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Racial Gaps in Voter Turnout Are Growing — and Undermining Democracy

The difference in turnout between white and nonwhite voters has soared since 2008, especially in regions once covered by strict Voting Rights Act protections.

• Arlyss Herzig

• Vote Suppression

A new  report from the Brennan Center  shows just how far we are from fulfilling the country’s promise of a democracy equally open to all Americans. The racial turnout gap — the difference between white and nonwhite turnout rates in elections — has been consistently growing since at least 2008, reaching 18 percentage points in the 2022 midterm elections. If the gap did not exist, nearly 14 million additional ballots would have come from voters of color that year.

And although the 2022 midterms saw the largest gap in recent years, the disparity in turnout isn’t new: the 2012 election was the only election in recent times to reach near parity between the turnout rates for Black and white voters, and at no point in at least 16 years has the turnout gap closed for any other group.

To better understand how the racial turnout gap has grown across the nation since 2008, we used nearly 1 billion vote records between 2008 and 2022. This voter snapshot data offers a better understanding of turnout compared to even the largest survey samples. The data also helped us observe how regional and socioeconomic factors influence the racial turnout gap. We found evidence that the ballot box reflects the voices of different racial groups at decidedly unequal rates, disproportionately uplifting white citizens.

While the turnout gap has never fully closed, it was smaller during the Obama presidential years than today. In presidential elections, the racial turnout gap has grown consistently since at least 2008 (the first election for which nationwide voter files are available). In 2008 the gap was 9 points, and by 2020 it had grown to 12 points. Things were no better in the midterms: in 2022, even with the  second highest turnout  for a midterm election in 20 years, both the white-Latino gap and white-Black gap were the largest they have been since at least 2010.

Importantly, these turnout gaps exist even when accounting for regional differences, income, and education. Though these factors account for some of the gap, they cannot fully explain it. White voter turnout has exceeded that of any nonwhite group since 2014 in every geographic region. While turnout tends to vary based on a neighborhood’s median household income and proportion of residents with a college degree, we still find racial turnout gaps in high- and low-income neighborhoods alike. The same is true of neighborhoods with high and low levels of education.

Myriad factors drive the nationwide racial turnout gap, like political disaffection, high rates of incarceration, inaccessibility at polling places, and language barriers, among other disenfranchising forces. However, the widening of the gap is occurring alongside a wave of restrictive voting laws going into effect. Within the past decade alone, almost  100 restrictive voting laws  have been enacted in at least 29 states. And the mounting attacks on democracy are not felt equally by all — when access to the ballot is restricted,  voters of color  are  more likely  to be  disenfranchised .

One key driver of the growth in the turnout gap in recent years is the Supreme Court’s 2013 decision in  Shelby County v. Holder.  The decision severely weakened the Voting Rights Act by gutting Section 5, which required federal approval for any changes to election rules and practices in areas with a history of racially discriminatory voting policies, a process known as preclearance. Pointing to Black turnout surpassing white turnout in covered states in the 2012 election, the ruling claimed that federal approval was no longer necessary to protect the right to vote.

The same day the Supreme Court announced its decision,  Texas  passed a discriminatory voter ID law that had formerly been blocked by the federal government.  Mississippi, Alabama, and North Carolina  soon followed with similar legislation that established voter ID and other restrictive voting laws aimed at Black communities. A federal court later  described  North Carolina’s law as “target[ing] African Americans with almost surgical precision.” The decision also allowed for many more local changes to go unchallenged. Prior to 2013, county administrators needed permission to move polling places, draw school board districts, and make other administrative and policy changes.

We use advanced statistical tools to prove the cumulative, negative effect of all the changes made in the aftermath of  Shelby County . As the chart below shows, the changes in the white-Black turnout gap moved in tandem in counties that were covered and not covered by Voting Rights Act approval requirements during the years before  Shelby County . After the decision, however, the gap has grown more quickly in the formerly covered counties. Though the racial turnout gap has increased everywhere since the  Shelby County  decision, it has grown almost twice as quickly in formerly covered jurisdictions than in similar, non-covered ones.

In fact, the white-Black turnout gap in formerly covered regions was 5 percentage points greater in 2022 than it would have been if the preclearance had applied, while the white-nonwhite gap was about 4 points higher. These gaps are especially significant in light of the fact that, according to Brennan Center research, since  Shelby County  was decided at least 62 Senate, gubernatorial, and presidential races in the states containing areas subject to preclearance have been decided by fewer than 5 percentage points.

We show that these effects were largest in counties where state  and  local practices were constrained by Section 5, as well as in counties that had racially discriminatory policies blocked by Section 5 in the years before the  Shelby County  decision. The effect is clear: the ruling cost hundreds of thousands of votes from voters of color in formerly covered counties in the 2022 midterm election.

Discriminatory voting policies and practices further remove us from America’s vision of a representative democracy. These laws target voters of color, likely increasing the racial turnout gap, impacting the ability of communities of colors to elect representatives that are responsive to their needs. Despite the Supreme Court’s view that racial differences in political participation are a thing of the past, this study provides compelling evidence that the  Shelby County  decision has accelerated this disturbing trend in democracy.

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Growing Racial Disparities in Voter Turnout, 2008–2022

The gap is increasing nationwide, especially in counties that had been subject to federal oversight until the Supreme Court invalidated preclearance in 2013.

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Republican Gains in 2022 Midterms Driven Mostly by Turnout Advantage

1. voter turnout, 2018-2022, table of contents.

• Other key findings from the study
• Political preferences differ a lot by race and ethnicity … and so does voter turnout
• Voters and nonvoters
• Voting methods in the 2022 election
• Gender, race and the 2022 vote
• Education and voting preferences
• Age and the 2022 election
• Party, ideology and the 2022 election
• Urban, suburban, rural voting in 2022
• Religion and the 2022 election
• Racial and ethnic composition of 2022 voters
• Rural, suburban and urban composition of 2022 voters
• Educational composition of 2022 voters
• Age composition of 2022 voters
• Religious composition of 2022 voters
• Acknowledgments
• The American Trends Panel survey methodology

American Trends Panel: Pew Research Center’s online probability survey panel , which consists of more than 12,000 adults who take two to three surveys each month. Some panelists have been participating in surveys since 2014.

Defectors/Defection: People who either switch their vote to a different party’s candidate from one election to the next, or those who in a given election do not support the candidate of the party they usually support. Also referred to as “vote switching.”

Drop off/Drop-off voters: People who vote in a given election but not in a subsequent election. The term commonly refers to people who vote in a presidential election but not in the next midterm. It can also apply to any set of elections.

Midterm elections: General elections held in all states and the District of Columbia in the even-numbered years between presidential elections. All U.S. House seats are up for election every two years, as are a third of U.S. Senate seats (senators serve six-year terms).

Mobilize: Efforts by candidates, political campaigns and other organizations to encourage or facilitate eligible citizens to turn out to vote.

Nonvoter: Citizens who didn’t have a record of voting in any voter file or told us they didn’t vote.

Panel survey: A type of survey that relies on a group of people who have agreed to participate in multiple surveys over a time period. Panel surveys make it possible to observe how individuals change over time because the answers they give to questions in a current survey can be compared with their answers from a previous survey.

Party affiliation/Party identification: Psychological attachment to a particular political party, either thinking of oneself as a member of the party or expressing greater closeness to one party than another. Our study categorizes adults as Democrats or Republicans using their self-reported party identification in a survey.

Split-ticket voting/Straight-ticket voting: Voters typically cast ballots for more than one office in a general election. People who vote only for candidates of the same party are “straight-ticket” voters, while those who vote for candidates of different parties are “split-ticket” voters.

Turnout: Refers to “turning out” to vote, or simply “voting.” Also used to refer to the share of eligible adults who voted in a given election (e.g., “The turnout in 2020 among the voting eligible population in the U.S. was 67%”).

Validated voters/Verified voter: Citizens who told us in a post-election survey that they voted in the 2022 general elections and have a record for voting in a commercial voter file. (The two terms are interchangeable).

Voter file: A list of adults that includes information such as whether a person is registered to vote, which elections they have voted in, whether they voted in person or by mail, and additional data. Voter files do not say who a voter cast a ballot for. Federal law requires states to maintain electronic voter files, and businesses assemble these files to create a nationwide list of adults along with their voter information.

The elections of 2018, 2020 and 2022 were three of the highest-turnout U.S. elections of their respective types in decades. About two-thirds (66%) of the voting-eligible population turned out for the 2020 presidential election – the highest rate for any national election since 1900. The 2018 election (49% turnout) had the highest rate for a midterm since 1914. Even the 2022 election’s turnout, with a slightly lower rate of 46%, exceeded that of all midterm elections since 1970.

While sizable shares of the public vote either consistently or not at all, many people vote intermittently. Given how closely divided the U.S. is politically, these intermittent voters often determine the outcome of elections and how the balance of support for the two major political parties swings between elections.

Overall, 70% of U.S. adult citizens who were eligible to participate in all three elections between 2018 and 2022 voted in at least one of them, with about half that share (37%) voting in all three.

Adults who voted in at least one election during the period divide evenly between Democrats and independents who lean toward the Democratic Party or Republicans and Republican-leaning independents in their current party affiliation (48% each). The subset who voted in all three elections are similarly divided (49% Democrats, 50% Republicans). Citizens who did not vote in any of the three tilt Republican by 46% to 41%.

Democrats outnumbered Republicans among the 8% of adult citizens who voted in 2018 and 2020 but not 2022 (55% Democratic, 40% Republican). A similar-sized group (8%) voted in 2020 and 2022 but not 2018, and this group’s composition tilts Republican (57%, vs. 40% Democratic). The 12% who voted in 2020 and opted out of both the 2018 and 2022 midterms were roughly evenly divided among Democrats (46%) and Republicans (43%).

Given the sizable number of intermittent voters and chronic nonvoters, as well as the fact that this group, collectively, is fairly evenly divided in partisan affiliation, both parties have plenty of potential supporters on the sidelines in any given election.

Race and ethnicity are fundamental dividing lines in American politics, with political preferences and electoral participation varying greatly by race and ethnicity.

In the current partisan alignment, Black, Hispanic and Asian voters are all majority Democratic groups, to different degrees, though Republican candidates have gained some ground in the past four years among Hispanic voters.

Black voters remain Democratic stalwarts, voting 93% to 5% for the party’s candidates for U.S. House in 2022. Hispanic and Asian voters clearly favored Democratic candidates as well, but by narrower margins: 60% to 39% for Hispanic voters, and 68% to 32% for Asian voters.

But White Americans are much more consistent voters than Black, Hispanic or Asian Americans. Compared with the national average of 37% who voted in 2018, 2020 and 2022, 43% of White citizens who were age eligible to vote in all three elections did so; just 24% did not vote in any of these.

Black, Hispanic and Asian adults lagged far behind, with 27% of Black, 19% of Hispanic and 21% of Asian age-eligible citizens voting in all three elections. Hispanic citizens were most likely to have not voted in any of the most recent three general elections (47%, compared with 36% for Black and 31% for Asian citizens ages 22 and older in 2022).

Differences by education

Over the past few election cycles, Republicans have gained ground among White adults who do not have a college degree, who make up 41% of eligible voters. This group is about average in its consistency of voter turnout, with 35% of those ages 22 and over in 2022 voting in 2018, 2020 and 2022, and 31% voting in none of these three elections. White voters without a college degree favored Republican House candidates 66% to 32% in 2022.

By contrast, White adults with college degrees vote at very high rates: 56% of those eligible turned out in all three elections and just 13% participated in none of them. College-educated White adults make up 24% of the eligible electorate but about a third of voters in 2022 (34%). White voters with college degrees had tilted Republican for several decades, but in the past four elections have favored Democratic candidates (52% to 47% in 2022).

The education gap in White voters’ preferences in 2022 was not apparent among either Black or Hispanic voters (the sample size of Asian voters without a college degree was too small to produce a reliable estimate). College-educated Black and Hispanic adults also voted at higher rates than Black and Hispanic adults without a college degree in each of the three elections.

A small education gap appeared in 2020 presidential preference among Hispanic voters: College-educated Hispanic voters preferred Biden by a margin of 69% to 29%, while Hispanic voters without a college degree preferred Biden by a somewhat narrower margin (58% to 39%). But no significant education gap in candidate preference was observed for Black or Hispanic voters in 2018 or 2022, nor for Black voters in 2020.

The upshot of racial differences in candidate preference and turnout patterns is that Republican candidates benefited from both the relatively large size of the White adult population without a college degree and their somewhat higher turnout rates compared with Black, Hispanic and Asian adults.

Growth in support for Democratic candidates among White voters with a college degree, along with the high turnout levels among this group, offset some of the growth in support for the Republican Party among White voters without a college degree. But college-educated White adults remain a smaller share of all eligible voters than White adults without a college degree.

The two most recent midterms – 2022 and 2018 – both featured unusually high turnout compared with nearly every other recent midterm election year. But the differences between those who turned out to vote in 2022 versus 2018 – and between those who did not vote – accounted for much of the difference in outcomes between the two elections.

The stark demographic differences between voters and nonvoters in 2022 are similar to those seen in previous U.S. elections.

Voters were much older, on average, than nonvoters. Adults under 50 made up 36% of voters, but 64% of nonvoters. This is very similar to the pattern seen in 2018 – although those under 50 made up a somewhat larger share of voters in 2018 (40%).

Turnout also differed by race and ethnicity.

Three-quarters of voters (75%) were White, non-Hispanic adults. But this group accounted for a smaller share (55%) of nonvoters. Hispanic adults and Black, non-Hispanic adults each made up 9% of voters, but slightly larger shares of nonvoters (18% and 15%, respectively). Asian Americans made up 3% of voters, and a slightly higher share (5%) of nonvoters. These differences are nearly identical to the patterns seen in 2018.

There are also large educational and income differences between voters and nonvoters. Adults with a college degree made up 43% of voters in 2022, but only 25% of nonvoters. Those without a college degree made up 56% of voters, but 74% of nonvoters.

As concerns over the COVID-19 pandemic declined, fewer voters reported having voted absentee or by mail in 2022 than in 2020.

In 2022, 43% of voters said they cast their ballot in person on Election Day. A somewhat smaller share (36%) said they cast an absentee or mail-in ballot, and 21% said they voted in person before Election Day.

In the 2020 election, held during the first year of the coronavirus pandemic, 45% of voters said they cast their ballots by absentee or mail-in ballot, while identical shares (27%) said they voted in person either on Election Day or beforehand.

As was the case in 2020, voters who supported Republican candidates were more likely to report having voted in person on Election Day than by other methods. About half (51%) of those who supported Republicans said they voted this way, while smaller shares said they voted by mail or absentee ballot (27%) or voted in person before Election Day (22%). In 2020, 38% said they voted in person on Election Day, while somewhat smaller shares said they voted by mail or absentee (32%) or voted in person before Election Day (30%).

Voters who supported Democratic candidates were more likely to say they cast absentee or mail-in ballots (45%). About one-third (34%) said they voted in person on Election Day and two-in-ten said they voted in person before Election Day. In 2020, a 58% majority said they voted by mail or absentee ballot, while just 17% said they voted in person on Election Day.

Reflecting these patterns, Republicans won a majority of votes among those who said they voted in person on Election Day, 60% to 38%. Democrats won – by an identical margin – voters who said they voted by mail or absentee ballot. Those who said they voted in person before Election Day were divided: 53% supported Republican candidates, while 46% voted for Democratic candidates.

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Voter Turnout Personalized Videos Created in PowerPoint

Oct 24, 2020 | Articles , Case Studies , DataPoint , Elections , Evergreen , Government , Non-profit

In this video, we show you how you can create personalized voter turnout videos using PowerPoint. The examples shown are for the US election, but will also work for any country, state, province, or municipal election. This could be used to increase voter turnout on a non-partisan basis or to improve voter turnout for your political party or to get people out to vote on a specific issue. The tool used in the video is DataPoint Enterprise edition.

Look at the video above to see how we created these personalized voter turnout videos and to see examples. So how did we do this?

Step 1: Gather Voter Turnout & Other Data

My first step whenever I am creating data-driven presentations or personalized videos is to gather the data I needed. In this case, my approach was to combine personal information about a voter with the issues important to them and then with the voter turnout numbers in their area in the last election.

The voter personal information and issues important to them could come from a poll or survey you did in their area or by having them fill out an online form such as a “Who should I vote for?” online quiz they could fill out and provide their name and email. 

In this case, I just put in some sample information. The names are made up and any relation to any real people in these areas is not intended.

The voter turnout information is readily available online and I grabbed the real data from a government website.

For simplicity’s sake, I put all the information in a single spreadsheet, but I could have easily had one spreadsheet with the voter information and issues and one spreadsheet with voter turnout and just combined them in the PowerPoint presentation. 

Here is what the spreadsheet looks like. 

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Voter turnout

What is 'voter turnout', measuring turnout, studying voter turnout, can reforms increase turnout, what if everyone voted, data sources, suggested readings.

Important legislation in the twentieth century, most notably the Voting Rights Act of 1965, has led to a long-term increase in the ability of Americans to participate in elections. The effects of other legislation intended to increase turnout, such as the National Voter Registration Act, have been more limited to specific administrative practices across states.

This explainer was last updated on April 28, 2021.

Because high voter turnout is considered a mark of a thriving democracy, policymakers and citizens often support electoral reform measures based on whether they will increase turnout, either overall or for particular groups.

Although the idea of voter turnout is simple, measuring it is complicated. And even if the number of people who voted in an election is accurately counted, it's often unclear what turnout should be compared to—the number of eligible voters? Registered voters?

Political debates often rage over whether particular reforms will raise or lower turnout, either overall or for particular groups. In the 2020 election particularly, the rapid changes in how elections were administered, due to the pandemic, resulted in particularly heated discussions over election reforms and their effects. 

Turnout can be measured in the aggregate by simply counting up the number who vote in an election. Data from the United States Elections Project (USEP) indicates that 159.7 million voters participated in the 2020 presidential election. While it was previously difficult to determine the number of ballots cast and instead had to rely on the most ballots cast in a “highest off” (i.e. the office with the most votes for a candidate), more and more states are reporting total ballots counted alongside the results of the election.  Nonetheless, in 2020, seven states (Kansas, Kentucky, Mississippi, Missouri, Oklahoma, Pennsylvania, and Texas) did not record how many people turned out to vote.    

With the number of voters determined, we can now discuss the selection of the denominator to calculate the turnout rate . Often, states and news sources will provide turnout numbers that use registration as the denominator. This results in inconsistent measurements across states due to inconsistent practices, policies, and/or laws around the maintenance of their voter registration lists. For a more consistent measure, it is better to use a measure that reflects the population of possible voters.

The easiest comparison is with the voting age population (VAP)-that is, the number of people who are 18 and older according to U.S. Census Bureau. However, VAP includes individuals who are ineligible to vote, such as non-citizens and those disfranchised because of felony convictions. Thus, two additional measures of the voting-eligible population have been developed:

• Citizen Voting Age Population (CVAP) , which is based on Census Bureau population estimates generated using the American Community Survey.
• Voting Eligible Population (VEP), which is calculated by removing felons (according to state law), non-citizens, and those judged mentally incapacitated.

The denominator one chooses to calculate the turnout rate depends on the purposes of the analysis and the availability of data. Usually, VEP is the most preferred denominator, followed by CVAP, and then VAP. The estimated VEP in 2020 was 239.4 million, compared to an estimated VAP of 257.6 million.

Figure 1 shows the nationwide turnout rate in federal elections, calculated as a percentage of VEP by the USEP , from 1980 to 2020. In addition to the variation across time, the most notable pattern in this graph is the difference in turnout between years with presidential elections ("on years") and those without presidential elections ("off years"). Elections that occur in odd-numbered years and at times other than November typically have significantly lower turnout rates than the ones shown on the graph. (The turnout rate in the 2020 presidential election was the greatest since 1904.)

Figure 2 shows turnout rates in the 2020 election for each state. Although there are exceptions, states with the highest turnout rates in presidential elections tend to be in the north, while states with lower turnout rates tend to be in the south.

Sometimes we want to measure the turnout rates of groups of voters, or study the factors that lead individual citizens to vote. In these cases, we need individual measures of turnout based on answers to public opinion surveys.

The chief difficulty in using public opinion surveys to ascertain individual voter turnout is the problem of social-desirability bias , whereby many respondents who did not vote will nonetheless say they did to look like good citizens. As a result, estimates of turnout rates based on surveys will be higher than those based on administrative records. (For example, 78% of respondents to the 2012 American National Election Studies survey reported voting, compared to the actual turnout rate of 58% as reflected in the graph above.) To guard against over-reporting turnout in surveys, some studies use voter registration records to independently verify whether respondents voted, but few do.

Even with the problems of over-reporting, public opinion surveys are usually the only way we can study the turnout patterns of subpopulations of voters, such as regional or racial groups. It would be safe to use these surveys if all groups over-report on whether they voted by equal amounts, but there is evidence they don’t.

One consequence of the secret ballot is the inability to directly tie demographic factors to an actually recorded vote. Instead, researchers have relied on Voting and Registration Supplement (VRS) of the U.S. Census Bureau’s Current Population Survey (CPS) to gather information about the demographic factors that affect turnout. The CPS is a monthly survey on employment and the economy. The VRS, which is administered every November in even-numbered years, asks respondents whether they voted in the most recent election. Because the CPS already has a rich set of demographic information about each voter and has been conducted for decades, this is often the best source of data

The dominant theory for why turnout varies focuses on a type of cost-benefit calculation as seen from the perspective of the voter. Voters balance what they stand to gain if one candidate beats another, vs. their economic or social costs of voting. Other scholarship has challenged this approach by showing that going to the polls is largely based on voting being intrinsically rewarding.

A long history of political science research has shown that the following demographic factors are associated with higher levels of voter turnout: more education, higher income, older age, and being married (see table below). Women currently vote at slightly higher levels than men.

Can particular election reforms such as Election Day registration, vote-by-mail, early voting, photo ID, etc., have an effect on voter turnout? Research results in most of these areas have been mixed at best. The one reform that is most consistently correlated with higher levels of turnout is Election Day registration (EDR) , although even here, there is disagreement over whether EDR causes higher turnout or if states with existing higher turnout levels are more likely to pass EDR laws (it’s probably a combination of the two).

What about the roles that campaigns play in stimulating voter turnout? This is most visible in presidential elections, where candidates pour disproportionate resources into campaigning in battleground states—those that are closely divided along partisan lines and thus are most likely to swing the result of the Electoral College vote. In 2020, the average turnout in the 8 states where the presidential margin of victory was 5 percentage points or less was 70%, compared to 59% in the nine states where the margin of victory was greater than 30 points. (For the states in-between, the average turnout rate was 68%.)

Field experiments to test the effects of campaign communications on voter turnout have shown that personalized methods work best in mobilizing voters and mass e-mails are virtually never effective in stimulating turnout.

We care about turnout levels for two reasons. First, they're considered a measure of the health of a democracy, so higher turnout is always better than lower turnout. Second, if we believe that lower turnout levels exclude citizens with particular political views, then increasing turnout would “unskew” the electorate.

Early research seemed to justify skepticism that increasing turnout in federal elections would radically change the mix of opinions among those who actually vote. However, more recent research suggests that voters in national elections are more likely to be Republican and to oppose redistributive social policies than non-voters. Differences between voters and non-voters on other issues such as foreign policy are much less pronounced.

When it comes to local elections, overall turnout rates tend to be much lower than elections held to coincide with federal elections, and the demographic characteristics of voters are much more skewed compared to non-voters.

American National Election Studies

The United States Election Project

U.S. Census Bureau—Voting and Registration

Aldrich, John H. 1993. "Rational Choice and Turnout."  American Journal of Political Science  37 (1):   246–278.

Green, Donald P., and, Alan S Gerber. 2015.  Get Out the Vote: How to Increase Voter Turnout . Washington D.C.: Brookings Institution Press.

Hur, Aram and Christopher H. Achen. 2013. “Coding Voter Turnout Responses in the Current Population Survey.” Public Opinion Quarterly 77(4); 985 – 993.

Leighley, Jan E., and Nagler, Jonathan . 2013.  Who Votes Now?: Demographics, Issues, Inequality, and Turnout in the United States . Princeton : Princeton University Press.

Silver, Brian D., Barbara A. Anderson, and Paul. R. Abramson. 1986. "Who Overreports Voting? "  American Political Science Review  80   (2): 613–624.

Riker, William H.,  and Peter C. Ordeshook. 1968. "A Theory of the Calculus of Voting."  American Political Science Review  62   (1): 25–42.

Wolfinger, Raymond E.,  and Steven J. Rosenstone. 1980. Who Votes? New Haven:   Yale University Press.

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Just 56 percent of eligible American voters in cast ballots for president in the 2016 election, and nearly two-thirds remained on the sidelines in the 2014 midterms.

With the November election approaching, a Harvard panel on Friday cited those and other statistics to highlight how low voter turnout remains a stubborn challenge to American democracy, while also suggesting possible solutions.

“The vote is the most fundamental act of American democracy and yet very few of us actually turn out to vote,” said Archon Fung, Winthrop Laflin McCormack Professor of Citizenship and Self-Government at the Harvard Kennedy School of Government.

Sponsored by the Kennedy School’s Ash Center for Democratic Governance and Innovation, the panel was part of HUBweek (Oct. 8‒14), the annual ideas festival co-founded by Harvard, The Boston Globe, Massachusetts General Hospital, and Massachusetts Institute of Technology.

Fung noted that in recent presidential elections, far fewer Americans voted for the winning candidate than cast no vote at all. And he said the U.S. ranks near the bottom of advanced nations with its voting rates.

“The equivalent of the moonshot for American democracy would be getting to 80 percent participation,” a goal the Kennedy School is promoting with other groups, Fung said.

“What if 80 percent of the adults in America actually voted? What would that be like and how could we get there?” asked Fung, who called voting not only a civic responsibility but critical to promoting equality, making government responsive, and restoring trust in government.

The panel included Archon Fung of Harvard (from left), Democracy Works CEO Kathryn Peters, Corley Kenna of Patagonia, and John Horton of Lyft. This year, Lyft is offering discounted rides to the polls, and partnering with nonprofits to offer free rides in underserved communities.

Policy initiatives could help raise voting rates, Fung said, but changing civic culture is also key.

“How can we get companies and schools and other kinds of organizations to think that it’s part of their civic responsibility to encourage full participation?” he said. Fung added that we also need to change the idea of voting “from a chore to a celebration.”

As an example of what organizations can do, Fung cited the Kennedy School’s commitment this year to get 90 percent of its voting-eligible students to register.

More like this

Corporate activism takes on precarious role

Kathryn Peters, co-founder and chief operating officer of Democracy Works, discussed her organization’s TurboVote web platform , which seeks to increase voting by providing people with voting information specific to their geographic area.

Because voting procedures vary across the country, Peters said, “With TurboVote, we’ve built effectively a voter concierge that lets anyone walk through and find the process to be as understandable and welcoming as possible.”

Peters said she has seen the value of online relationships in spurring people to vote.

“Snapchat deciding that they were going to be excited about voting this year is possibly one of the biggest stories in the 18- to 23-[year-old] new-voter demographic that I can think of,” she said.

Corley Kenna, senior director of Global Communications and Public Relations for Patagonia, described the outdoor retailer’s efforts to promote voting.

“Our company culture is a lot about being responsible stewards of our resources but also being responsible citizens and … one of the most important responsibilities that we have in our society is voting,” Kenna said. This election season, “We are talking to our community about how democracy requires showing up.”

As it did in 2016, the company is also closing its stores and giving employees Election Day off to send the message that “it’s more important to vote than to shop” that day, Kenna said. It also helped launch a campaign in which 300 companies have pledged policies that ensure that employees have time to vote.

John Horton, senior manager of East Coast community affairs for Lyft, outlined how the ride-hailing company became involved in boosting voter turnout.

“We’re all about solving problems,” he said, and since lack of transportation can be a barrier to voting, “we thought, here is a problem we can easily attack.”

Lyft this year is offering discounted rides to the polls, and partnering with nonprofits to offer free rides in underserved communities. The company is also closing part of its corporate offices on Election Day, and has registered 16,000 new voters.

Fung asked his fellow panelists what strategy they would advocate to meet the 80 percent voting goal. Peters said she would support Civic Nation’s #VoteTogether program, which throws parties during election seasons.

“That idea of having it festive, of civics not being dour, is really important,” said Peters, who would also like other states “to adopt a voting system that looks like Colorado,” which combines mail-in balloting and other measures to make voting easy.

Horton said educating people about where and when to vote, and setting uniform election dates, would be important.

Kenna said she would like to see a uniform, mail-in ballot system across the country and to make Election Day a national holiday where “we celebrate our democracy and the fact that it works when we vote.”

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Media Briefings

Trends in voter turnout

October 18, 2022

Journalists: Get Email Updates

What are Media Briefings?

More than   two thirds of American adults   voted in the 2020 presidential election—the highest   voter   turnout   rate this century. As we approach the 2022 midterms, political and special interest groups will be increasingly focused on “getting out the vote” among different demographic blocks. At SciLine’s media briefing, three experts discussed what the research says about demographic trends in   voter   turnout, including patterns specific to different age groups, the persistent gender voting gap, and additional complexities that arise at the intersection of gender and race. They also discussed voter-ID laws and other barriers that influence political participation among different populations and communities. Brief presentations were followed by reporter Q&A.

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High definition (mp4, 1920x1080)

Introduction

RICK WEISS: Hello, everyone. Welcome to SciLine’s media briefing on trends in voter turnout—a topic in the news with pretty big implications for what’s going to happen at the local and state polls in November. I’m SciLine’s director, Rick Weiss. And for those not familiar with us, SciLine is a philanthropically funded, editorially independent free service for journalists and scientists based at the nonprofit American Association for the Advancement of Science. Our mission is pretty straightforward. It’s just to make it easier for reporters like you to get more scientifically validated evidence into your news stories. And that means—again, as I often tell reporters I’m talking to, that means not just stories that are about science, per se, but really, any story that can be strengthened by some science, which is just about any story that we can think of.

Today’s briefing is actually a great example of that. You know, who turns out to the elections in November and how that affects the results is a political story, but social and political scientists have studied these dynamics, and their findings can and should inform your political reporting. So, I’m looking forward to hearing what these experts have to tell us today. Among other things you should know about SciLine before we get started is that we do offer a free matching service that helps connect you directly, one on one, to scientists who are both very knowledgeable in their fields and are great communicators. We do that for you on deadline or as needed. So, just go to the sciline.org website and click on I need an expert. And while you’re there, you can check out our other helpful reporting resources.

A couple of quick logistical details before we get started. We’re going to have three panelists who will make short presentations of just maybe six or seven minutes each before we open things up for Q&A. To enter a question either during or after their presentations, just hover over the bottom of your Zoom window, select Q&A, enter your name, news outlet and your question, and if you want to pose that question to a particular panelist, be sure to note that. A full video of this briefing should be available on our website by the end of the day today or early this evening. A time-stamped transcript should be up within a day or two after that. And if you’d like a raw copy of the recording more immediately than any of that, just submit a request through that Q&A box, and we’ll send you a link to that video by the end of today. You can also use the Q&A box to alert SciLine staff to any technical difficulties.

All right. To get started, I’m not going to take time to give full introductions to our speakers. Their bios are on the website. I just want to tell you that we will hear first from Dr. Jane Junn, who is the USC associates chair in social sciences and a professor of political science and gender and sexuality studies—everything you want to hear about today—at the University of Southern California. She’ll be speaking about voter turnout, gender gap and how that intersects with how race influences voter participation. Second, we’re going to hear from Dr. John Holbein, who is an associate professor of public policy, politics and education at the University of Virginia. He’s going to talk about age and voter turnout and how that relationship intersects with race as well and what’s known about what works to increase youth voter participation. And third, we’ll hear from Dr. Nazita Lajevardi who is an associate professor in the department of political science at Michigan State, and she’s going to talk to us about U.S. voter ID laws and other similar legislation in states across the country and what the research tells us about how these laws influence political participation, who’s most affected by them and some of the challenges, actually, to studying their actual impact. OK. Let’s get started. And over to you to start us off, Dr. Junn.

Gender and voter turnout

JANE JUNN: Great. Thank you. And I’m delighted to be here. Let’s just begin. I have a very brief presentation and just want to give you three things to think about—all the reporters, journalists in the room, just three things to think about when you consider the big and broader question of gender and voting in U.S. elections. That’s my email address there. And you probably also have it in the link where you can contact me.

So, I’ll give you the takeaways, and I’d like to give you three. The first one is that women are the modal voter—by that, meaning the biggest category of voters with respect to sex—in the American electorate. For example, in the last election—I mean, sometimes people find this surprising, but it’s been true now for quite a long time, really since the ’60s, particularly in presidential elections. So, as you all know, women gained the right to vote by constitutional amendment in the 19th Amendment, which was ratified and then became a part of federal law through the Constitution in 1920, a little more than a hundred years ago. And at that time, of course, they were zero part of the voting population for the most part, and now they make up 53.1% of the electorate—that is, people who come out to vote. That’s how we define the electorate. And that’s more than men, which are at about 47%—just under 40% of the electorate. And this is data from 2020’s presidential election, so not a midterm but 2020. But even in the midterms, what you’ll see is a similar distribution. And that’s even true at local levels and state levels as well. So, women are the modal voter, meaning the biggest category, and have been, outnumbering men in the electorate for many decades. These—and what’s relevant about that is, well, women voters are the most powerful group in the American electorate. Despite the fact that elected officials are disproportionately male, women are disproportionately a part of the electorate.

The second point I’d like to make is that the gender gap is a race gap. So, you all know that women overall support Democrats more than men, but that’s not true for all women. So, in other words, not all women support Democratic candidates by a majority. Instead—and you’ll see in the red highlight—white female voters support Republicans while women of color are very heavy Democrats. And this all begins to change when the gender gap appears in the 1980s. It changes because women of voters—women of color voters enter the electorate about 20 years after the Voting Rights Act and 20 years after the Immigration and Nationality Act are a part of federal law. And they carry white women with them, only making white female voters look to be more Democratic when, in fact, white female voters have voted majority Republican for president in every election since 1948, except for two. And we can talk about those, if you like. You can guess which ones those are. But—and the second point is that the gender gap is a race gap.

And the final point that I’d like to make—and this is related to gender and race together—that every election has a unique electorate. So, even if you’re looking at stuff—what happens in ’20, and you’ll say, well, it should repeat itself in ’22. Well, no, because every election is a unique electorate. And it’s important to make the following distinctions—that is, between voter turnout, which is driven heavily by mobilization efforts, and the distinction between voter turnout and what drives that and what drives partisan candidate choice. Second thing to distinguish that’s important are national patterns from states and localities. So, you might see California voting in a very particular way versus, let’s say, Georgia. And finally, contours of the electorate change over time. And in particular, these are related not only to age, as John will talk about in a moment, but also voter restrictions, as Nazita will talk about, but also the change in the population as a function of young people coming in and older people dropping out. And then in addition to that, perhaps the most important element of change in the electorate over time is the introduction of voters from diverse backgrounds as a function of immigration to the United States.

So, I’ll just give you a tiny little bit of data. It’s just a pie chart of the race and gender composition of the 2020 electorate. And you can see that white women are the biggest chunk of the pie, right? That’s a massive piece—looks like strawberry rhubarb pie. They’re a little bit pinker than the white men, who are the dark red category, because they’re a little less Democratic—I’m sorry, a little less Republican. And all the blues are Black women, Latina women, Black men. And that, actually—second to the last one on the legend should say Latino men and other. So, you can see that about a quarter of the population of voters—it’s the electorate—are voters of color. They’re heavily Democratic whereas whites are heavily Republican. And, in particular, white women make up the biggest slice of the pie. So, if you see advertising being directed at white female voters, that’s why. They’re the biggest slice of the pie.

So, the gender gap now—I just want to give you a brief way of—let’s look at 2020. This is vote-cast data. This is the kind of official data of exit polls conducted by the Associated Press and other associated media organizations, the National Opinion Research Center. What you’ll see here is those who voted for Biden—just look at the middle column, voted for Biden—46% of men, 55% of women, which yields a big gender gap, right? That shows that the difference between women’s and men’s support of the Democratic candidate is plus 9%, right? And you’ll see that just the reverse is true, where men are more supportive of Trump, the Republican, than for Biden. But the gender gap is nevertheless there. And as, again, you can see in the second column over which the share of voters—that women make up the largest share of voters in the population, 53 to 47.

Now having said that, you think, oh, well, you know, everybody’s—women are Democratic. But that’s not the case, right? So, this is just a disaggregation of the vote. A vote choice is given to exit polls, which is a great scientific study of voters on Election Day, whether they voted in person or by mail, for example. And it disaggregates the proportion of who voted for Biden versus who voted for Trump by race and gender together. And what you’ll see is whites, whether they’re male or female, voted for Trump by a majority, 59-52, men versus women. And then African American men, women, Latino men, Latina women and all others are heavily Democratic. And I think it’s a pretty clear indicator of the second point that I wanted to make, which is that the gender gap is a race gap.

Now, over time, the last thing I’ll call your attention to is the share of voters column. That’s the first column. You’ll see that white females, again, are the biggest share of the pie, and that over time what’s happened is that the proportion of voters who are nonwhite and, in this case, minority has increased. And this has gone up, you know, really dramatically since the 1980s. And that’s part of the reason why you see the gender gap appearing in the first place. That’s all I’ve got. I’d be happy to take your questions after the other presentations.

RICK WEISS: Fantastic—a lot of information there for us to mull and get questions around and, of course, a huge teaser about what are the two elections where white women went Democratic? We won’t—we’ll just let that dangle for a while. And I suspect we will get a question about that to satisfy people’s curiosity in the Q&A. Meanwhile, over to you, Dr. Holbein.

JOHN HOLBEIN: OK. Can you all hear me?

RICK WEISS: Yes.

Age and voter turnout

JOHN HOLBEIN: Perfect. So, thanks so much for being here. I’m going to be talking to you today about some of the age dynamics of voter participation. And all of these things that I’m going to be talking about either are included in my recent book, Making Young Voters , or built off of that.

So. in the United States, it has been sort of a truism that young people are less likely to vote than older citizens. So, this is true going back all the way to the 1980s and even further to the 1970s, when 18-year-olds first gained the right to vote. They have consistently—young people have consistently voted at a lower rate than older citizens. So, you can see here this difference between older citizens and younger citizens, with older citizens being on the top and younger citizens being on the bottom, and the gap between those two groups’ rates of voter participation being plotted with the black line. So, as you can see, in 2020, youth voter participation went up to a level that we haven’t seen in decades. But still, that gap between younger and older voters remained relatively constant within the span of about a 20 to 30 percentage point voter participation gap between older and younger citizens.

And so, you might say to yourself, you know, what is this in context to other places, or is this just a phenomenon that happens all around the world? And the answer is, it’s not. So, the United States’ gap between older and younger citizens is somewhat unique relative to other countries. And in fact, the United States stands out in kind of the bad way, having one of the lowest rates of youth voter participation in the world and one of the largest age gaps in voter participation that we have data on. One other phenomenon that you should be aware of when you’re writing about youth voter participation is that many of the cleavages that we see among all adults are already present when young people are coming to the polls for the first time. So, we can see large gaps in voters—the voter participation among young voters by socioeconomic status—measures of socioeconomic status, education and income and also by racial categorizations, as well. And these differences between white and minority young voters, high- and low-income youth voters and high education status young voters versus lower—they’re large. These gaps are quite large.

So, the question is, why don’t more young people in the United States vote? The conventional wisdom here is that young people are apathetic, disinterested and disengaged. So, I don’t have to provide you with a summary of what everyone has ever said about this, but all you need to do is go to Google and Google in millennials are, and you’ll see some search result kind of like this. They’ve often been called the Me Me Me Generation. And this is a common thread, going all the way back to the days of Aristotle and to our more contemporary democracy, where people lament how disinterested, how apathetic, how turned off to social issues young people are. And the problem is that—or, no, the implication of this, if we want to address youth voter participation, is to increase political interest—right?—to make them care more about who is running for office, to care more about politics or public affairs more generally. That’s the approach we would want to take. The problem is that this conventional wisdom is not accurate, or at least not an accurate reflection of young people.

So, what I’m plotting here for you are two measures of political interest that we have from the American National Election Study, a highly reputable political science poll that’s used for many different research questions. And as you can see, across time and across all years, young people have always been very interested in politics. And that is capping out almost at the highest level possible, with 90-plus percent of young people regularly saying that they’re interested in elections. They care about who holds office. They intend to vote. And in fact, I just looked up the statistic for 2022. Again, close to 90% of young people say that they are going to vote in the upcoming midterm elections. The problem is that they don’t, right? So, there’s this persistent gap, what we term in the book the follow-through gap, right?

So, this is the gap—the difference between good intentions or a desire to participate in politics and actually following through. And it turns out that young people are nearly twice—or more than twice as likely to not follow through on their intention to participate. So, it’s less about a problem about voter apathy. They say that they want to vote. They say they plan to vote. They say they care about the issues that are on the ballot, but they just don’t follow through. And there are a number of reasons why that’s the case. And these tie in to some of the solutions that we have in the—that are discussed in our book for addressing youth voter participation. So, on the one side, making voting easier increases the chances that young people will show up and follow through and actually cast the ballot and follow through on their intention, right? So, Nazita is going to talk a little bit more about some of those specific reforms. We believe the preview of what we find is that making registration, specifically, easier helps young people disproportionately.

And the second piece of this is trying to focus on civics education reforms that put a meaningful change or that change meaningfully the way in which young people approach politics. So, rather than hitting them over the head with facts and figures about American government and politics, mostly historical, and forcing them to memorize those facts, getting them more actively involved in a more active form of learning works to increase youth voter participation. So, we see this in models that were—are starting to pop up throughout the United States and elsewhere that are trying to rethink civics education. So, the Democracy Prep network of charter schools is trying a new model of youth civics education in the which young people are not just memorizing facts and figures about American democracy, but they’re getting involved in politics. They’re meeting with elected officials. They’re help canvassing to mobilize people who are old enough to register and to vote, even long before they’re able to do that themselves. And what we see is from good evidence-based research that that increases the chances that young people who are coming through those programs will vote later on in their lives. And it’s a fairly large increase that we see.

The second thing that schools can do as we’re rethinking civics education is to help them overcome this barrier of voter registration. So, we see that when schools do this, when they help their young people register—this evidence comes from a program called the First-Time Voter Program, which helped do that across the United States—that that increases youth voter participation as well. The final thing that I want to note here is something that I mentioned earlier—is that the choke point seems to be, for many young people, the voter registration step of the process. In our research, we interviewed many young people across the United States, and they said again and again, things like voter registration—it’s archaic. It’s complex. I’m worried about making mistakes. Many young people do make mistakes on their voter registration forms, and they see it as costly and time-intensive. So, many of the reforms that make voter registration easier that are on the ballot—we have less evidence about automatic voter registration, although I’m happy to talk a little bit about that. But from historical evidence we have, reforms that make registration easier, such as online registration, pre-registration of 16-, 17-year-olds and same-day registration increase the chances that young people will vote. So, with that, I’ll stop and say thanks and hand the microphone over to Nazita.

RICK WEISS: Fascinating and, again, a lot of great data there to work with—we’ll continue on over to Dr. Lajevardi.

Voter ID laws and voter turnout

NAZITA LAJEVARDI: Hi, everyone. It’s a pleasure to be here with you. I am going to talk a little bit about voter ID laws and voter turnout. So, I think it’s probably not surprising to those of us here today that voting is, of course, one of the most centermost tenets of American democracy. It’s important. It allows citizens to choose their leaders, to influence policy and ultimately shape the democracy in which they reside. It is also the case that, when citizens do not vote, representatives can ignore them. They can ignore their preferences. And so therefore, the laws that come about and that can regulate who can and cannot vote in the course—you know, in elections has been subject to debate throughout the course of American democracy. But it remains really central to our debate today about who can and cannot vote.

The reality is, of course, as we all know, that the United States has long excluded groups from participating in the franchise. I’m happy to talk more throughout history about these different types of barriers that once existed. But for now, we’re going to kind of jump ahead to the post-civil rights era where there was continued resistance, and there has been continued resistance that is aimed to curtail the voting rights of Black Americans in particular, but other racial and ethnic minorities. It is true that Black Americans and other racial and ethnic minorities have the vote, but there are efforts to reduce the influence of that vote. Whether intentional or not, we have to take a deep dive and have a look at those. The focus today, of course, is going to be on voter ID laws, which are a form of voter registration requirement. But there are many different ways in which we’ve seen resistance in this post-1965 era. You can think about gerrymandering at large elections, where polling locations are, etc., etc. But today, the focus is going to be on voter ID laws. Today, they represent one of the country’s most important barriers to voting and thus, I would argue, one of the nation’s most important civil rights issues.

So, voter ID laws—what are they? These are laws that require a person to provide some form of official identification before they are allowed either to register to vote or to receive a ballot, or to actually cast a ballot on Election Day. As of 2022, 35 states currently have a voter ID law in place. But the reality is, is that prior to 2006, no state actually required identification in order for your vote to count. These laws have proliferated across the country. They’ve become stricter. But the reality is, is that the types and severity of these laws, they vary by state. They vary in the years that they’re introduced. And so, they’re—you know, tracing them over time and trying to assess the impacts can be a bit tricky. We’ll get through the—we’ll get to the methodological issues in a moment. There is, of course, arguments on both sides. So, proponents of voter ID laws argue that these are warranted, that they do not really reduce the participation of citizens. And we should really be thinking about voter fraud here. They’re helpful in reducing voter fraud, and this could potentially be a widespread phenomenon. And so, these laws at least help to verify the identification of voters in an increasingly contentious election environment.

Opponents of voter ID laws argue that these are burdensome and that they are effective barriers, that they place material burdens on segments of the population and that, frankly, they operate like poll taxes because you do need means, in order to participate, to be able to get the right type of identification in order to participate in elections. There is a map of voter ID laws that’s currently in effect by state. I urge you to go to the link that I have in my slides here. They’re—the map is created by the National Conference of State Legislatures. What you’ll see there is that there are five categories of voter ID laws that they classify. You can have strict photo ID, strict non-photo ID, non-strict photo ID, non-strict non-photo ID or no photo ID law. The reality is, is that no state—no two state laws are identical. And there can be quite a bit of overlap across categories. So, the NCSL does provide really a simplified way of categorizing these laws. What you should know is that these laws vary typically on two dimensions. So, whether the state asks for a photo ID or whether it accepts ID without a photo as well, as well as what actions are available to the voter who does not have an ID. That’s an important component here. The strictest type of photo ID laws are laws that require registrants who are attempting to vote on Election Day to present a government-issued photo ID as well as qualifying identification at a time after they cast their ballot in order for their vote to count.

So, Bernard Fraga, in his book, The Turnout Gap , in 2018, he provides a really instructive example of how a strict photo ID law could be enforced in a state like Indiana. So, a voter in Indiana who doesn’t have the right type of ID who wishes to have a provisional ballot counted must within a week after the election visit the county election board in person and either produce photo ID or sign an affidavit indicating that they are indigent or have a religious objection to being photographed. The reality is, of course, that not all registrants have this type of photo ID. And scholars have spent a lot of time to try to identify and measure the impact of voter ID laws on turnout, especially minority turnout. And this is where my qualification comes out. We need to talk about methodology and voter ID laws. These laws are not being introduced in a vacuum.

So, Highton, 2017, notes that it is not possible to design and conduct an experiment where we randomize some states to have a strict photo ID law and some states to not have these laws. And as such, scholars are really trying to measure these effects of these laws in a number of different ways. These studies—they differ in their findings, in the samples that they look to, in their results and their methods. But on balance, most of them are generally discovering that the very, very strictest forms of strict ID laws—they do have negative effects on voter turnout.

Now, again, the literature is always evolving, and it’s always changing. But I think it’s important to put out there that we need to have many, many studies in order to evaluate how these laws are impacting voter turnout. I’m going to draw your attention to some of these studies. I personally have co-authored three studies that have looked at this question. Across all three, we find a negative effect on—a persistent negative significant effect on minority voter turnout. Our work is not alone. So, Fraga, 2018, also finds similar effects. I have a link here for you guys to have a look at these studies. This work is supplemented with surveys looking in particular states like Wisconsin or in other surveys like the Current Population Survey.

Fraga and Miller, 2022, a very recent paper that was just published, looks at Texas, and I think this is a very clever design. Texas was supposed to have their strict ID law go into effect, but in 2016 a court actually stayed that decision and allowed people without qualifying identification to cast ballots. But they had people fill out a declaration explaining why they didn’t have the right type of ID. And so, Fraga and Miller basically estimate that about 16,000 Texans would have been barred from casting a ballot in that election. And they find that these voters are more likely to be Black and Latinx. So, it’s not just that voter ID laws suppress turnout. There is other scholarship that matters here, too. It is also the case that minorities are less likely to possess valid forms of ID that are necessary to comply with these statutes. And so, the material burdens of actually acquiring an ID fall harder on them.

So, there’s a number of studies here that sort of delve into that. You’re free to have a look at those. And it is also the case that minorities are more likely than white Americans to be asked to present an ID at the polls. So, these laws are being applied differentially by race as well. It’s not just the existence of these laws. It is also the case that poll workers are interpreting them and executing them differentially as well. I’m just going to conclude about, you know, are we going to get voting rights legislation soon? I’m sorry to inform you that, no, we’re not. It doesn’t look so good. Hopefully the midterms will help us get some more movement, but it seems quite doubtful. I want to draw your attention to HR 1, as well as the John Lewis Voting Rights Act, that had made some efforts to try to strengthen our voting rights legislation. The John Lewis Voting Rights Act in particular was trying to restore Section 4(b) of the Voting Rights Act and was going to have a preclearance requirement for any change made to a voter ID law that was going to make ID law stricter. And so, that’s a bit of a depressing place to conclude. But I think it’s important to talk about the fact that, you know, in order to have a more representative democracy, you know, we have to consider the barriers that are enacted through these legal mechanisms.

What is being done well in press coverage of these issues, and where is there room for improvement?

RICK WEISS: Fantastic. Thank you, Dr. Lajevardi for a very data-rich presentation. I’ll remind reporters attending that these slides will go up on our website after the briefing, and you’ll be able to click on those various links and do that facilitated research that can help you with your story preparation. So, we’re going to start with the Q&A now. I’ll remind reporters that you can put your questions in the Q&A icon at the bottom of your screen. But while that starts to populate, I like to start things off here with my own question, which is meant to be something of practical helpfulness and value to the reporters attending. And I’d like to ask each of our speakers to answer the question of whether there’s something that they would point to from their experience looking at news stories on this topic that reporters could either do better than they’re doing now or maybe something that they’re doing great that you want to applaud them for. And why don’t we just go around the horn and see what kind of advice each of our speakers might have for reporters on this beat? Starting with you, Jane.

JANE JUNN: Great. Thank you, Rick. Looking forward to your questions. And I am going to talk about one of the things that I think reporters do really well, and that is talk to voters in the areas where they’re reporting from. I mean, as research scholars, most of us are—I mean, some of us are pretty close in to collecting data from individuals. But generally speaking, most of the data that you’ve looked at—and I think that’s true for all three of these academic presentations today—are based on large studies of, you know, thousands of people, and we aggregate them all into little data points, and then we give you slides which show points over time. What we lose in that is really the fine grain reasons for why people say that they do what they did and why they came out and all the very specific things that make politics interesting and make politics really about people and not just about national trends or what’s going to happen in a specific election or Supreme Court decision.

So, I think one of the things that journalists do exceptionally well is talk to regular people about what they think about politics and why they’re doing what they’re doing. And without that, I think that we would be much poorer off for it as scholars. I rely heavily on media reports from voters. I mean, I talk to as many people as I can, but it’s not nearly what we can cover across the whole 50 states and cities, rural areas and the United States as a whole. So, hats off to you for bringing us the truth from ordinary people about what they like and don’t like about politics.

RICK WEISS: Great. Thank you. John.

JOHN HOLBEIN: Great. Thanks. Yeah. I would just echo what Jane said, that it’s really, really valuable to get these stories, especially of young people and the challenges that they face, and I rely on that as well. I think one of the challenges about reporting on youth voting participation is that many of the things that influence and shape whether or not young people are going to cast the ballot are happening in between elections, right? So, they’re being exposed to civics education curricula. They’re being exposed to voter registration systems and have to—they have to navigate. They’re moving. They’re—you know, the things that are happening to young people don’t start and stop with a political campaign. And yet many of the—most of the attention that we get towards youth voter participation being so low is only something that comes up every four, two years. Whereas the challenges that are standing in the way of young people are ever-present.

The other thing is kind of low-hanging fruit, and it’s kind of a—it’s a caricature. But we do see this narrative show up that the reason why young people vote is that they just don’t care, that they’re just apathetic. They’re not interested in politics—that there’s just something fundamentally, morally wrong with the levels of how plugged-in young people are to politics. And that just doesn’t square with the data. So, I would say that those two things are two areas that could be improved. I realize the timing one is difficult because when readership cares about this is when elections are happening. But in as much as we can think about youth voter participation more than just during elections, but as a fundamental structural issue that we face in American politics, it will be to our benefit.

RICK WEISS: That’s some advice that should go to editors in addition to reporters. We’ll see if we can send some of that upstream. Nazita, over to you.

NAZITA LAJEVARDI: Thank you. Yeah, I guess, I think a lot of the reporting on voter ID laws sort of echoes what politicians and what political elites have to say about them. And any sort of reporting that does turn to scholarship often focuses on one study. And I was trying to make clear that there is a diversity of studies out there and that, you know, as scholars, you know, we’re sort of really trying to understand this issue, but it’s going to take many studies, you know, in order to draw more firm conclusions.

And so, I would urge journalists to have a look at expert testimony, actually. I think that’s where really good summaries and literature reviews of the impacts of voter ID on a number of different issues can be found. These laws are being litigated in courts. They’re being litigated in, you know, Congress right now. And there is so much expert testimony out there that’s just available. And I think that’s a really great way and resource for journalists to be able to understand and get, like, a good grasp of where the scholarship is at and really to sort of build on the totality of the circumstances and draw conclusions and maybe sort of focus a little bit away from the partisan debates at the elite level and look more at the scholarship in a more holistic way. I guess that would be my suggestion.

What factors influence youth voting patterns, other than ease of the voter registration process?

RICK WEISS: Great. Thank you. We’ll get into some questions now from our reporters. I’m going to start with one from Claudia Flisi, a freelancer based in Virginia. This is directed to you, Dr. Holbein, but others may want to chime in after. You say that U.S. youth are anomalous in their voting patterns compared to other countries. You also said that making voter registration easier would help, but voter registration is no easier in other countries. I vote in Europe as well as the U.S., and voting is harder than it is in the U.S. So, easier registration is not necessarily the problem or the solution. Can you address what else might be?

JOHN HOLBEIN: Thanks for this question, Claudia. This is a great, great point that I’m glad you brought up. I didn’t mean to say in my comments that voter registration was the only thing that caused this. And so, when we look at differences across countries, it’s very difficult to ascribe one factor as being the driving force. It’s—just sort of building on what Nazita was saying is we can only learn so much about the influence of, in this case, voter registration laws by looking at cross-country comparisons and raw voter participation rates. That can only tell us so much because there’s differences. Countries are different in many ways aside from their voter registration rates, and looking at it at that level can sort of skew our perception of the effect of voter registration law.

So, what we find is when we look within the United States, when voter registration laws do change in the easier direction, we see young people follow by increasing their rates to the polls. So, when we see these changes happening, there is a change. But this is a really good point. Young people don’t vote because of voter registration laws. They don’t vote because of the other things that are going on in their life. They’re highly mobile. They tend to be moving around a lot for college or work. So, there are other factors that are at play here, for sure.

Are poll workers being trained in their state voter ID laws?

RICK WEISS: Great. Here’s a question from Ian Stewart from VPM Public Media based in Richmond directed to you, Nazita. Do you know if poll workers are being trained in their state voter ID laws?

NAZITA LAJEVARDI: Yeah. So, I think this varies state to state, but my own observations are that they are being trained, but they are sort of also learning on the fly. The trainings are not very extensive, and people are also trying to understand how to apply these laws almost case by case. You know, situations present themselves that they haven’t necessarily been trained for, and they sort of kind of come together and try to understand and interpret the law together on Election Day. So, there is a lot of variation between states, but also by polling location. And so, I think—also I should just be clear, I think, of course, poll workers have the best of intentions, but who they apply the laws to do vary differentially.

Do issues like climate change or reproductive rights affect voter turnout?

RICK WEISS: Jane or John, anything to add on sort of implementation of voter laws right now? OK. Not your area. Let’s go here to Jacob Fischler from the D.C. bureau of States Newsroom. I’m curious about how particular issues, especially climate change, but other issues perhaps are affecting voter turnout. Is there any evidence that climate change is having any effect on turnout this year? How does that compare to previous election cycles? Are there—are other issues that are particularly salient this time around? And I think in particular, we can’t help but ask about whether the recent Supreme Court decision on reproductive rights might be driving increase in participation by women. Jane, is there anything you’ve got on that dynamic, to start?

JANE JUNN: Yeah, sure. I’ll start. I think it’s—and then maybe others can jump in about climate change—in particular climate change being an issue which is of high importance to young voters, in particular—many voters, but young voters in particular. Let’s talk about Dobbs, and that is the Supreme Court’s overturning of the precedent—privacy precedent set in Roe v. Wade for a woman’s right to choose, abortion in particular. And I think the jury is out about whether or not this will motivate either turnout or support for candidates who are not supportive of what the Supreme Court did.

But it’s important to remember a couple of things. I would caution against that kind of sentiment that—I mean, clearly, the Democratic Party thinks it’s going to work, and the Republican Party thinks it’s going to work in the opposite direction because many of the latter’s candidates are running by supporting the overturning of Roe and in support, in other words, of Dobbs—and running on that, along with state laws, which would then—I mean, let’s be clear that what the Supreme Court said was that it’s not a federal right and that any rights that would be—that would pertain to a woman’s right to choose reproductive freedom would then be based in states. But the Democratic Party, as well as many Democratic candidates, seem to be running on this. But I think that’s important to make a couple of points.

No. 1, abortion rights or reproductive rights more generally are not something that is a gendered issue. So, there’s a misconception that women are much more strong supporters of abortion rights. That’s not the case. There aren’t substantial differences between men and women. What you will find are differences in particular between older voters and younger voters, with younger voters having always grown up with the—with this particular right at the federal level maybe finding it to be more shocking. But on the basis of gender, there aren’t huge differences between men and women on this issue. And I think the second thing to keep in mind is that it may not be the most important issue. It could be very important to some people who talk to reporters and candidates who talk to reporters, but it’s not the most important issue for everyone. There could be other ones that are more important, whether it’s climate change or inflation or student loan debt or—among others. And so, I think while it is—I think it’s somewhat sobering to think about it in this way, it’s necessary to be reminded of the facts because this is a science briefing, and the facts don’t support the notion that all women are in support of abortion because if they were, they wouldn’t vote Republican.

RICK WEISS: Great points. John or Nazita, anything else about sort of issue-based voter turnout, maybe climate change in particular?

JOHN HOLBEIN: When I think about this issue of, like, will a curtain policy issue sort of solve our problems of low and unequal voter participation in the United States, it’s something that comes up every election cycle, it feels like. So, maybe my answer is a little bit too jaded here, but it seems like, in a way, we’re trying to have the political issues of the day save us from what are actually structural problems that are stopping people from casting a ballot. And so, I think these things can matter in the margins. I think we’ve seen, with young people especially, a lot of excitement and sparked interest in protests, obviously, around climate, not just in the United States, but elsewhere. And it’s possible that that will translate into increased voting. But it’s also possible that it won’t.

The conversations that we’ve had with lots of young people who are sparked to protest based on climate inaction would suggest that many of them see voting as futile. So, it’s—you know, it’s a real tension, right? Many young people say that they care about politics and they intend to cast a ballot and that—those numbers are already pretty high. And so, I would categorize this, like, issue-based approach where we hope that whatever is exciting in the next political campaign would save us from the low and unequal rates of voter participation to matter on the margins, but not necessarily to fundamentally alter the patterns that we see in American democracy for decades. So…

With new voting reforms in Florida, what might voter turnout look like among Hispanic voters and Black women voters in the state?

RICK WEISS: Thanks. We’ve got a question here that I think might be good for you, Nazita. This is from Antonio Fins from the Palm Beach Post. Florida approved new restrictions and reforms on voting, but Hispanic voter registrations have surged in Miami. And a Black woman candidate will top the ballot here for the first time ever. Any idea what voter turnout among these two critical bases will look like in November?

NAZITA LAJEVARDI: You know, Antonio, I don’t know. I think that’s just as honest as I can be. I think that looking at how Florida has, like, historically suppressed the vote—and with the changes in voter reform, you know, I think we’re all kind of looking to see what voter turnout will look like in Florida, especially among, for instance, former felons, you know, who are now having access to the ballot. So, you know, I think we can also be sure not to necessarily think that, you know, increase in Hispanic voter registrations necessarily means Democratic voters. I think we need to be careful in how we predict the subset of voters in different areas are going to turn out and for whom. And so, I guess I would ask Jane or John if they have any additional thoughts, but I’m not sure I have much more to offer.

RICK WEISS: Asking scientists to speculate is always a losing proposition. But if either of you want to add to that. All right, we’re not…

JOHN HOLBEIN: I mean, this is always the challenge, is we rely on historical data to try and study these topics of issues. And it’s hard in real time to know for sure. So, it’s a general phenomenon. I understand your frustration.

Is there a correlation between presidential visits to a state and the level of voter engagement in that state?

RICK WEISS: Here’s a question from Scott Morgan from South Carolina Public Radio. Is there any data that shows a correlation between frequent visits by a sitting president to a state and the level of engagement in that state among the president’s party’s voters? A lot of fuel is spent traveling around to states. Is it paying off?

JANE JUNN: I don’t know. Well, I mean, I think—are you—I mean, Scott, I wonder if this is in the context of natural disasters, maybe, because there are more presidential visits to places that have natural disasters. And my guess is that because—precisely because of the natural disaster, that would suppress turnout just because there’s been a natural disaster, right? You don’t need the polling place. You don’t have a place to live, whatever. But I’m not sure. I think the—I mean, we would call this an indigeneity problem because their frequent visits are driven, possibly, by the closeness of the election. So—or rather, you know, that a candidate that needs help, let’s say, running for governor or the Senate from the president of the United States—I mean, I think it—I think that I—my guess is no. But—and if there would be an effect, it would be a pretty small effect because that indicates a close election. Otherwise, it would be unnecessary. It’s like, Trump didn’t need to go to Alabama. He was going to win Alabama. Alabama was going to be won with the exception of Doug Jones during that one special. But, you know, other than that, I think a frequent visit would either be something related to a natural disaster, which would probably depress turnout, or it would be related to a close election, which it would be hard to then disentangle the effect of the frequent visits from the close election itself.

RICK WEISS: Great. Absolutely.

JOHN HOLBEIN: There was a recent—oh, sorry, Rick, to talk over you. There’s a recent study that confirms what Jane was saying by Alan Abramowitz and Costas Panagopoulos in Presidential Studies Quarterly of 2020. They looked at the effect of President Trump’s visits during the 2018 midterm elections and finds a very small, non-significant effect. And they speculate that, you know, in this context, the polarization that—many other things swamp the effect of campaign visits such—that would come along with rallies or visits, things along those lines. So, that’s in Presidential Studies Quarterly . It’s called “Trump On The Trail.” That’s the name of the article, in case you’re interested in digging a bit deeper.

NAZITA LAJEVARDI: A part of me wonders if those visits would matter more in primary elections, you know? When there’s two candidates in the same party, then maybe those visits matter, you know, to lend credibility to one candidate.

Are there surveys of people who don’t vote, to shed light on why that happened?

RICK WEISS: Great point. John, there’s a question here directed to you. You mentioned 90% of young people say they will vote, but they don’t. There are exit polls for people who vote. Are there any surveys of those who end up not voting to shed light on why that happened?

JOHN HOLBEIN: Yeah. Thanks a lot for this question. This is probably something I could also toss to Nazita and Jane. But there are a lot of survey data out there. That’s—in the current era of political science and political research, there’s no shortage of data. So, this is something that we can answer. So, the American National Election Study has been run for decades. And it surveys voters and nonvoters. The Current Population Survey—the CPS, November supplement specifically—asks questions about voting, participation. And there’s nonvoters and voters in that sample. And then the Cooperative Election Study—formerly the CCS, now the CES, so the Cooperative Election Study—surveys both voters and nonvoters. And all three of those have a rich array of data on characteristics of individuals and their context that they live that could be explored to look at differences between voters and nonvoters. So, that’s the ANES, the CPS and the CES.

JANE JUNN: I’ll add to that the Comparative Multiracial Post-Election Survey—CMPS—at UCLA. And the reason this one—so it’s got a similar set of measures. But the reason this one is so useful is because it oversamples populations of voters that are traditionally—have very small samples in national studies, such as Asian Americans, African Americans and Latinx. So, in a typical ANES survey, you might get a hundred people who are Black or Latino. But CMPS has several thousand in each one of those groups. And so, to get a better understanding of nonvoters and the difference between nonvoters and voters across the entire population of the United States—that is to say, all the races and ethnicities that are questioned there—I would refer you to the CMPS, Comparative Multiracial Post-Election Survey, at UCLA.

NAZITA LAJEVARDI: I could just add one more thing about the CMPS. It also has a module of young voters, so people—or young people who are not yet voters. So, I think they’re between 16 and 17 years old. And so, the same questions have been asked of them. So, if you’re interested in seeing kind of what are the issues that excite young voters, that’s a good place to go as well.

What are some potential solutions to increasing youth voter turnout?

RICK WEISS: Great. Another question here that, at least for starters, is directed to you, John, from Marie Cusick, from PBS NewsHour’s Student Reporting Lab, saying—noting that you mentioned rethinking civics education and democracy prep charter schools, making voter registrations easier, all as strategies to help youth register and vote. Are there any other solutions you’d like to add to increasing youth voter turnout that you can share?

JOHN HOLBEIN: So, there’s two—I like to group the set of interventions into two groups. One is sort of a longer-term change to civics. And one’s a shorter term or an easier approach. So, the easier—on the easier side of things, there’s been good research that suggests that providing young people with help registering to vote and providing them with voting systems demonstrations, taking a voting booth into the classroom and, you know, allowing young people to practice with it, see what it will be like when they’re going to cast a ballot—actually get that practical experience of what it would be like when they’re going to show up at the polls—that that matters, that that helps. And that’s a relatively low-hanging fruit type of intervention. In terms of, like, rethinking civics education, in our book, “Making Young Voters,” we talk a lot about, you know, this problem of low youth voter participation being driven by follow through, or a lack of follow through. And so, in the book, we talk a lot about skill development, helping young people develop the skills that they need to follow through in a given context where there will be obstacles that get in the way of them casting a ballot.

And so, many of these skills fall into the category of social and emotional or non-cognitive skills, the ability to persevere, grit, self—delayed gratification. These types of abilities are things that schools can foster and have in sort of pilot programs. And we’ve seen some really promising results for when those skills are developed. So, it’s social-emotional learning as sort of a longer—more focus on that in civics classrooms and in education more generally as a longer term strategy. But then also, sort of in the shorter term, schools can do a lot more to help familiarize young people with the voting process.

Has research shown that certain types of voter-turnouts efforts just don’t work?

RICK WEISS: Yeah. I think, as a parent, there’s also just browbeating your kid, like, get out and vote. I know I’ve been there for my teen. So, one last question I want to try to squeeze in, related, before we start to wrap up, and that is whether there’s been any research shown—this can be for any of you—for types of efforts that are intended to increase turnout but that don’t actually work. For example, do celebrity-driven campaigns targeting youth have an effect or not? Anything that you’ve seen out there that just is a waste?

JOHN HOLBEIN : I’ll let the other two panelists speak to this in their area, but I think the question—whoever asked this question has the right intuition because, you know, this sort of rock-the-vote approach for a—that has been taken for a long time to increase youth voter participation has limited—yielded limited results. So, I would say that this—that I categorize this in the, like, let’s get young people excited about voting. Let’s make voting cool or exciting. It’s like, well, young people already get it. They care about who’s going to run the—run their country, their state and local area. But it’s not so much about that kind of thing. The other thing that I would say is that many strategies that political campaigns use to get out the youth vote are not as effective. So, sending mailers or a variety of other light-touch interventions aren’t as useful as you might think. They help a little bit, but they’re not as helpful as you might think at mobilizing young people, specifically.

JANE JUNN: My favorite example of the mailer is there was some hypothesis that if you sent a postcard with Jesus on it, it might activate Catholic voters. And turns out, it didn’t matter. But the point is that there are a lot of things that we think might matter, like with your teenager trying to get them to study or get them to get up on time and vote or whatever, but part of the reason we don’t hear about them and we don’t know about them systematically is they’re not reported precisely because they didn’t work. And so, I think that when you see things that do work and consistently do, which is, essentially, mobilization by people that you know and that you care about because so much of how many celebrities will say something or put something out on TikTok, the one time that it might work is when we focus on it. But there are probably many, many others that don’t.

And I just wanted to add one thing about the focus on—earlier in my career, a long time ago, I worked on civic education many, many years ago. And one of the things that kind of bugs me about the way the conversation is, is there’s something wrong with them. There’s something wrong with the students. It’s quite possible if you look at all of—we need to consider the other side. All the solutions are geared at people, young voters, like, get out there and vote. Maybe they don’t have a reason to do it. And more of our focus and at least more of our critical ability should go toward why is the system, itself, so ponderous, so difficult, so unresponsive? That just completely gets, you know, elided. All of our focus goes on trying to fix these people who don’t vote. In many ways, they have a reason for not doing it because the government doesn’t respond to them.

And so, I think political scientists and journalists alike, all political observers, ought to try and more focus on the delivery of the provision of services and the representation of elected officials in government, which doesn’t provide an incentive for people. I’m not saying we shouldn’t focus on making people better civic citizens, but at the same time, we need to focus on making the government a better place where people want to participate.

What is one key take-home message for reporters covering this topic?

RICK WEISS: That’s a fascinating, very interesting point to add. And, you know, one thing that kids pick up on very well is whether something is serving their needs or is sort of blowing smoke. So, very interesting. Nazita, anything to add to this topic before we do a final round? Great. OK. Well, one thing I like to do at the end here is to just go around the horn one last time to get some take-home points for reporters, perhaps the most salient and quotable things we can give you in a briefing. I just want to remind the reporters before we start to wrap, that as you log off today, you will see a short survey pop up. It takes nothing longer than 30 seconds to fill it out, and it really helps us keep designing these briefings to be useful to you structurally and the topics that we cover. So, please take a moment to do that. And let’s go around and hear from each of our speakers just one or two real take-homes that you want to leave reporters with that are most important, that you want to make sure they don’t forget as they walk away today. And we’ll start with you, Jane.

JANE JUNN: I think I’d just reiterate the points that I started with, and that is that women—there are more women voters out there. So, if you see advertisements that look like laundry detergent ads, that’s why. Because women voters are the most powerful voters in the American electorate; that the gender gap is really a race gap; that white women are Republican voters, not Democrats, and women of color only make them look like Democrats; and finally, that each election is a unique electorate. And so therefore, you got to roll with the dynamism, embrace the dynamism, rather than being afraid of it.

RICK WEISS: Thank you. John.

JOHN HOLBEIN: Young people want to vote. They care about the political process. But there’s many things that get in the way. And anything we can do to make that process easier is going to pay big dividends for the next generation of Americans.

RICK WEISS: All right. Nazita.

NAZITA LAJEVARDI: Minority voters are suppressed by voter ID laws. We have to think about why minority voters are less likely to possess valid types of ID. And even when they’re at the polls, they’re being asked to present ID more than others. So, I think we should think about all the aspects of these laws on hampering turnout.

RICK WEISS: Fantastic. This has been such an informative and interesting briefing. Thank you so much for the generous contributions from our three panelists today. Thank you, reporters, for attending and for doing the work you’re doing to spread this kind of evidence-based information throughout your news stories as the election approaches. Please do visit our website at sciline.org. Follow us on Twitter—@realsciline. And we’ll see you again soon at our next SciLine media briefing. Thank you all.

Dr. John Holbein

University of Virginia

Dr. John Holbein is an associate professor of public policy at the Frank Batten School of Leadership and Public Policy at the University of Virginia. He studies political participation, democratic accountability, political representation, discrimination, and education policy. His book, Making Young Voters: Converting Civic Attitudes into Civic Action , explores why youth voter turnout is so low in the United States and outlines ways to help solve this problem.

Declared interests:

Dr. Jane Junn

University of Southern California

Dr. Jane Junn is a professor of political science at the University of Southern California. She has been the vice president of the American Political Science Association and a Fulbright Senior Scholar. Dr. Junn was a member of the Social Science Research Council National Research Commission on Elections and Voting and a member of the National Academy of Science Committee on the U.S. Naturalization Test Redesign. She was the director of the USC- Los Angeles Times poll during the 2010 California election. She is currently at work on a new book on the “gender gap” and voting in the United States.

Dr. Nazita Lajevardi

Michigan State University

Dr. Nazita Lajevardi is an associate professor of political science and attorney at Michigan State University. Her work focuses mainly on issues related to American political behavior and public opinion through the lens of racial and religious identity, and she has paid specific attention to voting rights. She is the author of numerous books and articles, and her scholarship has been featured in the popular media .

Dr. John Holbein slides

Dr. jane junn slides, dr. nazita lajevardi slides, more from social sciences , elections.

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Correlates of Voter Turnout

• Original Paper
• Open access
• Published: 20 May 2021
• Volume 45 , pages 607–633, ( 2023 )

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• Richard W. Frank   ORCID: orcid.org/0000-0003-4382-1039 1 &
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Despite decades of research, there is no consensus as to the core correlates of national-level voter turnout. We argue that this is, in part, due to the lack of comprehensive, systematic empirical analysis. This paper conducts such an analysis. We identify 44 articles on turnout from 1986 to 2017. These articles include over 127 potential predictors of voter turnout, and we collect data on seventy of these variables. Using extreme bounds analysis, we run over 15 million regressions to determine which of these 70 variables are robustly associated with voter turnout in 579 elections in 80 democracies from 1945 to 2014. Overall, 22 variables are robustly associated with voter turnout, including compulsory voting, concurrent elections, competitive elections, inflation, previous turnout, and economic globalization.

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Introduction

A common challenge in the study of comparative politics is balancing theoretical and empirical comprehensiveness with substantive importance. Consider voter turnout. If we ask what the most statistically significant and substantively important predictors of national-level voter turnout in democratic elections are, even after more than 50 years of comparative voter turnout research, there are few certainties beyond the fact that compulsory voting increases turnout. For example, several studies including Radcliff and Davis ( 2000 ) find larger district magnitudes increase turnout while others like Tavits ( 2008 ) find either no significant relationship or even a negative one (Fumagalli & Narciso, 2012 ).

One possible reason for these sorts of contradictory findings is that a topic has not received enough research attention for a consensus to emerge. This is not the case for voter turnout; it is one of the most studied topics in the discipline. Indeed, Cancela and Geys ( 2016 , p. 264) suggest “turnout scholarship witnessed a veritable explosion” in the last 15 years. A second possibility is that we lack a thorough understanding of the underlying explanatory factors. Again, this does not seem to be the case here given the profusion of turnout meta-analyses. A recent meta-analysis of 130 journal articles identifies over a hundred explanatory factors (Stockemer, 2017 ). Geys’s ( 2006 ) earlier meta-analysis of 83 studies focuses on fourteen correlates but identifies several dozen more; and more recently Cancela and Geys ( 2016 ) examine 102 studies and identify several dozen correlates. Therefore, although there are myriad possible factors driving voter turnout, it has been difficult to reach solid empirical conclusions. A third possibility is that the world’s contextual heterogeneity explains why some variables behave differently in certain contexts, driving contradictory findings. Although empirical conflicts can definitely arise from contextual differences, they do not tell the whole story. For if a goal of comparative politics is reaching solid and generalizable conclusions across contexts, it is important to systematically approach competing explanations for comparable outcomes while recognizing important contextual differences. The comparable outcome we explore here is national-level voter turnout.

In the national-level voter turnout literature, it is uncommon to claim that one empirical model trumps others (Temple, 2000 ). However, there are developed techniques to systematically evaluate the proposed factors for a political outcome including meta-analyses and extreme bounds analyses. Given the sizable, established literature on voter turnout, this paper’s contribution is synthesizing the recent literature and evaluating its proposed correlates of national-level voter turnout using an extreme bounds analysis.

Extreme bounds analysis (EBA) has been used in a wide variety of contexts to evaluate factors driving a number of political and economic outcomes (Leamer, 1983 ; Levine & Renelt, 1992 ; Sala-i-Martin, 1997 ). For example, it has been used to evaluate over 50 predictors of economic growth (Levine & Renelt, 1992 ), 20 possible factors contributing to human rights violations (Hafner-Burton, 2005 ), 59 predictors of democracy (Gassebner, Lamla, & Vreeland, 2013 ), 59 electoral integrity predictors (Frank & Martínez i Coma, 2017 ), 43 covariates of life expectancy and infant mortality (Carmignani et al., 2014 ), 53 determinants of health care expenditures (Hartwig & Sturm, 2014 ), and 23 factors behind the diffusion of coups (Miller, Joseph, & Ohl, 2018 ). While many proposed proxies in these areas are statistically significant when considered in isolation, when tested with other predictors such findings are often fragile (Leamer, 1983 ). Extreme bounds analysis allows us to systematically evaluate what factors are robust to different model specifications. Hence, a primary EBA goal is to show that the “assumed model specification is largely inconsequential for statistical inference” (Gassebner, Gutmann, & Voigt, 2016 , p. 295). Another goal is to reconcile the literature’s several (sometimes contradictory) findings.

To collect possible predictors of national-level electoral turnout, we analyze 44 articles on voter turnout published between 1986 and 2017 in leading political science journals. We identified 127 unique independent variables that may affect turnout, and we were able to collect data and run models using seventy of these variables in 579 elections in 80 democracies from 1945 to 2014. We then run over two million regressions with different combinations of these seventy predictors. Each variable was included in up to 41,660 models with various combinations of other variables. If a variable is a significant predictor across models, then we can conclude that its statistical significance is unlikely to be an artefact of model specification. To determine robust turnout predictors, we used the two most common EBA decision criteria proposed by Leamer ( 1983 ) and Sala-i-Martin ( 1997 ), and we find 7 and 22 variables respectively are robust according to these 2 sets of criteria across 2 model specifications. Footnote 1 The first series of models includes country fixed effects to control for unobserved country-level factors; the second series of models includes random effects which allows for the inclusion of sluggish or stationary country-level factors the literature suggests affects turnout. We also run a number of further sensitivity analyses excluding a lagged dependent variable, using a dependent variable with a different denominator (voting age population rather than registered voters), and running models on eight election subsamples.

This research is theoretically significant because we still lack a systematic and parsimonious explanation of voter turnout that can address the current inconclusive and sometimes contradictory nature of the literature’s empirical results. The aim of this paper is, therefore, threefold: (1) to shed light on the dozens of factors that can affect turnout; (2) assess the empirical robustness of the different explanatory factors; and (3) provide insight on which controls may be worth including in future work on voter turnout. Footnote 2 We proceed as follows. The next section briefly summarizes the current voter turnout literature. The third section examines meta-analysis’s strengths and weaknesses and describes how extreme bounds analysis compliments it. The research design section discusses our election sample, the dependent and independent variables, and several estimation considerations. Our main results are then presented and are followed by a series of further analyses. We then conclude with a discussion of our main findings and areas for future research.

What Do We Know About Voter Turnout?

The first national-level turnout studies explain variations in voter turnout by focusing on a selected sample of lower house elections in OECD countries. For example, Jackman ( 1987 ) analyzes 19 democracies, Jackman and Miller ( 1995 ) analyze 23, and Blais and Carty ( 1990 ) and Powell ( 1986 ) include 20. Normally, these studies focus on a series of factors such as “socio-economic environment, the constitutional setting, and the party system,” (Blais & Dobrzynska, 1998 , p. 241).

Recent years have seen a proliferation of potential theoretical factors shaping turnout as well as an expansion of coverage. For instance, voter turnout is depressed with the size of a political community (Remmer, 2010 ), economic globalization (Steiner, 2010 ), corruption (Stockemer, LaMontagne, & Scruggs, 2013 ), and ethnic diversity (Martínez i Coma & Nai, 2017 ). Terrorism, in turn, increases the electorates’ attention on national politics and, consequently, turnout increases (Robbins, Hunter, & Murray, 2013 ). Similarly, while previous studies focused mostly on economically developed democracies, Blais and Dobrzynska ( 1998 ) were the first to have a truly global collection of democratic elections. More recent analyses focus on other regions including Latin America (Fornos, Timothy Power, & Garand, 2004 ), post-Cold War Eastern Europe (Kostadinova, 2003 ), Africa (Kuenzi & Lambright, 2007 ), and Muslim-majority countries (Stockemer & Khazaei, 2014 ).

These works share a number of similarities including the use of three general types of independent variables: socioeconomic, institutional, and political (Geys, 2006 ; Blais & Dobrzyinska, 1998). Socioeconomic variables include factors like economic growth and urbanization. Institutional variables capture the institutional arrangements under which the election take place (e.g., electoral formula). Finally, political variables account for specific electoral outcomes (e.g., the margin of victory). Footnote 3

The literature’s limitations appear when comparing their results. For example, one of the most cited studies by Blais and Dobrzyinska (1998) find that turnout is significantly affected by economic development, literacy rates, population size and density, compulsory voting laws, minimum voting ages, the electoral system, the number of political parties seeking seats, and the election’s competitiveness. Endersby and Krieckhaus ( 2008 ) reach similar empirical conclusions but suggest that context is important. Along the same lines, Martínez i Coma ( 2016 ) confirms some of Blais and Dobrzyinska’s (1998) findings but not others—notably those relating to electoral systems and economic development.

Furthermore, and more important for the purpose of this paper, a consensus does not yet exist on the robustness of these variables and, consequently, on what variables should be considered for a core model of cross-national aggregate voter turnout. For example, of the eight socio-economic factors used in the three articles mentioned above, only one variable is considered in all three—population. Footnote 4 Put simply, comparative studies provide mixed evidence for the robustness of particular factors affecting voter turnout. In summary, after over 50 years of research the literature still has not coalesced around a core model of turnout; different sets of variables are used in different analysis. Footnote 5 The literature, “draw[s] on relatively small samples, differing sets of observations, divergent operationalizations of turnout, and a wide array of theoretical models, the literature has mainly converged around relatively commonsensical observations” (Remmer, 2010 , p. 277).

Why a Meta-analysis is Not Enough

One popular means of evaluating a literature’s lessons is a meta-analysis, which basically assesses whether (or not) an independent variable affects a dependent variable. Footnote 6 A meta-analysis, or an “analysis of analyses” (Glass, 1976 , p. 3), normally employs two procedures: “vote-counting” and “combined tests.” The former counts the number of times a given coefficient is significant and in the expected direction. In such cases, it is counted as a ‘success’; otherwise, it is considered an anomaly. The higher the success rate, the more likely it is that we are to be observing a real association between the variables. A combined test is “based on the summation of the actual test statistics provided in each study” (Geys, 2006 , p. 640). Footnote 7

Such meta-analyses have been done in the voter turnout literature. For example, Geys ( 2006 , p. 641) identifies twelve significant socio-economic, political, and institutional factors affecting turnout, while highlighting that none are “omnipresent in the literature.” Footnote 8 This is “partly due to the lack of a firm theoretical model at the basis of variable selection,” and he argues for the “construction of a ‘core’ model of turnout” (Geys, 2006 , pp. 641, 653). Along similar lines, Stockemer’s ( 2017 , p. 712) meta-analysis of 135 studies from 2004 to 2013 identifies over 100 potentially salient variables, thoroughly analyzes 10 of them, and concludes “no variable is omnipresent or appears in most studies. Rather, different variables are used in various contexts.” The divergence pointed out by Stockemer ( 2017 ) explains the different results from his and Geys’ ( 2006 ) meta-analysis. They assess the “success” or robustness of 18 variables, 5 of those common in both works. Even for those five common variables, Footnote 9 when comparing the studies’ success rates, there are three common results: compulsory voting and population size impact turnout while income inequality does not. They diverge on the impact of election closeness and PR system because Geys ( 2006 ) finds they affect turnout, while Stockemer ( 2017 ) does not. Unsurprisingly then, Stockemer ( 2017 , p. 712) acknowledges that the “literature is far from establishing a core turnout model.”

Meta-analysis’s main limitation is not procedural but conceptual. In the end, even the most exhaustive meta-analyses like those above are circumscribed by their samples. This implies that we are unsure about the robustness of such results, given that most researchers’ robustness checks are ad hoc. “They identify a set of competing explanations and see if their empirical results hold once they control for some variables that might be consistent with those explanations” (Hegre & Sambanis, 2006 , p. 509). In other words, given all possible variable combinations, we cannot be sure that the selected model and the results presented are the ‘right’ ones. Usually, sensitivity (or robustness) checks estimate a series of regressions with alternative specifications. However, the number of possible alternative specifications is, of course, substantially higher. For example, without assuming any initial knowledge of variable selection, 5 variables lead to 32 possible model specifications, 6 variables have 64 combinations, 7 variables have 5040, 8 have 40,320, etcetera. The number of permutations increases exponentially as the number of variables increases; therefore, any particular study is likely showing a tiny proportion of the possible combinations. Hence, even the most demanding and detailed meta-analysis will only cover a tiny percentage of the multiple possible combinations that may affect voter turnout.

The standard regression framework on which meta-analyses rely has two other limitations. First, a particular variable’s statistical significance may be sensitive to the inclusion/exclusion of other variables. As Leamer ( 1983 , p. 38) concludes, “an inference is not believable if it is fragile, if it can be reversed by minor changes in assumptions.” Second, even when theories point to particular mechanisms, they are not “refined enough to inform the choice of the empirical measure to be used to proxy for such factors/mechanisms” (Carmignani et al., 2014 , p. 516). For example, should we use GDP per capita (as four studies considered in this paper do) as a proxy, the log of GDP per capita (two studies), or the log of GDP at purchaser’s price parity (two studies)? Should we use one measure or two? How would results change, for example, if instead of using Laakso and Taagepera’s ( 1979 ) effective number of parties, one decides to use the disaggregated number of parties or the number of parties weighted by their vote share?

Why Extreme Bounds Analysis?

By contrast, one of extreme bounds analysis’ key characteristics is that it estimates all possible combinations of a set of predictors showing how slight changes in the included variables affect estimation results. Therefore, rather than focusing on a specific set of variables, by considering all possible variable combinations, EBA can suggest which predictors are systematically robust. What EBA cannot do as well as meta-analysis is theoretically and empirically highlight the relationship between a particular outcome and explanatory variable, including possible mediation and moderation by other factors.

An extreme bounds analysis estimates a set of regressions with the following functional form:

where Y is the dependent variable (in our case, voter turnout), I is a vector of core variables included in all models, M is the variable of interest, Z is a set of controls, and u is the error term (Levine & Renelt, 1992 ). I is the set of variables always included—the “base” or “core” variables—because the literature suggests a well-established relationship with the dependent variable. What is the I vector of variables for electoral turnout? Unfortunately, as we show below, less than those that one, a priori, may think.

EBA repeatedly estimates the equation with a different set of Z controls in each regression. Since every regression produces a coefficient for all included variables, all the regressions create a distribution of such coefficients. In order to decide whether the coefficients are robust, researchers have relied on two main criteria. The first by Leamer ( 1983 ) suggests that a variable should be considered robustly related to the outcome variable if, and only if, the lower and upper extremes of a variable’s coefficients have the same sign. Footnote 10 Specifically, the extreme upper (lower) bound is defined by the maximum (minimum) value of the variable of interest plus (minus) two standard deviations. If the variable of interest remains of the same sign at both upper and lower bounds, then such relation among the variables is said to be “robust.” When the variable of interest does not keep the same sign at both upper and lower bounds, then such relation among the variables is said to be “fragile.” In short, only after running all possible regressions including all variables and only if all estimates are in the same direction, are results considered robust. Sala-i-Martin ( 1997 ) finds Leamer’s standard to be overly restrictive in most cases because it is likely that if enough model specifications are analyzed, and assuming that the distribution of β has both some positive and negative support, it is likely that the signs of the coefficients will change at least once. In fact, following Leamer’s criteria if a single regression produces a coefficient of the opposite sign large enough to shift one of the bounds, then the variable is considered not robust. Sala-i-Martin ( 1997 ) proposes to look at the entire distribution of coefficients and conclude a variable is robustly related to the outcome variable when a large percentage—say 90–95%—of the coefficient’s distribution is either above or below zero.

These criteria, then, can lead to different substantive conclusions. In the extreme, if enough regressions are run and the distribution of the estimators have some “positive and some negative support, then one is bound to find one regression for which the estimated coefficient changes signs” (Sala-i-Martin, 1997 , p. 179). Indeed, by following Leamer’s approach, we may conclude that the knowledge about many social phenomena is scarce and thereby make a consequential Type-II error. In contrast, as Plümper and Traunmüller ( 2020 , p. 149) recently show, Leamer’s EBA has “an extremely low probability of producing false positives” while, Sala-i-Martin is “more likely to suffer from identifying false positives than the inferential rule it replaced” (Plümper & Traunmüller, 2020 , p. 149). Previous works have, in the main, relied on Sala-i-Martin’s rather than Leamer’s approach. Footnote 11 However, both criteria are informative, so we follow Hafner-Burton ( 2005 ) and report results according to both criteria. As is clear below, there are substantive differences in what is considered robust predictors of turnout using these different criteria, and we compare our results using both criteria.

Research Design

In the turnout literature, there has been a thorough discussion of how to best operationalize the phenomenon of interest. The two main options are the number of votes cast as a percentage of the voting age population (VAP) or the voting registered population (VRP). There are arguments for both. For example, Blais and Dobrzynska ( 1998 ) use the latter and argue that VAP is not adjusted for the alien population, which artificially downplays turnout. Endersby and Krieckhaus ( 2008 , p. 602), by contrast, recommend VAP because if registration is not automatic, and registration and voting are correlated, “then the ratio of voters to registered voters is a biased measure of citizen’s motivation to vote.” Among our 44 studies, 16 use VAP, 18 use VRP, 5 use both, 4 use other definitions Footnote 12 and 1 (Siaroff & Merer, 2002 ) does not provide a definition. In this article, we primarily use VRP, but we also use VAP measures in a series of robustness checks. Footnote 13 If after applying the same analysis on two related but different dependent variables, the results of the independent variables are similar, this would be a clear signal of a variable’s strength. Turnout data are from International IDEA ( 2017 ). According to IDEA, their data comes from the national election management bodies (EMBs) and national statistical bureaus. EMBs provide data from their official reports and web portals. IDEA’s population data comes from secondary sources. In order to be included in the dataset, the election has been held after 1945; must have been for national political office in an independent nation state; there must more than one party contesting the election; and the franchise must be universal. Footnote 14

In our data, the VAP and VRP turnout measures correlate at 0.68. Consistent with the literature we limit our sample to lower house elections in democracies (defined as a Polity value of six and above in the year before the observed election). Overall, our sample includes 579 elections in 80 democratic countries from 1945 to 2014.

Independent Variables

In order to identify the most common predictors of voter turnout, we rely on Geys ( 2006 ), Geys and Cancela (2016), and Stockemer ( 2017 ) meta-analyses of 83, 185, and 130 works, respectively. For our analysis, we included all English language, national-level, comparative, peer-reviewed journal articles focused on voter turnout published between 1980 and 2017. We therefore exclude case studies, studies focusing on local, regional, or provincial elections, book-length studies, and studies not in English. We focus on the national level because local dynamics are likely distinct from those at the national level. Furthermore, logistically it also makes sense to exclude works where the underlying data are not comparable to other cases: for example, exploring the effects of Norwegian school referendums (Kaniovski & Miller, 2006 ) on voter turnout in non-Norwegian countries is not possible. This decision implies that some factors, like campaign expenditures, cannot be examined given the almost total lack of available data outside the US. The 44 included studies are listed in the Appendix. Footnote 15

As mentioned above, turnout predictors are usually organized into three groups: socio-economic, institutional, and political; and we follow this approach when organizing 127 independent variables derived from the 42 articles we examine. Footnote 16 We find 41 socio-economic factors, 48 institutional factors, and 38 political factors. Such a large number of independent variables reinforce the diversity of empirical approaches in the literature and the need to clearly determine what robustly affects turnout (and what does not).

Tables 1 , 2 and 3 summarizes each group of variables. The first column presents the number of times a variable is used in the literature; the second column includes the variable name or concept; the third column show the ways in which the variable has been measured (if available); the fourth column present the directional effect in turnout—sub-divided in four sub-columns, one accounting for each possible result. When the variable had a positive impact for turnout, it is labelled as ‘positive’, ‘negative’ when the contrary; ‘NS’ suggests a non-significant result, while ‘mixed’ captures those results when the results vary depending on the model.

Table 1 includes 48 institutional factors. The most frequent factor is compulsory voting, which is measured in three different ways. Twenty-eight studies find that compulsory voting has a positive and significant effect while five find it not significant. Two other variables—the number of political parties and proportional representation (PR)—are the next frequent (19 times each). This illustrates the literature’s differences in measurement; the former is operationalized in 10 different ways, the latter in two. We have created a straightforward measure of agreement among studies by dividing the most frequent result by the number of studies that use such measure and multiplying it by 100. The higher the percentage, the more established the finding. Footnote 17 For compulsory voting, the degree of agreement is over 84%. Other variables are less established. For example, the agreement on the impact of the number of parties or the impact of proportional representation electoral systems is unclear, with about 53% agreement. Regarding the former, while 10 cases find that higher number of parties, lead to lower turnout, 8 do not find it significant. Likewise, 10 studies show that countries under proportional representation systems show higher turnout while 7 find it not significant. Table 2 ’s socio-economic variables include two sub-groups, socioeconomic characteristics and geographical dummies. First, the indicators gathering relevant socio-economic characteristics of a given society, such as size of population, GDP, GDP growth, and urbanization. Twenty-two studies include GDP as an independent variable. Eight find that GDP has a positive impact on turnout, while seven show a non-significant relationship, three report a negative influence, and four show mixed results. Results are not much better for the rest of such variables. Second, there are geographical dummies for specific countries or regions. Most notable about these variables is that including a variable for Switzerland and/or for the US almost always are negatively related to turnout. There are two patterns worth mentioning when discussing Table 3 ’s political variables. First, not many political variables appear in the articles we examined. This is surprising given the fundamentally political nature of turning out to vote. An exception is “closeness/competitiveness” that appears in 21 studies (almost half of our sample). Furthermore, the level of agreement for this variable is below 50%. Second, we only find a high level of agreement for the previous election turnout level (though such variable only appears in five studies).

In sum, three important findings arise from this initial literature review. First, out of 127 distinct variables, less than half (44%) appear more than once. Even the most frequently used indicator, compulsory voting, was included in less than 75% of the examined studies. Second, among the 55 variables that appear in more than 1 article, over half (57%) are measured in more than one way. Third, it seems more generally that turnout studies face a paradox—while voting is mainly a political act, the most common empirically tested arguments in the literature are of institutional or socioeconomic mechanisms. Only recently have some articles examined the impact of terrorist attacks (Robbins et al., 2013 ), corruption (Stockemer et al., 2013 ) or electoral dynamics (Martínez i Coma & Trinh, 2017 ) on turnout.

Table 4 condenses this information and also offers an overview of the distribution of the 70 variables for which we have data. These results strongly suggest that a standard model of turnout does not yet exist, and few factors—especially institutional and socioeconomic—have a consistently established effect on voter turnout.

Estimation Considerations

Including all 127 predictors discussed above in our empirical models is not possible due to data availability—several variables are available for only a short span of time, specific region (i.e., Europe) or a clear set of countries (i.e., OECD members). Footnote 18 The final line in Table 4 describes the distribution of the seventy variables we include, and Appendix Tables A2, A3 and A4 present these variables’ summary statistics, operationalization, and sources.

Given the absence of a commonly accepted model of voter turnout, in our selection of core variables we rely on several theoretical assumptions consistent with the literature and empirical regularities to include five variables in all of our models. From an institutional perspective, compulsory voting has been found to affect turnout. 84% of our 44 studies find that compulsory voting increases turnout, which is likely the literature’s most commonly accepted finding. The electoral system is also a recurrent variable of study under the (challenged) assumption that voter turnout is usually higher in proportional representation systems (Blais & Carty, 1990 ). We also include two socio-economic factors—per capita gross national income and population (both logged to control for outliers). The former accounts for the literature’s finding that economic development fosters turnout while the latter controls for the relationship between community size (an element assessed both by Geys, 2006 and Stockemer, 2017 ) and turnout. Finally, we include the level of turnout in the previous election because the literature suggests that turnout may be habit forming and has its own inertia (Geys, 2006 ). Footnote 19 While we acknowledge that more core variables could be included, we believe our core turnout predictors are consistent with the literature we survey.

Given that several variables measure similar concepts and/or that the measurement of such variables may be related, multicollinearity may be a risk when including such a large number of predictors. Levine and Renelt ( 1992 , p. 945) Footnote 20 rely on three common strategies to reduce this risk: (1) they limit the total number of explanatory variables to eight at most; (2) they limit the number of Z controls to seven; (3) and they further restrict their Z variables by excluding variables that a priori may measure the same phenomenon. Similarly, Hartwig and Sturm ( 2014 ) drop some variables when they are highly correlated. To avoid artificially inflating our estimates, we have followed their first and third strategies (we limit the total number of core and independent variables to eight and we exclude multiple measures of the same underlying phenomena). Footnote 21 Footnote 22

The final estimation consideration is what type of model to run. The most commonly used method in the 44 reviewed studies was ordinary least squares (OLS) with robust standard errors clustered by country (15 articles). Regression models with fixed effects and random effects were used in several (6 articles and 7 articles respectively) of the 44 articles examined in the literature review above. Footnote 23 Nevertheless, there are reasons to expect with eight predictors that there is the real risk of unobserved variable bias and risk of unmeasured unit-effects. Furthermore, like most of the literature we are interested in the reasons for differences in turnout both between and within countries. There is an ongoing methodological debate about which is appropriate for time-series cross-section data such as ours that is outside the scope of this article. Footnote 24 Therefore, we estimate two series of models. The first series of OLS models with fixed effects focuses on time-varying explanations for turnout. Our I vector includes 5 variables, and the Z vector has 53 variables. The second series of models use random effects and clustered standard errors by country to include time-invariant variables. A random effects approach relies on the strong assumption that the included predictors are uncorrelated with country-specific intercepts (i.e., unobserved factors), but it does allow for time-invariant factors’ effects on turnout to be estimated. Our I vector for the random effects models includes 5 variables, and the Z vector has 65 variables . In both series of models, continuous independent variables are standardized in order to aid the comparability of coefficients and a number of continuous predictors were log-transformed to reduce the effects of extreme values. Footnote 25

After running 1,170,324 regressions with fixed effects, the 5 core variables, and 22,096 unique combinations of 53 independent variables, nine variables are robust according to Sala-i-Martin’s criteria [the area under the general cumulative density function (CDF) that are on either side of zero is less than 0.05]. Footnote 26 One variable ( economic globalization ) also meets Leamer’s ( 1985 ) more demanding criteria. Table 5 summarizes the results for the variables our fixed effects analysis suggests are robustly associated with voter turnout. Footnote 27 The nine robust predictors are evenly split between institutional, socio-economic, and political factors. The three institutional variables are proportional representation, concurrent elections, and the number of years since universal suffrage was introduced—the first two increase turnout, while the last decreases turnout. As for socioeconomic conditions, economic globalization decreases turnout and inflation, and spending decentralization increases it. Among the three political factors (competitiveness, a dummy for elections before 1995, and a time trend variable), the most interesting result is that higher levels of competitiveness (measured as the difference between the first and the second party’s vote share) decreases turnout. Only one (proportional representation) of the core five variables was found to be robust. Finally, it is notable that several variables our analysis finds significant have been largely overlooked by the existing literature. For instance, economic globalization and spending decentralization were each included in only 1 of the 44 articles we analyze, yet they are both robust predictors in these and other models discussed below.

Our fixed effects model results suggest that 9 out of the 53 time-varying variables we measure are robustly associated with national-level turnout. However, as mentioned above these models exclude 12 time-invariant or sluggishly changing variables that previous research has found important. Therefore, we run an additional 2.9 million regressions with random effects that allow us to capture important variation in turnout across countries and regions. Our findings (summarized in Table 6 ) suggest that seven variables meet Leamer’s robustness criterion and 14 variables meet Sala-i-Martin’s. Footnote 28 Seven are location dummies—Eastern Europe, Latin America, and Switzerland dummies depress national-level voter turnout while Norway, New Zealand, Oceania, and Sweden dummies have the opposite effect. From the socio-economic indicators, higher ethnic fractionalization and levels of inequality are robustly associated with lower electoral turnout while inflation increases turnout. Additionally, the lagged dependent variable is a robust turnout predictor (unlike in the fixed effects models). Finally, two institutional factors (concurrent elections and compulsory voting) are also robustly associated with higher turnout.

The results for these two institutional variables (concurrent elections and compulsory voting) are also interesting because they highlight the different substantive conclusions that would be reached depending on which robustness threshold is chosen. Both variables are significant using Sala-i-Martin’s criterion but not Leamer’s. Let us explore for a bit why this is the case. In almost three million regressions using random effects, the concurrent election variable never has an estimated coefficient below zero (the smallest is 0.04). This is why this variable’s CDF never includes negative values and therefore why Sala-i-Martin would consider this variable a robust predictor of turnout. At the same time, because of concurrent election’s average standard deviation (7.01) is similar in size to its average coefficient (7.89), its lower extreme bound (two standard deviations below the mean) is less than zero and therefore not robust for Leamer. The story behind compulsory voting’s results is different because (out of 2.9 million random effects models) 9775 models (0.003%) do indeed produce a negative coefficient for this variable. Is there something systematically different about these 9775 models that lead to such a counter-intuitive result? Several things stand out. Footnote 29 First, the inclusion of several variables substantially reduce the sample size. Union density is in 9430 (96%) of these 9775 models, followed by human development index (6616, 68%) and the Oceania dummy (1500, 15%). Second, democracies with union density data are relatively rare (17 of 80) leading to the possibility that outliers can have undue effects on estimated coefficients. This might help explain why the average number of observations (40) in these 9775 models are significantly lower than for the other 2.9 million models including compulsory voting (360). Footnote 30 Figure  1 provides a striking visual of this difference in sample size. It is worth noting that (as in all articles using EBA that we are aware of) our regressions include varying sample sizes due to data availability. This is one of EBA’s main strengths—if a variable significantly affects turnout across a range of samples, this is a clear signal of the robustness of the estimated relationship.

Sample sizes for 384 models with negative coefficients for compulsory voting (top) and in all (2,955,366) random effects models with compulsory voting (bottom)

Turning now to other ways our results are consistent (or not) with the previous work summarized in Tables 1 , 2 and 3 , we reach several main conclusions. First, the mixed results for two of our core variables (population and GNI) is consistent with the literature. Take population. Table 2 suggests that population size has an inconclusive effect on turnout—seven studies find a negative effect and another seven find no significant effect. Our fixed effects and random effects results also suggest that population and economic development have no robust effect on turnout. Similarly, we do not find significant effects for several common socioeconomic variables including economic growth (nine studies) and literacy (seven studies). Regarding institutional variables, we do not find robust results for the number of parties, the different electoral formulae, the magnitude of the electoral district, the level of disproportionality, and the legal voting age, among others. Footnote 31 By contrast, our results suggest that economic globalization (one study) and inflation (two studies) affect turnout while receiving nowhere near as much attention as a number of institutional and political factors (e.g., 19 articles include the number of political parties in their models). It is also notable that (regardless of the criteria we rely upon and excluding geographical dummies) the most robust turnout predictors are political while (as we saw above) previous research focuses more on institutional and socioeconomic factors.

Finally, it is worth highlighting our models’ average substantive effects. Figure  2 summarizes the coefficient distributions for Table 5 ’s robust predictors. Each square represents a variable’s average estimated impact on national-level voter turnout, and the bars on either side of these coefficients represent Leamer’s upper and lower extreme bounds. Holding all else equal, concurrent elections increase turnout 7.9% over elections without candidates competing for executive office. Proportional representative election systems are associated with higher levels (5.2%) of voter turnout than states without this electoral system. The variables that (on average) depressed turnout the most in the fixed effects models are the time trend (which estimated a lowering of average turnout by 6.9% from 1945 to 2014) and economic globalization (− 4.3% average effect across its observed range).

Robust predictors of voter turnout, mean coefficients and extreme bounds. Note Results from fixed effects models reported in Table 5 . Table A7 includes complete results. *Identifies core variables in all fixed effects models. The only core variable found to be (Sala-i-Martin) robust is proportional representation. Population is a core variable but not included in figure due to the disproportionate size of its extreme bounds

Further Analyses

Our empirical results taken from over four million regressions using fixed and random effects provide compelling evidence for the robustness of a number of predictors of national-level voter turnout. These results, however, vary more in the variables the models included than their model specifications themselves, which lead to several potential limitations. First, we run our main models using fixed and random effects rather than the literature’s most common model specification—OLS regression using robust standard errors clustered by country. Second, they include a lagged dependent variable, which some (e.g., Achen, 2001 ) argue depress predictors’ statistical significance. Third, we focus on registered voter turnout, while the literature also suggests turnout as a percentage of voting age population could lead to different results. Fourth, these seventy factors may work differently in different types of countries (e.g., developed/developing, Western/non-Western), and fifth there may be over-time variation that is not captured by our time trend variables. We, therefore, re-run all or part of our analyses with fourteen additional model or sample variations. Figure  3 summarizes the results from these additional 11.8 million models for the variables included in Tables 5 and 6 . Footnote 32

Further analyses checks, coefficient and extreme bounds plots. Note Y-axis abbreviations represent distinct model series. Horizontal lines represent extreme bounds (+/− two standard deviations). All models (except those marked VTA) are limited to democracies. VTFE registered voters with fixed effects and lagged dependent variable (DV), VTRE registered voters with random effects and lagged DV, VTNLFE registered voters with fixed effects and no lagged DV, VTNLRE registered voters with random effects and no lagged DV, VT registered voter turnout, robust standard errors clustered by country, and lagged DV, VTNL registered voter turnout, robust standard errors clustered by country, and lagged DV, VAP voting aged population voter turnout, robust standard errors clustered by country, and lagged DV, VTA registered voter turnout, robust standard errors clustered by country, lagged DV and including all countries. Population’s extreme bounds for VTFE and VTNLFE models excluded due to their disproportionate size (− 3666.2 and 3681.1 for VTFE)

Using robust standard errors First, we run 2,433,115 models using robust clustered standard errors the core 5 variables, and 65 alternating M and Z variables. 11 and 20 variables were significant using either Leamer or Sala-i-Martin’s criteria. Fourteen of these 20 (70%) variables were also significant in either the fixed effects or random effects models. Six variables (boycott, a fifth or sixth election dummy, GNI growth, quality of democracy, union density, and unionization) were robust in these new models but not in our main models, although four of these six (fifth or sixth election, quality of democracy, union density, and unionization) would be considered robust in either the fixed or random effects models if we use a 0.1 level rather than 0.95. Thus, our first set of further analyses suggest that the majority of our results using fixed effects and random effects hold if we use robust standard errors clustered by country. Footnote 33

Removing the lagged dependent variable Next, we run an additional 4,228,480 regressions identical to the three series of models described above except that we lagged turnout. We included it above because the literature suggests today’s turnout is systematically related to previous turnout. However, as Achen ( 2001 ) explains, lagged dependent variables may suppress the explanatory power of relatively time-invariant independent variables, such as compulsory voting or population size. The results for models without a lagged dependent variable do vary in a few ways, a few variables lose robustness, while more than double are now found to be robust. Specifically, in the fixed effects models without a lagged dependent variable, concurrent elections now meet the Leamer robustness criteria while suffrage is no longer significant at either level. Otherwise, the other 54 variables in these fixed effects models (96%) have substantively identical results. Turning to the random effects models, economic globalization, the time trend, the 1945–1994 dummy, and competitiveness are now significant and in the same direction as the fixed effects models. Our population variable is also now significant, while the second election dummy loses significance. Overall, 57 of 69 variables (83%) have substantively identical results. Finally, in the models with robust standard errors and no lagged dependent variable concurrent elections and the second election loses significance without lagged dependent variable while population gains significance. Here, 58 of 69 variables (84%) have substantively similar results. Overall, these three series of models without lagged dependent variables produce quite substantively similar results as those models with a lagged term.

Changing denominators Next, we re-estimate the lagged dependent variables and clustered standard errors models using a different dependent variable denominator (voting aged population instead of registered voters). The literature summarized above splits almost evenly in its use of these denominators. Given that these variables’ correlation coefficient is 0.67, results may significantly vary depending on which denominator used. Overall, we find 51 of 70 (73%) variables’ robustness unchanged. Twelve of seventy variables are robust according to both criteria. Four are geographical dummies (Sweden, Switzerland, Easter Europe, and Latin America/Caribbean), and the others are now familiar (compulsory voting, concurrent election, ethnic fractionalization, inflation, previous turnout, second election, fifth or sixth election, time trend, and union density). Eight variables are no longer robust (boycott, GNI growth, the Latin American and Oceania dummies, quality of democracy, spending decentralization, unionization, and years 1945–1994), while 11 are now considered robust (the Asia, Norway, and USA dummies, economic globalization, female suffrage, plurality system, radios per capita, third election dummy, legislative seats, share of voters aged 30 to 69, and violence) in this series of over 2.3 million models.

Splitting samples Different samples of countries may also have systematically different correlates of national-level voter turnout (Stockemer, 2015 ). Therefore, we ran an additional 2,647,887 analyses breaking up our sample into different groups—democracies and all states, Western and non-Western states, established and newer democracies, and above and below median income states. Footnote 34 Our results (see Appendix) suggest that while we limit our main analysis to democratic states, turnout drivers are overwhelmingly similar in non-democracies. Footnote 35 Indeed, 64 of 70 variables’ robustness does not substantively change in models including anocracies and autocracies (i.e., states with a Polity score of less than six). Of the six variables with changes, four are robust in all-states models and not in democracy-only models (Asia, revenue decentralization, urban population, and violence), while two are no longer robust (boycott, and the 1945–1994 dummy). The vast majority (12 of 15) of variables in Western and non-Western democracies did not significantly change from the original models. For those that did change, boycotts, per capita GNI, and spending decentralization were robustly associated with turnout in Western states but not in non-Western states. The results for the latter are likely to be at least partly driven by increased data availability in Western states. The most notable differences in these series of split sample models were found between countries that had more or less than two decades since democratization. Take, for example, the Eastern Europe dummy that is robust in the main models, it robustly decreases expected turnout in established democracies but not in the new democracies. Relatedly, once the analysis is performed in newer democracies, regional differences fade away—a result consistent with previous studies (e.g., Kostadinova & Powers, 2007 ). Footnote 36 Additionally, the original findings hold for a number of variables by countries with above the median income than below it. Of the 22 robust predictors described above, the richer country models had 9 significant predictors while the poor states had 4. Specifically, compulsory voting increases turnout in rich states but not poor states. So too did ethnic fractionalization, economic growth, spending decentralization and inflation. Poorer states’ national voter turnout was robustly associated with boycotts (as in Western states) and concurrent elections while rich states’ turnout was not.

Variation over time Finally, there may be over-time variation that is not captured by the time-trend variables in our original analyses. We therefore ran two additional series of models, one with a linear time trend and a time squared variable in all models and the second with time, time squared, and time cubed. Substantive results for the other variables were virtually identical to our main model, and the time trend variable and time cubed variables are robust in their respective model series.

Take as a whole, the results of over 15 million regressions of national-level voter turnout on 70 unique variables using 16 distinct model (or sample) variations suggest that there are indeed a recurring series of country-level factors shaping national-level voter turnout. Some of these factors are consistent with a wide swath of the turnout literature (e.g., institutional factors like compulsory voting or concurrent elections) while others have only been included in one or two studies (e.g., socio-economic factors like economic globalization and inflation). The implications of our findings are twofold. First, our results suggest that there is room for theoretical reassessments of several frequently used institutional variables (e.g., electoral formulae or the number of parties). Second, our results provide a comprehensive evidence base for future turnout research.

Discussion and Conclusion

In this article we establish which social, institutional, and political factors driving national-level voter turnout are empirically robust using a wide vary of model specifications. This has three implications. First, it sheds light on what national political, institutional, or socio-economic factors are significantly associated with turnout. Second, it provides fodder for further inductive theory development. Third, our results suggest a set of potentially useful control variables for future turnout studies. Below, we develop these three points further.

First, robust results are essential to discriminate among the dozens of proposed mechanisms driving national-level voter turnout. When over a hundred factors potentially affecting turnout, it is hard to determine what robustly shapes turnout. We collect 127 literature-derived factors and empirically analyze 70 of them. We find that some of the literature’s results are highly sensitive to small changes in model specification while others are not. Overall, we find that compulsory voting, competitive elections, concurrent elections, economic globalization, inflation, previous turnout, proportional representation, spending decentralization, and some geographical dummies are robust predictors of turnout. Similar to other empirical assessments (e.g., Gassebner et al., 2013 ’s study of the emergence and survival of democracy) our results are humbling but provide an empirical foundation for future research.

Second, from a theory-building perspective, fragile results are important because they force us to reassess both the theoretical underpinnings of our hypotheses as well as “reconsider whether it is theoretically reasonable to expect robustness across the various sample populations being considered” (Hafner-Burton, 2005 , p. 696). Furthermore, reassessing the theories that generate our hypotheses requires taking into account other possible causes that have not received much attention in previous studies. For example, several economic factors (e.g., economic globalization and inflation) are robust in our models but have yet to receive much theoretical attention in the turnout literature.

We conclude by highlighting a number of possible areas for future research. For example, our data contain several different measures for the same concept. For practical reasons, we have relied on the most commonly used measures. A logical next step would be to use other sources and operationalizations and compare results. Relatedly, including all or some of the remaining 57 variables included in Tables 1 , 2 and 3 but not analyzed in this paper is a possibility. Furthermore, like previous studies (e.g., Hegre & Sambanis, 2006 ), we did not include any interaction effects in our models. Including theoretically informed interactions may provide for a more comprehensive analysis of the interplay of different explanatory factors and the way they may mediate or moderation certain relationships. Certainly, then, our analysis is limited and, by no means, definitive, but it is the largest systematic evaluation of factors shaping national-level voter turnout to date.

Using extreme bounds analysis, we examine the most robust factors correlated with turnout. Like previous studies using this methodology, we do not estimate a structural model, theorize the relationship between different variables, establish specific causal mechanisms, or improve turnout measurements.

Existing meta-analyses present contradictory results. In fact, Stockemer ( 2017 , p. 15) states “the fact that the influence of many factors … on turnout is inconclusive demands more contextual analysis.” While we agree with the need for context bound analysis, our research shows that even at the most general level there are common voter turnout correlates in democratic countries.

Such categorizations are neither exhaustive nor exclusive; rather they can be seen as a useful theoretical heuristic.

The other variables are population density, gross domestic (or national) product per capita, gross domestic product, literacy rate, life expectancy, and ethnic diversity.

Even when studies include the same variables, there is no consensus on how to measure some concepts. See below for a broader measurement discussion.

In discussing meta-analyses, we are not referring to quantitative analysis of a variable’s average treatment effect because this literature’s focus is on a broad spectrum of possible causes rather than any one particular cause.

A limitation of such an approach is it requires consistent reporting across the studies, but there are solutions. For example, see Geys ( 2006 ) and Smets and van Ham ( 2013 ).

Cancela and Geys ( 2016 ) expand on Geys’ ( 2006 ) by adding 102 new studies and differentiating between national and subnational elections.

The variables that only Geys ( 2006 ) considers are: population concentration, population stability, population homogeneity (ethnic diversity), previous turnout, campaign expenditures, political fragmentation, proportional representation electoral system, concurrent elections, and registration requirements. The variables that only Stockhemer (2017) considers are district magnitude, effective number of parties, important elections, education, and literacy rate.

A criterion used by Levine and Renelt ( 1992 ).

Sala-i-Martin’s ( 1997 ) focus on the entire coefficient distribution is a common approach used by Hegre and Sambanis ( 2006 ), Gassebner, Lamla and Vreeland ( 2013 ), Hartwig and Sturm ( 2014 ), Gassebner, Gutmann and Voigt ( 2016 ), and Miller, Joseph and Ohl ( 2018 ).

Such definitions measure turnout as “the total votes cast divided by the size of the electorate” (Blais & Carty, 1990 , p. 169); “the average turnout of each country” (Colomer, 1991 , p. 319); the proportion of the eligible electorate voting (Radcliff, 1992 , p. 445); and “the percentage of eligible voters that turned out at the respective country's national election” (Stockemer, 2015 , p. 87).

This debate is not new. In the United States, McDonald and Popkin ( 2001 ) proposed another measure, but no cross-national data for this measure exists. More recently, Stockemer (2016) created VEP for 500 elections in 116 countries. Given our focus here, we rely on the established measures.

Despite all the efforts, IDEA’s data are not perfect. For example, when two elections were held in a single year, IDEA does not report which election is captured. We thank a reviewer for highlighting this fact.

Appendix Table A1 lists the 285 studies that were excluded and the reasons for exclusion.

For example, see Geys ( 2006 ) and Blais and Drobzinska (1998).

This points us to a degree of agreement about a specific covariate. We define 70% or more as a “high level of agreement.” When comparing the common variables from Geys ( 2006 ) and Stockemer ( 2017 ), only compulsory voting shows a high level of agreement (84%), followed by income inequality (60%), PR (53%), vote closeness (47%) and population size (44%).

Over 80% of excluded variables appear in only one study.

We are aware of Achen’s ( 2001 ) warning regarding the inclusion of a lagged dependent variable. Our further analyses below address this concern.

This article examines economic growth and over 50 independent variables.

The third strategy to avoid multicollinearity is included in our design by default because we rely only on the most frequent source and variable operationalization.

We also lag time-varying independent variables to reduce the risks of endogeneity or reverse causality.

Models took an average of 12.5 days to run 2,433,115 regressions using Stata 16.1 on an Amazon Web Service Elastic Compute Cloud c3.large instance running 2019 Windows Server Edition.

See for example Bell and Jones ( 2015 ), Bell, Fairbrother and Jones ( 2019 ), Clark and Linzer ( 2015 ), Dieleman and Templin ( 2014 ), Imai and Kim ( 2019 ), and McNeish and Kelley ( 2019 ).

There are two possible sources of variation: (1) substantively important results to small changes in the variables included and (2) how fragile the inferences are to small changes in operationalization. Given that some variables appear nineteen times but are operationalized in ten different ways, we focus on the first source of variation. Even in this case, 70 variables in the random effects models lead to over 2 million regressions. We leave for future research the analysis of different operationalizations.

Sala-i-Martin ( 1997 , pp. 179–180) looks at both normal and generalized CDFs. Histograms of the 1.2 million estimated coefficients (available on request) suggest most coefficients are not normally distributed, so we use the generalized CDF.

Table A7 summarizes results for all 58 variables.

Complete results available in Table A8.

Table A15 includes the variables included in these 9775 models.

Negative coefficient model average sample size: 40 (s.d. 33.8); all models: 360 (s.d. 179.3).

It is important to note that all of these variables also had mixed results in the literature.

Appendix Tables A7 to A18 includes more detailed results for these analyses.

70% of variables in fixed effects models and 83% of random effects variables.

States that had Polity2 scores of six or greater for 20 years or more are considered “established”.

Turnout in non-democracies is rarely studied in a comparative framework. Exceptions include Martínez i Coma ( 2016 ) and Martínez i Coma and Morgenbesser ( 2020 ).

We follow Kostelka ( 2017 ) in believing that the differences here require an in-depth exploration of alternative explanations of voter turnout.

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Acknowledgements

We would like to thank Ignacio Lago, Ciaran O'Faircheallaigh, participants of the 2017 Australian Society of Quantitative Political Science Conference, the 2018 Doctores Miembro and former Post-Doctoral Fellows of Juan March Institute Conference, the Editor, and the anonymous peer reviewers for their valuable comments and suggestions. Replication materials are available at: https://dataverse.harvard.edu/dataverse/richardwfrank . All remaining errors are our own.

The authors acknowledge support for this research from the Australian Research Council’s Discovery Project Scheme (DP150102398, DP190101978).

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Frank, R.W., Martínez i Coma, F. Correlates of Voter Turnout. Polit Behav 45 , 607–633 (2023). https://doi.org/10.1007/s11109-021-09720-y

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A Roadmap for Engaging College Students in Our Nation’s Elections

Civic Education , Civic Engagement , Student Registration , Student Voters

1. Build a team. No one can engage an entire campus on their own. Create a core group to coordinate engagement efforts, divide up the work, and ensure key stakeholders communicate. This team should include administrators, faculty, staff, and student leaders. Building a team is important because teamwork is an effective vehicle for community change. Teams are flexible and responsive, build a sense of community, provide a means for community empowerment, and allow diversity to be valued (Druskat and Wolff 2001).

2. Register students to vote. It is the first step to student electoral participation. Set campus-wide goals and plan ways to implement them. To get started, organize events to register students during orientation, class registration, and other major activities. If students live on campus, register them at their residence hall addresses on move-in day. Libraries, cafeterias, and student unions are also great locations to set up registration drives. Ulbig and Waggener (2011) found that students who registered through an on-campus voter registration drive turned out to vote at a higher rate than similar young people nationwide.

3. Educate students on issues and candidates. Verba et al. (1995) have suggested that individuals do not participate politically because they either lack resources or knowledge, have not been recruited by others, or feel that their participation will not make a difference. In order to increase voter turnout, in other words, students need clear information on where candidates stand and why their vote matters. Campus Election Engagement Project has nonpartisan candidate guides for major statewide and national races. They also have a “ guide to the guides ” that describes ways campuses have successfully distributed them. In addition to these guides, Vote411 , Ballotpedia , and Rock the Vote are also excellent resources for non-partisan educational information.

4. Help students volunteer with partisan or nonpartisan campaigns of their choice. To get students to change the political landscape by doing more than just voting, encourage them to volunteer for a political campaign. Working for any kind of campaign (e.g., issue-based, mayoral, congressional, or presidential) is a way for students to directly help create governmental change. Volunteering can empower students by helping them increase their sense of personal control and concern with social influence (Rappaport 1987). To make this happen, make information about on- and off-campus volunteer opportunities widely available, ensuring that you give equal visibility to contacts for Republicans, Democrats, and third or fourth-party candidates if they have a significant electoral presence. Promote volunteer opportunities not just with political parties, but with grassroots groups as well.

5. Build election excitement and visibility leading up to Election Day, using every appropriate channel. Copeland and LaBand (2002) find that the excitement of voting can affect the public’s decision to vote or not. This means you should make voting a fun activity! Hold rallies and election-related festivals during the early voting stage and on Election Day. Party at the Polls is a great resource for event ideas. It has helped campuses across the country host block parties, BBQs, and parades. These events should be combined with concrete activities like registration drives and pledges to vote. Entertain as you engage and educate.

6. Get out the vote. Research shows that making a concrete plan significantly increases the likelihood of voting (Nickerson and Rogers 2010). Make sure students know where to vote, when to vote, and what to bring. Publicize voter ID requirements and help students secure all necessary materials. VoteRiders.org has downloadable wallet cards on ID rules. Mock polling events with sample ballots and voting machines are also great events to hold for students. These events allow students to practice voting and consider how they’ll vote on their real ballot. These “dry runs” can assure new voters they’re bringing the proper ID and filling out the ballot or using the machines as they intend.

7. Measure your impact. You are building momentum not just for the current election, but also for future ones. The more you document your efforts and keep your momentum going, the more effective you’ll be for the next election cycle. Have senior administrators sign up for the National Study of Learning, Voting, and Engagement , which studies patterns in college student voting and provides campuses with data on student political engagement. If you register students through the Rock the Vote or TurboVote tools, you can use the dashboard they provide to track how many students registered and voted.

Once you have your post-election results in hand, you should reflect with your team on the following questions:

• Which approaches worked best for the culture of our campus, both in terms of getting people excited about voting and in producing concrete results, like numbers of students registered and numbers who turned out to the polls?
• What approaches didn’t work well, or had less impact than we had hoped?
• Which approaches would we have wanted to do if we had more time?

Remember that student voting doesn’t just happen. It takes cultivation. Your efforts on your campus matter and are making a difference. For every student who votes because of your efforts, you have increased their chances of voting for a lifetime (Green and Gerber 2019).

Copeland, C., & Laband, D. N. (2002). Expressiveness and voting.  Public Choice ,  110 (3-4), 351-363.

Druskat, V. U., & Wolff, S. B. (2001). Building the emotional intelligence of groups. Harvard business review , 79 (3), 80-91.

Green, D. P., & Gerber, A. S. (2019).  Get out the vote: How to increase voter turnout . Brookings Institution Press.

Nickerson, D. W., & Rogers, T. (2010). Do you have a voting plan? Implementation intentions, voter turnout, and organic plan making.  Psychological Science ,  21 (2), 194-199.

Rappaport, J. (1987). Terms of empowerment/exemplars of prevention: Toward a theory for community psychology.  American journal of community psychology ,  15 (2), 121-148.

Ulbig, S. G., & Waggener, T. (2011). Getting registered and getting to the polls: The impact of voter registration strategy and information provision on turnout of college students.  PS: Political Science & Politics ,  44 (3), 544-551.

Verba, S., Schlozman, K. L., & Brady, H. E. (1995).  Voice and equality: Civic voluntarism in American politics . Harvard University Press.

Rachael Houston is a guest contributor for the RAISE the Vote Campaign . The views expressed in the posts and articles featured in the RAISE the Vote campaign are those of the authors and contributors alone and do not represent the views of APSA.

Rachael Houston is a PhD candidate in political science at the University of Minnesota, Twin Cities. She studies American politics and political methodology. For the last two years, Rachael has worked for Campus Election Engagement Project, a national nonpartisan project that helps administrators, faculty, staff, and student leaders at America’s colleges and universities engage students in federal, state, and local elections. 

The contributor for this post was recommended by the Elections, Public Opinion, and Voting Behavior Organized Section. Joining an APSA Organized Section, like this one, can provide an additional way to network and get the word out about your research, teaching, and other professional activities. Consider  joining  the Elections, Public Opinion, and Voting Behavior section today!

• Join the Campaign

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LinkedIn PowerPoint Quiz Answers (2024)

• Post last modified: 22 March 2023
• Reading time: 62 mins read
• Post category: LinkedIn Certification

The LinkedIn Skill Assessments feature allows you to demonstrate your knowledge of the skills you’ve added on your profile. Job posters on LinkedIn can also add Skill Assessments as part of the job application process. This allows job posters to more efficiently and accurately verify the crucial skills a candidate should have for a role.

The topics in the MS PowerPoint assessment include:

• Animation and Transitions
• SmartArt and WordArt
• Collaboration

Question Format

Multiple Choice

Table of Content

• 1.1 When you create a new, blank presentation, you begin with one blank slide. What is the layout name of this slide?
• 1.2 What do you use to align objects on a slide to one another?
• 1.3 Which option must be enabled if you want to manipulate the playback of a video file during a presentation?
• 1.4 Which feature analyzes the content of your slides and offers suggested options?
• 1.5 Where can you configure advanced PDF options?
• 1.6 Which tab is best for adding various types of objects to slides?
• 1.7 Which SmartArt category can you use to make informational graphics out of bullet points?
• 1.8 What is NOT checked when you run the Accessibility Checker?
• 1.9 How can you change the appearance of a table in one click?
• 1.10 Which option changes a text box so that it automatically changes shape to fit longer text?
• 1.11 Which three shadow properties can be adjusted in PowerPoint?
• 1.12 You want your presentation to play continuously on screen, What option must you set?
• 1.13 Which statement about the Compress Pictures command is true?
• 1.14 You have a Word document you would like to import as an outline into a PowerPoint presentation. How should you format the text in your Word document that you want to be the slide content?
• 1.15 In what manner can you filter comments?
• 1.16 Which Arrow Options choice will hide the mouse cursor when presenting, except when the cursor is moved?
• 1.17 Which chart type is best suited for displaying annual financial data for a 10-year period?
• 1.18 If you want to create an organizational chart, which SmartArt category works best?
• 1.19 When printing multiple handouts, which option will group multipage documents together and make them easier to distribute?
• 1.20 How can you adjust which data in a table is used when working on a chart?
• 1.21 Which view lets you see additional information on a second monitor when delivering a presentation?
• 1.22 A picture looks good in your slide show but pixelated when you print it. How can you fix this?
• 1.23 You have inserted a video on a slide and want the video to automatically start 2 seconds after the associated slide appears. How can you accomplish this?
• 1.24 When inserting pictures, how can you avoid distorting the pictureif it is resized later?
• 1.25 Which Protect Presentation option protects a presentation from accidental changes?
• 1.26 How can you change colors in your presentation all at once without changing the design?
• 1.27 What function allows you to borrow slides from a presentation fro an other presentation?
• 1.28 What is the term used for the arrangement of elements on a slide, such as Title and Content?
• 1.29 How would you change a list of text to a visual diagram on a slide?
• 1.30 You need to reorder the levels of shapes on a SmartArt diagram. How can this be done
• 1.31 Why would you trim a video?
• 1.32 How would you save your layouts, colors, fonts, effects, background styles and content for others to use with their presentations?
• 1.33 You are creating an industrial presentation of a new software program and would like to include an image of your program. How can you use PowerPoint to do this?
• 1.34 How can you add more layouts to the Layout Gallery?
• 1.35 Why would you use the Outline view?
• 1.36 The text on the left and right of the slide are cut off in print preview. How can you fix this in the printer settings?
• 1.37 In addition to organizing your slides, how else can sections increase your productivity?
• 1.38 You have many slides that you need to reorder. How would you accomplish this?
• 1.39 What can be seen in Presenter view?
• 1.40 How can you format the entire contents of a text box?
• 1.41 You have a large number of objects on your slide. How can you use the selection pane to help you identify the objects quickly?
• 1.42 How would you ensure a consistent appearance and placement across your slides even though the content might be different?
• 1.43 How can you add icons to SmartArt?
• 1.44 You are having trouble playing back a presentation smoothly. What should you do?
• 1.45 Which feature lets you configure multiple combinations of specific slides for playback from a larger slideshow?
• 1.46 When working with a mapchart, which options can be drawn?
• 1.47 You receive feedback from two clients on your presentation. Each person sent you an edited file. What’s the best way to review their changes?
• 1.48 How do you promote subbullets to top-level bullets?
• 1.49 After you select the chart icon in a placeholder, what is the next step to create a chart?
• 1.50 How would you show a correlation between the amount of chocolate a city consumes and the number of crimes committed?
• 1.51 You want to use a morph transition between two slides. How do you set up the slides?
• 1.52 Why would you use the Rehearse Timings command?
• 1.53 How can you make an inserted picture look like it has been sketched or painted?
• 1.54 How can you modify your presentations entire set of colors with a single change?
• 1.55 You want to print full-page slides to fill 8. 5-by-11-inch paper, but they appear too small in the preview. What is the best way to rectify this?
• 1.56 You want to import a Word document as an outline into a Powerpoint presentation. How should you format the Word documents text that you want to be the slide content?
• 1.57 What is not reviewed when you check a slide shows accessibility?
• 1.58 What is the easiest way to change the appearance of a table?
• 1.59 Why would you use Outline view?
• 1.60 Before publishing a presentation, which option should you use to scan a presentation and look for issues that may make it difficult for someone with a visual impairment to read?
• 1.61 How can you filter the series and/or categories in a chart?
• 1.62 You have an object that needs to follow a specific motion path – including curves, straight lines, and loops – on the slide. Which animation gives the capability to draw this?
• 1.63 When you add encryption to a PowerPoint presentation, what does it do?
• 1.64 Which type of shape allows you to add text that can be moved around, formatted with styles and effects, or grouped with other objects?
• 1.65 You need to apply a special effect that will display as the presentation moves from one slide to the next slide. Which should you apply?
• 1.66 Which option can be used to import selected slides from a previously created presentation into a new presention without manually copying and pasting them?
• 1.67 The picture on the left is the original image. The image on the right is smaller, but the flowers are the same size and part of the picture has been removed. What method was used to format this picture?
• 1.68 You are copying and pasting slides from another presentation into your presentation. How will you ensure the new slides are consistent in appearance with your existing slides?
• 1.69 What happens when you press the Esc key while playing a From Current Slide or From Beginning slide show?
• 1.70 When you save a presentation with a .potx file extension, which type of Powerpoint file is created?
• 1.71 You are presenting with two monitors. Which view displays a preview of next slide and speaker notes on the second monitor?
• 1.72 Which effect can a presenter use to add motion to an object on a slide?
• 1.73 When you copy data from an Excel worksheet into a slide to create a table, which Paste option will allow you to edit the table data in Excel?
• 1.74 You created a photo album with transitions and want to send it to friends, but the file size is too large for email. How can you use one command to fix this?
• 1.75 In a presentation about voter turnout, you are illustrating various data with charts. Which type of information would you present in a pie chart?
• 1.76 A few lines of text are overflowing the placeholder on your slide. How can you fix it on your slide?
• 1.77 If you want to create an organizational chart that illustrates the reporting relationships within the organization, which SmartArt category would you use?
• 1.78 What do the labels Introduction and ProjectsByMonth indicate?
• 1.79 There are objects and text hidden behind an image on your slide. With the image selected, what could you adjust to make the objects and text appear “through” the image?
• 1.80 How can you create a new presentation that includes prebuild layouts, colors, fonts, background styles, and content?
• 1.81 How can you reorder the levels of shapes in a SmartArt diagram?
• 1.82 When you insert a new slide in a presentation, where is it placed in relation to the exsisting slides?
• 1.83 In Normal view, which pane is used to add text and reference information for the speaker?
• 1.84 What is the best way to modify slide’s appearance or content while in Note Page view?
• 1.85 You want to group your slides based on their content to better organize your presentation. How would you accomplish this?
• 1.86 How do you remove the background of an inserted impage?
• 1.87 In a SmartArt graphic, how can you add text to the shapes other than directly in each shape?
• 1.88 You want to add two images to a presentation, but one is a bit blurry and another is quite dark. What feature on the Picture Format tab can you use to adjust them?
• 1.89 A colleague has given you a presentation file, and you want to use one of the slides from that presentation in your own. How would you do this?
• 1.90 Which view allows you to select individual objects on a slide?
• 1.91 When in Slide Show view, what happens when you hold Ctrl (Windows) or Command (Mac) and click the left mouse button?
• 1.92 Which PowerPoint feature is best used to set up slide timings so slides automatically advance?
• 1.93 You plan to distribute a presentation. In which format might you save it to ensure that it appears as an automatically playing slide show composed of individual slides, regardless of the recipients’ platform or software applications?
• 1.94 Which type of illustration can be rotated while in Slide Show view, allowing the audience to see all sides of an image?
• 1.95 You inserted audio into a presentation. Which two options must you enable for the audio to play throughout all slides and repeat until it is stopped?
• 1.96 Your presentation has a slide on which the text appears scrambled. What happened?
• 1.97 Your colleagues have collaborated in a presentation with you, adding new content and reusing slides from other presentations. You notice the layouts vary throughout the slides: differently sized and positioned containers, and different headers and footers. Where can you make a change that will automatically be applied to all slides in the presentation?
• 1.98 Your company logo appears on all slides in a presentation as part of the overall design template’s Slide Master. How can you prevent the logo from appearing on one particular slide?
• 1.99 Many PowerPoint slide backgrounds use a color effect that is gradually shaded from top to bottom. What is this effect called?

LinkedIn PowerPoint Quiz Answers

When you create a new, blank presentation, you begin with one blank slide. what is the layout name of this slide.

• Title slide
• Title and Content

What do you use to align objects on a slide to one another?

• Layout tools
• Arrange tools
• Design Ideas feature
• Picture tools

Which option must be enabled if you want to manipulate the playback of a video file during a presentation?

• Record Side Show
• Use Presenter View
• Show Playback Controls
• Show Media Controls

Which feature analyzes the content of your slides and offers suggested options?

• Design ideas
• Browse for Themes

Where can you configure advanced PDF options?

• the Options tab
• the Home tab
• the Print dialog box
• the Share tab

Which tab is best for adding various types of objects to slides?

Which smartart category can you use to make informational graphics out of bullet points.

• all of these answers

What is NOT checked when you run the Accessibility Checker?

• reading order
• slide titles
• missing alt text

How can you change the appearance of a table in one click?

• Apply a cell stye.
• Apply a graphic style.
• Apply a table style.
• Right-click a table and choose a new style.

Which option changes a text box so that it automatically changes shape to fit longer text?

• Resize shape to fit text
• Do not autofit
• none of these answers
• Shrink text on overflow

Which three shadow properties can be adjusted in PowerPoint?

A. Focal Point B. Depth C. Blur D. Angle E. Distance

You want your presentation to play continuously on screen, What option must you set?

• Use Presenter view
• Advance slides manually
• Advance slides automatically
• Loop continuously until Esc

Which statement about the Compress Pictures command is true?

• You can compress all images at the same time.
• You cannot delete cropped areas.
• There are no options for print output.
• You can compress only one image at a time.

You have a Word document you would like to import as an outline into a PowerPoint presentation. How should you format the text in your Word document that you want to be the slide content?

• Heading Style 2
• Content Style

In what manner can you filter comments?

Which arrow options choice will hide the mouse cursor when presenting, except when the cursor is moved, which chart type is best suited for displaying annual financial data for a 10-year period.

• donut chart
• line or area chart

If you want to create an organizational chart, which SmartArt category works best?

• Relationship

When printing multiple handouts, which option will group multipage documents together and make them easier to distribute?

• Print on Both Sides
• Print One-sided

How can you adjust which data in a table is used when working on a chart?

• Click the Switch Row/Column button.
• Click the Refresh Data button.
• Click the Select Data button.
• Click the Edit Data button.

Which view lets you see additional information on a second monitor when delivering a presentation?

• Normal view
• Reading view
• Slide Sorter view
• Presenter view

A picture looks good in your slide show but pixelated when you print it. How can you fix this?

• Compress the picture and use the Print (220 ppi) resolution setting.
• Select Best scale for slide show, increase the picture resolution and resize the picture to the original size
• Remove picture compression.
• Change the printer property settings to print in a higher resolution.

You have inserted a video on a slide and want the video to automatically start 2 seconds after the associated slide appears. How can you accomplish this?

• Use playback timings.
• Use animation timings.
• Use animation triggers.
• Use video timings.

When inserting pictures, how can you avoid distorting the pictureif it is resized later?

• Use best scale for slide show.
• Lock the aspect ratio.
• Compress the picture.
• Set the picture to be relative to the orignal picture size.

Which Protect Presentation option protects a presentation from accidental changes?

• Always Open Read-Only
• Add a Digital Signature
• Encrypt with Password
• Mark as Final

How can you change colors in your presentation all at once without changing the design?

• Apply a Color Template.
• Apply a Color Theme.
• Apply a Color Palette.
• Customize the Color Fills.

What function allows you to borrow slides from a presentation fro an other presentation?

• Borrow Slides
• There is no special fuction. You must use Copy and Pase
• Reuse Slides
• Find Slides

What is the term used for the arrangement of elements on a slide, such as Title and Content?

How would you change a list of text to a visual diagram on a slide.

• Convert to WordArt.
• Convert to Shapes.
• Convert to SmartArt.
• Convert to Picture.

You need to reorder the levels of shapes on a SmartArt diagram. How can this be done

• Retype the text
• Change the shapes from rifht to left.
• Promote Shapes and/or Demote Shapes.
• Move the shapes up and down.

Why would you trim a video?

• To crop the video thumbnail.
• To resize the video.
• To remove background noise in the video.
• To remove some of the beginning and/or end of the video.

How would you save your layouts, colors, fonts, effects, background styles and content for others to use with their presentations?

• Export the file as a template.
• Save the file as a PDF.
• Save the file as a POTX.
• Save the file as a PPTX.

You are creating an industrial presentation of a new software program and would like to include an image of your program. How can you use PowerPoint to do this?

• Use the Copy command.
• Use the Screenshot command.  (Office2019)
• Use the Print key.
• Use the Capture Screen command.

How can you add more layouts to the Layout Gallery?

• Customize the Layout Gallery.
• Add Slide Layouts to the Slide Master.
• Use the Add Layouts command.
• Use the Insert Layouts command.

Why would you use the Outline view?

• To see only the text in placeholders.
• To present the slides at a high level.
• To add text from a Word document.
• To organize your slides.

The text on the left and right of the slide are cut off in print preview. How can you fix this in the printer settings?

• Unselect High Quality.
• Select Scale to Fit Paper.
• Select Frame Slides.

In addition to organizing your slides, how else can sections increase your productivity?

• In addition to organizing your slides, how else can sections increase your productivity?.
• All the slides in a section can be selected at once to apply changes such as transitions, layouts or hiding slides.
• Sections can be exported as a separate presentation.
• You can resize all the slides in a section at once.

You have many slides that you need to reorder. How would you accomplish this?

• Move the slides in Slide Show view.
• Renumber the slides in Outline view.
• Move the slides in the Slide Sorter view.
• Reset the slide footers.

What can be seen in Presenter view?

• Side Navigator.
• all of these answers.

How can you format the entire contents of a text box?

• Click the dashed/dotted border of the text box.
• Right-click the text box and select Select All.
• Click the Fond button on the Home tab.
• Double-click any word in the text box.

You have a large number of objects on your slide. How can you use the selection pane to help you identify the objects quickly?

• Select the show/hide icons next to the objects.
• Rename the objects with meaningful names.
• Drag and drop the objects.
• Tab through the objects and note each object as it’s highlighted.

How would you ensure a consistent appearance and placement across your slides even though the content might be different?

• Use styles.
• Use bullet points.
• Use layouts.

How can you add icons to SmartArt?

• Select the picture icon in a SmartArt Picture Diagram.
• Select the icon in a SmartArt Icon Diagram.
• Select Insert Icon and place it on top of the SmartArt Diagram.
• Select a SmartArt shape and select Insert Icon.

You are having trouble playing back a presentation smoothly. What should you do?

• Copy the presentation to your internal hard drive.
• Clear the Disable hardware graphics acceleration option.
• Select the Show without animation option.

Which feature lets you configure multiple combinations of specific slides for playback from a larger slideshow?

• Record Slide Show
• Rehearse Timings
• Custom Slide Show

When working with a mapchart, which options can be drawn?

• Postal code
• All of these answers

You receive feedback from two clients on your presentation. Each person sent you an edited file. What’s the best way to review their changes?

• Review > Accept
• Review > Smart Lookup
• Review > Compare
• Review > Show Comments

How do you promote subbullets to top-level bullets?

• Press Shift+Tab.
• Right-click the bulleted text and select Promote.
• Press the Esc key.
• Press the Spacebar.

After you select the chart icon in a placeholder, what is the next step to create a chart?

• Select the chart elements.
• Select the chart type.
• Select the chart data in Excel.
• Select the chart style.

How would you show a correlation between the amount of chocolate a city consumes and the number of crimes committed?

• Use a bar chart.
• Use a column chart.
• Use a line chart.
• Use a scatter chart.

You want to use a morph transition between two slides. How do you set up the slides?

• The two slides must have multiple objects in common with different names in the Selection pane
• The two slides must have at least one obiect in common that is in a different position on the second slide
• The second slide must have different objects that are in the same position as the objects on the first slide
• The two slides must have at least one obiect in common that is in the same position on the second slide

Why would you use the Rehearse Timings command?

• to adiust animation timings
• to record your slide show
• to remove transition timings
• to determine how long it takes to present your slide show

How can you make an inserted picture look like it has been sketched or painted?

• Apply a picture effect
• Apply a draw effect
• Apply an artistic effect
• Apply a picture style

How can you modify your presentations entire set of colors with a single change?

• Apply a color template
• Customize a color fill
• Apply a color palette
• Apply a color theme

You want to print full-page slides to fill 8. 5-by-11-inch paper, but they appear too small in the preview. What is the best way to rectify this?

• Change the slide size to standard
• Repeatedly bump up the scale setting print option until it fills the page in the preview
• Change the scale setting print option to 100%
• Select the Scale to Fit Paper print option

You want to import a Word document as an outline into a Powerpoint presentation. How should you format the Word documents text that you want to be the slide content?

What is not reviewed when you check a slide shows accessibility, what is the easiest way to change the appearance of a table.

• Apply a graphic style
• Apply a table style
• Right-click a table and select a new style
• Apply a cell style

Why would you use Outline view?

• to quickly edit titles or bulleted text in the Slide pane
• to view the comments on all slides
• to add notes in the Slide pane
• to change the font on all slides

Before publishing a presentation, which option should you use to scan a presentation and look for issues that may make it difficult for someone with a visual impairment to read?

• Protect Presentation
• Inspect Document
• Check Compatibility
• Check Accessibility

How can you filter the series and/or categories in a chart?

• Edit the data to remove the data for the series or category.
• Switch the rows and columns.
• Use a filter so the data series or category does not display.
• Change the chart type.

You have an object that needs to follow a specific motion path – including curves, straight lines, and loops – on the slide. Which animation gives the capability to draw this?

• Custom Path.

When you add encryption to a PowerPoint presentation, what does it do?

• scrambles the text so it is unreadable when opened.
• shares the file in cloud-based platform.
• protects the file with a password.
• requires the user to log in to their Office 365 account.

Which type of shape allows you to add text that can be moved around, formatted with styles and effects, or grouped with other objects?

You need to apply a special effect that will display as the presentation moves from one slide to the next slide. which should you apply.

• design ideas

Which option can be used to import selected slides from a previously created presentation into a new presention without manually copying and pasting them?

• Reuse Slide
• Slides from Outline
• Add Section

The picture on the left is the original image. The image on the right is smaller, but the flowers are the same size and part of the picture has been removed. What method was used to format this picture?

• changing the width and height with aspect ration locked
• drapping a corner sizing handle
• using the  Crop to Shape  tool
• using the Crop tool

You are copying and pasting slides from another presentation into your presentation. How will you ensure the new slides are consistent in appearance with your existing slides?

• Select  Keep Source Formatting  when pasting the slides.
• Select  Use Destination Theme  when pasting the slides.
• Change both presentations to the same theme before copying and pasting.
• Select  Picture  when pasting the slides.

What happens when you press the Esc key while playing a From Current Slide or From Beginning slide show?

• The current slide is temporary hidden.
• The slide show ends and you return to your previous view.
• The next slide appears.
• Nothing happens.

When you save a presentation with a .potx file extension, which type of Powerpoint file is created?

• macro-eanbled presentation

You are presenting with two monitors. Which view displays a preview of next slide and speaker notes on the second monitor?

• Master view

Which effect can a presenter use to add motion to an object on a slide?

When you copy data from an excel worksheet into a slide to create a table, which paste option will allow you to edit the table data in excel.

• Keep Formatting
• Keep Text Only

You created a photo album with transitions and want to send it to friends, but the file size is too large for email. How can you use one command to fix this?

• Click  Export  in Backstage view and embed the presentation in an email.
• Compress all the pictures to the Email (96 ppi) resolution.
• Use the Share feature in Backstage view and petty email to automatically compress the pictures.
• Use the Share feature in Backstage view to create a pdf to send as an attachment.

In a presentation about voter turnout, you are illustrating various data with charts. Which type of information would you present in a pie chart?

• the decline of voter turnout by county
• trends in voter turnout over the part 10 years
• what percentage of the whole population voted
• how many people voted in various geographic regions on a map

A few lines of text are overflowing the placeholder on your slide. How can you fix it on your slide?

• Delete the overflowing text.
• Select  Stop Fitting Text to This Placeholder .
• Select Autofit Text to Placeholder.
• Click  Collapse .

If you want to create an organizational chart that illustrates the reporting relationships within the organization, which SmartArt category would you use?

What do the labels introduction and projectsbymonth indicate.

• section names
• hidden slides
• links to the next slide

There are objects and text hidden behind an image on your slide. With the image selected, what could you adjust to make the objects and text appear “through” the image?

• Compress Picture
• Transparency
• Corrections
• Artistic Effects

How can you create a new presentation that includes prebuild layouts, colors, fonts, background styles, and content?

• Use a theme.
• Use a design.
• Use a template.
• Use a style.

How can you reorder the levels of shapes in a SmartArt diagram?

• Promote or demote the shapes.
• Change shapes from left to right.
• Retype the text.

When you insert a new slide in a presentation, where is it placed in relation to the exsisting slides?

• as the first slide.
• before the current slide.
• at the end of the exsisting slide.
• after the currently selected slide.

In Normal view, which pane is used to add text and reference information for the speaker?

What is the best way to modify slide’s appearance or content while in note page view.

• Edit the thumbnail of the slide as needed.
• Slides cannot be modified while in Note Page view.
• Use the Zoom slider to magnify the slide contents
• Right-click the slide and select edit

You want to group your slides based on their content to better organize your presentation. How would you accomplish this?

• Create an outline in the outline view and rearrange slides.
• Add a table of contents slide and link the remaining slides to it.
• Add sections and move the slides into the appropriate sections.
• Create custom shows and add the slides into the shows.

How do you remove the background of an inserted impage?

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• v.119(29); 2022 Jul 19

Civilian national service programs can powerfully increase youth voter turnout

Cecilia hyunjung mo.

a Department of Political Science, University of California, Berkeley, CA 94720;

John B. Holbein

b Frank Batten School of Leadership and Public Policy, University of Virginia, Charlottesville, VA 22903;

Elizabeth Mitchell Elder

c Center for the Study of Democratic Politics, Princeton University, Princeton, NJ 08544;

d Bobst Center for Peace and Justice, Princeton University, Princeton, NJ 08544

Author contributions: C.H.M. and J.B.H. designed research; C.H.M. and J.B.H. performed research; C.H.M., J.B.H., and E.M.E. analyzed data; and C.H.M., J.B.H., and E.M.E. wrote the paper.

Associated Data

Nonproprietary anonymized replication data and code have been deposited in Harvard Dataverse ( 78 ). The nationwide voter file and TFA administrative data, which contain sensitive personally identifiable information, that are required to replicate our core findings can only be accessed through the Data Trust and TFA. The Data Trust data are proprietary and we have signed data use agreements that prohibit us from sharing them. The individual-level data can be provided by Bill Dune at The Data Trust pending scientific review and a completed material transfer agreement. Requests for the individual-level data should be submitted to [email protected] . The TFA admissions data are proprietary and we have signed data use agreements that prohibit us from sharing them. The individual-level data can be provided by TFA pending scientific review and a completed material transfer agreement. Requests for the individual-level data should be submitted to gro.aciremarofhcaet@hcraeser . All other data needed to evaluate the conclusions in this paper are present in this paper and/or supporting information.

Significance

Enrolling young people to participate as Teach For America (TFA) teachers has a large positive effect on rates of voter turnout among those young people who participate. This effect is considerably larger than many previous efforts to increase youth voter turnout. Each year, TFA places thousands of young adults in 2-y teaching positions in disadvantaged communities around the United States. After their 2 y of service, we find that these young adults vote at a rate 5.7 to 8.6 percentage points higher than that of similar nonparticipant counterparts. Our results suggest that civilian national service programs targeted at young people show great promise in narrowing the enduring participation gap between younger and older citizens in the United States.

Low rates of youth voting are a feature of contemporary democracies the world over, with the United States having some of the lowest youth turnout rates in the world. However, far too little is known about how to address the dismal rates of youth voter participation found in many advanced democracies. In this paper, we examine the causal effect of a potentially scalable solution that has attracted renewed interest today: voluntary national service programs targeted at the youth civilian population. Leveraging the large pool of young people who apply each year to participate in the Teach For America (TFA) program—a prominent voluntary national service organization in the United States that integrates college graduates into teaching roles in low-income communities for 2 y—we examine the effect of service participation on voter turnout. To do so, we match TFA administrative records to large-scale nationwide voter files and employ a fuzzy regression discontinuity design around the recommended admittance cutoff for the TFA program. We find that serving as a teacher in the Teach For America national service program has a large effect on civic participation—substantially increasing voter turnout rates among applicants admitted to the program. This effect is noticeably larger than that of previous efforts to increase youth turnout. Our results suggest that civilian national service programs targeted at young people have great promise in helping to narrow the stubborn and enduring political engagement gap between younger and older citizens.

Political engagement is one of the central pillars of democracy ( 1 – 4 ). Yet, the United States holds the dubious honor of having one of the lowest and most unequal rates of political participation in the world. One of the largest gaps in voter turnout is by age—in the United States, it is not altogether uncommon to see older citizens turn out at a rate twice as high as that of their younger counterparts ( 5 ). Fig. 1 puts this gap into perspective. Fig. 1 A shows the gap between older (i.e., those ages 60+) and younger (i.e., those ages 18 to 29) voters across the world; Fig. 1 B shows how various generations in the United States have aged into voting. As can be seen, the United States has the largest gap between older and younger voters in measured countries, and there is some evidence that youth turnout may be getting lower over time. Based on data from the Current Population Survey, young voters are turning out at rates lower than previous generations did at the same age. Whatever comparison point one uses, however, youth voter turnout in the United States is, in a word, dismal. This pattern has many known causes—including the uniquely high obstacles to registration and voting in the United States and other societal inequities ( 5 – 16 ).

Youth voter turnout in the United States is low and may be declining. ( A ) The age gap in voter turnout across 34 countries in the Comparative Study of Electoral Systems (Module 4; via ref. 5 ). Bars show the turnout rate for those 60+ y old minus those 18 to 29 y old in each country. ( B ) Voter turnout in the United States (1978 to 2014 midterms) by age and generation. Source : Current Population Survey November Supplement (recreated as reported by Pew Research Center and in ref. 5 ). Following Pew’s coding, Millennials are defined as those born between 1981 and 1996, Generation X as those born between 1965 and 1980, Baby Boomers as those born between 1946 and 1964, and the Silent Generation as those born between 1928 and 1945.

However, there exists far too little knowledge of what can be done—particularly at scale—to ameliorate this large, consequential, and stubborn gap in political participation. At present, we know that many campaign-based efforts to get out the vote have very modest effects ( 17 – 20 ). While some get-out-the-vote programs have shown promise at raising youth turnout (e.g., ref. 6 ), these interventions are few and far between and are often implemented only in select areas. In this paper, we take a step toward filling this lacuna in the scientific literature by exploring one of the core proposed public policies designed to help raise youth civic engagement: voluntary civilian (i.e., nonmilitary) national service programs, which we simply refer to as national service programs hereafter. We use an established and prominent national service program—Teach For America (TFA)—as our empirical case.

Given the core role that young people play in shaping the future health of civic life, young people have been the objects of many service-based efforts to inculcate the values and practices upon which democratic citizenship depends ( 21 ). When discussing national service programs, scholars have examined two types of programs—those voluntary and those nonvoluntary. Voluntary national service programs, like the Peace Corps and AmeriCorps, stand in contrast to compulsory national service programs, like those involving mandatory military service ( 22 – 24 ), in both the nature of their service activities and their noncompulsory element. Many voluntary national service programs have historically targeted and attracted young adults. For example, the National Civilian Community Corps (NCCC), an AmeriCorps program, requires that participants be between 18 and 26 y old, and about 85% of Peace Corps volunteers have historically been recent college graduates ( 25 ).

Numerous scholars have found evidence of a positive relationship between voluntary youth national service and future civic engagement. Indeed, there are many theoretical mechanisms by which national service may affect participation rates (for a full list, see SI Appendix , section B.1 ). Extant sociology research finds that participation in youth service is associated with increases in 1) feelings of civic duty ( 26 ); 2) understanding of civic issues facing a community, as well as feelings of obligation and connection to a community ( 27 , 28 ); 3) engagement in civic life (e.g., participating in a community action program, volunteering, working for a nonprofit, or participating in a community service organization) ( 29 – 31 ); and 4) a sense of duty to improve government systems for the common good ( 28 , 32 ).

Despite these promising findings from conditional-on-observable research designs, we currently lack large-scale studies that are designed to detect the causal effects of national service programs. This raises serious inferential concerns, as previous positive associations may be driven more by the types of people who select into national service programs rather than the independent effects of national service programs. Moreover, while some evidence indicates that service learning encourages participants to better understand the challenges communities face ( 33 ), some scholars have noted that there is less evidence on the impact of service learning on political behavior such as voting or interacting with government officials ( 34 ). More pointedly, one prominent study of a youth national service program recently argued that graduates of that program are engaged in fewer service activities/civic duties, vote at lower rates, and are employed in less “prosocial” jobs than an observationally similar comparison group ( 21 ). However, these less-positive studies did not examine validated voter turnout, nor did they identify the causal effect of participating in a civilian voluntary national service program.

To start to address these inferential gaps in studies of the effects of national service programs on civic engagement and political participation, we examine the causal effect of participating in TFA, a prominent national service program established in 1990 with a mission “to enlist, develop, and mobilize as many as possible of our nation’s most promising future leaders to grow and strengthen the movement for educational equity and excellence.” * To accomplish this goal, TFA places college graduates into teaching positions in low-income and underserved communities throughout the United States. Applicants who are admitted and matriculate into the program serve as teachers for a period of 2 y. † Alumni may, and often do, continue to serve as teachers at their assigned schools; however, active service time in TFA is limited to 2 y.

To evaluate the effect of this program, we utilize a large-scale, proprietary administrative dataset from TFA that contains information on all program applicants during the 2007 to 2015 period. Because nearly 80% of TFA applicants from this period are younger than 25 y old, this represents a unique opportunity to study the causal effects of a national service program on youth turnout. We match these TFA data with nationwide voter file data that contain information on all registered voters (N ≈ 200 million) in the United States. With this combined dataset, we take advantage of a natural experiment wherein we compare individuals who were marginally recommended to be accepted to participate (based on TFA’s proprietary admittance threshold) to those who were marginally recommended to be rejected from participating. This threshold allows us to employ a fuzzy regression discontinuity design, a method of causal inference developed and refined over the past 60 y that leverages continuity in potential outcomes around an arbitrary cutoff ( 35 – 40 ) and benchmarks well with randomized control trial estimates ( 41 ).

We find that participating in TFA has a large effect on youth political participation. Ceteris paribus, 2 y after applying for TFA (which is the duration of the TFA program), individuals who scored marginally above the admissions cutoff score and, hence, were recommended for admission into TFA, vote at a rate 5.7 to 8.6 percentage points higher than those who were marginally rejected (conventionally this estimate is called the intention to treat [ITT] effect). When we account for the fact that the admissions cutoff score has a probabilistic (rather than deterministic) relationship to likelihood of selection into the program, we estimate that participants in TFA are 30.1 to 42.3 percentage points more likely to vote than similar nonparticipants (conventionally this is called the complier average causal effect [CACE]). To put these estimates into perspective, the ITT effect is 3 to 14 times larger than standard get-out-the-vote (GOTV) programs and ∼20 to 30% of the entire turnout gap between old and young voters (which is about 30 percentage points, depending on the election considered). Regardless of the comparison point one uses, these effects are large.

Our work has important practical and conceptual implications. The House and the Senate are currently considering whether to expand national service to bolster COVID-19 pandemic recovery (see, for example, S.3964 and H.R.1162), and the expansion of national service is regularly discussed in Congress (for the full list of the over 250 bills having to deal with national service in recent Congresses, see SI Appendix , Table S2 ). States are also investing in regional service programs targeting youth. For example, in January 2022, the state of California announced the creation of the #CaliforniansForAll College Corps to create debt-free college pathways for students who commit to serve their communities ( 42 ). While future studies of the causal effect of different types of national and regional service programs are needed, our findings provide insight into the potential benefits of voluntary civilian service programs targeting youth. We find that the TFA experience catalyzes young participants to vote in a way rarely seen in previous studies. More broadly, these results help us understand how exposure to various social ills, paired with the act of serving to meet critical needs in disadvantaged communities, can affect the prosocial behavior of youth in American society. Voluntary civilian service experiences have the potential to change how youth act in the democratic domain and, in so doing, can help narrow the stubborn participatory gap between young and older citizens.

To estimate the effect of TFA admittance and participation on voter turnout, we linked a national voter file database from the Data Trust LLC (snapshot taken 29 September 2017) to records from TFA’s administrative data for the 2007 through 2015 admissions cycles and a 2015 to 2016 original survey of TFA applicants. TFA’s administrative data include an application quality score that strongly and discontinuously predicts admission into the program. We employ a regression discontinuity design around the TFA admittance cutoff to estimate the effect of admission to and participation in TFA on voting in subsequent national elections. (We describe the TFA admissions cutoff in greater detail in Materials and Methods and SI Appendix , sections A.3 and A.4 . There too, we describe the methods we used to link TFA and voter file records.)

Fig. 2 displays our regression discontinuity estimates, where the outcome of interest is whether one voted in either the 2012 or the 2014 election (see SI Appendix , section A.7 for an alternative presentation of these results). The green lines in Fig. 2 reflect a matching strategy, hereafter referred to as “Match 1,” which employs names from TFA’s application file and year of birth from the survey. The blue lines reflect an alternative matching strategy, hereafter referred to as “Match 2,” employing names and year of birth from TFA’s administrative file. In the case of Match 2, when year of birth was missing, we employed applicants’ graduation year to estimate their year of birth (see SI Appendix , section A.4 for more details). Overall, these matching strategies lead us to a similar conclusion and illustrate the robustness of our results.

Effect of TFA experience on voter turnout. ( Top ) The CACE of TFA acceptance on turnout. ( Bottom ) The ITT effect. Each panel shows the effect of the treatment on turnout before the treatment occurred, followed by the effect of treatment on turnout in elections 2 or more y after treatment.

In the left pair of estimates in each panel of Fig. 2 (see also SI Appendix , Fig. S6 A and B for regression discontinuity design [RDD] plots), we compare rates of voter turnout before TFA applicants were either admitted to or rejected by the program across the admissions cutoff. As can be seen, before applying to the TFA program, eventual marginal admits and marginal declines voted at rates that are statistically and substantively indistinguishable from one another. This can be seen by the insignificant and close-to-zero estimates of the CACE and ITT effect in the pretreatment period. In these pretreatment elections, the CACE ranges from –1.3 percentage points ( P = 0.96) to 0.2 percentage points ( P = 0.99), and the ITT effect ranges from –0.3 ( P = 0.93) to –0.2 percentage points ( P = 0.96). In other words, TFA participants were statistically equally as likely to participate in elections as nonparticipants when they applied to join—and before they had completed any parts of—the TFA program. This pretreatment balance lends support to the key assumption that barely admitted and barely rejected applicants would not have voted at different rates at the cutoff without the intervention of TFA.

In contrast, the second pair of estimates in each panel of Fig. 2 (see SI Appendix , Fig. S6 C and D for RDD plots) shows the rates of voter turnout for TFA applicants 2 or more y after their admittance into or rejection from the program (i.e., when a participant would have completed their TFA participation). We find that TFA participation increases voter turnout in the 2012 and 2014 elections for treated respondents. The intention to treat ( Fig. 2 , Bottom ) estimate ranges from 5.7 ( P = 0.057) to 8.6 percentage points ( P < 0.001). The complier average causal effect ( Fig. 2 , Top ) of TFA participation ranges from 30.1 ( P = 0.028) to 42.3 percentage points ( P < 0.001). If we examine whether TFA participation has a positive effect on the proportion of elections in which an applicant voted, we similarly find positive effects (see SI Appendix , Fig. S9 , and the rest of SI Appendix , section A.9 for other alternative dependent variables).

These estimated effects are substantively meaningful. They are large, but not unreasonable given the nature of the program. In considering the magnitude of our effects, we pause a moment here to note the size and scope of the TFA program and TFA’s known effects on the antecedents to voting. Unlike many get-out-the-vote programs, which draw the attention of voters for a matter of minutes (at most), the TFA experience is quite an intensive treatment. TFA is not a light nudge, but is, rather, a fully immersive 2-y treatment. Those who serve as TFA teachers spend all of their working—and many of their nonworking—hours being exposed to a whole new set of experiences, networks, and cultures. In this immersive environment, TFA teachers are exposed to many students, teachers, administrators, parents, communities, and/or contexts that are outside of their normal realm of experience. Previous research on the attitudinal effects of TFA has shown that this immersive experience fundamentally shifts some of the antecedents to voting. For example, past research on TFA shows that TFA substantially increases participants’ dissatisfaction with the current political system, while simultaneously increasing their ability to see the plight of disadvantaged communities and empowering them with the optimism and efficacy needed to believe that positive reform in the policy arena is possible ( 33 , 43 ). Unlike other get-out-the-vote programs ( 44 , 45 ), TFA fundamentally transforms treated subjects’ attitudes. In short, we have reason to expect that the effects we observe will be larger than those of many of the lighter-touch nudges to vote studied in the past. And this is, in fact, exactly what we observe.

There are several benchmarks against which we can compare our effect sizes. None of these are perfect—each comes with its own limitations and assumptions—but using them together, we can get a sense of the magnitude of our effect. Moreover, all end up pointing in the same direction—that the effect of TFA is large and meaningful. Below, we mostly benchmark our ITT effects to the ITT effects obtained from other comparable interventions intended to increase voter turnout, to make the comparisons as equivalent as possible.

First, we can compare the effect of TFA participation to the voter turnout gap between young and older voters. As Holbein and Hillygus ( 5 ) report, in the two most recent national elections, the gap between young (i.e., those ages 18 to 29) and older (i.e., those ages 60+) is somewhere between 28.0 (in 2016) and 32.9 (in 2018) percentage points. As such, the ITT effect of TFA on voter turnout represents 17.3 to 30.7% of the entire gap between younger and older voters (depending on which of the two estimates of the effect or which turnout gap estimate is used). This suggests that an effect of the size of TFA’s on all program applicants—if translated to youth more generally—would not completely close the gap between young and older voters, but it would take an important step toward doing so. Moreover, if we consider the effect size local to compliers (i.e., the CACE) instead and apply this to the broader youth population, then TFA would be sufficient to effectively close the gap between younger and older voters. (We discuss whether generalizing our effects to a broader population is wise in the Discussion. )

Second, we can compare our effects to other more immersive education-based treatments. We first compare our effects to estimates of the average effects of civics education ( 46 – 48 ). Holbein and Hillygus ( 5 ) report that the average effect of implementing Advanced Placement (AP) Government courses on turnout ranges from 0.5 to 3.1 percentage points and the average effect for implementing AP US History courses ranges from 1.9 to 2.5 percentage points. Our effects are somewhere between 1.8 and 17.2 times as large as the effects of these commonly taken courses that are often advocated as a means of increasing youth engagement. Our effect also compares favorably to those of more tailored pilot civics education programs. The First-Time Voter program, which uses in-classroom voting and registration tutorials, increases youth turnout by 5.7 percentage points (ITT effect) on average ( 5 , 6 ). Our ITT effects benchmark well with those from this program—being right in line or somewhat larger. Our effects are also right on par with the lottery-based ITT estimate of Democracy Prep Charter Schools, which increase youth turnout by 7.2 percentage points ( 5 , 49 ). In short, TFA appears to be much more effective than the average standard civics curricula and right in line with (if not, in some cases, slightly larger than) other educational programs of similar duration and/or intensity.

Finally, we can compare our effects to other commonly used strategies to get out the vote (i.e., GOTV)—such as phone calls, canvassing, and mailers. Although these GOTV interventions are clearly not the same as a national-service–based TFA treatment—being significantly smaller in their treatment intensity than the TFA program—GOTV efforts are the most widely studied approach to increase voter turnout and the one approach that most frequently uses methods for causal inference ( 50 ). To benchmark our effects to GOTV interventions, we draw on a recent meta-analysis conducted by Green, McGrath, and Aronow ( 51 ) that pulled together effect estimates from 75 phone-banking experiments, 147 mailer experiments, and 73 canvassing experiments. Fig. 3 plots our Match 2 effect size relative to these GOTV experiments. The effect of TFA is 14.3 times larger than that of the average phone-banking GOTV intervention, 12.3 times larger than that of the average mailer GOTV intervention, and 3.4 times larger than that of the average canvassing GOTV intervention. (The comparable numbers for Match 1 are 9.5 times larger, 8.1 times larger, and 2.3 times larger, respectively.) Compared to the whole distribution of GOTV effects, the average TFA effect falls at the 93rd percentile of phone-banking interventions, the 69th percentile of canvassing interventions, and the 98th percentile of mailer interventions. Even if we cherry pick the most effective youth canvassing approach found in Green, McGrath, and Aronow’s dataset (51)—which finds a CACE of about 22 percentage points, on average—our effects are still noticeable, being about 1.4 to 1.9 times larger than in-person contact treatment effects.

The effect of TFA experience relative to other GOTV interventions. Data from GOTV interventions come from 75 phone-banking experiments, 147 mailer experiments, and 73 canvassing experiments included in Green, McGrath, and Aronow ( 51 ). ( Left ) The effect of TFA (match 2) as a coefficient with corresponding 95% CIs next to the distributions of the GOTV effects. ( Right ) A lollipop chart that places the average effect of TFA relative to the average effect of each GOTV treatment.

In short, all comparison points suggest that TFA’s effect on voter turnout is large and meaningful. Indeed, national service experience through TFA likely does not completely close the gap between young and older voters, but it does make a meaningful step toward doing so. In interpreting the magnitude of these effects, however, one important point about treatment scope and exposure is worth reiterating. TFA is a 2-y program, whereas many previous voting treatments are much shorter. The effects that we observe are wholly consistent with larger, more immersive programs having larger effects on voter turnout.

Our effects are robust to various alternate specifications. For example, when we use the admissions score as an instrument for TFA program completion rather than matriculation, we see that the effects are stronger ( SI Appendix , Fig. S14 ). Further, while we prioritize the 2012 and 2014 elections given that those elections are closest to when participants were surveyed to get reliable information on their state of residence, if we expand the analysis to include the 2008 and 2010 elections, results largely remain the same ( SI Appendix , Figs. S12 and S13 ). Moreover, we note that one may be concerned that effect sizes are overestimates if nonadmits are pursuing work that may socialize individuals to participate less in politics. However, they could also be underestimates if nonparticipants systematically worked in sectors that may also encourage greater political participation. To explore this question, we examine the job sectors of nonparticipants since 2007, the first cohort year in our study. As seen in SI Appendix , Figs. S15–S17 , over one-third of nonparticipants pursued work in the education sector. The next two most-represented sectors are the nonprofit and legal sectors. Nearly half of nonparticipants entered the legal, nonprofit, and education sectors, and there are no documented reasons that these three sectors would depress voter turnout. As such, it is unlikely that we overestimate the effect of TFA due to the career trajectory of nonparticipants.

When it comes to treatment effect heterogeneity, the effect of TFA is similar for Whites and non-Whites and across several geographic areas in the United States, but effects appear to be larger among men than women and among those with higher rather than lower socioeconomic status, as measured by federal Pell Grant status ( SI Appendix , Fig. S7 ). However, as with most tests of treatment effect heterogeneity, statistical power may be an issue here. Moreover, we do not see consistent evidence that the positive effects we detect are present after receiving a smaller “dose” of the program (i.e., receiving only a few months or 1 y of the program), although we interpret these results with caution given the reduced sample size ( SI Appendix , Figs. S10 and S11 ).

We consider potential mechanisms in SI Appendix , section B.1 . We note here that mechanism testing is inherently difficult as observing all potential mechanisms is difficult, if not impossible ( 52 ). We note (briefly) here one mechanism that is unlikely to be driving our results—direct mobilization of TFA participants by the TFA organization itself. Explicit efforts from the national service organization to increase political participation are not a likely mechanism for the effects we observe, as the Serve America Act explicitly prohibits national service programs from doing so ( 53 ) and, as such, no mobilization/registration programs occur under the umbrella of the TFA organization. That said, it is possible that while enrolled in TFA, participants are exposed to mobilization from their peers and organizations that oversee and interact with teachers. The shift in the network with which TFA participants interact likely plays some role in the effects we see, along with the fundamental attitudinal transformation that follows participating in TFA ( 33 , 43 ). While the mechanisms driving the effects we observe are likely many and complex—and worthy of examination in future research—this much is clear: For many young people, participating in TFA substantially increases the chances that they will be active in the democratic process.

Participating in Teach For America—as thousands of young people across the United States do each year—substantially increases the odds that these young people will participate in politics. Pairing comprehensive records of those who have applied to TFA with a unique nationwide voter file, we have shown compelling causal evidence that serving as a TFA teacher increases voter participation substantially. Individuals who participate in TFA vote at a rate 5.7 to 8.6 percentage points higher than those who do not. Regardless of the comparison used to benchmark these estimates, this effect is large—suggesting that national service programs like TFA have the potential to fundamentally change the civic engagement patterns of those who choose to participate. This is significant, as a democracy requires citizens that actively participate in public deliberation rather than citizens that are apathetic, alienated from the political process, and withdrawn into the private sphere of family, career, and personal pursuits ( 54 ).

Our work uses a natural experiment unique in its scale and size to study the causal effect of national service participation on youth voter turnout; however, it should not be the last to examine this important topic. Future research is needed to explore questions of external validity. While we established that the effects of TFA participation are positive across many subsets of the population of applicants, we cannot estimate how effects would differ in other populations. This may be especially important, as individuals who apply to national service programs may differ from the general population in important ways. Future work should also explore whether effects like those we document here generalize to other subsets of compliers, like to noncollege graduates and to older citizens (to name a few). That said, scholars doing such work in the future would do well to note that, to a certain extent, all individual voluntary youth service programs—given their voluntary nature—likely experience selection effects into application. Conversely, while mandatory national service programs may cover a broader subset of participants—being mandatory, after all—they may struggle to produce all-else-equal counterfactual comparisons. Moreover, mandatory national service programs may have different effects on participation given differences in the potential attitudinal effects of compelling service. Future research would also do well to see whether we observe similar causal effects of other voluntary service programs beyond TFA, like the Peace Corps. Doing so would allow us to answer questions like whether the effects we document are larger for national service programs that involve teaching, as there is suggestive evidence that under certain conditions, being a teacher increases voter turnout ( 55 ) (see SI Appendix , section B.1 for a discussion of how the act of teaching is unlikely to be driving the entirety of our effect). Simply put, the magnitude of effects may differ by the type of service one is charged to provide.

In 1910, US philosopher William James argued that youth service could be a mechanism by which “a stable system of morals of civic honor builds itself up” (ref. 56 , p. 24). And since President John F. Kennedy famously challenged Americans—“Ask not what your country can do for you, ask what you can do for your country” ( 57 )—national service programs have multiplied, with over 1.3 million Americans answering Kennedy’s call to serve ( 25 , 58 ). However, 1.3 million is a small fraction of the US adult population eligible to apply to such programs. Currently, an expansion of national service is being proposed by the House of Representatives and the Senate—via the CORPS Act (see, for example, S.3964 and H.R.1162; for the full list of over 250 bills having to deal with national service over the past 13 Congresses, see SI Appendix , Table S2 )—as a necessary response to bolster COVID-19 recovery. The intention of these efforts is to create more opportunities for young people to help the nation combat, and recover from, the devastation of the COVID-19 pandemic ( 59 ). In the midst of the pandemic, recognizing how polarized Americans are today, the Editorial Board of the New York Times recently wrote that young Americans should be required to give a year of service in response to a “bitterly divided America struggling with a pandemic ” ( 60 ). Moreover, campaigns like Serve America Together have been initiated to make 1 y of full-time public service a common expectation of and opportunity for all young Americans. Our results speak to the promise of these efforts.

We find that TFA, which explicitly aims “to improve lives, strengthen communities, and foster civic engagement through service and volunteering” ( 61 ), meaningfully increases civic participation. These results are vitally important given the low rates of civic participation in the United States. Young people in the United States have some of the, if not the, lowest rates of civic engagement in the world. Moreover, looking at voter turnout by age and generation, we see that each generation is voting less than its previous generation at the same age. Our results suggest that service interventions, like TFA, which targets youth for a voluntary national service experience, have great potential to increase the chances that citizens participate in politics.

Materials and Methods

Our analysis leverages administrative data from TFA, a nationwide voter file, and a survey of TFA applicants. The Committee for Protection of Human Subjects (CPHS) at University of California, Berkeley provided institutional review board approval to conduct this research (2020-02-13026). TFA maintains detailed selection data (e.g., name, date of birth, telephone number, current address, undergraduate university, application year, selection score, admissions decision, matriculation decision, and demographic characteristics) at the time a given applicant applied to the program, and we use this information for all applicants who were competitive to join the program. We focus on the set of applicants who made it to the final round of interviews for the 2007 to 2015 application cycles and, hence, received a selection score. This amounts to 120,329 applicants. All of these applicants were also targeted by Mo and Conn ( 33 ) between 1 October 2015 and 31 March 2016 to complete an online survey. Over one-quarter of the targeted applicants (27.1%; American Association for Public Opinion Research R2 response rate) responded to the survey, which importantly asked about their current state of residence. After removing noncitizens and individuals who did not provide a current state of residence, we were left with a sample of 28,662 potential voters (see SI Appendix , Table S1 for survey sample demographic characteristics). Crucially, there was no difference in response rates of admits and nonadmits close to the admissions cutoff ( SI Appendix , Fig. S1 and sections A.1 and A.2 give further information on the sample and measures).

In the United States, voting is public record and, as such, individual states make the list of registered voters in their state available to researchers. The voter file data that we use have been collected and collated by the data and analytics firm Data Trust. We use their snapshot of the nationwide voter file from 29 September 2017. Much like other large-scale voter-file vendors—like Catalist, L2, and Aristotle—Data Trust appends voting information from all 50 US states along with the District of Columbia into a single file. This appended dataset contains voting and registration histories of all registered voters in the United States—with ∼200 million voters contained therein. Whereas other firms share 1% samples of their voter files with researchers, we have the entire Data Trust file for all 200 million individuals, thus making it possible to match individuals who applied to participate in TFA. There is evidence that the Data Trust file is high quality; indeed, extant research ( 62 – 67 ) notes a high degree of fidelity between historical and contemporary measures found in Data Trust’s data and official reports of demographics, political partisanship, and turnout. This file also has good coverage on the inputs we use for matching and has relatively few potential duplicate individuals in the file. We use data on validated voter turnout from this file as our outcome of interest.

To merge the TFA applicants with their voting records in Data Trust, we used the fastLink package in R to probabilistically identify matches based on an applicant’s name, date of birth, and state of residence ( 68 ). Given that the survey we use began data collection in October 2015, we focus on turnout in the 2012 and 2014 elections (see SI Appendix , section A.5 for more details). We focus on the two most recent general elections to minimize the time elapsed between the elections in our voter file data and information on individuals’ current state of residence from the 2015 to 2016 survey. For all survey respondents, we searched for voting records in the current states of residence reported in the survey (more information about the matching process can be found in SI Appendix , section A.4 ). Following previous practice, if no voting record is found, we assume that person did not vote; this allows us to avoid issues with posttreatment bias ( 69 – 73 ).

Our core analyses are restricted to those who completed the survey conducted by Mo and Conn ( 33 ), as we have the most current information on geographic location for our population of interest among survey participants. However, we employ different geographic location data for estimating pretreatment effects and treatment effects. The majority of TFA applicants moved during the period between their application and the survey, and applicants who participated in TFA moved at even higher rates—perhaps because participating in TFA often involves moving to a different state. When we assess whether the state of residence information in the survey matches the state of residence indicated in the TFA’s administrative files from the application stage, we see that 61% of TFA participants moved states, compared to 50% of nonparticipants. Previous research has shown that increased mobility contributes to lower levels of voter turnout ( 74 , 75 ). Moreover, the mobility of young people also makes record linkage tricky. Since our matching process blocks on state, inaccurate state information will lead to less accurate turnout estimates ( 76 ). We therefore rely on geographic information from TFA’s administrative file when estimating pretreatment effects (i.e., rate of political participation before any TFA national service could take place), as location information from the applications is an accurate source of an applicant’s location shortly before and when applying to TFA. When estimating treatment effects, we utilize the state information from the survey ( 33 ), as state of residence in 2015/2016 is more likely to be the state of residence during the 2012 and 2014 election for the cohorts for whom we can estimate posttreatment effects (2007 to 2012 application cycles) than the state of residence at the time of their application recorded in the admissions administrative file.

Participating in TFA is distinctly nonrandom. As individuals choose whether or not to participate in TFA, simply comparing the vote history of individuals who participate to those who do not—even if we control for observable characteristics—leaves open the possibility that any estimated effect of TFA would actually reflect selection bias rather than a systematic program effect. As such, to estimate the causal effect of TFA participation on voter turnout, we employ a natural experimental design. Specifically, we use a regression discontinuity design (RDD). RDDs are a natural experimental technique that have grown in popularity in recent years, especially in contexts where formal randomization is not/cannot be conducted. The design leverages scenarios where there is an arbitrary value of some variable—a cutoff—that sorts individuals into treatment and control groups; individuals on one side of the cutoff are in the treatment group, while individuals on the other are in the control group. As has been well established, regression discontinuity designs exploit continuity in potential outcomes around an arbitrary cutoff (35 to 40; see SI Appendix , section A.6 for more on the design’s application to our case). As long as the (often modest) assumption that the potential outcomes of the control and treatment groups are continuous around the cutoff is met, RDDs will provide causal estimates that benchmark remarkably well with randomized control trial estimates ( 41 ). As we show below, our RDD appears likely to satisfy the assumptions underlying this methodological approach.

The discontinuity that we leverage in this paper is found in the selection scores given to all of the young people who apply to participate in TFA. As background, in 2007 TFA instituted a selection process that assigns a continuous score to all applicants. This score represents TFA’s holistic assessment of how effective the applicant will be in the classroom based on the applicant’s application materials. This is based on applicants’ educational history, extracurricular activities, transcripts, personal statements, and interviews. Individuals who score above a certain preset score are recommended to be admitted to the TFA program, whereas those who score below the cutoff are not recommended to be admitted, although the score is not the only factor that determines whether an applicant will be admitted to the program (see SI Appendix , section A.3 for more details). Importantly, this cutoff and the weight given to the constituent parts that go into determining one’s score are not public knowledge to the interviewees and the interviewers. This provides us with a strong precondition for estimating the causal effect of TFA using an RDD: It is unlikely that individuals will be able to precisely sort around the admittance cutoff ( 39 ). As we would expect if this cutoff were (locally) sorting individuals as good as randomly, observable covariates ( SI Appendix , Fig. S3 ) and the density of the running variable ( SI Appendix , Fig. S4 ) are balanced at the cutoff. This approach allows us to overcome issues of selection bias, endogeneity, and/or simultaneous/reverse causation and estimate the causal effect of being chosen to participate in TFA (the ITT effect) and actually participating in TFA (the CACE). As is standard practice, the CACE is estimated via a fuzzy regression discontinuity design that instruments program participation with program selection ( 77 ).

Supplementary Material

Supplementary file, acknowledgments.

We thank scholars at the University of California, Berkeley; at the Stanford Graduate School of Business; at the University of Virginia’s Frank Batten School of Leadership and Public Policy, School of Education and Human Development, and EdPolicyWorks; at the Midwest Political Science Association’s (2020) Annual Meeting; at the American Political Science Association’s (2021) Annual Meeting; and at the European Consortium for Political Research’s (2021) Annual Meeting for their helpful comments. We are particularly grateful to Brittany Anlar, Daphna Bassook, David Broockman, Emanuel Coman, John Gerring, Donald Green, Brian Hamel, Dominik Hangartner, Karl-Oskar Lindgren, Isaac Mbiti, Charles T. McClean, Edward Miguel, Sven Oskarsson, Mikael Persson, Jay Schimshack, Beth Schueler, Sebastian Tello-Trillo, Craig Volden, and James Wycoff for their feedback. We thank the Data Trust for providing access to the voter file data used in this project and Donald Green, Mary McGrath, and Peter Aronow for publicly sharing their meta-analysis data. We are grateful to representatives from Teach For America, especially Tameka Brigham, Yoon Ha Choi, Raegen Miller, Amy Nasr, Becky Smerdon, Grant Van Eaton, and Johann von Hoffman for providing us with access to TFA’s administrative data, providing information regarding the organization, and providing us with assistance to collect the survey data. We thank representatives from Teach For All, especially Jean Arkedis and Robbie Dean for their partnership. Finally, we acknowledge the truly fantastic research assistance provided by Julia Christensen in the data collection, data cleaning, data merging, and initial data analyses stages of the research process; she was an indispensable research assistant. Financial support from the NSF (SES-1657821), Vanderbilt University’s Discovery Grant Program, and the Berkeley Institute for Young Americans (52801-13279-40-SPCHM) is gratefully acknowledged.

The authors declare no competing interest.

This article is a PNAS Direct Submission.

* This was TFA’s mission statement at the start of the organization; however, the language of their core mission has recently changed. With that said, the noted mission or goals in several of TFA’s regional office uses this original language. For example, see TFA San Diego’s website ( https://www.teachforamerica.org/where-we-work/san-diego/our-work [accessed 15 June 2022] and TFA Eastern North Carolina’s website ( https://www.teachforamerica.org/where-we-work/eastern-north-carolina/our-work [accessed 15 June 2022]).

† When examining the 2007 to 2015 cohorts, we find that 87% of those who matriculated into the program completed the full 2-y program commitment.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2122996119/-/DCSupplemental .

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microsoft-power-point-quiz.md

Latest commit, file metadata and controls, microsoft powerpoint, q1. when you create a new, blank presentation, you begin with one blank slide. what is the layout name of this slide.

• Title slide
• Title and Content

Q2. What do you use to align objects on a slide with each other?

• Layout tools
• Arrange tools
• Design Ideas feature
• Picture tools

In MS PP 2019 it's called "Drawing Tools". There is no such answer but there is a function called "Arrange"

Q3. Which option must be enabled if you want to manipulate the playback of a video file during a presentation?

• Record Side Show
• Use Presenter View
• Show Playback Controls
• Show Media Controls

Q4. Which feature analyzes the content of your slides and offers suggested options?

• Design ideas/ Designer
• Browse for Themes

Q5. Where can you configure advanced PDF options?

• the Options tab
• the Home tab
• the Print dialog box
• the Share tab

Should be in the printer window not in the options menu

Q6. Which tab is best for adding various types of objects to slides?

Q7. which smartart category can you use to make informational graphics out of bullet points.

• All of these answers

Q8. What is NOT checked when you run the Accessibility Checker?

• Reading order
• Slide titles
• Missing alt text

Q9. How can you change the appearance of a table in one click?

• Apply a cell stye.
• Apply a graphic style.
• Apply a table style.
• Right-click a table and choose a new style.

Table Tools -> Design Tab -> Table Styles

Q10. Which option facilitates a text box to automatically changes shape to fit longer text?

• Resize shape to fit text
• Do not autofit
• Shrink text on overflow
• None of these answers

Q11. Which three shadow properties can be adjusted in PowerPoint?

Q12. if you want your presentation to play continuously on screen, what option must you set.

• Use Presenter view
• Advance slides manually
• Advance slides automatically
• Loop continuously until 'Esc'

Slide Show Tab -> Set Up Slide Show -> Show options -> Loop continuously until 'Esc'

Q13. Which statement about the Compress Pictures command is true?

• You can compress all images at the same time.
• You cannot delete cropped areas.
• There are no options for print output.
• You can compress only one image at a time.

Q14. You have a Word document you would like to import as an outline into a PowerPoint presentation. How should you format the text in the Word document that you want to be the content of the?

• Heading Style 2
• Content Style

Q15. In what manner can you filter comments?

Q16. which arrow options choice will hide the mouse cursor while presenting, except when the cursor is moved.

If hidden is selected the courser stays hidden no matter what.

Q17. Which chart type is best suited for displaying annual financial data for a 10-year period?

• Donut chart
• Line or area chart

Q18. If you want to create an organizational chart, which SmartArt category works best?

• Relationship

Q19. When printing multiple handouts, which option will group multipage documents together and make them easier to distribute?

• Print on Both Sides
• Print One-sided

Q20. How can you adjust which data in a table is used when working on a chart?

• Click the Switch Row/Column button.
• Click the Refresh Data button.
• Click the Select Data button.
• Click the Edit Data button.

Q21. Which view lets you see additional information on a second monitor when delivering a presentation?

• Normal view
• Reading view
• Slide Sorter view
• Presenter view

Q22. A picture looks good in your slide show but pixelated when you print it. How can you fix this?

• Compress the picture and use the Print (220 ppi) resolution setting.
• Select Best scale for slide show, increase the picture resolution and resize the picture to the original size
• Remove picture compression.
• Change the printer property settings to print in a higher resolution.

Personal Hint: File Tab -> Options -> Advanced -> Image Size and Quality -> Do not compress images in file File Tab -> Options -> Advanced -> Print -> High quality

Q23. You have inserted a video on a slide and want the video to automatically start 2 seconds after the associated slide appears. How can you accomplish this?

• Use playback timings.
• Use animation timings.
• Use animation triggers.
• Use video timings.

Q24. When inserting pictures, how can you avoid distorting the picture if it is resized later?

• Use best scale for slide show.
• Lock the aspect ratio.
• Compress the picture.
• Set the picture to be relative to the orignal picture size.

Q25. Which Protect Presentation option protects a presentation from accidental changes?

• Always Open Read-Only
• Add a Digital Signature
• Encrypt with Password
• Mark as Final

Q26. How can you change colors in your presentation all at once without changing the design?

• Apply a Color Template.
• Apply a Color Theme.
• Apply a Color Palette.
• Customize the Color Fills.

Q27. What function allows you to borrow slides from a presentation fro an other presentation?

• Borrow Slides
• There is no special fuction. You must use Copy and Pase
• Reuse Slides
• Find Slides

Q28. What is the term used for the arrangement of elements on a slide, such as Title and Content?

Q29. how would you change a list of text to a visual diagram on a slide.

• Convert to WordArt.
• Convert to Shapes.
• Convert to SmartArt.
• Convert to Picture.

Q30. How can you reorder the levels of shapes in a SmartArt diagram?

• Retype the text
• Change the shapes from rifht to left.
• Promote or Demote the shapes
• Move the shapes up and down.

Q31. Why would you trim a video?

• To crop the video thumbnail.
• To resize the video.
• To remove background noise in the video.
• To remove some of the beginning and/or end of the video.

Q32. How would you save your layouts, colors, fonts, effects, background styles and content for others to use with their presentations?

• Export the file as a template.
• Save the file as a PDF.
• Save the file as a POTX.
• Save the file as a PPTX.

Personal Hint: There are multiple file suffixes for templates (.potx, .potm, .pot).

Q33. You are creating an industrial presentation of a new software program and would like to include an image of your program. How can you use PowerPoint to do this?

• Use the Copy command.
• Use the Screenshot command. (Office2019)
• Use the Print key.
• Use the Capture Screen command.

Q34. How can you add more layouts to the Layout Gallery?

• Customize the Layout Gallery.
• Add Slide Layouts to the Slide Master.
• Use the Add Layouts command.
• Use the Insert Layouts command.

Q35. Why would you use the Outline view?

• To see only the text in placeholders.
• To present the slides at a high level.
• To add text from a Word document.
• To organize your slides.

Q36. The text on the left and right of the slide are cut off in print preview. How can you fix this in the printer settings?

• Unselect High Quality.
• Select Scale to Fit Paper.
• Select Frame Slides.

Q37. In addition to organizing your slides, how else can sections increase your productivity?

• In addition to organizing your slides, how else can sections increase your productivity?.
• All the slides in a section can be selected at once to apply changes such as transitions, layouts or hiding slides.
• Sections can be exported as a separate presentation.
• You can resize all the slides in a section at once.

Q38. You have many slides that you need to reorder. How would you accomplish this?

• Move the slides in Slide Show view.
• Renumber the slides in Outline view.
• Move the slides in the Slide Sorter view.
• Reset the slide footers.

Q39. What can be seen in Presenter view?

• Side Navigator.
• all of these answers.

Q40. How can you format the entire contents of a text box?

• Click the dashed/dotted border of the text box.
• Right-click the text box and select Select All.
• Click the Fond button on the Home tab.
• Double-click any word in the text box.

Q41. You have a large number of objects on your slide. How can you use the selection pane to help you identify the objects quickly?

• Select the show/hide icons next to the objects.
• Rename the objects with meaningful names.
• Drag and drop the objects.
• Tab through the objects and note each object as it's highlighted.

Q42. How would you ensure a consistent appearance and placement across your slides even though the content might be different?

• Use styles.
• Use bullet points.
• Use layouts.

Q43. How can you add icons to SmartArt?

• Select the picture icon in a SmartArt Picture Diagram.
• Select the icon in a SmartArt Icon Diagram.
• Select Insert Icon and place it on top of the SmartArt Diagram.
• Select a SmartArt shape and select Insert Icon.

Q44. You are having trouble playing back a presentation smoothly. What should you do?

• Copy the presentation to your internal hard drive.
• Clear the Disable hardware graphics acceleration option.
• all of these answers
• Select the Show without animation option.

Q45. Which feature lets you configure multiple combinations of specific slides for playback from a larger slide show?

• Record Slide Show
• Rehearse Timings
• Custom Slide Show

Q46. When working with a mapchart, which options can be drawn?

• Postal code

Q47. You receive feedback from two clients on your presentation. Each person sent you an edited file. What's the best way to review their changes?

• Review > Accept
• Review > Smart Lookup
• Review > Compare
• Review > Show Comments

Q48. How do you promote subbullets to top-level bullets?

• Press Shift+Tab.
• Right-click the bulleted text and select Promote.
• Press the Esc key.
• Press the Spacebar.

Q49. After you select the chart icon in a placeholder, what is the next step to create a chart?

• Select the chart elements.
• Select the chart type.
• Select the chart data in Excel.
• Select the chart style.

Q50. How would you show a correlation between the amount of chocolate a city consumes and the number of crimes committed?

• Use a bar chart.
• Use a column chart.
• Use a line chart.
• Use a scatter chart.

Q51. You want to use a morph transition between two slides. How do you set up the slides?

• The two slides must have multiple objects in common with different names in the Selection pane
• The two slides must have at least one obiect in common that is in a different position on the second slide
• The second slide must have different objects that are in the same position as the objects on the first slide
• The two slides must have at least one obiect in common that is in the same position on the second slide

Q52. Why would you use the Rehearse Timings command?

• to adiust animation timings
• to record your slide show
• to remove transition timings
• to determine how long it takes to present your slide show

Q53. How can you make an inserted picture look like it has been sketched or painted?

• Apply a picture effect
• Apply a draw effect
• Apply an artistic effect
• Apply a picture style

Q54. How can you modify your presentations entire set of colors with a single change?

• Apply a color template
• Customize a color fill
• Apply a color palette
• Apply a color theme

Q55. You want to print full-page slides to fill 8. 5-by-11-inch paper, but they appear too small in the preview. What is the best way to rectify this?

• Change the slide size to standard
• Repeatedly bump up the scale setting print option until it fills the page in the preview
• Change the scale setting print option to 100%
• Select the Scale to Fit Paper print option

Q56. You want to import a Word document as an outline into a Powerpoint presentation. How should you format the Word documents text that you want to be the slide content?

Q57. what is not reviewed when you check a slide shows accessibility.

• missing alt text
• slide titles
• reading order

Q58. What is the easiest way to change the appearance of a table?

• Apply a graphic style
• Apply a table style
• Right-click a table and select a new style
• Apply a cell style

Q59. Why would you use Outline view?

• to quickly edit titles or bulleted text in the Slide pane
• to view the comments on all slides
• to add notes in the Slide pane
• to change the font on all slides

Q60. Before publishing a presentation, which option should you use to scan a presentation and look for issues that may make it difficult for someone with a visual impairment to read?

• Protect Presentation
• Inspect Document
• Check Compatibility
• Check Accessibility

Q61. How can you filter the series and/or categories in a chart?

• Edit the data to remove the data for the series or category.
• Switch the rows and columns.
• Use a filter so the data series or category does not display.
• Change the chart type.

Q62. You have an object that needs to follow a specific motion path - including curves, straight lines, and loops - on the slide. Which animation gives the capability to draw this?

• Custom Path.

Q63. When you add encryption to a PowerPoint presentation, what does it do?

• scrambles the text so it is unreadable when opened.
• shares the file in cloud-based platform.
• protects the file with a password.
• requires the user to log in to their Office 365 account.

Q64. Which type of shape allows you to add text that can be moved around, formatted with styles and effects, or grouped with other objects?

Q65. you need to apply a special effect that will display as the presentation moves from one slide to the next slide. which should you apply.

• design ideas

Q66. Which option can be used to import selected slides from a previously created presentation into a new presention without manually copying and pasting them?

• Slides from Outline
• Add Section

Q67. The picture on the left is the original image. The image on the right is smaller, but the flowers are the same size and part of the picture has been removed. What method was used to format this picture?

• changing the width and height with aspect ration locked
• drapping a corner sizing handle
• using the Crop to Shape tool
• using the Crop tool

Q68. You are copying and pasting slides from another presentation into your presentation. How will you ensure the new slides are consistent in appearance with your existing slides?

• Select Keep Source Formatting when pasting the slides.
• Select Use Destination Theme when pasting the slides.
• Change both presentations to the same theme before copying and pasting.
• Select Picture when pasting the slides.

Q69. What happens when you press the Esc key while playing a From Current Slide or From Beginning slide show?

• The current slide is temporary hidden.
• The slide show ends and you return to your previous view.
• The next slide appears.
• Nothing happens.

Q70. When you save a presentation with a .potx file extension, which type of Powerpoint file is created?

• macro-eanbled presentation

Q71. You are presenting with two monitors. Which view displays a preview of next slide and speaker notes on the second monitor?

• Master view

Q72. Which effect can a presenter use to add motion to an object on a slide?

Q73. when you copy data from an excel worksheet into a slide to create a table, which paste option will allow you to edit the table data in excel.

• Keep Formatting
• Keep Text Only

Q74. You created a photo album with transitions and want to send it to friends, but the file size is too large for email. How can you use one command to fix this?

• Click Export in Backstage view and embed the presentation in an email.
• Compress all the pictures to the Email (96 ppi) resolution .
• Use the Share feature in Backstage view and petty email to automatically compress the pictures.
• Use the Share feature in Backstage view to create a pdf to send as an attachment.

Q75. In a presentation about voter turnout, you are illustrating various data with charts. Which type of information would you present in a pie chart?

• the decline of voter turnout by county
• trends in voter turnout over the part 10 years
• what percentage of the whole population voted
• how many people voted in various geographic regions on a map

Q76. A few lines of text are overflowing the placeholder on your slide. How can you fix it on your slide?

• Delete the overflowing text.
• Select Stop Fitting Text to This Placeholder .
• Select Autofit Text to Placeholder .
• Click Collapse .

Q77. If you want to create an organizational chart that illustrates the reporting relationships within the organization, which SmartArt category would you use?

Q78. what do the labels introduction and projectsbymonth indicate.

• section names
• hidden slides
• links to the next slide

Q79. There are objects and text hidden behind an image on your slide. With the image selected, what could you adjust to make the objects and text appear "through" the image?

• Compress Picture
• Transparency
• Corrections
• Artistic Effects

Q80. How can you create a new presentation that includes prebuild layouts, colors, fonts, background styles, and content?

• Use a theme.
• Use a design.
• Use a template.
• Use a style.

Q81. How can you reorder the levels of shapes in a SmartArt diagram?

• Promote or demote the shapes.
• Change shapes from left to right.
• Retype the text.

Q82. When you insert a new slide in a presentation, where is it placed in relation to the exsisting slides?

• as the first slide.
• before the current slide.
• at the end of the exsisting slide.
• after the currently selected slide.

Q83. In Normal view, which pane is used to add text and reference information for the speaker?

Q84. what is the best way to modify slide's appearance or content while in note page view.

• Edit the thumbnail of the slide as needed.
• Slides cannot be modified while in Note Page view.
• Use the Zoom slider to magnify the slide contents
• Right-click the slide and select edit

Q85. You want to group your slides based on their content to better organize your presentation. How would you accomplish this?

• Create an outline in the outline view and rearrange slides.
• Add a table of contents slide and link the remaining slides to it.
• Add sections and move the slides into the appropriate sections.
• Create custom shows and add the slides into the shows.

Q86. How do you remove the background of an inserted impage?

• Select the image and, on the Design tab, use the Format Background feature
• Select the image and, on the Picture Format tab, use the Compress Picture feature
• Select the image and, on the Picture Format tab, click the Remove Background button
• On the Drawing Tools Format tab, select Graphics Fill > Remove Background

Q87. In a SmartArt graphic, how can you add text to the shapes other than directly in each shape?

• Open th Text Pane for the graphic and enter the text.
• Create a bulleted list and then convert it to a shape.
• Select all of the shapes and start typing.
• You can only add text by typing directly into each shape.

Q88. You want to add two images to a presentation, but one is a bit blurry and another is quite dark. What feature on the Picture Format tab can you use to adjust them?

• Corrections.
• Picture Effects.
• Trasparency.

Q89. A colleague has given you a presentation file, and you want to use one of the slides from that presentation in your own. How would you do this?

• Use the Reuse Slides command.
• Use the Import slides command.
• Use the Insert Slides command.
• Use the Duplicate Slides command.

Q90. Which type of shape allows you to add text that can be moved around, formatted with styles and effects, or grouped with other objects?

Q91. which view allows you to select individual objects on a slide.

• Slide Show view
• Design view

Q92. When in Slide Show view, what happens when you hold Ctrl (Windows) or Command (Mac) and click the left mouse button?

• You activate the laser pointer
• You end the slide show
• You toggle the mouse pointer on/off
• You terminate animations

Q93. Which PowerPoint feature is best used to set up slide timings so slides automatically advance?

• Timing on the Animations tab
• Advanced Animation on the Animations tab
• Reharse Timings on the Slide Show tab
• Timing on the Transitions tab

Q94. You plan to distribute a presentation. In which format might you save it to ensure that it appears as an automatically playing slide show composed of individual slides, regardless of the recipients' platform or software applications?

• PowerPoint Show (.ppsx)
• OpenDocument Presentation (.odp)
• MPEG-4 Video (.mp4)

Q95. Which type of illustration can be rotated while in Slide Show view, allowing the audience to see all sides of an image?

• action button
• animated image

Q96. You inserted audio into a presentation. Which two options must you enable for the audio to play throughout all slides and repeat until it is stopped?

Q97. your presentation has a slide on which the text appears scrambled. what happened.

• The text direction has been changed to Vertical.
• The text direction has been changed to Stacked.
• The text direction has been changed to 360 degrees.
• The text direction has been changed to 90 degrees.

Q98. Your colleagues have collaborated in a presentation with you, adding new content and reusing slides from other presentations. You notice the layouts vary throughout the slides: differently sized and positioned containers, and different headers and footers. Where can you make a change that will automatically be applied to all slides in the presentation?

• Quick Styles
• Slide Master
• Header & Footer
• Handout Master

Q99. Your company logo appears on all slides in a presentation as part of the overall design template's Slide Master. How can you prevent the logo from appearing on one particular slide?

• Hide the graphic using Design > Format Background.
• Right-click the logo (Windows) or select the logo (Mac), and then select Delete.
• Insert a blank shape to mask the logo.
• Remove the logo graphic from the Slide Master template.

Q100. Many PowerPoint slide backgrounds use a color effect that is gradually shaded from top to bottom. What is this effect called?

• pattern fill
• texture fill
• gradient fill

Q101. The picture on the left is the original image. Which type of picture format has been applied to the picture on the right?

• Picture Styles
• Picture Layout
• Picture Effects

Q102. To create a stacked image with the campfire icon on top of the yellow circle, select the campfire icon, then drag it to the center of the yellow circle, What is the next step?

• On the graphics format tab, press Enter (windows) or Return (mac)
• on the graphics format tab, click the send backward arrow and select Send to back
• On the graphics format tab, click the Bring forwarrd arrow and select bring to front
• On the graphics format tab, in the selection pane, drag the campfire icon below the orange circle

Q103. In this image, a link to _ is being added?

• create a new file
• an existing file

Q104. You are creating columns in a text box using the Format Shape menu. What properties of the column can you adjust under the Text Options tab?

• space between lines of text and space between columns
• number of columns and length of columns
• only the number of columns
• number of columns and space between columns

Q105. You would like several of the slides in your presentation to use the same font for the title text. The slides all use the same slide layout. What is the most efficient way to apply the desired font to those slides?

• Change the font on each individual slide as it is added.
• Use the Duplicate Selected Slides command.
• Change the font on the Slide Master for that layout.

Q106. Which tool provides the easiest way to to create and insert an organizational chart into a presentation

Q107. what can you enable to help align objects on a slide.

• Arrange all
• Fit to window

Q108. Which Bullets and Numbering function should you use to start a numbered list at a value other than 1 ?

• Custom Numer
• Align Number

Q109. While in Outline view, what does "promoting"" a paragraph do to its placement?

• It copies the paragraph to the PowerPoint clipboard.
• It moves the paragraph down one level in the outline.
• It moves the paragraph up one level in the outline.
• It moves the paragraph to the top of the outline.

Q110. You want to create the image on the right from the one on the left - i.e., place the blue star behind both of the others. How can you do this with a single command?

• Select the blue star and click Send Backward.
• Select the blue star and click Send to Back.
• Select the gold star and click Bring to Front.
• Select the red star and click Bring Forward.

Q111. How would you ensure a uniform appearance and placement across your slides even though the content might be different?

• Use embedded fonts.
• Use consistent layouts.
• Use numbered lists. reference

Q112. When you provide alt text for an image, what type of control are you including?

• password protection
• accessibility
• presentation

Q113. In Normal view, which pane is used to add text and reference information for the speaker?In Normal view, which pane is used to add text and reference information for the speaker?

• CommentsComments
• ReadingReading

Q114. You want to use a chart from an existing Excel file in your presentation. You need to ensure that the data is always up to date in PowerPoint, even when the workbook is updated in Excel. Which method should you use to do this and include only the chart?You want to use a chart from an existing Excel file in your presentation. You need to ensure that the data is always up to date in PowerPoint, even when the workbook is updated in Excel. Which method should you use to do this and include only the chart?

• Copy the chart in Excel and in the presentation, and select Paste Special using the Link Data option.Copy the chart in Excel and in the presentation, and select Paste Special using the Link Data option.
• Copy the chart in Excel and in the presentation, Paste Special using an Embed Workbook option.Copy the chart in Excel and in the presentation, Paste Special using an Embed Workbook option.
• Copy the chart in Excel and in the presentation, Paste Special as a Picture.Copy the chart in Excel and in the presentation, Paste Special as a Picture.
• In the presentation, insert an object and select Create from File.In the presentation, insert an object and select Create from File.

Q115. Which type of shape allows you to add text that can be moved around, formatted with styles and effects, or grouped with other objects?Which type of shape allows you to add text that can be moved around, formatted with styles and effects, or grouped with other objects?

• 3D model3D model

Q116. Which choice describes the result of importing this Word outline into a presentation?

• Two slides will be created from the outline.
• One slide will be created with seven bullet items.
• Two sections will be created, one with three slides and one with two slides.
• Seven slides will be created from the outline.

Q117. How do you eliminate the blank columns on the char, as indicated by the arrow in this image?

• In the Chart in Microsoft PowerPoint window, use the sizing handler in the lower-right corner of the range, dragging it up to cell D4.
• In the Chart in Microsoft PowerPoint window, select the range A5:D5 and press Delete.
• Click the chart and then, on the Shape format tab, click Edit Shape. Then crop the chart to show only A1:D4.
• Click Change Chart Type.

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Law Day puts spotlight on voter participation

TOPEKA, Kan. (WIBW) - The laws that impact our lives depend on who we elect to the offices that make those laws.

The connection is being stressed on this year’s Law Day. Tana Griffith and LeTiffany Obozele with the Topeka Bar Assoc. visited Eye on NE Kansas to share more about the connection.

Law Day falls on May 1, 2024. Griffith and Obozele said the national theme from Voices of Democracy aims to deepen understanding of the electoral process, foster discussions on issues and promote voter turnout.

The Topeka Bar Assoc. will mark Law Day by visiting area high schools to engage students and encourage them to participate in the democratic process as they get older.

Topeka Bar Assoc. members are still available to give presentations at area high schools. If you would like to schedule someone, contact 785-233-3945 or email [email protected].

Copyright 2024 WIBW. All rights reserved.

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Erie County sees 25% voter turnout for Tuesday's primary. How that compares to recent years.

Erie County saw 25% voter turnout for Tuesday's primary, an election that featured few local marquee races and a presidential contest that was essentially a done deal in a closed primary.

"I was hoping for (turnout in) the low 20s," said Erie County Clerk Karen Chillcott, who oversees elections in the county. "I think that's realistic. The (presidential) nominees had already been decided."

Pennsylvania is one of the 10 states nationwide that conducts closed primaries, which means you must be a registered member of a major party to participate. The Nov. 5 general election will almost certainly see a much larger turnout.

The unofficial turnout data factors in mail-in ballots.

According to county officials, 19,913 mail-in ballots were requested by Erie County voters for Tuesday’s election. As of Wednesday morning, 15,996 of those, or 80%, had been counted.

Prep for the polls: See who is running for president and compare where they stand on key issues in our Voter Guide

Turnout breakdown

According to unofficial results from the Erie County Board of Elections, 42,847 votes were cast in Tuesday's primary, either by mail or at polling sites countywide.

That includes 25,154 votes cast by Democrats — 12,550 of those by mail and 12,604 via in-person voting.

There are 79,992 registered Democrats in Erie County. That means the overall Democratic turnout was 31%.

Unofficial results show that Republicans on Tuesday cast 17,673 total votes— 14,227 in person and 3,446 by mail. GOP turnout was 26%.

"I think there were people who weren't as motivated to come out," Chillcott said. She also noted that's not unusual in primaries, especially in a presidential election year.

"We're going to see it heat up pretty quickly for the fall," Chillcott said. "There's always a pretty significant difference between a primary and a general election."

For example, according to county records, the 2020 primary saw 36% voter turnout. But the November election, which saw Democrat Joe Biden defeat incumbent Republican Donald Trump in the race for president, the countywide turnout was 68%.

How Tuesday's turnout compares with other recent primaries

Erie County's 2023 primary saw total turnout of 24%, with nearly 42,000 total votes cast. The November 2023 general election saw countywide voter turnout of 35%.

Here are other Erie County primary turnout totals from recent years:

The lowest primary turnout occurred in 1998, when just 13 percent of Erie County’s registered voters cast ballots.

Erie County's record high turnout for a November election came in 1992, when 87% of the county's registered voters cast ballots and President Bill Clinton won a first term.

Contact Kevin Flowers at  [email protected] .  Follow him on X at  @ETNflowers .

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Jammu, Apr 25 (PTI) With the terror threat on the wane and changing dynamics in the Kashmir region, Jammu and Kashmir Chief Electoral Officer Pandurang K Pole is hopeful of a significant surge in voter participation during the Lok Sabha elections in the Valley.

The traditional narrative of poll boycotts orchestrated by separatist groups, prevalent for more than three decades, appears to be losing momentum and Pole expressed optimism regarding a manifold increase in voter turnout in the Valley.

Historically, factors such as the emergence of militancy in the early 1990s and threats of violence, including incidents such as the mutilation of inked fingers and stone-pelting near polling stations, deterred voters from exercising their franchise.

In an interview with PTI Video, Pole highlighted the current favourable environment created by the proactive engagement of political parties and candidates with the electorate, signalling a potential surge in voter participation.

“This time, the overall atmosphere is great and, the way the political parties and the candidates are reaching out to the people, a manifold increase in voter turnout is expected in the Valley,” Pole said.

Prime Minister Narendra Modi, during an election rally in Udhampur on April 12, credited the improved ground situation in the Valley to the revocation of Article 370 in August 2019.

He emphasised that the Lok Sabha polls in the Union Territory are anticipated to transpire without the shadow of terrorism, disruptions or cross-border tensions.

Significant shifts within separatist factions, notably the separatist amalgam Hurriyat Conference, have also contributed to the evolving political landscape.

Divisions in the conglomerate in 2003 and subsequent developments, including the passing of influential leaders such as Syed Ali Shah Geelani in 2021 and the incarceration of key figures on terror-related charges, have altered the region’s political landscape.

Despite historical trends of low voter turnout in Kashmir, 2014 showed promising signs with assembly polls in the then-state witnessing a record turnout of more than 65 per cent in the region.

Pole emphasised that the ongoing aggressive campaigning by political parties and the encouraging response from the public are likely to lead to improved voter participation.

He said the assembly polls, unlike the parliamentary elections, mostly used to have good voter turnout ranging between 50 and 60 per cent in Jammu and Kashmir.

“Voting in north Kashmir was relatively higher than south and central Kashmir, maybe because of local issues, presence of more candidates and active politics … What we are seeing today is that the political parties and the candidates are canvassing aggressively and the response of the people is also encouraging,” the chief electoral officer said.

With preparations in place to ensure security and build public confidence, Pole anticipates a notable improvement in polling percentages, particularly in north Kashmir where Omar Abdullah of the National Conference is contesting against separatist-turned-politician and former minister Sajjad Gani Lone of the People’s Conference.

The Anantnag-Rajouri seat goes to the polls on May 7, Srinagar on May 13 and Baramulla on May 20.

Udhampur-Kathua, which went to the polls in the first phase on April 19, recorded a turnout of 68.27 per cent. On Friday, polling will be held for the Jammu seat.

Pole also spoke about various initiatives under the Systematic Voters’ Education and Electoral Participation (SVEEP) Programme that have been launched to boost voter engagement, particularly in areas with historically low turnout, underscoring efforts to promote democratic participation and inclusivity in the electoral process.

Amid these developments, Jammu and Kashmir gears up for a potentially transformative electoral exercise, with hopes pinned on a vibrant and participatory democratic process shaping the region’s political future. PTI TAS SKL TAS SZM

This report is auto-generated from PTI news service. ThePrint holds no responsibility for its content.

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Lok Sabha Polls 2024: 74% Voter Turnout Recorded In Arunachal Pradesh Repolling

The poll percentage, however, is likely to go up as hundreds of voters stood in queues to exercise their franchise even after the scheduled time for voting was over.

The repolling was necessitated following reports of violence

An estimated 74 per cent voter turnout was recorded in the repolling at eight polling stations across four assembly constituencies of Arunachal Pradesh conducted on Wednesday amid elaborate security arrangements, an election official said.

The total number of registered voters was 4,469 in the eight polling stations where voting began at 6 am and ended at 2 pm.

The repolling was necessitated following reports of violence, including damage to electronic voting machines (EVMs), during the simultaneous Lok Sabha and assembly elections in the state on April 19, the official said.

The Election Commission had ordered repolling in Sario under the Bameng assembly constituency in East Kameng district, Longte Loth in the Nyapin segment in Kurung Kumey, and Bogne and Molom booths under the Rumgong seat in Siang. Dingser, Bogiya Siyum, Jimbari and Lengi under the Nacho constituency in Upper Subansiri were also on the list of booths where repolling was carried out, Chief Electoral Officer Pawan Kumar Sain said.

Elaborate security arrangements were made for the repolling.

"For these eight polling stations, we had extensive deployment of security personnel and we will ensure that this time, if something happens or if anybody tries to snatch or touch the EVMs unlawfully, strict action will be initiated," he said.

An estimated 82.71 per cent of the total 8,92,694 voters had exercised their franchise on April 19 to elect 50 MLAs for the 60-member assembly in the northeastern state.

The ruling BJP has already won ten assembly seats unopposed. The turnout in the Lok Sabha polls in the state was recorded at 77.51 per cent.

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Counting of votes for assembly elections would be held on June 2, while that of Lok Sabha polls would be on June 4.

(Except for the headline, this story has not been edited by NDTV staff and is published from a syndicated feed.)

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