Voters of Color Helped Turn Northern Virginia Blue. Now, They’re Starting to Turn it Back Red

Donald Trump’s second inauguration marks a historic moment, capping off a wild and unpredictable 2024 campaign. Two extraordinary events defined the race: Joe Biden’s abrupt departure from the race after a disastrous first debate and Donald Trump narrowly surviving a serious assassination attempt. Yet, if one factor stands out as the key to Trump’s victory, it’s the GOP’s surging support among minority voters—particularly the growing wave of Hispanic support. On the day of his inauguration, it feels fitting to reflect on this pivotal realignment of coalitions—a shift poised to define American politics throughout the 2020s.

It Wasn’t Supposed to be This Way

Five years ago, after Democrats secured sweeping victories in suburbs nationwide during the 2018 midterms—and appeared poised to replicate this success in the 2020 presidential election—the media was filled with stories about the growing political influence of Hispanic and Asian voters moving into suburban areas and shifting them blue.

This New York Times story by Sabrina Tavernise and Robert Gebeloff was a good read at the time, I remember enjoying it while bored during high school on the day it came out and it always stuck with me as outlining the biggest political theme of 2008-2018. In summary, Northern Virginia (NoVA) was once a stronghold for affluent white Republicans. However, between 1990 and 2018, an influx of Hispanic and Asian voters transformed the region, shifting it decisively blue. NoVA became a vibrant symbol of the new, diverse Democratic coalition, expanding from urban areas to suburbs.

The Wikipedia page on the Great Replacement theory in America describes it as a “conspiracy theory,” which is a fair characterization. However, it did stem from two very real trends: (1) the United States was becoming increasingly diverse over time, and (2) this growing diversity helped Democrats as they performed extremely well with voters of color, including the rapidly growing Hispanic and Asian populations. Republicans diagnosed these problems in the much famed “RNC Autopsy” of 2013. It warned that the growing non-white population in the United States could make it increasingly challenging for Republicans to win elections unless the party made efforts to engage with these voters. Trump won in 2016, sure, but his victory just relied on boosting GOP support among white voters to another level. However, this did little to address the GOP’s long-term challenges with non-white voters, who continued to grow as a share of the electorate.

So, what’s happened since 2016? Affluent white voters in Northern Virginia have become increasingly liberal, while Republicans have started to win over both affluent and working class Asian and Hispanic voters. By 2024, we reached an inflection point: Harris won a higher percent of white voters than non-white voters in Northern Virginia. The realignment of political coalitions in 2024 was striking and dramatic, and it will likely take years for people to adjust to the idea that Democrats are becoming less and less of the party of minorities and more and more of the party of elite white voters over time. In 2016, only about 10% of Trump’s voters were people of color, but by 2024, that number had grown to around 25%.

Estimates

Modeling suggests that Harris actually outperformed Biden’s 2020 performance with white voters in the Northern Virginia area. However, she faced significant challenges with Black voters and saw sharp declines in support among Asian and Hispanic voters.

When analyzing county-level data, this result might seem surprising, as Harris actually gained ground in very few areas. However, if you look at the precinct-level data, it makes more sense. Harris flipped Purcellville, outperformed Biden by 8 points in McLean, and ran it up in the Prince William County exurbs near Dale City. Basically, she ran particularly strongly in the whitest areas of Northern Virginia.

The plots below illustrate the modeled raw margin shifts by race, normalized for the overall turnout decline in the region. Harris made significant gains among well-educated, affluent white voters in the inner-ring suburbs, particularly in areas like McLean and Vienna. Meanwhile, Trump improved in Prince William County and held steady in Arlington and inner Loudoun County. Trump made gains among Black voters across the region, though the overall impact was limited due to the smaller Black population. In the diversifying suburbs around Dale City, Harris increased the Black margin primarily due to growth in the raw Black population. Democrats suffered heavy losses with Hispanic and Asian voters across the region, particularly in eastern Loudoun County and central Fairfax County. Middle-class Vietnamese voters in the Springfield area shifted away from Democrats significantly, as did affluent Indian voters in Loudoun County and affluent Korean voters around Centerville.

Ecological Inference

Ecological inference (EI) is a statistical method used to infer individual-level behavior or characteristics from aggregate-level data, particularly when direct individual data is unavailable. In this case, the central question revolves around how different demographic groups voted in the 2024 election. While exact data isn’t available, EI allows us to estimate voting patterns using precinct-level data. The graphs below illustrate the core concept of EI, which examines the relationship between racial demographics and vote percentages across precincts to uncover correlations.

The estimates above are based on my recently completed Statistics Senior Thesis at Yale, which focused on developing a new method for ecological inference. You can read the full thesis here for a detailed explanation of the methodology. All the estimates presented above are based on election and demographic data sourced from Dave’s Redistricting App, which outlines its data sources here.

Local Estimate Disclaimer

It’s important to emphasize that these are not national estimates. The populations of White, Hispanic, Asian, and Black residents in Northern Virginia (NoVA) differ significantly from their national counterparts. For instance, the highly educated white voters in NoVA are far more liberal than white voters nationally and likely represent one of the strongest regions for Democratic gains among white voters in the country—comparable to areas like the inner northern suburbs of Salt Lake City, Utah, and the inner western suburbs of Milwaukee, Wisconsin.

Similarly, Black voters in NoVA are markedly different from Black voters nationwide. They tend to be more affluent and highly educated, which actually makes them more conservative. Inner-city Black voters, who tend to be Democrats’ strongest supporters, shifted rightward by a smaller margin. Based on my preliminary estimates, inner-city Black voters were about D+90 compared to D+73 for NoVA Black voters in 2020, and D+83 compared to D+60 for NoVA Black voters in 2024.

The Hispanic and Asian voter samples in NoVA are somewhat more similar to their national counterparts, particularly in how they voted in 2024, but notable differences remain.

Virginia Precinct Data Disclaimer

In Virginia, absentee ballots (AB) are reported at the countywide level. Results collection services then allocate these ballots to specific precincts, often using their own methodologies. For the 2020 election, I relied on estimates from the Harvard VEST dataset, which describes its approach for Virginia as follows:

"Absentee ballots and provisional votes were reported at the county or city level throughout the state. These were distributed by candidate to precincts based on their share of the precinct-level reported vote."

This methodology is widely regarded as reliable, but there were known issues in Prince William County (PWC). Specifically, the allocation process led to an overestimation of Democratic support in the western parts of the county and an overestimation of Republican support in the eastern parts. These discrepancies can be addressed using a simple linear regression model (R2 of approximately 0.96) to recalibrate the 2020 results based on data from the 2018 Senate election and the 2021 gubernatorial election using precinct results from surrounding areas. By applying this approach in PWC, I significantly improved the accuracy of the allocations.

To further investigate potential precinct aggregation errors in PWC’s 2020 data, I analyzed the correlations between precinct-level Democratic and Republican vote shares in the 2018 Senate election and the 2020 presidential election results. Compared to other counties in Northern Virginia, PWC exhibited notably weaker correlations, suggesting potential aggregation issues. Specifically, the correlation for Democratic vote number was 0.613 in PWC versus 0.771 in neighboring counties, while the correlation for Republican vote number was 0.743 in PWC versus 0.918 elsewhere.