Patterns of Gun Ownership in the United States

The events in Newtown, Connecticut, give us all pause.  It has led me to examining data on gun ownership beginning with the 2010 General Social Survey conducted every other year by the National Opinion Research Center at the University of Chicago.  In all some 3,207 respondents were asked in 2010 whether they owned a gun, and if so, whether they owned a rifle, shotgun or a “pistol”. I will use the more-common term “handgun” for “pistol.”

Let us begin with some basic information on patterns of gun ownership.  Many surveys like Gallup report figures for “gun” ownership without differentiating among the various kinds of guns people possess.  We might imagine a group of “sportsmen” who own rifles and shotguns but not handguns.  There are also people who own only a handgun,  people who own both handguns and long guns, and people who own no guns at all.

Two-thirds of the households report owning no guns at all.   Only six percent of Americans, or 18% of gun owners, possess just a handgun. Eleven percent of households own only a long gun, with preferences about equal for either a rifle or a shotgun or both.  A majority of handgun owners also have both a rifle and a shotgun in their possession.   Here is a reorganized version of the data above that divides American households into three categories — people who own only long guns, people who own only handguns, and people who own both.

About a fifth of American households own a handgun while a quarter own a rifle or shotgun.  Only three out of ten handgun owners do not also own a long gun.  The reverse combination is more common; 44% of long gun owners do not own a handgun.

Health Insurance Coverage in the American States – I

With this post I begin a new series of reports based on my analysis of state-level data on health insurance coverage.  I began this research in the summer after reading this article on a blog over at the The Atlantic but put it aside during the furore of the election campaign. In that article Richard Florida examines a map of data on insurance coverage from Gallup and finds an “uninsured belt” running through “much of the deep south and the Sunbelt.” In contrast, states in the Northeast and the industrial Midwest have larger proportions of their populations who are covered by health insurance.

Mr. Florida then goes on to report positive correlations between the proportion of a state’s population without health insurance and political attitudes like the proportion of a state’s residents that identify themselves as conservatives in polling or that voted for McCain in the 2008 election. Patterns like these make it tempting to infer that health insurance coverage reflects a pattern of Republican “stinginess” and Democratic “generosity.” The truth, as it turns out, is much more complicated.

What is left out of these “big picture” analyses of ties between Republican opinion and healthcare coverage is any explanation of the mechanisms by which popular opinions are converted into actual policy outcomes like rates of insurance coverage. The most obvious hypothesis is that states with strong Republican leanings elect Republican legislatures and governors that implement policies leading to lower rates of coverage in the states they govern. While that might be the most obvious mechanism, it is simply untrue. There is no evidence that states where the Republicans controlled the levers of government have systematically lower levels of insurance coverage.

For this research I relied on the extensive set of state-level figures on insurance coverage based on Census data as maintained by the Kaiser Family Foundation.  I will start with the coverage rates based on the state’s total population.  Later I will narrow the focus to one specific group, adults 19-64 without dependents, who constitute the most difficult group to insure.

To test this hypothesis, I categorized states based on the number of years between 1990 and 2010 one party or the other controlled both houses of the state legislature and the governor’s office.  A state was scored +1 for each year both houses and the governorship were held by the Republicans; years where the Democrats were in control were scored as -1.  Any years of split control were coded zero.  I then averaged these figures over the two decades to construct an index of partisan control and computed the average percent of the population without insurance for five categories.  Here are the results:

As we might expect, states with consistent Republican control also had higher rates of support for John McCain in 2008.  However there is simply no relationship between partisan control of state governments and the percent of the population without health insurance.  What does influence health insurance coverage rates will be the subject of the postings to follow.


The New Delusion: Sandy Elected Obama

Over the weekend Republican-leaning commentators at Nate Silver’s 538 Blog have been advancing the theory that President Obama’s victory can be attributed to “Superstorm” Sandy which hit the Northeast on October 29th, just a week before Election Day.  What especially irked Republicans was the reaction of New Jersey Governor Chris Christie who embraced the President in the wake of the storm despite having delivered the keynote speech at the Republican National Convention and being an active spokesman for Governor Romney.

The view that Christie “threw Romney under the bus” and thus assured the President’s re-election was the polar opposite of Republican commentary right after the storm hit.  Spokesmen like Rudy Giuliani criticized the President for campaigning in Western states like Nevada when New Jersey and New York had been ravaged. Liberal commentators like John Cassidy at The New Yorker naturally took the opposite view, suggesting that the President’s “handling of Sandy has raised his standing, and his poll ratings.”

I find neither of these arguments convincing.  First, many of the claims about a late Sandy-based surge in the polls rely on differences in the President’s level of support that fall well within the margin of error.  Cassidy, for instance, points to a three-point swing in Pew’s results as evidence, a change that could just as easily be attributed to sampling variability.  Including a term in my model for polls conducted after Sandy showed a  pro-Obama effect of one percent, but it, too, did not achieve statistical significance.

Rather than looking at the pre-election polls, I suggest we examine the exit polls taken on Election Day.  Looking at the national exit polls, we might conclude that Sandy had a large effect.  Fully 15% of voters reported that Sandy was “the most important factor” in determining how they voted, and 73% of those people reported voting for Obama.  To anyone accustomed to analyzing surveys, this 15% figure seems implausibly high. For 15% of the national electorate to attribute their vote to Sandy, when the vast majority of them lived outside the affected area, is hard to comprehend. Moreover, despite the obvious correlation between attitudes about the storm and support for the President, the direction of causality is questionable.  An equally plausible explanation for this correlation is that both answers reflect the partisanship of the person being interviewed.

We might thus wonder what effect the storm might have had on voters in swing states like Ohio, or even Florida with its history of hurricanes.  Unfortunately, if we drill down to the individual state exit polls, we find that this question was asked only among voters in New York and New Jersey, states whose outcomes were never in doubt.  Even voters in neighboring states like Pennsylvania and Connecticut were not asked about Sandy. (No exit polling was conducted in Louisiana this year so we cannot assess the claim that voters in that state might have been more attuned to the issue because of their experience with Katrina.  Mississippi voters were polled but not asked the Sandy question.)

In order for the hurricane to have affected the outcome of the election, we would need to see its effects in the swing states where the election was actually decided.  Since we do not have direct measures of peoples’ opinions about the effects of the storm in these states, and because such answers are inherently untrustworthy, we have to look at a more indirect measure of Sandy’s effects.  We can compare the opinions of voters in the swing states who reported deciding between the candidates in the last days before the election and see whether we can detect a late movement toward the President in the aftermath of the hurricane.  Here are the results from the exit polls in the nine swing states I examined before.


The chart shows quite clearly that, if anything, late deciders broke more in favor of Governor Romney than President Obama.  Of voters who made up their minds in October or earlier, the President’s average lead in these nine swing states was about five points.  Among voters who decided during “the last few days” before the election or on Election Day itself, his margin shrank to just over a percentage point.

More importantly, we can determine how large a contribution each group of voters made to the overall margin between the candidates.  Take Wisconsin as an example.  Late deciders made up 10% of the electorate in that state, and those voters favored Romney by eight points.  Taken together we can attribute an effect on the overall margin of 10% times 8%, or 0.8% in favor of Mr. Romney.  The other 90% of Wisconsin’s voters favored the President by 10%, for an overall effect on the margin of 9.0% in favor of the President.

The average effect of the decisions of late deciders in these nine states is zero compared to a four percent margin in the President’s direction among voters who decided in October or before.  Thus there is absolutely no measurable effect of Superstorm Sandy on the results in these swing states.  If anything voters who decided after the storm hit showed a slight preference for Mr. Romney in comparison to voters who decided before.