Weather Shocks, Agriculture, and Crime: Evidence

from India

David S Blakeslee⇤ Ram Fishman†

January 31, 2017

Abstract

We use detailed crime, agriculture, and weather data from India during the years 1971-

2000 to conduct a systematic analysis of the relationship between weather shocks and

multiple categories of crime. We find that drought and heat exert a strong impact

on virtually all types of crimes, that the impact on property crimes is larger than for

violent crimes, and that this relationship has been relatively stable over three decades

of economic development. We then use seasonal and geographical disaggregations of

weather and agricultural cultivation to examine the prevailing hypothesis that agricul-

tural income shocks drive the weather-crime relationship in developing countries. The

patterns we find are consistent with this hypothesis in the case of rainfall shocks, but

suggest additional mechanisms may play an important role in driving the heat-crime

relationship, consistent with evidence from industrialized countries.

Keywords: Crime, Income Shocks, Weather Shocks, Climate, Agriculture

JEL Codes: O10, O13, Q54

⇤New York University - Abu Dhabi. Email: davidsblakeslee@nyu.edu

†Tel Aviv University and George Washington University. Email: ramf@post.tau.ac.il

1

1 Introduction

Growing interest in the potential impacts of climate change has spurred a substantial liter-

ature on the connection between weather conditions and conflict. This literature has now

produced a substantial body of evidence that weather shocks increase civil conflict (Hsiang,

Burke, and Miguel, 2013; Burke et al., 2009), which has dovetailed neatly with a related

literature on the economic determinants of civil conflict (Collier and Hoeffler, 1998, 2004;

Miguel, Satyanath, and Sergenti, 2004). A smaller number of papers, focused mostly on in-

dustrialized countries, have examined the relationship between weather and personal crime

(Ranson, 2014).

In this paper, we use weather and crime data spanning several hundred Indian districts

over three decades (1971-2000) to conduct a systematic analysis of the connections between

heat, drought, and a range of property and violent crimes that is unique in its spatial

and temporal scale; in the range of crimes investigated; and in the integrated analysis of

potentially correlated temperature and rainfall variability.

We find strong evidence that both elevated heat and drought lead to substantial increases

in crime rates of virtually all categories. During years in which precipitation is more than

one standard deviation below the local long-term mean, crime rates increase by about 5%,

while temperatures one standard deviation above the local mean are associated with a

3% increase in crime. We also find that, though average crime rates have declined over

time, the relationship between crime and weather has remained remarkably stable, despite

the considerable economic development and structural change occurring across these years.

This stability suggests that economic development, as it has hitherto unfolded, may have

limited ability to shield vulnerable sectors of society from the warming and increased rainfall

variability that is expected to occur over the coming decades as a result of climate change.

Having established the effect of weather shocks on crime, we make use of the spatial and

temporal extent of our data to make headway in understanding the mechanisms at work,

an issue which remains a major gap in the literature (Hsiang, Burke, and Miguel, 2013).

Previous studies of weather-crime connections in industrialized countries have tended to be

confined to temperature fluctuations, and to explain their correlation with crime through

a psychological mechanism relating heat to aggression (Anderson et al., 2000; Auliciems

and DiBartolo, 1995; Card and Dahl, 2011; Cohn and Rotton, 1997; Jacob, Lefgren, and

Moretti, 2007; Kenrick and MacFarlane, 1986; Larrick et al., 2011; Mares, 2013; Ranson,

2

2014; Rotton and Cohn, 2000). In contrast, the few studies of weather-crime connections

in developing countries have generally been confined to rainfall fluctuations, and invoked

an income mechanism to explain their results (Miguel, 2005; Mehlum, Miguel, and Torvik,

2006; Sekhri and Storeygard, 2014; Fetzer, 2014). The latter hypothesis is based on the sen-

sitivity of agricultural productivity, wages, and employment to rainfall fluctuations in many

developing countries, including in India (Guiteras, 2009; Jayachandran, 2006; Kaur, 2014;

Fishman, 2016), and the large share of agriculture in both rural income and employment.

It is also well grounded in the economic theory of crime, pioneered by Becker (1968), which

predicts that individuals experiencing negative income shocks will be more likely to resort

to criminal activity.1

As plausible as these hypotheses may be, previous studies have tended to assume rather

than test them.2 Moreover, their validity has increasingly been called into question by the

growing literature on the multi-faceted impacts of weather shocks. Temperature shocks in

particular are known to have deleterious effects on a range of economic outcomes (Dell, Jones,

and Olken, 2014), including not only agricultural output (Deschenes and Greenstone, 2007;

Lobell, Schlenker, and Costa-Roberts, 2011; Schlenker and Lobell, 2010; Guiteras, 2009;

Fishman, 2016; Colmer, 2016), but also labor markets and productivity in non-agricultural

sectors (Hsiang, 2010; Dell, Jones, and Olken, 2012; Sudarshan and Tewari, 2013; Zivin and

Neidell, 2014; Deryugina and Hsiang, 2014); while also affecting a number of psychological

(Anderson et al., 2000), cognitive (Zivin, Hsiang, and Neidell, 2015; Park, 2016), and health-

related (Burgess et al., 2013) outcomes. Because of the multiple channels through which

temperature shocks may affect crime and other forms of conflict, particularly in developing1Beginning with Becker (1968), an extensive literature has invoked individual utility optimization to

explain the economic factors driving the decision to engage in criminal activities. Particular emphasis is

given to the opportunity costs of engaging in crime, which consist of the foregone income and other penalties

in the event of capture, with the general prediction that reductions in legal income due to economic shocks

will reduce the opportunity cost of crime and thereby increase its incidence. A number of studies have

subsequently sought to empirically test the posited relationship between economic incentives and crime

(see Freeman (1999), for a review); and have generally found a relationship between economic distress and

the incidence of crime (Gould, Weinberg, and Mustard, 2002; Machin and Meghir, 2004; Grogger, 1997).

Virtually all of these studies are based on industrialized countries, however, with the result that little is

known about how income shocks affect crime in the developing world, precisely those places most susceptible

to highly disruptive economic shocks, and in which individuals are least able to insure themselves against

large drops in income. Moreover, since economic conditions can themselves be influenced by the prevalence

of crime (Bourguignon, 2000), and because unobservable characteristics such as institutions and culture may

influence both the dependent and independent variables, identifying the causal relationship between the two

is a perennial challenge.

2In fact, the well established relationship between rainfall and agricultural income has led some re-

searchers (e.g., Miguel, Satyanath, and Sergenti, 2004) to use rainfall incidence as an instrumental variable

for estimating the causal relationship between income and civil conflict, which effectively assumes that rain-

fall does not affect conflict by any channel other than agricultural income. Recent studies have sought

to test this assumption more closely by inspecting whether infrastructure (Sarsons, 2015) or social policy

(Fetzer, 2014) that can protect income from rainfall shocks weaken the rainfall-civil conflict relationship,

and find conflicting results.

3

countries, it essential to gather evidence that would shed light on the actual mechanisms at

work.

Our analysis focuses on the role and relative importance of agricultural income in driving

the associations we observe between heat, drought, and crime. Our approach consists of

a systematic comparison of various types of heterogeneity in the patterns relating weather

(temperature and precipitation) and crime, on the one hand, and those relating weather and

agricultural production, on the other. Disagreement between these patterns is counted as

evidence against the agricultural income hypothesis, while correspondences are interpreted

as strengthening it. Four types of heterogeneity are examined. The first makes use of the

seasonal structure of the Indian agricultural calendar, wherein agricultural production de-

pends most vitally on weather during the monsoon season. This approach contrasts with

that of previous studies, which have tended to rely on annual weather, thereby facilitating a

closer analysis of the relevant mechanisms at play, while simultaneously improving precision

by removing the noise introduced through the inclusion of weather events of little economic

importance.3 The second makes use of geographical variation in climatic conditions, includ-

ing average precipitation, which varies widely across different regions, and the existence of

a second monsoon in parts of southern and eastern India. The third utilizes geographical

differences in cultivation practices, including in the extent of dry-season cultivation and the

use of irrigation, both of which have strong implications for the predicted effects of weather

shocks on crime. The fourth compares property and non-property crime rates, and tests

the simple theoretical prediction that property crimes should be more strongly responsive

to income shocks than violent crimes

In the case of precipitation, these comparisons generally yield results consistent with the

agricultural income hypothesis. Drought during the principal rainy season, the Southwest

Monsoon, which is also the main cultivation period, has strong negative effects on agricul-

tural production and strong positive effects on crime rates. The effect on property crimes is

larger, in a statistically significant way, than on non-property crimes. Similar patterns are

found for drought occurring during a second rainy season, the Northeast Monsoon, in those

parts of India subject to it:4 when the Northeast Monsoon is weak, agricultural production

suffers and crime rates increase. Excessive rainfall during the primary rainy season also3In results not shown, we find that the estimated impacts of weather shocks on crime are reduced by the

use of the annual, as opposed to seasonal, measures of weather variation.

4This monsoon sweeps along the eastern seaboard up through northeast India just as the summer monsoon

is subsiding across much of the subcontinent.

4

leads to reductions in agriculture and increases in crime, though the effects are smaller than

for negative rainfall shocks. Moreover, this result obtains only in those areas having drier

climates and therefore specializing in “dryland crops” which are more susceptible than other

crops to damage in the face of extreme rainfall.5 Finally, irrigation dams, which are asso-

ciated with lower weather-sensitivity of agricultural output in downstream districts, appear

to lower the impact of negative rainfall shocks on crime as well, though insufficient power

prevents us from establishing this result conclusively.

The parallel evidence in the case of temperature shocks is also broadly consistent with

the existence of an agricultural mechanism. High temperature shocks that occur during

the primary agricultural season, which lead to substantial declines in agricultural output,

also lead to an increase in crime, and the estimated impact is larger for property than non-

property crime. In addition, the increase in crime with rainy season temperature shocks

is substantially larger than that found with positive temperature shocks occurring during

equally hot periods of the year that lie outside the primary agricultural season, suggesting

that an agricultural mechanism is at least partially responsible for the primary season re-

sults. Even the smaller impact on crime outside the primary season is consistent with an

agricultural mechanism: while the extent of cultivation during these periods is lower, yields

also suffer from the same temperature shocks that increase crime; and the effect of these

off-monsoon temperature shocks on crime is larger in those parts of the country where off-

monsoon cultivation is more extensive. These comparisons are strikingly consistent with an

agricultural income channel, though power limitations render verdicts of varying statistical

precision.

The agricultural income channel, however, is unable to explain all of the patterns we

observe for the effect of temperatures. Positive temperature shocks during the second mon-

soon, for example, lead to an increase in crime despite there being no corresponding de-

crease in agricultural income. In addition, negative temperature shocks occurring during

the monsoon season, which if anything increase agricultural income, simultaneously lead

to an increase in certain types of crime. The latter result, though not readily explicable

in terms of income-based channels, could potentially be explained through the effects of

favorable temperatures on social interactions, a phenomenon for which evidence has been5

Papaioannou (2016) provides historical evidence in favor of such an interpretation.

5

found in the U.S. (Ranson, 2014).

We interpret the sum of the evidence emerging from these various comparisons to be

consistent with the agricultural income hypothesis, i.e. that agricultural productivity shocks

drive at least part of the observed effects of weather on crime . Even though our analysis

doest not claim to, and is unable to fully explain each and every impact of weather shocks,

it constitutes an important step forward in the establishing some of the the mechanisms

mediating the weather-crime relationship. Our results should not be taken as precluding

the presence of channels of influence other than agricultural income, but they do support the

proposition that agricultural incomes are a central, if not exclusive, mechanism mediating

the weather-crime relationship.

The remainder of the paper is organized as follow. Section 2 describes our data and the

empirical approach. Section 3 presents results relating weather shocks to both agriculture

and crime. Section 4 investigates the agricultural income hypothesis further by comparing

agro-climatic heterogeneities in the impacts of weather shocks on crime and agriculture.

Section 5 discusses how the effects of weather on crime have changed across time, and

section 6 concludes.

2 Data and Empirical Strategy

2.1 Data Sources

2.1.1 Crime Data

Data on crime rates was obtained from India’s National Crime Records Bureau (INCRB),

housed under the Ministry of Home Affairs. INCRB produces annual documents on national

and sub-national crime trends, including detailed statistics on the annual incidence of various

types of crimes at the district levels, beginning in 1971.6 The types of crime for which data is

available and we will use here include burglary, robbery, banditry, theft, riots, murder, rape,

and kidnapping.7 Our main outcome variable is the logarithm of the number of incidents

occurring per 100,000 people in each district and year (population data are extrapolated

linearly from Indian census data). Figure A.1 displays the geographical distribution of the6Crime data is also available before 1971, but only at the state level.

7Robbery is distinguished from theft in that it involves violence in the commission of the crime. Banditry

is distinguished from robbery in that it is committed by five or more individuals.

6

average rates of the different crime categories in our data. Figure A.2 presents plots of India-

wide average crime rates over time, and table 1 presents some summary statistics. The three

columns report the incidence of the indicated crime across the three decades spanning 1971-

2000, and indicate a general decline in the incidence of most property crimes, particularly in

burglary, banditry, thefts, and robbery. Kidnapping was relatively stable across this period,

while riots have slightly declined. Murder and rape show a small increase.

A small number of observations (less than 2% of the sample for most crimes)8 report

zero crime incidence, making the outcome variables, the logarithm of crime rates, undefined.

We follow the approach of Pakes and Griliches (1980), who replace this outcome variable

with zero where the crime incidence is zero, and then include a binary indicator of this

sub-sample in the regressions.

2.1.2 Weather Data

Weather data is based on gridded daily precipitation and daily mean temperature data pro-

duced by the Indian Meteorological Department (Rajeevan et al., 2005; Srivastava, Rajeevan,

and Kshirsagar, 2009) and converted to district-wise figures by area-weighted averaging over

grid points falling within a given district (see Fishman, 2016 for more details). We make

use of this daily data in order to calculate summary measures of the annual weather real-

ization (precipitation and temperature) in each district, which we then use in the regression

analysis.

Our measure of precipitation consists of the total seasonal amount. Almost all rainfall

in India occurs during the Southwest Monsoon season (figure 1), which lasts from June to

September, though some parts of southern and eastern India receive a substantial portion

of their rainfall during a second monsoon season, the Northeast Monsoon, which occurs

between October and December (figure A.3). We follow the terminology of the Indian

meteorological department and denote June-September as the “monsoon” season, October-

December as the “post-monsoon” season, and March-May as the “pre-monsoon” season.9

Our interest in the linkage between weather, agriculture, and crime leads us to make

use of the growing season Degree Days as our measure of heat exposure. This measure is

considered to be more relevant for crop growth than average temperatures, and is commonly

used in empirical studies of weather-agriculture relationships, including in India (Schlenker,8The one exception to this is banditry, for which zero values are encountered 7% of the time.

9This classification scheme is described at https://en.wikipedia.org/wiki/Climate_of_India, which gives

useful details of the Indian seasonal calendar.

7

Hanemann, and Fisher, 2006; Guiteras, 2009; Fishman, 2016). Degree Days are defined by:

DDS =X

d

D(Tavg,d

)

D(T ) =

8>>>>>><

>>>>>>:

0 if T 8oC

T � 8 if 8oC < T 32oC

24 if T > 32oC

,

where Tavg,d

is the mean temperature in day d. The daily resolution of our temperature data

allows us to calculate degree days explicitly in each district, over the time period of interest,

which will usually consist of the monsoon season, but we will also make use of degree days

calculated during the pre and post-monsoon periods. We note that the pre-monsoon season

is the hottest period of the year in most of India (figure 1).

2.1.3 Agricultural Data

The main agricultural growing season in India, the Kharif, coincides with the monsoon

(June to September), when rain-fed cultivation is possible. For the poor in particular, who

lack access to irrigation, this season is the primary source of agricultural income. Rainy

season crops, of which rice is the predominant one, are typically planted in June to July

and harvested between September and December, depending on the specific crop and region

(figure 1). Production during this season is sensitive to both monsoon precipitation and

temperatures.

The secondary agricultural season, during which the Rabi crop is cultivated,10 begins in

November-January (sowing times vary by crop and region) and can last until as late as May.

Crops grown during this season, of which wheat is the predominant one, rely on monsoon

precipitation for soil moisture (or irrigation water), but they are also sensitive to in-season

(pre-monsoon) heat. In those parts of India that receive an additional rainy season in the

post-monsoon period (October-December), the agricultural calendar may differ somewhat,

since these secondary rains enable either a longer duration Kharif crop, or an additional

crop outside the monsoon period. As a result, production in these regions in both the main

and secondary seasons can be affected by low rainfall in the post-monsoon period.

District level agricultural data is obtained from two sources. The first consists of the10

In some regions there is even a third crop, which is grown at the end of the Rabi season during the hot

months prior to the monsoon

8

data used by Duflo and Pande (2007), which contains information on aggregate production,

yields (production per unit cultivated area) and wages. Production and yield are measured

in Rupees, and calculated as the total value of the production of the principal crops, using

constant 1961-5 prices. These data are defined on an annual basis, and are aggregated over

the main season of the year in question and the following secondary season, even though

the latter technically occurs in the following calendar year.

To complement this data with seasonally disaggregated agricultural output measures,

which are of importance to our analysis, we also make use of the India Harvest database

produced by the Center for the Monitoring of the Indian Economy (see Fishman, 2016 for

a complete description), which contains information on the crop-specific area, production,

and yield of major crops during both of the two agricultural seasons.11 Summary statistics

reported in table 1 show the substantial improvements in agricultural production which

occurred during the period of our study. We note that each of the two agricultural data sets

contain substantial numbers of missing observations (see Duflo and Pande, 2007).

These two sets of measures each have advantages and disadvantages as indicators of

agricultural performance. While outcomes for individual crops provide a richer and more

precise seasonal disaggregation, they may fail to capture shifts in cropping decisions across

crops, which may be correlated with weather. In addition, numerous crops are cultivated in

India, and it is not clear how to balance potentially divergent responses to weather shocks

across crops. Annually aggregated production provides a summary measure of output that

accounts for shifts in cropped areas and cross-seasonal impacts, but relies on the specific

weights used for aggregation (we follow Duflo and Pande, 2007, and Guiteras, 2009, in using

1960-65 prices).

Determining the appropriate agricultural indicator for an analysis of crime poses addi-

tional challenges. Ideally, one would observe the distribution of household-level agricultural

income within a district-year, and compare income shocks and crime incidence. Because such

data is unavailable at the required spatio-temporal resolution, we instead use district-level

data on agricultural output and wages, which have proven elsewhere to capture important

dimensions of rural welfare in India (Jayachandran 2006; Kaur 2014). Without information

on the timing of crime and the identity of its perpetrators, it is difficult to know which agri-

cultural outcome is most relevant. For example, if landless agricultural laborers are those

most likely to resort to crime, then agricultural wages or employment may be the most11

This data set has the drawback that it is unbalanced for all but the few most important crops.

9

relevant agricultural variable. If crime is instead typically being committed by land-owning

smallholders, then the yields of the crops typically cultivated by these farmers might be of

primary interest. Uncertainty regarding the identity of perpetrators also has implications

for the relevant timing of weather shocks as well: wage laborers may be most economically

vulnerable to early season weather fluctuations, when cropping decisions are made; whereas

smallholder farmers would be vulnerable to shocks occurring throughout the agricultural

cycle.

In what follows, we remain agnostic on the specific household-level mechanisms driving

the crime response, and report estimates for several agricultural outcomes, including aggre-

gate annual production, yield, and wage, and the production of the primary crop in each

season (main and secondary).12 The various agricultural indicators tend to display effects

that are well correlated in sign. In the appendix we report impacts on the production of

specific crops.

2.1.4 District Partitioning

An important issue in empirical studies on India for which the unit of observation is the

district is the substantial partitioning of districts that has occurred over time. This makes

it challenging to combine district-level data sets and to assign spatial data to observations

correctly. It also raises the question of the correct determination of the relevant unit for

capturing time-invariant unobservables. One approach is to fix district boundaries at a

certain early date, and then aggregate outcomes occurring after districts are partitioned

to the original boundaries. We prefer to follow a more conservative approach, in which a

distinct district is observed only during the continual period of time in which its boundaries

remained un-modified. Whenever a district is partitioned into two new districts, for example,

we consider the two new districts as separate from the original one, assigning separate fixed

effects to these separate districts in our panel regressions. In this way, we do not make any

assumptions about the resulting changes in institutions, district population, resources, and

local governance, which can be substantial. To implement this approach, we use detailed

records of the partitioning and formation of districts over the period of the study Kumar and

Somanathan (2009), and assign every district the appropriate agricultural and crime data.

Weather data is coded using the year 2000’s district boundaries, but we employ weighted12

The primary crop for a given district-season is defined as the crop that occupies the largest average area

in a district in a given season.

10

averaging to assign it to historical districts. Overall, our data spans 590 districts and a 30

year time period. However, given the way we define districts, every district is “observed”

only during its period of actual existence. The average number of observations per district

is 16, and the total sample size is about 9200.

2.2 Empirical Strategy

The basic structure of the models we estimate takes the form:

Log(Zist

) = ↵+ �W

ist

+ �

t

+ �

i

+ f

s

(t) + ✏

ist

. (1)

Here, Z is an outcome variable of interest, indexed by district (i), state (s), and year (t).

These outcomes will consist of the rates of various crime, and of agricultural outcomes

(production, yield, etc.). The main explanatory variable, W , is a vector of summary mea-

sures of annual weather (temperature and precipitation) constructed from daily data. The

model also includes district fixed effects, year fixed effects, and state specific time trends

(�i

, �t

, and f

s

(t), respectively),13 in order to absorb time-invariant district characteristics,

sample-wide annual shocks, and secular time trends that can lead to spurious correlations

between productivity, crime, and weather. As explained above, separate district fixed ef-

fects are assigned to every administrative district, and districts created from partitions or

combinations of existing districts are considered to be distinct, and assigned independent

fixed effects. Because our main outcome variable is defined in terms of per-capita crime oc-

currence, we weight each observation by that district’s population, as reported in the 1971

census.

In calculating the regression’s standard errors, we employ a two-way clustering procedure

in order to simultaneously account for within-district serial correlation in errors and within-

year spatial correlation in weather shocks (Conley and Molinari, 2007; Hsiang, 2010; Fetzer,

2014). In our benchmark specification, we allow for arbitrary serial correlation, and spatial

correlations extending up to 300 KMs.

2.2.1 Specifying Weather Shocks

There are two aspects of the manner in which we specify weather realizations that warrant

discussion. First, we choose to specify weather variables not in terms of their physical levels,13

Are main specification uses linear, state specific time trends, Specifications using quadratic time trends

were also estimated, and yield nearly identical results.

11

but in terms of the magnitude of their deviation from the local mean of this weather variable,

calculated in terms of the localized standard deviation over the 1970-2005 period. We refer

to these as the standardized variables.14 This seems a natural approach to the modeling of so

extreme a behavioral response as crime, to which individuals presumably resort only when

other, less extreme coping mechanisms that are typically available to economically distressed

households have been exhausted. From this perspective, it is reasonable to expect crime

to respond more highly to shocks that are extreme in terms of their rarity, rather than in

terms of their physical levels, as one might expect for the purely physical response of crop

yields to weather. In an appendix, we test the robustness of our main results to alternative

specifications.

Second, we choose to specify the functional form of these standardized weather deviations

through the use of two binary variables (for each weather variable), which we denote by

W

+and W

�, that indicate whether they differ from the local long-term mean by more

than one standard deviation in either direction.15 This specification is motivated by the

non-linear relationship between weather shocks, crime, and agriculture, suggested by figures

5-7, which portray local linear regressions of crime incidence and agricultural output on

our two main weather variables (standardized). We also estimate richer semi-parametric

regressions that include a larger number of binary indicators to capture more sub-ranges at

which standardized weather deviations may occur. Both of these aspects of our specification

are similar to those used in other papers that investigate the impacts of rainfall shocks on

district level outcomes in India, which make use of binary indicators of the extremity of a

rainfall shock in terms of the long-term localized rainfall distribution (e.g. Jayachandran,

2006; Kaur, 2014). For completeness, however, we also test additional weather specifications

used in other studies (e.g., Duflo and Pande, 2007; Jayachandran, 2006) in an appendix.

As explained above, we decompose annual weather into the three seasons of the monsoon,

the pre-monsoon, and the post-monsoon, and include separate shock indicators for each of

them.14

Note that while, as discussed above, we observe crime and agriculture in a given district only during the

years in which the district actually exists as an identical administrative units, we calculate these standardized

weather deviations by using the long-term (35 years) weather observed in the same location.

15More explicitly, the binary variable indicate if the standardized variable is higher than 1 or lower than

-1.

12

2.2.2 Temporal Structure

Figure 2 displays the temporal structure of our model specification. As depicted at the

bottom of the figure, our principal agricultural outcome, annual production in year t, is

defined as the sum of production during the primary agricultural season (generally June-

December of year t) and the following secondary agricultural season (January-May of year

t + 1). Production in the primary season is affected by rainfall and temperatures during

the monsoon; while production in the secondary season is affected both by weather in the

preceding monsoon16 as well as temperatures during the pre-monsoon period (as depicted

in figure 2), with little rainfall occurring during the secondary season itself. In order to

properly capture the effects of weather shocks on agriculture, our agricultural regressions

therefore use monsoon weather shocks occurring during the rainy season of year t, and

pre-monsoon weather shocks that occur during the calendar year t + 1 as the explanatory

variables. As we will discuss below, in some parts of India there is a secondary monsoon

season that occurs from October to December. In these areas, rainfall during this period

can affect crops grown during both the primary season and the following secondary season.

Our crime outcome variable consists of crime incidence over the course of a given calendar

year t. In our main specifications, we include weather shocks occurring within the same

calendar year in which crime is reported. These include: weather shocks occurring during

the monsoon season of year t (June-September), which impact the production of crops that

are planted around June and harvested up to December of year t; pre-monsoon weather

shocks occurring during March-May of calendar year t, which affect the production of crops

planted around January of year t and harvested up to May; and, in some specifications, post-

monsoon shocks occurring in October-December of year t, which impact the production of

crops grown during both the main season of year t and secondary season of year t+ 1.

There is a possibility that weather shocks occurring in the previous calendar year, t� 1,

may also affect crime rates in year t. For example, in areas experiencing a secondary

monsoon season (Northeast Monsoon) during October-December, weather shocks occurring

during the latter season of year t � 1 may affect crops that are grown and harvested in

the secondary season of year t; which, in turn, may lead to an increase in crime in year

t . The primary monsoon season (Southwest Monsoon) too may exert a lagged effect on

crime, as weather shocks during June-September of year t�1 also affect agricultural output16

The secondary season crops rely on moisture derived from the accumulation of rain water in soils,

aquifers, and surface water bodies.

13

during the dry season (January-May) of year t, though their influence is smaller than that of

secondary monsoon shocks. We therefore examine these effects in additional specifications

that control for “lagged” weather shocks.

2.2.3 Specification of Crime Regressions

We estimate two types of regressions in which crime rates are the outcome variable. In the

first, we pool together all crime categories in a single regression; in the second we estimate

the same specification separately for each crime, with fixed effects and time effects adjusted

accordingly. To be explicit, the specification employed is the pooled model is:

ln(ycist

) = ↵+ �1 · P+it

+ �2 · P�it

+ �3 · T+it

+ �4 · T�it

+ �

c,t

+ �

c,i

+ f

c,s

(t) + "

cist

(2)

where the natural log of the incidence of crime c (per 100k people) in district i, state s,

year t is regressed on binary indicators of positive and negative precipitation (P+, P

�) and

degree-days shocks ( T

+, T

�) in year t, district i. In estimating these pooled regressions,

we allow errors associated with different types of crime observations to be correlated, in

addition to employing temporal and spatial clustering.

As discussed above, simple theoretical considerations suggest income shocks should have

a larger impact on property crimes. We will therefore also separately estimate regressions

for property and non-property crime rates. Of the crimes included in the data, we classify

burglary, banditry, theft, robbery, and riots as property crimes, and murder, rape, and kid-

napping as non-property crimes. Of these, kidnapping and rioting would seem to occupy

an ambiguous place; however, closer scrutiny justifies this classification. Kidnapping, for

example, is disproportionately targeted against women, for reasons not entirely, or even

principally, economic.17 Riots are known to occur during times of economic duress, par-

ticularly in response to heightened food prices, and are often characterized by widespread

looting.18

17The data indicates that the 74 % of kidnappings are targeted against women, though this disaggregation

is only reported after 1987. Though others have classified this as an economic crime, such a classification

overlooks this important non-economic component.

18Note that these are distinct from the Hindu-Muslim riots analyzed by Bohlken and Sergenti (2010).

14

3 The Effects of Weather Shocks on Agriculture and

Crime

In this section, we examine whether weather shocks that disrupt agricultural production also

tend to increase crime rates. To do so, we regress both agricultural production and crime

rates on a range of weather shocks and compare the estimates. In most tables that follow,

we facilitate the comparison by reporting, side by side, regressions of the two outcomes on

the same set of weather shocks. Our analysis unfolds by gradually increasing the range of

weather shocks considered, thereby scrutinizing the thesis that agriculture meditates the

effects on crime in greater detail. For crime outcomes, we report results that pool all types

of crimes into a single regression; results that pool all property crimes together; results

that pool all non-property crimes together; and separate estimates for individual crime

categories. For agricultural outcomes, we focus on measures of agricultural production used

by Duflo and Pande (2007): the logarithm of the total value of production of the main crops,

calculated using 1960-65 prices, the logarithm of the yield (production per area), and the

agricultural labor wage rate. In addition, for the purpose of seasonal disaggregation, we also

report impacts on the (logarithm) of the yield of the primary crop in both the main and

secondary seasons, the primary crop in each district being defined as the crop that occupies

the largest average area in that district in the season in question.19 We also report, in

Appendix table A.1 results for the yields of the most important specific crops.

3.1 Weather Shocks in the Main Growing Season (Monsoon)

We begin by estimating a model that follows much of the weather-agriculture literature in

India in that it is focused on deviations in two variables: total precipitation and degree-days

in the main growing season (the monsoon season of June-September). We also control for the

same weather indicators calculated separately during the pre-monsoon season (March-May),

for reasons that will become clearer when we discuss their estimates in the next sub-section.

Regression results are reported in table 2. Column (1) reports estimates that pool all

types of crime into a single regression. Columns (2)-(6) report results of similar models

in which the dependent variables are the agricultural outcomes mentioned above. The

statistically significant estimates in columns (1) and (2) indicate that the same monsoon19

We are unable to calculate a complementary measures of seasonal aggregate production because pro-

duction data is missing for a substantial number of observations for all but the few most important crops.

15

weather shocks that reduce agricultural production also tend to increase crime. Negative

rainfall shocks are associated with a 15% decrease in production, a 9.5% decline in yields,

a 2.5% decline in the wage rate, and a 4.8% increase in crime incidence. Positive rainfall

shocks are associated with an (imprecise) 2.8 % decrease in production, 2.4% decrease in

yields, 2.6% decrease in wages, and a 1.5% increase in crime. Positive temperature shocks

are associated with a 8.4% decrease in production, a 4.8% decline in wages, and a 3.3%

increase in crime. In appendix table (A.1) we report the impact of these weather shocks on

the yields of specific crops, and find similar effects.

The impacts we observe on agricultural production are in line with basic agronomic

considerations and with previous statistical studies of weather-agriculture linkages in India

and elsewhere (Guiteras, 2009; Auffhammer, Ramanathan, and Vincent, 2012; Fishman,

2016). Drought (negative rainfall shocks) limits moisture availability for crops grown during

both the monsoon season and the subsequent season; excessive rainfall (positive rainfall

shocks), under certain conditions, can damage crop yields (more on this later); and excessive

heat (positive shocks to degree days) harms crop maturation and reduces yields.20

In table 3 we report results that disaggregate property and non-property crimes. Column

(1) reports estimates that only includes property crimes, column (2) reports estimates that

only includes non-property crimes, and column (3) reports the p-value for a test of the

equality of the coefficients for property and non-property crimes. The estimates in columns

(1) and (2) exhibit point estimates that tend to be larger for property crimes than for

non-property crimes: whereas negative rainfall shocks lead to a 5.8% increase in property

crimes, they lead to an almost 50% smaller (3.1%) increase in non-property crimes, with the

difference between the two being statistically significant at the 10% level (p-value=0.07).

Similarly, whereas positive temperature shocks lead to a 3.8% increase in property crimes,

they lead to a smaller (and statistically insignificant) 2.4% increase in non-property crimes,

though the difference is imprecisely estimated (p-value=0.32).

We next estimate the effects of the same weather shocks on each individual crime cate-

gory. In addition to the inherent intrigue of the effects for individual crimes, these estimates

are instructive for two reasons. First, they allow us to assess whether the differential effect

on property versus non-property crimes is sensitive to the classification adopted for kidnap-

ping and riots, which, as noted in the introduction, are of somewhat ambiguous economic20

Though the yield effect of high temperatures isn’t evidenced in this table, appendix table (A.1) shows

the expected effect when looking at individual crops.

16

content. Second, they allow us to explicitly observe effects on two crime categories, murder

and robbery, which are considered to be less susceptible to under-reporting (Fajnzylber, Le-

derman, and Loayza, 1998),21 and thereby provide a partial check of concerns about biased

reporting of other crime categories. The results are reported in columns (4)-(11) of table 3,

and plotted in figure 5. All five property crimes and two of the three non-property crimes

display a statistically significant increase with negative rainfall shocks. As was found in the

pooled regressions, the statistically significant effects on property crimes tend to be larger

than the effects on non-property crimes: riots increase by 7.0%, burglary by 6.7%, banditry

by 7.3%, thefts by 3.5%, and robbery by 4.2%. Among non-property crimes, murder in-

creases by 3.0%, kidnapping by 3.4%, and rape by a statistically insignificant 2.2%. Positive

rainfall shocks only increase banditry in a statistically significant way (by 4.7%). Positive

temperature shocks are associated with a 7.6% increase in banditry, a 5.8% increase in riots,

and a 3.0% increase in murder. We do not find evidence that the impacts on robbery and

murder stand out in comparison to other types of crime, suggesting that under-reporting is

unlikely to be systematically biasing our estimates.

We also estimate the relationship between weather shocks and crime using a richer

model that categorizes weather deviations into a larger number of “shock categories,” or

z-score bins: shocks that are greater than 0.5, 1, and 1.5 standard deviations above and

below the local mean. Table 4 reports the results of these semi-parametric regressions for

the pooled crime categories. Appendix figures A.4 and A.5 and table A.2 report similar

estimates for individual crime categories. Crime rises steadily with larger negative rainfall

deviations, while positive rainfall 1.5 standard deviations above the mean is associated with

a 3.8% increase in crime. The relationship between crime and temperature shocks is noisier:

while crime increases with positive temperature shocks in the 1.0-1.5 z-score bin, it drops

precipitously thereafter, to become indistinguishable from zero.

There is also an evident increase in crime with low temperature shocks, which is driven

by increases in burglary, banditry, robbery, and murder. This increase is not accompanied

by similar responses of agricultural production; and while we are unable to explain its

occurrence through an income mechanism, we note that previous studies have hypothesized

that favorable weather conditions may also lead to increases in crime by increasing social21

Though under-reporting is generally a concern in such contexts, there are good reasons to think that it

would, if anything be biasing our results downwards: specifically, it is not implausible that under-reporting

would increase at precisely those times when crime rates are high, which would have the effect of reducing

the absolute magnitude of the coefficient for adverse weather shocks.

17

interactions (Ranson, 2014).22 Whatever the merit of this thesis, we stress again that we

do not claim here to explain all aspects of the weather-agriculture relationship. Rather,

our purpose is to test the hypothesis that weather shocks that affect agriculture also affect

crime rates, and whether this occurs because they affect agriculture. This hypothesis does

not preclude the possibility that weather shocks have effects on crime which operate through

additional channels. The relative magnitude of the estimated impacts of weather shocks on

agriculture and crime are noteworthy. Figure 6 displays a scatter plot of the estimated

impacts of the four weather shocks that are found to have significant effects on crime (table

2). Though not supporting a definitive conclusion, the correlation of both sets of effects

is rather striking, and consistent with a single underlying mechanism linking the effects of

weather on income and crime. Thus interpreted, all four sets of point estimates point to an

elasticity of crime with respect to agricultural product which is in the order of 0.3-0.55.

3.2 Weather Shocks in the Secondary Growing Season (Pre-Monsoon)

The pre-monsoon period (March-May) is as hot, if not more so, than the June-September

period (figure 1), and experiences little rain. Consequently, the extent of cultivation during

this period is substantially lower than during the monsoon, and weather deviations have

a concomitantly smaller effect on annual agricultural incomes. The estimates reported in

table 2 show that pre-monsoon positive temperature shocks have a significant effect on crime

rates (1.9% increase). The smaller magnitude of this impact in comparison to monsoon

temperature shocks, despite average temperatures across the two periods being equally hot,

is therefore suggestive of the presence of an agricultural mechanism driving at least part

of the effect of monsoon temperatures on crime (though the difference between the two is

imprecisely estimated).

It is important to note, however, that even though cultivation during this time is not as

prevalent as during the main growing season, crops grown during the secondary season are

still likely to be in the field during the pre-monsoon period, and their yields can therefore

be sensitive to temperatures during this time. Indeed, while the estimated impact of pre-

monsoon positive temperature shocks on annual agricultural production is a negative but

imprecise 3.4%, crop-specific estimates, reported in appendix table A.1, show significant

impacts on the yields of of prominent dry-season crops. Therefore, the increase in crime22

In the hot climate prevailing across most of India, cooler than normal temperatures, especially during the

Monsoon, often qualify as favorable, and therefore are likely to increase the incidence of social interactions.

18

with pre-monsoon positive temperature shocks may also be consistent with an agricultural

mechanism, with cultivation in the pre-monsoon period simply constituting a far smaller

share of annual agricultural income. Below, we examine this possibility more carefully by

comparing the impacts on crime and agriculture in areas which have intensive dry season

cultivation to those that do not.

3.3 The Second Monsoon and Weather Shocks in the Post-Monsoon

We now turn to an examination of the impacts of post-monsoon weather shocks (October-

December) on crime and agriculture. In most of India, rainfall is almost entirely concen-

trated within the June-September monsoon season (the “Southwest Monsoon”). In those

areas, October-December temperatures can affect main season crops that are in the final

stages of their maturation, as well as secondary season crops in early stages of cultivation,

depending on the exact local agricultural calendar in each area. Some parts of southern

and eastern India also experience a second monsoon (the “Northeast Monsoon”), which

commences in October, just as the summer monsoon is subsiding, and continues through

November and December. Rainfall during the second monsoon facilitates the cultivation of

a longer main season crop, or of a second crop outside the monsoon (without the neces-

sity of using irrigation), which constitutes an important component of annual agricultural

incomes in these areas. Appendix figure A.3 plots the location of areas experiencing this

second monsoon.23 In these regions, rainfall during the post-monsoon period can have a

substantial effect on agricultural incomes, and potentially crime, and we therefore include

it as an explanatory variable in an analysis that is limited to these areas .

Table 5 reports the estimated impacts of post-monsoon weather shocks on crime, agricul-

tural product and yields. Estimates are conducted separately for the full sample (columns

1-3) and the second monsoon sample (columns 4-6); and, for the latter, second monsoon

rainfall shocks are also controlled for. The results establish that negative rainfall shocks

during the second monsoon reduce agricultural production by 6.4% and also increase crime

by 5.5%, lending further support to the agricultural income hypothesis for rainfall shocks.

On the other hand, post-monsoon positive temperature shocks display divergent effects on

agricultural product and crime. In the full sample, positive temperature shocks reduce pro-

duction but do not seem to affect crime. In the second monsoon sample, they increase crime23

To be precise, we define the second monsoon sample to consist of those districts receiving more than

150 millimeters of rainfall during the post-monsoon

19

rates (5.7%), even though they increase agricultural output. This divergence is hard to

reconcile with either a purely agricultural or a purely psychological channel.

3.4 The Effect of Lagged Weather Shocks

Our principal specification only includes contemporaneous weather shocks, i.e., those occur-

ring within the same calendar year in which crime is observed. Here, we examine the impacts

of weather shocks occurring in the previous calendar year. There are several reasons why

these “lagged” weather shocks might affect crime. First of all, criminal activity may display

serial correlation over time (Jacob, Lefgren, and Moretti, 2007), and could thus be affected

by lagged weather shocks that have increased crime in the previous year.24 Conversely, if

weather shocks merely shift the timing of criminal acts forward, lagged weather shocks may

have an effect on crime which is opposite in sign to the contemporaneous shocks (Jacob,

Lefgren, and Moretti, 2007). Both of these effects are not specific to any one weather shock,

and can therefore be expected to be similarly reflected in the lagged versions of all weather

shocks that are found to impact crime contemporaneously.

Second, weather can also have delayed direct effects on crime through the timing of

economic outcomes. Negative income shocks may be temporarily offset through drawing

down savings or accessing credit, with individuals resorting to crime only after these means

of smoothing consumption have been exhausted. In addition, weather shocks occurring in

calendar year t � 1 may affect the production of crops that are harvested in year t, and

thereby affect crime rates in that year as well;25 Areas experiencing a secondary monsoon

season (October-December) are a particularly plausible candidate for such a delayed effect,

though even weather shocks during the Monsoon period (June-September of year t�1) may

generate a lagged increase in crime, as they too affect crops grown during the dry season

(January-May) of year t, though to a smaller extent.

Table A.3 reports estimates of a specification similar to the baseline specification, ex-

cept that it also includes weather shocks occurring in the previous calendar year. Column

(1) reports estimates that include the full sample, and column (2) reports estimates that

use the “second monsoon” sample discussed above, and include variables for post-monsoon24

Persistence could occur, for example, if criminal acts reduce the opportunity cost of engaging in addi-

tional illicit activities, whether by generating crime-specific human capital, or reducing outside employment

options.

25This can happen if individuals or markets fail to fully predict and capture these future yield effects.

While planting decisions and agricultural labor markets, for example, are likely to react to early season

weather shocks, it seems plausible that some of the impacts of within season weather shocks cannot be fully

predicted and may only become manifest at the time of harvest.

20

weather shocks. We do not find statistically significant evidence for a lagged effect of Mon-

soon rainfall or temperature shocks on crime (column 1). It should be noted, however, that

the point estimate of the lagged Monsoon negative rainfall shock for crime, though impre-

cisely estimated, is indeed negative, and of a magnitude that is consistent with its relative

contribution to annual agricultural production (secondary season crops not only occupy a

smaller area, but as evident from table A.1, they also suffer smaller yield losses as a result of

monsoon rainfall shocks). There is also no indication of a lagged effect of positive Monsoon

temperature shocks on crime: the point estimate is very small and imprecise. However,

there is less reason to expect these lagged temperature shocks to affect secondary season

production in the following year than in the case of rainfall. It is therefore difficult to

interpret this estimate as pointing against an agricultural income channel.

When we examine the lagged effect of the secondary monsoon on crime in the appropriate

sub-sample (column 4), we find a statistically significant estimate of the lagged negative

rainfall shock (6.7%) that is remarkably close in magnitude to the contemporaneous effect.

The appearance of both contemporaneous and delayed effects of this shock on crime is

supportive of an agricultural income channel, whereby declines in secondary monsoon rainfall

affect income both before and after yields are realized, over a a physiological channel that

would be expected to only have contemporaneous impacts on crime. In contrast, there is

no apparent effect of lagged post-monsoon temperature shocks on crime in the following

year. This casts further doubt on the possibility that an agricultural income mechanism

is responsible for the contemporaneous effect of these shocks on crime, discussed in the

previous sub-section.

4 Agro-Climatic Heterogeneities

The analysis of the previous section compared the effects of seasonal rainfall and temperature

shocks on crime and agricultural production, and showed that shocks occurring during the

monsoon and pre-monsoon seasons had corresponding effects on the two outcomes, consis-

tent with an agricultural income mechanism as the driver of the weather-crime relationship.

In this section, we test this correspondence further by comparing agro-climatically derived

heterogeneity in the impacts of weather on these two outcomes. We first examine whether

crime in areas in which pre-monsoon cultivation is more prevalent is more responsive to

pre-monsoon temperature shocks. We then test whether crime is more responsive to posi-

21

tive rainfall shocks in areas that are drier and are therefore mostly cultivated with dryland

crops that are more vulnerable to excessive rain. Finally, we examine whether irrigation

attenuates the effect of negative rainfall shocks on crime.

4.1 Areas with Extensive Secondary Season Cultivation

In the previous section, we found that positive temperature shocks occurring during the

pre-monsoon periods increase crime, and raised the possibility that this effect is driven

by the corresponding impact of these temperature shocks on secondary season agricultural

production. This agricultural effect is manifest in the crop specific estimates reported in

table A.1. To test this possibility more carefully, we decompose our sample into districts

in which (average) secondary season cultivation does and does not constitute a significant

share of annual cultivation, and re-estimate the effect of weather shocks on agriculture and

crime in each.

The results are reported in table 6. Column (1) repeats the prior estimates of the

effect of summer temperatures on crime in the full sample. In column (2), the sample is

restricted to districts where the extent of secondary season cultivation relative to primary

season cultivation is below the median, and in column (3) to districts where it is above the

median. We find that positive pre-monsoon temperature shocks are only associated with a

statistically significant increase in crime in those districts in which dry season cultivation is

substantial, with little evidence of any such effect where this cultivation is low (though the

difference between the estimates in these two sub-samples is statistically insignificant).

4.2 Dry and Wet Areas

As was noted in our baseline results, there is some evidence of an increase in crime with

positive rainfall shocks, which we hypothesized to be due to income losses sustained from

excess rainfall or flooding (Hidalgo et al., 2010; Papaioannou, 2016). To explore this possi-

bility further, we exploit heterogeneities in the responsiveness of different crops to excessive

rainfall, and examine whether crime responds to these shocks in a stronger manner in areas

in which more susceptible crops tend to be grown. We first disaggregate our sample into

districts that are characterized by mean rainfall that is above and below the median, which

we will refer to as the ‘dry’ and ‘wet’ samples. Cropping patterns in India differ substan-

tially between areas with high and low mean rainfall: in areas with low rainfall, the so

22

called ‘dryland crops’ of Sorghum (Jowar), Millet (Bajra), and Maize are more commonly

cultivated; whereas in areas with higher rainfall, the cultivation of rice is more common.

In appendix table A.1, we examined the sensitivities of individual crops’ yields to weather

shocks, and found that the yields of dryland crops are more susceptible to the negative

effects of excessive rainfall shocks than other crops. This disparity is even more apparent

in semi-parametric estimates, reported in appendix table A.4, which show dryland crops

suffering steep declines in yields with excessive rainfall. This suggests that in dry areas,

agricultural production should be more sensitive to positive rainfall shocks; and that crime

may be expected to respond accordingly.

In table 7, we report some differences between the dry and wet samples (columns 1 and

2). In the first row we compare the (long-term average) fraction of cultivated area that is

sown with these crops across the two samples; and, as anticipated, find that this fraction is

larger by 34% in dry areas (56% in dry areas vs. 22% in wet areas). If the effect of positive

rainfall shocks on crime estimated in the previous section operates through an agricultural

channel, one would expect the effect of positive rainfall shocks on crime to also be larger

in the dry sample. This prediction is supported by the regression estimates: while positive

rainfall shocks increase crime by 3.5% in the dry sample, they have a small and insignificant

effect on crime in the wet sample (the difference between the two estimates is statistically

significant, p=0.016).

The semi-parametric estimation, reported in appendix table A.4, shows that both the

decline in agricultural production and the increase in crime in the dry sample occur when

rainfall exceeds 1.5 standard deviations above the mean, consistent with the flooding hy-

pothesis suggested above. In the dry districts, excessive rainfall is associated with a 10.5%

decrease in agricultural yields, and a 5.8% increase in crime. In wet districts, there is a

far smaller decline in yields and no evidence for an increase in crime. These results are

consistent with historical accounts of increases in crime with both drought and floods in

British colonial Asia (Papaioannou, 2016).

4.3 Irrigated and non-Irrigated Areas

Access to irrigation simultaneously increases agricultural income and reduces its susceptibil-

ity to rainfall shocks. If the effect of rainfall shocks on crime is mediated by an agricultural

channel, this may imply that crime in irrigated districts will simultaneously experience a

23

level reduction and a lower sensitivity to negative rainfall shocks. Testing this hypothesis,

though for civil conflict rather than individual crime, Sarsons (2015) finds that Hindu-

Muslim riots are in fact no less likely to occur with negative rainfall shocks in districts that

are located downstream from irrigation dams (and therefore having a greater extent of irri-

gation), thereby calling into question the validity of the agricultural income hypothesis as

driving civil conflict, and by implication the use of weather shocks as instrumental variables

in the income-conflict literature.

Here we follow a similar approach to Sarsons (2015), expanding the analysis to the

domain of individual crime incidence. Like Sarsons (2015), we make use of the data and

identification approach used by Duflo and Pande (2007), who employ geographical variation

to identify the causal effects of irrigation on agriculture, income, and poverty. Calculating

the predicted number of dams in a district (see Duflo and Pande, 2007, for details),26 we

estimate a regression that includes an interaction of our rainfall shocks with this variable.27

The results are reported in appendix table A.5.

Like Duflo and Pande (2007) and Sarsons (2015), we find that the adverse effects of

negative rainfall shocks are substantially mitigated by the presence of upstream dams: the

interaction term of negative rainfall shocks with upstream dams is 9.3% for agricultural

product (marginally insignificant) and 11.1% for agricultural yield (significant at the 5%

level). However, we estimate the interaction of negative rainfall shocks and the presence

upstream dams crime to also be negative, albeit imprecisely. While this result is a sug-

gestively consistent with the agricultural income mechanism emphasized throughout this

paper, limits of statistical power prevent us from testing this difference more conclusively.

It should be noted that the prediction that irrigation should break the rainfall-crime

relationship may depend on strong assumptions about the micro-dynamics of crime. For

example, irrigation coverage, as measured by the fraction of gross cultivated area that is

irrigated, is quite low almost everywhere in India, including in districts served by upstream26

The first-stage in Duflo and Pande (2007) is

Dist = ↵1 +4X

k=2

↵2k(RGrki ⇤Dst) + ↵3(Mi ⇤Dst) +4X

k=2

↵rk(RGrki ⇤ lt) + vi + µst + !ist,

where, Dist is the number of dams in district i of state s at time t, �i are district fixed effects, µst are state-

year fixed effects. RGrki are the river gradient variables, which determine the suitability of the district

for dam placement, and the other variables account for other relevant characteristics. Deriving imputed

dams from this regression,

dDist, we then interact the continuous measure of imputed dams, both within the

district, as well as in upstream districts, with our rainfall variables.

27Our specification differs from the one used by these authors in the way rainfall shocks are specified,

namely that we use the same binary indicators of rainfall shocks used throughout this paper rather than a

continuous linear function of rainfall.

24

dams. Indeed, on average, districts that are downstream from a dam irrigate just 23.5%

of the cultivated land, meaning that even ostensibly ‘irrigated’ districts have a substantial

amount of un-irrigated cultivation. With larger landowners being most likely to capture

the benefits of irrigation expansions, poorer farmers may continue to be disproportionately

dependent on rainfed cultivation, thereby muting the income-smoothing effect of irrigation

for the most vulnerable populations,28 and the potential for a lower impact on crime. While

the absence of detailed micro-data on criminal offenders prevents us from directly testing

these hypotheses, it is important to highlight that the prediction that crime incidence should

be less sensitive to rainfall shocks in more irrigated districts is not a priori self-evident.

5 Crime Responsiveness across Time

We finally turn to an analysis of whether there have been changes over time in the effects of

weather shocks on crime. Given the substantial changes occurring in the Indian economy, it

is natural to expect that there will have been changes in the sensitivity of crime to rainfall

and temperature deviations. For example, with more individuals living in urban areas and

employed in non-farm related activities, fewer households will be directly dependent on

weather for their incomes, while in the agricultural sector, technological and infrastructural

advancement may shield productivity from weather deviations . These changes have the

potential to reduce the sensitivity of crime to weather deviations, as has been found elsewhere

with respect to health outcomes (Barreca et al., 2016; Burgess et al., 2013).29 Countervailing

forces exist, however, that might work against these benefits; such as, for example, higher

urbanization rates, which generate new governance challenges and social antagonisms, or

the expansion of irrigation, which may increase rural inequality or attract landless laborers

from elsewhere.

Whether the remarkably consistent effects of climate on conflict found across a broad

literature might be mitigated by increased prosperity and reduced agricultural dependence

is a question of considerable import (Hsiang, Burke, and Miguel, 2013). Most previous

research has focused on economically static countries, or on short time spans, so that it has

not been possible to identify how the estimated effects might change with economic and28

In fact, such farmers might ultimately suffer larger income declines when rainfall is low, as the lower

effect on average yields in these districts might also reduce the accompanying increase in prices that partially

compensates for the reduction in output.

29Barreca et al. (2016) find that mortality rates in the US have become less responsive to high temperatures

due to the expansion of air conditioning.

25

human development. Climate change increases the urgency of understanding this issue, as

individuals will be increasingly exposed to abnormal weather deviations. Mean temperatures

in India expected to increase by an amount which corresponds to several standard deviations

above their long-run mean, and precipitation is expected to become more variable (Fishman,

2016). To shed light on the this matter, we examine how the effects of weather on crime

have varied across the three decades covered by our data.

Figure 7 shows the relationship between crime and weather shocks across the three

intervals: 1971-1980; 1981-1990; and 1991-2000. The effect of negative rainfall shocks is

relatively stable, remaining around 3-4% across these decades, though with a small increase

across time. In contrast, the effect of temperature shocks drop from 6.3% in the 1970s,

down to 0.5% in the 1980s, then 4.1% in the 1990s. Table A.6 reports the evolution of the

disaggregated impacts of property and non-property crimes, as well as those of specific crime

types. The decline of the temperature effect occurs primarily for property crimes; violent

crimes, in contrast, are as responsive to positive temperature shocks in the1990s as in the

1970s (3.8%), though have no effect in the 1980s.

6 Conclusion

We provide some of the first evidence on the relationship between weather and crime in

developing countries, using a data set that covers a population of nearly a billion people,

and spanning three decades of economic and social transformation. Variation in temperature

and rainfall are found to cause substantial increases in the incidence of both property and

violent crime. Weather shocks associated with increases in crime are also generally found

to reduce agricultural incomes, plausibly suggesting a role for agricultural income shocks in

mediating the observed relationship, consistent with a lengthy literature on the economic

determinants of crime and group conflict.

To test this hypothesis more rigorously, we explore the weather-crime-agriculture rela-

tionship across an array of seasonal and geographical disaggregations of our data. None of

these tests yield evidence at odds with the agricultural hypothesis; and, for precipitation

shocks, they reveal patterns in the responsiveness of agriculture and crime that are in re-

markable accord. In the case of temperature shocks, the evidence is more mixed, providing

some evidence for the operation of an agricultural income channel, but also for the pres-

ence of other mechanisms. Due to limitations in the temporal precision of the data, we are

26

unable to draw more definitive conclusions about the relative roles of agriculture and other

mechanisms in driving the effects of temperature on crime. Additional studies, perhaps uti-

lizing micro-data on crime and income, would facilitate further progress in resolving these

questions.

Finally, our results show that, despite the higher incomes, greater access to consumption

smoothing instruments, and reduced susceptibility of agriculture to climatic variability that

accompany economic growth, crime has not become substantially less responsive to extreme

weather than it was prior to these improvements. This may be taken as evidence that,

despite India’s remarkable gains in human and economic development, the poorest members

of society continue to remain highly vulnerable to aggregate economic shocks, reinforcing

existing concerns about the equity of India’s remarkable economic growth.

27

References

Anderson, Craig A, Kathryn B Anderson, Nancy Dorr, Kristina M DeNeve, and Mindy

Flanagan. 2000. “Temperature and aggression.” Advances in experimental social psychol-

ogy 32:63–133.

Auffhammer, Maximilian, Veerabhadran Ramanathan, and Jeffrey R Vincent. 2012. “Cli-

mate change, the monsoon, and rice yield in India.” Climatic Change 111 (2):411–424.

Auliciems, A and L DiBartolo. 1995. “Domestic violence in a subtropical environment: Police

calls and weather in Brisbane.” International Journal of Biometeorology 39 (1):34–39.

Barreca, Alan, Karen Clay, Olivier Deschenes, Michael Greenstone, and Joseph S Shapiro.

2016. “Adapting to Climate Change: The Remarkable Decline in the US Temperature-

Mortality Relationship over the Twentieth Century.” Journal of Political Economy

124 (1):105–159.

Becker, Gary S. 1968. “Crime and Punishment: An Economic Approach.” Journal of

Political Economy 76 (2):169–217.

Bohlken, Anjali Thomas and Ernest John Sergenti. 2010. “Economic growth and ethnic

violence: An empirical investigation of Hindu-Muslim riots in India.” Journal of Peace

research .

Bourguignon, Francois. 2000. “Crime, violence and inequitable development.” In Annual

World Bank Conference on Development Economics 1999. 199–220.

Burgess, Robin, Olivier Deschenes, Dave Donaldson, and Michael Greenstone. 2013. “The

unequal effects of weather and climate change: Evidence from mortality in india.” Un-

published working paper .

Burke, Marshall B, Edward Miguel, Shanker Satyanath, John A Dykema, and David B

Lobell. 2009. “Warming increases the risk of civil war in Africa.” Proceedings of the

national Academy of sciences 106 (49):20670–20674.

Card, David and Gordon B Dahl. 2011. “Family violence and football: The effect of

unexpected emotional cues on violent behavior.” The Quarterly Journal of Economics

126 (1):103.

28

Cohn, Ellen G and James Rotton. 1997. “Assault as a function of time and temperature:

A moderator-variable time-series analysis.” Journal of Personality and Social Psychology

72 (6):1322.

Collier, Paul and Anke Hoeffler. 1998. “On economic causes of civil war.” Oxford economic

papers 50 (4):563–573.

———. 2004. “Greed and grievance in civil war.” Oxford economic papers 56 (4):563–595.

Colmer, Jonathan. 2016. “Weather, labour reallocation, and industrial production: Evidence

from india.” Tech. rep., Mimeo.

Conley, Timothy G and Francesca Molinari. 2007. “Spatial correlation robust inference with

errors in location or distance.” Journal of Econometrics 140 (1):76–96.

Dell, Melissa, Benjamin F Jones, and Benjamin A Olken. 2012. “Temperature shocks and

economic growth: Evidence from the last half century.” American Economic Journal:

Macroeconomics 4 (3):66–95.

———. 2014. “What do we learn from the weather? The new climate–economy literature.”

Journal of Economic Literature 52 (3):740–798.

Deryugina, Tatyana and Solomon M Hsiang. 2014. “Does the environment still matter?

Daily temperature and income in the United States.” Tech. rep., National Bureau of

Economic Research.

Deschenes, Olivier and Michael Greenstone. 2007. “The economic impacts of climate change:

evidence from agricultural output and random fluctuations in weather.” The American

Economic Review 97 (1):354–385.

Duflo, Esther and Rohini Pande. 2007. “Dams.” The Quarterly Journal of Economics

122 (2):601–646.

Fajnzylber, Pablo, Daniel Lederman, and Norman Loayza. 1998. Determinants of crime

rates in Latin America and the world: an empirical assessment. World Bank Publications.

Fetzer, Thiemo. 2014. “Social insurance and conflict: evidence from India.” Job Market

Paper, November 14.

Fishman, Ram. 2016. “More uneven distributions overturn benefits of higher precipitation

for crop yields.” Environmental Research Letters 11 (2):024004.

29

Freeman, Richard B. 1999. “The economics of crime.” Handbook of labor economics 3:3529–

3571.

Gould, Eric D, Bruce A Weinberg, and David B Mustard. 2002. “Crime rates and local

labor market opportunities in the United States: 1979–1997.” Review of Economics and

statistics 84 (1):45–61.

Grogger, Jeff. 1997. “Market wages and youth crime.” Tech. rep., National Bureau of

Economic Research.

Guiteras, Raymond. 2009. “The impact of climate change on Indian agriculture.”

Manuscript, Department of Economics, University of Maryland, College Park, Maryland

.

Hidalgo, F Daniel, Suresh Naidu, Simeon Nichter, and Neal Richardson. 2010. “Economic

determinants of land invasions.” The Review of Economics and Statistics 92 (3):505–523.

Hsiang, Solomon M. 2010. “Temperatures and cyclones strongly associated with economic

production in the Caribbean and Central America.” Proceedings of the National Academy

of sciences 107 (35):15367–15372.

Hsiang, Solomon M, Marshall Burke, and Edward Miguel. 2013. “Quantifying the influence

of climate on human conflict.” Science 341 (6151):1235367.

Jacob, Brian, Lars Lefgren, and Enrico Moretti. 2007. “The dynamics of criminal behavior

evidence from weather shocks.” Journal of Human resources 42 (3):489–527.

Jayachandran, Seema. 2006. “Selling labor low: Wage responses to productivity shocks in

developing countries.” Journal of political Economy 114 (3):538–575.

Kaur, Supreet. 2014. “Nominal wage rigidity in village labor markets.” Tech. rep., National

Bureau of Economic Research.

Kenrick, Douglas T and Steven W MacFarlane. 1986. “Ambient temperature and horn

honking a field study of the heat/aggression relationship.” Environment and behavior

18 (2):179–191.

Kumar, Hemanshu and Rohini Somanathan. 2009. “Mapping Indian districts across census

years, 1971-2001.” Centre for Development Economics Working Paper (176).

30

Larrick, Richard P, Thomas A Timmerman, Andrew M Carton, and Jason Abrevaya. 2011.

“Temper, temperature, and temptation heat-related retaliation in baseball.” Psychological

Science .

Lobell, David B, Wolfram Schlenker, and Justin Costa-Roberts. 2011. “Climate trends and

global crop production since 1980.” Science 333 (6042):616–620.

Machin, Stephen and Costas Meghir. 2004. “Crime and economic incentives.” Journal of

Human Resources 39 (4):958–979.

Mares, Dennis. 2013. “Climate change and levels of violence in socially disadvantaged neigh-

borhood groups.” Journal of Urban Health 90 (4):768–783.

Mehlum, Halvor, Edward Miguel, and Ragnar Torvik. 2006. “Poverty and crime in 19th

century Germany.” Journal of Urban Economics 59 (3):370–388.

Miguel, Edward. 2005. “Poverty and witch killing.” The Review of Economic Studies

72 (4):1153–1172.

Miguel, Edward, Shanker Satyanath, and Ernest Sergenti. 2004. “Economic shocks and civil

conflict: An instrumental variables approach.” Journal of political Economy 112 (4):725–

753.

Pakes, Ariel and Zvi Griliches. 1980. “Patents and R&D at the firm level: A first report.”

Economics letters 5 (4):377–381.

Papaioannou, Kostadis J. 2016. “’Hunger Makes a Thief of Any Man’: Poverty and Crime

in British Colonial Asia.” Available at SSRN 2780577 .

Park, Jisung. 2016. “’Human Stress and Human Capital Production.” Unpublished

Manuscript, Harvard University Economic Department, Working Paper .

Rajeevan, M, Jyoti Bhate, JD Kale, and B Lal. 2005. “Development of a high resolution

daily gridded rainfall data for the Indian region.” Met. Monograph Climatology 22:2005.

Ranson, Matthew. 2014. “Crime, weather, and climate change.” Journal of environmental

economics and management 67 (3):274–302.

Rotton, James and Ellen G Cohn. 2000. “Violence is a curvilinear function of temperature

in Dallas: a replication.” Journal of personality and social psychology 78 (6):1074.

31

Sarsons, Heather. 2015. “Rainfall and conflict: A cautionary tale.” Journal of development

Economics 115:62–72.

Schlenker, Wolfram, W Michael Hanemann, and Anthony C Fisher. 2006. “The impact of

global warming on US agriculture: an econometric analysis of optimal growing conditions.”

Review of Economics and statistics 88 (1):113–125.

Schlenker, Wolfram and David B Lobell. 2010. “Robust negative impacts of climate change

on African agriculture.” Environmental Research Letters 5 (1):014010.

Sekhri, Sheetal and Adam Storeygard. 2014. “Dowry deaths: Response to weather variability

in India.” Journal of development economics 111:212–223.

Srivastava, AK, M Rajeevan, and SR Kshirsagar. 2009. “Development of a high resolution

daily gridded temperature data set (1969–2005) for the Indian region.” Atmospheric

Science Letters 10 (4):249–254.

Sudarshan, Anant and Meenu Tewari. 2013. “The economic impacts of temperature on

industrial productivity: Evidence from indian manufacturing.” Tech. rep., Working Paper.

Zivin, Joshua Graff and Matthew Neidell. 2014. “Temperature and the allocation of time:

Implications for climate change.” Journal of Labor Economics 32 (1):1–26.

Zivin, Joshua S Graff, Solomon M Hsiang, and Matthew J Neidell. 2015. “Temperature

and human capital in the short-and long-run.” Tech. rep., National Bureau of Economic

Research.

32

0"

50"

100"

150"

200"

250"

300"

0"

5"

10"

15"

20"

25"

Jan" Feb" Mar" Apr" May" Jun" Jul" Aug" Sep" Oct" Nov" Dec"Precipita

?on"(m

m)"

Tempe

rature"(D

egrees"Celsiu

s)"

Figure 1: Average climate in India. Monthly average temperature (black drop lines) andprecipitation (bars). Monsoon crops are usually sown June-September and harvested be-tween October and December. The blue and green shades represent the breakup of weatherindicators used in the empirical analysis

33

Crime&Observa,on&(Annual)&

}&Pre6Monsoon&

Temp.&&Shocks&

}&Monsoon&&Rain+Temp.&

Shocks&

Second&Monsoon&&Rain&

&Shocks.&&

}&}&

Monsoon&&Rain+Temp.&&

Shocks&&

}&Jun&&Jul&&Aug&&Sep&&Oct&&Nov&&Dec&&&Jan&&Feb&&Mar&&Apr&&May&&Jun&&Jul&&Aug&&Sep&&Oct&&Nov&&Dec&&&&&

Main&&Season&

Secondary&&Season&

Main&&Season&

Secondary&&Season&

Second&Monsoon&&Rainfall&&Shocks.&

&

Ag.&Produc,on&Observa,on&(Annual)&Ag.&Produc,on&Observa,on&(Annual)&

Figure 2: Temporal Structure of the model specification.

34

Figure 3: Local regressions of crime incidence on standardized (z-score) Monsoon temper-ature (degree-days, top panel) and rainfall (bottom panel) deviations. To produce theseplots, the pooled (logarithm of) crime rate and the weather deviation in question are bothregressed on all other explanatory variables in equation (2). The residual terms from thecrime regression is then locally regressed on the residual from the weather deviation re-gression using a locally weighted polynomial regression with Epanechnikov kernel functions(using the STATA command lpolyci). Note: 95% confidence intervals are based on errors(dashed lines) that are not clustered as in our main specifications.

35

Figu

re4:

Loca

lreg

ress

ions

ofag

ricu

ltur

alpr

oduc

tion

(top

pane

ls)a

ndw

ages

(bot

tom

pane

ls)o

nst

anda

rdiz

ed(z

-sco

re)M

onso

onte

mpe

ratu

re(d

egre

e-da

ys,r

ight

pane

ls)

and

rain

fall

(lef

tpa

nels

)de

viat

ions

.To

prod

uce

thes

epl

ots,

the

agri

cultur

alou

tcom

ean

dth

ew

eath

erde

viat

ion

inqu

estion

are

both

regr

esse

don

allo

ther

expl

anat

ory

vari

able

sin

equa

tion

(1).

The

resi

dual

term

from

the

agri

cult

ural

outc

ome

regr

essi

onis

then

regr

esse

don

the

resi

dual

from

the

wea

ther

devi

atio

nre

gres

sion

usin

ga

loca

llyw

eigh

ted

poly

nom

ialr

egre

ssio

nw

ith

Epa

nech

niko

vke

rnel

func

tion

s(u

sing

the

STA

TAco

mm

and

lpol

yci).

Not

e:95

%co

nfide

nce

inte

rval

sar

eba

sed

oner

rors

(das

hed

lines

)th

atar

eno

tcl

uste

red

asin

our

mai

nsp

ecifi

cation

s.

36

Estim

ated

Impa

ct o

n C

rime

Rat

es

-0.05

0

0.05

0.1

0.15

Burglar

y

Bandit

ryThe

fts

Robbe

ryRiot

s

Kidnap

ping

Rape

Murder

Estim

ated

Impa

ct o

n C

rime

Rat

es

-0.05

0

0.05

0.1

0.15

Burglar

y

Bandit

ryThe

fts

Robbe

ryRiot

s

Kidnap

ping

Rape

Murder

Negative Rainfall Shocks

Positive Temperature Shocks

Figure 5: Estimated coefficients from regressions of disaggregated crime rates on negativemonsoon rainfall shocks (top) and positive monsoon temperature shocks (bottom). Propertycrimes coefficients are indicated by green markers, and violent crime coefficients are indicatedby red markers Error bars depict 95% confidence intervals.

37

Figure 6: Estimated coefficients from regressions of pooled crime rates (horizontal axis) andagricultural product (vertical axis) for Monsoon and Pre-Monsoon weather shocks for whichsignificant impacts on crime were found. Each square represents one point estimate. Allestimates are obtained from table 2

38

Figure 7: Coefficients from regressions of pooled crime incidence on Monsoon negative rain-fall and positive temperatures variables indicating weather deviations more than 1 standarddeviation from the mean. To produce these plots equation 2 was estimated for each decadeindependently. The coefficients from the regressions were then plotted, along with the 95%confidence intervals for these coefficients.

39

Table 1: Summary Statistics1970-2000 1970s 1980s 1990s

Mean S.D. Mean Mean Mean

Crime Rate (per 100,000 pop.)

Burglary 20.5 (19.1) 33 20.2 13.2

Banditry 1.4 (2.0) 2.1 1.6 0.9

Theft 42.2 (46.2) 64.2 42.7 28.2

Robbery 3.2 (3.7) 4 3.4 2.5

Riots 11.6 (11.7) 11.9 12.8 10.5

Kidnapping 2 (2.2) 1.9 1.9 2.2

Rape 1.2 (1.3) 0.7 1.1 1.6

Murder 3.8 (2.8) 3.3 3.7 4.2

Weather

Monsoon Rainfall (mm) 855 (490) 837 846 873

Monsoon Temerpature (Degree Days per day) 20.86 (1.47) 20.68 20.88 20.93

Agriculture

Annual Production (Rupees, 1960-65 prices) 22,801 (23,091) 17,582 22,854 26,845

Yield (Annual Production per Hectare, Rupees) 76 (51) 55 72 98

Agricultural Wage Labour (Ruppes per hour) 5.62 (3.25) 4.29 5.41 7.29

40

Tabl

e2:

The

Effe

cts

ofW

eath

erSh

ocks

onA

gric

ultu

rean

dC

rim

eC

rim

eP

rodu

ctY

ield

Wag

eSe

ason

alY

ield

Ann

ual

Ann

ual

Mai

nSe

cond

ary

(1)

(2)

(3)

(4)

(5)

(6)

Monsoon

Neg

Rai

n0.

048*

**-0

.150

***

-0.0

95**

*-0

.025

**-0

.159

***

-0.0

47**

*(0

.010

)(0

.023

)(0

.015

)(0

.011

)(0

.020

)(0

.013

)Po

sR

ain

0.01

5*-0

.028

-0.0

24-0

.026

**-0

.017

0.01

3(0

.009

)(0

.021

)(0

.015

)(0

.013

)(0

.013

)(0

.010

)N

egTe

mp

0.01

40.

039*

*0.

029*

*0.

023

0.00

1-0

.007

(0.0

11)

(0.0

16)

(0.0

13)

(0.0

16)

(0.0

16)

(0.0

13)

Pos

Tem

p0.

033*

*-0

.080

**-0

.016

-0.0

48**

*-0

.002

0.00

1(0

.015

)(0

.032

)(0

.018

)(0

.015

)(0

.022

)(0

.014

)P

re-M

onsoon

Neg

Tem

p0.

010

0.03

9**

0.00

70.

004

0.02

70.

015

(0.0

13)

(0.0

20)

(0.0

13)

(0.0

12)

(0.0

19)

(0.0

13)

Pos

Tem

p0.

019*

-0.0

34-0

.029

-0.0

050.

004

-0.0

36**

*(0

.011

)(0

.036

)(0

.019

)(0

.014

)(0

.014

)(0

.014

)

R-S

quar

ed0.

131

0.09

20.

089

0.15

50.

117

0.09

9N

o.of

Obs

erva

tion

s73

295

6503

6502

3892

5920

7294

Col

umn

1re

port

ses

tim

ates

ofre

gres

sion

(2)

whi

chin

clud

esw

eath

ersh

ocks

inth

eM

onso

onan

dP

re-M

onso

onse

ason

s.C

olum

ns2-

4re

port

esti

mat

esof

regr

essi

ons

assp

ecifi

edin

equa

tion

(1)in

whi

chth

eou

tcom

eva

riab

les

are

the

loga

rith

mof

annu

alag

ricu

ltur

alpr

oduc

tion

(col

umn

2),y

ield

(col

umn

3)an

dw

ages

(col

umn

4).

Inco

lum

ns(5

)-(6

),si

mila

rre

gres

sion

sar

ees

tim

ated

inw

hich

the

outc

omes

are

the

yiel

dsof

the

prim

ary

crop

inth

em

ain

and

seco

ndar

yse

ason

s.St

anda

rder

rors

,clu

ster

edin

tim

ean

dsp

ace

(see

text

),ar

ere

port

edin

pare

nthe

ses.

Star

sin

dica

test

atis

tica

lsig

nific

ance

:*

p<

0.1,

**p

<0.

05,*

**p

<0.

01.

41

Tabl

e3:

The

Effe

cts

ofW

eath

erSh

ocks

onIn

divi

dual

Cri

me

Cat

egor

ies

Pro

pert

yN

on-P

rope

rty

Bur

glar

yB

andi

try

The

fts

Rob

bery

Rio

tsK

idna

ppin

gR

ape

Mur

der

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Monsoon

Neg

Rai

n0.

058*

**0.

031*

**0.

07*

0.06

7***

0.07

3***

0.03

5**

0.04

2*0.

070*

**0.

034*

*0.

022

0.03

0***

(0.0

13)

(0.0

11)

(0.0

18)

(0.0

22)

(0.0

18)

(0.0

25)

(0.0

26)

(0.0

17)

(0.0

20)

(0.0

11)

Pos

Rai

n0.

016

0.01

20.

700.

008

0.04

7**

0.01

80.

004

0.00

30.

021

0.00

80.

003

(0.0

11)

(0.0

10)

(0.0

12)

(0.0

21)

(0.0

12)

(0.0

18)

(0.0

18)

(0.0

16)

(0.0

17)

(0.0

10)

Neg

Tem

p0.

023

-0.0

010.

180.

013

0.04

50.

011

0.05

3**

-0.0

09-0

.002

-0.0

050.

008

(0.0

15)

(0.0

12)

(0.0

16)

(0.0

31)

(0.0

16)

(0.0

25)

(0.0

24)

(0.0

19)

(0.0

21)

(0.0

13)

Pos

Tem

p0.

038*

*0.

024

0.32

-0.0

030.

076*

*0.

026

0.04

50.

058*

*0.

018

0.03

00.

030*

(0.0

18)

(0.0

16)

(0.0

19)

(0.0

33)

(0.0

22)

(0.0

31)

(0.0

28)

(0.0

22)

(0.0

24)

(0.0

15)

Pre-M

onsoon

Neg

Tem

p0.

011

0.00

80.

840.

033*

0.05

2*-0

.004

-0.0

19-0

.009

0.02

4-0

.021

0.02

1(0

.017

)(0

.013

)(0

.018

)(0

.027

)(0

.023

)(0

.032

)(0

.027

)(0

.020

)(0

.022

)(0

.014

)Po

sTe

mp

0.02

00.

017

0.80

0.00

10.

010

0.02

20.

051*

*0.

019

0.00

90.

036*

0.01

0(0

.013

)(0

.012

)(0

.019

)(0

.028

)(0

.014

)(0

.024

)(0

.023

)(0

.018

)(0

.021

)(0

.012

)

R-S

quar

ed0.

203

0.11

00.

420

0.50

70.

182

0.23

00.

234

0.17

90.

355

0.05

0N

o.of

Obs

erva

tion

s45

725

2757

091

7191

1791

1791

7391

4791

8691

9191

93

Col

umns

1-2

repo

rtes

tim

ates

ofre

gres

sion

(2)

whi

chin

clud

esw

eath

ersh

ocks

inth

eM

onso

onan

dP

re-M

onso

onse

ason

s,re

stri

cted

topr

oper

ty(c

olum

n1)

and

non-

prop

erty

(col

umn

2)cr

imes

.C

olum

n3

repo

rts

p-va

lues

for

diffe

renc

esbe

twee

nth

etw

ose

tsof

coeffi

cien

ts.

Col

umns

4-11

repo

rtes

tim

ates

ofre

gres

sion

sas

spec

ified

ineq

uati

on(1

)in

whi

chth

eou

tcom

eva

riab

les

are

the

loga

rith

ms

ofth

era

tes

ofin

divi

dual

crim

eca

tego

ries

.St

anda

rder

rors

,cl

uste

red

intim

ean

dsp

ace

(see

text

),ar

ere

port

edin

pare

nthe

ses.

Star

sin

dica

test

atis

tica

lsig

nific

ance

:*

p<

0.1,

**p

<0.

05,

***

p<

0.01

.

42

Table 4: The Effects of Weather Shocks on Agriculture and Crime (Multiple Bins)Crime Product Yield Wage

(1) (2) (3) (4)Rainfall

< -1.5 sd 0.064*** -0.278*** -0.172*** -0.038**(0.020) (0.046) (0.034) (0.015)

-1.5 to -1.0 0.051*** -0.109*** -0.077*** -0.014(0.012) (0.023) (0.015) (0.012)

-0.5 to -1.0 0.016 -0.045** -0.039*** -0.009(0.010) (0.020) (0.010) (0.009)

05. to 1.0 0.010 -0.006 -0.011 0.010(0.010) (0.014) (0.011) (0.012)

1.0 to 1.5 0.006 -0.014 -0.009 -0.022(0.012) (0.020) (0.014) (0.015)

> 1.5 sd 0.038*** -0.062* -0.066*** -0.031*(0.013) (0.034) (0.025) (0.019)

Temperature

< -1.5 sd -0.009 0.039 0.029 0.045**(0.021) (0.025) (0.021) (0.022)

-1.5 to -1.0 0.038*** 0.040** 0.035** 0.003(0.012) (0.018) (0.015) (0.022)

-0.5 to -1.0 0.025** 0.005 0.015 -0.005(0.012) (0.020) (0.014) (0.014)

05. to 1.0 -0.006 -0.018 -0.043** -0.008(0.011) (0.028) (0.020) (0.012)

1.0 to 1.5 0.041** -0.055 -0.020 -0.026(0.018) (0.037) (0.019) (0.016)

> 1.5 sd 0.019 -0.121** -0.043 -0.091***(0.023) (0.051) (0.029) (0.024)

R-squared 0.131 0.098 0.099 0.164No. of Observations 73295 6503 6502 3892

Column 1 reports estimates of regression (2) which includes multiple bins for weather shocksin the Monsoon and Pre-Monsoon seasons. Columns 2-4 report estimates of regressions asspecified in equation (1) in which the outcome variables are the logarithm of agriculturalproduction (column 2), yield (column 3) and wages (column 4). Standard errors, clustered intime and space (see text), are reported in parentheses. Stars indicate statistical significance:* p < 0.1, ** p < 0.05, *** p < 0.01.

43

Table 5: The Effects of Second Monsoon Weather Shocks on Agriculture and CrimeFull sample Second Monsoon Sample

Crime Product Yield Crime Product Yield(1) (2) (3) (4) (5) (6)

Monsoon

Neg Rain 0.045*** -0.150*** -0.098*** 0.055** -0.147*** -0.051**(0.010) (0.023) (0.015) (0.021) (0.045) (0.024)

Pos Rain 0.016* -0.026 -0.023 0.003 -0.006 0.005(0.009) (0.021) (0.015) (0.019) (0.040) (0.022)

Neg Temp 0.015 0.034** 0.025** 0.009 0.012 0.015(0.011) (0.016) (0.013) (0.028) (0.041) (0.023)

Pos Temp 0.035** -0.069** -0.016 0.002 -0.105* 0.008(0.016) (0.032) (0.018) (0.036) (0.057) (0.029)

Post-Monsoon

Neg Rain 0.055** -0.064* -0.052**(0.022) (0.036) (0.023)

Pos Rain 0.016 0.018 0.046**(0.021) (0.035) (0.022)

Neg Temp -0.008 0.071*** 0.064*** -0.026 0.049 0.030(0.014) (0.019) (0.015) (0.028) (0.035) (0.025)

Pos Temp 0.009 -0.086* -0.021 0.057** 0.150* 0.035(0.012) (0.052) (0.022) (0.028) (0.085) (0.030)

R-Squared 0.125 0.096 0.094 0.149 0.075 0.184No. of Observations 75071 6531 6530 15265 1242 1241

Columns 1-3 reports estimates of regressions for which the outcomes are crime (pooled),agricultural product and yield, and which control for Monsoon (June-September) rainfalland temperature shocks and for Post-Monsoon (October-December) temperature shocks.Columns 4-6 report similar estimates except that the sample is limited to those districtsthat receive a second monsoon (figure A.3) and the regressions also control for Post-Monsoonrainfall weather shocks. Standard errors, clustered in time and space (see text), are reportedin parentheses. Stars indicate statistical significance: * p < 0.1, ** p < 0.05, *** p < 0.01.

44

Table 6: The Heterogenous Effects of Weather Shocks on Crime by Dry Season CultivationAll Low Rabi High Rabi(1) (2) (3)

Monsoon

Neg Rain 0.048*** 0.034*** 0.059***(0.010) (0.013) (0.013)

Pos Rain 0.015* 0.007 0.025**(0.009) (0.013) (0.011)

Neg Temp 0.014 0.023 0.008(0.011) (0.015) (0.015)

Pos Temp 0.033** 0.054*** 0.021(0.015) (0.019) (0.018)

Pre-Monsoon

Neg Temp 0.010 0.011 0.045***(0.013) (0.016) (0.017)

Pos Temp 0.019* 0.010 0.033**(0.011) (0.012) (0.015)

R-Squared 0.131 0.134 0.138No. of Observations 73295 33374 39178

Columns 1-3 report estimates of regression 2 for the full sample (column 1), areas with high(column 2) and low (column 3) extent of cultivation in the dry season. All regression controlfor monsoon and pre-monsoon season weather shocks. Standard errors, clustered in timeand space (see text), are reported in parentheses. Stars indicate statistical significance: * p< 0.1, ** p < 0.05, *** p < 0.01.

45

Table 7: The Effects of Weather Shocks on Crime in Wet and Dry AreasCrime

Dry Wet(3) (4)

Share, Dryland Crops 0.56 0.22

Neg Rain 0.058*** 0.028**(0.016) (0.013)

Pos Rain 0.035** -0.007(0.014) (0.011)

Neg Temp -0.014 0.042***(0.019) (0.012)

Pos Temp 0.008 0.063***(0.024) (0.018)

R-Squared 0.145 0.110No. of Observations 38058 35395

Column 1-2 compare estimates of regression (2) which includes monsoon season weathershocks between the “wet” and “dry” sub-samples (see text for definitions). The first rowreports the average share occupied by “dryland crops” in cultivated area in the two sub-samples. Standard errors, clustered in time and space (see text), are reported in parentheses.Stars indicate statistical significance: * p < 0.1, ** p < 0.05, *** p < 0.01.

46

Additional Figures and Tables

47

Figu

reA

.1:

The

spat

iald

istr

ibut

ion

ofav

erag

e19

71-2

000

crim

era

tes

(cri

mes

com

mit

ted

per

100,

000

popu

lati

on).

48

Figure A.2: Plots of the (logarithm) of India-wide average crime incidence (per 100,000population) over time.

49

Figure A.3: A map of the districts included in the “Second Monsoon” analysis.

50

Figu

reA

.4:

Loca

lreg

ress

ions

ofin

divi

dual

crim

eca

tego

ryin

cide

nce

onst

anda

rdiz

ed(z

-sco

re)

mon

soon

tem

pera

ture

(deg

ree-

days

).To

prod

uce

thes

epl

ots,

the

pool

ed(log

arithm

of)

crim

era

tean

dth

ew

eath

erde

viat

ion

inqu

estion

are

both

regr

esse

don

allo

ther

expl

anat

ory

vari

able

sin

equa

tion

(2).

The

resi

dual

term

from

the

crim

ere

gres

sion

isth

enre

gres

sed

onth

ere

sidu

alfr

omth

ew

eath

erde

viat

ion

regr

essi

onus

ing

alo

cally

wei

ghte

dpo

lyno

mia

lreg

ress

ion

wit

hE

pane

chni

kov

kern

elfu

ncti

ons

(usi

ngth

eST

ATA

com

man

dlp

olyc

i).

Not

e:95

%co

nfide

nce

inte

rval

sar

eba

sed

oner

rors

(das

hed

lines

)th

atar

eno

tcl

uste

red

asin

our

mai

nsp

ecifi

cati

ons.

51

Figu

reA

.5:

Loca

lreg

ress

ions

ofin

divi

dual

crim

eca

tego

ryin

cide

nce

onst

anda

rdiz

ed(z

-sco

re)

mon

soon

rain

fall.

Topr

oduc

eth

ese

plot

s,th

epo

oled

(log

arit

hmof

)cr

ime

rate

and

the

wea

ther

devi

atio

nin

ques

tion

are

both

regr

esse

don

allo

ther

expl

anat

ory

vari

able

sin

equa

tion

(2).

The

resi

dual

term

from

the

crim

ere

gres

sion

isth

enre

gres

sed

onth

ere

sidu

alfr

omth

ew

eath

erde

viat

ion

regr

essi

onus

ing

alo

cally

wei

ghte

dpo

lyno

mia

lreg

ress

ion

wit

hE

pane

chni

kov

kern

elfu

ncti

ons

(usi

ngth

eST

ATA

com

man

dlp

olyc

i).

Not

e:95

%co

nfide

nce

inte

rval

sar

eba

sed

oner

rors

(das

hed

lines

)th

atar

eno

tcl

uste

red

asin

our

mai

nsp

ecifi

cation

s.

52

Tabl

eA

.1:

The

Effe

cts

ofW

eath

erSh

ocks

onIn

divi

dual

Cro

ps’Y

ield

Mon

soon

Cro

psD

rySe

ason

Cro

psR

ice

Jow

arB

ajra

Mai

zeA

rhar

Ric

eJo

war

Baj

raM

aize

Whe

at(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)M

onsoon

Neg

Rain

-0.1

76**

*-0

.110

***

-0.2

02**

*-0

.105

***

-0.0

92**

*-0

.021

-0.1

09**

*-0

.146

**-0

.025

-0.0

59**

*(0

.023

)(0

.028

)(0

.044

)(0

.032

)(0

.021

)(0

.016

)(0

.033

)(0

.064

)(0

.027

)(0

.014

)Pos

Rain

0.01

0-0

.054

***

-0.0

41-0

.076

**0.

007

0.01

3-0

.008

-0.0

230.

011

0.02

3**

(0.0

12)

(0.0

19)

(0.0

36)

(0.0

32)

(0.0

17)

(0.0

18)

(0.0

22)

(0.0

35)

(0.0

17)

(0.0

10)

Neg

Tem

p0.

024

0.01

10.

163*

*0.

017

0.07

1***

0.00

1-0

.016

0.07

20.

017

0.02

2*(0

.018

)(0

.025

)(0

.066

)(0

.040

)(0

.027

)(0

.019

)(0

.042

)(0

.076

)(0

.026

)(0

.012

)Pos

Tem

p0.

001

-0.0

10-0

.075

-0.0

48-0

.024

0.02

70.

043

-0.0

67-0

.047

*0.

019

(0.0

25)

(0.0

35)

(0.0

61)

(0.0

36)

(0.0

25)

(0.0

26)

(0.0

41)

(0.0

66)

(0.0

27)

(0.0

14)

Pre-M

onsoon

Neg

Tem

p0.

028

0.07

3**

0.00

50.

011

-0.0

35-0

.006

0.04

9-0

.039

0.00

1-0

.004

(0.0

20)

(0.0

30)

(0.0

47)

(0.0

35)

(0.0

22)

(0.0

17)

(0.0

31)

(0.0

59)

(0.0

32)

(0.0

13)

Pos

Tem

p0.

013

0.00

60.

043

0.12

0***

0.02

7-0

.039

**-0

.037

-0.0

040.

013

-0.0

41**

*(0

.017

)(0

.025

)(0

.041

)(0

.033

)(0

.022

)(0

.016

)(0

.027

)(0

.058

)(0

.023

)(0

.016

)

R-Squared

0.15

70.

046

0.05

60.

062

0.09

60.

111

0.08

20.

117

0.34

10.

156

N57

8447

0512

9524

7667

3723

0019

6638

611

0869

85

Col

umns

1-10

repo

rtes

tim

ates

ofre

gres

sion

ofth

eyi

elds

ofin

divi

dual

crop

son

mon

soon

and

Pre

-mon

soon

wea

ther

shoc

ks.

Stan

dard

erro

rs,c

lust

ered

inti

me

and

spac

e(s

eete

xt),

are

repo

rted

inpa

rent

hese

s.St

ars

indi

cate

stat

isti

cals

igni

fican

ce:

*p

<0.

1,**

p<

0.05

,***

p<

0.01

.

53

Tabl

eA

.2:

The

Effe

cts

ofW

eath

erSh

ocks

onIn

divi

dual

Cri

me

Cat

egor

ies

(Mul

tipl

eB

ins)

Pro

pert

yN

on-P

rope

rty

Bur

glar

yB

andi

try

The

fts

Rob

bery

Rio

tsK

idna

ppin

gR

ape

Mur

der

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Rainfall

<-1

.5Sd

0.08

2***

0.03

5*0.

045

0.13

0***

0.05

3**

0.06

30.

119*

**0.

030

0.01

00.

045*

*(0

.025

)(0

.020

)(0

.030

)(0

.045

)(0

.026

)(0

.048

)(0

.042

)(0

.032

)(0

.031

)(0

.021

)-1

.5To

-1.0

0.06

2***

0.03

3**

0.07

7***

0.08

3***

0.03

7*0.

054*

*0.

059*

0.04

8**

0.02

10.

024*

*(0

.015

)(0

.013

)(0

.019

)(0

.027

)(0

.022

)(0

.026

)(0

.033

)(0

.019

)(0

.024

)(0

.012

)-0

.5To

-1.0

0.01

40.

017

-0.0

010.

032

0.01

30.

016

0.01

10.

024

0.01

30.

014

(0.0

12)

(0.0

10)

(0.0

16)

(0.0

23)

(0.0

13)

(0.0

19)

(0.0

19)

(0.0

15)

(0.0

17)

(0.0

11)

05.

To1.

00.

024*

*-0

.013

-0.0

120.

075*

**0.

018

0.04

6**

-0.0

030.

004

-0.0

45**

-0.0

03(0

.012

)(0

.011

)(0

.015

)(0

.023

)(0

.012

)(0

.020

)(0

.018

)(0

.017

)(0

.018

)(0

.011

)1.

0To

1.5

0.00

8-0

.000

-0.0

120.

029

-0.0

01-0

.006

0.01

80.

009

-0.0

11-0

.009

(0.0

14)

(0.0

13)

(0.0

17)

(0.0

28)

(0.0

15)

(0.0

23)

(0.0

19)

(0.0

21)

(0.0

21)

(0.0

13)

>1.

5Sd

0.04

4***

0.02

8*0.

021

0.11

9***

0.05

6***

0.04

5*-0

.013

0.04

5*0.

017

0.02

0(0

.016

)(0

.014

)(0

.017

)(0

.030

)(0

.018

)(0

.027

)(0

.028

)(0

.024

)(0

.024

)(0

.014

)Tem

perature

<-1

.5Sd

-0.0

07-0

.013

-0.0

19-0

.037

-0.0

040.

028

-0.0

13-0

.004

-0.0

320.

001

(0.0

28)

(0.0

23)

(0.0

27)

(0.0

48)

(0.0

29)

(0.0

43)

(0.0

40)

(0.0

29)

(0.0

38)

(0.0

23)

-1.5

To-1

.00.

050*

**0.

019

0.04

8***

0.07

1**

0.02

60.

097*

**0.

012

0.00

10.

022

0.03

5**

(0.0

16)

(0.0

15)

(0.0

17)

(0.0

36)

(0.0

16)

(0.0

28)

(0.0

28)

(0.0

24)

(0.0

25)

(0.0

15)

-0.5

To-1

.00.

027*

0.02

1*0.

034*

*0.

001

0.01

40.

059*

*0.

027

0.01

30.

011

0.03

3***

(0.0

15)

(0.0

12)

(0.0

16)

(0.0

30)

(0.0

16)

(0.0

24)

(0.0

22)

(0.0

18)

(0.0

22)

(0.0

13)

05.

To1.

0-0

.011

0.00

3-0

.021

-0.0

300.

013

-0.0

02-0

.017

-0.0

280.

012

0.02

2*(0

.013

)(0

.012

)(0

.017

)(0

.025

)(0

.014

)(0

.026

)(0

.020

)(0

.018

)(0

.019

)(0

.012

)1.

0To

1.5

0.05

2**

0.02

60.

005

0.08

6**

0.04

5*0.

071*

0.06

7**

0.00

70.

041

0.02

7(0

.021

)(0

.020

)(0

.025

)(0

.036

)(0

.027

)(0

.042

)(0

.032

)(0

.026

)(0

.030

)(0

.021

)>

1.5

Sd0.

015

0.02

6-0

.022

0.03

90.

015

0.02

40.

023

0.01

10.

018

0.06

3***

(0.0

26)

(0.0

23)

(0.0

32)

(0.0

46)

(0.0

31)

(0.0

46)

(0.0

38)

(0.0

32)

(0.0

38)

(0.0

20)

R-S

quar

ed0.

204

0.11

10.

421

0.50

90.

184

0.23

10.

235

0.18

00.

356

0.05

3N

4572

527

570

9171

9117

9117

9173

9147

9186

9191

9193

Col

umns

1-2

repo

rtes

tim

ates

ofre

gres

sion

(2)w

hich

incl

udes

wea

ther

shoc

ksin

the

mon

soon

and

Pre

-mon

soon

seas

ons,

rest

rict

edto

prop

erty

(col

umn

1)an

dno

n-pr

oper

ty(c

olum

n2)

crim

es.

Col

umns

3-10

repo

rtes

tim

ates

ofre

gres

sion

sin

whi

chth

eou

tcom

eva

riab

les

are

the

loga

rith

ms

ofth

era

tes

ofin

divi

dual

crim

eca

tego

ries

.In

allr

egre

ssio

ns,m

ulti

ple

bins

for

the

valu

eof

stan

dard

ized

wea

ther

shoc

ksin

the

mon

soon

and

Pre

-mon

soon

seas

ons

are

incl

uded

.St

anda

rder

rors

,clu

ster

edin

tim

ean

dsp

ace

(see

text

),ar

ere

port

edin

pare

nthe

ses.

Star

sin

dica

test

atis

tica

lsig

nific

ance

:*

p<

0.1,

**p

<0.

05,*

**p

<0.

01.

54

Table A.3: The Effects of Lagged Weather Shocks on CrimeEntireSample

SecondaryMonsoon

(1) (2)Same Year

Monsoon

Neg Rain 0.047*** 0.051**(0.010) (0.021)

Pos Temp 0.031** 0.005(0.015) (0.038)

Post-Monsoon

Neg Rain 0.067***(0.022)

Pos Temp 0.059**(0.029)

Previous Year

Monsoon

Neg Rain 0.012 0.020(0.010) (0.019)

Pos Temp -0.005 -0.001(0.012) (0.025)

Post-Monsoon

Neg Rain 0.067***(0.022)

Pos Temp 0.005(0.031)

R-Squared 0.131 0.153No. of Observations 73295 15041

Columns 1 and 2 reports estimates of regression (2) which include weather shocks in the yearof observation and the previous year. Regressions reported in column 1 include Monsoonseason shocks, and those reported in column 2 also include Post-Monsoon season shocks. Incolumns 2, the sample is limited to those districts that receive the second monsoon (figure??). Standard errors, clustered in time and space (see text), are reported in parentheses.Stars indicate statistical significance: * p < 0.1, ** p < 0.05, *** p < 0.01.

55

Tabl

eA

.4:

The

Effe

cts

ofW

eath

erSh

ocks

onC

rim

ean

dA

gric

ultu

rein

Wet

and

Dry

Are

as,M

ulti

ple

Rai

nfal

lBin

sC

rim

eA

gric

ultu

ralP

rodu

ctIn

divi

dual

Agr

icul

tura

lYie

lds

Dry

Wet

Dry

Wet

Dry

land

Cro

psW

etC

rops

Jow

arB

ajra

Mai

zeR

ice

Arh

ar(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)R

ainfall

<-1

.5Sd

0.06

2*0.

056*

*-0

.345

***

-0.1

94**

*-0

.232

***

-0.3

34**

*-0

.224

***

-0.2

31**

*-0

.136

***

(0.0

33)

(0.0

22)

(0.0

78)

(0.0

36)

(0.0

53)

(0.0

59)

(0.0

71)

(0.0

42)

(0.0

43)

-1.5

To-1

.00.

069*

**0.

022

-0.1

10**

*-0

.083

***

-0.1

06**

*-0

.209

***

-0.0

83**

-0.1

74**

*-0

.076

***

(0.0

18)

(0.0

15)

(0.0

30)

(0.0

32)

(0.0

28)

(0.0

52)

(0.0

32)

(0.0

26)

(0.0

24)

-0.5

To-1

.00.

012

0.01

3-0

.045

**-0

.036

-0.0

68**

*-0

.101

***

-0.0

00-0

.065

***

-0.0

33**

(0.0

14)

(0.0

13)

(0.0

22)

(0.0

34)

(0.0

19)

(0.0

36)

(0.0

23)

(0.0

11)

(0.0

17)

05.

To1.

00.

003

0.02

0*-0

.034

*0.

014

-0.0

350.

056

-0.0

350.

026*

*0.

033*

(0.0

15)

(0.0

12)

(0.0

19)

(0.0

17)

(0.0

21)

(0.0

36)

(0.0

28)

(0.0

11)

(0.0

17)

1.0

To1.

50.

021

-0.0

10-0

.024

-0.0

15-0

.050

**-0

.000

-0.0

67*

0.01

1-0

.016

(0.0

18)

(0.0

12)

(0.0

29)

(0.0

25)

(0.0

22)

(0.0

42)

(0.0

36)

(0.0

14)

(0.0

21)

>1.

5Sd

0.05

8***

0.01

0-0

.093

**-0

.001

-0.0

99**

*-0

.081

*-0

.098

*-0

.010

0.03

8(0

.019

)(0

.015

)(0

.045

)(0

.026

)(0

.030

)(0

.048

)(0

.050

)(0

.017

)(0

.024

)

R-S

quar

ed0.

146

0.11

10.

106

0.11

10.

052

0.07

70.

058

0.17

00.

100

N38

058

3539

535

8729

2847

0512

9524

7657

8467

37

Aco

mpa

riso

nof

the

effec

tsof

rain

fall

shoc

kson

crim

ean

dag

ricu

ltur

ein

the

“wet

”an

d“d

ry”

sub-

sam

ples

(see

text

for

defin

itio

ns)

usin

gm

ulti

ple

shoc

kca

tego

ries

(or

z-sc

ore

bins

).C

olum

ns1-

2re

port

esti

mat

esin

whi

chth

eou

tcom

eva

riab

leis

the

pool

edcr

ime

rate

.C

olum

ns3-

4re

port

esti

mat

esin

whi

chth

eou

tcom

eva

riab

leis

the

loga

rith

mof

annu

alag

ricu

ltur

alpr

oduc

t.C

olum

ns5-

9re

port

esti

mat

esin

whi

chth

eou

tcom

esar

eth

eyi

elds

ofth

em

ain

“dry

land

crop

s”(c

olum

ns5-

7)an

d“w

etcr

ops”

(col

umns

8-9)

.St

anda

rder

rors

,cl

uste

red

inti

me

and

spac

e(s

eete

xt),

are

repo

rted

inpa

rent

hese

s.St

ars

indi

cate

stat

istica

lsig

nific

ance

:*

p<

0.1,

**p

<0.

05,*

**p

<0.

01.

56

Table A.5: The Effects of Weather Shocks in Irrigated DistrictsCrime Product Yield Wage

(1) (2) (3) (4)Neg Rain 0.051*** -0.046*** -0.024** -0.016

(0.016) (0.017) (0.012) (0.011)Pos Rain 0.024** -0.033** -0.005 -0.013

(0.012) (0.015) (0.011) (0.011)

Upstream Dam ⇥ Neg Rain -0.037 0.093 0.111** 0.019(0.045) (0.065) (0.050) (0.019)

Upstream Dam ⇥ Pos Rain -0.018 0.028 0.002 -0.015(0.027) (0.047) (0.036) (0.019)

R-Squared 0.911 0.917 0.906 0.876No. of Observations 55322 6487 6486 3881

Column 1 reports estimates of regression (2) which includes monsoon rainfall shocks andtheir interaction with the presence of an irrigation dam upstream (the presence of damswithin the district is also controlled for). Columns 2-4 report similar regressions in which theoutcome variables are the logarithm of agricultural production (column 2), yield (column 3)and wages (column 4). Standard errors, clustered in time and space (see text), are reportedin parentheses. Stars indicate statistical significance: * p < 0.1, ** p < 0.05, *** p < 0.01.

57

Tabl

eA

.6:

The

Effe

cts

ofM

onso

onW

eath

erSh

ocks

onIn

divi

dual

Cri

me

Cat

egor

ies

Acr

oss

Dec

ades

Pro

pert

yN

on-P

rope

rty

Bur

glar

yB

andi

try

The

fts

Rob

bery

Rio

tsK

idna

ppin

gR

ape

Mur

der

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Negative

Rainfall

1970

S0.

036

0.01

60.

003

0.08

7**

-0.0

100.

029

0.07

0**

0.03

20.

006

0.00

7(0

.022

)(0

.022

)(0

.037

)(0

.040

)(0

.039

)(0

.035

)(0

.033

)(0

.027

)(0

.044

)(0

.021

)

1980

S0.

043*

*0.

014

0.09

0***

0.08

8**

0.03

2-0

.001

0.00

40.

015

-0.0

010.

029*

*(0

.017

)(0

.013

)(0

.023

)(0

.035

)(0

.022

)(0

.032

)(0

.049

)(0

.023

)(0

.022

)(0

.013

)

1990

S0.

039*

*0.

031*

*0.

044*

0.02

80.

027*

0.05

5**

0.04

90.

061*

*0.

006

0.02

7(0

.018

)(0

.016

)(0

.025

)(0

.031

)(0

.016

)(0

.027

)(0

.032

)(0

.024

)(0

.028

)(0

.019

)

Positive

Tem

perature

1970

S0.

079*

*0.

038

0.04

30.

123*

0.08

0**

0.01

00.

150*

**0.

020

0.07

30.

026

(0.0

31)

(0.0

26)

(0.0

32)

(0.0

64)

(0.0

39)

(0.0

47)

(0.0

51)

(0.0

35)

(0.0

46)

(0.0

25)

1980

S0.

012

-0.0

060.

019

-0.0

15-0

.044

0.11

6*-0

.018

-0.0

360.

036

-0.0

18(0

.028

)(0

.023

)(0

.035

)(0

.047

)(0

.043

)(0

.066

)(0

.047

)(0

.041

)(0

.035

)(0

.021

)

1990

S0.

046*

**0.

038*

*0.

019

0.06

3*0.

030

0.09

7***

0.03

00.

081*

**-0

.021

0.05

4***

(0.0

18)

(0.0

15)

(0.0

22)

(0.0

34)

(0.0

18)

(0.0

27)

(0.0

23)

(0.0

21)

(0.0

23)

(0.0

19)

Est

imat

edco

effici

ents

ofne

gati

vera

in(t

op6

row

s)an

dpo

siti

vete

mpe

ratu

re(b

otto

m6

row

s)sh

ocks

onpr

oper

ty(c

olum

n1)

,non

-pro

pert

y(c

olum

n2)

and

indi

vidu

alcr

ime

cate

gori

es(c

olum

ns3-

10)

inea

chof

the

thre

ede

cade

sin

the

sam

ple

(eac

hes

tim

ate

isfr

oma

sepa

rate

regr

essi

on).

Stan

dard

erro

rs,c

lust

ered

intim

ean

dsp

ace

(see

text

),ar

ere

port

edin

pare

nthe

ses.

Star

sin

dica

test

atis

tica

lsig

nific

ance

:*

p<

0.1,

**p

<0.

05,*

**p

<0.

01.

58

7 Alternative Rainfall Specifications

In table B.1, we report results of estimates that use alternative specifications of rainfall

deviations in the monsoon season. First, we replace binary indicators of rainfall totals

exceeding one localized standard deviation above and below the local long-term mean, with

similar indicators of rainfall totals falling in the 80th and 20th percentiles of the localized

long-term distribution, following Jayachandran (2006)and Kaur (2014). The results (panel

A) are similar, and indicate that both types of shocks increase crime in a statistically

significant manner, but that the impact of negative rainfall shocks is larger. Second, we

use a continuous measure of the fractional deviation of total rainfall from its long-term

local mean, following Duflo and Pande (2007). We find a negative impact, as expected

(panel B), but it is imprecisely estimated, possibly because of the non-monotonic effect of

rainfall. We therefore repeat the estimation, but separately estimate slopes in the positive

and negative shock domains; and find, once again, that both are positive and significant,

but that the negative effect is larger. Third, we conduct a similar estimation, but now using

the localized continuous z-score instead of the fractional deviation. Once again, we find

negative and positive shocks to have significant impacts, with a stronger effect for negative

shocks (panel C). Finally, we test the effect of an alternative measure of precipitation that

replaces the total rainfall amount with the number of rainy days (days having at least 0.1

mm of precipitation). Such measures have been recently shown to perform well as predictors

of agricultural productivity (Fishman, 2016). We divide the range of possible values of this

variable into 7 intervals and include binary indicators of each of those in the regression. We

find that small number of rainy days below 55 (out of a maximum possible 120) lead to

increases in crimes. The lack of impacts of positive rainfall shocks in this specification is

consistent with the nature of the variable. While excessive total rainfall can damage crops,

large numbers of rainy days imply a smooth distribution of rainfall and can therefore have

monotonic positive impacts on crop yields, as shown by Fishman (2016).

59

Table B.1: Alternative Rainfall Specifications(Logarithm) of Pooled Crime Rates

(1) (2) (3) (4) (5)A Bottom Quintile 0.035***

(0.009)Top Quintile 0.014*

(0.008)B Fractional Deviation -0.020

(0.013)Neg Fract Deviation 0.134***

(0.029)Pos Fract Deviation 0.055***

(0.021)C Neg Z-Score Deviation 0.034***

(0.008)Pos Z-Score Deviation 0.016**

(0.006)D 0 To 45 Wet Days 0.057**

(0.028)45 To 55 Wet Days 0.025*

(0.013)55 To 65 Wet Days 0.020

(0.012)65 To 75 Wet Days -0.013

(0.010)85 To 95 Wet Days 0.012

(0.010)95 To 105 Wet Days 0.000

(0.013)>105 Wet Days -0.043

(0.027)

R-Squared 0.132 0.130 0.131 0.131 0.131N 73131 73295 73295 73295 73295

Estimated effects of rainfall shocks on crime in alternative specifications. Panel A: Ex-planatory variables are binary indicators for the top and bottom quintiles of the localizeddistribution of rainfall over time (Jayachandran, 2006; Kaur, 2014). Panel B: Explanatoryvariable is the continuous fractional deviation (Duflo and Pande, 2007)of rainfall in relationto its long-term localized mean (column 2), or the same deviation, estimated separately fornegative and positive deviations (column 3). Panel C: The continuous z-score of rainfallin relation to its long-term localized mean and standard deviation, estimated separatelyfor negative and positive deviations. Panel D: binary indicators for the number of rainydays (precipitation above 0.1 mm) occurring during the monsoon (Fishman, 2016). Allregressions control for temperature shocks as in the main sepcification. Standard errors,clustered in time and space (see text), are reported in parentheses. Stars indicate statisticalsignificance: * p < 0.1, ** p < 0.05, *** p < 0.01.

60