Do Drop Boxes Improve Voter Turnout? Evidence from King County, Washington

Keywords: Voter Turnout; Vote By Mail; Drop Box; Accessibility

I. Introduction

Voting is an essential component of vibrant democracies. Yet voter turnout in many places across the United States, especially during off election years, has been in decline (Becker

2016), with some cities witnessing the lowest turnout in decades (Maciag 2014). Low turnout typically means uneven turnout, with some groups (typically minority, low income, and younger residents) turning out at especially low rates (Rosenstone and Hansen 1993). Evidence shows that such voting disparities may lead to political outcomes that are biased against groups that don’t vote (Hajnal 2010).

Considerable interest among academics and practitioners alike centers around identifying ways to improve voter turnout and voting parity (Becker 2016). As more and more locales introduce alternative forms of voting -- such as vote by mail and early voting -- assessing the impact of such alternatives on voting behavior becomes more important. One relatively new form of alternative voting is drop box voting, whereby voters drop their mail-in ballots into a drop box at designated sites. Washington State is one of 22 states with a vote-by-mail (VBM) provision, and since

2013, all voting in the state has been conducted by mail. As is true with most places with mail-in voting, voters typically stamp their own ballot. However, counties in Washington are required to provide some opportunity for voters to leave their ballot in a drop box, a large secure box where unstamped ballots can be deposited anytime, day or night. Washington State’s largest county,

King County, recently underwent an expansion in the number of these drop boxes, in 2016 growing from a county-wide total of 10 to a total of 43 boxes. The stated purpose of these additions was to improve citizens’ access to voting so that (in the words of the King County

Executive), “everyone’s voice is heard in our region.” (King County Newsroom 2016).

This study measures the effect this expansion in drop boxes has had both in terms of overall voter turnout in King County, as well to the turnout among those residing close to a new drop box. Our intent is to estimate the effect these additional drop boxes have on voter turnout overall.

We begin with a brief description of turnout and trends in turnout in the United States, followed by a review of literature assessing the relationship between the form voting takes and voter turnout. A third section describes recent voting in King County, its switch to all-mail voting, and its recent expansion of drop boxes. Section four details our data and methodology, and section five presents our results. We discuss the results in a sixth section, and a final section concludes with suggestions for future research.

II. Low Voter Turnout and Its Consequences

Voter turnout in the United States is among the lowest in the developed world. In the

2012 presidential election, just 53.6% of the voting age population turned out to vote (Desilver 2016). In non-presidential election years, the rate falls even lower, with just 36.3% turning out to vote in the 2014 midterm elections - a 70-year low (DelReal 2014). This leads scholars to question how well American democracy can represent its citizens when so few are making their voices heard through electoral participation (Bartels 2009). Particularly troubling is the research into who votes and who does not (Wolfinger and Rosenstone 1980). If the voting population differs significantly from those who do not vote, then a potentially large portion of the electorate may not be having their political preferences represented in the officials, laws, and policies that result from the electoral process (Verba and Nie 1987; Stimson, Mackuen, and Erikson 1995).

Nationwide, voting turnout varies by race, with voting rates (defined as the percent of registered voters who vote) generally highest among whites, with African Americans close behind.1 Voting rates among Hispanic voters tends to be low, with recent national evidence showing declining turnout each mid-term election (File 2015). However, research suggests candidate mobilization and ethnic identity can enhance Latino turnout (Barreto and Collingwood

2015; Valenzuela and Michelson 2016). Voting rates also tend to increase with age, and in midterm elections, has been exhibiting a downward trend among all age groups except those 65 and older. In 2014, 59 percent of registered voters voted, although among those 18-34 it was a low of 23 percent (File 2015). Women are more likely to vote than men (43 versus 41 percent), and native citizens vote more often than do naturalized citizens. Finally, large differences emerge across socio-economic status. Voting rates are especially low among the unemployed, those separated from a spouse, those never married, and those without a high school diploma.

Voting rates rise steadily both with educational level and with income (File 2015; Rosenstone and Hansen 1993; Leighley and Nagler 2014).

1According to File (2015), in 2014 only about 65 percent of citizens eligible to vote were registered voters. The implication of uneven demographic turnout differential is consequential because voters differ in ideological and policy preferences from non-voters. For instance, one recent study finds that “marginal voters” (or those who are less likely to turn out) are over 20% more supportive of the Democratic Party than “regular voters” (Fowler 2015). Evidence suggests that social more than economic policy preferences drive “irregular voters” as compared to “regular voters” (Nawara 2016). However, non-voters have also been shown to have significantly more liberal views on redistributive policies such as government provision of health insurance and federal aid for schools (Leighley and Nagler 2014).

There is a growing consensus among scholars that policy makers pay more attention to the preferences of those who vote over those who do not, and particularly to those with most affluence (Gilens 2012). One study, for instance, found that the preferences of voters were more likely to predict the aggregate roll-call behavior of Senators, while the preferences of non-voters did not (Griffin and Newman 2005). Another study found that members of Congress specifically try to allocate federal resources to areas that provide the best voter returns (Martin 2003). Other studies show that political outcomes are biased against groups that don’t vote (Hajnal 2010).

Voter turnout, therefore, is both a significant problem in the United States, and one that has a serious potential to bias public policy against those who systematically vote at lower rates.

In response, some scholars, government officials, and activists have been seeking ways to target non-voting populations and improve turnout among them.

III. Can Voter Turnout Be Increased?

One reason for low voter turnout in the United States could be the opportunity costs associated with voting. State voting laws vary significantly, with many requiring that votes be cast in-person at a precinct location, although 22 do allow some elections to be conducted by mail (National Conference of State Legislatures 2016). In three states, Oregon, Washington, and

Colorado, all elections are completely conducted by mail. The rational voter model of Anthony

Downs (1957) argues that voters calculate the benefits of their vote compared to the costs associated with the act of voting. If the benefit outweighs the cost then the person in question will vote. There are a few issues with this theory, the most glaring being the disappearingly small likelihood that any vote cast will be the deciding one, which means that the actual expected benefit of that individual vote is basically zero, which would be outweighed by even the smallest of costs (Dyck & Gimpel 2005). However, Riker and Ordeshook (1968) suggest that voters may get some sort of social and psychological benefit from political participation, which is why many people vote despite the apparent irrationality of doing so. Furthermore, most of the research on non-voting behavior finds that it is most strongly linked with social or psychological factors such as a lack of motivation, cynicism, a weak attachment to parties or candidates, and a lack of mobilization (Rosenstone & Hansen 1993), rather than to the cost of voting itself.

However, institutional factors that make it harder to vote can play a role in the act of voting, especially among voters who only need a nudge to vote (Barreto, Nuno, and Sanchez

2009). One of the most important of these is geography. How close to or far a voter is from a polling location can increase or decrease the convenience of voting, which in turn can increase or decrease the likelihood of voting. Distance has been shown to influence voting behavior and it provides some insight into how voter participation could be increased. In a study of Clark

County, Nevada, Dyck and Gimpel (2005) found that as the distance between a voter’s home and their precinct voting site increased, the likelihood of non-voting went up, although this did not explain the non-voting behavior in Las Vegas, where most voters lived relatively close to polling locations but turnout was low nonetheless.

Brady and McNulty (2011) took advantage of a natural experiment to examine the role distance played in voter turnout. They drew on data from the 2003 California recall election in

Los Angeles, for which voter precincts were consolidated so that there was a 64 percent decline in the total number of polling locations. They found that increases in distance from one’s old polling location decreased the likelihood of voting by 3.03 percent, while it increased absentee voting by 1.18 percent, leaving an overall decline of 1.18. Gimpel and Schuknecht (2003) found that in metropolitan areas, distance played the largest role in voting behavior for those in the suburbs and middling areas where voters had to contend with both distance and travel impediments like congestion. Those in rural areas where it is less difficult getting to the polls, the likelihood of voting did not decrease despite a distance of 6 to 10 miles to the polls. The access of voters to an automobile can also mitigate the effects of distance in urban areas. If no car is available, Haspel and Gibbs Knotts (2005) found that the likelihood of voting drops from .664 at .01 miles from a poll, to .418 at the median distance of .69 miles from a poll. If a car is available, the importance of distance to poll on voting behavior declines noticeably.

These findings suggest that distance and time to poll can depress voter turnout.

Accordingly, one way to increase voter participation should be to reduce travel costs by improving access to voting locations. However, in Laramie County, Colorado in 2003, precincts were replaced with Election Day vote centers which reduced the number of voting locations, but also allowed voters to cast their vote at any of the vote centers. This change resulted in an estimated 2.6 percent increase in voter participation, a calculation arrived at by comparing voting turnout in another country that did not make this change. Haspel and Gibbs Knotts (2005) similarly found that an increase in the number of precincts in Atlanta from 160 to 168 led to increased voting turnout. Both findings show that the convenience of voting influences voting behavior. The more locations there are, or the more conveniently located are these locations, the more likely it is that people will vote.

Based on this evidence, one would predict that the increasing alternative forms of voting

—whether by mail or by early voting-- should eventually lead to improved voter turnout. Indeed, evidence does suggest that voters will use alternative methods of voting should they be given the opportunity. In 2000, an estimated 10 percent of ballots cast nationwide were cast by mail, and by 2012, this figure had risen to nearly 20 percent (Pew Charitable 2015). And by 2014, nearly one-third of voters used some form of alternative voting, whether via the mail or by early voting

(File 2015).

At least 22 states have some provision for citizens to vote by mail (NCSL 2016).

Washington, Oregon and Colorado are currently the only states conducting all elections exclusively by mail (NCSL 2016), although in some states, such as California, a majority of votes come via mail-in ballots (DiCamillo 2016). The overwhelming evidence thus far suggests that mail-in ballots do indeed increase voter turnout in both candidate and non-candidate elections (Hamilton, 1988; Jeffe & Jeffe, 1990; Karp & Banducci, 2000; Magleby, 1987; Mutch,

1992; Rosenfeld, 1995; Southwell & Burchett, 1997; Southwell & Burchett, 2000; Southwell,

2016). For instance, Southwell and Burchett (2000) examined the impact of all-mail elections on turnout by analyzing data from 46 different statewide elections in Oregon between 1960 and

1996, three of which were all-mail.2 They found that on average during these three all-mail elections, voter participation increased by 10 percent compared to elections relying on traditional

2 Oregon did not become a fully all-mail state until 1998. in-person voting. The authors posit the increase is attributable to the increased convenience of voting and the removal of the cost of time and travel from voters’ decisions.

Building on the work of Southwell and Burchett, Karp and Banducci (2000) drew on data from Oregon for 27 statewide elections between 1986 and 2000 to assess the impact of mail-in ballots. For high-salience races, like the 2000 presidential race, VBM had a negligible impact on turnout, but for low-salience local elections, they estimate that vote by mail increased turnout by a very large 26.5 percent. Southwell similarly found that in both Oregon and Washington, VBM had the largest positive effect on special elections, but also a positive effect on presidential elections, while it had little influence on voting in off-year Congressional elections (Southwell,

2009; Southwell, 2010; Southwell, 2011). There is also evidence that mail-in ballots decrease the number of voters who abstain from voting for down-ballot measures, something called roll-off voting (Southwell, 2009).

In terms of who was more likely to vote, Karp and Banducci (2000) found that the largest increases in turnout occurred among those demographic groups already more likely to vote --

White, educated, older and had higher incomes voters. On the other hand, VBM had little impact on the likelihood of minorities and those of lower education or income voting in Oregon, though

Southwell (2010) did find positive effects for Black and Latino voting in Colorado as a result of mail-in ballots. This suggests the possibility that while the move to alternative forms of voting has increased voting turnout, it may also be increasing inequities in voting behavior. In a review of literature on voting reforms, Berinsky (2005) found that most tend to retain voters rather than stimulate new voters, meaning that while they may result in increased turnout, they do not increase the diversity of the electorate, a finding mirrored by Karp and Banducci’s study of VBM in Oregon. The effect of convenience has also been questioned by some scholars. In a comprehensive study of state-level voter turnout between 1972 and 2002, Fitzgerald (2005) found that there was no effect for either early voting and unrestricted absentee voting, while not significant, had a negative coefficient in regard to turnout. Fitzgerald argues that these findings suggest that lowering the costs associated with voting through increased convenience has little impact on turnout. Similarly, Gronke et al. (2007) found that no-excuse absentee balloting, permanent absentee balloting, and early in-person voting had no effect on turnout in U.S. elections between 1980 and 2004. They did find that VBM in Oregon did increase turnout by

4.7% in presidential years, but point out that this was less than half the increase that was reported by Southwell and Burchett (2000). Returning to an examination of Southwell and Burchett’s findings in 2012, Gronke and Miller found that the 10% increase that was found because of

VBM in Oregon was instead a novelty effect for the first three contests it was available. Taking this into account, VBM was only significant for special elections. Drawing on a natural experiment in California where some voters in less populous precincts are assigned to VBM,

Kousser and Mullin (2007) matched these precincts to others where voters must go to a polling place to cast their ballot. They found that those assigned to VBM actually turned out at lower rates than those who had to go to a polling place in general elections, but that VBM did increase turnout in special elections. All of this suggests that it is still unclear whether the convenience associated with VBM has a significant effect on turnout and if it does whether this is due simply to retention or by drawing in new voters.

One aspect of VBM that has received little scholarly attention is the additional convenience of drop boxes for mail-in ballots. In VBM elections, a stamp is almost always required of voters, as ballots are generally collected via the US Postal Service. No such stamp is necessary with a drop box, one must simply slide their ballot into the box slot. While the use of mail-in voting options is growing throughout the United States, where 24 hour drop boxes are available, these have proven very popular (Pew Charitable). In one survey, the most common reason given for using ballot drop boxes was their convenience, followed by the fact that postage was not required. The third most common reason given was the greater trust that one’s vote was actually being cast (Pew Charitable 2015).

Counties in VBM states increasingly are providing drop boxes as alternatives to the use of stamped envelopes. In Oregon, 56 percent of votes are cast in some alternative to the post office, and in Washington, 39 percent are, although this number inclined to 50 percent in the

2016 general election. In Pierce County, Washington, just south of King County, over 50 percent of ballots are returned via drop boxes. Thus, as states look to expand voting convenience, ballot drop boxes appear to be a popular alternative to traditional Election Day voting and more standard VBM systems.

While drop boxes are heavily used when available, it isn’t apparent whether their existence causes people to vote who otherwise would not, or simply diverts ballots from the stamped route to the unstamped route to the county elections office. Generally the expansion of drop boxes is incremental, making any measurement of their effect difficult. However, in 2016

King County, Washington, expanded their stock of drop boxes from 10 to 43, offering an opportunity to examine the impact of these boxes on voter participation.

We do not test the mechanism of drop box usage in this paper; rather we examine whether drop boxes influence turnout – and if so amongst whom. Given the extant research on convenience voting, we hypothesize that drop boxes will positively enhance turnout – specifically those voters who physically reside closer to a drop box will be more likely to vote all else equal. We think this drop box effect will be enhanced in lower salience elections where the drop box provides visual cues and reminders to those who live near them. While these cues are present in all elections, general elections in presidential years have so many cues and reminders that the added benefit of a drop box is miniscule whereas in an off-year and primary election the cues should be more distinct and therefore stand out more. In addition, low salience elections may well be perceived as less important by many voters so voters are less likely to make the effort to vote. Given that a closer drop box likely makes it a bit easier to vote, we should see a drop box turnout effect when the election overall is perceived to be less important (i.e., primaries and off-year generals).

Finally, fitting with the extant convenience voting literature, we think drop boxes will increase voting overall but are unlikely to shift the makeup of the voting pool much. That is, low income, younger, and racial ethnic/minorities – groups that historically exude lower turnout rates than wealthy, older whites – will not be more likely to turnout because of a drop box – all else equal. We hypothesize that drop boxes will not produce low-propensity voter heterogeneous treatment effects.

IV. King County, Washington

King County, Washington, with Seattle as its seat, has around two million residents, making it the United States’ 13th largest county. With about 30 percent of Washington’s residents, it is also the largest in Washington, having experienced rapid population growth in recent years (see Table 1). Relative to national and state averages, the county’s population is disproportionately of working age. Residents are also ethnically and racially diverse, with about

17 percent Asian, 10 percent Hispanic, 7 percent African American, 21 percent foreign born, and a quarter speaking some language other than English at home. It’s also well-educated, with almost half of its 25 and older residents having a college degree, compared with 29 percent at the national level. Median household income ($73,000) and per capita income ($41,000) is nearly

50 percent above the national average, and significantly above the state average; the poverty rate is also below both (see Appendix). With a density of over 900 people per square mile, the county is about nine times denser than both the US average and the state average.

King County has about 1 million registered voters. In terms of votes cast, King County residents strongly lean toward Democratic candidates and issues, although some suburbs in the east and to the south are more moderate. Since 2000, over 60 percent of Presidential votes cast have been for Democratic candidates, with Barack Obama receiving about 70 percent of the votes cast in both 2008 and 2012.

In terms of turnout rates, defined as the percent of registered voters who vote, the size and pattern varies by the election type, which can be categorized into four groups. First are elections held every four years when voters decide on the US President. Turnout for the last several general Presidential elections have been rising slightly, from 75 percent in 2000 to 85 percent in 2012 (Table 1). A second type of election is the midterm election when Congressional seats are determined. For this cycle, turnout in King County typically ranges from 55 to 75 percent (Figure 1). Third, Washington State also holds odd-year elections where voters decide ballot initiatives and referenda, as well as cast votes for local offices, like Seattle Mayor. As is typical nationwide, turn-out for general off-year elections is low, averaging around 50 percent, with the occasional very low turnout of 36 percent in 2003 and 40 percent in 2015. A final fourth category of elections is the primaries that occur during any of the three election cycles above Primary turnout is low in all years, averaging about 40 percent during Presidential elections, 33 percent during midyear elections, and 29 percent during off years (Table 1).3

[INSERT TABLE 1 ABOUT HERE]

Like most locales, King County exhibits very uneven voting patterns at the precinct level.

In the 2014 election, 53 percent of registered voters voted. However a number of precincts experienced less that 30 percent turnout, whereas others had over 71 percent turnout. Figure 2 color-codes turnout by precinct for 2014, and Figures 2A and 2B shows the same information for

2015 when the county-wide turnout was 39 percent. If drop boxes do increase turnout, does it also improve the distribution of voting across King County, or do they widen them?

[INSERT FIGURE 2, 3A, AND 3B ABOUT HERE]

V. Drop Boxes and Their Expansion

As is true across the nation, King County has turned increasingly to alternative forms of voting. In 1974, all voters in Washington could request an absentee ballot, and in 1985 that request could be made permanent. In 1983, jurisdictions could request a vote by mail for special elections, a right given to counties for local elections in 1987, and for statewide elections in

1993. In 2005, Washington counties could conduct all elections by mail (Secretary of State

2007), and by 2007 all King County voters received their ballots in the mail, although one-third

3 There are also special elections, not discussed here. Turnout for these is low but variable, occasionally reaching above 50 percent. still chose to vote in person. In 2009, the County closed all precinct voting, and from that year on, all voting has been by mail. In 2011, the Washington legislature passed a law requiring all counties to conduct their vote by mail, and since that year the entire state has exclusively voted by mail.

Washington State requires all counties to provide at least two drop box locations where voters can drop off their ballot any time of the day, and almost all provide many more drop boxes than this. King County has been slower than most to expand its drop boxes beyond this minimum. By 2015 county residents had access to 10 permanent drop boxes and 13 temporary ballot-drop vans with limited hours. This compared with 30 permanent boxes in nearby Pierce

County, which has only half of King County’s population. This difference in availability of drop boxes explains why over half of all Pierce County residents typically vote via drop boxes, while as of 2015 in King County only about a quarter do.

During the 2015 general election for the director of King County elections, the two candidates campaigned on a platform of expanding drop box locations, and the eventual winner set a goal of 40 by the following 2016 general election (Connelly 2015). The stated purpose of the expansion was to ensure that 90 percent of residents lived within three miles of a drop box

(Bahain 2016). On drop box expansion, newly-elected Director of Elections, Julie Wise, stated,

“We should make it as easy as possible to exercise the right to vote and this is a good step in that direction” (Capital Hill Seattle 2016). Throughout the summer and fall of 2016, these drop boxes were installed somewhat equitably throughout the county (see Figure 4), although the final selected locations were not determined randomly.4 Early results from the primary, which

4 According to a report from the King County Elections Office produced in early 2016, the drop box locations were determined by site from Elections staff to over 100 possible sites across King County. The office established a set of criteria with which to score each possible location, which included: alignment with department and county goals, operational effectiveness, general occurred during the middle of the drop box expansion (29 of the total 43 drop boxes were in place), showed their popularity: 36 percent of primary voters used the drop box, compared with

26 percent during the 2015 general election (King County 2016).

We next outline our data, methods, and identification strategy to determine whether ballot drop boxes increase turnout and if these effects differ by election type. On balance, we anticipate that drop boxes -- as measured by one’s distance between their residence and their closest drop box -- are associated with higher voter turnout. However, drop box effects are likely to vary as a function of election salience. To this end, turnout is already approaching 80 percent in

Washington State in highly competitive presidential elections. Voters know an election is happening, as the media coverage is overwhelming, mobilization is high, and social media accounts are active. Moreover, the psychological and social benefits one gains from voting is likely to be relatively high compared to voting in elections perceived to be less important. Thus, in this environment, the addition of a local drop box is unlikely to propel someone to vote who otherwise would not. However, voters and the media tend to pay less attention during primary and off-year general elections, resulting in lower turnout. Under these scenarios, drop boxes may provide a reminder or stimulant of the upcoming election. The convenience of a nearby drop box may well propel some people to vote who might not otherwise.

[INSERT FIGURE 4 ABOUT HERE]

VI. Data and Methodology accessibility and continuity of service. Once all sites had been scored on this basis, a short list of locations was drawn up which also took into account geographic equity and site owner input. To assess whether drop boxes affect voter turnout, we contracted with a nationally- known voter file vendor (Labels and Lists, or L2), who maintains lists of Washington’s voters taken directly from that state’s Secretary of State office. When voters register to vote, several bits of information are required of them, including their age, gender, address, and some states require party identification and race (the latter are not requested in Washington). These files are augmented with numerous variables, including precinct, registration date, census and commercial data that provide estimates of income, education, and race/ethnicity, and vote history for the past twelve years. The voter file records whether each voter cast a ballot in every possible election over this 12-year window. Given our interest, we restrict the Washington voter file to

King County.

Our goal is to determine whether the placement of drop boxes has influenced voter turnout. But because drop box effects likely vary by type of election (i.e., primary versus general, presidential versus off-year), we construct several dependent variables to measure voting in a variety of scenarios. We analyze four elections (2015 general, 2015 primary, 2016 general, and

2016 primary), where in each, our dependent variable is a binary indicator equal to one if an individual is recorded as voting or zero otherwise.5 In our estimation strategy, which we describe below, we collapse the four dependent variables into two models (a general election model and a primary election model). To assess heterogeneous treatment effects, we conduct discrete subgroup analyses for the following variables: age, gender, income, race/ethnicity.

The creation of our independent variable is somewhat more complex. We wish to measure the average treatment effect a drop box has on voter turnout. To do so, we craft a

5 We subset the data to only voters who were eligible to vote in both election years. distance measure for each voter to her closest drop box for the 2015 primary (when there were

10 total boxes), 2015 general (10 boxes), 2016 primary (29 boxes), and 2016 general (43 boxes) elections (CITATION RETRACTED). While it is certainly possible voters will not use the drop box closest to their residence, for instance -- voters may use the drop box closest to their work -- voter residence is the only geographic characteristic available. Besides, distance to polling location has been the measure traditionally employed in similar analyses (e.g., Gimpel and

Schuknecht 2003; Stein and Vonnahme 2008).

Because the number of drop boxes in the two 2015 elections are the same -- 10 boxes -- this distance measure is also the same for the 2015 elections. However, King County expanded to 29 drop boxes by the 2016 primary, and to 43 by the 2016 general. Thus, each voter has three separate distance measures, which we exploit to estimate the average treatment effect. To calculate a voter’s distance to their nearest drop box, we began by using a geocoding process to calculate the longitude and latitude coordinates of all 43 drop boxes in King County. The location of each drop box was given to us by the King County board of elections, and is mapped in Figure 1. We repeated this geocoding process with the address of every registered voter. Next, we calculated the haversine (in meters) between each voter to all drop boxes, for that particular election. Similar to Euclidean or cosine distance, haversine is a formula used to measure the distance between two latitude and longitude coordinates on a sphere, such as the earth (Robusto

1957). The measure is particularly well-suited for distances relatively close to one another, such as distances within a county. For each voter, we then selected the smallest haversine distance between their residence and drop box, which becomes the drop box distance for that voter in that particular election. Unfortunately, the additional drop boxes were not placed randomly around the county, but instead were installed in areas where planners anticipated higher demand, and in areas farther from existing drop box locations. This means that if we simply compare turnout of voters living in precincts with new drop boxes to voters living in precincts without drop boxes, pretreatment selection effects might actually show new drop boxes having a negative effect on turnout. In other words, other variables that correlate with voter participation might also correlate with drop box placement.

To address this problem, we employ a conditional logit model to estimate the relationship between distance to the nearest drop box and an individual’s propensity to vote. The conditional logit model exploits the fact that we have multiple observations for most of the voters in our data set. Generally speaking, the conditional logit model is designed to deal with case-control comparisons within “grouped” data. In our context, we treat each individual as a “group” observed over several time periods. This allows us to estimate the effect of a change in distance to the nearest drop box by treating the observation of each individual prior to the installation of new drop boxes as their own “control.” This effectively controls for all time-invariant individual characteristics (e.g. race, gender, etc.) that might otherwise influence the propensity to vote.

Furthermore, we subset the data to include only voters who had registered by January 1,

2015 and had not moved. This ensures that any change in voting behavior is not due to a change in registration status. We also include a fixed effect for the year 2016 to capture any change in the propensity to vote between 2015 and 2016 that might be common across all individuals.

Since voters are generally more likely to turn out in Presidential election years, we expect this fixed effect will be positively related to the propensity to vote. We also estimate the models separately for primary and general elections. We do so because we suspect that the overall propensity to vote, as well as the effect of distance to drop box on the propensity to vote, varies between primary and general elections. In each model, the independent variable of interest is the individual’s distance to closest drop box, which changes between the 2015 general (10 total boxes) and 2016 general (43 total boxes), and then also between the 2015 primary (10 total boxes) and the 2016 primary (29 total boxes).

The estimated relationships are expressed in terms of odds ratios, whereby a value of 1 indicates no relationship between distance to drop box and the propensity to vote. A value greater than 1 suggests greater distance to the nearest drop box increases the propensity to vote, and a value below 1 indicates greater distance to the nearest drop box decreases the propensity to vote. We also use these estimates to generate and plot predicted probabilities of voting, holding unobserved individual characteristics fixed, for each type of election.

We expect an odds ratio greater than one on our year fixed effect and an odds ratio lower than one on our measure of distance to the nearest drop box. We also expect a larger effect (odds ratio closer to zero) for the primary election. Given the salience of the presidential race, we expect drop box distance to play a limited role in the 2016 general election compared to the primary or the 2015 general. These are all lower salience elections.

VII. Results

Our primary interest is to assess whether drop boxes increase turnout in elections, and if so, if the effects are stronger in low salient competitions. Then, we want to assess whether these turnout effects vary by relevant social characteristics. The estimated odds ratios for the baseline model are reported in Table 2. Values in parentheses are p-values calculated using robust standard errors clustered at the individual level. The results conform to our expectations, and all results are statistically significant at the 1% level. The estimated odds ratio on our year dummy in column (1) is 42.66, implying that the odds of voting in the 2016 general election was 42.66 times higher than the odds of voting in the 2015 general election, holding all else constant. This, of course, comports with turnout rates. The odds ratio on the year dummy for the primary election was only 3.36, implying a much smaller change in the odds of voting in the primary elections between 2015 and 2016.

[INSERT TABLE 2 ABOUT HERE]

We also find that greater distance to the closest drop box was associated with a lower propensity to vote. The odds ratio on our distance measure in column (1) is 0.98, implying a one mile increase in the distance from the nearest drop box will decrease the odds an individual will vote by approximately 2%. The effect is larger for primary elections, as reported in column (2).

Here, we find that a one mile increase in the distance from the nearest drop box will decrease the odds an individual will vote by approximately 4%.

[INSERT FIGURES 5 ABOUT HERE]

It is difficult to interpret the magnitude of these effects based only on the reported odds ratios. For ease of interpretation, Figure 5 plots the predicted probabilities of voting in the four analyzed elections, based on the estimates reported in Table 2.6 The bottom right panel presents voting probabilities during the 2016 general election as a function of distance from the nearest

6 We also estimated all models among respondents within 30 miles and 15 miles of a drop box, respectively. The findings are essentially identical. Predicted probability plots are presented in the appendix. drop box. These estimates hold time-invariant individual characteristics constant, and show the relationship between distance to the nearest drop box and likelihood of voting for the average registered voter in the estimation sample. We can see from the figure that the probability of voting declines very slowly until we start looking at individuals more than 20 miles away from their nearest drop box. However, an examination of the rug at the bottom of the plot (voter distance density) reveals that very few voters live more than 20 miles from the nearest drop box.

We can get an even stronger sense of the magnitude of the drop box effect in the bottom right panel of Figure 6, where focus on the region of the plot is one standard deviation above and below the mean value of distance to the nearest drop box. This figure shows that a one standard deviation increase or decrease from the mean changes the likelihood of voting by less than one percentage point. In short, the drop box effect on turnout in the 2016 general was extremely minimal, as hypothesized.

[INSERT FIGURES 6 ABOUT HERE]

The upper right quadrants in Figures 5 and 6, respectively, repeat this exercise for the

2015 general election. The plots show that the likelihood of voting is generally lower in 2015 (as indicated by the odds ratio on our year dummy), and also more sensitive to changes in the distance to the nearest drop box. In Figure 5, we can see that the likelihood of voting varies between 20% and 50% over the whole estimation sample. Figure 6 shows the change in the likelihood of voting for one standard deviation above and below the mean of the distance variable. This figure shows that a one standard deviation decrease in distance to the nearest drop box relative to the mean increases the likelihood to vote by approximately 1.5 percentage points. The bottom left quadrants of Figures 5 and 6, respectively, plot the relationship between distance to the nearest drop box and the likelihood of voting for the 2016 primary election.

Compared to the 2016 general, the probability of voting is generally lower, and much more sensitive to changes in distance to the nearest drop box. The likelihood of voting varies between approximately 30% and 80% over the whole estimation sample. The bottom left quadrant in

Figure 6 focuses on changes from one standard deviation above and below the mean distance to the nearest drop box. We find that decreasing the distance to the nearest drop boxes by one standard deviation relative to the mean increases the likelihood to vote by approximately two percentage points.

Finally, the top left quadrants of Figures 5 and 6, respectively, plot the relationship between the likelihood of voting and distance to the nearest drop box for the 2015 primary election. This election shows the lowest overall rates of turnout and the greatest sensitivity to changes in distance to the nearest drop box. This is the election with the lowest salience; thus, the turnout magnitude comports with our expectations. As shown in Figure 5, the likelihood of voting ranges between 10% and 50% for the entire estimation sample, and drops off sharply as the distance increases. Figure 6 shows that a one standard deviation decrease in distance to the nearest drop box relative to the mean increases the likelihood of voting by approximately four percentage points. Thus, the overall pattern of findings indicates that drop boxes have greater turnout effects in less salient elections, fitting with our theoretical priors.

The secondary focus of our analysis is to assess heterogeneous treatment ballot drop box turnout effects. To examine this, we replicate the above analysis among different subsets of groups, including age (three categories), income (three categories), gender (two categories), and race/ethnicity (four categories). However, we should note that the determination of an individual’s race/ethnicity and income are based on a combination of surname, neighborhood characteristics, and other consumer data, so the match is not necessarily exact.7 Given the extant literature on VBM and convenience voting, ceteris paribus, we do not expect voters from groups lower in socio-economic status to disproportionately be influenced by the presence of a drop box.

Table 3 presents results for age divided into three subgroups: younger voters (18-36), middle-age votes (37-55), and older voters (56 or older). In each case, we subset the data to just voters fitting that particular age range and estimate a conditional logit model. Here, we focus on the odd’s ratios by noting that middle-age (odds ratio = 0.975, p < 0.01) and older voters (0.963, p < 0.01) are actually more likely to vote the closer they are to a drop box in general elections

(top part of Table 3), and even more so in primary elections (.951, and .934, respectively, p <

0.01). Thus, fitting with extant convenience voting literature, it seems that groups who are already likely to vote (older voters) are precisely those who gain in turnout ever so slightly from the presence of a drop box, all else equal. Importantly, we test the joint equivalence of the estimated odds ratios on our distance variable – across groups. The null of this test is that the coefficients are equal, whereas a statistically significant finding (p < .10) indicates the presence of a heterogeneous treatment effect. The p-value is significant in both general and primary elections indicating that drop box placement differentially influences voters by age.

[INSERT TABLE 3 ABOUT HERE]

7 Unfortunately, the voter file vendor could not provide us with precise methodology for flagging voters by race/ethnicity and income. Table 4 presents results from an identical analysis but split by race/ethnicity for the following groups: Asian, Hispanic, White/European, Black/African-American. Given the ethnic/racial population base, and accuracy of race/ethnic predictions in the voter file, the size of classified voters based on race for Asian, Hispanic, and black is relatively small lending to no statistically significant subgroup effects in general elections (top part of Table 4). However, whites/Europeans report an odd’s ratio of .980 indicating they were a bit more likely to turnout in general elections as a function of a drop box. However, the equivalence of distance coefficient is not statistically significant, so we cannot say for sure that drop boxes had a racial effect in general elections.

The primary findings are extremely interesting, as we see that drop boxes positively influence turnout for all groups, disproportionately aiding Asians (.901, p < 0.01), blacks (0.934, p < 0.01), whites (0.944, p < 0.01), and Hispanics (0.958, p < 0.01). Furthermore, the test of equivalence indicates these distance effects are statistically significant. These primary results actually contradict our expectations, in that whites are not necessarily the group most likely to be positively influenced by turnout. That said, given the relatively small case-size, we take these results with some caution, and suggest further recent in this area is necessary to further support these heterogeneous effects.

[INSERT TABLE 4 ABOUT HERE]

Table 5 and Table 6 present the results by gender and income, respectively. Overall, we find evidence that suggests men are more likely to be positively influenced by drop boxes than are women, but only in primary elections (the equivalence of distance is statistically significant at p = 0.0031). This comports with our general expectations. Finally, as presented in Table 6, perhaps most critical to the discussion of convenience voting and voter turnout, we do not find, however, any heterogeneous effects on income, as evidenced by non-statistically significance p- values on the two equivalency tests. This means that, at least in this analysis, drop boxes do not disproportionately increase voting among poor voters or rich voters.

[INSERT TABLE 5 ABOUT HERE]

[INSERT TABLE 6 ABOUT HERE]

Overall, the subgroup analyses provide mixed results for our priors. Older voters and males seem to benefit slightly from drop boxes, whereas some under-represented ethnic groups

(Asians and African-Americans) appear to benefit in terms of small increases in turnout. We do not, however, find heterogeneous effects by income. To be sure, while these results are telling, further research in different locations and even different subgroups should further investigate these heterogeneous effects.

VIII. Discussion

Our estimation results support our basic hypothesis that increased proximity to drop boxes increase the likelihood that an individual will vote. This result is robust to controls for time-invariant individual characteristics that might otherwise affect the probability of voting (e.g. education, gender, race, etc.). The magnitude of this effect depends critically on the type of election being considered. If we focus on the changes associated with a one standard deviation decrease in the distance to the nearest drop box, we see that the effects were greatest in the 2015 primary, a much less salient election compared to the 2016 general.

This suggests that the mechanisms through which drop boxes affect voter behavior can be

“crowded out” by other characteristics of the election. The election in 2016 was contentious and saturated US media. Voters were apparently so motivated to turn out that proximity to the nearest drop box was not an important consideration. A primary election or an off-year general election

(which is actually common on Washington State) will receive less media coverage, and voters may be more sensitive to the increased convenience or security the drop box provides. Therefore, installing new drop boxes can help increase turnout in low-salience (i.e. off-year or primary) elections, but appears unlikely to increase turnout in presidential contests.

Interestingly, we found mixed results for drop box heterogeneous treatment effects – some confirming our expectations while others challenging expectations. By splitting our data by variables into several subgroups, we examined whether certain groups of voters were more likely to vote as a function of how close they live to the nearest drop box. Older men benefitted from drop box distance, whereas treatment by income had no differential effects. Finally, it seems that

Asians and African-Americans may well be more affected by drop box placement relative to non-Hispanic whites and Hispanics. Given that researchers and practitioners are often looking to improve turnout among under-represented populations (i.e., racial and ethnic minorities), this finding is a welcome development.

IX. Conclusions

In this paper, we set out to evaluate whether vote by mail drop boxes influence turnout.

King County recently installed 33 new boxes around the county, and it is likely that the state will invest greater resources in this method of voting. Other vote by mail states, such as Oregon, have also implemented similar drop box voting methods. However, before this study, no one really knew if installing new drop boxes actually had any impact on voter turnout. One could simply not look at variation in turnout in places where drop boxes existed or did not – because auditors and election administrators did not select drop box sites at random. In the end, are drop boxes a good use of government spending if the boxes simply shift the burden of voting to placing a ballot in a steel box as opposed to placing it in the post, if the new method has no overall democratic effect?

Our results clearly show that drop boxes have a democratic effect – both in terms of increasing overall turnout and turnout among key under-represented racial minorities. In a time when many states are opting to restrict the electorate through voter identification laws and reducing early voting hours, Washington State is leading the way to achieving a more realized democracy. While the effects of boxes in presidential general elections were found to be minimal, turnout effects in primaries and off year elections are clearly noticeable as seen in

Figures 5 and 6. Moving from one standard deviation distance below the mean to one standard deviation above the mean, we observe an increase in probability of voting by 2-7 percentage points (depending on the election). However, this is not surprising, as a significant body of research shows that distance to polling location has a measurable effect on turnout – primarily in off-year and primary elections.

Moving forward, scholars should investigate whether drop boxes influence turnout similarly in other jurisdictions, elections, and states. For instance, are drop boxes more effective in cities where voters are more likely to commute via buses, trains, and bicycles? Or, are drop boxes more likely to influence voting behavior in high density locations than in low-density locations?

Other questions are important to investigate: 1) Is the distance to closest drop box a valid measure of average box treatment effect? If election administrators agree to track which voters use which drop boxes, we can calculate the percentage of the time voters who use drop boxes use their nearest box, and further test which boxes get the most traffic. Answering these types of questions will no doubt inform future drop box placement. 2) Are our findings in this paper simply a function of box advertising? With so many boxes going in in 2016, the King County auditor certainly spent significant efforts to let voters know that the boxes were there. Thus, it is possible that drop box voting might not be so impressive if new installations do not come with advertising. Or, do drop box effects dissipate over time as the newness of them is forgotten?

Future research can begin to evaluate advertising effects through mailer studies. 3) It is possible that drop boxes are more likely to influence turnout in certain types of neighborhoods than in other types of neighborhoods, or among specific types of voters. Placing a box in a high-income area may have no effect on turnout, as voters there are not worried about stamp costs, and may be more likely to trust government institutions, such as the post office. Our analysis of heterogeneous treatment effects suggests that box placement affects rich and poor equally. 4)

Finally, through focus groups and survey research, we can provide an answer to why voters might prefer voting via drop box than through the mail.

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