The simplest method is to examine the number of spoiled ballots by precinct. For precincts that use the same voting machine type and ballot design, the fraction of spoiled ballots should be nearly the same. When spoilage in certain precincts is higher than in others, either there is a concealed difference in voting machine type or ballot design, or there may have been a violation of equal protection under the law. Since the Voting Rights Act requires that disability not be a factor in access to voting, the age, eyesight, or intelligence of voters is not an excuse for unusual ballot spoilage. Race, too, is excluded as a legitimate excuse. Defenders of the status quo may fall back on education as an excuse, but there is very little evidence that education affects the ability to vote.
Professor Allan Lichtman developed the basic technique used to determine discriminatory effect. Called ecological regression, this technique as applied here amounts to graphing the amount of spoilage versus the percentage of a minority population in a voting district and drawing the best-fit straight line through the data points. One can refine this procedure in a number of ways, such as weighting the voting district by size.
In “Bush’s Fifth Ace: a Crooked Panhandle,” we demonstrated that ballot or machine tampering strategies have predictable effects on electoral outcomes. One of the most interesting and generally useful of these was the effect on crossover. In crossover voting, a voter who votes Democratic for one race decides to vote Republican for another or vice versa.
In Florida 2000, a study of 3 million ballots by Dan Keating of The Washington Post (11/12/01) concluded that crossover was, on average, only 20% for Republicans and slightly less than that for Democrats in the race for US Senate. In a precinct in which 100% of the voters voted for Gore, 20% might vote for Republican Bill McCollum for Senate. In a precinct in which 100% of voters voted for George Bush, 20% might vote for Democrat Bill Nelson for Senate. Even those extreme values overstate the situation, since very few precincts voted more than 80% for any presidential candidate.
On the other hand, there could be some regional variation in crossover. One might expect Bill McCollum to be more popular near the congressional district he represented in northeast Florida, while Bill Nelson might be more popular in Tallahassee where he served, or in central Florida where he grew up and served at NASA. Still, by looking at precincts rather than counties, one should get around the issue of regional bias.
What we found was striking. In certain precincts, there was crossover well outside the norm. In heavily Republican precincts, an unusual number of voters who voted for Democratic Senate nominee Bill Nelson also voted for George Bush. Additionally, we found that the counties in which we found this unusual sort of crossover had very high levels of spoilage in Democratic precincts. Combining the two factors, which we proved were independent of one another, we were able to explain almost all of Gore’s performance. The patterns were so regular that they did not resemble what is seen in nature. They looked systematic, as if produced by machine. We called this strange crossover “pseudocrossover” to reflect our opinion that it was probably the result of tampering with ballots, counting machines, or final tallies.
The value of this work was that it identified a small number of precincts that were the most likely locations of electoral fraud and estimated the scale of fraud. In 2000, we estimated that 7100 votes could have been stolen in 11 northern counties and more votes in other counties. This is not massive, but it certainly was enough to swing the election.
In more recent work, we have shown that spoilage was all but eliminated in 2004, but pseudocrossover was not, pseudocrossover cannot be explained by simple models of crossover, pseudocrossover cannot be explained by “Dixiecrat voting” (whatever that is),
and
there is an atmosphere of official lawlessness in Florida consistent with fraud.
Conclusions
Scholars and lay researchers have developed effective strategies for identifying electoral fraud. To effect change, however, requires both a political as well as an analytic strategy. Efforts to force a serious investigation of the Florida vote have failed so far because Floridians have not been willing to do the on-the-ground investigation needed to translate statistical analysis into information useful in a criminal investigation. Thanks to The Free Press and The Alliance for Democracy, Ohioans in 2004 have made more progress. The battle, though, is far from won.
If elections are being stolen—a suspicion that must be regarded as not yet proven—the theft will only stop when someone pays a heavy price. If elections are not being stolen, then a proper investigation might reestablish some degree of faith in our institutions. It is not rational to wait for a favorable election cycle for change to be ushered in. Faith in fair elections is essential to a free country, since without confidence that votes will be counted, voters have no reason to go to the polls.
In a recent Newtopia article, Diane Perlman suggested a number of reasons why voters may be apathetic in the face of the possibility that elections are being stolen. If they can be involved in efforts to monitor elections, that could change. Certainly the Florida effort we have mounted needs volunteers for specific tasks. Many of these, such as building database queries, contacting counties to request vote information, or collating information available online can be done over the Internet.
Interested parties should contact OliverDawshed(at)aol.com.
Note: Oliver Dawshed and Elizabeth Jordan are pseudonyms.
