Complainant Identity and Criminal Justice in India

Gender, Violence, and Triage:Complainant Identity and Criminal Justice in India

Nirvikar Jassal∗

October 1 2021

Please Do Not Cite/Circulate Without Permission

Abstract

Are women hindered vis-a-vis accessing justice? I provide evidence of institutional triagein which particular complaints are disadvantaged when passing through nodes of a justicesystem in which multiple administrators utilize discretion to discriminate. Using an originaldataset of roughly half a million Indian crime reports, merged with court files, I find thatwomen’s complaints are significantly more likely to be delayed and dismissed at the policestation and courthouse compared to men. Suspects that female complainants accuse ofcrime are less likely to be convicted and more likely to be acquitted, an imbalance thatpersists even when accounting for cases of violence against women (VAW). The applicationof machine learning to cases reveals—contrary to intuitions of policymakers or judges—that VAW, including the extortive practice of dowry, are not “petty quarrels,” but mayinvolve starvation, poisoning, and marital rape. To make a causal claim about the impact ofcomplainant identity on outcomes, I utilize a matching technique that uses high-dimensionaltext data; it underscores why those who su↵er from cumulative disadvantage in society maybe likely to face challenges whilst seeking punitive justice via formal state institutions.

Keywords: Gender, Crime, Policing, Violence Against Women, Sexual Assault, India

∗Postdoctoral Fellow, Stanford University ([email protected]). I thank Aprajit Mahajan, Paul Novosad,Sam Asher, Irfan Nooruddin, Alison Post, Abhijit Banerjee, and Margit Tavits, as well as Elliott Ash andChristoph Goessmann for supplementing the data. I am grateful for excellent research assistance at Stanford byEmily Wu and Shirley Cheng.

[email protected]

Introduction

Are minorities disadvantaged in accessing justice, and if so how? These are questions of theoret-ical and policy relevance, without clear answers. In the largest democracy of India, journalistsregularly report that women and minorities are discriminated against when seeking help fromthe state. Yet, aside from challenges in accessing data that can tackle these puzzles, it remainsambiguous as to whether any disparities are attributable to the types of cases registered by suchgroups or their identity. If women are discriminated against, is it because of their gender or thecontent of their complaints, e.g. harder-to-prove cases of violence against women (VAW)1

Not only is there limited research on crime and policing in political science, but also few dis-cussions about inequities in state responses to violence (Htun and Weldon 2012). Investigationsinto VAW in economics (Jayachandran 2015), sociology (Armstrong, Gleckman-Krut, and John-son 2018), or criminology (Khan et al. 2020), are typically carried out through the prism of sexualassault (McDougal et al. 2018). In political science, scholarship on VAW has exclusively focusedon rape in conflict or post-conflict settings (Karim 2020; Cohen 2013; Agerberg and Kreft 2020),rather than gradations of everyday abuse (Khan et al. 2020). And, while an emerging body ofwork has sought to re-prioritize attention toward criminal justice, most studies experimentallytest the impact of police interventions,2 rather than paint a portrait of the broader system.

I ask whether women in India are less likely than men to access justice when turning tothe state, i.e. police and judiciary. I advance a theory of ‘institutional triage’ to explain howo�cials use discretion to filter cases as complaints funnel through nodes of the justice system.This triage, deployed at specific junctures, marginalizes those who may already su↵er fromcumulative disadvantage, compounding existing inequalities, including those rooted in gender.To illustrate, I create an original micro-level dataset of the universe of crime from Haryana, partof the Hindi-speaking heartland, and merge them with court files, thereby tracing cases from thesecond a victim enters a police station until (potentially years) later when a verdict is issued.

The article combines several research questions—e.g., on police accountability toward mi-norities and/or judicial bias against women—into one holistic study. By linking all arms of thesystem for the first time, I establish a series of facts, e.g. cases of VAW are likely to be delayedin terms of police registration and court verdict compared to non-gendered crime. Unlike the av-erage 18% conviction for non-gendered cases, VAW results in only 7-10% conviction for suspects.Strikingly, even accounting for VAW, female complainants are significantly more likely to havetheir cases dismissed, delayed, or result in a suspect’s acquittal compared to male complainants.I attempt to provide credible evidence that this is causally identifiable.

The paper aims to make additional contributions. Scholarship has pointed to social imped-iments hindering women from coming forward to authorities (Iyer et al. 2012; Green, Wilke,and Cooper 2020; Jassal and Barnhardt 2020), with an implicit assumption that if only theycan be encouraged to report crime, the state may be accommodating. The findings herein notonly hint at why VAW carries on with impunity, but also suggest that hesitancy in reportingcould be grounded in calculations about the low probability of punitive justice at the conclusionof an arduous process. “Gatekeeping” decisions by police in terms of case registration, while

1. India has been dubbed the most unsafe country for women (Goldsmith and Beresford 2018); 28%, 6.6%,and 78.4% of women report physical violence, sexual assault, and fear of their spouse, respectively (DHS 2017).The UN definition of VAW is, “any act of gender-based violence that results in, or is likely to result in, physical,sexual or mental harm or su↵ering to women, including threats of such acts, coercion or arbitrary deprivation ofliberty, whether occurring in public or in private life” (WHO, n.d.). Sexual assault is one component of VAW.

2. E.g. community policing, representation, and training (Blair, Karim, and Morse 2019; EGAP 2019).

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important, may ultimately have little to do with punishment for crime (Spohn and Tellis 2019).The study supplements work on bureaucratic discrimination, much of which has focused on

ethnicity or involved audit experiments (Butler and Broockman 2011; White, Nathan, and Faller2015), rather than administrative data (Emeriau 2021). I use the universe of registrations todepict the true “ground reality” for women facing challenges as complaints are being processed,simultaneously quantifying the duration of police investigations, court hearings, and other out-comes, i.e. granular points of interest to scholars of state capacity and South Asia. The workalso expands research on gender disparities—which in India have focused on education, income(Calvi 2020), health (Dupas and Jain 2021), and property (Brule 2020)—to justice delivery.

Another novelty of the study is that it applies unsupervised machine learning to police re-ports, each of which contain ⇡500-word first-person testimonies (Roberts, Stewart, and Airoldi2016; Roberts, Stewart, and Nielsen 2020). While such methods have been used to probe thecontent of Arabic fatwas (Lucas et al. 2015), Indian rural deliberation (Parthasarathy, Rao, andPalaniswamy 2019), or UK parliamentary debate (Sanders, Lisi, and Schonhardt-Bailey 2017),they have not been applied to the study of crime. The benefits of a text-as-data approach arethree-fold. First, it amplifies victims ’ voices, minimizing the researcher’s involvement. Second,topic modeling disentangles VAW carried out in and out of the household, summarizing ac-tual triaged cases, e.g. marital rape or abuse related to women’s extortion for dowry. Third,topic-matching diminishes confounding to attempt causal inference using text (Feder et al. 2021).

The study is structured as follows: I outline the theory, contextualize the Indian criminaljustice system, and explain the merging process of two distinct records. I present quantitativetests of the argument, utilizing descriptive and OLS analyses, topic modeling, and matching. Idiscuss the insights, as well as the research agenda that the findings illuminate.

Institutional Triage in Criminal Justice

In a review essay, Kurlychek and Johnson (2019) note that existing studies on U.S. criminaljustice tend to examine isolated stages or “episodic disparities” rather than the reproductionof inequality from one body to the next. U.S. studies—which look at either the police orjudiciary—show that African Americans are disadvantaged with regard to bail, sentencing, andincarceration (Arnold, Dobbie, and Yang 2018; Alesina and La Ferrara 2014; Abrams, Bertrand,and Mullainathan 2012; Knox, Lowe, and Mummolo 2020). One reason for the imbalancesis what legal scholars call “triage,” i.e. lawyers’ de-prioritization of minorities’ cases (Brown2004; Richardson 2016). Because public defenders are overworked, their implicit biases produceshortcuts in allocating time or resources, e.g. delaying interviews of witnesses or carrying outshoddy investigations for cases seemingly predisposed to an outcome (Richardson and Go↵ 2012).

I define institutional triage as a form of system-wide discrimination wherein administrators—e.g. from the constable to the judge—leverage the discretion at their disposal to filter or de-prioritize specific complaints as they move through nodes in the chain. In criminal justice, thesenodes might include (a) police registration, e.g. citizens may be turned away or dissuaded fromcase filing; (b) police investigation, e.g. o�cers may delay inquiries or persuade the complainantto withdraw the report; (c) preliminary hearing, e.g. judges may stall arbitration or postponetrial dates; and (d) court decision, e.g. judges may acquit rather than convict suspects. Broadly,triage manifests in non-episodic unequal outcomes (exclusion), or a disproportionately tryingprocess (burdens) across stages (Olsen, Kyhse-Andersen, and Moynihan 2020).

I aim to make a distinction between mere discrimination and triage. First, unlike discrimina-

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tion that may occur as citizens avoid the authorities (e.g. tra�c stops or arbitrary arrest), triageexhibits when individuals actively turn to the state for grievance redressal (Kruks-Wisner 2021).Second, discrimination may describe single-stages (e.g. stop-and-frisk), whereas triage encapsu-lates the “squeezing” of requests through multi-nodal agencies (Figure 1). Unlike, say, obtaininga driver’s license wherein one agency provides all services, criminal justice is a paradigmaticsetting in which triage might manifest because at least two linked bureaucracies are involved inproviding services for the same complaint.

Register Investigate Verdict

Standard Access to Justice

Register Investigate Verdict

Triaged Complaints

Figure 1: Standard access to justice versus “triage” wherein requests spend longer in-between nodes and havea lower probability of transitioning (as seen in the progressively smaller size of boxes).

However, the theory does not speak to administrator motivation. While triage might cer-tainly be rooted in taste-based discrimination, o�cials may also be embedded within a milieu(e.g. where domestic violence is seen as a “family matter”), or constrained by resource scarcity(Dasgupta and Kapur 2020). Indeed, low levels of development and layered bureaucracies canresult in misgovernance without actors behaving with repressive intent (Banerjee 1997; Sloughand Fariss 2021). O�cials may even display preference-based discrimination (paternalism), “pro-tecting” victims from the complex (and public) process of accessing formal justice (Bindler andHjalmarsson 2020). Regardless of motives, a testable implication of triage is that economicallyor socially disadvantaged groups in society will see a diminished speed and likelihood of theircases crossing the desks of disparate o�cials, each of whom retain varying levels of discretion.

Charting cases in this way may lead to greater precision. For instance, if police mishandleinvestigations, judges may have limited evidence; consequently, looking only at a single-stagedataset of judicial verdicts may lead to a misleading conclusion that judges are to blame (Langand Spitzer 2020).3 Yet, because triage can only be probed by tracing complaints across timeand space, it has been challenging to show because of the inability to link multiple nodes.4 Forthe first time, I follow administrator decisions sequentially across bureaucracies, which Holland(2016) refers to as “enforcement process tracing.” The approach determines, “the number of andtype of cases that feed up to the next step of the process until ultimately resulting in a sanction”(Bozcaga and Holland 2018, 303), thereby highlighting bottlenecks and sources of “leakage.”

I look at Haryana, a patriarchal region of north India (Jassal 2021). Here, women may beless likely to have organizational support such as access to lawyers (Tellez, Wibbels, and Krishna2020; Roychowdhury 2021), and cases of VAW may be perceived as di�cult to prove and a strainon bureaucratic resources. Culturally, administrators may see women’s cases, including VAWthat takes place inside the home such as dowry,5 as a threat to marriage and male dominance.

3. Spohn and Tellis (2019) show how numerous sexual assault cases for which the LAPD have probable causenever yield arrest but are rejected by the District Attorney prior to felony charges.

4. See Rehavi and Starr (2014) for a notable exception of multi-nodal data linkage in the United States.5. Unlike bride-price, dowry involves a wife being coerced, often violently, into providing resources to her spouse

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The framework would thus predict that women’s cases and VAW will face obstacles vis-a-visthe process and outcomes associated with formal justice delivery from the stage of entry (policeregistration) to exit (judicial verdict). I test two sets of hypotheses:

1a: At the stage of entry, women’s cases and gendered crime will be more likely to havebeen delayed vis-a-vis police registration than men’s cases and non-gendered crime.

1b: Conditional on police registration, women’s cases and gendered crime will be lesslikely to be sent to court than men’s cases and non-gendered crime.

2a: Conditional on entering court, women’s cases and gendered crime will be morelikely to be delayed vis-a-vis resolution than men’s cases and non-gendered crime.

2b: At the stage of exit, women’s cases and gendered crime will be less likely to resultin a suspect’s judicial conviction than men’s cases and non-gendered crime.

Gender and the Indian Criminal Justice System

Crime registration is a citizen’s primary step toward formal justice. Registration occurs at policestations run by a head station o�cer, who is supported by sta↵ (e.g. sub-inspectors). The policeare supposed to file all complaints whether they believe them to be valid or not, but in practicehave leeway as to which cases are registered. When filed, a case is assigned to a deputy, and,depending on the crime-type, investigations have to be completed within a time-window (e.g. 90days). If the case is not dropped, or withdrawn, it is sent to the next wing.

The judiciary is related to other former British colonies wherein the Supreme Court sits at theapex of a hierarchy that includes roughly two dozen High Courts, and 7000 district/subordinatecourts. Every police station is located within a jurisdiction of a district court; crime reports andany evidence collected during police investigations are assigned to a jurisdictional judge (Ashet al. 2021). These judges may be of the rank District and Sessions Judge down to a CivilJudge–Junior Division. On appeal, a case may travel to a High Court or the Supreme Court.

Figure 2 presents a stylized illustration. Level A represents the abstract concept of all crime,which can never be precisely measured. Level B signifies those who came forward to report(e.g. at a station or help-desk). Within Level 1—when reported crime transition to registeredcases—there are two sub-categories: women’s complaints and gendered crime (or VAW).6 (Thisis illustrated in a Venn diagram because not all VAW is reported by women.7) Cases in Level 2represent those that, after a preliminary investigation, survive police cancellation. The remainingcases, once investigated, enter the judiciary in Level 3. There, unless stalled or dismissed, averdict may be issued after trials that (dis)favors the complainant in the original crime report.

Judges have greater discretion as to how cases are handled compared to law enforcement. Forthe police, there are explicit rules that mandate registration of “cognizable” or serious crimes,8

some introduced after an infamous 2012 gang-rape of a Delhi college student. Police are requiredto register all gendered complaints—including acid attacks, sexual harassment, tra�cking, and

(Anderson 2007; Rao 1993, 1997; Srinivasan and Bedi 2007). Historically associated with small tokens or giftsand originally a practice among the upper caste (Srinivas 1956), it is among the most common gendered crimesin India today (Jassal and Barnhardt 2020). The practice has been linked to wife-beating, murder, and “missinggirls” (Rao 1997; Srinivasan and Bedi 2007; Rose 1999; Bhalotra, Chakravarty, and Gulesci 2020).

6. In criminology, the gap between Levels 1-A is called “the dark figure of crime” (Biderman and Reiss 1967).7. VAW can be further subdivided: abuse inside the household involves the spouse, family, or in-laws.8. Section 154 of Code of Criminal Procedure.

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Figure 2

Female

V AW

(A) All Crime

(B) Reported

(1) Registered

(2) Police (3) Court

Formal Access to Justice

Note: The process of accessing justice in India. Light and dark blue represent police jurisdiction; brown representsthe judiciary. Arrows signify nodes that connect the system. The analyses focus on all steps from Levels 1-3.

rape—with the threat of one-year jail time and fine for the o�cer.9 Manuals mandate that rapeinvestigations be completed within two-months of filing.10 Aside from being pressured “fromabove” via such guidelines, the police are also constrained “from below” where, for example,activists and NGOs assist victims in filing cases, especially VAW (Roychowdhury 2021). Thejudiciary is exempt from such pressures11 or from juries, which were formally abolished in 1973.12

During registration, police o�cers stamp Penal Codes to case registrations in order to signalwhat laws are alleged to have been broken. Gendered Penal Codes (and related “acts”) includeSection 326-A (acid throwing), Section 376 (rape),13 Protection of Women from Domestic Vi-olence Act, and others.14 An important law is Section 498-A. In 1983, a new provision made“cruelty” by a husband (or in-laws) against a wife a crime (Oldenburg 2002).15 While intendedfor dowry harassment, the law was applicable to domestic violence.16 Some politicians argue that

9. Section 166A of the Penal Code.10. Section 173 of Code of Criminal Procedure.11. Law enforcement is also constrained and subservient to the bureaucracy (or Administrative Service) and, in

practice, answerable to local politicians who hold sway over promotions and transfers (Iyer and Mani 2012).12. Jury trials had been in operation since British India to 1959. See 1973 Code of Criminal Procedure.13. See Table A1 for full list. While Section 497 (adultery) might not be considered VAW, I classify all gendered

sections as VAW from o�cial lists. This clause was ruled unconstitutional in 2018 (Jassal and Chhibber 2019).14. There are implicit distinctions between ‘heinous’ and ‘non-heinous’ violations. Non-heinous cases include

‘compoundable’ sections where police are not forced to take action if the victim settles. Gendered cases suchas Section 497 (adultery) or Section 312 (causing miscarriage) are compoundable. Bailable, compoundable, andnon-cognizable laws are considered the least serious. Section 320 of the Code of Criminal Procedure.15. Some feminists criticized the clause because it was restricted to married women, and retained a vague

definition (Kothari 2005). ‘Cruelty’ is defined as conduct that drives a woman to suicide, causes grave injury,or endangers life. Section 498-A was followed with Section 304-B or “dowry death,” wherein violence related toextortion for dowry culminates in the victim’s suicide or murder.16. The law enabled “dowry” to become a metaphor for all violence in the marital home. In 2005, the Pro-

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women exaggerate when registering such cases, even noting, “Many families are destroyed or ru-ined under such [gendered] provisions, and the legal proceedings go on for years. Men’s rightsorganizations are working to raise awareness...in opposition to women...men should be arrestedafter proper inquiries rather than on the basis of the woman’s complaint” (Verma 2017).

These sentiments are not restricted to politicians. (All-male) benches of the Supreme Courthave ruled that domestic violence provisions are, “a license for unscrupulous persons to wreckpersonal vendetta or unleash harassment [against men],” and a form of “legal terrorism [bywomen].”17 The Court has noted, “...complaints under Section 498-A are filed in the heat of themoment over trivial issues without proper deliberations. The learned members of the Bar haveenormous social responsibility and obligation to ensure that the social fiber of family life is notruined or demolished,”18 and that women should not file cases to, “satisfy the ego and angerof the complainant.”19 These pronouncements imply that women’s cases are (a) frivolous, (b)reported in the heat of the moment, (c) submitted by those with an agenda, or (d) best resolvedthrough reconciliation (Basu 2012). I scrutinize these assumptions using two sources of data.

The First-Information-Report Dataset + Judicial Records

In a push for transparency, India made crime or First-Information-Reports (FIRs) accessible(Court 2016). Over several years, I harvested and parsed millions of records; the present studyutilizes all 418,190 registrations in Haryana from January 2015-November 2018.20 I focus onthis state for which I translated reports into English, and worked with the local police to collectinformation about o�cers and previously inaccessible cases.21 Aside from particulars aboutvictims, suspects, and o�cers, FIRs contain descriptions of the incident, generally una↵ected bysocial desirability.22 Because few people in the Subcontinent have meaningful interaction withlaw enforcement (CSDS and Cause 2018), crime reports, unlike survey measures, enable us tozero in on individuals who interacted with state o�cials.23

I then merged FIRs with judicial records. India has made (semi-) public the universe ofjudicial files on a platform called E-Courts, similar to a domain established by China (Liebmanet al. 2020). Judicial records contain details about the date of filing/first appearance in courtfor FIRs, judges assigned, and verdict (if any). With support from scholars at ETH Zurichand the Development Data Lab—who compiled the universe of 80 million records from 2010-2018—I merged these files via the particulars of the police station, complainant name, and otheridentifiers.24 Out of 418,190 crime reports, I merged precisely 251,804 or 60.2% to court files, afigure that accurately represents registered cases that were sent to court.25

tection of Women from Domestic Violence Act expanded the definition of domestic violence, but also prioritized‘counseling’ abused women. Agnes and D’Mello (2015, 80) argue, “...counseling is based on a patriarchal premiseand is laden with anti-women biases...advised to “save the marriage” even at the cost of danger to her life.”17. Sushil Kumar Sharma v. Union of India, No. 141, 2005.18. Preeti Gupta & Anr. v. State of Jharkhand, Appeal No. 1512, Criminal Appellate Jurisdiction, 2010.19. Rajesh Sharma v. State of Uttar Pradesh, Appeal No. 1265, Criminal Appellate Jurisdiction, 2017.20. I anonymize the dataset in replication files.21. The police are exempted from releasing details on ‘sensitive’ cases involving sexual assault or insurgency.22. Citizens would have had to provide as much detail to o�cers to initiate investigation.23. Victims of VAW, for instance, do not turn to the police as one of the top five sources for help (DHS 2017).24. Documents produced by each wing are formatted di↵erently, requiring manual re-coding. As a check, Penal

Code violations in FIRs were fuzzy matched with those in the court files to ensure cases were correctly merged.25. As a validation exercise, I show that a third of cases of VAW could not be matched to court, reinforcing

research based on internal police memos demonstrating ⇡30% of crime as cancelled (Jassal 2020).

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Level 3Conviction or Acquittal [H2b]outcome

Duration in Court [H2a]process

Level 2-3Dismissal [H2b]outcome

Investigation Duration [H2a]process

Level 1Cancelled or Sent to Court [H1b]outcome

Registration Duration [H1a]process

Figure 3: Measures of Institutional Triage and Corresponding Hypotheses

Research Design: OLS, STM & Topical Inverse Regression Matching

To evaluate H1a, I examine the duration of time it took to file an FIR. Each report has datesof case registration, as well as when the complainant told an o�cer the crime began or ended.Registration Duration reflects the di↵erence between registration date and incident, thus pro-viding an estimate vis-a-vis delays in police filings. To test H1b, I examine the likelihood of aregistered case being sent to court. Specifically, non-merged cases are categorized as Cancelled,illustrating that law enforcement did not send them to the next branch.

For H2a, I create two measures. First, Investigation Duration—the di↵erence (in days)between FIR registration and preliminary hearing in court—estimates the time of police inves-tigation. Second, I create a numeric variable corresponding to the number of days from thepreliminary to latest court hearing on file (Duration in Court). To evaluate H2b, I createthree indicator variables of judicial review, i.e. whether the case was ejected by a judge at aninitial (bail) hearing (Dismissal); or whether, after subsequent trials, the outcome resulted in asuspect’s Conviction or Acquittal. I utilize variations of the following OLS model:

Yi = ↵ + �1Femalei + �2V AWi + �3(Female · V AW )i +�!� Ss +�!⌘ Cc + ✏i (1)

Y is a binary or numeric outcome for crime report i. Female is an indicator representingwhether the case involved a woman as the primary complainant, while VAW signifies whethera gendered Penal Code was a�xed to the FIR. Ss and Cc are a set of station- and court-level covariates, e.g. dummies for police station, district, month-year (of registration), rankof investigator, rank of presiding judge, and whether the area in which the case was tackledis urban. When excluding VAW, I include fixed e↵ects for the primary26 Penal Code violation,enabling me to compare di↵erences between complainants within categories of crime (e.g. theft).The interaction allows us to observe the di↵erence between men and women for gendered andnon-gendered crime. In the Appendix, I breakdown the results for four common types of VAW:female kidnapping, rape, dowry harassment, and criminal force. The standard errors for allmodels are clustered at the district level. Figure 3 provides a breakdown of the measures.

I also estimate structural topic models (STM) that, in a regression-type framework, can pre-dict whether cases devoted to a topic (e.g. rape) are functions of covariates, e.g. the probability

26. As seen in Appendix Figure A3, most FIRs are combinations of multiple Penal Code clauses, with the firstlisted generally indicating the case type. There are approximately 1000 unique Penal Codes and special acts.

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of being dismissed (Roberts, Stewart, and Tingley 2019; Roberts et al. 2014; Roberts, Stew-art, and Airoldi 2016).27 Unsupervised machine learning de-emphasizes categorizations of crimebased on coarse Penal Codes and disaggregates crime, e.g. domestic violence from attemptedmurder. To do this, I compiled and parsed text from each FIR into an R-readable format, andthen translated the (primarily) Hindi text for 418,190 reports (200 million words or ⇡450,000A4-size single pages) using Google Translate.28

It is possible that fixed e↵ects OLS models and STM might still lead to imprecise estimatesabout the impact of complainant gender on, say, conviction. There may be concerns aboutomitted variable bias or inframarginality (Arnold, Dobbie, and Yang 2018), i.e. even withincrime type (e.g. theft), women may report distinct sub-types of cases (e.g. chain-snatching)compared to men (e.g. motorcycle robbery). Consequently, I utilize a third method: topicalinverse regression matching (TIRM), introduced by Roberts, Stewart, and Nielsen (2020), thatallows one to condition on the content within FIRs, thereby diminishing confounding.

To implement TIRM, I estimate a STM with a “treatment” (a woman’s crime report) asa content covariate. This estimates the relationship between having a female complainant andwords in the corpus, as well as how FIRs registered by women discuss topics di↵erently (Roberts,Stewart, and Airoldi 2016). Following Roberts, Stewart, and Nielsen (2020), I extract topicproportions for control FIRs as though they were treated,29 attaching an estimated propensityscore to the topic-proportion vector for every FIR, and then performing coarsened exact matching(Iacus, King, and Porro 2012), in order to fit models predicting conviction or acquittal.

Descriptive Statistics

Figure 4 displays the top Penal Codes appearing in cases registered by female complainants aswell as in the category of VAW.30 Women registered 38,828 or 9% of all FIRs. Descriptively, thereare di↵erences in the types of cases registered by women and men (Appendix Figure A1). Forinstance, for men, the top substantive31 Penal Codes relate to theft, rash driving, burglary, andpublic intoxication/bootlegging. The top substantive Penal Code for women is Section 498-A;domestic violence/dowry-related abuse perpetrated by a spouse (or in-laws) was present in 15% oftheir registrations.32 Other common gendered Penal Codes include abduction (e.g. kidnappinga woman “to compel her into marriage”/“procuring a minor girl”),33 “obscene acts/songs,”34

“criminal force against a woman,”35 rape, “insulting the modesty of a woman,”36 stalking, “intent

27. For most analyses, I specify 35-40 topics. As seen in Figure 4 and Appendix Figure A1, most crimes can beslotted into roughly two-dozen Penal Code classifications. I see more repeat topics for values greater than 40.28. I analyze translations because (a) machine learning, including the STM, were designed primarily for English,

and (b) to ease pre-processing, i.e. stemming, lemmatization, and ejection of stop- or common words.29. The content covariate in the STM knows the weight of each word and topic-word combination. The projection

for an FIR would then be the sum of its weighted word counts normalized by FIR length.30. Appendix Figure A2 presents a heat map illustrating the locations of registrations.31. Most sections relate to concrete violations, e.g. theft and murder. There are additional clauses that are non-

substantive and attached as supplements, e.g. Section 323 (causing hurt), invoked for rash driving to extortion.32. Many Penal Codes are registered in conjunction with Section 498-A, e.g. “unnatural”/anal sex (for marital

rape), or dowry death (when domestic violence culminates in suicide or murder). See Appendix Figure A3.33. Invoked from cases ranging abductions to young women eloping or running away with boyfriends.34. Invoked in cases that may include lewd behavior in front of, or towards a woman, as well as ‘obscenity.’35. Invoked in cases ranging from acting aggressively to attempted rape.36. Invoked in a range of cases, including exhibitionism and invasion of privacy.

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to disrobe,” sexual harassment, and “unnatural” (anal) sex.37

Figure 4: Top Indian Penal Code Sections Listed [Female Complainants and Gendered Crime]

intimidation

voluntarily causing hurt

act done by several persons

dowry harassment/cruelty by husband or relatives

breach of trust

theft

burglary

wrongful confinement/missing person

trespassing/preparation for hurt or assault

cheating

rash driving

trespassing by night

causing hurt

unlawful assembly

wrongful restraint

criminal force to woman with intent to outrage her modesty

kidnapping

rioting, armed with weapon

criminal conspiracy

mischief, causing damageIPC 1860;427IPC 1860;120−B

IPC 1860;148IPC 1860;365IPC 1860;354IPC 1860;341IPC 1860;149IPC 1860;337IPC 1860;457IPC 1860;279IPC 1860;420IPC 1860;452IPC 1860;346IPC 1860;380IPC 1860;379IPC 1860;406

IPC 1860;498−AIPC 1860;34

IPC 1860;323IPC 1860;506

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40Proportion

Indi

an P

enal

Cod

e (IP

C)

Top 20 Penal Codes [Female Complainants]A)

intimidation


dowry harassment/cruelty by husband or relatives

acts done by several persons

breach of trust

kidnapping from guardianship

procuration of minor girl

obscene acts/songs

criminal force to woman with intent to outrage her modesty

kidnapping a woman to compel her to marriage

trespassing/preparation for hurt or assault

criminal conspiracy

rape

word, gesture or act intended to insult modesty of a woman

dowry death

stalking

wrongful restraint

sexual harassment

criminal force to woman with intent to disrobe

unnatural (anal) sexIPC 1860;377IPC 1860;354−B

IPC 1860;354−A(1)IPC 1860;341

IPC 1860;354−DIPC 1860;304−B

IPC 1860;509IPC 1860;376

IPC 1860;120−BIPC 1860;452IPC 1860;366IPC 1860;354IPC 1860;294

IPC 1860;366−AIPC 1860;363IPC 1860;406IPC 1860;34

IPC 1860;498−AIPC 1860;323IPC 1860;506

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Proportion

Indi

an P

enal

Cod

e (IP

C)

Top 20 Penal Codes [Gendered Cases]B)

Note: Top twenty Penal Codes attached to women’s cases (N=38,828) and gendered crime or VAW(N=20,869). See Appendix Figure A1 for male complainants and non-gendered crime.

Table 1 presents descriptive statistics for the FIR dataset. Distance reveals that crime takesplace, on average, 6 kilometers from a station. Cases likely have 2 suspects, with crimes registeredby women, and VAW, more likely to have a female suspect (Female Suspects). (As Jassal andBarnhardt (2020) show, cases of dowry-related oppression may involve the complainant’s mother-in-law.) While o�cers do not always note the ages of victims, non-missing data suggest thatcomplainants are, on average, in their 30s. VAW is likely to have more Penal Codes appended(No of Sections), and complainants wait longer at the station in anticipation of registration(9.3 hours). The variables prefixed with ‘R:’ represent investigator ranks; women’s cases are lesslikely to be assigned to constables (who cannot charge-sheet cases).

Unlike Pre-Registration Duration, which reflects the di↵erence between registration dateand when a crime first began,38 Registration Duration can be seen a measure of police hesi-tancy in registration. The median days between crime occurrence and registration is 1, with amean of 28. However, women’s cases, as well as VAW, have means of 69 and 113, respectively. Inother words, a complainant may have visited a police station to register an FIR but asked to dropthe case, or be forced to return at a later date.39 Prima facie, Pre-Registration Duration andRegistration Duration challenge the assumption that gendered cases are filed, “in the heat ofthe moment.”40 No Record shows 32% of VAW is cancelled at the police-level.41

Table 2 highlights variables created post-merging. Investigation Duration reflects daysbetween registration and preliminary hearing. The mean number of days spent in the judiciary(Duration in Court) is just under a year (336 days), with women’s cases, and VAW, spending

37. Invoked in cases of sodomy; this clause was repealed from the statutes in 2018 (Jassal and Chhibber 2019).38. Therefore potentially illustrative of how long a complainant waited to file a case and/or duration of abuse.39. See Appendix Figure A4 and A5 for a graphical illustration of the inter-quartile range.40. Preeti Gupta & Anr. v. State of Jharkhand, Appeal No. 1512 (Criminal Appellate Jurisdiction, 2010.41. While it is possible certain cases have transitioned to the judiciary, the FIRs cover 2015-2018. Investigations

are supposed to be carried out within 90-days, and the E-Courts database was downloaded in mid-2020. Conse-quently, the analyses in this study ‘allow’ a two-year window, i.e. far longer than time allotted for investigation.

9

longer.42 While most cases are assigned to Judicial Magistrate 1st Class, women’s cases, andVAW, are more likely to be assigned to senior judges, e.g. Addl. District Sessions Judge.

Figure 5 illustrates judicial outcomes, which fall into roughly seven categories. Acquittedrefers to whether the suspect is absolved; Allowed denotes if the case entered the judiciary buta trial has not been set; Convicted denotes that a suspect was convicted, while Dismissedunderscores if the case was ejected at a preliminary (or bail) hearing. Untraced representswhether the suspect could not be found or brought to court. The remaining outcomes areclassified as Disposed, indicating that a decision was taken (e.g. fine issued) but further detailsare unavailable. The cross-tabulations in Figure 5 show that—whether as a function of allregistrations (Panels A and B) or simply those in the court docket (Panels C and D)—women’scomplaints (as well as VAW) are more likely to be listed as on-going (stalled), dismissed, or resultin a suspect’s acquittal, and less likely to see a suspect sent to prison.

Figure 5: Crime Reports Statuses [Split by Complainant Gender and Crime Type]

2.9

13.5

4.2

26

4 3.2 4.6

41.7

10.8 10.1

2.6

22.8

4.6 2.86.6

39.6

05

101520253035404550

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

no_record

Court Status of Crime Reports by Complainant GenderA)

3

17.4

5.8

29.4

2.6 4.3 5.1

32.3

10.4 10.1

2.6

22.8

4.6 2.86.5

40.2

05

101520253035404550

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

no_record

Court Status of Crime Reports by Crime TypeB)

5

23.2

7.2

44.5

6.8 5.57.9

17.9 16.8

4.3

37.8

7.64.7

11

05

101520253035404550

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

female other

Court Status of Crime Reports by Complainant Gender [Court Docket]C)

4.5

25.7

8.6

43.3

3.96.4 7.5

17.5 16.9

4.3

38.1

7.84.7

10.9

05

101520253035404550

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

gendered nongendered

Court Status of Crime Reports by Crime Type [Court Docket]D)

Note: Judicial outcomes for cases (% on Y axis). Panels A and B reflect outcomes conditional on police registration.Panel A is separated by female (N=38,828) and male/other complainants (N=379,362). Panel B reflects gendered(N=20,869) and non-gendered crime (N=397,321). Panels C and D reflect outcomes conditional on entering thecourt docket. Panel C is separated by female (N=22,648) and male/other complainants (N=229,156), and Panel Dgendered (N=14,134) and non-gendered crime (N=237,670). 95% confidence intervals included.

42. See Figure A16 for a graphical display.

10

Table 1: Descriptive Statistics on Select Variables: First-Information-Report (FIR) Dataset

ALL CRIME

Complainant N Mean SD Crime Type N Mean SD N Mean SD MedianPre-Registration Duration Female 33738 181.78 580.08 Gendered 17254 346.80 773.18 381668 49.38 310.38 1.00

Other 347930 36.54 266.81 Nongendered 364414 35.30 261.17Registration Duration Female 33766 68.94 341.50 Gendered 17269 112.88 440.69 381836 27.78 225.87 1.00

Other 348070 23.79 210.88 Nongendered 364567 23.75 209.46Word Count Female 38828 577.41 421.49 Gendered 20869 722.44 526.30 418189 452.30 257.86 381.00

Other 379361 439.49 230.98 Nongendered 397320 438.11 226.72Distance Female 36868 5.96 12.45 Gendered 19585 6.96 14.01 400345 5.52 13.83 3.00

Other 363477 5.48 13.96 Nongendered 380760 5.45 13.82Female Suspects Female 22022 0.70 1.07 Gendered 17676 0.75 1.08 220943 0.18 0.72 0.00

Other 198921 0.12 0.65 Nongendered 203267 0.13 0.66Total Suspects Female 22022 2.74 2.35 Gendered 17676 2.78 2.26 220943 2.00 2.42 1.00

Other 198921 1.92 2.41 Nongendered 203267 1.93 2.42Victim Age Female 17953 35.93 10.39 Gendered 9131 34.35 10.29 192939 38.40 9.07 38.00

Other 174986 38.65 8.88 Nongendered 183808 38.60 8.96No. of Sections Female 38828 2.57 1.59 Gendered 20869 3.31 1.65 418190 2.11 1.42 2.00

Other 379362 2.07 1.39 Nongendered 397321 2.05 1.37Urban Female 38141 0.60 0.49 Gendered 20028 0.53 0.50 417322 0.59 0.49 1.00

Other 379181 0.59 0.49 Nongendered 397294 0.59 0.49Hours Waited at PS Female 38690 7.51 62.08 Gendered 20775 9.32 79.25 416045 7.06 52.72 0.68

Other 377355 7.01 51.67 Nongendered 395270 6.94 50.95Hour Registered Female 38828 17.37 4.80 Gendered 20869 17.00 5.07 418190 17.20 5.32 19.00

Other 379362 17.19 5.38 Nongendered 397321 17.21 5.34Hour Arrived Female 38828 16.50 4.83 Gendered 20869 16.14 5.11 418190 16.35 5.43 18.00

Other 379362 16.34 5.48 Nongendered 397321 16.36 5.44R:Head Constable Female 36959 0.29 0.46 Gendered 19621 0.16 0.36 400086 0.43 0.49 0.00

Other 363127 0.44 0.50 Nongendered 380465 0.44 0.50R:Ass. Sub-Inspector Female 36959 0.52 0.50 Gendered 19621 0.58 0.49 400086 0.44 0.50 0.00

Other 363127 0.43 0.50 Nongendered 380465 0.44 0.50R:Sub-Inspector Female 36959 0.16 0.36 Gendered 19621 0.22 0.41 400086 0.10 0.30 0.00

Other 363127 0.10 0.30 Nongendered 380465 0.10 0.30R:Inspector Female 36959 0.03 0.17 Gendered 19621 0.04 0.21 400086 0.02 0.16 0.00

Other 363127 0.02 0.15 Nongendered 380465 0.02 0.15No Record/Not Sent to Court Female 38828 0.42 0.49 Gendered 20869 0.32 0.47 418190 0.40 0.49 0.00

Other 379362 0.40 0.49 Nongendered 397321 0.40 0.49Note: Descriptive statistics for variables in the FIR dataset, split by female/other complainants, as well as gendered/nongendered crime. The term‘Other’ is used because a small fraction of cases may be brought forward by organizations or institutions rather than individuals. Gendered crimemay be brought forward by male or female complainants.

11

Table 2: Descriptive Statistics: First-Information-Report Dataset Merged With Court Records

ALL CRIME

Complainant N Mean SD Crime Type N Mean SD N Mean SD MedianInvestigation Duration Female 22471 133.77 206.57 Gendered 14007 113.66 185.91 248920 127.95 204.38 54.71

Other 226449 127.38 204.15 Nongendered 234913 128.81 205.40Dismissed Female 22648 0.07 0.26 Gendered 14134 0.09 0.28 251804 0.05 0.21 0.00

Other 229156 0.04 0.20 Nongendered 237670 0.04 0.20Ongoing Female 22648 0.44 0.50 Gendered 14134 0.43 0.50 251804 0.38 0.49 0.00

Other 229156 0.38 0.48 Nongendered 237670 0.38 0.49Acquitted Female 22648 0.23 0.42 Gendered 14134 0.26 0.44 251804 0.17 0.38 0.00

Other 229156 0.17 0.37 Nongendered 237670 0.17 0.37Convicted Female 22648 0.05 0.22 Gendered 14134 0.04 0.21 251804 0.17 0.37 0.00

Other 229156 0.18 0.38 Nongendered 237670 0.17 0.38Duration in Court Female 22522 377.37 368.07 Gendered 14120 378.43 362.50 250287 336.18 365.50 205.00

Other 227765 332.10 364.99 Nongendered 236167 333.65 365.52No. of Hearings Female 20077 9.82 9.04 Gendered 12852 10.41 9.49 195480 9.84 9.15 7.00

Other 175403 9.84 9.17 Nongendered 182628 9.80 9.13R:Civil Judge Junior Division Female 22634 0.06 0.25 Gendered 14124 0.06 0.24 251629 0.07 0.25 0.00

Other 228995 0.07 0.25 Nongendered 237505 0.07 0.25R:Judicial Magistrate 1st Class Female 22634 0.43 0.50 Gendered 14124 0.39 0.49 251629 0.46 0.50 0.00

Other 228995 0.47 0.50 Nongendered 237505 0.47 0.50R:Sub-Divis. Judicial Magistrate Female 22634 0.08 0.27 Gendered 14124 0.07 0.26 251629 0.09 0.29 0.00

Other 228995 0.09 0.29 Nongendered 237505 0.09 0.29R:Addl. Chief Judicial Magistrate Female 22634 0.09 0.29 Gendered 14124 0.08 0.26 251629 0.11 0.31 0.00

Other 228995 0.11 0.31 Nongendered 237505 0.11 0.31R:Chief Judicial Magistrate Female 22634 0.13 0.33 Gendered 14124 0.09 0.29 251629 0.14 0.35 0.00

Other 228995 0.14 0.35 Nongendered 237505 0.14 0.35R:Addl. District Sessions Judge Female 22634 0.17 0.37 Gendered 14124 0.29 0.45 251629 0.11 0.31 0.00

Other 228995 0.10 0.30 Nongendered 237505 0.10 0.30R:District Sessions Judge Female 22634 0.03 0.16 Gendered 14124 0.01 0.10 251629 0.02 0.14 0.00

Other 228995 0.02 0.13 Nongendered 237505 0.02 0.14Duration in CJ System Female 22492 573.19 383.60 Gendered 14110 568.78 381.88 249462 508.71 392.22 435.71

Other 226970 502.32 392.49 Nongendered 235352 505.11 392.54Note: Descriptives statistics for select variables in merged dataset of crime and judicial records, split by female and other complainants, as well asgendered and non-gendered crime. The term ‘Other’ is used because a small fraction of cases may be brought forward by organizations or institutionsrather than individuals. Gendered crime may be brought forward by male or female complainants.

12

OLS Results

Female Complainants and VAW

Table 3 tests hypotheses outlined in Level 1 (Figure 3). Columns 1-2 show that women’s caseshave a lag of over a month between incident and registration (significantly longer than thebaseline of 24 days). In columns 5-6, when interacting Female with an indicator for a caseinvoking a gendered Penal Code, the gap increases. Put di↵erently, in non-gendered contexts,the gap between crime occurrence and registration is a week longer for women; this gap exceeds100 days when complaints involve VAW. While this may be reflective of hesitancy in reporting, atthe node when cases have not formally entered the books, the police has discretion in forwardingcomplainants to counseling centers or asking citizens to return later to avoid registration.

Columns 7-8 of Table 3 reveal that women’s cases are significantly less likely than men’s tobe sent to court. However, this does not apply to VAW. Conditional on registration, cases ofVAW are 7-8% more likely to be sent to the judiciary than non-gendered crime. Police o�cersare bound by rules to ensure (registered) cases of VAW transition or are investigated quickly.For instance, in columns 3-4 of Table 4, cases of VAW are investigated, on average, roughlytwo-weeks sooner than non-gendered crime (compared to a baseline of 128 days). Columns 6-7reveal that it is women’s non-gendered complaints for which investigations are ⇡20-days slower.

Figure 6 presents average marginal e↵ects in an easy-to-interpret plot. Panel A suggests thatcases of VAW (brought forward by female complainants) have the longest lag between incidentand registration. Nevertheless, cases of VAW are, conditional on registration, allowed to passthrough the early stages (Panel B and C). At the police-level, gender imbalances for registeredcases largely hold in non-gendered contexts, settings where o�cers are bound by fewer rules.

Table 3: Process and Outcomes: Level 1

Registration Duration Cancelled After Registration

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Female 45.148⇤⇤⇤ 40.778⇤⇤⇤ 9.100⇤⇤⇤ 6.858⇤⇤⇤ 0.021⇤⇤⇤ 0.025⇤⇤⇤ 0.068⇤⇤⇤ 0.063⇤⇤⇤

(6.353) (7.248) (2.263) (2.494) (0.008) (0.008) (0.010) (0.009)

VAW 89.135⇤⇤⇤ 85.401⇤⇤⇤ 27.459⇤⇤⇤ 22.906⇤⇤ -0.079⇤⇤⇤ -0.065⇤⇤⇤ -0.046⇤⇤⇤ -0.034⇤⇤

(13.777) (16.049) (7.740) (8.986) (0.014) (0.011) (0.017) (0.014)

Female:VAW 111.601⇤⇤⇤ 117.971⇤⇤⇤ -0.120⇤⇤⇤ -0.114⇤⇤⇤

(17.815) (18.085) (0.018) (0.019)

Constant 23.788⇤⇤⇤ 8.933⇤⇤⇤ 23.749⇤⇤⇤ 6.915⇤⇤ 23.128⇤⇤⇤ 5.133⇤ 0.396⇤⇤⇤ 0.338⇤⇤⇤ 0.402⇤⇤⇤ 0.397⇤⇤⇤ 0.397⇤⇤⇤ 0.392⇤⇤⇤

(2.385) (2.901) (2.344) (2.994) (2.350) (2.801) (0.018) (0.013) (0.018) 0.013) (0.018) (0.013)

Obs. 381,836 360,022 381,836 360,022 381,836 360,022 418,190 382,265 418,190 382,265 418,190 382,265R2 0.003 0.015 0.007 0.019 0.010 0.022 0.0002 0.111 0.001 0.112 0.003 0.113

Controls N Y N Y N Y N Y N Y N YPS FE N Y N Y N Y N Y N Y N YMonth Yr FE N Y N Y N Y N Y N Y N Y

Note: Controls include a numeric variable for distance of crime from station, investigator rank, and urban. Standarderrors clustered by district. ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

Nonetheless, this dynamic changes by the time of the first hearing in court. At this node,having entered the purview of judges where there are few constraints on administrators, triagebecomes even more apparent. Columns 7-12 in Table 4 suggest that women’s cases—whetherin non-gendered or gendered contexts—begin to yield negative outcomes for the complainant.

13

Figure 6 expresses this in Panel D. Specifically, even though women’s cases in non-gendered con-texts are 1-2% more likely to be dismissed than related cases brought forward by men (comparedto a baseline of 4%), this gap persists for VAW.

Table 4: Process and Outcomes: Level 2

Investigation Duration Court Dismissal

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)Female 6.390 9.015⇤⇤⇤ 18.875⇤⇤⇤ 19.507⇤⇤⇤ 0.029⇤⇤⇤ 0.014⇤⇤⇤ 0.024⇤⇤⇤ 0.013⇤⇤⇤

(4.841) (3.278) (4.159) (2.550) (0.003) (0.004) (0.003) (0.003)

VAW �15.146⇤⇤ �11.009⇤ �9.306 �4.214 0.043⇤⇤⇤ 0.010 0.049⇤⇤⇤ 0.004(6.946) (5.880) (7.090) (6.004) (0.006) (0.006) (0.007) (0.007)

Female:VAW �27.209⇤⇤⇤ �29.353⇤⇤⇤ �0.032⇤⇤⇤ �0.002(6.155) (6.700) (0.006) (0.007)

Constant 127.378⇤⇤⇤ 116.458⇤⇤⇤ 128.807⇤⇤⇤ 118.368⇤⇤⇤ 127.631⇤⇤⇤ 117.171⇤⇤⇤ 0.043⇤⇤⇤ 0.007 0.043⇤⇤⇤ 0.008 0.041⇤⇤⇤ 0.007(5.926) (16.774) (5.924) (16.800) (6.010) (16.736) (0.003) (0.008) (0.003) (0.008) (0.003) (0.008)

Obs. 248,920 227,315 248,920 227,315 248,920 227,315 251,804 229,954 251,804 229,954 251,804 229,954R2 0.0001 0.069 0.0003 0.069 0.001 0.070 0.002 0.084 0.002 0.083 0.003 0.084

Controls N Y N Y N Y N Y N Y N YPS FE N Y N Y N Y N Y N Y N YMonth-Yr FE N Y N Y N Y N Y N Y N Y

Note: Controls include a numeric variable for distance of crime from station, investigator rank, judge rank, andurban. Standard errors clustered by district. ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

Columns 1-2 in Table 5 reveal that—for complaints that survive the node of preliminary courtdismissal—women’s cases spend longer in the judiciary by over a month (compared to a baselineof just under a year). Graphically, Panel E in Figure 6 shows that cases of VAW brought forwardby women spend the longest time stalled (⇡390 days), regardless of whether a verdict was issued.

To investigate whether punitive justice was ultimately meted out, I pay attention to convictionand acquittal. Columns 5-6 in Table 6 demonstrate that cases brought forward by women in non-gendered contexts are 5-6% more likely to result in suspect acquittal (from a baseline of 17%), afigure which is pulled higher if it involves VAW. Women’s cases are associated with 10-13% fewerconvictions of suspects compared to a baseline of 18% (columns 7-8). Figure 6 summarizes thefindings where, in Panel F, we see conviction rates for men who register VAW (e.g. for family orfriends) drop from their non-gendered base, but not to the same level as women who have onlya 7-10% chance of a suspect being convicted in either category. The results largely hold whenincluding dummies for over a thousand primary Penal Codes (Appendix Table A2).

Heterogeneous E↵ects Across Gendered Crime

VAW is a broad category. It is plausible that violence perpetrated by a spouse, family, or in-lawswould be most likely to be triaged. Consequently, I disaggregate VAW into the four commoncase types: (a) dowry harassment, (b) female kidnapping, (c) criminal force, and (d) rape.43

Appendix Table A3 suggests that cases of female kidnapping and “criminal force” are registered

43. These Penal Codes have the least overlap between them, providing variation in gendered crime registered.Dowry (Section 498-A) always involves the spouse or extended family, but this does not apply to rape (Section 376)which is stamped when a non-spouse commits assault. Female kidnappings (Section 366) are usually registeredby family/relatives of the complainant rather than the primary victim.

14

Table 5: Process: Level 3

Duration in Court

(1) (2) (3) (4) (5) (6)

Female 45.264⇤⇤⇤ 40.953⇤⇤⇤ 27.042⇤⇤ 33.423⇤⇤⇤

(11.042) (8.827) (12.088) (10.229)

VAW 44.781⇤⇤⇤ 33.761⇤⇤⇤ 4.677 9.866(10.738) (10.885) (10.194) (11.084)

Female:VAW 47.649⇤⇤⇤ 14.743(8.335) (10.695)

Constant 332.103⇤⇤⇤ 548.265⇤⇤⇤ 333.650⇤⇤⇤ 550.261⇤⇤⇤ 331.976⇤⇤⇤ 547.495⇤⇤⇤

(12.660) (32.900) (12.347) (32.835) (12.755) (32.800)

Obs. 250,287 228,542 250,287 228,542 250,287 228,542R2 0.001 0.200 0.001 0.200 0.002 0.200

Controls N Y N Y N YPS FE N Y N Y N YMonth-Yr FE N Y N Y N YNote: Controls include a numeric variable for distance of crime from station, investigator rank, judgerank, and urban. Standard errors clustered by district. ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

Table 6: Outcomes: Level 3

Acquittal Conviction

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Female 0.064⇤⇤⇤ 0.055⇤⇤⇤ 0.056⇤⇤⇤ 0.054⇤⇤⇤ �0.129⇤⇤⇤ �0.106⇤⇤⇤ �0.123⇤⇤⇤ �0.108⇤⇤⇤

(0.009) (0.007) (0.008) (0.006) (0.012) (0.010) (0.011) (0.010)

VAW 0.088⇤⇤⇤ 0.068⇤⇤⇤ 0.100⇤⇤⇤ 0.080⇤⇤⇤ �0.130⇤⇤⇤ �0.081⇤⇤⇤ �0.122⇤⇤⇤ �0.065⇤⇤⇤

(0.012) (0.008) (0.010) (0.008) (0.014) (0.012) (0.014) (0.012)

Female:VAW �0.071⇤⇤⇤ �0.067⇤⇤⇤ 0.095⇤⇤⇤ 0.065⇤⇤⇤

(0.010) (0.009) (0.011) (0.011)

Constant 0.168⇤⇤⇤ 0.393⇤⇤⇤ 0.169⇤⇤⇤ 0.394⇤⇤⇤ 0.165⇤⇤⇤ 0.391⇤⇤⇤ 0.179⇤⇤⇤ 0.267⇤⇤⇤ 0.175⇤⇤⇤ 0.261⇤⇤⇤ 0.182⇤⇤⇤ 0.269⇤⇤⇤

(0.017) (0.018) (0.017) (0.018) (0.017) (0.018) (0.014) (0.028) (0.014) (0.028) (0.015) (0.028)

Obs. 251,804 229,954 251,804 229,954 251,804 229,954 251,804 229,954 251,804 229,954 251,804 229,954R2 0.002 0.124 0.003 0.124 0.004 0.125 0.010 0.101 0.006 0.097 0.012 0.102

Controls N Y N Y N Y N Y N Y N YPS FE N Y N Y N Y N Y N Y N YMonth Yr FE N Y N Y N Y N Y N Y N Y

Note: Controls include a numeric variable for distance of crime from station, investigator rank, judge rank, andurban. Standard errors clustered by district. ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

15

Figure 6: Average Marginal E↵ects for Interactions

23.23

46.13

30.08

170.96

50

100

150

Nongendered Gendered

Day

s

Registration DurationA)

0.397

0.363

0.459

0.3120.30

0.35

0.40

0.45

Nongendered GenderedPe

rcen

t

No Record in Court/Cancelled at PSB)

127.22

123.01

146.73

113.16

110

120

130

140

150


Day

s

Investigation DurationC)

0.043

0.047

0.056

0.058

0.045

0.050

0.055

0.060


Perc

ent

Court/Preliminary DismissalD)

334.45

344.32

367.88

392.49

330

350

370

390


Day

sDuration in CourtE)

0.176

0.111

0.068 0.068

0.06

0.09

0.12

0.15

0.18


Perc

ent

ConvictionF)

a aMale FemaleNote: Marginal e↵ects based on regressions in columns 7 or 14 in Tables 3-6. All models include controls,month-year, and police station fixed e↵ects. Standard errors clustered by district.

sooner than the baseline, with rape registered around the same time as the average non-genderedcase. Dowry/domestic violence is the exception: the lag between the incident and registrationcan exceed 270 days, and almost a year if the complainant is a woman (Appendix Figure A28),providing suggestive evidence that law enforcement may have initially delayed or diverted com-plainants.44 Appendix Table A4 (Figure A29) show that, conditional on registration, VAW ismore likely to appear in court records than non-gendered cases, while Table A5 (and Figure A30)illustrate that VAW—except female kidnapping45—are investigated (relatively) quickly.

Nevertheless, by the preliminary hearing, VAW, especially dowry harassment, begin to bedismissed at high rates. If complaints happen to cross this node, all four types of VAW spendsignificantly longer stalled (Table A6). Dowry/domestic violence is among the least likely case toresult in conviction (0.7%), comparable to culpable homicide (e.g. rash driving) and real estate

44. This validates the use of Registration Duration as a measure of police reluctance in registration; if it onlyreflected women’s anxiety in coming forward, we should also have seen similar lags for rape or “criminal force.”45. Interviews with Haryana police suggest that a large proportion of cases involving Section 366 involve girls,

14-18, who allegedly ran away with partners. O�cers believe these cases are not bona fide kidnapping but insteadteenagers “rebelling” in conservative settings where there are restrictions on women’s mobility. These cases areregistered by family members of the victim. One policewoman explained, “Parents refuse to accept that [a womanfell in love] and get an FIR against the boy... As per law, a minor’s consent is not consent even if given voluntarily,and thus once police trace the couple or they come back on their own, we get the girl’s statement recorded. Many atimes, they allege forceful abduction and rape under the coercion of family members...police remain less interestedin such crimes. However, they’re more responsive if, say, a girl below 10-12 years is missing... According to theKhap [village council] rules, girls are forbidden to marry in same gotras and nearby villages; apparently all areconsidered brothers and sisters in a village. Hence, young girls feel compelled to break free, desires which haveonly been amplified with technology and internet.” Personal interview, Crimes Against Women Desk, Haryana.

16

disputes (Appendix Figure A24 and A21). As Table A6 and Figure A34 demonstrate, the initialvariation in how VAW is accommodated at the police-level dissipates such that the sub-typesbegin yielding higher acquittal rates,46 and lower convictions (with rape as an exception).47

The coe�cient on Female remains significant in every single model. Further, triage appearsmost extreme in the mid- to late-stages of justice delivery, “the last mile” at which complaints(considered serious to have been registered/investigated) have spent e↵ort to reach later stages.

Text-As-Data: Structural Topic Modeling

Aside from the usual caveats associated with OLS, there are two challenges. First, categorizationsof crime have hitherto relied on Penal Codes. Second, even if we accept that there is a strikinggender imbalance, perhaps female complainants are more likely to register cases without merit,which the criminal justice system happens to be e�ciently weeding out.

To investigate, I apply unsupervised machine learning on victims’ testimonies. The techniqueprecludes myself or the administrator (e.g., the o�cer who stamped Penal Codes) from insertingthemselves into the research. Topic modeling estimates relationships between meta-data andtopics from the corpus (Roberts, Stewart, and Tingley 2019),48 thereby facilitating hypothe-sis testing. Are there, for instance, particular topics within the testimonies—including thosegenerally associated with female complainants—that yield lower conviction rates?49

As highlighted in Table 1, complaints brought forward by women are longer (VAW has amean word count of 722).50 Appendix Figures A37 highlights the kinds of topics that emergefrom the entire corpus. For women, Figure 7 presents the highest probability as well as FREX(frequent and exclusive) words. Among the top topics that emerge from women’s cases involve“fighting” (Topic 14), usually domestic violence. The word clouds for this topic in AppendixFigure A46-A48 underscore terms such as: wife, hospital, kill, beaten, domest, husband, hurt,blunt. The kind of theft that female complainants often register is distinct from those associatedwith men; for women, the most common form of theft is “chain-snatching” (Topic 15), as opposedto auto-theft for men (Topic 22 in Appendix Figure A37).

Figure 8 presents two visualizations. Panel A is a STM of women’s complaints with anindicator for conviction as a predictor. Non-gendered cases such as “cheating,” “chain-snatching,”or “public intoxication” yield better outcomes. Panel B shows correlations (when topics arelikely to co-occur within an FIR). Cases involving dowry are clustered at the bottom, with otherforms of gendered crime (e.g. rape, domestic violence, and “criminal force”) immediately above,suggesting overlap in the kinds of abuse perpetrated in and out of the household.51

46. In Appendix Figure A7, five of the top ten Penal Codes that have the longest gap between incident andregistration are gendered, with dowry being the most delayed case (Appendix Figure A6, Figure A14 and A15).47. Appendix Figure A21 highlights that, while cases of child sexual assault and dowry death have higher

conviction percentages (10-17%), cases where a female victim is not alleged to have been raped (by a non-spouse), or not perceived to be grievously injured, have lower conviction rates (e.g. “word or acts intended toinsult the modesty of women” (1.3%), and sexual harassment (3.4%). Also see Appendix Figure A21-A22).48. The method uses the ‘bag of words’ assumption where each document is a vector containing the count of a

word type without reference to order. The resulting Document Term Matrix (DTM) is one where a row representsa document, and a column represents a word (Lucas et al. 2015; Grimmer and Stewart 2013).49. I utilize the universe of FIRs, and create indicators for whether they eventually resulted in conviction or

acquittal (as opposed to analyzing only those in the court docket).50. See Appendix Figure A36 for a graphical visualization of the spread.51. Other clusters include cases involving finances, e.g. phishing, real estate and development disputes.

17

Figure 9 breaks down VAW. Topics range from the extortion of women with compromisingphotographs/videos (Topic 18) to “tra�cking” or being sold into prostitution (Topic 12). Whiletopics involving abuse inside the household appear to be unlikely to result in formal punishment(e.g. dowry), cases involving child abuse and rape have better outcomes (vis-a-vis conviction)(Figure 10). Still, both forms of VAW—in or out of the household—are likely to yield high ratesof acquittal and dismissal (Appendix Figure A45), supporting the OLS analyses.

A theme that emerges from the STM exercise is the prioritization of sons over daughters.Specifically, Topic 7 refers to abandoning or killing babies (“killing the girl child”), Topic 14refers to (illegal) sex selective diagnostic technologies, and Topic 5 includes unlicensed doctorsperforming abortions. As highlighted in the word clouds of the Appendix (Figures A51-A53),common words in these categories include: children, child, medic, drug, abort, kill, patient,ultrasound, pregnant. A number of inter-correlated topics involve dowry (Topics 6, 23, 13, 9, and5) in Figure 10. Appendix Figures A46-A48 shows that common words include: dowry, tortur,parent, cash, daughter, greed, kill, demand, cruelti, in-law, assault. “Mother-in-law” appearsrepeatedly, indicating that abuse perpetrated against the victim invariably involves the in-lawsas opposed to just an intimate partner.

When disaggregating dowry, the machine is able to separate abuse relating to mental andphysical abuse (Topics 1 and 2) from others involving, for instance, violence perpetrated when avictim is pregnant (Topic 3). Topics 6 involves harassment in conjunction with spousal rape; thiscan be seen in the FREX words of Panel B of Figure 9 that accentuate terms such as unnatur(or anal) and sexual. Topic 16 is illustrative of FIRs in which complainants explain that theytried to register a case before but were instead asked to reconcile (Jassal 2020). Topics 19 and 20refer to abusers either deserting their wives or absconding (so as to extract dowry from anothervictim), and Topic 20 represents cases where suspects starve their wives for extortion. Whilecases related to rape (by a non-spouse) have a better likelihood of being disciplined (Topic 10),when similar acts are perpetrated by family (Topic 6), triage by the criminal justice systembecomes more apparent. The only type of dowry-related abuse that is associated with higherlevels of conviction is Topic 9, i.e. when harassment has culminated in either a victim’s suicideor killing (equivalent to murder).

18

Figure 7: Top Topics (Female Complainants, N=38,828)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Female Complainant Top Topics

Expected Topic Proportions

(20) MISCELLANEOUS: sister, sisterinlaw, food, grain, ambedkar, cook(8) DEVELOPMENT: plot, widow, wife, possess, registri, estat

(16) CRIMINAL FORCE: woman, wife, daughterinlaw, domest, femal, bride(24) ALCOHOL: drink, alcohol, liquor, drunk, bottl, drug(4) UNLICENSED: fake, medic, board, educ, record, certif(5) DOWRY−A: parent, marriag, demand, dowri, famili, matrimoni

(2) POISONING: hospit, uncl, matern, medicin, doctor, eat(23) DOWRY−E: father, inlaw, money, child, lakh, marriag(9) DOWRY−C: women, daughter, harass, marri, cell, marriag(7) REAL ESTATE: land, amount, properti, compani, cheat, fal(1) VILLAGE PROBLEM: sikh, panchayat, sarpanch, farm, fire, field(13) DOWRY−D: motherinlaw, husband, fatherinlaw, inlaw, brotherinlaw, beat(17) RAPE: phone, forc, famili, mobil, room, rape

(3) CHEAT: note, gali, park, compani, jain, imit(18) RUNAWAY/SUICIDE: husband, shop, children, death, phone, wife(21) PHISHING: money, bank, rupe, atm, lakh, check(6) DOWRY−B: dowri, parent, demand, marriag, beat, lakh

(10) CHILD ABUSE/RAPE: girl, daughter, mother, children, marri, child(22) DRIVING ACCIDENT: hospit, driver, bus, drive, accid, hit(15) CHAIN−SNATCH: motor, cycl, motorcycl, boy, bike, snatch

(12) DOMESTIC VIOLENCE: kill, threaten, famili, abus, life, wife(14) FIGHTING: wife, hospit, fight, injuri, hit, blunt

(11) BURGLARY: stolen, gold, bag, railway, theft, good(19) MISSING PERSON: wife, home, imit, gone, wear, search

A)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Female Complainant Top FREX


(20) MISCELLANEOUS: ambedkar, sister, grain, lamp, anguri, pale(8) DEVELOPMENT: plot, widow, registri, allot, ganga, estat

(16) CRIMINAL FORCE: grandson, woman, daughterinlaw, sweeti, prathiya, manpratiya(24) ALCOHOL: bottl, alcohol, drink, drunk, liquor, intox(4) UNLICENSED: princip, examin, educ, board, patient, salochana(5) DOWRY−A: matrimoni, merciless, maltreat, humili, cruel, wed

(2) POISONING: medicin, poison, uncl, bathroom, muslim, health(23) DOWRY−E: father, expen, pregnant, abort, inlaw, jewelri(9) DOWRY−C: women, mahila, cell, counsel, divorc, harass(7) REAL ESTATE: sale, chequ, payment, forg, construct, loan(1) VILLAGE PROBLEM: sarpanch, field, farm, gram, pistol, crop(13) DOWRY−D: motherinlaw, fatherinlaw, brotherinlaw, inlaw, husbandinlaw, husband(17) RAPE: rape, video, scare, obscen, vulgar, hotel

(3) CHEAT: jain, note, bazar, park, gali, thakur(18) RUNAWAY/SUICIDE: shop, husband, hang, murder, death, dead(21) PHISHING: atm, bank, branch, check, withdraw, money(6) DOWRY−B: dowri, settl, demand, parent, taunt, donat

(10) CHILD ABUSE/RAPE: girl, pocso, seduc, mother, children, babi(22) DRIVING ACCIDENT: driver, scooti, accid, truck, drive, auto(15) CHAIN−SNATCH: motor, cycl, bike, motorcycl, snatch, neck

(12) DOMESTIC VIOLENCE: kill, threaten, dirti, protect, life, abus(14) FIGHTING: xray, blunt, injuri, stick, fight, hurt

(11) BURGLARY: stolen, theft, railway, bag, thief, steal(19) MISSING PERSON: search, feet, bodi, wear, salwar, tenant

B)

Figure 8: Conviction Rate and Correlation of Topics Associated with Women’s Cases

−0.10 −0.05 0.00 0.05

Female Complainant Case Conviction

Not Convicted ... Convicted

(1) VILLAGE PROBLEM(2) POISONING

(3) CHEAT(4) UNLICENSED

(5) DOWRY−A(6) DOWRY−B

(7) REAL ESTATE(8) DEVELOPMENT

(9) DOWRY−C(10) CHILD ABUSE/RAPE

(11) BURGLARY(12) DOMESTIC VIOLENCE

(13) DOWRY−D(14) FIGHTING

(15) CHAIN−SNATCH(16) CRIMINAL FORCE

(17) RAPE(18) RUNAWAY/SUICIDE

(19) MISSING PERSON(20) MISCELLANEOUS(21) PHISHING

(22) DRIVING ACCIDENT(23) DOWRY−E

(24) ALCOHOL

A) Correlation

(1) VILLAGE PROBLEM

(2) POISONING

(3) CHEAT

(4) UNLICENSED

(5) DOWRY−A

(6) DOWRY−B

(7) REAL ESTATE

(8) DEVELOPMENT

(9) DOWRY−C

(10) CHILD ABUSE/RAPE

(11) BURGLARY

(12) DOMESTIC VIOLENCE

(13) DOWRY−D

(14) FIGHTING

(15) CHAIN−SNATCH

(16) CRIMINAL FORCE

(17) RAPE

(18) RUNAWAY/SUICIDE

(19) MISSING PERSON

(20) MISCELLANEOUS

(21) PHISHING

(22) DRIVING ACCIDENT

(23) DOWRY−E

(24) ALCOHOL

B)

A) STM with binary indicator for conviction. B) Topic correlations and magnitude of regression coe�cients.19

Figure 9: Top Topics (Gendered Crime, N=20,869)

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Gendered Crime Top Topics


(3) DOWRY−PREGNANCY: child, pregnant, money, thousand, stomach, rupe

(5) UNLICENSED (SEX SELECTION): note, decoy, ultrasound, fake, rupe, ladi

(6) DOWRY−RAPE: mental, physic, tortur, forc, marriag, parent

(1) DOWRY−MENTAL: parent, husband, mother, money, marriag, father

(14) SEX SELECTION/ABORTION: hospit, medic, doctor, medicin, treatment, abort

(20) DOWRY−STARVATION: daughter, famili, marri, wife, home, money

(13) BLACKMAIL: photo, fal, jain, cheat, video, land

(12) TRAFFICKING: room, manag, driver, sikh, hotel, women

(19) DOWRY−DESERTION: marriag, demand, parent, dowri, matrimoni, home

(9) DOWRY DEATH: sister, die, marri, death, dowri, poison

(4) DOWRY−ECONOMIC: gold, lakh, father, rupe, cash, marriag

(7) KILLING GIRL CHILD: children, shop, woman, telephon, railway, plot

(9) ALCOHOL: abus, drink, dirti, alcohol, bus, liquor

(16) DOWRY−POST COUNSELING: dowri, marriag, beat, daughter, harass, cell

(2) DOWRY−PHYSICAL: dowri, demand, parent, marriag, father, beat

(15) DOWRY−EXTENDED: husband, motherinlaw, fatherinlaw, inlaw, father, brotherinlaw

(18) LEWD PHOTOS: threaten, kill, phone, life, famili, mobil

(17) RAPE: mother, forc, father, home, rape, room

(10) HURT/DOMESTIC VIOLENCE: wife, husband, hit, beat, kill, fight

(11) KIDNAPPING: girl, home, wife, marri, imit, daughter

A)

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Gendered Crime Top FREX


(3) DOWRY−PREGNANCY: child, stomach, deliveri, pregnant, babi, defend

(5) UNLICENSED (SEX SELECTION): decoy, note, currenc, ultrasound, seal, gender

(6) DOWRY−RAPE: physic, mental, unnatur, tortur, sexual, atroc

(1) DOWRY−MENTAL: salari, australia, loan, earn, shagun, honeymoon

(14) SEX SELECTION/ABORTION: hospit, medicin, doctor, medic, treatment, drug

(20) DOWRY−STARVATION: daughter, sell, famili, prayer, sad, panchayati

(13) BLACKMAIL: facebook, jain, photo, video, blackmail, defam

(12) TRAFFICKING: sikh, manag, prostitut, hotel, immor, driver

(19) DOWRY−DESERTION: matrimoni, merciless, maltreat, prohibit, lac, cruelti

(9) DOWRY DEATH: sister, poison, hang, death, die, dead

(4) DOWRY−ECONOMIC: gold, chain, silver, lakh, jeweleri, cupboard

(7) KILLING GIRL CHILD: children, railway, shop, auto, plot, market

(9) ALCOHOL: drunk, alcohol, dirti, drink, profan, liquor

(16) DOWRY−POST COUNSELING: cell, assault, dairi, mediat, harass, counsel

(2) DOWRY−PHYSICAL: settl, panchayat, demand, dowri, motorcycl, greedi

(15) DOWRY−EXTENDED: fatherinlaw, motherinlaw, husband, inlaw, brotherinlaw, sisterinlaw

(18) LEWD PHOTOS: threaten, protect, properti, phone, mobil, kill

(17) RAPE: pocso, rape, bike, mother, cri, advi

(10) HURT/DOMESTIC VIOLENCE: blunt, injuri, hit, stick, xray, attack

(11) KIDNAPPING: girl, seduc, search, feet, wear, niec

B)

Figure 10: Conviction Rate and Correlation of Topics Associated with Gendered Crime

−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15

Gendered Crime Conviction


(1) DOWRY−MENTAL

(2) DOWRY−PHYSICAL

(3) DOWRY−PREGNANCY

(4) DOWRY−ECONOMIC(5) UNLICENSED (SEX

SELECTION)(6) DOWRY−RAPE

(7) KILLING GIRL CHILD

(9) DOWRY DEATH

(9) ALCOHOL(10) HURT/DOMESTIC

VIOLENCE(11) KIDNAPPING

(12) TRAFFICKING

(13) BLACKMAIL(14) SEX SELECTION/

ABORTION(15) DOWRY−EXTENDED

(16) DOWRY−POSTCOUNSELING

(17) RAPE

(18) LEWD PHOTOS

(19) DOWRY−DESERTION

(20) DOWRY−STARVATION

A) Correlation

(1) DOWRY−MENTAL



(4) DOWRY−ECONOMIC

(5) UNLICENSED (SEX SELECTION)

(6) DOWRY−RAPE


(9) DOWRY DEATH

(9) ALCOHOL

(10) HURT/DOMESTIC VIOLENCE

(11) KIDNAPPING

(12) TRAFFICKING

(13) BLACKMAIL

(14) SEX SELECTION/ABORTION

(15) DOWRY−EXTENDED

(16) DOWRY−POST COUNSELING

(17) RAPE

(18) LEWD PHOTOS



B)

A) STM with binary indicator for conviction. B) Topic correlations and magnitude of regression coe�cients.

20

Text-as-Data: Topical Inverse Regression Matching

The methods used thus far cannot fully account inframarginality, i.e. case-types between com-plainant gender may be distinct. While an imagined experiment would be to randomly assignindividuals to crime, a realistic approach is to leverage the data to match cases on textual(and non-textual) dimensions.52 Then, after qualitatively ensuring that the technique correctlymatched cases (Grimmer and Stewart 2013), compare outcomes.

I use the entire corpus of registered FIRs for topical inverse regression matching (TIRM) in-troduced by Roberts, Stewart, and Nielsen (2020).53 Figure 11 is the first balance-test. The greybars—which highlight the di↵erence between female minus male complainants in the unmatcheddata—reveal stark di↵erences. Women are more likely to discuss dowry violence (Topic 24),whereas men cases of bootlegging or drunkenness (Topic 4). There are topics that a↵ect bothequally, e.g. Topic 13 (“cheating”). Figure 11 shows that while projection matching somewhatimproves balance, TIRM is more successful in minimizing di↵erences, similar to topic matchingonly (despite also balancing on propensity scores).

As a second test, I randomly select and present 12 matched testimonies in Table 7. This is ahard test for balance, and adds a qualitative component to the study. It is a hard test becausethe machine matched cases without any reference to Penal Codes; and still, after TIRM, wesee similarities in the Codes simply based on content. In fact, the machine is more successfulat categorizations than police o�cers.54 [[An outgrowth of this research is that administratorsmay now be able to use machine algorithms to ensure correct Penal Codes are being utilized,instead of relying on o�cers’ discretion, who may use memory or manuals to classify crimes,potentially “under-weighting” the seriousness of cases or making mistakes. An online tool, calledthe Indian-Penal-Code Classifier under development at Stanford University may (a) ensureaccurate charging decisions are applied, and (b) reduce the cognitive load for o�cers.]]

In rows 2, 3 and 6 of Table 7, we see generic cases registered by either a male or femalecomplainant [identifying information censored]. Row 2 depicts scooter theft, and row 3 a hit-and-run. In the cases of hit-and-run, the machine correctly matched cases not only based onthe fact that a crash occurred, but also that the complainants recognize the suspect. Still,despite being topically similar, there remain dissimilarities that the machine cannot (and shouldnot) perfectly match on; for instance, in row 7, the treated and control group involve confidence-tricksters, but the type of con is distinct. The treatment group in the dowry murder case involvesthe killing of a wife, but in the control condition a wife and her child have been found dead.

The language in rows 1,4, and 5 is rich, and allows for a brief interpretative exercise. In row1, we see (relatively less violent) dowry cases wherein victims have been extorted and beaten.Consider the way in which class is foregrounded. In the control group of row 1, the father—who is registering a case on behalf of his child—notes that his daughter is well-educated. Thecomplainant in the treatment group is registering a case against a lawyer and judge, whichsuggests not only that the perpetrators have influence, but also that they are well-educated;and yet, the suspects allegedly believe they are owed luxury vehicles in view of their “status.”Similarly, in row 4, the complainant in the treatment group notes that the in-laws (in likely an

52. I view matching as an additional test rather than a preferred analysis, since it rests on certain assumptions(Sekhon 2009). One also has to consider immpanipulable categories like gender as a “treatment” (Neil andWinship 2019), and potentially minimize the greater hurdles for disadvantaged groups for having come forward.53. If matched only on propensity scores, treated/non-treated cases may not be topically similar, e.g. extortion

might be lumped with bag-snatching because, say, they have equal probability of being registered by women.54. E.g., Table 7 (row 3), the o�cer did not attach Section 338 as may have been warranted based on testimony.

21

Figure 11: Balance Check 1

Note: Balancing estimated topics, and comparison of TIRM with full data set and other matching techniques.

arranged marriage) had been given material goods in accordance “with their status.”55 A puzzlearises as to how justice would vary across these contexts; would the system provide re-distributivejustice (financial compensation), especially for losses in the dowry and cheating cases?

Particularly striking in the treatment group of row 5 is that the perpetrators previouslywent to prison. This raises concerns about the type of punishment that led to the predictablekilling of a woman despite the glaring warnings. The reports shed light on criminal impunity,where individuals may be abducted from families in broad daylight, or killed in defiance ofthe authorities. Many victims are threatened with further violence if they dare to reveal theiroppression (e.g. row 5). Clearly, victims in these reports face challenges for breaking theirsilence, thereby not only hinting at the courage required to register, but also the number oflikely unreported cases. The example dowry murders (a type of o↵ense that happens to have thehighest probability of suspect acquittal, Appendix Table A22), add depth to preceding analysesby illustrating how real human beings are impacted.

In Table 8, Female remains significant. Columns 1, 4 show results with only TIRM matching,while Columns 2-3, 5-6 add controls. The results add confidence to the notion that complainantidentity specifically yields dissimilar responses to requests for help from the state.

55. More well-to-do individuals might demand luxury vehicles as dowry—which for a less upwardly mobile groupcould involve a motorcycle instead of car—in addition to the mandatory jewelry and household e↵ects.

22

Table 7: Balance Check 2 (Hard Test): Matched Cases and Penal Codes [Identifying Information Censored]

Treated First-Information-Report Matched Control First-Information-Report

...I, Anuja , daughter of late ...cruelty and violence which has completely left metraumatized and I am constantly living in fear for my own life...went to my parental house forPag Phera and returned back at night to my matrimonial house, in Ambala. In the eveningall the leftover jewelry (which I was wearing) was taken by my sister-in-law on pretext thatit is better to be kept safe with in-laws... After marriage I realised that my husband andmy in-laws were downright greedy as they started making more illegal demands for dowry...They used to persistently taunt and harass me for not bringing su�cient amount of cash and

gifts. My husband and his father also demanded that they have not been given acar according to their ‘status,’ and should be given a Mercedes or Pajero in dowry. Father-

in-law ...is one of the leading lawyers in the town...his elder son is judge posted asCivil Judge Cum JMIC. My husband...taunting that my parents had not spent money...Sincethen health has started deteriorating, my mother-in-law and father-in-law became angry andbeat me...IPC 323/406/498-A/506

Mr. Sir... is my daughter who has studied up to M.Sc., B.Ed. and whose

marriage we had with Maqsood from Delhi on . We had an engagement ceremonywhich cost Rs.3,00,000 / and gave the boy a gold chain, a gold ring and Rs. 1,51,000 / cash.They then demanded a Scorpio. When we expressed our inability to deliver the Scorpio

vehicle, he asked to meet after two days, and I met him on , he said that we alsowant Rs.5,00,000 / - cash with the Scorpio. On our refusal, he refused to bring a procession.But we had completed the wedding preparations. Some relatives had arrived. We had bookedconfectioners, tents, banquet hall... we already spent Rs.10,00,000 /. Then I, and my boy

, my brother-in-law , our neighbor met them. Sitting and talking, they refusedto marry without Rs. 5,00,000 /...The culprits refused to marry my girl after being engagedin the greed of dowry, and I was humiliated and my Rs.20,00,000/ has been lost. Therefore,I pray that legal action should be taken against him and FIR should be lodged...my goods,cash should be returned...Dowry Prohibition Act, 1961;4/3.

Dow

ryHarassm

ent

I am Ankita , daughter of Ashok Colony, , Punjab. I live in

Gurgaon. I work in company sector . On date at 10am I came to company for duty on my scooty. I parked my scooty in the parking lot, andI went to o�ce. When I came back at around 6:00 pm, my scooty could not be found. My

scooty color was Gray Model 2014, License Engine No . I do not know whotook it. Please register an FIR for my stolen scooty. IPC 379.

I am Kapil , son of from Nagina. I have a scooty number in white.

I left my scooty on in a plot near University. I was giving exam from 2-5 o’clock when Icame back, Scooty was not standing there... After that, I had gone to my hometown for someurgent work, and now I am submitting to police. I do not remember the Scooty’s engine orchassis number, all papers were in Scooty itself. Please register an FIR for my theft. Phone

No. . IPC 379.

Scooter

Theft

I am Vandana, wife of Caste Kamboj, resident of Village . I am 30. Yes-

terday, my boy had gone to for tutoring. I was going to pick him up at 6.00 pm on my

Activa, License No. . While taking U-turn in front of Gupta Petrol Pump, a motorcycledriver from Yamunanagar crashed into me. I fell on the road, and my left leg was seriously

injured... My brother noted the License number ...got admitted to Rama Kr-ishna Hospital Jagadhri for treatment. I am in full consciousness now. The motorcyclist ranaway, but I can recognize him if he comes in front of me... IPC 279/337/338.

I am Harsha , son of Pradeep, Caste ...I study in B.T.Class. On date at

around 9:40 PM, I was riding my cycle (License ) from Sector 13 to Mohan Nagar.

Behind me my friend Jagjit , son of , caste Jat, was sitting and I wasdriving. When we reached the telephone exchange, a car came from behind with great speedand carelessness, and hit me, from which I bounced o↵ bike. My head went into the electric

pole, and my friend fell on the road. The car no. was , a Honda I10...the driver’sname is Kartik...Strictest legal action should be taken against him. IPC 279/337.

Hit-an

d-R

un

I am . Late Shri married his girl Puja to , resident of on 21.04.2009. Accordingto his status, everything was given, but after a few months, the accused started harassingthe family and demanded a motorcycle. Her family members started beating her. In 2010,

he tried to kill her by pouring kerosene on her, but she escaped. For this, and his father

were caught and sent to jail, but later they started living together again. and

brought Puja to Delhi and started harassing her again, saying they want Rs. 1 lakh

from her family to start business. The father and mother-in-law Devi...started to behavemore wrongly till Puja was hanged. Shrimanji is requested to investigate this and please getjustice...information was received from Safdarjung Hospital that Puja has died...IPC 304-B.

I have come to complain that my sister Shilpa was wife of Sahil , resident of

Ground. She was married to Sahil 3 years ago at age 24. Today at 4 o’clock in theevening, we got the news that she and her son Rihansh, aged 2 years, have both been found

dead in the bathroom. We got a call from the hospital...Go to as soon as possible - we aresure that the death has been caused by dowry demands. We got a call from Shilpa on date

from Poonam, a resident of Delhi. Shilpa told her that she was being bullied fordowry - Rs. 10 lakh and a vehicle was being demanded...she was being beaten...Please fully

investigate that Shilpa’s husband Sahil has definitely killed Shilpa and her son Rihansh.We hope to take immediate action from you. IPC 304-B.

Dow

ry/Murder

...Mr. Sir...I am Bimala, wife of from Sonipat. This morning my daughter, whose

name is , was abducted by Sagar aka and family. She’s been taken away. I am

getting phone from No. . Sagar has threatened to kill her, and said that give 5 lakhrupees or else she will die. We do not know where she is, but the number is telling locationChandigarh. I pray to you that the police administration is involved and it is registered,

please do not delay it. Phone no. . IPC 365.

Mr. Sir...I am a resident of Road Punhana, Mewat, Khan. I am a man of peace

who abides by the law. On the date at around 1 o’clock at night, Hakku son of

of ... asked me to open the door...there were two or three others. The men camein and put a katta [knife] on my neck and started saying that “if you make noise, we will killyou and your family.” They took my girl Shabnam by force and cash of Rs. 32,000 / - andput my girl in a Scorpio. They said they will kill her if we go to police...when we went toHakku in the morning, he told us that he will not give her at any price...I request, Janab, totake legal action against the people and return my girl to a poor man. IPC 363/366-A.

Abduction

I am a Indira wife of Mr. from Colony, Hisar. I work as an assistant in

. In January 2016, I got a call from Sachin , JGS India Trading andMarketing PVT Ltd...a good scheme...where government employees have a big advantage...deposit two lakh twenty thousand rupees in the account of this company, you will get 8000 ru-pees per month for 12 months...He said that we have benefited thousands of people...Account

...IFSC Code ...Sachin threatened me...stole Rs 2,20,000... IPC 406/420

Mr. Sir...I am Gulzar son Mr. Sadhu , resident of , Ambala city. I haveknown the suspects for 15-20 years. They said they would help me file to go to Canada in2015......they told me that they work to send poor people abroad, and with down-paymentof Rs 1,50,000 - to 2,00,000. / - one can easily earn more abroad...told me that you shouldgive me all the documents...My shop is located in Grain Mandi...They took my money andnow saying they will kill me...retrieve my money which is Rs.6,50,000/... IPC 406/420.

Cheatin

g

23

Table 8: Impact of Complainant Gender on Conviction/Acquittal After Text-Matching

Convicted Acquitted

(1) (2) (3) (4) (5) (6)

Female �0.008⇤⇤⇤ �0.008⇤⇤⇤ �0.012⇤⇤⇤ 0.010⇤⇤⇤ 0.014⇤⇤⇤ 0.021⇤⇤⇤

(0.001) (0.001) (0.002) (0.002) (0.002) (0.003)

Constant 0.036⇤⇤⇤ 0.037⇤⇤⇤ 0.052⇤⇤⇤ 0.124⇤⇤⇤ 0.466⇤⇤⇤ 0.682⇤⇤⇤

(0.0003) (0.007) (0.012) (0.001) (0.012) (0.020)

Observations 337,056 309,008 179,335 337,056 309,008 179,335R2 0.0002 0.037 0.066 0.0001 0.093 0.135Controls N Y Y N Y YPS FE N Y Y N Y YMonth-Yr FE N Y Y N Y YJudge Rank N N Y N N Y

Note: Controls include a numeric variable for a crime’s distance from a station, investigator rank,as well as whether the registering station is urban. ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

Discussion

Political science has had limited purchase, even basic descriptive evidence, as to whether thestate treats minorities seeking justice di↵erently, especially in the Global South. This papercharts the full trajectory of complaints from the very second that citizens enter a police stationuntil a verdict is issued by court. Having created an original dataset of the universe of crimerecords from a major Indian state, and then combining it with judicial files, I show that womenface a more onerous process and unequal outcomes.

Unlike medicine, where individual doctors may prioritize patients that have the highest chanceof survival, triage in criminal justice I argue reflects a group or complaint-type’s relationship withmultiple administrators such that episodic discriminations cumulate. Specifically, I find thatwomen may be disadvantaged in terms of (1) delays in registering cases, (2) lower likelihood ofcases being sent to court, (3) delays in police investigations, (4) higher levels of case dismissals, (5)delays in court hearings and verdict issuance, (6) higher levels of acquittals and lower convictionsfor suspects. While VAW is less likely to be cancelled by law enforcement, both categories ofcrime registered by women are likely to be triaged in the judiciary. Text-matching providesadditional evidence on the impact of complainant identity on sanctions for suspects.

I contend that triage may occur when marginalized groups approach formal institutions forgrievance redressal; discrimination may not be restricted to a single-stage, but might exhibit ascomplaints transition or “squeeze” through the discretionary purview of connected o�cials, whomay utilize tactics at their disposal to (dis)favor complaints. In South Asia, these strategiescould include deflecting cases of sexual assault to counseling centers,56 while in the United Statesthey may comprise securing plea deals to lesser charges (Ransom 2021).

The findings illustrate the importance of being attentive to the workings of criminal justice

56. Mueller-Smith and T. Schnepel (2021) note that Texas may “divert” perpetrators of low-level (drug andproperty) o↵enses to community service instead of prison. In India, however, diversion is more often applied tocomplainants rather than perpetrators, and for gender-based violence (Jassal 2020).

24

institutions when complaints are being processed, long after initial registration. In post-colonialcontexts, for instance, the state may retain patchworks of red-tape through which triage cansustain. In India, I demonstrate that triage is most extreme at the judicial level where there arefew pressures from either “above” or “below.”57 And so, interventions at mitigating discrimina-tion in any one agency may be ine↵ective unless the manner in which other administrators caninfluence the same case’s trajectory is accounted for.

Furthermore, the study expands discussions of VAW—which largely focus on sexual assaultin (or after) conflict—by highlighting gradations of daily abuse. Dowry, for instance, is a caselikely to be triaged; yet, topic modeling reveals that such crimes are not “petty quarrels,” butmay involve heinous acts including marital rape. This dynamic is evocative of a double-bind:on the one hand, women may be faced with marital violence, and even (dowry) death, in ane↵ort to extract resources from their natal homes; yet, delaying or avoiding marriage comeswith its own costs (Carpena and Jensensius 2020; Corno, Hildebrandt, and Voena 2020). Whilestudies on VAW in India have focused on property rights (Panda and Agarwal 2005; Chin 2012),alcohol consumption (Luca, Owens, and Sharma 2015), and culture (Fernandez 1997), a questionemerges as to whether perpetrators are aware of the inability (or unwillingness) of the state toprovide punitive justice, and if this knowledge predisposes them to act.

Subsequent scholarship might systematically probe the motivations of administrators too.Are o�cials repressive, e.g. triaging cases because of supposed privilege that women exude bycoming forward (e.g. without male support)? Or, are they constrained by resource scarcity in anoverburdened system? Can cultural forces be at play, e.g. formal justice for women as a threatto male dominance? Do structural barriers have a bearing, e.g. limited access to lawyers, lackof autonomy to follow-up at station- and court-houses, and/or inability to pay bribes?

Aside from opening a research agenda, the data consist of a modern archive that may be usefulnot only in the present, but also to social scientists and historians a century from now. The casescapture—often in deeply poignant terms—the helplessness of victims, who invariably expressthat they have turned to formal institutions as a last resort, despite uncertainty in a system’sability to help when much seems lost or destroyed. Other questions worth exploring include: Howdoes gender interact with caste or ethnicity? Is north India representative of other parts of theSubcontinent? Can state policies that make the criminal justice system more demographicallyrepresentative (for women and minorities) a↵ect the base-line statistics outlined herein? Canfiner-grained measures of justice delivery (e.g. monetary compensation) be generated throughsurveys, especially since many of the complaints remain active?

While the notion that women face hardship in India may be unsurprising to some, others,including judges and policymakers, have vociferously argued that female complainants send mento prison for “petty” o↵enses, that the Penal Code is stacked in their favor, and that a burgeoning“men’s rights movement” should be supported in deterring women’s “legal terrorism” (Lodhia2014; Naishadham 2018). The findings cast doubt on many of these assumptions. Furthermore,the study aims to make a theoretical case for exploring the junctures at which linked institutionsare connected, and the varying discretionary authority of bureaucrats across those bodies, inorder to understand deeper, multi-layered patterns of discrimination. Exploring what criminaljustice triage entails, and where it manifests across institutional designs, may promote theory-building and target reform58 aimed at improving justice delivery and the quality of democracy.

57. A nationally representative Indian survey shows respondents blaming the judiciary (Appendix Figure A35).58. While 30% of gendered cases are dismissed by law enforcement in Haryana, newspapers report prosecutors

dropped 49% of sexual assault cases in New York City in 2019 (Ransom 2021).

25

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Online Appendix

Table of Contents1 Additional Data on Police Files ii

2 Triage: PROCESS vi

2.1 Investigation Duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

2.2 Duration in Court and Entire Criminal Justice System . . . . . . . . . . . . . . xii

3 Triage: OUTCOMES (Function of Court Docket) xviii

3.1 Cross-Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii

3.2 Court Dismissal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix

3.3 Conviction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xx

3.4 Acquittal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi

3.5 Ongoing Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxii

4 OUTCOMES (Function of All Registered Crime) xxiii

4.1 Cross-Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiii

4.2 Cancelled at Station/No Record in Court . . . . . . . . . . . . . . . . . . . . . xxiv

4.3 Conviction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxv

4.4 Acquittal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxvi

5 Additional Tests/Heterogenous E↵ects xxvii

6 Text-as-Data xxxviii

6.1 STM on Corpus of Crime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxxix

6.2 Female Complainants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xlix

6.3 Gendered Crime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . liv

i

1 Additional Data on Police Files

Figure A1: Top Indian Penal Code Sections [Male Complainants]

theft

rash driving

intimidation


burglary

offenses related to illicit liquor

acts done by several persons

causing hurt

trespassing by night

cheating

causing death by negligence

non−appearence in response to order


wrongful confinement/missing person

unlawful assembly


offenses related to arms/ammunition

wrongful restraint

mischief, causing damage

rioting, armed with weaponIPC 1860;148IPC 1860;427IPC 1860;341

ARMS ACT, 1959;25PUNJAB EXCISE ACT, 1914;14

IPC 1860;149IPC 1860;346

PUNJAB EXCISE ACT, 1914;1IPC 1860;174−AIPC 1860;304−A

IPC 1860;420IPC 1860;457IPC 1860;337

IPC 1860;34PUNJAB EXCISE ACT, 1914;68

IPC 1860;380IPC 1860;323IPC 1860;506IPC 1860;279IPC 1860;379

0.00 0.05 0.10 0.15 0.20 0.25Proportion

Indi

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enal

Cod

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C)

Top 20 Penal Codes [Male/Other Complainants]

Note: Top twenty Indian Penal Code sections attached to cases brought forward by men/other (N=379,362).The top substantive sections include theft, rash driving, burglary, and illicit liquor/bootlegging.

ii

Table A1: Description of Sections & Special Acts Considered Gendered or ‘Crimes Against Women’

Section DescriptionIPC 1860;294 obscene acts or songsIPC 1860;304-B dowry deathIPC 1860;313 causing miscarriage without woman’s consentIPC 1860;314 death caused by act done with intent to cause miscarriageIPC 1860;315 act done to prevent child from being born aliveIPC 1860;316 death of unborn childIPC 1860;318 concealment of birth by secret disposal of dead bodyIPC 1860;354 sexual harassmentIPC 1860;366 kidnapping, abducting a woman to compel her to marriageIPC 1860;366-A procuration of minor girlIPC 1860;366-B importation of girl from foreign countryIPC 1860;376 rapeIPC 1860;376-B intercourse by husband upon his wife during separationIPC 1860;376-C intercourse by person in authorityIPC 1860;376-D gang rapeIPC 1860;376-E punishment for repeat o↵endersIPC 1860;497 adulteryIPC 1860;498 enticing or taking away a married womanIPC 1860;498-A husband or relative subjecting woman to crueltyIPC 1860;509 word, gesture or act intended to insult modesty of a womanIPC 1860;306 abetment of suicideIPC 1860;317 exposure or abandonment of childIPC 1860;326-A acid throwingIPC 1860;326-B attempted acid throwingIPC 1860;363 kidnapping from guardianshipIPC 1860;377 “unnatural” sex (anal sex/sodomy)IPC 1860;494 marrying again during lifetime of husband or wifeIPC 1860;495 concealment of marriageIPC 1860;496 ceremony gone through without lawful marriageThe Child Marriage Restraint Act, 1929The Immoral Tra�c (Prevention) Act, 1956The Dowry Prohibition Act, 1961The Commission of Sati (Prevention) Act, 1987Protection of Women Against Domestic Violence Act, 2005The Information Technology Act, 2000The Indecent Representation of Women (Prohibition) Act, 1986Protection of Children from Sexual O↵enses Act, 2012

Note: Gendered crimes or ‘crimes against women’ listed in o�cial government documents. IPC refers toIndian Penal Code. All cases that have one or more of the foregoing Penal Codes appended are categorizedas VAW or gendered crime.

iii

Figure A2: Crimes With Female Complainants

Note: Map depicting locations of all Haryana police stations in which female complainants have had casesregistered. Dots vary in intensity depending on the total crimes registered for female complainants, 2015-2018(N=38,828).

iv

Figure A3: Top Cases Registered by Female Complainants and ‘Gendered’ Crime or VAW

12

34

56

78

910

1112

1314

151617181920IPC 1860;363/IPC 1860;366−A

IPC 1860;323/IPC 1860;34/IPC 1860;341/IPC 1860;506IPC 1860;379/IPC 1860;420

IPC 1860;420IPC 1860;356/IPC 1860;379

IPC 1860;323/IPC 1860;34/IPC 1860;452/IPC 1860;506IPC 1860;380/IPC 1860;454IPC 1860;406/IPC 1860;420

IPC 1860;380IPC 1860;379A

IPC 1860;174−AIPC 1860;323/IPC 1860;506

IPC 1860;323/IPC 1860;34/IPC 1860;506IPC 1860;323/IPC 1860;406/IPC 1860;498−A/IPC 1860;506

IPC 1860;365IPC 1860;323/IPC 1860;34/IPC 1860;406/IPC 1860;498−A/IPC 1860;506

IPC 1860;279/IPC 1860;337IPC 1860;380/IPC 1860;457

IPC 1860;346IPC 1860;379

0.000 0.025 0.050 0.075

Indi

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Cod

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C)

Top 20 Cases [Female Complainants]A)

12

34

56

789

1011

1213

1415

1617

181920IPC 1860;294

IPC 1860;294/THE PUNJAB EXCISE ACT, 1914, 1 OF 1914;68IPC 1860;323/IPC 1860;34/IPC 1860;498−A

IPC 1860;304−B/IPC 1860;34/IPC 1860;498−AIPC 1860;318

IPC 1860;34/IPC 1860;406/IPC 1860;498−AIPC 1860;304−B/IPC 1860;34

IPC 1860;323/IPC 1860;498−A/IPC 1860;506IPC 1860;323/IPC 1860;34/IPC 1860;406/IPC 1860;498−A

IPC 1860;294/IPC 1860;506IPC 1860;366

IPC 1860;323/IPC 1860;406/IPC 1860;498−AIPC 1860;406/IPC 1860;498−A/IPC 1860;506

IPC 1860;34/IPC 1860;406/IPC 1860;498−A/IPC 1860;506IPC 1860;406/IPC 1860;498−A

IPC 1860;323/IPC 1860;34/IPC 1860;498−A/IPC 1860;506IPC 1860;323/IPC 1860;406/IPC 1860;498−A/IPC 1860;506

IPC 1860;363/IPC 1860;366IPC 1860;323/IPC 1860;34/IPC 1860;406/IPC 1860;498−A/IPC 1860;506

IPC 1860;363/IPC 1860;366−A

0.000 0.025 0.050 0.075

Indi

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Cod

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C)

Top 20 Cases [Gendered]B)

Note: Top twenty cases with female complainants (N=38,828) and ‘gendered’ crime (N=20,869). Mostcases are combinations of multiple Penal Code sections. The first Penal Code in the list typically providesan indication of the kind of case, but not always.

v

2 Triage: PROCESS

a) Registration Duration

Figure A4: Di↵erence in Days by Complainant Gender

Note: Box plots depicting di↵erence in the date from when the complainant was able to register a casecompared to the date the victim told the o�cer the last incident related to the o↵ense began or ended. Eachdot is a registered crime report. Inter-quartile range is depicted, mean cannot be displayed. Women’s caseshave a longer lag in registration.

Figure A5: Di↵erence in Days by Crime Type

Note: Box plots depicting di↵erence in the date from when the complainant was able to register a casecompared to the date the victim told the o�cer that the o↵ense began or ended. Each dot is a registeredcrime report. Inter-quartile range depicted, mean cannot be displayed. Gendered cases have a longer lag inregistration.

vi

Figure A6: Delays in Case Registration for Particular Gendered Crimes

Note: Box plots depicting days waited by specific gendered crime, where each dot is a registered report(FIR). Dowry or Section 498-A (N=7,674); rape or Section 376 (N=1,094); female kidnapping or Section366 (N=3,754); “criminal force with intent to outrage a woman’s modesty” or Section 354 (N=3,804). Thedi↵erence in days since the last incident related to dowry occurred and when the report was registered isa median of 16 days (mean of 326). Panel B of A6 highlights that the median number of days since theabuse first began for dowry harassment/domestic violence is 712 days (mean of 1023.6) or 2.8 years, almostan order of magnitude greater than other gendered crimes.

vii

Figure A7: Di↵erence in Days by Select Penal Code Violations

0.00.00.00.00.00.00.00.00.00.0

1.00.0

1.01.0

0.00.0

1.01.01.01.01.01.01.0

5.00.00.0

1.00.0

2.01.01.01.01.01.01.0

6.0

0.30.30.70.7

2.12.52.8

33.43.64.14.24.34.64.6

5.65.9

66.5

77

10.410.6

11.612

14.815.3

17.418.118.3

21.621.9

26.928.6

34.6

theft (N=89,222)

rash driving (N=47,921)

theft/burglary (N=33,758)

trespassing (N=21,905)

cheating (N=19,957)

wrongful confinement/missing person (N=18,149)

culpable homicide (N=17,282)

Excise Act/offenses related to liquor (N=54,194)

unlawful assembly (N=16,462)

wrongful restraint (N=13,357)

rioting (N=13,010)

Arms Act/offenses related to weapons (N=14,033)

trespassing at night (N=10,590)

kidnapping and abducting (N=7,334)

causing hurt with weapons (N=6,805)

Electricity Act/offenses related to electricity theft (N=7,142)

Gambling/offenses related to gambling (N=16,039)

assault on public servant (N=3,862)kidnapping (N=3,634)attempt to murder (N=3,206)

murder (N=2,774)

Narcotics Act/offenses related to narcotics (N=7,562)

procuration of minor girl (N=2,042)

obscene acts/songs (N=1,595)

wrongful confinement (N=1,995)

robbery (N=1,796)

abetment of suicide (N=1,466)

public misconduct by drunkard (N=1,335)

Damage to Public Property Act (N=4,743)Cruelty to Animals Act/offenses related to cattle/cow slaughter (N=3,880)

word, gesture or act intended to insult modesty of a woman (N=821)

Regulation of Urban Areas Act (N=1,127)

dowry death (N=637)

stalking (N=616)sexual harassment (N=1,195)

Prevention of Atrocities Act/hate crime (N=1,245)

Protection of Children from Sexual Offenses Act (N=1,184)

narcoticipc_510

gamblingexcise

armsipc_304a

public_propertyanimalsipc_457ipc_353ipc_363ipc_307ipc_279

ipc_366aipc_380ipc_392ipc_324ipc_341ipc_379ipc_149ipc_148

pocsoipc_452

electricityipc_306ipc_302ipc_365

ipc_304bipc_346ipc_294

scstipc_509ipc_342

ipc_354aipc_354dipc_420

development

0 25 50 75 100Days

Indi

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enal

Cod

e/Sp

ecia

l Act

Registration Duration

Note: Box plots depicting di↵erence in the date from when the complainant was able to register a case compared to the date the victim told the o�cer the lastincident related to the o↵ense occurred (split by various violations of the Penal Code). Mean in red. Five of the top crimes with the longest lag are genderedcrimes.

viii

Figure A8: Di↵erence in Days (2) by Select Penal Code Violations

0.00.00.00.01.0

0.00.00.00.01.01.01.01.01.01.02.0

1.01.01.01.01.01.0

6.01.01.02.0

1.02.0

1.02.0

1.01.02.0

1.01.0

32.0

0.30.30.80.8

2.93.13.43.93.94.75.36.1

77.17.47.98.18.1

99.910

14.414.7

19.823.1

24.824.9

25.929.3

39.262.463.2

65.7102

129.2

theft (N=89,222)




cheating (N=19,957)


culpable homicide (N=17,282)Excise Act/offenses related to liquor (N=54,194)


wrongful restraint (N=13,357)rioting (N=13,010)







assault on public servant (N=3,862)

kidnapping (N=3,634)

attempt to murder (N=3,206)

murder (N=2,774)





robbery (N=1,796)



Damage to Public Property Act (N=4,743)

Cruelty to Animals Act/offenses related to cattle/cow slaughter (N=3,880)



dowry death (N=637)




narcoticipc_510

gamblingexcise

ipc_304aanimals

armspublic_property

ipc_353ipc_279ipc_457

ipc_366aipc_392ipc_380ipc_307ipc_379ipc_324ipc_363ipc_341ipc_148ipc_149ipc_452

electricityipc_302

pocsoipc_346ipc_306ipc_365ipc_294

scstipc_509ipc_342

ipc_354dipc_354aipc_304bipc_420

development

0 50 100 150Days

Indi

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enal

Cod

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ecia

l Act

Pre−Registration Duration

Note: Box plots depicting di↵erence in the date from when the complainant was able to register a case compared to the date the victim told the o�cer thatthe first o↵ense related to the crime began to occur (split by various violations of the Penal Code). Mean in red. Five of the top ten crimes are gendered.

ix

2.1 Investigation Duration

Figure A9: Days Until First Court Appearence

Note: FIRs that could be merged with judicial records. Figures represent the di↵erence in days from thefirst date that the case appeared in the court files to the date of original crime report registration. PanelA is split by female (N=22,648), and male/other complainants (N=229,156). Panel B is split by gendered(N=14,134), and nongendered crime (N=237,670).

Figure A10: Days Until First Court Appearance for Particular Gendered Crimes

Note: Figure reflects the di↵erence between the first hearing date in the judicial records with date of regis-tration for dowry (N=5,541), rape (N=804), female kidnapping (N=1,685), and “criminal force” (N=2,648).Female kidnapping cases take longer to investigate.

x

Figure A11: Days Until First Court Appearance for Select Penal Code Violations

23.725.728.725.733.735.7

28.742.7

30.768.7

36.744.750.747.756.7

35.746.755.752.7

45.764.759.754.764.7

83.752.7

65.782.794.7

80.7147.7

125.7136.7

221.7158.7

249.7324.7

42.648.348.854.863.263.364.776.882.383.383.883.885.888.390.292.492.596.8101102

102.4103.4104.8

106125

144153.4155.1

196.4199.4

233244.5

259.5267.7

305.6357.4

422.3

theft (N=38,888)


theft/burglary (N=13,578)trespassing (N=8,906)

cheating (N=10,346)






rioting (N=10,007)










murder (N=1,922)





robbery (N=1,107)

abetment of suicide (N=844)





Regulation of Urban Areas Act (N=586)

dowry death (N=542)

stalking (N=443)

sexual harassment (N=834)

Prevention of Atrocities Act/hate crime (N=814)

Protection of Children from Sexual Offenses Act (N=993)

narcoticipc_510

gamblingexcise

public_propertypocso

ipc_294ipc_354d

animalsipc_304bipc_354aipc_452ipc_324ipc_341

armsscst

ipc_353ipc_279ipc_342ipc_509ipc_148ipc_307

ipc_304aipc_149ipc_302ipc_306

ipc_366aipc_392ipc_420ipc_363ipc_379ipc_380ipc_457

developmentipc_365

electricityipc_346

0 500 1000 1500 2000Days

Indi

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enal

Cod

e/Sp

ecia

l Act

Days in the Criminal Justice System (FIR to First Court Appearence)

Note: Box plots for di↵erence in date from when the complainant was able to register a case compared to when it first entered the court (split by variousviolations of the Penal Code). Mean in red. Cases such as missing persons and kidnapping take longest to investigate, whereas cases such as public intoxicationand drug-use take the shortest.

xi

2.2 Duration in Court and Entire Criminal Justice System

Figure A12: Days in Court for Particular Gendered Crimes

Note: Duration of a case investigation: case registration with police until the date of the first hearing incourt. Dowry cases have the longest gap in terms of investigation (even though the suspect—unlike femalekidnapping—is generally known).

xii

Figure A13: Days in Court for Select Penal Code Violations

0.00.0102.5103.045.0129.075.0

195.083.0158.5176.5169.0

218.0178.5196.0

170.0182.0

156.0194.0

269.5308.0

245.5294.5303.0294.0298.0315.0321.0328.0

395.0362.0357.0

422.0492.0488.0

531.0511.0

86.5137.6

197.9203207

211.9213.9240.2244.1253.3262.9274.2281.9297.2304.2

313323

334.8353.4368.7377.3386.5386.8389.5393.2396.7411.9412.8417.3420.5441.6442.6473.2514.5519.1531.8539.7

theft (N=38,888)



cheating (N=10,346)






rioting (N=10,007)









attempt to murder (N=2,629)murder (N=1,922)





robbery (N=1,107)







dowry death (N=542)

stalking (N=443)sexual harassment (N=834)


Protection of Children from Sexual Offenses Act (N=993)theft (N=38,888)



cheating (N=10,346)






rioting (N=10,007)









attempt to murder (N=2,629)murder (N=1,922)





robbery (N=1,107)







dowry death (N=542)

stalking (N=443)sexual harassment (N=834)



ipc_346gamblingipc_363

electricityexcise

ipc_366aipc_365

pocsoipc_379ipc_510

scstipc_306

ipc_304bipc_307ipc_302ipc_457ipc_380animalsipc_420

ipc_354aipc_354dipc_392ipc_294ipc_509ipc_342ipc_324ipc_452ipc_148ipc_149

developmentarms

ipc_341ipc_353narcoticipc_279

public_propertyipc_304a

0 1000 2000 3000 4000Days

Indi

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Cod

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l Act

Days in Court (First to Most Recent Hearing)

Note: Box plots for di↵erence in the date from when the case entered court and its most recent hearing.Mean in red.

xiii

Figure A14: Days in the Entire Criminal Justice System for Select Penal Code Violations

56.7120.7

213.7361.7

432.7412.2440.2445.2

411.7456.7

440.7415.7

450.7466.7455.2

524.2485.7493.7519.7536.7531.7549.7

530.7548.7561.2563.7

600.7606.7624.7619.7643.7638.7631.7637.7657.7658.7

787.7

192.9269.9

313.3415.8

473.6489.7489.8492.6

506510.6515.1515.1515.6531.3536.6563.9564.5568.5591.6595.9597.7601.1601.5607.6624.8626.6628.9643.1657.3658.9670.6671.8678.9681.8699.4716.2

752.2

theft (N=38,888)




cheating (N=10,346)






rioting (N=10,007)










murder (N=1,922)


procuration of minor girl (N=1,143)obscene acts/songs (N=1,595)


robbery (N=1,107)







dowry death (N=542)

stalking (N=443)




gamblingexcise

ipc_510pocso

ipc_366aipc_294ipc_363

scstanimals

ipc_304bipc_354aipc_379

ipc_354dipc_509ipc_346ipc_306ipc_324ipc_452ipc_341ipc_342ipc_380

electricityipc_457ipc_365ipc_149ipc_148

public_propertyipc_279narcotic

armsipc_304aipc_353ipc_307ipc_420ipc_302ipc_392

development

0 1000 2000 3000Days

Indi

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enal

Cod

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ecia

l Act

Days in the Criminal Justice System (FIR to Recent Date in Judicary)

Note: Figure presents box plots for di↵erence in the date from when the complainant was able to registera case compared to most recent hearing date in the judiciary, i.e. including on-going cases (split by variousviolations of the Penal Code). Mean in red.

xiv

Figure A15: Days Until a Final Decision is Reached for Select Penal Code Violations

45.772.7

150.7200.2

339.7327.7

366.7317.2

371.7346.7

409.2421.2

376.7431.7

385.7422.7

386.7460.7

415.7439.2434.7442.7450.7453.7452.7461.7

515.2565.2

532.7555.7559.7573.2

590.7595.2

641.7636.7634.7

144.8192.7

244.2370.9

392.3413.1426.4429.3430.4

449.6450.3454.7

470.7470.7474.5479.4479.5

500.3504.6

514519

526.2532.1536.7

539547.9

577.3581.1592.6604.8614.7639

640.8645.4

668.2677.6681.8

theft (N=28,187)



cheating (N=5,330)wrongful confinement/missing person (N=3,981)





rioting (N=4,978)










murder (N=1,447)Narcotics Act/offenses related to narcotics (N=2,973)

procuration of minor girl (N=893)

obscene acts/songs (N=873)

wrongful confinement (N=719)

robbery (N=752)


public misconduct by drunkard (N=773)





dowry death (N=437)

stalking (N=212)




gamblingexcise

ipc_510animals

pocsoipc_294

ipc_354adevelopment

scstipc_379

ipc_354dipc_366aipc_509ipc_363ipc_452

ipc_304bipc_324ipc_306ipc_341ipc_342ipc_420ipc_346ipc_149ipc_148ipc_380ipc_457ipc_365

public_propertyelectricity


armsipc_307narcoticipc_302

ipc_304a

0 500 1000 1500 2000Days

Indi

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enal

Cod

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ecia

l Act

Days in the Criminal Justice System (FIR to Last Court Decision)

Note: Figure presents box plots for di↵erence in the date from when the complainant was able to register a case compared to the date a decision was made,i.e. excluding on-going cases (split by various violations of the Penal Code). Mean in red.

xv

Figure A16: Days in the Criminal Justice System

Note: FIRs that could be merged with judicial records. Figures represent the di↵erence in days from themost recent date of the case in the court files from the date of original crime report registration with lawenforcement. Panel A is split by female (N=22,648), and male/other complainants (N=229,156). Panel Bis split by gendered (N=14,134), and nongendered crime (N=237,670). Women’s cases and genderedcrime spend longer in the criminal justice system.

Figure A17: Days Until a Decision Was Reached by a Judge

Note: FIRs that ultimately had a decision reached by a judge. Figures represent the di↵erence in days fromthe date a decision was reached from the date of original crime report registration with law enforcement.Panel A is split by female (N=12,572), and male/other complainants (N=142,585). Panel B is split bygendered (N=8,008), and nongendered crime (N=147,149). Women’s cases and gendered crime takelonger to reach a verdict.

xvi

Figure A18: Days in the Criminal Justice System for Particular Gendered Crimes

Note: Panel A reflects all cases with court files, and reflects the di↵erence between the most recent hearingdate in the judicial records with date of original crime registration for dowry (N=5,541), rape (N=804),female kidnapping (N=1,685), and criminal force (N=2,648). Panel B reflects only those cases that resultedin a decision (excluding on-going cases) for dowry (N=2,680), rape (N=608), female kidnapping (N=1,367),and criminal force (N=1,339). Panel A reveals that gendered cases, especially dowry/domestic violence, aremore likely to have a later date associated with the case in the judiciary with a mean of 644 days in thecriminal justice system. Of the cases that did in fact reach a decision (including acquittal or dismissal),dowry/domestic violence cases wait, on average, 550 days before a judge issues a final ruling.

xvii

3 Triage: OUTCOMES (Function of Court Docket)

3.1 Cross-Tab

Figure A19: Crime Report Statuses in the Judicial System [Conditional on Having a Court Record]

1

21.2

9.2

51.7

3.86.4 6.6

05

1015202530354045505560

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

Status of Crime Reports [Dowry, Court Docket]A)

11.7

39.4

8

24.4

2.26.5 7.8

05

1015202530354045505560

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

Status of Crime Reports [Rape, Court Docket]B)

5

27.6

16.118.9

10.9 10.1 11.5

05

1015202530354045505560

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

Status of Crime Reports [Female Kidnapping, Court Docket]C)

3.4

27.5

6.4

49.4

2.45 5.8

05

1015202530354045505560

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

Status of Crime Reports [Criminal Force, Court Docket]D)

Note: Breakdown of case statuses for crime reports that have a record in court/could be merged withjudicial files, broken down by specific gendered crimes. Panel A reflects dowry cases or those that invokedSection 498-A (N=5,541); Panel B highlights rape cases or those that invoked Section 376 (N=804); PanelC represents female kidnapping or Section 366 (N=1,685); Panel D reflects criminal force with intent tooutrage a woman’s modesty (N=2,648). Gendered cases have low rates of conviction, with the highest in thecategory of rape (by a non-spouse).

xviii

3.2 Court Dismissal

Figure A20: Dismissal Rates of Crime Reports Based on Specific Penal Code Violations [Court Docket]

5.2

1.3

55.1

16.4

29.9

1.5

0.7

5.4

3.7

5.65.5

4.8

20.3

4.8

9.4

0.6

5.6

14.6

11

21.7

3.2

12.2

3.4

7.5

8.2

14.5

0.3

1

4.4

6.15.6

8.7

4.7

6.7

11.8

7.7

theft (N=38,888)



cheating (N=10,346)






rioting (N=10,007)Arms Act/offenses related to weapons (N=11,493)









murder (N=1,922)





robbery (N=1,107)





word, gesture or act intended to insult modesty of a woman (N=559)Regulation of Urban Areas Act (N=586)

dowry death (N=542)

stalking (N=443)




ipc_510gambling

excisepublic_property

ipc_279ipc_304a

narcoticipc_294ipc_341animals

ipc_354dipc_324ipc_452ipc_380ipc_457ipc_379ipc_149

armsipc_148ipc_353

developmentipc_509

ipc_354aipc_342

pocsoipc_392

ipc_304belectricity

ipc_307scst

ipc_366aipc_306ipc_363ipc_420ipc_365ipc_302ipc_346

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Percent

Indi

an P

enal

Cod

e/Sp

ecia

l Act

Dismissal Percentages (Court Docket)

Note: FIRs that could be merged with judicial records. Figure reveals dismissal rates by cases subset byparticular Penal Code violations.

xix

3.3 Conviction

Figure A21: Conviction Rates of Crime Reports Based on Specific Penal Code Violations [Court Docket]

7.3

4.2

8.89.4

1.2

2.2

0.6

40

1.6

2.2

1.6

4

1.9

3.2

2.5

2.2

72.1

3.2

5.4

6.9

14.512.3

5.8

8.3

2.8

6

2.6

33.8

2.8

36.6

1.3

0.3

10.1

2.5

3.4

4.4

16.8

theft (N=38,888)



cheating (N=10,346)






rioting (N=10,007)














robbery (N=1,107)







dowry death (N=542)

stalking (N=443)




developmentipc_304a

ipc_420ipc_509ipc_149ipc_148ipc_452ipc_346ipc_341

electricityipc_354d

ipc_324ipc_306

public_propertyipc_342ipc_365ipc_353

ipc_354aarms

ipc_279scst

ipc_363ipc_366a

ipc_392ipc_307ipc_379ipc_294ipc_380ipc_457

ipc_304bnarcoticipc_302

pocsoipc_510animals

excisegambling

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Percent

Indi

an P

enal

Cod

e/Sp

ecia

l Act

Conviction Percentages (Court Docket)

Note: FIRs that could be merged with judicial records. Figure reveals conviction rates by cases subset byparticular Penal Code violations. The figure reveals heterogeneity in the types of gendered cases that resultin higher rates of conviction. Cases perceived as ‘heinous’ that involve death (e.g. dowry death) or child rape(Protection of Children from Sexual O↵enses Act) have higher convictions than cases seen as ‘non-heinous’,e.g. sexual harassment or ‘insulting the modesty of women.’

xx

3.4 Acquittal

Figure A22: Acquittal Rates of Crime Reports Based on Specific Penal Code Violations [Court Docket]

13.1

23.3

13.513.9

4.65.5

21.7

13.1

26.8

33.2

27.7

21.8

31.9

12.1

34.4

26.2

8.1

20.1

27.1

22.3

19.315

32.2

29.7

26.726.3

25.9

22.1

24.4

8.5

20.2

2.4

43

28

30.1

29

38.8

theft (N=38,888)








rioting (N=10,007)













wrongful confinement (N=1,350)robbery (N=1,107)







dowry death (N=542)

stalking (N=443)




developmentipc_420ipc_346

gamblinganimalsipc_365

exciseipc_379ipc_380ipc_457narcoticipc_302ipc_353ipc_509

ipc_304aarms


public_propertyipc_306

electricityipc_392ipc_342ipc_149ipc_363ipc_148

ipc_354dscst

ipc_294ipc_354a

ipc_452ipc_366a

ipc_341ipc_324

pocsoipc_304b

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Percent

Indi

an P

enal

Cod

e/Sp

ecia

l Act

Acquittal Percentages (Court Docket)

Note: FIRs that could be merged with judicial records. Figure reveals acquittal rates by cases subset byparticular Penal Code violations. Gendered crime have the highest acquittals, whether they are percievedas ‘heinous’ (e.g. dowry death) or not (sexual harassment).

xxi

3.5 Ongoing Cases

Figure A23: On-Going Rates of Crime Reports Based on Specific Penal Code Violations [Court Docket]

27.5

61.9

36.234.3

48.5

8.8

66.4

20.4

51.9

48.8

50.3

52.2

49.5

25.8

46.3

10.911.9

56.8

20.2

31.2

24.7

57.7

21.9

45.3

46.7

32.1

29.7

36.8

65.7

33

60.1

71.5

19.4

52.1

46.3

33

22

theft (N=38,888)



cheating (N=10,346)






rioting (N=10,007)





Electricity Act/offenses related to electricity theft (N=2,470)Gambling/offenses related to gambling (N=13,795)




murder (N=1,922)





robbery (N=1,107)







dowry death (N=542)

stalking (N=443)




ipc_346electricitygamblingipc_304b

ipc_363excise

ipc_366apocso

ipc_302ipc_365ipc_379ipc_306ipc_307ipc_392animals

scstipc_457ipc_380ipc_510ipc_294

ipc_354aipc_324ipc_342ipc_420ipc_341ipc_452ipc_148ipc_149

ipc_354darms

ipc_353narcoticipc_509ipc_279

public_propertyipc_304a

development

10 20 30 40 50 60 70 80 90 100Percent

Indi

an P

enal

Cod

e/Sp

ecia

l Act

Percentages of Cases Ongoing (Court Docket)

Note: FIRs that could be merged with judicial records. Figure reveals rates of cases ongoing subset by particular Penal Code violations.

xxii

4 OUTCOMES (Function of All Registered Crime)

4.1 Cross-Tab

Figure A24: Crime Reports Statuses in the Judicial System [Specific Gendered Crime]

0.7

15.2

6.6

37

2.7 4.6 4.7

28.4

05

1015202530354045505560

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

no_record

Status of Crime Reports [Dowry]A)

8.6

29

5.9

17.9

1.64.8 5.8

26.5

05

1015202530354045505560

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

no_record

Status of Crime Reports [Rape]B)

2.2

12.47.2 8.5

4.9 4.5 5.1

55.1

05

1015202530354045505560

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

no_record

Status of Crime Reports [Female Kidnapping]C)

2.4

19.2

4.5

34.4

1.7 3.5 4

30.4

05

1015202530354045505560

convictedacquitted

dismissed_cancelled

ongoing

untraced_abated

allowed

disposed_other

no_record

Status of Crime Reports [Criminal Force]D)

Note: FIRs that could be merged with judicial records. Panel A reflects dowry cases or those that invokedSection 498-A (N=7,674); Panel B highlights rape cases or those that invoked Section 376 (N=1,094); PanelC represents female kidnapping or Section 366 (N=3,754); Panel D reflects criminal force with intent tooutrage a woman’s modesty or Section 354 (N=3,804). 30% of gendered cases, except for female kidnapping,are cancelled at the stage of law enforcement.

xxiii

4.2 Cancelled at Station/No Record in Court

Figure A25: No Record Rates of Crime Reports Based on Specific Penal Code Violations

56.4

26.5

59.859.3

48.2

76

30.330.5

24.5

21.823.1

18.1

21

68.3

20.1

65.4

14

24.1

54.5

18

30.7

7.1

44

20.1

24.8

34.3

42.4

8.3

16.7

14

31.9

48

14.9

28.130.2

34.6

16.1

theft (N=89,222)



cheating (N=19,957)


culpable homicide (N=17,282)Excise Act/offenses related to liquor (N=54,194)


wrongful restraint (N=13,357)rioting (N=13,010)










murder (N=2,774)





robbery (N=1,796)







dowry death (N=637)




narcoticipc_510animals

gamblingipc_304b

pocsopublic_property

ipc_307arms

ipc_294ipc_324ipc_452ipc_341ipc_148ipc_353ipc_149ipc_342ipc_279

ipc_354dipc_354aipc_304a

exciseipc_302ipc_509ipc_392

scstipc_306

ipc_366adevelopment

ipc_420ipc_363ipc_379ipc_457ipc_380

electricityipc_365ipc_346

10 20 30 40 50 60 70 80 90 100Percent

Indi

an P

enal

Cod

e/Sp

ecia

l Act

Percentages of Cases from All FIRs That Do Not Have a Court Record

Note: Figure reveals rates of cases in the FIR dataset that could not be merged with court records/had no record in the judiciary, subset by particular PenalCode violations.

xxiv

4.3 Conviction

Figure A26: Conviction Rates of Crime Reports Based on Specific Penal Code Violations

3.23.1

3.53.8

0.60.50.4

27.8

1.2

1.7

1.2

3.3

1.5

1

2

0.8

62

2.52.4

5.6

10.111.5

3.2

6.6

2.1

3.9

1.5

31

2.3

31.5

0.9

0.2

8.6

1.8

2.3

2.9

14.1

theft (N=89,222)rash driving (N=47,921)


cheating (N=19,957)wrongful confinement/missing person (N=18,149)culpable homicide (N=17,282)




rioting (N=13,010)







assault on public servant (N=3,862)kidnapping (N=3,634)






robbery (N=1,796)







dowry death (N=637)

stalking (N=616)

sexual harassment (N=1,195)



developmentipc_304aipc_346ipc_420

electricityipc_509ipc_365ipc_149ipc_148ipc_306ipc_452ipc_341

ipc_354dipc_324ipc_342

public_propertyipc_354aipc_363ipc_353

scstipc_279ipc_379

ipc_366aarms

ipc_380ipc_457ipc_392ipc_307ipc_294

ipc_304bipc_302narcotic

pocsoexcise

ipc_510animals

gambling

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Percent

Indi

an P

enal

Cod

e/Sp

ecia

l Act

Conviction Percentages (All FIRs)

Note: Figure reveals conviction rates by cases subset by particular Penal Code violations, as a function ofall registered crime.

xxv

4.4 Acquittal

Figure A27: Acquittal Rates of Crime Reports Based on Specific Penal Code Violations

5.7

17.1

5.45.6

2.41.3

15.2

9.1

20.2

25.9

21.3

17.9

25.2

3.8

27.5

9.1

7

15.2

12.4

18.3

13.4

13.9

18

23.7

20.1

17.3

14.9

20.320.3

7.3

13.8

1.2

36.6

20.1

21

19

32.5

theft (N=89,222)








rioting (N=13,010)










murder (N=2,774)





robbery (N=1,796)


public misconduct by drunkard (N=1,335)Damage to Public Property Act (N=4,743)




dowry death (N=637)

stalking (N=616)

sexual harassment (N=1,195)



developmentipc_346ipc_420ipc_365ipc_380ipc_457ipc_379

gamblinganimals

electricityexcise

ipc_363ipc_302ipc_509narcoticipc_306

ipc_304aipc_353ipc_279ipc_392

armsipc_366aipc_307

scstipc_342

ipc_354dipc_149

public_propertyipc_510

ipc_354aipc_148ipc_294ipc_452ipc_341ipc_324

pocsoipc_304b

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100Percent

Indi

an P

enal

Cod

e/Sp

ecia

l Act

Acquittal Percentages (All FIRs)

Note: Figure reveals acquittal rates by cases subset by particular Penal Code violations, as a function of allregistered crime. Dowry death and child sexual assault have the highest rate of acquittals.

xxvi

5 Additional Tests/Heterogenous E↵ects

Table A2

E↵ects Controlling for Primary Penal Code

(1:Registration) (2:Cancellation) (3:Investigation) (4:Court Dismissal) (5: Court Duration) (6: Acquittal) (7: Conviction)

Female 31.529⇤⇤⇤ 0.022⇤⇤⇤ 0.622 0.004⇤ 6.934⇤⇤ 0.001 �0.010⇤⇤⇤

(5.666) (0.005) (2.438) (0.002) (2.999) (0.005) (0.002)

Constant �15.810⇤⇤ 0.450⇤⇤⇤ 135.789 �0.098⇤⇤⇤ 369.940⇤⇤⇤ 0.253⇤⇤ 0.485⇤⇤

(7.935) (0.093) (87.218) (0.035) (130.477) (0.128) (0.189)

Controls Y Y Y Y Y Y YPS FE Y Y Y Y Y N YMonth-Yr FE Y Y Y Y Y Y YTop/Primary IPC FE Y Y Y Y Y Y Y

Note: ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

xxvii

Table A3

Registration Duration

(1) (2) (3) (4)

Female 8.532⇤⇤⇤ 6.515⇤⇤

(2.294) (2.552)

Dowry 301.380⇤⇤⇤ 311.979⇤⇤⇤ 248.411⇤⇤⇤ 252.896⇤⇤⇤

(35.408) (36.706) (38.566) (41.591)

Rape 5.707 �2.533 3.304 �2.934(6.477) (6.253) (9.196) (10.010)

Fem Kidnapping �19.833⇤⇤⇤ �24.250⇤⇤⇤ �19.777⇤⇤⇤ �23.954⇤⇤⇤

(2.498) (2.480) (2.409) (2.405)

Criminal Force �9.600⇤⇤ �10.981⇤⇤ �10.051⇤ �9.656⇤

(4.200) (4.715) (5.314) (5.577)

Female:Dowry 61.501⇤⇤ 71.882⇤⇤⇤

(24.675) (23.058)

Female:Rape �3.205 �4.353(10.863) (11.494)

Female:Fem Kidnapping �5.088⇤ �4.745⇤

(2.972) (2.428)

Female:Criminal Force �6.809 �7.846(6.369) (7.702)

Constant 23.683⇤⇤⇤ �0.724 23.075⇤⇤⇤ �1.290(2.326) (3.038) (2.334) (3.045)

Observations 381,836 360,022 381,836 360,022R2 0.025 0.038 0.026 0.038

Controls N Y N YPS FE N Y N YMonth-Yr FE N Y N Y

Note: Controls include a numeric variable for how far the crime took place from a station, investigatingo�cer rank, as well as whether the registering station is urban. PS stands for police station. Standard errorsare clustered by district for all models. Dowry has longest lag between incident and registration, while femalekidnapping is registered sooner. ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

xxviii

Table A4

No Record in Court

(1) (2) (3) (4)

Female 0.055⇤⇤⇤ 0.051⇤⇤⇤

(0.010) (0.009)

Dowry �0.109⇤⇤⇤ �0.083⇤⇤⇤ �0.104⇤⇤⇤ �0.090⇤⇤⇤

(0.017) (0.014) (0.022) (0.021)

Rape �0.125⇤⇤⇤ �0.125⇤⇤⇤ �0.117⇤⇤⇤ �0.099⇤⇤⇤

(0.015) (0.022) (0.027) (0.037)

Fem Kidnapping 0.154⇤⇤⇤ 0.161⇤⇤⇤ 0.163⇤⇤⇤ 0.175⇤⇤⇤

(0.028) (0.026) (0.028) (0.028)

Criminal Force �0.079⇤⇤⇤ �0.083⇤⇤⇤ �0.103⇤⇤⇤ �0.117⇤⇤⇤

(0.013) (0.008) (0.024) (0.018)

Female:Dowry �0.054⇤⇤⇤ �0.033⇤⇤

(0.015) (0.016)

Female:Rape �0.047 �0.066(0.047) (0.052)

Female:Fem Kidnapping �0.074⇤⇤⇤ �0.098⇤⇤⇤

(0.028) (0.031)

Female:Criminal force �0.008 0.008(0.026) (0.026)

Constant 0.400⇤⇤⇤ 0.395⇤⇤⇤ 0.395⇤⇤⇤ 0.390⇤⇤⇤

(0.018) (0.013) (0.018) (0.013)

Observations 418,190 382,265 418,190 382,265R2 0.002 0.113 0.003 0.114

Controls N Y N YPS FE N Y N YMonth-Yr FE N Y N Y

Note: Controls include a numeric variable for how far the crime took place from a station, investigatingo�cer rank, as well as whether the registering station is urban. PS stands for police station. Standard errorsare clustered by district for all models. Most gendered crime types are likely to be sent to court, except femalekidnapping which is significantly likely to be cancelled by law enforcement. ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

xxix

Table A5

Investigation Duration Dismissal

(1) (2) (3) (4) (5) (6) (7) (8)

Female 14.214⇤⇤⇤ 16.011⇤⇤⇤ 0.024⇤⇤⇤ 0.011⇤⇤⇤

(4.195) (2.780) (0.003) (0.003)

Dowry �4.337 �0.105 �7.392 �2.633 0.047⇤⇤⇤ 0.037⇤⇤⇤ 0.039⇤⇤⇤ 0.007(6.999) (6.940) (10.275) (9.465) (0.009) (0.010) (0.010) (0.011)

Rape �42.884⇤⇤⇤ �44.607⇤⇤⇤ �38.742⇤⇤ �30.830⇤ 0.022⇤⇤⇤ �0.083⇤⇤⇤ 0.040⇤⇤ �0.074⇤⇤⇤

(10.722) (9.212) (17.220) (16.439) (0.007) (0.011) (0.018) (0.020)

Fem Kidnapping 85.726⇤⇤⇤ 85.916⇤⇤⇤ 87.767⇤⇤⇤ 90.886⇤⇤⇤ 0.117⇤⇤⇤ 0.043⇤⇤ 0.121⇤⇤⇤ 0.047⇤⇤

(17.214) (13.304) (19.419) (15.965) (0.013) (0.018) (0.014) (0.020)

Criminal Force �36.472⇤⇤⇤ �33.762⇤⇤⇤ �31.151⇤⇤ �26.876⇤⇤ 0.013⇤⇤⇤ 0.002 0.023⇤⇤⇤ 0.004(7.210) (7.055) (13.342) (13.406) (0.005) (0.004) (0.008) (0.008)

Female:Dowry �8.074 �9.884 �0.010 0.030⇤⇤⇤

(8.264) (8.506) (0.008) (0.009)

Female:Rape �14.728 �27.627⇤⇤ �0.039⇤ �0.019(10.799) (10.887) (0.020) (0.022)

Female:Fem Kidnapping �18.056 �31.868 �0.036⇤⇤⇤ �0.024(19.970) (21.005) (0.013) (0.018)

Female:Criminal Force �18.076 �21.552 �0.031⇤⇤⇤ �0.011(12.501) (13.865) (0.009) (0.010)

Constant 127.997⇤⇤⇤ 117.278⇤⇤⇤ 127.036⇤⇤⇤ 116.103⇤⇤⇤ 0.043⇤⇤⇤ 0.007 0.042⇤⇤⇤ 0.006(5.830) (16.823) (5.946) (16.729) (0.003) (0.008) (0.003) (0.008)

Observations 248,920 227,315 248,920 227,315 251,804 229,954 251,804 229,954R2 0.002 0.071 0.002 0.071 0.003 0.085 0.004 0.085

Controls N Y N Y N Y N YPS FE N Y N Y N Y N YMonth-Yr FE N Y N Y N Y N YNote: Controls include a numeric variable for how far the crime took place from a station, investigatingo�cer rank, judge rank, as well as whether the registering station is urban. PS stands for police station.Standard errors are clustered by district for all models. Rape (by a non-spouse) is investigated quickest, whilefemale kidnapping takes longest. There are rules in place that mandate that IPC 376 cases be investigatedwithin 2-3 months. ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

xxx

Table A6

Duration in Court Acquittal Conviction

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Female 29.068⇤⇤ 34.631⇤⇤⇤ 0.061⇤⇤⇤ 0.056⇤⇤⇤ �0.121⇤⇤⇤ �0.103⇤⇤⇤

(12.129) (10.227) (0.008) (0.006) (0.011) (0.010)

Dowry 112.164⇤⇤⇤ 73.441⇤⇤⇤ 83.907⇤⇤⇤ 52.500⇤⇤⇤ 0.032⇤⇤ 0.012 0.072⇤⇤⇤ 0.043⇤⇤⇤ �0.154⇤⇤⇤ �0.125⇤⇤⇤ �0.151⇤⇤⇤ �0.108⇤⇤⇤

(13.098) (11.907) (19.044) (18.829) (0.016) (0.011) (0.017) (0.014) (0.013) (0.016) (0.015) (0.022)

Rape �55.407⇤⇤⇤ �55.553⇤⇤⇤ �82.536⇤⇤⇤ �60.725⇤⇤⇤ 0.204⇤⇤⇤ 0.141⇤⇤⇤ 0.173⇤⇤⇤ 0.112⇤⇤⇤ �0.019 0.063⇤⇤⇤ �0.038 0.058(15.271) (16.090) (18.203) (22.357) (0.026) (0.024) (0.042) (0.038) (0.023) (0.018) (0.033) (0.036)

Fem Kidnapping �140.816⇤⇤⇤ �97.035⇤⇤⇤ �146.490⇤⇤⇤ �102.858⇤⇤⇤ 0.098⇤⇤⇤ 0.087⇤⇤⇤ 0.095⇤⇤⇤ 0.080⇤⇤⇤ �0.121⇤⇤⇤ �0.062⇤⇤⇤ �0.127⇤⇤⇤ �0.064⇤⇤⇤

(15.037) (16.850) (16.174) (20.384) (0.023) (0.017) (0.024) (0.019) (0.013) (0.015) (0.014) (0.016)

Criminal Force 42.188⇤⇤⇤ 33.545⇤⇤⇤ 43.012⇤⇤⇤ 28.422⇤⇤⇤ 0.094⇤⇤⇤ 0.083⇤⇤⇤ 0.089⇤⇤⇤ 0.075⇤⇤⇤ �0.117⇤⇤⇤ �0.081⇤⇤⇤ �0.127⇤⇤⇤ �0.084⇤⇤⇤

(12.649) (8.678) (13.319) (9.590) (0.023) (0.018) (0.023) (0.017) (0.014) (0.011) (0.019) (0.017)

Female:Dowry 11.691 �1.691 �0.104⇤⇤⇤ �0.088⇤⇤⇤ 0.099⇤⇤⇤ 0.064⇤⇤⇤

(15.842) (16.686) (0.011) (0.012) (0.013) (0.016)

Female:Rape 13.122 �18.552 �0.004 �0.003 0.108⇤⇤⇤ 0.077⇤⇤

(17.300) (19.244) (0.033) (0.034) (0.029) (0.037)

Female:Fem Kidnapping 10.542 10.175 �0.017 0.006 0.097⇤⇤⇤ 0.062⇤⇤⇤

(16.527) (29.938) (0.020) (0.028) (0.015) (0.017)

Female:Criminal Force �23.479⇤ �18.987⇤ �0.038⇤ �0.031⇤ 0.105⇤⇤⇤ 0.081⇤⇤⇤

(12.356) (11.307) (0.022) (0.018) (0.016) (0.017)

Constant 334.373⇤⇤⇤ 549.745⇤⇤⇤ 332.429⇤⇤⇤ 547.205⇤⇤⇤ 0.171⇤⇤⇤ 0.397⇤⇤⇤ 0.167⇤⇤⇤ 0.392⇤⇤⇤ 0.173⇤⇤⇤ 0.262⇤⇤⇤ 0.181⇤⇤⇤ 0.270⇤⇤⇤

(12.250) (32.884) (12.730) (32.827) (0.017) (0.018) (0.017) (0.018) (0.014) (0.028) (0.015) (0.028)

Observations 250,287 228,542 250,287 228,542 251,804 229,954 251,804 229,954 251,804 229,954 251,804 229,954R2 0.003 0.201 0.004 0.201 0.002 0.123 0.004 0.125 0.006 0.098 0.012 0.102

Controls N Y N Y N Y N Y N Y N YPS FE N Y N Y N Y N Y N Y N YMonth-Yr FE N Y N Y N Y N Y N Y N YNote: Controls include a numeric variable for how far the crime took place from a station, investigating o�cer rank, judge rank, as well as whether theregistering station is urban. PS stands for police station. Standard errors are clustered by district for all models. Dowry spends longest stalled in court.Generally, all gendered sub-types are significantly more likely to have a suspect acquitted rather than convicted. ⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

xxxi

Figure A28: Average Marginal E↵ects (Table A3)

29.2

353.98

22.8

275.7

100

200

300

Non−Dowry Dowry

Day

s

Registration DurationA)

33.49

26.2126.1423.2

0

20

40

60

Non−Rape Rape

Day

s

Registration DurationB)

33.74

5.04

26.35

2.39

−10

0

10

20

30

Non−Fem Kidnapping Fem Kidnapping

Day

s

Registration DurationC)

33.62

16.12

26.21

16.55

0

10

20

30

Non−Criminal Force Criminal Force

Day

s

Registration DurationD)

a aMale Female

Note: Based on column 4, in Table A3.

xxxii

Figure A29: Average Marginal E↵ects (Table A4)

0.4

0.31

0.45

0.32

0.30

0.35

0.40

0.45

Non−Dowry Dowry

Perc

ent

Cancelled at PSA)

0.39

0.29

0.44

0.28

0.25

0.30

0.35

0.40

0.45

Non−Rape Rape

Perc

ent

Cancelled at PSB)

0.39

0.57

0.44

0.52

0.40

0.45

0.50

0.55


Perc

ent

Cancelled at PSC)

0.4

0.28

0.44

0.34

0.25

0.30

0.35

0.40

0.45


Perc

ent

Cancelled at PSD)

a aMale Female

Note: Based on column 4, Table A4 .

Figure A30: Average Marginal E↵ects (Table A5, Investigation Duration)

126.93124.3

142.44

129.93

120

130

140

Non−Dowry Dowry

Day

s

Investigation DurationA)

126.97

96.14

142.37

83.92

75

100

125

Non−Rape Rape

Day

s

Investigation DurationB)

126.29

217.17

141.82

200.83

150

180

210


Day

s

Investigation DurationC)

127.14

100.27

142.68

94.25100

120

140


Day

s

Investigation DurationD)

a aMale Female

Note: Based on column 4, Table A5 .

xxxiii

Figure A31: Average Marginal E↵ects (Table A5, Court Dismissal)

0.04

0.050.05

0.09

0.04

0.06

0.08

Non−Dowry Dowry

Perc

ent

Court DismissalA)

0.04

−0.03

0.05

−0.04

−0.06

−0.03

0.00

0.03

0.06

Non−Rape Rape

Perc

ent

Court DismissalB)

0.04

0.09

0.05

0.08

0.04

0.06

0.08

0.10


Perc

ent

Court DismissalC)

0.04

0.05

0.05

0.05

0.03

0.04

0.05

0.06


Perc

ent

Court DismissalD)

a aMale Female

Note: Based on column 8, Table A5.

xxxiv

Figure A32: Average Marginal E↵ects (Table A6, Duration in Court)

334.46

386.96

368.91

419.72

350

375

400

425

Non−Dowry Dowry

Day

s

Duration in CourtA)

335.64

274.92

370.12

290.84

250

300

350

Non−Rape Rape

Day

s

Duration in CourtB)

336.14

233.28

370.5

277.82

250

300

350


Day

s

Duration in CourtC)

335.19

363.61

369.8

379.23

340

360

380


Day

s

Duration in CourtD)

a aMale Female


Figure A33: Average Marginal E↵ects (Table A6, Acquittal)

0.17

0.21

0.23

0.180.18

0.20

0.22

Non−Dowry Dowry

Perc

ent

AcquittalA)

0.17

0.28

0.23

0.33

0.20

0.25

0.30

0.35

Non−Rape Rape

Perc

ent

AcquittalB)

0.17

0.25

0.23

0.31

0.20

0.25

0.30

0.35


Perc

ent

AcquittalC)

0.17

0.25

0.23

0.27

0.20

0.25


Perc

ent

AcquittalD)

a aMale Female


xxxv

Figure A34: Average Marginal E↵ects (Table A6, Conviction)

0.17

0.070.07

0.03

0.05

0.10

0.15

Non−Dowry Dowry

Perc

ent

ConvictionA)

0.17

0.23

0.07

0.21

0.10

0.15

0.20

0.25

Non−Rape Rape

Perc

ent

ConvictionB)

0.17

0.11

0.07 0.07

0.05

0.10

0.15


Perc

ent

ConvictionC)

0.17

0.09

0.07 0.07

0.050

0.075

0.100

0.125

0.150

0.175


Perc

ent

ConvictionD)

a aMale FemaleNote: Based on column 12, Table A6. Women are unlikely to have a suspect convicted in allcategories compared to men.

xxxvi

Figure A35: CSDS-Common Cause Survey: Which Institution is to Blame?

9.9

24.2

46.3

2.3 1.7

15.5

0

5

10

15

20

25

30

35

40

45

50

55

1: Police 2: Court 3: Both 4: Neither 5: Others 8: Don't know

We know the process of justice often gets delayed. Which institution is responsible for this delay?

Note: Distribution of responses based on the Center for the Study of Developing Societies (CSDS)-CommonCause Survey 2017 (N=15,548).

xxxvii

6 Text-as-Data

Figure A36: Word Count by Complainant Gender and Crime Type

Note: Figure presents box plots for the word count by complainant gender and crime type, where eachdot is a registered report (FIR). Y-axis is scaled to a maximum of 3000 words, for ease of visualization.1st quartile, median, and third quartile included. Mean in red. Women’s cases and gendered crimeare significantly longer in terms of the first-person testimonies/contain more detail about theo↵ense.

xxxviii

6.1 STM on Corpus of Crime

Topics that the machine generated can be identified with highest probability words (Panel A) aswell as FREX or frequent and exclusive words to specific topics (Panel B). The top five most com-mon crime types include “public intoxication” and “bootlegging” (Topic 19), “burglary” (Topic16), “auto theft” (Topic 22/23), and “kidnapping” (Topic 27). As indicated in the FREX words,kidnapping cases usually involve women or girls as victims.59 Self-explanatory topics include“fighting” (Topic 17),“gambling” (Topic 28), “phone theft” (Topic 26), “driving misdemeanor”(Topic 14), “robbery” (Topic 29), drugs or “narcotics” (Topic 31), and “phishing” (Topic 4).

Machine generated topics that may require additional context include the following: “elec-tricity theft” refers to the illegal connection of wires to power grids (Topic 30). Topic 13 or“injury” can include cases in which a complainant has been hurt from hit-and-runs to construc-tion accidents. Topic 15 refers to absconding from law enforcement or ‘jumping bail.’ Topic6 represents cases related to the sand or mining mafia that smuggle or steal natural resources.Topic 24 refers to cases involving fraud and deception, typically financial.60 Topic 18 or “arms”refer to cases involving unlicensed weapons manufacture and smuggling. The machine coded allcases involving the word ‘Muslims’ in Topic 25 or “minorities.” Topic 1 or “unlicensed” refers tocases involving unlicensed doctors, fraudulent certificates, and fake medical exams. Topic 5 or“cattle” is illustrative of illegal smuggling of cows as well as cattle slaughter. Topic 9 or ‘railway’refers to crimes committed in trains or railway platforms, while “accident/attack” or Topic 10involves someone being attacked, including with a weapon. Topic 20 or “property” and Topic 32or “real estate” refer to cases involving property and real estate disputes, respectively. Relatedly,Topic 12 or “development” represent illegal land purchases, including by corporations.

Appendix Figure A39 highlight the top topics that are disproportionately associated withfemale complainants. These include “dowry-A” and “dowry-B” (Topics 3 and 8), as well as“lewd behavior” (Topic 11).61 ‘Lewd behavior’ encapsulates cases from blackmailing womenin releasing compromising photos62 to harassing women in public places. The case most likelyassociated with female complainants are dowry cases, wherein a victim complains to the policeabout the physical, mental, and emotional abuse her husband and in-laws perpetrate, usually inorder to extort money from her natal home. Appendix Figures A41-A44 highlight word cloudsassociated with each of the topics.

Figure A38 highlight the likelihood of conviction based on the topic metadata, as well asthe correlation between topics. In Panel B we see that the machine correctly estimated therelationship between topics where, for instance, Topic 10 and Topic 13 (‘accident’ and ‘injury’)are related to each other, as are “cattle” and “minorities,” suggesting that Muslims are dis-proportionately victimized for alleged o↵enses related to cow slaughter or smuggling. Similarly,“dowry-A,” “dowry-B” and “lewd behavior” are all highly correlated in terms of the languageused in the crime report. Cases involving public intoxication, fake currency, and gambling havehigher rates of conviction. Nevertheless, the plot suggests that topics related to gendered crime,as well as those brought by female complainants, are unlikely to lead to formal punishment.

59. Kidnapping may also be closely connected with cases classified as missing persons.60. These cases generally invoke Indian Penal Code Section 420.61. Other cases associated with female complainants include phishing (Topic 4), fighting (Topic 17), kidnapping

(Topic 27), robbery (Topic 29), and missing persons (Topic 7).62. Cases associated with the Information Technology Act.

xxxix

Figure A37: Top Topics (All Crime)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

All Crime Top Topics


(32) REAL ESTATE: land, sale, fake, sold, sell, cheat(3) DOWRY−A: famili, threaten, life, fal, justic, pressur(20) PROPERTY: properti, sikh, gurudwara, dealer, bullet, sale(23) AUTO THEFT−B: scooti, scooter, landlord, bolero, rent, scene(12) DEVELOPMENT: plot, estat, develop, allot, construct, build(11) LEWD BEHAVIOR: photo, panchayat, sarpanch, forc, fake, gram(8) DOWRY−B: dowri, marriag, parent, husband, father, motherinlaw(24) CHEAT: compani, amount, cheat, loan, fraud, payment(9) RAILWAY: railway, train, bazar, passeng, control, telephon(25) MINORITIES: muslim, anim, gujjar, chandni, buffalo, slum(18) ARMS: pistol, possess, bodi, butt, illegitim, control(29) CHAIN−SNATCH: boy, caught, hotel, pump, snatch, petrol(5) CATTLE: trial, shock, cow, manag, telephon, milk(1) UNLICENSED: shop, market, grain, medic, medicin, fake(31) DRUGS: bag, possess, seal, polythen, weigh, substanc(26) PHONE THEFT: mobil, phone, compani, guard, prison, secur(13) INJURY: hospit, father, uncl, die, telephon, dead(4) PHISHING: money, bank, rupe, atm, lakh, thousand(7) MISSING PERSON: wife, husband, mother, daughter, woman, brotherinlaw(6) RESOURCE MAFIA: driver, tractor, auto, vehicl, drive, load(15) FUGITIVE: room, note, morn, execut, imit, child(2) CURRENCY: rupe, note, slip, bogus, bet, gambl(14) DRIVING MISDEMEANOR: bus, chamar, telephon, brahmin, nishad, potter(10) ACCIDENT/ATTACK: injuri, hospit, blunt, xray, treatment, hit(30) ELECTRICTY THEFT: farm, night, fire, field, theft, stolen

(17) FIGHTING: kill, fight, beat, threaten, abus, noi(27) KIDNAPPING: girl, home, gone, wear, sister, feet(21) DRIVING ACCIDENT: driver, hospit, truck, accid, drive, hit

(28) GAMBLING: imit, note, help, park, templ, rajput(16) BURGLARY: stolen, night, good, gold, theft, lock

(22) AUTO THEFT−A: motorcycl, motor, cycl, bike, park, stolen(19) ALCOHOL: liquor, bottl, drink, contract, mouth, alcohol

A)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

All Crime Top FREX


(32) REAL ESTATE: land, patwari, registri, tadadi, khatauni, crop(3) DOWRY−A: matrimoni, suicid, sen, intimid, threat, maltreat(20) PROPERTY: taxat, moga, tibbi, dadu, vial, kulan(23) AUTO THEFT−B: scooti, scooter, tejveer, bhishma, sighana, jaroth(12) DEVELOPMENT: estat, allot, quota, fighter, plot, warwidow(11) LEWD BEHAVIOR: sarpanch, journalist, sarmpach, video, bpl, khadak(8) DOWRY−B: dowri, inlaw, motherinlaw, fatherinlaw, parent, marriag(24) CHEAT: chequ, refund, visa, loan, financ, fund(9) RAILWAY: railway, mithun, marsala, artilleri, luharu, bazar(25) MINORITIES: buffalo, islam, sadiq, shamshad, arshad, razak(18) ARMS: pistol, butt, magazin, blueprint, cartridg, revolv(29) CHAIN−SNATCH: petrol, pump, boy, helmet, hotel, muana(5) CATTLE: cow, ashram, calv, erickshaw, mirchpur, swami(1) UNLICENSED: shop, shopkeep, grain, decoy, market, bomb(31) DRUGS: polythen, smack, heroin, opium, bag, substanc(26) PHONE THEFT: prison, bhaudasi, mobil, htc, phone, iphon(13) INJURY: kamaldeep, siyaram, somi, uncl, hyderabadi, charit(4) PHISHING: atm, jain, bank, withdrawn, debit, withdraw(7) MISSING PERSON: wife, rubi, salochana, domest, rina, dks(6) RESOURCE MAFIA: tractor, auto, jeep, overweight, crasher, tractortrolley(15) FUGITIVE: ink, publish, subodh, gera, akhil, bailabl(2) CURRENCY: bogus, bet, bookmak, currenc, gambl, specul(14) DRIVING MISDEMEANOR: bus, roadway, depot, bhatla, dhakal, mandola(10) ACCIDENT/ATTACK: xray, injuri, blunt, tsm, achina, fractur(30) ELECTRICTY THEFT: wire, copper, coil, lamin, kahnor, moonak

(17) FIGHTING: attack, stick, rescu, fight, sword, stab(27) KIDNAPPING: salwar, niec, girl, blond, shoe, seduc(21) DRIVING ACCIDENT: accid, ambul, truck, threewheel, crash, colli

(28) GAMBLING: templ, asavata, manpur, srnote, thakur, hgate(16) BURGLARY: lock, luggag, cupboard, invert, cylind, silver

(22) AUTO THEFT−A: dlx, palla, seplend, haejeh, ezn, suplend(19) ALCOHOL: liquor, bottl, beer, smash, wine, gut

B)

Note: Top topics for entire corpus (N=418,190).

xl

Figure A38: Conviction Rate and Correlation of Topics Across Corpus

−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15

All Crime Conviction


(1) UNLICENSED(2) CURRENCY

(3) DOWRY−A(4) PHISHING

(5) CATTLE(6) RESOURCE MAFIA

(7) MISSING PERSON(8) DOWRY−B

(9) RAILWAY(10) ACCIDENT/ATTACK

(11) LEWD BEHAVIOR(12) DEVELOPMENT

(13) INJURY(14) DRIVING MISDEMEANOR

(15) FUGITIVE(16) BURGLARY

(17) FIGHTING(18) ARMS

(19) ALCOHOL(20) PROPERTY

(21) DRIVING ACCIDENT(22) AUTO THEFT−A

(23) AUTO THEFT−B(24) CHEAT(25) MINORITIES

(26) PHONE THEFT(27) KIDNAPPING

(28) GAMBLING(29) CHAIN−SNATCH

(30) ELECTRICTY THEFT(31) DRUGS

(32) REAL ESTATE

A) Correlation

(1) UNLICENSED

(2) CURRENCY

(3) DOWRY−A

(4) PHISHING

(5) CATTLE

(6) RESOURCE MAFIA

(7) MISSING PERSON

(8) DOWRY−B

(9) RAILWAY

(10) ACCIDENT/ATTACK

(11) LEWD BEHAVIOR

(12) DEVELOPMENT

(13) INJURY

(14) DRIVING MISDEMEANOR

(15) FUGITIVE

(16) BURGLARY

(17) FIGHTING

(18) ARMS

(19) ALCOHOL

(20) PROPERTY

(21) DRIVING ACCIDENT

(22) AUTO THEFT−A

(23) AUTO THEFT−B

(24) CHEAT

(25) MINORITIES

(26) PHONE THEFT

(27) KIDNAPPING

(28) GAMBLING

(29) CHAIN−SNATCH

(30) ELECTRICTY THEFT

(31) DRUGS

(32) REAL ESTATE

B)

Note: Left: STM estimation of all cases with binary indicator for whether the topic resulted in conviction.Right: Network of correlated topics where node color indicates magnitude of regression coe�cients (reddernodes indicate positive and bluer negative). Edge width is proportional to the strength of correlation. Fakecurrency and public intoxication have better conviction rates, conditional on registration.

xli

Table A7: Top Word Stems by Topic With FREX (All Crime)

Topic Top Words(1) UNLICENSED Highest Prob: shop, market, grain, medic, medicin, fake, food

FREX: shop, shopkeep, grain, decoy, market, bomb, mphw(2) CURRENCY Highest Prob: rupe, note, slip, bogus, bet, gambl, amount

FREX: bogus, bet, bookmak, currenc, gambl, specul, baj(3) DOWRY-A Highest Prob: famili, threaten, life, fal, justic, pressur, suicid

FREX: matrimoni, suicid, sen, intimid, threat, maltreat, compromi(4) PHISHING Highest Prob: money, bank, rupe, atm, lakh, thousand, check

FREX: atm, jain, bank, withdrawn, debit, withdraw, dairi(5) CATTLE Highest Prob: trial, shock, cow, manag, telephon, milk, munshi

FREX: cow, ashram, calv, erickshaw, mirchpur, swami, morni(6) RESOURCE MAFIA Highest Prob: driver, tractor, auto, vehicl, drive, load, rajasthan

FREX: tractor, auto, jeep, overweight, crasher, tractortrolley, amw(7) MISSING PERSON Highest Prob: wife, husband, mother, daughter, woman, brotherinlaw, children

FREX: wife, rubi, salochana, domest, rina, dks, hemlata(8) DOWRY-B Highest Prob: dowri, marriag, parent, husband, father, motherinlaw, demand

FREX: dowri, inlaw, motherinlaw, fatherinlaw, parent, marriag, taunt(9) RAILWAY Highest Prob: railway, train, bazar, passeng, control, telephon, ticket

FREX: railway, mithun, marsala, artilleri, luharu, bazar, srm(10) ACCIDENT/ATTACK Highest Prob: injuri, hospit, blunt, xray, treatment, hit, hurt

FREX: xray, injuri, blunt, tsm, achina, fractur, natija(11) LEWD BEHAVIOR Highest Prob: photo, panchayat, sarpanch, forc, fake, gram, villag

FREX: sarpanch, journalist, sarmpach, video, bpl, khadak, grafer(12) DEVELOPMENT Highest Prob: plot, estat, develop, allot, construct, build, municip

FREX: estat, allot, quota, fighter, plot, warwidow, elig(13) INJURY Highest Prob: hospit, father, uncl, die, telephon, dead, death

FREX: kamaldeep, siyaram, somi, uncl, hyderabadi, charit, poison(14) DRIVING MISDEMEANOR Highest Prob: bus, chamar, telephon, brahmin, nishad, potter, mob

FREX: bus, roadway, depot, bhatla, dhakal, mandola, surewala(15) FUGITIVE Highest Prob: room, note, morn, execut, imit, child, destroy

FREX: ink, publish, subodh, gera, akhil, bailabl, bhog(16) BURGLARY Highest Prob: stolen, night, good, gold, theft, lock, morn

FREX: lock, luggag, cupboard, invert, cylind, silver, laptop(17) FIGHTING Highest Prob: kill, fight, beat, threaten, abus, noi, stick

FREX: attack, stick, rescu, fight, sword, stab, noi(18) ARMS Highest Prob: pistol, possess, bodi, butt, illegitim, control, iron

FREX: pistol, butt, magazin, blueprint, cartridg, revolv, wood(19) ALCOHOL Highest Prob: liquor, bottl, drink, contract, mouth, alcohol, control

FREX: liquor, bottl, beer, smash, wine, gut, patio(20) PROPERTY Highest Prob: properti, sikh, gurudwara, dealer, bullet, sale, prevent

FREX: taxat, moga, tibbi, dadu, vial, kulan, sukhjit(21) DRIVING ACCIDENT Highest Prob: driver, hospit, truck, accid, drive, hit, treatment

FREX: accid, ambul, truck, threewheel, crash, colli, oxid(22) AUTO THEFT-A Highest Prob: motorcycl, motor, cycl, bike, park, stolen, theft

FREX: dlx, palla, seplend, haejeh, ezn, suplend, mblhaameh(23) AUTO THEFT-B Highest Prob: scooti, scooter, landlord, bolero, rent, scene, sheep

FREX: scooti, scooter, tejveer, bhishma, sighana, jaroth, jupit(24) CHEAT Highest Prob: compani, amount, cheat, loan, fraud, payment, paid

FREX: chequ, refund, visa, loan, financ, fund, infrastructur(25) MINORITIES Highest Prob: muslim, anim, gujjar, chandni, bu↵alo, slum, cruelti

FREX: bu↵alo, islam, sadiq, shamshad, arshad, razak, sahabuddin(26) PHONE THEFT Highest Prob: mobil, phone, compani, guard, prison, secur, manag

FREX: prison, bhaudasi, mobil, htc, phone, iphon, emei(27) KIDNAPPING Highest Prob: girl, home, gone, wear, sister, feet, children

FREX: salwar, niec, girl, blond, shoe, seduc, feet(28) GAMBLING Highest Prob: imit, note, help, park, templ, rajput, gali

FREX: templ, asavata, manpur, srnote, thakur, hgate, banchari(29) CHAIN-SNATCH Highest Prob: boy, caught, hotel, pump, snatch, petrol, forc

FREX: petrol, pump, boy, helmet, hotel, muana, mimarpur(30) ELECTRICTY THEFT Highest Prob: farm, night, fire, field, theft, stolen, electr

FREX: wire, copper, coil, lamin, kahnor, moonak, sirsal(31) DRUGS Highest Prob: bag, possess, seal, polythen, weigh, substanc, search

FREX: polythen, smack, heroin, opium, bag, substanc, narcot(32) REAL ESTATE Highest Prob: land, sale, fake, sold, sell, cheat, registri

FREX: land, patwari, registri, tadadi, khatauni, crop, acr

xlii

Figure A39: All Crime I

−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15

Male/Female Complainants

Male ... Female


(3) DOWRY−A(4) PHISHING(5) CATTLE

(6) RESOURCE MAFIA(7) MISSING PERSON

(8) DOWRY−B(9) RAILWAY

(10) ACCIDENT/ATTACK(11) LEWD BEHAVIOR

(12) DEVELOPMENT(13) INJURY

(14) DRIVING MISDEMEANOR(15) FUGITIVE(16) BURGLARY




(23) AUTO THEFT−B(24) CHEAT

(25) MINORITIES(26) PHONE THEFT

(27) KIDNAPPING(28) GAMBLING

(29) CHAIN−SNATCH(30) ELECTRICTY THEFT

(31) DRUGS(32) REAL ESTATE

A)

−0.05 0.00 0.05

Rural/Urban

Rural ... Urban














(27) KIDNAPPING(28) GAMBLING

(29) CHAIN−SNATCH(30) ELECTRICTY THEFT

(31) DRUGS(32) REAL ESTATE

B)

Note: Missing persons, dowry, fighting, kidnapping are likely to have a female complainant. In Panel B,economic o↵enses (e.g. phishing, development and real estate disputes) are more likely to be urban.

xliii

Figure A40: All Crime II

−0.04 −0.02 0.00 0.02 0.04

Acquittal

Unacquitted ... Acquitted



(5) CATTLE(6) RESOURCE MAFIA(7) MISSING PERSON

(8) DOWRY−B(9) RAILWAY

(10) ACCIDENT/ATTACK(11) LEWD BEHAVIOR

(12) DEVELOPMENT(13) INJURY

(14) DRIVING MISDEMEANOR(15) FUGITIVE

(16) BURGLARY(17) FIGHTING

(18) ARMS(19) ALCOHOL

(20) PROPERTY(21) DRIVING ACCIDENT

(22) AUTO THEFT−A(23) AUTO THEFT−B

(24) CHEAT(25) MINORITIES

(26) PHONE THEFT(27) KIDNAPPING

(28) GAMBLING(29) CHAIN−SNATCH


(32) REAL ESTATE

C)

−0.05 0.00 0.05

Dismissal

Not Dismissed ... Dismissed














(27) KIDNAPPING(28) GAMBLING(29) CHAIN−SNATCH


(32) REAL ESTATE

D)

Note:

xliv

Figure A41: Word Cloud for 1-8 Top Topics (All Crime)

goodtabl

clothinject

bombmedic

pregnantbarber

furnitur

healthgarment

stool

marketsugar

fakegrainshopkeep

shopultrasoundbaniya

vegetghee

food

medicin

buy

dispensari

cigarett

decoy

chickendrugcosmet

jewel sabzi

coldraidcereal

confectioneri

billrecord

abort

1

customchildthrew

wall

sum

bogusmango

raid

gambldeductpossess

hair

cardboard

money

passersbi

wornhandov

leav

currenc

amount

gestur

control

search

fake

manag

pocket

bookmak

rupehundr

help di

stanc

true

notecash

newspap

captur

bet

slipbook

loss

2

condit

matrimoniharass

threatenlife

merciless

prayerhome

repeatpressur

amountdemand

rai

suicid

physic

protect

giftscare

return

compromiforc

forcibl

pray

love

relat

conspiraci

justic

jurisdict

savemental

intimid

threat

death

natur

fal

famili

sentortur

fearconsequ

3

credittransferremovonlinatm

cctv

voter

book

strictrupe

cashfakedismiss

help

cheatfraud

bank

checkduplic

thousand

debit

sackfraudul

hundr

transactjain

aadhaar

branchlakh

deposit

cell

footagwithdrawn

cyber

withdraw

mob

manag

return

dairi

money4

ashramcalv

skin

meat

fakeleav

smugglerbara

munshidisobey

shock

cow

sceneimit

khairi

minist

manag

gaushala

trialprotectbullock

pale

rope

recorddelay

raw

true

condit

busi

milk

telephon

partner

violat

prosecut

escap

controlmissil

swam

i

christian

rador

5

trafficvehiclrickshawgadi

auto

partner

train

checkcarload

block

distanc

jeep

bossriverdrove

leav

ride

rajasthan

damagdrivercontrol

taxsantro

blockadtransport trackdrive

life

stone

escapslip

conductor

cut

tractor

manag

cart

imithelp

good

6

bride

ladi

cloth

husbandadvi

forcwomen

pocso

famili

wifedaughter

domest

babirape

sisterinlaw

household

die

home

poem woman

upset

daughterinlawsack

armi

childrenjustic

neckmarri

strict

motherchildimit

brotherinlawtelephon

mahila

grandson

crirubi

room

femal

7

girlbeatenabus

goldmatern

motherinlaw

mother

husband

good

dowridaughter

brotherinlaw

settlinlawlakh

cloth

panchayat

father

marri

money

demandfamili

home

life

sister

cell

harass

women

torturbeatfatherinlaw

killhindu

child

taunt

parent

marriag rupethreaten

cash

8

xlv


ticketident

travel

obstruct

manag

minist

conditpoultri

railwaycorp

sellcontrol

babyalmutter

mithunendow

aadhaar

abus

noi

climb

trainmarsala

room

passeng

pocketprevent

incomgroom

commut

telephon

jaiswal

help

bazar

militariladi

uppal maliciarmi track

claimant

9

faintbed

doctor

true

treatmentshoulderfractur

blunt

return

knee

arrang

hospittelephon

scenechest

legwaist

xrayarmsafe

home

foreheadcontrol

conscious

medic

treat

hard

injur

room

hithurt

eye

felltrauma

injurisuffer

instrument

scan

farm

pain

10

religigramplead

presidsarpanch

villagdirtipunish

labormischievdismiss

strict

drain

vulgar

fake

obstruct

misbehavpension

photo

obscen

panchayati

forc

panchayat

remov

facebook

cutsoilvideojustic

indec

protect

minist

distribut

educ

book

wall

manag

sackprofan

record

11

estatdevelopfighter

roadreserv

forward

lifefraudul

warwidowviolat

plotduplic

true

constitut

discretionari

handicap

foundat build

fraud

cheatallotwidow

nakulatcountri

war controlconditin

dustri

fal

permissquota

cell

bearerrai

construct

schedulcertif

eligdisabl

municip

12

faintbloodconditroomhard

dead

unclconscious

eathealth

fatherpatient

death

grandfath morn

familidoctormarri

treatment

bodimurder medichome

destruct

medicin

harijan

even

matern

lieunconsci

telephon

bullet

mama

hospitcorp night

die

stomach poisonaunt

13

imam

nishad

brahmin

mob

nandram

potter har

imit

ride

bhaini

gajanand

bhatla

roadwayharijan

siha

bahbalpur

aheer

depot

telephon

basau

processgah

malign

fake

tempo

nakulat

sisay

aslicontrol

chancellor

bus

fountainchamarsurewala

groom

god

brahman

scene

rampratap

banshil

14

roomexecutnote

instrumentduplic

food

satisfimisleadabscond

destroyregistr

morn

child imiteduc

declar

treat

repeat

board

furnishsurrend

adopt

ink

univ

undertak

princip

publish record

wait

bond

minist

floorjuvenil

forward

exam

nakulat

expir

bail

suffer

examin

15

goldsilver

stealmob

sackcupboard

famili

thief

stolefloor

batteri

scene

mornthousand

stolenforccctv

chaingood

sleptmall

theftbroken

roomsearch cash

lock

homebreak

belong

earring

even

broke

imit

jeweleri

goneluggag

night

thiev

laptop

16

xlvi


telephonrodmedic

beat

noiweapon

fightkickironroom

beatenfell kill

life

fought

strict

threatenmouth

broke

arm

quarrel

rescu

attack

slap

stickcaught

brick

protect

hit assault famili

lathi

knifepunch

leav

save

even

imit

night

abus

17

passersbi

possess

woodenbranchcapturwe

ar

buttsearch

worncatch

driverblueprint

illegitimknife

cartridg

paint

true

bodi

pistolcountriweapon

verandah

sealwood

bypassseiz

fire

imit

experi

control

pocket

help

magazin

telephon

manag

ironarm

revolv

guard

walk

18

seizhelpbottl

drink

liquortruebeer

control threwmouth exci

cardboardcheck

countri

drunken walkcut

contractmangoroyal

possessgutexperi

glass

manag

wine

pour

broke

partner

drunk

verandah

femal seal

chokepermissshoulder

smash

imit

alcohol

drank

19

panniwalanikka

mogaodhan

commenc

gurudwarataxat

chhindalabelpa

rashar

dwl

lohgarh

bullet

poli

sikhdadu

propertiexci

dealergurunanakpura

santnagar

nathamob

bhakra

satyadev

kulanroadi

jayadev

preventraisikhdivana

kulvind

tax

salebarad

capsul

pardipvialtibbi

gurmit

20

injurleav

drove

medic

passersbi

doctordamaghurt

colli

fall

crashfarmneglig

truckthreewheelbodi

death

hardhospit

diefamili

night

driverfell

accid

brotherinlaw

driven

room

dead

hit

telephon

rider

suffercontrol

drive

walk

treatment

propo

ambul

bicycl

21

recordbikepurcha

pleadsearchpark

prayerpresid

scenehelp

motorcyclist

chase

forc

morn

ministimit

sack theft

motoreven

cycl

palac

nakulat

duplic

dismiss

belong

pollut

stolethiefsteal

gone

motorbik

companileav

fake

stolen

mob

bicycl

passion

motorcycl22

tejveermob

scootibhishma

singhana

prangar

kabulpur

solan

landlordrentsweat nurwala

hajipur

gudangwa

scenesighana

lohia

goat

ayush

baddijasmin

harishchandra

jaiprakash

muzaffarpur

chandraprakashnalagarh sheepgyanendra

jaroth

buri

sumesh

sulendra

telephon

neelkanth

asok

scooter boleropahlad

hukamchand

fnagar

23

securforg

promifalinstal

busifakerefund

billpayment

financ

chequ fraudul

purcha

companipaidexecut

transfer

amount

loan

accountconspiraci

lakh

visa

floor

agencpassport

check

lac

losstrust

tax

cheat

fraud manag

demand

sum

returnforgerideposit

24

xlvii


mallahimitmercilessliaquatmuzaffar

control

qureshinag

animcut

razak

preventleg

ruthlesssahabuddin

mohamadsham

shad

manag

arshadatali

nakulat

briberopehous

buffalo

slum

cruelti

cattl

lukm

an

chandnisadiq

mosqu

islamtrunk

muslimmasjid

mouth

martyr

sahe

gujjar

25

bathroombedinsertprison

batteriphone

gopalpur

search

securduplic

room

mobil

switch

naseebpur

conver

compani

bangariphonkhatana

lost guard

htc

charg

managwarden

pocketbelt

spice

bhaudasi

dismissbaldevanagar

remot

golden

bill

calcutta

gulia

engag detaincell

technician

26

forchair

daughterfeet

search

childrenrelatniec

girlimit

seduchang strong

mornsack

kidnapgone

fair

scene

hard

bodi

mob

nakulat cloth

wheatsister

leg

paint

leavsalwar

wear

return

familieye

even

marri

jeanfree

shoe

home

27

ganga

aslihbc

thakurambedkar disgust

templ

notefree

nehrubrahman

groundministfake

tank

mandir

gali

sabzi

pedest

help

tenantkhatri

rajput

god

nakulat

galli imam

scene

partner

cinema

delay

valmiki

heart

bhagwan

control

park

imit

computbaniya

harijan

28

shootpetroltelephon

escappush

imithelmet

neckroompartnerdhaba

pumppocket

leav

scene

boycover

scare lootwalk

taxi

caught

urinkisaneat

rider

pleadchaincloth

boss

food

snatch

forciblpour

hotel

eye

fear

forc

sack

distanc

29

lieestimpipegone

theftnightburnt bodi

farm

electrwireburn

tubecut

stolen

agricultur

dispatch

copperduplicmob

fire

lost

ground

wheatreturn

field

check

minist

power

farmer

board

loss

damag

coil

telephon

thievrod

good

nakulat

suffer

30

driverdrugstolesellcaptur

manag

telephon

hang cloth

electron

control

intoxverandah

blockad

separ

cut

search smack

experi

femal

caught

seiz

narcot

weighsubstanc

worn

possessseal

opium

seizur

walk

polythenpul

gram

pocket

good

baghelp

branch

heroin

31

threatencheatpaymentplowfal

fake

fraudul

pay

possessforg

contract

jurisdict

acr

cultiv

revenu

land

dism

iss

fraudsell

conspiraci

purcha

sale

killlawyer

agricultur

patwariharvest

fee

paid

help

transfer

documentconsist

justic

sold

sow

cropregistriground

power

32

xlviii

6.2 Female Complainants

Table A8: Top Word Stems by Topic With FREX (Female Complainants)

Topic Top Words(1) VILLAGE PROBLEM Highest Prob: sikh, panchayat, sarpanch, farm, fire, field, land

FREX: sarpanch, field, farm, gram, pistol, crop, cow(2) POISONING Highest Prob: hospit, uncl, matern, medicin, doctor, eat, die

FREX: medicin, poison, uncl, bathroom, muslim, health, gujjar(3) CHEAT Highest Prob: note, gali, park, compani, jain, imit, floor

FREX: jain, note, bazar, park, gali, thakur, bhagwan(4) UNLICENSED Highest Prob: fake, medic, board, educ, record, certif, princip

FREX: princip, examin, educ, board, patient, salochana, fake(5) DOWRY-A Highest Prob: parent, marriag, demand, dowri, famili, matrimoni, home

FREX: matrimoni, merciless, maltreat, humili, cruel, wed, expect(6) DOWRY-B Highest Prob: dowri, parent, demand, marriag, beat, lakh, rupe

FREX: dowri, settl, demand, parent, taunt, donat, greedi(7) REAL ESTATE Highest Prob: land, amount, properti, compani, cheat, fal, loan

FREX: sale, chequ, payment, forg, construct, loan, forgeri(8) DEVELOPMENT Highest Prob: plot, widow, wife, possess, registri, estat, sold

FREX: plot, widow, registri, allot, ganga, estat, pension(9) DOWRY-C Highest Prob: women, daughter, harass, marri, cell, marriag, justic

FREX: women, mahila, cell, counsel, divorc, harass, mediat(10) CHILD ABUSE/RAPE Highest Prob: girl, daughter, mother, children, marri, child, home

FREX: girl, pocso, seduc, mother, children, babi, posco(11) BURGLARY Highest Prob: stolen, gold, bag, railway, theft, good, lock

FREX: stolen, theft, railway, bag, thief, steal, lock(12) DOMESTIC VIOLENCE Highest Prob: kill, threaten, famili, abus, life, wife, beat

FREX: kill, threaten, dirti, protect, life, abus, save(13) DOWRY-D Highest Prob: motherinlaw, husband, fatherinlaw, inlaw, brotherinlaw, beat, home

FREX: motherinlaw, fatherinlaw, brotherinlaw, inlaw, husbandinlaw, husband, nanand(14) FIGHTING Highest Prob: wife, hospit, fight, injuri, hit, blunt, xray

FREX: xray, blunt, injuri, stick, fight, hurt, noi(15) CHAIN-SNATCH Highest Prob: motor, cycl, motorcycl, boy, bike, snatch, chain

FREX: motor, cycl, bike, motorcycl, snatch, neck, boy(16) CRIMINAL FORCE Highest Prob: woman, wife, daughterinlaw, domest, femal, bride, burn

FREX: grandson, woman, daughterinlaw, sweeti, prathiya, manpratiya, bride(17) RAPE Highest Prob: phone, forc, famili, mobil, room, rape, photo

FREX: rape, video, scare, obscen, vulgar, hotel, facebook(18) RUNAWAY/SUICIDE Highest Prob: husband, shop, children, death, phone, wife, die

FREX: shop, husband, hang, murder, death, dead, hemlata(19) MISSING PERSON Highest Prob: wife, home, imit, gone, wear, search, bodi

FREX: search, feet, bodi, wear, salwar, tenant, rajput(20) MISCELLANEOUS Highest Prob: sister, sisterinlaw, food, grain, ambedkar, cook, lamp

FREX: ambedkar, sister, grain, lamp, anguri, pale, hbc(21) PHISHING Highest Prob: money, bank, rupe, atm, lakh, check, thousand

FREX: atm, bank, branch, check, withdraw, money, withdrawn(22) DRIVING ACCIDENT Highest Prob: hospit, driver, bus, drive, accid, hit, treatment

FREX: driver, scooti, accid, truck, drive, auto, bus(23) DOWRY-E Highest Prob: father, inlaw, money, child, lakh, marriag, parent

FREX: father, expen, pregnant, abort, inlaw, jewelri, womb(24) ALCOHOL Highest Prob: drink, alcohol, liquor, drunk, bottl, drug, intox

FREX: bottl, alcohol, drink, drunk, liquor, intox, drug

xlix

Figure A45: Female Complainant Cases

−0.05 0.00 0.05

Rural/Urban

Rural ... Urban










(19) MISSING PERSON(20) MISCELLANEOUS

(21) PHISHING(22) DRIVING ACCIDENT

(23) DOWRY−E(24) ALCOHOL

A)

−0.05 0.00 0.05

Acquittal

Unacquitted ... Acquitted










(19) MISSING PERSON(20) MISCELLANEOUS



B)

−0.05 0.00 0.05

Dismissal

Not Dismissed ... Dismissed









(17) RAPE(18) RUNAWAY/SUICIDE(19) MISSING PERSON(20) MISCELLANEOUS



C)

Note: Gendered crime (dowry, fighting, domestic violence) have higher acquittals, unlike cases such asburglary or real estate disputes.

l

Figure A46: Word Cloud for 1-8 Top Topics (Female Complainants)

sarpanch

wheatmunshiarm

developbreak

sikhtelephonbullet

field

drain weaponmanag

broke

grampanchayati

tractor

help vehicl

wall

panchayat

nightland

wood

cow

wire

pistol

wife

groundcrop fa

rmdamag

firedestroy

cut

animburnt

stonetank

villag1

substancdeath

poisonliecondit

roomuncl

stomach

grandfath

gujjar

evenpain familim

atern

treatmentbed

muslim

milktelephon

bathroom

blood

treat

minist

bullet

night

mouth

healthconscious

forc

fed

hospitmedicin

eathome

unconsci

doctor

faint

aunt

die

morn

2

partnerroomgroundfloor

dairi

execut

rajasthan

rent

jain

morn

galli

trialbill

slip

surrend

compani

thakurelectrse

cur

bazarnehru

mandir

agenc

market

noteimit

galipocket

bhagwan

guard

powe

r

help

parknakulat

mob

mall

manag

delay

ashram

bail

3

duplicrecord

bookprove

scienc

ident

board

examin

conduct

fakemanag

test

principlost

schedul

minasoniya

mechantelephon

salochana

nominuniv

lalitha

medicsarikaexam

dispatch

ultrasound

municip

academi

photo

mansi

certif

presidpatient

caught

convent

sujata

aadhaareduc

4

marriaggift

cruelparent

torturfamili homelac

harass cashhindudemand

mother

amount

treat

behavior

child

pressur

taunt

merciless

praypanchayat

dowri

abus

daughter

gold

wife

physic

jurisdict

returnlife

crueltirelat

fathermental

matrimoni

threaten

cloth

beat

humili

5

rupecash

cloth

hindu

good

badplead

greedi

dowrimatern

daughter

behavior

assault

mentaltaunt

threaten

demandpossess

gold

inlaw

donatparent

wife

relatkill

jeweleri

thousandfamilimarriag

panchayat

beatphysicbeaten

marrimotorcycl

money

tortur

harass

lakh

settl

6

frauduljusticfloor

forg

demandsoldforgeri

document

sale

lossfraud developfalcheat

moneylacreturn

constructpaymentland

purchapray

loan

paid

amountfinanc

chequ dealer

busi

companiexecut

properti

conspiraci

jurisdict

transfer

possesssell

trust

contract

promi

7

contractcondit

wifeestat

patwarivacant

sold

war

widow

purchaminist

reserv

photo

ishtagasa

dismiss

pension

landprice

plotbeneficiari

fake

rajwati

dharmapatni

possess

misleadduplic

sell

trial

allot

lifemasonri

fal

interf

forcibl

registri

foundat

handicap

capturganga

hous

8li


femalassaultdeskcell

hindu

apolog

fight

famili

womenbeaten

expel

daughterladimistakup

set

help marribeat

teamental

marriag

mahiladismiss

dowrijustic child

harass

wife

mediat

presid

repeat

tortur

divorc

protect

duplic

strict

counsel

fed

comprom

ilife

9

roomsoniaim

it riya

posco

marri

seduc labor

night

rubi

sleptfamili

shabnam

motherboy

drivenhome

girlchild

even

hard

unmarribabi

poemhelpwife

children

slum

khushbu

salma

leav

daughtercwc

cri

die

manag

pocso

telephon

bed

love

10

broke

rupe

laptopgood

telephon

mobbreak

batteri

theftea

rring

silverimit

thief

bag lock

stealphone

control

sackmobil

thousandgold

luggag

belong

jewelri

night

morn

stolencloth

railwaycash

cupboardtrainchainladiroom

scene

jeweleristole

broken

11

attackthreatforciblho

me

molest

nakulat

tore

dismissbadpropertidirti

protectgrabfa

mili

childrenpush

assaultcloth

savebeat

beatennoi

abus

threaten

killimit

forc

quarrelwife

hair

filthichest

lifecaughtslap

night

rescu

justicsackstrict

12

devar

brotherinlaw

beaten

eye

famili

fatherinlawhusband

nanand

slap

beat

hair

marri

sisterinlaw

forcgrab

mothermatern

motherinlaw

nando

push

susar

daughter

quarrel

inlaw

hitfight

hindu

taunt

tortur

kill

liferoom

abusmoney

home

kick

husbandinlaw

pressur

night marriag

13

caughtattackdomest

hospitcontrolsave

treatment

iron kickbed

fell

telephon

roomboyweapon

stick

injur

slapbeat

quarrel

fighthit

beatenchamar

kill

brotherinlaw

bluntpunch

doctormedicrescu

injurinoi

scene

wife

imit

waist

xray

abushurt

14

imitmobilhelphelmet

telephoneven

wornwife

noi

veget

khatri

sack

scene

escaprickshaw

walk

cycl

ridermarketmotor

bikegolden

neckleav

wear

snatch phonecaught

templgold

boy

forc

mob

broke

handkerchiefbicycl

control

chainmotorcycl

catch

15

kerosentelephon

wifelumpremov

prathiya

bride gooddi

pour

sharada

grandson

eyemanpratiya

caughtburnsprinkl

promilaprabhiya

plead

likeshila

tantric

god

illegitim

justic

femalbeauti

prayiya famili

cut

daughterinlaw

clothprayerdomest

ladi

parthiya

sweeti

woman

violenc

love

16lii


eye

relat

lawyer

exploit

mobilintimid

physic

vulgar

obscenconsent

indec

photo facebook

famili

lovesexual

video

marriforcibl

fear

sex

threaten

mentalnight

complain

advi

defam

pressur

fal

conver

room

scare

rapeunnatur

internet

hotel

phone

forcrepeat

suicid

17

tenantevenjeanmorn

sack

freebrahminnakulat

salwarscene

harijanshoebodi

shock

duplicfeet

strong

leavleg

belong

home

trial

relat

chamar

search

wife

return imitgalli

mobilcloth

phone

help

wheat

wear

forc

gone

mob

kidnap

rajput

18

salwarduplicclothtenant

nakulatfreerelat

shockleavkidnap

wear

shoe

sack

homegalli

imit

scene

belong

search morn

phone

mob

mobil

boditrial

forc

leg

gone

chamarwifeharijan

strong

jean

wheat

rajput

brahmin

help

evenreturn

feet

19

elacahbcbitch

lamp

dolirosigentlmilk

kitchen

marripooja

sisterinlaw

priyankaambedkargrain

ddno

sistersure

proud

neetahome

pale

room

clothbhavana

european

bombrajwantisuman

processcurtain

cook

depot

anguriworship

foodcoilkid

ear

night

20

transactcellmob

salarivisa

rupe

book

mobilwife

debit

withdrawn

bank

photo

manag

atmcheatcctv

passportphone

onlinfraudul

market

footag

hundr

cash

lakh

checktransfer

withdraw

amountthousand

return

credit

fraud

cyber

account

moneyhelp

deposit

branch

21

treatdeadhurt

hospit

petrol

passersbiaccid

famili scootitrain

scene

diedoctorscooterhitcontrol

treatment

injuri

auto

drivensuffer

tractor

drive

fall

threewheeldriver

telephon

drove

fell

injur

pump

busmotorcycl

imit

neglig

domest

room

colli

ambul

truck

22

beat

relat

deliveriuncl slapp

regnant

father

home

rupe

abort

matern

daughter

salari

cloth

expen

inlawpressur

marriagtreat

mental

cash

money

taunt

child

condit

husband

life

jewelriapologmother

famili

parent

engagstomach

tortur food

night marri

lakhabus

23

drinkgambl

addictdrug

abus

broke

bottlrashmi

liquor

contract

cold

possess

selldrank

imit

habit

mouth

daili

drinker

night

beaten

telephon

smackalcohol

beer

control

drunk

royal

smoke

walk

verandah

countri

help

glass

smash

drunkenintox seal

fightwine

24

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6.3 Gendered Crime

Table A9: Top Word Stems by Topic with FREX (Gendered Crime)

Topic Top Words(1) DOWRY-MENTAL Highest Prob: parent, husband, mother, money, marriag, father, famili

FREX: salari, australia, loan, earn, shagun, honeymoon, atm(2) DOWRY-PHYSICAL Highest Prob: dowri, demand, parent, marriag, father, beat, panchayat

FREX: settl, panchayat, demand, dowri, motorcycl, greedi, illegitim(3) DOWRY-PREGNANCY Highest Prob: child, pregnant, money, thousand, stomach, rupe, babi

FREX: child, stomach, deliveri, pregnant, babi, defend, thousand(4) DOWRY-ECONOMIC Highest Prob: gold, lakh, father, rupe, cash, marriag, money

FREX: gold, chain, silver, lakh, jeweleri, cupboard, earring(5) UNLICENSED (SEX SELECTION) Highest Prob: note, decoy, ultrasound, fake, rupe, ladi, seal

FREX: decoy, note, currenc, ultrasound, seal, gender, custom(6) DOWRY-RAPE Highest Prob: mental, physic, tortur, forc, marriag, parent, pressur

FREX: physic, mental, unnatur, tortur, sexual, atroc, bad(7) KILLING GIRL CHILD Highest Prob: children, shop, woman, telephon, railway, plot, market

FREX: children, railway, shop, auto, plot, market, sarpanch(9) DOWRY DEATH Highest Prob: sister, die, marri, death, dowri, poison, telephon

FREX: sister, poison, hang, death, die, dead, murder(9) ALCOHOL Highest Prob: abus, drink, dirti, alcohol, bus, liquor, drunk

FREX: drunk, alcohol, dirti, drink, profan, liquor, filthi(10) HURT/DOMESTIC VIOLENCE Highest Prob: wife, husband, hit, beat, kill, fight, noi

FREX: blunt, injuri, hit, stick, xray, attack, rescu(11) KIDNAPPING Highest Prob: girl, home, wife, marri, imit, daughter, seduc

FREX: girl, seduc, search, feet, wear, niec, salwar(12) TRAFFICKING Highest Prob: room, manag, driver, sikh, hotel, women, woman

FREX: sikh, manag, prostitut, hotel, immor, driver, tra�c(13) BLACKMAIL Highest Prob: photo, fal, jain, cheat, video, land, fake

FREX: facebook, jain, photo, video, blackmail, defam, fraudul(14) SEX SELECTION/ABORTION Highest Prob: hospit, medic, doctor, medicin, treatment, abort, drug

FREX: hospit, medicin, doctor, medic, treatment, drug, termin(15) DOWRY-EXTENDED Highest Prob: husband, motherinlaw, fatherinlaw, inlaw, father, brotherinlaw, beat

FREX: fatherinlaw, motherinlaw, husband, inlaw, brotherinlaw, sisterinlaw, matern(16) DOWRY-POST COUNSELING Highest Prob: dowri, marriag, beat, daughter, harass, cell, women

FREX: cell, assault, dairi, mediat, harass, counsel, mahila(17) RAPE Highest Prob: mother, forc, father, home, rape, room, daughter

FREX: pocso, rape, bike, mother, cri, advi, posco(18) LEWD PHOTOS Highest Prob: threaten, kill, phone, life, famili, mobil, wife

FREX: threaten, protect, properti, phone, mobil, kill, threat(19) DOWRY-DESERTION Highest Prob: marriag, demand, parent, dowri, matrimoni, home, beat

FREX: matrimoni, merciless, maltreat, prohibit, lac, cruelti, jurisdict(20) DOWRY-STARVATION Highest Prob: daughter, famili, marri, wife, home, money, father

FREX: daughter, sell, famili, prayer, sad, panchayati, adopt

liv

Figure A49: Gendered Crime I

−0.05 0.00 0.05

Male/Female Complainants

Male ... Female

(1) DOWRY−MENTAL






(9) DOWRY DEATH


VIOLENCE

(12) TRAFFICKING


ABORTION(15) DOWRY−EXTENDED(16) DOWRY−POST

COUNSELING(17) RAPE

(18) LEWD PHOTOS



A)

−0.05 0.00 0.05

Urban

(1) DOWRY−MENTAL






(9) DOWRY DEATH



(12) TRAFFICKING




(17) RAPE

(18) LEWD PHOTOS



B)

Note:.

lv

Figure A50: Gendered Crime II

−0.05 0.00 0.05

Acquitted

(1) DOWRY−MENTAL






(9) DOWRY DEATH



(12) TRAFFICKING




(17) RAPE

(18) LEWD PHOTOS



C)

−0.05 0.00 0.05

Dismissed

(1) DOWRY−MENTAL






(9) DOWRY DEATH



(12) TRAFFICKING


ABORTION(15) DOWRY−EXTENDED(16) DOWRY−POST

COUNSELING(17) RAPE

(18) LEWD PHOTOS



D)

Note:.

lvi

Figure A51: Word Cloud for 1-8 Top Topics (Gendered Crime)

pressuramount

loanabuslife

hoteltortur

household

companimarri

salari

parentbehavior

help

demand

familihusband

expen

mental divorc

engag

cloth

forcbusi

jewelri

relatphoneaustraliabank

marriag

floor

moneyhome

harassearn

save

father

giftpaid

mother1

hinduharasspleadkill

marriagbeatwoman

rupebad

motorcyclreturnmatern

kick

illegitim

panchayat

daughtertauntcash

parentcloth

motor

wife

famili

demand

cycl

behavior fatherlakhgreedi

threaten

good

inlaw

dowribeaten

hungri

pray

marri

settlpossess

donat

2

maternmiscarriagparent

pregnantlakh

stomach

pregnanc

killpressur

conditparthiya

moneybeat

relatwife

mother

motorbabi

deliveri

cycl

child

fight

fatherwombdefendhome

abort

pain

marri

thousand

beatentreat

rupe

santrofood

marriag

expen

milkdie

maik

3

marrifathercupboard

home

rupemoney

wifemother

good

palac

food

demandbed

grandfath

thousand

gift

jewe

leri

golddowri

lakhdaughter

matern

marriagfurnitur

utensilfamili

householdinlaw

chain

earring

parent cloth

tabl

relat

cash

uncl

engag

jewelri

processsilver

4

amount

informhealth

ultrasoundmoneynote

bushundr

pregnant

determin

decoy

helppocketfake

caughtseal

gender check

womenthousand

custompossess

phone

womanbroker

currenc

envelop

mobilshalini

shop

raid

womb

patient

seiz

rupeladi

sex

wife

seizur

slip

5

beatforcroom

protestmarri

husbandkill

conditexploit

torturintox

atroc

marriag

bad

mental

pressur

unnatur

physic

assault

nighthome

relatdowri

forcibl

suffer

save

familitaunt

abus

behaviorthreaten

life

money

sexualtreat

harass

sexhelleye

parent

6

boyscene

companisackmornliedriver

petrol

sarpanch

babi

deadprinciphard park

farm

pump

telephondrive

boardwalk

shop

husband

labor

guard

child

market

justic

plotbazar

acid

help mob

scooti

woman

imit

railway

auto

grain

cow

children

7

hindusuicidpoisonbrotherinlawdeaddeath

boditelephon

beatenliescenehang

corp

harass

hospitmurder

wife

sisterinlaw

neck

marri

inlaw

fire

burnt

father

motherdestructfed

bed

morn

sisterroom

dowri

substanc

familidie

phone

moneykillbeat

marriag

8

lvii


control

throwdirtimisbehav

boycontractdrankvulgar

addict

motorcyclnakulat

obscen

drunken

buscheat

bottlmouthashil

wine

night

asham

help

intox

broke

filthi

profandrunk

women

indecsack

abus

nake

telephon

glass

drinkliquor

gestur

imitthrew

alcohol9

toretelephonhusband

imitbeaten

arm neck

beat

hurtdomest

hit

xray

injurinjurinoi

wifekick

slap

blunt

boy

fire

brotherinlaw

kill

hairattack

fell

fight

quarrel

assault

grab

broke

stick

punch

caught

clothpush

rescu

home

chest

save

10

sackdrivenweargali

help

muslim

feetdrivemobil

hard

nakulat

scenechildren

nightharijan

search

seduc

tenant

evenrajput h

omeboy

bodi

bus

mobwife

niec

phone

salwardaughter

famili

girlgone

forc

imit

marriagmarrimorn

chamar

relat

11

woman

cloth

custom

tenantwomen

true sikhmahilakhatri

dhaba

controlgalli

driver

immor

roomfemal

preventtrainrent

boy

gurudwara

pocket

busiimit

hotel

traffic

bogus

help

landlordpossess

money

wife

bodi

rajput

gadi

prostitut

floor

managride

gali

12

recordjaindefamstolenvideo

hurt

land

mobilcheck

bookreligi

pornographrelatdivorc

justic

falmarri

univ

moneyfraud

consent

bank

fraudul

cheatphoto

facebook

obscen

cyber

famili

educ

commentblackmail

internet

wife

fakecertif

compani

media

branch

conspiraci13

healthfaintcosmet

home

certif

controlpill

medicin

hospitpain

record

blood

drugterminpregnanc

treatment

imitsale conscious

examin

doctornur

medictelephon

purchaundergo

disea

bill

abort

forc

unconscihusband

patient

eat

worsen

injectbullet

wife

condittreat

14

wifemothermarriag

lifeinlawrupe

home

fatherhindu

relat

room

daughterinlawdemandmotherinlaw

fight

night

lakh

fatherinlaw

eatdaughtergrandfath

taunt

abus

uncl

brotherinlawfamiliparentdowri

matern

beaten

food

sisterinlaw

money

marri

kill

harass kick

husbandinlaw

beat

husband

15

mediatfightparent

life

justicrepeat

strictbeat money

mahila kill

marriag

marri

taunt hindutortur

demand

maternmother

wifeinlaw

women

home

beatendowri

famili

femal

harass

divorc

dismissassault

ladi

celldaughter

counselthreatenhusband

gali

dairi

woman

16

lviii


leavchamarmouth

cloth

pocso cycl

femal

templroomwomen

daughter

boy

fathernight forcibl

telephonadvi

noiladi

lawyermorn

gone

scare forc

motor

mothermotorcyclfear

unclposcohome

imitkillcri

rapewidow

justic

even

caughtbike

17

homeupsetduplictrial

abus wife

night

minist

prayersackpresid

famili

lifeprotect

phone

scare

good

whatsappsecur

nakulat

husband

indecdismiss

mobil threatharass

forcfearwoman

imitrecord

threatenkill

strictrepeatplead

properti

justicsave complain

18

loverelat

matrimonimarriag

cloth

tortur

beat

famili

gift

fathertaunt

humili

jurisdict gold

wifepanchayat

maltreat amount

pray

demand

lifedaughter

cash

return

homechild

merciless

motor

motheraffect

dowri

parent

threaten

hindu

cruelticycl

harasslac

cruel

behavior

19

marriagapolog

soldbeat

husband

life

daughterinlawdiequarrel

sad

homemothernight

sell

daughterlove

conditjustic

threatenfood

room eat

wife

treat

gamblfamili

land

relatpanchayat

money

prayer

harass

mistak

pressur

beatenfather

marri

panchayati

adopt

kill

20

lix

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