NBER WORKING PAPER SERIES DOES VOTING TECHNOLOGY AFFECT ELECTION OUTCOMES? TOUCH-SCREEN VOTING AND THE 2004 PRESIDENTIAL ELECTION David Card Enrico Moretti Working Paper 11309 http://www.nber.org/papers/w11309 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 May 2005 We are grateful to Ted Miguel for very helpful comments on a previous draft and for sharing his data on presidential election. We also thank Ken Chay and Alex Mas for useful conversations, Stephen Ansolabehere for sharing some corrections to our voting technology data, and Daniel Hartley and David Walton for outstanding research assistance. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. 2005 by David Card and Enrico Moretti. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including notice, is given to the source.
Does Voting Technology Affect Election Outcomes? Touch-screen Voting and the 2004 Presidential Election David Card and Enrico Moretti NBER Working Paper No. 11309 May 2005 JEL No. H0, J0 ABSTRACT Supporters of touch-screen voting claim it is a highly reliable voting technology, while a growing number of critics argue that paperless electronic voting systems are vulnerable to fraud. In this paper we use county-level data on voting technologies in the 2000 and 2004 presidential elections to test whether voting technology affects electoral outcomes. We first show that there is a positive correlation between use of touch-screen voting and the level of electoral support for George Bush. This is true in models that compare the 2000-2004 changes in vote shares between adopting and nonadopting counties within a state, after controlling for income, demographic composition, and other factors. Although small, the effect could have been large enough to influence the final results in some closely contested states. While on the surface this pattern would appear to be consistent with allegations of voting irregularities, a closer examination suggests this interpretation is incorrect. If irregularities did take place, they would be most likely in counties that could potentially affect statewide election totals, or in counties where election officials had incentives to affect the results. Contrary to this prediction, we find no evidence that touch-screen voting had a larger effect in swing states, or in states with a Republican Secretary of State. Touch-screen voting could also indirectly affect vote shares by influencing the relative turnout of different groups. We find that the adoption of touch-screen voting has a negative effect on estimated turnout rates, controlling for state effects and a variety of county-level controls. This effect is larger in counties with a higher fraction of Hispanic residents (who tend to favor Democrats) but not in counties with more African Americans (who are overwhelmingly Democrat voters). Models for the adoption of touch-screen voting suggest it was more likely to be used in counties with a higher fraction of Hispanic and Black residents, especially in swing states. Nevertheless, the impact of non-random adoption patterns on vote shares is small. David Card Department of Economics University of California Berkeley, CA 94720-3880 and NBER card@econ.berkeley.edu Enrico Moretti Department of Economics University of California Berkeley, CA 94720-3880 and NBER moretti@econ.berkeley.edu
1. Introduction The difficulty of counting the ballots in Florida during the 2000 presidential election drew the nation s attention to the issue of voting technology. Backed by funding from a new federal law the Help America Vote Act of 2002 many counties across the U.S. responded by installing touch-screen voting machines. 1 Supporters of this new technology (also known as direct recording electronic voting) point to several advantages, including the instant availability of vote counts, accessibility for disabled voters, and ease of implementing last minute ballot changes. 2 Nevertheless, before, during and after the most recent presidential election concerns were raised that touch-screen voting may be vulnerable to fraud. 3 Distrust in electronic voting is not limited to conspiracy theory web sites. It is shared by the mainstream press, some Representatives in Congress, and is substantiated by peer-reviewed academic studies. A recent technical analysis showed that the software in one brand of touch-screen voting machinery is insecure, and noted that in the absence of a paper trail vote tampering may be impossible to detect (Kohno et al., 2004). Critics concerns increased when, just prior to the 2004 election, the CEO of a leading touch-screen manufacturer told Republicans he was committed to helping Ohio deliver its electoral votes to the president. 4 The day after the election some of these concerns seemed validated by the discovery of an error in electronic voting machinery that gave President Bush more votes than had actually been cast in one Ohio precinct. 5 Reports of voting irregularities prompted a formal objection to the Ohio election results by a number of Democratic Congressmen. 6 Although not all the controversies surrounding the 2004 election are related to voting technology, touch-screen voting machines seem to have generated the most widespread debate. 7 A recent New York Times editorial summarized the state of public skepticism on this new voting 1 HAVA provides funding for adopting one of two electronic voting systems: so called optical scan systems, which rely on a paper ballot marked with a pencil, and touch screen systems. See Katz and Bolin (2005). 2 See for example the Customer Comments on the Patriot voting system sold by Unilect Corporation at www.unilect.com/. 3 Reflecting these concerns, California s Secretary of State published guidelines in December 2003 that required all touch-screen voting systems purchased by cities or counties after July 2005 to include an accessible voter verified paper audit trail. Harris (2003) presents a critique of touch-screen voting systems. 4 The Aug. 14, 2004 letter from Walden O'Dell of Diebold Inc. - who was active in the re-election effort of President Bush - prompted Democrats to question the propriety of allowing O'Dell's company to calculate votes in the 2004 presidential election. 5 The error occurred in a precinct in Columbus, Ohio. The original electronic tally showed Bush receiving 4,258 votes to Democrat John Kerry's 260 votes. However, only 638 voters cast ballots in that precinct. The error was later corrected by election officials. 6 Explaining her decision to support the objection, Senator Barbara Boxer (D-CA) commented We cannot keep turning our eyes away from a flawed system (San Francisco Chronicle, January 7, 2005). Problems with electronic voting systems have continued after the presidential election. A recent New York Times article (April 1 2005) noted that a computer glitch caused Miami-Dade electronic voting machines to throw out hundreds of ballots in a special election on March 8, 2005 and raised questions about votes in five other municipal elections. The problem came to light when officials noticed a large number of undervotes in the election. 7 Many newspapers articles and web sites alleged voting irregularities in Florida counties that use optical-scan voting machines. These reports were never officially substantiated. 1
technology, concluding that these ATM-style machines make a lot of sense for the manufacturers because they are expensive. But touch screen machines are highly vulnerable to being hacked or maliciously programmed to change votes. 8 With a narrowly divided electorate, even minor problems in the accuracy of polling could tip election results for one party or the other. More generally, the issue of voting technology threatens to erode trust in the democratic process, leading to voter disengagement and reduced turnout. One of the reasons why the choice of voting technology is so controversial is that there is very little systematic empirical evidence on the relationship between voting technology and election outcomes. 9 In this paper we use county-level data on voting technologies in the 2000 and 2004 presidential elections to test whether voting technology affects electoral outcomes. Clearly, due to its illicit nature, it is difficult to find direct evidence of vote tampering. Instead, following the approach of other recent studies that have tried to detect illegal behavior, we use publicly available data on voting outcomes across counties to test for patterns that could be suggestive of manipulation. 10 In particular, we focus on interactions between the use of touch-screen machinery and the incentives faced by local officials to tip the vote in one direction or the other. We begin by comparing the change in the Republican two-party vote share between 2000 and 2004 in counties that adopted touch-screen voting technology and counties that did not, relative to the trends in the same counties between 1996 and 2000. The results suggest that the gain in the share of votes cast for George Bush between 2000 and 2004 was greater in counties with touchscreen voting. The gap is fairly large, accounting for 1.2-2.7 percentage points of the 2000-2004 gain in the Republican two-party vote share enough to have affected the final outcome of the election. Of course these results do not necessary prove that voting technology matters. An alternative explanation is touch-screen voting was more likely to be adopted in counties where support for Republican presidential candidates was accelerating. We therefore consider models for the change in the Republican vote share that control for a wide variety of observed election determinants, including state fixed effects, controls for the Republican and third party vote shares in the county since 1992, income, church membership, presence of military personnel and racial composition. In these models the difference in the change in the 2000-2004 Republican two-party vote share between counties with and without touch-screen voting falls to 0.2-0.3 percentage points, 8 New York Times, March 10, 2005. 9 The only paper that we are aware of that directly studies the effect of technology on election outcomes is Hout et al. (2004). McDonald (undated) presents a critique of this study. Brady et al (2001) and a group at CalTech-MIT (2001) focus on the question of which voting technology generates the lowest fraction of invalid votes. 10 Recent examples include Levitt and Duggan (2002), Fisman and Wei (2004), Jacob and Levitt (2003), Hsieh and Moretti (2005), Di Tella and Schargrodsky (2003), Fisman (2001), and Reinikka and Svensson (2004). 2
but remains marginally significant. Although small, this effect would have been large enough to influence the final result in some closely contested states, and therefore the final election outcome. Is the gap in Republican vote share between counties with and without touch screen voting evidence of systematic irregularities on the part of Republicans election officials, or just a spurious correlation? To provide further insights we turn to more direct tests based on the notion that incentives for vote manipulation vary widely across counties. If irregularities did take place, they would be most likely in counties that could potentially affect statewide election totals, or in counties where election officials had incentives to affect the results. For example, there are few incentives for vote tampering in solidly Democratic or solidly Republican states like California or Texas, since small changes in a county s vote tally have no effect on the final outcome. On the other hand, incentives are higher in states like Ohio or Florida, where minor changes in the tallies from a small number of counties could affect the outcomes under the winner-take-all electoral college system. Similarly, in the presence of irregularities associated with voting technology, one would not expect touch-screen voting to favor Republicans in states where election officials are Democrats. In this spirit, we estimate models that include interactions between an indicator for touchscreen voting and indicators for whether the state was a swing state and whether the Secretary of State (or the Governor) was Republican. We find no evidence that these interaction effects are positive. Indeed, if anything, the touch-screen voting effect is smaller in swing states, and in states with a Republican Secretary of State. These results are inconsistent with the irregularity hypothesis. As a further check, we estimate a parallel set of models for the change in the share of voters registered as Republicans. Trends in registration presumably reflect trends in voter sentiment but should not be affected by voting technology. Thus any relation between touch screen voting and the change in the Republican share of registered voters suggests a problem with unobserved heterogeneity that could also bias the relation between touch screen voting and vote shares. 11 Results from this investigation suggest that changes in the relative shares of registered voters are unrelated to use of touch screen voting, although the power of the exercise is limited by the lack of complete data on county-level voter registration. If the touch-screen voting effect cannot be explained by voting irregularities, or by omitted variables, why is there a relationship between touch-screen voting and changes in support for Bush? One possible link is through voter turnout: if touch screen voting affects the relative turnout of groups with systematically different voter preferences, it could affect vote share outcomes. We find that touch-screen voting is associated with lower turnout rates, and that this effect is larger in counties with a larger fraction of Hispanics. Since Hispanics tend to vote for Democrats, this 11 A similar test could be performed with exit poll data. However, county-level exit poll data are unavailable. 3
turnout effect may ultimately affect the election outcomes. On the other hand, there is no similar interaction with the fraction of Black residents in a county. Finally, we present models where technology adoption is the dependent variable. Controlling for state effects, these models suggest that touch-screen voting was more likely to be adopted in counties with higher fractions of Black and Hispanic voters. Consistent with a possible partisan motive, the Hispanic effect is generally stronger in swing states, though not in states with a Republican governor. Overall, we reach two main conclusions. First, although there is some evidence that use of touch-screen voting is correlated with the change in the Republican vote share in a county, caution is needed in interpreting these patterns. While this evidence would appear superficially consistent with voting manipulation, more direct tests for systematic voting irregularities show no evidence that the Republican gains are correlated with local incentives to raise the Republican vote share. We stress that our empirical strategy is intended to test for systematic voting irregularities, and cannot detect voting irregularities in only in one or two counties. Second, touch-screen voting can affect election outcomes indirectly by affecting the relative turnout of different voter groups. The evidence suggests that touch-screen voting reduces overall turnout, with a larger effect in counties with more Hispanic residents. The fact that touch-screen voting seems to have been adopted more quickly in counties with more Hispanics (particularly in swing states) may point to systematic effort to influence election outcomes, though regardless of intention the overall effect on election outcome was small. 2. The Relationship Between Voting Technology and Presidential Elections Outcomes In this section we begin by describing our main data sources and presenting an overview of trends in county-level vote shares in the presidential elections since 1992. We then present four sets of empirical results. First, we show some simple difference-in-difference and triple differenced estimates, where we compare the 2000-2004 change in the two-party Republican vote share between counties with and without touch-screen voting, and these changes relative to the trends between the 1996 and 2000 elections. We then present estimates from regression models where the 2000-2004 change in the two-party Republican vote share is regressed on an indicator for touchscreen voting technology and a rich set of covariates. Third, we present models where touch-screen voting is interacted with a number of state or county characteristics that might be expected to be associated with irregularities. These models with interactions provide our most direct test of possible voting irregularities. Finally, we present models where the dependent variable is voter registration. 4
2.1 Summary Statistics We begin in Table 1 by showing means for some key variables in counties that are classified by whether or not they used direct recording electronic (DRE) technology in the 2004 presidential election. Our use of counties as a unit of analysis is dictated by the fact that in most states, voting technology is selected by officials at the county level, and is homogeneous within counties. 12 Data on voting technology for the 2004 election were obtained from ElectionOnLine.com. We validated these data for all swing states and several other states using information collected directly from the Secretaries of State. We also compared the data with information provided by Election Data Service. We found relatively few discrepancies between the three data sources. Similar data for the 2000 presidential election were purchased from Touch-screen Voting Technology, and corrected using information generously provided by Stephen Ansolabehere. 13 County data on religious adherents are from Jones et al. (2002). The remaining data on county characteristics are from the 2000 Census of Population. The top panel of Table 1 shows the prevalence of different voting technologies. Just over 20% of US counties used DRE technology in the most recent election. When we weight by county population (in the right-hand set of columns), this fraction increases to 27%, indicating that larger counties are more likely to adopt DRE. The difference in county size is illustrated directly in the second last row of the table, which shows that counties with DRE voting had an average population of about 119,000 in 2000 compared to an average population of 82,000 in non-dre counties. The other rows of the top panel in Table 1 show the prevalence of other voting technologies, including optical scanning technology (used by the 48% of counties), paper ballots (10.5% of counties), lever voting machines (7.3% of counties) and punch cards (10.5% of counties). Finally, we show the rates of use of DRE technology in the 2000 election. Between the 2000 and 2004 elections the fraction of counties using DRE roughly doubled. Interestingly, a small number of counties (116) used DRE in 2000 and not in 2004. (These are the 4.7% of counties in the DRE=0 column that used DRE in 2000). The next set of panels show election outcomes from the past four presidential elections. Consistent with the electoral outcomes, the Republican vote share whether expressed as a fraction of the two-party vote or as a fraction of all votes case has risen in recent elections, though the 12 In 8 states Connecticut, Maine, Massachusetts, Michigan, New Hampshire, New York, Vermont, and Wisconsin choice of technology is made at a lower level typically at the township level. Of the 281 counties in these states, 166 had the same voting technology throughout the county in 2004. Thus, there are only 115 counties with multiple technologies. We were able to obtain sub-county data on technology choices for 5 of these counties and use the fraction of townships with DRE technology (instead of an indicator of DRE choice) as a measure in these counties. 5
gains are overstated by looking at the unweighted county tabulations, since smaller (mainly rural) counties have become more Republican than larger counties. Comparisons of trends by DRE status point to two key facts. The first is that longer-run trends in vote shares are slightly different in counties that used DRE technology in 2004 and those that did not. For example, the unweighted average Republican share of the two-party vote rose by 7.6 percentage points between 1992 and 2000 in counties that used DRE in 2004, versus 8.7 percentage points in counties that did not. A second fact is that estimated trends in vote shares are sensitive to whether county-level outcomes are weighted by population. Looking again at the 1992-2000 change in the Republican share of the two-party vote, the gain was only 1.6 percentage points using population-weighted vote shares in DRE counties, versus 2.6 percentage points in the non-dre counties. Interestingly, however, the relative trends between 1992 and 2000 in DRE and non-dre counties are about the same using weighted or unweighted vote tallies. The lower panels of Table 1 present means of some county level characteristics that may be correlated with electoral outcomes. DRE technology is more likely to be used in counties with a higher fraction of black residents, and also in counties with higher income and a lower fraction of religious adherents. The fractions of Hispanic residents, residents with a college degree, and residents employed by the military are also somewhat different in DRE and non-dre counties, though the direction of the gaps depends on whether the data are weighted or unweighted. Most striking are the regional differences in the adoption of DRE technology. Southern counties are disproportionately represented among DRE adopters, accounting for 78% of counties using DRE in the 2004 election versus 38% of counties using other technologies. 2.2 Graphical Analysis and Difference in Difference Estimates We now turn to the relationship between use of DRE technology in the 2004 election and the change in the Republican share of the 2-party vote between 2000 and 2004. Figure 1 plots the 2004 Republican share of the 2-party vote in a given county against the same measure in the 2000 election, using circles for counties that had DRE technology in 2004 and dots for counties that did not, and varying the sizes of the symbols to indicate the relative sizes of the different counties (based on 2000 population). While many points in the figure fall very close to the 45 degree line indicating little change in the Republican share there are many counties above and below the line. Figures 3-6 show similar plots for some of the main swing states, again using different plot symbols for DRE and non-dre counties with scaling to represent the relative populations of different counties. In Florida there are two large and heavily Democrat counties Palm Beach and 13 There is some controversy over the reliability of the 2000 technology data see Brady et al. (2001). Unfortunately, 6
Broward Counties where the Republican vote share is noticeably higher than what one would have predicted based on the 2000 share. Both counties used DRE voting in the 2004 election. Variants of this figure were noted in Hout et al. (2004) and have been widely cited as informal evidence of a problem with touch-screen voting. Data for 7 other swing states with sizeable numbers of DRE and non-dre counties (Ohio, Iowa, and New Mexico, Pennsylvania, Wisconsin, Michigan, and Minnesota) show little or no evidence of a connection between DRE voting and the change in the Republican vote share. Below, we present some statistical tests aimed at determining whether DRE adoption had an effect on voting outcomes for the major swing states. Across all counties in the US, the gain the Republican share of the two-party vote between 2000 and 2004 was larger in counties that used touch-screen voting in 2004 than in other counties. As shown by the entries in Table 1, the gain was 3.2 percentage points (standard error = 0.2) in DRE counties versus 1.8 percentage points (standard error=0.07) in non-dre counties. The difference in the change in vote shares between DRE and non-dre counties is illustrated in Figure 2, which plots the histograms for the two groups of counties. The distribution in the DRE counties appears to be shifted to the right. A potential problem with the simple comparisons in Figures 1-6 is that counties that adopted DRE may have had pre-existing trends in voting sentiment that differ from the trends in non-dre counties. A standard method for controlling for such trends is to difference the change in the vote share between 2000 and 2004 from the change between 1996 and 2000. To the extent that countyspecific underlying trend factors tend to persist over time, a comparison of the change in trends between DRE and non-dre counties (a difference-in-difference-in-differences approach) will lead to valid inferences about the true effect of touch-screen voting. Table 2 summarizes the results from this exercise. The entries in column 2 of the top panel of Table 2 indicate that counties with touch-screen voting in 2004 experienced a 3.18 percentage point average increase in the Republican share of the two-party vote, and a 4.01 percentage point gain in the Republican share of all votes. The corresponding gains for counties without touchscreen voting were 1.79 and 3.22 percentage points (column 3). Thus the difference-in-differences is 1.39 percentage points, using the Republican share of the two-party vote (column 4), or 0.79 percentage points (using the overall Republican share). The relative gains for Republicans are substantially smaller using population-weighted averages (in the four right hand columns of Table 2), and are insignificantly different from 0. Consistent with the existence of county-specific trends, however, the data in the middle panel in Table 2 suggest that counties that used DRE technology in 2004 experienced a decline in support for Republican candidates between 1996 and 2000, relative we were unable to obtain corrections made by Brady et al. 7
to counties that used other technologies. Interestingly, the magnitude of the 1996-2000 relative trend is quite similar whether the Republican share is taken over all votes, or only the votes cast for the two major parties, and whether the individual county vote shares are weighted or not. The bottom panel shows the difference-in-differences within each of the county subgroups, obtained by subtracting the 1996-2000 difference from the 2000-2004 difference, as well as the triple-differenced comparisons. Using the two-party vote share the estimated contrast between DRE and non-dre counties is 2.71 percentage points (standard error = 0.26) or 1.35 percentage points (standard error=0.19), depending on whether we weight or we do not weight by population. Using the overall Republican vote share the relative contrasts are similar. The triple differenced comparisons in Table 2 suggest that relative to longer run trends, the adoption of DRE voting by a county in the 2004 election was associated with a 1.2-2.7 percentage point increase in votes for the Republican candidate. The estimates are relatively precise (standard errors in the range of 0.2 to 0.3 percentage points) and highly statistically significant. Nevertheless, the interpretation of these estimates is not unambiguous. On the one hand, this finding is consistent with concerns raised by some Democrats that the adoption of touch-screen voting helps Republicans. If voting technology is unbiased, we would not expect to observe a systematic relationship between election outcomes and voting technology, holding other factors constant. This interpretation is particularly troublesome because the magnitude of the estimated coefficient is large enough to influence the final result in Ohio and possibly other contested states, thus affecting the final election outcome. On the other hand, it is possible that other things are not held constant, and that the adoption of touch-screen voting is correlated with other determinants of electoral outcomes that are not fully accounted for by earlier vote trends in the county. Alternatively, it is possible that county-level trends in vote shares are mean-regressive, rather than fully persistent, so that triple-differencing over-controls for the 1996-2000 trends. This can be addressed by including lagged vote shares as regressors in a model for the change in vote shares between 1996 and 2000, rather than assuming that the lagged changes enter with a coefficient of 1.0, as is implicit in the triple-differencing approach. 2.3 Econometric Specifications. To try to shed more light on this issue, we now turn to a more formal econometric analysis. We begin by estimating variants of the following model 8
(1) V cs = β 1 DRE cs2004 + β 2 V cs2000 + β 3 V cs1996 + β 4 V cs1992 + β 5 T cs2000 + β 6 T cs1996 + β 7 T cs1992 + β 8 X cs + d s + e cs where V cs is the 2000-2004 change in the Republican 2-party vote share in county c in state s, DRE cs2004 is an indicator for whether county c used touch-screen voting in 2004; V cst is the 2-party vote share in county c in year t (t=2000, 1996, or 1992); T cst is the third party vote share in county c in year t; X cs is a vector of county characteristics that might affect electoral outcomes (including percent in the military, percent who are religion adherents, percent blacks, percent Hispanics, median income, percent college graduates, percent in agriculture, and county population), and d s is a vector of state dummies. By focusing on the change in the Republican vote share, we are differencing out all permanent differences across counties in voter sentiment. By controlling for state effects, we absorb any unobserved state-specific shocks that might have affected the 2000-2004 change in vote shares in that state. Identification of the DRE effect in model (1) comes from the fact that in many states there is county-level variation in voting technology. Specifically, identification comes from variation across counties in the subset of 23 states that have both DRE and non-dre voting technologies. 14 By including lagged Republican and third party vote shares, we control for preexisting county-specific trends in voter sentiment that might be correlated with use of touch-screen voting. Finally, by adding the covariates we hope to account for county-specific economic and cultural factors that could affect the rate of change in voter sentiment and might also be correlated with technology adoption. We have also fit all of our models using as a dependent variable the change in the Republican vote share (rather than the Republican share of the 2-party vote) and found results that are quite similar to the ones that we report here. An implicit assumption in model (1) is that the only voting technology that matters is touchscreen voting. Other possible technology choices (optical scanning, punch cards, level voting machines, and paper ballots) are combined together as the omitted reference group. Given the controversy over touch-screen voting we believe this specification is reasonable. It also simplifies the interpretation of our results, since otherwise one has to specify which of the voting technologies is used as the baseline for comparing the enumerated choices. Our models identify the effect of touch-screen voting relative to an average of the other technologies. 14 A total of 5 states used only touch screen voting in 2004 (Delaware, Georgia, Maryland, Nevada). Another 20 states and the District of Columbia had no counties with DRE voting. Two states (New York and Wisconsin) with townshiplevel choice of voting technology had some counties with partial use of DRE, but no counties with full DRE adoption. We have no data on Alaska. 9
Another specification issue in model (1) is the potential effect of touch-screen voting in the 2000 election. Just over a third of counties with DRE in 2004 also used touch-screen voting in 2000. It is unclear whether one should assume that touch screen voting had no effect in the 2000 election, or had the same effect as in 2004. Thus, we estimate a second model where we include both an indicator for DRE in 2004 and an indicator for DRE in 2000: (2) V cs = β 1 DRE cs2004 + β 2 DRE cs2000 + β 3 V cs2000 + β 4 V cs1996 + β 5 V cs1992 + β 6 T cs2000 + β 7 T cs1996 + β 8 T cs1992 + β 9 X cs + d s + e cs This model encompasses the two possibilities that touch screen voting had no effect in 2000 (β 2 =0) or that it had the same effect in both 2000 and 2004, implying an equal and opposite effect of the DRE indicators for the two years on the change in vote shares (i.e., β 2 =-β 1 ). When we present estimates of equation 2, we also test whether β 2 =-β 1. A final specification issue is that equations 1 and 2 implicitly restrict the effect of touchscreen voting to be the same across all counties. For a number of reasons this may be an inappropriate restriction. In particular, if one is concerned about voting irregularities associated with the adoption of DRE, it is implausible that these irregularities occurred in every county. If there were such manipulations, we should see them only where they could have made a difference for the overall election outcome, or in states where elections officials had an incentive and the opportunity to favor one candidate. To test this possibility, we estimate models that include interactions of the change in the use of DRE with state or county characteristics that one would expect to be associated with an increase the chances of frauds in favor of Republicans: (3) V cs = β 1 DRE cs + β 2 DRE cs * Z cs + β 3 Z cs + β 4 V cs2000 + β 5 V cs1996 + β 6 V cs1992 + β 7 T cs2000 + β 8 T cs1996 + β 9 T cs1992 + β 10 X cs + d s + e cs where DRE cs is the 2000-2004 change in DRE status and Z is an interaction term. To keep the specification parsimonious, models with interactions restrict the coefficient on DRE in 2000 and the coefficient on DRE in 2004 to be the same, with opposite sign. We experiment with many different interaction terms including: the Republican vote share in 2000; the party affiliation of the governor or the Secretary of State; whether the state is a swing state; whether the governor is Republican and the state is a swing state (triple interaction); county population; county income; percent black in the 10
county; percent black in the county interacted with whether the state is a swing state (triple interaction); percent Hispanic; percent college graduates; and indicators for selected swing states. 2.4 Results. a. Basic Models Table 3 present estimates of a number of variants of equation 1. All the models are estimated by weighted least squares, using as a weight the county s population in 2000. 15 The model in column 1 regresses the 2000-2004 change in the Republican 2-party vote share on a single indicator for use of touch-screen voting in 2004. This model yields an estimate that is the same as the difference-of-differences estimate in the top panel of Table 2. As we have noted, the interpretation of this estimate is potentially problematic. For example, counties in the South were more rapid adopters of DRE. At the same time, it is possible that Republican party support has been growing more rapidly in the South than in other regions, leading to a spurious correlation between DRE and the change in the Republican vote share. To address this concern, the model in column 2 adds a set of state dummies, which control for any factors that are common across counties in the same state. The addition of the state effects reduces the measured effect of DRE, to a negative effect. 16 To control for heterogeneity in the longer-run trends in voter sentiment across counties, the model in column 3 adds the 1992-2000 values of the Republican vote share and the third party vote share in each county. These lagged vote outcomes particularly the third-party vote share measures are very strong predictors of the changes in Republican support between 2000 and 2004. Interestingly, the addition of these factors leads to a positive DRE coefficient. Finally, in column 4 we present our most complete specification, which includes the lagged vote share variables, state effects, and a total of 8 other county-level control variables, all measured in 2000: percent black, percent Hispanic, percent with a college degree, percent in the military, percent religious adherents, percent working in agriculture, mean personal income, and county population. The addition of the extra controls leads to a small increase in the estimated size of the DRE coefficient. The estimated DRE effect implies that use of touch-screen technology was associated with a 0.21 percentage point higher Republican share of the 2-party vote. The estimate is statistically significant at conventional levels (t=2.1). Estimates from an unweighted model yield a slightly larger coefficient: 0.28. It is interesting to note that the regression models with unrestricted coefficients on the lagged vote shares lead to estimated DRE effects that are closer in magnitude to 15 Unweighted models are generally similar. 16 If we add region effects (for 3 of the 4 regions of the country) rather than state effects the weighed estimate falls to 0.02 percentage points. 11
the simple difference-in-differences estimates in Table 2 than to the triple differenced estimates. Examination of the coefficients associated with the lagged vote shares suggests that contrary to the persistent trends assumption of a triple-differenced comparison, the regression estimates show much smaller effects of the lagged Republican vote share variables on future changes. Table 4 presents estimates of equation 2, in which we include indicators for use of DRE voting in both the 2000 and 2004 elections. In general, the coefficients associated with use of touch-screen voting in 2004 are similar to the ones in Table 1, while the coefficients associated with touch-screen voting in 2000 are negative. In most cases, we cannot reject that the effects in 2000 and 2004 are equal and opposite (i.e., that β 1 = -β 2 ). In column 4, where all the controls are included, the coefficient on DRE in 2004 and 2000 are, respectively, 0.21 percentage points and 0.00 percentage points, but the p-value for the test of equal and opposite coefficients is 0.43. b. Models with Interactions Like the models in Table 3, the most fully parameterized models in Table 4 suggest that use of touch-screen voting is associated with a gain the Republican vote share on the order of 0.2 percentage points. While this is consistent with the allegations of voting irregularities raised by Democrats during and after the election, the interpretation is unclear because DRE status is not randomly assigned. To better understand the sources of this effect we turn to models that include interactions of the change in the DRE indicator with a variety of state and county-level characteristics. Each row of Table 5 presents an alternative version of equation 3 in which we have included our full set of control variables, the change in use of DRE voting, and the interaction of the change in DRE voting with the variable identified in the row heading. 17 We use the change in DRE status, as opposed to DRE status in 2000 and in 2004, for ease of interpretation and because the tests in Table 4 suggest that in the more general models we cannot reject the differenced specification. Inspection of the estimates in column 2 of Table 5 suggests that most of interaction terms are either zero or negative. For example, the coefficient of the interaction with Republican Secretary of State is -0.21 percentage points, while the interaction with a Republican governor is 0.23. Neither estimate is statistically different from zero. Similarly, the coefficient of the interaction between change in DRE and swing state status is -0.62 percentage points. The coefficient on the triple interaction between change in DRE, Republican governor, and swing state is also significantly negative. These findings are inconsistent with the hypothesis that touch-screen 12
voting was manipulated by Republican election officials in order to tip the 2004 election in favor of George Bush. We also report the interactions between the change in DRE status and various county characteristics, including county population, county income, the fractions of black and Hispanics in the county, and the percent of county residents with a college degree. Interestingly, there is no evidence that the DRE voting effect is larger (or smaller) in counties with more black or Hispanic residents or college graduates. The interaction with county population is positive, but the interaction with population* swing state is insignificant. In the last four rows of the Table, we focus on interactions of the change in DRE voting with selected swing states. Although we have shown that the effect of DRE is on average negative when all swing states are considered, it is possible that the effect is different in individual states. Overall, however, the results for the four selected states confirm the picture from our pooled national samples. In the case of Florida, for example the estimated interaction coefficient is -0.52 percentage points (standard error=0.33 points). Contrary to the impression conveyed by the data in Figure 3, a richer model of voting outcomes suggests that Florida counties using touch-screen voting did not experience larger gains for Bush than non-dre counties, on average. The interactions effects for Ohio, Iowa and New Mexico are likewise negative or zero. To summarize, we find that use of touch-screen voting in a county is associated with a small gain in the Republican vote share in the 2004 election. The precise magnitude of the gain is sensitive to which specific model is adopted, but the estimated effect is significant in our richest specifications which control for state effects, lagged vote shares, and various county-level characteristics. On closer inspection, however, we find virtually no indication that the gain arose in counties or states where one could argue that election officials had the greatest incentive to tip the election in favor of Republicans. Thus, we conclude that it is implausible that the positive association between DRE voting and the Republican vote share reflects direct manipulation of DRE machines by Republican officials. c. Voter Registration As an further check for the potential influence of unobserved trends in voter sentiment across counties that happen to be correlated with the adoption of DRE voting, we estimated a series of models for trends in county-level voter registration. Changes in the fraction of voters registered as Republicans presumably reflect the same forces that influence trends in vote shares. However, voter registration patterns are unlikely to be affected by choice of voting technology. Thus, a test 17 Note that the main effects of any of the state-level variables used as interactions are absorbed by the state effects 13
for the effect of touch-screen voting on the relative fraction of voters registered as Republicans provides a specification test of our basic regression framework. In particular, the finding of a positive effect of touch screen voting on the Republican share of registered voters would suggest a spurious correlation between underlying voter preferences and technology choice that could also confound our vote share models. We collected the data on registration by contacting the secretary of state of each state. A limitation of these data is that not all the states provided data on registration by party. We have only been able to assemble 2004 voter registration data by party for a subset of 1123 counties (36% of our main sample). The number of counties with 2000 and 2004 registration data is even lower (only 478, or 15% of our main sample). The regression models in Table 6 take as a dependent variable the fraction of voters registered as Republicans in a county in 2004, or the change in the Republican share of registered voters between 2000 and 2004. For simplicity, we present only our richest specifications, which include state effects, lagged vote shares, and county-level characteristics. The model for the Republican share of registered voters in 2004 (column 1) shows a positive correlation between use of touch-screen voting and the share of voters registered as Republicans. In the differenced specification (column 2), however, the DRE effect drops virtually to zero. Since our vote share models use the change in vote shares as a dependent variable, we interpret the results in Table 6 as supportive of our basic framework. We also fit a set of models similar to the ones in Tables 3 and 4, but taking as a dependent variable the difference between the change in Republican vote share and the corresponding change in the Republican share of registered voters in a county. By deviating the vote share from the share of registered voters, these models potentially eliminate many of the unobserved components of voter sentiment that could confound the estimated effects of touch-screen voting. 18 Unfortunately, the lack of complete data on voter registration rates presents a problem for this exercise, since baseline models similar to the ones in Table 3 or 4 but restricted to the sub-sample of counties with 2000 and 2004 voter registration data give rise to estimated DRE effects that are somewhat different from the estimates over the full sample of counties. Nevertheless, models for the change in the vote share relative to the change in the Republican share of registered voters show a positive and marginally significant effect of DRE use in 2000 on the change in the Republican vote share relative to the change in the Republican share of registered voters between 2000 and 2004. included in the model. 18 Voters may not necessarily switch their party of registration, even when they have firmly realigned their election preferences. Thus, the level or trend in voter registration is at best only a partial control for voter sentiment. 14
(Estimates available on request.) Given the limitation of the registration data, however, it is difficult to draw strong conclusions from these estimates. 3. Models of Turnout and DRE Adoption a. Overview So far we have focused on the effect of voting technology on the share of Republican votes, effectively conditioning on the sample of citizens who go to the polls and whose vote is recorded as valid. We have shown that there is a small positive effect of touch-screen voting technology on the level of electoral support for George W. Bush. Looking at models that interact the use of DRE with county-level characteristics, however, we conclude that this finding is unlikely to be explained by systematic voting irregularities on the part of Republican election officials. If the DRE effect cannot be explained by voting irregularities, why is there a relationship between DRE and changes in support for Bush? Part of the explanation may be a spurious correlation between underlying trends in voter preferences and choice of voting technology, although our results on voter registration seem to suggest that this effect may be limited. Another, potentially more interesting, explanation is that the adoption of new voting technology may affect the mix of voters at the polls, or the mix of voters whose vote is counted as a valid vote. In this section we investigate whether voting technology has a differential impact on the turnout rates of different subgroups. If certain subgroups of voters---for example, minorities---are more or less likely to turn out when balloting is conducted with touch screen machinery, or if their vote is more or less likely to be counted as valid, there could be an effect on election outcomes. We also try to assess whether there is evidence of any systematic effort to affect election outcomes through this channel. Specifically, we test whether the adoption of touch screen voting technology is higher (or lower) in counties that have more minorities. What are the channels that would lead the adoption of touch screen voting technology to affect voter turn out rates of some subgroups? First, it is possible that electronic machines are perceived as confusing or intimidating by subgroups that have limited familiarity with computers and ATM machines, or have limited English proficiency. Second, some minorities, especially African-American, may be particularly suspicious of this new controversial technology, given the many allegations surrounding DRE technology and the many historical episodes of disenfranchisement of African-American voters. Third it is possible that the probability of making mistakes while voting is affected by the technology. Although proponents of electronic voting argue that it should increase the overall fraction of valid votes, it is possible that the opposite is true for 15
some sub-groups. 19 In the presence of language or cultural barriers, electronic voting may in fact result in fewer valid votes. In our empirical analysis, we define turn-out broadly, as the ratio of valid votes cast on county population. Our measured effect of DRE on turn-out therefore includes both the effect on the number of voters who go to the polls (if any), and the effect on the number of votes that are counted as invalid (if any). Because we do not have data on number of invalid votes by county, we can not separate these two margins. In order to better understand how voting technology may affect electoral outcomes through its effect on turn out, consider the following simple model. Let V=number of voters in county. Then V = V m + V o, where m denotes a particular subgroup of voters whose turn out may be affected by the new technology (for example, minority voters) and o denotes others. Denote V m /V = g m, the share of minorities among all voters. If S is the Republican share of votes in a county, and S m and S o are the vote shares for the two groups, then S = S m V m + S o V o = S o + g m (S m - S o ). A mechanical effect of relative turnout on voting outcomes arises if minorities (or some other group whose turnout is differentially affected by voting technology) tend to vote differently than other voters (i.e., if S m S o ) If minorities are less likely to vote Republican, for example, anything that reduces the relative share of minority voters increases the Republican vote share. We note that there may be another effect too, if the voters who are on the margin of turning out have different preferences than other voters in the same group. For simplicity we will ignore this possibility. Let t m and t o denote the turnout rates of minority and non-minority voters, and let f m denote the fraction of minorities in the voter-eligible population. It is easy to show that g m = f m + f m (1- f m ) (t m!t o )/t where t is the average turnout rate. Finally, note that 19 Indeed, the CalTech/MIT Voting Technology Project (2001) found that DRE voting has about the same error rates as 16