KEY POINTS FROM THIS ARTICLE

— The predictive power of demographics makes county margins strongly correlated and thus inferable from each other. Comparing the actual results to the expected results based on county demographics gives us a better idea of candidate performance.

— In the 2020 presidential election, Democrats overperformed in states with high numbers of educated white voters, such as Texas, Arizona, and Georgia. They also began to show signs of hitting their electoral floors in much of Appalachia.

— Strong Republican showings with evangelicals, non-college whites, and Hispanics helped Trump overperform in Florida, Iowa, and Ohio.

Where Biden and Trump overperformed in 2020

Was the swing in South Texas’ Rio Grande Valley really all that surprising in the context of the 2020 election results? Were the sharp swings left in New England really anything extraordinary, given how the rest of the nation voted?

Candidates and parties often chalk up results in individual areas to factors solely dependent on those areas themselves, but in 2020, the predictive power of demographics has made county margins stunningly correlated and inferable. Counties do not vote independently of each other; in fact, given nationwide results and an area’s underlying demographics, we can actually predict an individual county’s 2020 partisanship at the presidential level to within an average of 2.4%!

In this article, we will use a regression-based model that regresses 2020 results against the demographics, trends, and partisanship on a county-level basis, and we will use this to examine candidate performance in an area. Put simply, it will tell us how much a county’s margin deviated from the expected result in the context of the national environment.

This helps us establish a better baseline for expected results on a statewide basis, which helps us better evaluate how well a party or a candidate actually did in a given state compared to what demographics and the national environment would have predicted.

For example, Texas is a state in which many Democrats expected to do better than the R +5.6 margin that actually occurred, but the nationwide results suggest that they actually did extremely well in keeping it as close as they did: The modeled underlying factors would have actually suggested an R +8.2 margin in the Lone Star State.

In this manner, examining the results for the two major presidential candidates on a county-level basis against what was expected will give us a better picture of candidate over and under-performance. In this article, we will do this for several regions of interest in the 2020 election. The factors we consider will be race, religion, age, income, education, partisanship, urbanization, 2016 partisanship, and the demographic and partisan trends of an area between 2012 and 2016. In calculating performance above expected, we will be comparing the true two-party partisan margin to the expected one.

Below, we see a national map showing each candidate’s overperformance by county (you can click on any of the maps to see a larger version).

Map 1: Where Biden or Trump did better than model expectations

With this, we will be examining the Percent Above Expected (PAE) for Joe Biden and Donald Trump in the 2020 presidential election across five different regions; namely, Appalachia, the South, the Western Sun Belt, the Midwest, and New England.

APPALACHIA

Map 2: Biden/Trump performance relative to model expectations in Appalachia

Interestingly enough, some of Biden’s strongest overperformances came in Appalachia, where there appears to be significant evidence that Democrats are now hitting their electoral floor; demographically similar areas in the upper Midwest saw significantly harder swings right than the ones in Appalachia, with much higher rates of attrition in Democratic support. In Kentucky (+2.6 Biden PAE), Trump’s margins were significantly lower than expected across the state, with several areas in the east seeing rightward swings that were significantly less pronounced than expected and several areas in the west seeing swings to the left that were far greater than expected. This could hint that the rate of erosion of support for the Democratic Party in these places ancestrally favorable to them is significantly declining.

This theory is supported by Biden’s performances in West Virginia (+2.4 Biden PAE) and Tennessee (+0.6 Biden PAE). The mountain areas of both states saw a Biden overperformance despite being relatively racially homogenous, rural, and with a high concentration of low-income, religious non-college whites. These are the types of areas that swung rightward across the nation, but in the core Appalachian states, Biden managed to buck this trend to some degree (even as Trump still won these states in landslides overall).

The Appalachian overperformances extended to north Georgia, north Alabama, and western North Carolina as well, giving some hints that the non-Midwestern parts of the region as a whole, which have been more Republican-leaning for quite some time, may now be hitting a floor in Democratic support.

THE SOUTH

Map 3: Biden/Trump performance relative to model expectations in the South

Perhaps no states were as hotly debated in 2020 as the trio of North Carolina, Georgia, and Florida. However, the electoral results in these states were very different, in part due to the diverging demographics and electoral trends seen in each state.

Georgia (+1.9 Biden PAE) was arguably the biggest Democratic success story of the cycle, with Democrats picking up two Senate seats in the runoff and flipping the state at the presidential level for the first time since 1992. Biden’s significant strength with white, well-off social liberals in the suburban Atlanta area was key to powering this flip; had it voted in line with expectations, Republicans would have actually held the state. Biden’s overperformance here was manifested in his doing five-and-a-half points above expected in Gwinnett County and eight points above expected in Henry, which showed just how different the state’s voting patterns are as compared to those seen even eight years ago. Interestingly, we see that Democrats also overperformed in the Appalachian region of north Georgia, which is consistent with the results observed in Kentucky, West Virginia, and Tennessee and may hint at a plateau of Republican support being hit in these areas.

Meanwhile, in Florida (+3.8 Trump PAE), the Republican Party overperformed all across the state, and this is best shown by Trump making significant inroads into the Democratic bases in Palm Beach, Broward, and Miami-Dade counties (the three big southeastern counties situated along the Atlantic coast), where he performed anywhere between seven to nine points above expected, depending on the county. Trump’s strength with Cubans and other voters with Caribbean ancestry meant Democrats did not hit the necessary margins in any of these three counties that they needed in order to win the state, which negated their gains in other parts of the state, such as the Jacksonville and Pensacola areas. Had Florida voted as demographics predicted, Democrats may have won the state; as it was, however, they suffered their largest loss at the presidential level there since 2004.

North Carolina (+0.8 Trump PAE) is another fascinating case study, as Democrats appear to have suffered from extremely low rural Black turnout in the east of the state. This, combined with their underperformances in Durham, Mecklenburg (Charlotte), and Wake (Raleigh) — between one to three percentage points below expected, depending on the county — cost them the state. Interestingly, the western part of North Carolina, which is part of Appalachia, saw an overperformance consistent with those seen in Kentucky, Georgia, Tennessee, and West Virginia, again providing more evidence for the notion that Democrats may be near or rebounding from their floor in these areas.

Finally, on an interesting note, Arkansas (+2.2 Trump PAE) saw one of the largest Trump overperformances. It is possible that Hillary Clinton, the state’s former first lady, may have actually had some residual strength in the state that prevented it from sliding as far right as it may have otherwise done in 2016; thus, in 2020, Trump swung more votes than one may have expected, enabling him to carry the state by 28 points, a slight improvement on his 2016 showing.

THE WESTERN SUN BELT

Map 4: Biden/Trump performance relative to model expectations in the Western Sun Belt

A lot of focus was put on Texas (+2.7 Biden PAE) and Arizona (+1.2 Biden PAE) in the build-up to the election. However, while Biden’s overperformance in counties with heavy amounts of white college voters was evident in both states, their relative partisanships meant they went to differing parties. In Arizona, his overperformance in Maricopa (Phoenix), where he did roughly 2% better than expected, was crucial to fueling his flip of the state, which voted for Trump by about 3.5 points in 2016.

Texas, however, voted for Trump by nine points in 2016, and the heavy vote firewall that Republicans held meant that even a sharp swing left overall still saw Trump carry the state by 5.6 points. Biden was hurt in part by heavy swings towards the Republicans in the Rio Grande Valley. However, in the context of national results, Biden actually overperformed quite significantly in the state despite what the raw vote margin may have indicated, aided largely by his strength with white college voters in the Austin, Dallas-Ft. Worth, San Antonio, and Houston metro areas. In particular, he performed nine points above average in San Antonio, between three to six points above average in the Dallas-Fort Worth suburban quad (depending on the county), and six points above average in suburban Hays County, near Austin. All of these areas have a heavy concentration of white college-educated voters, but even accounting for that demographic group’s leftward turn across the nation, Biden still swung them by far more than what one would have expected given national results.

The Rio Grande Valley saw a pronounced swing rightward, but much of it was actually expected based on the national Hispanic swing rightward. In particular, Webb County and Hidalgo County swung 28 and 23 points to the right, respectively, but each voted within 1% of what was expected. Meanwhile, Cameron County, which saw a 19-point swing rightward, actually saw Biden doing a point better than one would expect given national results. This suggests that the swing in the Rio Grande Valley was not necessarily reflective of issues limited to just that region of South Texas and was, in fact, part of a larger national swing right among Hispanics, especially among those who are religious and do not have a four-year college degree.

MIDWEST

Map 5: Biden/Trump performance relative to model expectations in the Midwest

The Midwest was perhaps the most electorally significant area where both Trump and Biden sold themselves as having unique strengths: Biden due to his wins with Barack Obama in 2008 and 2012 and connection to his hardscrabble childhood home of Scranton — Pennsylvania is included here as a Midwestern state because of its electoral similarities to the other, classically Midwestern states — and Trump due to his 2016 victory, which saw the area take a sharp lurch rightwards. However, the picture was far more mixed for both candidates in 2020.

Biden overperformed in Michigan (+0.6 Biden PAE), Indiana (+0.8 Biden PAE), Missouri (+0.5 Biden PAE), and Minnesota (+0.4 Biden PAE). Interestingly, in carrying both Michigan and Minnesota, Biden’s overperformances were not only in areas with concentrations of educated white voters — Kent County, Michigan and Anoka County, Minnesota are both prime examples of this — but also in secular working-class counties such as Sibley County, Minnesota, in which Biden actually improved on Clinton’s margins, contrary to expectations. However, Biden’s relative overperformance in Michigan and Minnesota, while perhaps interesting to note, is still relatively slight as a whole; much of the swing left is explained entirely by the demographics of the two states, each of which saw better turnout and a sharp, yet expected, swing left by college whites, rather than any region-specific strength or weakness of either candidate.

In contrast to his overperformances in Michigan and Minnesota, however, Biden underperformed in Illinois (+1.6 Trump PAE), Ohio (+2.3 Trump PAE), Wisconsin (+1.9 Trump PAE), Iowa (+1.6 Trump PAE), and Pennsylvania (+0.8 Trump PAE). Interestingly, however, these underperformances come for differing reasons, showcasing the coalitions that each candidate drew their respective strength from.

Biden’s Wisconsin and Pennsylvania underperformances came primarily due to the suburban counties not swinging nearly as much as predicted. Biden underperformed quite significantly in the Philadelphia collar and in the suburban Wisconsin counties of Waukesha, Ozaukee, and Washington, which ensured that the states were far closer than one would have envisioned given the nationwide demographic swings among college-educated white voters. To the extent that any expected “home state bounce” materialized for Biden in Pennsylvania, it was largely limited to the northeast of the state, in and around his hometown of Scranton, shown by him doing three points better than expected in Lackawanna County.

Compared to the results seen in Pennsylvania and Wisconsin, Trump’s overperformances in Iowa and Ohio are notable because they come in areas with heavy rates of religiosity with previously high wells of Democratic support. These areas lurched far more sharply to the right than expected, and they were more than enough to ensure that the pre-election possibility of Biden carrying the two states would not come to fruition.

It appears as if the Republican Party’s strength with evangelicals and religious, working-class whites may be beginning to take hold in areas where the realignment had not hit quite as strongly, and the rates of erosion in Democratic support were thus significantly faster than one might have expected given the other 2020 results across the nation. It is worth noting, also, that the Appalachian parts of Ohio and Pennsylvania saw Biden underperformances that diverged significantly from much of the rest of Appalachia, which has recently been (and still is) more Republican. It may be that these northern areas of Appalachia are continuing to align with the rest of the region’s voting patterns.

NEW ENGLAND

Map 6: Biden/Trump performance relative to model expectations in New England

At the presidential level, New England saw some of the most pronounced shifts leftwards, with New Hampshire swinging seven points to the left. However, data suggests that Biden’s overall performance was roughly in line with what was expected given the demographics of the area; with a heavier-than-usual dose of secular and educated white voters, New England was full of the types of voters that swung strongly to the left in the 2020 election.

The only states where Biden performed more than a point above or below expectations were Maine (+1.7 Biden PAE) and Massachusetts (+1.1 Trump PAE). The others, Connecticut (+0.3 Biden PAE), Rhode Island (0.0 Biden PAE), New Hampshire (+0.8 Biden PAE), and Vermont (+0.7 Biden PAE), performed more in line with expectations. While the slight leftward swings in the latter four states were expected given their demographics, Biden’s performance was surprisingly stronger in Maine, where the proportion of college-educated voters is significantly lower than in other New England states, and weaker than usual in Massachusetts, which is highly educated in general and thus was expected to swing further to the left than it really did.

Conclusion

Overall, we see that the uniquely predictive power of demographics make swings extremely strongly correlated across the nation. The factors that caused Hispanics to swing right in areas like Southern California and Miami-Dade also were evident in the swing seen in the Rio Grande Valley, while the factors that saw white college voters take a sharp turn left were evident in states ranging from Michigan to Georgia to Texas. By isolating out these factors with a demographic regression model, we can better analyze overperformances with respect to the national environment, which helps us better understand electoral trends as well as the candidates’ strengths and weaknesses unique to states and areas.

KEY POINTS FROM THIS ARTICLE

— Swing voters are not the same as swing states. This article discusses a metric called “elasticity” for counties, which tracks a county’s variance in vote margin, to help us better identify and draw the distinction between these two.

— In the 2020 election, Republicans face an extremely tough challenge in holding Wisconsin, as the highly elastic nature of the state, combined with the heavily Democratic environment, open up too many holes to cover in order to maintain their 2016 margins. Similarly, Arizona may be difficult to hold for the GOP, given the leftward lurch of Maricopa County.

— In inelastic states like Florida and North Carolina, both parties are heavily reliant on turnout from their bases in order to carry the state. Biden’s strength, however, may be in his ability to more closely match Barack Obama’s performance in the Republican areas of the states, which are generally more elastic than the Democratic areas.

Introduction

The concept of a “swing state” is thought to be an easily-understood notion in politics — it’s a state that could tip either way in any given election. But not every voter in a swing state is actually a swing voter, and it’s important to draw the distinction. Some states, like Wisconsin, do have a lot of actual swing voters. But other swing states, like Florida and North Carolina, are home to relatively few swing voters. The closely contested nature of these states comes instead from a relatively equal set of committed partisans on each side, and election victories in such states generally go to the candidate that can turn out the most voters on their side.

This is a tough thing to measure, however — how can we understand which category certain states fall into? One thing we can examine is the tendency for the state’s counties to swing between parties across elections.

For example, a county could vote for the Republican by a five-point margin for governor and vote for the Democrat by a 10-point margin for president, indicating a high degree of openness to voting for any candidate, regardless of party. Examining this tendency should thus help us gauge the partisan loyalty of a county’s voters across offices and would thus provide a rough, but vote-based estimate of the types of voters in an area and their electoral “elasticity.” With context, this would greatly help us in identifying how persuadable voters in counties really are.

Understanding and quantifying the elasticity of voters in areas (a concept discussed by Nate Silver at FiveThirtyEight) is an extremely important thing for candidates, as it helps them decide on resource allocation and helps in setting campaign strategy for maximizing the ultimate number: votes. For example, Joe Biden would likely be wasting his time airing ads about bipartisanship in a place like Florida’s Broward County, a Democratic stronghold whose voters are already fairly entrenched in their views. But he might be better off airing those ads in Wisconsin, where more swing voters exist, and investing in a heavy turnout machine in Broward County to turn out his voting base instead.

To measure this tendency, we’ll introduce a concept called “elasticity” for counties. This concept measures the deviations in a county’s percentage-based vote margin across a set of elections, which we use as a proxy for the openness of a county’s voters to voting for candidates across the political spectrum, regardless of political affiliation.

It is important to note that what this metric measures is the vote-based electoral “bipartisanship” of counties across offices — i.e. “How much has this county’s vote varied across elections?” This is different from the concept of “swing counties.” You can imagine a county being fairly elastic as it oscillates between R+20 and R+50, while being reliably red — we see that a fair amount of voters are open to voting for the Democrat, even if the county appears to be solidly Republican in each of those hypothetical elections. In a closely-contested election, those margins can make all the difference.

The metric is computed as follows: For any set of elections {A, B, C, D, …}, we plot the (Republican, Democratic) vote by county on an (x, y) coordinate scale and compute the pairwise Euclidean distance between all points. These distances are then all summed to obtain an elasticity score for the county. Basically, the more distance between the points, the more elastic the county is, because that means there’s been more variance in how the county has voted in recent elections.

In this column, our set of elections will consist of the most recent Senate election, the most recent governor election, and the two most recent presidential elections. We will be analyzing four closely-watched states in this year’s presidential election: Wisconsin, Florida, Arizona, and North Carolina. The elasticity of a county will be denoted by (E number), and we will have five tiers: very inelastic (E < 15), moderately inelastic (E 15-25), slightly elastic (E 25-40), moderately elastic (E 40-60), and highly elastic (E > 60).

Let’s explore this in more detail by looking at the elasticity metrics of two counties: Vernon County in Wisconsin (a moderately elastic area), and Pinellas County in Florida (a moderately inelastic one).

Let’s first collect the set of results we need to calculate Vernon County’s elasticity, with the vote for each election represented in (Republican, Democrat) format. For ease of viewing, we will round results to the nearest whole number.

The 2018 governor results were (48% Republican, 50% Democratic), the 2018 Senate results were (42, 58), the 2016 presidential results were (49, 45), and the 2012 presidential results were (42, 56). We will now calculate the pairwise Euclidean distance between all these points and sum them up to get the elasticity of the county, as shown in the table below. For information on how the two-dimensional Euclidean distance used is calculated, we recommend taking a quick glance here.

Table 1: Elasticity of Vernon County, Wisconsin

The elasticity of Vernon County becomes especially clear when contrasted with that of a county like Pinellas in Florida, which has a far less variable vote margin between elections.

Table 2: Elasticity of Pinellas County, Florida

We can also contrast the elasticity of two counties with the visual below — essentially, elasticity is the sum of the edge lengths in a graph that connects all four points, plotted by (R, D) vote percentages on the (x, y) coordinate plane, with each other. The longer the edges, the more elastic the county is. Thus, an elastic county would have a “stretchier” shape, as the set of vertices would be farther apart.

Table 3: Elasticity of Vernon County, WI vs. Pinellas County, FL

With that explanation out of the way, let’s look at some of these key states.

Wisconsin

Map 1: Wisconsin elasticity by county, 2012-2018

Wisconsin is the classic example of a traditional swing state: one comprised of moderate swing voters who swing between parties based on candidacies and the national mood. Trump’s 2016 victory in Wisconsin was by the slimmest of margins (less than 25,000 votes), and even the most minor swing away could spell doom for him. However, an unfavorable national environment and the highly elastic nature of the state in many of Trump’s 2016 counties bode for some serious trouble — in such an environment, one can reasonably expect the president to sustain some losses in elastic areas like western and northeastern Wisconsin — counties like Marinette (E 73.4) and Trempealeau (E 70.7) are microcosms of the problems the incumbent may face. Republicans must prevent Joe Biden from coming close to Obama’s 2012 margin or to Sen. Tammy Baldwin (D-WI)’s 2018 margins if they hope to carry the state.

The Democratic counties of Dane (E 33.0) and Eau Claire (E 37.4) do have some degree of elasticity, but there is reason to believe that this is cause for Republican concern rather than celebration. Recall that elasticity gives us a proxy for the possibility of divergence from the average — this is to say, if a county is normally 15 points Democratic but with an elasticity of 50, the margin may rise or fall by a fair amount in either direction.

The problem? There is reason to believe that Democrats underperformed here in 2016 relative to what we can expect in 2020, and that the high elasticity of this state indicates a fair amount of room to grow for the party. The elasticity of these areas is largely due to the sharp Democratic improvement in 2018, showing that more latent Democratic votes exist than previously thought possible. For example, while Clinton won Eau Claire and Dane by 7 and 47 points, respectively, now-Gov. Tony Evers (D) won them by 12 and 51, and Baldwin by 22 and 55. In a national environment that more closely mirrors 2018 than 2016, it is reasonable to expect that the voters who swung from Trump to Baldwin/Evers may stick with that switch.

Republican problems in the state are further compounded by the problems they may face with keeping votes in the suburbs. Racine (E 30.0) and Kenosha (E 37.5), both counties Obama won in 2012, were won by Trump in 2016, but Baldwin won them in 2018, and they are elastic enough to allow for further erosion among Trump’s base. The Waukesha (E 33.7), Washington (E 30.0), and Ozaukee (E 42.7) counties (WOW counties) are elastic enough to the point where Republicans could see losses of two to three percentage points in margin compared to 2016 — in fact, this would mirror Tammy Baldwin’s 2018 victory and build on signs seen in Jill Karofsky’s 2020 Supreme Court victory (Karofsky won as a Democratic-backed candidate in a technically nonpartisan race).

Such a margin may seem small, but it is, in fact, one of the biggest red flags for the party in 2020; a moderately elastic area with 350,000 votes is far more dangerous to a statewide margin than a highly elastic area with 40,000 votes simply due to the sheer volume of votes in the former. If just 4% of voters swung away from Trump’s 2016 margin in the WOW counties, the Democrats would carry the state by merely maintaining their margins elsewhere.

Because of the national environment, however, continued improvement in the WOW counties combined with Democrats regaining the ground they lost in 2016 in western Wisconsin is an entirely plausible scenario.

Florida

Map 2: Florida elasticity by county, 2012-2018

Florida is the perfect example of a turnout-based swing state. In a state like this, swing voters are actually relatively sparse, and statewide control goes to the party with the better organizing and ground game.

Notice that elasticity tells us the proportion of voters in a county that have shown an openness to voting for either party across elections. This gives us a proxy for how reliable a party’s support base is and whether they can expect any defections from their expected strength across the set of elections. This is especially important for a state as closely contested as this one.

In Florida, the Democrats would be gladdened to see that their support bases in Broward (E 11.2) and Miami-Dade (E 21.3) are very and moderately inelastic, respectively. This means that their support is locked in and that there is little room for them to fall in these areas. Obama won the state twice thanks to an incredibly strong turnout machine that brought base voters to the polls in droves, and Biden will seek to replicate that.

The Republicans, however, cannot feel as safe. The Florida Panhandle, a reliably Republican area, is one of the more elastic parts of this turnout-based state, and the trio of counties above Tampa Bay (Pasco, Hernando, and Citrus) are fairly elastic counties that are generally double-digit Republican strongholds — in fact, Hernando (E 46.3) and Citrus (E 41.1) are two of the five most elastic counties in Florida. If Biden is able to siphon away a significant amount of votes from these areas, holding the state would become an incredibly tall ask for Republicans.

North Carolina

Map 3: North Carolina elasticity by county, 2012-2018

Another turnout-based swing state, North Carolina is a state that skews more Republican than the rest of the nation, but with enough Democratic voters to stay competitive. Flipping this state will be the tallest order for Democrats in 2020; however, it is once again important to note that the traditionally Democratic areas in central, southern, and northeastern North Carolina are far less elastic than the Republican base in the southeastern and western part of the state. Outside of the eastern part of the state, the only truly elastic county is Robeson County (E 53.8) — despite being a historically Democratic stronghold, Trump managed to win the county by four percentage points only four years after Obama carried it by 18.

However, although Democrats can expect to regain a fair amount of the ground lost in 2016 in these areas under the current national environment, flipping the state on swing voters alone in these areas will be a nigh-impossible task. Although a bluer-than-usual national environment may help them along the way, flipping North Carolina is a tougher task for Democrats and will rely on them turning out their voting base in the counties of Wake (E 29.7), Guilford (E 21.8), and Cumberland (E 15.3), and continuing the swing of suburban voters in Wake County (one of the few populous areas with any elasticity for them to capitalize on).

Arizona

Map 4: Arizona elasticity by county, 2012-2018

Arizona falls in the middle of the spectrum of states we are analyzing — although it is nowhere near as elastic as Wisconsin, it is certainly more elastic than Florida.

Pinal (E 47.3) and Maricopa (E 46.9) are the two most interesting counties to examine. Traditionally Republican, the high elasticity of these counties would be a source of concern for several Republicans, as they can ill-afford to lose too many votes here if they wish to hang on to the state at the Senate and presidential levels. Maricopa, in particular, is home to Phoenix and its suburbs and cast more than 1.5 million votes in 2016. The county includes significant pockets of white college-educated voters, a group that Biden has been making gains with in polls.

The exceptionally high elasticity of Maricopa, when combined with the sheer volume of voters present, makes this a particularly appealing target county for Democrats, as investment here could flip an incredibly high amount of voters, and Kyrsten Sinema used this to great effect in her 2018 Senate victory over Martha McSally. Conversely, a popular Republican incumbent with high approval ratings could see a victory like Gov. Doug Ducey’s double-digit win in the 2018 gubernatorial race; however, with current trends, this is increasingly unlikely for Republicans, meaning their focus needs to be on stemming the bleed of swing voters instead.

Conclusion

Elasticity is a metric that helps us quantify the voting nature of states and the reliability of a support base. We can see through this metric that states like Florida are turnout-based and have relatively little divergence in voting margins between elections on a county-level basis, implying a relative lack of swing voters. Meanwhile, states like Wisconsin are highly elastic, indicating the presence of swing voters that must be won over in order to carry the state. In conjunction with electoral trends and context, elasticity helps us gauge the possibility of latent votes for either party in the 2020 election and helps identify key areas that campaigns must target to carry the state.