Precinct Closing Times in Floridaduring the 2012 General Election∗

Michael C. Herron† Daniel A. Smith‡

October 21, 2014

Abstract

The Supreme Court’s decision in Shelby County v. Holder to strike down Section 4 of theVoting Rights Act has spurred a search for measures of election performance that extendbeyond race-based registration and turnout rates. We contribute to this endeavor by study-ing patterns of precinct congestion in Florida during the 2012 General Election. With precinctclosing times as proxies for congestion, our study covers 5,302 total Election Day precincts inFlorida. We show that there was tremendous variance in closing times in Florida on ElectionDay in 2012 and that precincts with greater proportions of Hispanic voters closed dispropor-tionately late. This finding holds even controlling for the number of pollworkers/precinct.Broadly speaking, voting place congestion in the 2012 General Election did not affect allFloridians equally, and most notably the post-Shelby electoral environment in the United Statescontinues to reflect racial disparities. With the loss of the Voting Rights Act’s retrogressionstandard, our analysis illustrates how precinct congestion data can be used to assess whetherdifferent racial/ethnic groups face different barriers to voting.

∗An earlier version of this paper was presented at the 2013 Annual Meeting of the American Political ScienceAssociation. We draw heavily on Herron and Smith (2013a), a report commissioned by Advancement Project andpresented by Katherine Culliton-Gonzalez of Advancement Project to the Presidential Commission on Election Ad-ministration on June 28, 2013. The authors thank Advancement Project for its generous support, Brian Amos forresearch assistance, and Linda Fowler, Melissa Herman, Michael Martinez, and seminar participants at DartmouthCollege and the University of Wisconsin, Madison, for helpful comments on earlier drafts.†Professor of Government, Dartmouth College. 6108 Silsby Hall, Hanover, NH 03755-3547

(michael.c.herron@dartmouth.edu).‡Professor of Political Science, University of Florida. 234 Anderson Hall, Gainesville, FL 32611-7325

(dasmith@ufl.edu).

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Introduction

In June, 2013, the United States Supreme Court in Shelby County v. Holder struck down Section

4 of the Voting Rights Act (VRA). The five-person majority on the Court argued that the VRA’s

pre-clearance coverage formula defining which jurisdictions in the United States needed permis-

sion from the federal Department of Justice before changing any aspects of election administration

was not “grounded in current conditions” and hence was unconstitutional. The Court’s majority in

Shelby noted that black voter registration and turnout rates in some jurisdictions requiring preclear-

ance under Section 5 of the VRA were roughly equivalent to, or even higher than, corresponding

white rates and that it was accordingly not fair to penalize these jurisdictions for antiquated racial

voting problems.1

The Court’s assertions about black registration and turnout rates were grounded in survey esti-

mates published by the United States Census Bureau. However, as the Shelby majority conceded,

registration and turnout rates are just one piece of a broad electoral canvass: “At the same time,

voting discrimination still exists; no one doubts that. The question is whether the [Voting Rights]

Act’s extraordinary measures, including its disparate treatment of the States, continue to satisfy

constitutional requirements.” This point was also articulated by Justice Ginsberg in her Shelby

dissent; she contended that “increases in voter registration and turnout” are not “the whole story”

in the matter of assessing racial regularities in electoral participation.

Shelby’s elimination of Section 5 preclearance shifted to plaintiffs the burden of proof when

arguing under Section 2 of the VRA that minority voting rights are being harmed by existing

laws or practices. Such plaintiffs will need to anchor their arguments in measures of electoral

performance that transcend the types of registration and turnout arguments criticized in Shelby.

This lends a sense of importance to the task of exploring new measures of election performance,

measures that can identify sources of bias in electoral processes or show the lack thereof.

1The opinions in Shelby County v. Holder are available at http://www.supremecourt.gov/opinions/12pdf/12-96_6k47.pdf (last accessed August 12, 2013).

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The extent of Election Day precinct congestion is one measure of electoral performance, and

the question that interests us is whether the degree of congestion faced by Florida voters in the

2012 General Election varied by voter type, in particular by voter race/ethnicity. Although we

comment briefly on the relationship between congestion and party affiliation, our primary interest

in race/ethnicity reflects the legacy of discrimination in the voting history of the United States and

the importance of these constructs as articulated in the VRA.

A clearer understanding of disparate levels of precinct congestion may open new avenues for

state or federal oversight of electoral processes across the country. Moreover, excessive congestion

may potentially disenfranchise some voters,2 and congestion is effectively a “time tax” (Mukher-

jee, 2009). Indeed, if precinct congestion is understood as an impediment to voting, it is important

to document whether congestion at the polls has a disparate impact on minority voters. Standard

measures of minority voter participation, like voter registration and turnout rates, certainly can

serve as important indicators of racial discrimination, but these indicators may belie the discrimi-

natory effects of other state laws or administrative decisions that affect election performance and

precinct congestion, among other things.

We explain shortly how we measure precinct congestion, but for the moment it suffices to note

that one sign of congestion is the presence of voting lines. Indeed, lengthy lines at polling stations

may be one of the more memorable themes that many Americans associate with the 2012 General

Election. Within the politically important state of Florida, there was extensive press coverage of

Election Day lines with the The Tampa Tribune reporting that, “[L]ines were especially long in

Broward, Lee, Miami-Dade and Orange counties, and voters in Lee and Miami-Dade cast ballots

even after Republican challenger Mitt Romney had conceded to President Barack Obama.”3 Lines

2See, for example, “Analysis: 201,000 in Florida didn’t vote because of long lines,” Or-lando Sentinel, January 29, 2013, available at http://articles.orlandosentinel.com/2013-01-29/business/os-voter-lines-statewide-20130118_1_long-lines-sentinel-analysis-state-ken-detzner (last accessed August 15, 2013).

3See “In Florida, not every precinct is created equally,” Tampa Bay Tri-bune, November 16, 2012, available at http://tbo.com/ap/politics/in-florida-not-every-precinct-is-created-equally-566822 (last accessed June 26, 2013).

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during the early voting period in Florida were also prominent, and following complaints from

voters “who sometimes [had] waited seven hours to vote or who did not vote at all because they

could not wait for so long to do so,” the Florida Democratic Party filed a federal lawsuit on the last

day of early voting.4 President Obama even commented on voting lines after the 2012 Election,

saying during his post-election acceptance speech on the morning of November 7, “By the way, we

have to fix that.”5 Then on March 28, 2013, President Obama signed an executive order establishing

a Presidential Commission on Election Administration whose goal is to “promote the efficient

administration of Federal elections and to improve the experience of all voters.”6

The problem of precinct congestion is not unknown to the administrators who run American

elections. Indeed, half a year after the 2012 General Election two of Florida’s most populous

counties, Broward and Miami-Dade, decided to “reorganiz[e] hundreds of voting precincts with

the goal of reducing the long lines of voters that plagued last November’s presidential elections

and embarrassed the state.”7 Still, despite an abundance of anecdotes from the 2012 General

Election, there is at present very little systematic information about many aspect of voting lines

and the broader subject of precinct congestion.8 How do precinct line lengths evolve over the

course of Election Day? Are line lengths different in predominantly white precincts compared

than in precincts with many black and Hispanic voters? Finally, at what point do long lines deter

4The lawsuit is described in “Democrats Sue to Extend Floridas Early Voting,” The New YorkTimes, November 4, 2012, available at http://thecaucus.blogs.nytimes.com/2012/11/04/democrats-sue-to-extend-floridas-early-voting/ (last accessed June 26, 2013).

5See “President Obamas acceptance speech (Full transcript),” The Washington Post, November 7,2012, available at http://articles.washingtonpost.com/2012-11-07/politics/35506456_1_applause-obama-sign-romney-sign (last accessed June 26, 2013).

6The text of the executive order is available at http://www.whitehouse.gov/the-press-office/2013/03/28/executive-order-establishment-presidential-commission-election-administr(last accessed August 12, 2013). The Commission’s final report is available at https://www.supportthevoter.gov/files/2014/01/Amer-Voting-Exper-final-draft-01-09-14-508.pdf (last accessed October 20, 2014).

7See “Counties reorganize precincts to help reduce voting lines,” Miami Herald, July 14,2013, available at http://www.miamiherald.com/2013/07/14/v-fullstory/3499848/counties-reorganize-precincts.html (last accessed July 15, 2013).

8For a recent addition to the literature, seeFamighetti, Melillo and Perez (2014) and Government AccountabilityOffice (2014).

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people from voting and lead to what is known as reneging? There are precious few answers to

these important questions and others similar to them, and this hampers efforts to identify patterns

of precinct congestion, diminish congestion to the extent that it exists, and maximize the ability of

precincts to handle voters in an expeditious and fair way.

The reason that so little is known about precinct congestion is simple: a lack of data that

researchers can use to study this subject. With this in mind, we seek to characterize the nature

of precinct congestion in one state, Florida, during the recent 2012 General Election. While the

analysis here does not extend to the entirety of this state, it does cover 43 of Florida’s 67 counties

and 5,302 of the 6,147 Election Day precincts that existed in Florida on November 6, 2012.9 Why

one state and why Florida in particular? We recognize that there are tradeoffs between analyzing

(a majority of) the counties in one state versus a selection of counties or other geographical units

across a variety of states. Nonetheless, as we describe shortly, gathering precinct congestion data

from even one state has proven to be complicated, hence our coverage of approximately two-

thirds of Florida’s counties (although, as we note later, almost 93 percent of the citizen voting age

population in the state). Given the dearth of data across the United States on precinct congestion,

the corresponding lack of scholarship, and the present lack of standards that could in theory specify

how congestion and in particular voting lines should be measured, we believe that our initial focus

on one state is appropriate. It is our opinion that a research design concentrating on one state, where

we have some idea of the set of potential confounds that might affect our results, is preferable to

a design that would entail casting a wide net across the United States (e.g., Nicholson-Crotty and

Meier, 2003). We hope that our Florida-based research on precinct congestion will help establish

criteria that assist other researchers in broader efforts aimed at this subject.

Even with our focus on precinct congestion in a single state and in one election, we have not

been able to resolve fully the types of data quandaries that regularly affect election administration

96,147 is the total number of precincts that existed in Florida as of December 31, 2012, according to the officialFlorida statewide voter file. We discuss this file and our use of it later. There were 5,950 precincts in Florida with atleast 20 registered voters as of the end of December, 2012.

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research. While we would like to present a study of the correlates of Election Day voting lines

in Florida, there are almost no data on Florida voting lines per se outside of anecdotes from press

coverage and results from a small number of public opinion polls. Indeed, in the course of our

attempting to collect data on such lines across Florida counties, we quickly learned that, with only

a few exceptions, neither county nor precinct administrators in Florida appear to keep track of

Election Day line lengths, maximum or average wait times, much less the times when final voters

were processed. Our brief attempts to look for voting line data beyond Florida suggest that this

appears to be the case for counties across the United States as well. Nonetheless, we were able to

gather from 43 Florida counties a set of what are called precinct closing times. There are subtleties

in how closing time is defined, but the idea is simple: a closing time measures when on Election

Day a precinct finished processing voters or tabulating votes.

Briefly, we find evidence of significant variation across Florida in Election Day precinct closing

times from the 2012 General Election. With respect to race/ethnicity in particular, we find strong

evidence that Florida precincts with many Hispanic Election Day voters had later closing times

than precincts with many white voters. Our results hold even controlling for pollworkers per

precinct and they are also conditional on county; because key election features in Florida are

for the most part held constant within counties, the variance in closing times that we observe

is presumably indicative of resource malapportionment that transcends pollworker allocations or

unanticipated turnout surges.

The remainder of this paper is organized as follows. In the next section we discuss existing

literature on precinct congestion and voting lines, and we explain how our results contribute to

this literature. We then describe how we collected our data on Florida precinct closing times,

and the next section of the paper presents. We conclude with suggestions for research and for

data standards that could help alleviate the current lack of understanding in the matter of precinct

congestion and its consequences.

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Existing literature on precinct congestion

There exists at present a small literature on precinct congestion. We have already noted that this

literature is hamstrung by a lack of data, and with respect to voting lines in particular (Stewart

III, 2013, p. 10) summarizes the situation nicely: “[T]he study of waiting times is in its infancy

because measures of waiting to vote are primitive.”

To circumvent a lack of data on lines and other aspects of precinct congestion some scholars

have relied on surveys. These surveys incorporate voter self-reports and have been conducted via

telephone and the Internet. A reliance on voter self-reports in the matter of precinct congestion is

probably not ideal: whether voters are unbiased observers when it comes to estimating how long

they themselves waited in line before voting is unknown. Furthermore, whether voters would admit

to engaging in a socially problematic behavior like reneging is similarly not clear. Nonetheless,

large-scale surveys of voter experiences have the advantage of covering many jurisdictions across

the United States, and survey data on precinct congestion is certainly better than no data at all.

Stewart III (2013) is an excellent example of the value that surveys bring to the study of precinct

performance and voter wait times. Focusing on the 2012 General Election, Stewart III draws on two

large-scale surveys and offers several conclusions.10 One, waiting times to vote vary significantly

across the United States with some jurisdictions suffering from line problems and others not. Two,

long voting lines tend to crop up in urban areas and areas with large numbers of minority voters.

Relatedly, Alvarez et al. (2009), a survey-based study of the 2008 General Election, finds that 70

percent of Election Day respondents reported waiting fewer than ten minutes to vote, that four

percent waited at least one hour, and that early voting lines were in general longer than Election

Day lines.

Kimball (2013, p. 17) also uses survey data, finding that urban voters across the country suf-

fer from the longest lines and that “urban voters contend with a disproportionate share of voting

10In particular, the 2008 and 2012 editions of the Survey of the Performance of American Elections and the Coop-erative Congressional Election Study.

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difficulties in the United States.” These findings are very similar to those in the aforementioned

Stewart III and Alvarez et al. Finally, Claassen et al. (2008) use exit poll data in a study which

argues that overall voter sentiment about voting experiences is sensitive to the quality of precinct

administration and wait times; this study does not focus on the causes of wait times per se but

rather on the extent to which voters find lines troubling.11

To the best of our knowledge, Spencer and Markovits (2010) is the only study that measures

voting line lengths with an eye on the factors that lead to long lines. In particular, Spencer and

Markovits focus on precinct performance during the 2008 California presidential primary and

highlight the substantial variance in voter processing rates across a set of 30 precincts. Most

importantly from our perspective, they show that precinct voting line lengths in 2008 fluctuated

across Election Day and were longest around 6:00pm. Spencer and Markovits also find an overall

renege rate of 1.89 percent—this is the percentage of voters who turned out to vote and then left

a precinct without having voted—and they observe a positive correlation between the number of

people in line when a voter arrived to vote and the probability of reneging.

Reneging can have political consequences if the probability that a voter reneges is not uniform

across all voter types. This assertion is an underlying part of the analysis in Highton (2006),

who argues that Franklin County, Ohio, precincts with relatively few voting machines in the 2004

General Election suffered from low voter turnout, ceteris paribus. Highton also argues that a

lack of voting machines in Franklin County cost Democratic presidential candidate John Kerry in

the neighborhood of 6,000 votes (this quantity would not have been pivotal to Kerry’s 2004 loss

in Ohio). A natural interpretation of Highton’s findings is that precincts with disproportionately

fewer voting machines had long lines and hence greater reneging and/or balking rates. Viewed

from this perspective, Highton seeks to document the impact of precinct congestion, a goal similar

to ours.11We are not aware of surveys on wait times that posed questions about the times ballots were cast in conjunction

with voting delays encountered. Whether precinct congestion varied in time is beyond the purview of extant surveyresearch on wait times.

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We contribute to the literature on precinct congestion in several ways. First, our results draw

on observed election features, namely Election Day precinct closing times from a set of counties

in Florida; our findings do not rely on voter self-reports, and accordingly we see our results as

complementing the detailed survey-based findings in Stewart III and others. Overall we believe that

the literature on precinct congestion should reflect a mix of research based on observed quantities

and research that queries voters about their experiences. Second, we seek to understand precinct

correlates of late closing times across a set of several thousand precincts. While these precincts are

drawn from one state, they vary geographically and in the racial/ethnic breakdown of the voters

that they serve. This provides us with confidence that our results on the correlates of congestion

are not idiosyncratic to a particular precinct profile.

Measuring precinct congestion

Precinct congestion has many facets, and as already noted the proxy we use for congestion is

precinct closing times. We now describe how we gathered closing time data from multiple counties

in Florida and after this discuss how we integrated these data with the Florida statewide voter files

compiled and published by the Florida Department of State (FDOS).

Election Day precinct closing times in 43 Florida counties

There are 67 counties in Florida, and elections within these units are governed by county-level

Supervisors of Elections (SOEs). All but the Miami-Dade County SOE are elected, and most

SOEs run for office as partisan candidates.12

To each Florida SOE we sent a public records request for information on precinct administra-

tive capacity. In particular, we asked for a list of active precincts used during the 2012 General

12See the June 28, 2013, testimony of Penelope Townsley, Miami-Dade Supervisor of Elections, to the Presi-dential Commission on Election Administration at a hearing at the BankUnited Center, University of Miami CoralGables, Florida. The testimony is available at http://www.supportthevoter.gov/files/2013/06/PCEA-Miami-Dade-Supervisor-Penelope-Townsley1.pptx (last accessed August 13, 2013).

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Election, the times at which each precinct closed, the number of pollworkers per precinct, the

number of optical scan voting machines per precinct, and the number of electronic poll books per

precinct. Approximately one-third of our public records requests generated responses. Some of

the responses we received included machine-readable data on precinct closing times, and others

provided scanned images that we had to enter by hand. The types of information we received

varied greatly by county.

Beyond precinct-level data gleaned via public records requests, Scott Powers and David Dam-

ron of the Orlando Sentinel provided us with a copy of a spreadsheet that their newspaper as-

sembled immediately following the 2012 General Election. Orlando Sentinel staff members had

sent requests to the 25 largest counties in Florida asking for information on precinct closing times,

and we use the Orlando Sentinel data to supplement the data we received directly from SOEs in

Florida.13 When we have data from both the Orlando Sentinel and from a public records request,

we use data from the latter. The only exception to this is Miami-Dade County. For reasons that

are unclear to us, the precinct closing times we received from our Miami-Dade public records re-

quest did not include closing times for several precincts mentioned in a report titled, “Elections

After Action Report – November 6 Presidential Election,” that was commissioned by Miami-Dade

County in the aftermath of the 2012 General Election.14 Insofar as these precincts were specifically

mentioned in the aforementioned report, and insofar as the report notes that these precincts closed

relatively late compared to other Miami-Dade precincts, we believe we would be remiss not to

include them. Hence, for Miami-Dade County we use Orlando Sentinel data as opposed to data

we gleaned from our requests.

In total we have data from 43 of 67 Florida counties, and these counties are listed in Table 1.13The Orlando Sentinel data are the basis of “Analysis: 201,000 in Florida didn’t vote be-

cause of long lines,” Orlando Sentinel, January 29, 2013, available at http://articles.orlandosentinel.com/2013-01-29/business/os-voter-lines-statewide-20130118_1_long-lines-sentinel-analysis-state-ken-detzner (last accessed July 2, 2013).

14The Miami-Dade report is available at http://www.miamidade.gov/mayor/library/elections-after-action-report.pdf (last accessed July 2, 2013).

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The 43 counties are displayed graphically in Figure 1; the counties in this figured are shaded by

the fraction of precincts for which we have closing time data (we discuss these fractions later).

The white areas in Figure 1 denote counties for which we do not have closing times, and many of

the white-colored counties in the figure contain very few people. With the exception of the Florida

Panhandle and Florida Keys (Monroe County), the North-Central Florida counties for which we

have not been able to obtain closing time data are rural and sparsely populated. Similarly, the

cluster of counties in South-Central Florida from which we lack closing tima data are largely

agricultural and include large portions of Lake Okeechobee and the Everglades.

According to data from the 2008-2012 American Community Survey, our 43 counties contain

17,536,930 citizens of voting age, and in contrast the 24 Florida counties from which we have not

been able to gather closing time data contain 1,348,225 such citizens. Therefore, even though we

have data on only approximately 64 percent of Florida counties—and this includes the ten most

populous Florida counties in terms of citizen voting age—these counties contain approximately

92.9 percent of Florida’s citizens of voting age. In terms of race and ethnicity, our 43 counties

when aggregated are approximately 15.6 percent black while the 24 excluded counties are ap-

proximately 11.5 percent black; with respect to Hispanic citizens of voting age, the corresponding

figures are 23.3 percent and 11.3 percent. It follows from these figures that our excluded 24 coun-

ties are disproportionately white compared to our included 43 counties. We comment on possible

consequences for our results of this type of non-representativeness.15

Florida Election Day precincts open at 7:00am, and voters are not permitted to join voting

queues after 7:00pm. All registered voters in line as of 7:00pm are permitted to vote, and this

means that precincts with long lines as of 7:00pm may conduct voting operations for several hours

after the last person joined a voting queue.

15Data from the 2008-2012 American Community Survey were downloaded from https://www.census.gov/rdo/data/voting_age_population_by_citizenship_and_race_cvap.html (last accessedAugust 4, 2013). The ten most populous Florida counties in terms of citizen voting age population are Miami-Dade,Broward, Palm Beach, Hillsborough, Orange, Pinellas, Duval, Lee, Polk, and Brevard (in decreasing order by popula-tion).

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Figure 1: Closing time coverage by county

Note: shading represents the fraction of precinct by county for which we have closing time data.

In the process of collecting closing time data from counties across Florida we encountered

a variety of definitions for closing time. These definition included the time that a precinct’s last

voter signed in to vote; the last time a vote was recorded; the time when optical scan machines were

powered off; and, the time that results were reported or uploaded to a central location. Regarding

Hillsborough County, for example, the Orlando Sentinel spreadsheet describes the precinct times

at which voting results were uploaded to a central server administered by the Hillsborough SOE.

In contrast, the Hillsborough County SOE provided data to us that indicates when the county’s

various optical scan machines, located across many precincts, were shut down. We prefer machine

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shutdown times to data upload times because we suspect that the former more closely approximate

what closing times are. Still, in our judgment the problem with differing definitions of closing

times is a lack of standardization as opposed to the existence of problematic definitions.16

In our dataset of 43 counties, the most common definition of precinct closing time is the afore-

mentioned data upload time; 21 of our counties—approximately 46 precent—employed it. When

we present results we first analyze the data upload counties separately because the many closing

times within these counties are comparable. After examining the data upload counties, we compute

standardized closing times for all 43 counties and analyze these times. As will be clear shortly, our

key results do not depend on whether we analyze the 21 comparable data upload counties or our

43 counties overall, and this implies that the lack of closing time definition standardization across

Florida, while unfortunate, is not a driving factor in our results.

Our use of closing times as a proxy for precinct congestion and wait times is intuitive has its

limitations. In theory voters may have spent time in line well before 7:00pm, the time at which

voting lines were formally capped on November 6, 2012. Thus, closing times do not necessary

capture the extent to which precincts were busy on Election Day morning. We would be able to

control for this issue, but we do not know the times at which Florida Election day voters cast their

ballots. To the extent, though, that precincts congested at night were also busy in the morning,

our use of closing times as a proxy for wait times is most likely conservative. That is, there

were probably Florida precincts in the 2012 General Election that, according to our closing time

measures, were not congested yet suffered from morning lines. It is hard to imagine, however, that

there were a large number of late-closing precincts that were fully line-free in the afternoon and

morning of Election Day.

16Even with respect to machine shutdown times there are subtleties. Many precincts have multiple optical scanmachines, and we cannot always be sure that closing times based on machine shutdown times reflect first machineshutdowns or the last machine shutdowns. This is the sort of issue that should be addressed in standards promulgatedby the FDOS or perhaps the Florida State Association of Supervisors of Elections.

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Florida voter files

Florida voter registration records are maintained in what are called statewide voter files, and via

a public records request to FDOS we acquired a Florida voter file dated December, 2012. Florida

voter files are updated monthly, and each file contains demographic and participation records on

every Florida vote registered in the state as of the file’s promulgation. The December, 2012, file

includes participation codes for elections through the 2012 General. This means that the December,

2012, file has codes that describe who among the registered voters in Florida voted early in the 2012

General Election, who voted on Election Day, who voted absentee, who did not vote at all, who

cast a provisional ballot, and so forth. The November 2012 voter file does not contain participation

records for the 2012 General Election, hence our use of the December, 2012, file.

We acquired the December, 2012, voter file in January, 2013, and subsequently we learned via

email communications with the FDOS on June 3, 2013, that this file contains errors that affect

the 2012 General Election participation codes for seven Florida counties.17 With this in mind we

acquired a copy of a later voter file (March, 2013) and from this constructed a hybrid Florida voter

file that includes data from the December, 2012, file (60 counties) and the March, 2013 file (seven

counties). Henceforth when we refer to a voter file we mean the hybrid voter file described here.18

17The seven problematic counties are, alphabetically, Broward, Duval, Lee, Leon, Palm Beach, Polk, and Sarasota.We were informed by an FDOS staff member that the 2012 General Election participation codes were fixed as of theMarch 2013 edition of the Florida voter file. Polk exemplifies the importance of using this voter file when examining2012 General Election participation codes for the seven aforementioned counties. According to the December 2012voter file, there were only 94 Election Day voters in Polk. However, this figure is approximately 128,000 in the March2013 file.

18Florida voter files consist of what are called extract and history files. The former contain lists of voters, theiraddresses, and their demographics, and each voter in the extract file (technically, one file per county) is associatedwith a unique voter identification number. These numbers link to voter history files (again, one file per county) whichcontain election participation codes. We use the December, 2012, extract file in our hybrid file because we were toldthat the errors affecting the seven aforementioned counties affect only history codes. The FDOS publishes what wehave referred to as voter files, called VH01 files, and what the FDOS calls election recap files, so-called VH03 files.When we use the term “voter file” we are referring to a VH01 file. Our correspondence with the FDOS suggests thatonly four counties, as opposed to seven, had errors in the December, 2012, VH01. However, we are concerned thatcounties listed as having VH03 errors also had VH01 errors. To be conservative, we assume that any county known tohave 2012 General Election participation code errors in either the December, 2012, VH01 or VH03 file is problematic.We replace 2012 General Election participation codes for these counties with data from the March, 2013, VH01.

From our hybrid voter file we generated a list of all precincts used in each of Florida’s 67 counties; there are 6,147such precincts. We then merged this list of precincts with the precinct closing times from our 43-county closing time

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Our coverage across Florida is described in Table 1. Among other things this table lists for our

set of 43 counties the number of precincts per county based on our hybrid voter file as well as the

number of precincts per county for which we have usable data, meaning usable closing time data,

usable racial demographics, and so forth.

Despite the complications in merging our voter file with county-level data on precinct closing

times, our coverage rates by county tend to be very high. In Miami-Dade County, for example, we

have usable data on 98.5 percent of the precincts that exist in this county according to the FDOS.

Moreover, in many cases we have coverage for all of the precincts in a county. The only outlier

here is Broward County; we have usable data on 77.6 percent of the precincts that exist in Broward

according to the FDOS. Our efforts to rectify this situation have not been successful.

The various delay columns in Table 1 represent closing delays past 7:00pm based on counties’

methods of determining closing times. A precinct that was reported to have closed ten minutes late

might have closed its doors at 7:00pm yet uploaded its data ten minutes after that time. Hence one

should not compare across counties in Table 1.

Table 1 highlights significant within-county variability. For example, in Palm Beach County,

precinct closing delays ranged from 27 to 285 minutes with an average of almost 84 minutes; in

Hillsborough County the range of closing days was one minute to 167 minutes; in Miami-Dade,

closing delays ranged from one minute to 420. We seek to explain some of this variance and in

particular we consider whether precincts with relatively long days (i.e., late closing times) had

particular racial profiles.

dataset. This merge was not perfect, and there exist voter file precincts for which we have no closing times and closingtimes reported by counties that do not appear to correspond to actual precincts according to our hybrid voter file. Onemight reasonably inquire as to why this situation exists. We cannot be sure of the explanation but suspect that the rootof this problem lies in coordination and communication difficulties between the FDOS, which maintains the statewideFlorida Voter Registration System (known in the state as the FVRS) and individual county SOEs. See, for example,“Florida Voter Registration System,” available at http://election.dos.state.fl.us/hava/fvrs (lastaccessed October 8, 2013). Furthermore, there are three Palm Beach County precincts in the Florida voter file thathad very small yet positive numbers of voters on Election Day but a total of zero black, Hispanic, and white ElectionDay voters. We dropped these precincts from our analysis. This is obviously not ideal, but we note that Palm BeachCounty had almost 800 precincts total.

15

Table 1: Summary of Analyzed Counties and Precincts

Existing Analyzed Percent Minimum Maximum Zero AverageCounty Precincts Precincts Coverage Delay Delay Delay DelayMiami-Dade 801 789 98.5 1 420 0 72.4968Palm Beach 770 735 95.5 27 285 0 83.8902Broward 795 617 77.6 0 190 34 25.0551Hillsborough 347 347 100.0 1 167 0 30.1844Pinellas 299 298 99.7 7 120 0 41.0436Orange 233 227 97.4 4 318 0 85.7841Duval 198 197 99.5 0 170 1 34.8883Polk 167 163 97.6 4 194 0 39.1273Leon 136 129 94.9 14 137 0 40.7364Marion 127 127 100.0 6 144 0 26.5354Volusia 125 125 100.0 10 458 0 75.1600Lee 126 125 99.2 3 474 0 115.0160Brevard 166 116 69.9 11 216 0 62.6552Manatee 113 112 99.1 14 202 0 53.3304Pasco 111 109 98.2 0 308 1 41.1835Lake 104 102 98.1 5 84 0 20.3398Sarasota 100 99 99.0 5 197 0 36.0505Seminole 80 80 100.0 2 87 0 25.3500Escambia 79 79 100.0 0 33 1 11.3418Charlotte 79 78 98.7 0 0 78 0.0000Osceola 71 70 98.6 0 242 13 36.4143St. Lucie 65 65 100.0 17 381 0 96.1692Collier 62 61 98.4 0 305 44 8.2459Alachua 70 61 87.1 14 117 0 33.5902Clay 49 48 98.0 0 52 3 12.9792St. Johns 47 46 97.9 0 0 46 0.0000Bay 44 44 100.0 4 101 0 24.3182Indian River 37 37 100.0 6 56 0 17.6757Citrus 31 31 100.0 0 0 31 0.0000Martin 31 31 100.0 0 0 31 0.0000Sumter 24 24 100.0 9 162 0 46.0000Nassau 16 16 100.0 0 3 15 0.1875Taylor 14 14 100.0 0 0 14 0.0000Jackson 14 14 100.0 1 21 0 8.4286Bradford 14 14 100.0 0 0 14 0.0000Wakulla 12 12 100.0 0 0 12 0.0000Okeechobee 11 11 100.0 0 0 11 0.0000Union 11 11 100.0 0 0 11 0.0000Baker 9 9 100.0 0 15 5 5.0000Franklin 8 8 100.0 0 0 8 0.0000Hamilton 8 8 100.0 1 17 0 5.1250Holmes 8 8 100.0 1 16 0 8.1250Lafayette 5 5 100.0 0 0 5 0.0000

16

Results

As noted in our initial discussion of Shelby, we are motivated here primarily by an interest in the

racial correlates of precinct closing times. However, before turning to this matter we describe

basic patterns in closing times across Florida. Our analysis starts with the set of Florida counties

that define closing time based on data upload, and we begin here because closing times in these

counties are comparable.

Precinct closing times in data upload counties

There are 21 so-called data upload counties, and in 20 of these there was variance in closing times

on November 6, 2012. The exception to this is St. Johns County, which contained 47 precincts on

Election Day 2012. As noted in Table 1 we have data on 46 of these precincts, and the St. Johns

SOE reported that each of these precincts uploaded its data at 7:00pm on November 6. St. Johns

County had the fewest precincts of all data upload counties in 202.

We define a closing delay as the number of minutes past 7:00pm that a precinct uploaded its

data (all of Florida is in the eastern time zone). We do not expect closing delays of exactly zero

even in precincts that had cleared out their voters by 7:00pm on November 6. In such precincts it

is natural that there was a short delay between official closing and data upload time.

Figure 2 contains boxplots of closing delays in 20 Florida counties, namely, all of the data

upload counties except for St. Johns. The counties are sorted by number of precincts with Miami-

Dade County accordingly at the top

The solid, vertical line in a county’s boxplot in Figure 2 denotes its median closing delay across

all precincts for which we have closing times. As the figure makes clear, there was significant

variance across counties in median delays. For example, Collier County had a median delay of

zero minutes while the median delay in Lee County was approximately 100 minutes. The extensive

variance in delays by county is also evident in the fact that some counties—Miami-Dade, Orange,

17

Figure 2: Closing delays across 20 Florida data upload counties

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18

and Lee, for example—contained many precincts with delays of greater than 150 minutes. Other

key statistics pictured Figure 2’s boxplots—i.e., hinges, which describe the first and third quartiles

of closing delays—also vary dramatically by county.

Another notable feature of Figure 2 is that almost every county pictured had some precincts

with long closing delays. The aforementioned Collier County is an exception as is Escambia

County; both of these counties had relatively few precincts, though, and this is evident in their

placement in the bottom portion of Figure 2. Among the many-precinct counties that appear in the

top half of the figure, all had at least one precinct with a closing delay of at least 100 minutes.

Because the closing times in Figure 2 are comparable, we can rank counties based on median

delay. Lee County had the longest median delay followed by Hillsborough and Orange Counties.

We have already noted that Collier and St. Johns County had median delays of zero. We will

consider shortly the matter of whether these delays reflect county idiosyncrasies or the types of

voters who live within the counties.

Figure 2 does not contain every county in Florida, but the 20 counties pictured in it include

some of the most populous counties in Florida, e.g., Miami-Dade, Broward, Hillsborough, and

Orange Counties. Still, there are counties for which we have closing times data (Table 1 has the

complete list), counties that did not use the data upload method for defining closing times. While

we cannot compare median closing times for this latter group of counties, we can inquire about

variance within county in closing delays. This will become clear shortly.

Election Day race/ethnicity and closing delays

It could in principle be argued that closing delays are not problematic with respect to the VRA,

or even more generally, as long as they are uniformly distributed among voters. With such an

argument in mind, we now turn to the racial/ethnic profiles of Floridians who on Election Day in

2012 voted at precincts with relatively long (or relatively short) closing delays. We now ask, that is,

whether the types of closing delays pictured in the boxplots of Figure 2 affected some racial/ethnic

19

groups more than others.

Consider a single Florida county, say, Orange. This county appears fifth from the top in Figure

2 and is physically located approximately in the middle (north-south) of Florida. Home to the city

of Orlando, Orange County has 227 precincts in our dataset as noted in Table 1. Each of these

precincts has a closing time and an associated delay and is characterized by three racial/ethnic

variables, namely, fraction black voters on Election Day, fraction Hispanic voters, and fraction

white voters. These three fractions sum to almost one for all Orange County precincts; they do

not sum to one exactly because some registered voters in Florida do not provide race/ethnicity

and some provide a race/ethnicity that is neither black, Hispanic, nor white. Nonetheless, we plot

Orange County closing delays and the racial/ethnic composition of Election Day voters in a ternary

diagram. See Figure 3a.

Within this figure and in the other three ternary diagrams in Figure 3, each dot represents an

Election Day precinct. Moreover, a dot’s location connotes the associated precinct’s racial/ethnic

distribution of Election Day voters. For example, a precinct with a distribution of Election Day

voters that was exactly one-third black, one-third Hispanic, and one-third white would be repre-

sented by a dot precisely in the middle of a ternary diagram; a precinct whose Election Day voters

were completely Hispanic would be located on a Hispanic vertex; and, a precinct that was split

between black and white voters with no Hispanics at all would be located on a line connecting

black and white vertices.

The shading of the precinct dots in Figure 3’s four ternary diagrams represents closing time

delays, and in particular darker colors connote later times after 7:00pm. The colors of the dots

in Figure 3 cannot be directly compared across counties, and this is because an individual ternary

diagram’s dot shading scheme is determined by making comparisons of precinct closing times

within a county. We have already noted that Florida counties do not employ identical standards for

reporting precinct closing times, and because of this we often cannot compare closing times across

counties. What is important in Figure 3, then, is not absolute closing delays themselves but rather

20

Figure 3: Election Day race/ethnicity and closing times in four Florida counties

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21

the within-county variance in these times.

With respect to the Orange County ternary diagram in Figure 3a, several points are notable.

First, there are many heavily white precincts in the county, a small number of heavily black

precincts, and very few, if indeed any, heavily Hispanic precincts; one can see this from the density

of dots in Figure 3a, irrespective of dot color. Second, the darkest dots in the figure are closer to

the Hispanic vertex than the white vertex. Indeed, of the few Orange County precincts that have

many Hispanics, almost all of them had long closing delays. In contrast, of the many heavily

white precincts in Orange County, only a few of them had long delays. Third, the heavily black

precincts in Figure 3a did not have long closing delays. Broadly speaking, Figure 3a shows that the

distribution of Election Day closing delays in Orange County was not uniform across precincts.

Figure 3b displays a similar ternary diagram for Osceola County, a county adjacent to Orange

but one that is much smaller. Osceola County is one of the most Hispanic (largely Puerto Rican)

and fastest growing counties in the state. The darkest dots in the Osceola ternary diagram are

relatively close to the Hispanic vertex, and this implies that Osceola precincts with many Hispanic

Election Day voters had relatively late closing times. In this way Orange and Osceola Counties

are similar. One way in which they are different is that Osceola County lacks precincts which had

large numbers of black Election Day voters.

There are no obvious racial patterns in Figure 3c, which describes Hillsborough County, a

previously a VRA Section 5 jurisdiction in Florida. There is certainly little evidence that black

voters in Hillsborough disproportionately voted at late-closing precincts on Election Day 2012.

However, Figure 3d shows that the distribution of precinct closing times in Miami-Dade County

was not uniform across racial/ethnic groups. To wit, the darkest dots in this figure are located

closest to the Hispanic vertex.

There are many race/ethnicity ternary diagrams that we could have shown, and the ones dis-

played in Figure 3 illustrate typical patterns that obtain across many Florida counties. Indeed,

across ternary plots we often find evidence that Hispanics voted in precincts that had relatively late

22

closing times. To investigate this possibility in greater detail we now turn to a set of regression

model.

Tobit analysis of closing delays in data upload counties

Parallel to the previous section, we begin with a regression analysis of the 20 data upload counties

that have comparable closing delays. The dependent variable in the analysis is the number of

minutes beyond 7:00pm that a precinct reported being closed, and we thus use a tobit regression

(the lowest value for a precinct’s closing delay is zero) . The independent variables in our tobit

regression are racial/ethnic and partisan fractions of election day voters; for example, one key

variable is the fraction of the election day electorate at a precinct that was Hispanic. We also

control for the raw number of individuals who voted on election day, and our tobit model has

county fixed effects as well. For this reason we drop St. Johns County for the purposes of our tobit

regression; in such a model, the fixed effect for a county in which all precincts had zero closing

delays cannot be identified. Regression results are in Table 2.

The main covariates in Table 2 are motivated by our interest in race/ethnicity and the VRA,

and they consist of the fraction of a precinct’s election day voters that was black and the fraction

Hispanic. Of interest beyond racial/ethnic variables are two party affiliation variables which repre-

sent the fraction of Election Day voters who were Democratic and fraction with no party affiliation

(NPA). These latter two affiliations in conjunction with Republican are at present the three domi-

nant party affiliations in Florida. We are not particularly interested in party estimates per se—party

affiliation in not formally part of the VRA, and party groups are not protected classes of voters like

racial/ethnic groups—but it is important to control for partisan composition of the Election Day

electorate. Finally, we use heteroskedastic-consistent standard errors in Table 2 because our tobit

models may suffer from heteroskedasticity at the county-level.

Our tobit regression results have several notable implications. First, the Hispanic finding that

we observed in various ternary plots is readily apparent in Table 2. To be precise, the Hispanic

23

Table 2: Tobit regression analysis of 20 data upload counties

Dependent variable:

Minutes open past 7:00pm

(1) (2)

Voted at polls 0.031∗∗∗

(0.003)Hispanic fraction 68.042∗∗∗ 61.352∗∗∗

(7.146) (6.830)Black fraction 1.345 −5.282

(7.259) (7.194)Democratic fraction 29.744∗∗∗ 46.050∗∗∗

(10.711) (10.898)NPA fraction 81.439∗∗∗ 74.202∗∗∗

(16.339) (15.696)Brevard County 34.107∗∗∗ 29.887∗∗∗

Broward County −24.024∗∗∗ −10.298∗∗∗

Collier County −63.023∗∗∗ −59.730∗∗∗

Dade County 1.177 16.199∗∗∗

Duval County 5.590∗ 9.148∗∗∗

Escambia County −14.672∗∗∗ −13.495∗∗∗

Hillsborough County 31.092∗∗∗ 41.452∗∗∗

Lake County −6.884∗∗ 2.321Lee County 85.030∗∗∗ 83.102∗∗∗

Leon County 9.882∗∗∗ 18.650∗∗∗

Manatee County 25.883∗∗∗ 28.960∗∗∗

Martin County 0.416 8.772∗∗∗

Orange County 41.667∗∗∗ 42.388∗∗∗

Pasco County 9.878∗∗ 11.420∗∗∗

Pinellas County 9.032∗∗∗ 19.227∗∗∗

Sarasota County 8.348∗∗ 12.573∗∗∗

Seminole County −12.295∗∗∗ −17.470∗∗∗

St. Lucie County 62.749∗∗∗ 67.226∗∗∗

Volusia County 39.943∗∗∗ 40.585∗∗∗

Constant 0.974 −30.721∗∗∗

(5.901) (6.862)

Observations 3,866 3,866Log Likelihood −20,042.700 −19,933.080

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01heteroskedastic-consistent standard errorsin parentheses, omitted for county indicators

24

estimate in the table is positive and easily statistically significant at conventional confidence levels

(z ≈ 9.0, based on the second model in the table). This means that, within our set of 20 data upload

counties, precincts with large numbers of Hispanic Election Day voters closed disproportionately

late compared to precincts with large numbers of white voters. Given the county-fixed effects in

Table 2, this finding cannot be explained by the possibility, say, that the most heavily Hispanic

counties in Florida had late-closing precincts for an extraneous reason.

It is important to note that our Hispanic findings does not directly imply that Hispanic voters

themselves had long Election Day waits in 2012. Concluding this from Table 2 would risk an

ecological fallacy. It is possible, for example, that non-Hispanics who live in Hispanic precincts

vote disproportionately in the evening and in so doing cause precincts to close late. We do not

know of any evidence that would support this conjecture, but we note it nonetheless. When the

literature on election administration develops better results on the arrival times of voters and the

extent to which different racial/ethnic groups of voters cast ballots at different times of the day, this

issue will be resolvable.

Second, we do not find evidence that precincts with large numbers of black Election Day voters

closed disproportionately late in 2012. Namely, the black estimate in Table 2 is negative and not

significant at conventional confidence levels (z ≈ 0.73). The lack of evidence that heavily black

precincts in Florida closed disproportionately late compared to precincts with large numbers of

white voters on Election Day may seem surprising given the considerable controversy surrounding

the passage in Florida of House Bill 1355, a piece of election-reform legislation that became state

law in mid-2011 (e.g., Herron and Smith, 2013b). We have more to say about this null finding

below.

Third, and regarding the party variables in Table 2, our tobit model contains two party compo-

sition variables with an omitted category of Republican. Because both the Democratic and NPA

estimates in the table are positive and significant (z ≈ 4.3 and z ≈ 4.7, respectively), it follows

that precincts with large numbers of Republican voters tended to close relatively early on Election

25

Day, all things equal. Such a political bias in closing times could be problematic and have subtle

effects on vote shares if late closing times are associated with long lines. Nonetheless, as we noted

earlier party affiliation is not protected by the VRA and is not our primary focus.19

Fourth, there is a significant variation in closing times across data upload counties, even con-

ditioning on racial and partisan composition of the election day electorate and on the number of

election day voters. The omitted county in Table 2 is Alachua, and as such Lee County’s precincts,

for example, closed on average approximately 85 minutes after Alachua’s. Orange and St. Lucie

Counties had precincts with long closing delays as well, ceteris paribus, but Broward and Collier

County had precincts that closed disproportionately early, ceteris paribus.

Earlier we ranked data upload counties based on median closing delay, but this ranking was

confounded by the fact that data upload counties did not have identical Election Day electorates.

With this in mind, the county effects in Table 2 control for electorate composition, and we thus see

that Lee County had the latest closing precincts among data upload counties, ceteris paribus. St.

Lucie had precincts with the second worst delay, ceteris paribus, and the third-ranked county with

for late delays is Orange. On the other side, and beyond St. Johns County, precincts in Collier and

Seminole closed early, ceteris paribus.

The American model of election administration is state-based, and in Florida election admin-

istration is relatively highly decentralized. The key units in Florida are counties, and there are

many possibilities for the variance in county effects that is evident in Table 2. Counties can vary in

precinct training protocols, staffing, technology, budgets, and so forth. County-level variance is to

a large extent beyond our scope, but we do want to address one possibility, that precinct resources

are the source of the county effects we have observed.

To this end we now augment our tobit model with a measure of pollworkers. When we queried

all 67 Florida counties regarding precinct closing times, we also requested data on pollworkers

19We do not explicitly engage partisanship here because of our focus on the VRA. However, in many locales in theUnited States partisanship and race are so correlated that it is difficult to speak of one without invoking the other. Onthis point see Hasen (2014).

26

per precinct. Only a small number of SOEs supplied us with pollworker data, and only four of

the data upload counties are in this group; these counties are Alachua, Brevard, Miami-Dade, and

Pinellas. This situation is far from ideal, and any model that relies on only four counties should be

treated very cautiously. We note that an abundance of missing data is nonetheless not uncommon

when studying aspects of election administration across multiple jurisdictions. In our conclusion

we return to the subject of data availability, but for now we estimate the tobit model in Table 2

using only pollworker counts and the four data upload counties that provided us with pollworker

data. Results are in Table 3, which augments our earlier tobit with pollworker counts and squared

counts, the latter included because the effects of pollworkers on precinct closing times may have

diminishing returns to the extent that such effects exist at all.20

Table 3’s substantive implications regarding race/ethnicity are the same as those seen earlier,

and the county fixed effects in the table are very similar to those previously discussed. This is

notable insofar as it implies, for example, that precincts with large numbers of Hispanic Election

Day voters closed disproportionately late even conditional on numbers of pollworkers. We note

that conditioning in this way is not obviously appropriate from a voting rights perspective. The

assertion that a given racial/ethnic group in Florida faced a problematic voting environment—

one characterized by late-closing precincts, for example—would not be obviously ameliorated (or

perhaps excused) if one were to argue, say, that said environment were simply a reflection of low

staffing.

The pollworker estimate in Table 3 is puzzling, however. The estimate is positive and sig-

nificant at the 0.05 level, and this suggests that precincts with more pollworkers closed dispro-

portionately late, ceteris paribus. The quadratic pollworker estimate in Table 2 is not significant

at conventional confidence levels. Overall, our pollworker results based on data upload counties

20There are two reasons that we use pollworkers as a measure of precinct capacity. First, it appears from ourFlorida-wide data gathering exercise that the definition of pollworker is defined similarly across counties. Second, ourcoverage of voting machine data is not extensive. It would be ideal to conduct a study like Highton (2006) for theentire state of Florida, but this will have to wait until better data are available.

27

Table 3: Tobit regression analysis of data upload counties with pollworker counts

Dependent variable:

Minutes open past 7:00pm

(1) (2)

Voted at polls 0.035∗∗∗

(0.006)Hispanic fraction 57.379∗∗∗ 47.949∗∗∗

(10.136) (9.661)Black fraction −9.206 −19.452

(14.292) (14.432)Democratic fraction 43.061∗∗ 55.645∗∗

(21.436) (22.130)NPA fraction 94.320∗∗∗ 90.440∗∗∗

(27.943) (27.218)Pollworkers 5.223 6.884∗∗

(3.397) (3.278)Pollworkers-squared −0.048 −0.162

(0.129) (0.123)Brevard County 11.840∗∗ 13.330∗∗

(5.326) (5.873)Dade County −0.620 17.310∗∗∗

(5.373) (6.117)Pinellas County 0.699 13.145∗∗∗

(2.943) (4.063)Constant −50.162∗∗ −87.998∗∗∗

(22.751) (23.682)

Observations 1,263 1,263Log Likelihood −6,849.513 −6,817.253

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01heteroskedastic-consistent standard errorsin parentheses

28

are not especially intuitive, but it is hard to know how seriously to take these findings given their

dependence on a selection of only four Florida counties. Shortly we show how to include more

counties in analyses, and upon doing so our pollworker estimates become much more intuitive.

Regression analysis of closing delays across Florida

We have noted repeatedly the problem caused by the fact that Florida’s counties use different

definitions of precinct closing time. One solution to this problem, exemplified above, is for us to

focus on a select group of counties that use a common definition of closing time. This resolves the

definitional problem but at a cost of reducing our purview. We now offer an analysis of standardized

closing times, one that expands our coverage of counties but at the cost of losing across-county

comparability. This is a small cost to pay in light of our interest in the relationship between

race/ethnicity and closing times.

To this end, for each county in our dataset of 43 counties we create a set of standardized

precinct closing times; such times are based on the difference between an observed closing time

and a county’s average closing time, the difference then divided by the standard deviation of a

county’s closing times. For those counties that reported no variance in closing times (standard

deviations are zero), we set standardized closing times for relevant precincts to be zero.

We now in Table 4 present estimates from a linear regression akin to the tobit models seen

earlier, but now we no longer need the tobit framework; standardized closing times are not bounded

below by zero. We also do not include county fixed effects in our linear regression since, by

construction, the average standardized closing time per county is zero.

The key results from Table 4 are similar to those seen earlier in our data upload county analysis.

Namely, this table shows that precincts with large numbers of Hispanic Election Day voters closed

disproportionately late, ceteris paribus, and the same applies to precincts with many Democratic

and NPA voters. Table 4’s black fraction estimates are negative and are marginally significant at

conventional confidence levels; this suggests that heavily black precincts closed disproportionately

29

Table 4: Regression analysis of counties using standardized precinct closing times

Dependent variable:

Standardized minutes open past 7:00pm

(1) (2)

Voted at polls 0.001∗∗∗

(0.00004)Hispanic fraction 0.340∗∗∗ 0.374∗∗∗

(0.076) (0.072)Black fraction −0.092 −0.209∗

(0.108) (0.108)Democratic fraction 0.330∗∗ 0.751∗∗∗

(0.139) (0.144)NPA fraction 0.797∗∗∗ 0.897∗∗∗

(0.194) (0.185)Constant −0.336∗∗∗ −0.871∗∗∗

(0.063) (0.076)

Observations 5,307 5,307R2 0.015 0.066Adjusted R2 0.014 0.065

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01heteroskedastic-consistent standard errorsin parentheses

early on Election Day 2012. Most importantly, the substantive racial/ethnic implications of Table

4 mirror those seen earlier. This means that we need not worry that the combination of precinct

closing times from many counties, not all of which use the same definition of closing time, is

driving our results.

We noted earlier that the set of counties for which we have closing time data includes ap-

proximately 92% of voting-eligible Floridians. The remainder are disproportionately white, and

it is natural to ask how this might affect our results, namely, the regression results in Table 4. If

precincts that had disproportionately many white voters closed relatively early, as the table implies,

than the exclusion from our dataset of many heavily white precincts will diminish the estimated

effect of large fractions of Hispanic and black Election Day voters on closing times. In general, ex-

30

cluding relatively white precincts from a regression analysis will make it harder for the regression

to identify white-Hispanic or white-black differences. Thus, the biases induced by the fact that we

have coverage of only 43 of 67 Florida counties will push our racial/ethnic results in a conservative

direction. In other words, the Hispanic and black effects in Table 4 are likely underestimates of

true Hispanic and black effects.

As before, we now augment our regression results with pollworker counts, and Table 5 contains

corresponding regression results. Controlling for pollworkers has the advantage of ensuring that

our results reflect a conceivably important precinct-level covariate, but this comes at a cost of

decreasing the observations at our disposal due to the fact that many counties did not respond to

our requests for pollworker data.

The racial/ethnic implications in Table 5 are similar to those we have seen in various other

contexts: precincts with many Election Day Hispanic voters closed late, ceteris paribus. The same

thing applies to precincts with Democratic and NPA-registered voters. Table 5’s black coefficients

estimates are negative, like others we have seen, and significant at conventional confidence levels.

That is, this table implies that there is a negative relationship between the percentage of black

Election Day voters and closing times. We discuss this shortly and propose an explanation based

on the fact that many black voters voted early in 2012.

Finally, we note that the pollworker estimates in Table 5 are intuitive, unlike our other poll-

worker estimates that were based on a set of four counties. Namely, for the range of pollworkers

in our data, Table 5 shows that, the more pollworkers, the earlier a precinct closed, ceteris paribus.

This follows from the negative and significant estimate on the basic pollworker count in Table 5.

The two quadratic estimates in the table are positive, and this is consistent with diminishing returns

of pollworkers counts on precinct closing times.

In summary, with various tobit and linear regression models we have addressed the question,

did voters of a particular racial/ethnic group vote in precincts that had late or early closing times,

all things equal? We have shown that Hispanic Election Day voters in particular seem to have

31

Table 5: Regression analysis of counties using standardized precinct closing times and pollworkercounts

Dependent variable:

Standardized minutes open past 7:00pm

(1) (2)

Voted at polls 0.0005∗∗∗

(0.0001)Hispanic fraction 0.192∗∗ 0.271∗∗∗

(0.085) (0.084)Black fraction −0.454∗∗∗ −0.412∗∗∗

(0.147) (0.147)Democratic fraction 0.433∗∗ 0.551∗∗∗

(0.196) (0.199)NPA fraction 0.615∗∗ 0.718∗∗∗

(0.242) (0.243)Pollworkers −0.065∗∗∗ −0.108∗∗∗

(0.021) (0.021)Pollworkers-squared 0.004∗∗∗ 0.005∗∗∗

(0.001) (0.001)Constant −0.117 −0.153

(0.137) (0.138)

Observations 2,457 2,457R2 0.026 0.052Adjusted R2 0.024 0.049

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01heteroskedastic-consistent standard errorsin parentheses

32

been disadvantaged in terms of where they voted in Florida, and this is notable in light of how

we motivated this paper. To be precise, in the post-Shelby County world it is incumbent on elec-

tion scholars to seek measures of election performance that are sensitive to the presence of racial

barriers in voting. Our estimates from Florida suggest that Election Day congestion in the 2012

General Election may have been such a barrier with respect to Hispanic voters. Of course we

are not making a causal argument about Hispanics nor an argument about election administrators

who operate precincts in heavily Hispanic neighborhoods. Rather, we suspect in accordance with

Spencer and Markovits (2010) that precinct features and resources beyond the level of pollworkers

are associated with closing times and that the distribution of these resources is key to variability in

closing times.

Election Day versus early and absentee voting

An important point to keep in mind about our results is that voters in Florida choose when they

want to vote. Namely, registered Floridians can vote on Election Day, vote absentee (no excuse

needed), or vote early. Different types of voters tend to vote at different times, e.g., blacks are

heavy users of early voting (Herron and Smith, 2012, 2014), and Republican voters often vote

absentee.21 This means that the Election Day closing times we observe may have been confounded

by the fact that some voters may have selected out of Election Day voting while others opted in.

This is conceivably important for the following reason. Recall our statistically significant find-

ing in Table 5 that precincts with large concentrations of black voters tended to close dispropor-

tionately early, ceteris paribus; we see hints of this finding in Tables 3 and 4 as well. This finding

may reflect the fact that many black voters chose to vote early in the 2012 General Election and

could not have been in line on Election Day because they had already voted. Accordingly, the pos-

itive black fraction estimate in Table 5 may reflect the fact that precincts with many black Election21See, for example, “Error and Fraud at Issue as Absentee Voting Rises,” New York Times,

October 6, 2012, available at http://www.nytimes.com/2012/10/07/us/politics/as-more-vote-by-mail-faulty-ballots-could-impact-elections.html?pagewanted=all&_r=0 (last accessed August 13, 2013).

33

Day voters were also places that had large contingents of black early voters.

We see evidence of this confound in the two panels of Figure 4. In particular, Figure 4a plots

for Florida precincts the black Election Day voting rate against the black early voting rate.22 Note

the negative slope in the regression line that is superimposed on the precincts depicted in Figure

4a. The implication here is that precincts with more black early voters had fewer black election

day voters. This statement is not tautological: it is possible that precincts with more early voters

had more election day voters and correspondingly fewer absentee voters. Nonetheless, the more

that a precinct’s black voters voted early, the fewer blacks in line on Election Day. This is only

part of the story since a plot (not shown) like Figure 4a for Hispanic voters shows a qualitatively

identical pattern.

Figure 4: Evidence for black shifts to early voting

(a) Black Election Day and Early Voting Rates

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0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Black Early Voting Rate

Bla

ck E

lect

ion

Day

Vot

ing

Rat

e

(b) Black and Hispanic Early Voters

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0.00 0.05 0.10 0.15 0.20 0.25

0.00

0.05

0.10

0.15

0.20

0.25

Precinct Fraction Hispanic Early

Pre

cinc

t Fra

ctio

n B

lack

Ear

ly

Note: each dot represents a Florida precinct, and dot sizes are proportional to the number ofregistered voters per precinct. The left panel contains a superimposed regression line, and theright panel, a 45 degree line.

22To calculate the black early voting rate by precinct we divided the number of black early voters in a precinct bythe number of registered black voters. Black Election Day voting rates were calculated similarly.

34

Although both black and Hispanic voters displayed similar patterns in tradeoffs between early

and Election Day voting, one difference between these two groups is that, on average, black voters

make up larger fractions of precincts. To see this, consider Figure 4b. This figure plots the fraction

of black early voters divided by total precinct registration against the fraction of Hispanic early

voters divided by total precinct registration. Most dots in the figure lie above the pictured 45-

degree line, and the dots above the line extend higher on the black axis (vertical) than the Hispanic

axis (horizontal).23 This shows that black early voters constituted larger portions of precincts

than did Hispanic early voters. Moreover, we can calculate the average difference between the

black early voting fraction in Figure 4b and the Hispanic early voting fraction, and this difference

is approximately 0.0234 (s = 0.0726). A simple difference-in-means test based on the 5,889

precincts in the figure implies that the difference here has a z statistic of almost 25. Therefore,

black early voters on average made up more of a precinct in 2012 compared to Hispanic early

voters. As such, after black voters cast early ballots, there were fewer eligible voters available on

Election Day. This is of course true for Hispanic voters as well, but the effect is greater for black

voters.

Conclusion

This paper is one of the first statistical analyses of observed precinct closing times that covers a

major state—Florida—in an important electoral context—the 2012 General Election. Both poll

closing and wait times are proxies for precinct congestion, and our findings contribute to a small

but developing literature on the distribution of congestion and the types of voters who have to

deal with congested voting environments. Precinct congestion is a wide-ranging topic, and closing

times constitute only one facet of this topic. Nonetheless, given the dearth of literature on precinct

congestion, even rough measures of this phenomenon will contribute to our understanding of the

23The axes in Figure 4b are truncated at 0.25 so as not to distort the scale of the figure. There are accordingly someprecincts in Florida not pictured.

35

extent to which voters had varying experiences while voting.

This paper was motivated in part by the recent Supreme Court decision in Shelby County v.

Holder that struck down Section 4 of the VRA. The Shelby decision has sparked efforts to develop

measures of election performance that can study the role of race and ethnicity in ways that extend

beyond registration and turnout, and to that end we have shown that Hispanic voters in Florida

who voted on Election Day 2012 tended to use precincts that closed disproportionately late. This

conclusion holds conditional on county and controlling for various precinct-level features.

On the subject of differential levels of Election Day congestion in Florida in the 2012 Gen-

eral Election, our findings may interest several parties in the post-Shelby landscape with regard

to the weakened VRA. Those interested may include members of Congress keen on revamping

outdated Section 4 geographical targeting and preclearance criteria, voting rights activists pursu-

ing VRA Section 2 anti-discrimination litigation to address racially biased voting practices, and

the Department of Justice, which can use Section 3 of the VRA to bail-in jurisdictions with the

aim of preventing persistent infractions of voting rights. At the time of this paper’s writing, the

Department of Justice is challenging a voter law in North Carolina, and in principle the types of

precinct congestion measures described here may be useful in VRA litigation in North Carolina

and beyond.24

We have identified patterns of racial bias in precinct closing times and have shown that these

patterns exist even when controlling for one measure of precinct capacity, namely, pollworkers.

Pollworkers notwithstanding, we are currently engaged in research that is designed to dig more

deeply into within-precinct processes so that we can better understand what features of precincts

are associated with long lines and extended closing times. To this end we offer three suggestions

that might be able to alleviate data problems and lead to research which improve voting processes

24See, for example, “Parts of North Carolina Law Limiting Vote Are Restored by Justices,” TheNew York Times, October 8, 2014, available at http://www.nytimes.com/2014/10/09/us/parts-of-north-carolina-law-limiting-vote-are-restored-by-justices.html?_r=0(last accessed October 19, 2014).

36

in the United States.

Our first suggestion is to standardize the definition of precinct closing time. We suggest in

particular that precincts record the precise time when the last voter processed completes his or her

voting process, meaning, has his or her ballot counted by a machine. In Florida, where optical

scan voting is the norm, this would generally mean the last time that a voter has a ballot read by

an optical scanner. This time, we believe, is an intuitive definition of closing time. If, say, all

counties in Florida were to adopt this definition and make public their closing times, then scholars

and election administrators could easily compare closing times within and across counties so as

to better understand what leads to late-closing precincts. This could in theory lead to a better

allocation of election resources. Furthermore, it could assist county officials in their efforts aimed

at developing best practices as they pertain to precinct congestion.

A second suggestion is for Election Day precincts (either all precincts in a county or a random

selection thereof) to establish a regular schedule for monitoring lines. This suggestion appears

as well in the final report of the Presidential Commission on Election Administration: “Election

officials should keep track of wait times at individual polling places using simple management

techniques, such as recording line length at regular intervals during Election Day and giving time-

stamped cards to voters during the day to monitor turnout flow (p. v).” For example, at the top

of every hour precincts could count the number of people in line to vote. These data could be

made public, either during an election or thereafter, and such data would shed light on waiting line

evolution and the types of precincts that tend to have long lines. If scholars knew line lengths at

regular intervals on Election Day, then they could characterize the evolution of lines throughout

the day.

Our third suggestion is to resolve the aggregate data problems that affect some of our analyses.

Before each election, the Florida Department of State could designate a set of precincts in which

a line monitor would collect or write down the name of every voter (or a randomly selected set

of voters) who joined a voting queue. A monitor at the egress of a precinct would then request

37

names of voters as they left. With timing monitors like these—and various electronic systems may

be feasible as well, i.e., voters send a text message to a provided number upon entering a line and

then a text message upon leaving—it would be possible to determine at an individual-level whether

certain types of prospective voters wait disproportionately long time to vote. It would be important

in such an investigation to ensure that all voters, even those who do not own, say, text messaging

devices, were provided the opportunity to check in and then check out of their precincts. The

Florida Department of State, for example, could also randomly select 10,000 voters to monitor

during the run-up to Election Day with the idea being that these voters would be surveyed about

their waiting periods either during or after a general election’s voting period.

We have argued that congestion at the polls in the 2012 General Election was not uniformly

distributed across Florida but rather had a disproportionate impact on people of color, especially

Hispanic voters. Our conclusions are based on precinct closing times, and we have been forthright

about the limitations in these data. Limitations notwithstanding, our evidence casts doubt on the

claim that there is no longer racial discrimination in the matter of voting in the United States.

In what is now the post-Shelby era, policymakers seeking measures which are sensitive to the

presence or absence of racial/ethnic discrimination in elections might begin by looking critically

at patterns in precinct congestion and identifying the voters most affected by wait times and delays

at the polls.

38

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