# Education, ethnicity and the county shift, 2016 to 2024: report

Chapter 8.2 of The Public Mind, by Vishal Singh (NYU Stern). Generated from the chapter's data files; the reading version is at https://vishalsingh.org/politics/education-ethnicity-and-the-county-shift. Table n of this report is figure n of the chapter.

## Identification

- Title: Education, ethnicity and the county shift, 2016 to 2024
- Chapter: 8.2 (Part VIII, Places and the people in them; Votes, giving and places)
- Question: Which county characteristics tracked the presidential swing before and after 2016?
- Dek: 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.
- Grain: places
- Status: draft
- Published: 2026-09-30
- Updated: not revised
- Data sources:
  - County election returns. County presidential returns, 2000-2024: Algara and Amlani (2000-2020) and MIT Election Data and Science Lab (2024), repaired file; years: 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
  - County demographics panel. County demographics panel, 2000-2024: education, Hispanic, foreign-born and Black shares, income and rural share at the earlier election of each pair; years: 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)
  - Precinct votes on census geographies. Precinct votes allocated to ZIP code areas, 2016 and 2020, with the census atlas measures; years: 2016 and 2020 (contiguous United States only); weights: 2020 presidential votes; areas with 100 votes or fewer dropped
  - Cooperative Election Study. Cooperative Election Study cumulative file, 2012, 2016, 2020 and 2024 waves; years: 2006-2024 (every year; self-reported turnout is available in the 10 even years); weights: weight (year-specific); sensitivity: weight_post and vvweight
- Canonical URL: https://vishalsingh.org/politics/education-ethnicity-and-the-county-shift
- Report: https://vishalsingh.org/politics/education-ethnicity-and-the-county-shift/report
- Markdown: https://vishalsingh.org/politics/education-ethnicity-and-the-county-shift/report.md
- BibTeX key: singh2026educationethnicityandthecountyshift

```bibtex
@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.}
}
```

## Principal estimates

The facts the dek and the opening prose cite, in order of appearance.

| Estimand | Population | Period | Value | Interval | n | Weights | Source | Fact id |
| --- | --- | --- | ---: | --- | ---: | --- | --- | --- |
| vote-weighted correlation between the Hispanic share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | −0.66 | 95% CI −0.76 to −0.46 | 3,102 | two-party votes in the later election | County demographics panel | r_hispanic_2024_w |
| vote-weighted correlation between the Hispanic share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 | +0.45 | 95% CI 0.31 to 0.53 | 3,112 | two-party votes in the later election | County demographics panel | r_hispanic_1216_w |
| vote-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 swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 | +0.69 | 95% CI 0.62 to 0.78 | 3,112 | two-party votes in the later election | County demographics panel | r_college_1216_w |
| vote-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 swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | +0.02 | 95% CI −0.08 to 0.13 | 3,102 | two-party votes in the later election | County demographics panel | r_college_2024_w |
| count of counties matched on FIPS | counties with returns in both elections of the pair, Alaska excluded | 2020 to 2024 | 3,111 |   | 3,111 | none (full counts) | County election returns | n_matched_2024 |
| count of counties in the correlations and regressions | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 3,102 |   | 3,102 | none (full counts) | County demographics panel | n_analysis_2024 |
| matched counties with no 2020 county characteristics (the eight Connecticut counties) | counties with returns in both elections of the pair, Alaska excluded | 2020 to 2024 | 8 |   | 3,111 | none (full counts) | County demographics panel | n_no_comp_2024 |

## Findings

In 2020 to 2024, counties with more Hispanic residents moved further toward the Republicans than other counties did. The vote-weighted correlation of a county's Hispanic share with its swing in the presidential margin was −0.66, against +0.45 in 2012 to 2016, when counties with a higher Hispanic share had moved further toward the Democrats. The vote-weighted correlation with college share fell from +0.69 in 2012 to 2016 to +0.02 (95% CI −0.08 to 0.13) in 2020 to 2024. The timing of that fall depends on the weighting. Counting each county once instead of by its votes, the college correlation stays near its 2012 to 2016 level through 2016 to 2020 and falls only in 2020 to 2024.

A county's swing is the change in its two-party margin from one presidential election to the next, in points, and a positive swing is a move toward the Democrats. The national swing is the change in the summed margin of all counties with returns in both elections, the measure used in Chapter 6.1, Nationalization of the county vote (https://vishalsingh.org/politics/nationalization-of-the-county-vote). A correlation with the swing equals the correlation with the swing minus the national swing, so a positive value means that counties with more of a characteristic moved further toward the Democrats than other counties did, whichever way the nation moved. A vote-weighted correlation counts each county in proportion to its two-party votes in the later election, so the largest counties count most, and a county-equal correlation counts every county once.

The chapter holds three kinds of evidence, which answer different questions. The first is county composition, the share of adults with a bachelor's degree or more and the Hispanic, foreign-born and non-Hispanic Black shares of residents, each measured at the start of the pair. The second is the county swing, weighted by votes or counted once. The third, at the end, is the change in the reported vote of survey respondents by race, the only one that describes voters. The 95% confidence interval of an estimate follows it in parentheses, and the note beside Table 1 says how it is computed. Companion 8.1a, The diploma map (https://vishalsingh.org/politics/the-diploma-map) takes up the level of the vote by education, and Chapter 8.1, Five axes of the county vote (https://vishalsingh.org/politics/five-axes-of-the-county-vote) ranks the axes together. (Note: The last pair, 2020 to 2024, has 3,111 matched counties, and 3,102 are used here. The 8 Connecticut counties have no county characteristics for 2020, and Jackson County, Missouri is left out because its Kansas City votes are a separate unit in 2024, which gives it a swing that is not its own. Both stay in the national swing so that it matches the other chapter.)

### Vote-weighted Hispanic and foreign-born correlations turned negative after 2016

Table 1 plots the correlation of the swing with each characteristic over six pairs of elections from 2000 to 2024. The vote-weighted Hispanic correlation is positive in 2004 to 2008, 2008 to 2012 and 2012 to 2016 (+0.45 in the last) and negative in 2016 to 2020 (−0.48 (95% CI −0.62 to −0.20)) and 2020 to 2024 (−0.66 (95% CI −0.76 to −0.46)). The foreign-born correlation follows the same path, from +0.58 in 2012 to 2016 to −0.50 and −0.69. The college correlation has a positive point estimate in all six pairs in both weightings and is highest, vote-weighted, in 2012 to 2016. The intervals for 2020 to 2024 (vote-weighted) and 2008 to 2012 (county-equal, +0.09 (95% CI −0.01 to 0.20)) include zero. (Note: The intervals resample whole states, because counties in one state share candidates, campaigns and election law, and a resample of counties would give a narrower interval. The resampling repeats the calculation on many draws of the states with replacement, which is a bootstrap. The returns are full counts, so an interval says how much a correlation depends on which states are in the set and is not sampling error of the vote. The characteristics are census and American Community Survey estimates interpolated to the election years, so they carry sampling error that no interval includes.)

Table 1. Vote-weighted, the college correlation fell after 2016 while the Hispanic and foreign-born correlations turned negative

Counting each county once, the Hispanic correlation also changes sign, from +0.33 in 2012 to 2016 to −0.30 (95% CI −0.51 to 0.10) in 2016 to 2020, though the later interval includes zero. The county-equal foreign-born correlation is −0.03 (95% CI −0.20 to 0.22) in 2016 to 2020 and −0.34 (95% CI −0.47 to −0.14) in 2020 to 2024. Black share has a vote-weighted correlation of +0.16 (95% CI 0.03 to 0.31) in 2012 to 2016 and −0.07 (95% CI −0.25 to 0.09) in 2020 to 2024, and counting each county once it is −0.24 (95% CI −0.36 to −0.14) in 2020 to 2024.

The spread of county swings was also smaller in 2020 to 2024. The vote-weighted standard deviation of county swings, measured as in Chapter 6.1, Nationalization of the county vote (https://vishalsingh.org/politics/nationalization-of-the-county-vote), was 10.8 points in 2012 to 2016 and 4.5 in 2020 to 2024, so the correlations describe how the counties are ordered and not how far they moved. The differences in points are in Table 4, where the 103 counties with a Hispanic share of 50% or more averaged −7.6 (95% CI −12.2 to −5.3) points relative to the national swing, vote-weighted.

One account of the 2016 campaign holds that the parties' coalitions were already dividing along racial and ethnic identities and that the Trump campaign stressed race, immigration and religion [1]. A study that combined survey and county data found the educational composition of counties to be 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.

### Counties furthest below the national swing had high Hispanic shares

Table 2 colors each county by its swing minus the national swing, for 2012 to 2016, 2016 to 2020 and 2020 to 2024. The national swing in 2020 to 2024 was −6.1 points, a move toward the Republicans, and 75.6% of counties were within 5 points of it, against 24.6% in 2012 to 2016. Among the 584 counties with 50,000 or more votes in 2024, the ten furthest below the national swing all have a Hispanic share of 28.0% or more and a foreign-born share of 22.9% or more. They lie along the Rio Grande (Webb, Hidalgo, Cameron and El Paso counties), in the Imperial Valley, in the New York City area (Bronx, Queens, Passaic and Hudson counties) and in Miami-Dade. Without the vote cut, the ten furthest below still all have a Hispanic share of 28.0% or more. Four small Texas counties (Maverick, Starr, Dimmit and Culberson) enter the ten, which brings the lowest foreign-born share among them down to 9.0%. Of the ten furthest above the national swing, 5 are in Georgia, and the swing of Henry County was +15.2 points relative to the nation. This chapter does not explain why the Georgia counties sit at that end.

Table 2. In 2020 to 2024, 75.6% of counties were within 5 points of the national swing, and the counties furthest below it were high-Hispanic

The 2012 to 2016 frame has different places at its ends. Among the counties with 50,000 or more votes, 8 of the ten furthest below the national swing are in Pennsylvania, Ohio or Michigan, and 7 of the ten furthest above are in Utah or northern Virginia.

### The timing of the college fall depends on the weighting

Vote-weighted, the college correlation has already fallen in 2016 to 2020 (+0.25 (95% CI 0.11 to 0.41)). Counting each county once, it is +0.57 in 2012 to 2016 and +0.51 in 2016 to 2020 and falls only in 2020 to 2024, to +0.16 (95% CI 0.07 to 0.24), an interval that excludes zero. Only the vote-weighted series reaches zero. The third view of Table 1 shows the two series together. The county-equal correlation is not a fixed fraction of the vote-weighted one. The two differ by more than 0.2 in 4 of the 24 comparisons of a characteristic and a pair, by as much as 0.47, and they have opposite signs in 2 cells where both are near zero.

A vote-weighted correlation counts the largest counties most. In 2020 to 2024 the 134 counties with 250,000 or more votes cast 46.3% of the votes. Table 3 shows that, inside each size group and counting each county once, the 2020 to 2024 college correlation is similar, +0.23 among the large counties and +0.22 among the rest. The groups differ in level. The large counties have a higher mean college share (40.7%, against 22.8%) and a lower mean swing (−5.5 points, against −3.3). The vote-weighted correlation across all counties, +0.02, is lower than either group's.

Table 3. The 2020 to 2024 college correlation is similar in large and other counties and is near zero only across all counties weighted by votes

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.

### The Hispanic gradient steepens at high shares

Table 4 plots each county's 2020 to 2024 swing minus the national swing against its Hispanic share, with the mean of four groups of counties. The vote-weighted mean is +3.3 (95% CI 2.8 to 3.9) points among the 2,285 counties under 10% Hispanic and +1.7 (95% CI 0.3 to 3.4) among the 503 counties from 10% to under 25%. It is −1.9 (95% CI −3.6 to −0.3) among the 211 counties from 25% to under 50% and −7.6 (95% CI −12.2 to −5.3) among the 103 counties at 50% or more. Those last counties moved −13.7 (95% CI −18.3 to −11.4) points in their own margin, against −6.1 nationally. Counting each county once, the four means are +3.1 (95% CI 2.6 to 3.5), +2.9 (95% CI 2.1 to 3.7), +1.0 (95% CI −0.7 to 1.9) and −3.9 (95% CI −7.5 to −2.9), and only the last has an interval below zero. In both weightings the drop from one group to the next gets larger as the Hispanic share rises. A single correlation (−0.66 vote-weighted, −0.39 county-equal) summarizes a relationship that is shallow where Hispanic shares are low and steeper where they are high.

Table 4. County swing relative to the nation fell with Hispanic share in 2020 to 2024, and fell faster at higher shares

The college-share view of the same figure shows little slope in the vote-weighted means. The vote-weighted mean is +1.4 (95% CI 0.2 to 2.5) points among counties under 20% college, +1.1 (95% CI −0.2 to 2.8) from 20% to under 40% and +1.7 (95% CI 0.4 to 3.3) at 40% or more. Counting each county once, it is +1.9 (95% CI 1.4 to 2.5), +3.3 (95% CI 2.7 to 3.8) and +3.2 (95% CI 2.1 to 4.3), so the county-equal correlation of +0.16 is a difference between the counties with the lowest college shares and the rest.

### Holding other shares fixed, college share re-emerges

The four characteristics are correlated across counties, so Table 5 compares each correlation with its standardized coefficient in one regression on all four. Coefficient intervals use standard errors clustered by state. In 2020 to 2024, vote-weighted, college share goes from +0.02 alone to +0.31 (95% CI 0.24 to 0.38) with the others fixed. Among counties with the same Hispanic, foreign-born and Black shares, a county with a college share one standard deviation higher had a swing +0.31 standard deviations further toward the Democrats. The foreign-born coefficient is −0.69 (95% CI −0.93 to −0.44) and the Hispanic coefficient −0.18 (95% CI −0.35 to −0.01). Vote-weighted, the Hispanic coefficient is smaller as characteristics are added, from −0.66 alone to −0.35 (95% CI −0.50 to −0.20) in a regression on Hispanic and foreign-born share only and −0.18 with all four. The R-squared of a regression is the share of the variance of county swings that its fitted values reproduce. Vote-weighted, it is 60.2% for the four characteristics together in 2020 to 2024 and 0.0% for college share alone, and 28.1% and 2.4% counting each county once. In 2012 to 2016 the vote-weighted values were 70.6% and 47.3%, and the county-equal values 58.3% and 32.2%.

Table 5. With the other three shares held fixed, college share tracks the 2020 to 2024 swing although its vote-weighted correlation alone is near zero

In 2016 to 2020, vote-weighted, college share also goes from +0.25 alone to +0.56 (95% CI 0.46 to 0.65). The foreign-born coefficient is −0.77 (95% CI −0.99 to −0.55) and the Hispanic coefficient is +0.08 (95% CI −0.16 to 0.31), an interval that includes zero. The Hispanic correlation in that pair, −0.48, goes to −0.24 (95% CI −0.42 to −0.07) with foreign-born share added and to +0.08 with all four. Counting each county once in 2016 to 2020, the coefficients are close to the correlations, +0.51 for college share and −0.30 for Hispanic share. Counting each county once in 2020 to 2024, the coefficients are +0.24 for college share, −0.22 for Hispanic share, −0.29 for foreign-born share and −0.24 for Black share. Black share has a coefficient different from zero in that version but not in the vote-weighted one (−0.04 (95% CI −0.16 to 0.07)).

### ZIP code areas reproduce the Hispanic and foreign-born correlations

The 2016 to 2020 swing can be measured for 29,323 ZIP code areas, which are far smaller than counties, using precinct votes allocated to each area [3]. The two college measures are not identical. The county panel reports a bachelor's degree share, which averages 21.7% across counties, and the ZIP code atlas reports a college-education share with no documented definition that averages 54.7% across areas, so the college correlations are not like for like. Table 6 compares the vote-weighted correlations at the two grains. The Hispanic correlation is −0.50 (95% CI −0.60 to −0.29) in ZIP code areas and −0.48 (95% CI −0.62 to −0.20) in counties, and the foreign-born correlation is −0.46 (95% CI −0.61 to −0.14) and −0.50 (95% CI −0.67 to −0.10). The Hispanic and foreign-born correlations are reproduced at the finer grain. The college correlation is +0.47 (95% CI 0.42 to 0.55) in ZIP code areas, against +0.25 (95% CI 0.11 to 0.41) in counties. The 14 areas with a missing characteristic are dropped.

Table 6. ZIP code areas give Hispanic and foreign-born correlations similar to counties for 2016 to 2020, and a larger college correlation with a different college measure

No ZIP code area returns exist for 2024, so the pair in which the vote-weighted college correlation is near zero, 2020 to 2024, cannot be checked below the county.

### High-Hispanic counties swung furthest toward the Republicans

Table 7 sorts the 2020 counties by rules, applied in this order, into four groups. High-Hispanic counties are 25% Hispanic or more. Of the rest, high-Black counties are 25% non-Hispanic Black or more, and affluent suburban counties have 35% or more of adults holding a bachelor's degree, a median household income of $75,000 or more and fewer than 25% of residents in rural areas. Every other county is in a fourth group. The figure plots each group's mean swing minus the national swing, minus the mean over all counties in the same weighting. That all-county mean is +1.3 (95% CI 0.2 to 2.8) points vote-weighted and +2.7 (95% CI 2.2 to 3.2) county-equal. It is not zero because the national swing is the change in the summed margin, which large counties dominate, so each group is compared with the all-county mean and not with zero.

Table 7. High-Hispanic counties swung 4.2 points further toward the Republicans than the average county, vote-weighted

The 314 high-Hispanic counties averaged 4.2 (95% CI 3.0 to 6.2) points further toward the Republicans than the all-county mean vote-weighted, and 3.3 (95% CI 2.4 to 4.7) points further county-equal. The 379 high-Black counties differ from the all-county mean by +0.9 (95% CI −0.8 to 2.1) vote-weighted and −1.8 (95% CI −2.9 to −1.0) county-equal, so their position depends on the weighting. The 152 affluent suburban counties differ by +0.8 (95% CI −0.1 to 1.7) and +0.3 (95% CI −0.7 to 1.3), and both intervals include zero. The county-equal difference for the high-Black counties has the same sign as the county-equal Black-share correlation, −0.24 (95% CI −0.36 to −0.14), and the vote-weighted Black-share correlation is −0.07 (95% CI −0.25 to 0.09).

The high-Hispanic difference stays below the all-county mean when the cut is 15%, at −3.1 (95% CI −4.2 to −2.1) vote-weighted and −2.1 (95% CI −2.9 to −1.3) county-equal. The high-Black difference keeps its signs under each of 6 other cuts from 15% to 50%, from +0.8 to +1.9 vote-weighted and from −2.0 to −1.4 county-equal. The affluent suburban difference is at or above zero vote-weighted under 6 other sets of college, income and rural cuts, from 0.0 to +1.0, and changes sign county-equal, from −0.8 to +0.6.

Kings County, New York, has a Black share of 28.0% and is in the high-Black group, where it has the most votes. Its swing was −5.2 points relative to the nation, so it moved further toward the Republicans than the nation did and lowers the group's vote-weighted mean. Without it the group's mean swing minus the national swing rises from +2.2 to +2.5. Wayne County, Michigan (−2.5) and Essex County, New Jersey (−4.2) are also below the national swing, and without all three the mean is +2.9. Fulton, Cobb, DeKalb, Gwinnett and Henry counties in the Atlanta area are all above the national swing, the smallest at +3.4 points, and without them the mean is +1.8. Of the ten high-Black counties with the most votes, 2 are below the national swing, Kings and Wayne. The group's vote-weighted mean combines large counties on both sides of the national swing.

### The Democratic margin fell among Hispanic and Black respondents

The county correlations do not say which voters moved. The Cooperative Election Study (CES) asks respondents how they voted and how they describe their race, and Table 8 reports the change in the Democratic margin among respondents who reported a major-party vote, using survey weights. Each wave is a separate sample, so the figure compares two groups of respondents and does not follow the same people. (Note: The intervals use the Kish effective sample size [4], treat the weighted sample as a simple random sample, and combine the standard errors of the two waves because they are separate samples of respondents. The CES is an opt-in panel and not a probability sample, so the intervals describe sampling variation among its respondents and leave out how those respondents came to join it. The default weight is the year-specific weight, the only one present in all four waves. It is the weight of the pre-election wave, applied here to a post-election vote question.) Among Hispanic respondents the margin went from +32.5 in 2020 to +9.5 in 2024, a change of −23.0 (95% CI −30.5 to −15.5) points. Among Black respondents it went from +82.9 to +63.8, a change of −19.0 (95% CI −24.1 to −14.0). Among white respondents the change was −7.0 (95% CI −9.1 to −4.8) in 2012 to 2016, +2.2 (95% CI 0.3 to 4.2) in 2016 to 2020 and −1.9 (95% CI −4.0 to 0.2) in 2020 to 2024, and among Black respondents it was −6.5 (95% CI −9.9 to −3.1) in 2012 to 2016.

Table 8. The Democratic margin fell among Hispanic and Black respondents in 2020 to 2024 and among white and Black respondents in 2012 to 2016

The size of these changes depends on the weight. With the validated-voter weight, which covers respondents matched to a voter file, the 2020 to 2024 changes are −14.9 (95% CI −24.1 to −5.6) among Hispanic respondents, −13.9 (95% CI −19.5 to −8.3) among Black respondents and −4.1 (95% CI −6.2 to −1.9) among white respondents. With the post-election weight, the 2012 to 2016 changes are −4.4 (95% CI −6.7 to −2.2) among white respondents and −6.8 (95% CI −10.5 to −3.0) among Black respondents. The Hispanic and Black margins fall in 2020 to 2024 under both weights, and the white change in 2020 to 2024 has an interval that includes zero under the year-specific weight and excludes it under the validated-voter weight.

The Hispanic result agrees in sign with the county correlation for 2020 to 2024. The Black margin fell by −19.0 points among respondents while county Black share has a vote-weighted correlation of −0.07 with the swing, so a county correlation does not measure how a group voted. Chapter 3.3, Hispanic, Black and Asian voters (https://vishalsingh.org/politics/hispanic-black-and-asian-voters; planned) takes up the votes of each group.

### What these data cannot show

Every correlation above 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 inference. Robinson [5] showed that a correlation across places can have the opposite sign from the correlation across individuals. The foreign-born population includes residents who are not citizens and cannot vote, so the foreign-born correlation says where the swing was large and not how immigrants voted. The characteristics describe residents, not voters, and 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 (https://vishalsingh.org/politics/composition-and-behavior-in-county-change; planned) separates composition from behavior with other data.

The dates are the dates of pairs. A correlation for 2012 to 2016 and one for 2016 to 2020 cannot say when between the two elections a county moved, and a change of sign between two pairs says that the ordering of counties changed, not why. The four characteristics are correlated with each other and with others that are not in the regression, among them income, age and rurality, which Planned, Income and the county vote (https://vishalsingh.org/politics/income-and-the-county-vote; planned) and Planned, Rural counties (https://vishalsingh.org/politics/rural-counties; planned) take up. A coefficient therefore describes one regression and not a separate effect. The characteristics are census and American Community Survey estimates interpolated between census and survey years, so they carry sampling error, most in small counties, that no interval here includes. The county set is not harmonized across boundary changes, and the county groups are threshold rules whose group means move with the cut. A swing is not adjusted for the county's earlier margin, and a county that began with an almost one-sided margin had less room to move in one direction than a competitive county did.

The intervals describe dependence on which states are in the set, and the returns themselves are counts. The intervals on group means are of the same kind. The ZIP code check covers only 2016 to 2020, and its votes are allocated from precincts. The CES is an opt-in online panel with self-reported vote, so its estimates depend on how well the weights correct for who joins and on which weight is used. The data used here hold no measure of turnout by group, of attitudes or of campaign activity, so they cannot say why the swing lined up with Hispanic and foreign-born share in 2020 to 2024 and with other characteristics in 2012 to 2016.

## Tables

### Table 1. 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

| 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 |

Views: Vote-weighted (weighting = Vote-weighted); County-equal (weighting = County-equal); College share, both weightings (characteristic = College share).

Note: Election returns are full counts of votes, so they carry no sampling error. The 95% intervals come from a bootstrap that resamples whole states, so they show how much a number depends on which states are in the set. They do not include the sampling error of the county characteristics, which are census and American Community Survey estimates interpolated between years.

Note: The pair 2020 to 2024 leaves out the eight Connecticut counties, which have no county characteristics for 2020, and Jackson County, Missouri, whose swing is not its own.

Source: County presidential returns, 2000-2024 (Algara and Amlani; MIT Election Data and Science Lab), full counts of votes, and county demographics (decennial census and American Community Survey estimates interpolated to election years); weighted by two-party votes in the later election or county-equal, as stated

Data: https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/correlation_by_pair.json

### Table 2. In 2020 to 2024, 75.6% of counties were within 5 points of the national swing, and the counties furthest below it were high-Hispanic

County swing in the Democratic minus Republican margin, minus the national swing, in percentage points, for the pairs 2012 to 2016, 2016 to 2020 and 2020 to 2024

| Pair of elections | Position | County | Swing minus national swing (points) | Swing (points) | Hispanic share (%) | Foreign-born share (%) | College share (%) | Two-party votes, later election |
| --- | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
| 2012–16 | Ten furthest below the national swing | Schuylkill, PA | −29.3 | −31.2 | 3.2 | 1.8 | 14.6 | 60,771 |
| 2012–16 | Ten furthest below the national swing | Trumbull, OH | −28.0 | −29.9 | 1.5 | 1.5 | 17.2 | 92,038 |
| 2012–16 | Ten furthest below the national swing | St. Clair, MI | −24.1 | −26.1 | 3.0 | 2.7 | 16.6 | 73,604 |
| 2012–16 | Ten furthest below the national swing | Mahoning, OH | −23.4 | −25.4 | 5.0 | 3.0 | 21.9 | 110,997 |
| 2012–16 | Ten furthest below the national swing | Luzerne, PA | −23.0 | −25.0 | 8.1 | 5.2 | 21.3 | 130,395 |
| 2012–16 | Ten furthest below the national swing | Monroe, MI | −22.4 | −24.4 | 3.2 | 2.1 | 18.2 | 70,124 |
| 2012–16 | Ten furthest below the national swing | Jefferson, MO | −22.3 | −24.2 | 1.7 | 1.7 | 17.7 | 100,519 |
| 2012–16 | Ten furthest below the national swing | Lackawanna, PA | −22.1 | −24.0 | 5.6 | 4.9 | 25.6 | 100,367 |
| 2012–16 | Ten furthest below the national swing | Androscoggin, ME | −21.4 | −23.3 | 1.7 | 3.0 | 19.6 | 51,164 |
| 2012–16 | Ten furthest below the national swing | Fayette, PA | −21.3 | −23.2 | 0.9 | 0.9 | 14.4 | 52,536 |
| 2012–16 | Ten furthest above the national swing | Salt Lake, UT | +35.0 | +33.0 | 17.4 | 12.3 | 31.8 | 313,906 |
| 2012–16 | Ten furthest above the national swing | Davis, UT | +28.3 | +26.4 | 8.7 | 4.8 | 34.7 | 90,995 |
| 2012–16 | Ten furthest above the national swing | Utah, UT | +25.7 | +23.8 | 10.9 | 7.1 | 36.9 | 130,704 |
| 2012–16 | Ten furthest above the national swing | Arlington, VA | +25.5 | +23.6 | 15.2 | 23.0 | 72.5 | 112,202 |
| 2012–16 | Ten furthest above the national swing | Weber, UT | +21.4 | +19.4 | 17.2 | 7.3 | 22.7 | 63,366 |
| 2012–16 | Ten furthest above the national swing | Alexandria, VA | +20.2 | +18.2 | 16.4 | 26.4 | 61.3 | 70,527 |
| 2012–16 | Ten furthest above the national swing | Fairfax, VA | +19.6 | +17.7 | 15.8 | 29.8 | 59.2 | 512,843 |
| 2012–16 | Ten furthest above the national swing | Travis, TX | +18.7 | +16.7 | 33.6 | 17.7 | 45.3 | 435,469 |
| 2012–16 | Ten furthest above the national swing | Orange, CA | +17.5 | +15.5 | 33.9 | 30.4 | 37.5 | 1,106,504 |
| 2012–16 | Ten furthest above the national swing | Gwinnett, GA | +17.4 | +15.5 | 20.3 | 25.1 | 34.7 | 313,142 |
| 2016–20 | Ten furthest below the national swing | Webb, TX | −32.2 | −29.6 | 95.5 | 26.0 | 18.1 | 67,718 |
| 2016–20 | Ten furthest below the national swing | Hidalgo, TX | −27.2 | −24.7 | 91.9 | 27.1 | 18.4 | 218,726 |
| 2016–20 | Ten furthest below the national swing | Miami-Dade, FL | −25.4 | −22.9 | 67.3 | 53.1 | 29.0 | 1,150,697 |
| 2016–20 | Ten furthest below the national swing | Cameron, TX | −22.9 | −20.4 | 89.4 | 23.3 | 17.5 | 113,095 |
| 2016–20 | Ten furthest below the national swing | Imperial, CA | −21.8 | −19.2 | 83.7 | 31.0 | 14.7 | 55,525 |
| 2016–20 | Ten furthest below the national swing | Bronx, NY | −15.0 | −12.5 | 55.7 | 34.8 | 19.7 | 423,114 |
| 2016–20 | Ten furthest below the national swing | Osceola, FL | −14.5 | −12.0 | 52.5 | 20.0 | 20.9 | 170,777 |
| 2016–20 | Ten furthest below the national swing | El Paso, TX | −12.2 | −9.7 | 82.2 | 24.6 | 22.9 | 262,457 |
| 2016–20 | Ten furthest below the national swing | Queens, NY | −11.7 | −9.1 | 27.9 | 47.2 | 32.0 | 781,703 |
| 2016–20 | Ten furthest below the national swing | Kings, NY | −10.6 | −8.1 | 19.1 | 36.4 | 36.5 | 906,082 |
| 2016–20 | Ten furthest above the national swing | Henry, GA | +13.6 | +16.2 | 6.7 | 7.4 | 27.8 | 121,702 |
| 2016–20 | Ten furthest above the national swing | Forsyth, GA | +13.5 | +16.0 | 9.4 | 16.5 | 51.6 | 127,331 |
| 2016–20 | Ten furthest above the national swing | Douglas, GA | +11.8 | +14.3 | 9.4 | 8.7 | 27.1 | 68,268 |
| 2016–20 | Ten furthest above the national swing | Kaufman, TX | +11.6 | +14.1 | 20.8 | 7.7 | 19.8 | 56,029 |
| 2016–20 | Ten furthest above the national swing | Hamilton, IN | +11.3 | +13.9 | 3.9 | 8.1 | 57.8 | 189,977 |
| 2016–20 | Ten furthest above the national swing | El Paso, CO | +11.0 | +13.6 | 16.8 | 7.0 | 37.6 | 364,769 |
| 2016–20 | Ten furthest above the national swing | Fayette, GA | +10.7 | +13.3 | 7.2 | 9.7 | 46.3 | 71,018 |
| 2016–20 | Ten furthest above the national swing | Collin, TX | +10.7 | +13.3 | 15.2 | 20.2 | 51.7 | 483,263 |
| 2016–20 | Ten furthest above the national swing | Paulding, GA | +10.5 | +13.0 | 6.1 | 5.8 | 24.3 | 84,212 |
| 2016–20 | Ten furthest above the national swing | Stafford, VA | +10.5 | +13.0 | 12.3 | 9.8 | 39.3 | 77,881 |
| 2020–24 | Ten furthest below the national swing | Imperial, CA | −19.7 | −25.8 | 85.3 | 30.9 | 15.7 | 52,629 |
| 2020–24 | Ten furthest below the national swing | Webb, TX | −19.6 | −25.7 | 95.3 | 25.4 | 20.1 | 65,336 |
| 2020–24 | Ten furthest below the national swing | Bronx, NY | −16.4 | −22.5 | 55.6 | 34.6 | 21.3 | 359,844 |
| 2020–24 | Ten furthest below the national swing | Queens, NY | −14.9 | −21.0 | 28.0 | 47.2 | 34.8 | 701,910 |
| 2020–24 | Ten furthest below the national swing | El Paso, TX | −14.3 | −20.4 | 82.7 | 23.5 | 25.0 | 248,280 |
| 2020–24 | Ten furthest below the national swing | Hidalgo, TX | −14.0 | −20.1 | 92.1 | 26.2 | 20.0 | 215,277 |
| 2020–24 | Ten furthest below the national swing | Passaic, NJ | −13.6 | −19.7 | 43.1 | 32.6 | 30.2 | 196,110 |
| 2020–24 | Ten furthest below the national swing | Cameron, TX | −13.0 | −19.1 | 89.6 | 22.9 | 20.1 | 115,249 |
| 2020–24 | Ten furthest below the national swing | Miami-Dade, FL | −12.8 | −18.9 | 68.7 | 54.3 | 32.5 | 1,085,945 |
| 2020–24 | Ten furthest below the national swing | Hudson, NJ | −11.9 | −18.0 | 41.7 | 43.2 | 46.4 | 224,678 |
| 2020–24 | Ten furthest above the national swing | Henry, GA | +15.2 | +9.2 | 7.7 | 8.3 | 28.8 | 128,235 |
| 2020–24 | Ten furthest above the national swing | Douglas, GA | +12.3 | +6.2 | 11.1 | 9.3 | 29.5 | 70,236 |
| 2020–24 | Ten furthest above the national swing | Kaufman, TX | +12.3 | +6.2 | 24.9 | 9.5 | 23.3 | 68,789 |
| 2020–24 | Ten furthest above the national swing | Paulding, GA | +11.3 | +5.2 | 7.7 | 7.1 | 26.7 | 94,571 |
| 2020–24 | Ten furthest above the national swing | Island, WA | +10.7 | +4.6 | 8.4 | 7.7 | 35.4 | 50,562 |
| 2020–24 | Ten furthest above the national swing | Henderson, NC | +10.4 | +4.3 | 11.6 | 7.5 | 34.0 | 68,858 |
| 2020–24 | Ten furthest above the national swing | Newton, GA | +10.3 | +4.2 | 6.5 | 6.8 | 22.9 | 58,732 |
| 2020–24 | Ten furthest above the national swing | Mesa, CO | +9.9 | +3.8 | 15.1 | 4.1 | 29.9 | 89,412 |
| 2020–24 | Ten furthest above the national swing | Fayette, GA | +9.8 | +3.7 | 8.0 | 10.6 | 48.3 | 73,999 |
| 2020–24 | Ten furthest above the national swing | Buncombe, NC | +9.8 | +3.7 | 7.6 | 6.4 | 43.6 | 157,678 |

The map draws every unit from https://vishalsingh.org/politics/data/education-ethnicity-and-the-county-shift/swing_map.json; the rows above are the subset the figure file carries.

Note: Election returns are full counts of votes, so the map draws no intervals. Counties are drawn on 2020 boundaries and matched across elections on FIPS code; a county with no return in one of the two elections draws as no data. Jackson County, Missouri is left blank in 2020 to 2024 because Kansas City's votes are a separate unit in 2024. The data table lists the ten counties at each end for each pair, not the whole map.

Source: County presidential returns, 2012-2024 (Algara and Amlani; MIT Election Data and Science Lab), full counts of votes, not weighted; tooltip characteristics from the county demographics panel (decennial census and American Community Survey estimates interpolated to election years)

Data: https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/swing_map.json

### Table 3. The 2020 to 2024 college correlation is similar in large and other counties and is near zero only across all counties weighted by votes

Correlation of the college share and the foreign-born share with the county swing, with the mean college share and mean swing of each group of counties, for 2016 to 2020 and 2020 to 2024

| Counties and weighting | Pair of elections | Counties | Share of two-party votes (%) | College correlation | College, low | College, high | Foreign-born correlation | Foreign-born, low | Foreign-born, high | Mean college share, start of pair (%) | Mean swing (points) |
| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| All counties, vote-weighted | 2016–20 | 3,113 | 100.0 | +0.25 | +0.11 | +0.41 | −0.50 | −0.67 | −0.10 | 32.2 | +2.5 |
| All counties, county-equal | 2016–20 | 3,113 | 100.0 | +0.51 | +0.45 | +0.60 | −0.03 | −0.20 | +0.22 | 21.7 | +1.1 |
| Large counties, county-equal | 2016–20 | 142 | 48.8 | +0.21 | +0.02 | +0.38 | −0.65 | −0.78 | −0.37 | 37.6 | +3.2 |
| Other counties, county-equal | 2016–20 | 2,971 | 51.2 | +0.53 | +0.48 | +0.63 | −0.03 | −0.24 | +0.26 | 20.9 | +1.0 |
| All counties, vote-weighted | 2020–24 | 3,102 | 100.0 | +0.02 | −0.08 | +0.13 | −0.69 | −0.77 | −0.49 | 34.6 | −4.8 |
| All counties, county-equal | 2020–24 | 3,102 | 100.0 | +0.16 | +0.07 | +0.24 | −0.34 | −0.47 | −0.14 | 23.6 | −3.4 |
| Large counties, county-equal | 2020–24 | 134 | 46.3 | +0.23 | 0.00 | +0.40 | −0.70 | −0.80 | −0.54 | 40.7 | −5.5 |
| Other counties, county-equal | 2020–24 | 2,968 | 53.7 | +0.22 | +0.15 | +0.29 | −0.28 | −0.40 | −0.07 | 22.8 | −3.3 |

Note: Intervals resample whole states; see Methods. Intervals within a size group resample states from the counties of that group.

Note: Counties are those in the correlations for the pair; the eight Connecticut counties and, in 2020 to 2024, Jackson County, Missouri are left out.

Source: County presidential returns, 2000-2024 (Algara and Amlani; MIT Election Data and Science Lab), full counts of votes, and county demographics (decennial census and American Community Survey estimates interpolated to election years); weighted by two-party votes in the later election or county-equal, as stated

Data: https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/size_classes.json

### Table 4. County swing relative to the nation fell with Hispanic share in 2020 to 2024, and fell faster at higher shares

Each dot is a county with characteristics for 2020; the vertical axis is its swing in the Democratic minus Republican margin between 2020 and 2024, minus the national swing, in percentage points

| County | Hispanic share, 2020 (%) | College share, 2020 (%) | Swing minus national swing (points) | Two-party votes, 2024 |
| --- | ---: | ---: | ---: | ---: |
| Autauga, AL | 3.3 | 28.7 | +4.5 | 27,923 |
| Baldwin, AL | 5.2 | 32.8 | +1.9 | 120,732 |
| Barbour, AL | 5.3 | 11.1 | −1.0 | 9,764 |
| Bibb, AL | 3.1 | 11.5 | −0.4 | 9,191 |
| Blount, AL | 9.8 | 14.5 | +5.2 | 27,930 |
| Bullock, AL | 7.0 | 9.2 | +2.1 | 4,084 |
| Butler, AL | 1.6 | 14.7 | −0.9 | 8,423 |
| Calhoun, AL | 4.2 | 19.8 | +0.5 | 48,106 |
| Chambers, AL | 3.3 | 14.7 | −1.5 | 14,116 |
| Cherokee, AL | 1.8 | 14.7 | +3.5 | 12,911 |
| Chilton, AL | 9.0 | 15.0 | +1.5 | 19,618 |
| Choctaw, AL | 0.9 | 14.6 | −2.2 | 6,618 |
| Clarke, AL | 0.9 | 14.4 | +1.0 | 11,892 |
| Clay, AL | 3.2 | 13.2 | −1.3 | 6,727 |
| Cleburne, AL | 2.3 | 16.5 | +3.2 | 7,593 |
| Coffee, AL | 8.8 | 22.1 | +1.5 | 22,096 |
| Colbert, AL | 3.2 | 19.5 | −1.3 | 26,851 |
| Conecuh, AL | 0.8 | 13.6 | −0.5 | 6,003 |
| Coosa, AL | 1.9 | 12.7 | −3.6 | 5,236 |
| Covington, AL | 1.9 | 16.4 | +1.9 | 16,991 |
| Crenshaw, AL | 2.2 | 18.6 | −0.6 | 6,457 |
| Cullman, AL | 4.7 | 17.6 | +3.3 | 42,743 |
| Dale, AL | 6.9 | 19.3 | +0.3 | 18,960 |
| Dallas, AL | 0.6 | 17.1 | +1.0 | 15,426 |
| DeKalb, AL | 15.9 | 14.0 | +2.2 | 29,391 |
| Elmore, AL | 3.2 | 25.6 | +2.4 | 41,148 |
| Escambia, AL | 2.4 | 13.0 | −2.8 | 14,848 |
| Etowah, AL | 4.7 | 17.9 | +0.9 | 45,680 |
| Fayette, AL | 0.7 | 13.6 | +1.5 | 8,300 |
| Franklin, AL | 19.1 | 15.4 | −1.2 | 11,985 |
| Geneva, AL | 4.7 | 13.2 | +3.0 | 12,320 |
| Greene, AL | 1.4 | 12.6 | −1.2 | 4,018 |
| Hale, AL | 0.7 | 16.6 | −6.0 | 7,237 |
| Henry, AL | 2.4 | 20.4 | −1.6 | 9,252 |
| Houston, AL | 4.0 | 22.6 | +1.2 | 43,821 |
| Jackson, AL | 3.3 | 16.1 | +2.4 | 23,349 |
| Jefferson, AL | 4.7 | 35.1 | +3.3 | 293,235 |
| Lamar, AL | 1.8 | 11.7 | +2.3 | 6,839 |
| Lauderdale, AL | 3.1 | 27.4 | −0.5 | 43,034 |
| Lawrence, AL | 2.7 | 15.5 | −1.5 | 16,007 |
| Lee, AL | 4.6 | 39.4 | −1.6 | 71,818 |
| Limestone, AL | 6.7 | 27.6 | +5.1 | 52,468 |
| Lowndes, AL | 0.3 | 14.5 | −2.5 | 5,625 |
| Macon, AL | 1.3 | 20.8 | −1.6 | 7,766 |
| Madison, AL | 5.9 | 45.1 | +5.1 | 193,254 |
| Marengo, AL | 2.6 | 18.3 | +1.0 | 9,626 |
| Marion, AL | 2.9 | 13.3 | +2.5 | 13,442 |
| Marshall, AL | 15.7 | 21.1 | +3.5 | 39,987 |
| Mobile, AL | 3.2 | 25.2 | +1.5 | 172,814 |
| Monroe, AL | 0.3 | 12.6 | −1.2 | 9,747 |
| Montgomery, AL | 4.2 | 34.1 | +5.3 | 88,423 |
| Morgan, AL | 9.5 | 22.3 | +3.0 | 52,841 |
| Perry, AL | 1.1 | 16.8 | +0.5 | 4,443 |
| Pickens, AL | 4.2 | 13.8 | −1.0 | 8,853 |
| Pike, AL | 2.5 | 28.8 | −1.7 | 13,123 |
| Randolph, AL | 2.9 | 17.7 | 0.0 | 11,022 |
| Russell, AL | 5.8 | 17.8 | +1.3 | 20,500 |
| St. Clair, AL | 2.7 | 19.7 | +6.2 | 43,141 |
| Shelby, AL | 6.6 | 45.5 | +5.9 | 112,753 |
| Sumter, AL | 0.9 | 18.7 | −1.3 | 5,267 |
| Talladega, AL | 2.4 | 15.9 | −2.1 | 32,998 |
| Tallapoosa, AL | 2.7 | 19.6 | −0.1 | 19,859 |
| Tuscaloosa, AL | 4.7 | 31.6 | +0.5 | 84,123 |
| Walker, AL | 3.2 | 15.0 | +2.5 | 29,566 |
| Washington, AL | 1.0 | 15.7 | −0.7 | 8,397 |
| Wilcox, AL | 0.2 | 12.3 | 0.0 | 5,242 |
| Winston, AL | 3.4 | 13.8 | +4.6 | 11,075 |
| Apache, AZ | 6.5 | 14.5 | −8.8 | 31,667 |
| Cochise, AZ | 35.1 | 26.8 | +2.6 | 58,232 |
| Coconino, AZ | 14.9 | 39.0 | +1.7 | 69,080 |
| Gila, AZ | 18.5 | 19.0 | +2.7 | 27,405 |
| Graham, AZ | 31.9 | 14.7 | +2.9 | 15,044 |
| Greenlee, AZ | 47.0 | 18.8 | −0.8 | 3,262 |
| La Paz, AZ | 26.6 | 12.6 | +0.9 | 7,571 |
| Maricopa, AZ | 31.2 | 35.0 | +0.4 | 2,031,547 |
| Mohave, AZ | 16.9 | 14.3 | +1.9 | 109,764 |
| Navajo, AZ | 11.3 | 17.6 | −2.9 | 50,234 |
| Pima, AZ | 37.0 | 35.2 | +2.5 | 507,119 |
| Pinal, AZ | 30.1 | 21.7 | +1.4 | 207,582 |
| Santa Cruz, AZ | 82.9 | 22.0 | −11.0 | 18,964 |
| Yavapai, AZ | 15.0 | 28.3 | +1.6 | 148,063 |
| Yuma, AZ | 64.5 | 16.1 | −8.2 | 67,568 |
| Arkansas, AR | 3.7 | 15.0 | +1.8 | 5,454 |
| Ashley, AR | 6.0 | 12.5 | +2.0 | 6,919 |
| Baxter, AR | 2.7 | 18.6 | +3.0 | 20,594 |
| Benton, AR | 17.6 | 36.2 | +5.7 | 125,138 |
| Boone, AR | 2.9 | 17.5 | +3.4 | 16,822 |
| Bradley, AR | 15.7 | 14.6 | −1.8 | 3,174 |
| Calhoun, AR | 3.6 | 15.3 | −2.3 | 2,053 |
| Carroll, AR | 15.4 | 20.6 | +3.4 | 11,282 |
| Chicot, AR | 6.1 | 16.3 | −2.6 | 3,454 |
| Clark, AR | 5.2 | 25.7 | −0.2 | 7,485 |
| Clay, AR | 1.7 | 14.5 | +5.2 | 4,875 |
| Cleburne, AR | 2.7 | 17.6 | +4.8 | 12,536 |
| Cleveland, AR | 0.7 | 17.8 | +0.6 | 3,328 |
| Columbia, AR | 3.1 | 21.2 | +1.4 | 7,833 |
| Conway, AR | 4.4 | 18.8 | +1.9 | 8,342 |
| Craighead, AR | 5.7 | 28.4 | +4.1 | 36,362 |
| Crawford, AR | 8.3 | 19.1 | +4.7 | 23,368 |
| Crittenden, AR | 3.0 | 17.2 | +1.0 | 14,390 |
| Cross, AR | 2.2 | 16.5 | +4.7 | 6,395 |
| Dallas, AR | 1.4 | 11.2 | +0.2 | 2,280 |
| Desha, AR | 6.8 | 13.3 | −1.2 | 3,443 |
| Drew, AR | 4.0 | 24.1 | 0.0 | 6,253 |
| Faulkner, AR | 4.9 | 33.5 | +3.4 | 53,109 |
| Franklin, AR | 3.3 | 12.9 | +5.0 | 6,814 |
| Fulton, AR | 1.3 | 14.6 | +1.3 | 4,946 |
| Garland, AR | 6.6 | 24.1 | +3.9 | 41,374 |
| Grant, AR | 2.9 | 20.9 | +4.6 | 7,947 |
| Greene, AR | 3.3 | 17.1 | +5.0 | 15,552 |
| Hempstead, AR | 14.1 | 16.2 | +0.9 | 5,969 |
| Hot Spring, AR | 3.8 | 16.4 | +2.7 | 12,044 |
| Howard, AR | 12.6 | 15.0 | +1.7 | 4,404 |
| Independence, AR | 7.7 | 18.3 | +5.4 | 13,712 |
| Izard, AR | 2.7 | 17.1 | +2.7 | 5,803 |
| Jackson, AR | 3.1 | 13.2 | +1.5 | 4,692 |
| Jefferson, AR | 2.4 | 19.0 | +4.2 | 21,270 |
| Johnson, AR | 13.9 | 17.2 | +4.1 | 8,873 |
| Lafayette, AR | 1.8 | 11.5 | +2.5 | 2,287 |
| Lawrence, AR | 1.9 | 14.5 | +2.5 | 5,573 |
| Lee, AR | 2.9 | 8.0 | +4.7 | 2,451 |
| Lincoln, AR | 4.6 | 10.2 | +0.3 | 3,315 |
| Little River, AR | 3.8 | 15.7 | +1.4 | 4,828 |
| Logan, AR | 3.3 | 14.5 | +3.9 | 8,031 |
| Lonoke, AR | 5.0 | 21.3 | +6.1 | 30,015 |
| Madison, AR | 6.2 | 12.5 | +3.2 | 7,376 |
| Marion, AR | 2.5 | 15.5 | +3.1 | 7,731 |
| Miller, AR | 3.8 | 16.5 | +1.9 | 15,611 |
| Mississippi, AR | 4.6 | 14.6 | −3.0 | 10,537 |
| Monroe, AR | 2.0 | 12.7 | +4.8 | 2,387 |
| Montgomery, AR | 4.6 | 15.9 | +2.9 | 3,632 |
| Nevada, AR | 2.4 | 12.9 | −1.4 | 2,851 |
| Newton, AR | 1.3 | 15.8 | +4.5 | 3,707 |
| Ouachita, AR | 2.5 | 14.9 | +0.7 | 8,468 |
| Perry, AR | 3.3 | 17.1 | +2.2 | 4,482 |
| Phillips, AR | 2.0 | 15.2 | −0.3 | 4,852 |
| Pike, AR | 7.3 | 18.7 | +1.2 | 4,306 |
| Poinsett, AR | 3.5 | 11.4 | +2.8 | 6,966 |
| Polk, AR | 6.8 | 16.5 | +4.2 | 8,132 |
| Pope, AR | 10.1 | 24.6 | +4.2 | 23,605 |
| Prairie, AR | 2.1 | 16.7 | +1.3 | 3,152 |
| Pulaski, AR | 7.3 | 36.6 | +5.7 | 150,015 |
| Randolph, AR | 2.4 | 12.9 | +4.1 | 6,505 |
| St Francis, AR | 4.5 | 12.6 | +1.6 | 5,862 |
| Saline, AR | 6.1 | 28.5 | +7.3 | 56,345 |
| Scott, AR | 7.9 | 12.6 | +3.5 | 3,338 |
| Searcy, AR | 2.4 | 13.0 | +3.1 | 3,816 |
| Sebastian, AR | 15.2 | 23.3 | +4.2 | 44,371 |
| Sevier, AR | 34.5 | 11.4 | −1.3 | 4,634 |
| Sharp, AR | 2.4 | 12.2 | +4.1 | 7,294 |
| Stone, AR | 2.1 | 14.2 | +2.3 | 5,897 |
| Union, AR | 4.4 | 20.1 | +2.5 | 15,215 |
| Van Buren, AR | 3.3 | 15.4 | +2.8 | 7,460 |
| Washington, AR | 17.8 | 34.2 | +3.3 | 94,022 |
| White, AR | 4.7 | 20.6 | +3.9 | 30,155 |
| Woodruff, AR | 0.8 | 15.6 | +1.6 | 2,273 |
| Yell, AR | 20.9 | 14.1 | +4.8 | 6,360 |
| Alameda, CA | 22.8 | 50.5 | −1.7 | 640,340 |
| Alpine, CA | 14.6 | 39.7 | +6.6 | 722 |
| Amador, CA | 15.2 | 20.3 | +2.5 | 21,801 |
| Butte, CA | 18.4 | 30.1 | +1.2 | 91,407 |
| Calaveras, CA | 13.5 | 21.8 | +1.6 | 25,806 |
| Colusa, CA | 61.3 | 15.6 | −5.9 | 6,845 |
| Contra Costa, CA | 26.7 | 44.3 | −0.9 | 511,316 |
| Del Norte, CA | 20.0 | 18.4 | +5.2 | 10,265 |
| El Dorado, CA | 13.7 | 37.6 | +2.8 | 108,812 |
| Fresno, CA | 53.9 | 23.2 | −6.4 | 317,552 |
| Glenn, CA | 43.6 | 14.8 | −2.0 | 10,164 |
| Humboldt, CA | 13.0 | 32.0 | +1.3 | 61,359 |
| Imperial, CA | 85.3 | 15.7 | −19.7 | 52,629 |
| Inyo, CA | 23.6 | 28.9 | +2.9 | 8,669 |
| Kern, CA | 55.2 | 17.9 | −5.1 | 276,120 |
| Kings, CA | 56.6 | 14.3 | −4.9 | 40,593 |
| Lake, CA | 23.0 | 17.3 | −1.8 | 25,955 |
| Lassen, CA | 20.9 | 12.9 | +3.2 | 11,097 |
| Los Angeles, CA | 48.4 | 34.8 | −5.0 | 3,606,971 |
| Madera, CA | 59.6 | 16.6 | −3.4 | 53,325 |
| Marin, CA | 17.8 | 60.6 | +4.0 | 140,206 |
| Mariposa, CA | 13.2 | 29.4 | +3.0 | 9,247 |
| Mendocino, CA | 26.6 | 24.6 | −2.8 | 37,577 |
| Merced, CA | 61.8 | 14.7 | −9.2 | 84,145 |
| Modoc, CA | 15.3 | 21.5 | +3.9 | 3,892 |
| Mono, CA | 27.1 | 32.1 | +4.2 | 5,816 |
| Monterey, CA | 60.3 | 27.2 | −5.3 | 142,286 |
| Napa, CA | 35.2 | 38.5 | +0.7 | 63,569 |
| Nevada, CA | 10.1 | 39.6 | +3.6 | 59,961 |
| Orange, CA | 34.0 | 42.7 | −0.4 | 1,346,546 |
| Placer, CA | 15.0 | 42.0 | +4.1 | 227,899 |
| Plumas, CA | 9.8 | 23.5 | +5.7 | 9,745 |
| Riverside, CA | 50.3 | 24.4 | −3.3 | 915,459 |
| Sacramento, CA | 23.8 | 32.8 | +0.6 | 633,704 |
| San Benito, CA | 61.2 | 22.1 | −5.9 | 26,881 |
| San Bernardino, CA | 54.5 | 22.3 | −7.0 | 740,530 |
| San Diego, CA | 34.3 | 41.3 | +0.1 | 1,434,642 |
| San Francisco, CA | 15.7 | 59.6 | −0.3 | 386,313 |
| San Joaquin, CA | 42.1 | 21.0 | −9.0 | 255,643 |
| San Luis Obispo, CA | 23.9 | 38.0 | +3.9 | 146,246 |
| San Mateo, CA | 24.5 | 52.9 | −0.7 | 319,573 |
| Santa Barbara, CA | 47.0 | 35.9 | +0.8 | 179,019 |
| Santa Clara, CA | 25.0 | 55.0 | −0.8 | 721,668 |
| Santa Cruz, CA | 34.4 | 42.9 | +0.9 | 128,976 |
| Shasta, CA | 11.1 | 22.9 | +2.6 | 86,669 |
| Sierra, CA | 10.5 | 22.6 | +3.2 | 1,707 |
| Siskiyou, CA | 13.2 | 22.7 | +2.4 | 20,790 |
| Solano, CA | 28.2 | 27.9 | −1.2 | 184,342 |
| Sonoma, CA | 28.5 | 37.5 | +1.1 | 243,026 |
| Stanislaus, CA | 48.5 | 18.7 | −6.0 | 192,333 |
| Sutter, CA | 32.1 | 20.5 | −9.3 | 38,388 |
| Tehama, CA | 27.3 | 18.4 | −0.2 | 25,918 |
| Trinity, CA | 6.8 | 20.8 | +1.9 | 5,428 |
| Tulare, CA | 65.9 | 15.6 | −7.1 | 135,075 |
| Tuolumne, CA | 13.1 | 23.5 | +2.9 | 28,119 |
| Ventura, CA | 43.6 | 35.0 | +0.1 | 376,325 |
| Yolo, CA | 32.5 | 43.7 | +1.2 | 89,249 |
| Yuba, CA | 29.8 | 17.9 | +1.8 | 29,216 |
| Adams, CO | 41.6 | 27.1 | −1.4 | 227,080 |
| Alamosa, CO | 47.6 | 28.5 | −4.3 | 7,301 |
| Arapahoe, CO | 20.6 | 45.0 | +1.5 | 316,036 |
| Archuleta, CO | 17.5 | 40.3 | +7.9 | 9,122 |
| Baca, CO | 11.1 | 23.3 | +5.1 | 1,961 |
| Bent, CO | 32.4 | 15.4 | +0.8 | 2,141 |
| Boulder, CO | 14.3 | 63.6 | +5.6 | 190,907 |
| Broomfield, CO | 13.4 | 58.2 | +7.3 | 45,497 |
| Chaffee, CO | 9.9 | 41.3 | +13.0 | 14,026 |
| Cheyenne, CO | 10.6 | 24.8 | +3.6 | 1,038 |
| Clear Creek, CO | 7.5 | 54.2 | +9.8 | 5,916 |
| Conejos, CO | 51.0 | 25.6 | −4.5 | 3,985 |
| Costilla, CO | 58.0 | 24.4 | −6.5 | 2,005 |
| Crowley, CO | 30.5 | 10.6 | +6.0 | 1,653 |
| Custer, CO | 7.9 | 38.9 | +7.1 | 3,771 |
| Delta, CO | 14.8 | 24.2 | +7.6 | 18,978 |
| Denver, CO | 28.8 | 53.4 | +1.0 | 353,399 |
| Dolores, CO | 12.0 | 27.1 | +4.8 | 1,401 |
| Douglas, CO | 9.6 | 60.3 | +6.3 | 237,859 |
| Eagle, CO | 29.8 | 50.7 | +0.6 | 27,091 |
| Elbert, CO | 8.3 | 35.1 | +5.3 | 19,977 |
| El Paso, CO | 18.1 | 40.3 | +7.2 | 370,530 |
| Fremont, CO | 13.3 | 20.2 | +7.5 | 24,839 |
| Garfield, CO | 30.6 | 32.8 | +5.9 | 29,621 |
| Gilpin, CO | 8.0 | 48.6 | +9.7 | 3,983 |
| Grand, CO | 9.7 | 41.4 | +7.1 | 9,565 |
| Gunnison, CO | 9.8 | 61.1 | +5.1 | 10,576 |
| Hinsdale, CO | 5.7 | 45.7 | +9.5 | 589 |
| Huerfano, CO | 32.2 | 25.0 | +0.2 | 4,311 |
| Jackson, CO | 10.8 | 31.3 | +8.1 | 807 |
| Jefferson, CO | 15.8 | 49.2 | +7.0 | 351,380 |
| Kiowa, CO | 8.7 | 20.6 | +8.3 | 846 |
| Kit Carson, CO | 19.5 | 19.0 | +1.9 | 3,639 |
| Lake, CO | 29.4 | 44.9 | +2.0 | 3,756 |
| La Plata, CO | 12.9 | 46.7 | +7.1 | 34,701 |
| Larimer, CO | 12.3 | 50.3 | +8.3 | 219,056 |
| Las Animas, CO | 39.9 | 19.6 | +2.4 | 7,604 |
| Lincoln, CO | 18.6 | 19.4 | +4.3 | 2,522 |
| Logan, CO | 17.1 | 20.4 | +5.2 | 9,953 |
| Mesa, CO | 15.1 | 29.9 | +9.9 | 89,412 |
| Mineral, CO | 5.1 | 52.9 | +7.3 | 734 |
| Moffat, CO | 16.4 | 20.4 | +6.9 | 6,525 |
| Montezuma, CO | 12.6 | 30.2 | +7.5 | 14,765 |
| Montrose, CO | 21.1 | 26.6 | +9.9 | 25,058 |
| Morgan, CO | 37.3 | 17.5 | −1.3 | 13,121 |
| Otero, CO | 42.1 | 19.4 | +1.9 | 8,684 |
| Ouray, CO | 4.4 | 52.8 | +7.8 | 4,015 |
| Park, CO | 7.0 | 37.1 | +6.6 | 11,669 |
| Phillips, CO | 29.7 | 28.5 | +2.2 | 2,300 |
| Pitkin, CO | 10.8 | 62.0 | −1.4 | 10,924 |
| Prowers, CO | 39.5 | 18.9 | +1.2 | 5,060 |
| Pueblo, CO | 42.7 | 24.3 | −0.9 | 83,016 |
| Rio Blanco, CO | 10.6 | 24.7 | +10.3 | 3,625 |
| Rio Grande, CO | 42.5 | 30.2 | +1.3 | 6,049 |
| Routt, CO | 8.0 | 51.8 | +6.4 | 15,884 |
| Saguache, CO | 36.4 | 26.9 | −4.3 | 3,281 |
| San Juan, CO | 21.1 | 42.8 | +13.7 | 564 |
| San Miguel, CO | 11.3 | 60.7 | +1.7 | 4,683 |
| Sedgwick, CO | 19.2 | 23.9 | +6.3 | 1,331 |
| Summit, CO | 15.8 | 54.3 | +3.7 | 17,006 |
| Teller, CO | 7.4 | 36.7 | +5.8 | 15,921 |
| Washington, CO | 11.1 | 22.9 | +5.0 | 2,765 |
| Weld, CO | 30.3 | 30.4 | +3.1 | 175,221 |
| Yuma, CO | 27.1 | 23.0 | +7.2 | 4,565 |
| Kent, DE | 7.7 | 26.0 | +4.0 | 86,680 |
| New Castle, DE | 11.0 | 38.2 | +1.5 | 271,568 |
| Sussex, DE | 10.4 | 31.2 | +6.4 | 145,861 |
| District Of Columbia, DC | 11.5 | 62.0 | +3.8 | 315,261 |
| Alachua, FL | 11.4 | 46.4 | −0.1 | 134,517 |
| Baker, FL | 3.4 | 16.1 | +3.5 | 14,908 |
| Bay, FL | 7.9 | 27.1 | +2.4 | 96,698 |
| Bradford, FL | 4.7 | 12.7 | +1.8 | 13,866 |
| Brevard, FL | 11.4 | 32.6 | +1.7 | 357,766 |
| Broward, FL | 31.5 | 34.9 | −6.7 | 866,280 |
| Calhoun, FL | 5.6 | 11.3 | +0.8 | 6,388 |
| Charlotte, FL | 7.9 | 25.4 | −1.3 | 122,930 |
| Citrus, FL | 6.4 | 19.8 | +1.3 | 97,632 |
| Clay, FL | 10.8 | 27.2 | +4.1 | 125,637 |
| Collier, FL | 28.1 | 38.1 | −2.3 | 214,987 |
| Columbia, FL | 7.2 | 15.9 | +1.1 | 33,358 |
| DeSoto, FL | 30.8 | 11.5 | −4.9 | 12,413 |
| Dixie, FL | 4.4 | 10.1 | +1.7 | 8,103 |
| Duval, FL | 11.3 | 32.6 | +0.7 | 465,650 |
| Escambia, FL | 6.4 | 28.4 | +1.7 | 161,008 |
| Flagler, FL | 11.1 | 28.5 | −1.4 | 79,445 |
| Franklin, FL | 5.6 | 24.2 | −0.5 | 6,701 |
| Gadsden, FL | 11.4 | 19.8 | +0.2 | 21,698 |
| Gilchrist, FL | 7.0 | 13.8 | +2.0 | 10,593 |
| Glades, FL | 23.4 | 12.5 | −1.0 | 5,256 |
| Gulf, FL | 4.7 | 23.4 | +2.6 | 8,654 |
| Hamilton, FL | 10.6 | 9.6 | −1.2 | 5,691 |
| Hardee, FL | 43.1 | 10.6 | −5.2 | 8,087 |
| Hendry, FL | 56.0 | 10.2 | −9.3 | 13,349 |
| Hernando, FL | 15.5 | 20.1 | −0.8 | 109,877 |
| Highlands, FL | 21.3 | 19.0 | −0.3 | 51,609 |
| Hillsborough, FL | 29.7 | 36.3 | −4.0 | 663,472 |
| Holmes, FL | 3.3 | 11.2 | +5.0 | 9,075 |
| Indian River, FL | 13.2 | 32.2 | +0.3 | 98,391 |
| Jackson, FL | 5.2 | 14.0 | −1.1 | 21,966 |
| Jefferson, FL | 4.6 | 21.2 | −5.7 | 8,440 |
| Lafayette, FL | 14.5 | 9.6 | +1.7 | 3,737 |
| Lake, FL | 17.4 | 26.1 | +1.5 | 225,046 |
| Lee, FL | 23.3 | 29.8 | −3.2 | 389,901 |
| Leon, FL | 7.4 | 47.8 | −0.5 | 154,917 |
| Levy, FL | 9.5 | 15.3 | +1.5 | 24,239 |
| Liberty, FL | 5.5 | 15.0 | −0.4 | 3,464 |
| Madison, FL | 5.9 | 13.9 | −3.3 | 9,105 |
| Manatee, FL | 17.4 | 32.8 | −1.4 | 227,160 |
| Marion, FL | 15.4 | 22.0 | +0.4 | 212,609 |
| Martin, FL | 14.8 | 35.3 | −0.5 | 97,660 |
| Miami-Dade, FL | 68.7 | 32.5 | −12.8 | 1,085,945 |
| Monroe, FL | 24.6 | 36.9 | −4.4 | 43,997 |
| Nassau, FL | 5.1 | 33.0 | +5.2 | 65,088 |
| Okaloosa, FL | 10.4 | 33.5 | +3.1 | 112,383 |
| Okeechobee, FL | 26.0 | 15.1 | −3.3 | 15,986 |
| Orange, FL | 33.0 | 37.4 | −3.5 | 599,086 |
| Osceola, FL | 55.3 | 26.5 | −9.3 | 170,918 |
| Palm Beach, FL | 23.5 | 38.8 | −6.0 | 739,348 |
| Pasco, FL | 17.2 | 26.9 | +0.9 | 315,229 |
| Pinellas, FL | 10.6 | 34.6 | +0.6 | 511,924 |
| Polk, FL | 26.4 | 22.0 | −0.2 | 345,923 |
| Putnam, FL | 10.7 | 14.0 | −0.4 | 36,054 |
| St. Johns, FL | 8.2 | 47.7 | +1.4 | 195,621 |
| St. Lucie, FL | 20.6 | 24.6 | −1.5 | 183,810 |
| Santa Rosa, FL | 6.3 | 29.6 | +2.1 | 111,349 |
| Sarasota, FL | 10.0 | 38.6 | −1.7 | 275,887 |
| Seminole, FL | 22.9 | 41.9 | −0.3 | 250,452 |
| Sumter, FL | 6.1 | 33.7 | +4.6 | 104,685 |
| Suwannee, FL | 10.7 | 16.2 | +1.9 | 21,778 |
| Taylor, FL | 4.3 | 13.7 | +0.4 | 9,945 |
| Union, FL | 5.6 | 10.8 | +3.4 | 6,195 |
| Volusia, FL | 15.4 | 26.4 | −1.6 | 307,823 |
| Wakulla, FL | 4.4 | 20.9 | +2.7 | 19,687 |
| Walton, FL | 7.5 | 32.5 | +0.1 | 49,257 |
| Washington, FL | 3.9 | 11.8 | +1.9 | 12,510 |
| Appling, GA | 10.4 | 10.4 | +0.9 | 8,321 |
| Atkinson, GA | 26.7 | 10.6 | −0.8 | 3,050 |
| Bacon, GA | 8.4 | 9.8 | +5.9 | 4,831 |
| Baker, GA | 4.4 | 19.0 | +2.0 | 1,473 |
| Baldwin, GA | 2.6 | 24.3 | +2.6 | 18,733 |
| Banks, GA | 7.2 | 17.2 | +6.4 | 10,494 |
| Barrow, GA | 13.4 | 20.7 | +9.3 | 43,679 |
| Bartow, GA | 9.8 | 22.0 | +6.2 | 57,213 |
| Ben Hill, GA | 6.5 | 12.0 | +0.4 | 6,480 |
| Berrien, GA | 5.7 | 14.5 | +3.1 | 8,050 |
| Bibb, GA | 4.1 | 25.8 | +4.6 | 68,830 |
| Bleckley, GA | 3.5 | 18.9 | +4.0 | 6,024 |
| Brantley, GA | 2.7 | 10.7 | +5.3 | 8,480 |
| Brooks, GA | 6.4 | 16.1 | +0.1 | 7,189 |
| Bryan, GA | 7.9 | 34.5 | +5.3 | 24,517 |
| Bulloch, GA | 4.7 | 30.0 | +1.0 | 32,499 |
| Burke, GA | 3.4 | 15.0 | −1.5 | 11,021 |
| Butts, GA | 3.6 | 12.7 | +4.7 | 12,968 |
| Calhoun, GA | 5.2 | 12.4 | +2.9 | 2,053 |
| Camden, GA | 7.3 | 23.5 | +1.6 | 26,224 |
| Candler, GA | 12.3 | 13.7 | +0.9 | 4,562 |
| Carroll, GA | 7.8 | 22.9 | +4.3 | 60,170 |
| Catoosa, GA | 3.5 | 23.6 | +7.1 | 34,854 |
| Charlton, GA | 11.3 | 12.6 | +0.9 | 4,614 |
| Chatham, GA | 7.4 | 36.0 | +5.2 | 140,094 |
| Chattahoochee, GA | 17.1 | 28.3 | +3.3 | 1,685 |
| Chattooga, GA | 5.5 | 10.8 | +4.3 | 10,665 |
| Cherokee, GA | 11.5 | 39.0 | +6.7 | 160,980 |
| Clarke, GA | 11.3 | 46.4 | +2.0 | 52,346 |
| Clay, GA | 0.2 | 20.2 | +2.8 | 1,434 |
| Clayton, GA | 13.8 | 20.8 | +4.1 | 111,080 |
| Clinch, GA | 6.3 | 9.9 | +2.2 | 2,903 |
| Cobb, GA | 14.1 | 49.6 | +6.6 | 397,083 |
| Coffee, GA | 12.6 | 13.0 | +1.1 | 15,683 |
| Colquitt, GA | 20.1 | 14.6 | +3.3 | 16,565 |
| Columbia, GA | 7.4 | 37.5 | +6.5 | 85,281 |
| Cook, GA | 6.7 | 16.8 | +0.3 | 7,330 |
| Coweta, GA | 7.6 | 34.7 | +8.0 | 85,315 |
| Crawford, GA | 3.7 | 12.6 | +2.7 | 6,324 |
| Crisp, GA | 3.6 | 14.5 | +5.2 | 8,092 |
| Dade, GA | 2.6 | 17.1 | +4.6 | 8,147 |
| Dawson, GA | 6.1 | 35.0 | +9.2 | 19,465 |
| Decatur, GA | 6.6 | 14.6 | −0.8 | 11,512 |
| DeKalb, GA | 9.5 | 46.5 | +3.4 | 362,252 |
| Dodge, GA | 3.7 | 16.2 | +1.9 | 8,330 |
| Dooly, GA | 7.7 | 13.9 | +4.5 | 4,164 |
| Dougherty, GA | 3.1 | 22.5 | +7.0 | 33,735 |
| Douglas, GA | 11.1 | 29.5 | +12.3 | 70,236 |
| Early, GA | 1.5 | 14.2 | −0.3 | 4,876 |
| Echols, GA | 24.6 | 11.8 | +0.3 | 1,434 |
| Effingham, GA | 5.7 | 25.5 | +7.1 | 36,087 |
| Elbert, GA | 5.9 | 14.0 | −0.7 | 9,560 |
| Emanuel, GA | 4.7 | 12.4 | +0.7 | 9,592 |
| Evans, GA | 12.1 | 15.3 | +0.7 | 4,225 |
| Fannin, GA | 2.9 | 23.1 | +6.2 | 16,039 |
| Fayette, GA | 8.0 | 48.3 | +9.8 | 73,999 |
| Floyd, GA | 11.9 | 23.6 | +5.5 | 44,493 |
| Forsyth, GA | 9.8 | 56.0 | +6.3 | 136,790 |
| Franklin, GA | 5.2 | 17.4 | +3.4 | 12,106 |
| Fulton, GA | 7.7 | 56.5 | +4.5 | 529,407 |
| Gilmer, GA | 12.0 | 23.1 | +7.4 | 18,389 |
| Glascock, GA | 2.4 | 13.7 | +2.1 | 1,667 |
| Glynn, GA | 7.2 | 31.1 | +3.4 | 43,702 |
| Gordon, GA | 16.2 | 17.4 | +5.5 | 27,477 |
| Grady, GA | 12.4 | 15.9 | −0.2 | 10,675 |
| Greene, GA | 6.6 | 34.2 | +3.7 | 12,729 |
| Gwinnett, GA | 22.5 | 38.2 | +4.3 | 415,548 |
| Habersham, GA | 15.8 | 22.9 | +5.7 | 23,178 |
| Hall, GA | 28.8 | 25.6 | +5.9 | 101,338 |
| Hancock, GA | 1.1 | 9.8 | −2.5 | 4,228 |
| Haralson, GA | 2.0 | 17.1 | +6.1 | 16,304 |
| Harris, GA | 4.4 | 35.2 | +4.6 | 22,259 |
| Hart, GA | 4.0 | 19.0 | +1.0 | 14,274 |
| Heard, GA | 2.8 | 11.0 | +3.0 | 6,194 |
| Henry, GA | 7.7 | 28.8 | +15.2 | 128,235 |
| Houston, GA | 7.1 | 31.7 | +7.4 | 80,997 |
| Irwin, GA | 5.7 | 16.2 | +3.0 | 4,326 |
| Jackson, GA | 9.5 | 25.6 | +9.5 | 46,969 |
| Jasper, GA | 4.5 | 17.1 | +1.1 | 9,084 |
| Jeff Davis, GA | 13.2 | 12.1 | +1.7 | 5,859 |
| Jefferson, GA | 3.7 | 12.1 | −2.0 | 7,439 |
| Jenkins, GA | 7.8 | 7.7 | +1.6 | 3,396 |
| Johnson, GA | 2.2 | 7.7 | −0.3 | 3,979 |
| Jones, GA | 2.0 | 20.4 | +2.1 | 16,038 |
| Lamar, GA | 3.0 | 16.6 | +1.5 | 10,370 |
| Lanier, GA | 6.9 | 19.5 | +1.8 | 3,721 |
| Laurens, GA | 3.0 | 18.1 | +1.7 | 23,280 |
| Lee, GA | 3.2 | 30.6 | +7.4 | 17,612 |
| Liberty, GA | 12.7 | 19.9 | −0.8 | 22,900 |
| Lincoln, GA | 1.7 | 14.7 | −1.1 | 4,910 |
| Long, GA | 12.2 | 14.0 | +3.3 | 7,033 |
| Lowndes, GA | 6.5 | 27.1 | +0.2 | 47,568 |
| Lumpkin, GA | 5.4 | 31.5 | +3.1 | 17,695 |
| McDuffie, GA | 3.4 | 16.4 | +0.5 | 10,499 |
| McIntosh, GA | 1.5 | 18.7 | −1.5 | 7,375 |
| Macon, GA | 4.5 | 10.5 | +0.9 | 4,671 |
| Madison, GA | 6.6 | 20.1 | +4.7 | 16,704 |
| Marion, GA | 7.9 | 15.6 | +2.5 | 3,601 |
| Meriwether, GA | 1.6 | 12.6 | +1.2 | 11,748 |
| Miller, GA | 1.9 | 20.0 | +2.3 | 2,715 |
| Mitchell, GA | 4.9 | 13.0 | +0.3 | 8,851 |
| Monroe, GA | 2.6 | 26.8 | +2.5 | 17,643 |
| Montgomery, GA | 7.2 | 18.2 | +3.2 | 3,960 |
| Morgan, GA | 3.5 | 26.9 | +2.1 | 13,122 |
| Murray, GA | 15.6 | 12.8 | +4.1 | 17,424 |
| Muscogee, GA | 8.1 | 29.0 | +5.3 | 80,029 |
| Newton, GA | 6.5 | 22.9 | +10.3 | 58,732 |
| Oconee, GA | 5.6 | 53.2 | +3.9 | 27,044 |
| Oglethorpe, GA | 5.8 | 22.5 | +2.7 | 8,770 |
| Paulding, GA | 7.7 | 26.7 | +11.3 | 94,571 |
| Peach, GA | 8.6 | 22.4 | +4.7 | 13,397 |
| Pickens, GA | 3.6 | 24.7 | +6.6 | 20,803 |
| Pierce, GA | 5.1 | 16.8 | +4.0 | 9,744 |
| Pike, GA | 2.0 | 20.5 | +4.1 | 12,512 |
| Polk, GA | 13.6 | 14.1 | +2.9 | 19,101 |
| Pulaski, GA | 4.3 | 13.8 | +4.6 | 4,317 |
| Putnam, GA | 6.7 | 27.3 | +5.0 | 12,832 |
| Quitman, GA | 1.1 | 11.9 | +0.3 | 1,136 |
| Rabun, GA | 8.4 | 29.8 | +7.0 | 10,373 |
| Randolph, GA | 3.8 | 14.3 | +4.6 | 2,974 |
| Richmond, GA | 5.4 | 23.5 | +4.8 | 83,129 |
| Rockdale, GA | 10.5 | 29.8 | +12.7 | 44,876 |
| Schley, GA | 3.9 | 17.1 | +2.6 | 2,423 |
| Screven, GA | 2.5 | 14.7 | −0.1 | 6,906 |
| Seminole, GA | 2.2 | 14.4 | +0.7 | 4,002 |
| Spalding, GA | 5.4 | 18.3 | +10.3 | 32,863 |
| Stephens, GA | 3.8 | 16.6 | +2.4 | 13,036 |
| Stewart, GA | 10.0 | 13.2 | +3.2 | 2,024 |
| Sumter, GA | 6.0 | 20.7 | +3.5 | 12,005 |
| Talbot, GA | 0.7 | 12.3 | −2.5 | 3,371 |
| Taliaferro, GA | 4.4 | 8.9 | −0.8 | 882 |
| Tattnall, GA | 11.3 | 14.7 | +1.7 | 8,482 |
| Taylor, GA | 0.9 | 15.0 | +2.1 | 3,966 |
| Telfair, GA | 13.2 | 8.5 | −2.3 | 4,204 |
| Terrell, GA | 2.8 | 11.6 | +1.7 | 4,328 |
| Thomas, GA | 3.8 | 23.4 | +1.6 | 22,017 |
| Tift, GA | 12.6 | 19.1 | +4.3 | 16,934 |
| Toombs, GA | 11.8 | 17.5 | +0.9 | 10,882 |
| Towns, GA | 3.1 | 31.8 | +4.5 | 8,804 |
| Treutlen, GA | 1.7 | 14.1 | −0.8 | 3,114 |
| Troup, GA | 4.2 | 21.9 | +3.7 | 31,149 |
| Turner, GA | 4.8 | 10.3 | +2.5 | 3,822 |
| Twiggs, GA | 1.3 | 10.4 | −1.2 | 4,444 |
| Union, GA | 3.6 | 24.8 | +7.1 | 17,786 |
| Upson, GA | 2.5 | 14.5 | +0.6 | 13,626 |
| Walker, GA | 2.6 | 20.2 | +6.6 | 31,898 |
| Walton, GA | 5.6 | 23.5 | +9.7 | 58,012 |
| Ware, GA | 4.6 | 14.8 | +3.6 | 14,347 |
| Warren, GA | 0.6 | 11.4 | −0.7 | 2,586 |
| Washington, GA | 2.4 | 16.0 | +3.4 | 9,467 |

The first 500 of 3102 rows; every row is in https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/swing_by_share.json.

Views: Hispanic share; College share.

Note: Intervals resample whole states; see Methods. The number of counties in each group is in the tooltip.

Note: Counties without 2020 characteristics (the eight Connecticut counties) and Jackson County, Missouri are left out. Points are shown for all remaining counties.

Source: County presidential returns, 2000-2024 (Algara and Amlani; MIT Election Data and Science Lab), full counts of votes, and county demographics (decennial census and American Community Survey estimates interpolated to election years); weighted by two-party votes in the later election or county-equal, as stated

Data: https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/swing_by_share.json

### Table 5. With the other three shares held fixed, college share tracks the 2020 to 2024 swing although its vote-weighted correlation alone is near zero

Correlation of each characteristic with the county swing, alone and as a standardized coefficient in one regression on the four characteristics; two pairs of elections and two weightings

| Characteristic | Pair | Weighting | Correlation alone | Alone, low | Alone, high | Coefficient, all four | Together, low | Together, high | Counties |
| --- | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| College share | 2016–20 | County-equal | +0.51 | +0.45 | +0.60 | +0.51 | +0.43 | +0.59 | 3,113 |
| Hispanic share | 2016–20 | County-equal | −0.30 | −0.51 | +0.10 | −0.30 | −0.60 | +0.01 | 3,113 |
| Foreign-born share | 2016–20 | County-equal | −0.03 | −0.20 | +0.22 | −0.02 | −0.19 | +0.15 | 3,113 |
| Black share | 2016–20 | County-equal | −0.12 | −0.26 | −0.02 | −0.10 | −0.17 | −0.04 | 3,113 |
| College share | 2020–24 | County-equal | +0.16 | +0.07 | +0.24 | +0.24 | +0.15 | +0.34 | 3,102 |
| Hispanic share | 2020–24 | County-equal | −0.39 | −0.51 | −0.16 | −0.22 | −0.36 | −0.08 | 3,102 |
| Foreign-born share | 2020–24 | County-equal | −0.34 | −0.47 | −0.14 | −0.29 | −0.48 | −0.10 | 3,102 |
| Black share | 2020–24 | County-equal | −0.24 | −0.36 | −0.14 | −0.24 | −0.35 | −0.13 | 3,102 |
| College share | 2016–20 | Vote-weighted | +0.25 | +0.11 | +0.41 | +0.56 | +0.46 | +0.65 | 3,113 |
| Hispanic share | 2016–20 | Vote-weighted | −0.48 | −0.62 | −0.20 | +0.08 | −0.16 | +0.31 | 3,113 |
| Foreign-born share | 2016–20 | Vote-weighted | −0.50 | −0.67 | −0.10 | −0.77 | −0.99 | −0.55 | 3,113 |
| Black share | 2016–20 | Vote-weighted | −0.13 | −0.29 | 0.00 | −0.09 | −0.16 | −0.01 | 3,113 |
| College share | 2020–24 | Vote-weighted | +0.02 | −0.08 | +0.13 | +0.31 | +0.24 | +0.38 | 3,102 |
| Hispanic share | 2020–24 | Vote-weighted | −0.66 | −0.76 | −0.46 | −0.18 | −0.35 | −0.01 | 3,102 |
| Foreign-born share | 2020–24 | Vote-weighted | −0.69 | −0.77 | −0.49 | −0.69 | −0.93 | −0.44 | 3,102 |
| Black share | 2020–24 | Vote-weighted | −0.07 | −0.25 | +0.09 | −0.04 | −0.16 | +0.07 | 3,102 |

Views: 2020–24, vote-weighted (pair = 2020–24, weighting = Vote-weighted); 2020–24, county-equal (pair = 2020–24, weighting = County-equal); 2016–20, vote-weighted (pair = 2016–20, weighting = Vote-weighted); 2016–20, county-equal (pair = 2016–20, weighting = County-equal).

Note: Intervals resample whole states; see Methods.

Note: Each characteristic is standardized with the weighted mean and standard deviation of the weighting shown, so a coefficient from a regression on one characteristic equals its correlation. The four characteristics are correlated across counties, so a coefficient depends on which others are in the regression. Non-Hispanic white share is not in the regression.

Source: County presidential returns, 2000-2024 (Algara and Amlani; MIT Election Data and Science Lab), full counts of votes, and county demographics (decennial census and American Community Survey estimates interpolated to election years); weighted by two-party votes in the later election or county-equal, as stated

Data: https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/correlation_and_model.json

### Table 6. ZIP code areas give Hispanic and foreign-born correlations similar to counties for 2016 to 2020, and a larger college correlation with a different college measure

Vote-weighted correlation of four characteristics with the swing in the Democratic minus Republican margin between 2016 and 2020, at county and ZIP code area grain

| Characteristic | County | County, low | County, high | Counties | ZIP code area | ZIP, low | ZIP, high | ZIP code areas |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| College share | +0.25 | +0.11 | +0.41 | 3,113 | +0.47 | +0.42 | +0.55 | 29,323 |
| Hispanic share | −0.48 | −0.62 | −0.20 | 3,113 | −0.50 | −0.60 | −0.29 | 29,323 |
| Foreign-born share | −0.50 | −0.67 | −0.10 | 3,113 | −0.46 | −0.61 | −0.14 | 29,323 |
| Black share | −0.13 | −0.29 | 0.00 | 3,113 | −0.18 | −0.29 | −0.11 | 29,323 |

Note: Intervals resample whole states; see Methods.

Note: ZIP code area votes are estimates allocated from precinct returns, so they carry allocation error. There is no ZIP code area release for 2024, so the 2020 to 2024 pair cannot be checked below the county. The 14 areas for which the atlas codes one of the four characteristics as missing, with a negative value, are dropped.

Source: County presidential returns, 2000-2024 (Algara and Amlani; MIT Election Data and Science Lab), full counts of votes, and county demographics (decennial census and American Community Survey estimates interpolated to election years); weighted by two-party votes in the later election or county-equal, as stated; ZIP code areas from precinct votes allocated to census geographies (Fekrazad 2025), 2016 and 2020, weighted by 2020 presidential votes

Data: https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/zip_code_check.json

### Table 7. High-Hispanic counties swung 4.2 points further toward the Republicans than the average county, vote-weighted

Mean county swing in the Democratic minus Republican margin minus the national swing, minus the mean over all counties of the same weighting, in percentage points, by county type, weighted by votes and counting each county once

| County type | Weighting | Minus all-county mean (points) | Difference, low | Difference, high | Mean swing minus national swing (points) | Interval low | Interval high | All-county mean, same weighting (points) | Counties | Two-party votes, 2024 (millions) |
| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| High-Hispanic | Vote-weighted | −4.2 | −6.2 | −3.0 | −2.9 | −4.8 | −1.2 | +1.3 | 314 | 36.6 |
| High-Hispanic | County-equal | −3.3 | −4.7 | −2.4 | −0.6 | −2.0 | +0.4 | +2.7 | 314 | 36.6 |
| High-Black | Vote-weighted | +0.9 | −0.8 | +2.1 | +2.2 | +0.6 | +3.4 | +1.3 | 379 | 17.5 |
| High-Black | County-equal | −1.8 | −2.9 | −1.0 | +0.9 | −0.2 | +1.7 | +2.7 | 379 | 17.5 |
| Affluent suburban | Vote-weighted | +0.8 | −0.1 | +1.7 | +2.1 | +0.7 | +3.7 | +1.3 | 152 | 36.2 |
| Affluent suburban | County-equal | +0.3 | −0.7 | +1.3 | +3.0 | +1.9 | +4.3 | +2.7 | 152 | 36.2 |
| All other | Vote-weighted | +1.9 | +0.8 | +2.7 | +3.2 | +2.6 | +3.8 | +1.3 | 2,257 | 59.7 |
| All other | County-equal | +0.7 | +0.4 | +1.1 | +3.4 | +3.0 | +3.9 | +2.7 | 2,257 | 59.7 |

Note: Intervals resample whole states; see Methods. The groups are rules applied in the order given in the text, and each county is in one group; the thresholds are not fitted to the swing.

Note: Counties are assigned by their 2020 characteristics. Jackson County, Missouri and the eight Connecticut counties are left out. The data table also gives each group's mean minus the national swing, with its interval, and the all-county mean.

Source: County presidential returns, 2000-2024 (Algara and Amlani; MIT Election Data and Science Lab), full counts of votes, and county demographics (decennial census and American Community Survey estimates interpolated to election years); weighted by two-party votes in the later election or county-equal, as stated

Data: https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/county_types.json

### Table 8. 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

| 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 |

Views: Default weight (plotted = true, weights = Year-specific weight); Other weights (plotted = true, weights = Post-election weight, validated-voter weight for 2020 to 2024).

Note: The Cooperative Election Study is an opt-in online panel with post-stratification weights, so the intervals use the Kish effective sample size and assume a weighted simple random sample. They describe sampling variation among its respondents and leave out how those respondents came to join the panel. The year-specific weight is the pre-election weight, applied here to a post-election vote question, and it is the only weight present in all four waves.

Note: Respondents are those who reported voting for the Democratic or Republican presidential candidate, so people who did not vote and people who voted for others are left out. Race is self-reported. A dot compares two separate samples of respondents, so it does not follow the same people over time.

Source: Cooperative Election Study, 2012, 2016, 2020 and 2024 waves; weighted by the year-specific weight, and in the second view by the post-election weight (2012 to 2016 and 2016 to 2020) or the validated-voter weight (2020 to 2024)

Data: https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/ces_by_race.json

## All estimates

Every fact in facts.json (169), sorted by id.

| Fact id | Value | Unit | Estimand | Population | Period | Interval | n | Weights | Source | Note |
| --- | ---: | --- | --- | --- | --- | --- | ---: | --- | --- | --- |
| aff_ba_cut | 35% | percent of adults 25 and older | definition of an affluent suburban county | threshold for affluent suburban counties | 2020 |   |   | none (full counts) | County demographics panel |   |
| aff_inc_cut | 75,000 | dollars of median household income, 2024 prices | definition of an affluent suburban county | threshold for affluent suburban counties | 2020 |   |   | none (full counts) | County demographics panel |   |
| aff_rural_cut | 25% | percent of residents in rural areas | definition of an affluent suburban county | threshold for affluent suburban counties | 2020 |   |   | none (full counts) | County demographics panel |   |
| atlanta_min_rel | +3.4 | county swing minus national swing, percentage points of the two-party margin | smallest county swing minus national swing among the five counties | Fulton, Cobb, DeKalb, Gwinnett and Henry counties, Georgia | 2020 to 2024 |   | 5 | none (full counts) | County election returns |   |
| b2_hispanic_1620_w | −0.24 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on Hispanic share in a vote-weighted regression of the county swing on Hispanic share and foreign-born share only | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI −0.42 to −0.07 | 3,113 | two-party votes in the later election | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b2_hispanic_2024_w | −0.35 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on Hispanic share in a vote-weighted regression of the county swing on Hispanic share and foreign-born share only | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.50 to −0.20 | 3,102 | two-party votes in the later election | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_black_2024_u | −0.24 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on Black share in a county-equal regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.35 to −0.13 | 3,102 | none; each county counted once | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_black_2024_w | −0.04 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on Black share in a vote-weighted regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.16 to 0.07 | 3,102 | two-party votes in the later election | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_college_1620_u | +0.51 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on college share in a county-equal regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI 0.43 to 0.59 | 3,113 | none; each county counted once | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_college_1620_w | +0.56 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on college share in a vote-weighted regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI 0.46 to 0.65 | 3,113 | two-party votes in the later election | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_college_2024_u | +0.24 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on college share in a county-equal regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI 0.15 to 0.34 | 3,102 | none; each county counted once | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_college_2024_w | +0.31 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on college share in a vote-weighted regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI 0.24 to 0.38 | 3,102 | two-party votes in the later election | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_foreign_1620_w | −0.77 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on foreign-born share in a vote-weighted regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI −0.99 to −0.55 | 3,113 | two-party votes in the later election | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_foreign_2024_u | −0.29 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on foreign-born share in a county-equal regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.48 to −0.10 | 3,102 | none; each county counted once | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_foreign_2024_w | −0.69 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on foreign-born share in a vote-weighted regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.93 to −0.44 | 3,102 | two-party votes in the later election | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_hispanic_1620_u | −0.30 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on Hispanic share in a county-equal regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI −0.60 to 0.01 | 3,113 | none; each county counted once | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_hispanic_1620_w | +0.08 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on Hispanic share in a vote-weighted regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI −0.16 to 0.31 | 3,113 | two-party votes in the later election | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_hispanic_2024_u | −0.22 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on Hispanic share in a county-equal regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.36 to −0.08 | 3,102 | none; each county counted once | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| b_hispanic_2024_w | −0.18 | standardized regression coefficient, standard deviations of swing per standard deviation of the characteristic | coefficient on Hispanic share in a vote-weighted regression of the county swing on the four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.35 to −0.01 | 3,102 | two-party votes in the later election | County demographics panel | Interval: 95% normal interval with errors clustered by state. |
| band | 5 | percentage points of the two-party margin | definition of near the national swing | half-width of the band around the national swing | 2012 to 2024 |   |   | none (full counts) | County election returns |   |
| big_cut | 250,000 | two-party votes in the later election | definition of a large county | threshold for a large county | 2016 to 2024 |   |   | none (full counts) | County election returns |   |
| big_n_2024 | 134 | counties | count of large counties | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 |   |   | none (full counts) | County election returns |   |
| big_share_2024 | 46.3% | percent of two-party votes | share of the later election's two-party votes cast in large counties | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 |   | 134 | none (full counts) | County election returns |   |
| black_cut | 25% | percent of residents | definition of a high-Black county | threshold for high-Black counties | 2020 |   |   | none (full counts) | County demographics panel |   |
| black_ex_atlanta | +1.8 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | high-Black counties without Fulton, Cobb, DeKalb, Gwinnett and Henry counties | 2020 to 2024 |   | 374 | two-party votes in the later election | County election returns |   |
| black_ex_kings | +2.5 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | high-Black counties without Kings County | 2020 to 2024 |   | 378 | two-party votes in the later election | County election returns |   |
| black_ex_three | +2.9 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | high-Black counties without Kings, Wayne and Essex counties | 2020 to 2024 |   | 376 | two-party votes in the later election | County election returns |   |
| black_top10_neg | 2 | counties | count with a swing below the national swing | the ten high-Black counties with the most votes | 2020 to 2024 |   | 10 | none (full counts) | County election returns |   |
| ces_black_1216 | −6.5 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | black respondents who reported voting for a Democratic or Republican presidential candidate | 2012 to 2016 | 95% CI −9.9 to −3.1 | 6,938 | CES year-specific weight | Cooperative Election Study | Interval: 95% normal interval on the Kish effective sample size, model-based. |
| ces_black_2024 | −19.0 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | black respondents who reported voting for a Democratic or Republican presidential candidate | 2020 to 2024 | 95% CI −24.1 to −14.0 | 7,822 | CES year-specific weight | Cooperative Election Study | Interval: 95% normal interval on the Kish effective sample size, model-based. |
| ces_black_m2020 | +82.9 | Democratic minus Republican margin, percentage points of the two-party margin | weighted Democratic minus Republican margin among presidential voters | black respondents who reported voting for a Democratic or Republican presidential candidate | 2020 |   | 3,898 | CES year-specific weight | Cooperative Election Study |   |
| ces_black_m2024 | +63.8 | Democratic minus Republican margin, percentage points of the two-party margin | weighted Democratic minus Republican margin among presidential voters | black respondents who reported voting for a Democratic or Republican presidential candidate | 2024 |   | 4,069 | CES year-specific weight | Cooperative Election Study |   |
| ces_hispanic_2024 | −23.0 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | hispanic respondents who reported voting for a Democratic or Republican presidential candidate | 2020 to 2024 | 95% CI −30.5 to −15.5 | 7,642 | CES year-specific weight | Cooperative Election Study | Interval: 95% normal interval on the Kish effective sample size, model-based. |
| ces_hispanic_m2020 | +32.5 | Democratic minus Republican margin, percentage points of the two-party margin | weighted Democratic minus Republican margin among presidential voters | hispanic respondents who reported voting for a Democratic or Republican presidential candidate | 2020 |   | 3,788 | CES year-specific weight | Cooperative Election Study |   |
| ces_hispanic_m2024 | +9.5 | Democratic minus Republican margin, percentage points of the two-party margin | weighted Democratic minus Republican margin among presidential voters | hispanic respondents who reported voting for a Democratic or Republican presidential candidate | 2024 |   | 4,078 | CES year-specific weight | Cooperative Election Study |   |
| ces_white_1216 | −7.0 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | white respondents who reported voting for a Democratic or Republican presidential candidate | 2012 to 2016 | 95% CI −9.1 to −4.8 | 61,083 | CES year-specific weight | Cooperative Election Study | Interval: 95% normal interval on the Kish effective sample size, model-based. |
| ces_white_1620 | +2.2 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | white respondents who reported voting for a Democratic or Republican presidential candidate | 2016 to 2020 | 95% CI 0.3 to 4.2 | 63,584 | CES year-specific weight | Cooperative Election Study | Interval: 95% normal interval on the Kish effective sample size, model-based. |
| ces_white_2024 | −1.9 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | white respondents who reported voting for a Democratic or Republican presidential candidate | 2020 to 2024 | 95% CI −4.0 to 0.2 | 64,714 | CES year-specific weight | Cooperative Election Study | Interval: 95% normal interval on the Kish effective sample size, model-based. |
| cesv_black_1216 | −6.8 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | black respondents who reported voting for a Democratic or Republican presidential candidate | 2012 to 2016 | 95% CI −10.5 to −3.0 | 6,938 | CES post-election weight | Cooperative Election Study | Sensitivity to the weight. Interval: 95% normal interval on the Kish effective sample size, model-based. |
| cesv_black_2024 | −13.9 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | black respondents who reported voting for a Democratic or Republican presidential candidate | 2020 to 2024 | 95% CI −19.5 to −8.3 | 5,828 | CES validated-voter weight | Cooperative Election Study | Sensitivity to the weight. Interval: 95% normal interval on the Kish effective sample size, model-based. |
| cesv_hispanic_2024 | −14.9 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | hispanic respondents who reported voting for a Democratic or Republican presidential candidate | 2020 to 2024 | 95% CI −24.1 to −5.6 | 5,294 | CES validated-voter weight | Cooperative Election Study | Sensitivity to the weight. Interval: 95% normal interval on the Kish effective sample size, model-based. |
| cesv_white_1216 | −4.4 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | white respondents who reported voting for a Democratic or Republican presidential candidate | 2012 to 2016 | 95% CI −6.7 to −2.2 | 61,083 | CES post-election weight | Cooperative Election Study | Sensitivity to the weight. Interval: 95% normal interval on the Kish effective sample size, model-based. |
| cesv_white_2024 | −4.1 | change in the two-party Democratic margin, percentage points of the two-party margin | difference between two waves in the weighted Democratic minus Republican margin among presidential voters | white respondents who reported voting for a Democratic or Republican presidential candidate | 2020 to 2024 | 95% CI −6.2 to −1.9 | 53,387 | CES validated-voter weight | Cooperative Election Study | Sensitivity to the weight. Interval: 95% normal interval on the Kish effective sample size, model-based. |
| county_ba_mean | 21.7% | percent of adults 25 and older with a bachelor's degree or more | mean across counties of the bachelor's degree share in 2016 | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 |   | 3,113 | none; each county counted once | County demographics panel |   |
| diff_cut | 0.2 | difference between two correlations | definition of a large difference | cut-off for a large difference between weightings | 2000 to 2024 |   |   | none (full counts) | County demographics panel |   |
| essex_rel | −4.2 | county swing minus national swing, percentage points of the two-party margin | county swing minus national swing | Essex County, New Jersey | 2020 to 2024 |   | 1 | none (full counts) | County election returns |   |
| ext_all_fb_min_2024 | 9.0% | percent of residents | smallest foreign-born share among the ten counties | the ten counties furthest below the national swing, with no minimum on votes | 2020 to 2024 |   | 10 | none (full counts) | County demographics panel |   |
| ext_all_hisp_min_2024 | 28.0% | percent of residents | smallest Hispanic share among the ten counties | the ten counties furthest below the national swing, with no minimum on votes | 2020 to 2024 |   | 10 | none (full counts) | County demographics panel |   |
| ext_high_georgia_2024 | 5 | counties | count in Georgia | the ten counties with 50,000 or more votes furthest above the national swing | 2020 to 2024 |   | 10 | none (full counts) | County election returns |   |
| ext_high_utva_2016 | 7 | counties | count in Utah or Virginia | the ten counties with 50,000 or more votes furthest above the national swing | 2012 to 2016 |   | 10 | none (full counts) | County election returns |   |
| ext_low_fb_min_2024 | 22.9% | percent of residents | smallest foreign-born share among the ten counties | the ten counties with 50,000 or more votes furthest below the national swing | 2020 to 2024 |   | 10 | none (full counts) | County demographics panel |   |
| ext_low_hisp_min_2024 | 28.0% | percent of residents | smallest Hispanic share among the ten counties | the ten counties with 50,000 or more votes furthest below the national swing | 2020 to 2024 |   | 10 | none (full counts) | County demographics panel |   |
| ext_low_rust_2016 | 8 | counties | count in Pennsylvania, Ohio or Michigan | the ten counties with 50,000 or more votes furthest below the national swing | 2012 to 2016 |   | 10 | none (full counts) | County election returns |   |
| ext_min_votes | 50,000 | two-party votes in the later election | minimum size of a county in the ranking | counties named among the furthest above or below the national swing | 2012 to 2024 |   |   | none (full counts) | County election returns |   |
| ext_n_2024 | 584 | counties | count of counties | counties with 50,000 or more two-party votes in the later election | 2020 to 2024 |   |   | none (full counts) | County election returns |   |
| g_ba_20_40_rel_u | +3.3 | county swing minus national swing, percentage points of the two-party margin | county-equal mean of county swing minus national swing | counties 20 to under 40 percent college in 2020 | 2020 to 2024 | 95% CI 2.7 to 3.8 | 1,528 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_ba_20_40_rel_w | +1.1 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | counties 20 to under 40 percent college in 2020 | 2020 to 2024 | 95% CI −0.2 to 2.8 | 1,528 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_ba_40p_rel_u | +3.2 | county swing minus national swing, percentage points of the two-party margin | county-equal mean of county swing minus national swing | counties 40 percent or more college in 2020 | 2020 to 2024 | 95% CI 2.1 to 4.3 | 228 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_ba_40p_rel_w | +1.7 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | counties 40 percent or more college in 2020 | 2020 to 2024 | 95% CI 0.4 to 3.3 | 228 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_ba_lt20_rel_u | +1.9 | county swing minus national swing, percentage points of the two-party margin | county-equal mean of county swing minus national swing | counties under 20 percent college in 2020 | 2020 to 2024 | 95% CI 1.4 to 2.5 | 1,346 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_ba_lt20_rel_w | +1.4 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | counties under 20 percent college in 2020 | 2020 to 2024 | 95% CI 0.2 to 2.5 | 1,346 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_cut10 | 10% | percent of residents | definition of a county group | Hispanic share cut-off for the county groups | 2020 |   |   | none (full counts) | County demographics panel |   |
| g_cut15 | 15% | percent of residents | definition of an alternative high-Hispanic county | alternative Hispanic share cut-off for high-Hispanic counties | 2020 |   |   | none (full counts) | County demographics panel |   |
| g_cut20ba | 20% | percent of adults with a bachelor's degree or more | definition of a county group | college share cut-off for the county groups | 2020 |   |   | none (full counts) | County demographics panel |   |
| g_cut40ba | 40% | percent of adults with a bachelor's degree or more | definition of a county group | college share cut-off for the county groups | 2020 |   |   | none (full counts) | County demographics panel |   |
| g_cut50 | 50% | percent of residents | definition of a county group | Hispanic share cut-off for the county groups | 2020 |   |   | none (full counts) | County demographics panel |   |
| g_hisp_10_25_n | 503 | counties | count of counties | counties 10 to under 25 percent Hispanic in 2020 | 2020 to 2024 |   |   | none (full counts) | County demographics panel |   |
| g_hisp_10_25_rel_u | +2.9 | county swing minus national swing, percentage points of the two-party margin | county-equal mean of county swing minus national swing | counties 10 to under 25 percent Hispanic in 2020 | 2020 to 2024 | 95% CI 2.1 to 3.7 | 503 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_hisp_10_25_rel_w | +1.7 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | counties 10 to under 25 percent Hispanic in 2020 | 2020 to 2024 | 95% CI 0.3 to 3.4 | 503 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_hisp_25_50_n | 211 | counties | count of counties | counties 25 to under 50 percent Hispanic in 2020 | 2020 to 2024 |   |   | none (full counts) | County demographics panel |   |
| g_hisp_25_50_rel_u | +1.0 | county swing minus national swing, percentage points of the two-party margin | county-equal mean of county swing minus national swing | counties 25 to under 50 percent Hispanic in 2020 | 2020 to 2024 | 95% CI −0.7 to 1.9 | 211 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_hisp_25_50_rel_w | −1.9 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | counties 25 to under 50 percent Hispanic in 2020 | 2020 to 2024 | 95% CI −3.6 to −0.3 | 211 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_hisp_50p_n | 103 | counties | count of counties | counties 50 percent or more Hispanic in 2020 | 2020 to 2024 |   |   | none (full counts) | County demographics panel |   |
| g_hisp_50p_rel_u | −3.9 | county swing minus national swing, percentage points of the two-party margin | county-equal mean of county swing minus national swing | counties 50 percent or more Hispanic in 2020 | 2020 to 2024 | 95% CI −7.5 to −2.9 | 103 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_hisp_50p_rel_w | −7.6 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | counties 50 percent or more Hispanic in 2020 | 2020 to 2024 | 95% CI −12.2 to −5.3 | 103 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_hisp_50p_swing_w | −13.7 | change in the two-party margin, percentage points of the two-party margin | vote-weighted mean of the county swing | counties 50 percent or more Hispanic in 2020 | 2020 to 2024 | 95% CI −18.3 to −11.4 | 103 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_hisp_lt10_n | 2,285 | counties | count of counties | counties under 10 percent Hispanic in 2020 | 2020 to 2024 |   |   | none (full counts) | County demographics panel |   |
| g_hisp_lt10_rel_u | +3.1 | county swing minus national swing, percentage points of the two-party margin | county-equal mean of county swing minus national swing | counties under 10 percent Hispanic in 2020 | 2020 to 2024 | 95% CI 2.6 to 3.5 | 2,285 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| g_hisp_lt10_rel_w | +3.3 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | counties under 10 percent Hispanic in 2020 | 2020 to 2024 | 95% CI 2.8 to 3.9 | 2,285 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| henry_rel | +15.2 | county swing minus national swing, percentage points of the two-party margin | county swing minus national swing | Henry County, Georgia | 2020 to 2024 |   | 1 | none (full counts) | County election returns |   |
| hisp_cut | 25% | percent of residents | definition of a high-Hispanic county | threshold for high-Hispanic counties | 2020 |   |   | none (full counts) | County demographics panel |   |
| kings_black | 28.0% | percent of residents | non-Hispanic Black share of residents | Kings County, New York | 2020 |   | 1 | none (full counts) | County demographics panel |   |
| kings_rel | −5.2 | county swing minus national swing, percentage points of the two-party margin | county swing minus national swing | Kings County, New York | 2020 to 2024 |   | 1 | none (full counts) | County election returns |   |
| map_within_1216 | 24.6% | percent of counties shown | share of counties whose swing is within the band of the national swing | counties with returns in both elections of the pair, Alaska excluded | 2012 to 2016 |   | 3,112 | none (full counts) | County election returns |   |
| map_within_2024 | 75.6% | percent of counties shown | share of counties whose swing is within the band of the national swing | counties with returns in both elections of the pair, Alaska excluded | 2020 to 2024 |   | 3,110 | none (full counts) | County election returns |   |
| max_diff | 0.47 | difference between two correlations | largest absolute difference between the vote-weighted and county-equal correlation | four characteristics in six pairs | 2000 to 2024 |   |   | none (full counts) | County demographics panel |   |
| n_aff_alts | 6 | sets of cut-offs | count of alternative sets | alternative college, income and rural cut-offs for affluent suburban counties | 2020 |   |   | none (full counts) | County demographics panel |   |
| n_analysis_2024 | 3,102 | counties | count of counties in the correlations and regressions | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 |   | 3,102 | none (full counts) | County demographics panel |   |
| n_black_alts | 6 | cut-offs | count of alternative cut-offs | alternative non-Hispanic Black share cut-offs from 15 to 50 percent | 2020 |   |   | none (full counts) | County demographics panel |   |
| n_cells | 24 | correlations | count of pairs of vote-weighted and county-equal correlations | four characteristics in six pairs | 2000 to 2024 |   |   | none (full counts) | County demographics panel |   |
| n_diff_02 | 4 | correlations | count of characteristic-pair cells where the two weightings differ by more than 0.2 | four characteristics in six pairs | 2000 to 2024 |   |   | none (full counts) | County demographics panel |   |
| n_matched_2024 | 3,111 | counties | count of counties matched on FIPS | counties with returns in both elections of the pair, Alaska excluded | 2020 to 2024 |   | 3,111 | none (full counts) | County election returns |   |
| n_no_comp_2024 | 8 | counties | matched counties with no 2020 county characteristics (the eight Connecticut counties) | counties with returns in both elections of the pair, Alaska excluded | 2020 to 2024 |   | 3,111 | none (full counts) | County demographics panel |   |
| n_opposite | 2 | correlations | count of cells where the two weightings have opposite signs | four characteristics in six pairs | 2000 to 2024 |   |   | none (full counts) | County demographics panel |   |
| n_pairs | 6 | pairs of consecutive presidential elections | count of pairs | elections from 2000 to 2024 | 2000 to 2024 |   |   | none (full counts) | County election returns |   |
| nat_swing_2024 | −6.1 | change in the two-party margin, percentage points of the two-party margin | change in the summed two-party margin of the matched counties | counties with returns in both elections of the pair, Alaska excluded | 2020 to 2024 |   | 3,111 | none (full counts) | County election returns |   |
| r2_all_1216_u | 58.3% | share of the variance of county swings | county-equal R-squared of the regression of the county swing on all four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 |   | 3,112 | none; each county counted once | County demographics panel |   |
| r2_all_1216_w | 70.6% | share of the variance of county swings | vote-weighted R-squared of the regression of the county swing on all four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 |   | 3,112 | two-party votes in the later election | County demographics panel |   |
| r2_all_2024_u | 28.1% | share of the variance of county swings | county-equal R-squared of the regression of the county swing on all four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 |   | 3,102 | none; each county counted once | County demographics panel |   |
| r2_all_2024_w | 60.2% | share of the variance of county swings | vote-weighted R-squared of the regression of the county swing on all four characteristics | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 |   | 3,102 | two-party votes in the later election | County demographics panel |   |
| r2_college_1216_u | 32.2% | share of the variance of county swings | county-equal R-squared of the regression of the county swing on college share alone | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 |   | 3,112 | none; each county counted once | County demographics panel |   |
| r2_college_1216_w | 47.3% | share of the variance of county swings | vote-weighted R-squared of the regression of the county swing on college share alone | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 |   | 3,112 | two-party votes in the later election | County demographics panel |   |
| r2_college_2024_u | 2.4% | share of the variance of county swings | county-equal R-squared of the regression of the county swing on college share alone | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 |   | 3,102 | none; each county counted once | County demographics panel |   |
| r2_college_2024_w | 0.0% | share of the variance of county swings | vote-weighted R-squared of the regression of the county swing on college share alone | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 |   | 3,102 | two-party votes in the later election | County demographics panel |   |
| r_black_0812_w | +0.43 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the non-Hispanic Black share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2008 to 2012 | 95% CI 0.34 to 0.53 | 3,113 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_black_1216_w | +0.16 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the non-Hispanic Black share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 | 95% CI 0.03 to 0.31 | 3,112 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_black_1620_w | −0.13 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the non-Hispanic Black share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI −0.29 to 0.00 | 3,113 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_black_2024_u | −0.24 | correlation coefficient, from −1 to +1 | county-equal correlation between the non-Hispanic Black share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.36 to −0.14 | 3,102 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_black_2024_w | −0.07 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the non-Hispanic Black share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.25 to 0.09 | 3,102 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_college_0812_u | +0.09 | correlation coefficient, from −1 to +1 | county-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 swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2008 to 2012 | 95% CI −0.01 to 0.20 | 3,113 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_college_1216_u | +0.57 | correlation coefficient, from −1 to +1 | county-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 swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 | 95% CI 0.49 to 0.66 | 3,112 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_college_1216_w | +0.69 | correlation coefficient, from −1 to +1 | vote-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 swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 | 95% CI 0.62 to 0.78 | 3,112 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_college_1620_u | +0.51 | correlation coefficient, from −1 to +1 | county-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 swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI 0.45 to 0.60 | 3,113 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_college_1620_w | +0.25 | correlation coefficient, from −1 to +1 | vote-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 swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI 0.11 to 0.41 | 3,113 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_college_2024_u | +0.16 | correlation coefficient, from −1 to +1 | county-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 swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI 0.07 to 0.24 | 3,102 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_college_2024_w | +0.02 | correlation coefficient, from −1 to +1 | vote-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 swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.08 to 0.13 | 3,102 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_foreign_1216_w | +0.58 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the foreign-born share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 | 95% CI 0.44 to 0.69 | 3,112 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_foreign_1620_u | −0.03 | correlation coefficient, from −1 to +1 | county-equal correlation between the foreign-born share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI −0.20 to 0.22 | 3,113 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_foreign_1620_w | −0.50 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the foreign-born share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI −0.67 to −0.10 | 3,113 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_foreign_2024_u | −0.34 | correlation coefficient, from −1 to +1 | county-equal correlation between the foreign-born share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.47 to −0.14 | 3,102 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_foreign_2024_w | −0.69 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the foreign-born share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.77 to −0.49 | 3,102 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_hispanic_1216_u | +0.33 | correlation coefficient, from −1 to +1 | county-equal correlation between the Hispanic share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 | 95% CI 0.25 to 0.42 | 3,112 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_hispanic_1216_w | +0.45 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the Hispanic share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2012 to 2016 | 95% CI 0.31 to 0.53 | 3,112 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_hispanic_1620_u | −0.30 | correlation coefficient, from −1 to +1 | county-equal correlation between the Hispanic share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI −0.51 to 0.10 | 3,113 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_hispanic_1620_w | −0.48 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the Hispanic share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2016 to 2020 | 95% CI −0.62 to −0.20 | 3,113 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_hispanic_2024_u | −0.39 | correlation coefficient, from −1 to +1 | county-equal correlation between the Hispanic share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.51 to −0.16 | 3,102 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| r_hispanic_2024_w | −0.66 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the Hispanic share of residents at the start of the pair and the county swing | counties with returns in both elections of the pair and county characteristics, Alaska excluded | 2020 to 2024 | 95% CI −0.76 to −0.46 | 3,102 | two-party votes in the later election | County demographics panel | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| sd_w_1216 | 10.8 | standard deviation, percentage points of the two-party margin | vote-weighted standard deviation of county swings about their weighted mean | counties with returns in both elections of the pair, Alaska excluded | 2012 to 2016 |   | 3,112 | two-party votes in the later election | County election returns |   |
| sd_w_2024 | 4.5 | standard deviation, percentage points of the two-party margin | vote-weighted standard deviation of county swings about their weighted mean | counties with returns in both elections of the pair, Alaska excluded | 2020 to 2024 |   | 3,111 | two-party votes in the later election | County election returns |   |
| sens_aff_max_du | +0.6 | difference between the group mean and the all-county mean, percentage points of the two-party margin | highest of the county-equal differences between the group mean of county swing minus national swing and the all-county mean, across the alternative cut-offs | affluent suburban counties under the alternative college, income and rural cut-offs | 2020 to 2024 |   |   | none; each county counted once | County election returns |   |
| sens_aff_max_dw | +1.0 | difference between the group mean and the all-county mean, percentage points of the two-party margin | highest of the vote-weighted differences between the group mean of county swing minus national swing and the all-county mean, across the alternative cut-offs | affluent suburban counties under the alternative college, income and rural cut-offs | 2020 to 2024 |   |   | two-party votes in the later election | County election returns |   |
| sens_aff_min_du | −0.8 | difference between the group mean and the all-county mean, percentage points of the two-party margin | lowest of the county-equal differences between the group mean of county swing minus national swing and the all-county mean, across the alternative cut-offs | affluent suburban counties under the alternative college, income and rural cut-offs | 2020 to 2024 |   |   | none; each county counted once | County election returns |   |
| sens_aff_min_dw | 0.0 | difference between the group mean and the all-county mean, percentage points of the two-party margin | lowest of the vote-weighted differences between the group mean of county swing minus national swing and the all-county mean, across the alternative cut-offs | affluent suburban counties under the alternative college, income and rural cut-offs | 2020 to 2024 |   |   | two-party votes in the later election | County election returns |   |
| sens_black_max_du | −1.4 | difference between the group mean and the all-county mean, percentage points of the two-party margin | highest of the county-equal differences between the group mean of county swing minus national swing and the all-county mean, across the alternative cut-offs | high-Black counties under the alternative non-Hispanic Black share cut-offs | 2020 to 2024 |   |   | none; each county counted once | County election returns |   |
| sens_black_max_dw | +1.9 | difference between the group mean and the all-county mean, percentage points of the two-party margin | highest of the vote-weighted differences between the group mean of county swing minus national swing and the all-county mean, across the alternative cut-offs | high-Black counties under the alternative non-Hispanic Black share cut-offs | 2020 to 2024 |   |   | two-party votes in the later election | County election returns |   |
| sens_black_min_du | −2.0 | difference between the group mean and the all-county mean, percentage points of the two-party margin | lowest of the county-equal differences between the group mean of county swing minus national swing and the all-county mean, across the alternative cut-offs | high-Black counties under the alternative non-Hispanic Black share cut-offs | 2020 to 2024 |   |   | none; each county counted once | County election returns |   |
| sens_black_min_dw | +0.8 | difference between the group mean and the all-county mean, percentage points of the two-party margin | lowest of the vote-weighted differences between the group mean of county swing minus national swing and the all-county mean, across the alternative cut-offs | high-Black counties under the alternative non-Hispanic Black share cut-offs | 2020 to 2024 |   |   | two-party votes in the later election | County election returns |   |
| sens_hisp15_du | −2.1 | difference between the group mean and the all-county mean, percentage points of the two-party margin | county-equal mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | counties 15 percent Hispanic or more in 2020 | 2020 to 2024 | 95% CI −2.9 to −1.3 | 555 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| sens_hisp15_dw | −3.1 | difference between the group mean and the all-county mean, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | counties 15 percent Hispanic or more in 2020 | 2020 to 2024 | 95% CI −4.2 to −2.1 | 555 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| sz_college_2024_big | +0.23 | correlation coefficient, from −1 to +1 | county-equal correlation between the share of adults 25 and older with a bachelor's degree or more and the county swing within the group | counties with 250,000 or more two-party votes in the later election, Alaska excluded | 2020 to 2024 | 95% CI 0.00 to 0.40 | 134 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states. |
| sz_college_2024_small | +0.22 | correlation coefficient, from −1 to +1 | county-equal correlation between the share of adults 25 and older with a bachelor's degree or more and the county swing within the group | counties with fewer two-party votes than that in the later election, Alaska excluded | 2020 to 2024 | 95% CI 0.15 to 0.29 | 2,968 | none; each county counted once | County demographics panel | Interval: 95% percentile interval from a bootstrap over states. |
| sz_mean_college_2024_big | 40.7% | percent of adults with a bachelor's degree or more | county-equal mean college share | big counties in 2020 | 2020 to 2024 |   | 134 | none; each county counted once | County demographics panel |   |
| sz_mean_college_2024_small | 22.8% | percent of adults with a bachelor's degree or more | county-equal mean college share | small counties in 2020 | 2020 to 2024 |   | 2,968 | none; each county counted once | County demographics panel |   |
| sz_mean_swing_2024_big | −5.5 | change in the two-party margin, percentage points of the two-party margin | county-equal mean county swing | big counties | 2020 to 2024 |   | 134 | none; each county counted once | County election returns |   |
| sz_mean_swing_2024_small | −3.3 | change in the two-party margin, percentage points of the two-party margin | county-equal mean county swing | small counties | 2020 to 2024 |   | 2,968 | none; each county counted once | County election returns |   |
| t_aff_n | 152 | counties | count of counties in the group | affluent suburban counties | 2020 to 2024 |   |   | none (full counts) | County demographics panel |   |
| t_all_u | +2.7 | county swing minus national swing, percentage points of the two-party margin | county-equal mean of county swing minus national swing | counties in the analysis | 2020 to 2024 | 95% CI 2.2 to 3.2 | 3,102 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| t_all_w | +1.3 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | counties in the analysis | 2020 to 2024 | 95% CI 0.2 to 2.8 | 3,102 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| t_black_n | 379 | counties | count of counties in the group | high-black counties | 2020 to 2024 |   |   | none (full counts) | County demographics panel |   |
| t_black_w | +2.2 | county swing minus national swing, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing | high-black counties in 2020 | 2020 to 2024 | 95% CI 0.6 to 3.4 | 379 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| t_hisp_n | 314 | counties | count of counties in the group | high-hispanic counties | 2020 to 2024 |   |   | none (full counts) | County demographics panel |   |
| td_aff_u | +0.3 | difference between the group mean and the all-county mean, percentage points of the two-party margin | county-equal mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | affluent suburban counties in 2020 | 2020 to 2024 | 95% CI −0.7 to 1.3 | 152 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| td_aff_w | +0.8 | difference between the group mean and the all-county mean, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | affluent suburban counties in 2020 | 2020 to 2024 | 95% CI −0.1 to 1.7 | 152 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| td_black_u | −1.8 | difference between the group mean and the all-county mean, percentage points of the two-party margin | county-equal mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | high-black counties in 2020 | 2020 to 2024 | 95% CI −2.9 to −1.0 | 379 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| td_black_w | +0.9 | difference between the group mean and the all-county mean, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | high-black counties in 2020 | 2020 to 2024 | 95% CI −0.8 to 2.1 | 379 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| td_hisp_u | −3.3 | difference between the group mean and the all-county mean, percentage points of the two-party margin | county-equal mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | high-hispanic counties in 2020 | 2020 to 2024 | 95% CI −4.7 to −2.4 | 314 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| td_hisp_u_abs | 3.3 | points of the two-party margin below the all-county mean | absolute value of the county-equal mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | high-Hispanic counties in 2020 | 2020 to 2024 | 95% CI 2.4 to 4.7 | 314 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| td_hisp_w | −4.2 | difference between the group mean and the all-county mean, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | high-hispanic counties in 2020 | 2020 to 2024 | 95% CI −6.2 to −3.0 | 314 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| td_hisp_w_abs | 4.2 | points of the two-party margin below the all-county mean | absolute value of the vote-weighted mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | high-Hispanic counties in 2020 | 2020 to 2024 | 95% CI 3.0 to 6.2 | 314 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| td_other_u | +0.7 | difference between the group mean and the all-county mean, percentage points of the two-party margin | county-equal mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | all other counties in 2020 | 2020 to 2024 | 95% CI 0.4 to 1.1 | 2,257 | none; each county counted once | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| td_other_w | +1.9 | difference between the group mean and the all-county mean, percentage points of the two-party margin | vote-weighted mean of county swing minus national swing in the group, minus the same mean over all counties in the analysis | all other counties in 2020 | 2020 to 2024 | 95% CI 0.8 to 2.7 | 2,257 | two-party votes in the later election | County election returns | Interval: 95% percentile interval from a bootstrap over states, 2,000 replicates. |
| wayne_rel | −2.5 | county swing minus national swing, percentage points of the two-party margin | county swing minus national swing | Wayne County, Michigan | 2020 to 2024 |   | 1 | none (full counts) | County election returns |   |
| z_black | −0.18 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the Black share and the ZIP code area swing | ZIP code areas with more than 100 presidential votes in 2020, contiguous United States | 2016 to 2020 | 95% CI −0.29 to −0.11 | 29,323 | presidential votes in 2020 | Precinct votes on census geographies | Interval: 95% percentile interval from a bootstrap over states. |
| z_college | +0.47 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the college share (ZCTA atlas measure) and the ZIP code area swing | ZIP code areas with more than 100 presidential votes in 2020, contiguous United States | 2016 to 2020 | 95% CI 0.42 to 0.55 | 29,323 | presidential votes in 2020 | Precinct votes on census geographies | Interval: 95% percentile interval from a bootstrap over states. |
| z_foreign | −0.46 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the foreign-born share and the ZIP code area swing | ZIP code areas with more than 100 presidential votes in 2020, contiguous United States | 2016 to 2020 | 95% CI −0.61 to −0.14 | 29,323 | presidential votes in 2020 | Precinct votes on census geographies | Interval: 95% percentile interval from a bootstrap over states. |
| z_hispanic | −0.50 | correlation coefficient, from −1 to +1 | vote-weighted correlation between the Hispanic share and the ZIP code area swing | ZIP code areas with more than 100 presidential votes in 2020, contiguous United States | 2016 to 2020 | 95% CI −0.60 to −0.29 | 29,323 | presidential votes in 2020 | Precinct votes on census geographies | Interval: 95% percentile interval from a bootstrap over states. |
| zcta_college_mean | 54.7% | percent, atlas college measure | mean across areas of the atlas college-education share | ZIP code areas in the check | 2016 to 2020 |   | 29,323 | none; each area counted once | Precinct votes on census geographies |   |
| zcta_n | 29,323 | ZIP code areas | count of areas | ZIP code areas with more than 100 presidential votes in 2020 and all four characteristics | 2016 to 2020 |   |   | none (full counts) | Precinct votes on census geographies |   |
| zcta_n_dropped | 14 | ZIP code areas | count of areas dropped because the atlas codes a characteristic as missing (-1) | ZIP code areas with more than 100 presidential votes in 2020 | 2016 to 2020 |   |   | none (full counts) | Precinct votes on census geographies |   |

## Definitions

- Correlation: A 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.
- Swing: The change in a place's two-party margin between two elections. In this book a positive swing is a move toward the Democrats.
- County two-party margin: The 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.
- Percentage point: The 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.
- National swing: The 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.
- Confidence interval: A range around an estimate that would contain the true value in 95% of repeated samples if the sampling model held. The book reports 95% intervals unless it says otherwise.
- Matched counties: The 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.
- Standard deviation: A measure of how far values typically fall from their average. A larger standard deviation means the values are more spread out.
- Standardized coefficient: A regression coefficient measured in standard deviations of the variables involved. It lets effects on differently scaled variables be compared.
- Clustered standard errors: Standard errors that allow answers within the same group, such as the same county or the same week, to be related. They are wider than ordinary errors when members of a group resemble each other.
- ZCTA: ZIP Code Tabulation Area: the Census Bureau's area approximation of a ZIP code, built from the census blocks that fall inside it. It is the one neighborhood geography that lines up between the 2016 and 2020 vote estimates.
- Survey weights: Numbers 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.
- Effective sample size: The number of equally weighted respondents that would give the same precision as the weighted sample. It is smaller than the raw count when the weights vary a lot, and it sets how wide the confidence interval is.
- Ecological inference: Drawing 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.

## Methods

Sample: presidential returns for the 7 elections from 2000 to 2024 (Algara and Amlani for 2000 to 2020, MIT Election Data and Science Lab for 2024), from the repaired county file whose 2024 totals are corrected for 173 counties in South Carolina, Wisconsin and West Virginia. The 41 Alaska rows (election districts) are dropped before any calculation; in these years no other row lacks a FIPS code, a vote count or two-party votes. A pair of consecutive elections uses the counties present in both, matched on FIPS code, which gives 3,111 to 3,114 counties; county boundaries are not harmonized. County characteristics come from the county demographics panel (decennial census and American Community Survey estimates, interpolated to election years, so they carry sampling error that no interval here includes) at the earlier election of the pair: the share of adults 25 and older with a bachelor's degree or more, the Hispanic share of residents (any race), the foreign-born share of residents and the non-Hispanic Black share of residents; the county groups also use median household income in 2024 dollars and the rural share of residents. Eight Connecticut counties have no characteristics for 2020, and Jackson County, Missouri loses its Kansas City votes to a separate unit in 2024; both are left out of the 2020 to 2024 correlations, regressions, groups and scatter and stay in the national swing, so that the swing matches chapter 10.1; Alaska is not in any county statistic. Estimand: the margin is 100 times the Democratic minus the Republican votes over their sum; a county's swing is its margin in the later election minus its margin in the earlier one; the national swing is the same change in the summed margin of the matched counties; the relative swing is the county swing minus the national swing. A correlation with the swing equals the correlation with the relative swing. Weights: two-party votes in the later election (vote-weighted) or one per county (county-equal). Correlations are Pearson correlations across counties. Regressions are weighted least squares of the swing on the four characteristics, each variable standardized with the weighted mean and standard deviation of the weighting used; non-Hispanic white share is not a regressor. A second regression uses Hispanic and foreign-born share only. R-squared is the share of the variance of the standardized swing that the fitted values reproduce, from the weighted fit. Intervals: 95% percentile intervals from a bootstrap over whole states for correlations, for the mean swing of county groups and for the difference between a group mean and the all-county mean (2,000 replicates, seed 12345, 48 states and the District of Columbia in the 2020 to 2024 pair; for a group mean the states are resampled from all states in the analysis, so a group can gain or lose states), and normal intervals with errors clustered by state for regression coefficients. The returns are full counts, so these intervals show how much a number depends on which states are in the set; they are not sampling error of the vote and they do not include the sampling error of the county characteristics. County groups: high-Hispanic (25% or more Hispanic in 2020), high-Black (25% or more non-Hispanic Black and not high-Hispanic), affluent suburban (35% or more of adults with a bachelor's degree, median household income of $75,000 or more, rural share below 25%, and neither of the first two) and all other counties. The county-type figure plots each group's mean relative swing minus the mean over all counties in the analysis, in the same weighting, because the all-county mean is not zero (the national swing is the change in the summed margin, which large counties dominate). Threshold check: the difference from the all-county mean is recomputed for the high-Hispanic group with a 15% cut, for the high-Black group with six other cuts (15, 20, 30, 35, 40 and 50%), and for the affluent suburban group with six other sets of cuts that move the college cut (to 30 or 40%), the income cut (to $60,000 or $90,000) or the rural cut (to 10 or 50%) one at a time from the baseline. Group means of the swing by Hispanic share (under 10, 10 to under 25, 25 to under 50 and 50% or more) and by college share (under 20, 20 to under 40 and 40% or more) are drawn at the group's median share, the same for both weightings. Large counties: 250,000 or more two-party votes in the later election; correlations inside a size class count each county once, and the table of size classes also gives the mean college share and mean swing of each class, and for all counties in each weighting. ZIP code areas: the 2016 to 2020 swing surface from precinct votes allocated to ZIP code areas (Fekrazad 2025; the swing is the 2020 margin minus the 2016 margin, in points), areas with more than 100 presidential votes in 2020 (29,323 areas), weighted by 2020 presidential votes; its college measure is the atlas's college-education share, which the atlas file does not define and is not the county bachelor's degree share; areas with a negative sentinel in any of the four measures are dropped. Survey check: Cooperative Election Study respondents who reported a Democratic or Republican presidential vote in 2012, 2016, 2020 and 2024, the year-specific weight (the pre-election weight, the only weight present in all four waves, applied to a post-election vote item), the change in the weighted margin between two waves by self-reported race (the waves are separate samples of respondents; the chart shows White, Black and Hispanic respondents and the data table also has Asian and other respondents), with intervals from the Kish effective sample size (two independent waves, standard errors combined in quadrature). Sensitivity to the weight: the post-election weight for 2012 to 2016 and 2016 to 2020 (not in the file for 2024) and the validated-voter weight for 2020 to 2024 (respondents matched to a voter file). Suppression: none for county or ZIP code area figures; a survey cell needs at least 100 respondents and an effective sample of at least 100, and every race-by-wave cell meets both.

## What these data cannot show

- Every correlation above 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 inference.
- Robinson [5] showed that a correlation across places can have the opposite sign from the correlation across individuals.
- The foreign-born population includes residents who are not citizens and cannot vote, so the foreign-born correlation says where the swing was large and not how immigrants voted.
- The characteristics describe residents, not voters, and 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 (https://vishalsingh.org/politics/composition-and-behavior-in-county-change; planned) separates composition from behavior with other data.
- The dates are the dates of pairs.
- A correlation for 2012 to 2016 and one for 2016 to 2020 cannot say when between the two elections a county moved, and a change of sign between two pairs says that the ordering of counties changed, not why.
- The four characteristics are correlated with each other and with others that are not in the regression, among them income, age and rurality, which Planned, Income and the county vote (https://vishalsingh.org/politics/income-and-the-county-vote; planned) and Planned, Rural counties (https://vishalsingh.org/politics/rural-counties; planned) take up.
- A coefficient therefore describes one regression and not a separate effect.
- The characteristics are census and American Community Survey estimates interpolated between census and survey years, so they carry sampling error, most in small counties, that no interval here includes.
- The county set is not harmonized across boundary changes, and the county groups are threshold rules whose group means move with the cut.
- A swing is not adjusted for the county's earlier margin, and a county that began with an almost one-sided margin had less room to move in one direction than a competitive county did.
- The intervals describe dependence on which states are in the set, and the returns themselves are counts.
- The intervals on group means are of the same kind.
- The ZIP code check covers only 2016 to 2020, and its votes are allocated from precincts.
- The CES is an opt-in online panel with self-reported vote, so its estimates depend on how well the weights correct for who joins and on which weight is used.
- The data used here hold no measure of turnout by group, of attitudes or of campaign activity, so they cannot say why the swing lined up with Hispanic and foreign-born share in 2020 to 2024 and with other characteristics in 2012 to 2016.

## References

1. Sides, John; Tesler, Michael; Vavreck, Lynn (2018). Identity Crisis: The 2016 Presidential Campaign and the Battle for the Meaning of America. Princeton University Press. https://press.princeton.edu/books/hardcover/9780691174198/identity-crisis
2. Zingher, Joshua N. (2022). Diploma Divide: Educational Attainment and the Realignment of the American Electorate. Political Research Quarterly 75(2): 263-277. https://doi.org/10.1177/10659129221079862
3. Fekrazad, Amir (2025). A dataset of US precinct votes allocated to Census geographies with precision. Scientific Data 12: 794. https://doi.org/10.1038/s41597-025-05140-3
4. Kish, Leslie (1965). Survey Sampling. John Wiley and Sons.
5. Robinson, W. S. (1950). Ecological Correlations and the Behavior of Individuals. American Sociological Review 15(3): 351-357.

## Provenance

- Pipeline version: 1.0.0
- Build script: book3/chapters/education-ethnicity-and-the-county-shift/build.py (sha256 f5f2e746959f3a841962ddacad9e93d8c3e96e4b09d54bc29efb65bdb0db0388)
- Built on: 2026-09-30
- Site commit: 16414b2c7257546a7042d0b1734657f208999f60

Inputs:

| File | Size (bytes) | SHA-256, first 1 MB | SHA-256, last 1 MB |
| --- | ---: | --- | --- |
| data/cces/cces_clean.parquet | 53,454,868 | 9430e88b3196c9ca2250bdda1908d3920e80e276ca0c529c514e2fc2bdd00806 | f61097c727c38fd8d7c8e53fe86935fb626f351a64d99ccb1ae0b56715b2c405 |
| data/county/county_demographics_2000_2024.parquet | 4,334,220 | 45cdaf63128da1aa0c12e1196d7e60835697d7432ab9bff1acd2f5559fdb5130 | 90293a64867bf37645b53eaf5ab343f3b284c0d2c3fc3b8e0f38d32f1649fee4 |
| data/county/pres_county_1868_2024.parquet | 6,295,675 | bec11c7a0cb5d4180bbd6809d048bc892dd00dfcdde527fa4da871b1dd3f835f | b2a4f000f2eea72d09af8939555289e924875cca40cd95ec8869104cd575bed7 |
| data/precinct/census_estimates/zcta_swing_atlas.parquet | 11,850,164 | ca87f58b5b27fc0ebd644d594a8b93971f85d40f95132ac641ffc84c5021684e | 6db8ad81722c59d7fc7436e5563bfd33b9a60fe03aff344e763af8a25df38651 |

Outputs:

| File | SHA-256 |
| --- | --- |
| facts.json | 7464f5e3872aba0a22081c0d29fb15a918851de8df78ff45d13c776e5e5043dd |
| figures/ces_by_race.json | c6a252485706a8746b15c92d54aea7daa700584da2f8ce9852e8a7d2aa05e539 |
| figures/correlation_and_model.json | 6a5e17b116f8bb2669531108516970013f5ed8a0a444b533cb8b5b8a76f2e494 |
| figures/correlation_by_pair.json | bbef30d1d9a8bef56396b87678ba67a66bd63d153ac1045cad280913885e87b2 |
| figures/county_types.json | 1a9ecfe991a3f7d3c092b1a25e3348ecada6ca26e6c27acc55e416483ef9372f |
| figures/size_classes.json | 3c58f16849b38b5544184571334cdd6ec878c4e7d93091a96e7686388e951ea6 |
| figures/swing_by_share.json | 24ec23e62848bdc8db2946e19f2879cd82f35d5262a661e3796b9702011cba51 |
| figures/swing_map.json | 4bf2f17dfc1f486b70e949ac9c05fd009403c4be378e90f35093908c37c70bf6 |
| figures/zip_code_check.json | 7a1b7792c746cac843cba3a651416cf81f3c8f49fb1d0000a14cad546b048e25 |
| meta.json | b0fca8d301e58168ec3d3c931c99f07dd0274ab1df23a4eca870c35c56d8c0d5 |
| public/data/education-ethnicity-and-the-county-shift/swing_map.json | 463109502e6e43b2430741cf9880dfb154f576691591cbe85e5d24769376eec1 |
| refs.json | bdb9a736095e79a8f41f0f4161b0472c8804962220f14e7b8d6b27114063b76d |

## Machine formats

- Report (HTML): https://vishalsingh.org/politics/education-ethnicity-and-the-county-shift/report
- Report (Markdown): https://vishalsingh.org/politics/education-ethnicity-and-the-county-shift/report.md
- Every estimate (JSON): https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/facts.json
- Table 1, figure "correlation_by_pair" (JSON): https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/correlation_by_pair.json
- Table 2, figure "swing_map" (JSON): https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/swing_map.json
- Table 3, figure "size_classes" (JSON): https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/size_classes.json
- Table 4, figure "swing_by_share" (JSON): https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/swing_by_share.json
- Table 5, figure "correlation_and_model" (JSON): https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/correlation_and_model.json
- Table 6, figure "zip_code_check" (JSON): https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/zip_code_check.json
- Table 7, figure "county_types" (JSON): https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/county_types.json
- Table 8, figure "ces_by_race" (JSON): https://vishalsingh.org/politics/report/education-ethnicity-and-the-county-shift/figures/ces_by_race.json
- Payload of "swing_map" (JSON): https://vishalsingh.org/politics/data/education-ethnicity-and-the-county-shift/swing_map.json
- Index of every report: https://vishalsingh.org/politics/llms.txt; all reports in one file: https://vishalsingh.org/politics/llms-full.txt