Rainfall'Shocks'and'Property'Crimes'in'Agrarian'Societies:' Evidence'from'India' David!S!Blakeslee 1! Ram!Fishman 2!

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1 Rainfall'Shocks'and'Property'Crimes'in'Agrarian'Societies:' Evidence'from'India' ' DavidSBlakeslee 1 RamFishman 2 ' We examine the effects of rainfall shocks on various the incidence of crime in India from the years 1971@2000. We find that the incidence of most crimes increases with negativerainfallshocks.positiverainfallshocks,incontrast, leadtoincreasesinpropertycrimes,buthavenoeffecton non@property crimes. These findings are consistent with economic models of crime emphasizing both opportunity costsandreturns. ' Introduction' The Economic theory of crime (starting with Becker 1974) postulates that individuals decisions to engage in criminal activity depend on the associated benefits and costs. In the case of property crime, benefits include the prospects of profitableloot, and costs include the potential consequences of being caught and punished. Positiveaggregateincomeshocksmayincreaseboththecostsandthebenefits of criminal activity individuals with higher non@criminal income have more to lose ifcaught,butthereturnsfrompredatoryactivityarealsolikelytobehigher 1 ColumbiaUniversity. 2 GeorgeWashingtonUniversity. 1

2 there is more to gain. The economic theory of crime suggests, therefore, that in principle,bothpositiveandnegativeaggregateincomeshocksmightincreasecrime rates. In this paper, we use panel data on crime rates in India to show that both positiveandnegativeweather (rainfall)shocks,whichstrongly affect agricultural income,canincreasecrimerates.weconsideravarietyoftypesofcriminalactivity, and in accordance with theory, find that property crime increases in response to both types of shocks, whereas violent crime, for which the benefits are independent,increasesonlyinresponsetonegativeshocks,whenindividualshave lesstolose. Theimpactofnegativeincomeshocksoncrimerateshasbeendemonstratedby several authors. For example, Miguel (2003) finds that negative weather shocks increase murder rates ( witch killing ) in rural Tanzania. In India, Bohlken and Sergenti (2011) and Sarsons (2011) find an association between negative rainfall shocksandtheincidenceofhindu@muslimriots,andsekhriandstoreygard(2010) findanassociationbetweennegativerainfallshocksandcrimesagainstwomenand vulnerable minorities. A related literature shows civil conflict can also arise as a result of weather related income shocks (for example Miguel et al For a reviewseeburkeandhsiang2012).severalpapers(angrist&kugler2008,dube& Vargas2008,Nunn&Qian2012,Lei&Michaels2011)havealsoprovidedevidence ofrapacity@increasedconflictinresponsetopositiveincomeshocksinsectorsthat are relatively less labor intensive(so the more to gain effect dominates over the moretolose effect).ourresultsareuniqueinthattheysuggestthatpositiveand 2

3 negativeincomeshockswithinthesamesectorcanbothincreasecrimerates. Likemuchoftheliterature,thispaperisbasedonevidencefromadeveloping country.forpositiveincomeshockstoaffectpropertycrime,itseemsnecessaryfor incomestobeeasilyobservable.ruralareasofdevelopingcountries,whereincome institutions may be relatively weak, therefore present a likely setting where these patterns can be observed. In addition, agricultural income is highly sensitive to exogenousweathershocks,facilitatingidentification. Conceptual'Framework' We present a highly simplified model of criminal activity. A continuum of individuals,indexedby0<i<mandorderedbytheirincomesπi,0(theindex0stands foranaverageyear),makedecisionsonwhethertoengageincriminalactivity.for propertycrimes,weassumethatthehighestincome@earnerscanbeidentifiedand targeted,sothattherewardsfromcrime,ifsuccessful,areproportionaltoπm,0.the probability of success is p, and the probability of failure(arrest and punishment), assumed to be welfare equivalent to an income of π=0, is 1@p. Utility is rising concavefunctionofincomeu(π).anindividualcomparesthebenefitsfromcriminal activitytothosefromproductiveactivity,anddecidestoengageincriminalactivity if, < ", +, Ifu(π)issufficientlysteepatπ=0andsufficientlyflatatπ=M,thenitseasytoshow thereisanintermediatevalueofπforwhichtheinequalityissatisfiedforalllower 3

4 valuesandnotsatisfiedforallhighervalues sufficientlypoorindividualsengagein crime. Consider now a positive or negative income shocks, for which the income distributionsbecomeπi,+ πi,0andπi,@ πi,0.weassumeallindividualsarebetteror worseoff,respectively,asaresultoftheseshocks,butthatwealthierindividualsare disproportionallyaffected:theygainmorefrompositiveshocksandarelessaffected bynegativeshocks,i.e.foralli:,,,,,,,, Undertheseconditions,itispossibleforbothnegativeorpositiveincomeshocksto raise crime rates. To illustrate, consider risk neutral individuals, for whom the conditionforcrimebecomes (1 ), < ", Obviously,theaboveconditionsguaranteethateveryindividualwhochoosescrime inanormalyearwillalsochoosecrimeineitherapositiveoranegativeshockyear. Note that for violent crime (not property related), the benefit of crime B is not dependentonincome,sotheconditionforchoosingcrimeis, < ",, andthereforeonlyanincreaseinanindividual sincomewillmakeherlesslikelyto choosecrime. Inequalityandtheaboveconditions(whichessentiallyincreaseinequalityina shockyear)areneededinthemodelinorderforbothtypesofshockstoincrease 4

5 crime. Homogenous shocks to an equal society will not do much to change crime rates, especially for positive income shocks. However, the above assumptions are quitenaturalintheagriculturalcontext.forexample,accesstoirrigation,whichis oftenwealthandclassstratifiedinindia,canbothreducetheimpactsofanegative rainfall shock, and enhance the positive impact of bountiful rain by using stored watertoexpanddoublecroppinginthedryseason. Therearealsoothermodelsthatcanleadtoincreasesincrimefrombothtypes of shocks. For example, there could be two sectors, with restrictions on labor movement, with one sector(agriculture) more sensitive to rainfall than the other. When rainfall is good, it induces labor to turn to crime against agriculturalists,andwhenrainfallispoor,itinducesfarmer,whohavelittletolose, toturntocrime. 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, and including detailed statisticsontheincidenceofvariouscrimesatthedistrictlevels,beginningin Mostmajorclassesofcrimesareavailablecontinuouslyfrom1971onwards.Ofthe crimesincludedinthedata,weconsiderburglary,banditry,theft,androbberytobe property crimes, and murder, rape, and riots to be purely violent crimes. Other 3 Crimedataisalsoavailablebefore1971,butonlyatthestatelevel. 5

6 typesofcrime,suchascheatingandkidnapping,fitlesseasilyintothisclassification scheme. Rainfall figures were based on gridded precipitation and temperature data producedbytheindianmeteorologicaldepartment(rajeevanetal2005,srivastava gridpointsfallingwithinagivendistrict. Agricultural data on the production of rice and wheat were obtained from the Indian Harvest database produced by the Center for the Monitoring of the Indian Economy. While these figures do not provide a complete indicationofruralincome,theyarelikelytobereasonableproxies,giventhatrice and wheat are the largest crops (in terms of cultivated area) in India s two main agriculturalseasons. Table1providessummarystatisticsofthecrimesincludedinourdataset.The crime variables are measured as the number of incidents per 100,000 people. The threecolumnstabulatetheincidenceoftheindicatedcrimeacrossthethreedecades spanning 1970@2000, and indicate a general decline in the incidence of most property crimes, with substantial declines in burglary, banditry, thefts, robbery, 4 andcontractviolations( breachoftrust ).Cheatingandkidnapping,however,were 4 Robberyisdistinguishedfromtheftinthatitincludesviolenceinthecommission ofthecrime.banditryisdistinguishedfromrobberybyitsinvolving5ormore individualsinthecommissionofthecrime. 6

7 relativelystableacrosstheseperiods.murderandrapeincreasedsomewhatduring thisperiod,whilehomicides 5 werestable,andriotsdeclineslightly. ' ' Results' A.'Rain'Shocks' OurprimaryempiricalspecificationisthePoissonregression: "# = exp( + "#$%&' " + "#$%&' " + + "#$%$"&' " + " ). Theincidenceofeachcrime(per100,000people)indistricti,statesandyeartis regressed on dummy variables for positive and negative rainfall shocks 6 in constituency i at time t, which take the value of 1 when rainfall is one standard deviationaboveorbelowthedistrictmean.districtfixedeffectsareincluded,asare state@levelquadratictimetrends. Estimated coefficients for rainfall shocks(β1 and β2) are reported in Table 2. Columns(1)@(2)reportestimatesthatincludeonlytherainfalldummies;incolumns (3)@(4),districtfixedeffectsareadded;andincolumns(5)@(6)thestate@leveltime trendsareincludedtoformourpreferredspecification. Crimeratesinmostcategoriesrespondpositivelytodeficientrainfalldeviations in statistically significant ways. For example, burglary rates increase by about 5% 5 Homicidesaredefinitionallyequivalentto manslaughter inbritishcommonlaw, andconsistofnon@culpabledeaths.includedamongstthesearedeathscausedin self@defense. 6 WeusefluctuationsinMonsoonrainfall.Approximately90%ofannualrainfall occursduringthemonsoonseason,anditisuponthisthatagricultureisprincipally dependent.assuch,theinclusionofrainfallfromtimesoftheyearwhenitisnot agriculturallybeneficialaddsnoisetotherainfallmeasure. 7

8 during dry years, banditry by about 10%, thefts by 4%, and robberies by 12%. Similarly, kidnapping, 7 riots, rape, murder and homicide rates increase by an estimated4%,5%,3%,7%and22%(butthelatterisnotstatisticallysignificant). During rainier than normal years, however, results are more mixed. Over all, it seemsthatpropertycrimerates(includingburglary,banditry,theftsandrobberies) respond positively (with statistically significant coefficients) in excessively rainy years, whereas violent crimes (riots, murders, rape, and kidnapping) show no statisticallysignificantresponsetoabundantrain. The one exception to this pattern is homicides, where both negative and positive rainfall shocks are associated with increases in incidence, though the former is statistically insignificant and the latter significant at the 10% level. This finding is not inconsistent with our thesis, as murders due to self@defense are classified as homicides, and therefore might be expected to rise with increases in property crime. In any case, the homicide variable is clearly somewhat noisy, as evidencedbythelargecoefficientsandstandarderrors,whichisunsurprisinggiven thatitincludesaccidentaldeaths,whichareunlikelytobe much influenced by economicfactors. Asarobustnesscheck,weestimatetwoadditionalspecifications.Inthefirst,we replace the dummy rainfall measures with continuous variables: the negative (positive) rainfall shock variable takes the value of zero when monsoon rainfall is 7 Kidnapping,itshouldbenoted,isambiguousastoitseconomiccontent. Kidnappingsforthesakeofextortingransomswillhaveobviouseconomiccontent, whereasthekidnappingsofwomentendtohavenon@economicmotivations.the dataindicatesthatthepreponderanceofkidnappingsareofwomen(74%),though thisdisaggregationisonlyreportedafter

9 above(below)thedistrictmean,andtakesthevalueofthedeviationwhenrainfallis below (above) the district mean. In the second, the rainfall variables are again specified as dummies indicating shocks 1 standard deviation above and below the mean,buttheregressionsarenowspecifiedasols.toaccountfortheproblemof observations for which the crime takes the value of zero, we specify the outcome variable as ln(1 + "#$%), and then include dummies equaling 1 for observations in which"#$% = 0. The results from these two specifications are giveninappendixtablesa1anda2andareconsistentwiththoseobtainedfromour preferredspecification. B.'Mechanisms' To examine whether the impact of rainfall shocks on crime rates is mediated through income channels, we estimated parallel regressions for the production of rice and wheat, India s most prevalent crops (in terms of cultivated areas) on rainfallshocks.thisisparticularlyimportantforpositiverainfallshocks,sincethese couldbesuspectedofactuallyreducingincomethroughfloodingandwaterlogging, forexample 8.InIndia,however,wefindthattheaverageimpactofpositiverainfall shocksontheproductionofriceandwheatisindeedpositive,lendingsupporttothe incomechannelthesis. The findings are reported in table 3. As with the crime regressions, the two rainfall variables are specified as dummies taking the value of 1 for positive and 8 Forexample,Hidalgo,Naidu,NichterandRichardson(2010)findanassociation betweenbothtypesofrainfallshocksandlandinvasions,andalsoshowthatboth typesofshocksreduceagriculturalincomes. 9

10 negativerainfallshocks,respectively. In the outcome used is(the logarithm of) total output; in columns (log) yield per hectare; and in columns the (log) area of cultivation. The successive columns add district fixed effects and state time trends. Panel A gives the results for rice cultivation; panelbforwheatcultivation. As expected, negative rainfall shocks are associated with significant declines in agricultural output: a standard deviation decline in rainfallleadstoanapproximately40percentagepointsdeclineinriceoutput,anda 25percentagepointsdeclineinwheat.Positiverainfallshocksareassociatedwith increasesinagriculturaloutput:astandarddeviationincreaseinrainfallcausesan approximately10percentagepointsincreaseinbothriceandwheatproduction. Theseagriculturaleffectsareconsistentwiththemechanismpositedbywhich rainfall shocks influence criminal conduct. The increases in crime found with negative rainfall shocks are consistent with the large disruptions in agricultural outputcausedbyalackofrain.similarly,theincreasesinpropertycrimeassociated withpositiverainfallshocksareconsistentwiththesubstantialincreasesinoutput causedbyhighlevelsofrainfall. Conclusion' Weprovidesuggestive evidence for the operation of two distinct and important mechanisms in the occurrence of property crimes in India. On the one hand, opportunitycostmodelsofcrimearevalidated,withmosttypesofcrimeincreasing during times of economic duress. On the other side of the ledger, we also find evidence for the validity of models emphasizing the returns to crime as a driving 10

11 mechanism. A host of property crimes are found to increase during times of economicprosperity,withnon@propertycrimesshowingnochange. 11

12 References' Becker, G. S. (1974). Crime and punishment: An economic approach. Essays' in' the' Bohlken, A. T. and Sergenti, E. J.(2010). Economic growth and ethnic violence: An empirical investigation of hindu muslim riots in india. Journal' of' Peace' Research, 47(5): Burke, M. and Hsiang, S. M.(2012). Climate, Conflict, and Social Stability: What do thedatasay? Hidalgo, F., Naidu, S., Nichter, S., and Richardson, N. (2010). Economic minantsoflandinvasions.thereviewofeconomicsandstatistics,92(3): Miguel,E.,Shanker,S.,andSergenti,E.(2004).Economicshocksandcivilconflict:An Angrist,J.D.andKugler,A.D.(2008).Ruralwindfalloranewresourcecurse?Coca, income, and civil conflict in Colombia. The'Review'of'Economics'and'Statistics 90.2 Dube,O.andVargas,J.(2008).Commoditypriceshocksandcivilconflict:Evidence fromcolombia.unpublished'manuscript'harvard'university. Nunn,N.andQian,N.(2012)Aiding'Conflict:'The'Impact'of'US'Food'Aid'on'Civil'War. WorkingPaperNo.w17794.NationalBureauofEconomicResearch. Lei, Y. and Michaels, G. (2011). "Do giant oilfield discoveries fuel internal armed conflicts?". 12

13 Rajeevan,M.,etal.(2005)"Developmentofahighresolutiondailygriddedrainfall datafortheindianregion."met.'monograph'climatology22:2005. Sarsons, H. (2011). Rainfall and conflict. Sekhri,S.andStoreygard,A.(2010).TheImpactofClimateVariabilityonVulnerable Populations: Evidence on Crimes against Women and Disadvantaged Minorities in India. Srivastava, A. K., Rajeevan, M.andKshirsagar, S. R.(2009)."Development of a high resolution daily gridded temperature data set( ) for the Indian region." Atmospheric'Science'Letters10.4:249@

14 Table 1: Summary Statistics: Crime (per 100k) 1970s 1980s 1990s property crimes burglary banditry thefts robbery breach of trust counterfeiting cheating non-property crimes kidnapping riots rape murder homicide dowry death grievous hurt agriculture irrigation wheat product wheat yield wheat area rice product rice yield rice area cotton product cotton yield cotton area sugarcane product sugarcane yield sugarcane area

15 Table 2: Monsoon Rainfall Shocks and Crime monsoon shocks > 1sd outcome: neg pos neg pos neg pos (1) (2) (3) (4) (5) (6) property crimes burglary 0.083*** *** 0.073*** 0.049*** 0.086*** (0.026) (0.026) (0.020) (0.022) (0.018) (0.018) banditry 0.079** *** 0.113*** 0.101*** 0.122*** (0.038) (0.037) (0.029) (0.025) (0.029) (0.024) thefts 0.060* *** 0.044* 0.040** 0.063*** (0.032) (0.033) (0.024) (0.026) (0.020) (0.023) robbery 0.132*** *** 0.038* 0.115*** 0.038** (0.038) (0.032) (0.034) (0.020) (0.033) (0.019) breach of trust 0.081** *** 0.084** ** (0.032) (0.044) (0.028) (0.042) (0.027) (0.041) cheating *** (0.033) (0.031) (0.026) (0.022) (0.023) (0.019) non-property crimes kidnapping ** ** (0.032) (0.030) (0.020) (0.017) (0.020) (0.017) riots ** *** 0.054*** ** 0.048*** (0.026) (0.026) (0.018) (0.016) (0.016) (0.014) rape *** *** ** (0.029) (0.028) (0.020) (0.020) (0.015) (0.014) murder ** 0.056*** *** (0.020) (0.022) (0.012) (0.015) (0.011) (0.014) homicide 0.272** * * (0.135) (0.068) (0.168) (0.044) (0.169) (0.044) district FEs no no yes yes yes yes state time trends no no no no yes yes 15

16 Table 3: Monsoon Rainfall, Temperature Shocks, and Agricultural Output total output yield per hectare area (1) (2) (3) (4) (5) (6) (7) (8) (9) Panel A: Rice rainfall neg ** *** *** *** *** *** 0.139** *** *** (0.073) (0.025) (0.023) (0.019) (0.015) (0.012) (0.069) (0.020) (0.019) rainfall pos *** 0.092*** 0.079*** 0.047*** 0.049*** *** 0.045*** (0.067) (0.022) (0.021) (0.017) (0.013) (0.011) (0.062) (0.018) (0.017) R-squared N Panel B: Wheat rainfall neg *** *** *** *** *** *** *** *** *** (0.066) (0.024) (0.021) (0.016) (0.010) (0.008) (0.058) (0.020) (0.018) 16 rainfall pos 0.151** 0.093*** 0.099*** 0.057*** 0.025*** 0.028*** 0.100* 0.070*** 0.074*** (0.062) (0.021) (0.019) (0.015) (0.010) (0.007) (0.055) (0.018) (0.017) R-squared N district FEs no yes yes no yes yes no yes yes state time trends no no yes no no yes no no yes

17 Appendix Tables Table 1: Monsoon Rainfall Shocks and Crime: Continuous Measure monsoon shocks outcome: neg pos neg pos neg pos (1) (2) (3) (4) (5) (6) property crimes burglary 0.108*** 0.056*** 0.086*** 0.072*** 0.045*** 0.057*** (0.026) (0.021) (0.016) (0.015) (0.014) (0.013) banditry 0.189*** 0.108*** 0.125*** 0.102*** 0.109*** 0.100*** (0.034) (0.027) (0.022) (0.017) (0.021) (0.016) thefts 0.113*** 0.046* 0.088*** 0.053*** 0.042** 0.041*** (0.034) (0.025) (0.021) (0.018) (0.017) (0.015) robbery 0.144*** 0.055*** 0.102*** 0.055*** 0.076*** 0.047*** (0.031) (0.020) (0.022) (0.013) (0.021) (0.012) breach of trust 0.092*** *** (0.027) (0.036) (0.021) (0.031) (0.020) (0.031) cheating * (0.029) (0.021) (0.019) (0.014) (0.016) (0.012) non-property crimes kidnapping (0.029) (0.020) (0.019) (0.011) (0.019) (0.011) riots *** ** (0.024) (0.018) (0.015) (0.011) (0.014) (0.010) rape ** *** (0.025) (0.021) (0.015) (0.014) (0.012) (0.009) murder *** *** (0.017) (0.014) (0.009) (0.008) (0.009) (0.008) homicide * (0.136) (0.039) (0.112) (0.030) (0.113) (0.030) district FEs no no yes yes yes yes state time trends no no no no yes yes 17

18 Table 2: Monsoon Rainfall Shocks and Crime: OLS monsoon shocks > 1sd outcome: neg pos neg pos neg pos (1) (2) (3) (4) (5) (6) property crimes burglary 0.088*** * 0.101*** *** 0.034** (0.022) (0.021) (0.017) (0.015) (0.015) (0.014) banditry 0.036** *** 0.035*** 0.047*** 0.039*** (0.017) (0.016) (0.011) (0.010) (0.011) (0.010) thefts 0.047** *** 0.067*** ** 0.034*** (0.022) (0.021) (0.016) (0.014) (0.014) (0.012) robbery 0.035* * 0.066*** 0.019* 0.056*** 0.019* (0.019) (0.018) (0.013) (0.012) (0.012) (0.011) breach of trust 0.068*** *** *** (0.015) (0.014) (0.013) (0.012) (0.011) (0.010) cheating *** ** ** (0.016) (0.015) (0.012) (0.011) (0.011) (0.010) non-property crimes kidnapping *** * ** (0.014) (0.013) (0.009) (0.008) (0.009) (0.008) riots *** 0.065*** *** 0.063*** (0.024) (0.022) (0.014) (0.013) (0.014) (0.012) rape *** *** (0.013) (0.012) (0.009) (0.008) (0.007) (0.006) murder *** *** 0.025*** *** (0.013) (0.012) (0.008) (0.007) (0.008) (0.007) homicide 0.043*** 0.023** * 0.012* (0.010) (0.010) (0.007) (0.006) (0.007) (0.006) district FEs no no yes yes yes yes state time trends no no no no yes yes 18