We make use of national and statewide data on gun sales as well as a nationally representative survey to look at the relationship between perceived threats to men’s gender identities and guns. In doing so, we are able to test the generalizability of past work on gender identity threat and guns and show that in areas and in times with greater economic strain on men, gun sales increase. Similarly, as men’s perceptions of threat to male social dominance increase, so too does their support for laxer gun laws.

Past qualitative work on gun ownership (Carlson 2015 ) has shown that gun owners often justify owning or carrying a gun through fears of violent crime; however, over the same period in which gun purchases have risen, violent crime has dropped. In 1999, the Federal Bureau of Investigation (FBI) reports that there were 523 violent crimes for every 100,000 U.S. residents; in 2017, there were 383. If gun ownership is about protection, why has it increased, even as the apparent need for protection has decreased?

Despite highly publicized mass shootings and the removal of firearms from some national retailers, the number of guns sales in the United States has continued to rise. In January 1999, the National Instant Criminal Background Check System (NICS) reported 591,355 background checks, each corresponding to one attempt to buy one or more firearms through a licensed dealer. In January 2018, that figure had risen to more than 2.1 million.

In these studies focusing on masculine identities, loss or threat of loss of masculine identity looms large. Connell ( 1995 ) refers to this as alternative or marginal masculinities: attempts by men to construct a gender identity by men who cannot meet the standards of hegemonic masculinity.

Studies have also pointed out the role of gun ownership in protecting the self and families against real or imagined threats of violence (Carlson 2015 ; O’Neill 2007 ; Stroud 2012 ). While gun owners perceive that gun ownership among others is associated with crimes—home invasions, gang violence, and gun violence in general—they see their own gun ownership as a way to protect themselves, their families, and their communities against armed violence. Of course, these attempts to construct masculine identities are raced and classed (Carlson 2015 ; Connell and Messerschmidt 2005 ; Stroud 2012 ), as white men attempt to protect white women and children against threats from black, Latino, and immigrant men (Carlson 2015 ; Collins 2004 , 2006 ; Stroud 2012 ). Metzl ( 2019 ) links support of gun rights directly to the anxieties of white men, especially in the face of threats to their racial privilege. As he points out, though, white men are also most directly impacted by the accessibility of guns, suffering disproportionately higher suicide rates and loss of life as a result.

Melzer’s ( 2009 ) qualitative work points to the active role of the National Rifle Association (NRA) in promoting a masculinities narrative for gun ownership. Borrowing from Connell’s hegemonic masculinity, Melzer attended NRA meetings and events and analyzes NRA literature, finding that the NRA actively invokes constructed memories of frontier masculinities. These appeals are made mostly to working‐class white men who are threatened by women’s rights and civil rights movements: in the preferred narrative, gun ownership allows these men to construct masculine identities based on an imagined frontier masculinity and American freedoms.

As of 2017, some 42% of people in the United States have a firearm in their household (Enten 2018 ), and about 17 million Americans have a permit to carry a concealed weapon (Guns to Carry 2019 ). The fact that American gun owners are overwhelmingly male (Carroll 2005 ) has led to studies exploring the relationship between gun ownership and masculine identity (Carlson 2015 ; Connell 1995 ; Gibson 1994 ; Jeffords 1994 ; Melzer 2009 ; Stroud 2012 ). In general, this line of research argues that men who, for whatever reason, cannot conform to societally acceptable forms of masculinity may embrace guns or violence as an alternative way of performing masculinity (Britton 2011 ; Connell 1995 ; Connell and Messerschmidt 2005 ).

However, men can also compensate by doubling down on aspects of masculine identity that they are able to attain. For instance, Besen‐Cassino and Cassino ( 2014 ) show that men who lose breadwinner status reduce the amount of housework they provide. Similarly, Cassino ( 2018 ) finds that when men face the threat of losing their breadwinner status, they become less likely to support a female candidate in the 2016 U.S. presidential election and less likely to support racially egalitarian social programs. Willer et al. ( 2013 ) find that men subjected to gender identity threat become more likely to engage in all manner of compensatory behaviors, ranging from homophobia and support of war to interest in buying a sports utility vehicle (SUV). In essence, threats to breadwinner status make men more likely to embrace attitudes and behaviors that allow them to symbolically assert a masculine identity.

In the face of such a threat to one of the pillars of “package deal masculinity” (Townsend 2002 ), many have opted for “repackaging the deal” (Carlson 2015 ; Randles 2013 ; Townsend 2002 ). Men may renegotiate their masculine identities when facing a threat to the breadwinner status. Faced with an inability to do so, some men double down on being an engaged father, involved caregiver, and emotional‐support provider for children (Gallagher and Smith 1999 ; Roy 2004 ).

This matters because of the close link between relative earnings within the household and current constructions of masculinity (Besen‐Cassino and Cassino 2014 ; Townsend 2002 ). Townsend ( 2002 ), for instance, finds that breadwinner status is one of the four central components of masculinity for many men along with homeownership, marriage, and fatherhood. Michniewicz, Vandello, and Bosson ( 2014 ) find that U.S. men believe that others will see them as “less of a man” if they lose their jobs, believe that the loss of gender status will be larger than U.S. women think it will be, and overestimate how much others will perceive the loss of status. That is, even though society does not perceive a strong link between men’s gender identities and their employment status, men do see a link, and thus, any threat of unemployment is a threat to masculinity.

In recent years, shifts in the economy have led to more men being unable to meet the role of household breadwinner (Wang, Parker, Taylor, 2013 ). In the Great Recession of 2008–2010, more men than women experienced unemployment or faced reduced wages, resulting in a loss of breadwinner status; as the U.S. economy shifts away from manufacturing and toward service sector and knowledge‐based jobs, the movement away from breadwinner status has become permanent for more men (Hoyner, Miller, and Schaller 2012 ). Both the changing economy and culture result in higher labor force participation rates for women and fewer male breadwinners (Hoyner et al. 2012 ; Legerski and Cornwall 2010 ; Schanzenbach et al. 2014 ; Townsend 2002 ).

We should also note that while we refer throughout the studies to the behavior of “men,” the raced nature of gun ownership—as when men in interviews talk about the need to defend themselves from racially defined threats, as discussed previously—means that we are mostly talking about “white men.” While there is no reason to believe that the general mechanism of men engaging in compensatory behaviors in order to buttress a threatened masculinity is limited to any particular racial or ethnic group, the social nature of these compensatory behaviors means that they would likely vary between groups. In much of our data, we cannot differentiate between effects pertaining to white men and those pertaining to men generally, but we would caution that the qualitative work driving our hypotheses is based on work with white men, so it is among them that our claims are strongest.

Our work also draws upon Mencken and Froese’s ( 2017 ) analyses of the Baylor Religion Survey in which they find a relationship between economic distress and positive beliefs about guns. We carry out a conceptual replication of this study, looking at perceived threat from the status of women in society (rather than economic threat), and extend it by looking at the revealed preferences of actual behavior, rather than statements in response to a survey question.

While qualitative work such as that of Stroud ( 2012 ) and Carlson ( 2015 ) captures the highly racialized and gendered everyday mechanisms of this identity negotiation, their results draw on relatively small studies in certain areas of the United States. The three sets of analyses included in the studies that follow, across two studies, draw directly from this qualitative work, starting with the key relationship between men’s gender identities and gun ownership uncovered in their work. The men in both Stroud’s and Carlson’s studies see guns as a way to assert their gender identity; as such, men who face threat to their gender identities, and thus have a greater need to assert it, should be more likely to buy guns. However, we are not simply replicating the past qualitative research but looking at the external validity of these studies. Valuable as it is, the qualitative analyses offered by Stroud, Carlson, Metzl, and others is geographically and temporally constrained, limited to particular times and places, and critically, to places in which guns are fairly prevalent. Our goal is to conceptually replicate these studies by extending them in time, as far back as the data will allow, and geographically, to the entire United States. We also explore the conditionality of these findings to see the extent to which men are using gun purchases as a compensatory mechanism changes in different social environments. The results of Stroud, Carlson, and Metzl all imply that guns function as a more powerful compensatory mechanism in areas with a strong gun culture, but their work is unable to test this. The geographic variance in our data allows us to do so.

These findings have been echoed in research that has looked at attitudes about guns in the United States. Mencken and Froese ( 2017 ) find that white men facing economic threat become more likely to say that guns are emotionally important, as well as say that they have the capacity to solve societal problems.

By carrying a gun, symbolically, these men renegotiate their masculine identities as protectors of the family (Baker 2005 ). This protection‐based identity is highly classed and raced: the narratives of men highlight they have an obligation as “good,” “decent,” and “responsible” men to protect their families against highly racialized crime (Carlson 2015 ). Gun ownership is a symbolic to preserve a mythologized past—“mourning Mayberry”—that never existed (Coontz 1993 ). Crucially, in both Stroud’s and Carlson’s work, the men purchasing or carrying the firearms are doing so in areas in which guns are accepted. For a symbolic act to have power, it must be recognized, by the individual as much as those around him. As such, gun ownership may be most useful as a way of symbolically asserting masculinity in areas in which gun ownership is already relatively common.

As women become income earners and displace men as sole breadwinners, men may respond to their eroding labor opportunities and the changing social status of women but emphasizing protection. An emphasis on protection helps men claim a privileged relation vis‐à‐vis women and children (by protecting them) and vis‐à‐vis other men (by protecting against them) alongside, or even in place of, provision. In this way, masculine protectionism operates as a form of hegemonic masculinity, allowing some men to legitimate neo‐patriarchal relationships. (Carlson 2015 :391)

Masculinity, guns, and violence have historically been closely linked; violence is a mechanism through which men physically and symbolically establish dominance (Katz 2003 ). Scholars have explored the role of masculinity in acts of violence and domination through force (Burbick 2006 ; Katz 2003 ; Kimmel and Mahler 2003 ), but gun ownership, as Carlson ( 2015 ) argues, is a racialized and classed experience in which men come closer to the ideal of hegemonic masculinity by protecting their families from perceived crime. By keeping their family safe, protecting women and children, gun ownership helps men negotiate masculinity and power (Melzer 2009 ; O’Neill 2007 ; Stroud 2012 ). Stroud’s ( 2012 ) work in Texas shows that some male gun owners with concealed carry permits stress the importance of protecting their families and their role as protectors of their families in their decisions to carry concealed weapons. She finds that while they are not likely to use a gun to actually protect their wives and children, the belief that they may need to do so, and need to be ready to do so, is an important part of how they construct their gender identity. The act of carrying a gun makes them a good father and a good protector, regardless of whether it is ever used. While the concealed weapon may not be visible to everyone, the audience for the symbolic act is the carrier himself, as it helps him to construct his masculinity.

Carlson’s ( 2015 ) qualitative study of 60 male gun carriers in Michigan shows that gun ownership is a symbolic act, rather than a practical decision against rising crime rates or protection. She argues that especially in an area like Michigan with declining manufacturing jobs and rising men’s economic instability, men use gun ownership to symbolically negotiate their masculine identities through their duties to protect their families. Borrowing from Young’s ( 2003 ) theoretical trope of “masculinist protection,” Carlson argues that men reconstruct masculine identities in the face of economic threat by assuming the role of household protector.

STUDIES

We present two studies to test the relationship between perceived gender role threat among men and attitudes and behaviors regarding guns. In the first, we use survey data to demonstrate a link between perceived threat to men’s place in society and opposition to gun control among men. This study serves to establish a link between gender‐based threat, rather than the pure economic threat used in some other studies (e.g., Mencken and Froese 2017) and guns. We then test to see if threat leads to an increase in actual gun‐buying behavior among men, rather than simply responses to a survey, by looking at the link between relative unemployment among married men and the FBI background checks that accompany most gun purchases.

Study 1 In study 1, we make use of individual‐level survey data to test the conceptual relationships established in the past literature. But rather than look at economic threat (as in study 2 and in Mencken and Froese 2017), we make use of items measuring the degree of gender‐based threat perceived by individual men and how this relates to their stated attitudes about gun control.

Data The data used in this study were collected prior to the November 2018 midterm election by Data for Progress. The survey made use of a representative, though nonprobability, online sample of registered voters in the United States from YouGov. The data were provided to interested researchers. The overall sample size of the study was 3,215 (unweighted 45% male, 81% white, 38% Democratic without leaners, 30% Republican). Importantly, the survey contains a three‐item hostile sexism scale (Glick and Fiske 1997). Respondents are asked to agree or disagree (on a 5‐point scale, ranging from strong agreement to strong disagreement) with the following statements: “Most women interpret innocent remarks or acts as being sexist,” “Women are too easily offended,” and “Most women fail to appreciate fully all that men do for them.” These items were equally weighted and combined into a single 0–1 scale, with 0 representing strong disagreement with all of the statements, and 1 representing strong agreement with all of the statements (mean of 0.40; standard deviation of 0.30). As might be expected, men have higher scores on this measure than women (mean of 0.46 vs. 0.34), and Republicans (mean of 0.58) have a higher score than Democrats (mean of 0.23). While this scale is generally used to measure hostile sexism, it does so by asking respondents about whether women are pushing too hard, threatening the social status of men (by not appreciating what men do for them, or misinterpreting remarks, or getting offended too easily). Glick and Fiske (1997:130) argue that “for men, these beliefs reflect the desire to dominate women, see themselves as superior to women, and exploit women as sexual objects—all of which promote hostility toward nontraditional women.” Hostile sexism, in essence, is being measured by the extent to which men perceive that women are usurping men’s rightful place in society. As Glick and Fiske (1994:124) note, “The combination of Hostile Sexism and Benevolent Sexism resembles a protection racket in which men provide both the threat (Hostile Sexism) and the solution to that threat (Benevolent Sexism), with the price being women’s compliance with conventional gender roles and acceptance of patriarchy.” In addition to the sexism items, the survey included an item asking, “Which of the following is closest to your view on gun regulations?” with options on a 5‐point scale, ranging from “It should be more difficult to buy all types of guns” to “It should be less difficult to buy all types of guns.” Overall, 38% of respondents were at the lowest (more difficult) point in the scale, with only 13% in the two highest categories combined (4.8% in the next‐to‐highest category and 8.0% in the highest).

Hypothesis We expect that men who perceive higher levels of threat—as evidenced by higher scores on the hostile sexism scale—will be less likely to support increased restrictions on gun sales and more likely to support reduced restrictions.

Analysis We test our hypothesis with an ordered logit regression model, as presented in Table 1, in which the dependent variable is response to the gun control item described above, and the key independent variable of interest is the interaction between gender and score on the sexism scale. Control variables are whether the respondent is unemployed (3.1% of respondents are), party identification (on a standard 7‐point scale), age (on a 5‐point scale; mean of 3.9), education (6‐point scale; mean of 4.0), and political ideology (5‐point scale; mean of 3.1). Table 1. Individual‐Level Ordered Logit Results, Data for Progress Data Coef. Std Error Z Gender −0.317 0.133 −2.39 Sexism Scale 1.699 0.204 8.33 Gender x Sexism Scale −0.530 0.250 −2.12 Unemployed? 0.077 0.202 0.38 Party ID 0.254 0.025 10.04 Age −0.122 0.033 −3.71 Education −0.056 0.027 −2.04 Ideology 0.563 0.048 11.77 Cut Points 1 1.503 0.219 2 3.182 0.228 3 5.024 0.238 4 5.567 0.242 The model has an overall sample size of 2,962, with a pseudo‐R2 of 0.18. Nearly all of the independent variables are significant predictors of gun control attitudes, but for our purposes, the most important effect is that of the interaction between gender and sexism score. The relative sign of the coefficient indicates that sexism is not as powerful a driver of gun‐control attitudes for women as it is for men.

Results The expected values resulting from the regression (with the top two and bottom two categories collapsed for ease of presentation) are in Fig. 1. Seventy‐one percent of men at the low end of the sexism scale (at the 10th percentile) say that it should be more difficult to buy guns; that figure falls to 41% among the men with the highest levels of sexism (99th percentile). Similarly, the percentage of men who think that it should be easier to buy guns increases from 7% among men scoring low on the sexism scale to 23% among those men scoring highly on the sexism scale. Figure 1 Open in figure viewer PowerPoint Expected Views of Gun Control, Data for Progress Data To put these effects in context, 73% of strong Democrats support making it more difficult to buy guns, compared with 45% of strong Republicans. How important is sexism in determining men’s views of gun control? About as important as party identification. These results allow us to reject the null hypothesis established for the second study, as men with higher scores on the sexism scale do, indeed, have greater support for restrictions on gun sales.

Study 2 In study 2, we make use of aggregated data to look at the relationship between firearms sales and the degree of economic threat experienced by men.

Hypotheses As breadwinner status is an important aspect of hegemonic masculinity in the United States for married men, we expect that increases in the number of men living in households where men are unemployed, but women are not, will be linked with an increase in the number of gun sales. Second, as there are many ways in which men may choose to assert their masculinity in the face of a threat to their gender identity, the effect of relative unemployment on gun sales should be mediated by the prevalence of guns in the area. Specifically, gun sales should be more closely linked to gendered relative unemployment in states where gun ownership is already more common.

Data In order to properly isolate the effects of an increased number of households in which men have become unemployed, it is necessary to include a number of indicators of unemployment, all retrieved from the U.S. Bureau of Labor Statistics. It might be tempting to look only at the overall unemployment rate or the gendered unemployment rate, but neither of these measures, on their own, would allow us to isolate the number of households in which men have become unemployed while their partners have not. Our goal is to determine the degree of gender identity threat faced by men, which requires us to go deeper into the data. We acknowledge that this limits the applicability of our findings to heterosexual couples, but given the data collected by the federal government and the fact that same‐sex marriage was illegal in most of the United States for most of the period studied, this is unfortunately unavoidable. To better establish the validity of our results, we make use of data on both the state and national levels. On the state levels, reliable monthly unemployment data broken down in the ways we need simply are not available. While there are estimates of unemployment by state on a monthly basis, the quality of the estimates varies widely by the size of the state, and subgroup information is generally unavailable. As a result, we make use of annual data for the states, while using monthly data for the national level of analysis. In the national data, overall unemployment ranges between 3.5% and 10.6%, with a median value of 5.2% (mean of 5.8; standard deviation of 1.8). Men’s unemployment ranges from 3.5% to 12.3%, with a median of 5.4 (mean of 6.0; standard deviation of 2.0). The change in men’s unemployment from one month to the next has a mean value of –0.2 points but drops by as much as 1.5 points from one month to the next and increases by as much as 2.1 points. This figure, the extent to which men’s employment prospects are getting better or worse, relative to the employment prospects of women, works as a measure of gender identity threat based on breadwinner status. But since we have the capacity to measure the degree of threat dynamically, we can also look at how this degree of threat changes over time. The change in this measure indicates the extent to which the employment environment is more or less friendly to men relative to women. It has shifted by as much as 1 point down in a month and by as much as 0.9 points up (reflecting an environment getting worse for men relative to women). The mean and median rate of change is 0, and the standard deviation is 0.36. We make use of the same indicator in the state‐level data, though on an annual, rather than a monthly, basis. In both cases, the constructed variable indicates an increase or decrease in the number of households in which married men do not have jobs, but their wives do, relative to the previous time period. An increase in this figure indicates more households in which men are newly experiencing the possibility of gender identity threat resulting from loss of breadwinner status. Unfortunately, the nature of the data means that we cannot look at the potential role of underemployment in these same households. While some past work (e.g., Besen‐Cassino and Cassino 2014) point to underemployment and loss of relative income as being drivers similar to unemployment, data on underemployment at the level of detail necessary for these analyses simply are not available. Gun sale indicators are rather simpler. Since late 1998, the federal government has maintained records of the number of background check queries made through NICS, broken down by the state in which the query was made and the type of firearm purchased. While these data are consistent, they are not as complete as we might like, as they do not include gun sales outside of licensed firearm dealers, such as private sales, and most sales at gun shows, which are not required to make use of the background check system. Additionally, since the NICS system tracks queries rather than number of firearms sold, one background check can cover multiple weapons: in the data, a purchase of one handgun and a purchase of five handguns leave the same footprint (a mixture of different types of weapons is recorded). Finally, some states also make use of the NICS system in the issuance of concealed carry permits, which may increase the number of checks in those states (most of which are already on the high end of the number of NICS checks). All this means that the data are missing some unknown proportion of gun sales; what is important is that there is no reason to believe that the degree of bias in the data has changed over time. A recent study, based on a probability‐based online sample, found that 22% of gun owners had obtained their firearms (often as a gift or inheritance) without a background check, but only about 13% had purchased a firearm without a background check (Miller, Hepburn, and Azrael 2017). This would suggest that our data are capturing most of the firearms purchases made in the United States, but it is entirely possible that the figure varies by subgroup and almost certain that it varies by state, as state laws differ. Essentially, the NICS data are a biased but consistent estimate of the number of gun sales (rather than the number of guns sold) and an unbiased estimate of the number of gun sales at licensed dealers. In either case, it is appropriate for regression analysis, though the results are likely to underestimate movement in the number of guns sold due to exogenous factors. While better estimates of gun sales are desirable, and apparently are circulated among firearms manufacturers and associated groups, federal law restricts government collection and dissemination of any detailed data about gun sales, leaving the NICS data as the best available data source. However, in addition to the exogenous factors, the NICS data are also subject to endogenous factors, at least on the monthly level. Background checks peak annually in December (presumably for holiday gift giving) and at the start of hunting season. The monthly series, seen in Fig. 2, also shows significant autoregressive properties of the series at one and two lags: in plain terms, gun sales tend to be driven by what they were last month and the month before that. There were no significant endogenous factors identified in the state‐level data, which are aggregated to the annual level. Figure 2 Open in figure viewer PowerPoint NICS Background Checks by Month, 1998–2018 The series shows remarkable growth over time. In 1999, there were about 920,000 background checks per month. In 2009, there were about 1.2 million per month. In 2018, that figure was 2.1 million. Over the whole series, running from December 1998 through October 2018, there were 1.07 million background checks per month at the median (1.25 million mean; standard deviation of 596,000). Finally, the use of state‐level data, in addition to the national‐level data, means that we can look at what characteristics of a state might result in a greater, or fewer, number of background checks occurring in response to increased numbers of households in which men have become unemployed. Partisanship is often used as a mediating factor in analyses like this (e.g., Cassino 2018), but given the relatively long time frame of this study and variation in the ideological content of the parties over that period, measures of partisanship do not work here. For instance, Mississippi, now among the most Republican states, had Democratic control in both houses of the legislature until 2010 and had unified Democratic control of the state from 2000 until 2003. By most measures, it would be considered a more Democratic state than Massachusetts for most of the period studied, despite major differences in the ideological underpinnings of Massachusetts and Mississippi Democrats. Therefore, rather than draw on partisanship, we make use of a direct measure of the prevalence of gun culture in a state, the number of firearms per thousand residents in that state, drawn from Bureau of Alcohol, Tobacco, Firearms and Explosives data. These figures range from 3.83 per thousand in New York, up to 229.24 per thousand residents in Wyoming (mean of 21.09; standard deviation of 31.4).

Analysis We test our hypotheses with four models, one looking at the number of background checks carried out nationally, on a monthly basis, and three looking at the number of background checks within each state (one with and one without prevalence of firearms as a mediating factor, and one adding in an interaction effect between prevalence of firearms and change in the unemployment rate of interest). The first model uses four unemployment‐based independent variables (overall unemployment, men’s unemployment, the difference between married men’s and married women’s unemployment, and the change in the last), along with AR(1,2) as endogenous factors to model the change in the number of background checks from one month to the next. The key variable here is the change in the difference between married men’s and married women’s unemployment, which indicates the number of households in which married men—but not married women—are newly unemployed. This model, presented in Table 2, has a sample size of 238 (the number of months in the period covered by the data). While standard goodness‐of‐fit indicators such as R2 are not available for autoregresssive integrated moving average models, the Wald chi‐squared statistic (84.26 against 6 degrees of freedom; p < .000) indicates that the model has significant explanatory power. Table 2. Effect of Monthly Change in Unemployment Rates on Change in Background Checks, National Coef. Std Error Z Change in Married Men ‐ Married Women's Unemployment 2,01,316 42,134 4.8 Married Men ‐ Married Women's Unemployment −1,50,323 68,211 −2.2 Men's Unemployment 1,20,259 1,14,389 1.1 Overall Unemployment −1,07,071 1,17,738 −0.9 Constant −1,23,081 46,026 −2.7 Endogenous AR(1) −0.263 0.050 −5.3 AR(2) −0.266 0.056 −4.7 Sigma 2,00,850 5,450 36.9 Controlling for differential unemployment rates between married men and women, the overall and the gendered unemployment rates have no significant effect on the change in the number of background checks carried out in a month. However, both the existing level of unemployed married men relative to the level of unemployed married women and the change in that figure do have a significant impact on the change in the number of background checks carried out. Essentially, an increase in the number of households in which men, but not women, are unemployed leads to a significant increase in the number of background checks carried out in that month. While looking at these figures on a month‐to‐month basis may give a stronger sense of how these series are linked, there is also reason to believe that the relationship should be mediated, at least somewhat, by local factors, such as the prevalence of firearms. After all, men have many ways in which they could potentially assert a masculine identity in the face of threat, and firearms should be a more common choice in areas where they are already more prevalent, due to differences in availability, permitting requirements and symbolic value. To account for this, we make use of a second set of models, examining the same indicators on a state‐by‐state basis. The state‐level analyses do require some other changes. First, as discussed before, they make use of annual, rather than monthly, data. Second, because of wide variability in the number of background checks carried out in various states, the state‐level analyses make use of percentage changes in the number of background checks as the dependent variable, rather than change in the actual number of background checks (as in the national‐level data). The District of Columbia is excluded from the analysis, as strict gun policies and a relatively small populations means that it has result in a very small number of background checks being carried out annually (often fewer than 10), and thus, there is an extremely high variability on a percentage basis. Given the very small number of checks taking place in the District of Columbia—about four ten‐thousandths of what we see in a state like California or Texas—this does not seem like a significant problem for the analysis overall. There is certainly a place for a detailed analysis of how changes in the law impacted the number of background checks and gun sales in the district, but this is not it. Endogenous factors are not included in the model; as most of them are based on seasonality, they cancel out when the data are taken annually (also, no significant autoregressive or moving average functions are present in the data). Finally, as the data consist of 20 separate annual measurements for each state, all of the results are clustered by state in order to ensure that the results correctly account for sample size. Table 3 presents three models of this relationship. The first does not include the mediating variable of firearms per capita or its interaction with the main variable of interest. The second includes the main effect of firearms per capita but not the interaction. The third includes the main and interacting effect of firearms per capita in the state. Multiple specifications of the model are included to assuage any fears about p‐hacking, or significance of results based only on certain specifications of the variables. Other variations not presented in table form include the use of year as a predictor variable (in both this and the national model), but this did not have any substantive effect on the interpretation of the model. Table 3. Effect of Annual Change in Unemployment Rates on Percent Change in Background Checks, States Model One Model Two Model Three Coef. Std Error Z Coef. Std Error Z Coef. Std Error Z Change in Married Men ‐ Married Women's Unemployment 0.019 0.008 2.4 0.019 0.008 2.4 0.017 0.008 2.0 x Firearms per capita 0.000 0.000 2.4 Married Men ‐ Married Women's Unemployment −0.007 0.011 −0.7 −0.008 0.011 −0.8 −0.008 0.011 −0.7 Men's Unemployment −0.016 0.019 −0.8 −0.015 0.019 −0.8 −0.015 0.019 −0.8 Overall Unemployment 0.024 0.020 1.2 0.024 0.020 1.2 0.024 0.020 1.2 Firearms per capita 0.000 0.000 −1.2 0.000 0.000 −1.3 Constant 0.010 0.012 0.8 0.011 0.013 0.9 0.011 0.013 0.9 Sample size for all of the models is 700, with standard errors adjusted for 50 state clusters. All of these models point to the same conclusions: increases in the number of households in which married men, but not married women, become unemployed are linked with increases in the number of background checks carried out in that state. This is exacerbated by the number of guns per capita in the state, with stronger effects in states that already have many firearms.