Psychological Mechanisms in the Temperature-Crime Relationship in Mexico

30 Pages·2017·7.07 MB·English

Losing your Cool: Psychological Mechanisms in the
Temperature-Crime Relationship in Mexico
Gordon C. McCord∗
Aleister Montfort†
February 17, 2017
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Abstract
We investigate the role of weather (cid:29)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(cid:27)ect of weather on violence, looking at di(cid:27)erent mechanisms (eco-
nomic structure, social exclusion and lack of access to electricity) through which weather shocks might
play a role in human con(cid:29)ict. We are the (cid:28)rst paper to add daily level analysis to this literature, allow-
ing us to uniquely distinguish between slower-moving income channel e(cid:27)ects of weather as opposed to
same-day e(cid:27)ects in line with human psychology literature. We (cid:28)nd that hotter days have an immediate
same-daye(cid:27)ectonhomicides,supportinghypothesesfocusingonpsychologicale(cid:27)ectsofheat. However,
themagnitudeofthesame-daye(cid:27)ectisone-fourththemagnitudeofthemonthlye(cid:27)ect,andevensmaller
compared to a 6-month e(cid:27)ect, suggesting that slower-moving mechanisms - such as an income e(cid:27)ect on
crime-aremoreconsequentialthanthepsychologicale(cid:27)ect. Inthecaseofrainfall,thee(cid:27)ectonhomicides
is concentrated in municipalities with large proportions of the labor force working in agriculture, which
suggests an economic mechanism linking rainfall to violence. We (cid:28)nd no evidence that the temperature
e(cid:27)ectonhomicidesisattenuatedbypenetrationofairconditioning. Resultssuggestthatclimatechange
willlikelyincreasehomicidesthroughbothpsychologicalandincome-relatede(cid:27)ectsoncriminalbehavior.
Keywords: violence, climate, Mexico.
∗[email protected],SchoolofGlobalPolicyandStrategy,UniversityofCalifornia,SanDiego
†[email protected]
1
1 Introduction
Recent research has documented the e(cid:27)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),
laborproductivity(Gra(cid:27)ZivinandNeidell,2014;Somanathanetal.,2015),andmortality(Deschenes,2014;
Barreca,2012). Agrowingbodyofworkhasshownacausalrelationshipbetweenweatherandhumancon(cid:29)ict
(Hsiangetal.,2013;Migueletal.,2004). Studiesfromdiversedisciplinesincludingpsychology,anthropology,
andmorerecently,economicsandpoliticalscience,havedocumentedthee(cid:27)ectofambientclimaticconditions
on interpersonal con(cid:29)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(cid:27)ect violence by changing incomes (particularly from
agriculture) and thus the opportunity cost of engaging in criminal behavior, others have argued that the
psychologicale(cid:27)ectofhightemperatureonaggressivebehaviorplaysaprominentrole. Unpackingtherelative
importanceofdi(cid:27)erentmechanismthroughwhichweathervariationa(cid:27)ectscon(cid:29)ictinthedevelopingcountry
setting is important for policy prescriptions. On the one hand, e(cid:27)ects of a weather shock on con(cid:29)ict via
an income channel suggests that instruments such as cash transfers might be appropriate to mitigate the
e(cid:27)ect. Ontheotherhand, apsychological e(cid:27)ect ofheaton aggressivenessmotivates investments inadaptive
responses to high temperatures, such as through di(cid:27)usion of air conditioning.
To our knowledge, this paper is the (cid:28)rst to use daily data on criminal behavior to distinguish between
same-daye(cid:27)ectsoftempeartureshocksasopposedtoe(cid:27)ectsoverlongertimeperiods. Weexplorethecausal
e(cid:27)ectofweatheronviolencewhilelookingatdi(cid:27)erentmechanisms(economicstructure, socialexclusionand
lack of access to electricity) through which weather shocks might play a role in human con(cid:29)ict. We (cid:28)rst
aggregate the data to the monthly level to show that when municipalities experience temperatures above
thatlocation’saverage,orbelowaveragerainfall,thelikelihoodofhomicidesincreases. Inthecaseofrainfall,
the e(cid:27)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(cid:27)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 (cid:28)nd that hot days have an immediate e(cid:27)ect on homicides,
suggestingthatthee(cid:27)ectoftemperaturepartlyoperatesthroughapsychologicalmechanism. Themagnitude
ofthesame-daye(cid:27)ectisone-fourththemagnitudeofthemonthlye(cid:27)ect,suggestingthattemperaturechanges
a(cid:27)ect homicides both immediately and through mechanisms that do not operate on the same day.
2
Section 2 presents a literature review of the academic work that has studied the relationship between
con(cid:29)ictandclimate. Section3describesthedataandtheeconometricapproachemployed. Section4presents
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(cid:29)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
extensivelybypsychology. Forexample,Anderson(2001)showshow,incontrolledexperiments,temperature
hasanon-lineare(cid:27)ectonaggressivebehavior. KenrickandMacFarlane(1986)alsoconductanexperimentto
explorethee(cid:27)ectoftemperatureondi(cid:27)erentexpressionsofaggressiveness,and(cid:28)ndthathottertemperatures
make drivers more prone to using horns.
Otherresearchershavedocumentedthee(cid:27)ectofweatheroncrimewhileemphasizingtheincomechannel
as the likely causal mechanism. Mehlum (2015) uses rainfall as an instrument for rye prices in Bavaria in
the 19thcentury, and shows thata one standarddeviation inthe rye priceincreased property crimes by 8%,
but reduced violent crimes. The latter e(cid:27)ect is explained by an increase in the beer prices. Blakeslee and
Fishman (2017) (cid:28)nd that negative rainfall shocks lead to an increase in most types of crimes in India, while
positive rainfall shocks have a negative e(cid:27)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 (cid:28)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(cid:27)ects of climate on con(cid:29)ict across social groups or geographic
zones. Mares (2013) (cid:28)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-
borhoodsarepredictedtoexperience50%oftheclimatechange-relatedincreasesinviolence. 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(cid:29)ict is extensive, few works have studied the e(cid:27)ect of weather
oninterpersonalcon(cid:29)ictindevelopingcountries. SheetalandStoreygard(2011)analyzethee(cid:27)ectofrainfall
shocks in India and (cid:28)nd that a one standard deviation decline in annual rainfall increases domestic violence
3
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) (cid:28)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 (cid:28)nd that temperature has the largest
e(cid:27)ect on drug tra(cid:30)cking-related homicides, and explore whether the e(cid:27)ect is consistent with an income
mechanism. They(cid:28)ndthathomicidesinpoorerstatesareequallysensitivetotemperatureshockscompared
to rich states, whereas temperature has a stronger e(cid:27)ect on suicides in richer states. They also (cid:28)nd that
rainfall has no e(cid:27)ect on homicides. In addition, they (cid:28)nd that hotter temperatures during the growing
season have a negative e(cid:27)ect on homicides (which is opposite to the expected e(cid:27)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(cid:28)cant e(cid:27)ect.
They conclude that economic factors have a limited e(cid:27)ect on explaining the observed e(cid:27)ect of temperature
on violence, and the psychological channels are likely important. This paper extends their inquiry using
dailydatafromadi(cid:27)erentsourceinordertobetterdisentangletherelativee(cid:27)ectsofsame-daypsychological
e(cid:27)ects of weather variation compared to e(cid:27)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(cid:28)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
1TheoriginaldatawascleanedanddeathcodesstandardizedwithWHOGlobalBurdenofDiseasecodesbytheCenterfor
US-MexicoStudiesattheUniversityofCalifornia,SanDiego.
4
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
arrivedtopowerandinitiateda’warondrugs’policy. Duringthefollowingmonths,violentdeathsdecreased
andreachedaminimuminFebruary2007. Someobserversattributethisshort-termreductiontoa‘surprise’
e(cid:27)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-
surementswithin300km,wheretheweightsaretheinversesquaredistancefromcentroidstostations. 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),
aswellastemperaturebinsforeverymunicipality. FollowingBarrecaetal.(2013),wecreate10temperature
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
Iftherelationshipbetweenweatherandviolencecomesfrombehaviorresponsestoincomeshocks, wewould
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, (cid:28)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).
5
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(cid:27)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(cid:27)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(cid:28)veyears. TheCONAPOdatafromthe2000censuswasassignedtoyears1998to2004inoursample,
the2005datawasassignedtotheyears2005-2009,andthe2010censusdatawasmergedwiththe2010-2012
in our data.
The (cid:28)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 (cid:28)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(cid:27)ect of weather on violent con(cid:29)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(cid:29)ict. The
set of controls include time (cid:28)xed e(cid:27)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 (cid:28)xed
e(cid:27)ects captures all potential trends and seasonality at the national level, we add state-year (cid:28)xed e(cid:27)ects to
(cid:29)exibly allow for di(cid:27)erent trends in crime and weather subnationally. We use the homicide rate per 100,000
populationasthedependentvariable. Giventheabundanceofzeroesinthedata(78%ofthemunicipal-month
observations and 98% of the municipal-day observations had no homicides) and the skewed distribution, we
3Theindexincludescomponentssuchasthepercentageofilliteratepopulationabove15yearsold,peoplewithnoprimary
education,percentageofhousingoccupantswithnoaccesstoelectricity,pipedwater,orwithdirty(cid:29)oor,percentageofpopulation
thatliveinlocalitiesoflessthan5,000inhabitants,andpercentageofworkingpopulationwithearningsupto2minimumwages.
Foradetailedexplanationofthismethodology,seeCONAPO(2010).
6
opt for using the inverse hyperbolic sine in order to interpret the coe(cid:30)cients as if the dependent variable
were logarithmic (Burbidge et al., 1988).
The preferred speci(cid:28)cation is the following regression:
H =α+φTEMP +θPREC +ϕ +ν +ρ ∗δ +ε (1)
Mt Mt Mt M t S year Mt
whereH istheinversehyperbolicsine(IHS)oftheaveragedailyhomiciderate(homicidesper100,000
Mt
inhabitants) in municipality M in month t in the month-level regression. In the regression using daily data,
H is the IHS of the daily homicide rate in municipality M on day t. Note that the monthly regression
Mt
uses the average daily homicide rate insteade of the monthly homicide rate to facilitate comparison of
estimates across the monthly and daily regressions. TEMP is the average temperature in Celsius degrees
Mt
in a municipality M at time t; and PREC is the average precipitation (in mm) in municipality M at
Mt
time t. Many of potential confounders are controlled for with a set of (cid:28)xed e(cid:27)ects: ϕ is the vector of
M
municipal (cid:28)xed e(cid:27)ects, which account for time-invariant municipal characteristics that may be correlated to
homicide levels and average weather. ν is the time (cid:28)xed e(cid:27)ect (unique for each month in the sample in the
t
monthly regressions, or for day in the sample in the daily regressions) to (cid:29)exibly capture the global trend
and seasonality a(cid:27)ecting both the rate of violence and climatic conditions in all municipalities. Finally, ρ
S
are state-by-year (cid:28)xed e(cid:27)ects to allow for (cid:29)exible subnational trends. For example, there could be di(cid:27)erent
trends across states in the development of their state capacity to have reliable and e(cid:30)cient police corps,
which may a(cid:27)ect the rate of homicides. ε is the error term.
Mt
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(cid:27)ect allows us to test the causal e(cid:27)ect of climate
on violence as measured by φ and θ. In a separate speci(cid:28)cation, we divide the temperature into national
deciles and regress each bin separately in order to account for non-linear e(cid:27)ects of temperature on con(cid:29)ict.
This approach does not impose a functional form but rather allows the data to determine the relationship
between temperature and con(cid:29)ict.
7
5 Results
5.1 Summary Statistics
ThedataaresummarizedinTable1. Homicides,homiciderates,temperatureandprecipitationarepresented
atbothmunicipality-monthandmunicipality-daylevels,matchingthetwosetsofanalyses. Dataontherural
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-
censusyearsareassignedthenearestcensusyear). Dataonairconditioningpenetrationarefromtwosources.
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(cid:28)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(cid:28)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(cid:27)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(cid:28)cationsincludemunicipal(cid:28)xede(cid:27)ectstocontrolfortime-invariantmunicipalcharacteristicsthatmay
be correlated with levels of homicides, as well as time (month) (cid:28)xed e(cid:27)ects in order to (cid:29)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
8
e(cid:27)ect of temperature on violence, where a one-Celsius degree increase in the monthly average temperature
increasestheaveragedailyhomiciderateby0.12%. Thewithin-municipalitystandarddeviationofhomicide
rate is 1.55 and of temperature is 2.86, so the e(cid:27)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(cid:27)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(cid:27)ect for a 1 SD higher temperature is
signi(cid:28)cantly smaller than the 4% result in Hsiang et al. (2013). Column (II) adds state-year (cid:28)xed e(cid:27)ects to
(cid:29)exibly allow for subnational trends at the state level; the resulting e(cid:27)ect size of temperature becomes even
smaller by 50%.
The e(cid:27)ect of precipitation is also strongly signi(cid:28)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(cid:27)ect size of -0.0005 suggests that a 1σ(cid:118) decrease in monthly rainfall leads to a 0.2% increase
in the mean violence rate.
5.2.1 Testing for Income-Related Mechanisms
Weproceedtotestwhethertheassociationbetweenweathershocksandviolenceislikelytooperatethrough
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(cid:27)ect of temperature and weather variation in agricultural municipalities
would be consistent with an economic mechanism driving the e(cid:27)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(cid:28)cant, suggesting that the temperature e(cid:27)ect is consistent in urban and rural
municipalities. The interaction with precipitation is negative and signi(cid:28)cant, suggesting that the e(cid:27)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(cid:28)cant, suggesting that the e(cid:27)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(cid:28)cant and negative, again suggesting that the e(cid:27)ect of precipitation
variation on homicide rates is greater in agricultural than non-agricultural municipalities. Taken together,
theseresultssuggestthattemperatureisnotoperatingthroughanincome-relatedchannelthata(cid:27)ectsfarm-
ers di(cid:27)erently from non-farmers, while precipitation does seem to operate through an income channel by
9
showing a stronger e(cid:27)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(cid:27)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(cid:28)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(cid:28)cant e(cid:27)ect on the daily homicide rate, though it is of small magnitude. This e(cid:27)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(cid:27)ects violence than is the case for income-related channels.
5.3 Daily Level Analysis
Inordertotestwhethertemperaturevariationisa(cid:27)ectinghomicideratesatthedailylevel(thussuggestinga
roleofphysiologicalmechanismsasopposedtoincome-relatedmechanismsthatwouldnotvarydaily),Table
3repeatsthemonth-levelanalysisusingdailydata. Column(I)regressestheIHSofthehomiciderateonthe
same day’s temperature and precipitation, and (cid:28)nds that both are strongly associated (to 99% con(cid:28)dence)
with the within-municipality variation in the homicide rate. The coe(cid:30)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(cid:27)ect size of the monthly regressions. Column (II) adds state-by-year (cid:28)xed e(cid:27)ects to (cid:29)exibily allow
for di(cid:27)ering subnational trends. This slightly reduces the coe(cid:30)cient on temperature, but both coe(cid:30)cients
remainstronglysigni(cid:28)cant. Figure7plotsthenon-parametricestimateoftherelationshipbetweenhomicide
rates and the same-day temperature, after partialing out municipality and time (cid:28)xed e(cid:27)ects, as well as
precipitation. The (cid:28)gure suggests that while the e(cid:27)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(cid:28)cation to explore whether di(cid:27)erent temperatures have di(cid:27)erent
marginal e(cid:27)ects. The data are binned into temperature deciles; the Figure shows that the marginal e(cid:27)ect of
moving across temperature bins is roughly consistent over the range of temperatures in the data (regression
10