Chapter 8.2
Education, ethnicity and the county shift, 2016 to 2024
In 2020 to 2024, counties with more Hispanic residents moved further toward the Republicans than other counties did, a vote-weighted correlation of −0.66 with the swing, against +0.45 in 2012 to 2016.
Webb, Hidalgo, Cameron and El Paso counties lie along the Rio Grande in Texas. Of the counties with 50,00050,000 two-party votes in the later electionEstimandminimum size of a county in the rankingWeightsnone (full counts)Populationcounties named among the furthest above or below the national swingPeriod2012 to 2024SourceCounty election returns or more votes in 2024, the ten whose presidential vote moved furthest toward the Republicans against the nation between 2020 and 2024 include those four. All ten have a Hispanic share of 28%28.0% percent of residentsEstimandsmallest Hispanic share among the ten countiesn10Weightsnone (full counts)Populationthe ten counties with 50,000 or more votes furthest below the national swingPeriod2020 to 2024SourceCounty demographics panel or more, and the rest lie in the Imperial Valley, the New York City area and Miami-Dade. A county's swingSwingThe change in a place's two-party margin between two elections. In this book a positive swing is a move toward the Democrats. is the change in its Democratic minus Republican two-party marginCounty two-party marginThe Democratic vote minus the Republican vote, divided by the sum of the two, so a positive value is a Democratic lead. Votes for other candidates are left out. from one presidential election to the next, in pointsPercentage pointThe unit for the difference between two percentages. A change from one share to another is stated in points, not in percent, so that absolute and relative change are not confused., with a positive swing toward the Democrats. The national swingNational swingThe change between two elections in the Democratic minus Republican margin for the country as a whole. A county's own swing is compared with it to show whether the county moved with the nation or against it., the same change in the summed margin of every county with returns in both elections, was −6−6.1 change in the two-party margin, percentage points of the two-party marginEstimandchange in the summed two-party margin of the matched countiesn3,111Weightsnone (full counts)Populationcounties with returns in both elections of the pair, Alaska excludedPeriod2020 to 2024SourceCounty election returns points in 2020 to 2024, a move toward the Republicans, and each of these counties moved further in that direction than the nation did. A county's swing combines all of its voters, along with people who began or stopped voting or moved, so the returns from these counties cannot say how much their Hispanic voters changed.
Counties with more Hispanic residents moved further toward the Republicans
The chapter sorts the counties of 2020 into four groups by Hispanic share, the highest being those where 50%50% percent of residentsEstimanddefinition of a county groupWeightsnone (full counts)PopulationHispanic share cut-off for the county groupsPeriod2020SourceCounty demographics panel or more of residents were Hispanic. Weighted by their votes, the counties in that group moved −14−13.7 change in the two-party margin, percentage points of the two-party marginEstimandvote-weighted mean of the county swingInterval95% CI −18.3 to −11.4n103Weightstwo-party votes in the later electionPopulationcounties 50 percent or more Hispanic in 2020Period2020 to 2024SourceCounty election returnsNoteInterval: 95% percentile interval from a bootstrap over states, 2,000 replicates. points between 2020 and 2024, further toward the Republicans than the nation did. Across all counties, the vote-weighted correlationCorrelationA number from minus 1 to plus 1 for how closely two measures move together in a straight-line pattern. Zero means no straight-line pattern; a correlation does not say that one measure causes the other. between Hispanic share and the swing was −0.66−0.66 correlation coefficient, from −1 to +1Estimandvote-weighted correlation between the Hispanic share of residents at the start of the pair and the county swingInterval95% CI −0.76 to −0.46n3,102Weightstwo-party votes in the later electionPopulationcounties with returns in both elections of the pair and county characteristics, Alaska excludedPeriod2020 to 2024SourceCounty demographics panelNoteInterval: 95% percentile interval from a bootstrap over states, 2,000 replicates. in 2020 to 2024, against +0.45+0.45 correlation coefficient, from −1 to +1Estimandvote-weighted correlation between the Hispanic share of residents at the start of the pair and the county swingInterval95% CI 0.31 to 0.53n3,112Weightstwo-party votes in the later electionPopulationcounties with returns in both elections of the pair and county characteristics, Alaska excludedPeriod2012 to 2016SourceCounty demographics panelNoteInterval: 95% percentile interval from a bootstrap over states, 2,000 replicates. in 2012 to 2016. In that earlier pair, counties with more Hispanic residents had moved further toward the Democrats than other counties did. A negative correlation means that counties with more of a characteristic moved further toward the Republicans than other counties did, whichever way the nation moved. A vote-weighted correlation counts each county by its two-party votes in the later election, so the largest counties count most; a county-equal correlation counts every county once. Figure 1 places each county by its Hispanic share and its swing minus the national swing. In both weightings the drop from one group to the next gets larger as the Hispanic share rises. One correlation therefore summarizes a relationship that is shallow where Hispanic shares are low and steeper where they are high.
Counted votes, census shares and intervals that resample states
These are counted votes, not survey answers. The margins are county presidential returns for the seven elections from 2000 to 2024, with Alaska left out. They come from the Algara and Amlani county file through 2020 and the MIT Election Data and Science Lab for 2024. Each pair of consecutive elections uses the matched countiesMatched countiesThe counties that report a vote in both elections being compared. Changes are computed on them so that a county created, merged or renamed between the two elections does not distort the comparison., the ones that reported in both, about 3,1003,102 countiesEstimandcount of counties in the correlations and regressionsn3,102Weightsnone (full counts)Populationcounties with returns in both elections of the pair and county characteristics, Alaska excludedPeriod2020 to 2024SourceCounty demographics panel of them in 2020 to 2024. The Connecticut counties, which have no 2020 characteristics, and Jackson County, Missouri, whose Kansas City votes became a separate unit in 2024, are set aside but stay in the national swing. The four characteristics are the share of adults with a bachelor's degree or more and the Hispanic, foreign-born and non-Hispanic Black shares of residents, measured at the start of each pair. They are decennial census and American Community Survey estimates, interpolated to election years. A full count has no sampling error, so the intervals here answer a different question, how much a number depends on which states are in the set, and they come from resampling whole states. The weighting is the qualification that changes the reading, because it decides whether the largest counties or the many small ones set the result. The report gives every estimate with its interval and sample size.
When the college correlation fell depends on the weighting
In 2012 to 2016 the counties with more college graduates moved further toward the Democrats than other counties did. The vote-weighted college correlation was higher in that pair than in any other of the six. Which pair it fell in depends on the weighting. Vote-weighted, it had already fallen in 2016 to 2020 and was +0.02 (95% CI −0.08 to 0.13)+0.02 correlation coefficient, from −1 to +1Estimandvote-weighted correlation between the share of adults 25 and older with a bachelor's degree or more at the start of the pair and the county swingInterval95% CI −0.08 to 0.13n3,102Weightstwo-party votes in the later electionPopulationcounties with returns in both elections of the pair and county characteristics, Alaska excludedPeriod2020 to 2024SourceCounty demographics panelNoteInterval: 95% percentile interval from a bootstrap over states, 2,000 replicates. in 2020 to 2024, an interval that includes zero. Counting each county once, it stayed near its 2012 to 2016 level through 2016 to 2020 and fell only in 2020 to 2024, to +0.16 (95% CI 0.07 to 0.24)+0.16 correlation coefficient, from −1 to +1Estimandcounty-equal correlation between the share of adults 25 and older with a bachelor's degree or more at the start of the pair and the county swingInterval95% CI 0.07 to 0.24n3,102Weightsnone; each county counted oncePopulationcounties with returns in both elections of the pair and county characteristics, Alaska excludedPeriod2020 to 2024SourceCounty demographics panelNoteInterval: 95% percentile interval from a bootstrap over states, 2,000 replicates., an interval that excludes zero. In 2020 to 2024 the counties with 250,000250,000 two-party votes in the later electionEstimanddefinition of a large countyWeightsnone (full counts)Populationthreshold for a large countyPeriod2016 to 2024SourceCounty election returns or more votes cast 46%46.3% percent of two-party votesEstimandshare of the later election's two-party votes cast in large countiesn134Weightsnone (full counts)Populationcounties with returns in both elections of the pair and county characteristics, Alaska excludedPeriod2020 to 2024SourceCounty election returns of the votes, and they had a higher mean college share and a lower mean swing than the rest. Inside each size group, counting each county once, the college correlation is similar, and the vote-weighted correlation across all counties is lower than either group's (Table 3). A statement that the college correlation is near zero in 2020 to 2024 therefore holds for the vote-weighted correlation across all counties and not for the correlation inside either size group.
Figure 2
Vote-weighted, the college correlation fell after 2016 while the Hispanic and foreign-born correlations turned negative
Correlation between a county characteristic at the start of a pair of presidential elections and the county's swing in the two-party margin, 6 pairs from 2000 to 2004 through 2020 to 2024
Correlation of the characteristic with county swing
Show data
| Pair of elections | Characteristic | Weighting | Correlation | Interval low | Interval high | Counties |
|---|---|---|---|---|---|---|
| 2000–04 | College share | Vote-weighted | +0.44 | +0.31 | +0.57 | 3,113 |
| 2004–08 | College share | Vote-weighted | +0.25 | +0.11 | +0.38 | 3,113 |
| 2008–12 | College share | Vote-weighted | +0.10 | +0.02 | +0.18 | 3,113 |
| 2012–16 | College share | Vote-weighted | +0.69 | +0.62 | +0.78 | 3,112 |
| 2016–20 | College share | Vote-weighted | +0.25 | +0.11 | +0.41 | 3,113 |
| 2020–24 | College share | Vote-weighted | +0.02 | −0.08 | +0.13 | 3,102 |
| 2000–04 | Hispanic share | Vote-weighted | −0.03 | −0.15 | +0.09 | 3,113 |
| 2004–08 | Hispanic share | Vote-weighted | +0.27 | +0.07 | +0.40 | 3,113 |
| 2008–12 | Hispanic share | Vote-weighted | +0.33 | +0.17 | +0.46 | 3,113 |
| 2012–16 | Hispanic share | Vote-weighted | +0.45 | +0.31 | +0.53 | 3,112 |
| 2016–20 | Hispanic share | Vote-weighted | −0.48 | −0.62 | −0.20 | 3,113 |
| 2020–24 | Hispanic share | Vote-weighted | −0.66 | −0.76 | −0.46 | 3,102 |
| 2000–04 | Foreign-born share | Vote-weighted | +0.01 | −0.18 | +0.24 | 3,113 |
| 2004–08 | Foreign-born share | Vote-weighted | +0.21 | +0.05 | +0.35 | 3,113 |
| 2008–12 | Foreign-born share | Vote-weighted | +0.45 | +0.28 | +0.56 | 3,113 |
| 2012–16 | Foreign-born share | Vote-weighted | +0.58 | +0.44 | +0.69 | 3,112 |
| 2016–20 | Foreign-born share | Vote-weighted | −0.50 | −0.67 | −0.10 | 3,113 |
| 2020–24 | Foreign-born share | Vote-weighted | −0.69 | −0.77 | −0.49 | 3,102 |
| 2000–04 | Black share | Vote-weighted | +0.05 | −0.06 | +0.15 | 3,113 |
| 2004–08 | Black share | Vote-weighted | +0.03 | −0.09 | +0.16 | 3,113 |
| 2008–12 | Black share | Vote-weighted | +0.43 | +0.34 | +0.53 | 3,113 |
| 2012–16 | Black share | Vote-weighted | +0.16 | +0.03 | +0.31 | 3,112 |
| 2016–20 | Black share | Vote-weighted | −0.13 | −0.29 | 0.00 | 3,113 |
| 2020–24 | Black share | Vote-weighted | −0.07 | −0.25 | +0.09 | 3,102 |
| 2000–04 | College share | County-equal | +0.40 | +0.30 | +0.49 | 3,113 |
| 2004–08 | College share | County-equal | +0.34 | +0.26 | +0.41 | 3,113 |
| 2008–12 | College share | County-equal | +0.09 | −0.01 | +0.20 | 3,113 |
| 2012–16 | College share | County-equal | +0.57 | +0.49 | +0.66 | 3,112 |
| 2016–20 | College share | County-equal | +0.51 | +0.45 | +0.60 | 3,113 |
| 2020–24 | College share | County-equal | +0.16 | +0.07 | +0.24 | 3,102 |
| 2000–04 | Hispanic share | County-equal | −0.09 | −0.21 | +0.07 | 3,113 |
| 2004–08 | Hispanic share | County-equal | +0.19 | +0.11 | +0.29 | 3,113 |
| 2008–12 | Hispanic share | County-equal | +0.20 | +0.11 | +0.30 | 3,113 |
| 2012–16 | Hispanic share | County-equal | +0.33 | +0.25 | +0.42 | 3,112 |
| 2016–20 | Hispanic share | County-equal | −0.30 | −0.51 | +0.10 | 3,113 |
| 2020–24 | Hispanic share | County-equal | −0.39 | −0.51 | −0.16 | 3,102 |
| 2000–04 | Foreign-born share | County-equal | +0.05 | −0.05 | +0.16 | 3,113 |
| 2004–08 | Foreign-born share | County-equal | +0.24 | +0.15 | +0.33 | 3,113 |
| 2008–12 | Foreign-born share | County-equal | +0.26 | +0.17 | +0.35 | 3,113 |
| 2012–16 | Foreign-born share | County-equal | +0.51 | +0.44 | +0.58 | 3,112 |
| 2016–20 | Foreign-born share | County-equal | −0.03 | −0.20 | +0.22 | 3,113 |
| 2020–24 | Foreign-born share | County-equal | −0.34 | −0.47 | −0.14 | 3,102 |
| 2000–04 | Black share | County-equal | −0.08 | −0.21 | +0.04 | 3,113 |
| 2004–08 | Black share | County-equal | −0.04 | −0.17 | +0.09 | 3,113 |
| 2008–12 | Black share | County-equal | +0.49 | +0.36 | +0.59 | 3,113 |
| 2012–16 | Black share | County-equal | +0.29 | +0.20 | +0.38 | 3,112 |
| 2016–20 | Black share | County-equal | −0.12 | −0.26 | −0.02 | 3,113 |
| 2020–24 | Black share | County-equal | −0.24 | −0.36 | −0.14 | 3,102 |
The College share line peaks in 2012 to 2016 at +0.69+0.69 correlation coefficient, from −1 to +1Estimandvote-weighted correlation between the share of adults 25 and older with a bachelor's degree or more at the start of the pair and the county swingInterval95% CI 0.62 to 0.78n3,112Weightstwo-party votes in the later electionPopulationcounties with returns in both elections of the pair and county characteristics, Alaska excludedPeriod2012 to 2016SourceCounty demographics panelNoteInterval: 95% percentile interval from a bootstrap over states, 2,000 replicates. and falls to near zero by 2020 to 2024. The Hispanic share and Foreign-born share lines cross zero between 2012 to 2016 and 2016 to 2020 and are negative in both later pairs. Each point is a vote-weighted correlation across matched counties between a characteristic at the start of a pair of elections and the swing in that pair. Bands are 95% intervals from resampling states, drawn for college and Hispanic share. The third view, college share in both weightings, draws the vote-weighted line falling between 2012 to 2016 and 2016 to 2020 and the county-equal line falling between 2016 to 2020 and 2020 to 2024. Values in Table 1.
What a county correlation says, and what it does not
The chapter holds three kinds of evidence, and they answer different questions. The first is county composition, the shares of residents in each county at the start of a pair. The second is the county swing, weighted by votes or counted once, which the correlations and the group means describe. Sorted by rules into four types, the high-Hispanic counties sat below the all-county mean swing in both weightings, and the affluent suburban counties had differences from it whose intervals include zero (Table 7). Among counties with the same Hispanic, foreign-born and Black shares, those with a higher college share swung further toward the Democrats in 2020 to 2024, vote-weighted (Table 5). The college share that drops out of the single correlation reappears once the other shares are held fixed. One account of the 2016 campaign holds that the parties' coalitions were already dividing along racial and ethnic identities [1]. A study that combined survey and county data found county educational composition among the strongest county-level indicators of shifts in the presidential vote from 2000 to 2020 [2]. The correlations here do not test either account.
The third kind is the change in the reported vote of survey respondents by race, the only one that describes voters. Each wave of the Cooperative Election Study is a separate sample, so Figure 3 compares two groups of respondents and does not follow the same people. Among Black respondents the Democratic margin changed by −19.0 (95% CI −24.1 to −14.0)−19.0 change in the two-party Democratic margin, percentage points of the two-party marginEstimanddifference between two waves in the weighted Democratic minus Republican margin among presidential votersInterval95% CI −24.1 to −14.0n7,822WeightsCES year-specific weightPopulationblack respondents who reported voting for a Democratic or Republican presidential candidatePeriod2020 to 2024SourceCooperative Election StudyNoteInterval: 95% normal interval on the Kish effective sample size, model-based. points between 2020 and 2024, using survey weightsSurvey weightsNumbers that scale each respondent so the sample matches the population on known traits such as age, race, education and region. A weighted average counts a respondent with a large weight for more than one with a small weight., while the vote-weighted correlation of county Black share with the swing was −0.07 (95% CI −0.25 to 0.09)−0.07 correlation coefficient, from −1 to +1Estimandvote-weighted correlation between the non-Hispanic Black share of residents at the start of the pair and the county swingInterval95% CI −0.25 to 0.09n3,102Weightstwo-party votes in the later electionPopulationcounties with returns in both elections of the pair and county characteristics, Alaska excludedPeriod2020 to 2024SourceCounty demographics panelNoteInterval: 95% percentile interval from a bootstrap over states, 2,000 replicates., an interval that includes zero. A county correlation therefore does not measure how a group voted. The Hispanic result agrees in sign across the two kinds of evidence. The margin fell among Hispanic respondents, and the counties with more Hispanic residents swung further toward the Republicans. The agreement does not tie the two together, because a county's Hispanic share does not identify how its Hispanic voters changed, and the county swing includes every other voter in the county. Chapter 3.3, Hispanic, Black and Asian voters (planned) takes up the votes of each group.
Figure 3
The Democratic margin fell among Hispanic and Black respondents in 2020 to 2024 and among white and Black respondents in 2012 to 2016
Change in the Democratic minus Republican margin among White, Black and Hispanic respondents who reported a major-party presidential vote, in percentage points, with one panel for each pair of elections
2012–16
2016–20
2020–24
Change in Democratic minus Republican margin (points)
Show data
| Race | Pair of elections | Weight | Change in margin (points) | Interval low | Interval high | Respondents, both waves combined |
|---|---|---|---|---|---|---|
| White | 2012–16 | Year-specific weight | −7.0 | −9.1 | −4.8 | 61,083 |
| Black | 2012–16 | Year-specific weight | −6.5 | −9.9 | −3.1 | 6,938 |
| Hispanic | 2012–16 | Year-specific weight | +4.3 | −3.6 | +12.3 | 6,340 |
| Asian | 2012–16 | Year-specific weight | +11.0 | −3.0 | +25.0 | 1,502 |
| Other | 2012–16 | Year-specific weight | −7.1 | −17.9 | +3.7 | 2,817 |
| White | 2016–20 | Year-specific weight | +2.2 | +0.3 | +4.2 | 63,584 |
| Black | 2016–20 | Year-specific weight | −2.4 | −6.1 | +1.4 | 7,967 |
| Hispanic | 2016–20 | Year-specific weight | −5.7 | −12.9 | +1.5 | 7,866 |
| Asian | 2016–20 | Year-specific weight | −12.5 | −22.0 | −3.1 | 2,208 |
| Other | 2016–20 | Year-specific weight | −2.5 | −12.3 | +7.3 | 3,138 |
| White | 2020–24 | Year-specific weight | −1.9 | −4.0 | +0.2 | 64,714 |
| Black | 2020–24 | Year-specific weight | −19.0 | −24.1 | −14.0 | 7,822 |
| Hispanic | 2020–24 | Year-specific weight | −23.0 | −30.5 | −15.5 | 7,642 |
| Asian | 2020–24 | Year-specific weight | −9.8 | −21.1 | +1.4 | 2,210 |
| Other | 2020–24 | Year-specific weight | −6.7 | −17.2 | +3.9 | 3,492 |
| White | 2012–16 | Post-election weight, validated-voter weight for 2020 to 2024 | −4.4 | −6.7 | −2.2 | 61,083 |
| Black | 2012–16 | Post-election weight, validated-voter weight for 2020 to 2024 | −6.8 | −10.5 | −3.0 | 6,938 |
| Hispanic | 2012–16 | Post-election weight, validated-voter weight for 2020 to 2024 | +10.1 | +1.9 | +18.3 | 6,340 |
| Asian | 2012–16 | Post-election weight, validated-voter weight for 2020 to 2024 | +11.0 | −3.3 | +25.4 | 1,502 |
| Other | 2012–16 | Post-election weight, validated-voter weight for 2020 to 2024 | −2.6 | −13.7 | +8.4 | 2,817 |
| White | 2016–20 | Post-election weight, validated-voter weight for 2020 to 2024 | +3.6 | +1.5 | +5.7 | 63,583 |
| Black | 2016–20 | Post-election weight, validated-voter weight for 2020 to 2024 | −4.3 | −8.6 | +0.1 | 7,967 |
| Hispanic | 2016–20 | Post-election weight, validated-voter weight for 2020 to 2024 | −4.7 | −12.3 | +3.0 | 7,866 |
| Asian | 2016–20 | Post-election weight, validated-voter weight for 2020 to 2024 | −11.5 | −22.0 | −1.0 | 2,208 |
| Other | 2016–20 | Post-election weight, validated-voter weight for 2020 to 2024 | +4.7 | −6.5 | +15.8 | 3,138 |
| White | 2020–24 | Post-election weight, validated-voter weight for 2020 to 2024 | −4.1 | −6.2 | −1.9 | 53,387 |
| Black | 2020–24 | Post-election weight, validated-voter weight for 2020 to 2024 | −13.9 | −19.5 | −8.3 | 5,828 |
| Hispanic | 2020–24 | Post-election weight, validated-voter weight for 2020 to 2024 | −14.9 | −24.1 | −5.6 | 5,294 |
| Asian | 2020–24 | Post-election weight, validated-voter weight for 2020 to 2024 | −3.1 | −15.6 | +9.4 | 1,408 |
| Other | 2020–24 | Post-election weight, validated-voter weight for 2020 to 2024 | +8.1 | −4.0 | +20.2 | 2,777 |
In the panel for 2020 to 2024 the dots for Hispanic and Black respondents sit to the left of zero with intervals that do not reach it, and the interval for white respondents includes zero. Among Hispanic respondents the Democratic margin changed by −23.0 (95% CI −30.5 to −15.5)−23.0 change in the two-party Democratic margin, percentage points of the two-party marginEstimanddifference between two waves in the weighted Democratic minus Republican margin among presidential votersInterval95% CI −30.5 to −15.5n7,642WeightsCES year-specific weightPopulationhispanic respondents who reported voting for a Democratic or Republican presidential candidatePeriod2020 to 2024SourceCooperative Election StudyNoteInterval: 95% normal interval on the Kish effective sample size, model-based. points. Each dot is the difference in the weighted Democratic minus Republican margin between two waves, among respondents who reported a major-party presidential vote, with the year-specific weight. The second view uses other weights, under which the Hispanic and Black margins still fall in 2020 to 2024. Values in Table 8.
What these returns cannot tell you
Every correlation here is a property of counties, and a pattern across places can differ from the pattern among the people who live in them, a limit known as ecological inferenceEcological inferenceDrawing a conclusion about individuals from data on groups or places, such as counties. A pattern across counties can differ from the pattern among the people who live in them.. Robinson [3] showed that a correlation across places can have the opposite sign from the correlation across individuals. The characteristics describe residents, not voters, and the foreign-born population includes residents who cannot vote, so the foreign-born correlation says where the swing was large and not how immigrants voted. A swing combines people who changed their vote with people who began or stopped voting or moved in or out; Planned, Composition and behavior in county change (planned) separates composition from behavior with other data. The dates are the dates of pairs, so a change of sign between two pairs says that the ordering of counties changed, not why. The data hold no measure of turnout by group, of attitudes or of campaign activity.
Go deeper
- The report: every estimate with its interval and sample size.
- Tables: every figure of the report as a table.
- Neighborhoods: the explorer for this part.
Same question at another grain:
- Race and ethnicity
- Chapter 3.3, Hispanic, Black and Asian voters (planned) Individuals
- Planned, Rural counties (planned) Places
Reading paths that include this chapter:
Data sources
- County election returnsCounty presidential returns, 2000-2024: Algara and Amlani (2000-2020) and MIT Election Data and Science Lab (2024), repaired fileYears: President 1868-2024 (40 elections); House and Senate 1990-2024 · Weights: none (full counts of votes); county swings weighted by two-party votes in the later election, or county-equal · License: The Heideman file is CC BY 4.0 (doi:10.5281/zenodo.22976553), but the state records behind it keep their own terms, and the sites of Florida, Indiana, Colorado and Wisconsin restrict reuse.
- County demographics panelCounty demographics panel, 2000-2024: education, Hispanic, foreign-born and Black shares, income and rural share at the earlier election of each pairYears: 2000-2024 (election years, interpolated between census and survey anchors) · Weights: none (census and American Community Survey estimates, interpolated to election years; sampling error not carried) · License: US government source data, public domain. The interpolated panel is derived work.
- Precinct votes on census geographiesPrecinct votes allocated to ZIP code areas, 2016 and 2020, with the census atlas measuresYears: 2016 and 2020 (contiguous United States only) · Weights: 2020 presidential votes; areas with 100 votes or fewer dropped · License: CC0 (data doi:10.7910/DVN/Z8TSH3), as stated in the project README.
- Cooperative Election StudyCooperative Election Study cumulative file, 2012, 2016, 2020 and 2024 wavesYears: 2006-2024 (every year; self-reported turnout is available in the 10 even years) · Weights: weight (year-specific); sensitivity: weight_post and vvweight · License: See the provider's terms of use.
References
- Sides, John; Tesler, Michael; Vavreck, Lynn (2018). Identity Crisis: The 2016 Presidential Campaign and the Battle for the Meaning of America. Princeton University Press. press.princeton.edu/books/hardcover/9780691174198/identity-crisis
- Zingher, Joshua N. (2022). Diploma Divide: Educational Attainment and the Realignment of the American Electorate. Political Research Quarterly 75(2): 263-277. doi:10.1177/10659129221079862
- Robinson, W. S. (1950). Ecological Correlations and the Behavior of Individuals. American Sociological Review 15(3): 351-357.
How to cite
@misc{singh2026educationethnicityandthecountyshift,
author = {Singh, Vishal},
title = {Education, ethnicity and the county shift, 2016 to 2024},
year = {2026},
url = {https://vishalsingh.org/politics/education-ethnicity-and-the-county-shift},
note = {In The Public Mind. Published 30 September 2026.}
}