exit polls, in partic- ular black voters. Education level results show increasing polarization of the electorate between those with and without a college degree. Next, the paper uses regression analysis to decompose the support structures of candidates across time and isolate possible explanations for why turnout and partisan support patterns have changed. First, results suggest a shift in deterministic power away from economic variables such as income, unemployment, and income inequality toward identity-related variables such as race, country of birth, and education. Second, analysis of turnout changes between 2012 and 2016 suggest minorities and college graduates turned out more heavily in well-off precincts with large foreign-born populations while high school graduate turnout surged in working class precincts with high manufacturing, construction, and natural resource-based economies. Black turnout slumped in cities while Hispanic turnout surged in urban areas. Third, white voters became more Republi- can in 2016 in less educated and economically tepid precincts while diversity and wealth promoted more white Clinton support. Hispanics and Asians, however, became more Republican in the presence of high rates of foreign born individuals. Last, the paper uses an instrumental variable approach to estimate causal effects of

4 the fracking boom on changes in turnout and partisan support between each election. Re- sults of this method show strong retrospective voting tendencies, with voters most likely to benefit from the fracking boom increasing turnout and Republican support. Groups likely opposed or harmed by increased fracking production such as Native Americans and graduate degree holders show some evidence of increased Democratic support. An aggregate analysis of the data implies pro-Republican effects most notable in Pennsylvania, where the cumulative effect of eight years of fracking additions may have been sufficient to account for Trump’s entire vote margin.

2 Data

2.1 Precinct Data Background and Collection

Every county in the United States is subdivided into one or more geographical units for the purposes of election administration, voter registration, and redistricting higher-level legislative districts3 which are commonly referred to as “precincts.”4 Election precincts do not generally conform to other geographies such as census blocks, zip codes, and municipalities.56 State-level election oﬃcials may aggregate precinct-level election results and geographies but often do not. On a national level, the only election with a publicly-available set of practicably-compelete results and geographies is the 2008 presidential election. This set is maintained by the Harvard Election Data Archive and was accomplished with the assistance of the Census Bureau collecting and releasing Voting Tabulation District (VTD)

3The exception being Alaska, where precincts are nested within State House Districts and may cross borough boundaries. Delaware and Hawaii also nest precincts within State House Districts, but these districts tend to also nest within counties or be sub-precincted by the intersection of these districts and counties. A further exception is Kalawao County, Hawaii, which sees its entire election process administered by neighboring Maui County; Kalawao nevertheless typically maintains a separate precinct. 4They have alternate oﬃcial names in some states, such as “election districts” in New Jersey and New York and “wards” in Wisconsin. 5With the exception of being nested within townships in New England and parts of the Northeast and Midwest. 6Most precincts, unlike counties, maintain approximate population equality within a state, meaning the density of precincts follows population density as of the most recent redrawing of precinct lines. Precincts vary in size from a few city blocks in Manhattan to hundreds of square miles in the rural Mountain West. Precincts may have one unique polling place assigned, may share with neighboring precincts, or may not relate to polling places in vote-by-mail states.

5 shapefiles, which approximate precincts, in conjunction with the 2010 Decennial Census.7 Data for 2008 for this paper was drawn from this source and from Dave Bradlee’s Redis- tricting App, which supplements Census geographies with census block group estimates for the 2008 election in these three states. Precinct-level results for the 2012 and 2016 presidential elections were hand-collected for this paper through an extensive process of web-scraping and contacting state Secre- taries of State, Boards of Election, and other statewide and county-wide electoral authorities. For states which do not compile precinct-level election result data, individual county clerks were contacted by email, phone, fax, or in person. These precinct-level votes were mapped to precinct polygonal shapefiles using Geo- graphic Information Systems (GIS) software.8 This process was iteratively updated to be as complete as practicable given the author’s time, manpower, and financial limitations. The resultant dataset covers over 99% of votes in more than 173,000 precincts in 99.9% of counties in each of these two elections.9 The results of these three elections were merged with each other and with demographic data from the 2005-2009, 2008-2012, and 2012-2016 American Community Surveys10 and 2010 Decennial Census through dasymetric re-aggregation. Dasymetric mapping substantially reduces re-aggregation error compared to inclusion-exclusion techniques such as centroid containment or naive re-aggregation algorithms such as areal interpolation (Zandbergen and Ignizio, 2010).11 This methodology closely approximates characteristics of the current precincts across time and, as most of the re-aggregated characteristics

7Even the Census’s collection is incomplete as Census files do not contain precinct geographies for all or large portions of Montana, Oregon, and Rhode Island. 8Shapefiles for twenty-four states were obtaining in whole from state authorities or academic institu- tions. The remaining states required substantial modification from out-of-date shapefiles or wholesale creation by hand. This process often started with state-level 2010 VTD shapefiles which were then extensively updated with county-level files from local authorities or through hand modification and dig- itization of paper or electronic static map files. Many precinct boundary lines varied between 2012 and 2016 and had to be modified separately. Some minor township-level aggregation was required in the Northeast and Midwest. 9Further information of the gaps in coverage can be found in Section 2.2. 10These were the closest datasets available to the three elections at the time of writing. 11Using the 2016 precinct boundaries as a base, these datasets were first mapped in their native geographies, then projected onto census block centroids. These census block-projections were then weighted according to their population as of the 2010 Decennial Census and summed up to the 2016 precinct polygons.

6 are rates or proportions, minimally distorts re-aggregated values. With the re-aggregated 2008 and 2012 results, we can deﬁne “swing”12, a measure of the change in voting outcomes from an election in time t − 1 to t in 2016 precinct i, as

1 Di,t − Ri,t Di,t−1 − Ri,t−1 Si,t = − (1) 2 Di,t + Ri,t + Oi,t Di,t−1 + Ri,t−1 + Oi,t−1

where Dt, Rt, and Ot represent the total votes for the Democratic candidate, Republican candidate, and all other candidates, respectively. This measure is arbitrarily deﬁned such that an increase in the percentage margin of victory for the Democratic candidate increases the swing and vice versa. Figure 1a maps the swing in precinct-level voting between the 2012 and 2016 presidential elections while Figure 1b does the same for the 2008 and 2012 elections, with all data re-aggregated to 2016 boundaries. The 2012-16 swing map exhibits a strong Republican swing in much of the rural Midwest and Northeast, with dark blue Democratic swings in the Mormon Corridor and in the suburbs of large cities. The 2008-2012 swing map sees fewer strong swings, with the largest pro-Republican swings occurring in coal mining regions in Appalachia and southern Illinois and Indiana, in Utah, and in upper regions of the Mountain West. The most visible Democratic swings in the latter map occur in western Indian reservations, the southern Black Belt, the Rio Grande Valley, lower Ohio, and upper New York.

2.2 Precinct Data Limitations

There are three limiting factors in the dataset for the 2012 and 2016 presidential elections. First, there exist several classes of votes which may not be assigned to sub-county geographies by local election oﬃcials. Ballots which were cast as absentee, provisionally, prior to election day, by overseas citizens, by members of the armed forces, or by individuals who changed residences close to an election may not be assigned to a precinct but simply generalized to the county level. This non-assignment tends to be uniform within a state

12This deﬁnition of swing is commonly used in international elections, but less often in American elections.

7 but not between states. These types of ballots may comprise a large share of the vote in certain circumstances, and these votes often diﬀer in composition and candidate share from other votes within a county. As shown in Table1, these ballot types comprised approximately 3% of all votes cast in both 2012 and 2016. The unallocable vote types tended to be 3-6 points more Democratic-leaning than allocable votes. Second, precinct geographies were not obtainable for two rural counties: Lake County, Oregon and Walworth County, South Dakota. An additional seven rural counties in Arkansas, Alabama, and Kentucky did not readily release precinct-level votes in 2012, but did in 2016. These counties were aggregated, and precinct-level swings were assigned on the basis of county swings. Last, a small portion of votes were unable to be allocated to geographies due to author limitations in obtaining precinct shapeﬁles with the latest divisions of previous precincts.13 In most cases, when precincts are added, these new precincts exist as subsets of previous precincts; when the exact nature of this division could not be discerned, the new precinct may have been omitted. This error accounts for 0.3% and 0.7% of all votes in the 2016 and 2012 elections, respectively. These votes tended to lie in Republican-leaning rural areas which swung toward Trump relative to Romney. Figure A1a maps the total unallocable absentees and related vote types by county, averaged between 2012 and 2016. Idaho, Louisiana, Maryland, New Jersey, South Car- olina, and Virginia were the only states where more than 10% of total votes were of this type. Figure A1b maps the average omitted vote rate by county for the two elections. The largest errors lie in select counties in Alabama and West Virginia. In Alabama, 4-5% of all votes were omitted, while 4% of West Virginia votes were omitted. Approx- imately 80% of counties in both years were completely error-free, and 93% of counties were missing no more than 1% of votes.

13Control over drawing precinct boundaries falls to county or other local election officials under general guidelines put forth by state governments; boundaries are renamed, redrawn, and renumbered both following decennial censuses and in the intercensal period. This redrawing process is often only publicized locally, and up-to-date maps and election result data may be very difficult to obtain for many counties. Difficulties may include opaque and varying responsibilities for election management, frequent turnover in local election officials, frequently obsolete contact information, fees for data, lengthy Freedom of Information Act requirements, opaquely differing housing of maps and GIS data apart from election officials, lack of digitized data, and lack communication and transmission faculties.

8 Despite these limitations, both the 2012 and 2016 datasets include 96.7% of all votes cast, and the summed totals closely align with actual national averages. This fact, combined with their limited scale, assuages most concerns about data quality. It may still be possible for these limitations to bias estimates, but their systematic nature may be accounted for through empirical techniques in later sections.

2.3 Fracking Background and Data

Hydraulic fracturing, commonly known as “fracking”, is a technique using non-vertical or “directional” drilling and subterranean pressurized fluid injection to extract otherwise inaccessible hydrocarbon deposits such as shale and tight oil plays. The technology and economic conditions necessary for fracking’s profitability arose rapidly during the mid-2000s. As shown in Figure2, the number of directional wells in the United States increased by 32% from 1996 to 2000 and by 39% from 2000 to 2004 before rising to a 67% growth rate between 2004 and 2008, 65% between 2008 and 2012, and 41% between 2012 and 2016. The overall number of such wells climbed from 47,127 in 2000 to 270,511 in 2017. The geographic extent of growth in fracking-related wells is directly limited by the exogenous location of shale plays which benefit from this extraction technique. The most notable region affected by the fracking boom is the Bakken Formation in western North Dakota and eastern Montana which received a large surge of in-migration during this period, although other regions were also affected. Feyrer, Mansur, and Sacerdote (2017) document large and persistent wage and job gains in regions affected, leading to substantial wage migration (Wilson, 2016) and an increase in birth rates but not marriage rates (Kearney and Wilson, 2018). Montana and North Dakota made large swings to the right in the 2012 and 2016 presidential elections, which might be expected given an influx of workers in the natural resource extraction industry, traditionally aligned with the Republican Party. However, these swings were not constrained to areas within the Bakken Formation at the county level, and environmental issues and Native American land rights issues have arisen in

9 reaction to the fracking boom, implying potential polarizing effects. Income effects and increased population may also be forces which drive voters leftward. An a priori predic- tion, therefore, might be an increase in polarization in fracking boom areas. While Fedaseyeu, Gilje, and Strahan (2015) use county-level data in only seven states to find shale booms increase support for conservative candidates, otherwise conservative counties on the Great Plains and in the Mountain West often have dense pockets of Democratic-leaning Native American or environmentalist voters whose responses may be obscured by using more highly-aggregated data. Their study also does not account for voting behavior in the highly unique 2016 presidential election. Proprietary data on hydrocarbon well locations, characteristics, and output were provided by the company DrillingInfo through their academic outreach initiative. Their data provides the latitude and longitude, orientation, activity status, quarterly output, and other characteristics of each well, but does not state explicitly if a well uses hydraulic fracturing. Following Wilson (2016) and Kearney and Wilson (2018), fracking wells are identified through the intersection of directional wells with those situated over a shale or tight oil play. These regions were identified by GIS shapefiles provided by the United States Energy Information Agency (EIA).

2.4 Summary Statistics

2.4.1 Precinct Data

Of the 173,355 precinct geographies collected for the 2016 election, 168,825 saw nonzero votes cast. Table2 displays summary statistics for these vote-casting precincts. The mean precinct cast 782.6 votes in 2016, 375.4 for Clinton and 361.1 for Trump, and gave Trump a 1.9% larger margin than Romney; the mean precinct voted about ﬁve points to the left of the median precinct, implying a larger share of strongly-Clinton precincts than strongly-Trump precincts. The median precinct cast more votes in 2008 than in either 2012 or 2016, bottoming out in 2012. This roughly corresponds to overall turnout

10 patterns between the three elections.14 The gap between the median and mean Democratic margins widened from 2008 to 2012 and again from 2012 to 2016, from 4.26% to 5.11% to 5.30%. This widening can be explained by an increasing reliance of the Democratic Party on highly unanimous areas at the expense of more balanced precincts. This observation corroborates a geographic partisan sorting narrative wherein Democratic voters inefficiently pack themselves in dense, urban precincts at the expense of influence in swing districts. This process is sometimes referred to as “unintentional gerrymandering” (Chen and Rodden, 2013). The increasing spatial polarization of the electorate can also be observed in the increasing standard deviations of each party’s share. The standard deviation of the Democratic share steadily increased from 20.77% to 22.49% to 24.04% across the three cycles. Figure 3 displays a histograms comparing the distribution of Democratic shares in 2012 and 2016; the 2016 distribution is noticeably flatter, implying higher polarization.15 Appendices A.1 and A.2 contrast observed demographic and political covariate means in the precinct-level dataset with comparable county and survey data. Precinct data is more representative than either and substantially moreso than county data. These sections also discuss additional methodological advantages precinct data exhibits over these alternative data types.

2.4.2 Fracking Data

Between the 2008 and 2012 presidential elections, 182,226 total wells were drilled across the United States, of which 55,347—30.4%—were identiﬁed by the fracking well criteria. These fracking wells generated a mean 31,111.7 barrel of oil equivalents (BOEs) per year compared to 15,547.1 BOEs per year overall. Between the 2012 and 2016 presidential elections, an additional 134,585 wells were added; 56,343 or 41.9% were fracking wells. The yearly output of these wells was higher at a mean 58,095.7 BOEs per year due to

14The mean and standard deviation for turnout numbers are skewed and artiﬁcially inﬂated due to heavier county-level aggregation in 2012. 15The only exception is in the far right tail of the Democratic share, where Obama surpassed Clin- ton considerably. This can be explained both by Clinton’s relative under-performance in highly black precincts and by stronger third party performances in 2016. See Section A.3 for more details.

11 diminishing returns over time. Figure4 maps these newly added fracking wells and their associated shale plays. As shown in Table3, 16% of precincts intersected at least partially with a shale or tight oil play, but only 1.4% of precincts saw a fracking well drilled between the 2008 and 2012 elections, and 1.0% of precincts saw the same between the 2012 and 2016 elections. The average precinct saw 0.32 fracking wells added in each period, with a mean 410 BOEs per year added between 2008 and 2012 and 657 BOEs per year added between 2012 and 2016. Precincts with fracking wells added were substantially more Republican than both the nation and within shale plays in general, topping 60% for the Republican candidate in each cycle. These precincts swung further to the right by 4-6% between the three races, a swing larger than shale plays generally. Precincts which added a fracking well between 2008 and 2012 swung 4.2% toward Romney while those which did not add a fracking well during this period but did during 2012 to 2016 swung 4.0% to Romney, implying a substantial portion of this swing may be unrelated to fracking wells themselves or due to spatial spillover. Precincts which added a fracking well in the ﬁrst period, but did not in the second period swung 5.2% to Trump while those which added in both swung 5.6% to Trump. Precincts which only added a fracking well in the second period swung 7.4% to Trump.

3 Empirical Methods

3.1 Ecological Inference

One widely agreed-upon ecological inference procedure does not exist. Goodman (1953) first operationalized the ecological regression (“ER”), which assumes homogeneous turnout and subgroup partisan support patterns. These assumptions typically do not hold in reality. ER is also without upper or lower bound, permitting impossible estimates. ER results can be tamed by incorporating spatial fixed effects to isolate intra-regional variation, but the unbounded property remains after aggressive spatial controls. Thomsen (1987) used a logistic regression approach to correct the boundedness issue and allowed for partial

12 heterogeneity by averaging across geographic regions wherein homogeneity is assumed. This procedure was updated by Park (2008) with more ﬂexible non-linear substitution patterns (“Thomsen-Park” or “TP”). Freedman (1991) introduced the neighborhood model (“NM”), a simple and bounded method which assumed complete heterogeneity at the precinct level such that all subgroup patterns were driven solely by geography.16 King (1997), later generalized by King et al. (1999) and Rosen et al. (2001) (“King- Rosen” or “KR”), developed a two-stage “method of bounds” estimator which allows precinct-level heterogeneity, estimates turnout by subgroup and precinct, and is bounded. KR relies on an assumed distributional form—truncated normal in King (1997) or multinomial- Dirichlet in Rosen et al. (2001)—while producing “untamed” estimates which may not represent a large improvement over other methods (Freedman et al., 1998; Tam Cho, 1998) and cannot be used as a dependent variable due to consistency issues without an adequate covariate describing the aggregation structure (Herron and Shotts, 2003). KR also produces biased estimates in the presence of “extreme spatial heterogeneity” which spatial weighting may be able to solve (Anselin and Tam Cho, 2002). Calvo and Esco- lar (2003) suggest running the KR procedure with the precinct-level coeﬃcient estimate from a local geographically-weighted regression (“GWR”) as a covariate, producing a spatial-KR model (“SKR”) which generates consistent estimates.17

The SKR procedure begins with the accounting identity that turnout rate Ti is a weighted sum of group population shares d. For j groups, the turnout rate in precinct i is X Ti = θi,jdi,j (2) j P where di,j = 1. As the θi,j values are the only unknowns, we can reduce the dimen- sionality by one by solving for θi,k as a function of θi,−k. With perfect information and under homogeneous subgroup preferences across precincts, the each j − 1-dimensional

16Heterogeneous voting preferences within races and ethnic groups are well-established, but ill- measured. For example, Cuban and Vietnamese descendants tend to be far more Republican than other Hispanic or Asian subgroups. The extent to which this heterogeneity varies by geography is poorly understood. 17This process presages later models equating ecological inference problems with instrumental variable methods for estimating causal eﬀects (Spenkuch, 2018).

13 hyperplane should intersect at a unique point. With heterogeneous preferences, a unique intersection will not exist, but the density of intersections will be higher near the “true”

θi,j values. To estimate these values, SKR uses a three-stage hierarchical Bayesian procedure:

first, assume Ti follows a multinomial distribution and define the contribution of precinct i’s results to the likelihood function as the product of all θi,j. Next, define φi,j,p as the share of the precinct’s population voting in both subgroup j and voting for candidate p

and assume φi,j,p is distributed Dirichlet as a function of covariate Zi. The probability

density function of φi,j,p is

Γ ω P exp(δ + ζZ) j p Y f(φ) = φωj exp(δ+ζZ)−1 (3) Q Γ (exp(δ + ζZ)) p p

where F () is the gamma function. Both δ, ζ are given uniform priors while ω is given a exponential prior. By Bayes’s theorem, the posterior distribution is

f(θ, φ, Z) = L(θj|φj,p)f(φj,p|δj,p, ζj,p, ωj)p(δj,p, ζj,p, ωj) (4)

This posterior is maximized using Monte Carlo simulations based on a Gibbs sampler using a Metropolis algorithm.

The covariate Zi in the SKR model is generated by ﬁrst running ER, then regressing a geographically weighted regression projecting ER-predicted turnout on the ER residual. The GWR estimator, which mirrors a generalized least squares estimator, is

0 −1 0 ˆ ˆ ˆ ER Zi = Ti wiTi Ti wiεˆi (5)

where the spatial weighting matrix wi weights nearby precincts with a positive value if they fall with a bandwidth distance and zero otherwise. The optimal kernel size is based on the degree of spatial non-stationarity and is frequently found by minimizing the Akaike information criterion. The entire procedure can be run in one step or as two steps, ﬁrst predicting turnout and then partisan vote shares using the predicted electorate

14 composition. KR and SKR uniquely produce heterogeneous precinct-level estimates of turnout and vote shares for each subgroup. Predicted electorate compositions can used in the other procedures, increasing estimate accuracy compared to assuming turnout reﬂects voting- age population (VAP). The SKR model thus contains the most comprehensive set of a priori characteristics desirable in an ecological inference estimator and be the primary basis of this section’s analysis, although its estimates will be compared to other procedures.

3.2 Determinants of Subgroup Voting

While ecological inference can deduce how groups changed behavior between election cycles, less can be said about why these changes occurred. Regression analysis can illuminate correlates which may have motivated these outcomes. While causal effects cannot be claimed in this section, results presented here point toward plausible rationales for behavior or suggest there may not exist relationships previously thought. Specifying a regression design requires addressing several issues. First, the data limitations in Section 2.2 must be accounted for. Unallocable absentee votes operate primarily at the state level, but sometimes at the county level. Because these types of votes tend to be disproportionately Democratic-leaning, they may bias the remaining results rightward. County-level fixed effects serve to correct for this type of bias. County-level fixed effects should also correct for much of the error introduced by author limitations. A more conservative approach would be to also weight observations by the likelihood these errors will affect observational accuracy. The following error weight EW based on the percentage of missing votes MV for each precinct i in county c for the 2012 and 2016 elections is used:

2 EWi,t = (1 − MVi|c,t)(1 − MVi|c,t−1) (6) where the entire term is squared to more aggressively punish author-introduced errors.18 18In practice, results are strongly robust to exponent choice and weight inclusion or exclusion. The

15 This weight is equal to one if all non-absentee votes in a county in both years are assigned to precinct geographies, which is true for 80% of counties. Because erroneous boundaries within a county affect multiple precincts by definition, errors are likely correlated within counties, necessitating the use of county-level clustered standard errors. Clustering at the county level also mimics the sample design of county-by-county data collection, justifying clustering at this level (Abadie et al., 2017). A fixed effects approach takes advantage of the unique and rich structure of precinct- level data by focusing on the previously unobservable variation hidden by county data; however, this approach differs from the multilevel methodology often employed in political science. Multilevel modeling is ideal with small samples as subgroup effects are smoothed based on geographic nesting, but the large number of precincts per county removes the need to assume a distributional form for and forcedly attenuate subgroup coefficient estimates. Random effects also assume exogeneity of subgroups effects, a rather implausible assumption which would interfere with later causal analyses while fixed effects make no such assumption. A second issue is the large variation in the number of voters across precincts. A notable number of precincts–3.8% in 2016–had fewer than ten voters in any given election, creating noisy values which may not yet approach the true mean of the political views of all voters in these precincts. A solution to this issue is to weight values by the number of total votes cast, but any form of aggregation may bias estimates toward these super- precinct geographies. One option therefore would be to use the natural logarithm of total votes in order to account for both of these factors. This weight can be multiplied by

EWi,t to account for both issues. Another potential issue is spatial autocorrelation. Spatial methods based on weighting matrices are computationally burdensome with large datasets as matrix size increases with the square of observations. Data size even limits the application of sparse and banded weighting matrices. This constraint limits the available spatial methods, as do exception is if the exponent is suﬃciently large as to entirely omit these precincts. Results in this scenario are qualitatively similar for almost all variables, but do vary somewhat quantitatively, likely due to biasing results away from Republican precincts. See Table A11 for a comparison across weight types.

16 other properties of the dataset. Given the strong self-sorting processes seen in electoral geography, the exogeneity of any spatial weighting matrix with the independent variable matrix is likely violated, rendering “spatial lag of X” and spatial Durbin models inconsistent (Gibbons and Overman, 2012). As spatial error models increase efficiency but do not effect consistency while spatial lags affect consistency, only the latter will be focused on. An approximation of a spatial lag model can be constructed by determining precinct-by-precinct the spatial structure of nearby precincts. This section employs a k- nearest neighbors specification with observations weighted by the inverse of their centroid distance. For computational parsimony, only the ten nearest neighbors are considered. Results suggest spatial autocorrelation is present, although it does not qualitatively affect model estimates outside of a few instances. One final issue concerns using SKR estimates as dependent variables. While the GWR covariate allows SKR to produce consistent estimates, thereby allowing for consistent estimation with SKR estimates as dependent variables, the estimates will be inefficient and have overly small standard errors. To correct for this, coefficient estimates based on SKR estimates will be bootstrapped with a thousand repetitions. The following model specifies the base regression approach for V ∈ {D,R,O} in election t:

Vi = γXi + θi|c + ρVk∈δ(i) + εi (7)

where X is a matrix of ones and explanatory variables as appropriate, θi|c is the county- level ﬁxed eﬀect, and δ(i) is the set of ten nearest precincts to precinct i. Regressions explaining swings in SKR variables will follow the form:

∆Vi,t = γXi,t + β (Xi,t − Xi,t−1) + θi|c + ρSk∈δ(i),t + εi,t (8)

which accounts for changes in independent variables between elections.

17 3.3 Retrospective Voting and the Fracking Boom

As voting patterns correlate with most human activity, there is strong reason to suspect endogeneity exists between added fracking production and changes in voting behavior. This is especially true given the politicized nature of natural resource extraction and fracking, where Republican or Republican-trending states might be more willing to allow exploitation than Democratic areas. An instrumental variables (IV) approach can address this concern by leveraging fea- tures beyond human control or use. Feyrer, Mansur, and Sacerdote (2017) and Kearney and Wilson (2018) create a “simulated fracking production” instrument from an indicator identifying whether an area intersects with a shale or tight oil play and a time trend. As these hydrocarbon deposits formed naturally thousands of years ago, their location will be exogenous to human activity. These formations also satisfy the exclusion restriction for the very reason they were not utilized until the fracking boom: there was no economically feasible way to extract molecularly-bonded shale oil from these deposits and no expectation to do so in the future, implying settlement and voting patterns should not be affected. As there is no other way to utilize these types of deposits, exclusion should be maintained. While previous literature uses panel data and can interact shale play intersection with a time trend, the data structure used here does not afford such variation. An alternate dimension can substitute: space. This new instrument uses the distance between the precinct’s boundary and the centroid of its shale play as a proxy for the volume of hydrocarbons within its play. The measure is zero if the precinct is not within a play, then counts upward from the outside of a shale play inward. This measure is maximized for a precinct containing the centroid of the largest shale play. As the instrument’s variation remains fundamentally reliant on the exogenous location where the play formed and introduces no additional endogeneity concerns, the above instrument validity conditions will remain satisfied.

18 4 Results

4.1 Ecological Inference

This section uses various ecological inference techniques to determine how subgroups of voters changed behavior between elections and which changes were most responsible for election outcomes, particularly for the 2016 election.

4.1.1 Turnout

Table4 displays results of the SKR procedure estimating turnout by race and education level for the three elections. All estimates adjust for votes unallocated to precincts by assuming these votes reﬂect the distribution of votes allocated to precincts in the same county. The table also depicts the electorate composition based on these estimates. SKR estimates suggest overall turnout as a percentage of estimated VAP stood at 56.4% in 2008, dropped to 55.7% in 2012, then rose to 58.4% in 2016. This pattern is largely driven by changes in white turnout, which comprised nearly three-quarters of voters in each election. White turnout crossed 60% in 2008 before declining to 59% in 2012 and surging to 63.5% in 2016. Minority turnout peaked in 2008, receding from 2008 to 2012 and again from 2012 to 2008 for every group except Hispanics, who increased turnout rates slightly in 2016 from 2012. Black turnout shrunk by 3% from 2008 to 2012 and by 5% from 2012 to 2016. Asian turnout saw a 6% drop from 2008 to 2012, but declined less than a point from 2012 to 2016. Native American turnout cratered between 2008 and 2012, plummeting over 15%, but only declined 1% the following cycle. These turnout patterns generated an increasing white electorate share between each cycle despite whites comprising a shrinking share of VAP. Estimates suggest an electorate which is more white than exit polls indicate, more in line with CPS estimates or voter list statistics (Cohn, 2016). The table’s estimates point toward an increasingly educated electorate. Turnout estimates for most education attainment statuses were fairly consistent across cycles, re- ﬂecting overall trends. The exception is those without a high school diploma, whose

19 turnout dropped between each election. This group dropped from 12% of the electorate in 2008 to under 9% in 2016. This eﬀect is likely driven both by increasing education of the populace overall and by declining minority turnout. Those with only a high school diploma also saw a decrease in electorate share. The electorate moved from 33.4% with a bachelor’s degree or more in 2008 to 36% in 2016. Figure5 demonstrates heterogeneity in racial turnout in diﬀering racial contexts for the 2016 election by local regression. Estimated white turnout rates were highest in predominately white areas and highly non-white precincts, but dipped in areas with a near equal share of whites and non-whites. Black turnout followed a similar pattern, although with a minimum near 35% white. Hispanic turnout was estimated to peak in highly white areas and decline as the non-white population increased. Asian turnout was positively related to white share of VAP.

4.1.2 Partisan Support by Race

Table5 presents estimates of each race’s partisan support across the three elections as calculated by several different methodologies followed by official exit polls results, when available. Columns are arranged from most assumed homogeneity to most assumed heterogeneity as follows: global ER, TP averaging across states, ER with zip code-level fixed effects, GWR with zip code-level fixed effects, KR, SKR, and NM. Additional results can be found in Tables A1 and A2 which compare GWR estimates at varying levels of fixed effects. Estimates show substantial variation with heterogeneity level. Global ER produces sharply polarized estimates which often exceed the possible zero-one bounds. State-level TP produces estimates most similar to exit polls outcomes, but whether this is the best representation of reality is an open question.19 By contrast, all estimates allowing for lower levels of heterogeneity produce less polarized estimates than global ER and state- TP, with NM producing the least polarization. KR and SKR estimates resemble NM but slightly more polarized while both zip code estimates give similar results.

19Table A3 displays the correlation of estimates between the various models. State-TP has the highest correlation with exit polls of any of the models shown.

20 Zip code-level models, KR, SKR, and NM all suggest racial voting may not be as extreme as exit polls report. This effect is most pronounced for blacks. While conventional wisdom and exit polls report fewer than 10% of blacks vote for Republicans, these models suggest black Republican support may be two to five times higher. This result arises from estimates that blacks in the South, West, and majority-white areas are far less Democratic than blacks in the Midwest, Northeast, and majority-black urban areas. A few external pieces of evidence suggest this result may be plausible: first, polling suggests a quarter or more of blacks consider themselves “conservative” and this rate also matches answers concerning black views on current race relations and whether the Civil Rights Movement succeeded in its goals (Smith, 2017; Associated Press, 2018, De Pinto et al., 2018). Second, only 41% of blacks live in heavily-Democratic urban areas while 40% live in more Republican suburban areas, meaning voting behavior of blacks in non-black areas should be weighted roughly equivalent to areas typically used to justify black unanimity. If exit polls or other surveys rely heavily on insufficiently geographically-diverse black populations in their sampling, a biased estimate of black support may be unintentionally generated. Alternatively, social perceptions of black Democratic hegemony may induce misreporting of black voting preferences on surveys due to social desirability bias. If stigma-related bias in surveys exists, answers to related questions which may obliquely reveal preferences may be more accurate than direct questioning. Regardless of the reasoning, this is a subject which deserved further study. Figure6 displays local regression curves of SKR estimates of Clinton support in 2016 for each race by precinct white electorate share. SKR estimates suggest all races become more Republican as white share rises. Blacks are consistently the most Democratic group, reaching approximately 85% Clinton share in fully non-white precincts while dropping to a slim majority in near-fully white precincts. White voters appear most Democratic when they comprise about 20% of the electorate before Democratic share falls off in an increasing manner with increased white share. Hispanics and Asians exhibit differing behavior in non-white precincts, with Hispanics clearing 70% Clinton support, but the two groups converge together as white share rises, both reaching about 46% Clinton share

21 in near-fully white precincts. Estimates from each model point toward similar swings between elections for each racial group with a few exceptions. The models, not including exit polls, unanimously agree that: whites swung toward Trump despite no change in margin in exit polls; whites swung toward Romney; blacks swung toward Obama in 2012, in disagreement with exit polls; and Native Americans swung toward Trump in 2016 and Obama in 2012. All but one of the models also agreed that blacks swung toward Trump, Hispanics swung toward Obama, and Asians swung toward Clinton—the latter disagreeing with exit polls. The only two instances of widespread model disagreement concern the 2016 Hispanic swing and the 2012 Asian swing. The SKR models suggest Hispanics swung toward Clinton and Asians toward Romney while zip code-level ER and GWR estimates suggest the opposite swings. The SKR model suggest widening white-minority polarization in most cases, but regression-based approaches disagree. Figure7 depicts SKR estimates of partisan support by race and state for the three elections. White support largely mimics overall state voting with a slight Republican tilt; white voting patterns become more Republican over time in nearly every state. The black vote nears or crosses 80% Democratic in a broad belt from Massachusetts to Wisconsin and Missouri, but is also strongly Democratic in Louisiana, Tennessee, California, and Nevada. Hispanic support for Democratic candidates is highest in the urban Northeast, Maryland, Illinois, and California and tilts Democratic by a lesser margin in Hispanic-heavy states such as Arizona, Texas, and Florida. The Asian vote largely follows urbanization patterns, but is stronger than expected in North Carolina and Idaho. Native American support for Democratic candidates is highest in states with high Reservation populations: Alaska, Montana, the Dakotas, Arizona, and New Mexico, but Republicans appear to win Native votes in Oklahoma and Alabama.

4.1.3 Partisan Support by Education

Table6 reports parallel results by education attainment. These estimates appear less sensitive to the level of assumed heterogeneity, likely due to lower geographic sorting by

22 education relative to race and ethnicity. As education cleavages tend to be less extreme than racial voting divides, more disagreement exists over the victors among education groups, especially in the middle of the educational ladder. Each model agrees that Clinton won those without a high school diploma in 2016, as did Obama in both campaigns. For no other group does a unanimous picture emerge with regard to plurality support. Heterogeneous models, global ER, and state-TP all suggest Clinton won those with bachelor’s or graduate degrees while Trump won those with a high school diploma or some college experience. Zipcode-level ER and GWR models point in the opposite direction for all four groups. In both 2008 and 2012, every model except global ER suggests Obama won both those with a high school diploma and some college, while a similar divide to 2016 appears with respect to college graduates between models. The models do mostly agree with respect to swing patterns between the three elections. All models suggest all groups with some college or less swung toward Trump and those with a graduate degree swung toward Clinton. Heterogeneous models, state-TP, and global ER all suggest bachelor’s degree holders swung toward Clinton while zipcode-level models suggest a modest swing to Trump. All models agree that high school graduates and college graduates swung toward Romney in 2012, all but global ER agree that those with some college swung toward Romney, but disagreement exists about the behavior of those without a high school diploma based on heterogeneity—precinct-level models suggest a swing toward Romney while more aggregated models imply a swing toward Obama. Figure8 shows SKR estimates for each state by education level. Far less variation exists in estimates across levels compared to the previous racial maps, although the 2016 maps ﬁnd a widening educational divide. The less than high school completion map shows a distinctively higher level of Democratic support along the southern border than imme- diately higher education levels. This is due to Hispanics making up a disproportionately large share of this education grouping.

23 4.1.4 Was 2016 Determined by Turnout or a Partisan Swing?

Table7 investigates whether changes in racial or education-based turnout were more important than internal swings within races or education groups. It simulates results based on the state-level aggregations of the SKR estimates while holding either electorate composition or partisan behavior constant, displaying counterfactual election outcomes under differing conditions. The results shown imply intra-group partisan support played a larger factor than turnout and electorate composition. Holding racial support constant, increasing minority turnout to 2012 levels minimally changes the national popular vote and appear to change the mean outcome in only Michigan and Wisconsin, which do not have enough electoral votes to flip the election to Clinton. Holding educational support constant, reverting to 2012 turnout among education groups does not change the mean outcome in any state. Holding turnout constant and reverting racial support to 2012 levels shifts the popular vote nearly to the 2012 outcome and generates a mean result flipping every states which changed hands in 2016 back to the Democrats. Doing the same procedure with education-based support produces a slightly larger shift in the popular vote, but flips one fewer state’s mean outcome—Florida remains in the Republican column even with 2012 education patterns while every Midwest flip returns to Democratic hands.

4.2 Determinants of Subgroup Voting

This section uses regression analysis to determine the key factors motivating the changes in turnout and partisan support found through ecological inference. The section is struc- tured as follows: ﬁrst, a model comparing the correlates of Democratic support across years; second, results for changes in SKR racial and education-based turnout between cycles; and third, explanations of changes in SKR Republican support between election.

4.2.1 Overall Candidate Support

Table8 displays selected notable variable estimates explaining support for Clinton in 2016 and Obama in 2012 and 2008 while estimates for all covariates can be found in

24 Tables A4-A10.20 Coefficient estimates for SKR electorate composition by race are largest in absolute magnitude for the 2012 election, suggesting racial polarization increased in 2012 but declined in 2016. By contrast, estimates for SKR electorate composition by education steadily increase in absolute magnitude between all three elections, suggesting voting cleavages are increasingly organized along education-related lines. Other variables see- ing statistically significant increases in absolute magnitude for each election include the proportion female, median age, average commute time, proportion commuting by driving alone and walking, and the constant. Variables whose coefficients which have continually attenuated include Vietnamese descent and the spatial lag. The former points to the erosion of idiosyncratic Republican support among the Vietnamese population relative to other Asian subgroups. The latter suggests precincts are becoming less similar to their neighbors over time, which points toward self-sorting and increasing inter-precinct polarization. Large sample sizes render most coefficients statistically significant, making the ones which are not perhaps more notable. Several variables return significant estimates in 2008 and 2012, but not in 2016. Notable among them are median household income, the Gini coefficient, and unemployment rate. This result, combined with increasing polarization along education, gender, and age lines suggests a notable reorganization of the deepest electoral cleavages: they depict a spike in the importance of identity-related factors at the expense of economic characteristics.

4.2.2 Changes in SKR Turnout

Table9 displays selected notable variable estimates explaining changes in turnout by race in 2016 while Table 10 explains turnout changes by education level; estimates for all covariates can be found in Tables A12-A16 for racial turnout and Tables A17-A21 for education-based turnout. White turnout, which generally increased, saw the largest increases in 2016 compared

20Estimates explaining support for Republican candidates are essentially negations of these estimates.

25 to 2012 in precincts with high levels of college completion, high non-white populations, younger populaces, lower unemployment, lower household sizes, high employment in science and technology, and high employment in arts and entertainment—all trends which likely beneﬁted Clinton. At the same time, white turnout also increased in precincts with high disability rates, long commute times, high rates of motorcycle commuting, male populaces, and employment in agriculture, natural resource extraction, construction, and manufacturing—trends which likely beneﬁted Trump. Black turnout, which declined in 2016, fell most in urban, heavily black precincts. Hispanic and Asian turnout surged dramatically in areas with high foreign born populations and high rates of college completion. Hispanic turnout increased in highly popu- lated urban areas, especially areas with substantially poverty and large household sizes. Dominican and Cuban turnout increased relative to Mexican and Puerto Rican descendants. Asian turnout gains were larger among Filipino and Japanese descendants relative to Asian Indians and Koreans. College graduates turned out at greater rates in 2016 relative to 2012 in precincts with higher rates of marriage, higher foreign born populations, higher incomes, higher income inequality, lower unemployment, lower commute times, higher rates of commuting by bicycle, much higher rates of science and technology employment, and large populations of Russian descent. Graduate degree holders especially increased their turnout in areas with high education employment, likely located close to universities. High school graduates and those with some college experience turned out at increased rates in precincts larger disabled populations, lower incomes, lower inequality, longer commutes, more transportation and science employment, and higher “American” and German ancestries. High school graduate turnout especially surged in precincts with high employment in agriculture, natural resource extraction, construction, and manufacturing while slumping in precincts with large foreign born populations. Some college turnout increased in precincts with high rates of motorcycle commuting and Norwegian and Polish ancestries. Turnout from those without a high school diploma increased in poor precincts with large disabled and foreign born populations, large households, more employment in

26 agriculture and mining, and many “American” and Pennsylvania Dutch descendants.

4.2.3 Changes in SKR Subgroup Candidate Support

Table 11 displays selected notable variable estimates explaining changes in Republican support by race in 2016 while Table 12 explains Republican support changes by education level; estimates for all covariates can be found in Tables A22-A27 for racial support and Tables A28-A33 for education-based support. White voters shifted toward Trump relative to Romney in precincts with high minority populations—especially black populaces—and in areas with higher rate of college non- completion. Whites in areas which saw increased white and Hispanic turnout moved toward the Democratic Party, but those in precincts with increased college turnout shifted toward the Republicans. Whites became more Democratic in precincts with more women, more people born overseas, higher incomes and less poverty, and more employment in science, education, and entertainment. White became more Republican in precincts with higher disabled populations, more unemployment, larger households, and more ancestries originating in France, Italy, Poland, Portugal, and Ukraine. Black voters without a high school diploma and in areas with high poverty and high disability shifted toward Republicans while black voters in precincts with more women and higher income inequality moved toward the Democrats. Hispanic and Asian voters in precincts with high incomes swung toward Clinton, but those in precincts with high foreign born populations moved more to Trump. Dominicans and Asian Indians saw large movements toward the Democratic Party. Hispanics in precincts heavy in working class occupations such as construction, manufacturing, and transportation swung strongly toward Trump. Voters without a high school diploma moved more Republican in areas with high poverty and disability rates while trending more Democratic in precincts with large Dutch populations and rates of marriage. High school graduates and those with some college swung more Democratic as median income and income inequality rose while swinging more Republican in precincts with high “American” populations. High school graduates

27 became more Republican in precincts with large construction, utilities, and transportation industries. College graduates also became more Democratic with higher incomes and income inequality, but also in areas rich in Cuban, Dutch, and English descendants.

4.3 The Fracking Boom

The following section analyzing whether the fracking boom causally influenced local changes in SKR turnout and partisan support. This section serves as one example of how this data and methodology can be applied to geography-specific economic or social shocks. OLS and IV results for the effects of added fracking production per square mile on SKR turnout and Republican support rates can be found in Table 13. First stage results at the bottom of the table show simulated fracking production strongly predicts actual added fracking output. Excluded-instrument F -statistics and Cragg-Donald F -statistics both suggest this instrument is statistically strong. Coefficient estimates display the effect on SKR estimates per 100,000 BOEs of additional yearly fracking production. IV estimates in this case tend to be larger in absolute terms relative to their OLS counterparts, which is unsurprising given the large amount of heterogeneity in both fracking utilization within and between shale plays and its political responses. This heterogeneity causes OLS’s average treatment effect estimates to vary from IV’s local average treatment effect estimate. IV estimates suggest white voters increased their turnout and support for Republican candidates, although this effect is only statistically significant for turnout changes between 2012 and 2016 and for support changes between 2008 and 2012. Estimates suggest, albeit at the 10% level of significance, that Hispanics may have become more supportive of Romney relative to McCain due to added fracking while Native Americans became less supportive of Trump relative to Romney in fracking-heavy precincts. The latter effect may relate to the Standing Rock protests with regard to the Dakota Access Pipeline’s construction. The strongest and most consistently estimated effect of added fracking production

28 occurs for high school graduates, the group most likely to see employment gains in the industry. High school graduates significantly increased their rate of turnout between all three elections and became more Republican in both cycles due to the fracking boom. The pro-Republican effect appears to extend to those with some college education in 2016. The results also suggest a backlash against Republicans may have occurred among those with graduate degrees in precincts with fracking utilization. Aggregating these effects, as shown in Table 14, suggests the aggregate effects of these changes were in most cases pro-Republican. Texas and Pennsylvania saw by far the largest total changes in votes cast, followed by Louisiana, West Virginia, and Ohio. These effects played a larger role in the 2012 election than in 2016 as more additional fracking production was added from 2009 to 2012 than from 2013 to 2016. It does not appear that these effects were large enough to change the outcome in any state by themselves, although the cumulative effect between 2008 and 2016 may be sufficient to account for Trump’s entire margin in the state of Pennsylvania. These results strongly support the retrospective voting hypothesis, suggesting the voters most likely to be positively impacted increased both turnout and support for the party most associated with policies enabling the utilization of fracking techniques. Groups who may have been harmed or were more likely to normatively disagree with fracking expansion show some evidence of punishing the party most responsible.

5 Conclusion

This paper documents, analyzes, and applies a novel dataset of precinct-level American presidential election results from 2008, 2012, and 2016 to isolating the key groups and factors responsible for each electoral outcome. It ﬁrst discusses the collection of precinct data and its limitations, then summarizes the broad patterns of the data. Increasing levels of spatial polarization are observed. Ecological inference estimates vary with heterogeneity level, but imply increasing racial and education-based polarization, albeit from a less polarized starting point than previously believed. Aggregating the ecological infer-

29 ence results suggests changing voter partisanship accounted for more of the 2016 swing than turnout changes did. Analysis of the determinants of racial and education-based voting behavior changes reveals potential explanations and motivations for voter choices. The declining role of economic characteristics as electoral predictors is noted. The foreign born population in a precinct played a key role in aﬀecting voter turnout and partisanship, but its impact varied by race and education. Industrial factors and ancestries, acting both directly and as a proxy for class or social status, also were key motivators. Particularly strong were working class occupations for whites and those without college degrees, although less for manufacturing compared to construction and transportation. Causal analysis of the fracking boom shows strong evidence of retrospective voting behavior. Whites and high school graduates show large increases in turnout rates and Republican support in precincts which gained fracking production between cycles. Groups harmed or often in opposition to fracking development such as Native Americans and graduate degree holders may have increased Democratic support in these localities. While this paper contributes to our understanding of the previous three American presidential elections, its largest contribution is the creation of a widely applicable dataset for future research. This paper uses precinct data primarily as a dependent variable, ignoring the wide applicability of precinct data as an explanatory characteristic. Available high-frequency, micro-level data with geolocation properties has strongly risen through tech and social media in the previous decade, a trend which is expected to continue. This type of data is especially well-suited toward pairing with political data which varies at small spatial levels. Studies such as Chen and Rohla (2018) have already begun to take advantage of this intersection and such studies will only become more feasible as time passes.

30 References

Abadie, Alberto, Susan Athey, Guido Imbens, and Jeffrey Wooldridge. “When should you adjust standard errors for clustering?” NBER Working Paper No. 24003, 2017. Alford, John, Peter Hatemi, and John Hibbing. “The politics of mate choice.” The Journal of Politics, 73, no. 2 (2011): 362-379. Anselin, Luc and Wendy Tam Cho. “Spatial effects and ecological inference.” Political Analysis, 10, no. 3 (2002): 276-297. Associated Press. “The February 2018 AP-NORC Center Poll.” February 19, 2018. apnorc.org/PDFs/AP-NORC%20Omnibus%20February%202018/February%202018%20 Custom%20Poll%20Topline First%20AP%20Story.pdf Augenblick, Ned and Scott Nicholson. “Ballot position, choice fatigue, and voter behaviour.” Review of Economic Studies, 83, no. 2 (2016): 460-480. Autor, David, David Dorn, Gordon Hanson, and Kaveh Majlesi. “A note on the effect of rising trade exposure on the 2016 presidential election.” Working Paper. 2016. Bagues, Manuel and Berta Esteve-Volart. “Politicians luck of the draw: evidence from the Spanish Christmas lottery.” Journal of Political Economy, 124, no. 5 (2016): 1269-1294. Bor, Jacob. “Diverging life expectancies and voting patterns in the 2016 US presidential election.” American Journal of Public Health, 107, no. 10 (2017): 1560-1562. Brunner, Eric, Stephen Ross, and Ebonya Washington. “Economics and policy preferences: causal evidence of the impact of economic conditions on support for redistribution and other ballot proposals.” The Review of Economics and Statistics, 93, no. 3 (2011): 888-906. Burnett, Craig and Vladimir Kogan. “The politics of potholes: service quality and retrospective voting in local elections.” The Journal of Politics, 79, no. 1 (2017): 302-314. Calvo, Ernesto and Marcelo Escolar. “The local voter: a geographically weighted approach to ecological inference.” American Journal of Political Science, 47, no. 1

31 (2003): 189-204. Chen, Jowei. “Voter partisanship and the effect of distributive spending on political participation.” American Journal of Political Science, 57, no. 1 (2013): 200-217. Chen, Jowei and Jonathan Rodden. “Unintentional gerrymandering: political geography and electoral bias in legislatures.” Quarterly Journal of Political Science, 8 (2013): 239-269. Chen, M. Keith and Ryne Rohla. “The effect of partisanship and political advertising on close family ties.” Science, 360, no. 6392 (2018): 1020-1024. Cohn, Nate. “There are more white voters than people think. That’s good news for Trump.” The New York Times, June 6, 2016. nytimes.com/2016/06/10/upshot/there-are-more-white-voters-than-people-think- thats-good-news-for-trump.html Cole, Shawn, Andrew Healy, and Eric Werker. “Do voters demand responsive governments? evidence from Indian disaster relief.” Journal of Development Economics, 97, no. 2 (2012): 167-181. De Pinto, Jennifer, Fred Backus, Kabir Khanna, and Anthony Salvanto. “Americans view MLK as important, but believe only some of his goals have been achieved.” CBS News, April 2, 2018. cbsnews.com/news/americans-view-mlk-as-important-but-believe-only-some-of-his- goals-have-been-achieved DellaVigna, Stefano and Ethan Kaplan. “The Fox News effect: media bias and voting.” The Quarterly Journal of Economics, 122, no. 3 (2007): 1187-1234. Fedaseyeu, Viktar, Erik Gilje, and Philip Strahan. “Voter preferences and political change: evidence from shale booms.” NBER Working Paper No. 21789, 2015. Feigenbaum, James, Alexander Hertel-Fernandez, and Vanessa Williamson. “From the bargaining table to the ballot box: political effects of right to work laws.” NBER Working Paper No. 24259, 2018. Ferreira, Fernando and Joseph Gyourko. “Do political parties matter? evidence from U.S. cities.” The Quarterly Journal of Economics, 124, no. 1 (2009): 399-422.

32 Feyrer, James, Erin Mansur, and Bruce Sacerdote. “Geographic dispersion of economic shocks: evidence from the fracking revolution.” American Economic Review, 107, no. 4 (2017): 1313-1334. Fiorina, Morris. “Economic retrospective voting in American national elections: a micro- analysis.” American Journal of Political Science, 22, no. 2 (1978): 426-443. Freedman, David, Stephen Klein, Jerome Sacks, Charles Smyth, and Charles Everett. “Ecological regression and voting rights.” Evaluation Review, 15, no. 6 (1991): 673- 711. Freedman, David, Stephen Klein, Michael Ostland, and Michael Roberts. “On ‘solutions’ to the ecological inference problem.” Journal of the American Statistical Association, 93 (1998): 1518-1522. Fujiwara, Thomas, Kyle Meng, and Tom Vogl. “Habit formation in voting: evidence from rainy elections.” American Economic Journal: Applied Economics, 8, no. 4 (2016): 160-188. Gelman, Andrew, David Park, Stephen Ansolabehere, Phillip Price, and Lorraine Minnite. “Models, assumptions and model checking in ecological regressions.” Journal of the Royal Statistical Society, 164, no. 1 (2001): 101-118. Gentzkow, Matthew, Jesse Shapiro, and Michael Sinkinson. “The effect of newspaper entry and exit on electoral politics.” American Economic Review, 101, no. 7 (2011): 2980-3018. Gerber, Alan, Donald Green, and Ron Shachar. “Voting may be habit-forming: evidence from a randomized field experiment.” American Journal of Political Science, 47, no. 3 (2003): 540-550. Gerber, Alan, Daniel Kessler, and Marc Meredith. “The persuasive effects of direct mail: a regression discontinuity based approach.” The Journal of Politics, 73, no. 1 (2011): 140-155. Gervais, Will and Maxine Najle. “How many atheists are there?” Social Psychological and Personality Science, 9, no. 1 (2017): 3-10. Gibbons, Stephen and Henry Overman. “Mostly pointless spatial econometrics?” Journal

33 of Regional Science, 52, no. 2 (2012): 172-191. Gomez, Brad, Thomas Hansford, and George Krause. “The Republicans should pray for rain: weather, turnout, and voting in U.S. presidential elections.” The Journal of Politics, 69, no. 3 (2007): 649-663. Goodman, Leo. “Ecological regressions and the behavior of individuals.” American Sociological Review, 18 (1953): 663-666. Hawley, George. “Home aﬀordability, female marriage rates and vote choice in the 2000 US presidential election.” Party Politics, 18, no. 5 (2011): 771-789. Healy, Andrew and Neil Malhotra. “Random events, economic losses, and retrospective voting: implications for democratic competence.” Quarterly Journal of Political Science, 5, no. 2 (2010): 193-208. Hersh, Eitan and Clayton Nall. “The primacy of race in the geography of income-based voting: new evidence from public voting records.” American Journal of Political Science, 60, no. 2 (2016): 289-303. Herron, Michael and Kenneth Shotts. “Using ecological inference point estimates as dependent variables in second-stage linear regressions.” Political Analysis, 11, no. 1 (2003): 44-64. Kearney, Melissa and Riley Wilson. “Male earnings, marriageable men, and non-marital fertility: evidence from the fracking boom.” The Review of Economics and Statistics, forthcoming. King, Gary. A Solution to the Ecological Inference Problem: Reconstructing Behavior from Aggregate Data. Princeton, New Jersey: Princeton University Press, 1997. King, Gary, Ori Rosen, and Martin Tanner. “Binomial-Beta hierarchical models for ecological inference.” Sociological Methods and Research, 28 (1999): 61-90. Martin, Gregory and Ali Yurukoglu. “Bias in cable news: persuasion and polarization.” American Economic Review, 107, no. 9 (2017): 2565-2599. Mas, Alexandre and Enrico Moretti. “Racial bias in the 2008 presidential election.” American Economic Review: Papers & Proceedings, 99, no. 2 (2009): 323-329. McConnell, Christopher, Yotam Margalit, Neil Malhotra, and Matthew Levendusky.

34 “The economic consequences of partisanship in a polarized era.” American Journal of Political Science, 62, no. 1 (2018): 5-18. Nall, Clayton. “The political consequences of spatial policies: how interstate highways caused geographic polarization.” The Journal of Politics, 77, no. 2 (2015): 394-406. Oc, Burak, Celia Moore, and Michael Bashshur. “When the tables are turned: the effects of the 2016 U.S. presidential election on in-group favoritism and out-group hostility.” PLOS One, 13, no. 5 (2018): e0197848. Park, Won-ho. “Ecological inference and aggregate analysis of elections.” PhD diss., University of Michigan, 2008. Perez-Truglia, Ricardo. “Political conformity: event-study evidence from the United States.” The Review of Economics and Statistics, 100, no. 1 (2018): 14-28 Perez-Truglia, Ricardo and Guillermo Cruces. “Partisan interactions: evidence from a field experiment in the United States.” Journal of Political Economy, 125, no. 4 (2017): 1208-1243. Rosen, Ori, Wenxin Jiang, Gary King, and Martin Tanner. “Bayesian and frequentist inference for ecological inference: the RxC case.” Statistica Neerlandica, 55, no. 2 (2001): 134-156. Rutchick, Abraham. “Deus ex machina: the influence of polling place on voting behavior.” Political Psychology, 31, no. 2 (2010): 209-225. Scala, Dante and Kenneth Johnson. “Political polarization along the rural-urban continuum? the geography of the presidential vote, 2000-2016.” The ANNALS of the American Academy of Political and Social Science, 672, no. 1 (2017): 162-184. Shachar, Ron. “Party loyalty as habit formation.” Journal of Applied Econometrics, 18, no. 3 (2003): 251-269. Smith, Samantha. “Democratic voters are increasingly likely to call their views liberal.” Pew Research Center report, September 7, 2017. pewresearch.org/fact-tank/2017/09/07/democratic-voters-are-increasingly-likely-to- call-their-views-liberal Spenkuch, Jorg. “Causal ecological inferences.” Working paper. 2018.

35 kellogg.northwestern.edu/faculty/spenkuch/research/ei-iv.pdf Stokes, Leah. “Electoral backlash against climate policy: a natural experiment on retrospective voting and local resistance to public policy.” American Journal of Political Science, 60, no. 4 (2016): 958-974. Tam Cho, Wendy. “Iﬀ the assumption ﬁts: a comment on the King ecological inference solution.” Political Analysis, 7 (1998): 143-163. Thomsen, Soren. Danish Elections 1920-79: A Logit Approach to Ecological Analysis and Inference. Arhus, Denmark: Politica, 1987. Warf, Barney. “Class, ethnicity, religion and place in the 2008 U.S. presidential election,” in Revitalizing Electoral Geography, edited by Barney Warf and Jonathan Leib, 133- 156. London: Ashgate Press, 2011. Williams, Jhacova. “Historical lynchings and contemporary voting behavior of blacks.” Working paper. 2018. aeaweb.org/conference/2018/preliminary/paper/yAFZeHYn Wilson, Riley. “Moving to economic opportunity: the migration response to the fracking boom.” Working paper. 2017.

econweb.umd.edu/∼wilson/frackingmigration wilson 2017.pdf Zandbergen, Paul and Drew Ignizio. “Comparison of dasymetric mapping techniques for small-area population estimates.” Cartography and Geographic Information Systems, 37, no. 3 (2010): 199-214.

36 Table 1: Measuring the coverage of the dataset 2016 2012 Vote Type Votes Percent Votes Percent Allocated to geographies 131,950,061 96.64% 124,130,939 96.66% Unallocable absentees and related 4,227,344 3.10% 3,434,403 2.67% Unallocable due to author limitations 357,014 0.26% 857,577 0.67%

Partisan Composition Dem. Rep. Dem. Rep. Allocated to geographies 47.98% 46.13% 51.16% 47.39% Absentees, provisionals, and related 53.89% 41.64% 54.18% 43.85% Author limitations 41.70% 54.64% 48.82% 49.37%

37 Table 2: Summary statistics for political variables Variable N Mean S.D. Min. Median Max. 2016 Election Total Votes 168,825 782.58 713.89 1 611 20,857 Total Clinton Votes 168,825 375.38 387.13 0 279 14,519 Total Trump Votes 168,825 361.1 409.33 0 253 14,009 Total Johnson Votes 168,825 25.94 29.39 0 18 868 Total McMullin Votes 168,825 3.06 17.63 0 0 624 Total Stein Votes 168,825 8.27 11.24 0 5 689 Total Castle Votes 168,825 1.08 2.52 0 0 99 Clinton Proportion 168,825 0.4748 0.2404 0 0.4431 1 Trump Proportion 168,825 0.4663 0.2388 0 0.4876 1 Johnson Proportion 168,825 0.0321 0.0244 0 0.0298 1 McMullin Proportion 168,825 0.0046 0.0262 0 0 1 Stein Proportion 168,825 0.0111 0.0147 0 0.0089 1 Castle Proportion 168,825 0.0016 0.0056 0 0 1

2012 Election Total Votes 167,894 795.71 1,289.37 0 591 45,187 Total Obama Votes 167,894 403.50 612.63 0 297 27,739 Total Romney Votes 167,894 378.41 748.03 0 249 27,903 Obama Proportion 167,894 0.5134 0.2249 0 0.4875 1 Romney Proportion 167,894 0.4680 0.2241 0 0.4932 1

2008 Election Total Votes 167,895 798.98 783.12 0 617 36,840 Total Obama Votes 167,895 422.94 481.53 0 322 28,317 Total McCain Votes 167,895 365.97 408.42 0 258 12,450 Obama Proportion 167,895 0.5318 0.2077 0 0.5099 1 McCain Proportion 167,895 0.4540 0.2063 0 0.4747 1

Election-to-Election Swings 2012-2016 Swing 167,513 -0.0188 0.0839 -1 -0.0170 1 2008-2012 Swing 167,267 -0.0161 0.0614 -0.7918 -0.0149 1 Note: Data only for precincts with at least one vote cast in 2016. All data not from the 2016 presidential election re-aggregated to 2016 precincts. Positive swing represents a swing toward the Democratic candidate in the latter election. Standard errors for 2012 total votes are larger due to select county-level aggregations.

38 Table 3: Summary statistics for fracking well variables Variable Mean S.D. Min. Max. Precinct in Shale Play 0.1631 0.3694 0 1 Precinct Added Fracking Well, 2008-2012 0.0140 0.1175 0 1 Precinct Added Fracking Well, 2012-2016 0.0096 0.0977 0 1 Total Added Fracking Wells, 2008-2012 0.3211 8.88 0 1,366 Total Added Fracking Wells, 2012-2016 0.3260 10.50 0 1,803 Cumulative BOEs Added, 2008-2012 (in thousands) 71.28 2,005 0 344,659 Cumulative BOEs Added, 2012-2016 (in thousands) 68.24 2,140 0 402,335 BOEs Per Year Added, 2008-2012 (in thousands) 0.4100 5.15 0 320.82 BOEs Per Year Added, 2012-2016 (in thousands) 0.6569 10.07 0 575.79 Shale Play Distance Instrument 22.85 67.72 0 347.90 Note: N = 173, 355. All data aggregated to 2016 precincts. BOEs refers to barrel-of-oil equivalents. Well data provided by DrillingInfo.

39 Table 4: Spatial King-Rosen estimates of turnout by race and education VAP Turnout Electorate Composition 2008 2012 2016 2008 2012 2016 Overall 56.4% 55.7% 58.4%

By Race White 60.2% 59.4% 63.5% 72.3% 73.9% 75.0% Black 55.7% 52.7% 47.6% 11.8% 11.6% 10.1% Hispanic 39.4% 33.8% 34.9% 10.0% 9.2% 9.6% Asian 54.9% 48.7% 48.0% 4.8% 4.6% 4.7% Native 64.0% 48.7% 47.6% 0.9% 0.6% 0.5% Paciﬁc Islander 61.3% 51.4% 45.6% 0.2% 0.1% 0.1%

By Education Less than High School 44.0% 38.0% 36.5% 12.0% 10.1% 8.8% High School Diploma 52.0% 49.0% 51.3% 26.7% 25.3% 25.1% Some College 56.3% 55.5% 58.1% 27.8% 29.7% 30.1% Bachelor’s Degree 66.6% 65.6% 67.3% 20.6% 21.7% 22.2% Graduate Degree 71.0% 66.6% 68.7% 12.8% 13.2% 13.8% Note: Estimates extrapolated to include omitted absentee and missing votes on a county basis.

40 Table 5: Estimates of partisan vote by race and methodology 2016 ER TP ER GWR KR SKR NM Exit Poll D 30.6 38.1 40.3 40.3 42.4 42.0 42.1 37 White R 62.8 55.4 53.7 53.5 48.2 52.7 51.7 57 Other 6.5 6.6 6.0 6.1 9.4 5.3 6.2 6 D 99.1 90.3 81.5 81.6 65.2 73.7 69.2 89 Black R 0.2 8.5 13.9 13.7 27.5 21.1 27.0 8 Other 0.7 1.2 4.6 4.7 7.3 5.2 3.8 3 D 74.7 69.8 60.3 60.2 56.3 62.1 59.0 66 Hispanic R 19.5 25.2 33.5 33.6 35.7 29.7 35.2 28 Other 5.8 5.0 6.1 6.2 8.0 8.1 5.7 6 D 107.7 71.4 55.1 54.8 60.1 59.7 59.8 65 Asian R -14.0 22.5 38.9 39.2 30.5 28.6 34.1 27 Other 6.3 6.0 6.0 6.1 9.4 11.8 6.1 8 D 55.9 51.9 69.6 68.5 42.1 43.6 46.2 N/A Native R 29.5 38.4 21.0 21.7 37.7 35.1 45.9 N/A Other 14.6 9.7 9.4 9.8 20.2 21.3 7.9 N/A 2012 D 36.2 41.2 43.7 44.0 46.8 46.7 45.5 39 White R 61.5 56.9 54.4 53.9 52.1 52.2 52.7 59 Other 2.2 1.9 1.9 2.1 1.1 1.1 1.8 2 D 102.3 92.6 87.0 86.8 69.3 71.6 73.1 93 Black R -2.4 7.1 11.6 11.4 28.2 26.2 25.8 6 Other 0.1 0.4 1.3 1.8 2.5 2.2 1.1 1 D 75.6 70.4 63.4 63.8 59.2 59.8 60.9 71 Hispanic R 21.9 27.9 34.4 34.0 37.0 36.5 37.3 27 Other 2.5 1.7 2.2 2.2 3.9 3.7 1.8 2 D 97.7 66.4 55.6 56.9 57.1 58.4 59.5 73 Asian R -0.4 31.8 41.1 40.1 37.0 35.8 38.6 26 Other 2.7 1.8 3.3 3.0 5.9 5.8 1.9 1 D 71.1 61.3 72.9 73.3 46.5 47.1 52.6 N/A Native R 25.5 34.3 13.8 12.9 38.2 37.3 45.5 N/A Other 3.4 4.3 13.3 13.8 15.3 15.5 1.9 N/A 2008 D 40.1 43.2 46.5 46.6 49.5 49.4 47.9 43 White R 58.2 55.3 52.1 51.9 49.6 49.7 50.7 55 Other 1.7 1.5 1.5 1.5 0.9 0.9 1.4 2 D 100.4 92.3 85.1 85.1 69.5 69.6 73.1 95 Black R -0.4 7.5 13.9 13.9 28.9 28.8 26.1 4 Other 0.1 0.2 0.9 1.0 1.6 1.6 0.7 1 D 72.7 69.1 61.4 61.7 58.9 58.9 61.4 67 Hispanic R 26.4 29.8 37.1 36.8 38.6 38.6 37.5 31 Other 0.9 1.1 1.5 1.5 2.5 2.5 1.1 2 D 94 66.7 55.8 55.9 60.2 60.7 60.2 62 Asian R 4.7 32.4 42.8 42.6 35.7 35.2 38.5 35 Other 1.3 0.9 1.4 1.4 4.1 4.1 1.3 3 D 62.6 58.0 69.0 71.6 47.4 47.7 52.6 N/A Native R 35.8 39.2 29.2 26.8 41.0 40.5 46.2 N/A Other 1.7 2.8 1.8 1.6 11.6 11.8 1.3 N/A Heterogeneity Level N S Z Z P P P Assumed Turnout VAP KR KR SKR KR SKR VAP Heterogeneity level key: N - None, S - State, Z - Zipcode, P - Precinct Model key: ER - Ecological regression (Goodman, 1953), TP - Park (2008) model based on Thomsen (1987), GWR - geographically-weight regression (Calvo and Escobar, 2003), KR - Rosen et al. (2001) model generalizing King (1997), SKR - KR model with GWR covariate, NM - Neighborhood model (Freedman, 1991). 41 Table 6: Estimates of partisan vote by education and methodology 2016 ER TP ER GWR KR SKR NM Exit Poll D 137.0 53.9 55.8 55.1 48.7 49.7 51.8 N/A Less than HS R -38.0 42.8 38.4 39.2 43.9 42.1 42.9 N/A Other 1.0 3.3 5.8 5.7 7.4 8.3 5.2 N/A D -4.1 40.5 47.7 47.6 44.8 43.3 45.2 46 HS Diploma R 104.8 55.3 46.6 46.8 47.2 51.9 49.3 51 Other -0.7 4.1 5.6 5.6 8.0 4.8 5.6 3 D 34.0 45.4 48.8 48.6 45.8 46.9 46.4 43 Some College R 51.7 48.9 45.3 45.5 46.2 48.2 47.6 51 Other 14.3 5.7 5.9 5.9 8.0 4.9 5.6 6 D 60.6 51.7 44.0 43.7 47.8 50.6 49.4 49 Bach. Deg R 30.9 40.4 50.0 50.3 43.8 43.4 44.3 44 Other 8.5 7.9 6.0 6.1 8.4 6.1 6.3 7 D 90.7 56.8 42.5 42.3 50.7 53.4 51.8 58 Grad. Deg. R 6.9 35.2 51.3 51.7 41.2 37.8 42.1 37 Other 2.4 8.0 6.2 6.0 8.1 8.8 6.2 5 2012 D 121.8 62.4 61.5 61.0 54.0 54.6 55.9 64 Less than HS R -23.3 35.8 36.2 36.5 43.1 42.5 42.4 35 Other 1.5 1.8 2.3 2.6 2.9 2.9 1.7 1 D 19.3 51.0 53.9 53.8 50.2 50.4 50 51 HS Diploma R 80.3 47.2 44.2 44.4 48.3 48.1 48.3 48 Other 0.4 1.8 1.8 1.8 1.5 1.5 1.7 1 D 40.8 50.0 52.3 52.4 50.4 50.8 49.8 49 Some College R 54.8 48.4 46.1 46.0 48.2 47.9 48.5 48 Other 4.4 1.7 1.6 1.5 1.4 1.4 1.8 3 D 47.2 47.5 46 45.9 50.5 50.8 49.6 47 Bach. Deg R 51.1 50.8 51.9 51.9 47.8 47.4 48.6 51 Other 1.7 1.7 2.1 2.3 1.8 1.8 1.7 2 D 82.0 49.6 41.3 41.2 50.3 51 51.1 55 Grad. Deg. R 16.6 48.5 55.1 55.6 47.1 46.2 47.1 42 Other 1.4 1.9 3.6 3.3 2.7 2.7 1.8 3 2008 D 110.1 62.0 60.9 61.4 55.1 55.3 56.5 63 Less than HS R -10.3 36.5 37.5 37.1 42.9 42.7 42.3 35 Other 0.2 1.5 1.6 1.5 2 2 1.2 2 D 25.1 51.5 54.7 54.7 52.2 52.3 52 52 HS Diploma R 73.0 46.9 43.8 43.8 46.6 46.5 46.7 46 Other 1.9 1.6 1.5 1.5 1.2 1.2 1.3 2 D 41.6 51.2 53.5 53.5 52.8 52.6 51.8 51 Some College R 56.1 47.5 45.1 45 46.1 46.3 46.9 47 Other 2.3 1.3 1.4 1.4 1.1 1.1 1.3 2 D 51.6 51.3 49.4 49.1 53.3 53.3 52.6 50 Bach. Deg R 47.8 47.8 49.3 49.6 45.3 45.3 46.2 48 Other 0.6 0.9 1.3 1.4 1.4 1.4 1.2 2 D 87.5 53.8 46.1 46.1 54.4 54.5 54.3 58 Grad. Deg. R 11.7 45.3 52.7 52.7 43.5 43.4 44.5 40 Other 0.8 0.9 1.3 1.2 2 2.1 1.2 2 Heterogeneity Level N S Z Z P P P Assumed Turnout VAP KR KR SKR KR SKR VAP Heterogeneity level key: N - None, S - State, Z - Zipcode, P - Precinct Model key: ER - Ecological regression (Goodman, 1953), TP - Park (2008) model based on Thomsen (1987), GWR - geographically-weight regression (Calvo and Escobar, 2003), KR - Rosen et al. (2001) model generalizing King (1997), SKR - KR model with GWR covariate, NM - Neighborhood model (Freedman, 1991). 42 Table 7: SKR estimates of 2016 results with 2012 turnout and voteshares Model D R Flipped States With 2012 racial turnout 0.4829 0.4694 MI, WI With 2012 education turnout 0.4793 0.4611 None With 2012 racial partisan support 0.5097 0.4725 FL, IA, MI, OH, PA, WI With 2012 education partisan support 0.5106 0.4711 IA, MI, OH, PA, WI

43 Table 8: Selected determinants of Democratic precinct vote shares Variable Clinton Obama Obama 2016 2012 2008 SKR Electorate White Prop. 0.0077 0.0905 0.0933 (0.0268) (0.0691) (0.0554)* Black Prop. 0.2889 0.3636 0.3152 (0.0281)*** (0.0699)*** (0.0575)*** Hispanic Prop. 0.1666 0.2176 0.1669 (0.0263)*** (0.0693)*** (0.0563)*** Asian Prop. 0.1022 0.1646 0.1230 (0.0248)*** (0.0679)** (0.0548)** Native Prop. 0.2402 0.3514 0.2387 (0.0314)*** (0.0707)*** (0.0569)*** Less than HS Prop. -0.0930 -0.0429 -0.0263 (0.0053)*** (0.0042)*** (0.0039)*** HS Graduate Prop. -0.1041 -0.0528 -0.0320 (0.0046)*** (0.0039)*** (0.0038)*** Some College Prop. -0.1051 -0.0550 -0.0359 (0.0046)*** (0.0044)*** (0.0041)*** Bachelor Prop. -0.0524 -0.0423 -0.0280 (0.0043)*** (0.0039)*** (0.0036)***

Demographics Female 0.1028 0.0515 0.0137 (0.0095)*** (0.0102)*** (0.0081)* Median Age 0.0011 0.0009 0.0001 (0.0001)*** (0.0001)*** (0.0001) Median Household Income (in thousands) 0.0000 -0.0004 -0.0002 (0.0000) (0.0001)*** (0.0001)*** Gini Coefficient -0.0132 -0.0718 -0.0302 (0.0134) (0.0117)*** (0.0093)*** Unemployed 0.0242 0.1013 0.0792 (0.0181) (0.0188)*** (0.0114)*** Average Commute Time -0.0008 -0.0003 -0.0001 (0.0001)*** (0.0001)** (0.0001) Commute by Driving Alone -0.1167 -0.0511 -0.0352 (0.0213)*** (0.0191)*** (0.0190)* Vietnamese Descent -0.0929 -0.1358 -0.1827 (0.0157)*** (0.0183)*** (0.0157)*** Spatial Lag 0.4315 0.4538 0.5078 (0.0271)*** (0.0291)*** (0.0305)*** Constant 0.3647 0.3080 0.1791 (0.0423)*** (0.0769)*** (0.0636)*** N 167,526 167,109 167,214 R2 0.9009 0.8829 0.8715 Note: All estimates include county-level fixed effects and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1

44 Table 9: Selected determinants of SKR turnout change by race, 2012-16 Variable White Black Hispanic Asian Native White in 2012 -0.8996 -0.6212 -0.0465 -0.8206 -0.1775 (0.0820)*** (0.1684)*** (0.1356) (0.2307)*** (0.1712) Black in 2012 -0.4923 -1.1592 -0.0875 -0.8085 -0.2077 (0.0791)*** (0.1718)*** (0.1328) (0.2312)*** (0.1711) Hispanic in 2012 -0.3175 -0.5975 -1.3489 -0.8685 -0.1876 (0.0790)*** (0.1686)*** (0.1701)*** (0.2314)*** (0.1710) Asian in 2012 -0.2600 -0.5891 -0.1153 -2.5436 -0.1797 (0.0788)*** (0.1701)*** (0.1283) (0.2559)*** (0.1701) Native in 2012 -0.4560 -0.6841 -0.0337 -0.7376 -0.9632 (0.0800)*** (0.1701)*** (0.1398) (0.2362)*** (0.1958)*** Less than High School in 2012 -0.0912 -0.0182 0.0123 -0.0753 0.0143 (0.0097)*** (0.0143) (0.0147) (0.0170)*** (0.0126) High School Graduate in 2012 -0.1011 -0.0191 -0.0268 -0.0829 -0.0018 (0.0085)*** (0.0128) (0.0127)** (0.0145)*** (0.0115) Some College in 2012 -0.0990 -0.0248 -0.0408 -0.0982 -0.0001 (0.0093)*** (0.0128)* (0.0128)*** (0.0145)*** (0.0112) Bachelor’s Degree in 2012 -0.0530 -0.0246 -0.0260 -0.0516 0.0119 (0.0079)*** (0.0136)* (0.0131)** (0.0150)*** (0.0117) Population Density -0.0003 -0.0005 0.0006 -0.0002 -0.0001 (0.0002) (0.0003)** (0.0002)*** (0.0002) (0.0002) Female -0.0316 0.1096 -0.0466 -0.0741 -0.0324 (0.0118)*** (0.0238)*** (0.0270)* (0.0252)*** (0.0205) Married 0.1254 -0.1979 -0.0431 -0.0016 -0.0573 (0.0120)*** (0.0202)*** (0.0193)** (0.0174) (0.0145)*** Median Age -0.0010 -0.0001 0.0019 -0.0003 0.0004 (0.0002)*** (0.0004) (0.0004)*** (0.0003) (0.0003) Disabled 0.1237 0.0581 -0.0783 0.0155 0.0108 (0.0205)*** (0.0516) (0.0396)** (0.0445) (0.0358) Foreign Born -0.1329 -0.1623 0.1314 0.2619 -0.0020 (0.0249)*** (0.0377)*** (0.0240)*** (0.0269)*** (0.0173) Median Household Income -0.0003 -0.0000 -0.0005 -0.0001 -0.0001 (0.0001)*** (0.0001) (0.0001)*** (0.0001) (0.0001) Under Poverty Line -0.0864 0.0443 0.0685 0.0348 0.0193 (0.0112)*** (0.0219)** (0.0219)*** (0.0195)* (0.0156) Gini Coefficient 0.0294 0.0798 -0.0195 -0.0586 0.0354 (0.0134)** (0.0320)** (0.0293) (0.0293)** (0.0257) Unemployed -0.0975 0.4027 -0.0207 -0.0542 0.0447 (0.0270)*** (0.0505)*** (0.0433) (0.0499) (0.0355) Average Household Size -0.0404 0.0143 0.0469 0.0033 0.0124 (0.0061)*** (0.0060)** (0.0099)*** (0.0048) (0.0054)** Average Commute Time 0.0008 0.0010 -0.0002 0.0006 0.0002 (0.0002)*** (0.0003)*** (0.0003) (0.0003)** (0.0003) Employed in Agriculture 0.1436 -0.0067 -0.0534 -0.0547 0.0583 (0.0491)*** (0.0782) (0.1078) (0.0923) (0.0677) Employed in Oil and Mining 0.1783 -0.2308 -0.1346 -0.0369 -0.0381 (0.0491)*** (0.1040)** (0.1026) (0.1113) (0.0872) Employed in Construction 0.2510 -0.2233 -0.1549 -0.0837 0.0292 (0.0425)*** (0.0781)*** (0.0922)* (0.0885) (0.0682) Employed in Manufacturing 0.0948 -0.0236 -0.1072 -0.0085 0.0266 (0.0404)** (0.0768) (0.0796) (0.0857) (0.0646) Employed in Science and Technology 0.1045 -0.1765 -0.1440 -0.1147 0.0327 (0.0422)** (0.0733)** (0.0846)* (0.0896) (0.0657) Employed in Arts and Recreation 0.1191 -0.1820 -0.2326 -0.0896 0.1154 (0.0481)** (0.0951)* (0.1021)** (0.0950) (0.0783) Note: All estimates include county-level fixed effects and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1

45 Table 10: Selected determinants of SKR turnout change by education, 2012-16 Variable LTHS HS Graduate Some College Bachelor’s Deg. Graduate Deg. White in 2012 -0.6680 -0.2686 -0.5530 -0.5220 -0.9611 (0.1648)*** (0.1198)** (0.0967)*** (0.1517)*** (0.2054)*** Black in 2012 -0.6686 -0.2995 -0.5635 -0.5823 -1.0012 (0.1641)*** (0.1190)** (0.0965)*** (0.1511)*** (0.2045)*** Hispanic in 2012 -0.5765 -0.2977 -0.5490 -0.5696 -0.9770 (0.1649)*** (0.1205)** (0.0967)*** (0.1509)*** (0.2046)*** Asian in 2012 -0.6489 -0.2574 -0.4725 -0.4271 -0.8771 (0.1618)*** (0.1192)** (0.0993)*** (0.1498)*** (0.2036)*** Native in 2012 -0.4512 -0.2198 -0.4265 -0.3505 -0.8447 (0.1662)*** (0.1188)* (0.1021)*** (0.1519)** (0.2030)*** Less than High School in 2012 -1.8171 -0.0004 -0.0712 -0.0935 1.9081 (0.0469)*** (0.0156) (0.0128)*** (0.0219)*** (0.0464)*** High School Graduate in 2012 -0.0176 -1.2630 -0.0125 -0.0894 1.8928 (0.0143) (0.0188)*** (0.0118) (0.0214)*** (0.0462)*** Some College in 2012 -0.0422 0.0569 -1.4245 -0.0713 1.9060 (0.0145)*** (0.0136)*** (0.0136)*** (0.0214)*** (0.0468)*** Bachelor’s Degree in 2012 -0.0251 0.0049 -0.0081 -1.7934 1.9596 (0.0138)* (0.0142) (0.0114) (0.0425)*** (0.0549)*** Female -0.0711 -0.0185 -0.0382 -0.0375 0.0032 (0.0230)*** (0.0199) (0.0166)** (0.0215)* (0.0215) Married 0.0046 0.0766 -0.0515 0.0443 0.0463 (0.0170) (0.0148)*** (0.0114)*** (0.0143)*** (0.0179)*** Median Age -0.0005 0.0008 -0.0021 0.0002 0.0012 (0.0004) (0.0003)*** (0.0002)*** (0.0003) (0.0003)*** Disabled 0.3486 0.3835 0.2315 -0.4636 -0.5600 (0.0429)*** (0.0394)*** (0.0393)*** (0.0443)*** (0.0434)*** Foreign Born 0.2062 -0.0670 0.0240 0.1162 0.1333 (0.0385)*** (0.0208)*** (0.0301) (0.0275)*** (0.0388)*** Median Household Income -0.0006 -0.0015 -0.0014 0.0009 0.0022 (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** Under Poverty Line 0.1789 -0.0461 -0.0580 -0.0651 0.0458 (0.0195)*** (0.0147)*** (0.0158)*** (0.0173)*** (0.0198)** Gini Coefficient 0.0122 -0.1258 -0.2717 0.1980 0.3870 (0.0291) (0.0226)*** (0.0213)*** (0.0250)*** (0.0296)*** Unemployed 0.0652 0.0512 -0.0130 -0.2445 -0.0947 (0.0441) (0.0371) (0.0294) (0.0396)*** (0.0424)** Average Household Size 0.0329 0.0299 -0.0178 -0.0361 -0.0663 (0.0060)*** (0.0051)*** (0.0040)*** (0.0050)*** (0.0057)*** Average Commute Time 0.0002 0.0022 0.0017 -0.0006 -0.0018 (0.0003) (0.0003)*** (0.0002)*** (0.0003)** (0.0003)*** Employed in Agriculture 0.2289 0.3156 -0.0700 -0.2198 -0.0962 (0.0923)** (0.0684)*** (0.0711) (0.0846)*** (0.0885) Employed in Oil and Mining 0.2225 0.2678 -0.1060 -0.3212 -0.1240 (0.1029)** (0.0835)*** (0.0820) (0.1034)*** (0.1090) Employed in Construction 0.1467 0.3262 0.0955 -0.2736 -0.0874 (0.0844)* (0.0649)*** (0.0736) (0.0811)*** (0.0854) Employed in Manufacturing 0.1086 0.1682 0.0039 -0.2022 -0.0422 (0.0822) (0.0631)*** (0.0692) (0.0801)** (0.0835) Employed in Science and Tech. -0.0666 -0.2328 -0.3366 0.1815 0.5650 (0.0864) (0.0658)*** (0.0709)*** (0.0833)** (0.0845)*** Employed in Education -0.1806 -0.1621 -0.1688 0.0164 0.4847 (0.0810)** (0.0633)** (0.0706)** (0.0806) (0.0814)*** “American” Descent 0.0800 0.1500 0.0805 -0.0501 -0.0721 (0.0255)*** (0.0208)*** (0.0170)*** (0.0240)** (0.0252)*** German Descent -0.0319 0.1779 0.0496 0.0470 -0.0789 (0.0311) (0.0238)*** (0.0224)** (0.0246)* (0.0276)*** Note: All estimates include county-level fixed effects and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1

46 Table 11: Selected changes in SKR racial Republican support, 2012-16 Variable White Black Hispanic Asian Native White 0.0182 0.1656 0.1466 0.0264 -0.0337 (0.0560) (0.0827)** (0.0901) (0.0885) (0.1003) Black 0.1561 0.2096 0.2885 0.0941 0.0079 (0.0568)*** (0.0833)** (0.0909)*** (0.0886) (0.1008) Hispanic 0.1011 0.2058 0.1903 0.0656 -0.0215 (0.0562)* (0.0828)** (0.0904)** (0.0879) (0.0977) Asian 0.0878 0.1902 0.1846 -0.0222 -0.0370 (0.0521)* (0.0837)** (0.0889)** (0.0896) (0.0999) Native 0.0783 0.1232 0.2221 0.0449 0.0675 (0.0581) (0.0860) (0.0922)** (0.0927) (0.1015) Less than HS 0.0679 0.0268 0.0089 0.0187 -0.0122 (0.0073)*** (0.0144)* (0.0141) (0.0161) (0.0120) HS Grad. 0.0698 0.0100 -0.0036 0.0049 0.0094 (0.0063)*** (0.0129) (0.0133) (0.0137) (0.0109) Some Coll. 0.0602 0.0080 -0.0030 0.0049 0.0014 (0.0062)*** (0.0125) (0.0129) (0.0136) (0.0116) Bach. Deg. 0.0176 -0.0119 -0.0102 -0.0348 -0.0066 (0.0074)** (0.0130) (0.0141) (0.0169)** (0.0115) ∆ White Turnout -0.0439 -0.0154 -0.0116 -0.0102 0.0001 (0.0036)*** (0.0039)*** (0.0055)** (0.0046)** (0.0049) ∆ Black Turnout -0.0008 0.0246 0.0034 0.0023 -0.0002 (0.0008) (0.0027)*** (0.0025) (0.0023) (0.0018) ∆ Hispanic Turnout -0.0021 0.0052 0.0296 0.0011 0.0010 (0.0010)** (0.0022)** (0.0033)*** (0.0022) (0.0019) ∆ Asian Turnout -0.0003 0.0016 0.0005 0.0091 -0.0001 (0.0008) (0.0021) (0.0021) (0.0026)*** (0.0019) ∆ Native Turnout -0.0008 0.0030 -0.0002 0.0048 -0.0010 (0.0009) (0.0025) (0.0027) (0.0025)* (0.0023) Female -0.0212 -0.0524 -0.0116 -0.0039 0.0140 (0.0090)** (0.0196)*** (0.0194) (0.0198) (0.0193) Married 0.0164 -0.0295 -0.0674 -0.0398 -0.0026 (0.0065)** (0.0141)** (0.0153)*** (0.0135)*** (0.0133) Disabled 0.1047 0.1184 0.0418 0.0697 0.0096 (0.0162)*** (0.0334)*** (0.0331) (0.0342)** (0.0294) Foreign -0.0426 -0.0121 0.0449 0.0586 0.0051 (0.0178)** (0.0209) (0.0198)** (0.0221)*** (0.0239) Median Household Income -0.0002 -0.0001 -0.0002 -0.0005 0.0000 (0.0000)*** (0.0001)* (0.0001)*** (0.0001)*** (0.0001) Under Poverty Line -0.0162 0.0517 0.0270 -0.0208 0.0144 (0.0079)** (0.0154)*** (0.0153)* (0.0151) (0.0140) Gini Coefficient -0.0281 -0.0469 -0.1104 -0.0622 0.0070 (0.0098)*** (0.0222)** (0.0235)*** (0.0238)*** (0.0193) Unemployed 0.0422 0.0281 -0.0418 -0.0254 -0.0148 (0.0183)** (0.0356) (0.0392) (0.0382) (0.0374) Average Household Size 0.0080 -0.0022 -0.0109 -0.0071 0.0007 (0.0033)** (0.0040) (0.0041)*** (0.0044) (0.0039) Emp. in Construction 0.0226 0.0384 0.2034 -0.0640 -0.0168 (0.0345) (0.0699) (0.0767)*** (0.0724) (0.0603) Emp. in Manufacturing -0.0097 0.0225 0.2052 -0.0801 -0.0124 (0.0331) (0.0694) (0.0758)*** (0.0726) (0.0596) Emp. in Transportation 0.0013 0.0241 0.2219 -0.0124 -0.0008 (0.0340) (0.0706) (0.0770)*** (0.0753) (0.0624) Emp. in Sci. and Tech. -0.0716 0.0598 0.1982 -0.0137 0.0131 (0.0349)** (0.0755) (0.0763)*** (0.0718) (0.0620) Emp. in Education -0.0732 0.0096 0.1748 -0.0630 0.0069 (0.0344)** (0.0695) (0.0751)** (0.0721) (0.0602) Emp. in Arts and Rec. -0.0784 0.0617 0.2604 -0.0479 0.0033 (0.0389)** (0.0757) (0.0837)*** (0.0838) (0.0727) Note: All estimates include county-level fixed effects and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1 47 Table 12: Selected changes in SKR Republican support by education, 2012-16 Variable LTHS HS Graduate Some College Bachelor’s Deg. Graduate Deg. White 0.1873 0.0204 -0.0162 0.0296 0.0527 (0.0795)** (0.0536) (0.0645) (0.0616) (0.0713) Black 0.2238 0.0099 -0.0284 0.0956 0.1335 (0.0799)*** (0.0540) (0.0651) (0.0618) (0.0719)* Hispanic 0.2014 -0.0011 -0.0254 0.0732 0.0950 (0.0790)** (0.0528) (0.0651) (0.0631) (0.0725) Asian 0.2070 0.0330 -0.0395 0.0483 0.0791 (0.0804)** (0.0523) (0.0657) (0.0612) (0.0728) Native 0.1845 0.0317 -0.0689 0.0232 0.0574 (0.0829)** (0.0581) (0.0679) (0.0657) (0.0771) Less than HS -0.0654 0.0737 0.0549 0.0037 0.0364 (0.0152)*** (0.0138)*** (0.0131)*** (0.0133) (0.0158)** HS Grad. -0.0439 0.0378 -0.0017 -0.0412 0.0362 (0.0116)*** (0.0113)*** (0.0113) (0.0115)*** (0.0147)** Some Coll. -0.0349 0.0097 0.0313 -0.0273 0.0270 (0.0116)*** (0.0114) (0.0118)*** (0.0109)** (0.0146)* Bach. Deg. -0.0182 -0.0185 -0.0165 -0.0637 0.0312 (0.0127) (0.0114) (0.0123) (0.0133)*** (0.0160)* Female -0.0169 -0.0223 -0.0456 -0.0349 -0.0207 (0.0201) (0.0159) (0.0149)*** (0.0166)** (0.0180) Married -0.0556 0.0010 0.0348 0.0082 -0.0538 (0.0133)*** (0.0120) (0.0103)*** (0.0121) (0.0129)*** Median Age -0.0003 -0.0003 -0.0006 0.0002 -0.0002 (0.0003) (0.0002) (0.0002)*** (0.0002) (0.0003) Disabled 0.1186 0.1079 0.1776 0.1188 0.0490 (0.0348)*** (0.0271)*** (0.0278)*** (0.0289)*** (0.0325) Foreign Born -0.0240 -0.0504 -0.0526 0.0233 0.0243 (0.0298) (0.0178)*** (0.0173)*** (0.0189) (0.0211) Median Household Income -0.0001 -0.0003 -0.0005 -0.0003 -0.0005 (0.0001) (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** Under Poverty Line 0.0497 -0.0101 -0.0206 0.0203 0.0108 (0.0155)*** (0.0126) (0.0113)* (0.0125) (0.0140) Gini Coefficient -0.0418 -0.0659 -0.1031 -0.0735 -0.0796 (0.0219)* (0.0194)*** (0.0166)*** (0.0203)*** (0.0200)*** Emp. in Construction -0.0243 0.1226 0.0053 0.0716 0.0941 (0.0739) (0.0600)** (0.0515) (0.0590) (0.0668) Emp. in Transportation 0.0064 0.1253 -0.0612 0.0904 0.1065 (0.0784) (0.0640)* (0.0523) (0.0591) (0.0665) Emp. in Utilities -0.0138 0.1980 -0.1344 -0.0115 0.0548 (0.0971) (0.0773)** (0.0730)* (0.0788) (0.0841) “American” -0.0337 0.0755 0.0549 -0.0493 -0.0959 (0.0214) (0.0167)*** (0.0168)*** (0.0187)*** (0.0198)*** Dutch -0.2969 0.0286 0.0524 -0.1701 -0.2817 (0.0714)*** (0.0597) (0.0597) (0.0777)** (0.0673)*** English -0.1196 -0.0866 -0.0004 -0.1397 -0.1394 (0.0333)*** (0.0300)*** (0.0317) (0.0284)*** (0.0324)*** Cuban -0.1165 -0.0001 0.0217 -0.2270 -0.2023 (0.0307)*** (0.0283) (0.0214) (0.0881)** (0.0729)*** Note: All estimates include county-level fixed effects and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1

48 Table 13: Effects of added fracking production on SKR estimates Aﬀects change on: White Black Hispanic Asian Native OLS ∆ Turnout, 2008-12 0.0069 -0.0084 0.0078 0.0195 -0.0102 (0.0100) (0.0284) (0.0299) (0.0248) (0.0241) ∆ Turnout, 2012-16 0.0084 0.0122 -0.0029 0.0077 -0.0018 (0.0039)** (0.0123) (0.0115) (0.0117) (0.0095) ∆ Republican Support, 2008-12 0.0044 0.0426 -0.0453 0.0299 -0.0170 (0.0089) (0.0224)* (0.0229)** (0.0285) (0.0203) ∆ Republican Support, 2012-16 0.0036 -0.0065 0.0093 -0.0003 0.0113 (0.0027) (0.0091) (0.0120) (0.0104) (0.0122)

IV ∆ Turnout, 2008-12 0.1141 0.2922 0.1735 0.1566 0.0954 (0.1143) (0.2476) (0.2543) (0.2651) (0.1954) ∆ Turnout, 2012-16 0.1490 0.1722 -0.0891 0.1159 0.0005 (0.0578)** (0.1222) (0.1069) (0.1277) (0.0953) ∆ Republican Support, 2008-12 0.3839 -0.0185 0.2624 -0.2273 -0.1475 (0.1229)*** (0.2161) (0.1576)* (0.2189) (0.1825) ∆ Republican Support, 2012-16 0.0213 0.1140 0.0131 -0.0441 -0.1402 (0.0428) (0.0979) (0.0882) (0.0995) (0.0853)* LTHS HS Graduate Some College Bachelor’s Deg. Graduate Deg. OLS ∆ Turnout, 2008-12 0.0073 0.0696 -0.0399 0.0043 0.0443 (0.0226) (0.0190)*** (0.0201)** (0.0233) (0.0206)** ∆ Turnout, 2012-16 0.0107 0.0162 -0.0035 -0.0154 -0.0068 (0.0119) (0.0096)* (0.0063) (0.0103) (0.0105) ∆ Republican Support, 2008-12 -0.0243 -0.0079 0.0157 0.0082 -0.0155 (0.0182) (0.0156) (0.0241) (0.0208) (0.0194) ∆ Republican Support, 2012-16 0.0026 0.0154 0.0022 -0.0110 0.0028 (0.0097) (0.0072)** (0.0077) (0.0084) (0.0100)

IV ∆ Turnout, 2008-12 0.1506 0.4870 -0.0424 -0.4822 0.2032 (0.2271) (0.1869)*** (0.1938) (0.2039)** (0.1864) ∆ Turnout, 2012-16 0.1270 0.2825 0.0674 -0.0028 -0.0251 (0.0936) (0.0844)*** (0.0711) (0.1023) (0.1043) ∆ Republican Support, 2008-12 -0.1104 0.4262 0.0043 0.1069 0.0457 (0.2349) (0.1228)*** (0.1316) (0.1329) (0.1968) ∆ Republican Support, 2012-16 0.1879 0.1270 0.1368 0.0306 -0.1319 (0.1341) (0.0705)* (0.0651)** (0.0756) (0.0889)* Estimate F -stat C-D F -stat IV First Stage Sim. Production Added, 2008-12 0.0241 24.86 1,980.3 (0.0048)*** Sim. Production Added, 2012-16 0.0449 23.62 1,732.0 (0.0092)*** Note: Displays effect of an additional 100,000 yearly BOEs per square mile added between the noted elections. Each estimate originates from a separate regression. All estimates include county-level fixed effects, a spatial lag term, and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All models include control variables found in previous models. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1

49 Table 14: Aggregate change in Republican votes from new fracking production By model: 2012 Race 2012 Edu 2016 Race 2016 Edu Alabama 0 0 0 6 Arkansas 3,861 2,043 9 682 California 899 222 13 303 Colorado 2,796 913 60 2,762 Kentucky 432 173 0 4 Louisiana 13,761 10,100 -250 3,926 Mississippi 148 132 8 232 Montana 395 217 0 11 New Mexico 754 270 -10 415 New York 527 170 0 0 North Dakota 1,780 779 -82 690 Ohio 5,627 3,871 -3 7,607 Oklahoma 415 2,667 -406 2,990 Pennsylvania 39,908 27,428 142 16,854 Texas 60,725 31,404 -299 13,126 Utah 155 74 -4 38 Virginia 244 55 0 33 West Virginia 6,733 4,163 35 1,609 Wyoming 897 357 3 427 Note: All other states equal to zero. Based only on statistically signiﬁcant eﬀects.

50 Figure 1a. Partisan swing by precinct, 2012-2016

Figure 1b. Partisan swing by precinct, 2008-2012

51 Figure 2. Directional wells in the United States over time

52 Figure 3. Histograms of precinct Democratic shares in 2012 and 2016

53 Figure 4. Fracking wells added from 2009 to 2016 and shale plays

54 Figure 5. Spatial King-Rosen turnout estimates by precinct white share

55 Figure 6. SKR estimates by Democratic support by race and white share

56 Figure 7. Spatial King-Rosen estimates of state partisan vote by race

57 Figure 8. Spatial King-Rosen estimates of state partisan vote by education

58 Appendix

A Precinct Data Notes

A.1 Precinct Data Compared to County Data

Using precinct-level data presents a large gain over county-level data on at least two fronts: first, precinct-level data considerably extends the support of observed demographic characteristics of geographies compared to county-level data which tends to aggregate highly diverse urban areas. This expanded range increases observations in extreme regions, allowing for a better sense of how specific demographic groups voted and how more factors may have impacted individual decisions. As a corollary, the fifty-five-fold increase in observations dramatically increases explanatory power of regression analyses. Second, precinct-level data correlates with underlying individual characteristics at a much higher level than county-level data, which exhibits a strong bias toward rural, white, and Republican areas.21 Figure A2 displays a histogram of Clinton vote shares for counties and precincts,22 demonstrating this Republican bias at the county level. As shown in Table ??, precinct-level data better approximates national averages than county- level data for almost every variable.23 Precinct-level means reduced absolute error from the county-level means by a median 84.8%. An example of these advantages is visualized as shown in Figure A3. This figure plots a local polynomial regression of Clinton’s proportion of the vote at the county and precinct levels compared to median household income. At the precinct level, additional information at low and high levels of income extend the defined support for the local regression. A large increase in observations at higher levels of income smooths the predicted Clinton share. Further, this figure suggests that county-level data may bias estimates of Clinton’s share among those at lower income levels downward while inflating estimates

21This bias arises as counties favor area-equalization over population-equalization 22This histogram only includes precincts with at least one vote cast. 23The only exception being the vote share for Evan McMullin. This can be explained as McMullin was often a write-in vote in many states; individual write-in votes are frequently reported at the county-level but not the precinct level.

59 at higher incomes relative to data closer to the individual level.

A.2 Precinct Data Compared to Survey Data

The two most commonly utilized individual-level surveys in the study of American election behavior are the American National Election Study (ANES) and the Cooperative Congressional Election Study (CCES). The ANES has run since 1948, providing a very long panel to compare changes across time, but only samples approximately 5,000 individuals each election cycle using a combination of face-to-face and internet-based sampling. The CCES, dating back to 2005, samples a much larger universe of voters–typically over 60,000–though an entirely online methodology. While the CCES’s larger sample size lends itself to more precise estimates, its ability to do so wildly varies between states; many states receive fewer than 200 responses, a showing far lower than that used by most pre- election polls, implying a positive relationship between reliability and population. While internet polls have improved dramatically within the past ten years, they are nonetheless susceptible to convenience sampling and other selection issues (Hill et al., 2007). Precinct data has multiple advantages over these surveys. First, national survey data is, by construction, ill-suited to identify impacts of geography-speciﬁc factors unless intentional oversampling takes place. Second, survey data relies on truthful and accurate recollection of past behavior; social desirability bias has been known to plague political surveys and exit polls in regards to how individuals vote (Carsey and Jackson, 2001), whether individuals voted at all in the past (Belli et al., 1999), and what their true demographic attributes are (Panagopoulos, 2013). The sample of the 2016 CCES, despite its large size, skews far from population means on several fronts: among those who reported answers, the sample records a 7.5% margin for Clinton over Trump compared to a true national margin of 2.1%, a 20.7% margin for Obama over Romney compared to a true margin of 3.9%, a 72% white population compared to a true 62%, a 8% Hispanic population compared to a true 17%, a 3% less than high school completion rate compared to a true 13%, and a 23% bachelor’s degree completion rate compared to 19%. For all of these variables, precinct-level means better approximate true national means.

60 Precinct data is not subject to such biases, instead suﬀering from other potential sources of bias such as Tiebout sorting, the ecological fallacy, and the modiﬁable areal unit problem. Despite these issues, much valuable research arises from both of these sources; the topic set compatible with survey data has little overlap with the topic set compatible with a highly spatially-disaggregated data source like precinct data.

A.3 Graphical Relationships

Figure A4 uses Epanechnikov kernel-weighted local polynomial regressions (Fan and Gi- jbels, 1996) to predict Democratic share for a variety of demographic characteristics across all three elections. The left column displays relationships with racial and ethnic categories while the right column contains responses to education, income, density, and income inequality. Democratic vote share monotonically diminishes with an increase in the white proportion of the population. Moving from 2008 to 2012, the predicted Obama share curve pivots counterclockwise around 42% white, increasing racial polarization by diminishing his proportion given higher white shares and increasing for lower white shares. Moving from 2012 to 2016, Clinton experienced a broadly parallel downward shift compared to the second Obama victory, returning to Obama 2008 levels in low white precincts. The one exception is in highly-white precincts, where Clinton underperformed Obama’s 2012 curve by far more than elsewhere. Among highly-white precincts, Democratic performance somewhat declined from 2008 to 2012 and sharply declined from 2012 to 2016. The predicted Democratic share curve with respect to black proportion is relatively similar across all three elections. Obama slightly declined at low levels of black share from 2008 to 2012 while gaining in mid-levels. Obama’s share as black proportion approaches one was constant between cycles. Clinton experienced a near-parallel downward shift across the entire black curve, with a slightly stronger decline for low levels of black share. This shift from 2012 to 2016 would be consistent with either time-constant preferences but lower turnout or a slight drop-oﬀ in black support mirroring non-black voters. With respect to Hispanic proportions, the Democratic curve pivoted between 2008 and

61 2012 toward heightened polarization and shifted downward with a slight hint of further polarization between 2012 and 2016. Obama gained dramatically in highly Hispanic precincts between 2008 and 2012, and most of this gain was maintained by Clinton in 2016. Clinton again sharply underperformed in less Hispanic precincts compared to both Obama campaigns. The Democratic curve with respect to Asian proportion showed a similar trend, with Obama increasing from 2008 to 2012 in areas of high proportion and Clinton maintaining the majority of this gain; the diﬀerence seems to be that there was no diﬀerence between Obama 2012 and Clinton 2016 in moderately Asian precincts– between approximately 30% Asian and 55% Asian– whereas Clinton underperformed both Obama runs in moderately Hispanic precincts. The most dramatic racial changes seem to belong to the Democratic curve with respect to Native American proportion. Obama again experienced heightened racial polarization with a large jump in vote share in highly Native American precincts between 2008 and 2012, but Clinton lost ground at much larger rate. She under-performed Obama 2012 in areas with low Native proportions while simultaneously under-performing Obama 2008 in areas with high levels of Native Americans. The overall decline in Democratic proportion between 2012 and 2016 is a mostly parallel shift, indicating perhaps a large decline in turnout rates or a systematic drain to a third party candidate such as Jill Stein, who did well in many reservation precincts. The Democratic curve shifted considerably along educational attainment lines over time. Obama experienced an inward rotation of his curve with respect to bachelor’s degree attainment between 2008 and 2012, with a large reduction in support in areas with high levels of college completion. This trend disappears entirely from 2012 to 2016, with Clinton’s curve experiencing a large polarization relative to Obama’s 2012 curve, surpassing Obama’s 2008 share at high levels of education and strongly lagging Obama’s 2012 share at lower levels. The Democratic curves with respect to the proportion with a high school diploma or less broadly inverts the above trends, with some additional detail at high levels. Below $40,000 of median income, the Obama 2008 and 2012 curves are nearly identical,

62 but diverge increasingly as income rises, likely due to Romney’s appeal among upper income voters. Clinton’s curve deviates from this pattern, increasingly lagging Obama’s curves as income initially rises, with a maximum decline near $50,000, before rapidly rising to cross the Obama 2012 curve near $100,000 and coincide with Obama’s 2008 performance among voters in precincts with a median household income above $130,000. This rise in income-based polarization between 2012 and 2016 also appears when income inequality is taken into consideration. All three Democrats performed better in precincts with higher levels of income equality, but Clinton trailed both Obama runs in regions with low and moderate levels of inequality while outperforming Obama 2012 among areas with high levels of inequality. Both 2012 and 2016 experienced a decline in the Democratic share among voters in areas with lower population densities, although the decline from 2012 to 2016 outpaced the decline from 2008 to 2012. All three cycles saw a similar performance for the Democratic candidate in areas with high population density. This heightened urban-rural polarization adds to the lengthy list of lines by which the 2016 election was more polarized than both elections preceding it.

63 Table A1: GWR estimates by race and heterogeneity level 2016 National State County Zip Code Tract D 32.7 34.0 38.6 40.3 40.4 White R 61.4 59.9 55.2 53.5 53.6 Other 5.9 6.1 6.2 6.1 6.0 D 97.6 100.6 90.5 81.6 81.1 Black R 1.0 -3.7 6.1 13.7 14.2 Other 1.3 3.1 3.3 4.7 4.7 D 75.4 72.5 67.3 60.2 58.3 Hispanic R 19.7 22.4 27.1 33.6 35.7 Other 5.0 5.2 5.6 6.2 6.0 D 97.1 82.6 60.3 54.8 54.1 Asian R -2.5 11.7 33.8 39.2 39.9 Other 5.4 5.6 5.8 6.1 6.0 D 53.0 65.7 76.0 68.5 76.9 Native R 32.1 24.6 14.4 21.7 13.3 Other 14.9 9.7 9.6 9.8 9.8 2012 D 38.5 38.6 42.7 44.0 44.0 White R 59.5 59.3 55.1 53.9 54.1 Other 2.0 2.2 2.2 2.1 1.9 D 98.3 101.9 96.9 86.8 86.0 Black R 1.4 -2.9 1.8 11.4 12.3 Other 0.4 1.0 1.3 1.8 1.8 D 74.3 74.1 72.1 63.8 61.4 Hispanic R 23.5 24.2 25.9 34.0 36.5 Other 2.2 1.7 2.0 2.2 2.1 D 86.7 75.1 60.3 56.9 55.4 Asian R 10.6 22.8 37.0 40.1 41.3 Other 2.7 2.1 2.7 3.0 3.3 D 64.7 75.3 82.5 73.3 81.9 Native R 28.1 18.3 8.2 12.9 9.2 Other 7.2 6.3 9.3 13.8 8.9 2008 D 41.4 41.8 45.0 46.6 46.9 White R 57.0 56.6 53.5 51.9 51.6 Other 1.6 1.6 1.5 1.5 1.5 D 95.6 100.1 95.3 85.1 84.6 Black R 4.2 -0.6 4.0 13.9 14.4 Other 0.2 0.5 0.7 1.0 1.0 D 67.8 68.7 66.6 61.7 60.3 Hispanic R 31.3 30.0 31.9 36.8 38.2 Other 1.0 1.3 1.5 1.5 1.5 D 81.7 71.2 57.1 55.9 55.7 Asian R 16.9 27.9 41.6 42.6 43.0 Other 1.4 0.9 1.4 1.4 1.3 D 54.6 67.6 77.4 71.6 77.2 Native R 43.5 30.5 21.0 26.8 21.9 Other 1.9 1.9 1.5 1.6 0.9 Note: Assumed electorate based on SKR estimates.

64 Table A2: GWR estimates by education and heterogeneity level 2016 National State County Zip Code Tract D 59.0 63.1 64.6 55.1 53.1 Less than HS R 37.6 32.5 30.3 39.2 41.2 Other 3.4 4.5 5.1 5.7 5.7 D 29.6 33.4 45.6 47.6 46.7 HS Diploma R 66.4 61.6 49.1 46.8 47.6 Other 4.0 5.1 5.3 5.6 5.7 D 41.4 41.6 47.3 48.6 47.9 Some College R 7.2 6.0 5.8 5.9 5.9 Other 7.2 6.0 5.8 5.9 5.9 D 54.1 51.2 38.5 43.7 45.2 Bach. Deg. R 38.2 41.6 54.7 50.3 48.6 Other 7.7 7.2 6.8 6.1 6.2 D 72.8 64.9 46.3 42.3 46.1 Grad. Deg. R 20.4 28.1 47.0 51.7 47.9 Other 6.8 7.0 6.7 6.0 6.0 2012 D 66.4 69.1 70.4 61.0 59.0 Less than HS R 31.2 28.4 27.0 36.5 38.4 Other 2.4 2.5 2.6 2.6 2.6 D 43.9 46.8 54.7 53.8 53.0 HS Diploma R 54.4 51.2 43.5 44.4 45.1 Other 1.7 2.0 1.8 1.8 1.9 D 46.9 48.1 52.4 52.4 52.1 Some College R 51.3 50.3 46.1 46.0 46.3 Other 1.9 1.6 1.5 1.5 1.6 D 50.2 47.9 39.6 45.9 47.1 Bach. Deg. R 47.6 50.0 58.2 51.9 50.7 Other 2.2 2.0 2.2 2.3 2.2 D 62.2 52.7 37.8 41.2 44.0 Grad. Deg. R 34.7 44.1 58.8 55.6 52.7 Other 3.1 3.2 3.3 3.3 3.3 2008 D 63.5 67.6 69.2 61.4 59.6 Less than HS R 35.1 30.8 29.3 37.1 38.8 Other 1.4 1.5 1.5 1.5 1.6 D 46.9 49.4 55.4 54.7 54.0 HS Diploma R 51.5 48.9 43.1 43.8 44.5 Other 1.6 1.7 1.5 1.5 1.5 D 48.2 49.4 52.6 53.5 53.4 Some College R 50.2 49.2 46.0 45.0 45.2 Other 1.6 1.5 1.4 1.4 1.4 D 53.4 51.2 44.0 49.1 49.9 Bach. Deg. R 45.3 47.7 54.7 49.6 48.7 Other 1.2 1.1 1.3 1.4 1.3 D 67.9 58.2 45.9 46.1 48.6 Grad. Deg. R 31.1 40.7 52.9 52.7 50.2 Other 1.0 1.1 1.3 1.2 1.2 Note: Assumed electorate based on SKR estimates.

65 Table A3: Model estimate correlations TP ERZ GWR KR SKR NM Exit ERN 0.846 0.750 0.750 0.763 0.783 0.770 0.848 TP 0.976 0.976 0.963 0.973 0.970 0.996 ERZ 0.999 0.972 0.974 0.979 0.974 GWR 0.972 0.973 0.979 0.974 KR 0.996 0.998 0.958 SKR 0.996 0.967 NM 0.966

66 Table A4: Determinants of Democratic precinct vote shares, 2008-2016 (1/7) Variable Clinton 2016 Obama 2012 Obama 2008 SKR Electorate White Prop. 0.0077 0.0905 0.0933 (0.0268) (0.0691) (0.0554)* Black Prop. 0.2889 0.3636 0.3152 (0.0281)*** (0.0699)*** (0.0575)*** Hispanic Prop. 0.1666 0.2176 0.1669 (0.0263)*** (0.0693)*** (0.0563)*** Asian Prop. 0.1022 0.1646 0.1230 (0.0248)*** (0.0679)** (0.0548)** Native Prop. 0.2402 0.3514 0.2387 (0.0314)*** (0.0707)*** (0.0569)*** Less than HS Prop. -0.0930 -0.0429 -0.0263 (0.0053)*** (0.0042)*** (0.0039)*** HS Graduate Prop. -0.1041 -0.0528 -0.0320 (0.0046)*** (0.0039)*** (0.0038)*** Some College Prop. -0.1051 -0.0550 -0.0359 (0.0046)*** (0.0044)*** (0.0041)*** Bachelor Prop. -0.0524 -0.0423 -0.0280 (0.0043)*** (0.0039)*** (0.0036)***

Change in SKR Turnout Change in White Turnout 0.0442 0.0374 (0.0027)*** (0.0022)*** Change in Black Turnout -0.0092 -0.0104 (0.0007)*** (0.0007)*** Change in Hispanic Turnout -0.0038 -0.0020 (0.0007)*** (0.0008)** Change in Asian Turnout 0.0004 0.0006 (0.0005) (0.0004) Change in Native Turnout -0.0006 -0.0016 (0.0005) (0.0006)*** Change in Less than HS Turnout -0.0014 -0.0029 (0.0005)*** (0.0006)*** Change in HS Graduate Turnout -0.0006 -0.0052 (0.0009) (0.0008)*** Change in Some College Turnout -0.0023 -0.0057 (0.0010)** (0.0005)*** Change in Bachelor Turnout -0.0066 -0.0035 (0.0007)*** (0.0011)*** Change in Graduate Turnout -0.0084 -0.0030 (0.0006)*** (0.0007)***

67 Table A5: Determinants of Democratic precinct vote shares, 2008-2016 (2/7) Variable Clinton Obama Obama 2016 2012 2008 Demographics Total Votes (in thousands) 0.0015 -0.0003 0.0021 (0.0007)** (0.0003) (0.0006)*** Tract Population (in thousands) -0.0010 -0.0008 -0.0007 (0.0002)*** (0.0003)*** (0.0002)*** Population Density (in thousands) 0.0003 0.0002 0.0002 (0.0002) (0.0001) (0.0002) Female 0.1028 0.0515 0.0137 (0.0095)*** (0.0102)*** (0.0081)* Married -0.1489 -0.1587 -0.1154 (0.0099)*** (0.0106)*** (0.0091)*** Median Age 0.0011 0.0009 0.0001 (0.0001)*** (0.0001)*** (0.0001) Disabled -0.0855 -0.0313 -0.0883 (0.0132)*** (0.0123)** (0.0126)*** Foreign Born 0.1087 0.1015 0.0551 (0.0224)*** (0.0263)*** (0.0214)**

Economic Characteristics Median Household Income (in thousands) 0.0000 -0.0004 -0.0002 (0.0000) (0.0001)*** (0.0001)*** Under Poverty Line 0.1021 0.0902 0.0896 (0.0081)*** (0.0078)*** (0.0076)*** Poverty Line to Double Pov. Line 0.0620 0.0670 0.0634 (0.0051)*** (0.0046)*** (0.0041)*** Gini Coeﬃcient -0.0132 -0.0718 -0.0302 (0.0134) (0.0117)*** (0.0093)*** Unemployed 0.0242 0.1013 0.0792 (0.0181) (0.0188)*** (0.0114)*** Not in Labor Force -0.0763 -0.0903 -0.0289 (0.0073)*** (0.0077)*** (0.0054)*** Average Household Size -0.0038 -0.0020 -0.0102 (0.0031) (0.0036) (0.0031)*** Occupied Housing -0.0037 0.0466 0.0638 (0.0297) (0.0306) (0.0289)** Seasonal Housing 0.0252 0.0630 0.0869 (0.0296) (0.0303)** (0.0288)*** Vacant Housing -0.0245 0.0140 0.0375 (0.0339) (0.0375) (0.0360)

68 Table A6: Determinants of Democratic precinct vote shares, 2008-2016 (3/7) Variable Clinton 2016 Obama 2012 Obama 2008 Commute Patterns Average Commute Time -0.0008 -0.0003 -0.0001 (0.0001)*** (0.0001)** (0.0001) Work at Home -0.0740 -0.0320 -0.0125 (0.0272)*** (0.0274) (0.0267) Commute by Driving Alone -0.1167 -0.0511 -0.0352 (0.0213)*** (0.0191)*** (0.0190)* Commute by Carpool -0.0570 0.0008 0.0046 (0.0203)*** (0.0175) (0.0187) Commute by Bus -0.0831 -0.1087 -0.0648 (0.0635) (0.0809) (0.0707) Commute by Subway 0.0108 0.0083 0.0416 (0.0870) (0.1034) (0.0957) Commute by Rail 0.1465 0.0930 0.1339 (0.0777)* (0.0938) (0.0860) Commute by Ferry 0.3419 0.2634 0.2669 (0.1080)*** (0.1319)** (0.1193)** Commute by Taxi 0.1429 0.1695 0.2008 (0.0821)* (0.0983)* (0.0981)** Commute by Motorcycle -0.2301 -0.1296 -0.1162 (0.0483)*** (0.0500)*** (0.0508)** Commute by Bicycle 0.2660 0.3650 0.3559 (0.0397)*** (0.0425)*** (0.0397)*** Commute by Walking -0.1399 -0.0992 -0.0637 (0.0231)*** (0.0219)*** (0.0237)***

69 Table A7: Determinants of Democratic precinct vote shares, 2008-2016 (4/7) Variable Clinton 2016 Obama 2012 Obama 2008 Industry Employment Employed in Agriculture -0.0166 -0.1488 -0.0250 (0.0400) (0.0244)*** (0.0262) Employed in Oil and Mining -0.0649 -0.0752 -0.0001 (0.0389)* (0.0188)*** (0.0320) Employed in Construction 0.0318 -0.0349 0.0517 (0.0333) (0.0146)** (0.0229)** Employed in Manufacturing 0.1362 0.0565 0.1321 (0.0316)*** (0.0124)*** (0.0256)*** Employed in Wholesale 0.0628 -0.0599 0.0383 (0.0329)* (0.0165)*** (0.0258) Employed in Retail 0.1000 -0.0112 0.0759 (0.0313)*** (0.0130) (0.0232)*** Employed in Transportation 0.0843 0.0017 0.0869 (0.0403)** (0.0258) (0.0335)*** Employed in Utilities -0.0530 -0.1336 -0.0098 (0.0389) (0.0295)*** (0.0277) Employed in Information 0.3179 0.1957 0.2368 (0.0628)*** (0.0442)*** (0.0474)*** Employed in Finance and Insurance 0.1294 -0.0455 0.0670 (0.0353)*** (0.0184)** (0.0267)** Employed in Real Estate 0.0951 -0.0999 0.0256 (0.0344)*** (0.0258)*** (0.0261) Employed in Science and Technology 0.3152 0.1647 0.2110 (0.0391)*** (0.0209)*** (0.0335)*** Employed in Management 0.2090 -0.0774 0.0736 (0.0759)*** (0.0930) (0.0772) Employed in Administration 0.1535 0.0424 0.1132 (0.0327)*** (0.0140)*** (0.0265)*** Employed in Education 0.1883 0.0428 0.1078 (0.0320)*** (0.0132)*** (0.0257)*** Employed in Health Care 0.1728 0.0460 0.1278 (0.0318)*** (0.0121)*** (0.0250)*** Employed in Arts and Recreation 0.2748 0.1337 0.1809 (0.0438)*** (0.0314)*** (0.0406)*** Employed in Accommodation 0.1588 0.0712 0.1521 (0.0311)*** (0.0159)*** (0.0231)***

70 Table A8: Determinants of Democratic precinct vote shares, 2008-2016 (5/7) Variable Clinton 2016 Obama 2012 Obama 2008 Ancestry “American” Descent -0.2333 -0.1805 -0.1881 (0.0116)*** (0.0120)*** (0.0119)*** Arab Descent -0.0594 -0.0489 -0.0953 (0.0802) (0.0847) (0.0692) Czech Descent -0.0947 -0.0414 -0.0042 (0.0413)** (0.0418) (0.0426) Danish Descent 0.1119 0.1311 0.1006 (0.0501)** (0.0498)*** (0.0488)** Dutch Descent -0.2571 -0.2726 -0.2675 (0.0307)*** (0.0317)*** (0.0308)*** English Descent -0.1932 -0.1837 -0.1899 (0.0155)*** (0.0158)*** (0.0155)*** French Descent -0.2893 -0.2066 -0.2284 (0.0319)*** (0.0360)*** (0.0382)*** German Descent -0.1616 -0.1323 -0.1274 (0.0152)*** (0.0157)*** (0.0155)*** Irish Descent -0.0693 -0.0378 -0.0773 (0.0187)*** (0.0215)* (0.0172)*** Italian Descent -0.1552 -0.0915 -0.1328 (0.0238)*** (0.0236)*** (0.0239)*** Norwegian Descent 0.0561 0.0719 0.0519 (0.0338)* (0.0365)** (0.0316) Pennsylvania Dutch Descent -0.2419 -0.1869 -0.1886 (0.0398)*** (0.0450)*** (0.0482)*** Polish Descent -0.0100 0.0685 0.0440 (0.0207) (0.0197)*** (0.0203)** Portuguese Descent -0.1277 -0.0260 -0.0854 (0.0436)*** (0.0493) (0.0497)* Russian Descent 0.2289 0.2355 0.2657 (0.0883)*** (0.0880)*** (0.0983)*** Ukrainian Descent -0.3865 -0.3241 -0.3210 (0.0653)*** (0.0646)*** (0.0666)*** Welsh Descent -0.0848 -0.1105 -0.1221 (0.0565) (0.0591)* (0.0531)** Cuban Descent -0.2253 -0.2331 -0.2287 (0.0406)*** (0.0490)*** (0.0418)*** Puerto Rican Descent 0.0554 0.0619 0.0272 (0.0165)*** (0.0177)*** (0.0193) Hispan˜o Descent -0.0340 0.0018 -0.0167 (0.0292) (0.0305) (0.0300) Asian Indian Descent -0.0773 -0.0546 -0.0273 (0.0221)*** (0.0204)*** (0.0158)* Chinese Descent -0.0686 -0.0345 -0.0513 (0.0190)*** (0.0221) (0.0205)** Filipino Descent -0.0668 -0.0703 -0.0944 (0.0306)** (0.0357)** (0.0434)** Hmong Descent -0.0319 -0.0412 -0.0610 (0.0401) (0.0415) (0.0317)* Japanese Descent 0.0406 0.0391 0.0365 (0.0399) (0.0645) (0.0661) Korean Descent -0.1249 -0.1257 -0.1122 (0.0436)*** (0.0529)** (0.0622)* Vietnamese Descent -0.0929 -0.1358 -0.1827 (0.0157)*** (0.0183)*** (0.0157)*** 71 Table A9: Determinants of Democratic precinct vote shares, 2008-2016 (6/7) Variable Clinton 2016 Obama 2012 Obama 2008 Demographic Change Change in White 0.0501 -0.0117 (0.0119)*** (0.0113) Change in Black -0.0767 -0.0312 (0.0116)*** (0.0132)** Change in Hispanic -0.0249 -0.0399 (0.0101)** (0.0128)*** Change in Asian 0.0016 -0.0069 (0.0127) (0.0119) Change in Native -0.0868 -0.1037 (0.0229)*** (0.0232)*** Change in Less Than HS 0.0155 -0.0076 (0.0053)*** (0.0051) Change in Some College 0.0567 -0.0064 (0.0073)*** (0.0046) Chande in Bachelor’s Degree 0.0018 -0.0037 (0.0057) (0.0057) Change in Graduate Degree -0.0222 -0.0376 (0.0084)*** (0.0069)*** Change in Population 0.0000 -0.0004 (0.0000)*** (0.0001)*** Change in Female -0.0424 0.0094 (0.0070)*** (0.0085) Change in Married 0.0736 0.0677 (0.0097)*** (0.0096)*** Change in Poverty Rate -0.0341 -0.0199 (0.0054)*** (0.0047)*** Change in Median Household Income 0.0000 0.0000 (0.0000)*** (0.0000)*** Change in Unemployment 0.0184 -0.0811 (0.0384) (0.0158)*** Change in NILF 0.0652 -0.0158 (0.0376)* (0.0126) Change in Gini Coeﬃcient 0.0067 -0.0437 (0.0082) (0.0188)** Change in Median Age -0.0003 0.0361 (0.0001)** (0.0088)*** Change in Foreign Born -0.0829 0.0000 (0.0123)*** (0.0000)*

72 Table A10: Determinants of Democratic precinct vote shares, 2008-2016 (7/7) Variable Clinton 2016 Obama 2012 Obama 2008 Industry Change Change in Agriculture and Mining 0.0541 -0.0436 (0.0371) (0.0235)* Change in Construction 0.0240 0.0493 (0.0355) (0.0219)** Change in Manufacturing -0.0304 -0.0283 (0.0361) (0.0197) Change in Wholesale 0.0058 -0.0797 (0.0369) (0.0186)*** Change in Retail -0.0088 -0.0257 (0.0347) (0.0203) Change in Transportation 0.0034 -0.0321 (0.0414) (0.0194)* Change in Utilities 0.0518 -0.0371 (0.0379) (0.0226) Change in Information -0.1238 0.0225 (0.0511)** (0.0270) Change in Finance and Insurance -0.0329 -0.1461 (0.0380) (0.0277)*** Change in Real Estate -0.0053 -0.0110 (0.0354) (0.0209) Change in Science and Technology -0.1114 0.0168 (0.0398)*** (0.0251) Change in Management -0.0226 -0.1303 (0.0764) (0.0210)*** Change in Administration -0.0510 0.0300 (0.0366) (0.0761) Change in Education -0.0633 -0.0579 (0.0372)* (0.0194)*** Change in Health Care -0.0515 -0.0699 (0.0366) (0.0190)*** Change in Arts and Recreation -0.1141 -0.0628 (0.0414)*** (0.0195)*** Change in Accommodation -0.0514 -0.1033 (0.0344) (0.0264)***

Spatial Lag 0.4315 0.4538 0.5078 (0.0271)*** (0.0291)*** (0.0305)*** Constant 0.3647 0.3080 0.1791 (0.0423)*** (0.0769)*** (0.0636)*** N 167,526 167,109 167,214 R2 0.9009 0.8829 0.8715 Note: All estimates include county-level ﬁxed eﬀects and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1

73 Table A11: Selected determinants of Clinton vote shares by weight type Variable None Error Votes Both SKR White -0.0370 -0.0372 0.0077 0.0077 (0.0391) (0.0391) (0.0269) (0.0268) SKR Black 0.2482 0.2476 0.2894 0.2889 (0.0400)*** (0.0400)*** (0.0281)*** (0.0281)*** SKR Hispanic 0.1261 0.1258 0.1667 0.1666 (0.0380)*** (0.0380)*** (0.0263)*** (0.0263)*** SKR Asian 0.0605 0.0604 0.1021 0.1022 (0.0366)* (0.0366)* (0.0248)*** (0.0248)*** SKR Native 0.1987 0.1996 0.2394 0.2402 (0.0432)*** (0.0433)*** (0.0315)*** (0.0314)*** SKR Less than HS -0.0915 -0.0923 -0.0923 -0.0930 (0.0062)*** (0.0062)*** (0.0053)*** (0.0053)*** SKR HS Graduate -0.1003 -0.1016 -0.1030 -0.1041 (0.0051)*** (0.0051)*** (0.0046)*** (0.0046)*** SKR Some College -0.1009 -0.1015 -0.1046 -0.1051 (0.0050)*** (0.0050)*** (0.0046)*** (0.0046)*** SKR Bachelor’s Deg. -0.0491 -0.0491 -0.0524 -0.0524 (0.0047)*** (0.0047)*** (0.0042)*** (0.0043)*** Total Votes 0.0029 0.0029 0.0015 0.0015 (0.0009)*** (0.0009)*** (0.0007)** (0.0007)** Tract Population -0.0011 -0.0011 -0.0010 -0.0010 (0.0002)*** (0.0002)*** (0.0002)*** (0.0002)*** Population Density 0.0003 0.0003 0.0003 0.0003 (0.0002) (0.0002) (0.0002) (0.0002) Female 0.0971 0.0967 0.1032 0.1028 (0.0099)*** (0.0099)*** (0.0095)*** (0.0095)*** Married -0.1473 -0.1468 -0.1492 -0.1489 (0.0101)*** (0.0102)*** (0.0099)*** (0.0099)*** Median Age 0.0010 0.0010 0.0011 0.0011 (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** Foreign Born 0.1140 0.1137 0.1090 0.1087 (0.0233)*** (0.0231)*** (0.0226)*** (0.0224)*** Average H.H. Size -0.0051 -0.0049 -0.0041 -0.0038 (0.0032) (0.0032) (0.0032) (0.0031) Median H.H. Income 0.0001 0.0001 0.0000 0.0000 (0.0000) (0.0000) (0.0000) (0.0000) Under Poverty Line 0.1037 0.1033 0.1027 0.1021 (0.0085)*** (0.0085)*** (0.0081)*** (0.0081)*** Gini Coeﬃcient -0.0090 -0.0085 -0.0137 -0.0132 (0.0134) (0.0135) (0.0133) (0.0134) Unemployed 0.0252 0.0258 0.0237 0.0242 (0.0184) (0.0184) (0.0181) (0.0181) Not in Labor Force -0.0731 -0.0738 -0.0755 -0.0763 (0.0077)*** (0.0077)*** (0.0074)*** (0.0073)*** Average Commute Time -0.0008 -0.0008 -0.0008 -0.0008 (0.0002)*** (0.0002)*** (0.0001)*** (0.0001)*** Spatial Lag 0.4311 0.4317 0.4309 0.4315 (0.0263)*** (0.0265)*** (0.0270)*** (0.0271)*** Constant 0.4520 0.4544 0.3628 0.3647 (0.0573)*** (0.0573)*** (0.0422)*** (0.0423)*** N 167,756 167,756 167,526 167,526 R2 0.8811 0.8811 0.9006 0.9009

74 Table A12: Determinants of change in SKR turnout by race, 2012-16 (1/5) Variable White Black Hispanic Asian Native Initial SKR Electorate White in 2012 -0.8996 -0.6212 -0.0465 -0.8206 -0.1775 (0.0820)*** (0.1684)*** (0.1356) (0.2307)*** (0.1712) Black in 2012 -0.4923 -1.1592 -0.0875 -0.8085 -0.2077 (0.0791)*** (0.1718)*** (0.1328) (0.2312)*** (0.1711) Hispanic in 2012 -0.3175 -0.5975 -1.3489 -0.8685 -0.1876 (0.0790)*** (0.1686)*** (0.1701)*** (0.2314)*** (0.1710) Asian in 2012 -0.2600 -0.5891 -0.1153 -2.5436 -0.1797 (0.0788)*** (0.1701)*** (0.1283) (0.2559)*** (0.1701) Native in 2012 -0.4560 -0.6841 -0.0337 -0.7376 -0.9632 (0.0800)*** (0.1701)*** (0.1398) (0.2362)*** (0.1958)*** LTHS in 2012 -0.0912 -0.0182 0.0123 -0.0753 0.0143 (0.0097)*** (0.0143) (0.0147) (0.0170)*** (0.0126) HS Grad. in 2012 -0.1011 -0.0191 -0.0268 -0.0829 -0.0018 (0.0085)*** (0.0128) (0.0127)** (0.0145)*** (0.0115) Some Coll. in 2012 -0.0990 -0.0248 -0.0408 -0.0982 -0.0001 (0.0093)*** (0.0128)* (0.0128)*** (0.0145)*** (0.0112) Bach. Deg. in 2012 -0.0530 -0.0246 -0.0260 -0.0516 0.0119 (0.0079)*** (0.0136)* (0.0131)** (0.0150)*** (0.0117)

Demographics Total Votes 0.0366 0.0159 0.0320 0.0212 -0.0007 (0.0031)*** (0.0025)*** (0.0034)*** (0.0033)*** (0.0022) Tract Population -0.0018 -0.0003 -0.0001 0.0001 0.0000 (0.0004)*** (0.0007) (0.0006) (0.0007) (0.0006) Population Density -0.0003 -0.0005 0.0006 -0.0002 -0.0001 (0.0002) (0.0003)** (0.0002)*** (0.0002) (0.0002) Female -0.0316 0.1096 -0.0466 -0.0741 -0.0324 (0.0118)*** (0.0238)*** (0.0270)* (0.0252)*** (0.0205) Married 0.1254 -0.1979 -0.0431 -0.0016 -0.0573 (0.0120)*** (0.0202)*** (0.0193)** (0.0174) (0.0145)*** Median Age -0.0010 -0.0001 0.0019 -0.0003 0.0004 (0.0002)*** (0.0004) (0.0004)*** (0.0003) (0.0003) Disabled 0.1237 0.0581 -0.0783 0.0155 0.0108 (0.0205)*** (0.0516) (0.0396)** (0.0445) (0.0358) Foreign Born -0.1329 -0.1623 0.1314 0.2619 -0.0020 (0.0249)*** (0.0377)*** (0.0240)*** (0.0269)*** (0.0173)

Economic Characteristics Median H.H. Income -0.0003 -0.0000 -0.0005 -0.0001 -0.0001 (0.0001)*** (0.0001) (0.0001)*** (0.0001) (0.0001) Under Poverty Line -0.0864 0.0443 0.0685 0.0348 0.0193 (0.0112)*** (0.0219)** (0.0219)*** (0.0195)* (0.0156) Poverty Line to Double -0.0446 0.0381 0.0775 0.0291 0.0084 (0.0098)*** (0.0190)** (0.0186)*** (0.0190) (0.0155) Gini Coeﬃcient 0.0294 0.0798 -0.0195 -0.0586 0.0354 (0.0134)** (0.0320)** (0.0293) (0.0293)** (0.0257) Unemployed -0.0975 0.4027 -0.0207 -0.0542 0.0447 (0.0270)*** (0.0505)*** (0.0433) (0.0499) (0.0355) Not in Labor Force -0.0122 0.0640 -0.0047 -0.0171 -0.0257 (0.0100) (0.0170)*** (0.0188) (0.0189) (0.0153)* Average H.H. Size -0.0404 0.0143 0.0469 0.0033 0.0124 (0.0061)*** (0.0060)** (0.0099)*** (0.0048) (0.0054)**

75 Table A13: Determinants of change in SKR turnout by race, 2012-16 (2/5) Variable White Black Hispanic Asian Native Commute Patterns Average Commute Time 0.0008 0.0010 -0.0002 0.0006 0.0002 (0.0002)***(0.0003)*** (0.0003) (0.0003)** (0.0003) Work at Home -0.0132 -0.1175 0.0700 -0.1595 -0.0595 (0.0362) (0.0713)* (0.0783) (0.0744)** (0.0610) Commute by Driving Alone -0.0228 -0.1451 0.0797 -0.0467 -0.0933 (0.0331) (0.0642)** (0.0659) (0.0654) (0.0552)* Commute by Carpool -0.0549 -0.0865 0.0725 -0.0308 -0.1363 (0.0350) (0.0660) (0.0661) (0.0693) (0.0566)** Commute by Bus -0.1143 0.5686 -0.1291 0.2113 0.2642 (0.1060) (0.3008)* (0.1571) (0.2368) (0.1923) Commute by Subway -0.0275 0.2314 -0.2361 0.3044 0.1910 (0.1312) (0.2977) (0.1929) (0.2632) (0.2035) Commute by Rail 0.0011 0.2640 -0.1300 0.2982 0.2341 (0.1106) (0.3070) (0.1708) (0.2601) (0.2002) Commute by Ferry 0.0869 0.3085 -0.0269 0.3636 0.1973 (0.1335) (0.3806) (0.2756) (0.3154) (0.2152) Commute by Taxi 0.2444 0.2529 -0.0930 0.2896 0.2821 (0.1256)* (0.1552) (0.1639) (0.2075) (0.1306)** Commute by Motorcycle 0.3464 -0.2219 0.2213 0.4541 -0.2160 (0.0818)*** (0.1975) (0.1945) (0.2202)** (0.1645) Commute by Bicycle 0.1105 -0.3111 0.2063 -0.2044 -0.1058 (0.0609)* (0.1028)*** (0.1304) (0.1044)* (0.0903) Commute by Walking -0.0407 -0.2561 0.1478 0.0416 -0.0842 (0.0364) (0.0706)***(0.0794)* (0.0746) (0.0608)

76 Table A14: Determinants of change in SKR turnout by race, 2012-16 (3/5) Variable White Black Hispanic Asian Native Industry Employment Emp. in Agriculture 0.1436 -0.0067 -0.0534 -0.0547 0.0583 (0.0491)*** (0.0782) (0.1078) (0.0923) (0.0677) Emp. in Oil and Mining 0.1783 -0.2308 -0.1346 -0.0369 -0.0381 (0.0491)*** (0.1040)** (0.1026) (0.1113) (0.0872) Emp. in Construction 0.2510 -0.2233 -0.1549 -0.0837 0.0292 (0.0425)*** (0.0781)*** (0.0922)* (0.0885) (0.0682) Emp. in Manufacturing 0.0948 -0.0236 -0.1072 -0.0085 0.0266 (0.0404)** (0.0768) (0.0796) (0.0857) (0.0646) Emp. in Wholesale 0.0711 -0.2492 0.0040 0.0113 0.0589 (0.0458) (0.0864)*** (0.0919) (0.0950) (0.0740) Emp. in Retail 0.0754 -0.1065 -0.0929 -0.0203 0.0277 (0.0407)* (0.0749) (0.0842) (0.0856) (0.0659) Emp. in Transportation 0.0164 0.1566 -0.1226 -0.0008 0.0527 (0.0475) (0.0795)** (0.0851) (0.0892) (0.0679) Emp. in Utilities 0.1848 -0.1188 -0.1579 -0.1219 -0.0387 (0.0546)*** (0.1095) (0.1151) (0.1204) (0.0968) Emp. in Information 0.1721 -0.0361 -0.1984 -0.0997 0.0939 (0.0663)*** (0.0921) (0.1274) (0.1002) (0.0782) Emp. in Finance 0.0279 -0.0849 -0.0717 0.0112 0.0470 (0.0441) (0.0794) (0.0852) (0.0895) (0.0739) Emp. in Real Estate 0.1272 0.0330 -0.2539 -0.1980 0.1162 (0.0495)** (0.0848) (0.1096)** (0.0939)** (0.0797) Emp. in Sci. and Tech. 0.1045 -0.1765 -0.1440 -0.1147 0.0327 (0.0422)** (0.0733)** (0.0846)* (0.0896) (0.0657) Emp. in Management -0.1353 -0.2744 0.7556 0.2582 -0.1656 (0.1425) (0.2949) (0.3204)** (0.3101) (0.2927) Emp. in Administration 0.0960 0.0978 -0.1614 -0.0455 -0.0089 (0.0428)** (0.0812) (0.0910)* (0.0896) (0.0690) Emp. in Education 0.0680 -0.0452 -0.1950 -0.0784 0.0635 (0.0406)* (0.0739) (0.0821)** (0.0850) (0.0673) Emp. in Health Care -0.0010 0.1438 -0.1402 -0.0002 0.0696 (0.0398) (0.0750)* (0.0835)* (0.0850) (0.0661) Emp. in Arts and Rec. 0.1191 -0.1820 -0.2326 -0.0896 0.1154 (0.0481)** (0.0951)* (0.1021)** (0.0950) (0.0783) Emp. in Accommodation 0.0719 -0.1311 -0.1201 -0.0106 0.0392 (0.0404)* (0.0740)* (0.0910) (0.0858) (0.0669)

77 Table A15: Determinants of change in SKR turnout by race, 2012-16 (4/5) Variable White Black Hispanic Asian Native Ancestry “American” 0.3379 (0.0152)*** Arab 0.2787 (0.0321)*** Czech 0.4121 (0.0711)*** Danish 0.2972 (0.0880)*** Dutch 0.2611 (0.0472)*** English 0.3416 (0.0245)*** French 0.3852 (0.0413)*** German 0.2913 (0.0171)*** Irish 0.3335 (0.0200)*** Italian 0.3038 (0.0205)*** Norwegian 0.3349 (0.0485)*** Pennsylvania Dutch 0.2064 (0.0634)*** Polish 0.3879 (0.0350)*** Portuguese 0.3181 (0.0512)*** Russian 0.4743 (0.0595)*** Ukrainian 0.3575 (0.0697)*** Welsh 0.3012 (0.0874)***

78 Table A16: Determinants of change in SKR turnout by race, 2012-16 (5/5) Variable White Black Hispanic Asian Native Ancestry (Cont.) Mexican 0.5467 (0.0315)*** Dominican 0.7415 (0.0800)*** Cuban 0.7530 (0.1039)*** Puerto Rican 0.5432 (0.0572)*** Central American 0.3923 (0.0509)*** South American 0.3071 (0.0685)*** Hispan˜o 0.4364 (0.0848)*** Asian Indian 0.7371 (0.0784)*** Chinese 0.8999 (0.0810)*** Filipino 1.0598 (0.0753)*** Hmong 0.8001 (0.1691)*** Japanese 1.1586 (0.2216)*** Korean 0.6348 (0.0892)*** Vietnamese 0.9499 (0.0935)***

Spatial Lag 0.1558 -0.0495 -0.0486 -0.0694 -0.0626 (0.0227)*** (0.0160)*** (0.0160)*** (0.0146)*** (0.0196)*** Constant 0.7620 0.7134 -0.0478 0.9864 0.1805 (0.0826)*** (0.1649)*** (0.1566) (0.2385)*** (0.1679) N 167,553 167,553 167,553 167,553 167,553 R2 0.2329 0.0811 0.1545 0.1178 0.0720 Note: All estimates include county-level ﬁxed eﬀects and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1

79 Table A17: Determinants of change in SKR turnout by education, 2012-16 (1/5) Variable LTHS HS Grad. Some Bach. Grad. Coll. Initial SKR Electorate White in 2012 -0.6680 -0.2686 -0.5530 -0.5220 -0.9611 (0.1648)*** (0.1198)** (0.0967)*** (0.1517)*** (0.2054)*** Black in 2012 -0.6686 -0.2995 -0.5635 -0.5823 -1.0012 (0.1641)*** (0.1190)** (0.0965)*** (0.1511)*** (0.2045)*** Hispanic in 2012 -0.5765 -0.2977 -0.5490 -0.5696 -0.9770 (0.1649)*** (0.1205)** (0.0967)*** (0.1509)*** (0.2046)*** Asian in 2012 -0.6489 -0.2574 -0.4725 -0.4271 -0.8771 (0.1618)*** (0.1192)** (0.0993)*** (0.1498)*** (0.2036)*** Native in 2012 -0.4512 -0.2198 -0.4265 -0.3505 -0.8447 (0.1662)*** (0.1188)* (0.1021)*** (0.1519)** (0.2030)*** LTHS in 2012 -1.8171 -0.0004 -0.0712 -0.0935 1.9081 (0.0469)*** (0.0156) (0.0128)*** (0.0219)*** (0.0464)*** HS Grad. in 2012 -0.0176 -1.2630 -0.0125 -0.0894 1.8928 (0.0143) (0.0188)*** (0.0118) (0.0214)*** (0.0462)*** Some Coll. in 2012 -0.0422 0.0569 -1.4245 -0.0713 1.9060 (0.0145)*** (0.0136)*** (0.0136)*** (0.0214)*** (0.0468)*** Bach. Deg. in 2012 -0.0251 0.0049 -0.0081 -1.7934 1.9596 (0.0138)* (0.0142) (0.0114) (0.0425)*** (0.0549)***

Demographics Total Votes 0.0246 0.0371 0.0385 0.0526 0.0320 (0.0035)*** (0.0034)*** (0.0036)*** (0.0041)*** (0.0035)*** Total Population -0.0023 -0.0028 0.0005 -0.0003 -0.0009 (0.0008)*** (0.0006)*** (0.0005) (0.0006) (0.0006) Population Density -0.0000 -0.0003 -0.0003 0.0005 0.0001 (0.0002) (0.0002) (0.0002)** (0.0003)* (0.0003) Female -0.0711 -0.0185 -0.0382 -0.0375 0.0032 (0.0230)*** (0.0199) (0.0166)** (0.0215)* (0.0215) Married 0.0046 0.0766 -0.0515 0.0443 0.0463 (0.0170) (0.0148)*** (0.0114)*** (0.0143)*** (0.0179)*** Median Age -0.0005 0.0008 -0.0021 0.0002 0.0012 (0.0004) (0.0003)*** (0.0002)*** (0.0003) (0.0003)*** Disabled 0.3486 0.3835 0.2315 -0.4636 -0.5600 (0.0429)*** (0.0394)*** (0.0393)*** (0.0443)*** (0.0434)*** Foreign Born 0.2062 -0.0670 0.0240 0.1162 0.1333 (0.0385)*** (0.0208)*** (0.0301) (0.0275)*** (0.0388)***

Economic Characteristics Median H.H. Income -0.0006 -0.0015 -0.0014 0.0009 0.0022 (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** Under Poverty Line 0.1789 -0.0461 -0.0580 -0.0651 0.0458 (0.0195)*** (0.0147)*** (0.0158)*** (0.0173)*** (0.0198)** Poverty Line to Double 0.1921 0.0256 -0.0268 -0.1445 -0.0338 (0.0183)*** (0.0150)* (0.0135)** (0.0161)*** (0.0188)* Gini Coeﬃcient 0.0122 -0.1258 -0.2717 0.1980 0.3870 (0.0291) (0.0226)*** (0.0213)*** (0.0250)*** (0.0296)*** Unemployed 0.0652 0.0512 -0.0130 -0.2445 -0.0947 (0.0441) (0.0371) (0.0294) (0.0396)*** (0.0424)** Not in Labor Force 0.1293 0.0460 -0.0986 -0.0951 0.0220 (0.0187)*** (0.0148)*** (0.0133)*** (0.0164)*** (0.0174) Average H.H. Size 0.0329 0.0299 -0.0178 -0.0361 -0.0663 (0.0060)*** (0.0051)*** (0.0040)*** (0.0050)*** (0.0057)*** 80 Table A18: Determinants of change in SKR turnout by education, 2012-16 (2/5) Variable LTHS HS Some Bach. Grad. Grad. Coll. Commute Patterns Average Commute Time 0.0002 0.0022 0.0017 -0.0006 -0.0018 (0.0003) (0.0003)***(0.0002)***(0.0003)** (0.0003)*** Work at Home -0.1959 -0.1231 -0.0842 0.1739 0.0278 (0.0738)***(0.0574)** (0.0489)* (0.0712)** (0.0662) Commute by Driving Alone -0.0513 0.0318 0.0569 -0.0523 -0.1654 (0.0668) (0.0515) (0.0465) (0.0613) (0.0593)*** Commute by Carpool -0.0055 -0.0023 0.0501 -0.1072 -0.0958 (0.0686) (0.0548) (0.0455) (0.0660) (0.0601) Commute by Bus 0.2026 0.0729 0.0717 -0.0379 -0.2254 (0.1848) (0.1428) (0.1914) (0.2109) (0.2433) Commute by Subway 0.1840 0.1298 0.0545 -0.0199 -0.2171 (0.1897) (0.1440) (0.1955) (0.2104) (0.2316) Commute by Rail 0.2550 0.1265 0.1137 -0.0649 0.1133 (0.2095) (0.1454) (0.1916) (0.2238) (0.2564) Commute by Ferry 0.2213 0.0068 -0.0930 0.3250 0.2071 (0.2804) (0.2131) (0.3093) (0.2943) (0.3156) Commute by Taxi -0.0273 -0.0174 0.0721 -0.0421 0.2725 (0.1766) (0.1869) (0.1187) (0.1429) (0.1568)* Commute by Motorcycle 0.0060 0.0752 0.3752 -0.2435 0.0895 (0.1975) (0.1775) (0.1450)*** (0.1835) (0.1991) Commute by Bicycle 0.0906 -0.1146 -0.3990 0.2452 0.4752 (0.1164) (0.0891) (0.0905)***(0.1065)** (0.1214)*** Commute by Walking -0.0929 0.0352 0.0767 -0.0705 -0.0537 (0.0697) (0.0538) (0.0528) (0.0672) (0.0700)

81 Table A19: Determinants of change in SKR turnout by education, 2012-16 (3/5) Variable LTHS HS Grad. Some Bach. Grad. Coll. Industry Employment Emp. in Agriculture 0.2289 0.3156 -0.0700 -0.2198 -0.0962 (0.0923)** (0.0684)*** (0.0711) (0.0846)*** (0.0885) Emp. in Oil and Mining 0.2225 0.2678 -0.1060 -0.3212 -0.1240 (0.1029)** (0.0835)*** (0.0820) (0.1034)*** (0.1090) Emp. in Construction 0.1467 0.3262 0.0955 -0.2736 -0.0874 (0.0844)* (0.0649)*** (0.0736) (0.0811)*** (0.0854) Emp. in Manufacturing 0.1086 0.1682 0.0039 -0.2022 -0.0422 (0.0822) (0.0631)*** (0.0692) (0.0801)** (0.0835) Emp. in Wholesale 0.0945 0.0923 -0.0152 -0.0630 -0.1193 (0.0873) (0.0736) (0.0741) (0.0900) (0.0937) Emp. in Retail -0.0110 0.0307 0.0613 -0.1740 -0.0755 (0.0811) (0.0633) (0.0707) (0.0805)** (0.0808) Emp. in Transportation 0.0416 0.2342 0.1561 -0.3329 -0.2700 (0.0833) (0.0686)*** (0.0703)** (0.0837)*** (0.0850)*** Emp. in Utilities 0.0491 0.2099 0.0751 -0.2082 -0.1762 (0.1203) (0.0889)** (0.0897) (0.1113)* (0.1122) Emp. in Information -0.1471 -0.0579 -0.0640 0.1204 0.0835 (0.0998) (0.0763) (0.0697) (0.0976) (0.1308) Emp. in Finance -0.0761 -0.1712 -0.0943 0.1600 0.0823 (0.0872) (0.0677)** (0.0714) (0.0862)* (0.0840) Emp. in Real Estate -0.1052 -0.1565 -0.0267 0.0437 0.0805 (0.0881) (0.0771)** (0.0780) (0.0981) (0.0970) Emp. in Sci. and Tech. -0.0666 -0.2328 -0.3366 0.1815 0.5650 (0.0864) (0.0658)*** (0.0709)*** (0.0833)** (0.0845)*** Emp. in Management 0.3423 -0.4083 -0.6040 0.2562 -0.0075 (0.3508) (0.2776) (0.2646)** (0.2716) (0.2883) Emp. in Administration 0.0644 0.0947 0.0302 -0.1720 0.0255 (0.0864) (0.0664) (0.0729) (0.0885)* (0.0867) Emp. in Education -0.1806 -0.1621 -0.1688 0.0164 0.4847 (0.0810)** (0.0633)** (0.0706)** (0.0806) (0.0814)*** Emp. in Health Care -0.0543 -0.0708 -0.0380 -0.1033 0.1328 (0.0803) (0.0631) (0.0711) (0.0825) (0.0818) Emp. in Arts and Rec. -0.1060 -0.1098 -0.1171 0.0905 0.1414 (0.0880) (0.0699) (0.0732) (0.0950) (0.1027) Emp. in Accommodation 0.0208 0.0131 0.0203 -0.0842 -0.0545 (0.0822) (0.0653) (0.0682) (0.0810) (0.0809)

82 Table A20: Determinants of change in SKR turnout by education, 2012-16 (4/5) Variable LTHS HS Grad. Some Coll. Bach. Grad. Ancestry “American” 0.0800 0.1500 0.0805 -0.0501 -0.0721 (0.0255)*** (0.0208)*** (0.0170)*** (0.0240)** (0.0252)*** Arab -0.2145 -0.2345 0.0083 0.1964 0.0982 (0.0874)** (0.0556)*** (0.0374) (0.0448)*** (0.0732) Czech -0.0060 -0.0704 -0.0211 0.4277 0.0153 (0.1828) (0.1409) (0.1217) (0.1466)*** (0.1644) Danish -0.3269 0.1038 0.0723 0.3282 -0.2067 (0.2668) (0.2056) (0.1629) (0.2187) (0.2281) Dutch -0.1021 0.1152 -0.0273 0.1145 -0.1761 (0.0774) (0.0604)* (0.0524) (0.0679)* (0.0800)** English -0.1597 -0.1170 0.0536 0.2156 0.0608 (0.0441)*** (0.0378)*** (0.0301)* (0.0422)*** (0.0508) French -0.0133 0.0528 0.0813 -0.0848 -0.2257 (0.0941) (0.0791) (0.0633) (0.0820) (0.0924)** German -0.0319 0.1779 0.0496 0.0470 -0.0789 (0.0311) (0.0238)*** (0.0224)** (0.0246)* (0.0276)*** Irish -0.0003 0.0334 -0.0130 0.1721 0.0440 (0.0357) (0.0332) (0.0296) (0.0318)*** (0.0421) Norwegian 0.2552 0.0111 0.1501 0.2222 0.0182 (0.0948)*** (0.0744) (0.0697)** (0.0825)*** (0.0776) Pennsylvania Dutch 0.5710 -0.0581 -0.0628 -0.3966 -0.3058 (0.1905)*** (0.1040) (0.0859) (0.1535)*** (0.1883) Polish 0.0963 0.0911 0.0811 0.0009 -0.0615 (0.0452)** (0.0531)* (0.0320)** (0.0381) (0.0395) Portuguese -0.2067 0.0214 -0.0101 -0.2043 -0.0841 (0.0806)** (0.1056) (0.0613) (0.0636)*** (0.0691) Russian -0.2382 -0.3416 -0.3235 0.2232 0.7495 (0.1080)** (0.0774)*** (0.0689)*** (0.0823)*** (0.1206)*** Ukrainian -0.0746 -0.1569 0.0454 0.2854 -0.2042 (0.1420) (0.1230) (0.1133) (0.1021)*** (0.1163)* Welsh -0.5106 -0.2593 0.0122 0.3622 -0.0434 (0.2866)* (0.2377) (0.1628) (0.2083)* (0.2425)

83 Table A21: Determinants of change in SKR turnout by education, 2012-16 (5/5) Variable LTHS HS Grad. Some Coll. Bach. Grad. Ancestry (Cont.) Cuban -0.0765 -0.1314 -0.0604 0.1355 -0.0766 (0.0341)** (0.0367)*** (0.0288)** (0.0328)*** (0.0569) Puerto Rican 0.0835 -0.0570 -0.0242 0.0618 -0.0212 (0.0564) (0.0360) (0.0504) (0.0328)* (0.0436) Hispan˜o -0.0551 0.0192 0.0674 0.0224 0.0407 (0.0717) (0.0637) (0.0500) (0.0628) (0.0671) Asian Indian -0.2146 -0.0794 -0.1209 -0.0250 0.1154 (0.0504)*** (0.0498) (0.0417)*** (0.0523) (0.0498)** Chinese -0.1548 -0.0786 -0.0848 -0.0245 0.0605 (0.0580)*** (0.0466)* (0.0307)*** (0.0492) (0.0374) Filipino -0.1513 -0.0383 0.0950 0.2004 -0.1984 (0.0699)** (0.0554) (0.0447)** (0.0430)*** (0.0548)*** Hmong 0.0185 -0.0550 -0.1324 -0.1934 -0.0358 (0.1362) (0.1294) (0.1330) (0.1149)* (0.1252) Japanese -0.4085 -0.0599 -0.2239 0.1975 0.2143 (0.0955)*** (0.1072) (0.0799)*** (0.0808)** (0.1457) Korean -0.1399 -0.1874 -0.1495 0.0881 0.0470 (0.0767)* (0.0580)*** (0.0706)** (0.0525)* (0.0605) Vietnamese 0.0057 -0.1160 0.0366 0.0130 -0.1376 (0.0745) (0.0380)*** (0.0404) (0.0589) (0.0481)***

Spatial Lag -0.0258 0.0503 0.0403 -0.0237 -0.0557 (0.0121)** (0.0115)*** (0.0104)*** (0.0133)* (0.0131)*** Constant 0.7469 0.4176 1.3810 1.1115 -0.7421 (0.1716)*** (0.1285)*** (0.1099)*** (0.1619)*** (0.2073)*** N 167,553 167,553 167,517 167,553 167,553 R2 0.2446 0.3551 0.4865 0.2995 0.2096 Note: All estimates include county-level ﬁxed eﬀects and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1

84 Table A22: Change in SKR racial Republican support, 2012-16 (1/6) Variable White Black Hispanic Asian Native SKR Electorate White 0.0182 0.1656 0.1466 0.0264 -0.0337 (0.0560) (0.0827)** (0.0901) (0.0885) (0.1003) Black 0.1561 0.2096 0.2885 0.0941 0.0079 (0.0568)*** (0.0833)** (0.0909)*** (0.0886) (0.1008) Hispanic 0.1011 0.2058 0.1903 0.0656 -0.0215 (0.0562)* (0.0828)** (0.0904)** (0.0879) (0.0977) Asian 0.0878 0.1902 0.1846 -0.0222 -0.0370 (0.0521)* (0.0837)** (0.0889)** (0.0896) (0.0999) Native 0.0783 0.1232 0.2221 0.0449 0.0675 (0.0581) (0.0860) (0.0922)** (0.0927) (0.1015) Less than HS 0.0679 0.0268 0.0089 0.0187 -0.0122 (0.0073)*** (0.0144)* (0.0141) (0.0161) (0.0120) HS Grad. 0.0698 0.0100 -0.0036 0.0049 0.0094 (0.0063)*** (0.0129) (0.0133) (0.0137) (0.0109) Some Coll. 0.0602 0.0080 -0.0030 0.0049 0.0014 (0.0062)*** (0.0125) (0.0129) (0.0136) (0.0116) Bach. Deg. 0.0176 -0.0119 -0.0102 -0.0348 -0.0066 (0.0074)** (0.0130) (0.0141) (0.0169)** (0.0115)

Change in SKR Turnout ∆ White Turnout -0.0439 -0.0154 -0.0116 -0.0102 0.0001 (0.0036)*** (0.0039)*** (0.0055)** (0.0046)** (0.0049) ∆ Black Turnout -0.0008 0.0246 0.0034 0.0023 -0.0002 (0.0008) (0.0027)*** (0.0025) (0.0023) (0.0018) ∆ Hispanic Turnout -0.0021 0.0052 0.0296 0.0011 0.0010 (0.0010)** (0.0022)** (0.0033)*** (0.0022) (0.0019) ∆ Asian Turnout -0.0003 0.0016 0.0005 0.0091 -0.0001 (0.0008) (0.0021) (0.0021) (0.0026)*** (0.0019) ∆ Native Turnout -0.0008 0.0030 -0.0002 0.0048 -0.0010 (0.0009) (0.0025) (0.0027) (0.0025)* (0.0023) ∆ LTHS Turnout 0.0014 -0.0026 -0.0024 0.0007 0.0006 (0.0009) (0.0021) (0.0021) (0.0022) (0.0020) ∆ HS Grad. Turnout 0.0023 -0.0020 0.0022 0.0060 -0.0018 (0.0014)* (0.0026) (0.0031) (0.0029)** (0.0025) ∆ Some Coll. Turnout 0.0050 0.0021 -0.0007 0.0044 -0.0018 (0.0014)*** (0.0029) (0.0030) (0.0028) (0.0028) ∆ Bach. Deg. Turnout 0.0077 0.0010 0.0042 0.0059 0.0030 (0.0013)*** (0.0025) (0.0025)* (0.0025)** (0.0022) ∆ Grad. Deg. Turnout 0.0075 -0.0008 -0.0010 0.0013 0.0009 (0.0011)*** (0.0024) (0.0023) (0.0026) (0.0021)

85 Table A23: Change in SKR racial Republican support, 2012-16 (2/6) Variable White Black Hispanic Asian Native Demographics Total Votes -0.0038 -0.0121 -0.0103 -0.0138 -0.0027 (0.0008)*** (0.0021)*** (0.0024)*** (0.0024)*** (0.0017) Tract Population -0.0002 -0.0016 -0.0018 -0.0003 0.0004 (0.0003) (0.0006)*** (0.0006)*** (0.0005) (0.0005) Population Density 0.0000 0.0001 0.0001 0.0002 -0.0000 (0.0001) (0.0001) (0.0001) (0.0001) (0.0003) Female -0.0212 -0.0524 -0.0116 -0.0039 0.0140 (0.0090)** (0.0196)*** (0.0194) (0.0198) (0.0193) Married 0.0164 -0.0295 -0.0674 -0.0398 -0.0026 (0.0065)** (0.0141)** (0.0153)*** (0.0135)*** (0.0133) Median Age 0.0001 -0.0002 -0.0003 -0.0004 0.0001 (0.0001) (0.0003) (0.0003) (0.0003) (0.0002) Disabled 0.1047 0.1184 0.0418 0.0697 0.0096 (0.0162)*** (0.0334)*** (0.0331) (0.0342)** (0.0294) Foreign -0.0426 -0.0121 0.0449 0.0586 0.0051 (0.0178)** (0.0209) (0.0198)** (0.0221)*** (0.0239)

Economic Characteristics Median Household Income -0.0002 -0.0001 -0.0002 -0.0005 0.0000 (0.0000)*** (0.0001)* (0.0001)*** (0.0001)*** (0.0001) Under Poverty Line -0.0162 0.0517 0.0270 -0.0208 0.0144 (0.0079)** (0.0154)*** (0.0153)* (0.0151) (0.0140) Poverty Line to Double 0.0030 0.0529 0.0181 0.0339 0.0070 (0.0066) (0.0147)*** (0.0146) (0.0144)** (0.0119) Gini Coeﬃcient -0.0281 -0.0469 -0.1104 -0.0622 0.0070 (0.0098)*** (0.0222)** (0.0235)*** (0.0238)*** (0.0193) Unemployed 0.0422 0.0281 -0.0418 -0.0254 -0.0148 (0.0183)** (0.0356) (0.0392) (0.0382) (0.0374) Not in Labor Force -0.0239 -0.0221 -0.0507 -0.0802 -0.0188 (0.0066)*** (0.0155) (0.0152)*** (0.0144)*** (0.0138) Average Household Size 0.0080 -0.0022 -0.0109 -0.0071 0.0007 (0.0033)** (0.0040) (0.0041)*** (0.0044) (0.0039)

86 Table A24: Change in SKR racial Republican support, 2012-16 (3/6) Variable White Black Hispanic Asian Native Commute Patterns Average Commute Time 0.0007 -0.0000 0.0001 0.0003 -0.0002 (0.0001)*** (0.0002) (0.0002) (0.0002) (0.0002) Work at Home -0.0529 -0.0274 -0.1477 0.0416 -0.0114 (0.0281)* (0.0600) (0.0655)** (0.0565) (0.0510) Commute by Driving Alone -0.0267 -0.0015 -0.1244 0.0802 -0.0128 (0.0253) (0.0538) (0.0567)** (0.0494) (0.0459) Commute by Carpool -0.0275 0.0094 -0.1312 0.1214 -0.0121 (0.0255) (0.0559) (0.0579)** (0.0521)** (0.0483) Commute by Bus -0.0852 0.2121 -0.1878 0.0177 0.1729 (0.0845) (0.1623) (0.1082)* (0.1411) (0.1619) Commute by Subway -0.0644 0.2582 -0.0859 0.0402 0.2492 (0.0888) (0.1650) (0.1247) (0.1499) (0.1602) Commute by Rail -0.2023 0.4665 -0.0190 0.1766 0.1450 (0.0880)** (0.1806)*** (0.1441) (0.1594) (0.1584) Commute by Ferry -0.0829 0.2018 -0.1596 0.1238 0.1406 (0.1005) (0.2023) (0.1980) (0.1994) (0.1961) Commute by Taxi -0.1205 0.0392 0.1178 0.3232 -0.2030 (0.0572)** (0.1437) (0.1297) (0.1896)* (0.1321) Commute by Motorcycle -0.0162 -0.4060 -0.3366 0.1023 -0.0748 (0.0648) (0.1609)** (0.1608)** (0.1679) (0.1440) Commute by Bicycle 0.0197 0.1845 0.0337 0.2565 0.0400 (0.0478) (0.0821)** (0.0888) (0.0765)*** (0.0725) Commute by Walking -0.0426 -0.0461 -0.1933 0.1108 -0.0294 (0.0270) (0.0575) (0.0623)***(0.0541)** (0.0492)

87 Table A25: Change in SKR racial Republican support, 2012-16 (4/6) Variable White Black Hispanic Asian Native Industry Employment Emp. in Agriculture -0.0023 -0.0068 0.1810 -0.0111 0.0040 (0.0359) (0.0704) (0.0767)** (0.0754) (0.0610) Emp. in Oil and Mining -0.0035 -0.1942 0.1433 -0.1683 -0.0078 (0.0454) (0.0935)** (0.1002) (0.0883)* (0.0829) Emp. in Construction 0.0226 0.0384 0.2034 -0.0640 -0.0168 (0.0345) (0.0699) (0.0767)*** (0.0724) (0.0603) Emp. in Manufacturing -0.0097 0.0225 0.2052 -0.0801 -0.0124 (0.0331) (0.0694) (0.0758)*** (0.0726) (0.0596) Emp. in Wholesale -0.0418 -0.0403 0.1507 -0.2554 0.0154 (0.0356) (0.0745) (0.0838)* (0.0813)*** (0.0660) Emp. in Retail -0.0353 -0.0103 0.1753 -0.0527 -0.0256 (0.0335) (0.0683) (0.0740)** (0.0730) (0.0591) Emp. in Transportation 0.0013 0.0241 0.2219 -0.0124 -0.0008 (0.0340) (0.0706) (0.0770)*** (0.0753) (0.0624) Emp. in Utilities 0.0549 -0.0186 0.0912 -0.0424 0.0701 (0.0449) (0.0974) (0.0975) (0.1004) (0.0812) Emp. in Information 0.0037 0.0407 0.2893 0.0704 0.0535 (0.0436) (0.0813) (0.0849)*** (0.0885) (0.0783) Emp. in Finance -0.0939 -0.0541 0.0832 -0.1390 -0.0070 (0.0355)*** (0.0762) (0.0804) (0.0789)* (0.0641) Emp. in Real Estate -0.0765 -0.0276 0.1241 -0.1673 -0.0706 (0.0384)** (0.0825) (0.0874) (0.0832)** (0.0675) Emp. in Sci. and Tech. -0.0716 0.0598 0.1982 -0.0137 0.0131 (0.0349)** (0.0755) (0.0763)*** (0.0718) (0.0620) Emp in Management -0.2205 -0.2155 0.5403 -0.9601 0.2419 (0.1268)* (0.2921) (0.2913)* (0.2635)*** (0.2408) Emp. in Administration -0.0182 -0.0092 0.1548 -0.0319 -0.0213 (0.0359) (0.0743) (0.0762)** (0.0754) (0.0596) Emp. in Education -0.0732 0.0096 0.1748 -0.0630 0.0069 (0.0344)** (0.0695) (0.0751)** (0.0721) (0.0602) Emp. in Health Care -0.0389 -0.0227 0.1770 -0.0644 -0.0148 (0.0345) (0.0690) (0.0746)** (0.0721) (0.0580) Emp. in Arts and Rec. -0.0784 0.0617 0.2604 -0.0479 0.0033 (0.0389)** (0.0757) (0.0837)*** (0.0838) (0.0727) Emp. in Accommodation -0.0199 -0.0081 0.1771 -0.0754 0.0098 (0.0359) (0.0685) (0.0757)** (0.0729) (0.0595)

88 Table A26: Change in SKR racial Republican support, 2012-16 (5/6) Variable White Black Hispanic Asian Native Ancestry “American” 0.0765 (0.0095)*** Arab 0.0453 (0.0182)** Czech 0.0967 (0.0557)* Danish -0.0818 (0.0542) Dutch 0.0431 (0.0312) English 0.0098 (0.0163) French 0.0993 (0.0329)*** German 0.0307 (0.0104)*** Irish 0.0320 (0.0154)** Italian 0.0850 (0.0138)*** Norwegian 0.0124 (0.0208) Pennsylvania Dutch 0.0411 (0.0358) Polish 0.1041 (0.0256)*** Portuguese 0.1094 (0.0380)*** Russian -0.0525 (0.0630) Ukrainian 0.1822 (0.0781)** Welsh -0.0691 (0.0577)

89 Table A27: Change in SKR racial Republican support, 2012-16 (6/6) Variable White Black Hispanic Asian Native Ancestry (Cont.) Mexican -0.0196 (0.0109)* Dominican -0.1071 (0.0359)*** Cuban -0.0341 (0.0250) Puerto Rican 0.0137 (0.0265) Central American -0.0184 (0.0300) South American -0.0598 (0.0390) Hispan˜o 0.0970 (0.0511)* Asian Indian -0.2033 (0.0363)*** Chinese 0.0301 (0.0326) Filipino 0.0286 (0.0350) Hmong 0.0564 (0.1006) Japanese 0.0198 (0.0703) Korean -0.0788 (0.0431)* Vietnamese 0.0024 (0.0389)

Spatial Lag 0.2130 -0.0868 -0.0563 -0.0566 -0.0735 (0.0186)*** (0.0150)*** (0.0147)*** (0.0148)*** (0.0204)*** Constant -0.0473 -0.1745 -0.1149 0.0248 0.0304 (0.0593) (0.0934)* (0.1070) (0.1040) (0.1038) N 167,517 167,517 167,517 167,517 167,517 R2 0.2687 0.0575 0.0656 0.0629 0.0662 Note: All estimates include county-level ﬁxed eﬀects and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1

90 Table A28: Change in SKR Republican support by education level, 2012-16 (1/6) Variable LTHS HS Grad. Some Bach. Grad. Coll. SKR Electorate White 0.1873 0.0204 -0.0162 0.0296 0.0527 (0.0795)** (0.0536) (0.0645) (0.0616) (0.0713) Black 0.2238 0.0099 -0.0284 0.0956 0.1335 (0.0799)*** (0.0540) (0.0651) (0.0618) (0.0719)* Hispanic 0.2014 -0.0011 -0.0254 0.0732 0.0950 (0.0790)** (0.0528) (0.0651) (0.0631) (0.0725) Asian 0.2070 0.0330 -0.0395 0.0483 0.0791 (0.0804)** (0.0523) (0.0657) (0.0612) (0.0728) Native 0.1845 0.0317 -0.0689 0.0232 0.0574 (0.0829)** (0.0581) (0.0679) (0.0657) (0.0771) Less than HS -0.0654 0.0737 0.0549 0.0037 0.0364 (0.0152)*** (0.0138)*** (0.0131)*** (0.0133) (0.0158)** HS Grad. -0.0439 0.0378 -0.0017 -0.0412 0.0362 (0.0116)*** (0.0113)*** (0.0113) (0.0115)*** (0.0147)** Some Coll. -0.0349 0.0097 0.0313 -0.0273 0.0270 (0.0116)*** (0.0114) (0.0118)*** (0.0109)** (0.0146)* Bach. Deg. -0.0182 -0.0185 -0.0165 -0.0637 0.0312 (0.0127) (0.0114) (0.0123) (0.0133)*** (0.0160)*

Change in SKR Turnout ∆ White Turnout -0.0040 0.0092 0.0013 0.0103 0.0104 (0.0044) (0.0035)*** (0.0041) (0.0041)** (0.0043)** ∆ Black Turnout -0.0021 -0.0007 0.0006 -0.0033 -0.0034 (0.0021) (0.0019) (0.0017) (0.0018)* (0.0020)* ∆ Hispanic Turnout -0.0024 -0.0001 -0.0029 -0.0005 0.0001 (0.0020) (0.0017) (0.0016)* (0.0018) (0.0019) ∆ Asian Turnout -0.0024 -0.0033 0.0013 0.0009 -0.0007 (0.0020) (0.0016)** (0.0015) (0.0017) (0.0019) ∆ Native Turnout 0.0024 -0.0038 0.0049 -0.0024 0.0022 (0.0022) (0.0018)** (0.0018)*** (0.0020) (0.0022) ∆ LTHS Turnout 0.0462 -0.0015 -0.0026 -0.0007 0.0026 (0.0035)*** (0.0018) (0.0016) (0.0018) (0.0019) ∆ HS Grad. Turnout 0.0017 0.0107 -0.0052 -0.0057 -0.0052 (0.0030) (0.0043)** (0.0024)** (0.0023)** (0.0025)** ∆ Some Coll. Turnout -0.0011 -0.0012 0.0164 -0.0012 -0.0001 (0.0028) (0.0023) (0.0036)*** (0.0023) (0.0025) ∆ Bach. Deg. Turnout 0.0012 0.0062 0.0063 0.0276 -0.0004 (0.0026) (0.0019)*** (0.0022)*** (0.0035)*** (0.0022) ∆ Grad. Deg. Turnout 0.0010 0.0063 0.0039 0.0019 0.0212 (0.0022) (0.0018)*** (0.0017)** (0.0019) (0.0030)***

91 Table A29: Change in SKR Republican support by education level, 2012-16 (2/6) Variable LTHS HS Grad. Some Bach. Grad. Coll. Demographics Total Votes -0.0075 -0.0088 -0.0062 -0.0026 -0.0083 (0.0021)*** (0.0016)*** (0.0014)*** (0.0017) (0.0020)*** Tract Population -0.0014 -0.0011 -0.0007 -0.0001 -0.0010 (0.0005)*** (0.0004)*** (0.0004)* (0.0005) (0.0005)** Population Density 0.0000 -0.0000 0.0000 -0.0002 -0.0001 (0.0001) (0.0001) (0.0001) (0.0001)** (0.0001) Female -0.0169 -0.0223 -0.0456 -0.0349 -0.0207 (0.0201) (0.0159) (0.0149)*** (0.0166)** (0.0180) Married -0.0556 0.0010 0.0348 0.0082 -0.0538 (0.0133)*** (0.0120) (0.0103)*** (0.0121) (0.0129)*** Median Age -0.0003 -0.0003 -0.0006 0.0002 -0.0002 (0.0003) (0.0002) (0.0002)*** (0.0002) (0.0003) Disabled 0.1186 0.1079 0.1776 0.1188 0.0490 (0.0348)*** (0.0271)*** (0.0278)*** (0.0289)*** (0.0325) Foreign Born -0.0240 -0.0504 -0.0526 0.0233 0.0243 (0.0298) (0.0178)*** (0.0173)*** (0.0189) (0.0211)

Economic Characteristics Median Household Income -0.0001 -0.0003 -0.0005 -0.0003 -0.0005 (0.0001) (0.0001)*** (0.0001)*** (0.0001)*** (0.0001)*** Under Poverty Line 0.0497 -0.0101 -0.0206 0.0203 0.0108 (0.0155)*** (0.0126) (0.0113)* (0.0125) (0.0140) Poverty Line to Double 0.0611 0.0177 -0.0163 0.0169 0.0429 (0.0149)*** (0.0107)* (0.0107) (0.0122) (0.0139)*** Gini Coeﬃcient -0.0418 -0.0659 -0.1031 -0.0735 -0.0796 (0.0219)* (0.0194)*** (0.0166)*** (0.0203)*** (0.0200)*** Unemployed 0.0237 -0.0151 0.0271 0.0449 -0.0213 (0.0360) (0.0262) (0.0254) (0.0307) (0.0300) Not in Labor Force -0.0455 -0.0364 -0.0246 -0.0369 -0.0585 (0.0139)*** (0.0118)*** (0.0107)** (0.0126)*** (0.0119)*** Average Household Size -0.0042 -0.0021 -0.0001 0.0049 0.0003 (0.0044) (0.0035) (0.0029) (0.0036) (0.0034)

92 Table A30: Change in SKR Republican support by education level, 2012-16 (3/6) Variable LTHS HS Some Bach. Grad. Grad. Coll. Commute Patterns Average Commute Time 0.0003 0.0006 0.0009 0.0004 0.0004 (0.0003) (0.0002)***(0.0002)***(0.0002)** (0.0002)** Work at Home -0.0370 -0.0481 -0.0199 -0.0300 -0.0973 (0.0574) (0.0468) (0.0388) (0.0490) (0.0518)* Commute by Driving Alone -0.0055 0.0169 0.0487 -0.0379 -0.1103 (0.0516) (0.0399) (0.0355) (0.0458) (0.0461)** Commute by Carpool 0.0145 0.0044 0.0396 -0.0342 -0.0858 (0.0525) (0.0425) (0.0386) (0.0475) (0.0480)* Commute by Bus -0.2539 0.0989 0.1324 0.0442 -0.1444 (0.1795) (0.1519) (0.1107) (0.1063) (0.1726) Commute by Subway -0.2527 0.1209 0.1866 0.0872 -0.1330 (0.1914) (0.1531) (0.1120)* (0.1117) (0.1774) Commute by Rail -0.1584 0.0412 0.0476 -0.0232 -0.2118 (0.1790) (0.1542) (0.1262) (0.1184) (0.1761) Commute by Ferry -0.1868 -0.0103 0.1976 0.1949 -0.2281 (0.2668) (0.2328) (0.1665) (0.1398) (0.2597) Commute by Taxi 0.0838 -0.0863 0.3306 -0.1035 -0.1513 (0.1934) (0.1086) (0.1104)*** (0.1031) (0.1140) Commute by Motorcycle -0.2636 0.1813 0.0346 0.0570 -0.0524 (0.1572)* (0.1205) (0.1105) (0.1398) (0.1450) Commute by Bicycle -0.0307 0.0125 0.0685 0.0705 0.0404 (0.0742) (0.0623) (0.0582) (0.0779) (0.0664) Commute by Walking -0.0585 0.0104 0.0520 -0.0401 -0.1878 (0.0555) (0.0452) (0.0401) (0.0487) (0.0481)***

93 Table A31: Change in SKR Republican support by education level, 2012-16 (4/6) Variable LTHS HS Grad. Some Bach. Grad. Coll. Industry Employment Emp. In Agriculture -0.0435 0.1043 -0.0629 0.0320 0.0701 (0.0755) (0.0634) (0.0519) (0.0596) (0.0649) Emp. in Oil and Mining 0.0078 0.0874 -0.0902 0.0606 0.0965 (0.0934) (0.0859) (0.0670) (0.0763) (0.0873) Emp. in Construction -0.0243 0.1226 0.0053 0.0716 0.0941 (0.0739) (0.0600)** (0.0515) (0.0590) (0.0668) Emp. in Manufacturing -0.0032 0.0872 -0.0965 0.0361 0.0999 (0.0743) (0.0602) (0.0502)* (0.0569) (0.0634) Emp. in Wholesale -0.0979 -0.0038 -0.1267 -0.0174 0.0583 (0.0826) (0.0656) (0.0553)** (0.0656) (0.0717) Emp. in Retail -0.0335 0.0867 -0.1186 0.0354 0.0791 (0.0731) (0.0608) (0.0491)** (0.0568) (0.0628) Emp. in Transportation 0.0064 0.1253 -0.0612 0.0904 0.1065 (0.0784) (0.0640)* (0.0523) (0.0591) (0.0665) Emp. in Utilities -0.0138 0.1980 -0.1344 -0.0115 0.0548 (0.0971) (0.0773)** (0.0730)* (0.0788) (0.0841) Emp. in Information 0.0387 0.1272 -0.1370 0.0938 0.1727 (0.0788) (0.0714)* (0.0549)** (0.0671) (0.0747)** Emp. in Finance -0.0537 -0.0347 -0.1686 -0.0531 0.0638 (0.0768) (0.0611) (0.0541)*** (0.0598) (0.0674) Emp. in Real Estate -0.1002 0.0246 -0.1235 -0.0723 0.0380 (0.0870) (0.0681) (0.0578)** (0.0658) (0.0736) Emp. In Sci. and Tech. -0.0045 0.0233 -0.1579 -0.0488 0.0450 (0.0763) (0.0665) (0.0516)*** (0.0574) (0.0679) Emp. in Management -0.0102 -0.1336 0.0436 -0.2204 -0.3480 (0.2685) (0.2080) (0.2026) (0.1976) (0.2439) Emp. in Administration 0.0014 0.0559 -0.1137 0.0108 0.0531 (0.0759) (0.0644) (0.0528)** (0.0599) (0.0662) Emp. in Education -0.0455 0.0436 -0.1303 -0.0115 0.0837 (0.0738) (0.0602) (0.0502)*** (0.0576) (0.0642) Emp. in Health Care -0.0582 0.0198 -0.1074 0.0270 0.0899 (0.0746) (0.0596) (0.0498)** (0.0573) (0.0645) Emp. In Arts and Rec. -0.1008 0.0565 -0.1457 0.0444 0.1241 (0.0810) (0.0702) (0.0563)*** (0.0630) (0.0676)* Emp. in Acommodation -0.0174 0.0226 -0.1331 0.0501 0.1027 (0.0755) (0.0610) (0.0501)*** (0.0592) (0.0629)

94 Table A32: Change in SKR Republican support by education level, 2012-16 (5/6) Variable LTHS HS Grad. Some Coll. Bach. Grad. Ancestry “American” -0.0337 0.0755 0.0549 -0.0493 -0.0959 (0.0214) (0.0167)*** (0.0168)*** (0.0187)*** (0.0198)*** Arab -0.0100 -0.0128 -0.0107 0.0066 0.0682 (0.0608) (0.0377) (0.0431) (0.0519) (0.0526) Czech -0.2758 -0.0728 0.0520 0.0564 -0.1455 (0.1462)* (0.1301) (0.1154) (0.1340) (0.1200) Danish 0.2033 -0.3905 0.1027 0.0870 0.2564 (0.1918) (0.1447)*** (0.1453) (0.1447) (0.1742) Dutch -0.2969 0.0286 0.0524 -0.1701 -0.2817 (0.0714)*** (0.0597) (0.0597) (0.0777)** (0.0673)*** English -0.1196 -0.0866 -0.0004 -0.1397 -0.1394 (0.0333)*** (0.0300)*** (0.0317) (0.0284)*** (0.0324)*** French -0.0004 0.1130 0.1043 -0.0581 -0.0663 (0.0741) (0.0587)* (0.0556)* (0.0719) (0.0782) German -0.1175 0.0324 0.0769 -0.0740 -0.1010 (0.0232)*** (0.0201) (0.0180)*** (0.0204)*** (0.0215)*** Irish -0.0124 -0.0024 0.0137 -0.0309 -0.0308 (0.0314) (0.0227) (0.0263) (0.0360) (0.0342) Italian -0.0210 0.0154 0.0871 0.0644 0.0297 (0.0254) (0.0307) (0.0229)*** (0.0219)*** (0.0260) Norwegian 0.0658 -0.0511 -0.0586 0.0288 0.0028 (0.0582) (0.0458) (0.0451) (0.0572) (0.0550) Pennsylvania Dutch 0.1000 0.2241 0.0095 -0.1536 -0.1762 (0.1497) (0.1006)** (0.0897) (0.1014) (0.1339) Polish 0.0458 0.0441 0.1346 0.0821 0.0041 (0.0454) (0.0420) (0.0320)*** (0.0439)* (0.0538) Portuguese -0.0405 0.1810 0.1561 0.0268 0.0902 (0.0921) (0.1381) (0.0656)** (0.0851) (0.0632) Russian 0.1150 0.0890 0.1611 -0.1823 0.0814 (0.0659)* (0.0712) (0.0801)** (0.1112) (0.0773) Ukrainian -0.1695 -0.0156 0.2782 0.0298 0.0426 (0.1102) (0.0763) (0.0922)*** (0.1290) (0.1464) Welsh -0.4114 -0.0096 -0.1000 -0.1407 -0.4185 (0.2286)* (0.1722) (0.1507) (0.1790) (0.1912)**

95 Table A33: Change in SKR Republican support by education level, 2012-16 (6/6) Variable LTHS HS Grad. Some Coll. Bach. Grad. Ancestry (Cont.) Mexican -0.0144 -0.0062 -0.0246 -0.0134 -0.0226 (0.0134) (0.0122) (0.0098)** (0.0104) (0.0115)** Dominican 0.0150 -0.0888 0.0041 -0.0332 0.0303 (0.0365) (0.0237)*** (0.0202) (0.0314) (0.0284) Cuban -0.1165 -0.0001 0.0217 -0.2270 -0.2023 (0.0307)*** (0.0283) (0.0214) (0.0881)** (0.0729)*** Puerto Rican 0.0297 -0.0194 0.0098 0.0747 0.0434 (0.0280) (0.0207) (0.0207) (0.0252)*** (0.0268) Central American 0.0169 -0.0083 -0.0154 -0.0879 -0.0046 (0.0287) (0.0237) (0.0278) (0.0299)*** (0.0338) South American 0.0667 0.0425 -0.0234 0.0407 0.0457 (0.0480) (0.0287) (0.0327) (0.0374) (0.0344) Hispan˜o -0.0189 -0.0388 0.0082 -0.0628 -0.0229 (0.0504) (0.0473) (0.0502) (0.0459) (0.0544) Asian Indian -0.0034 0.0658 0.0318 -0.1156 -0.0366 (0.0325) (0.0258)** (0.0322) (0.0261)*** (0.0311) Chinese 0.0225 0.0337 0.0467 0.0138 0.0152 (0.0237) (0.0224) (0.0283)* (0.0365) (0.0316) Filipino 0.0152 0.0104 -0.0556 0.0030 0.0255 (0.0477) (0.0392) (0.0308)* (0.0338) (0.0319) Hmong 0.1751 -0.1269 0.1127 -0.0779 0.0508 (0.0900)* (0.0666)* (0.0536)** (0.0885) (0.1009) Japanese 0.0344 -0.1094 0.1129 -0.0048 0.1934 (0.0717) (0.0622)* (0.0611)* (0.0648) (0.0740)*** Korean -0.0337 0.0699 0.0089 -0.0656 -0.1788 (0.0547) (0.0661) (0.0436) (0.0434) (0.0450)*** Vietnamese -0.0256 -0.0632 -0.0684 -0.0605 -0.0832 (0.0454) (0.0302)** (0.0303)** (0.0269)** (0.0309)***

Spatial Lag -0.0520 -0.0033 -0.0263 -0.0942 -0.0594 (0.0138)*** (0.0132) (0.0140)* (0.0167)*** (0.0144)*** Constant -0.0357 -0.0196 0.1326 0.0391 0.0169 (0.0952) (0.0708) (0.0744)* (0.0706) (0.0831) N 167,517 167,517 167,517 167,517 167,517 R2 0.0534 0.0854 0.0862 0.0677 0.0813 Note: All estimates include county-level ﬁxed eﬀects and are weighted by the interaction of the logarithm of total votes in a precinct and the error weight. All demographic variables re-aggregated from census block groups or tracts and originate from the 2012-2016 ACS, 2008-2012 ACS, and 2005-2009 ACS respectively. County-level clustered standard errors in parentheses: *** p <0.01, ** p <0.05, * p <0.1