Abstract. The present work uses data from 1992-2004 to develop standard methodology to understand voting trends. The focus was on crossover from U.S. Senator to President in Florida elections, but other races were considered. A normalized internal standard method was found to be most widely applicable.

The stimulus for this study was a controversy arising out of Election 2004 regarding the voting behavior of a number of rural north Florida counties. It was asserted that racial attitudes guide voting patterns, with the implication that residual white racism plays such a dominant role that other factors are negligible. In the present work, this is called the “Dixiecrat hypothesis.”  

In particular, it was asserted that significant numbers of people in some counties vote for the Democratic candidate for U.S. Senate but cross over to vote for the Republican presidential candidate. This assertion fails very basic tests. As judged by the Shapiro-Wilk test, the crossover distribution is essentially normal, with no unambiguous outliers. To the extent it deviates from normality, it does not seem to be consistently due to this effect alone. The most generous ad hoc interpretation might allow that perhaps five counties are in some way different.

Furthermore, the criterion suggested by proponents of the Dixiecrat hypothesis - disparity between registration and voting behavior - is found to be unsuitable. This criterion is characteristic of about half of Florida counties. It is also distributed in a bimodal fashion, making it difficult to treat by many statistical measures. Crossover, by contrast, is essentially normally distributed. 

It is always possible and never wise to construct post hoc analyses to support a viewpoint. The work presented here suggests that crossover behavior is extremely complex, not easily characterized by a phrase. Perhaps Dixiecrat voting does influence crossover. That conclusion, however, is not clear from the data, and if there is a trend or pattern, it is not evident in recent history.

Proponents of the Dixiecrat hypothesis, quick to assert the pre-eminence of Dixiecrat voting as an explanatory variable,  have been very slow to concede that there might be other explanations for what they claim to see. One simple example is that north Florida crossover patterns at the county level could be somewhat different than those of the rest of the state because many of the Democrats who sought a seat in the US Senate, notably Bob Graham, Betty Castor, and Bill Nelson, served in Tallahassee before running for the Senate. Some of their Republican opponents were either from south Florida (Martinez) or served in Washington (McCollum). So, the Democrats may have enjoyed an advantage in name recognition, access to local media, and shorter travel distance from their homes to north Florida political events.

At the precinct level, however, very few reasons suffice to explain unusual crossover. Regional advantages, such as access to local media and convenient travel for campaigning cannot be explanatory. Within a county, demographic differences are also substantially compressed. Although housing segregation can certainly follow racial and economic lines, one might expect genuine segregation to manifest itself in the form of clusters of precincts with high crossover and others with low, not as a smooth, linear, normal distribution. So, one is left in a quandary if the pattern observed is the latter.

Nor does the Dixiecrat hypothesis explain why the pattern of crossover within a county should change substantially and unpredictably from election to election, nor can it explain why the unusual crossover is observed in locations far outside of rural north Florida, many of which meet none of the criteria proposed for qualifying as Dixiecrat. Finally, the Dixiecrat hypothesis cannot explain why certain counties show crossover ratios greater than 1, representing voters who vote Republican at the senatorial level but cross over to vote Democratic at the presidential level.

The present work investigates whether crossover in north Florida, or in any Florida county, can be considered to be a clear, historical trend (or pattern). Means of analyzing voting patterns involving crossover are explored and objective statistical criteria by which to fairly assess the question are presented.

2.0   Introduction. The present work investigates whether crossover in north Florida, or in any Florida county, can be considered to be a clear, historical trend (or pattern). Longitudinal studies of voting behavior are notoriously difficult, so discerning trends (or patterns) in voting is particularly challenging. The candidate roster changes, as do perceptions of individual candidates. Consider that in 1992, an incumbent Republican president faced a Democratic challenger in the face of a strong third party challenge.  In 1996, an incumbent Democratic president faced a Republican challenger in the face of a weak third party challenge.  In 2000, neither presidential candidate enjoyed the benefits of incumbency, nor was there a serious third party challenge. In 2004, a Republican incumbent faced a Democratic challenger with no serious third party challenge. While some voters will come out to vote for the candidate of their chosen party no matter what, many may stay home or switch between parties depending on the circumstances.

Factors other than those inherent in the candidates also affect voting. Different segments of the electorate are energized or indifferent in a given year. The demographics of a region may change. Voting machine technology evolves, altering the number of miscast or spoiled ballots. A minor factor, but one that complicates analysis, is the fact that more votes are typically cast for the candidates at the top of the ticket than for candidates down the ballot. For these and other reasons, accurately apprehending trends in voting behavior is a remarkably difficult task. 

There are many ways to try to control for the principal factors. One of the simplest is the use of percentages, which controls for changes in the size of the electorate.  A further refinement is normalization, in which all data are displayed on a fixed scale, typically zero to 1.  This is done by determining the minimum and the maximum and calculating for each data point the expression:

So, if the presidential vote percentage for a candidate in a state ranges from 20% to 70%, and a particular county has a value of 45%, this becomes (45-20)/(70-20) = 0.5.  This approach has the valuable effect of diminishing the influence of third party candidates on the resultant statistics.  If a third party takes either a given number of percentage points or a proportional fraction of a party’s vote in each county, then both numerator and denominator of eq. 2.0.1 stay the same.  

A third means of controlling for factors involves an internal standard. As described in earlier work, the ratio of votes between two candidates on the same ticket should be more or less constant across a state.  If a presidential candidate receives 90,000 votes and a senatorial candidate receives 100,000, the crossover ratio is 90,000/100,000 = 0.9. Deviations from the statewide average crossover ratio in one county may signal that one candidate or the other has a local preference, either positive or negative.   

Finally, it is often helpful to convert data into Z-scores.  The Z-score expresses the distance of a single observation from the mean in units of standard deviations. 

3.0   Results

3.1     Categorization of Florida counties according to presidential preference

When Democratic presidential performance (expressed as a percent of total vote) in Florida counties for the elections 1992-2004 is normalized according to equation 2.0.1, the slope of change taken, and the slopes rank-ordered, 11 counties are found to show increasing relative Democratic strength by more than one standard deviation, but less than two, from the mean.^a These are St. Lucie, Martin, Orange, Osceola, Monroe, Indian River, Seminole, Palm Beach, Brevard, Sarasota, and Pinellas.  Thirteen counties show the opposite trend.  Twelve are more than one standard deviation from the mean (Washington, Nassau, Levy, Columbia, Sumter, Holmes, Baker, Suwannee, Bradford, Union, Lafayette, and Gilchrist), while Dixie County is more than two standard deviations from the mean. 

When Republican presidential performance is taken through the same procedure, a different list is obtained.  Relative Republican strength increased by more than one standard deviation in Okeechobee, Glades, Putnam, Union, Calhoun, Taylor, Sumter, Walton, Suwannee, Levy, and Lafayette and by more than two standard deviations in Gilchrist and Dixie.  Republican strength waned by more than one standard deviation in Orange, Miami-Dade, Seminole, Duval, Collier, Martin, Palm Beach, Leon, and Clay.

Therefore, the counties in which Democratic strength at the presidential level is probably rising at the expense of Republican strength are Martin, Orange, Seminole, and Palm Beach.  The counties in which Republican strength at the presidential level is probably rising at the expense of Democratic strength are Sumter and a cluster on the eastern side of the Panhandle:  Union, Suwannee, Levy, Lafayette, Gilchrist, and Dixie. In the others, third party effects and defects inherent in the calculation process^b make debatable the conclusion as to waxing or waning political strength.  

Different patterns in trends are illustrated by the graphs above.  In the graph titled Dixie Presidential Trends, relative Democratic performance has declined as Republican performance has increased. In the graph titled Miami-Dade, relative Republican performance has fallen sharply, while relative Democratic performance is approximately constant. 

Other counties may have voted more heavily Republican in recent years.  The analysis of this section only indicates relative performance in a single race.  As described in a footnote,^a one of the virtues of using relative performance as a measure is that it compensates to some degree for the strength or weakness of a candidate in any given year, not to mention shifting national tides. Compared to the general trends, relatively few counties show the pronounced pattern of Democratic decline and Republican ascendance in presidential performance that Dixie does. 

3.2    The "Dixiecrat hypothesis"

3.2.1  -The conservative north

The trend in relative presidential performance across the state is, of course, only part of the story in understanding broader electoral trends. It has been proposed that in certain areas of Florida, voters vote Democratic for local candidates but Republican for presidential candidates, an idea termed “the Dixiecrat hypothesis.” The name comes from the 1948 Southern revolt under the flag of Strom Thurmond’s State’s Rights Party against the civil rights agenda proposed by Harry Truman. This is perhaps a misnomer, since Florida was not a major player in the Dixiecrat revolt, turning in one of the weakest performances for the State’s Rights Party of any southern state at 15% of the popular vote.   

There is, however, no question that north Florida has long been intensely conservative.  According to Dave Leip’s Atlas of presidential elections, as early as 1964 Barry Goldwater carried all of north Florida except Baker, Hamilton, Union, Bradford, Alachua, Gilchrist, Levy, and Dixie counties.

The right-wing hold on north Florida has not been absolute. Jimmy Carter, from neighboring Georgia, was able to carry much of the region in both 1976 and 1980, and Bill Clinton carried a number of the central Panhandle counties in 1992 and 1996.  Some counties in the central Panhandle (Gadsden, Jefferson, Madison) have high levels of African American participation that has helped Democratic efforts. Leon County, while largely white, is a strong regional Democratic contributor. So, north Florida is highly polarized, with most counties leaning Republican, at least at the presidential level.

One can see the shift toward voting Republican of the last 12 years manifest in the normalized presidential performance ratios of, for example, Dixie County shown in Fig. 3.1.a.  But are these trends distinguishable from a general conservative drift, as is implicitly claimed by the Dixiecrat hypothesis, or are they part of it? Proper understanding of this issue is critical to understanding whether unusual values of crossover are normal or not. 

Some statewide Democratic candidates below the presidential level have done well in north Florida. Still, it’s difficult to find evidence that there is a greater tendency of north Florida voters to vote for Democrats down the ballot than for national Democrats.^c  Will Kendrick, representing Dixie, Taylor, Madison, Hamilton and parts of five other counties is perhaps the only legislative Democrat from what might be considered a Dixiecrat area. Even so, it seems likely Kendrick draws much of his support from African Americans, something that Strom Thurmond and the Dixiecrats could never have achieved.  Based on the paucity of Democrats at any level, it would seem that the swing away from the FDR coalition and toward the Republicans that began in 1948 has about run its course and that the northern rural counties might better be described as Republican, rather than “Dixiecrat.” 

3.2.2    Applying the crossover ratio longitudinally

The question comes down to whether the voting patterns of rural north Florida are statistically unusual or whether they simply represent the conservative drift common to much of the South.  The “Dixiecrat hypothesis” can be framed as an issue of measuring crossover between the presidential vote and races down the ballot. 

The crossover ratio provides an excellent comparison of electoral performance across different precincts or counties, but it suffers some limitations in comparing performance longitudinally through time.  Candidates change from year to year, and their relative strength in different areas will depend on many factors.

Even with this limitation, one can calculate slopes for crossover and determine whether there is a trend or a stable pattern to the crossover ratio.  With only three data points, the uncertainty in slope is high and conclusions necessarily weak. However, Santa Rosa, Okaloosa, Collier, Martin, Miami-Dade, Hendry, Indian River, Clay, Osceola, Monroe, and Seminole may show a trend by more than one standard deviation toward higher crossover ratios, while  Sumter, Suwannee, Franklin, Holmes, Broward, Sarasota, Dixie, Gadsden, and Lafayette may show a trend toward lower crossover ratios. The remaining counties have to be regarded as stable. 

Also interesting is the result obtained by correlating the performance of each county with counties that reliably go for the Republican or the Democratic presidential candidate. Selecting Alachua, Broward, Gadsden, Jefferson, Leon, Miami, Palm Beach, St Lucie, and Volusia as Democratic counties, and selecting Escambia, Jackson, Lake, Lee, Okeechobee, and St. Johns as Republican counties, one discovers as expected that some counties correlate with the Democratic counties and against the Republican and some correlate with the Republican and against the Democratic. But some counties (Bay, Flagler, Nassau, Osceola, and Pinellas) correlate somewhat with both parties and one (Hardee) even anti-correlates to both.

3.2.3    The issue of bias by office level in the crossover ratio

There’s an additional point in interpretation. On average, more voters vote for president than vote for senate, independent of political party. As long as this tendency is small and distributed reasonably homogeneously, there’s no significant consequence to calculation of the crossover ratio. In the 2004 Florida election, total presidential votes exceeded senatorial by about 2%, while in 2000, it was about 1% and in 1992, about 3%. This introduces a small intrinsic upward bias into the crossover ratio but, ceterus paribus, this bias is removed by normalization.

These average vote totals, however, mask wide variations by year and location. The dispersity has sharply narrowed, with the standard deviation of total presidential/senatorial votes for the 67 counties falling from 6.4% of the mean in 1992 to 3.0% in 2000 and just 0.7% in 2004. Individual counties also show deviations from the mean. Bradford County recorded only 1% greater presidential than senatorial votes in 2004 but 4% fewer presidential than senatorial votes in 2000 (in 1992, the average was also below the mean, but only slightly).

An assumption implicit in this is that the tendency toward voting preferentially by office is distributed evenly in a geographic sense. This assumption could fail if, for example, rural voters tended were energized mostly by races other than the presidential. The narrow dispersity of 2004, however, suggests that this assumption held in that year. And, indeed, the Z-scores of only a few counties, averaged over the three elections, exceed 1 in magnitude (Bradford low; Broward, Martin, Miami-Dade, Sarasota high). So, the assumption that there is no intrinsic tendency of any county to turn out unusually high or unusually low values of the presidential/senatorial ratio is probably generally true.  

The direction in which the assumption might fail can be determined by looking at those counties recording more votes for president than for senate, on average,^c by at least one standard deviation. These were Bay, Broward, Calhoun, Franklin, Lee, Martin, Palm Beach, Pasco, Pinellas, Sarasota, and Sumter.  A number of counties recorded fewer votes for president than for senate by more than one standard deviation. These were Baker, Bradford, Dixie, Gilchrist, Hernando, Jefferson, Madison, and Santa Rosa. In many cases, these excursions from the mean are the result of a single year of unusual enthusiasm for candidates at either the presidential or senatorial level and probably have little importance. However, most of the counties in the group which showed a high president/senate ratio are urban and outside the north, while most of the counties in the group with a low president/senate ratio are rural and in the north.

What this means is that if there is any bias in the crossover ratio due to a preferential interest in certain races over others, it is toward northern, rural counties tending to have slightly lower crossover ratios than other counties. This tendency - if it exists - would be due to failing to vote for either candidate at the presidential level.  At any rate, the effect of bias by office level on geographic bias in the crossover ratio would appear to be small. 

3.2.4    -Counties exhibiting possibly unusual crossover

The very first test to establish whether a group of counties is in some way different than others is a test of the normality of the distribution. In 1992 and 2000, deviations of the crossover ratio from normality were not statistically significant, although in 1992, it was nearly so.  In 2004, the distribution was slightly skew, with the Shapiro-Wilk probability marginally significant at 0.03.^d A more detailed look shows a single low-end near outlier in 2004. If Lafayette and Holmes (which is nearly a near outlier) counties are eliminated from the calculation, the distribution becomes entirely normal.  In 1992, all but one of the outliers turn out to be at high crossover ratios in urban counties.  So, by the basic screening test used to decide whether there is anything unusual about a set of data, support for the hypothesis that the crossover ratios of some group of counties are unusual is not at all clear. 

Table 3.2.4.1, showing counties with unusual values of normalized crossover argues against the notion that the voters of north Florida are markedly different in their presidential preferences relative to their senatorial preferences.  The counties marked yellow are one standard deviation below the mean in crossover, while the counties marked red are two standard deviations below. The counties marked green are one standard deviation above the mean, while those marked blue are two standard deviations above. 

It is true that a number of rural north Florida counties show presidential/senatorial crossover ratios below the mean and that no urban, southern counties show crossover ratios below the mean.  However, in order to declare a value “different,” the usual requirement is that it be three standard deviations from the mean.  Only Holmes, Lafayette, and Santa Rosa counties are more than two standard deviations from the mean in any given year.  Of those, only Lafayette appears at greater than two standard deviations in more than one year.  Of counties that are more than one standard deviation from the mean, only Baker, Liberty, and Union are present in all three.  Baker, Liberty, Holmes, Lafayette, and Union show sufficiently unusual behavior to suggest they might be truly different from the rest of Florida’s counties - but only possibly so.  

Of course, there are confounding factors, especially spoilage and local preferences for a particular candidate. But until these are sorted out, one cannot say with confidence that the “Dixiecrat counties,” however they are defined, are obviously different from other conservative counties in their propensity to cross over at the senatorial level.    

3.2.5   -Longitudinal studies of crossover ^e

In principle, however, there may be a means of adapting the crossover ratio to longitudinal studies.  Using a technique called correlation, it is possible to characterize other counties as having voting patterns that are directly correlated or anticorrelated, weakly correlated, or uncorrelated to those of a reference county.  In other words, the voting patterns of the other counties could be classified as resembling the reference county, as diametrically opposed to those of the reference county, or as unrelated to the reference county. 

If the voting patterns of the reference county were very unstable, perhaps as a result of drastically changing demographics, the results might not be very meaningful. But if the voting patterns of the reference were reasonably stable across time, very interesting results could be obtained. For example, suppose the only factor in voter decisions was a candidate's political liberality or conservativism. Further suppose that the reference county were 60% conservative and 40% liberal.  Then, if the presidential candidate were liberal and the senatorial candidate conservative, the crossover ratio would be 40/60 or 0.67, while if the presidential candidate were conservative and the senatorial candidate liberal, the crossover ratio would be 60/40, or 1.5. A county that was 60% liberal and 40% conservative would show precisely opposite crossover ratios. If their choices were driven by conservative/liberal preferences, and those were stable over time, the two counties would anti-correlate, that is, move in opposite directions. 

Selecting arbitrarily nine counties in which John Kerry received over 50% (Alachua, Broward, Gadsden, Jefferson, Leon, Miami-Dade, Palm Beach, St Lucie, and Volusia) to serve as a Democratic reference, and six counties where George Bush did well in 2004 (Escambia, Jackson, Lake, Lee, Okeechobee, and St. Johns) to serve as a Republican reference, the unnormalized crossover ratio correlates to both Democratic and GOP counties, with the minimum observed value being 0.89. Since the major contributor to significance is presumably the difference between 1992, when a third party candidate strongly affected the presidential vote, this means (unsurprisingly) that the candidate roster is a key factor in the crossover ratio.

With normalized data, in which the importance of third party candidates is minimized, some clear patterns emerge, and there are a number of surprises. Figure 3.2.5.1, below, shows that a number of counties correlate to the Democratic and/or to the Republican counties in crossover. But a small number of counties anticorrelate to either or both. This suggests, unsurprisingly, that the individual candidates, not party, is the most important factor in crossover.

In the table below, Table 3.2.5.1, the counties shown are those ten at extremes above or below the mean.^f  Blue indicates counties that voted more than 50% for Kerry, green indicates counties that were 45-50% for Kerry, yellow indicates 35-45% for Kerry, and red indicates less than 35% for Kerry.  Correlation with Democratic counties means that the correlation coefficient of the normalized crossover coefficient was near 1 for a given county vs. Democratic counties, while correlation against Republican counties means that the correlation coefficient was nearer -1.  

In simple terms, a correlation near 1 means that a county agreed with Democratic counties as to whether the senatorial or the presidential candidate were the stronger, while a correlation near -1 means that a county agreed with Republican counties as to which was the stronger. Table 3.2.5.1 suggests that there are at least two kinds of rural, conservative, northern counties. Bay, Bradford, Gulf, Union and Washington - even though they vote Republican - see the strength of the senatorial candidate relative to the presidential the same way that Democratic counties do. Dixie, Hardee, Holmes, Lafayette, and Suwannee see the relative strengths least like Republican counties - but not as Democratic counties do. Sarasota, a more urban and Democratic county, and Hardee, a small conservative county have the distinction of being aligned against both parties.