The Primacy of Race in the Geography of Income-BasedVoting: New Evidence from Public Voting RecordsEitan Hersh and Clayton Nall12First Draft: August 24, 2012This Draft: February 12, 2013, 8:09am1Eitan Hersh is Assistant Professor, Department of Political Science, Yale University. Address: Institute forSocial and Policy Studies, Yale University, 77 Prospect Street, P.O. Box 208209, New Haven, CT 06520-8209;Email: eitan.hersh@yale.edu; Phone: 203-436-9061. Clayton Nall is Assistant Professor, Department of PoliticalScience, Stanford University. Address: 616 Serra Street Rm 100, Stanford CA 94305; Email: nall@stanford.edu;Phone: (650)725-4076.2We thank David Broockman, Anthony Fowler, Andrew Gelman, Justin Grimmer, Lauren Davenport, JacobHacker, David Laitin, and participants in the Stanford Political Science Methods Workshop for comments. Thanksto Yales Institution for Social and Policy Studies and the Center for the Study of American Politics for nancialsupport and to Jonathan Rodden for sharing a combined version of the Harvard Election Data Archive precinctdata.AbstractWhy does the relationship between income and partisanship vary across U.S. regions? Some answershave focused on economic context (in richer environments, economics is less salient), while others havefocused on racial context (in diverse environments, rich voters oppose support for poor minorities). Using73 million geocoded registration records and 185,000 geocoded precinct returns, we examine income-based voting across a diverse set of local areas. We show the political geography of income-based votingis inextricably tied to racial context,not economic context. Within homogeneously non-Black areas,an areas wealth has no bearing on the individual income-party relationship. The correlation betweenincome and partisanship is strong in heavily black areas of the Old South and weaker nearly everywhereelse, including urbanized areas of the South. Differences between racially diverse local areas of theSouth and comparable areas elsewhere account for state-level differences in income-based voting.1 IntroductionWhy are people in some places more likely to vote with their alleged income-based class interests thanpeopleinotherplaces? What isit about certainareasoftheUnitedStatesthat maketheincome-partisanship relationship stronger or weaker? While the political consequences of citizens alleged falseconsciousness has been a topic of intrigue at least since Marx, recent work in behavioral social sciencehas sought to explain deviations from class-based voting (typically operationalized in terms of voterincome) across U.S. states and across countries. Within American politics, these explanations fall pri-marily under two schools of thought. The rst, exemplied by the research of Gelman et al. (2008), hasfocused attention on how the wealth or modernization of a state corresponds to the income-partisanshiprelationship. The key nding is that within wealthier state jurisdictions, the relationship between incomeand partisanship is weaker than it is within poorer state jurisdictions. The second school has focusedattention on race. For example, Alesina and Glaeser (2004) suggest that much of the regional differencein support for social welfare policies and income redistribution (and support for the political party advo-cating redistribution) may be related to the level of racial heterogeneity in the region. The key ndingis that jurisdictions that are more racially diverse exhibit stronger partisan differences between rich andpoor.These two schools of thought offer useful paradigms for understanding cross-regional patterns inincome-based voting. The choice of paradigm yields different hypotheses about the micro-level factorsthat affect the voting patterns of rich and poor citizens. Under the paradigmfocused on economic context,we might look to how post-materialistic preferences, such as religion, affect voters in places that arepredominantly afuent versus areas that are predominantly poor. Under the racial context paradigm, wemight look to explanations of racial threat, sorting, and historical migration as factors that explain whyrich and poor vote the same in some places and differently in others.In this paper, we sort out these paradigms by showing that racial context trumps economic contextin structuring the geographic patterns of income and partisanship in the United States. Whatever micro-levelprocessesareattherootofjurisdictionaldifferencesintheincome-partisanrelationship, those1processes are closely tied to racial context, and they are only tied to economic context to the extent thateconomics and race are collinear. By narrowing the set of viable explanations for regional variationinincome-basedvotingtothosebearingonracialcontext, wemovetheliteratureastepforwardinunderstanding geographic variation in the political ssures between the wealthy and the poor.Building on research on racial heterogeneity and redistribution, our efforts here expand on the racial-contextual paradigm, focusing on geographic variation in racial and political orders, and capitalizingon new data sources that permit study of voter behavior at much lower geographic levels than couldpreviously be achieved using either survey data or aggregate-level election returns. Just as opposition toredistribution has been predicted by white reaction to the local prevalence of racial minorities, we expectthatthestrengthoftherelationshipbetweenincomeandsupportforthepartythatfavorseconomicredistribution will vary with local racial composition. We test this expectation using full-populationdatasets of all 73 million voters who register with one of the two parties in party registration states, aswell as a geographic le containing data on the 185,000 precincts from all 49 states in which electionreturns are organized by precinct and published. Once we account for the racial composition of smallgeographic areas within states, the relationship between the wealth of a jurisdiction and income-basedvoting vanishes.We show that variation in the income-partisan relationship is tied, foremost, to the racial compositionof local areas. In the homogeneously white areas where most Americans live, the relationship betweenincome and partisanship does not vary with the wealth of the community nor the wealth of the state. Onlyin local areas with substantial minority populations do regional differences emerge. In the Northeast andMidwest, more afuent voters living in districts with larger proportions of African-Americans are onlyslightly more Republican than low-income voters in these areas. However, in rural areas with high con-centrations of minorities, particularly in the Black Belt areas of the South and some agricultural areasof the West such as Californias Central Valley, the relationship between income and partisanship ismuch stronger than would be expected otherwise. This is not only because poor blackswho are over-whelmingly Democraticare a larger share of the population and contribute to a stronger income-partyrelationship, but because afuent non-blacks living in these areas tend to register and vote for Repub-2licans at much higher rates than those living among black voters elsewhere. We show that alternativeexplanations for this pattern, including religiosity and other indicators of social conservatism and liber-alism, provide only minimal additional predictive value. Regional differences between the preferencesof whites living in close proximity to racial minorities largely accounts for geographic variation in theincome-party relationship.2 Toblers Law, the Modiable Areal Unit Problem, and the Geog-raphy of the LeftOur impetus for exploiting variation in income-based voting at lowgeographic levels, and our hypothesesresultant from that decision, are rooted in three principles that inform us about the appropriate estimationof geographic aggregate statistics, contextual effects, and their inuence on redistribution preferences.Therstprinciple, knownasToblersLaw, states, Everythingisrelatedtoeverythingelse, butnearer things are more closely related than distant things (Tobler, 1970). Toblers Law helps us tounderstanding where the income-party relationship ought to be strongest:if racial or economic contextcorresponds with variations in income-base voting, we expect that variation to occur at the local level.Toblers Law is consistent with much of the behavior contextual effects literature, which has emphasizedthe importance of voter behavior within areas that are typically much smaller than states (Gimpel andSchuknecht, 2004). While much of that literature relies on arbitrary aggregate-level data (notably, metroareas and counties), it constitutes nearly a century of scholarship that has consistently demonstrated thatvoting behavior is geographically contingent and local, even if it has not established that differences invoter behavior are due to exogenous contextual effects.1As early as Tingstens study of Swedish voting behavior (Tingsten, 1937) and Keys scholarship oncounty-level variation in Democratic primary voting across the South (Key, 1949), research foundedon local-level election results and surveys has established that models that account for neighborhood,district, or county-level social context more accurately predict political behavior than analyses using1For recent scholarship on the specic geographies that voters use as their contexts, see Wong et al. (2012).3individual-level voter predictors alone (e.g., Lazarsfeld 1948; Katz and Lazarsfeld 1955; Huckfeldt andSprague 1995; Sampson, Raudenbush and Earls 1997; Oliver 1999; Gay 2006; Putnam 2007). Recentresearch based on geographically coded voter les suggests that Keys (1949) racial threat hypothesisappears in low-level neighborhood contexts as well (Enos, 2010). British political geographers of the1960s and 1970s found that class-based voting varied according to the geographic distribution of voters:working class voters living within more working-class communities tended to exhibit stronger supportfor the Labour Party than working class voters living elsewhere (Butler and Stokes, 1969, 146). Researchon racial contextual effects commonly identies effects at very local levels (Gay, 2002, 2006). Similarly,major changes that may predict differences in income effects, such as income segregation (Reardon andBischoff, 2011; Saez, 2008) and partisan gaps between core and periphery, have been observed withinmetropolitan areas, which are still substantially smaller geographic regions (in most cases) than the stateswidely used when relying on survey data (Chen and Rodden, 2011). All together, this work suggests thatwhen studying the relationship between geographic context and the income-party relationship, we shouldinvestigate contextual correlates at levels well below state lines.The second principle is a seemingly mundane regularity in the study of geographic statistical method-ology known as the Modiable Areal Unit Problem, or MAUP (Fotheringham and Wong, 1991). TheMAUP tells us that parameters estimated on aggregate data are vulnerable to how the data are groupedfor aggregation. Of special relevance to our claims here, the MAUP tells us that geographic correlationsbased on state-level data may obscure substantial within-state variation in these correlations. The MAUPmeans that as we dig beneath the state-level, we cannot take for granted that prior results identied at thestate- or country-level will map on to local areas.Models that assume the independent and identical distribution of voter preferences within states (evenconditional on demographics) may obscure key aspects of income-party relationship, particularly if thisrelationship is geographically contingent (Cho and Rudolph, 2008).2The caricature of the stereotypicalrich, secular New York Democrat and her rich, religious Alabama Republican counterpart may only hold2Weusethetermincomeeffectsandincome-basedvotingasashorthandforthebivariaterelationshipbetweenpersonal income and partisan outcomes, not to suggest that income has a causal effect on party and vote choice, or even thatincome is the primary consideration in voters minds.4within some subregions of each of those states. For example, unlike the urban areas of the South, theBlack Belta set of counties with a history of slave-based cotton plantations and, later, sharecroppingunder Jim Crow lawshas a high concentration of black poverty and some of the highest rates of overallDemocratic voting. Similarly, the persistence of Democratic voting among key white ethnic groups inthe Northeast (Gimpel and Cho, 2004) has helped maintain the partys dominance in those areas. Thesame can be said of areas dominated by ethnic and religious groups in which the Republican Party holdssway, such as the Mormon-dominated areas of Utah, parts of Idaho, and northern Nevada (Gimpel andChinni, 2011). In short, there are persistent political caricatures of voters in different regions of theU.S., but those caricatures have within-state, not just across-state, variants.3Our interest in local context,then, stems not just from the idea that local forces may be more potent in inuencing political behaviorthan coarser geographic phenomena, but that comparisons across large boundaries like states obscuresimportant within-state differences.The rst two principles, Toblers Law and the MAUP, justify our investigation of contextual effectsat local levels. When we examine the income-party relationship within local geographic areas,then,what do we expect to nd?Prior research, both in studies of countries and of U.S. states, falls withintwo primary schools of thought about the contextual correlates of income-based voting. One school iscentered around the effects of economic context. For example, Gelman et al. (2007), building on work byHuber and Stanig (2007) and Inglehart (1990), suggest that economic issues might well be more salientin poorer states (p. 74). The theory offered is that in areas that are more modernized, or that have higheraverage living standards, or that have higher incomes, economics becomes a less salient part of a voterspolitical calculus. Post-materialist concerns are not just a function of ones own economic conditionbut, under this view, they arise depending on the local economic conditions one sees on a day-to-daybasis. Beyond this line of reasoning,there are many other theories focused on the role of economiccontext in driving support for redistributive policies and/or support for parties that advocate or opposeredistribution. For example, research on income inequality (Melzer and Richard, 1981; Romer, 1975),3Wong et al. (2012) address the implications of choice of context for individual-level effects, assessing how individu-als dene the geographic scope of their neighborhoods. Their ndings are an extension of MAUP reasoning to individualpsychology, while our results are more focused on the basic statistical problem.5natural resource abundance (Auty and Gelb, 2000), and class bias in the electorate (Hill and Leighley,1992) test variation in political support by measures of economic context. Whether economic contextis measured by GDP, average income, inequality, or another measure, these theories share in common afocus on the economic conditions of a jurisdiction affecting individual-level attitudes or voting behavior.The main alternative school of thought, especially pertinent to American politics, is that preferencesfor and attitudes towards economic redistribution are inuenced by response to out-group threat or out-group dislike (e.g. Alesina and Glaeser 2004; Luttmer 2001; Gilens 1999). Hypotheses under this schoolof thought include that people are predisposed to feel sympathetic to those who look like them but notto those who look different, and that politicians capitalize on racial differences by pitting groups againstone another for electoral gain. Whatever the micro-level foundations, Alesina and Glaeser (2004) haveshown that racial heterogeneity in a jurisdiction explains much more of the variation in economic prefer-ences than do economic factors like inequality. As our third motivating principle, we apply Alesina andGlaesers result about the preeminent role of racial context, previously found to correlate with redistribu-tive preferences, to geographic variation in the income-party relationship. We argue that racial context,not economic context, ought to explain why in some places rich and poor vote alike and in other placesthey do not. Local areas are typically racially homogenous. In the absence of racial diversity in theselocal areas, we do not expect to see wide gaps between rich and poor. If nearly all voters in a local areaare white, for example, we expect to see small differences in average support for the Republican Partyacross income levels.We expect that most variation in income-based voting occurs in racially diverse localities. In suchareas, the poor end of the income spectrum is often overwhelmingly black, and black voters of all in-come levels everywhere are overwhelmingly Democratic (Dawson, 1995). In diverse local areas wherewealthier voters living near blacks (and, increasingly, other racial minorities) are politically liberal, therelationship between income and partisanship will be weak. But in areas where wealthy voters livingnear blacks are politically conservative, the relationship between income and partisanship will be quitestrong. The former condition is likely to be especially common urban areas with cosmopolitan voters (asGelman et al argues), whereas the latter condition rarely exists in central cities but may be more likely6in suburban and rural areas of the U.S. South. To the extent that the relationship between income andpartisanship varies across local jurisdictions in the United States, then, we expect that variation to becontained in racially diverse areas.Our contribution, then, is to test three empirical hypotheses about the role of local context in thestrength of income-based voting: First, after accounting for racial context, we do not expect income-base voting to vary with area-level income. Second, in homogenous white areas, we expect rich and poorvoters to have minimal differences in their political afliations and vote choices. Third, in heterogenousplaces, we expect to see regional variation in the income effect. In some parts of the country, poorminorities live in close proximity to rich, white, conservatives. In other parts, they live near rich, white,liberals. We expect strong income-base voting patterns only in areas that are both racially diverse andwhere the rich people are historically racially conservative, namely the South. Only when both theseconditions are met (diverse districts with racially conservative Whites) do we expect sizeable partisangaps between rich and poor.3 Data and MethodsWe test these hypotheses using data sources that have been unavailable to previous researchers. Pastworkonthepoliticalgeographyofincome-basedvotinghasreliedprimarilyonsurveymeasuresofself-reported income, party identication, and vote choice, or on geographically coarse aggregate-levelelection data. Because nationally representative surveys such as the American National Election Studyand even the National Annenberg Election Study have small sampleseach NES release is sample ofonly about 2,000 respondentsthe studies commonly sidestep questions of sub-state geographic variation(GelmanandLittle, 1997;Park, GelmanandBafumi, 2004;LaxandPhillips, 2009;Vigdor, 2006).In some cases, scholars supplement these data with analysis of aggregate correlations in counties andother similarly sized units below the state level (Gelman et al., 2008).4This paper expands on these4Among recent contributions to this literature, recent work has bridged across multiple surveys Tausanovitch and Warshaw(2011) to provide similar estimates within congressional districts. Other work has provided estimates of the voting behaviorof racial and economic subgroups within states (Ghitza and Gelman, 2012), but like many existing models, these have beenlimited to estimation of statewide effects within non-geographic subgroups.7research ndings using 73 million party-registration records and presidential election results from morethan 185,000 precincts. These data, which even a decade ago were not available due to limitations intechnology and data quality, now permit us to explore the relationship between geographic context andincome-based voting with extremely high resolution.The rst of our data sources is a voter le containing 73 million voter records from states that requirepartyregistration. Weapplyacombinationofnonparametriccomparisonsandmulti-levelmodelingto voter registration data from Catalist, a leading Democratic data vendor that compiles, cleans, andkeeps current registration records from every state.5Our analysis using these data is restricted to the29 states that invite registrants to register with a party (or as independent or unafliated voters). Thesecond data set, compiled through the Harvard Election Data Archive is a record of the 2008 presidentialelection returns from 185,000 precincts, which allows us to estimate the relationship between precinct-level income and the 2008 Republican presidential vote in the 49 states that have published and madeavailable their data (Ansolabehere and Rodden, 2011).The Catalist voter database permits us to make individual-level inferences about partisan identica-tion by linking party registration records to block-group level demographic characteristics. The versionof the Catalist database made available to academic researchers links individual registrants to the Cen-sus block-group of their residential addresses. The median household income of each registrants blockgroup in the 2000 Census is used as a proxy for their income class. These data are represented in theCatalist data as categorical variables that place each voter into a block-group income category at $20,000intervals, ranging from $20,000 or less to $200,000 income (the top-coded value reported in the Censusdata). Each voter i also associated with the racial composition of his or her block group.6To study the link between local racial and economic context and voter partisanship, we analyze theCatalist data at the state house district level. We have several reasons to study these relationships withinstate house districts. Foremost, these are the bodies that elect representatives to the lower chamber of5These data are explained in more detail in Ansolabehere and Hersh (2012).6Individual-level race data is only available in some Southern states. While we would prefer to have individual-leveldemographic information, given the level of racial segregation in the United States, using block-group level race data isunlikely to induce sufcient ecological bias to reverse our ndings. We discuss the merits of using block-group income as ameasure of economic class in the Online Appendix.8the state legislatures, making them the lowest-level geographic areas that elect politicians responsiblefor major policymaking. These legislators set state welfare policy and are responsible for state-to-localtransfers that result in both income redistribution and geographic redistribution. So, the relationshipbetween district income and partisanship within these districts has important implications for each state.State house districts are also useful to study because they are approximately comparable across statesand there are nearly 5,000 of them across the country. Because state house districts are numerous andtypically equal in population, they are especially useful in the study of metropolitan areas, which aregenerally contained within just a few populous counties but are almost always divided into many morestate legislative districts.7To measure district income, we take the unweighted mean of the median household block groupincome of every registered voter in each district. We similarly calculate the black population percentagein the district by averaging the black population percentage in registrants block groups in each district.8Thus, we study the relationship between a registered voters individual-level partisanship, the incomeof their block group,and the income and race of their district. For registrants in either of the majorparties, we have measures of party, income, racial and economic context, and geography for over 73million voters. Note that at the end of the manuscript and in a detailed online appendix, we offer adetailed justication for the use of party registration as a measure of partisanship, block group incomeas a measure of income, and state house districts as a measure of local area.Though recent scholarship has justied use of party registration states by arguing that they are repre-sentative of the nation at large (see Abrams and Fiorina 2012; McGhee and Krimm 2009), we supplementour analysis by using additional data from the Harvard Election Data Archive (hereafter, HEDA) (An-solabehere and Rodden, 2011), a GIS database containing 2008 presidential election results from 49states.9We merge the precinct-level data with block-group level income and race data from the 20007Unlike states and counties, the boundaries of state house districts are drawn strategically, subject to legal requirementsabout population size, racial composition, contiguity, compactness, and other criteria imposed by courts. That these districtsare drawn purposefully is not a weakness in our research design, as many so-called gerrymanders act to combine communi-ties of interest (Forest, 2004).8This will provide a registered-voter-weighted average of the the local population gures, which will differ slightly fromthe black proportion of the district population.9The omitted state, Oregon, conducts its elections by mail and does not release precinct-level voting returns.9Census using a spatial join in ArcGIS.1011After these Census data were aggregated by precinct, eachprecinct was linked the Census shapele of 2006 state house districts and the ESRI county shapele byway of a spatial join (of the Census, 2006). This was achieved by rst dening the precincts as points,then assigning them to the county or district in which they fell. The resulting precinct-level data setcontains 185,002 precincts distributed across districts and counties in all 49 states. Summary statisticsfor these data appear in Appendix Table A-3. Table 1 summarizes the two data sources.Table 1: Summary of Data SourcesCatalist Data HEDA DataIndividual 73,170,970 D. and R. Registrants 185,002 precinctsUnits with block group (BG) income measure with BG income measureAggregate State House Districts Counties and State House DistrictsUnits with aggregated BG inc. and race with aggregated BG inc. and raceCoverage 29 Party Registration States 49 States with Published DataTo test the relationship between racial context and income-based voting, we present a series of resultsthat vary in the strength of their assumptions and progressively bore below state boundaries. We adopttwo approaches in our analysis of the Catalist party registration data. First, we present nonparametricsummaries of Republican registration within different income groups, taking advantage of the abundantCatalist data. Then, adopting stronger modeling assumptions, we estimate a series of hierarchical linearmodels in which both the group level intercept and income coefcients are allowed to vary (Gelmanand Hill, 2007). We estimate these district-specic effects of income on Republican registration amongtwo-partyregistrantswithinstatehousedistrictsinthe29party-registrationstates, andforprecinctswithin districts (and in the appendix, within counties) for the 49 precinct-data states. We present themodel results using cartograms, maps in which the area of each geographic unit is proportional to itspopulation.Finally, we present a set of regressions in which district-level random-effect estimates of the district-level deviation in the rich-poor partisan gap are dened as the outcome variable, and various social and10Race and population data were obtained fromthe ESRI block group layer (ESRI, 2008). Aggregate and median householdincome for the block group were obtained from the National Historical GIS (Fitch and Ruggles, 2003). Block groups wereconverted to points, and data from all points within each precinct were averaged to generate the precinct-level data.11Block groups from states that do not appear in the HEDA data were excluded from the spatial matching procedure.10cultural variables are used as the explanatory. The purpose of this analysis is to test the marginal explana-tory value of competing social explanations commonly offered for income-based voting: religiosity, ruralculture, education, and family structure.4 Nonparametric Tests of the Local Contextual Income EffectsTobuildintuitionaboutthestrengthoftheincome-partyrelationshipwithindistricts, ourempiricalinvestigation begins with scatter plots based on aggregate-level regressions of district-level partisanshipagainst district-level income. Using the full dataset of registered Democrats and Republicans in 29 states,we calculate, in Figure 1, the percent Republican of each state house district and the average income levelof each district. We organize the party registration states into four approximately even groups, based onthe states income. We calculate state income by aggregating the block group median income value foreach voter in the jurisdiction. Similar to state income, the district income is calculated by averagingeach registered voters median block group income within a district. We plot the proportion of two-partyRepublican registration against the logged mean district income, and then t a lowess curve to eachstates set of state house districts.We display Figure 1 because it draws immediate attention to four facets of local political geographythat might be obscured by state-level analysis. First, the gure indicates the large variance in district-level income. State house districts in the U.S. differ in average income levels by as much as $130,000.By comparison, the richest and poorest states differ in average income by a much narrower $35,000.Second, in all but two of the 29 states plotted (Wyoming and Oregon), on a state-wise basis richer districtsare more Republican districts. Unlike states, rich localities are more Republican, and this holds in richliberal states like Connecticut as much as it holds in poor conservative states like Louisiana. Third, noticethat while there is state-by-state variation in the nature of the slope lines that are plotted, that variationappears to be completely unrelated to state-level income. Each quadrant of Figure 1 contains stateswith very steep curves, gradual curves, and exponential-like curves. Fourth, and perhaps most importantforourargument, whiletheconnectionbetweenstate-levelincomeandthebivariaterelationshipofdistrict income and district partisanship appears to be tenuous in Figure 1, observers familiar with the11Figure 1: Regardless of State Income, Rich Districts are Republican DistrictsKYKYKYKYKYKYKYKYKYKYKY KYKYKYKYKYKYKYKY KYKYKYKYKYKYKYKYKYKY KYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKY KYKYKYKYKYKYKYKYKYKYKYKYKYKYKYKY KYKYKYKYKYKYKYLALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALA LALALALALALALALALALALALALALALALALALALALALALALALALALALA LALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALALAOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOK OKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOK OKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOK OKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDSDWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWVWV WVWVWVWVWVWVWVWVWVWVWVMEMEMEME MEMEMEMEMEMEME ME MEME MEMEMEMEMEMEMEMEMEMEMEMEMEMEMEME MEME MEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEME MEMEMEMEMEMEMEMEMEMEME MEMEMEMEMEMEMEMEMEMEME MEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEMEME MEMEMENMNMNMNMNMNMNMNM NMNMNMNMNMNMNMNM NMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMNMWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWYWY WYWYWYWYWYWYWYWYWYWYWYWY0.2.4.6.81Pr. 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Republican of Two-Party Reg.Ln($12) Ln($20) Ln($33) Ln($55) Ln($90) Ln($150)Log of Mean District Income in $10KFLIANCNEORPARIMiddle-Poor StatesAZAZAZAZAZ AZAZAZAZAZAZAZAZAZAZAZAZAZAZAZAZAZAZAZAZAZAZAZAZAZDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDEDE DEDE DEDEDEDEDEDEDEDEDEDEKSKSKSKSKSKSKSKSKS KSKSKSKSKSKSKS KSKSKSKSKSKSKSKSKSKS KSKSKSKSKSKS KS KSKSKS KSKSKSKSKSKSKSKSKSKS KSKSKSKSKSKSKSKSKSKSKSKSKS KSKS KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKS KSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSKSNVNVNVNVNVNV NVNVNVNVNVNVNVNV NVNVNVNV NVNVNVNVNVNVNVNVNVNVNVNVNVNVNVNVNVNVNVNVNV NVNVNVNYNYNY NYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNY NYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNY NYNYNYNYNYNYNYNYNYNYNYNYNY NYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNYNY NYNYNYNYNYNYNY NYNYNY NYNYNYNYNYNYNYNYNYNYNYNYNY NYNYNYNY NYNYNYNYNYNYNYNYNYNYNYNYNY NY NYNYNYNYNYNYNYNYNY NYNY NYNY NYNYNYUTUTUTUTUTUTUTUTUTUTUTUTUTUTUTUTUTUT UTUTUTUTUTUTUTUTUTUTUTUTUTUTUTUTUTUTUT UTUTUTUTUTUTUTUTUTUTUTUTUT UTUTUTUTUTUTUTUT UT UTUTUTUTUTUTUTUTUTUTUTUTUT UTUTUTNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNH NHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNH NHNHNHNHNHNHNHNHNHNHNHNHNH NH NHNHNH NHNHNH NHNH NHNH NHNH NHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNHNH NH0.2.4.6.81Pr. Republican of Two-Party Reg.Ln($12) Ln($20) Ln($33) Ln($55) Ln($90) Ln($150)Log of Mean District Income in $10KAZDEKSNHNVNYUTMiddle-Rich StatesAKAKAKAKAK AK AKAKAKAKAKAKAKAKAKAKAKAKAKAKAKAKAKAK AKAKAKAKAKAKAKAKAKAKAKAKAKAKAK AKCACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACA CACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACACTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCT CTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCT CTCTCTCTCTCTCTCTCTCTCTCT CTCTCT CTCTCTCOCOCOCOCOCOCOCOCO COCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCO COCOCOCOCOCOCOCOCOCOCO COCOCOCOCO COCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCO MAMAMAMAMAMAMAMAMAMAMAMAMAMA MAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMA MAMA MAMAMAMAMAMA MAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMA MAMAMAMAMAMAMAMAMAMA MAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMA MAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMAMA MAMAMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMD MD MDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMDMD MDMDMDMDMDMDMDMDMDMDMDMDMDNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJNJ NJNJNJNJNJNJNJNJNJNJNJNJNJNJ0.2.4.6.81Pr. Republican of Two-Party Reg.Ln($12) Ln($20) Ln($33) Ln($55) Ln($90) Ln($150)Log of Mean District Income in $10KAKCACTCOMAMDNJRichest StatesNote: Figures are derived from the full population of registered Democrats and Republicans in party registration states. N=73,170,970. Average median household income is the unweighted average of the median household income of block groupsin the district; partisan registration is obtained from individual-level voter le records.American political landscape will notice a connection between the geography of race and the district-level relationship. The states that have very Republican wealthy districts and very Democratic poordistricts are states that have pockets of high-poverty minority areas. These include rich states like NewJersey and Connecticut as well as poor states like Louisiana and Oklahoma. States that have a relativelyat relationship include both rich and poor states, including Wyoming, Kentucky, Iowa, Oregon, UtahandNewHampshire. Allofthesestatesarelessthan2%African-American, withtheexceptionofKentucky, whichinthe2010CensushadthelowestAfrican-Americanpopulationshare(7.71%)ofany state south of the Mason-Dixon Line. Other states have not a linear relationship but a curvilinearrelationship. InSouthDakota, Nebraska, Delaware, andCalifornia, pocketsofpoorracial-minority12districts appear to explain the relatively at relationship between income and party in all but the poorestparts of the state.4.1 Local Racial Composition Matters to the Income-Party RelationshipHaving viewed the data in the aggregate, we now examine the rate of Republican registration amongvoters falling within each block-group income category within state house districts and within states. Westratify the state house districts based on their average income levels and on their racial compositions.Our rst empirical strategy is to exploit our massive data sets to adopt as few modeling assumptions aspossible and estimate the difference in partisan registration across income groups in different geographicsettings. Thelargenumberofobservationsinthevoterlegoesfarinoverwhelmingthecurseofdimensionality problem by brute force, allowing us to plot conditional means, without any models orsemiparametric smoothing techniques such as locally weighted regression (Cleveland, 1979).Figure 2 presents a nonparametric tests in the form of a set of graphs (lattice plots) that demon-strate the role of racial and economic context in the relationship between block-group median householdincome(expressedin$20,000incomeintervalsstartingatincomelevelsof$20,000andbelowandcapped at $100,000+) and the Republican proportion of two-party registrants within each income cate-gory.12These proportions are calculated and plotted for registered voters in block groups that fall withinthree state-house-district income categories: those with average block-group median income of less than$40,000, $40,000 to $60,000, and more than $60,000. Each of these categories is represented by a dif-ferent line style on the graph. Two sets of such lines are presented: red lines represent voters in thewealthier 14 party-registration states in our sample, while purple lines represent voters in states in the 15poorer party-registration states. Each of these estimates is further subdivided into graphs based on thedistricts black population percentage: 0 to 5 percent, 5 to 10 percent, 10 to 25 percent, and 25 percent ormore black. Block-group median household income is on the horizontal axis. To cite one example fromthe gure, the solid red line in the upper left plot shows the relationship between block-group income12These bins are not selected arbitrarily; rather they are how the data are grouped by Catalist. Because there are so fewblock groups with median household income above $100,000, income groups above this level were collapsed into the topcategory.Such plots summarize bivariate relationships within researcher-dened strata (Becker, Cleveland and Shyu, 1996).13and partisanship in districts that are poor, less than 5% black, and located in the rich states.13If there was a tendency of voters in poor states to vote their class interests more than voters in richstates, we would expect to see the purple lines (representing poor states) to have steeper slopes than theset of red lines (representing rich states). Likewise, if there was a tendency for voters in poor districts tovote with their class interests, we would see different slopes in the richest districts (shown with dottedlines) and the poorest districts (shown with solid lines). As the graph demonstrates, these patterns donotgenerallyhold. ThekeyexceptionisindistrictswithlargerproportionsofAfrican-Americans.In districts with more African Americans, we nally see a divergence between the richest half of thestates and the poorest half. While we are not attributing these ndings to a causal effect of contextualracial composition, the ndings presented here show that economic explanations are, at a minimum,descriptively explained by behavior among rich voters who live among black voters in rich states, versusthose who live among black voters in poor states.State-by-state differences in the income-party relationship appear to rest almost entirely on differ-encesinbehaviorinareaswithsizeableblackpopulations. OncestatehousedistrictswithsizeableAfrican American populations are excluded from the analysis, the link between income and partisanshipdoes not vary meaningfully with respect to either district-level or state-level contextual income. This factis important, because districts that are less than 5% black contain over half the U.S. population. Theseare typically white voters who live in rural and suburban areas and small cities and have minimal socialexposure to African-American citizens. The substantial overlap among block groups in both rich andpoor districts and rich and poor states indicates that homogeneously white districts are not the source ofmajor differences between so-called rich and poor states. A second, related nding is that the rich-poorgap is only meaningfully correlated with state income when we limit the analysis to state house districtsthat have sizable black populations. Unsurprisingly, places with a larger proportion of black voters havepoor voters who are much more Democratic than poor voters in other settings. This is unsurprisingbecause black voters everywhere register with the Democratic Party at rates of about 90 percent, andpoor voters in districts with large black minorities (or black majorities) are disproportionately black. In13In the Online Appendix (Table A-1), we conduct a version of this analysis using regression techniques.14Figure 2: The Income-Party Relationship is Closely Linked to Racial Context0.1.2.3.4.5.6Proportion Republican$0-20K Med. Household Income $100K+State House Districts 0-5% Black0.1.2.3.4.5.6Proportion Republican$0-20K Med. Household Income $100K+State House Districts 5-10% Black0.1.2.3.4.5.6Proportion Republican$0-20K Med. Household Income $100K+State House Districts 10-25% Black0.1.2.3.4.5.6Proportion Republican$0-20K Med. Household Income $100K+State House Districts 25%+ Blackin Varying Racial and Economic ContextsPartisanship by Block Group Income0.1.2.3.4.5.6Proportion Republican$0-20KMed. Household Income$100K+Dist. Income $60KRich StatesPoor StatesState House Districts 0-5% BlackNote: Figures are derived from the full population of registered Democrats and Republicans in party registration states. N=73,170,970.rich states, the poorest voters in the most black districts afliate with the Republicans at rates below tenpercent, a number that is only slightly higher in black areas in poor states.Here appear the sub-state origins of the red-state, blue-state paradox (Gelman et al., 2008) and,potentially, the local origins of opposition to redistributive policy in poor states: a huge gap existsbetween rich voters who live in more heterogeneous racial contexts in the poor states and rich voters wholive in more heterogeneous contexts in richer states. Voters with block group incomes over $100,000 inheavily black districts in rich states afliate with the Republicans, on average, at just over 20 percent,while voters in the same income group in poorer states afliate with Republicans at twice that rate.Several plausible explanations exist for these differences other than the legacies of local race relations15and white response to perceived racial threat, but man of these can be dismissed. One might think thatthesedifferencescanbeattributedtolargermiddleclassblackpopulationsinblackdistrictsinlesspoorstateswhomaybeonlyslightlymorelikelytobeRepublicanthanpoorblackvoters. Thisisunlikely, both because the black middle and upper class are increasingly geographically segregated frompoorblacks(ReardonandBischoff, 2011), andtheirpartisanvoting, whichremainsalmostentirelyDemocratic (Dawson, 1995), probably explains very little of the partisan difference between rich andpoor areas within such districts. Instead, it is much more likely that differences at the upper end of theincome spectrum can be explained by voter behavior among whites consistent with Keys racial threathypothesis. For our purposes, it is irrelevant whether this is a result of individual-level contextual effects,or is due to the persistence of partisanship arising from social interactions in these areas over multipledecades.14In the Appendix (Figures A-6 and A-7) we replicate Figure 2 but restrict the data to blockgroups that are less than 10% black and more than 90% non-Hispanic white. While the income-partyintercept is somewhat higher (due to the removal of most black voters in such districts) we nd that thedivergence in voting behavior in high-minority districts varies even when we limit the analysis to votersin block groups that are 90-100% white, allowing us to much more safely assume that the voters beingstudied are white voters. These results uphold the strength of the income-party relationship.15Another plausible reaction to the results presented in this section is that perhaps they are attributabledifferencesinahandfulofstates. Weaddressthisconcerninseveralways. First, intheAppendix(Figure A-5), we present a graphical summary of the party registration gap between rich and poor votersindistrictsofdifferentracialcompositionsbystate. Theresultsconrmthedivergenceinbehavioramong voters in rich and poor states, but the differences are concentrated in districts that are more than10% black. Second, we present the analysis in Figure 2 separately for each state, displaying estimates fornearly 2,000 conditional means. These graphs provide a transparent summary of the partisan registration14For an excellent discussion of the false choice of contextual versus compositional effects, see Sampson 2012, Ch. 1.15Another feature of Figure 2 establishes the primacy of race:in poor states, no state house districts with average block-group median incomes above $60,000 per year are more than 25% African-American. While this could be seen as a weaknessof the nonparametric analysis, in fact it reinforces our ndings that race and income are inseparable in the American context,and income effects should not be discussed without accounting for the geographic distribution of black poverty and its role inpartisan identication.16of voters in each race-income stratum in each group of state house districts for all party-registrationstates. Third, in the Appendix (Figure A-8), we replicate Figure 2 using precinct data from 49 states, andnd the same pattern. Finally, in the next section we adopt slightly stronger modeling assumptions byusing the Catalist data and presidential election results data in multi-level models.5 Testing Sub-State Variation Using Linear Mixed-Effect ModelsWe now continue to concentrate on local areas to demonstrate geographic clustering in the income-partyrelationship. For each state house district, the Catalist data provides counts of voters in each incomecategory, as well as state house district-level racial and income variables. We use these frequencies ina hierarchical linear model in which the Republican registration proportions in each income bin in eachdistrict are weighted according to the total number of two-party registrants in each district income bin.We then estimate a model in which mean Republican registration is the dependent variable and interceptand the effect of the categorical income variable are allowed to vary within district. The six incomecategories are included as a set of dummy variables in the six income categories, using as the omittedbase category block-group income of $0 to $20,000. Both the district level intercept, and district-levelcoefcients on the categorical variables are allowed to vary. This model can be written as follows:yij= + 2x2i + . . . + 6x6i + (2jx2ij + . . . + 6x6ij) + (2jx2i[j] + . . . + 6jx6i[j]) + ijwhere yij represents the Republican share of the two-party vote in for unit i situated in district j, repre-sents an overall intercept term, and 2 . . . 6 represent xed effects for each income category controllingfortheestimateddistrict-levelrandomeffects. Therandomintercept, j, istheinterceptcoefcientestimated by partially pooling the mean of observations i located in district j to the global mean inter-cept. Our primary substantive interest, though, is in the coefcients on the categorical income variables.These similarly vary by district and are represented by the term kj for each income category k = 2 . . . 6,where the base category is the bottom income category. These random effect estimates in each commu-nity lend themselves to easy interpretation. The coefcient on the top income category (for voters in17block groups with income of $100,000 or above) can be interpreted as the district-specic differencein the gap between the richest and poorest voters. Higher positive (negative) values indicate a higher(lower) income-party gradient between the poorest and richest voters than would be expected otherwise.Finally, the error term, ijis independently and identically distributed in each districtj. The randomeffects and errors are each assumed to be normally distributed with constant mean and variance in eachincome category k (Gelman and Hill, 2007, 258):kj N(k, 2k)kj N(k, 2k)

ij N(j, 2j)This model is represented using the R function lmer applied to panel data containing two-party regis-tration data and voter numbers in each district-income bin. (For additional details on the implementationof these models in the lme4 package (Bates, Maechler and Bolker, 2011), see the Online Appendix.)We apply a similar varying-intercept, varying-slope model to estimate the income-party relationshipusing HEDA precinct data clustered within state house districts, dening the McCain share of the two-party vote as the outcome variable and dening the income variable as the average of the block-grouplevel median household income values in each precinct. This model can then be estimated using thesame methods, allowing the relationship between precinct-level income and the two-party vote to varywithin state house districts. The hierarchical model for the income-party relationship for precinct i withindistrict j is thenyij= + xi + jxi[j] + jxi[j] + ijwhere is a general intercept term, is a general coefcient on xi, the precinct-level median householdincomevariableexpressedintensof thousandsofdollars(afteraccountingfordistrict-levelrandomeffects), andjandjare, respectively, the random intercept and income coefcients for precincts ineach district j. This model is estimated using similar assumptions using lmer (see Appendix).18We are most interested in the random effect for the income slope in each state house district, whichis analogous to an interaction term between income variable and the district or county dummy variable.Rather than presenting such estimates using the usual dot plots or condence interval plots, we integratethis with geographic knowledge by presenting our results in maps, but with one more added changetotraditionalapproaches. Whilebasicchoroplethmapshavebeenusedtopresentmodelestimates,the weakness of maps that preserve areas or distance is that the area of the units presented is rarelyproportional in area to the amount of data used. As a result, most maps of political quantities in theUnited States devote a majority of their area to unpopulated areas such as wheat elds of Kansas andthe barren Nevada and Utah high desert, while high-density population centers such as New York Cityand Long Island are nearly imperceptible to the naked eye. We avoid this problem by presenting ourresults using cartograms, maps in which geographic units have been distorted so that their areas areapproximately proportional to their population, using Gastner and Newmans diffusion-based cartogrammethod (Gastner and Newman, 2004) as implemented in ArcGIS (Gross, 2009).16The size of the districtpresented on each of these maps will be approximately proportional to the 2007 population of the countyor counties that it overlaps. For each model, the j random income effects estimated for each groupinggeography j are presented as a heat map constituting twelve equal intervals. Areas in which the marginaldistrict-level difference is below expectations appear at the green end of the scale, while areas with ahigher than expected income-party relationship appear at the redder end of the scale.5.1 Income-Based Partisanship and Voting Within State House DistrictsA cartogram of state-house-district level random income effects in Figure 3 visualizes interregional dif-ferences in partisan voting and highlights the importance of sub-state context. Here, we present 6,the random effect coefcient on the indicator for registered voters in block groups with incomes above$100,000 per year, relative to a base category of households earning $20,000 per year or less. Thus,thismapcapturesthemagnitudeoftherich-poorgaprelativetoanationalbaseline. Theserandomeffects on the Republican proportion of two-party registration between voters in the richest versus poor-16County-level population data was used to dene the cartogram transformation.19est block groups range from -0.34 to 0.45. Large swaths of the three party-registration states of theold ConfederacyNorth Carolina, Florida, and Louisianahave a larger income effect than would other-wise be expected, most notably in the rural Black Belt areas of those states. Similarly, the ethnicallydiverse and economically polarized areas of Californias Central Valley region, an area dominated bypoor Latino agricultural workers, have a strong relationship between income and party. The relation-ship between income and Republican partisanship is lower than would otherwise be predicted in mostmetropolitan areas, especially in districts of the Northeast megalopolis running from DC to Boston thathave been the subject of blue-state voter stereotyping. However, overall there is little evidence thatmajor differences in voting behavior are explained well by state-level contexts.What are the sources of these major differences? Based on the nonparametric analyses, we againexamine the explanatory value of the racial composition of places. We replicate the racial straticationin the earlier analysis by subsetting the random effects estimates to display only districts with the highestand lowest African-American populations. In Figure 4 we show districts that are at least 10% black andless than 5% black. This demonstrates the link between the racial composition of places and income-based voting. The variation in voting behavior in homogeneously white areas varies substantially withinstates, but does not differ dramatically across states (top panel). The regional divide in the income-basedparty afliation shows up, however, in the map of black-dominated state house districts (bottom panel).Except in a few urbanized areas of the South where urban blacks live among cosmopolitan whites, suchdistricts are in rural Black Belt areas where partisan afliation is strongly related to income. Note, inparticular, the large-magnitude effect in the districts along the Mississippi River in Louisiana and innorthern and eastern counties of North Carolina. Both of these areas have a legacy of slavery and blacksharecropping, particularly relative to the Appalachian areas of those states.These ndings are not merely a consequence of an accounting identity related to the nearly universalDemocratic afliation of black voters who fall near the bottom end of the economic spectrum. If weisolatetheresultstoincludedatafromblockgroupsthatarenotmorethan10%African-American(emulating the results that appear in Figure A-7), areas identied as having the strongest income-partydivide continue to stand out (see Figure A-9 in the appendix). Voters fromblock groups with fewAfrican-20Figure 3: Income Effects are Stronger than Expected in Districts with Rural Minority PovertyWithin-District Random Effect of Income on Republican RegistrationShift from 10% BlackWithin-District Random Effect of Income on Republican RegistrationShift from 5% BlackWithin-District Random Effect of Income on Republican RegistrationShift from