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The Geography of Racially Polarized Voting: Calibrating Surveys at the District Level [1]

['Shiro Kuriwaki', 'Stephen Ansolabehere', 'Angelo Dagonel', 'Soichiro Yamauchi']

Date: 2024-05-05  

The estimates offer a nuanced picture of the geography of racial voting preferences. Consistent with the group voting literature, Black voters overwhelmingly prefer Democrats and that preference does not appear to vary by geography. Consistent with the perspective that geography and context matter, the preferences of white and Hispanic voters vary considerably with place.

From coast to coast, the racial gap rises in the Midwest and the South and falls again in New England. The bottom panel of Figure 3 shows the same estimates of differences on a common vertical axis and shows the 80% credible interval for the uncertainty in the estimates. None of the districts have intervals that include zero. Footnote 6 In 2020, the racial gap was 7 points smaller and 5 out of 435 districts had intervals including zero (Appendix C of Supplementary Material).

Disaggregation by district highlights the variation within a state. For example, Chicago and the northern parts of Illinois have the lowest racial gap estimates in the Midwest, but districts in Southern Illinois adjacent to St. Louis have racial gaps as high as those in Tennessee and TX-01 (northeastern Texas, represented by Rep. Louie Gohmert). A similar pattern occurs in Virginia, where the DC suburbs of VA-08, VA-10, and VA-11 have low racial gap values similar to the Boston suburbs, but other Virginia districts have racial gap values that are in the top quarter of all districts.

Although the deep red parts of the map are visually striking, the interpretation of the gap and its magnitude depends on the context. A racial gap of 35 percentage points would arise from an electorate in which 70% of white voters vote for the Republican and 35% of non-white voters vote for the Republican candidate. Such a situation exhibits racially polarized voting and may require creation of a majority minority district under Section 2 of the VRA. However, a CD in which 95% of non-whites vote for Democratic and 60% of whites vote for Democratic has a similarly large racial gap (35 points) but is not racially polarized, because a majority of both groups prefer the same candidate.

The districts with the lowest racial gap tend to be urban areas. In 2016, the five least polarized districts include NY-12 (East side of Manhattan and parts of Queens Footnote 5 ), CA-12 (San Francisco, represented by Speaker Pelosi), CA-13 (Oakland, Rep. Barbara Lee [D]), and WA-07 (city of Seattle, Rep. Pramila Jayapal). In these districts, the racial gap ranges from 6 to 8 percentage points. The distribution of the racial gap is skewed, with large racial gaps in the Deep South pulling the average district’s racial gap to be about 2 percentage points higher than the median district (Appendix C of the Supplementary Material).

The dark red area of the map, centered in Louisiana, Mississippi, and Alabama, are the CDs that exhibit the highest racial gaps, in excess of 60 percentage points. The seven highest racial gap estimates all appear in Mississippi and Alabama. In these districts, the gap between white and non-white voters is close to 70 percentage points.

The gap between white and non-white voting preferences varies considerably across the United States. Figure 3 maps the racial gap between white and all non-white voters (defined in Equation 1 ) from our final estimates. Because CDs are roughly equal in population but vary in land area, we use the cartogram by Daily Kos that sizes districts equally while approximating each district’s location within a state. Footnote 4

Group Cohesion

The variation in the racial gap between white and non-white voters is largely driven by white voters and Hispanic voters. Black voters cohesively vote for the Democratic party in all CDs. Here, we examine the cohesiveness of each group nationally and across districts, states, and regions. Figure 4 decomposes the cohesion estimates of vote choice into the two-party vote share by the three major racial groups.

The voting tendencies of white voters in the urban areas of California, New York, and Illinois are a stark contrast to white voters in the Deep South, where over 80% of white voters voted for Trump. Hispanic voters also exhibit high levels of variation. Hispanic voters in Southern Florida are more Republican than the Hispanics in San Francisco or Chicago. Among districts where over 40% of the electorate is Hispanic, the district in which the Hispanic voters are the most Republican is FL-25, which has the largest Cuban American population in the United States and is represented by Rep. Díaz-Balart (R).

We use the term cohesion to refer to the voting behavior of a group (Atsusaka Reference Atsusaka2021; Pildes Reference Pildes2002). Cohesion equals the absolute deviation of the point estimate of the percentage of a group that votes for the Republican from 50%. For example, white voters voting 85% for Donald Trump will have the same cohesion value as Hispanic voters voting 15% for Trump: 35 percentage points. Figure 5 shows the range of these district cohesion scores for white, Hispanic, and Black voters. Black voters are highly cohesive in their preferred candidate, whereas Hispanic and white voters show considerable variation in the degree of cohesion across CDs. In 400 districts, Black voters’ cohesion is over 0.35 (i.e., 85% vote for one party); white voters have the same level of cohesion in only 13 districts and Hispanic voters in only 35. Using ecological inference on precinct-level election data produced higher cohesion estimates for white voters (by 6 percentage points on average) and Hispanic voters (12 percentage points), with lower cross-district variance.

Our goal is to measure the magnitude and patterns of racial group preferences, not to explain their source.Footnote 7 Nonetheless, it is worth noting that there is a clear urban–rural gradient evident in Figure 4. Our estimates reveal a substantial difference between the most urban and most rural CDs among white voters and among Hispanic voters. White voters in urban districts are more Democratic than white voters in suburban districts, and even more so than white voters in rural districts. That pattern is consistent with research on group preferences as a function of ZIP code density and distance to large cities (Gimpel et al. Reference Gimpel, Lovin, Moy and Reeves2020) and it is consistent with the notion that voting rights law needs to be narrowly tailored to the voting patterns in particular areas. Even still, within urban, suburban, and rural CDs, a 20-percentage point difference between white and Hispanic voters remain. Substantial racial group differences, then, are not just a matter of where people live.

Districts are, in turn, nested within states and regions. Table 1 shows our point estimates of vote by race at the national level, then separates this estimate to the four U.S. Census regions, then by the nine Census divisions, and then by each of the 50 states. Our statewide and national estimates differ from other surveys such as the National Exit Polls by only a few percentage points (Appendix B of the Supplementary Material).Footnote 8 Our procedure increases the precision, that is, decreases the standard error, in subgroup estimates, relative to the raw or poststratification-weighted survey data at the CD or state level. The standard error of our state-level estimates ranges from about 0.005 to 0.10, inversely proportional to the size of the group (Appendix C.4 of the Supplementary Material). For example, there are roughly $ n=300 $ Alabama voters in our survey data, a quarter of whom are Black. That implies a standard error of about 0.033 for white voters in Alabama and 0.06 for Black voters when the sample is taken as-is with no modeling. The comparable standard error of our modeled estimates is about 0.01 for white voters and 0.023 for Black voters.

In 2016, the white–non-white gap was 37 percentage points: 59% of white voters voted for Trump, whereas only 29% of Hispanic voters and 7% of Black voters voted for Trump. But whites in the Northeast were 8 points less likely to support Trump than whites in the Midwest (North Central), and 18 points less likely than whites in the South (Table 1a). Within the Northeast, moreover, voting among whites differed by 10 points between New England (45%) and the Middle Atlantic states (54%) of New York, New Jersey, and Pennsylvania (Table 1b). At the state level, we see further variation. The Republican voting patterns among white voters in the Deep South states are different from those in the peripheral South states (McKee and Springer Reference McKee and Springer2015), as well as different within other regions.

Overall, Table 1 shows how measuring the racial gap only at the state or national level masks important variation in large states. With state-level estimates, white voters appear solidly Republican: in all states but five in New England and four in the Pacific West, the majority of white voters vote for Donald Trump in 2016. But state groupings mask other differences in CD constituencies like the urban–rural divide.

An analysis of variance (ANOVA) is an ideal framework for summarizing the relative importance of racial group differences and nested geographic group differences (Gelman and Hill Reference Gelman and Hill2006, chap. 22). An ANOVA can partition the overall variation in our estimates by the variation explained by a race-level average, a geography-level average, or the interaction of race and geography. Recall that we have $ G\times J\times M $ estimates of vote choice $ {\overset{\sim }{\tau}}_{gj} $ for each racial group g and CD j. We first consider the following three-part decomposition:

(4) $$ \begin{array}{rl}{\overset{\sim }{\tau}}_{gj}=\mu +{\phi}_g+{\eta}_j+{\gamma}_{gj}+{\varepsilon}_{gj},& \end{array} $$

where we partition the estimates of vote choice into the racial group component ( $ \phi $ ), the geography component ( $ \eta $ ), and their interaction ( $ \gamma $ ). The error term $ \varepsilon $ represents sampling variation. By decomposing district–race vote shares in this way, we are defining the geography component as a single Republican vote share for a given district j that does not vary by racial group. It can be thought of as the normal partisan lean of the entire district. The definition is agnostic as to whether this partisan lean is due to factors such as rurality, or place-based identities.

We are interested in how much variation in the patterns we found is explained by each component. In ANOVA, these relative importance measures are given by

$$ \begin{array}{rll}{\hat{\kappa}}_{\mathtt{race}}=\frac{J{\displaystyle \sum_{g=1}^G}{\hat{\phi}}_g^{\hskip0.3em 2}}{\mathrm{TSS}(\overset{\sim }{\tau })}\hskip1em \mathrm{and}\hskip1em {\hat{\kappa}}_{\mathtt{cd}}=\frac{G{\displaystyle \sum_{j=1}^J}{\hat{\eta}}_j^{\hskip0.3em 2}}{\mathrm{TSS}(\overset{\sim }{\tau })},& & \end{array} $$

where TSS is the total sums of squares $ \mathrm{TSS}(\overset{\sim }{\tau })={\sum}_g{\sum}_j{({\overset{\sim }{\tau}}_{gj}\hskip1.5pt -\hskip1.5pt \hat{\mu})}^2 $ . The components are estimated via OLS with the sum-to-zero constraint on each group of coefficients, $ {\sum}_g{\phi}_g={\sum}_j{\eta}_j={\sum}_j{\sum}_g{\gamma}_{gj}=0 $ .Footnote 9 We interpret the $ \kappa $ term for each component as a proportion because they sum to 1.Footnote 10 A large value of $ {\hat{\kappa}}_{\mathtt{race}} $ would indicate that the variation in the final estimate is largely explained by a single national racial group difference in Republican vote share that does not vary with geography, whereas a large value of $ {\hat{\kappa}}_{\mathtt{region}}+{\hat{\kappa}}_{\mathtt{state}}+{\hat{\kappa}}_{\mathtt{cd}} $ would imply that a region, state, or district-level vote share explains more of the total variation in the estimates. A large value of $ {\hat{\kappa}}_{\mathtt{residual}} $ would imply that much of variability is posterior estimation uncertainty rather than anything systematic.

The simple model in Equation 4 can be made more elaborate to partition the geographic component $ \eta $ to states and regions, in addition to districts. This amounts to estimating separate terms for state and region components—for example, $ {\eta}_{\mathrm{state}[j]} $ and $ {\eta}_{\mathrm{region}[j]} $ . If a single value for an entire state or entire region is sufficient to entirely explain district-level geographic variation, all of the variation previously attributed to $ {\eta}_{\mathrm{cd}} $ will shift to the variation explained by $ {\eta}_{\mathrm{state}} $ .

The ANOVA results are summarized in Table 2. Model 1, corresponding to Equation 4, shows that about 60% of the total variation in the district- and race-level votes is explained by a national race pattern, 28% is explained by geography, and the interaction of the two (i.e., differences between races that vary depending on the geography) explains the remaining 5%. Model 2 decomposes the geographic component into districts (nested within states), states (nested within regions), and regions. CDs explain 15% of the variation above and beyond larger geographies, whereas states explain only 6% and regions explain 7% Estimation uncertainty accounts for about 5 percent of the variation in our data.

The weight of geography (i.e., CD, state, and region) also varies within each racial group. Models 3–5 use the estimates of one racial group at a time so that there is no race-level variation. The penultimate row shows that the total variation in the Trump vote among white and Hispanic voters is more than five times larger than the total variation observed for Black voters. In all racial groups, the district level accounts for more than twice as much variability in the total estimates than state- or region-level averages. White and Hispanic voters vary more than Black voters and the bulk of the variation occurs at the district level.

At a high level, then, differences in the average vote choice of racial groups nationwide explain twice as much of the systematic variation in voting as does the CD, state, or region. Sixty percent of the variation in the vote of racial groups within a particular CD can be accounted for by the average national vote of the groups. The normal vote of a CD, state, or region explains 30% of the remaining variation. A final 10% of the variation is due to the fact that voting patterns of a given racial group change by geography. In other words, while we do find substantial substate variation, geography (i.e., CD, state, and region) explains at most a third of the total variation across all district–race combinations.

Ecological inference estimates yield different conclusions on the explanatory power of geography. We applied the same ANOVA modeling to the posterior sample of ecological inference estimates, which were generated from applying EI one district at a time, described in Appendix B of the Supplementary Material. The national race component explains roughly the same amount of variation across both methods (both 0.52 in 2020). However, state, region, and district explain twice as much of the variation using MRP than using EI (0.34 vs. 0.15). Because MRP models district-level variation as a random effect, it allows for more efficient estimation of the district component $ {\kappa}_{\mathtt{cd}} $ . EI implemented at the district level forces that variation onto the interaction component $ {\kappa}_{\mathtt{race}\times \mathtt{cd}} $ . One may improve EI by allowing partial pooling across districts, but that is not conventionally done.

Our analysis examines a group’s voting behaviors, rather than beliefs, ideologies, or issue preferences. Our findings reaffirm the lack of variation in party vote among Black voters, but there are certainly diverse dynamics within that group beyond party choice (Jefferson Reference JeffersonForthcoming; White and Laird Reference White and Laird2020). Even still, we examine correlates of ideology or belief and the methods developed here open up opportunities to model any survey item to explore how ideology or public opinion may vary within groups.

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[1] Url: https://www.cambridge.org/core/journals/american-political-science-review/article/geography-of-racially-polarized-voting-calibrating-surveys-at-the-district-level/6BEF8C3000B763699C27A4F9E8590516

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