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REVISITING THE U.S. VOTING SYSTEM: A RESEARCH INVENTORY

November 27-28, 2006

Convened by the American Association for the Advancement of Science

Walter R. Mebane, Jr.

ABSTRACT FOR AAAS WORKSHOP ON THE US VOTING SYSTEM, WASHINGTON, DC, NOVEMBER 27-28, 2006

My recent research that is most relevant for this conference goes the heading “Election Forensics.” I am writing a book for which that is the working title. The book brings together work I have done since the 2000 American presidential election to develop statistical tools for diagnosing election anomalies and possibly detecting election fraud. Two tools I’ve developed include methods for robust estimation and outlier detection and methods that use the second digit Benford’s Law. Probably the most efficient way to communicate the scope of this work is to include the abstracts from a few of the papers I’ve produced or am working on.

The Butterfly Did It: The Aberrant Vote for Buchanan in Palm Beach County, Florida (with Jonathan N. Wand, Kenneth Shotts, Jasjeet S. Sekhon, Michael Herron and Henry E. Brady). 2001. American Political Science Review 95 (December): 793–810.

We show that the butterfly ballot used in Palm Beach County (PBC), Florida, in the 2000 presidential election caused more than 2,000 Democratic voters to vote by mistake for Reform candidate Pat Buchanan, a number larger than George W. Bush’s certified margin of victory in Florida. We use multiple methods and several kinds of data to rule out alternative explanations for the votes Buchanan received in PBC. Among 3,053 U.S. counties where Buchanan was on the ballot, PBC has the most anomalous excess of votes for Buchanan. In PBC Buchanan’s proportion of the vote on election-day ballots is four times larger than his proportion on absentee (non-butterfly) ballots, but Buchanan’s proportion does not differ significantly between election-day and absentee ballots in any other Florida county. Unlike other Reform candidates in PBC, Buchanan tended to receive election-day votes in Democratic precincts and from individuals who voted for the Democratic U.S. Senate candidate. Robust estimation of overdispersed binomial regression models underpins much of the analysis.

Robust Estimation and Outlier Detection for Overdispersed Multinomial Models of Count Data (with Jasjeet Sekhon) American Journal of Political Science 48 (April): 392–411.

We develop a robust estimator—the hyperbolic tangent (tanh) estimator—for overdispersed multinomial regression models of count data. The tanh estimator provides accurate estimates and reliable inferences even when the specified model is not good for as much as half of the data. Seriously ill-fitted counts—outliers—are identified as part of the estimation. A Monte Carlo sampling experiment shows that the tanh estimator produces good results at practical sample sizes even when ten percent of the data are generated by a significantly different process. The experiment shows that, with contaminated data, estimation fails using four other estimators: the nonrobust maximum likelihood estimator, the additive logistic model and two SUR models. Using the tanh estimator to analyze data from Florida for the 2000 presidential election matches well-known features of the election that the other four estimators fail to capture. In an analysis of data from the 1993 Polish parliamentary election, the tanh estimator gives sharper inferences than does a previously proposed heteroscedastic SUR model.

The Wrong Man is President! Overvotes in the 2000 Presidential Election in Florida. 2004. Perspectives on Politics 2 (September): 525–535.

Using ballot-level data from the NORC Florida ballots project and ballot-image files, I argue that overvoted ballots in the 2000 presidential election in Florida included more than 50,000 votes that were intended to go to either Bush or Gore but instead were discarded. The primary reason for this was defective election administration in the state, especially the failure to use systems that warn the voter when there are too many marks on the ballot and allow the voter to make corrections. If the best type of vote tabulation system used in the state in 2000—precinct-tabulated optical scan ballots—had been used everywhere in Florida, Gore would have won by more than 30,000 votes. The experience in Florida points to the need to gather ballot-level data to evaluate the success of election reform efforts now underway in much of the United States.

Inferences from the DNC Provisional Ballot Voter Survey. 2005. Section V of Democracy at Risk: The 2004 Election in Ohio. Democratic National Committee, Voting Rights Institute.

A survey conducted in Cuyahoga County, Ohio, shows that the single most important cause of voters casting a provisional ballot in the county in the November 2004 election was residential mobility. About 60 percent of the provisional ballots were cast by those who either were voting in Ohio for the first time or who had previously voted in Ohio but had since moved. Among those who had previously voted in Ohio and not moved since doing so, voters younger than 55 years of age were much more likely to cast a provisional ballot than older voters were. Among those who had previously voted in Ohio but since moved, African American voters were more likely than white voters were to cast a provisional ballot.

Ohio 2004 Election: Turnout, Residual Votes and Votes in Precincts and Wards (with Michael Herron). 2005. Section VI of Democracy at Risk: The 2004 Election in Ohio. Democratic National Committee, Voting Rights Institute.

During the first five months of 2005, the DNC Ohio 2004 Investigative Project collected extensive data from precincts throughout Ohio. Problems with election administration seriously affected the 2004 election. Not providing a sufficient number of voting machines in each precinct was associated with roughly a two to three percent reduction in voter turnout presumably due to delays that deterred many people from voting. Strong similarities at the precinct level between the vote for Kerry (instead of Bush) in 2004 and the vote for the Democratic candidate for governor in 2002 (Hagan) present strong evidence against the claim that widespread fraud systematically misallocated votes from Kerry to Bush. In most counties we also observe the pattern we expect in the relationship between Kerry’s support and other precinct-level factors: Kerry’s support across precincts increases with the support for the Democratic candidate for Senator in 2004 (Fingerhut), decreases with the support for Issue 1 and increases with the proportion African American. Only in Cuyahoga County is the relationship between Kerry’s vote and the support for Issue 1 significantly unusual. Over all precincts and wards in the analysis, the proportion voting for Kerry decreases as turnout in 2004 increases, even when turnout in the 2002 election is taken into account. This suggests that voter mobilization efforts focused on turnout on balance hurt Kerry, at least if one takes 2002 as the baseline. The presidential residual vote rate (here defined as the fraction of ballots without a vote for either Bush, Kerry, Bedarnik or Peroutka) is inversely related to the number of voting machines per registered voter in both DRE precincts and precincts using precinct-tabulated optical scan machines: more machines meant a lower residual vote rate. The mechanism that most likely produces this effect is easy to understand: with fewer machines per voter, polling places become more crowded and voters are less likely to take the time to check or correct their ballots.

Voting Machine Allocation in Franklin County, Ohio, 2004: Response to U.S. Department of Justice Letter of June 29, 2005. 2005. Working paper.

The allocation of voting machines in Franklin County was clearly biased against voters in precincts with high proportions of African Americans when measured using the standard of the November, 2004, electorate. In precincts with high proportions of African American voters there were 13.6 percent more active voters per voting machine than in precincts having low proportions of African American voters. While shortages of voting machines caused long delays in voting throughout the county, the allocation of voting machines among the county’s precincts affected different voters differently. The most severe effects in terms of reduced voter turnout were incident on voters in precincts that had high proportions of African Americans. The most conservative estimate—based on the reported size of the active electorate in November—is that typically the shortages of machines reduced voter turnout by slightly more than four percent in precincts in which high proportions of the voters were African American, while shortages in precincts where very few voters were African American reduced voter turnout by slightly less than 1.5 percent.

If the allocation of voting machines is compared to information about the size of the active electorate that was available to Franklin County election officials at the end of April, 2004, then the allocation of machines is not biased against voters who were active at that time in precincts having high proportions of African Americans. But if election officials did use that information to make their allocation plans, then they made plans that involved using a total number of machines that was nearly 45 percent too small. Even using the April measure of the size of the active electorate, 5,023 working voting machines were needed, not 2,800 machines as data supplied by the county indicate were actually deployed on election day.

Election Forensics: Vote Counts and Benford’s Law. 2006. Presented at the 2006 Summer Meeting of the Political Methodology Society, UC-Davis, July 20–22.

How can we be sure that the declared election winner actually got the most votes? Was the election stolen? This paper considers a statistical method based on the pattern of digits in vote counts (the second-digit Benford’s Law, or 2BL) that may be useful for detecting fraud or other anomalies. The method seems to be useful for vote counts at the precinct level but not for counts at the level of individual voting machines, at least not when the way voters are assigned to machines induces a pattern I call “roughly equal division with leftovers” (REDWL). I demonstrate two mechanisms that can cause precinct vote counts in general to satisfy 2BL. I use simulations to illustrate that the 2BL test can be very sensitive when vote counts are subjected to various kinds of manipulation. I use data from the 2004 election in Florida and the 2006 election in Mexico to illustrate use of the 2BL tests.

Election Forensics: The Second-digit Benford’s Law Test and Recent American Presidential Elections. 2006. Presented at the Election Fraud Conference, Salt Lake City, Utah, September 29–30.

While the technology to conduct elections continues to be imperfect, it is useful to investigate methods for detecting problems that may occur. A method that seems to have many good properties is to test whether the second digits of reported vote counts occur with the frequencies specified by Benford’s Law. I illustrate use of this test by applying it to precinct-level votes reported in recent American presidential elections. The test is significant for votes reported from some notorious places. But the test is not sensitive to distortions we know significantly affected many votes. In particular, the test does not indicate problems for Florida in 2000. Regarding Ohio in 2004, the test does not overturn previous judgments that manipulation of reported vote totals did not determine the election outcome, but it does suggest there were significant problems in the state. The test is worth taking seriously as a statistical test for election fraud.

Election Forensics: Statistics, Recounts and Fraud. 2007. To be presented at the Annual Meeting of the Midwest Political Science Association.

Often recounts are cited as an essential tool for detecting election fraud. Several states have laws that mandate random recounts of a fraction of the ballots cast. But recounts can detect only some kinds of fraud, and some plans for doing recounts are not very efficient. Supplementing a recount plan with auxiliary statistical analysis, or using statistical analysis to guide the recount plan, can do better. Statistics for outlier detection and using the second digit Benford’s Law can even detect fraud that a recount will overlook. I review the relevant statistical results and look at data from several cases from American, Mexican and other elections.

Election Forensics: Statistical Interventions in Election Controversies. 2007. To be presented at the Annual Meeting of the American Political Science Association.

Controversies about elections are not exactly commonplace, but neither are they rare. In recent years a suite of statistical tools has been developed for diagnosing election anomalies and possibly detecting election fraud. The tools include methods for outlier detection and methods that use the second digit Benford’s Law. These methods interact in complex ways with post-election recounts and audits. I review how such methods have been used (or almost used) in recent election controversies in various places including the United States, Mexico and Bangladesh.