Losing your Cool: Psychological Mechanisms in the

Temperature-Crime Relationship in Mexico

Gordon C. McCord∗

Aleister Montfort†

February 17, 2017

DRAFT - DO NOT CITE

Abstract

We investigate the role of weather �uctuations on homicides in Mexico, where climate change will al-

ter temperature and precipitation patterns and where violent crime has been a pressing issue in recent

decades. Using the most detailed panel of homicides in any developing country, spanning 15 years and

2,345 municipalities, we explore the e�ect of weather on violence, looking at di�erent mechanisms (eco-

nomic structure, social exclusion and lack of access to electricity) through which weather shocks might

play a role in human con�ict. We are the �rst paper to add daily level analysis to this literature, allow-

ing us to uniquely distinguish between slower-moving income channel e�ects of weather as opposed to

same-day e�ects in line with human psychology literature. We �nd that hotter days have an immediate

same-day e�ect on homicides, supporting hypotheses focusing on psychological e�ects of heat. However,

the magnitude of the same-day e�ect is one-fourth the magnitude of the monthly e�ect, and even smaller

compared to a 6-month e�ect, suggesting that slower-moving mechanisms - such as an income e�ect on

crime - are more consequential than the psychological e�ect. In the case of rainfall, the e�ect on homicides

is concentrated in municipalities with large proportions of the labor force working in agriculture, which

suggests an economic mechanism linking rainfall to violence. We �nd no evidence that the temperature

e�ect on homicides is attenuated by penetration of air conditioning. Results suggest that climate change

will likely increase homicides through both psychological and income-related e�ects on criminal behavior.

Keywords: violence, climate, Mexico.

∗gmccord@ucsd.edu, School of Global Policy and Strategy, University of California, San Diego†amontfor@ucsd.edu

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1 Introduction

Recent research has documented the e�ects of climate change on many social and economic outcomes (Car-

leton and Hsiang, 2016), including income per capita (Dell et al., 2012; Hsiang, 2010), growth rates (Hsiang

and Jina, 2014), agricultural output (D'Agostino and Schlenker, 2016), infectious disease (McCord, 2016),

labor productivity (Gra� Zivin and Neidell, 2014; Somanathan et al., 2015), and mortality (Deschenes, 2014;

Barreca, 2012). A growing body of work has shown a causal relationship between weather and human con�ict

(Hsiang et al., 2013; Miguel et al., 2004). Studies from diverse disciplines including psychology, anthropology,

and more recently, economics and political science, have documented the e�ect of ambient climatic conditions

on interpersonal con�ict, crime and social instability. In the case of Mexico, this question is particularly

relevant given both the prospect of climate change and the upward trend in violence during recent years.

While some research argues that weather shocks a�ect violence by changing incomes (particularly from

agriculture) and thus the opportunity cost of engaging in criminal behavior, others have argued that the

psychological e�ect of high temperature on aggressive behavior plays a prominent role. Unpacking the relative

importance of di�erent mechanism through which weather variation a�ects con�ict in the developing country

setting is important for policy prescriptions. On the one hand, e�ects of a weather shock on con�ict via

an income channel suggests that instruments such as cash transfers might be appropriate to mitigate the

e�ect. On the other hand, a psychological e�ect of heat on aggressiveness motivates investments in adaptive

responses to high temperatures, such as through di�usion of air conditioning.

To our knowledge, this paper is the �rst to use daily data on criminal behavior to distinguish between

same-day e�ects of tempearture shocks as opposed to e�ects over longer time periods. We explore the causal

e�ect of weather on violence while looking at di�erent mechanisms (economic structure, social exclusion and

lack of access to electricity) through which weather shocks might play a role in human con�ict. We �rst

aggregate the data to the monthly level to show that when municipalities experience temperatures above

that location's average, or below average rainfall, the likelihood of homicides increases. In the case of rainfall,

the e�ect on homicides is concentrated in municipalities with large proportions of the labor force working in

agriculture, which suggests an economic mechanism linking rainfall to violence. The e�ect of temperature

on homicides, on the other hand, is uniform across all types of municipalities, suggesting the possibility of

mechanisms other than income.

We then move to daily observations and �nd that hot days have an immediate e�ect on homicides,

suggesting that the e�ect of temperature partly operates through a psychological mechanism. The magnitude

of the same-day e�ect is one-fourth the magnitude of the monthly e�ect, suggesting that temperature changes

a�ect homicides both immediately and through mechanisms that do not operate on the same day.

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Section 2 presents a literature review of the academic work that has studied the relationship between

con�ict and climate. Section 3 describes the data and the econometric approach employed. Section 4 presents

the results and section 5 concludes.

2 Literature

This work builds on literature that has been succinctly detailed by Hsiang et al. (2013), who conduct a

meta-analysis of relevant studies on climate and con�ict and estimate that one standard deviation warmer

temperatures or absence of rainfall increases the frequency of interpersonal violence by 4%. The authors

underline that there is no consensus on the stories behind the causal mechanisms that link climate and

violence. On the one hand, the relationship between temperature and aggressiveness has been explored

extensively by psychology. For example, Anderson (2001) shows how, in controlled experiments, temperature

has a non-linear e�ect on aggressive behavior. Kenrick and MacFarlane (1986) also conduct an experiment to

explore the e�ect of temperature on di�erent expressions of aggressiveness, and �nd that hotter temperatures

make drivers more prone to using horns.

Other researchers have documented the e�ect of weather on crime while emphasizing the income channel

as the likely causal mechanism. Mehlum (2015) uses rainfall as an instrument for rye prices in Bavaria in

the 19th century, and shows that a one standard deviation in the rye price increased property crimes by 8%,

but reduced violent crimes. The latter e�ect is explained by an increase in the beer prices. Blakeslee and

Fishman (2017) �nd that negative rainfall shocks lead to an increase in most types of crimes in India, while

positive rainfall shocks have a negative e�ect on property crimes but not on non-property crimes. Jacob et

al. (2007) also use weather variation to test the inter-temporal correlation of crime in Dallas, and �nd that

a 10 percent increase in violent crime due to weather shocks in one week reduces criminal activity by 2.6%

in the following week.

Other authors explore the heterogenous e�ects of climate on con�ict across social groups or geographic

zones. Mares (2013) �nds that socially disadvantaged groups in St. Louis, Missouri are more prone to

experiencing high levels of violence as a consequence of climatic shocks: 20% of most disadvantaged neigh-

borhoods are predicted to experience 50% of the climate change-related increases in violence. Ranson (2014)

uses county-monthly data on crimes to show that there is a quasi-linear relationship between temperature

and most violent crimes, and a non-linear relationship for burglaries and larceny.

Although the literature on climate and con�ict is extensive, few works have studied the e�ect of weather

on interpersonal con�ict in developing countries. Sheetal and Storeygard (2011) analyze the e�ect of rainfall

shocks in India and �nd that a one standard deviation decline in annual rainfall increases domestic violence

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by 4.4% and dowry deaths by 7.8%. The increase in female homicides is a response to negative income

shocks (coming from rainfall shocks), since killings give households access to dowry payments. Similarly,

Miguel (2005) �nds that religiously motivated murders increase in Tanzania as a consequence of extreme

rainfall. Baysan et al. (2015) is the only work that explores relationship between weather and violence in

Mexico. They use data on homicides at the state-month level and �nd that temperature has the largest

e�ect on drug tra�cking-related homicides, and explore whether the e�ect is consistent with an income

mechanism. They �nd that homicides in poorer states are equally sensitive to temperature shocks compared

to rich states, whereas temperature has a stronger e�ect on suicides in richer states. They also �nd that

rainfall has no e�ect on homicides. In addition, they �nd that hotter temperatures during the growing

season have a negative e�ect on homicides (which is opposite to the expected e�ect if hotter temperatures

reduce agricultural incomes). Finally, the interactions of temperature with inequality, unemployment and

air conditioning (which has observations for only one year) do not show any statistically signi�cant e�ect.

They conclude that economic factors have a limited e�ect on explaining the observed e�ect of temperature

on violence, and the psychological channels are likely important. This paper extends their inquiry using

daily data from a di�erent source in order to better disentangle the relative e�ects of same-day psychological

e�ects of weather variation compared to e�ects over a longer term.

3 Data

3.1 Homicides

Data on homicides come from the Mexican National System Health Information (SINAIS) (2016).1 SINAIS

registered daily death certi�cates in Mexico from 1998 to 2012, including information on the cause of death,

location (state and municipality), day and time of occurrence, date of birth, sex, occupation, level of edu-

cation, and weight. For this analysis, we limit deaths to those in which the cause of death was 'intentional

injury', and excluded 'self-injury' (i.e. suicides) to focus on violent behavior against others. During this

period, there were 239,888 homicides, and the average number of homicides per municipality was 100 (0.6

per month). Figure 2 maps the average yearly homicide rate across Mexico's municipalities.

Violent deaths in Mexico increased dramatically after 2007. The causes are many, though scholars

underline President Calderon's `war on drugs', as well as structural factors (weak state capacity in local

governments) and the instability of drug cartels agreements (Escalante, 2009, 2011; Guerrero, 2009, 2011;

Merino, 2011; Hope, 2013). Figure 1 shows the time series of the monthly rate of intentional deaths. From

1The original data was cleaned and death codes standardized with WHO Global Burden of Disease codes by the Center forUS-Mexico Studies at the University of California, San Diego.

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1998 until 2007, intentional deaths were on a decreasing trend [see an explanation of the possible causes

and detailed state trends in Escalante (2009) and Escalante (2011)]. In December 2006, President Calderon

arrived to power and initiated a 'war on drugs' policy. During the following months, violent deaths decreased

and reached a minimum in February 2007. Some observers attribute this short-term reduction to a `surprise'

e�ect by which organized crime suddenly stopped violent disputes as a reaction to the government's use of

force. However, in 2007 a dramatic change in the trend of violent deaths began, reaching a maximum in

May 2011, the most violent year in recent Mexican history. After 2012, violent deaths stabilized.

3.2 Weather variables

Temperature and precipitation data come from the Mexican National Meteorology Institute (SMN).2 The

SMN has approximately 5,000 weather stations distributed across Mexico. The original data reports daily

minimum and maximum temperatures, as well as daily precipitation for each of these stations. To con-

struct municipal-level weather variables, GIS was used to calculate the distance from stations to municipal

population-weighted centroids, using gridded population data for 2010 from CIESIN (2016). Then, for each

municipality we calculated a distance-weighted average of all stations' temperature and precipitation mea-

surements within 300 km, where the weights are the inverse square distance from centroids to stations. Once

we have the distance-weighted average for minimum and maximum temperatures, we calculate the daily

average temperature as the average of the daily minimum and maximum temperatures. Figure 3 maps the

average temperature across Mexico's municipalities.

We construct both continuous measures of temperature (in degrees Celsius) and precipitation (in mm),

as well as temperature bins for every municipality. Following Barreca et al. (2013), we create 10 temperature

categories by partinioning the national distribution of temperature into deciles. This approach has the

advantage of taking into account nonlinearities in the temperature-mortality relationship (Barreca, 2012).

3.3 Agriculture variables

If the relationship between weather and violence comes from behavior responses to income shocks, we would

expect the relationship to be stronger in municipalities with more weather-dependent incomes. We test

whether agricultural areas are more prone to weather-induced violence by interacting the temperature and

precipitation variables with the percentage of the labor force working in the primary sector (agriculture,

cattle raising, forestry, �shing and hunting) from the 2010 census data (INEGI, 2010). Figure 4 maps the

percentage of the labor force in agriculture at the municipal level for 2010. As an alternative to the using

2Station-level data for daily temperature and precipitation was provided upon direct requets to the SMN(http://smn.cna.gob.mx).

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the labor force data, we also use census data on rural and urban population in each municipality in order to

test whether more rural municipalities are di�erent from urban ones in the weather-violence relationship.

3.4 Socioeconomic variables

Socioeconomic variables at the municipal level are used to test the channels through which weather a�ects

violence. The data come from the National Council of Population (CONAPO), which uses data from the

Census and Conteos of the National Institute of Statistics and Geography (INEGI). This data is available

every �ve years. The CONAPO data from the 2000 census was assigned to years 1998 to 2004 in our sample,

the 2005 data was assigned to the years 2005-2009, and the 2010 census data was merged with the 2010-2012

in our data.

The �rst variable we consider is the marginalization index, which is a measure of lack of access to

services in municipalities. CONAPO estimates it using principal components with indicators such as access

and quality of education, housing, and other services.3 A second variable is the percentage of households

without electricity, since household adaptation to warmer weather includes using fans or air conditioning.

We also incorporate two data sources on air conditioning penetration. The �rst is from INEGI's inter-

census survey in 2015, representative at the municipal level INEGI (2015). The second is from INEGI's

National Household Income and Expenditure Survey (ENIGH) for the years 2008, 2010, and 2012 INEGI

(2012). The survey is statistically representative at the state level.

4 Econometric strategy

Following a approach similar to Baysan et al. (2015), Barreca (2012) and Ranson (2014), we measure the

causal e�ect of weather on violent con�ict, controlling for unobservable characteristics at the municipal

level as well as for unobservable time factors that can be correlated both with climate and con�ict. The

set of controls include time �xed e�ects (one for every month in the sample in the month-level regressions

and one for every day in the sample in the day-level regressions). While the full set of time variable �xed

e�ects captures all potential trends and seasonality at the national level, we add state-year �xed e�ects to

�exibly allow for di�erent trends in crime and weather subnationally. We use the homicide rate per 100,000

population as the dependent variable. Given the abundance of zeroes in the data (78% of the municipal-month

observations and 98% of the municipal-day observations had no homicides) and the skewed distribution, we

3The index includes components such as the percentage of illiterate population above 15 years old, people with no primaryeducation, percentage of housing occupants with no access to electricity, piped water, or with dirty �oor, percentage of populationthat live in localities of less than 5,000 inhabitants, and percentage of working population with earnings up to 2 minimum wages.For a detailed explanation of this methodology, see CONAPO (2010).

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opt for using the inverse hyperbolic sine in order to interpret the coe�cients as if the dependent variable

were logarithmic (Burbidge et al., 1988).

The preferred speci�cation is the following regression:

HMt = α+ φTEMPMt + θPRECMt + ϕM + νt + ρS ∗ δyear + εMt (1)

where HMt is the inverse hyperbolic sine (IHS) of the average daily homicide rate (homicides per 100,000

inhabitants) in municipality M in month t in the month-level regression. In the regression using daily data,

HMt is the IHS of the daily homicide rate in municipality M on day t. Note that the monthly regression

uses the average daily homicide rate insteade of the monthly homicide rate to facilitate comparison of

estimates across the monthly and daily regressions. TEMPMt is the average temperature in Celsius degrees

in a municipality M at time t; and PRECMt is the average precipitation (in mm) in municipality M at

time t. Many of potential confounders are controlled for with a set of �xed e�ects: ϕM is the vector of

municipal �xed e�ects, which account for time-invariant municipal characteristics that may be correlated to

homicide levels and average weather. νt is the time �xed e�ect (unique for each month in the sample in the

monthly regressions, or for day in the sample in the daily regressions) to �exibly capture the global trend

and seasonality a�ecting both the rate of violence and climatic conditions in all municipalities. Finally, ρS

are state-by-year �xed e�ects to allow for �exible subnational trends. For example, there could be di�erent

trends across states in the development of their state capacity to have reliable and e�cient police corps,

which may a�ect the rate of homicides. εMt is the error term.

As has been discussed by Hsiang et al. (2013) and Dell et al. (2014), the exogenous variation in temper-

ature and precipitation conditional on time and location e�ect allows us to test the causal e�ect of climate

on violence as measured by φ and θ. In a separate speci�cation, we divide the temperature into national

deciles and regress each bin separately in order to account for non-linear e�ects of temperature on con�ict.

This approach does not impose a functional form but rather allows the data to determine the relationship

between temperature and con�ict.

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5 Results

5.1 Summary Statistics

The data are summarized in Table 1. Homicides, homicide rates, temperature and precipitation are presented

at both municipality-month and municipality-day levels, matching the two sets of analyses. Data on the rural

proportion of the population, proportion of the labor force in the primary sector, percent of the population

without electricity, and the marginalization index are available at the municipal level for census years (non-

census years are assigned the nearest census year). Data on air conditioning penetration are from two sources.

The INEGI data are at municipal level and only available for 2015. The ENIGH data are at state level and

available yearly from 2008-2012.

Figures 2 and 3 map homicide rates and temperature across Mexico. The highest rates are along the

central Paci�c coast in the states of Oaxaca, Guerrero, Michoacan, and Sinaloa, then inland and northern

states of Durango and Chihuaha. In the northeast, Tamaulipas and Nuevo Leon also exhibited areas of high

homicide rates. Note that the spatial correlation to temperatures match for the coastal Paci�c states from

Guerrero to Sinaloa, as well as the northeastern state of Tamaulipas. The homicide-temperature correlation

is far from strong, however, since the warmest parts of the country in the southeastern states have some of

the lowest homicide rates.

Figure 5 explores seasonality and crime by mapping the average homicide rate by month against the

average temperature and precipitation. With the exception of December's high homicide rate, the rest

of the year suggests a seasonality in homicides that is similar to the seasonality of temperature. The

relationship to precipitation weakly suggests that homicides rates are higher during the rainy season (April

to September) and the harvest season (October to December), though we will explore this relationship in

more detail when di�erentiating between agricultural and non-agricultural municipalities. Finally, Figure 6

separates municipalities by their average temperature, and shows that the half of municipalities with higher

temperatures do not systematically have higher homicide rates (with the exception years 2011 and 2012).

5.2 Month-Level Analysis

The relationship between weather and the rate of homicides at the monthly level is modeled in Table 2.

Speci�cations include municipal �xed e�ects to control for time-invariant municipal characteristics that may

be correlated with levels of homicides, as well as time (month) �xed e�ects in order to �exibly allow for

trends in homicides and weather at the national level (important given the seasonality of both weather and

homicides, as well as the trends in homicides in Mexico over the period). Column (I) shows the positive

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e�ect of temperature on violence, where a one-Celsius degree increase in the monthly average temperature

increases the average daily homicide rate by 0.12%. The within-municipality standard deviation of homicide

rate is 1.55 and of temperature is 2.86, so the e�ect size suggests that a one SD higher temperature in a

municipality increases the average daily homicide rate by 0.4%, or 0.0002 SD. The small size of this e�ect

is consistent with the fact that temperature is likely not a major driver of homicide variation (note that

the within-municipality R-squared is 0.0002). Note that this 0.4% e�ect for a 1 SD higher temperature is

signi�cantly smaller than the 4% result in Hsiang et al. (2013). Column (II) adds state-year �xed e�ects to

�exibly allow for subnational trends at the state level; the resulting e�ect size of temperature becomes even

smaller by 50%.

The e�ect of precipitation is also strongly signi�cant and negative, suggesting that abnormally low rain-

fall is associated with higher levels of homicides, consistent with a mechanism of negative income shocks

promoting violence in agricultural areas. The within-municipality standard deviation of precipitation is 3.5

mm/day, so the e�ect size of -0.0005 suggests that a 1σv decrease in monthly rainfall leads to a 0.2% increase

in the mean violence rate.

5.2.1 Testing for Income-Related Mechanisms

We proceed to test whether the association between weather shocks and violence is likely to operate through

economic channels by interacting the weather variables with the percentage of the labor force employed in

the agricultural sector. Since agriculture is more prone to weather-driven shocks to production than other

sectors of the economy, a stronger e�ect of temperature and weather variation in agricultural municipalities

would be consistent with an economic mechanism driving the e�ect of homicides. Column (III) interacts

the temperature and precipitation variables with a dummy variable for whether more than 15% of the mu-

nicipality's population lives in rural areas (around the 80% percentile in the data). The interaction term

with temperature is insigni�cant, suggesting that the temperature e�ect is consistent in urban and rural

municipalities. The interaction with precipitation is negative and signi�cant, suggesting that the e�ect of

lower precipitation on increased homicides is 50% stronger in rural municipalities than in urban ones. Col-

umn (IV) interacts the weather variables with the percentage of the labor force working in the agricultural

sector. Again, the interaction with temperature is insigni�cant, suggesting that the e�ect of temperature on

homicides operates equally in agricultural and non-agricultural municipalities. The interaction of precipita-

tion with the labor force variable is signi�cant and negative, again suggesting that the e�ect of precipitation

variation on homicide rates is greater in agricultural than non-agricultural municipalities. Taken together,

these results suggest that temperature is not operating through an income-related channel that a�ects farm-

ers di�erently from non-farmers, while precipitation does seem to operate through an income channel by

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showing a stronger e�ect in agricultural municipalities.

Column (V) interacts the weather variables with the marginalization index. Interestingly, it shows that

poorer municipalities (with higher values for the index) exhibit the same relationship between temperature

and homicides than wealthier municipalities. This suggests that wealthier households are not undertaking

defensive behaviors or investments that successfully mitigate the temperature e�ect. This is consistent

with the results in column (VI), which interacts the weather variables with the percentage of households

without access to electricity. The interaction with temperature is insigni�cant, suggesting that households

without electricity (and therefore without the possibility of mitigating high temperatures using fans or air

conditioning at home) do not experience a stronger response than other households with regards to changes

in homicide rates.

The results from the month-level analysis suggest that variation in temperature has a statistically sig-

ni�cant e�ect on the daily homicide rate, though it is of small magnitude. This e�ect is present among

all types of municipalities regardless of their level of urbanization, economic structure, level of poverty, or

electricity penetration, suggesting that a psychological mechanism may be playing a more important role in

how temperature a�ects violence than is the case for income-related channels.

5.3 Daily Level Analysis

In order to test whether temperature variation is a�ecting homicide rates at the daily level (thus suggesting a

role of physiological mechanisms as opposed to income-related mechanisms that would not vary daily), Table

3 repeats the month-level analysis using daily data. Column (I) regresses the IHS of the homicide rate on the

same day's temperature and precipitation, and �nds that both are strongly associated (to 99% con�dence)

with the within-municipality variation in the homicide rate. The coe�cient on temperature suggests that

a one degree increase in temperature leads to a 0.03% increase in the homicide rate. This is one-quarter

the e�ect size of the monthly regressions. Column (II) adds state-by-year �xed e�ects to �exibily allow

for di�ering subnational trends. This slightly reduces the coe�cient on temperature, but both coe�cients

remain strongly signi�cant. Figure 7 plots the non-parametric estimate of the relationship between homicide

rates and the same-day temperature, after partialing out municipality and time �xed e�ects, as well as

precipitation. The �gure suggests that while the e�ect of large positive temperature anomalies might be

slightly nonlinear, the linear model is not a bad approximation of the overall relationship. Figure 8 shows

the results of using a semiparametric speci�cation to explore whether di�erent temperatures have di�erent

marginal e�ects. The data are binned into temperature deciles; the Figure shows that the marginal e�ect of

moving across temperature bins is roughly consistent over the range of temperatures in the data (regression

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coe�cients are in Appendix Table 5), again supporting the use of the linear model.

Figure 9 presents the result of a distributed lag model using the daily-level data of homicides and temper-

ature after controlling for rainfall, time (day) and municipality �xed e�ects. The results suggest that higher

temperatures of one degree Celsius have a same-day e�ect of increasing the homicide rate, and the e�ect

does not continue into the following days. The fact that subsequent days do not have a negative coe�cient

show that the temperature increase does not lead to a death that would have happened anyway in the coun-

terfactural (that is, there is no evidence of �harvesting�). The fact that the homicide rate is only correlated

to the same-day temperature strongly points to a psychological mechanism between heat and violence, given

that a mechanism in which actors respond to expected income shocks would unlikely be operating at the

level of daily weather variation.

5.3.1 Testing for Income-Related Mechanisms

Columns (III)-(VI) in Table 3 test whether the same-day relationship between homicides and weather

vary across di�erent types of municipalities. In particular, di�erent e�ects between agricultural and non-

agricultural municipalities might suggest that the same-day e�ect operates through an income-related chan-

nel. Column (III) interacts the weather variables with a dummy variable for whether more than 15% of

the population in the municipality lives in rural areas. The coe�cient on the interaction with temperature

is negative and signi�cant, suggesting that a one degree increase in temperature raise that day's homicide

rate by 0.04% in urban municipalities, and 0.03% in rural municipalities. The e�ect of precipitation is equal

across rural and urban muncipalities. Columns (IV) interacts the weather variables with the labor force in

agriculture, and �nds that the interactions are not statistically signi�cant. This suggests that the same-day

e�ect of temperature on homicides is the same in agricultural and non-agricultural municipalities, lending

further evidence for a psychological mechanism driving the relationship. The absence of a di�erence across

agricultural and non-agricultural municipalities is consistent with the �ndings in Figure 10, which does not

�nd that the e�ect of temperature is di�erent during the growing season, the harvest season, and the rest

of the year. With the exception of June having a larger coe�cient, the other months of the year have

statistically consistent coe�cients.

Column (V) interacts the weather variables with the marginalization index. Interacting the weather

variables with the index as a continuous variable does not lead to signi�cant e�ects, instead we report the

interaction using a binary variable of whether the municipality is in the tenth percentile of marginalization.

The result shows that these poorest municipalities have a 50% larger e�ect of temperature on homicides.

Finally, Column (VI) interacts the weather variables with a binary for the 90th percentile of lack of electricity

penetration to test whether households with electricity are able to mitigate the e�ects of higher temperatures

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(perhaps through capital investments in fans or air conditioners). The interaction with temperature is

positive and signi�cant, and the magnitude indicates that municipalities with low electricity penetration have

a 50% larger e�ect. This suggest that households with access to electricity partly reduce the consequences

of high temperature (perhaps through reducing exposure using physical capital, or by spending less time

outside when their homes are cooler).

Table 4 directly tests whether municipalities with more penetration of air conditioning experience dif-

ferent e�ects of temperature shocks on homicides. There are two (imperfect) measures of air conditioning

penetration: the �rst is at the municipal level, but only for the year 2015 (three years after our study period).

The second is at the state level, and available every year from 2008-2012. We run both month-level and

day-level regressions, interacting temperature with these two a/c penetration variables in turn. Column (I)

presents a month-level regression, assigning the 2015 measure of a/c to each municipality, and limiting the

sample to 2008-2012. The interaction with temperature is negative, but not statistically signi�cant. Column

(II) assigns the state-level a/c penetration to each municipality within it, and again �nds no signi�cant

e�ect of a/c on the e�ect of temperature. Note that we do not include the state-by-year FE in this estimate

since that is the unit of observation of the a/c variable. Column (III) moves to daily data, and interacts

the municipal level a/c data with temperature, again �nding no signi�cant e�ect. Column (IV) uses the

state-level a/c data instead, and �nds the same null result on the interaction. Overall, we �nd no evidence

that a/c penetration leads to a strong attenuation of the e�ect of temperature on homicides, although the

a/c data available was not well matched to our study.

Finally, Figure 11 shows the geographic distribution of the marginal e�ect of temperature on violence.

This map was constructed by interacting temperature with indicator variables for each state. Most states

show a statistically signi�cant positive e�ect of same-day temperature on violence, suggesting that our results

are not being driven by a speci�c part of the country. Moreover, the distribution of magnitudes does not

match agricultural areas nor other relevant spatial patterns.

5.4 Varying Temporal Aggregation

As a �nal exercise, we measure the e�ect of temperature increases over various time spans prior to the

observed homicides. Figure 12 plots the coe�cients from regressing the daily homicide rate on average

temperature over various time spans preceeding the observation, from one day to one year. The coe�cient

decreases as the time span lengthens, and loses statistical signi�cance beyond two months. This suggests that

short-run e�ects of homicides on temperature dominate long-term e�ects. Figure 13 plots the coe�cients

on each 2 degree bin, adding the number of days in each bin over various time spans preceeding the daily

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observations (from one day to two months). The graphs shows that the strongest association between

temperature shocks and homicides occur on the day of the shock, while adding a warm day shows little

evidence on the homicided rate days or weeks later. This provides further evidence of a contemporaneous

e�ect of temperature on homicides, in line with a psychological mechanism.

5.5 Robustness

Given the count nature of homicide events, we include in our analysis a set of Poisson speci�cations using

the number of homicides in a given time period (month or day) as opposed to the homicide rate presented

in our main results. Table 6 presents regressions at the monthly level. The results are consistent with those

in our main speci�cation, with a one degree increase in temperature from the municipality's average that

month leading to a 0.5% increase in homicides. This e�ect is stronger in rural municipalities, but otherwise

consistent across locations regardless of how agricultural the economy is, or the degree of marginalization

or electri�cation. Table 7 shows the results of Poisson speci�cations using the daily data. The results are

consistent: a one degree increase in temperature increases the daily homicide count by 1%, with the e�ect

being strongest in rural areas. Note that in both the monthly and daily regressions, the Poisson speci�cations

lead to larger magnitudes than the main results.

6 Conclusion

Using panel data for Mexican municipalities, we are the �rst paper in the climate and crime literature to

employ daily level analysis in order to distinguish between slower-moving income channel e�ects of weather

as opposed to same-day e�ects operating through human psychology. Month-level analysis shows that abnor-

mally hot months have higher homicide rates, and the e�ect is consistent across all types of municipalities.

Months with lower than average rainfall experience higher homicide rates, in particular in municipalities

that are rural, agricultural, poorer and lacking electricity penetration. This is consistent with income shocks

being the mechanism through which weather a�ects homicide rates. Daily data suggest that the same-day

psychological e�ect of temperature on crime is one-fourth the magnitude of the e�ect at monthly resolu-

tion, and it is evident across all types of municipalities (particularly those with low electricity penetration).

While this analysis shows evidence for both income and psychological channels through which weather a�ects

violence, the magnitude of these e�ects is small.

13

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16

Figures and Tables

Figure 1: Total Homicides, by Month

Figure 2: Average Yearly Homicide Rate per 100,000 people, by Municipality

17

Figure 3: Average Temperature, by Municipality

Figure 4: % Labor Force in Agriculture

Source: INEGI (2015)

Legend

0% - 10%

11% - 25%

26% - 50%

51% - 75%

76% - 96%

18

Figure 5: Seasonality in Homicide Rates and Weather

Figure 6: 1998-2012 Homicide Rates, High vs. Low Avg. Temperature Municipalities

19

Figure 8: Semiparametric Estimate of Same-Day Temperature E�ect

Figure 7: Daily Temperature Deviations and Same-Day Homicide Rates

20

Figure 10: Same-Day E�ect of Temperature on Homicides, by Month

Figure 9: Daily Distributed Lag Model for Temperature

21

Figure

11:Same-Day

E�ectofTem

perature

onHomicides,byState

22

Figure 12: Coe�cient on Temperature, at varying temporal aggregation

Figure 13: Coe�cients on 2-Degree Temperature Bins, by Time Span

23

Table 1: Descriptive StatisticsVariable N Mean St. Dev. Min Max

Year 374,815 2005.5 4.2 1998 2012

Rural Population (% of total) 374,815 0.14 0.29 0 1

% labor force in agricultural sector 374,464 0.39 0.23 0.00 0.96

% population without electricity 374,562 0.07 0.11 0.00 0.99

Marginalization Index 374,562 -0.03 1.02 -2.45 4.50

Air Conditioning Penetration by Municipality in 2015 (INEGI) 373,031 0.05 0.13 0 0.87Air Conditioning Penetration by State-Year 2008-2012 (ENIGH) 137,943 0.08 0.15 0 0.78Monthly Level:

Homicides 374,815 0.64 3.92 0 487

Average Daily Homicide Rate per 100,000 population 374,815 0.06 0.5 0 69.6

Temperature 374,815 20.3 4.3 1.7 37.5

Precipitation (mm/day) 374,815 2.90 3.5 0.0 65.6

Daily Level:

Homicides 11,045,364 0.03 0.23 0 96

Homicide Rate per 100,000 population 11,045,364 0.05 1.54 0 1,268

Temperature 11,045,364 20.4 4.5 -13.6 39.0

Precipitation (mm)shows the 11,045,364 2.9 6.5 0 486

24

Table2:Weather

andviolence,Monthly

Level

DependentVariable

IHSofMonth'sAverageDailyHomicideRate

per

100,000

IndependentVariables

(I)

(II)

(III)

(IV)

(V)

(VI)

AverageDailyTem

perature

(C)

0.0012***

0.0007**

0.0006**

0.0007**

0.0009***

0.0008***

(min

andmax)

(0.0003)

(0.0003)

(0.0003)

(0.0003)

(0.0003)

(0.0002)

AverageDailyPrecipitation(m

m)

-0.0005***

-0.0004**

-0.0003

0.0006**

-0.0003

-0.0003

(0.0002)

(0.0002)

(0.0002)

(0.0003)

(0.0002)

(0.0002)

Tem

p*I(>15%

Rural)

0.0003

(0.0005)

Precip*I(>15%

Rural)

-0.0014***

(0.0004)

Tem

p*%

Laborin

Agriculture

0.0003

(0.0006)

Precip*%

Laborin

Agriculture

-0.002***

(0.0005)

MarginalizationIndex

0.001

(0.007)

Tem

p*MarginalizationIndex

0.0002

(0.0001)

Precip*MarginalizationIndex

-0.0005***

(0.0001)

%PopulationwithoutElectricity

0.02

(0.03)

Tem

p*%

noElectricity

0.0005

(0.001)

Precip*%

noElectricity

-0.003**

(0.001)

Observations

374,814

374,814

374,814

362,127

362,420

362,420

Within

R-squared

0.0002

0.0001

0.0002

0.0002

0.0002

0.0001

Number

ofmunicipalities

2,337

2,337

2,337

2,307

2,309

2,309

MunicipalFE

Yes

Yes

Yes

Yes

Yes

Yes

Tim

eFE

Yes

Yes

Yes

Yes

Yes

Yes

State*YearFE

No

Yes

Yes

Yes

Yes

Yes

Robust

standard

errors

clusteredatthemunicipallevel.Constantterm

andcoe�cients

onmunicipalandtimedummiesnotshown.

***p<0.01,**p<0.05,*p<0.1

25

Table3:Weather

andviolence,DailyLevel

DependentVariable

IHSofHomicideRateper

100,000

IndependentVariables

(I)

(II)

(III)

(IV)

(V)

(VI)

(VII)

AverageDailyTem

perature

(C)

0.0003***

0.0002***

0.0004***

0.0004***

0.0004***

0.0003***

0.0003***

(min

andmax)

(0.00005)

(0.00005)

(0.0001)

(0.0001)

(0.0001)

(0.0001)

(0.00004)

AverageDailyPrecipitation(m

m)

-0.0001***

-0.00005***

-0.0001***

-0.00003*

-0.00005***

-0.00005***

-0.0001***

(0.00001)

(0.00001)

(0.00001)

(0.00002)

(0.00001)

(0.00001)

(0.00001)

Tem

p*I(>15%

Rural)

-0.0001*

(0.00005)

Precip*I(>15%

Rural)

-0.00001

(0.00002)

Tem

p*%

Laborin

Agriculture

-0.0001

(0.0001)

Precip*%

Laborin

Agriculture

-0.00004

(0.00004)

I(MarginalizationIndex

Top10Ptile)

-0.002

(0.002)

Tem

p*I(Marg

Top10Ptile)

0.0002**

(0.0001)

Precip*I(Marg

Top10

Ptile)

-0.00005*

(0.00003)

I(>18%

Pop.withoutElectricity)

0.001

(0.002)

Tem

p*I(>18%

Pop.w/oElec)

0.0002**

(0.00009)

Precip*I(>18%

Pop.w/oElec)

-0.00007**

(0.00004)

Observations

11,378,626

11,378,626

11,378,646

11,367,986

11,371,040

11,371,040

11,3178,626

Within

R-squared

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

Number

ofmunicipalities

2,337

2,337

2,337

2,335

2,337

2,337

2,337

MunicipalFE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Tim

eFE

Date

Date

Date

Date

Date

Date

Yr,Mo&

DOW

Trend

No

State*YearFE

No

No

No

No

No

Robust

standard

errors

clusteredatthemunicipallevel.Constantterm

andcoe�cients

onmunicipalandtimedummiesnotshown.

***p<0.01,**p<0.05,*p<0.1

26

Table 4: Air Conditioning Penetration

Dependent VariableIHS of Daily Homicide Rate per 100,000

Independent Variables (I) (II) (III) (IV)Average Daily Temperature (C) 0.0007 0.0001 0.0002* 0.0002*(min and max) (0.0006) (0.0006) (0.0001) (0.0001)Average Daily Precipitation (mm) -0.0004* -0.0007*** -0.00008*** -0.00008***

(0.0003) (0.0003) (0.00002) (0.00002)Temp * Municipal A/C 2015 -0.0002 0.00003

(0.001) (0.0003)State A/C Penetration 2008-2012 0.38*** 0.12***

(0.07) (0.02)Temp * State A/C 2008-2012 0.001 0.0002

(0.001) (0.0002)Temporal Resolution Monthly Monthly Daily DailyYears Included 2008-2012 2008-2012 2008-2012 2008-2012Observations 137,284 137,942 4,179,133 4,199,162Within R-squared 0.0001 0.0001 0.0000 0.0001Number of municipalities 2,326 2,337 2,326 2,337Municipal FE Yes Yes Yes YesTime FE Date Date Date DateState*Year FE Yes No No NoRobust standard errors clustered at the municipal level. Constant term and coe�cients on municipal and time dummies not shown.

*** p<0.01, ** p<0.05, * p<0.1

27

Appendix

Table 5: Temperature BinsDependent Variable

IHS of Homicide Rate per 100,000Independent Variables (I)Temperature Bin 1 (T < 14.6) -0.0018***

(0.0004)Temperature Bin 2 (14.6< T <16.5) -0.0012***

(0.0003)Temperature Bin 3 (16.5< T <17.9) -0.0007***

(0.0003)Temperature Bin 4 (17.9< T <19.2) 0.000004

(0.0002)Temperature Bin 6 (20.3< T <21.4) 0.0004

(0.0003)Temperature Bin 7 (21.4< T <22.7) 0.0005*

(0.0003)Temperature Bin 8 (22.7< T <24.3) 0.0012***

(0.0003)Temperature Bin 9 (24.3< T < 26.4) 0.0024***

(0.0004)Temperature Bin 10 (> 26.4) 0.0036***

(0.0005)Precipitation -0.0001***

(0.00001)Observations 11,378,626Within R-squared 0.000Number of munid 2,337Municipal FE YesTime FE YesLinear Trend NoRobust standard errors clustered at the municipal level.

Constant term and coe�cients on municipal and time dummies not shown.

*** p<0.01, ** p<0.05, * p<0.1

28

Table6:PoissonSpeci�cation-Weather

andviolence,Monthly

Level

DependentVariable

Number

ofHomicides

IndependentVariables

(I)

(II)

(III)

(IV)

(V)

(VI)

AverageDailyTem

perature

(C)

0.0061

0.00515**

0.0052

0.0053**

0.0043

0.0057**

(min

andmax)

(0.0044)

(0.0025)

(0.0025)

(0.0024)

(0.0043)

(0.0024)

AverageDailyPrecipitation(m

m)

-0.0099*

-0.0012

-0.0008

0.0036

-0.0022

0.0013

(0.0058)

(0.0036)

(0.0037)

(0.0041)

(0.0033)

(0.0038)

Tem

p*I(>15%

Rural)

0.013**

(0.0063)

Precip*I(>15%

Rural)

-0.024***

(0.008)

Tem

p*%

Laborin

Agriculture

0.0021

(0.007)

Precip*%

Laborin

Agriculture

-0.018***

(0.006)

MarginalizationIndex

-0.36***

(0.12)

Tem

p*MarginalizationIndex

-0.0007

(0.002)

Precip*MarginalizationIndex

-0.0027**

(0.0012)

%PopulationwithoutElectricity

2.23***

(0.38)

Tem

p*%

noElectricity

0.0018

(0.014)

Precip*%

noElectricity

-0.039***

(0.015)

Observations

374,814

374,814

374,814

374,463

374,561

374,561

Number

ofmunicipalities

2,337

2,337

2,337

2,335

2,337

2,337

MunicipalFE

Yes

Yes

Yes

Yes

Yes

Yes

Tim

eFE

Yes

Yes

Yes

Yes

Yes

Yes

State*YearFE

No

Yes

Yes

Yes

Yes

Yes

Robust

standard

errors

clusteredatthemunicipallevel.Constantterm

andcoe�cients

onmunicipalandtimedummiesnotshown.

***p<0.01,**p<0.05,*p<0.1

29

Table7:PoissonSpeci�cation-Weather

andviolence,DailyLevel

DependentVariable

Number

ofHomicides

IndependentVariables

(I)

(II)

(III)

(IV)

(V)

(VI)

AverageDailyTem

perature

(C)

0.011***

0.009***

0.011***

0.010***

0.013***

0.011***

(min

andmax)

(0.002)

(0.002)

(0.002)

(0.003)

(0.003)

AverageDailyPrecipitation(m

m)

-0.003***

-0.002***

-0.003***

-0.0016

-0.004***

-0.002**

(0.001)

(0.001)

(0.001)

(0.001)

(0.001)

Tem

p*I(>15%

Rural)

0.011*

(0.006)

Precip*I(>15%

Rural)

-0.011***

(0.004)

Tem

p*%

Laborin

Agriculture

0.009

(0.006)

Precip*%

Laborin

Agriculture

-0.007***

(0.003)

MarginalizationIndex

0.35*

(0.18)

Tem

p*MarginalizationIndex

0.001

(0.001)

Precip*MarginalizationIndex

-0.0013**

(0.006)

%PopulationwithoutElectricity

2.23***

(0.50)

Tem

p*%

noElectricity

-0.003

(0.008)

Precip*%

noElectricity

-0.01**

(0.006)

Observations

11.4

million

11.4

million

11.4

million

11.4

million

11.4

million

11.4

million

Number

ofmunicipalities

2,337

2,337

2,337

2,335

2,337

2,337

MunicipalFE

Yes

Yes

Yes

Yes

Yes

Yes

Tim

eFE

Year&

Month

&DOW

FE

State*YearFE

No

Yes

No

No

No

No

Year*Month

FE

No

Yes

No

No

No

No

Robust

standard

errors

clusteredatthemunicipallevel.Constantterm

andcoe�cients

onmunicipalandtimedummiesnotshown.

***p<0.01,**p<0.05,*p<0.1

30