How many swing states in 2017

A return to just the turnout or support levels of would not have produced a win for Clinton in Recreating turnout levels would only have resulted in Democratic wins in Michigan and Wisconsin, while support levels would have netted just Michigan and Pennsylvania.

Recreating white non-college-educated support levels would have produced a large and relatively secure Electoral College win for Clinton. Another important shift observed in was among white non-college-educated voters. In aggregate, Clinton lost significant vote share among this group compared to Obama— These vote shifts, combined with the strong clustering of white non-college-educated voters in the Midwest and Appalachia see Figure 5 , played a pivotal role in the election.

Taken together, some have argued that Clinton could have avoided a loss if she were able to maintain prior levels of support among white non-college-educated voters. It not only produces a large Electoral College win, but also one that is particularly robust. Several of the states that Obama won in —Pennsylvania, Wisconsin, Michigan, Iowa, and Florida—would have stayed Democratic under these conditions.

In the first four of those states this simulation produces relatively robust wins, the narrowest of which is 3. In contrast, Florida is once again an extremely narrow flip—a margin of about 5, votes making the difference between a Democratic and a Republican win. Notably, this simulation does not produce a Democratic win in Ohio despite the fact that Obama won that state in While this second simulation produces secure wins in several key states, this exercise says nothing about the difficulty of achieving it.

The first group represents individuals who identified as Democrats in and voted for Obama in the last election, but changed their party affiliation and voted for Trump or a third-party candidate in According to data from the Pew Research Center, there has been a sharp decline in the number of white voters without a college degree who identify as Democrats in the last 10 years.

The shift among white voters with some college was less dramatic—from a 4-point Republican advantage in to a point advantage in —but still represents a large change in such a short time period.

The second category is made up of individuals who have traditionally identified as or voted for Republicans but voted for Obama in While people have grown accustomed to thinking about those who voted for Obama in but not Clinton in as Democratic defectors, the reality is that some portion of these voters were really Republican defectors in and have now returned to their customary voting behavior.

Taken together, there is substantial reason to think that a good portion of these white non-college-educated voters were unlikely to vote Democratic in While additional resources aimed at reaching out might have resulted in smaller shifts, it seems unlikely that any discrete intervention in would have fully recreated the margins observed in Let us assume for a moment that the difficulties described above are real. What if Democrats, even with a concerted effort, had only been able to split the difference between the and support rates of white non-college-educated voters?

Under these conditions, Clinton still would have taken Michigan, Wisconsin, and Pennsylvania, and therefore won the election. The size of these wins is obviously smaller, but the narrowest is still a 1. Our simulation predicts that Clinton still would have carried these three Rust Belt states, with Pennsylvania now going Democratic by a very narrow 0. On its own, Latino support returning to its levels would not have altered the outcome of the election or the outcome of any state.

The simulation clearly has the biggest effect in Florida, but results in no Electoral College change. In many ways was about the U.

No demographic exemplified that more than Latino voters. According to our analysis, the percent voting Democratic declined 3. While no one has seriously argued that the election hinged on changes in Latino voting behavior, we found this simulation worthwhile to explore given the rising influence and unique electoral features of this group. As expected, our results suggest that a return to levels of support would not have resulted in a win for Clinton.

Geographically, Latinos can almost be considered an inverse image of white non-college-educated voters. While the latter had an outsized influence on the election because of their geographic distribution, the former punches below its weight. Latino voters tend to be concentrated in a relatively small number of counties in the country and those counties tend to be located in non-swing states. The three states with the largest percentage of Latino voters—New Mexico, California, and Texas—were uncontested in and probably will be for at least several more presidential cycles.

Of the next three—Arizona, Florida, and Nevada—only Florida is a true swing state, at least for now. That said, our simulation shows that even in this relatively high population state, Latino voters shifting back to their support levels would not have closed the gap for Clinton.

It still would have missed the mark by about 5, votes. State-level demographic changes were not pivotal in , but they did create conditions that were generally more favorable for Clinton. Absent any changes in the eligible voter population, several states that Trump won narrowly would have been much safer for him. The simulation results in no Electoral College change.

Demographics may not be destiny, but in the short term it is reasonable to quantify the effects of demographic changes on election outcomes. Our fourth simulation measures this very thing: What would the election look like if there had not been any demographic changes in the past four years? We held turnout and support rates constant, but fed them into the demographics that were observed back in The effect is nearly universal—the demographic changes observed since have created an electoral landscape that is slightly more favorable to Democrats.

Had the population somehow remained unchanged during this time period, we expect Clinton would have won the national vote by 1. While these changes did not prove pivotal in any state, the estimated effect is still rather substantial. In our seven states of interest, this simulated population stability would have resulted in margins even more favorable for Trump.

In Michigan, Wisconson, and Pennsylvania Trump could have achieved margins that were 0. Although these states were not close, Ohio and Iowa would have seen a similarly sized 0. However, North Carolina and Florida, two states undergoing relatively rapid demographic shifts, were the most affected by changes to their eligible voter population.

Absent these changes, Trump would have expanded his win by 0. Broadening our horizons slightly, a number of states were significantly more Democratic in than they would have been given a stable population. Georgia and Maryland were even more extreme, with margins around 1. In fact, only two places were Republican-advantaged as a result of demographic changes since Washington, D.

Both have black populations that are shrinking as a share of eligible voters while less Democratic-leaning voters who are white and college-educated, Latino, and Asian or other race are growing.

The demographic churn within these states creates a unique scenario: populations that are simultaneously becoming more racially diverse and less Democratic. The findings from these data and simulations suggest that many of the existing intra-Democratic Party debates about the path forward have missed the mark.

Rather than deciding whether to focus on 1 increasing turnout and mobilization of communities of color, a key component of the Democratic base, or 2 renewing efforts to persuade and win back some segment of white non-college-educated voters and to increase inroads among the white college-educated population, Democrats would clearly benefit from pursuing a political strategy capable of doing both.

If black turnout and support rates in had matched levels, Democrats would have held Florida, Michigan, Pennsylvania, and Wisconsin and flipped North Carolina, for a to Electoral College victory. So increasing engagement, mobilization, and representation of people of color must remain an important and sustained goal of Democrats. They cannot expect to win and expand their representation in other offices without the full engagement and participation of voters who are black, Latino, and Asian American or other race.

Given the fact that the white non-college-educated voting population is almost four times larger as a share of the electorate than is the black voting population, it is critical for Democrats to attract more support from the white non-college-educated voting bloc—even just reducing the deficit to something more manageable, as Obama did in and Likewise, the apparent shift to third-party voting and potential disengagement among younger voters must be considered carefully if Democrats are to make gains against Trump and Republicans in President Trump can conceivably reconstruct his primarily white coalition from with very few changes and still eke out a narrow Electoral College victory in But this assumes that Democrats do little to either increase the turnout of voters of color or to make inroads with disaffected white Trump voters, particularly Obama-Trump voters.

Alternatively, both Trump and Republicans could expand their electoral advantages among white voters by focusing and delivering on their economic promises on infrastructure, jobs, and wages and doing more to help people with health care.

Given the trajectory of the current administration, this seems unlikely and could actually lead to a schism and third-party split among Republicans. Rob Griffin is the director of quantitative analysis at the Center for American Progress. The authors would like to thank Lauren Vicary, Emily Haynes, Will Beaudouin, Steve Bonitatibus, and Chester Hawkins for their excellent editorial and graphic design work on this report.

For this project we developed original turnout and support estimates by combining a multitude of publicly available data sources. We did this in order to deal with what we believe are systematic problems with some of the most widely available and widely cited pieces of data about elections.

One of the underappreciated problems in the world of election analysis is that some of the most reliable sources of data available on demographics, turnout, and support do not play very well together.

For example, if we combine some of the best data we have on demographics with the best data we have on turnout, we find that they vary from the actual levels of turnout observed on Election Day. These estimates are fully integrated with one another and, when combined, recreate the election results observed in and Below is a more detailed description of how each was created. We started off our process by collecting detailed demographic data at the county level from the U.

The goal of this process was to produce reasonable estimates about the composition of eligible voters within a given county. Specifically, we wanted to know how many eligible voters in each county fell into each our 32 demographic groups.

For example, data on the race and age distribution as well as data on the age and education level distribution within a county are available separately. To overcome this problem we employed a two-stage estimation process. We then used iterative proportional fitting IPF to make these various pieces of data that are available line up with one another. IPF is a form of adjustment that allowed us to make individual group counts—for example, the number of eligible voters in a county who are black, 18—29 years old, and have a college degree—line up with known population margins—for example, the number of eligible voters who are black and have a college degree, the number of eligible voters who are age 18—29 and have a college degree, and the number of eligible voters who are black and age 18— At this point in the process we had estimates on the eligible voter composition of each county, but there were several notable problems.

First, the use of the 5-year ACS was necessary in order to get estimates for every county in the United States, but it provides a somewhat blurry image of the year in question.

Data from the 5-year ACS are an amalgamation of data from —, while data are from — In short, the ACS provides the necessary coverage but at the expense of giving us an accurate picture of the population as it existed in the year in question. Second, the IPF process tends to spread certain characteristics—say, citizenship—somewhat indiscriminately across groups so long as the totals line up with other margins.

This is particularly problematic for something like education groups where—outside of the non-Hispanics white population—we see different rates of citizenship. Third, the IPF process inevitably generates estimates that are logically consistent within a county given the margins that have been provided, but does not collectively add up to the number of people one can expect to belong to a given group in a state.

To address all three problems we included an additional corrective step. Using the individual-level data from the and 1-year American Community Survey, we could accurately estimate the real state-level race, age, and education level composition of eligible voters. Logically, the numbers of eligible voters who fall into our 32 groups in the counties must add up to the number observed at the state level.

We once again employed IPF to make the frequencies in the counties collectively line up with the frequencies at the state level. These were used as our final estimates for eligible voter composition in each state.

The process of creating county-level and turnout rates for each of our 32 demographic groups began by generating state-level estimates for these groups. Using data from the and November Supplement of the Current Population Survey, or CPS, we ran cross-nested multilevel models that estimated the turnout rate for each year, state, race, age, and education level group represented in the data.

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So a state like New Hampshire, with its four electoral votes, was critical in a tightly projected race like the contest was expected to be. Nevada and Iowa were also important states in , where President Obama gained enough small Swing State votes to lessen the importance of bigger swing states like Florida and Ohio.

With a clean slate in June , the Democratic and Republican presidential candidates have already started targeting some battleground states. Trump endorsed Marchant when he ran unsuccessfully last year for Congress. If elected secretary of state, Marchant said, he would seek to end all early voting and ban the use of voting machines temporarily while the devices are examined for evidence of election-rigging.

Marchant could not provide evidence of fraud in Nevada when asked for it in an interview. In Wisconsin, businessman and secretary of state candidate Jay Schroeder is considered the frontrunner for the Republican nomination. He said in an interview that "there is lots of reasonable doubt" as to whether Biden won the election. The secretary of state in Wisconsin, unlike most other states, does not oversee elections. Schroeder is campaigning to change that: He advocates for stripping election oversight power from the bipartisan Wisconsin Elections Commission and giving it back to the secretary of state, which controlled elections until a decade ago.

If he gets his way, he said, he would get tough with counties that don't follow the law: "I would call for an audit, and if the county refused that, I would not certify their results. Georgia is shaping up to be a key battleground, with competitive Senate, governor and secretary-of-state races next year. These elections will be a major test of whether Republicans who crossed Trump can survive primaries - and whether those who backed his election-fraud falsehoods can win general elections against Democrats.

With Trump's support, Hice is seen as the frontrunner in Georgia's Republican nominating contest. In the hours after the Jan. Bossie's group supported Raffensperger in but now condemns his failure "to fight for what the overwhelming number of Republican voters in Georgia were demanding, which was ballot integrity," Bossie said.

Multiple recounts and audits have confirmed Biden won Georgia by about 12, votes. Since the vote, Raffensperger and his family have been inundated with threats of violence, causing them to go into hiding at one point and to take other precautions, including starting their car remotely to guard against bombs, the Reuters investigations revealed.