Breaking the Cycle: the Impact of Legal Reforms on Domestic Violence

Breaking the Cycle: The Impact of Legal Reforms on Domestic Violence

Claudio Ferraz∗ Laura Schiavon† PUC-Rio UFJF

February 15, 2019

Abstract

Domestic violence is a widespread phenomenon. Despite the implementation of several policies addressing its reduction, causal evidence on their effects is scarce and controversial. This paper evaluates the impact of a legal reform aimed at curbing domestic violence in Brazil. We exploit the fact that while trends of male and female domestic violence are very similar before its introduction, the law covers only women. The legal reform reduced female homicides due to household aggression by 9 percent. The effects were stronger for less educated and black women– those more likely to have low bargaining power within their households. Keywords: Domestic violence, homicides, intra-household bargaining JEL: K4, J1

∗Department of Economics, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rua Marquês de São Vicente, 225- Gávea Rio de Janeiro, RJ, 22453-900, Brasil. Phone: 552135271078. Email: [email protected]; and BREAD †Department of Economics, Universidade Federal de Juiz de Fora (UFJF), Rua José Lourenco Kelmer, s/n - São Pedro, Juiz de Fora, MG, 36036-900, Brasil. Phone: 553221023541. Email: [email protected] 1 Introduction

Violent crime has decreased significantly over the past centuries, specially in rich countries. Do- mestic violence, however, is still a major public policy concern in many societies. The World Health Organization estimates that one third of all women who have been in a relationship have experienced violence by their intimate partner (World Health Organization(2013)). Poor women, in particular, are significantly more likely to suffer from intimate-partner violence (IPV) due to the lack of exit options. The costs to society are enormous. Exposure to marital violence is correlated with suicide, depression and alcohol abuse among women. Victims are more likely to have complications during pregnancy and affected children suffer negative impacts as well as their peers (Carbone-López et al.(2006); Aizer(2011); Carrell and Hoekstra(2010); Carrell et al.(2016); Doyle Jr and Aizer(2018)). In extreme cases, domestic violence is a leading cause of female homicides. The World Health Organization estimates that almost 40 percent of worldwide mur- ders of women are originated from intimate-partner violence (World Health Organization(2013)). The prevalence of partner battering and homicide has contributed to the implementation of targeted policies in many countries. Probably due to the lack of identification or data, there are few impact evaluations of them.

This paper examines the effect of a legal reform aimed at curbing intimate-partner violence. We study the Maria da Penha Law, passed in Brazil in 2006. The new regulation introduced effective mechanisms for preventing and punishing domestic violence against women, providing social assistance to victims and aggressors, and changing social norms. The law was recognized as one of the best legislative initiatives of the sort by the United Nations (Women(2011)). It introduced emergency protective measures for women at risk and increased the penalty for domestic violence against women. It also fostered the creation of special courts and stimulated the improvement of public services and institutions for domestic violence victims.

We exploit a singular feature of this legal reform. Despite the fact that male and female domestic violence cases follow similar trends before 2006 in most of Brazilian municipalities, the law is exclusively applied for cases of aggression against women. We use a difference-in-differences model and compare the number of male and female homicides that occurred as a consequence of aggressions perpetrated within the household. We show that the parallel hypothesis is satisﬁed excluding the large cities of Brazil. Differently from a large literature that uses proxies for domestic violence such as self-reported cases of aggression or hospitalization records, homicides data are less likely to be biased or underestimated, are consistently collected over time, and include the universe of women. When a homicide occurs, the cause of death must be reported in ofﬁcial documents.

1 The homicides are registered as deaths caused by aggression. In contrast, self-reported data usually suffer from underreporting and may be biased according to the respondent’s characteristics. Self- reported data might also be affected by the introduction of laws aimed at curbing domestic violence and fail to pick up true impacts. Similarly, measures based on hospitalization may fail in the identiﬁcation of injuries caused by domestic violence, as in Brazilian datasets. Studies document that fatal and non-fatal intimate partner aggressions respond similarly to policy changes (Stevenson and Wolfers(2006); Miller and Segal(2018)).

We restrict our analysis to municipalities with less than 100,000 inhabitants comprising 5230 out of 5507 municipalities in Brazil. Capital cities and large municipalities have very different pre- intervention trends as a considerable share of violent crimes in these localities are driven by drug- related homicides. We use data from the Brazil’s National Health System (SUS) on male and female homicides that occurred as a consequence of aggression inside the household from 2001 to 2015. In addition to the cause of death, our microdata also provides information about the educational level and race of the victims that allow us to test different hypothesis that exist in the theoretical literature.

We find that the introduction of the Maria da Penha Law reduced female homicides due to household aggression by 9 percent with increasing effects over the years. The reduction in female homicides was concentrated in cases which the aggression was due to firearms, while we also find significant effects when looking at homicides caused by aggression without firearms. We conduct placebo tests and do not find effects on deaths caused by unintended aggressions or accidents within the household, suggesting the absence of manipulation of official statistics to omit domestic violence. The effects estimated were particularly large for poorer women with less bargaining power in the household. While the decrease in female homicides was 2.6 percent for women with primary school or more, the effect was a decline of 6.7 percent for women with less than primary schooling. Because of the high correlation of education and race in Brazil, the effects were concentrated in black women. Finally, consistent with theories that argue that women that have economic power are less susceptible towards suffering domestic violence, we find that the legal reform had a particularly large effect in localities where the women’s median wage was lower. While we do not have detailed data to pinpoint the different mechanisms that explain these effects, we gathered aggregate data that shows a very large increase in the number of hotline reports in aggression and the number of incarcerated men under the Maria da Penha Law.

Our ﬁndings indicate that the Maria da Penha Law had a signiﬁcant effect in protecting women that were most at risk. The literature on the determinants of domestic violence usually analyzes the risk

2 of aggression based on women’s bargaining position in the household, men’s desire for resources or partner control, and social norms. Corroborating the first view, a growing body of studies shows that increases in female income, reductions in gender wage gap, and governmental transfers significantly reduces intimate partner violence, with evidences of spillover effects (Bowlus and Seitz (2006);Aizer(2010); Hidrobo et al.(2016);Buller et al.(2018); Haushofer et al.(2018)). The reduction of costs associated with divorce also impacts women’s household bargaining power and reduces spousal violence (Stevenson and Wolfers(2006);Brassiolo(2016)). In line with the second view, there are evidences that increase in female educational level and income stimulate psychological violence, threats of violence, and other controlling behaviour (Bloch and Rao(2002);Erten and Keskin(2016); Hidrobo and Fernald(2013); Anderson and Genicot(2015)). Specifically, Bobonis et al.(2013) and Bobonis et al.(2015) find that conditional cash transfers (CCT) to women reduce physical abuse and increase violent threats in short run, with no long run effects. According to the social norm view, domestic violence is likely to be culturally transmitted, so it is difficult to break the cycle. Kids that grow up in violent households are more likely to repeat this pattern after growing-up (Abrahams and Jewkes(2005); Ehrensaft et al.(2003)). Moreover the use of violence against women is shaped by social norms that are highly persistent over time (Alesina et al. (2016)). In some contexts, however, changes in laws can induce changes in social norms by shifting the behavior of a large share of individuals into a new equilibrium (Acemoglu and Jackson(2017)).

Our study relates mostly to the incipient literature on policies aimed at combating domestic violence. The effect of policies that increase the probability or severity of punishment for domestic violence is ambiguous, since this kind of aggression is usually perpetrated by family members. By one hand, Iyengar(2009) provides evidence that laws that require the arrest of abusers when domestic violence is reported increase spouse homicides probably due to a reduction in report rates. By other hand, Aizer and Dal Bó(2009) concludes that policies which prohibit the prosecutor to drop charges of domestic violence increase the reporting and decrease the number of men murdered by intimates. Perova and Reynolds(2015) show that the creation of women’s police stations in Brazil is associated with a reduction in homicide rates of some groups of women. There are also evidences that an expansion of female participation in policing and legal bodies increases the reporting of gender crimes and reduces intimate partner fatal and non-fatal abuses (Miller and Segal (2018);Kavanaugh et al.(2018)). Speciﬁcally, the Maria da Penha Law was evaluated by Cerqueira et al.(2015) and Azuaga and Sampaio(2017). Similar to our results, both of them estimate a decrease of around 10% in the number of female homicides related to domestic assaults. Besides methodological differences, our analysis differs in that we investigate the response to a legal reform over the years and exploit heterogeneous effects, testing how results are related to the literature on

3 the determinants of domestic violence. Wolfers(2006) demonstrates that failures on explicitly modelling the response of a law over time can signiﬁcantly bias the treatment effect estimate.

The rest of the paper is organized as follows. Section 2 dicusses the Maria da Penha Law. Section 3 describes the data. Section 4 reports the empirical strategy and identiﬁcation assumptions. Section 5 discusses the results. Section 6 presents the concluding remarks.

2 Institutional background

2.1 Patterns of domestic violence

Domestic violence prevalence around the world has stimulated the study of its patterns. Many surveys report that usually the victims suffer repeated aggressions. Almost 60% of the women who call to the Brazilian 180 hotline relate that they suffer daily abuses (Conselho Nacional de Justiça (2013)). Physical aggressions are reported in most of these records. According to Waiselﬁsz(2011), more than half of the women who were hospitalized because of domestic violence in 2011 had been hospitalized before for the same reason. Abusive behaviors are associated with physical aggression to increase the abuser control of victim’s life. Most of them are poor and minority women (Aizer (2011); World Bank(2015)).

Brazil is the seventh country in the world ranking of crimes against women (Secretaria de Políticas para as Mulheres(2012)). In country, women are more prone to be victims of domestic violence than men. Among those hospitalized because of domestic violence, sexual abuse or related aggressions in 2009, more than 65% were women. Most of these episodes occurred within the victim’s household (Conselho Nacional de Justiça(2013)). Usually the abuser is the partner (Waiselﬁsz (2011)).

Psychological and some economic researches focus on the cyclicality of domestic violence episodes. Walker’s cycle theory of violence determines three phases of domestic aggression: the tension- building phase, the battering fase and the reconciliation phase (Walker(1980)). Aizer and Dal Bó (2009) discuss the time inconsistent preferences in the context of domestic violence. In line with this view, policies that offer psychological support to victims or that prohibit the drop of the charge may affect reporting and aggression incidence. Speciﬁcally, no-drop policies resulted in a increase in domestic violence reporting in the United States (Aizer and Dal Bó(2009)).

The increase in domestic violence report is a major policy concern. Many women who are victims of abuse do not report it to authorities. The main reasons for this choice are: fear of abuser, desire

4 to protect him, belief that violence is a private matter, and perception of criminal justice system as ineffective in dealing with this sort of crimes (Greenfeld et al.(1998)). In next subsection, we discuss the Maria da Penha Law measures to increase reporting, improve the criminal justice system effectiveness, protecting victims, and changing gender-norms.

2.2 The legal reform

The Maria da Penha Law was introduced in Brazil in August 2006. It implemented a set of measures to reduce domestic violence. It exclusively covers acts of aggression perpetrated against women. The cases of domestic violence against men are covered by the law number 9099/1995. Before 2006, domestic violence against women were also judged according to law number 9099/1995, with the exception of cases of sexual assault, severe aggression and homicides. This law establishes that mild domestic aggressions are minor offenses. The minor offenses are punished with less than two years of imprisonment. Calazans and Cortes(2011) report that around 90% of domestic violence cases tried in accordance with that law were closed. Among those punished, judicial decisions usually determined monetary penalties.

The main objective of Maria da Penha Law (law number 11340/2006) is to reduce domestic and family violence against women. Table1 summarizes the measures introduced by law. The strategy implemented is threefold: the improvement in criminal justice to effectively curb and punish domestic violence; the implementation of measures to support women at risk and aggressors; and the promotion of broad campaigns to change social norms. The legal reform brings about a set of innovative policies. First, it rules out the application of pecuniary penalties in cases of domestic violence against women. The new punishment is 1 to 4 years of imprisonment. Second, it creates domestic violence special courts. In districts without them, domestic violence cases are allocated to the criminal courts and have priority over other cases. Third, it promotes the specialization and integration of bodies in criminal justice system to deal with domestic violence cases. Fourth, it introduces emergency protective measures, which must be determined by the judge within less than 96 hours after the risk reporting to police. The main measures targeted at aggressors are the judicial orders for guaranteeing their eviction from home and physical separation and the possibility of pre-trial detention. On the other hand, the main measures targeted at victims are referrals to support and protection services. The law boosts the creation of legal bodies to help women at risk, such as women’s police stations, shelters and legal, psychological and health assistance centers. It also provides vulnerable women with the possibility of legal protection against dismissal and judicially determined access to social assistance beneﬁts. Fifth, reeducation and rehabilitation programs to aggressors are created. Finally, it fosters campaings, researches and educational programs about

5 gender-based violence, which was classiﬁed as human rights violation (Martins et al.(2015)).

Legal changes introduced by the law have been valid since August 2006. However, complemen- tary criminal justice institutions and assistance centers are being implemented gradually, as shown in Figure5 discussed in section 5. A hotline for assisting women in this situation, the toll-free number 180, was created in 2005 and assumed the role of a whistle blowing hotline in 2013 (Sec- retaria de Políticas para as Mulheres(2013)). In 2013, the service was available for 56% of Brazil- ian municipalities (Secretaria de Políticas para as Mulheres(2013)). In turn, the ﬁrst Brazilian women’s police stations were created in 1985 (Santos(2010)). The number of municipalities in Brazil that had women’s police stations was 443 in 2014, most of them concentrated in more de- veloped states, specially the state of São Paulo (IBGE(2014); Martins et al.(2015)). Perova and Reynolds(2015) show that the creation of women’s police stations in Brazilian municipalities is associated to a reduction in women homicide in metropolitan regions and among those aged 15 to 24 between 2004 and 2009. The availability of exclusive domestic violence special courts or specialized centers in criminal courts is also limited. According to the Justiça Aberta database (Conselho Nacional de Justiça(2015)), only 359 municipalities were located in districts with that judicial structure.

The effectiveness of the law instruments seems to have improved over the years. The number of men sentenced to prison for domestic violence increased from 1,825 in 2008 to 4,422 in 20141 (Ministério da Justiça(2014)). The number of domestic violence reports received by the 180 hotline totalled 46 thousand in 2006 and more than 1 million in 2016 (Secretaria de Políticas para as Mulheres(2013)). According to Senado Federal(2015), an opinion survey, the proportion of re- spondents who believed that anyone who is aware of aggression against women by family members should report rose from 42% in 2009 to 64% in 2015. They show that nearly all women interviewed in both waves were aware of the law. As discussed, the law affects domestic violence through three basic channels. First, the increase in probability of punishment and aggression penalties, through the promotion of the effectiveness of legal mechanisms. Second, the reduction of risk for victims and the mitigation of their economic vulnerability. Third, the the changing of social norms. The law impact on female homicide is consistent with deterrence and incapacitation mechanisms.

3 Data and descriptive statistics

1There were 9 women sentenced under Maria da Penha Law in 2008 and 39 in 2014 (Ministério da Justiça(2014)). This statistic may reﬂect a higher enforcement of the law or a higher number of domestic violent events.

6 3.1 Variables

We construct a pooled data panel containing annual information per gender for each municipality from 2001 to 2015 2. The measure of domestic violence used in this paper is the annual number of homicides occurred as a consequence of aggressions perpetrated within the household, calculated separately according to the sex of the victim for each municipality 3. There are evidences that homicides and other measures of domestic violence respond similarly to policy changes (Stevenson and Wolfers(2006); Miller and Segal(2018)). We exploit this measure because it is consistently collected over time and regions and have a high reporting rate in Brazil, since the cause of death must be reported in death certiﬁcate. There is a lack of high quality panel on self-reported data and hospitalization records of domestic violence in Brazil. These sources of crime data are usually interpreted carefully, since they are potentially underestimated and biased according to individuals’ characteristics. They also might be affected by targeted policies. To conduct a placebo test and investigate whether the cause of death is manipulated in ofﬁcial documents, we exploit the number of deaths caused by unintended aggression or accidental injury that occurred within the household.

Data on the different types of deaths from 2001 to 2015 were extracted from DATASUS, a health database maintained by the Ministry of Health (Ministério da Saúde(2015)). Their microdata iden- tifies firearm homicide and nonfirearm homicides and contains information on the educational level and race of victims, what allows us to estimate heterogeneous effects 4. We test which demographic group was more affected by the policy and relate our findings to the literature on domestic violence determinants. In order to estimate the heterogeneous effect of the legal reform for municipalities with low and high women’s median wage in 2000, we construct this variable considering the 15 years old or older women that work using Census (IBGE(2000)). To characterize municipalities, we also rely on the Census (IBGE(2000)) and use the median years of education for all individuals aged 24 or older and the proportion of black, indigenous and mixed-race population.

In our regressions, we include proxies for municipal labor market conditions, governmental income transfers and availability of ﬁrearms to control for these determinants of violence. We use a matched employer-employee data, RAIS (Ministério do Trabalho e Emprego(2015)), from 2001 to 2015 to construct the proportion of inhabitants aged 15 or older in the formal sector5. Municipal data on the number of families who are beneﬁciaries of the Bolsa Família (Brazilian conditional

2The number of Brazilian municipalities increased from 5507 in 2000 to 5570 in 2015. To construct the panel dataset, the data from these 63 new municipalities were considered as data from their original municipality. 3We also exploit the rate of homicides and the rate of homicides occurred as a consequence of aggressions perpetrated within the household per 100,000 inhabitants for descriptive purposes. 4Microdata on victims’ income are unavailable. 5There are no annual data on informal sector at municipal level

7 cash transfer program) available between 2004 and 2015 was extracted from the Matriz de Infor- mação Social (Ministério do Desenvolvimento Social(2018)). Following the crime literature, we use as proxy for availability of ﬁrearms the number of suicides by ﬁrearms, information collected from DATASUS (Ministério da Saúde(2015)).

We analyze the evolution of indicators of legal mechanisms to prevent and curb domestic violence over the years. We collect data on domestic violence special courts existence between 2009 and 2013 from the Justiça Aberta, a database managed by National Justice Council (Conselho Na- cional de Justiça(2015)). Information on the presence of women’s police station in each municipality is available in some waves of the survey Munic (IBGE(2014)). Data on number of hotline reports from 2006 to 2016 were extracted from ofﬁcial statements (Secretaria de Políticas para as Mulheres(2013)) and data on number of men sentenced to prison for domestic violence for available years are from Infopen (Ministério da Justiça(2014)). From DATASUS (Ministério da Saúde (2015)), we use the municipal average population as weights in our estimates.

3.2 Sample and descriptive statistics

Our analysis is conducted for municipalities with less than 100,000 inhabitants . We exclude capital cities and large municipalities because they have large drug markets and face important socio- economic challenges. As a consequence, in these localities our measure of homicides is not a good proxy for domestic violence since it is more likely to be affected by economic crimes. Table2 presents the weighted summary statistics of our annual municipal database from 2001 to 2015. Among the 5507 Brazilian municipalities, 5230 have less than 100,000 inhabitants and only 277 have 100,000 inhabitants or more. For this classiﬁcation we consider the municipal average population from 2001 to 2015. During the period studied, the ﬁrst group comprised around 46% of the Brazilian population, while the second group comprised the remainder of the population.

As shown in Table2, the average female homicide rate per 100,000 inhabitants is 3.4 for municipalities with less than 100,000 inhabitants and 5.1 elsewhere. The average male homicide rates per 100,000 inhabitants are 30.6 and 67.6, respectively. They are much higher in large cities than in our sample, what reinforces our argument that male and female exposure to violence follow very different patterns in large cities. On average, Brazilian municipalities present a rate of female homicides due to household aggression per 100,000 inhabitants of 1.1, which is the same for both group of municipalities, and a corresponding male rate of 4.1 for small municipalities and 4.8 for medium and large municipalities. We focus our analysis on the number of homicides due to household aggression and attest in section 5 that these measures for men and women follow a parallel trend

8 before the law implementation in our sample. Summary statistics for other variables are presented in Table2.

Figures1 and2 show the evolution of our measures of domestic violence, excluding large municipalities. We observe an increase in the number and rate of female homicides due to household aggression even after the law implementation in 2006. There were 441 deaths caused by this reason in 2005, 407 in 2006 and 622 in 2015. This increase mirrors the national trends of violence, explained by income shocks, failures in public security policies and other factors. In order to estimate the impact of Maria da Penha Law on domestic violence, it is necessary to conduct a counterfactual analysis. Our empirical strategy is explained in next section.

4 Empirical Strategy

This study aims to identify the treatment effect over the years of legal reforms to reduce domestic violence against women on the number of female homicides due to household aggression. We exploit the fact that the law is only applicable in cases where the victim is a woman. We obtain differences-in-differences estimators through the following model:

Ygmt = α + ∑γtYeart ∗Womeng +Yeart + µgm + εgmt (1) t where Ygmt denotes the log of number of homicides due to household aggression for gender g in municipality m and year t; Womeng equals 1 for women observations and 0 otherwise; Yeart denotes year ﬁxed effects; µgm denotes gender-municipality ﬁxed effect; and εgmt denotes the error term.

The coefficients of interest are γt, which estimate the percentage difference in the number of homicides between gender groups foreach year, attributable to the legal reform. The main identification hypothesis is the non-existence of unobserved sources of variation that disproportionally affect male or female fatal abuses occurred within the household. The validity of the parallel trends hypothesis before the law implementation is testable. It is satisfied if the interactions between the pre-2006 dummy of years and the gender indicator are not statistically different from zero. We test this hypothesis for the overall set of regressions. The effect of the law on male domestic homicides may also bias our estimates. On the one hand, we suppose that violence against men were not stimulated by the legal change, since the punishment for domestic violence against men remained constant. On the other hand, the policy may reduce male domestic homicides through the law di-

9 rect application for male victims, through the reduction of male deaths due to women self-defense or through changes in social norms. The ﬁrst seems to be inexpressive given the small number of women sentenced under Maria da Penha Law, as presented in Figure5. Even if the others are signiﬁcant, they introduce an attenuation-bias. In that case, the law effect is larger than estimated.

In order to obtain the average treatment effect for the whole treatment period, we also estimate the following regression:

0 Ygmt = α + γ Postt ∗Womeng +Yeart + µgm + εgmt (2) where Ygmt is the log of number of homicides due to household aggression for gender g in municipality m and year t; Womeng equals 1 for women observations and 0 otherwise; Postt equals 1 if year t is equal to 2007 or higher and 0 otherwise; Yeart denotes year fixed effects; µgm denotes the gender-municipal fixed effect; and εgmt is the error term. The coefficient of interest is γ, which estimates the treatment effect.

This is our reference model. To estimate heterogeneous effects and conduct placebo tests, we exploit other dependent variables, described in Section 3. We also estimate models including year fixed effect interacted with state dummies, controlling for state-specifics annual shocks. In order to control for convergence in domestic violence across municipalities, we include group-specific time trends in some specifications. More specifically, we interact the value of the dependent variable in 2001 and year fixed effects. It is worth noting that the treatment effect may be underestimated due to the inclusion of time trends. In some specifications, we include control for inhabitants aged 15 or older working in formal sector, number of beneficiaries of the Brazilian Conditional Cash Transfer program and number of suicides by firearms. These time-varying variables control for variations in individuals’ income and availability of guns. We estimate six specifications: the model reported in equation 2; this model with state-specific year fixed effects; the last model with group-specific time trends; and the last model with different combination of controls. We prefer the first specification, reported in equation 2, since the controls are only available for a restricted period and the time trends may partially absorb the treatment effect. As showed in Section 5, the results are similar among these regressions.

The models are estimated using weighted least squares. We weight for average municipal population in order to approximate the average partial effect for the whole population in the potential presence of hetereogeneous effects and heteroskedastic error terms. Weighting for population helps to identify the average partial effect in samples with underrepresented and overrepresented

10 subgroups (Solon et al.(2015)). As suggested by Solon et al.(2015), we study the heteronegeity and present treatment estimates for different groups of individuals separately, classiﬁed according to their socio-economic characteristics. The standard errors estimated are clustered at the municipal level due to a potential serial correlation of error terms for the same municipality over the years (Bertrand et al.(2004)). Finally, we also estimate Poisson models to check the robustness of our results because our dependent variable is a count variable and presents many zero observations. Poisson models are more appropriate for counting variables and are more sensitive to outlier observations than OLS models.

5 Results

5.1 Impact of legal reform on domestic violence

The expected effect of legal reforms on domestic violence is ambiguous, since they might affect the determinants of individuals’ decision to violate the law in different directions. According to Beckers’ seminal model, individuals decide rationally whether to commit a crime or not based on a comparison of their expected utility in each context (Becker(1968)). It depends basically on four factors: utility of non-committing crime, beneﬁts of crime, severity of penalties and probability of punishment.

Firstly, targeted policies might increase the utility of non-committing crime and reduce the beneﬁts of being involved in a crime without apprehension, what reduces crime through deterrence effect. Maria da Penha Law can modify social norms and individuals’ preferences through campaigns and educational programs. Secondly, the introduction of higher penalties directly affect felonies incidence through incapacitation and deterrence effects. This is the case of our treatment, as discussed in previous section. Thirdly, measures that increase the probability of punishment also reduce crime through incapacitation and deterrence effects. In the case of Maria da Penha Law, this change is directly promoted by the improvement in criminal justice institutions and by the introduction of temporary punishments aimed at the separation of victim and abuser. However, the net effect of legal reforms on the probability of punishment is hard to predict, since those policies might impact the probability of reporting in different ways. On the one hand, reporting might increase as a response to the introduction of campaigns to change social norms, mechanisms to reduce impunity, social assistance to victims and emergency protective measures. On the other hand, reporting might decrease as a response to the increase in the severity and probability of sanction because most of the victims have affective relationship with aggressors. As a consequence, victims are more likely

11 to protect aggressors against imprisonment and are more susceptible to threats.

In fact, there are empirical evidences that policies aimed at reducing domestic violence might incentivize or decrease its occurrence. Iyengar(2009) shows that mandatory arrest laws increased intimate partners homicide probably due to the reduction in reporting by victims. In contrast, Aizer and Dal Bó(2009) ﬁnd that no-drop policies of prosecution boost reporting and incarceration, while reduces the number of men intentionally murdered by their partners. The authors argue that women demand commitment devices to deal with intertemporally inconsistent preferences in a battering relationship. The presence of specialized institutions and professionals also have an important role in dealing with gender violence (Kavanaugh et al.(2018); Perova and Reynolds(2015); Miller and Segal(2018)).

Maria da Penha Law is considered innefective by the public opinion at large because the number of female homicides and those caused by aggression occurred within the household increased after the year of introduction of the law, as shown previously in Figures1 and2. This interpretation is inappropriate since the violence against women could be higher in the absence of the law. Violent deaths are correlated with other factors besides familiar abuses, such as weapon availability and illegal drug markets (Cerqueira(2013)). For this reason, it is necessary to compare the number of deaths after the law introduction with the expected number of deaths if the legal reform had not been passed.

In order to obtain the impact of the legal reform, we estimate the model specified in Equation1 for the dependent variable log of annual number of homicides due to household aggression, calculated by gender for each municipality. Table3 presents the law average treatment effect, estimated by the coefficient of the interaction between Women and Post 2006. All models include control for log of annual municipal population by gender, year fixed effect and municipality x gender fixed effect. Column 1 presents the baseline model, column 2 includes state-specific time dummies, column 3 introduces interactions of time quadratic polynomial with baseline values of our dependent, and columns 4 to 7 alternately control for the following annual variables: proportion of people aged 15 or older working in formal sector, number of suicides using firearms, and number of families who are beneficiaries of the Brazilian conditional cash transfer program (available for the period 2004-2015).

The results indicate that the law caused a 8% to 11% decrease in the number of female homicides due to household aggression. Our estimates remain strongly robust when we include controls for labor market conditions, firearms availability and number of conditional cash transfer program beneficiaries as well as when we include group-specific time trends. State-year dummies control

12 for distinct shocks across states and the differentials time polynomials allow heterogeneous time trends according to the baseline value of the dependent. Since some of the time-varying controls are only available for a restricted period and the treatment effect may be partially absorbed by the group-speciﬁc time trends, we prefer the speciﬁcation without controls and time trends. This is presented in column (1) and report a reduction of 9% in fatal cases of domestic violence.

Figure3 reports the coefficients of the the interaction between year dummies and gender dummy from Equation2. Firstly, the graph shows that the parallel trend hypothesis is satisfied, in other words, men’s and women’s trends of domestic violence are similar before the policy introduction in 2006. This is a necessary condition for our identification strategy. Secondly, these results indicate that the magnitude of the effect increases over the years. The number of female homicides caused by aggression occurred within the household decreases by over 10% from 2013 onwards.

The law prevents about 40 female deaths due to domestic violence per year in municipalities with 100,000 inhabitants or more. These towns are very important in country. More than 90% of Brazil- ian municipalities have less than 100,000 inhabitants. In addition, they comprise almost 50% of the national population. Despite the reduction in women’s homicides due to household aggression in those municipalities, they experienced an increase in violence in recent years. From 2000 and 2010, the homicides rates in small municipalities had an increase of more than 40%.

The progressive reduction in domestic violence over the years is potentially explained by the timing of implementation of measures to punish aggressors and protect women. The boost to the conﬁ- dence in these mechanisms and a social norm updating process may also explain this dynamic. These interpretations are consistent with some available data on criminal justice system, showed in 3. According to Ministério da Justiça(2014), the number of men sentenced to prison for domestic violence presented a gradual increase over the years. It took place in conjunction with the evolution of 180 hotline reports. The accumulated number of reports in 2012 was three times higher than that number in 2010 (Secretaria de Políticas para as Mulheres(2013)). Additionally, a survey on female aggression assault show an increase in the proportion of interviewees who believe that abuses must be reported to authorities. This suggest a transition in social acceptance of domestic violence. However, Figure5 shows that the number of municipalities with women’s police stations and domestic violence specialized courts remained almost stable during the period. There are few municipalities with those specialized bodies, what suggests that the effect of the law should be even greater with the expansion of them.

13 5.2 Comparing deaths caused by different reasons

One might think that our estimates are biased by gender differential effects of fluctuations in firearms availability that are not absorbed by controls. To conduct an additional test of the sen- sibility of our results to guns availability and obtain heterogeneous effects, we separately estimate the effect of the legal reform on female homicides classified according to the weapon involved in the case. In this case, we should not find a significant decrease in the number of female deaths caused by aggressions without firearm occurred within the household. For this reason, we estimate the models specified in Equations1 and2 for the dependents log of number of homicides due to household aggression with and without firearm. We use our preferred specification, which includes control for log of municipal population by gender, year fixed effects and municipality x gender fixed effects. These results are presented in Table4 and Figure4.

Columns (1) and (2) of Table4 show that the law reduced significantly the deaths caused by aggressions within the household with firearm and without firearm. The graphs presented in the top panel of Figure4 confirm this finding and the validity of parallel trend hypothesis for both of the variables. These results reinforce the robustness of our results to variations in the stock of arms. We find average decreases in female homicides caused and non-caused by guns of 7% and 3%, respectively. The correlation between victims’ exposure to firearms and probability of reporting or applying emergency protective measures might explain this heterogeneity. Additionally, we conduct a placebo test, testing whether the legal reform affected accidental deaths. Columns (3) and (4) of Table4 and the bottom panel of Figure4 show that the law had no impact on deaths due to unintended aggression occurred within household or deaths due to household accidents, as expected.

5.3 Who are the most affected women?

Poor and minority women are disproportionately affected by intimate parter violence around the world (Aizer(2011); World Bank(2015)). The prevalence of abuse among the poorest women are in line with the intra-household bargaining view of domestic violence. However, these evidences do not invalidate other views. Partner-control behavior and gender-related norms may be correlated with female economic empowerment and other characteristics of the victim.

In order to characterize the most affected women by the legal reform, we estimate the law heterogeneous effects across groups of individuals classiﬁed according to their educational level and race. As microdata on wages are not available, we also estimate the law effect on groups of municipalities classiﬁed according to their women’s median wage in 2000. We estimate the models presented

14 in Equations1 and2 using our preferred speciﬁcation. We break down the number of homicides due to household aggression by individuals’ educational level and race. Table5 shows that the most affected were the less educated and black women. As presented in Column (1) of Table5, we ﬁnd that the law reduced in 7% the fatal aggressions against women with uncompleted primary school or lower educational level. While we observe a 3% decrease in fatal aggressions against women with higher level of education. This difference is showed in Figure6, which reports the estimates of the annual effect of the legal reform and shows that homicides for each educational group of men and women followed similar pre-treatment trends. Column (2) and Figure7 show that the law effect was strongly concentrated in black, indigenous and mixed-raced women. It caused an average reduction of 8% in domestic violence against them and a 1% reduction in domestic violence against white and yellow women.

Additionally, we conduct an analysis of heterogeneous effects for groups of municipalities classi- ﬁed according to their women’s median wage in 2000. They are presented in Column (3) of Table 5 and8. We observe that the legal reform had a greater impact in municipalities where female median wage in 2000 is above the national median. Labor market participation and income may reduce domestic aggression through an increase in women’s intrahousehold bargaining level, since it is positively correlated with ﬁnancial independence and potentially correlated with knowledge on how to protect her rights. Couple’s wage, which are positively correlated, may affect gender identity norms and violence culture.

Heterogeneous effects analyses based on individual and municipal characteristics are, as usual, correlational analyses. These variables are correlated with other attributes which may have driven the differences in the estimated impact of the legal reform. However, our whole set of results are consistent with the literature on the determinants of domestic violence. Speciﬁcally, they are consistent with the views that emphasize the role of women’s intrahousehold bargaining power and the role of social norms. This fact reinforces the importance of women educational level and labor market outcomes on the incidence of domestic violence.

5.4 Poisson model

As robustness checks, we estimate our preferred speciﬁcation using a Poisson count model since this estimator is more sensitive to outliers and zero observations than OLS models. Table6 presents the results of both models for the dependent number of female homicides due to household aggression.

We ﬁnd a signiﬁcant reduction of 8.6% in the number of female homicides using an OLS model

15 and a signiﬁcant reduction of 9.4% using a Poisson model. This exercise reinforces the robustness of our results, since the effect of the legal reform remains strongly similar using a model which appropriately considers the elevated number of zeros in the dependent variable.

6 Conclusion

This paper analyzes the effect of legal reforms aimed at reducing domestic violence on female homicides due to household aggression. We investigate the Maria da Penha Law, passed in Brazil in 2006. The law introduced legal mechanisms for curbing domestic violence crimes and protecting the victims. One of its main innovation was the urgent protective measures for women at risk. The law also created domestic violence special courts and incentivized the creation of new women’s police stations. Concurrently, the punishment for this sort of crimes became more severe. In general, the legal reform introduced measures to increase abuse reports and criminal justice effectiveness in dealing with domestic violence. Furthermore, it stimulated campaigns against gender-related aggressions.

We use a differences-in-differences approach to estimate the effect of the law on domestic violence. We exploit the high degree of comparability between male and female household homicide before the introduction of the law, excluding large municipalities and capital cities. The results from all empirical models are quite consistent. We ﬁnd that the law caused a 9% reduction in female homicides due to household aggression. 6 This reduction was progressive. The gradual increase of the effect suggests that the law depends on the implementation of services to protect women, on popular conﬁdence in its effectiveness, and on awareness campaigns. They potentially affect the incidence of domestic violence through an increase in reporting, an increase in the probability of punishment itself, and cultural changes.

The law effect is larger for black women and for those with uncompleted primary school or lower levels of education. We also verify that its impact was greater where women presented lower wages. These are the more vulnerable women, according to the literature based on intrahousehold bargaining and social norms. Unfortunately, our study is unable to disentangle effective from innefective measures introduced by Maria da Penha Law.

6For our analysis, we consider municipalities with less than 100,000 inhabitants.

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20 Figures and Tables

Table 1: Innovations introduced by the legal reform

Objectives Measures Curbing and punishing Increasing penalties domestic violence Creation of domestic violence special courts Fostering the expansion of women’s police centers and specialized bodies Stimulus to the integration of specialized criminal institutions Capacitation of criminal justice workers to deal with gender-based violence Protection and social Possibility of pre-trial detention of aggressor assistance to victims Absence of contact between aggressor and victim (emergency protective Health, psychological, social and judicial support measures) Police protection to the victims Legal mechanisms to protect victims’ patrimonial and familiar rights Assistance to aggressors Introduction of reeducation and rehabilitation programs to aggressors Changes in social norms Fostering campaigns and researches about domestic violence Incentivizing educational programs in schools Classiﬁcation of gender-based violence as human rights violation

21 iue1 ubro eaehmcdsdet oshl aggression household to due homicides female of Number 1: Figure Note:

) ag uiiaiisaeexcluded. are municipalities Large (2015)). Saúde da (Ministério DATASUS from are Data Number of homicides 0 200 400 600

2001

2002

2003

2004

2005

2006

2007 22 2008

2009

2010

2011

2012

2013

2014

2015 iue2 aeo eaehmcdsdet oshl aggression household to due homicides female of Rate 2: Figure Note:

) ag uiiaiisaeexcluded. are municipalities Large (2015)). Saúde da (Ministério DATASUS from are Data Homicides rate 0 .5 1 1.5

2001

2002

2003

2004

2005

2006

2007 23 2008

2009

2010

2011

2012

2013

2014

2015 Table 2: Descriptive statistics

Municipalities Municipalities Variable All with less than with 100,000 100,000 inhabitants inhabitants or more Homicides Rate of female homicide 4.3 3.4 5.1 [5.1] [6.6] [3.1] Rate of male homicide 50.7 30.6 67.6 [42.2] [33.4] [41.5] Rate of female homicide due to household aggression 1.1 1.1 1.1 [2.7] [3.8] [1.1] Rate of male homicide due to household aggression 4.5 4.1 4.8 [6.2] [8.0] [4.1] Number of female homicide due to household aggression 5.6 0.2 10.1 [11.4] [0.5] [14.0] Number of male homicide due to household aggression 18.5 0.8 33.4 [34.8] [1.7] [41.7] Municipal characteristics in 2000 Years of education 5.1 3.2 6.7 [2.3] [1.4] [1.6] Proportion of black* residents 45.7 49.5 42.5 24 [22.2] [24.8] [19.2] Women’s median wage 232.2 149.0 302.0 [116.5] [76.3] [97.0] Annual municipal characteristics Proportion of people working in formal sector 25.6 16.0 33.6 [17.7] [12.7] [17.4] Number of suicides by ﬁrearms 6.2 0.2 11.2 [14.8] [0.6] [18.7] Number of CCT beneﬁciaries 36640.9 2772.8 65061.1 [71157.6] [2542.7] [86826.2] Number of municipalities 5507 5230 277

Notes: The table presents the means and standard deviations in brackets. They are weighted by municipal average population. Municipalities are classified according to their average population. Proportion of black includes black, indigenous and mixed-race population. CCT beneficiaries are families that are beneficiaries of Bolsa Família Program, the main Brazilian Conditional Cash Transfer (CCT) Program. Table 3: Effect of legal reform on female homicides due to household aggression

Homicides due to household aggression (1) (2) (3) (4) (5) (6) (7) Women = 1 × Post 2006 = 1 -0.0865∗∗∗ -0.0867∗∗∗ -0.107∗∗∗ -0.108∗∗∗ -0.107∗∗∗ -0.0845∗∗∗ -0.0843∗∗∗ (0.00750) (0.00748) (0.00716) (0.00715) (0.00717) (0.00858) (0.00858) Proportion of people working in formal sector 0.0416∗∗∗ 0.00331 (0.00389) (0.00668) Suicides by ﬁrearms 0.0280∗∗∗ 0.0318∗∗∗ (0.00870) (0.0102) CCT beneﬁciaries -0.00955 -0.00944 (0.0108) (0.0108) Observations 156900 156900 156900 156900 156900 125520 125520 R2 0.447 0.460 0.474 0.475 0.475 0.480 0.481 State × Year FE X X X X X X Dependent (2001) × time polynomial X X X X X

Notes: Standard errors clustered at municipal level in parenthesis. Regressions weighted for average municipal population. The regressions in columns 1 to 5 use data from 2001 to 2015 and the regressions in columns 6 and 7 use data from 2004 to 2015. All regressions control for log of municipal population by gender, year fixed effect and municipality x gender fixed effect. Additional controls included in some columns: proportion of people aged 15 or older working in formal sector, number of suicides using firearms and number of families who

25 are beneficiaries of the Bolsa Família, the main Brazilian conditional cash transfer program (available for the period 2004-2015). Group-specific time trends are included in some specifications. Significance levels: * 10%, ** 5%, *** 1% iue3 feto ea eomo eaehmcdsdet oshl grsinoe time over aggression household to due homicides female on reform legal of Effect 3: Figure r egtdfraeaemncplpplto.Alrgesoscnrlfrlgo uiia ouainb edr erfie fetadmncplt gender x municipality and effect fixed year gender, by Regressions population level. municipal municipal of at clustered log are for that control errors regressions standard All effect. using population. fixed constructed municipal intervals average confidence for percent weighted 95 are the represent lines Vertical estimates. point Note: h gr umrzsrgeso of regression summarizes figure The Law effect by year -.2 -.15 -.1 -.05 0 .05 2001 2003 oe 1 = Women × 2005 erFE Year nlgo ubro eaehmcdscue yhueodageso.Ga osso the show dots Gray aggression. household by caused homicides female of number of log on 2007 26 Year 2009 2011 2013 2015 Table 4: Effect of legal reform on female deaths caused by different types of aggression

Aggression with ﬁrearm Aggression without ﬁrearm Unintended aggression Accidental injury (1) (2) (3) (4) Women = 1 × Post 2006 = 1 -0.0740∗∗∗ -0.0330∗∗∗ -0.00200 -0.00173 (0.00679) (0.00529) (0.00373) (0.00643) Observations 156900 156900 156900 156900 R2 0.404 0.288 0.196 0.394

27 iue4 feto ea eomo eaedah asdb ifrn ye fageso vrtime over aggression of types different by caused deaths female on reform legal of Effect 4: Figure

Law effect by year Law effect by year

lsee tmncpllvl ersin r egtdfraeaemncplpplto.Alrgesoscnrlfrlgo uiia ouainb gender, by population municipal of log for control are regressions that All errors effect. standard fixed population. using gender municipal constructed x intervals average confidence municipality for percent and weighted 95 effect the are fixed represent Regressions year lines Vertical level. estimates. municipal point at the show clustered dots Gray household. the within Note: -.16-.12-.08-.04 0 .04 -.16-.12-.08-.04 0 .04 2001 2001 h gr umrzsrgeso of regression summarizes figure The 2003 2003 Aggression withfirearm Unintended aggression 2005 2005 2007 2007 Year Year 2009 2009 oe 1 = Women 2011 2011 × erFE Year 2013 2013 nlgo ubro eaehmcdscue ydfeettpso grsinta occurred that aggression of types different by caused homicides female of number of log on 2015 2015 28

Law effect by year Law effect by year -.16-.12-.08-.04 0 .04 -.16-.12-.08-.04 0 .04 2001 2001 2003 2003 Aggression withoutfirearm 2005 2005 Accidental injury 2007 2007 Year Year 2009 2009 2011 2011 2013 2013 2015 2015 IBGE ( Munic (2014)), Justiça da (Ministério Infopen from are and years speciﬁc for available are courts specialized and stations and police (2014)) women’s violence, domestic for prison Note: Number Number 0 100 200 300 400 0 500000 1000000 Municipalities withwomen'spolicestations .Dt nnme fmnsnecdto sentenced men of number on Data (2013). Mulheres as para Políticas de Secretaria from are reports hotline on Data municipalities. Brazilian all for data show Graphs 2006 2006 iue5 vlto fmcaim opeetadpns oetcviolence domestic punish and prevent to mechanisms of Evolution 5: Figure utç Aberta Justiça 2008 2008 ) respectively. (2015)), Justiça de Nacional (Conselho database Hotline reports 2010 2010 Year Year 2012 2012 2014 2014 2016 2016 29

Number Number 0 100 200 300 400 0 2000 4000 2006 2006 Municipalicities withspecializedcourts 2008 2008 Men sentencedtoprison 2010 2010 Year Year 2012 2012 2014 2014 2016 2016 Table 5: Heterogeneous effect of legal reform on female homicides due to household aggression

Individual’s Individual’s Municipal educational race women’s level median wage (1) (2) (3) Uncompleted primary White* Below school or less residents the median

Women = 1 x Post 2006 = 1 -0.0671∗∗∗ -0.0109∗∗∗ -0.108∗∗∗ (0.00601) (0.00486) (0.00805) Observations 156900 156900 116040 R2 0.386 0.380 0.394

Completed primary Black* Above school or more residents the median

Women = 1 x Post 2006 = 1 -0.0260∗∗∗ -0.0820∗∗∗ -0.0473∗∗∗ (0.00351) (0.00630) (0.0150) Observations 156900 156900 40860 R2 0.231 0.407 0.477

Notes:Standard errors clustered at municipal level in parenthesis. Regressions weighted for average municipal population. The regressions use data from 2001 to 2015. All regressions control for log of municipal population by gender, year fixed effect and municipality x gender fixed effect. The group called White includes white and yellow population. The group called Black includes black, indigenous and mixed-race population. Municipalities are classified according to women’s median wage in 2000. Significance levels: * 10%, ** 5%, *** 1%

30 iue6 feto ea eomo eaehmcdsdet oshl grsinb individuals’ by aggression household to due homicides female on level reform educational legal of Effect 6: Figure on siae.Vria ie ersn h 5pretcndneitrascntutduigsadr rosta r lsee tmncpllvl ersin r egtdfraverage for weighted effect. fixed are gender Regressions x level. municipality and municipal effect at fixed clustered year are group, and that gender errors by standard population using municipal constructed of intervals log for confidence control percent regressions 95 All the population. represent municipal lines Vertical estimates. point Note:

h gr umrzsrgeso of regression summarizes ﬁgure The Law effect by year -.15 -.1 -.05 0 .05 2001 2003 Uncompleted primaryschoolorless 2005 oe 1 = Women 2007 Year 2009 × erFE Year 2011 nlgo ubro eaehmcdscue yhueodageso o ahgopo oe.Ga osso the show dots Gray women. of group each for aggression household by caused homicides female of number of log on 2013 2015 31

Law effect by year -.15 -.1 -.05 0 .05 2001 2003 Completed primaryschoolormore 2005 2007 Year 2009 2011 2013 2015 iue7 feto ea eomo eaehmcdsdet oshl grsinb individuals’ by aggression household to due homicides female on reform race legal of Effect 7: Figure on siae.Vria ie ersn h 5pretcndneitrascntutduigsadr rosta r lsee tmncpllvl ersin r egtdfraverage for weighted effect. fixed are gender Regressions x level. municipality and municipal effect at fixed clustered year are group, and that gender errors by standard population using municipal constructed of intervals log for confidence control percent regressions 95 All the population. represent municipal lines Vertical estimates. point Note:

h gr umrzsrgeso of regression summarizes ﬁgure The Law effect by year -.15 -.1 -.05 0 .05 2001 2003 2005 oe 1 = Women 2007 White* Year 2009 × erFE Year 2011 nlgo ubro eaehmcdscue yhueodageso o ahgopo oe.Ga osso the show dots Gray women. of group each for aggression household by caused homicides female of number of log on 2013 2015 32

Law effect by year -.15 -.1 -.05 0 .05 2001 2003 2005 2007 Black* Year 2009 2011 2013 2015 iue8 eeoeeu feto ea eomo eaehmcdsdet oshl aggression household to due homicides female wage on median reform women’s legal municipal by of effect Heterogeneous 8: Figure l ersin oto o o fmncplpplto ygne,ya xdefc n uiiaiyxgne xdefc.Lclte r lsie codn otemda fmunicipal of median the population. to municipal according average classified for are weighted Localities are effect. Regressions fixed gender level. x municipality municipal and at effect clustered fixed are year gender, that by errors 2000. population standard in municipal wage using of median constructed women’s log intervals for control confidence regressions percent All 95 the represent lines Note: Law effect by year h gr umrzsrgeso of regression summarizes figure The 2001 -.25 -.2 -.15 -.1 -.05 0 .05 .1

2003 Low women'smedianwage 2005

2007 oe 1 = Women Year

2009 × erFE Year

2011 nlgo ubro eaehmcdscue yhueodageso.Ga osso h on siae.Vertical estimates. point the show dots Gray aggression. household by caused homicides female of number of log on

2013

2015 33

Law effect by year

2001 -.25 -.2 -.15 -.1 -.05 0 .05 .1

2003 High women'smedianwage 2005

2007 Year

2009

2011

2013

2015 Table 6: Poisson model

OLS Poisson (1) (2) Women = 1 × Post 2006 = 1 -0.0865*** -0.0945*** (0.00750) (9.71e-07) Observations 156,900 99,255 R2 0.447

Notes:Standard errors clustered at municipal level in parenthesis. Regressions weighted for average municipal population. All regressions control for log of municipal population by gender, year fixed effect and municipality x gender fixed effect. The regressions use data from 2001 to 2015. Significance levels: * 10%, ** 5%, *** 1%