Rainfall'Shocks'and'Property'Crimes'in'Agrarian'Societies:'
Evidence'from'India'
'
David!S!Blakeslee1!
Ram!Fishman2!
!
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!
negative!rainfall!shocks.!Positive!rainfall!shocks,!in!contrast,!
lead! to! increases! in! property! crimes,! but! have! no! effect! on!
non@property! crimes.! These! findings! are! consistent! with!
economic! models! of! crime! emphasizing! both! opportunity!
costs!and!returns.!
'
'
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!
profitable! loot,! and! costs! include! the! potential! consequences! of! being! caught! and!
punished.!!
Positive!aggregate!income!shocks!may!increase!both!the!costs!and!the!benefits!
of! criminal! activity! –! individuals! with! higher! non@criminal! income! have! more! “to!
lose”!if!caught,!but!the!returns!from!predatory!activity!are!also!likely!to!be!higher!–!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
1!Columbia!University.!
2!George!Washington!University.!
!
1!
there! is! more! “to! gain”.! The! economic! theory! of! crime! suggests,! therefore,! that! in!
principle,!both!positive!and!negative!aggregate!income!shocks!might!increase!crime!
rates.!
In! this! paper,! we! use! panel! data! on! crime! rates! in! India! to! show! that! both!
positive! and! negative! weather! (rainfall)! shocks,! which! strongly! affect! agricultural!
income,!can!increase!crime!rates.!We!consider!a!variety!of!types!of!criminal!activity,!
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! income@!
independent,!increases!only!in!response!to!negative!shocks,!when!individuals!have!
“less!to!lose”.!!
The!impact!of!negative!income!shocks!on!crime!rates!has!been!demonstrated!by!
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!
shocks!and!the!incidence!of!Hindu@Muslim!riots,!and!Sekhri!and!Storeygard!(2010)!
find!an!association!between!negative!rainfall!shocks!and!crimes!against!women!and!
vulnerable! minorities.! A! related! literature! shows! civil! conflict! can! also! arise! as! a!
result! of! weather! related! income! shocks! (for! example! Miguel! et! al! 2004.! For! a!
review!see!Burke!and!Hsiang!2012).!Several!papers!(Angrist!&!Kugler!2008,!Dube!&!
Vargas!2008,!Nunn!&!Qian!2012,!Lei!&!Michaels!2011)!have!also!provided!evidence!
of!rapacity!@!increased!conflict!in!response!to!positive!income!shocks!in!sectors!that!
are! relatively! less! labor! intensive! (so! the! “more! to! gain”! effect! dominates! over! the!
“more!to!lose”!effect).!Our!results!are!unique!in!that!they!suggest!that!positive!and!
!
2!
negative!income!shocks!within!the!same!sector!can!both!increase!crime!rates.!!
Like!much!of!the!literature,!this!paper!is!based!on!evidence!from!a!developing!
country.!For!positive!income!shocks!to!affect!property!crime,!it!seems!necessary!for!
incomes!to!be!easily!observable.!Rural!areas!of!developing!countries,!where!income!
is!dominated!by!agriculture!but!population!density!is!still!high,!and!law@protecting!
institutions! may! be! relatively! weak,! therefore! present! a! likely! setting! where! these!
patterns! can! be! observed.! In! addition,! agricultural! income! is! highly! sensitive! to!
exogenous!weather!shocks,!facilitating!identification.!
!
Conceptual'Framework'
We! present! a! highly! simplified! model! of! criminal! activity.! A! continuum! of!
individuals,!indexed!by!0<i<M!and!ordered!by!their!incomes!πi,0!!(the!index!0!stands!
for!an!average!year),!make!decisions!on!whether!to!engage!in!criminal!activity.!For!
property!crimes,!we!assume!that!the!highest!income@earners!can!be!identified!and!
targeted,!so!that!the!rewards!from!crime,!if!successful,!are!proportional!to!πM,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!
concave!function!of!income!u(π).!An!individual!compares!the!benefits!from!criminal!
activity!to!those!from!productive!activity,!and!decides!to!engage!in!criminal!activity!
if!!
!
!
! !!,! < !" !!,! + !!!,! + 1 − ! ! 0 !
If!u(π)!is!sufficiently!steep!at!π=0!and!sufficiently!flat!at!π=M,!then!its!easy!to!show!
there!is!an!intermediate!value!of!π!for!which!the!inequality!is!satisfied!for!all!lower!
!
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values!and!not!satisfied!for!all!higher!values!–!sufficiently!poor!individuals!engage!in!
crime.!!
Consider! now! a! positive! or! negative! income! shocks,! for! which! the! income!
distributions!become!πi,+!≥!πi,0!and!πi,@!≤!πi,0.!!We!assume!all!individuals!are!better!or!
worse!off,!respectively,!as!a!result!of!these!shocks,!but!that!wealthier!individuals!are!
disproportionally!affected:!they!gain!more!from!positive!shocks!and!are!less!affected!
by!negative!shocks,!i.e.!for!all!i:!
!!,!
!!,!
!≥!
!
!!,!
!!,!
!
!!,!
!!,!
!≥!
!
!!,!
!!,!
Under!these!conditions,!it!is!possible!for!both!negative!or!positive!income!shocks!to!
raise! crime! rates.! To! illustrate,! consider! risk! neutral! individuals,! for! whom! the!
condition!for!crime!becomes!!!!
(1 − !)!!,! < !"!!,! !
Obviously,!the!above!conditions!guarantee!that!every!individual!who!chooses!crime!
in!a!normal!year!will!also!choose!crime!in!either!a!positive!or!a!negative!shock!year.!
Note! that! for! violent! crime! (not! property! related),! the! benefit! of! crime! B! is! not!
dependent!on!income,!so!the!condition!for!choosing!crime!is!
!
!
! !!,! < !" !!,! , ! + 1 − ! ! 0 !
!and!therefore!only!an!increase!in!an!individual’s!income!will!make!her!less!likely!to!
choose!crime.!
Inequality!and!the!above!conditions!(which!essentially!increase!inequality!in!a!
shock! year)! are! needed! in! the! model! in! order! for! both! types! of! shocks! to! increase!
!
4!
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!
quite!natural!in!the!agricultural!context.!For!example,!access!to!irrigation,!which!is!
often!wealth!and!class!stratified!in!India,!can!both!reduce!the!impacts!of!a!negative!
rainfall! shock,! and! enhance! the! positive! impact! of! bountiful! rain! by! using! stored!
water!to!expand!double!cropping!in!the!dry!season.!!
There!are!also!other!models!that!can!lead!to!increases!in!crime!from!both!types!
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! non@agricultural! labor! to! turn! to! crime! against!
agriculturalists,!and!when!rainfall!is!poor,!it!induces!farmer,!who!have!little!to!lose,!
to!turn!to!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,! and! including! detailed!
statistics!on!the!incidence!of!various!crimes!at!the!district!levels,!beginning!in!1971.3!
Most!major!classes!of!crimes!are!available!continuously!from!1971!onwards.!Of!the!
crimes!included!in!the!data,!we!consider!burglary,!banditry,!theft,!and!robbery!to!be!
property! crimes,! and! murder,! rape,! and! riots! to! be! “purely”! violent! crimes.! Other!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
3!Crime!data!is!also!available!before!1971,!but!only!at!the!state!level.!
!
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types!of!crime,!such!as!cheating!and!kidnapping,!fit!less!easily!into!this!classification!
scheme.!
Rainfall! figures! were! based! on! gridded! precipitation! and! temperature! data!
produced!by!the!Indian!Meteorological!Department!(Rajeevan!et!al!2005,!Srivastava!
et!al!2009)!and!converted!to!district@wise!figures!by!area@weighted!averaging!over!
grid!points!falling!within!a!given!district.!
Agricultural! district@wise! 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!
indication!of!rural!income,!they!are!likely!to!be!reasonable!proxies,!given!that!rice!
and! wheat! are! the! largest! crops! (in! terms! of! cultivated! area)! in! India’s! two! main!
agricultural!seasons.!
Table!1!provides!summary!statistics!of!the!crimes!included!in!our!data!set.!The!
crime! variables! are! measured! as! the! number! of! incidents! per! 100,000! people.! The!
three!columns!tabulate!the!incidence!of!the!indicated!crime!across!the!three!decades!
spanning! 1970@2000,! and! indicate! a! general! decline! in! the! incidence! of! most!
property! crimes,! with! substantial! declines! in! burglary,! banditry,! thefts,! robbery,!4!
and!contract!violations!(“breach!of!trust”).!Cheating!and!kidnapping,!however,!were!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
4!Robbery!is!distinguished!from!theft!in!that!it!includes!violence!in!the!commission!
of!the!crime.!Banditry!is!distinguished!from!robbery!by!its!involving!5!or!more!
individuals!in!the!commission!of!the!crime.!!
!
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relatively!stable!across!these!periods.!Murder!and!rape!increased!somewhat!during!
this!period,!while!homicides5!!were!stable,!and!riots!decline!slightly.!
'
'
Results'
A.'Rain'Shocks'
Our!primary!empirical!specification!is!the!Poisson!regression:!
!!"# = exp!(! + !! !"#$%&'!" + !! !"#$%&'!" + !! +
!! ×! !"#$%$"&'!" + !!" ).!
The!incidence!of!each!crime!(per!100,000!people)!in!district!i,!state!s!and!year!t!is!
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!
deviation!above!or!below!the!district!mean.!District!fixed!effects!are!included,!as!are!
state@level!quadratic!time!trends.!!
Estimated! coefficients! for! rainfall! shocks! (β1! and! β2)! are! reported! in! Table! 2.!
Columns!(1)@(2)!report!estimates!that!include!only!the!rainfall!dummies;!in!columns!
(3)@(4),!district!fixed!effects!are!added;!and!in!columns!(5)@(6)!the!state@level!time!
trends!are!included!to!form!our!preferred!specification.!!
Crime!rates!in!most!categories!respond!positively!to!deficient!rainfall!deviations!
in! statistically! significant! ways.! For! example,! burglary! rates! increase! by! about! 5%!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
5!Homicides!are!definitionally!equivalent!to!“manslaughter”!in!British!common!law,!
and!consist!of!non@culpable!deaths.!Included!amongst!these!are!deaths!caused!in!
self@defense.!
6!We!use!fluctuations!in!Monsoon!rainfall.!Approximately!90%!of!annual!rainfall!
occurs!during!the!monsoon!season,!and!it!is!upon!this!that!agriculture!is!principally!
dependent.!As!such,!the!inclusion!of!rainfall!from!times!of!the!year!when!it!is!not!
agriculturally!beneficial!adds!noise!to!the!rainfall!measure.!!
!
7!
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!
estimated!4%,!5%,!3%,!7%!and!22%!(but!the!latter!is!not!statistically!significant).!!
During! rainier! than! normal! years,! however,! results! are! more! mixed.! Over! all,! it!
seems!that!property!crime!rates!(including!burglary,!banditry,!thefts!and!robberies)!
respond! positively! (with! statistically! significant! coefficients)! in! excessively! rainy!
years,! whereas! violent! crimes! (riots,! murders,! rape,! and! kidnapping)! show! no!
statistically!significant!response!to!abundant!rain.!!!
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!
evidenced!by!the!large!coefficients!and!standard!errors,!which!is!unsurprising!given!
that! it! includes! accidental! deaths,! which! are! unlikely! to! be! much! influenced! by!
economic!factors.!
As!a!robustness!check,!we!estimate!two!additional!specifications.!In!the!first,!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,!it!should!be!noted,!is!ambiguous!as!to!its!economic!content.!
Kidnappings!for!the!sake!of!extorting!ransoms!will!have!obvious!economic!content,!
whereas!the!kidnappings!of!women!tend!to!have!non@economic!motivations.!The!
data!indicates!that!the!preponderance!of!kidnappings!are!of!women!(74%),!though!
this!disaggregation!is!only!reported!after!1987.!
!
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above!(below)!the!district!mean,!and!takes!the!value!of!the!deviation!when!rainfall!is!
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,!but!the!regressions!are!now!specified!as!OLS.!To!account!for!the!problem!of!
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!
given!in!appendix!tables!A1!and!A2!and!are!consistent!with!those!obtained!from!our!
preferred!specification.!!
!
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!
rainfall!shocks.!This!is!particularly!important!for!positive!rainfall!shocks,!since!these!
could!be!suspected!of!actually!reducing!income!through!flooding!and!waterlogging,!
for!example!8.!In!India,!however,!we!find!that!the!average!impact!of!positive!rainfall!
shocks!on!the!production!of!rice!and!wheat!is!indeed!positive,!lending!support!to!the!
income!channel!thesis.!!
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!For!example,!Hidalgo,!Naidu,!Nichter!and!Richardson!(2010)!find!an!association!
between!both!types!of!rainfall!shocks!and!land!invasions,!and!also!show!that!both!
types!of!shocks!reduce!agricultural!incomes.!!
!
!
9!
negative! rainfall! shocks,! respectively.! In! columns! (1)@(3),! the! outcome! used! is! (the!
logarithm! of)! total! output;! in! columns! (4)@(6),! (log)! yield! per! hectare;! and! in!
columns! (7)@(9),! the! (log)! area! of! cultivation.! The! successive! columns! add! district!
fixed! effects! and! state! time! trends.! Panel! A! gives! the! results! for! rice! cultivation;!
panel! B! for! wheat! cultivation.! As! expected,! negative! rainfall! shocks! are! associated!
with! significant! declines! in! agricultural! output:! a! standard! deviation! decline! in!
rainfall!leads!to!an!approximately!40!percentage!points!decline!in!rice!output,!and!a!
25!percentage!points!decline!in!wheat.!Positive!rainfall!shocks!are!associated!with!
increases!in!agricultural!output:!a!standard!deviation!increase!in!rainfall!causes!an!
approximately!10!percentage!points!increase!in!both!rice!and!wheat!production.!!
These!agricultural!effects!are!consistent!with!the!mechanism!posited!by!which!
rainfall! shocks! influence! criminal! conduct.! The! increases! in! crime! found! with!
negative! rainfall! shocks! are! consistent! with! the! large! disruptions! in! agricultural!
output!caused!by!a!lack!of!rain.!Similarly,!the!increases!in!property!crime!associated!
with!positive!rainfall!shocks!are!consistent!with!the!substantial!increases!in!output!
caused!by!high!levels!of!rainfall.!!
!
Conclusion'
We! provide! suggestive! evidence! for! the! operation! of! two! distinct! and! important!
mechanisms! in! the! occurrence! of! property! crimes! in! India.! On! the! one! hand,!
opportunity!cost!models!of!crime!are!validated,!with!most!types!of!crime!increasing!
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!
!
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mechanism.! A! host! of! property! crimes! are! found! to! increase! during! times! of!
economic!prosperity,!with!non@property!crimes!showing!no!change.!!
!
!
!
!
!
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References'
Becker,! G.! S.! (1974).! Crime! and! punishment:! An! economic! approach.! Essays' in' the'
Economics'of'Crime'and'Punishment.!UMI,!1974.!1@54.!!
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):589–600.!
Burke,! M.! and! Hsiang,! S.! M.! (2012).! Climate,! Conflict,! and! Social! Stability:! What! do!
the!data!say?!
Hidalgo,! F.,! Naidu,! S.,! Nichter,! S.,! and! Richardson,! N.! (2010).! Economic! deter@!
minants!of!land!invasions.!The!Review!of!Economics!and!Statistics,!92(3):505–523.!
Miguel,!E.!(2005).!Poverty!and!witch!killing.!Review'of'Economic'Studies!72.4:!1153@
1172.!
Miguel,!E.,!Shanker,!S.,!and!Sergenti,!E.!(2004).!Economic!shocks!and!civil!conflict:!An!
instrumental!variables!approach.!Journal'of'political'Economy!112.4:!725@753.!
Angrist,!J.!D.!and!Kugler,!!A.!D.!(2008).!Rural!windfall!or!a!new!resource!curse?!Coca,!
income,! and! civil! conflict! in! Colombia.! The'Review'of'Economics'and'Statistics! 90.2!
(2008):!191@215.!
Dube,!O.!and!Vargas,!J.!(2008).!Commodity!price!shocks!and!civil!conflict:!Evidence!
from!Colombia.!Unpublished'manuscript'Harvard'University.!
Nunn,!N.!and!Qian,!N.!(2012)!Aiding'Conflict:'The'Impact'of'US'Food'Aid'on'Civil'War.!
Working!Paper!No.!w17794.!National!Bureau!of!Economic!Research.!
Lei,! Y.! and! Michaels,! G.! (2011).! "Do! giant! oilfield! discoveries! fuel! internal! armed!
conflicts?".!
!
12!
Rajeevan,!M.,!et!al.!(2005)!"Development!of!a!high!resolution!daily!gridded!rainfall!
data!for!the!Indian!region."!Met.'Monograph'Climatology!22:!2005.!
Sarsons, H. (2011). Rainfall and conflict.
!
Sekhri,!S.!and!Storeygard,!A.!(2010).!The!Impact!of!Climate!Variability!on!Vulnerable!
Populations:! Evidence! on! Crimes! against! Women! and! Disadvantaged! Minorities! in!
India.!
Srivastava,! A.! K.,! Rajeevan,! M.! and! Kshirsagar,! S.! R.(2009).! "Development! of! a! high!
resolution! daily! gridded! temperature! data! set! (1969–2005)! for! the! Indian! region."!
Atmospheric'Science'Letters!10.4:!249@254.!
!
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Table 1: Summary Statistics: Crime (per 100k)
1970s
1980s
1990s
property crimes
burglary
38.393
21.616
14.215
banditry
2.372
1.695
1.019
thefts
75.900
45.874
30.387
robbery
4.821
3.703
2.688
breach of trust
4.359
2.515
1.724
counterfeiting
0.099
0.286
0.225
cheating
3.183
2.591
3.370
non-property crimes
kidnapping
2.309
2.096
2.398
riots
13.709
13.771
11.340
rape
0.791
1.178
1.764
murder
3.724
4.036
4.593
homicide
0.511
0.585
0.495
dowry death
.
.
0.774
grievous hurt
.
.
24.486
agriculture
irrigation
0.514
0.580
0.610
wheat product
95.044
148.719
185.284
wheat yield
1.224
1.565
2.007
wheat area
69.389
77.476
73.879
rice product
131.768
165.486
214.185
rice yield
1.194
1.739
2.037
rice area
109.840
102.934
106.929
cotton product
0.061
23.030
45.546
cotton yield
0.167
0.221
0.256
cotton area
0.293
92.442
188.143
sugarcane product
0.081
54.674
442.360
sugarcane yield
30.438
40.612
56.543
sugarcane area
0.002
1.337
6.486
14
Table 2: Monsoon Rainfall Shocks and Crime
monsoon shocks > 1sd
outcome:
neg
(1)
pos
(2)
neg
(3)
pos
(4)
neg
(5)
pos
(6)
0.083***
(0.026)
0.005
(0.026)
0.089***
(0.020)
0.073***
(0.022)
0.049***
(0.018)
0.086***
(0.018)
banditry
0.079**
(0.038)
0.005
(0.037)
0.115***
(0.029)
0.113***
(0.025)
0.101***
(0.029)
0.122***
(0.024)
thefts
0.060*
(0.032)
-0.037
(0.033)
0.089***
(0.024)
0.044*
(0.026)
0.040**
(0.020)
0.063***
(0.023)
robbery
0.132***
(0.038)
-0.020
(0.032)
0.144***
(0.034)
0.038*
(0.020)
0.115***
(0.033)
0.038**
(0.019)
breach of trust
0.081**
(0.032)
0.024
(0.044)
0.074***
(0.028)
0.084**
(0.042)
0.025
(0.027)
0.084**
(0.041)
-0.171***
(0.033)
-0.028
(0.031)
-0.028
(0.026)
0.018
(0.022)
0.008
(0.023)
0.025
(0.019)
-0.082**
(0.032)
-0.022
(0.030)
0.020
(0.020)
0.019
(0.017)
0.039**
(0.020)
0.017
(0.017)
riots
-0.059**
(0.026)
-0.081***
(0.026)
0.054***
(0.018)
-0.038**
(0.016)
0.048***
(0.016)
-0.006
(0.014)
rape
-0.123***
(0.029)
-0.004
(0.028)
-0.057***
(0.020)
0.017
(0.020)
0.032**
(0.015)
0.003
(0.014)
murder
-0.017
(0.020)
-0.044**
(0.022)
0.056***
(0.012)
0.005
(0.015)
0.071***
(0.011)
0.005
(0.014)
homicide
0.272**
(0.135)
0.061
(0.068)
0.224
(0.168)
0.076*
(0.044)
0.227
(0.169)
0.079*
(0.044)
no
no
no
no
yes
no
yes
no
yes
yes
yes
yes
property crimes
burglary
cheating
non-property crimes
kidnapping
district FEs
state time trends
15
Table 3: Monsoon Rainfall, Temperature Shocks, and Agricultural Output
(1)
total output
(2)
(3)
(4)
yield per hectare
(5)
(6)
(7)
area
(8)
(9)
rainfall neg
-0.166**
(0.073)
-0.453***
(0.025)
-0.400***
(0.023)
-0.239***
(0.019)
-0.297***
(0.015)
-0.249***
(0.012)
0.139**
(0.069)
-0.091***
(0.020)
-0.089***
(0.019)
rainfall pos
0.110
(0.067)
0.116***
(0.022)
0.092***
(0.021)
0.079***
(0.017)
0.047***
(0.013)
0.049***
(0.011)
0.020
(0.062)
0.071***
(0.018)
0.045***
(0.017)
R-squared
N
0.0010
7257
0.9279
7082
0.9400
7082
0.0233
7226
0.6419
7052
0.7585
7052
0.0006
7226
0.9476
7052
0.9551
7052
rainfall neg
-0.275***
(0.066)
-0.267***
(0.024)
-0.228***
(0.021)
-0.079***
(0.016)
-0.123***
(0.010)
-0.083***
(0.008)
-0.203***
(0.058)
-0.145***
(0.020)
-0.147***
(0.018)
rainfall pos
0.151**
(0.062)
0.093***
(0.021)
0.099***
(0.019)
0.057***
(0.015)
0.025***
(0.010)
0.028***
(0.007)
0.100*
(0.055)
0.070***
(0.018)
0.074***
(0.017)
R-squared
N
0.0025
9076
0.9210
8334
0.9406
8334
0.0042
9058
0.7224
8319
0.8341
8319
0.0016
9058
0.9300
8319
0.9408
8319
no
no
yes
no
yes
yes
no
no
yes
no
yes
yes
no
no
yes
no
yes
yes
Panel A: Rice
Panel B: Wheat
16
district FEs
state time trends
Appendix Tables
Table 1: Monsoon Rainfall Shocks and Crime: Continuous Measure
monsoon shocks
outcome:
neg
(1)
pos
(2)
neg
(3)
pos
(4)
neg
(5)
pos
(6)
0.108***
(0.026)
0.056***
(0.021)
0.086***
(0.016)
0.072***
(0.015)
0.045***
(0.014)
0.057***
(0.013)
banditry
0.189***
(0.034)
0.108***
(0.027)
0.125***
(0.022)
0.102***
(0.017)
0.109***
(0.021)
0.100***
(0.016)
thefts
0.113***
(0.034)
0.046*
(0.025)
0.088***
(0.021)
0.053***
(0.018)
0.042**
(0.017)
0.041***
(0.015)
robbery
0.144***
(0.031)
0.055***
(0.020)
0.102***
(0.022)
0.055***
(0.013)
0.076***
(0.021)
0.047***
(0.012)
breach of trust
0.092***
(0.027)
0.049
(0.036)
0.060***
(0.021)
0.049
(0.031)
0.018
(0.020)
0.035
(0.031)
-0.056*
(0.029)
-0.006
(0.021)
-0.015
(0.019)
-0.007
(0.014)
-0.001
(0.016)
0.003
(0.012)
-0.011
(0.029)
0.004
(0.020)
0.019
(0.019)
0.008
(0.011)
0.027
(0.019)
0.009
(0.011)
riots
-0.006
(0.024)
-0.026
(0.018)
0.041***
(0.015)
-0.017
(0.011)
0.034**
(0.014)
0.001
(0.010)
rape
-0.055**
(0.025)
-0.019
(0.021)
-0.039***
(0.015)
-0.017
(0.014)
0.019
(0.012)
-0.004
(0.009)
murder
0.028
(0.017)
0.004
(0.014)
0.035***
(0.009)
0.003
(0.008)
0.039***
(0.009)
0.001
(0.008)
homicide
0.175
(0.136)
0.039
(0.039)
0.143
(0.112)
0.046
(0.030)
0.150
(0.113)
0.051*
(0.030)
no
no
no
no
yes
no
yes
no
yes
yes
yes
yes
property crimes
burglary
cheating
non-property crimes
kidnapping
district FEs
state time trends
17
Table 2: Monsoon Rainfall Shocks and Crime: OLS
monsoon shocks > 1sd
outcome:
neg
(1)
pos
(2)
neg
(3)
pos
(4)
neg
(5)
pos
(6)
0.088***
(0.022)
-0.036*
(0.021)
0.101***
(0.017)
0.021
(0.015)
0.072***
(0.015)
0.034**
(0.014)
banditry
0.036**
(0.017)
-0.011
(0.016)
0.053***
(0.011)
0.035***
(0.010)
0.047***
(0.011)
0.039***
(0.010)
thefts
0.047**
(0.022)
-0.059***
(0.021)
0.067***
(0.016)
0.012
(0.014)
0.029**
(0.014)
0.034***
(0.012)
robbery
0.035*
(0.019)
-0.030*
(0.018)
0.066***
(0.013)
0.019*
(0.012)
0.056***
(0.012)
0.019*
(0.011)
breach of trust
0.068***
(0.015)
-0.020
(0.014)
0.066***
(0.013)
0.015
(0.012)
0.031***
(0.011)
0.016
(0.010)
cheating
-0.103***
(0.016)
-0.022
(0.015)
-0.027**
(0.012)
0.011
(0.011)
-0.006
(0.011)
0.021**
(0.010)
-0.061***
(0.014)
-0.025*
(0.013)
0.007
(0.009)
0.007
(0.008)
0.022**
(0.009)
0.007
(0.008)
riots
-0.013
(0.024)
-0.065***
(0.022)
0.065***
(0.014)
-0.040***
(0.013)
0.063***
(0.014)
-0.008
(0.012)
rape
-0.057***
(0.013)
-0.005
(0.012)
-0.030***
(0.009)
-0.003
(0.008)
0.010
(0.007)
0.003
(0.006)
murder
-0.034***
(0.013)
-0.037***
(0.012)
0.025***
(0.008)
-0.006
(0.007)
0.040***
(0.008)
-0.001
(0.007)
homicide
0.043***
(0.010)
0.023**
(0.010)
0.009
(0.007)
0.010
(0.006)
0.012*
(0.007)
0.012*
(0.006)
no
no
no
no
yes
no
yes
no
yes
yes
yes
yes
property crimes
burglary
non-property crimes
kidnapping
district FEs
state time trends
18