Gender, Crime and Punishment:

Evidence from Women Police Stations in India

Sofia Amaral Sonia Bhalotra Nishith Prakash

June 19, 2018

SSDEV 2018

Motivation

• Law enforcement is typically considered as a male-dominant occupation

(Golden, 2015)

• Globally, women are under-represented in law enforcement

• 6% in India2013; 10% in the US2013; 29% in UK2016; 33% in Uganda2015

• International organizations have made efforts to feminize the police at

national and international level

• The U.N. Security Council aims to have 20% of female officers by 2020

We investigate if India’s all-women police stations help to prevent

violence committed against women

• Mixed evidence on the effectiveness of women in police across the world

Research questions

• Relationship between increased women’s presence in the police and violence

against women

• To answer this, we exploit the impact of an innovative form of policing:

All-Women Police Stations (WPS)

• WPS are designated police stations that only deal with violence against

women (VAW) and only employ female officers

• WPS is widely used in low and middle-income countries (for e.g.

Brazil, Ghana, South Africa and Uganda)

1. Do women-only police stations change women’s willingness to report a

crime?

2. Is there a difference in the ‘police effectiveness’ following the introduction of

WPS in the context of VAW?

Definitions

Why do we care about women in policing

• Female presence in the police is important to understand due to:

• Women’s access to justice and law & order is low and unequal in both

developed and developing countries

• High rates of under-reporting, low conviction rates, high legal attrition

rates

• Banerjee et al. 2012 find that in Rajasthan only 3% of females had

ever had any contact with the police

• Positive inter-generational impact of improving access to justice for women

• In general, economic growth (economic outcomes) are positively related to

trust in law & order and effective policing (Soares, 2012)

• From a policy perspective, identifying ways to improve reporting of crime

against women and deterrence extremely important and of first order

Why do we care about violence against women

• Labour and productivity losses associated with IPV are high

• CDC estimates losses averaging 7.2 days of work and an annual cost of 5.8

billion US dollars (Aizer and Dal Bo, 2009)

• VAW is a constraint to female empowerment (i.e., economic and own-body)

• Movement and FLP (Field et al., 2017); educational choices (Borker, 2017)

• Negative spillovers on children’s health and educational outcomes

• Birth weight (Aizer, 2011); test scores (Carrell and Hoekstra, 2010)

• Hospitalizations reduce birth weight by 163 g (Aizer, 2011)

• In-utero stress negatively affects the cognition and health (Aizer and Dal Bo,

2009)

• Mixed-evidence on the link between economic conditions and VAW

• Relative wages (Aizer, 2010); employment (Kotsdam and Villanger, 2018;

Anderberg et al., 2016; Amaral et al., 2015); rainfall (Abiona and

Koppensteiner, 2016) and cash-transfers (Bobonis et al., 2013; Ramos, 2016)

• Theories of “loss of control” (Card and Dahl, 2011; Anderberg and Rainer,

2013)

Summary of Results

• WPS led to an increase in the reporting of rate of total violence against

women

• Estimates range from 21% to 33% increase

• Due to increases in reports of female kidnappings and domestic violence

• No evidence of changes in other categories of crimes

• No evidence of changes in female mortality → suggests a change in

reporting behavior

• Modest improvements in measures of police deterrence such as arrests, esp.

female kidnappings

Contributions to the Literature

• Our study adds to a small number of papers on women’s access to justice

• Evidence from Peru: Kavanaugh et al. (2017)

• Effects of HH proximity to a Women’s only Justice Centers

• ↑ in female empowerment and child primary school enrollment

• ↓ in IPV

• Evidence from Brazil: Perova and Reynolds (2017)

• No average effect

• 17% ↓ in female homicides for women aged 15-24 in urban areas

• Evidence from the U.S.: Miller and Segal (2014)

• Increasing the female share of officers improves the quality of policing

• Increasing female officer representation prevents domestic violence

escalation

• ↑ in reported rate of IPV

• ↓ in IPV homicides

Background: Growth of WPS and Crime

Figure: Crime Reporting by Type and Growth of WPS in India

• Increase in WPS in India with sharp trajectory since 2009

• Rate of VAW is the fastest growing crime category

Crime Definitions - India

Background: Women in Policing

• Women first entered in the police in 1939

Figure: Police Strength and Female Strength by Rank in India

• Average share of female officers over 1995-2013 is approximately 5%

• There is no sorting of women into specific ranks

Background: All-Women Police Stations

• First woman police station in the world opened in 1973 in Kerala (no

surprise here!)

• Use of WPS is recommended by the federal government after the National

Police Commission of 1977 (Natarajan, 2016)

• Functioning of WPS:

• Typically only employs female officers

• Police station chief (SHO) is a female → decision over investigation

• Only handle crimes committed against women

• Officers at WPS have the same qualifications as other officers but

receive additional special training (for e.g. for victim support,

investigation, etc.) → state capacity is a constraint

• The opening of a WPS is widely mentioned in the local media

• Information on WPS and contacts of stations is also online

Background: Principal objectives of All-Women Police Stations

• Investigations by the police are expedited and monitored

• Family disputes are resolved or compromises reached through

counceling

• In case of serious crimes, the women police officer makes spot

enquiries, examines various witnesses, collects evidence and submits

the report with recommendations. Such investigations help in

providing immediate relief and justice to the victims of violence and

atrocities

• Regular contact with the complainant is made to know about her

satisfaction regarding action taken and record her comments in daily

diary report, which should be submitted to the Senior Officers

Background: Inside of an All-Women Police Stations

Figure: Visit to a Mahila Thana, Gandhi Maidan, Patna - December 27, 2017

• The station chief is a female officer

• All officers working at the station are female

Distribution of All-Women Police Stations by year-state

Year/State 2005 2006 2007 2008 2009 2010 2011 2012 2013

Andhra Pradesh 25 25 25 25 25 29 31 32 32

Assam 1 1 1 1 1 1 1 1 1

Bihar 0 0 0 0 0 0 0 40 40

Chhattisgarh 3 3 3 3 3 4 4 4 4

Gujarat 7 7 7 7 8 19 31 31 32

Haryana 1 1 1 1 1 2 2 2 2

Himachal Pradesh 0 0 0 0 0 0 0 0 0

Jammu and Kashmir 3 3 3 3 3 2 2 2 2

Jharkhand 0 22 22 22 22 22 22 22 22

Karnataka 10 10 10 10 10 10 10 10 10

Kerala 3 3 3 3 3 3 3 4 4

Madhya Pradesh 9 9 9 9 9 9 9 9 9

Maharashtra 0 0 0 0 0 0 0 0 0

Orissa 6 6 6 6 6 6 6 6 6

Punjab 0 0 0 0 0 5 5 6 7

Rajasthan 12 11 11 14 14 24 24 29 29

Tamil Nadu 194 196 196 196 196 196 196 196 196

Uttar Pradesh 11 12 12 12 12 42 68 71 71

Uttaranchal 2 2 2 2 2 2 2 2 2

West Bengal 0 0 0 0 0 0 0 10 10

Total 291 315 317 320 321 376 416 477 479

Background: All-Women Police Stations

Figure: Distribution of WPS across cities in 2005 and 2013

Background: All-Women Police Stations

• In 2013 there were 502 WPS

• Women police stations are almost exclusively in cities

• There is typically 1 women police station per city and has jurisdiction

over the entire district

• Urban areas had 4,513 regular stations → approximately 10% of

urban stations are WPS

• Average city has 1,786,728 million inhabitants

Data Source I

• Data on All-Women Police Stations:

• Bureau of Police Research and Development (BPRD) Reports on

Policing Organization: Provides the location of WPS across India and

its year of roll-out since 2005

• Ministry of Home Affairs

• State Police Headquarter

• Newspapers

• Endlessly calling/texting my IAS/IPS friends for Bihar, Uttar Pradesh,

and Punjab

• Assembling city-level data by various crime heads and WPS is a

contribution of this project

Data Source II

• Data on All Police Stations from Rajasthan (Banerjee et al., 2014):

• Sample of 152 police stations across 10 districts

• Over 20% of the police stations in the state and a population of almost

8 million people

• Collected detailed monthly data on crime reporting for years 2006–2007

• Despite not representative of India, this data allows us to credibly

separate ‘reporting’ from ‘actual crime’

• We are not aware of any other crime data in India, that can allow

researchers to separate ‘reporting’ from ‘actual crime’

Data Source III

• Data on Crime:

• National Crime Record Bureau (NCRB): Publishes yearly data on

reported crime by the Indian Penal Code (for e.g. violence against

women, non-gender based violence, property, etc.)

• Once an incident occurs and is reported, the police is required to

register a First Information Report (FIR)

• FIRs with IPCs are aggregated by each police station and then reported

to the SCRB and finally to NCRB

• NCRB aggregates the reported crime by categories at various

geographic units

• In addition, we also exploit data on arrests and chargesheets (proxy for

police effectiveness/deterrence)

• Data on Police Personnel, Political and Demographic:

• NCRB and BPRD: Provides information on police personnel by rank,

gender, and state

• Election Commission

• Census of India

Raw Crime Data

• Fieldwork Picture from Imbert, Kumar, Prakash and Vanden Eynde (2018)

Sample and Outcome Variables

• Main Analysis: City Level

• Sample consists of data on 76-89 cities from 2005–2013

• Secondary Analysis 1: Police Station Level

• Sample consists of data on 152 police stations across 10 districts in

Rajasthan from 2006–2007 (monthly)

• Secondary Analysis 2: State Level

• Sample consists of data on 16 major states from 1988–2013

• Outcomes:

• Violence against women per 100,000 population

• Non-gender based violence per 100,000 population

• Additional VAW outcomes (Female kidnapping, Domestic violence,

Dowry deaths, Molestation, Sexual harassment, and Rape)

• Arrest rate (VAW, Female kidnapping, and Rape)

• Chargesheet (VAW)

Identification Challenges I

• Is the timing of the opening of WPS across Indian cities exogenous?

• We are not aware of why WPS opened at a certain date/location →idiosyncratic

• Discussions with senior IPS officers suggests that the opening (esp.

timing) of a WPS across cities is exogenous

• The Superintendents of Police (SP) make a request to the State Police

Headquarters

• Director General of Police (DGP) then puts up the file to relevant

departments

• After a lengthy process/discussions with various departments, WPS is

approved in a city

Identification Challenges II

• Selection bias? → We test for the determinants of placement of theWPS

• No evidence of specific correlates with the probability of placement of

station in a given city-year

• No evidence of specific correlates with the probability of the roll-out in

a given state-year

• We exploit a plausibly exogenous variation in the timing and

placement of WPS across Indian cities

Evidence on timing of women entering the police and placement

of WPS

AP

AS

BH

GJ

KRN

KR

MP OR

PJ

RJ

TN

UP

WB

1940

1950

1960

1970

1980

Wom

en's

Inco

rpor

atio

n in

the

Polic

e (Y

ear)

1970 1980 1990 2000 2010Women Police Station Policy Initiation

WPS Policy YearFitted values

Women Entering the Police and WPS Policy

• No correlation between the timing of women entering the police and

placement of WPS

Evidence on Determinants of Placement and Roll-out of WPS in

Cities

Placement of WPS in Cities

Sex Ratio -0.088 0.387 0.064 0.577

(0.087) (0.634) (0.715) (0.679)

Literacy -0.150 -0.149 -0.177 -0.136

(0.185) (0.178) (0.179) (0.175)

Share of Female Officers (%) -0.005 -0.008** 0.003

(0.004) (0.003) (0.003)

Police Commissioner City 0.229 0.218

(0.145) (0.151)

Lagged VAW 0.017

(0.012)

N 689 667 667 588

Adj.R-sq. 0.073 0.077 0.134 0.080

City FE, Year FE Yes Yes Yes Yes

Identification Challenges III

• Time-varying unobservable factors → city-level interventions,

time-varying gender-norms, etc.

• Actual crime vs. reported crime:

• Since we only observe reported crime we cannot disentangle between

reporting and incidence → this is not unique to our study, but to entire

literature

• It is possible that an increase in reporting behavior following the

implementation of the WPS could be attributed to an increase in crime

• WPS led to a change in the supply of policing

• Increases police capacity (quantity effect)

• Changes the composition of police (quality effect)

Identification Strategy - Solutions

1. We exploit the natural experiment in the implementation of WPS

across large cities and use difference-in-differences (DiD) to estimate

the causal impact of WPS on various measures of crime against

women and police effectiveness/deterrence

2. We use various outcomes that are less likely to be driven by WPS to

disentangle between reporting and/or incidence effects

Empirical Specification: City Level Analysis

• Reduced-form estimation:

Crimect = α0 + δ1Post WPSct + βXct + γc + λt + φct + εct

• Crimect is rate of police-reported crimes per 100,000 population (in

logarithms) in city c in year t

• Post WPSct is a dummy that takes the value 1 in the years following the

opening of a WPS in given city

• Xct is a vector of city-level controls (for e.g. male/female ratio, literacy rate,

police commissioner dummy, etc.)

• γc is city fixed effects; λt is year fixed effects; φct is city-linear trends

• δ1 is the DiD coefficient → captures changes in crime rates after the

placement of WPS between treated and control areas

• Standard-errors are clustered at the city-level

Impact of WPS on VAW

Rate of VAW per 100,000 population

Post WPS 0.528*** 0.553*** 0.284** 0.284** 0.308** 0.194**

(0.150) (0.130) (0.123) (0.124) (0.122) (0.093)

N 652 652 634 634 612 630

Adj. R-sq. 0.941 0.944 0.957 0.957 0.957 0.488

Mean of Dep. Var 3.62

Effect Size 69.55 73.85 32.84 32.84 36.07 21.41

City FE Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes

Baseline Controls No Yes Yes Yes Yes Yes

State Linear Trends No No Yes Yes Yes No

Police Commissioner No No No Yes Yes Yes

Share of Fem. Officers No No No No Yes Yes

City Linear Trends No No No No No Yes

# of Cities 73 73 73 73 73 71

• VAW rates increased by 21% in relative to the mean

Impact of WPS on VAW: Event Study

• Evidence of a rise in crime rates following the placement of WPS

Impact of WPS on VAW: by Crime Heads

Female Kidnappings Domestic Violence Dowry Deaths

Post WPS 0.298** 0.201* 0.244** 0.197** 0.027 0.151*

(0.127) (0.115) (0.106) (0.078) (0.060) (0.077)

N 612 630 612 630 612 630

Adj. R-sq. 0.913 0.506 0.905 0.346 0.779 0.225

Effect Size 34.72 22.26 27.63 21.77 16.30

Molestation Sexual Harassment Rape

Post WPS 0.277 0.424** 0.229 0.033 -0.030 0.022

(0.231) (0.201) (0.235) (0.241) (0.080) (0.088)

N 612 630 612 630 612 630

Adj. R-sq 0.806 0.292 0.838 0.523 0.864 0.360

State Linear Trends Yes No Yes No Yes No

City Linear Trends No Yes No Yes No Yes

Reporting vs. Actual Crime?

• Is the increase in VAW driven by reporting or actual crime?

• WPS could lead to change in the reporting behavior

• For e.g. changes in women’s perception of the police; women feeling

comfortable dealing with female police officers

• Alternatively, WPS could also lead to a male-backlash

• Erten and Keskin (2016); Bobonis, Gonzalez-Brenes, and Castro (2013)

Reporting vs. Actual Crime?

• Use Police Stations level data from Rajasthan and estimate:

• Reporting of crime against women in WPS = ↑• Reporting of crime against women in General Police Stations

(non-WPS) = ↓• Reporting of crime against women by All Police Stations and General

Police Stations (non-WPS) → substitution

• We try to disentangle this by looking at VAW crimes with lowerreporting bias/costs of reporting

• Rates of female mortality (unnatural deaths + female suicides) and

murders due to love affairs are less likely to be driven by changes in

reporting behavior

Evidence on Reporting of Crime Against Women in WPS in

Rajasthan

Total VAW Rape Domestic Violence

District has WPS×Urban Station×WPS (δa) 8.354*** 8.160*** 8.355*** 0.075 -0.227 0.075 8.327*** 8.447*** 8.328***

(1.498) (1.589) (1.550) (0.230) (0.138) (0.238) (1.531) (1.548) (1.585)

District has WPS×Urban Station (δb) -1.348*** -1.103*** -1.345*** -0.274* -0.262 -0.272* -0.839*** -0.729*** -0.837***

(0.363) (0.405) (0.376) (0.145) (0.179) (0.150) (0.245) (0.258) (0.254)

N 3,243 3,243 3,243 3,243 3,243 3,243 3,243 3,243 3,243

R-squared 0.488 0.585 0.512 0.145 0.190 0.187 0.599 0.693 0.618

Mean (SE) Dep. Var 1.366 (2.126) 0.566 (0.961) 0.701 (1.800)

Mean (SE) Dep. Var General Urban Station 1.788 (3.274) 0.448 (0.808) 1.207 (3.118)

Mean (SE) Dep. Var WPS 8.874 (4.954) 0.568 (1.136) 8.295 (4.903)

District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Month-Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Station Monthly Trend No Yes No No Yes No No Yes No

District×Month-Year No No Yes No No Yes No No Yes

• (δa) >(δb) → evidence of shift of VAW cases from General Police

Stations to WPS + additional reports being reported in WPS

Evidence on Reporting of Crime Against Women in General Police

Stations in Rajasthan

Total VAW Rape Domestic Violence

District has WPS×Urban Station -2.285** -1.041** -1.022 -2.285** -0.238 -0.834 -2.285** -0.589 -0.445

(0.980) (0.491) (4.258) (0.980) (0.246) (2.481) (0.980) (0.362) (2.370)

District has WPS×Rural Station -0.022 -0.091 0.013 -0.022 0.025 -0.317 -0.022 -0.011 0.165

(0.873) (0.390) (4.203) (0.873) (0.190) (1.884) (0.873) (0.304) (2.537)

Observations 3,148 3,148 3,148 3,148 3,148 3,148 3,148 3,148 3,148

R-squared 0.141 0.392 0.976 0.141 0.299 0.979 0.141 0.289 0.967

District FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Month-Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Station Trends No Yes No No Yes No No Yes No

District×Month-Year No No Yes No No Yes No No Yes

Evidence on Reporting of Crime Against Women in ‘All Police

Stations’ and ‘General Police Stations’ in Rajasthan

Total VAW

All Police Stations General Police Stations Only

District has WPS×Urban Station×WPS 8.354*** 8.160*** 8.355***

(1.498) (1.589) (1.550)

District has WPS×Urban Station -1.348*** -1.103*** -1.345*** -2.285** -1.041** -1.022

(0.363) (0.405) (0.376) (0.980) (0.491) -4.258

District has WPS×Rural Station -0.022 -0.091 0.013

(0.873) (0.390) (4.203)

N 3,243 3,243 3,243 3,148 3,148 3,148

R-squared 0.488 0.585 0.512 0.141 0.392 0.976

Mean (SE) Dep. Var 1.366 (2.126)

Mean (SE) Dep. Var General Urban Station 1.788 (3.274) 1.788 (3.274)

Mean (SE) Dep. Var WPS 8.874 (4.954)

District FE Yes Yes Yes Yes Yes Yes

Month-Year FE Yes Yes Yes Yes Yes Yes

Station Monthly Trend No Yes No No Yes No

District×Month-Year No No Yes No No Yes

Evidence on Reporting of Crime Against Women with lower

reporting bias

• This suggests that the impact on VAW is driven by reporting and not actual crime

Robustness Checks

• Test for pre-trends on VAW, Female/Male Kidnappings, Domestic

Violence, Dowry Deaths, Molestation, Sexual Harassment, Rape at

city and state level

• Impact of WPS on non-VAW crime heads → WPS should have no

impact

• Use the spilt between Bihar (no WPS) and Jharkhand (has WPS) toestimate the impact of WPS on VAW

• In addition, we also compares border districts of Bihar and Jharkhand

• Estimate coefficient by iterative removal of states

• Remove Tamil Nadu from the analysis → point estimates similar

• Tamil Nadu has the highest number (196) of WPS

Test for Pre-Trends on VAW

Rate of VAW

Post WPS 0.412** 0.313**

(0.190) (0.121)

Pre (One Year) 0.276 0.185

(0.191) (0.125)

Pre (Two Years) 0.029 0.183

(0.077) (0.118)

N 630 416

Adj.R-sq. 0.488 0.959

Sample City State

Effect of WPS: NON-VAW vs. VAW

Rate of Non-Gender Based Violence VAW

Post WPS 0.051 0.079 0.238 0.308** 0.194**

(0.056) (0.072) (0.147) (0.122) (0.093)

N 634 612 630 612 630

Adjusted R-squared 0.959 0.961 0.437 0.957 0.488

Mean of Dep. Var 4.96 3.62

City FE Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes

Baseline Controls Yes Yes Yes Yes Yes

State Linear Trends Yes Yes No Yes No

Police Commissioner Yes Yes Yes Yes Yes

Share of Female Officers No Yes Yes Yes Yes

City Linear Trends No No Yes No Yes

Number of Cities 73 73 71 73 71

Placebo: Impact of WPS on Non-VAW Crime Heads

Male Kidnappings CBT Dacoity Counterfeiting Theft Arson Riots Hurt

Post WPS 0.131 -0.106 0.065 -0.062 0.134 -0.231 0.074 0.048

(0.083) (0.148) (0.154) (0.050) (0.100) (0.253) (0.066) (0.168)

Adj. R-sq. 0.673 0.732 0.622 0.670 0.755 0.703 0.793 0.731

N 670 670 670 670 670 670 670 670

City FE Yes Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes Yes

State Trends Yes Yes Yes Yes Yes Yes Yes Yes

Baseline Controls Yes Yes Yes Yes Yes Yes Yes Yes

Police Commissioner Yes Yes Yes Yes Yes Yes Yes Yes

Share in High Ranks Yes Yes Yes Yes Yes Yes Yes Yes

Robustness using Bihar and Jharkhand Split

• Bihar and Jharkhand were unified until November 2000

• Jharkhand is a newly created state comprising of 22 districts from Bihar

• The governance structure of the two states was identical until 2000

• The quality of governance in the two states was comparable for a few

years after the bifurcation

• Jharkhand rolled-out the WPS across districts in 2006 while Bihar did

not until 2012

• Estimation Sample:

• District level data from NCRB for the years 2001–2011 (5

pre-treatment and 6 post-treatment years)

• 22 districts in Jharkhand and 37 districts in Bihar

District Level Specification using Bihar and Jharkhand Split

• We use this split to compare the change in crime rates between

districts in Jharkhand and Bihar → similar in time-varying

unobservables

• The reduced-form specification is:

Crimedst = α0+δ3Jharkhanddst×Postt+α1Postt+βXdt+γd+λt+φst+εdst

• Jharkhanddst is a dummy variable takes values 1 if a district d is in

the state of Jharkhand s in year t

• Postt is a dummy variable that takes the value 1 after the year 2006

• δ3 is the DiD coefficient → captures for change in crime rates the

before-after the placement of WPS in a given district-year versus the

control districts in Bihar

Results using Bihar and Jharkhand Split

VAW Non-VAW

Total Kidnappings DV Murder Male Kidnappings

Panel A: All Districts

Post×Jharkhand 0.268*** 0.351*** 0.629*** 0.063 0.042

(0.093) (0.069) (0.139) (0.050) (0.085)

N 649 649 649 649 649

Adj. R-sq. 0.612 0.587 0.418 0.799 0.514

Panel B: Border districts

Post×Jharkhand 0.164 0.313*** 0.589** 0.073 0.063

(0.169) (0.106) (0.222) (0.076) (0.050)

N 198 198 198 198 198

Adj. R-sq. 0.524 0.549 0.246 0.542 0.384

District FE Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes

State Linear Trends Yes Yes Yes Yes Yes

Coefficient Estimates by Iterative Removal of States

State Level Analysis

• We use state level analysis to estimate the impact of WPS on

violence against women

• State-level crime information from 16 major states of India for the

years 1998–2013

• There are 3 control states; 2 treatment states (i.e. those that

implemented the policy before 1988) and 11 states that implemented

the policy at different points in time over the period in the sample

• Identifying Variation: We use the timing and state variation in the

initiation of the roll-out of WPS in states as a natural experiment to

identify the effects on gender-specific crime

State Level Variation in Roll-Out of WPS

201220102006200219941993199219911989No PolicyPre-1988No Data/Pre-2000 state policy

Empirical Specification: State Level Analysis

• Reduced-form estimation is:

Crimest = α0 + δ4Post WPS Policyst + βXst + γs + λt + φst + εst

• Post WPS Policyst is a dummy variable that takes the value 1 in the years

including and following a state initiation of the roll-out of the WPS in the

state

• γs is state fixed effects; λt is year fixed effects; φst is state-linear trends

• Xst include rich set of controls (sex ratio, literacy rate, state income per

capita, police per capita, election year dummies, the share of scheduled

castes and scheduled tribe)

• δ4 is the DiD coefficient → captures for change in crime rates the

before-after the roll-out of WPS in a given state-year versus the control

states

• Standard-errors are clustered at the state-level

Impact of WPS on VAW

Total Rate of VAW

Post WPS Policy 0.144 0.217** 0.210** 0.124* 0.195** 0.203** 0.201**

(0.107) (0.097) (0.077) (0.069) (0.074) (0.074) (0.072)

Share of Female Officers -0.005

(0.011)

Mean of Dep. Var 2.038

Effect Size 15.49 24.23 23.37 13.20 21.53 22.51 22.26

Adj. R-sq. 0.911 0.925 0.928 0.955 0.958 0.958 0.958

N 416 416 416 416 416 416 416

State, Year FE Yes Yes Yes Yes Yes Yes Yes

Controls No Yes Yes Yes Yes Yes Yes

Additional Controls No No Yes Yes Yes Yes Yes

State Linear Trends No No No Yes Yes Yes Yes

73rd Amendment No No No No Yes Yes Yes

NREGA×Star States No No No No No Yes Yes

• Also evidence of a increase in reporting of female kidnapping rates → Effect size of

10%

• No evidence of increase in reporting on rape

Further Evidence using DHS Data

• We use state-level specification to estimate the impact of WPS on

self-reported measure of incidence of intimate-partner violence (IPV)

using the 2005-2006 and 2015-2016 rounds of Demographic and

Health Surveys (DHS)

• We further use self-reported measure of use of formal/informalsupport services

• This module is asked to a small sample of women who respond to have

been victims of intimate-partner violence and asks respondents about

whether they have mentioned the problem to family members

(including the partner and/or former partner); police; neighbor; social

organization; lawyer; religious leader or a doctor

• Within the space of ten years, 5 states adopted the WPS policy; 11

states already had the WPS at the time of the first survey; and the

remaining 3 states had not yet implemented the policy

Further Evidence using DHS Data

• We do not find evidence that women’s exposure to domestic violencechanged and this result does not differ across other forms of IPV(emotional, physical or sexual)

• Suggestive evidence that the implementation of WPS led to a change

in measures of VAW that are sensitive to reporting incentives rather

than a change in incidence of violence

• We find that usage of support services increased which is consistent

with the WPS leading to an overall change in pro-active behaviors

that are correlated with increases in reports to the police

Effectiveness of Women Police Officers/WPS

• Are women police officers (or WPS) better at crime prevention, esp.

VAW?

• In particular, we look at the VAW arrest rates and First Information

Report rates of VAW crimes

• Women police officer exhibit less gender-biased attitudes but are they

more effective?

Effectiveness of Women Police Officers/WPS

Arrest Rate

VAW Female Kidnappings Rapes Chargesheet of VAW

Post WPS Policy -0.030 0.099* -0.032 -0.005

(0.109) (0.051) (0.072) (0.037)

N 399 383 400 351

Adj. R-sq. 0.795 0.799 0.786 0.096

# of States 16 16 16 16

State FE Yes Yes Yes Yes

Year FE Yes Yes Yes Yes

Controls Yes Yes Yes Yes

Additional Controls Yes Yes Yes Yes

State Linear Trends Yes Yes Yes Yes

73rd Amendment Yes Yes Yes Yes

NREGA×Star States Yes Yes Yes Yes

Share Yes Yes Yes Yes

• Female kidnappings arrest increase: small magnitude consistent with small effect

size in crime rates

Bottlenecks with All-Women Police Stations

• All Women Police Stations have certain inherent problems:

• Their coverage is entire sub-Division/District and they are ill equipped

in terms of communication and mobility to cover their jurisdiction

• For complainants, it is difficult to contemplate filing a complaint with

the WPS when the well known nearby police station could provide

necessary assistance

• The existence of a WPS is also a cause for diverting all such

complaints to the WPS by regular police stations which is also not

desirable → substitution

• The powers and intervention should not be limited to marital disputes

and but they could play the role as a specialized agency in addressing

all issues including the safety of women through consultations with the

schools, colleges, the market associations, the village panchayats, the

municipal bodies and can increase its scope by undertaking a variety of

activities to help, guide, empower and equip women while facing

violence, harassment etc.

Plausible Policy Implications

• Policy dilemmas:

• More All-Women Police Stations?

• More women in police?

• Mobile policing?

• We are collaborating with the Telangana State Police in India to test

the effectiveness of mobile policing on crime against women by

random varying the gender mix and the gender of the most senior

police officer in a team

• We also propose to test if it is more effective to have a women police

officer in the team vs. to train men to be sensitive to gender issues →how much training we need to get the same effects as having more

women in the police?

Alternative to All-Women Police Stations: Evidence from Odisha

State Police

“Here what we are doing is we are putting all mahila staff there and we

have encountered a lot of problems which are being faced by women

officers being posted there. Most of the cases which come to these police

stations are related to dowry, torture, rape, etc. where mostly these lady

officers have to travel, in a single case, to two-three locations all over

India, which is very difficult for them on their own. We also have a Mahila

Shishu Desk in each and every police station. My suggestion is that

instead of having exclusively Mahila Police Station it will be better if we

strengthen all the police stations to be gender sensitive and strengthening

them by posting adequate women officers and staff at that police station.”

Conclusions

• We find that the presence of WPS leads to more reporting of VAW

crime

• The increase in reporting of VAW crime is driven by:

• Female kidnappings

• Domestic violence

• We believe that the impact on VAW is driven by reporting and not

actual crime

• It appears that WPS is effective in getting women to report crime,

but ineffective in terms of effective policing (arrests and chargesheets)

• Our findings are consistent with results from the U.S. and Peru

Thank you!

Comments are welcome.

Definitions

1. Concepts of VAW

• Gender-based violence (GBV): all forms of violence against committed

against women where gender is the salient cause of incidence (e.g.

rape, molestation, dowry deaths, FGM etc)

• Intimate-Partner Violence (IPV): is a form of GBV. Refers to violence

committed by the intimate-partner (e.g. husband, boyfriend etc)

2. Measures

2.1 Police-reports of VAW → Changes with reporting incentives

2.2 IPV Index (or Binary): Count of the different forms of intimate-partner

violence reported by the respondent to trained enumerators• Includes physical, emotional and sexual violence: punch, kick, strangle, attack with weapon, threaten

with a weapon, push, or slap; humiliation, threats, insults and; forced sex, non approved sex acts

2.3 Attitudes: Count (or binary) of responses to tolerance of use of

violence towards/by the partner• Do you tolerate beating... if : i) cook poorly; ii) leaves the household without permission; iii) neglects

the children; iv) refuses sex

2.4 Hospitalizations: Limited availability, lower bias, tip of the iceberg

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Definitions - Crime in India

Table: Variable Definitions

Measure Description

Crimes against Women Includes the incidents registered under rape, kidnap-

ping and abduction of females, molestation, sexual

harassment, cruelty by husband and relatives

Female Mortality Female deaths due to dowry, accidental deaths due

to unnatural events and suicides

Non-gender based Violence Includes the incidents registered under riots, murder,

dacoity, male kidnappings, arson and hurt

Property Includes the incidents registered under under the cat-

egories of burglary, robbery, theft

Economic Includes the incidents registered under under the

categories of criminal breach of trust, cheating and

counterfeiting

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