Wound and Healing: The Social Consequence of Hate Crime and Role of Sexual Minority Politicians in Community Responses

This study investigates the impact of hate crimes on the mental health of American adults and explores LGBTQ political representation as a mitigation channel. Using US data, we analyze how hate crime exposure affects mental well-being, identifying sexual minority adults via household composition. Findings reveal that hate crime exposure correlates with worsened mental health, particularly in same-sex households, but does not significantly affect physical health or addictive behaviors. The presence of LGBTQ politicians, both Democratic and Republican, significantly alleviates mental health issues across different households. Democratic politicians notably benefit same-sex households, while Republican politicians reduce the duration of mental health issues. These results underscore the importance of LGBTQ political representation in bolstering community and public health against hate crimes. Wound and Healing: The Social Consequence of Hate Crime and Role of Sexual Minority Politicians in Community Responses

This study investigates the impact of hate crimes on the mental health of American adults and explores LGBTQ political representation as a mitigation channel. Using US data, we analyze how hate crime exposure affects mental well-being, identifying sexual minority adults via household composition. Findings reveal that hate crime exposure correlates with worsened mental health, particularly in same-sex households, but does not significantly affect physical health or addictive behaviors. The presence of LGBTQ politicians, both Democratic and Republican, significantly alleviates mental health issues across different households. Democratic politicians notably benefit same-sex households, while Republican politicians reduce the duration of mental health issues. These results underscore the importance of LGBTQ political representation in bolstering community and public health against hate crimes. 1 Introduction The prevalence of hate crimes in the United States has seen a troubling escalation in recent years. Officially defined by the Federal Bureau of Investigation (FBI) as criminal offenses driven by the offender’s biases against a range of demographic characteristics, including race, religion, sexual orientation, and disability, these incidents have been systematically recorded since the early 1990s. According to the latest FBI Hate Crimes Statistics Report, there were a total of 11,634 cases in 2022 alone, the highest number recorded since these statistics were first tracked (Federal Bureau of Investigation, 2023; Cineas, 2023). This significant increase underscores a growing societal issue that demands urgent attention - research consistently shows that the repercussions for victims extend far beyond the immediate physical harm, with many experiencing psychological impacts. These include a heightened incidence of depression, anxiety, post-traumatic stress disorder (PTSD), and other mental health disabilities (Berger and Sarnyai, 2015; Semlyen et al., 2016; Hudson et al., 2016; Chae et al., 2021; Bird et al., 2021; C´enat et al., 2023; C´enat, 2023), highlighting the necessity for comprehensive responses that address both prevention and aftercare.While research shows the influence of social and physical environments in shaping individual behaviors (Hummler and Ziller, 2024), the indirect impacts on individuals within violent environments, especially sexual minorities, remain underexplored. This gap highlights a critical area of study, as these environments may exert profound psychological effects on vulnerable populations, including those not directly involved in violent incidents. Events like the September 11 attacks not only traumatized direct witnesses but also the broader community through media exposure, creating “secondary witnesses”. Res These individuals, without direct experience of the crimes, are nonetheless exposed to traumatic information and can suffer comparable mental health consequences. A very recent study by Arnoso-Mart´ınez et al. (2024) demonstrated that people consuming media information will ignite the emotions responses such as hatred and fear. Another research in the UK, highlighted by (Paterson et al., 2018), used surveys, experiments, and interviews to investigate the indirect impacts of hate crimes on community mem bers, especially within the LGBT and Muslim communities. This study demonstrated that the impacts of hate crimes are often transmitted through emotional connections and shared feelings (Brown et al., 2018; Paterson et al., 2018). Further, Soni and Tekin (2023) addressed the broader adverse effects on communities exposed to mass shootings, suggesting that the societal costs for these underrepresented and vulnerable groups are significant and likely underestimated.As mental health issues become increasingly prevalent, the need for effective strategies and social support to mitigate the impacts of hate crimes is critical. Political figures significantly influence these outcomes (Hackett et al., 2020). On the one hand, politicians can exacerbate hate crimes and fuel hatred among people—hate crimes surged following the 2017 Charlottesville white supremacist rally, particularly after President Trump remarked that there were “Fine people on both sides” of the protest (Halpert, 2023). On the other hand, political leadership can also quell hatred and provide relief to affected communities. For instance, former President George W. Bush’s speech promoting tolerance toward Muslims in the days following 9/11 served as a powerful example of this mitigating effect. Research suggests that social support and political participation are vital for the mental health of vulnerable minorities (Rivers et al., 2010; Ojeda, 2015; McConnell et al., 2016; Burden et al., 2017; Ojeda and Pacheco, 2019; Pitman et al., 2022; Bernardi et al., 2023; Feir and Mann, 2024). Increased representation of minority politicians, as a specific form of social support, can draw significant social and policy attention, potentially alleviating mental health issues. However, direct investigations into the effect of political representation on reducing mental health consequences are still limited.This study delves into the relationship between hate crimes—a severe form of psychological and societal stressor—and their impacts on mental health, specifically examining their impact on mental health with a focus on sexual minority adults compared to their cisgender peers. We also examine the mitigating effects of political representation, specifically from sexual minority politicians. State level hate crime data is sourced from the FBI’s Hate Crime Statistics Program (HCSP), which includes crimes verified post-investigation. For individual mental health assessments, we use the Behavioral Risk Factor Surveillance System (BRFSS). This dataset allows us to distinguish between LGBTQ and cis-family groups, employing the methodology detailed by Carpenter et al. (2021) to conduct a comparative analysis of mental health outcomes.The results show that an increase in hate crimes as an environmental factor is positively associated with the probability of having extreme longer mental health issues and a longer duration of mental health issues, regardless of whether they are direct victims. When disaggregated by household type, the data show that sexual minority adults, proxy as individuals in same-sex households (SSH), are significantly more affected by the adverse mental health effects of hate crimes compared to adults in different-sex households (DSH), which are twice as large as the effects on individuals in DSHs.The second part explores the potential for mitigating the effects of hate crimes through enhanced political representation of minorities. We assesse the influence of LGBTQ politicians on the negative mental health outcomes associated with hate crimes by leveraging information from Wikipedia to identify the number and tenure of LGBTQ politicians within various states. Our analytical model incorporates variables representing the presence of LGBTQ politicians and their party affiliations—either Republican or Democratic. The findings reveal that the presence of LGBTQ politicians significantly alleviates the mental health impacts of hate crimes. This mitigating association is evident in both SSHs and DSHs, with more pronounced benefits observed in SSH. Notably, the presence of Democratic LGBTQ politicians consistently reduces mental health impacts among adults in SSHs. Although Republican LGBTQ politicians are less common, their presence is linked to significant decreases in the duration of mental health issues, highlighting the broad efficacy of minority political representation in addressing the repercussions of hate crimes.Our results reveal a clear correlation between hate crimes, mental health deterioration, and the mitigating effects of increased minority political representation. There is a notable positive correlation between hate crimes and mental health issues, particularly pronounced among adults in SSHs. The presence of LGBTQ politicians in office significantly alleviates these mental health challenges, with Democratic politicians demonstrating consistent effectiveness. Furthermore, our findings illuminate the broad community impacts of hate crimes and underscore the crucial role of LGBTQ political representation in advancing mental health equity. Research has consistently recommended involving the LGBTQ community in policy design to enhance accessibility and connection to minority groups (Semlyen and Rohleder, 2022). The influence of minority politicians extends beyond symbolic inclusion; it includes the implementation of empirical strategies that are specifically tailored to meet the needs of minority communities, thereby enhancing inclusion and accessibility. Our results also advocate for ongoing legislative efforts to support sexual minorities and the broader public, stressing the importance of inclusive policies.The subsequent sections of this chapter are organized as follows. Section 2 details the data sources utilized in this study and describes the construction of variables. Section 3 presents the empirical results, analyzing the associations identified through the research. Finally, Section 4 summarizes the key findings, discusses the policy implications derived from the results, and suggests potential avenues for future research. 2 Data 2.1 Mental health Our study uses data from the BRFSS, spanning from 2012 to 2022. This telephone-based survey collects detailed demographic and family composition information across all 50 states, the District of Columbia, and participating U.S. territories. It assesses respondents’ health status and behaviors, providing a comprehensive overview of various health dimensions. Conducted annually, the BRFSS surveys an equal number of families each month throughout the year, ensuring a consistent design that supports our longitudinal analysis of health trends.A key advantage of the BRFSS is its ability to identify sexual minority adults within families, a distinction that is critical for our research as it allows for focused analysis on this sexual minority group. Following the methodology of Carpenter et al. (2021), we use household composition information to identify sexual minority individuals. Data concerning the sex composition of households are gathered directly from respondents’ reports. A household qualifies as a Same-Sex Household if it comprises two adults of the same sex. To more accurately reflect the demographics of sexual minority households, our analysis includes only individuals aged 25 and above. This age criterion is employed to ensure that our sample predominantly represents settled adult households. As demonstrated by Carpenter et al. (2021), this approach enables us to ascertain that between 11 to 29 percent of individuals in our SSH sample are part of the sexual minority group.We derive our outcome variables from self-reported health data in the BRFSS, which queries respondents about “the number of days they experienced poor mental health” and “the number of days they experienced poor physical health”. Using these pieces of information, we construct a set of primary outcomes to gauge mental(physical) health impacts: extensive margin, extreme extensive margin, and intensive margin. The extensive margin is determined by whether respondents report any days of poor mental/physical health. The extreme extensive margin is assessed by whether the number of poor mental/physical health days reported by a respondent exceeds the national 75th percentile of mental health duration, thus identifying more severe cases. Lastly, the mental/physical health intensive margin directly quantifies the impact by using the actual reported number of days a respondent felt mentally/physically unwell. Additionally, drinking and smoking behaviors are captured using dummy variables based on whether respondents had at least one alcoholic drink in the past 30 days or smoked at least 100 cigarettes in their lifetime. All other individual characteristics captured from BRFSS including age, gender, general health status, race and ethnicity, income level, education level and employment status. 2.2 Hate Crime The hate crime data for this study is sourced from the FBI’s Hate Crime Statistics Program (HCSP), which annually compiles hate crime cases by state. This data is collected from local agencies via the Uniform Crime Reporting (UCR) program, originally established in the 1930s and including hate crime records since its inception. The HCSP dataset consists of crimes that, following a comprehensive police investigation, are confirmed as hate crimes. This substantiation provides a solid base for analyzing geographic and temporal trends in hate crime occurrences. The HCSP defines a hate crime as any criminal offense motivated fully or partly by the offender’s bias against characteristics such as race, religion, disability, sexual orientation, ethnicity, gender, or gender identity. For our analysis, we aggregate data across all hate crime categories to generate a cumulative count of incidents per state, facilitating a broad examination of patterns and impacts.The HCSP relies on local police and sheriff’s departments to report hate crime statistics, a system that is neither mandatory nor comprehensive. As noted by Holder (2024), some agencies report zero hate crimes annually, leading to potential under-reporting and under-counting, which is the case found in many states (Ruback et al., 2018; Pezzella et al., 2019) and in our raw data. Additionally, in 2021, the FBI transitioned its hate crime reporting to the National Incident-Based Reporting System (NIBRS), which resulted in fewer participating agencies and a significant decline in reported statistics. Our study includes data from 2021, and our preliminary analysis indicates that the reduction in reported hate crimes was not uniform across all states. For instance, California reported a drastic fluctuation, with hate crime counts falling from 1,339 in 2020 to 73 in 2021, before rising to 2,088 in 2022. Conversely, some other states did not experience significant changes. To enhance the reliability of our data, we incorporated state-level annual hate crime reports into our dataset. Specifically, for states with missing values or where the 2021 figures deviated more than twofold from 2020, such as New Jersey and California, we adjuste the figures using state-reported data. For Maryland and New York, where the series consistently showed lower counts than state reports, we replace the HCSP data with figures from state reports. This strategy, while not capable of fully restoring the complete landscape of state-level hate crimes, offers a more accurate representation of the variations in hate crime reporting, providing a clearer snapshot of the changes over time. 2.3 LGBTQ Politicians We collect data on LGBTQ politicians from Wikipedia11A full list of US LGBTQ politicians can be accessed as: List of LGBT politicians in the United States: https://en.wikipedia.org/wiki/List_of_LGBTQ_politicians_in_the_United_States. Wikipedia provides a comprehensive list of LGBTQ politicians in the United States. This resource outlines each politician’s name, state, and service level—from local to federal—along with their term duration and partisan affiliation. It also includes log notes for each politician, offering insights into their LGBTQ status. To mitigate potential confounding effects from national-level influences that could affect all states uniformly, our study specifically excludes federal-level legislators and officials. This exclusion helps ensure our analysis more accurately reflects the localized effects of state and local LGBTQ political representation.We align the data of LGBTQ politicians with the individual records in our main dataset using a matching strategy as follows. Firstly, we gather the information on LGBTQ politicians, focusing on their serving states and term durations. For each year a politician served in a state, we increment the count for that state. After processing all available politician data, we compile a table listing the number of LGBTQ politicians actively serving in each state throughout our study period. We repeat this process for each politician, categorizing them by party affiliation, Democratic or Republican. For instance, Kate Brown’s tenure as a Democratic politician in Oregon from 2015 to 2023 resulted in increments in both the general Oregon LGBTQ count and the specific count for Democratic politicians. This approach allows us to analyze the influence of LGBTQ political representation on individuals’ outcomes, adjusted for temporal and geographical variables. Subsequently, three tables (total, Democratic and Republican) were matched to our primary dataset based on state and year, ensuring that each individual’s data reflected the correct political representation during the corresponding timeframe.We also collect information on minority related events and resources such as LGBTQ related festivals, breast cancer benefits and minority friendly accommodations from Damron Events Panel and Damron Gay Guide. The Damron Gay Guide is one of the few continuously published sources detailing resources helping to navigate minority friendly communities (Knopp and Brown, 2021), which is often referred to as the LGBTQ green book. We incorporate this piece of information to control for the differences in the community environment. Other controlling characteristics include unemployment statistics from the Bureau of Labor Statistics (BLS), state sexual ratio and Medicaid expansion data from the Kaiser Family Foundation (KFF), and COVID-19 cases and deaths reported by The New York Times. 3 Empirical Strategy We employ regression analysis to explore the relationship between hate crimes and the incidence of mental health issues. The specifics of our regression model are as follows: g ( y ijt ) = β 0 + β 1 HateCrime jt + β 2 HateCrime j,t− 1 + Π ′ X + γ j + Λ t + ε ijt (1) for each individual i living in state j at time t . We examine the outcome y, which includes three mental or physical health outcomes as well as drinking and smoking behavior mentioned above and estimated using a different model, denoted as g (*). The extensive and extreme extensive margins as well as the probability of having drinking or smoking are modeled using a Logistic model, while the intensive margin is modeled using a Poisson model.The variable of interest, Hatecrime jt , captures the hate crime environmental factor and represents the number of hate crimes occurring in state j during year t . The estimated coefficient β 1 will measure the association between the prevalence of hate crimes and the extent of mental health issues. To control long-term trends in the hate crime environment, we include the number of hate crimes from the previous year, HateCrime j,t− 1 . Additional control includes respondent demographics (age, gender, general health status, race and ethnicity, income group, employment status and education level), and state-level information (population, male-to-female ratio, unemployment rate, number of law enforcement agencies, Medicare enrollment, Medicaid expansion status, Gay event and resources). Furthermore, we account for unobserved heterogeneity by including fixed effects: γ j for state-specific effects and Λ t for time-specific effects, which include both year and month. The error term ε ijt allows for correlations and is clustered at the state level to ensure robust estimation.To investigate if LGBTQ politicians in office will mitigate the mental burden of hate crime, we expand and add an interaction term into equation (1), which is specified as follow: g ( y ijt ) = β 0 + β 1 HateCrime jt × Politician jt + β 1 HateCrime jt × PoliticianPre j + π 1 HateCrime jt + π 2 HateCrime j,t− 1 + π 3 politician jt + Π ′ X + γ j + Λ t + ε ijt (2)In the extended version of Equation 1, HateCrime jt , HateCrime j,t− 1, X, γ j and Λ t are the same definition. Politician jt , when interacted with HateCrime jt , indicates the count of LGBTQ politicians actively holding office in state j at year t . This interaction term examine how the presence of LGBTQ politicians potentially buffers the mental health impacts associated with hate crimes. The coefficient β 1 now evaluates the association between the number of LGBTQ politicians and mental health issues induced by hate crime. A negative β 1 indicates a diminishing effect of hate crimes on mental health as the number of LGBTQ politicians increases, suggesting a mitigation role played by LGBTQ politicians. Additionally, we include PoliticianPre j , a control variable accounting for the number of LGBTQ politicians in office prior to the year 2010. This control ensures that the mitigating effects attributed to current politicians are not confounded by historical politician presence. 4 Results 4.1 Trends in Hate Crime, Mental Health and LGBTQ Politician We first present an overview of the trends of hate crime, the environment, mental health issues and number of LGBTQ politicians spanning from 2012 to 2022 in Figure 1.Sub-figure A presents the trends in average annual hate crimes as an environmental factor experienced by adults in DSHs and SSHs over the period from 2012 to 2022. During this period, both groups saw significant fluctuations in hate crime occurrences, with SSHs consistently witnessing higher averages. From 2012 to 2014, the number of hate crimes remained stable or even declined for both household types, with SSHs experiencing an average of 150 incidents and DSHs around 130 incidents in 2012. However, starting in 2014, there was a noticeable increase, peaking at 225 incidents for SSHs and 180 for DSHs by 2020. A subsequent drop in 2021 due to the reporting system transition mentioned above, but by 2022, there was a sharp increase in reported hate crimes, with numbers rising from 180 to nearly 300 for SSHs and from 175 to 250 for DSHs. This trend aligns with epidemiological statistics reported by (Semlyen and Rohleder, 2022) and the distinct shift in trend around 2014 identified in (Levin et al., 2022), indicating a pre-2014 decrease followed by a post-2014 increase.Sub-figure B illustrates the probabilities of experiencing mental health issues among adults in DSHs and SSHs, broken down into extensive and extreme extensive margins. The extensive margin represents the probability of experiencing any mental health issues, while the extreme extensive margin represents the probability of experiencing mental health issues for a duration longer than the national 75th percentile. Throughout the study period, SSHs consistently exhibit higher probabilities in both categories. For SSHs, the extensive margin remains relatively stable, fluctuating around 38%, while the extreme extensive margin shows a gradual decline from 30% in 2012 to about 20% in 2022. Conversely, DSHs display a similar but less grave trend. The extensive margin for DSHs maintains a steady level around 28% over the years, and their extreme extensive margin decreases from 20% in 2012 to just under 15% by 2022. These trends indicate a sustained higher vulnerability to mental health issues among SSHs compared to DSHs, although both groups show a slight reduction in the severity of these issues over time.Sub-figure C highlights the average monthly days with mental health issues, the intensive margin, for both DSH and SSH. As predicted, adults in SSHs consistently report more average mental health days compared to their DSHs peers, experiencing about 4-5 days of mental health issues. This number for adults in DSHs is 2.8 days.Sub-figure D offers a detailed view of the average annual number of LGBTQ politicians in office, categorized by their political affiliation: all LGBTQ politicians, Democratic LGBTQ politicians, and Republican LGBTQ politicians. Over the decade from 2012 to 2022, there has been a gradual increase in the total number of LGBTQ politicians, particularly among Democrats. The count rose from 67 LGBTQ politicians in 2012 to 91 in 2022. In contrast, the representation of Republican LGBTQ politicians has remained single count, typically ranging from one to two officeholders per year, and notably, there were no Republican LGBTQ politicians in office in 2020. This data highlights a significant disparity in political representation among LGBTQ politicians based on party affiliation. 4.2 Summary Statistics Next, we present summary statistics of selected control variables and outcomes for the total population, segmented by household types, as shown in Table 1. The average annual hate crime rate for DSH is 146 cases, compared to 168 cases for SSH, indicating a higher incidence of hate crimes in states where SSHs reside.Age distribution varies between DSHs and SSHs, with the majority in our dataset being older people aged above 65. Individuals aged 25 to 34 makeup 4.9% of DSH and 5.1% of SSH. Those aged 35 to 44 represent 11.6% of DSH and 8.8% of SSH. The 45 to 54 age group comprises 15.2% of DSH and 19% of SSH, while the 55 to 64 age group includes 25.7% of DSH and 27.8% of SSH. The largest group, those 65 or older, accounts for 42.7% of DSH and 39.3% of SSH. Females constitute a significantly higher proportion in SSH at 71.9%, compared to 54.8% in DSH. Income levels also differ, with majority of adults in DSHs residing in high income level and adults in SSHs being more equalized in all income groups: the lowest income bracket includes 1.6% of DSH and 6.7% of SSH, whereas the highest income bracket comprises 41.1% of DSH and 18.7% of SSHs.Looking at the state level characteristics, DSHs adults tend to live in states with less FBI agencies, a higher proportion of high school graduates or higher, a more balanced male-female ratio, lower unemployment rates, and are less likely to be in ACA expansion states than their SSHs counterparts.Focusing on mental health outcomes, the extensive margin affects 26.2% of DSHs and 36.7% of SSHs, and the extreme extensive margin affects 17.9% of DSHs and 27.6% of SSH. The average number of days under the mental health intensive margin is 2.5 for DSHs and 4.4 for SSH. These statistics correspond to the mental health trends depicted in Figure 1 and underscore the more severe mental health challenges faced by sexual minorities in SSHs. 4.3 Hate Crime Effects on Mental Health The section below presents the results from a regression analysis that explores the direct association between the occurrence of hate crimes in a given state and the occurrence as well as duration of mental health issues. These results are detailed in Table 2 (and in the following tables as well), which reports the marginal effects of an additional hate crime occurring in a state on three distinct types of mental health issues: the extensive margin (Ext., Column (1)), the extreme extensive margin (Extreme Ext., Column (2)), and the intensive margin (Int., Column (3)). The effects on the extensive and extreme extensive margins are presented in percentage points, whereas the effect on the intensive margin is adjusted to days per 1000 individuals.The results from the upper panel of the table indicate that an additional hate crime in a state is associated with a 0.0012 percentage point increase in the likelihood of having a mental health issue, a 0.0019 percentage point increase in the likelihood of having a mental health issue that lasts longer than the national 75th percentile, and an increase of 0.1911 days of mental health issues per 1000 individuals. The findings reveal that the effects for the extreme extensive margin and the intensive margin are statistically significant at the 5% level, whereas the effect for the extensive margin is not statistically significant.The lower panel of Table 2 disaggregates the regression results by household type, comparing SSHs to DSHs. The data demonstrate a clear pattern where increases in hate crimes within a state correlate positively with more severe mental health issues across both household types. Specifically, for adults in DSHs, an additional hate crime in their state is associated with an increase of 0.0010 percentage point in the likelihood of experiencing any mental health issues, an increase of 0.0017 percentage point in the likelihood of enduring longer-term mental health issues, and an additional 0.1596 days of mental health issues per 1,000 individuals. Both the extreme extensive margin and the intensive margin results are statistically significant at the 5% level. Conversely, the impact on individuals in SSHs is more severe. An additional hate crime in their state leads to an increase of 0.0031 percentage point in the extensive margin, 0.0038 percentage point in the extreme extensive margin, and 0.5478 more days of mental health issues per 1,000 individuals. These effects are statistically significant at both the 1% and 5% levels, indicating a stronger and more adverse impact of hate crimes on mental health in SSHs compared to DSHs.These findings substantiate the role of hate crimes as an environmental factor affecting mental health, aligning with research that highlights not only the direct victims but also those residing in environments with prevalent hate crimes can experience mental health issues, even without direct exposure (Paterson et al., 2018). This suggests that the ambient presence of hate crimes in a community may bring widespread mental health impacts, which is similar to the effects experienced by those directly subjected to racial or other forms of discrimination. Moreover, the data indicates that sexual minorities in SSHs are particularly vulnerable to the mental health impacts of hate crimes compared to adults in DSHs. The results from the lower panel, which differentiates between household types, reveal that the impacts on adults in SSHs are significantly more severe—ranging from double the effects in the extensive and extreme extensive margins to triple the effects in the intensive margin-compared to those in DSHs. This heightened vulnerability in SSHs suggests that increasing hate crimes not only raises the probability of experiencing mental health issues but also extends the duration of these issues.In addition to the mental health impacts, we hypothesize that increases in hate crime might also affect other aspects of individual health status. To explore this, we use available data from the BRFSS to examine potential associations between hate crime exposure and various health metrics, including physical health, as well as drinking and smoking behaviors. Physical health issues were assessed through responses to the question, ”how many days in a month have you experienced physical health issues?” We then transformed these responses into three distinct outcomes—extensive margin, extreme extensive margin, and intensive margin—similar to our approach for mental health outcomes. Drinking behavior was determined based on responses to whether individuals had ”had at least one drink of alcohol in the past 30 days,” and smoking behavior was gauged by whether respondents had ”smoked at least 100 cigarettes in their entire life.” Both of these measures were converted into dummy variables to indicate the presence of drinking and smoking behaviors.The results of these analyses are presented in Table 3 using the same regression analysis applied to mental health data. However, the findings indicate that the associations between hate crime exposure and these additional health and behavior outcomes are not statistically significant. This suggests that while hate crimes have a notable impact on mental health, they do not significantly affect other health statuses or health-related behaviors. 4.4 LGBTQ politicians, hate crime and mental health In this section, we investigate the association of having LGBTQ politicians in office on alleviating mental health impacts stemming from hate crimes. We first present the results of the marginal effects of having one more LGBTQ politician in office in the state on three mental health outcomes using the regression fully saturated with the hate crimes statistics. Results presented in Table 4 reveal that the presence of LGBTQ politicians is significantly associated with reductions in all three outcomes, with one more LGBTQ politician in office being associated with -0.1401 percentage point of extensive margin, -0.0994 percentage point of extreme extensive margin and 14.9132 days per 1000 individuals. All these outcomes are found to be statistically significant at 1% to 10% level. These outcomes suggest a strong negative correlation between the tenure of LGBTQ politicians and both the frequency and severity of mental health issues related to hate crimes. In subsequent analysis, we disaggregate the politician data by their political affiliation. Results presented in the lower panel of Table 4 indicate a varied figure of the mitigation effects from Democratic politicians v.s Republican politicians. Democratic LGBTQ politicians significantly reduce the prevalence of general and severity of mental health issues, with marginal effects of -0.2268 percentage point in the extensive margin and -0.1529 percentage point in the extreme extensive margin. Republican LGBTQ politicians also demonstrate a substantial impact, particularly in the intensive margin with a marginal effect of -100.6054 days of having mental health issues per 1000 individuals.The results show potential disparities in the intensity and mechanisms of impact by political alignment, controlling for the previous presences of LGBTQ politicians. This allows us to interpret the marginal effects as the incremental effect of electing an additional LGBTQ politician. Although the effects of adding one more Republican politician appear more pronounced (almost 10 times the marginal effects of intensive margin), this does not necessarily imply a greater overall mitigation impact compared to adding a Democratic LGBTQ politician. We propose two reasons for this observation: First, Republican politicians have long labelled themselves as associated with conservatism (Barber and Pope, 2019; Chapelan, 2020) and ”traditional” values (Freeman, 1993; Donegan, 2024), where individuals with such ideologies report greater prejudice and negative attitudes against sexual minorities than liberals (Poteat and Mereish, 2012). The emergence of an LGBTQ leader within the Republican Party is particularly significant due to its rarity, challenging traditional norms and enhancing public recognition of diversity and representation within a typically conservative milieu. This increased visibility may lead to greater acceptance and inclusivity, potentially alleviating the mental health burdens faced by LGBTQ individuals due to hate crimes. Second, first-time occurrences often elicit more pronounced effects. In our dataset, only four states—Maryland, New Hampshire, Ohio, and DC—each have one sexual minority Republican politician during the study period. The initial election or public acknowledgment of an LGBTQ Republican politician tends to have a pronounced impact because of its novelty and the heightened attention it garners. Over time, however, as more LGBTQ politicians gain office, the novelty of such events may wane, possibly reducing their unique mitigating effect on mental health issues. Next, we explore the mitigating effects of LGBTQ politicians on mental health outcomes resulting from hate crimes by household types. The results, presented in Table 5, assesses whether the increased representation of LGBTQ politicians exerts a differential impact on LGBTQ adults in SSH compared to their peers in DSH. Surprisingly, looking at the first section of Table 5, the findings indicate that the presence of LGBTQ politicians in office significantly reduces the incidence of mental health issues not only to adults in SSHs but also those in DSHs. Specifically, the extensive margin—which measures the probability of experiencing any mental health issues—shows a reduction of -0.1310 percentage point for DSHs and -0.2624 percentage point for SSHs. For the extreme extensive margin, which accounts for severe cases where mental health issues exceed the national 75th percentile, there is a decrease of -0.0894 percentage point for DSHs and -0.2345 percentage point for SSHs. Additionally, the intensive margin, indicating the number of days individuals experience mental health issues per 1,000 individuals, shows reductions of -12.7528 days for DSHs and -44.6288 days for SSHs. All these associations are statistically significant, highlighting the broad and positive impact of LGBTQ political representation on mental health. We further analyze the impact of LGBTQ politicians’ political affiliations on mitigating mental health outcomes associated with hate crimes, as detailed in the next four rows of the results. Rows 3 and 4 detail the effects of Democratic politicians in office. The presence of Democratic LGBTQ politicians is associated with reductions in all three mental health outcomes but only significant among the adults in SSHs. Rows 5 and 6 explore the impact of Republican LGBTQ politicians. The presence of Republican LGBTQ politicians only leads to statistically significant reductions in the intensive margin, demonstrating a marginal effect of -110.4796 days per 1000 individuals for adults in DSH and -175.5690 days for those in SSH.Two things can be concluded from the results of 5. First, the influence of an LGBTQ politicians extends beyond sexual minorities in SSH to also impact those in DSH. This suggests that the representation of sexual minority leaders transcends traditional boundaries of sexual orientation and household type, affecting a wider community. Second, the effectiveness of representation is particularly notable; the mitigation of mental health issues associated with hate crimes is more pronounced among sexual minorities in SSH than in DSH. This indicates that the visibility and representation of LGBTQ politicians have a significantly beneficial effect on the mental health of individuals in SSH. 5 Discussion and Conclusion In this study, we look into the potential harming effects of hate crimes as an environmental factor and examine the association between hate crimes and mental health issues. Then we focus on a potential mitigating role of sexual minority politicians. We compare these associations between sexual minority adults proxy by the SSHs to those in DSHs. Our analysis first shows a clear positive correlation between increased hate crime occurrences in a state and a higher likelihood of experiencing severe mental health issues and extended durations of distress. These effects are more pronounced among SSHs, who are more likely to be sexual minorities. Further analysis shows there is no significant association between hate crimes and physical health, drinking, or smoking behaviors.Subsequently, our study explores the association of LGBTQ politicians in office on mental health issues arising from hate crimes. We find that the presence of LGBTQ politicians is associated with a substantial reduction in mental health issues, decreasing the extensive margin by 0.1401 percentage point, the extreme extensive margin by 0.0994 percentage point and intensive margin by 14.9132 days per 1000 individuals, all of which are statistically significant. Further analysis by household type reveals that LGBTQ politicians confer mental health decreasing associations across both types of households, and yet the associations are more pronounced in SSHs. These findings underscore the broad impact of LGBTQ representation in mitigating the adverse mental health effects of hate crimes, benefiting not only sexual minorities but the general public as well. Additionally, political affiliation of LGBTQ politicians will have different effectiveness in the association with mental health impacts related to hate crimes. Democratic LGBTQ politicians lead to notable reductions in all mental health metrics but only among adults in SSHs. In contrast, Republican LGBTQ politicians demonstrate a more pronounced effect, significantly reducing the duration of mental health issues for adults in both types of households.These findings carry substantial policy implications. The pronounced mental health burdens linked to hate crimes, combined with the observable benefits from having LGBTQ politicians in office, underscore the urgent need for policies that effectively address hate crimes in communities and stay promoting diverse representation in the political arena. The benefits of increasing inclusive strategies extend beyond the minority populations and can positively impact a broader spectrum of the population. This underscores the dual importance of combating hate crimes not only to protect vulnerable groups but also to enhance the overall community resilience and mental health through inclusive political representation. Increasing minority representation, being a laissez faire way, can address fundamental issues that studies like (Semlyen and Rohleder, 2022) have highlighted, advocating for more collaborative methods to integrate diverse needs into existing healthcare systems. Additionally, enhancing representation can strengthen connections to sexual minorities, which, as evidenced by this study, benefits not only these groups but also the broader population. Such an approach not only aligns with calls for inclusivity but also promotes a healthier, more cohesive society by acknowledging and addressing the specific health disparities faced by underrepresented communities.In conclusion, the presence of LGBTQ politicians appears to play a significant role in alleviating the mental health burdens associated with hate crimes on the general public. The size of these effects highlights the importance of representation and inclusive policies in fostering mental health equity in the community. In 2024, the Democratic Party selected a presidential candidate from a minority community, marking a significant moment in political representation. Although the candidate did not succeed in her campaign, this nomination itself represents a step toward greater diversity and inclusivity at the highest levels of political engagement. This development aligns with ongoing discussions about the importance of minority representation in leadership roles, which not only reflects demographic diversity but also enriches decision-making processes by incorporating a broader range of perspectives and experiences. However, due to the research design and data limitations of this study, we exclude federal-level politicians and cannot evaluate the impact of having a minority president in office. Nonetheless, our findings suggest the potential for elected minority officials to improve not only the mental health of the minorities they represent but also that of the broader public. | Current Year Hate Crime | 146.589 (188.983) | 168.226 (210.404) | 147.995 (190.523) | | Individual Characteristics | ||| | Age Group | ||| | 25-34 | 42,457 (4.9%) | 3,066 (5.1%) | 45,523 (4.9%) | | 35-44 | 99,701 (11.6%) | 5,256 (8.8%) | 104,957 (11.4%) | | 45-54 | 130,807 (15.2%) | 11,347 (19.0%) | 142,154 (15.4%) | | 55-64 | 221,017 (25.7%) | 16,661 (27.8%) | 237,678 (25.8%) | | 65 and above | 367,369 (42.7%) | 23,542 (39.3%) | 390,911 (42.4%) | | Female | 471,916 (54.8%) | 43,038 (71.9%) | 514,954 (55.9%) | | General Health | ||| | Excellent | 154,613 (18.0%) | 7,680 (12.8%) | 162,293 (17.6%) | | Very good | 315,192 (36.6%) | 17,483 (29.2%) | 332,675 (36.1%) | | Good | 258,204 (30.0%) | 19,724 (32.9%) | 277,928 (30.2%) | | Fair | 96,482 (11.2%) | 10,369 (17.3%) | 106,851 (11.6%) | | Poor | 35,141 (4.1%) | 4,464 (7.5%) | 39,605 (4.3%) | | Don’t Know | 777 (0.1%) | 78 (0.1%) | 855 (0.1%) | | Refuse | 942 (0.1%) | 74 (0.1%) | 1,016 (0.1%) | | Race | ||| | White | 744,065 (86.4%) | 42,995 (71.8%) | 787,060 (85.4%) | | Black | 42,216 (4.9%) | 9,302 (15.5%) | 51,518 (5.6%) | | Hispanic | 10,592 (1.2%) | 1,293 (2.2%) | 11,885 (1.3%) | | Asian | 8,457 (1.0%) | 433 (0.7%) | 8,890 (1.0%) | | Others | 56,021 (6.5%) | 5,849 (9.8%) | 61,870 (6.7%) | | Income Level | ||| | Less than $10,000 | 13,390 (1.6%) | 4,004 (6.7%) | 17,394 (1.9%) | | $10,000-$15,000 | 17,754 (2.1%) | 4,746 (7.9%) | 22,500 (2.4%) | | $15,000-$20,000 | 32,717 (3.8%) | 6,702 (11.2%) | 39,419 (4.3%) | | $20,000-$25,000 | 54,224 (6.3%) | 7,503 (12.5%) | 61,727 (6.7%) | | $25,000-$35,000 | 79,412 (9.2%) | 8,176 (13.7%) | 87,588 (9.5%) | | $35,000-$50,000 | 129,028 (15.0%) | 9,048 (15.1%) | 138,076 (15.0%) | | $50,000-$75,000 | 166,421 (19.3%) | 7,940 (13.3%) | 174,361 (18.9%) | | $75,000 and above | 354,223 (41.1%) | 11,215 (18.7%) | 365,438 (39.7%) | | State Characteristics | ||| | Number of FBI Agencies | 316.214 (268.394) | 333.156 (276.874) | 317.315 (268.986) | | Population | 6,356,844.745 (7,044,301.555) | 7,208,238.100 (7,673,762.223) | 6,412,178.387 (7,090,012.936) | | High School Graduate or Higher | 89.246 (2.926) | 88.788 (2.928) | 89.216 (2.929) | | Male-Female Ratio | 0.965 (0.029) | 0.958 (0.028) | 0.964 (0.029) | | Unemployment Rate | 5.418 (1.831) | 5.619 (1.840) | 5.431 (1.833) | | ACA Enrollment | 1,039,461.400 (1,693,530.532) | 1,235,948.735 (1,923,527.981) | 1,052,231.478 (1,710,103.929) | | ACA Expansion State | 324,601 (37.7%) | 24,148 (40.3%) | 348,749 (37.9%) | | Mental Health Outcomes | ||| | Extensive Margin | 230,596 (26.8%) | 21,996 (36.7%) | 252,592 (27.4%) | | Extreme Extensive Margin | 153,984 (17.9%) | 16,551 (27.6%) | 170,535 (18.5%) | | Intensive Margin | 2.550 (6.614) | 4.412 (8.711) | 2.671 (6.785) | | N | 861,351 (93.5%) | 59,872 (6.5%) | 921,223 (100.0%) | NOTE: Table presents the summary statistics for selected control variables and outcomes. Three mental health outcomes include extensive margin (if the respondent has mental health issue, outcome in %, Ext.), extreme extensive margin (if the respondent has mental issue days more than national 75th quartile, outcome in %, Extreme Ext.) and intensive margin (days the respondent having mental health issue, outcome in 1000 individual, Int.). Sample encompass BRFSS 2012-2022. All controls include previous year hate crime rate, respondent demographics (age, sex, general health index, race, income group, employment status, education), state level information (population, male-to female ratio, unemployment rate, agency numbers, Medicare enrollment, and Medicaid expansion status). Table 2: Hate crime mental effects | Ext. | Extreme Ext. | Int. | | | Main | ||| | Current Year Hate Crime | 0.0012 | 0.0019** | 0.1911** | | (0.0010) | (0.0008) | (0.0804) | | | Different Sex Household | ||| | Current Year Hate Crime | 0.0010 | 0.0017** | 0.1596** | | (0.0010) | (0.0008) | (0.0813) | | | Same Sex Household | ||| | Current Year Hate Crime | 0.0031** | 0.0038*** | 0.5478** | | (0.0015) | (0.0013) | (0.2379) | | | N | 921,223 | 921,223 | 921,223 | NOTE: Table presents the estimated marginal effects of current year hate crime on three mental health outcomes. Column (1)-(3) contain the outcomes of extensive margin (if the respondent has mental health issue, outcome in %, Ext.), extreme extensive margin (if the respondent has mental issue days more than national 75th quartile, outcome in %, Extreme Ext.) and intensive margin (days the respondent having mental health issue, outcome in 1000 individual, Int.). Sample includes BRFSS 2012-2022. Extensive margin and extreme extensive margin are estimated using logistic regression model, and intensive margin is estimated using Poisson regression model. Control includes previous year hate crime rate, respondent demographics (age, sex, general health index, race, income group, employment status, education), state level information (LGBTQ events and resources, population, male-to-female ratio, unemployment rate, agency numbers, Medicare enrollment, Medicaid expansion status, Covid cases and Deaths). State fixed effects, year fixed effects and month fixed effects are included. ***Significant at 1% level. **Significant at 5% level. *Significant at 10% level. Table 3: Hate Crime Effects among Other Health Aspects | Physical Health Issue | ||| | Ext. | Extreme Ext. | Int. | | | Current Year Hate Crime | -0.0010 | -0.0001 | -0.0454 | | (0.0011) | (0.0006) | (0.1046) | | | Drinking Behavior | ||| | Current Year Hate Crime | 0.0018 | || | (0.0013) | ||| | Smoking Behavior | ||| | Current Year Hate Crime | 0.0007 | || | (0.0010) | NOTE: Table presents the estimated marginal effects of current year hate crime on three other health related outcomes. Column (1)-(3) contain the outcomes of extensive margin (if the respondent has mental health issue, outcome in %, Ext.), extreme extensive margin (if the respondent has men tal issue days more than national 75th quartile, outcome in %, Extreme Ext.) and intensive margin (days the respondent having mental health is sue, outcome in 1000 individual, Int.). Sample includes BRFSS 2012-2022. Extensive margin, extreme extensive margin, Drinking and Smoking behav iors are estimated using logistic regression model, and intensive margin is estimated using Poisson regression model. Control includes previous year hate crime rate, respondent demographics (age, sex, general health index, race, income group, employment status, education), state level information (LGBTQ events and resources, population, male-to-female ratio, unemploy ment rate, agency numbers, medicare enrollment, Medicaid expansion status, Covid cases and Deaths). State fixed effects, year fixed effects and month fixed effects are included. ***Significant at 1% level. **Significant at 5% level. *Significant at 10% level. Table 4: LGBTQ Politician Mitigation Effect on Hate Crime Mental Burden | Ext. | Extreme Ext. | Int. | | | All LGBTQ Politician # | -0.1401* | -0.0994** | -14.9132** | | (0.0743) | (0.0490) | (7.1015) | | | Dem. LGBTQ Politician # | -0.2268* | -0.1529* | -14.4024 | | (0.1319) | (0.0845) | (13.1505) | | | Rep. LGBTQ Politician # | -0.4120 | -0.1863 | -116.0931*** | | (0.5349) | (0.3668) | (28.5080) | | | N | 921223 | 921223 | 921223 | NOTE: Table presents the estimated marginal effects of the association of LGBTQ politician against hate crime mental health impacts. Column (1)-(3) contain the out comes of extensive margin (if the respondent has mental health issue, outcome in %, Ext.), extreme extensive margin (if the respondent has mental issue days more than national 75th quartile, outcome in %, Extreme Ext.) and intensive margin (days the respondent having mental health issue, outcome in 1000 individual, Int.). Sam ple includes BRFSS 2012-2022. Extensive margin and extreme extensive margin are estimated using logistic regression model, and intensive margin is estimated using Poisson regression model. Control includes previous year hate crime rate, respondent demographics (age, sex, general health index, race, income group, employment status, education), state level information (LGBTQ events and resources, population, male to-female ratio, unemployment rate, agency numbers, medicare enrollment, Medicaid expansion status, Covid cases and Deaths). State fixed effects, year fixed effects and month fixed effects are included. ***Significant at 1% level. **Significant at 5% level. *Significant at 10% level. Table 5: LGBTQ Politician Mitigation Effects against Hate Crime Impacts by Household Types | Ext. | Extreme Ext. | Int. | | | All LGBTQ Politician # | ||| | Different Sex Household | -0.1310* | -0.0894* | -12.7528* | | (0.0750) | (0.0490) | (6.8948) | | | Same Sex Household | -0.2624*** | -0.2345*** | -44.6288*** | | (0.0796) | (0.0640) | (11.8776) | | | Dem. LGBTQ Politician # | ||| | Different Sex Household | -0.2091 | -0.1342 | -10.7372 | | (0.1339) | (0.0852) | (12.9858) | | | Same Sex Household | -0.4513*** | -0.3910*** | -61.4193*** | | (0.1387) | (0.1129) | (20.3053) | | | Rep. LGBTQ Politician # | ||| | Different Sex Household | -0.3733 | -0.1586 | -110.4796*** | | (0.5793) | (0.3947) | (35.0116) | | | Same Sex Household | -0.7535 | -0.4147 | -175.5690** | | (0.8679) | (0.5693) | (80.9033) | | | N | 921223 | 921223 | 921223 | NOTE: Table presents the estimated marginal effects of the mitigation effects of LGBTQ politician against hate crime mental health impacts by household type, Different Sex Household v.s Same Sex Household. Column (1)-(3) contain the outcomesof extensive margin (if the respondent has mental health issue, outcome in %, Ext.),extreme extensive margin (if the respondent has mental issue days more than national75th quartile, outcome in %, Extreme Ext.) and intensive margin (days the respondenthaving mental health issue, outcome in 1000 individual, Int.). Sample includes BRFSS2012-2022. Extensive margin and extreme extensive margin are estimated using logisticregression model, and intensive margin is estimated using Poisson regression model.Control includes previous year hate crime rate, respondent demographics (age, sex,general health index, race, income group, employment status, education), state levelinformation (LGBTQ events and resources, population, male-to-female ratio, unemployment rate, agency numbers, medicare enrollment, and Medicaid expansion status,Covid cases and Deaths). State fixed effects, year fixed effects and month fixed effectsare included. ***Significant at 1% level. **Significant at 5% level. *Significant at 10%level. a

1. Table 1: Sample Summary Statistics Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Metrics & Citations Metrics Article Usage 224views 76downloads Citations Download citation Peiyuan Li, Ke Zeng. Wound and Healing: The Social Consequence of Hate Crime and Role of Sexual Minority Politicians in Community Responses. Authorea. 15 January 2025. DOI: https://doi.org/10.22541/au.173698037.70989027/v1 DOI: https://doi.org/10.22541/au.173698037.70989027/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.