Millennials Still Eschew Guns

The 2014 General Social Survey is now publicly available, so I have updated the charts that appear in my earlier postings on gun ownership by age and “generation.”

Younger millennials still show the lowest rates of gun ownership of any group in the survey.  Older millennials look more like their GenX age peers.  As I discussed in my earlier pieces on this subject, this could be a “life-cycle” effect where people buy guns as they age, though older generations did not show such trends.

It is still the case that millennials under thirty who were interviewed in 2012 and 2014 show higher ownership rates than those interviewed in 2008 or 2010.


However the growth in ownership we saw among the youngest groups between 2008 and 2012 seems to have reached a plateau in 2014.

The Rhythm of Senate Elections

From reading media reports of the 2014 election results you might believe the nation has experienced a political change of cataclysmic proportions. Republicans won 23 of the 36 states where a senatorial election was held, enough to give them control of the Senate for the next two years. Yet we need look back only to another strong Republican year, 2010, to see nearly identical results. In that year the Republicans took 24 of the 37 states where elections were held.

Historically the parties’ shares of Senate elections have swung back and forth quite substantially with the last decade appearing unusually unstable.  Here are the results for Senate elections back to 1936:



The Democrats reached their peak in the 1964 Johnson landslide, though this election merely confirmed the Democrats dominance of the Senate “class” elected six years during the 1958 Eisenhower recession.  Republicans have won about two-thirds of the seats in half-a-dozen elections over the same period of time.  The 2014 result is quite similar to the Republican margins in 2010, 2002, 1980, 1952, and 1946.

If you look carefully at this graph, you’ll see a certain rhythm in these results, one created by the six-year length of a Senatorial term and the power of incumbency.  In fact, if we slide the graph forward six years and superimpose the results, we get this:


Now we see how the partisan split in a Senate “class” helps explain the variation from election to election.  Because incumbents have an advantage when it comes time for re-election, the partisan composition of a Senate class tends to repeat at six-year intervals.  The Republican edge in 1946 was replicated six years later when Eisenhower won the White House. Six years after that a major political shift occurs.  The largely Republican class of 1946 and 1952 was replaced with a largely Democratic class during the Democrats sweep of the 1958 off-year elections. The Republicans’ victories in 1980 constituted a similar shift for their party.

Of course the partisanship of the class facing re-election just sets the stage on which each year’s set of electoral forces plays out.  We would expect that factors like broader trends in partisanship and the state of the economy might also influence the outcome of Senate elections.  I turn to those influences in the next article.



Millennials and Guns Updated

Just about a year ago I wrote here about the decline in gun ownership among those Americans who have come of age since the Kennedy Administration. Starting with the “Boomers,” rates of gun ownership by household have fallen by about ten percent each generation. Among those born since the turn of the 21st Century, the “Millennials,” only 19% reported living in a household with a gun present.

Those results were based on the responses to the General Society Survey, which has been conducted periodically by the National Opinion Research Center since 1972. My earlier results were based on the combined surveys from 1972 through 2010. Now that the 2012 installment is available, I have reproduced the results of my earlier analysis with those respondents included. The entries report the percentage of households with a gun present for each combination of generational cohort and age when interviewed.


The new data gives us the ability to estimate the ownership rate for two groups not present in the 2010 sample, but the size of each sample is fairly small. Boomers now reaching retirement age show a higher rate of gun ownership than younger people in the same cohort, but that 50% for the oldest Boomers is based on just 34 people and is not statistically reliable. A chi-squared test of ownership against age group among Boomers does not reach significance.

More disturbing perhaps is the reported 28% rate of ownership among the 53 Millennials just now reaching their thirties. That figure is eight points higher than the rate for younger Millennials, but again the difference between the age groups does not meet the usual criteria for statistical significance (p < 0.20). Still if we see continue to see increasing ownership rates among Millennials as they age, my earlier rejection of a role for life-cycle factors in the decision to own a gun may have been premature.

Since all the over-29 Millennials were interviewed in 2012, their higher rate of ownership could be the result of historical events that took place between 2010 and 2012. Evidence from sources like Gallup suggest that household ownership rates spiked in 2011 after years of stagnation. FBI records of the number of background checks performed show a similar spike in purchases that year. Breaking out the rates of ownership by year of interview in the GSS shows some corroboration for those trends.
age_trends_2008-2012The reported rate of ownership in 2012 for the country as whole hovered in the mid-thirties during the Obama years. Ownership estimates for the oldest cohorts declined sharply in 2010 then rebounded in 2012. For the “New Deal” cohort, aged 83 or older in 2010, these fluctuations can be attributed to small sample sizes. That is a less plausible explanation for the “Silents” (aged 65-82 in 2010); their samples each year ranged from 196 in 2008 to 144 in 2012. I have no explanation for the variation among these older Americans.

More striking is the consistent increase in household gun ownership rates among the Millennial generation, rising from 17% in 2008 to 25% four years later. With a sample of 243 people in 2008 and 310 people in 2012, this eight percent increase in gun exposure is “statistically significant” at the 0.02 level. Future posts will look more deeply into the reasons for this rise in gun exposure.

The Virginia Gubernatorial Election

On Tuesday, the Democrats won the governorship in the Commonwealth of Virginia. Though Terry McAuliffe held a substantial lead in the polls over Republican Attorney General Ken Cuccinelli throughout most of the campaign, McAuliffe’s actual margin of victory was just 2.5%, winning 48.0% to 45.5%. The narrowing of the lead during the final days of the campaign has led to much speculation over the “meaning” of this election.

This was a race between two unpopular major party candidates to succeed a Republican governor, Bob McDonnell, whose own tenure has been rocked by influence-peddling scandals. (Both McDonnell and his wife have been indicted by Federal prosecutors since I wrote this originally.) McAuliffe, a former Chair of the Democratic Party, raised prodigious amounts of money during the Clinton Administration and was accused of shady business practices. Even left-leaning commentators like Stephanie Mencimer of Mother Jones had little good to say about the Democratic candidate for governor of Virginia:

McAuliffe represents an unseemly slice of Washington. His primary role in politics for the past two decades or more has been raising money—most notably, for the Clintons. He cooked up the idea of essentially renting out the Lincoln bedroom during the Clinton administration as a fundraising vehicle, and he smashed all previous presidential fundraising records in the process. When McAuliffe was the Dems’ top fundraiser, a campaign finance scandal besieged the Clinton White House. Coincidence? No. McAuliffe was all about pushing the envelope when it came to the political money chase.

That alone might not be enough to render him a distasteful political candidate. What’s different about McAuliffe is his brazen mixing of his campaign fundraising activity and attempts to enrich himself personally. Many of McAuliffe’s business deals have come about due to his place in the political cosmos, not because he possesses a wealth of business skill. That tangled history has linked him to a long list of unsavory characters.

McAuliffe’s Republican opponent brought his own substantial baggage to the campaign. Long a crusader for right-wing causes, Cuccinelli built a reputation by aggressively opposing abortion rights, same-sex marriage, immigration reform, and gun control. In its post-mortem on the election the Washington Post put the blame for Cuccinelli’s loss directly at the feet of the candidate himself:

Fundamentally, what caused Tuesday’s Republican wipeout was Mr. Cuccinelli himself and the record he compiled — a clear, consistent right-wing agenda forged over a decade in Richmond.

The Cuccinelli record had nothing to do with job-creation or the state’s economic well-being or alleviating deepening transportation problems, all of which are central to Virginians’ well-being. It was mainly about bashing homosexuals, harassing illegal immigrants, crusading against abortion, denying climate change, flirting with birthers and opposing gun control. A hero to the tea party and a culture warrior of the first rank, Mr. Cuccinelli lost because he was among the most polarizing and provocative figures in Richmond for a decade. That made him the wrong candidate for Virginia.

Recent polls show the levels of voter dissatisfaction with the choice between McAuliffe and Cuccinelli. In the final pre-election poll by Public Policy Polling, both major candidates were viewed unfavorably by 52% of likely voters. However Cuccinelli was by far the loser among voters who viewed both candidates unfavorably; in that 15% of the Virginia electorate, McAuliffe outpolled Cuccinelli by 61% to 16%.

What factors influenced this result?

Pundits have proposed a variety of explanations for McAuliffe’s slim victory. Some pointed to the sustained turnout of African-American voters, who constituted 20% of the voters on November 5th, a figure equal to their share of the electorate in the 2012 Presidential election. For an election being held in an “off-off” year, that is a substantial tribute to the mobilization of black voters by the Democrats in Virginia, and particularly the efforts of the Obama campaigns, that began in 2008.

Another common argument concerned the government shutdown that took place in the weeks leading up to the November election. Virginia has a large number of government workers, not only in the suburbs around Washington, but in places like Norfolk where defense industries constitute an important part of the local economy. Observers suggested that disgruntled government workers turned out for McAuliffe to express their unhappiness with the government shutdown which they, and most other Americans, largely blamed on the Republicans, particularly the Party’s Tea Party wing. The dissatisfaction with the Tea Party spilled over onto Cuccinelli with his own strong ties to that faction.

Despite these factors that advantaged McAuliffe, the margin between the two major party candidates narrowed as the election drew near. The Democrat held a single-digit lead during most of September and early October, but by the middle of that month his margin ballooned into a lead of 10-17%. The highest figure recorded for McAuliffe rather surprisingly came from Rasmussen Reports, the Republican polling firm which consistently over-estimated support for Mitt Romney during the 2012 campaign.
During the last weeks of the campaign Cuccinelli took a cue from Tea Party politicians like Ted Cruz and began to stress his opposition to the Affordable Care Act, better known as “Obamacare.” Cuccinelli had led the charge against the law in 2010 when he filed suit to declare it unconstitutional, an action that was quickly followed by similar suits from Republican attorneys general across the country. Conservative commentators have since argued that Cuccinelli waited too late to bring the ACA to the forefront of his campaign, and had he done so sooner, he would have defeated McAuliffe.

The Sarvis Vote

Another complication for understanding this election was the candidacy of Libertarian Robert Sarvis. Sarvis had once run as a Republican but left the party because he thought it was too socially conservative. He holds a rather mixed bag of issue positions, some considered left-wing like his support for the legalization of marijuana, and some from the right like his opposition to income taxes and gun control. Conservative commentators have suggested that Sarvis was instrumental to McAuliffe’s victory by siphoning votes away from Cuccinelli. Some pointed to campaign contributions to a Libertarian PAC from a highly-placed Obama fundraiser in Texas as evidence for Sarvis’s role as a stalking horse for the Democrats.

Whether Sarvis was closer ideologically to Cuccinelli or McAuliffe probably did not matter much in voters’ decision-making calculus. One constant in the Virginia polling data was how little the public knew about Sarvis or his positions on the issues. In a late-October pre-election poll by the Washington Post, fully 43% of likely voters reported knowing “nothing at all” about Sarvis, and another 35% chose the “just a little” option. Only 3% reported knowing “a lot” about Sarvis, with another 18% saying they knew “a fair amount.” That 21% of voters who claimed to know something about Sarvis stands in stark contrast to the two major-party candidates, where the comparable figures were 81% for McAuliffe and 86% for Cuccinelli. While some of the nearly 7% of Virginians who cast ballots for Sarvis on November 5th probably supported him on the issues, it seems likely that most of his voters were largely ignorant of his positions.

Evidence from the Election Results

I have subjected some of these arguments to empirical testing by examining the vote by jurisdiction as reported by the Virginia State Board of Elections for the 133 counties and cities in Virginia. The model below estimates the vote for Cuccinelli as a function of the vote for Republican Bob McDonnell in the 2009 gubernatorial election and a few demographic factors like the proportion of African-Americans and Hispanics in each jurisdiction and the fraction of the population in each locale that were employed by the Federal government. Because the government shutdown affected civilian and defense workers differently, I have estimated separate effects for both groups. I also include the vote for Sarvis to see whether his candidacy influenced the margin between the two major-party candidates.

I start with a model that includes only the 2009 vote and demographics.

Data: 133 Virginia Counties and Cities
Dependent variable: Vote for Cuccinelli

               coefficient   std. error   t-ratio   p-value 
  Constant     −0.996052     0.0633928    −15.71    9.50e-32 ***
  McDonnell-09  0.827063     0.0334505     24.73    8.75e-51 ***
  Afr-Amer     −0.0889980    0.0128968     −6.901   2.09e-10 ***
  Hispanic     −0.139432     0.0192821     −7.231   3.77e-11 ***

Mean dependent var   0.040395   S.D. dependent var   0.583154
Sum squared resid    4.267090   S.E. of regression   0.181874
R-squared            0.904941   Adjusted R-squared   0.902731

All variables are measured as "logits."

Unsurprisingly the 2009 gubernatorial vote has by far the greatest predictive power, but both the demographic factors show significant effects as well. Of particular note is that the size of the Hispanic population in a jurisdiction has a slightly larger effect than the size of the African-American population. A statistical test for the equality of the two effects shows that the Hispanic coefficient is significantly larger than the one for African-Americans.

If we now add in the measure of Federal employment in each jurisdiction we find it adds no additional explanatory power to our simple demographic model.

                coefficient   std. error   t-ratio   p-value 
  Constant      −1.04497      0.0760838    −13.73    6.07e-27 ***
  McDonnell-09   0.816998     0.0345162     23.67    1.37e-48 ***
  Afr-Amer      −0.0918772    0.0131170     −7.004   1.26e-10 ***
  Hispanic      −0.129533     0.0210656     −6.149   9.21e-09 ***
  Fed_Emp       −0.0199454    0.0172078     −1.159   0.2486  

Mean dependent var   0.040395   S.D. dependent var   0.583154
Sum squared resid    4.222768   S.E. of regression   0.181633
R-squared            0.905929   Adjusted R-squared   0.902989

While the coefficient for Federal employment has the expected negative sign, it is only slightly larger than its standard error and falls well short of the conventional p<0.05 measure of statistical significance.

This is a rather surprising result given the journalistic coverage of the effects of the government shutdown on the race. It turns out that jurisdictions with larger proportions of Federal employees are also ones with larger Hispanic populations (r=0.42). If we exclude the Hispanic measure from the model, the coefficient for Federal employment then becomes significant.

                coefficient   std. error   t-ratio   p-value 
  Constant      −0.829620     0.0765776    −10.83    7.57e-20 ***
  McDonnell-09   0.824725     0.0391062     21.09    1.24e-43 ***
  Afr-Amer      −0.112898     0.0143573     −7.863   1.30e-12 ***
  Fed Emp       −0.0628453    0.0178336     −3.524   0.0006   ***

Mean dependent var   0.040395   S.D. dependent var   0.583154
Sum squared resid    5.470150   S.E. of regression   0.205923
R-squared            0.878141   Adjusted R-squared   0.875307

However this model does a poorer job of explaining variations in the Cuccinelli vote than the model that includes the Hispanic measure. Additional tests separating out civilian and defense employment show no improvement in explanatory power. Observers who saw a correlation between Federal employment levels and the gubernatorial vote were likely reacting to the more significant correlation with the size of the Hispanic population.

Finally I include the vote for Robert Sarvis, which definitely has a significant and negative effect on the vote for Cuccinelli.

               coefficient   std. error   t-ratio   p-value 
  Constant     −1.21892      0.123557     −9.865    2.03e-17 ***
  McDonnell-09  0.831222     0.0330809    25.13     2.51e-51 ***
  Afr-Amer     −0.0882480    0.0127363    −6.929    1.85e-10 ***
  Hispanic     −0.138816     0.0190369    −7.292    2.82e-11 ***
  Sarvis       −0.0833661    0.0398514    −2.092    0.0384   **

Mean dependent var   0.040395   S.D. dependent var   0.583154
Sum squared resid    4.126027   S.E. of regression   0.179540
R-squared            0.908084   Adjusted R-squared   0.905212

As I mentioned earlier, most voters had little idea who Sarvis was or what he stood for. Rather than representing support for either the candidate or his policy positions, the Sarvis vote more likely represents dissatisfaction with one or both of the major party candidates.

As it turns out, I found a similar pattern in voting for the British Liberal Party during the 1960s and early 1970s. In that article, and in my dissertation research, I showed that voters who switched their support from the Conservative or Labour parties to the Liberals often had specific issue disagreements with their party of origin. Since most voters viewed the Liberals as ideologically “between” the major parties, unhappy supporters of those parties could cast a protest ballot without having to cross all the way over to the opposition. Moreover, in many cases these switchers held positions that differed from the Liberals’ own positions on the issues. For instance, voters who opposed Britain’s entry into the Common Market were more likely to move to the Liberals despite the fact that the Liberal Party was itself the most pro-Market of the three main British parties.

The theoretical basis for this argument follows from the work of American political scientist V. O. Key who argued that elections were primarily retrospective evaluations of past performance and not mandates for future policy. Seen in this light, it makes eminent sense that voters for Sarvis were more likely to come from the ranks of potential Republican voters than Democratic ones. Voters who might otherwise have supported Cuccinelli, but were unhappy about the scandals plaguing the McDonnell administration, found it convenient to vote for Sarvis rather than having to cast a vote for McAuliffe. The same pressures would not have applied as strongly to Democrats despite their mixed views about Terry McAuliffe. While some Democrats might have been turned off by his rather smarmy reputation, the pressure to “rally round the flag” and oust the Republicans probably kept many Democrats from defecting to Sarvis. As a result, the Sarvis coalition contained more likely Republicans than likely Democrats, resulting in the negative coefficient we see in the results above.

Most Vulnerable Republicans Voting against the Shutdown Resolution


This table presents those Republicans who voted against H.R. 2775, the resolution to end the government shutdown and extend the debt ceiling until February 7, 2014.  The “incumbent” column indicates whether the Member already held the seat during the 2012 election.  Eight of the twenty are newcomers, though all but one come from districts carried by Mitt Romney in last year’s Presidential election.

According to Roll Call, Michelle Bachmann has already announced her intention to retire. The remainder have made no announcements about whether they will run for re-election in 2014.

Voting on the Amendment to Restrict NSA Data Collection

(Revised, July 30, 2013)

In an America where partisan polarization has reached levels unseen since Reconstruction, the vote on July 24th to restrict funding for some of the surveillance activities conducted by the National Security Agency offered an welcome counterpoint.

Representative Justin Amash (R-MI) proposed an amendment to the defense appropriation bill that would have blocked funding for the wholesale collection of telephone calling records, so-called “metadata,” by the NSA. Despite heavy lobbying from the President, senior members of his Administration, and the leadership in Congress who all favored the amendment’s defeat, in the end it lost by only twelve votes, 205-217.


Fully a majority of Democrats voted in favor of ending the policy of indiscriminate NSA collection of telephone records despite admonitions by the President that the program be maintained while undertaking “a reasoned review of what tools can best secure the nation.”  Over in the House both Speaker John Boehner and former Speaker Nancy Pelosi voted against the amendment, with Boehner delivering a especially forceful defense of the current surveillance programs.  (Usually the Speaker refrains from voting in roll calls, so Boehner must have thought this an important vote to cast.)  Despite their leader’s strong opposition to the amendment, about two out of five Republican Members also voted to end the surveillance program.

All sorts of theories have been suggested by the pundits for the pattern of votes on this amendment.  Since the usual left-right dimension fails to predict the divisions within the parties, we must look elsewhere for explanations.  One common theory is that surveillance and other civil liberties issues form a “second dimension” of political conflict.  This view pits civil libertarians on both the left and the right against those who believe that, in some circumstances, the demands of national security outweigh individual privacy rights.

To examine these theories we would need a way to determine the position of each Member of Congress on these dimensions of political conflict.  Luckily some smart political scientists have devoted quite a bit of their professional lives to developing a method of scoring legislators based on the roll call votes they cast.  The scores for every Congress through the one that just ended are available on the Voteview site.  That limits my analysis to the 342 Members who served in the preceding Congress and voted on the Amash amendment.*

In an earlier blog posting, these analysts suggested that the second dimension their method identifies might be correlated with voting on domestic surveillance issues.  In a reply to a posting by Nate Silver, they analyzed two votes from the last Congress, one to renew the Patriot Act in 2011, and one to renew FISA in 2012.  In this posting they suggested that the mysterious “second dimension” might help differentiates Members’ votes on security and surveillance issues:

Previous conflicts like civil rights in the mid-twentieth century and bimetallism in the late-nineteenth century emerged as “second dimension” conflicts, meaning that the underlying (first) liberal-conservative dimension is insufficient to explain cleavages over these issues. It is too early too tell whether the second dimension is truly capturing an “establishment vs. outsider” divide over issues like domestic surveillance and the debt ceiling in contemporary congressional voting or merely fitting noise on a special subset of roll call votes, but the evidence so far is suggestive that the second dimension may be real and important.

However Voteview’s analysis this week of the Amash vote found little evidence of this second dimension influencing Members’ votes on the issue.  Rather they saw a pattern of support among more extreme Members on both sides of the aisle.  Using the approach I describe in detail in the Technical Appendix, we can plot the estimated probability of voting for the Amash amendment by ideological position like this:


This chart is based on estimating the influence of ideology separately for Democrats and Republicans.  The median members of each party are represented by blue and red vertical lines respectively.  The model predicts the chances that the median Democrat would vote for the amendment at about two-to-one; the median Republican opposes the amendment with about the same two-to-one odds.  The much narrower range of opinion within the Democratic delegation helps explain the much steeper slope we see for Democrats.  The horizontal lines along the X-axis represent the range of ideology scores within one standard deviation of the median. A change of 0.1 on the ideology measure represents a larger difference of opinion within the Democratic delegation than it does among the Republicans.

We can also identify two groups, one within each caucus, that display higher predicted rates of voting for the amendment.  Among Democrats, those Members elected alongside President Obama were substantially more likely to favor restricting the NSA.  Of the fourteen Members elected for the first time in 2008, twelve of them voted in favor of the amendment including the most centrist member of the group, Bill Owens of upstate New York.

Across the aisle, the equivalent contingent of 2008 Republicans were also more likely to favor the amendment as were those Republicans elected for the first time in the 2010 landslide.  There are many more Members in this group, 88 in all, with DW-NOMINATE scores running from 0.386 for Jon Runyan of New Jersey to a 1.0 for South Carolina’s Mick Mulvaney, both members of the class of 2010.  (Mulvaney voted in favor of the amendment as did the only other Member of the House scored to Mulvaney’s right, Jim Sensenbrenner of Wisconsin, the author of the PATRIOT Act.  The New York Times reports that Sensenbrenner’s brief floor speech in favor of the Amash amendment strongly influenced wavering Members.)

This analysis shows clearly the generational divide within both parties, and particularly within the Republicans.  The few Democrats elected in 2010 showed no greater support for the amendment than other Democrats with equivalent ideological beliefs.  For Republicans, those elected in 2008 and those elected in 2010 were both more likely to favor NSA restrictions at a statistically identical rate.

Two other factors not shown in the graph also influenced votes on the Amash amendment.  Members of both the House Armed Services Committee and, especially, the Select Intelligence Committee were substantially more likely to oppose the Amash amendment than Members with equivalent ideological positions.

For details of the model, see the Technical Appendix for this posting.


* People who know how high House re-election rates are might be surprised that only 349 Members of those voting or absent Wednesday night were returning incumbents.  The turnover rate for the 113th Congress was the highest it has been for decades.  The rate of retirements skyrocketed, while the re-election rate for incumbents declined. Redistricting played a role especially in states like California where incumbents were forced to run against each other. The substantial influx of newcomers in 2013 led one observer to call this Congress one of the “most inexperienced in history.”

Divergent Estimates in Gun Ownership Trends

Only two organizations have provided continuous estimates of the proportion of American households that own a gun over an extended period of time. One of the these is the well-known pollster, The Gallup Organization. Much less known among ordinary Americans, but quite well known and respected by academic researchers, is the General Social Survey conducted every other year by the National Opinion Research Center at the University of Chicago.

These two survey organizations use very different methods to compile their samples of American adults. Gallup relies on telephone interviewing while the General Social Survey interviews people in their homes.  Naturally the two organizations’ estimates of gun ownership rates have differed over the years, but they have diverged sharply since about 2000.

As this graph shows, the GSS data* show a consistent decline in gun ownership rates since the late 1970s.  At that time estimates from the GSS actually exceeded those reported by Gallup.  However, beginning in the mid-1980s Gallup reported higher ownership rates than the GSS, a difference which has persisted ever since.  Despite this divergence both organizations documented a fairly steep decline in household ownership rates during the 1990s.


Starting around 1990, though, the two organizations began reporting quite different rates of gun ownership, and since 2000 the GSS has consistently reported ownership rates some seven or more points lower than Gallup’s.  The graph below reports differences between the two organization’s estimates for years where both surveys are available.  Since the “expanded” question was not asked by Gallup until 1991, I have relied on the narrower item, “do you have a gun in your home,” without including people who reported having a gun stored in a location outside the home.  Including those data would increase the divergence in estimates between the two organizations.


It is difficult to find other estimates of gun ownership rates to which we might compare the figures from Gallup and NORC.  The only data from a Federal agency that I can find so far is one set of figures for 2001 from the Behavioral Risk Factor Surveillance System maintained by the Centers for Disease Control. In 2001 a question on gun ownership was inserted into that survey of nearly 202,000 respondents nationwide.  That BRFSS reports an overall gun ownership rate for the United States of 32% in 2001, essentially identical to the 33.4% average of the figures from the 2000 and 2002 GSS. In contrast Gallup reported a 40% figure for 2001. I am continuing to review data from other Federal agencies, but, under pressure from the National Rifle Association, Congress has restricted the ability of agencies like the Centers for Disease Control to conduct studies of gun violence.


*The GSS figures come from my tabulation of the combined 1972-2010 GSS dataset used in this earlier posting on generational trends in gun ownership rates. I have treated the small number of refusals as missing data and report the percentage of people who gave a yes or no answer to the question about the presence of a gun in the household.  My estimates differ slightly from the rates reported by a similar tabulation from the Violence Policy Center.  Their figures may be based on the full cross-sectional samples for each year, but the VPC report does not provide details on exactly which dataset(s) they employed. (Return)

Millennials “just say no” to guns

Updated with 2012 Data

Updated, January 12, 2013

A person’s age can be thought of as summarizing two different sets of influences in our lives.  One set derives from our position in the life-cycle as we move from young adulthood through middle age and beyond.  These experiences should be rather similar no matter when someone is born.  However different generations share different formative experiences, so we might expect that people born in a particular historical period also share common beliefs and patterns of behavior.  These generational explanations rely on looking at date of birth, and not current age, when looking for patterns in survey data.

Disentangling the two factors is obviously impossible in just a single survey, but if we aggregate comparable surveys taken over many years we can separate out the effects of generational experiences and position in the life-cycle.  As it turns out, the General Social Survey provides exactly the kind of data we need if we want to study how gun ownership has varied over time and across age groups.

I have followed the Pew Research Center’s definitions of American political generations to label four “cohorts” who became adults after World War II.  Pew terms Americans who came of age between the War and the Kennedy Assassination the “Silent” generation.  They are followed by the “Boomers,” who came of age between 1964 and 1982, then “Generation X” (1983-1998) and finally the “Millennials,” who reached adulthood at the turn of the twenty-first century or thereafter.  To these groups I have added all Americans who came of age before 1946, which I call the “New Deal” generation since the Depression, FDR, and World War II were all significant influences on their adult lives.

In the table below I present the reported rate of household gun ownership for each of these five generations using the 33,154 respondents ever asked this question in the NORC General Social Survey since 1972.  With so many interviews conducted over such a long span of time we can disentangle the separate effects of both generation and life-cycle on gun ownership patterns.


Looking first at the highlighted column we see that gun ownership has declined substantially in each generation following the Silents, half of whom reported living in a household with a gun.  On average gun ownership has declined by about ten percent in every generation that came of age after 1963.  Gun ownership reaches its nadir in the youngest generation with only one-in-five “Millennials” reporting that they live in a household with a gun present.  If that trend continues over the next two generations, hardly any Americans coming of age in mid-century will choose to own a gun.

The right-hand side of the table shows how gun ownership varies over the course of the life cycle.  We might expect people to purchase guns as they age and become more settled, so that the low rate of ownership among current 18-29 year-old Millennials may not persist into their later years.  The GSS data provide no support for such a notion.  Within each generation gun ownership rates remain essentially flat across all age groups with perhaps a slight decline after retirement age.  That suggests the low rate of ownership among current Millennials should persist as they grow older.

As a validity check on these results, I compiled the same table for respondents to the 2010 General Social Survey.  I have excluded people who were interviewed as part of the time-series study and are thus included in the table above.  That leaves a separate sample of 2,857 people of whom 1,944 were asked the gun ownership question.  Respondents in the three youngest cohorts report slightly higher rates of gun ownership than we found in the time-series data, but the differences are small and the pattern identical to what we observed using all respondents interviewed since 1972.


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.