Monday, March 22, 2010
Same-sex marriage referenda from within... You only need three
Any modern student of political science has read Andrew Gelman's et al. magnum opus Red State, Blue State, Rich State, Poor State. In the book, Gelman et al. illustrate one of the more interesting facets of American political life: rich states vote Democratic, while poor states vote Republican. It is a bit counter-intuitive (as Democrats are supposedly the party of the workingman), but it turns out that it all makes sense in the end. WITHIN each state, rich voters are more Republican, while poor voters are more Democratic. You might be wondering what this has to do with Gay marriage bans. The answer is pretty simple: what works to explain electoral phenomenon between states does not necessarily work to explain what happens within them.
For those that follow this blog, predicting (and explaining) support for same-sex marriage referenda has been a favorite topic. Before I discovered that polling data worked so well to predict same-sex marriage referenda, I tried a buffet of other explanatory variables. With the ever-expanding research in this subsection of LGBTQ studies, I mostly concentrated on the findings of Patrick Egan and Ken Sherrill in their 2008 paper "California Proposition 8: What Happened and What Does the Future Hold". The paper was published after Californians voted in favor of Proposition 8 to dispel the myth that African-Americans were responsible for the passage of Proposition 8. Egan and Sherril found that race had very little predictive or explanatory power in understanding a person's vote on Proposition 8. Instead, people's votes were much more likely to breakdown along party identification and level of religiosity (tendency to attend church). One variable they did not (and could not) check (because their dataset did not have a question on it) was level of education. Yet, in the less discussed, but equally as close Florida Amendment 2 to ban same-sex marriage (and civil unions), education was found to be the main factor in people's vote. In fact, Daniel Smith found that in the Florida vote, "education level was five times more important than race in determining how people voted. The more educated people were, the more likely they were to oppose the amendment". Thus, we had three variables that predicted how people voted in California and Florida: education, party identification, and religiosity.
Wanting to make a big splash (as I always want to do), I decided to plug these variables into a large model containing the votes of same-sex marriage ban referenda nationwide. Since 1998, there have been 33 such referenda. For each referendum, I have collected the percentage of people who consider religion to be an important part of their lives in each state, the percentage of people in each state who had a bachelor’s degree, and the partisan lean in each state measured by the Cook Partisan Voting Index (many states do not have voter registration by party identification).
In a linear regression model of these variables predicting support in the aforementioned 33 elections, we can explain about 65% of the differences between states' support. That is pretty decent, but it is not anywhere near the 95% of our polling model. More than that, only religiosity and the percentage of people with bachelor’s degrees really add anything to the model. That is, the partisan nature of each state does little to predict the changes in support for the referenda from state to state.
But this comparison is a bit unfair. Nate Silver's research tells us that earlier same-sex marriage elections as well as those that just ban same-sex marriages (not both same-sex marriages and civil unions) are more likely to garner higher percentages of the vote. What happens when we control for the year and whether an election is a ban on just same-sex marriage or a ban on same-sex marriage and civil unions?
Now, we see that the partisan nature of a state, religiosity, and education level of a state do have statistically significant prediction (explanatory) power with 90% confidence (the effect is not due to chance) of support for same-sex marriage referenda. As we would expect, Republican states, religious states, and states with less educated people tended to vote for same-sex marriage bans in higher percentage. In addition, this model explains 83% of the difference in vote between different elections. However, it should be noted that it is really religion, year, and the nature (just a marriage or marriage and union ban) of the election that have the greatest impact on understanding the vote in each state. To me, this model does not do much to add to our understanding or predictive value. Our polling model does a much better job.
Still, I remembered what Gelman et al. with concern to income and state. I asked myself if the differences between any two states' votes cannot be explain by a model because the dynamics between the elections played out differently. That is, a highly publicized vote in California is going to be different by nature than say one in South Dakota. At the same time, religion might be played up more in some elections than in others. It would be very difficult for a simple regression model playing off of mostly demographic data to be able to pick up on these differences. Fortunately, within any one election, the dynamics would be the same. Most people would read the same newspaper endorsements, watch the same television advertisements, and be delivered the same mailers.
To test my theory, I have decided to check the county-by-county differences in vote in three recent same-sex marriage elections. The massive volume of counties that have voted in same-sex marriage referenda elections is 1,000+, so that it is not within the scope of this study to look at all of them. Instead, I downloaded data from the Arizona 2006, California 2008, and Maine 2009's same-sex marriage elections. For each election, I collected the percentage of people with bachelor degrees in each county, percentage of people who are religious adherents, and percentage of the vote each county gave to Obama in 2008 (a measure of party identification). Within each state (5 of 15 in Arizona, 18 of 58 in California, and 1 of 16 in Maine), some counties do not have a percentage of bachelor degree measurement because of a lack of population. Still, each state has more than 2/3’s of the counties covered, and there is no reason (I can think of) that not having these counties would greatly affect our models. With these counties and the variables I had, I made three linear regressions for each state: percentage of county supporting a ban on same-sex marriage = percentage of people with a bachelor degree within each county + percentage of vote Obama earned in each county + percentage of people who are religious adherents in each county.
It turns out that this model does a very good job at explaining the differences in vote within each state. In each state, over 91% of the difference in vote between the counties is explained by these three variables alone. In all three states, all three variables were statistically significant with at least 90% confidence. Counties with more religious adherents, less Obama supporters, and less educated people vote were more likely to vote for the same-sex marriage bans. Perhaps it is intuitive, but due to the nature of regression we also know such things as more religious Democratic counties are more likely to vote for bans than less religious Democratic counties.
In the 2006 Arizona election, 94.7% of the vote differences between counties can be understood with these three variables. Obama vote and bachelor degree are statistically significant with 99% confidence, while religion is significant with 90% confidence.
In the 2008 California election, I confirm the finding of Egan and Sherrill and find that religion and partisanship are statistically significant in their prediction of voting yes on Proposition 8 with well over 99% confidence. Education, a variable that they did not check, is also significant with well over 99% confidence. Overall, 94.0% of the vote difference between counties can be explained by these three variables.
In the 2009 Maine election, 91.5% of the differences between the 15 counties I checked are explained by these three variables. Both religious adherence and Obama vote are statistically significant with near 99% confidence, while the percentage of people with bachelor degree is significant with greater than 99% confidence.
All of these results make a lot of sense. Democrats are likely to see same-sex marriage as a civil rights issue, and therefore support it. Education affects views on same-sex marriage because, as Daniel Smith put it, "Education is so important because it increases exposure to those who are different. Studies show very clearly that the more educated people are, the more tolerant they are of differences". Religious people are more likely to oppose same-sex marriage on "moral grounds".
Going forward, it will be interesting to see if these three states are merely aberrations or more indicative of a larger trend. One thing I do caution is extrapolating these results to the individual (person) level. The research indicates that education, political affiliation, and religiosity do impact people's stances on the issue of same sex marriage, but it may not hold in all these states (with the exception of California where Egan and Sherrill have found it to); however, I would guess that they do.
1. Religiosity was measured in two ways. For the state-by-state model, religiosity is measured by the percentage of adults in a state who considered religion an important part of their daily lives in a 2008 Gallup study. I think this a very good measure, but it is not available by county. For the county data, I had to settle for the percentage of religion adherents per county. The estimate comes from the Association of Statisticians of American Religious Bodies (ASARB) The data has been “adjusted” by Roger Finke and Christopher P. Scheitle of Pennsylvania State University to account “for the historically African-American denominations and other religious groups not listed in the ASARB totals”.
2. Education is measured by the percentage of people with at least a bachelor's degree in a given county or state. Since this data only goes back to 2001 (and it would take a lot of time to gather the data by year of election for the state level... something I was not too interested in obtaining for this study), I used data from the 2008 American Community Survey. For the county data, I used the American Community Survey data from 2006 (the year of the election) for Arizona, 2008 (the year of the election) data for California, and an average of 2006-2008 (in order to obtain data from more counties) for Maine.
3. I prefer the Cook Partisan Voting Index as a measure of partisanship (as it combines data from the 2004 and 2008 Presidential Elections), but it is not published county-by-county. Therefore, I just use Obama vote share by county.4. In case you are wondering, the addition of a race variable has little predictive power. In Arizona, it adds nothing to the model. In California, it explains an additional one percent of the vote. Counties that had less white people were more likely to vote for the ban. In Maine (not exactly a diverse state racially), counties with less white people were actually LESS likely to vote for the ban. Overall, race just did not seem to have that much of an effect.
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