metadata: authors: 0: author: Behrer,Arnold Patrick
1: author: Bolotnyy,Valentin
count: United States
docna: 0: docna: Heat, Crime, and Punishment
docty: Policy Research Working Paper
subtopic: Crime and Society,Adaptation to Climate Change,Climate Change and Health,Science of Climate Change,Climate Change and Environment,Urban Housing,Urban Governance and Management,Municipal Management and Reform,Urban Housing and Land Settlements
teratopic: Social Development,Communities and Human Settlements,Environment,Science and Technology Development,Public Sector Development,Urban Development,Health, Nutrition and Population
abstracts: cdata!: Using administrative criminal records
            from Texas, this paper shows how heat affects criminal
            defendants, police officers, prosecutors, and judges. It
            finds that arrests increase by up to 15 percent on hot days,
            driven by increases in violent crime. There is no evidence
            that charging-day heat impacts prosecutorial decisions.
            However, working alone, judges dismiss fewer cases, issue
            longer prison sentences, and levy higher fines when ruling
            on hot days. Higher incomes, newer housing, more teamwork,
            and less accessible weapons may decrease these adverse
            effects of heat. Even with adaptation, the paper forecasts
            that climate change will increase crime and have substantial
            distributional consequences.
geo_reg_key: 119228,517191
repnme: repnme: Heat, Crime, and Punishment
display_title: Heat, Crime, and Punishment
listing_relative_url: /research/2022/01/33711960/heat-crime-punishment
topicv3: Climate Change m1326829 567,Criminal Justice m1326993 1014,Land and Housing m1327648 817,Climate Change Adaptation m1327767 574,Crime m1439217 3041
publishtoextweb_dt: 2022-01-24T00:00:00Z
dois: 10.1596/1813-9450-9909
historic_topic: Social Development,Communities and Human Settlements,Environment,Science and Technology Development,Public Sector Development,Urban Development,Health, Nutrition and Population
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 82
author_keys: 0: author_key: 1561699
1: author_key: NA
model_extractions: Policy Research Working Paper 9909 Adapting to Heat Evidence from the Texas Criminal Justice System _A. Patrick Behrer Valentin Bolotnyy_ Development Economics Development Research Group January 2022
Policy Research Working Paper 9909

# **Abstract**

Using administrative criminal records from Texas, we show that heat increases crime in a heterogeneous way across neighborhoods with different housing and economic characteristics. The heterogeneity allows us to predict how effective certain forms of adaptation will be at reducing the impacts of climate change on criminal activity. The simulations show adaptation reducing, but not completely offsetting, these impacts. Differential rates of adaptation across neighborhoods will likely exacerbate the consequences of already unequal exposure to climate change across society.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at abehrer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Adapting to Heat:

# Evidence from the Texas Criminal Justice System<sup>_∗_</sup>

## A. Patrick Behrer<sup>1</sup><sup>_,_2</sup> and Valentin Bolotnyy<sup>3</sup>

> 1Stanford University, Center for Food Security and the Environment

> 2The World Bank

> 3Stanford University, Hoover Institution

**Keywords:** Climate change, adaptation, extreme heat, criminal justice **JEL Codes:** Q5, H75, K42, D91.

Originally published in the Policy Research Working Paper Series on _January 2022._ This version is updated on _October 2023._ To obtain the originally published version, please email prwp@worldbank.org.

> _∗_ We thank Elise Breshears, Alex Bunin, Marshall Burke, Natalia Emanuel, Rebecca Goldstein, Adrienne Harrold, Peter Hong, Wallis Nader, Ishan Nath, and Jisung Park, as well as members of ECHOLab at Stanford, participants in the LAGV conference, the N. American Urban Economics Association meeting, the Midwest Economics Association meeting, and UCLA’s Climate Adaptation Research Symposium for their assistance and suggestions. We are also grateful to Robin Robinson for her excellent research assistance. The study was approved by Stanford University’s Institutional Review Board as Protocol 56777. An earlier version of this study was circulated as a working paper titled “Heat, Crime, and Punishment.” The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The data used for the project are proprietary and cannot be freely posted online. Code used to analyze the data will be posted in the World Bank’s replication database. We can facilitate other researchers’ potential access to the data. Other researchers can gain access to these data for replication purposes by contacting the Texas Department of Public Safety (TDPS). Neither Behrer nor Bolotnyy have anything to disclose in line with JHR’s disclosure policy.
# **1 Introduction**

A robust literature demonstrates that heat increases criminal activity, yet our understanding of the role that adaptation can play in moderating this relationship remains nascent.<sup>1</sup> Climate change will increase the frequency and intensity of hot days around the world (IPCC, 2014) and it will consequently, absent adaptation, also increase total criminal activity. The well documented relationship between heat and crime make this a good setting to examine how different adaptive paths shape social welfare.

Evidence for the specific mechanisms through which adaptation will mitigate the adverse effects of heat in all contexts, not only crime, is sparse, despite the abundant evidence that heat has adverse impacts on human well-being (Park et al., 2020a; Hsiang et al., 2017b; Burke and Emerick, 2016; Carleton, 2017; Carleton et al., 2020). We also lack evidence for how adaptation will differ in its speed and success across locations. While it is clear that adaptation will mitigate at least some of the adverse consequences of climate change, this will not be an instantaneous process, some forms of adaptation will require substantial changes to the built environment (for instance, Barreca et al. (2015)). Understanding the speed at which adaptation is currently occurring, and the effects of this pace of adaptation, are crucial for identifying potential instances where policy intervention can productively accelerate adaptation. It may also be the case that differential rates of adaptation exacerbate inequality in the impacts of climate change and warrant a policy response that helps certain groups of people adapt. It is well known that exposure to the extremes of a changing climate is higher at the bottom of the income distribution (Hallegatte et al., 2018) but differences in the rate of adaptation introduce an additional driver of inequality in who will suffer the consequences of climate change. Thus, understanding how heat affects human behavior and how adaptation has worked so far is key to formulating public policies that prepare us for a

> 1For evidence on the relationship between heat and crime in the United States see Ranson (2014); Harries et al. (1984); Anderson (1989); Anderson et al. (2000); Anderson (2001). Other work has extended these findings to middle (Bruederle et al., 2017; Garg et al., 2020) and low income settings (Blakeslee et al., 2018). Heilmann and Kahn (2019) provides one of the first, limited, examinations of the role of adaptation.

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hotter world.

Various explanations for why and how heat affects crime have been offered. Early economics work uses a Becker-style model to focus on potential reductions in the likelihood of being caught, because heat increases the costs of police effort, or on the increased relative benefits of crime due to heat-driven reductions in economic payoffs from other activities. Work in psychology, meanwhile, has focused on the role of heat in driving aggressive behavior (Anderson, 1989; Anderson et al., 2000; Baron and Bell, 1976). More recent economics work has examined how the impact of heat on crime varies across neighborhood characteristics, suggesting that heat’s differential effects may be a manifestation of differential investment in early childhood coupled with underlying psychological mechanisms (Heilmann and Kahn, 2019).<sup>2</sup> In this paper, we re-examine heat’s impacts on criminal activity using the most comprehensive data set yet brought to bear on this topic. Using data on the universe of more than 10 million arrests across the state of Texas from 2010 to 2017, we examine how heat impacts the commission of crimes. Our data are unique in providing detail at the individual defendant level across a large geographic region, with meaningful temperature variation across space and over time. The richness of our data on individual defendants allows us to better understand how heat affects human behavior and to conduct the most extensive examination to date of how adaptation could mitigate the effects of future climate change on crime.

Our data contain demographic information on the arrested individual, including their home address, race, and date of birth, as well as information on the charge at arrest. Cru-

> 2A psychological mechanism for heat’s impact on crime is consistent with the rapidly growing body of literature in economics that shows heat has a meaningful impact on cognitive and non-cognitive skills in a variety of contexts. Stress from any source, along with emotions like anger and sadness, adversely affects judgement, decision making, and cognition (for instance, Keltner et al. (1993); McEwen and Sapolsky (1995); Card and Dahl (2011); Lerner et al. (2015)). The weather, in turn, shapes emotions. Sunshine, for example, has a positive effect on mood (Schwarz and Clore, 1983), in a way that measurably increases optimism among stock market investors (Hirshleifer and Shumway, 2003). Heat also exerts a direct impact on human emotion. Higher temperatures have been shown to increase negative affect around the world (Baylis, 2020; Denissen et al., 2008; Kenrick and MacFarlane, 1986). Lab studies have confirmed existing work suggesting that heat increases aggression (Anderson, 2001; Alm ̊as et al., 2019). In other contexts, heat has been shown to decrease cognitive ability and test performance (Wyon et al., 1996; Fang et al., 2004; Seppanen et al., 2006; Cheema and Patrick, 2012; Fesselmeyer, 2019; Park et al., 2020a,b).

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cially, these data provide dates associated with major decisions, including the date of offense and date of arrest. Combining these data with detailed daily temperature data allows us to measure the causal effect of heat on the probability of arrest for different types of crime.

Our approach offers several advances on the existing literature. Previous work on the impacts of heat on crime has been restricted by data limitations to focusing either on a wide geographic region with little detail on individual defendants or temporal resolution (for instance, Ranson (2014)), or to using detailed information for a smaller geography, generally a single city (for instance, Heilmann and Kahn (2019); Harries et al. (1984)).<sup>3</sup> Our data have the advantages of both of these approaches. We are able to cover the geographic scope of the second largest state in the United States, with 28 million residents living across an area that is larger than any country on the European continent aside from Russia. Despite this large geographic scope, we are not constrained to analyzing monthly or geographic aggregates of crime. Rather, we have individual-level crime data, including the address at which the defendant resided, on a daily scale. These two factors allow us to examine the impact of heat on crime across a variety of climates and where the entire study area is not subject to the same temperature shocks (as might be the case with data from an individual city).

The individual scale of our data also allows us to conduct richer analyses of adaptation than existing work. Much of existing work has relied on reported crime data (for instance, Ranson (2014); Heilmann and Kahn (2019)) and so uses information about the location of the crime (or report), but not the residence of the defendant.<sup>4</sup> This makes it difficult to link defendants to a specific built environment in order to examine how this built environment might mediate the impact of heat. Because we know the address of the defendant at the time they commit a crime, we are not subject to this limitation and conduct a variety of heterogeneity analyses to understand how income, poverty, and the age of the housing stock

> 3Blakeslee et al. (2018) is a notable exception to this, using detailed daily data for the entire Indian state of Karnataka.

> 4Levy et al. (2020) demonstrates that where someone commits a crime and where someone resides are often different areas. We thus believe that our approach does a better job capturing who is driven by heat to commit crimes than earlier studies.

4
mediate the relationship between heat and crime.

It is well-known that the short-run response to weather shocks is not an accurate estimate of the impacts of future climate change (Hsiang, 2016) due to the adaptation that will occur in response to climate change. But there is currently little understanding of the mechanisms by which this adaptation will occur and the consequences that it will have for individuals during the period of transition. In much of the existing literature, adaptation is a black box process that simply reduces the negative impacts of, for example, heat on mortality in places that are more accustomed to high temperatures (for instance, Heutel et al. (2021)). Comparing impacts in places at different equilibrium levels of adaptation provides an estimate of the scope for adaptation to reduce impacts, but ignores the process of reaching that equilibrium and how adaptation may occur at differential rates in different places.

Differences in the transitional path to an adapted state may have important welfare implications. For example, consider the seminal work on heat and mortality by Barreca et al. (2015). The paper demonstrates that adaptation in the form of air conditioning has substantially reduced the mortality impacts of heat in the United States over time. The U.S. has now obtained an equilibrium level of adaptation in which mortality from heat exposure is substantially lower than prior to the widespread adoption of air conditioning. But the adoption of air conditioning was not a uniform process and the effects of heat on mortality were reduced differentially for different populations during the transition period. It is possible that this differential rate of adaptation _increased_ inequality in the mortality burden of heat during the transition period relative to a counterfactual of no adaption or uniform adaptation..

We examine how the process of adaption will occur and how it will vary across income, race, and ethnicity. We offer some of the first estimates of the distributional consequences of differences in _adaptation_ , rather than differences in exposure to climate change. To do so, we measure how the impact of heat varies across different levels of a range of neighborhood adaptation markers and use the variation in impacts to predict the effectiveness of future 5
adaptation. Crucially, we do not assume that levels of adaptation are fixed or that they evolve uniformly. Rather, we predict how adaptation will evolve independently across individual units in our data. Combined with the output of more than 40 global circulation models (GCM), we use these predictions to examine how adaptation evolving at different rates differentially mitigates the impact of predicted changes in exposure to high temperatures in Texas.

Our work confirms that temperatures above 65<sup>_◦_</sup> F lead to increases in crime. These increases are driven almost entirely by violent crime, with arrests for such offenses as traffic violations and larceny unaffected. The fact that arrests for traffic violations do not increase (or decrease) suggests that we are in fact measuring increases in crime, and not the effect of heat on police behavior.<sup>5</sup> We also do not find any evidence that heat increases police killings of civilians, a proxy for police aggression.

We find evidence that heat not only increases violent crime, but that it does so by interacting with the presence of deadly weapons. Within the violent crime category, heat has some of its largest effects on weapons charges and assault with a weapon. We observe that heat has larger effects on gun-specific charges after January 1st, 2016, when Texas made it easier to carry guns in public places.

Though these heat effects on crime are largest for individuals who live in the poorest census block groups in Texas, they are also significant for those who live in the wealthiest census blocks. The effects, however, are concentrated in block groups with the oldest homes. These are the homes with the lowest levels of air conditioning generally and lowest levels of central air conditioning specifically. Further, when we examine the joint distribution of housing stock age and income, we find that the effects are concentrated in block groups with the oldest housing stock, regardless of income. Effects in the block groups with older housing stocks are relatively consistent across income groups. Thus, the observed variation in the impact of heat across neighborhoods is likely due to differences in the ability of individuals

> 5A companion paper, Behrer and Bolotnyy (forthcoming), examines the impact of heat on non-defendant actors in the criminal justice system, including police, using these same data.

6
to protect themselves from heat that arise from differences in housing stock, rather than from differences in childhood investments (Heilmann and Kahn, 2019).

We also examine whether heat’s impact on all of the outcomes we study varies by the race or ethnicity of the defendant. Focusing on White non-Hispanic, Black, and Hispanic defendants, we do not find any evidence for variation in heat’s impact. Heat increases crimes at similar rates for all three groups and crimes increase in similar ways in neighborhoods with a majority population of each of these three groups.

Turning to adaptation, we estimate how future climate change will impact arrests inclusive of the mitigating effects of adaptation. We predict how proxies for the level of adaptation will evolve individually in every block group in our sample from 2030 to 2050, combining these predictions with predictions of how the climate will change from global circulation models. We find that climate change will increase arrests for violent crimes across Texas over this time period. Adaptation reduces the impact of unmitigated climate change by approximately 25%, but even with adaptation annual violent crime arrests still increase by 9% by 2050.

Our analysis confirms that the rate of adaptation is not even across the population. Lower income areas see increases in crime that are roughly 70% larger than high income areas, and majority-minority neighborhoods see increases that are about 20% larger than majorityWhite neighborhoods. This is a result of differences in the speed of adaptation rather than differences in the physical rate of climate change. Temperatures change at roughly the same rate across all neighborhoods, but adaptation occurs much faster in wealthier, majorityWhite neighborhoods.

Our results provide a more detailed understanding than the existing literature of how heat’s impacts vary across neighborhoods and racial or ethnic groups. These detailed heterogeneities provide additional evidence for the regressive impacts of climate change. They also enable us to incorporate a more informed estimation of adaptation into our examination of how climate change will change crime rates. Our results highlight that both climate mit7
igation policies (Peng et al., 2021) and differences in climate adaptation can create winners and losers.

The rest of the paper is structured as follows. Section 2 provides the conceptual framework for our study, Section 3 describes the data in detail, and Section 4 lays out our empirical strategy. Section 5 reports the effects of heat on arrests. Section 6 presents estimates for how the probability of arrest will change due to climate change and discusses the role of adaptation in reducing these effects. Section 7 concludes.

# **2 Conceptual Framework**

There is a robust literature both in and outside of economics on the adverse relationship between heat and crime. Hotter days have been shown to increase crime across a range of settings in the United States (Ranson, 2014; Heilmann and Kahn, 2019; Harries et al., 1984; Anderson, 1989; Anderson et al., 2000; Anderson, 2001). Other work has extended these findings to middle (Bruederle et al., 2017; Garg et al., 2020) and low income settings (Blakeslee et al., 2018). In much of this work, heat appears to increase non-property crimes more than property-focused crimes.

Different hypotheses have been advanced to explain this well-documented empirical relationship. Broadly, these can be classified as pointing to a “rational” economic channel for the impact of heat in the mold of Becker (1968) or towards an explanation that focuses on the role that heat plays in reducing psychological control. Arguments in favor of the former mechanism situate the decision to commit a crime in an expected utility framework and focus on the role that heat may play in changing either the costs or benefits of crime. For example, heat may change the effort that police exert in pursuing criminal complaints by making effort more costly (Obradovich et al., 2018) and so reduce the expected cost of committing a crime by reducing the likelihood of being caught. Heilmann and Kahn (2019) uses data on criminal and police activity in Los Angeles to test this hypothesis and finds 8
little support for it. Alternatively, heat may have negative impacts on legal sources of income (for instance, by reducing crop yields) and so increase the benefits of committing crime.

The hypothesis that heat increases criminal activity by reducing psychological control also has a long history. Work in psychology has documented the role of heat in increasing aggression for decades (Baron and Bell, 1976), succinctly summarized by Boyanowsky (1999): “aggression in heat is mediated by emotions, cognitions, and stress from affectivethermoregulatory conflict that produces violently aggressive behavior.” These results have been supplemented by more recent work in experimental economics showing the same (Alm ̊as et al., 2019). Observational work using billions of data points from Twitter has shown that heat increases negative sentiment (Baylis, 2020) in countries around the world. Arguments for a psychological mechanism linking heat and crime combine the observation that heat can increase irritability, anger, and hostility (Anderson, 1989; Anderson et al., 2000; Denissen et al., 2008; Larrick et al., 2011) with the evidence that a large share of crime, especially violent crime, is due to a non-rational response to stimuli (Heller et al., 2017).

Heat, thus, makes individuals more likely to respond to a given stimulus with violence.<sup>6</sup> This is particularly consistent with the evidence that heat has much larger impacts on violent crimes, or crimes of passion, than property crimes (Ranson, 2014; Heilmann and Kahn, 2019; Blakeslee et al., 2018; Mukherjee and Sanders, 2021). Heilmann and Kahn (2019) suggests that the psychological mechanism can also explain observed poverty and income gradients in the impact of heat on crime due to lower levels of investment in the development of noncognitive skills in childhood in higher poverty neighborhoods (Fletcher and Wolfe, 2016).

> 6Cohen et al. (2018) suggests that increased consumption of alcohol on hot days drives some of the observed increases in crime. To the extent that alcohol and heat both reduce psychological control, this explanation is not mutually exclusive with the psychological explanation.

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# **3 Data**

## **3.1 Texas Department of Public Safety (TDPS) Data**

We use confidential data on arrests reported to the Texas Department of Public Safety (TDPS) by arresting agencies every 7 to 30 days, as required by the Texas Code of Criminal Procedures, Chapter 66.252. Our data span 2010 through 2017 and cover every Texas county. Texas state law requires that counties maintain at least a 90% data completeness rate over a rolling five year period in order to be eligible for certain state funds. Completeness means that the data reflect the most up-to-date status or disposition of each case. We received our data in 2019, so at least 90% of the cases through 2017 have been deemed to accurately reflect their most up-to-date status in our data (Department of Public Safety, 2019).

The TDPS arrest disposition data come in several parts. We combine files providing data on the individual arrested, the circumstances of the arrest, details of any prosecution, details of any court trial, and details of the subsequent sentencing or appeal. The individual data provide a unique ID for each individual arrested in our data, as well as the sex, race, ethnicity, and date of birth. The arrest data include the date of arrest, the date of offense, the arresting agency, the level of the arrested offence (for instance, misdemeanor A), the arrest charge (for instance, manslaughter), and the address of the defendant at the time of the arrest.<sup>7</sup> Each arrest charge is given a unique entry in the data. For example, if an individual is arrested on October 1, 2010 and charged with assault and resisting arrest, we will have two records for that individual, one for each charge. If they are then arrested again in 2011 for another charge, we will have a third entry for them. Each of these incidents can be linked to the same individual with their unique ID and each incident has a unique incident ID.

We drop all arrests and charges for which we do not have court outcome data (i.e., the

> 7About 25% of the arrests do not have a date of offense recorded, so we conduct our analysis using the date of arrest. For the arrests for which we have both date of arrest and date of offense, these dates are the same 84% of the time. We run robustness checks using the date of offense and find broadly similar results.

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arrest charge does not have a match in the court data) and charges for which the court has not issued a decision.<sup>8</sup> We also drop misdemeanor C cases as these are inconsistently reported in our data. This leaves us with 2.6 million arrests. We geocode the addresses provided with the address information and match each arrest to the county in which the individual lived when they were arrested. We then collapse the data to the count of arrests at the county-day level. This leaves us with a balanced panel of 742,188 county-day observations from 2010 through 2017. We maintain separate counts of crimes by category of the arresting charge (for instance, violent crimes, assaults, etc).

Arresting charges are defined in the raw TPDS data. When we conduct analyses on total crimes, we pool all of these charges together. We also aggregate some of these charges into violent and non-violent crimes and analyze these separately. We consider the following crimes to be violent: assault, aggravated assault, homicide, manslaughter, kidnapping, domestic assault, and weapons crimes. We define non-violent crimes as: larceny, burglary, stolen property, traffic (excluding those resulting in manslaughter charges), marijuana possession, and marijuana dealing.

Our definition of violent and non-violent crimes is not exhaustive. There are some crimes that we do not consider violent or non-violent. Robbery, for example, is generally defined as “the action of taking property unlawfully from a person or place by force or threat of force.” Thus, while clearly a property crime, it might also be considered a violent crime due to the threat or use of force. Not all robberies, however, involve the actual use of force or violence. These crimes are included in our analysis of heat’s impact on total crime and we analyze heat’s impact on some of these uncategorized crimes individually, but we exclude them from our analyses of violent and non-violent crimes.

> 8These are indicated as cases where the result is “pending” or “no determination.” Dropping nonmatching court cases drops 11% of the arrests in our raw sample. Including these and conducting our main arrest analysis does not change our results.

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# **3.2 Weather Data**

We match our daily arrest counts with daily weather data from the PRISM Climate Group’s gridded re-analysis product. The PRISM product provides daily information on minimum and maximum temperature, minimum and maximum vapor pressure deficit, dew point, and precipitation on a 4km by 4km grid for the continental United States. We aggregate these measures to the county level by taking the average across the grid points within the county. We assign daily maximum temperature to one of 12 5<sup>_◦_</sup> F temperature bins from 40<sup>_◦_</sup> F up to 100<sup>_◦_</sup> F. Days below 40<sup>_◦_</sup> F and above 100<sup>_◦_</sup> F are included in separate bins. We also bin daily precipitation to control for the impacts of particularly rainy days. We assign days to four exclusive precipitation bins: no precipitation, less than half an inch, one half to one inch, and more than one inch.

# **3.3 Socio-economic and Neighborhood Data**

We collect socio-economic data at the census block group level from the 5-year American Community Survey (ACS) for every year in our sample. We use the geocoded addresses from the arrest data to assign individuals to census block groups and categorize arrests based on the characteristics of the block group in which the defendant lives at the time of the arrest. We focus in particular on median income, the poverty rate, and the median housing age. These allow us to classify arrests as occurring in block groups based on income or poverty rates and by the age of the housing stock, which we take as a proxy for the presence of air conditioning. We also use the share of residents in a block group living in an urban environment to classify block groups as urban or rural. We define block groups as urban if at least 80% of their residents live in an environment defined as urban by the ACS.

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# **3.4 Summary Statistics**

In Table 1 we present summary statistics for our primary measure of temperature - daily maximum temperature - for aggregate crimes, and for aggregate crimes by race and ethnicity. Roughly 60% of the days in our sample experience a maximum temperature above 70<sup>_◦_</sup> F and the majority of days in the sample have no precipitation. Figure A1 shows how days are distributed across temperature bins within an average year across all counties in our sample in aggregate and separately by race and ethnicity. We summarize the spatial distribution of both crimes and hot days in Figure 1. Arrests are broadly distributed across the state.

High temperature is also evenly distributed across the state. We show the average annual number of days over 90<sup>_◦_</sup> F. Counties in the Rio Grande Valley have, on average, the largest number of these days, but every county in Texas experiences at least 40 such days in an average year. Figure A2 underlines the variation in temperatures within counties across years in our sample and across months within a given year. Panel A shows the number of days above 90<sup>_◦_</sup> F in each year of our sample for three counties selected from each tercile of the distribution of 90<sup>_◦_</sup> F+ days. While there is clear separation in the number of days as you move down the distribution - Taylor County never experiences a year with as many hot days as the coolest year in Starr County, and Aransas County experiences only one year matching Taylor’s coolest year - there is also clear variation within each county across years in the number of hot days. On average these three counties experience yearly deviations of as many as 25 days on each side of their average number of 90<sup>_◦_</sup> F+ days.

Looking at the distribution of hot days within the same three counties across months of the year, it is clear there is also variation in when days become hot and cease to be hot within a year. Starr County experiences 50 such days in March during our sample, while Aransas and Taylor experience almost no such days in March. All experience a substantial number of 90<sup>_◦_</sup> F+ days in August, but while these decline to zero by October in Aransas it takes until January to reach zero days above 90<sup>_◦_</sup> F in Starr.

There are roughly 250,000 crimes per year in our data and counties experience an average 13
of 2.7 crimes per day over our sample period. White, non-Hispanic individuals commit the largest number of crimes in our data, reflecting their plurality in the overall population. Figure A3 shows that the distribution of crimes across time in our sample is relatively constant. The average number of daily crimes does not vary substantially across years or across months within the average year.

# **4 Empirical Approach**

In all of our analyses, we rely on day-to-day variation in local temperatures within a county to identify the impact of hotter temperatures on our outcomes of interest. Identification rests on the assumption that day-to-day variations in temperature within a county are plausibly exogenous with respect to our outcome of interest. We control for annual trends and monthto-month seasonality in temperature.

## **4.1 Arrest Analysis**

We follow much of the existing literature in assuming that crimes _Cidmy_ in county _i_ on day _d_ of month _m_ of year _y_ follow a Poisson distribution (for instance, Ranson (2014)). We assume the standard exponential form for the conditional mean ( _μ_ � **X** _idmy_ �) of crimes ( _Cidmy_ ) given our covariates ( **X** _idmy_ ) and take the logs of both sides to get our estimating equation: where _Tidmyk_ is an indicator for whether the maximum temperature in county _i_ on day _d_ in month _m_ and year _y_ is in the _k_<sup>_th_</sup> temperature bin. We use one bin for temperatures below 40<sup>_◦_</sup> F and one for those above 100<sup>_◦_</sup> F. Bins in between are in 5<sup>_◦_</sup> F increments and we omit the 60-65<sup>_◦_</sup> F bin. _Ridmyl_ is an indicator for whether the day falls in the l<sup>_th_</sup> precipitation bin. We omit the highest bin in our estimation. _ηd,_ Ω _m, δy,_ and _ψi_ are day-of-week, month, calendar year, and county fixed effects. Our county fixed effects absorb any time invariant location 14
specific determinants of crime. Our daily and monthly fixed effects account for variation in crimes over the course of a week (for instance, there may be more crimes on Fridays) and the year (for instance, there is less outdoor activity in the winter and generally lower crime). Our results are robust to several alternative sets of fixed effects, including a month _×_ year fixed effect. To alleviate concerns about smooth variation in temperature within months, we also show that our results are robust to including a day-of-year fixed effect (see Table A1).

_βk_ is the coefficient of interest and measures the approximate percentage change in daily crimes if the maximum temperature is in temperature bin _k_ relative to the 60-65<sup>_◦_</sup> F bin. We cluster standard errors at the county level (Abadie et al., 2017) and weight our regressions by the total population of the county in each year, as captured in the ACS.

We estimate this fixed effects Poisson model using maximum likelihood (Hausman et al., 1984; Wooldridge, 1999; Correia et al., 2019). We choose a Poisson model both because of the count and skewed nature of the outcome crime data and because of the properties of the fixed effects Poisson estimator. Since there are many county-days with no crimes, our data has many zeros. The Poisson model accounts for these zeros more easily than a linear fixed effects model with log( _Cidmy_ ). It also avoids the bias caused, when the share of zeros is non-trivial, by some common methods of transforming data to account for zeros (Nichols et al., 2010).

In our primary analysis _Cidmy_ represents the count of total crimes in a county on a given day. We conduct several alternative analyses to examine how heat impacts different types of crimes or impacts crimes in neighborhoods with different characteristics. In those analyses the specification is the same, but we change the outcome to be the count of crimes in a particular category. For example, _Cidmy_ can be the count of violent or non-violent crimes. We also conduct analyses where _Cidmy_ is the count of crimes in block groups where the median house was built prior to 1990 or after 2000 or in block groups within various income and poverty bins.

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# **5 Results: Heat and Arrests**

We present our results in several sections. We start with the average impact of heat on arrests before examining how heat impacts violent and non-violent crimes individually. We then turn to an examination of how the introduction of an open carry law in Texas in 2016 interacted with heat’s impact on crimes related to weapon use. Finally, we examine how the impact of heat varies by neighborhood characteristics, in particular the age of the housing stock and neighborhood income.

## **5.1 Heat Increases Arrests**

Our primary result is that heat increases arrests. We find that arrests increase roughly monotonically as temperature increases from 70<sup>_◦_</sup> F to 100<sup>_◦_</sup> F (see Figure 2 and Table A1). Days above 90<sup>_◦_</sup> F increase arrests by approximately 5% relative to days between 60-65<sup>_◦_</sup> F. We find a substantial decline in arrests on days below 55<sup>_◦_</sup> F, consistent with the notion that cooler days discourage the kinds of activities that can lead to crime.<sup>9</sup> These results are robust to alternative fixed effects specifications, including weekend rather than day-of-week fixed effects and county-specific time trends, as well as to controls for humidity like dew point and vapor pressure deficit (see columns 8 and 9 of Table A1). They are also robust to examining weekends separately from week days (Table A2), using data on the date of offense rather than the date of arrest (Table A3), and to including up to 5 lags of daily temperature (Table A4).<sup>10</sup>

> 9In contrast, the mechanisms driving the relationship between temperature and mortality, for example, are physiological rather than economic, and therefore are unlikely to be impacted by changes in activity at lower temperatures. We focus on absolute, rather than relative, measures of temperature because there is little evidence that it is exposure to high temperatures relative to a seasonal average, rather than high absolute temperatures themselves, that have the most adverse effects (for instance, Kim et al. (2021)).

> 10Some existing work has found evidence that heat shifts crimes forward in time (Jacob et al., 2007), such that crime increases on a hot day but declines on subsequent days. We do not find evidence that our effects on violent crime are due to shifting. In Table A5, we show that temperatures do not appear to impact the number of violent crimes on the subsequent three days.

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# **5.2 Heat’s Impacts are Largest for Violent Crime Arrests**

We find the impact of heat on total crime is driven almost entirely by heat’s impact on violent crime.<sup>11</sup> As Figure 3 shows, hot temperatures substantially increase violent crime, with a day above 100<sup>_◦_</sup> F increasing violent crimes by more than 10%. We do not find impacts of heat on non-violent crime that are significantly different from zero for any of our high temperature bins. These results are strongly suggestive that the impact of heat on crime operates by increasing aggression. Cool days appear to influence violent and non-violent crimes similarly, providing support for the hypothesis that the reduction in crimes on cool days is due to reductions in activity outside of the home.

To assess how much of our effect is driven by hot days leading to more activity outside of the home, we look at the impacts on crime of days that are both high temperature and high precipitation days (see Figure A5). We find that the effects of temperature on these days are attenuated across the entire temperature distribution. Days between 65<sup>_◦_</sup> F and 80<sup>_◦_</sup> F no longer have a significant impact on crime if there is also high precipitation, consistent with the idea that the effects at these mild temperatures are driven primarily by increased human interaction. However, our effects at higher temperatures are robust to looking at temperature’s impact on high precipitation days, with effects roughly 2/3rds the size of our base effect. This is consistent with the hypothesis that there is both a psychological and a human activity mechanism at play in how heat increases crime.

The richness of our data allows us to examine how heat impacts detailed categories of violent and non-violent crimes as well (for a discussion of non-violent crime results see Appendix 1). These detailed results indicate that the increase in crime is driven specifically by increases in assaults (Figure A6). Both aggravated assaults and assaults increase by between 10 and 20% at high temperatures. Heat also appears to significantly increase the frequency with which individuals reach for weapons, with both general weapons crimes and

> 11Figure A4 shows the count of a selection of specific violent and non-violent charges in our sample. While the most common crimes are traffic related, assault and aggravated assault are also among the most common crimes in our data. In general, both violent and non-violent crimes are well-represented in our data.

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aggravated assaults with a weapon increasing by between 10 and 20% on very hot days.<sup>12</sup>

# **5.3 Heat’s Impacts are Amplified by Open-Carry**

To further examine whether heat interacts with the presence of weapons we examine how the impact of heat on weapons charges changes after January 2016, when Texas made it significantly easier to carry weapons.<sup>13</sup> The law likely made guns more salient and may have made it more likely for individuals impacted by heat to reach for guns. We have shown that heat appears to increase violent crimes, most likely due to the documented impact of heat on mood, cognition, and aggression. If guns became more easily accessible in the heat of the moment after 2016, this may have increased the frequency of crimes that involve a gun.

To test this hypothesis, we specifically examine instances where individuals are charged with crimes like “brandishing a weapon.” We use the same Poisson fixed effects specification described in the analysis of heat on crimes, but we make two minor changes. First, we increase our temperature bin size to 10<sup>_◦_</sup> F and group all temperatures above 90<sup>_◦_</sup> F. We do this because there are relatively few gun-related arrests in our data and finer bins result in a lack of power, particularly at the highest temperatures. Second, we estimate an event study specification where we examine how the impact of a day in each temperature bin changes after January 1<sup>_st_</sup> , 2016.

We find that after 2016 the impact of a day over 90<sup>_◦_</sup> F on gun crimes increases by between 14% and 39%. By comparison, we see a 1% increase in the impact of heat on assaults and a 2% increase in the impact of heat on aggravated assaults (Table 3).<sup>14</sup> The range in effects is

> 12While other violent crimes increase - in particular homicides and kidnapping - our estimates of these increases are much less precise than our results on assaults. However, this imprecision is likely due to a relatively small number of these kinds of crimes in our data. For example, our data include roughly 5,000 homicides, but more than 100,000 assaults.

> 13Texas House Bill 910 went into effect on January 1 _st_ , 2016, allowing for the open-carry of handguns in areas in which only concealed-carry had previously been permitted. The law did not change the types of guns that were legal to possess or the process for acquiring a gun or a concealed-carry permit. It simply made it legal to openly carry a gun that previously had to be concealed. This made it easier to legally carry a gun in public and made weapons more visible. It may have also made it more likely for gun owners without permits to carry their guns in the expectation that police would assume anyone carrying a weapon had a permit.

> 14 Gun charges are their own category of charges (for instance, “brandishing a firearm”) and not a subset

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generated by differences in how we define a gun crime. The smaller effect is estimated on a broad subset of crimes related to weapons. The larger impact is estimated using a subset of crimes that we believe were particularly likely to be impacted by the law change.<sup>15</sup> Both of these results indicate that heat has a larger impact on the commission of gun crimes after 2016 than before, not that gun crimes increased by 14%-39% after the passage of the law. In other words, if prior to 2016 a day above 90<sup>_◦_</sup> F led to a 10% increase in gun crimes relative to a 60-65<sup>_◦_</sup> F day, our conservative estimate suggests that after the passage of the law the same day led to an 11.4% increase in gun crimes relative to a 60-65<sup>_◦_</sup> F day.

We cannot rule out that this increase in the sensitivity of gun crimes to heat was due to something other than the policy change. However, we believe this is strongly suggestive evidence that by reducing the administrative requirements for carrying a gun in public, and therefore increasing the number of individuals carrying handguns outside their homes, the 2016 law substantially increased the likelihood that individuals reached for a gun when aggravated on a hot day. The lack of a meaningful change in the impact of a 90<sup>_◦_</sup> F+ day on either assaults or aggravated assaults suggests that the increase we observe is not due to a general increase in policing following 2016 or a secular trend in the number of violent crimes.

Our findings are in line with concurrent work by Colmer and Doleac (2022), which utilizes geographic differences to show that more restrictive gun laws mitigate the impact of heat on homicides. Also related is work by Donohue et al. (2022), which finds Right-to-Carry laws increase violent crime through gun thefts and lower police effectiveness. Our approach focuses on a specific policy change, that of making open-carry easier, and on crimes that directly involve gun usage. We find effects on crimes directly affected by the policy change that are smaller (14-39% vs. 50%) than what Colmer and Doleac (2022) finds on homicides writ large. The difference could be due to the fact that Colmer and Doleac (2022) bundle different levels of open-carry restrictions into a binary variable, whereas we look at a marginal change in restrictions. It is also possible that the effect in our setting will grow over time,

> of assaults or aggravated assaults.

> 15The list of these specific charges is included in Table A11.

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as Texans become more aware of the increased ease of openly carrying guns.

# **5.4 Heat Impacts by Neighborhood Characteristics**

The impact of heat on individuals, and their psychological state, is likely mediated through their built environment. Changes in the built environment are likely to be a key adaptation to climate change. A detailed understanding of how the built environment mediates the effects of heat today provides insight into the capacity of adaptation to reduce the negative impacts of climate change.

While we do not have data on the specific structures in which defendants reside, we can study the characteristics of the census block groups in which they live. Block groups are the second smallest census unit and contain between 600 and 3,000 people. We assign the characteristics of these block groups to defendants who reside in them and analyze how variations in neighborhood characteristics mediate the impact of heat on crimes.

We focus our analysis on two neighborhood characteristics: the age of the housing stock and median income. We choose neighborhood housing age as the best proxy for the presence of air conditioning, a frequently suggested adaptation to mitigating the impact of heat on cognitive and non-cognitive skills (Park et al., 2020b).<sup>16</sup> We can and do examine whether housing age acts as a good proxy for the presence of air conditioning using CoreLogic data.<sup>17</sup> In Figure A9, we show that while more than 95% of new houses in Texas have central air, the penetration among houses built prior to 1980 averages around 80%. Conditional on having any air conditioning, older houses are also more likely to have window or other noncentral air conditioning units. Further, while we cannot test this directly, it is plausible that older houses have worse insulation and are less well sealed against the outside environment,

> 16Housing age is also correlated with other determinants of behavior, in particular lead paint. We examine whether there are larger effects in areas with houses built prior to the phase out of leaded paint and find no

> 17CoreLogic data are proprietary data on the U.S. property market that we access through Stanford University’s subscription. These data are assembled primarily from mortgage applications and include details about the financed properties. The data set does not include information on every property in Texas, but it does cover more than five million parcels there.

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making air conditioning less effective in older houses.

Turning to income, higher impacts in lower income neighborhoods may be expected as a result of lower penetration of air conditioning or lower utilization of the air conditioning that is possessed (Davis and Gertler, 2015). Housing quality along other dimensions may vary with income in ways that increase the impact of heat as well. To examine how the impact of heat varies by income, we assign block groups to income quartiles within each year based on their median income relative to other block groups in the same year.

We focus here on an examination of the joint impacts of income and housing age. Because these two neighborhood characteristics are highly correlated, Heilmann and Kahn (2019) is unable to fully separate their impacts on the relationship between heat and crime.<sup>18</sup> While we lack experimental variation in housing age across income bins, the scope of our data provides broader support across all combinations of housing age and income bins and allows us to conduct a deeper examination of their joint impact. Doing so can help us determine whether the observed income gradient in the impacts of heat on crime is driven by correlation between income and housing quality (and adaptive capacity), or by lower levels of investment in childhood education programs in lower-income neighborhoods. To tease apart the individual impacts of building age and income, we separately examine the impact of heat on arrests in block groups in each income quartile and with housing stock built before 1990 and after 2000.<sup>19</sup> To deal with the imprecision that comes from having smaller cell sizes, we widen our temperature bins from five degrees to ten degrees.

On average, we observe the largest impacts in older neighborhoods within each income quartile.<sup>20</sup> There is no statistical difference between the impacts of a day above 90<sup>_◦_</sup> F on

> 18 When we examine block group income and housing age independently, we find that heat has much larger impacts in older and lower-income neighborhoods, consistent with the results in Heilmann and Kahn (2019). We discuss these results at length in Appendix 2.

> 19In these analyses we omit neighborhoods with housing stock built between 1990 and 2000. The correlation between neighborhood housing age and income in our sample is not especially high ( _ρ ≈_ 0 _._ 3). Moving from the median to the 90<sup>_th_</sup> percentile of the housing age distribution increases median income by about 25% or moves one from the median of the income distribution to roughly the 65<sup>_th_</sup> percentile.

> 20We present results for heat’s impact on arrests for violent crimes in Table 2 and leave results on total crime and nonviolent crime for Tables A7 and A8. In all three cases, the pattern is the same.

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violent crimes in neighborhoods with older housing stock across all four income quartiles.<sup>21</sup> In these neighborhoods, a day above 90<sup>_◦_</sup> F increases violent crime by roughly 13%. In newer neighborhoods, with the notable exception of our estimates for the second income quartile, hotter days lead to no increase in violent crime in any income quartile.

Our results have the opposite implication of those in Heilmann and Kahn (2019), which, to the extent they unpack the joint impact, suggest that income is slightly more important than housing age in determining the effect of heat on crime. Our results indicate that it is the quality of the built environment, rather than income, that drives differences in the impact of heat on violent crime. The observed gradient in heat’s impact across neighborhoods of different incomes, when testing income individually, appears to be driven by the correlation between neighborhood income and housing quality.

Housing age and income are not the only neighborhood characteristics that may be important in determining how criminal activity responds to higher temperatures. We examine whether the relationship differs in rural and urban neighborhoods or those with higher levels of baseline violence. We also examine whether heat has differential impacts by race or ethnicity. We discuss these results in Appendix 3, but find no major differences across rural and urban neighborhoods, by baseline levels of violence, or by race and ethnicity.

# **6 What Can We Expect From Climate Change?**

Climate change, even under the most optimistic scenarios (see Figure A12), is expected to result in an increase in the number of days over 70<sup>_◦_</sup> F by mid-century (2050). We’ve shown that such days increase arrests for violent crimes relative to days between 60<sup>_◦_</sup> F and 65<sup>_◦_</sup> F. We should thus expect an increase in arrests for violent crime due to increased temperatures by mid-century. However, as temperatures increase, individuals and society will adapt to mitigate at least some of the impacts of these hotter temperatures. These adaptations will

> 21Consistent with our hypothesis that high incomes don’t fully offset the effects of older housing Burke et al. (2022) finds that older housing has higher rates of infiltration (which implies construction that makes AC less effective).

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take many forms, but two relevant ones are the increased installation of air conditioning (the extensive margin response) and improved housing conditions that make cooling less costly and more efficient on the intensive margin (for instance, better insulation, passive cooling, central air vs. window units, etc.). As a result, the increase in violent crime arrests is likely to be smaller than a naive projection of our marginal impacts would suggest. It is even possible, if adaptation is sufficiently effective, that the number of arrests due to hotter temperatures is smaller by mid-century than today.

Different areas will, however, adapt at different rates. Wealthier areas may be more able to adapt because of better access to credit to make the necessary investments. These differences in adaptive capacity increase the dimensions along which the impacts of climate change may be regressive. It is well known that poorer areas are more exposed to marginal climates and are likely to be more exposed to future climate extremes (Hallegatte et al., 2018; Hsiang et al., 2017a). But even if more vulnerable areas are not more exposed to future climate extremes, the impacts of climate change may be regressive if these areas are less able to adapt as effectively as a wealthier or less vulnerable area.

To examine this question, we account for the adaptive response to climate change in concert with projections of how future increased temperatures will impact arrests for violent crimes. We allow adaptive responses to evolve individually for every administrative unit in our data, in line with their baseline income and housing quality, as in Carleton et al. (2020). This approach allows us to examine the regressivity of future exposure to temperature and account for the adaptive response of an individual block group. Our projections for future temperatures come from data created by Rasmussen et al. (2016) and used in Hsiang et al. (2017a). These data collect the output of between 28 and 44 global circulation models (GCM) for each of the RCP scenarios and measure the number of days the maximum temperature is in each of the 1<sup>_◦_</sup> C bins for every county in the continental United States from 1981 to 2100. We use data on temperatures in Texas from 2000 to 2050 to examine how future climate change will change arrests.

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In line with existing work (for instance, Heutel et al. (2021)) we measure adaptation by examining how the marginal impact of hot days varies across different areas within our sample that we believe are more or less adapted. This approach captures adaptation to the extent that more adapted areas suffer smaller consequences from a given hot day than less adapted areas. The gradient in effects between more and less adapted places reveals how much we can expect adaptation to moderate the impacts of future climate change.

Our results in Section 5.4 suggest that impacts decline in areas with higher incomes and newer houses. We use both measures, plus their joint distribution, to examine adaptation. Higher levels of income likely enable more retroactive protective investments (for instance, installation of air conditioning in older housing) (Davis and Gertler, 2015), while newer housing stock is more likely to have air conditioning. The joint distribution of income and building age may capture variation in adaptive choices (for instance, how often to run AC vs. whether or not to install it) that is missed by income and housing age individually. For all three measures of adaptation we estimate the marginal impact of maximum temperatures in 10<sup>_◦_</sup> F bins separately across four quantiles of the income distribution, across three bins of housing age, and across the joint distribution of both. For full details see Appendix 4.<sup>22</sup> To measure future adaptation, we assign block groups to future income and building age bins based on projections of their current levels of income and median building age and the growth rate of these levels over our sample. We detail these projections in the Appendix. We estimate two different scenarios: (1) A base scenario in which incomes and building age evolve according to the observed growth rates in our sample and (2) a “high adaptation” scenario in which we assume that they will evolve at a growth rate 10x higher than what we have observed. We calculate the impact of temperature on arrests for violent crime as the change in violent crime arrests due to changes in temperatures in every year from 2030 to 2050 relative to the 2000-2010 average, times the marginal effect in each bin according

> 22Past work has found that historic exposure to temperature is a meaningful predictor of adaptation (Heutel et al., 2021) We consider historic exposure to high temperatures, but do not find evidence that hotter areas within Texas are more adapted than less hot areas (Table A12).

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to the level of income, building age, or both in the given block group-year. We do this for the temperature projections from each of the climate models in our data under the RCP2.6, RCP6.0, and RCP8.5 scenarios. Our mean projections are the average of the projections within each RCP scenario across all model runs.

We find that adaptation reduces the impacts of heat on violent crime, but does not eliminate the impact of heat or temperature more generally. In the RCP6.0 scenario, for example, assuming that income and building age evolve at their current growth rates, we find that without adaptation climate change will lead to approximately a 12% increase in violent crime arrests by 2050. When we consider our income-based measure of adaptation, this falls to 11%. Considering adaptation measured by building age or the joint distribution of building age and income further reduces the impact to about 9%. This represents a meaningful 25% reduction in violent crime arrests, but does not indicate that adaptation will eliminate the impact of temperature on violent crime arrests. It may, however, slightly understate the true impact of adaptation. As we show in Panel A of Figure 4, the gap between the base (no adaptation) scenario and the joint (considering both building age and income) adaptation scenario varies from year to year and the total reduction in violent crime arrests over the period 2025-2050 due to adaptation is likely larger than 25%.

The impacts of future warming are likely to be unevenly distributed. In Panel B of Figure 4 we show how our projected impacts vary by race and ethnicity. We consider where in Texas the average person of each racial or ethnic heritage lives and allow incomes by these groups to evolve separately. Our estimates of the annual percentage increase in arrests for violent crime by race lie within the same confidence intervals, but our point estimates suggest that White, non-Hispanic Texans may be less impacted by future warming than Black or Hispanic Texans.

However, these annual percentage estimates disguise differences in the total, aggregate impact that climate change will have on these groups. When we calculate the projected total increase in arrests for violent crimes over the period from 2020 to 2050, we find meaningful 25
and statistically significant differences by race and ethnicity. Specifically, Black and Hispanic Texans experience 25.2% ( _t_ -value: 8.6) and 21.0% ( _t_ -value: 9.4) more violent crime arrests than White Texans over this time period, respectively. These differences are driven by differences in the level of adaptation that we project among Black and Hispanic communities relative to White communities. They do not appear to be driven by differences in future exposure across these groups (Table A13).

Separately from disparities in impacts by race and ethnicity, we find substantial disparities in impacts by income as well. Areas with below median household income today will experience 69.0% ( _t_ -value: 9.8) more arrests for violent crime from 2020 to 2050 than areas above the median household income. This again appears to be driven by the slower uptake of adaptation among lower income communities rather than by differences in exposure by income (Table A13).

These results highlight the fact that climate change is likely to have significant distributional consequences due not only to differences in exposure, but also to differences in adaptive capacity. We estimate substantial differences in total impact across income, racial, and ethnic groups despite the fact that our projections suggest that in Texas these groups will be exposed to similar increases in hot days. Adaptation itself will not necessarily reduce the disparate impacts of climate change and may increase them as some areas or communities are more able to adapt than others.

One might have expected adaptation to have a larger mitigating impact than what we measure here. So what is driving these relatively small impacts of adaptation? First, a brief note about what is not driving the results. Our results are not due to the lack of a gradient in the marginal effect of a hot day between the least and most adapted areas. The marginal impact of a 90<sup>_◦_</sup> F day in low income areas is 55% higher than in high income areas. In block groups with older houses, the marginal effect of the same day is 423% larger. The gap between the most and least adapted areas using the joint distribution of income and building age is similar. Areas with newer houses do appear to be better adapted to heat, 26
with marginal effects of hot days that are close to zero.

Rather, our results appear to be driven by the slow take-up of adaptation as measured by our projections and the changes in the full distribution of temperature. As we show in Table A14, in our base scenario the average block group has not reached the highest level of adaptation as measured by income or building age by 2050. This relatively slow growth in income and slow rate of housing turnover appears to be a major driver of the small mitigating impacts of adaptation by 2050. A second important factor is the overall shift in the temperature distribution. Our estimates indicate that temperatures across the distribution have an impact on arrests for violent crime. Days above 70<sup>_◦_</sup> F generally increase arrests, while those below 60<sup>_◦_</sup> F generally reduce them. This implies that the increase in arrests due to more days above 70<sup>_◦_</sup> F in the future is only half the story. The reduction in days below 60<sup>_◦_</sup> F will also lead to an increase in arrests for violent crimes. As we show in Panel C of Figure 4, future climate change will lead to substantial increases in days above 70<sup>_◦_</sup> F along with nearly equal declines in the number of days below 60<sup>_◦_</sup> F. This does not totally offset the benefits of adaptation to higher temperatures (Panel D, Figure 4), but it does reduce some of the Our results under the aggressive adaptation scenario, in which we impose that income and building age grow at 10x the observed rate, are qualitatively similar (Figure A14). Under this scenario, all block groups have new housing by 2050 and the overwhelming majority are in the highest income bin (Figure A14). However, the overall impact of heat under the joint adaptation scenario is still positive and still increases violent crime arrests by more than 5%. In the aggressive adaptation scenario, the reduction in low temperature days is even more important than in the base scenario. Adaptation substantially reduces the impact of days above 90<sup>_◦_</sup> F, but this is offset by increases in arrests due to reductions in cool days. Importantly, even in the aggressive adaptation scenario, there remain large gaps in impacts across income, race, and ethnicity. Areas with below median household income today still experience 17.4% ( _t_ -value: 5.4) more arrests under an aggressive adaptation scenario, while 27
Black and Hispanic Texans experience 19.9% ( _t_ -value: 5.3) and 11.3% ( _t_ -value: 5.4) more violent crime arrests respectively than White Texans.

Overall, our examination of adaptation suggests three things. The first is that unabated, climate change will increase the number of arrests of violent crimes and increase the probability that individuals end up convicted of violent crimes (Behrer and Bolotnyy, forthcoming). This is due both to the increase in hot days as well as to the reduction in cooler days. However, the second implication is that adaptation will mitigate some of these effects. In our base scenario, adaptation reduces the impacts of increased temperature by about 25% relative to a simple projection of our pooled estimates. The third implication is that adaptations may be able to further reduce these impacts, but income growth and housing stock turnover will need to be faster than what has been observed in the past. We take this as suggestive evidence of a role for policy to encourage adaptive investments. The historic rate of building turnover, in particular, at least in part reflects observed changes in climate over the last 20 years. If the current trend continues, however, there will be a substantial benefit from adaptation “left on the table” by mid-century. Policy may be able to encourage faster turn-over or uptake of adaptive investments, capturing benefits that might otherwise be unrealized.

Our results also highlight the fact that climate change will shift the entire temperature distribution and reductions in low temperatures, not just increases in extremely hot temperatures, may also have adverse impacts depending on the outcome being examined. We observe substantial increases in arrests due to reductions in cooler days by mid-century, so while it may be generally true that reductions in cold temperatures will lead to improved outcomes (for instance, workplace safety), this will not always be the case.

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# **7 Conclusion**

We study how the adverse effects of heat on cognition, mood, and emotional state in turn affect criminal behavior. We find that heat significantly increases arrests, especially for violent crimes. Heat additionally interacts with the presence and availability of weapons. When the Texas open-carry gun law goes into effect in 2016, hot days see a 14-39% increase in gun crimes, compared to a 1-2% increase in non-weapon related assaults.

Our findings show that universal climate control is not a panacea when it comes to mediating the effects of heat on emotion, cognition, and behavior. Adaptation through increases in income and construction of new housing has the potential to blunt the effects of heat on violent crime by over 25%. On current trends, however, adaptation will not eliminate these effects. Differences in the rate of adaptation across vulnerable and nonvulnerable communities may make the realized impacts of climate change more regressive than simple changes in exposure would suggest.

Policy-driven approaches to adaptation may both increase the mitigating impact of future adaptation and reduce disparities across communities, ensuring that future impacts of climate change are reduced for all Texans. Policies that make weapons less readily available in heated moments or facilitate investments that accelerate the turn-over of old, inefficiently cooled housing stock will help communities further adapt to the increasing frequency of hot days. Without additional adaptation, however, we estimate that Texans in an RCP6.0 world will see their annual probability of arrest and conviction increase by 12%.

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# **8 Tables and Figures**

## **8.1 Tables**

Table 1: Summary statistics

||Mean|SD|Min|Max|
|---|---|---|---|---|
|**Annual averages of weather measures**|||||
|T above 100F|17.10|20.18|0|138|
|T 95-100F|36.75|14.41|0|94|
|T 90-95F|49.50|13.36|8|102|
|T 85-90F|45.26|12.17|13|121|
|T 80-85F|42.94|10.34|17|80|
|T 75-80F|37.18|9.06|13|87|
|T 70-75F|31.75|7.15|11|60|
|T 65-70F|27.26|6.24|9|46|
|T 55-60F|17.07|5.33|2|37|
|T 50-55F|13.06|5.32|1|31|
|T 45-50F|9.15|4.48|0|24|
|T 40-45F|6.50|3.99|0|21|
|T below 40F|8.89|8.15|0|38|
|Days with no prec|232.53|31.23|125|313|
|Days with less than 0.5 in|19.67|7.49|1|64|
|Days with 0.5 to 1 in|5.78|2.70|0|17|
|Days with_>_1in|107.27|28.44|25|201|
|**Daily crime averages**|||||
|Total crimes|3.24|11.10|0|213|
|Violent crimes|0.57|2.10|0|46|
|Non-violent crimes|1.59|5.65|0|137|

Notes: We aggregate our weather variables to the annual level and report averages across all counties and years in the sample. Thus, “Mean“, for example, indicates the average number of annual days in a temperature bin across all counties and years in the sample. Daily crime average statistics are daily averages across all Texas counties.

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Table 2: Impact of heat on violent crimes by income and building age

||1<sup>_st_ </sup>q|uartile|2<sup>_nd_ </sup>q|uartile|3<sup>_rd_ </sup>q|uartile|4<sup>_th_ </sup>q|uartile|
|---|---|---|---|---|---|---|---|---|
||Pre-1990|Post-2000|Pre-1990|Post-2000|Pre-1990|Post-2000|Pre-1990|Post-2000|
|T above 100F|0.120|-0.156|0.137|0.465|0.136|-0.104|0.077|0.025|
||(0.028)|(0.103)|(0.027)|(0.225)|(0.029)|(0.093)|(0.037)|(0.055)|
|T 90-100F|0.111|0.011|0.108|0.357|0.125|-0.037|0.091|0.011|
||(0.017)|(0.097)|(0.018)|(0.119)|(0.029)|(0.015)|(0.062)|(0.030)|
|T 80-90F|0.080|-0.003|0.085|0.074|0.097|-0.020|0.120|-0.002|
||(0.011)|(0.042)|(0.010)|(0.076)|(0.023)|(0.032)|(0.056)|(0.020)|
|T 70-80F|0.048|-0.069|0.035|-0.055|0.017|-0.004|0.085|0.017|
||(0.011)|(0.082)|(0.021)|(0.041)|(0.018)|(0.020)|(0.032)|(0.024)|
|N|672,060|99,348|721,734|131,490|701,280|189,930|479,208|175,320|
|Outcome mean, T60-65|0.17|0.00|0.13|0.01|0.09|0.02|0.04|0.03|
|Fixed Efects:|||||||||
|County|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Month|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|DOW|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: All columns report the results of a Poisson fixed effects specification. Errors are clustered at the county level and are reported in parentheses. All regressions are weighted by the total population in each county-year. Coefficients for bins below 75<sup>_◦_</sup> F are suppressed for parsimony, but all regressions include the full set of temperature bins and the full set of precipitation bins. Outcome mean indicates the average number of arrests on days in the omitted bin. 100 _×_ the coefficient estimate indicates the percent change on days in each bin relative to the baseline in the omitted bin. Building age refers to the median year (Pre-1990 or Post-2000) of home construction in the block group in which the arrested individual resided at the time of arrest. Income quartiles indicate the quartile of the block group in which the arrested individual resided. We calculate quartiles each year based on the distribution of median incomes by block group. The first quartile includes the lowest income block groups. Quartile thresholds vary by year.
Table 3: Change in gun crime charges after 2016

||Gun charges|Narrow gun charges|Assault charges|Agg. assault charges|
|---|---|---|---|---|
|T above 90F=1 _×_ Post 2016=1|0.144|0.391|0.009|0.003|
||(0.033)|(0.095)|(0.024)|(0.066)|
|T 80-90F=1 _×_ Post 2016=1|0.017|0.129|0.018|0.022|
||(0.044)|(0.125)|(0.013)|(0.043)|
|T 70-80F=1 _×_ Post 2016=1|-0.009|-0.001|-0.017|-0.058|
||(0.030)|(0.056)|(0.015)|(0.036)|
|N|709,307|601,940|734,151|702,732|
|Fixed Efects:|||||
|County|Yes|Yes|Yes|Yes|
|Month|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|DOW|Yes|Yes|Yes|Yes|

Notes: All columns report the results of a Poisson fixed effects specification. Errors are clustered at the county level and are reported in parenthesis. All regressions are weighted by the total population in each county-year. Coefficents for bins below 70<sup>_◦_</sup> F are suppressed for parsimony but all regressions include the full set of temperature bins and the full set of precipitation bins. 100 _×_ the coefficient estimates indicate the percent change in the impact of a day in each temperature bin on arrests relative to a day in the omitted bin after 2016 relative to pre-2016. “Gun charges” refers to all charges that we categorize as involving guns based on there National Crime Information Center (NCIC) and Texas Uniform Offense Classification codes. “Narrow gun charges” refer to those charges that are specifically related to possessing, discharging, or displaying a gun. Assault and aggravated assault charges are categorized based on their NCIC and Texas Uniform Offense Classification codes.

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