DOMESTIC VIOLENCE: a MATTER of POWER OR POVERTY? Empirical Evidences in Mayan Communities of Mexico

DOMESTIC VIOLENCE: A MATTER OF POWER OR POVERTY? Empirical evidences in Mayan communities of Mexico

Valentina Costa * † June 25, 2016

Abstract

I use data of the World Bank project Programático enfoque para la política de medio ambiente y cambio climatic (2014) developed in Mexico in order to study the determinants of domestic violence against Mayan women. Cases of domestic violence have been increased within the last years in Mexico, particularly amongst Mayan and poor people. Almost 40% of the women of the sample reported conditions or episodes of economic, psychological, physical or multiple violence in their lives. In line with most of the economic literature on this topic, my findings suggest that labour opportunities outside home increase the woman’s relative income that positively shifts bargaining power and reservation utility of the wife. In Mayan communities, this may lead to both an increase and a reduction of domestic violence. On one hand, whenever the women’s economic empowerment is accompanied by a rejection of the rule-setting decided by the man, the increase of relative income may lead to higher domestic violence, as supported by backlash theorists. However, the violent man knows that divorce is a credible threat in this context, especially if women become economically independent from their husbands. On the other hand, if the increase of the woman’s relative income boosts the exercise of female decision-making in domestic sphere, then domestic violence is expected to be reduced, as sustained by feminists’ standard predictions. In the latter case, in fact, the economic empowerment does not threaten the traditional role of the husband, although it empowers women’s decisions. The main contributions of this paper to the literature are the followings. First of all, I use a mix- methodology of capturing and analysing domestic violence that combines both ethnographic and economic methods. Secondly, I integrate two different sociological definitions of domestic violence sustaining that, in Mayan context, domestic violence is explained by both situational-economic and patriarchal values. Finally, since findings show that higher relative income may lead to both an increase and a reduction of domestic violence, I use non-cooperative economic models in support of both standard feminist and backlash predictions.

JEL Classification: C78, J12, J15 Key words: bargaining power, domestic violence, Mayan women

* Dept. of Economics Law and Institutions, University of Rome Tor Vergata, via Columbia, 2 – 00133 Rome. Email: [email protected]. † I am grateful to my advisor prof. Pasquale Lucio Scandizzo for guidance and important suggestions. I acknowledge the economic support from the World Bank and the in loco academic support from the University of Quintana Roo (Mexico). 1

1. INTRODUCTION

In Mexico, 40.6% of the women has suffered psychological violence by their partners at least ones in their lives, 23.8% has suffered economic violence, 13.5% physical violence and, lastly, 7.3% sexual violence (ENDIREH 2011). This high prevalence rate makes crucial the understanding of the determinants of domestic violence in order to better design policies focused on the issue. For this reason, the paper investigates whether the woman’s relative income may explain domestic violence and female decision making within the household. Since domestic violence has a multidimensional nature, the analysis starts by showing, on sociological perspective, that domestic violence suffered by Mayan women is both patriarchal terrorism and common couple violence. On one hand, patriarchal terrorism is exercised over the wife regardless her actions and even if she respects the traditional reproductive role of mother Johnson (1995). Patriarchal terrorism is a product of patriarchal traditions that support the idea to exercise control over the woman who belongs to the man. Domestic violence, thus, is perpetrated continuously by the husband in order to keep his supremacy status. Moreover, with an improvement in the wife’s economic status, the husband may increase the spousal violence also if divorce is a credible threat. This would explain why women in their productive ages are usually more exposed to domestic violence (Castro et al. 2008) especially if they work outside the home and in urban areas (Avila Burgos et al. 2009, Coker et al. 1998, Eswaran et al. 2009, Eng et al. 2009, Gonzalez-Brenes 2004, Mbugua 2014, Oduro et al. 2012, Okasha et al. 2014, Panda et al. 2005, Rapp et al.2012, Semahegn 2013). On the other hand, common couple violence is less patriarchal and more a product of nowadays economic stress beyond any specific gender discrimination (Strauss & Smith 1990). Socio- economic and health problems, in fact, may also exaggerate domestic violence. In low-income households the risk of domestic violence is higher than in any other household and disease generates imbalances in the household economy, increasing health expenses and cases of domestic abuse. (Bertocchi et al. 2012, Bloch et al. 2002, Gonzalez-Brenes 2004, Koenig et al. 2014, Oduro et al. 2012, Okasha et al. 2014, Rao 1998, Rapp et al.2012). This is especially true for diabetes of type 2 which has one of the highest rates in Mexico, with 12% of the population suffering from this condition and the majority of them lives in the Mayan Region. On economic perspective, the household in developing countries is usually modelled as conﬂictual where violent relationships tend to be unstable, the dominant person does not necessarily assure to the other person the minimum utility level required for the individual to stay, and domestic violence may be an internal threat while divorce is the ultimate threat when

2 bargaining fails (Folbre 1986, Sen 1990). The most recent economic theories focus on non- cooperative bargaining models where both partners join non-cooperative process where the tacit division of responsibilities follows traditional gender roles. Each partner accepts to be responsible for gender-specific set of household activities as long as the his (or her) reservation utility is assured. Empirical literature shows that the reservation utility is usually influenced by women’s relative income (Income Sharing Rule), female education (Education Mayan Women), female asset at marriage (House Ownership), economic support by the Government (Oportunidades and Land Ownership), and woman’s satisfaction with her closest network of friends and relatives (Parents and Friends), people from the community and local infrastructures (Neighbours, Health Services and Public Services). The and many other variables contribute in modifying people’s expectations on their own reservation utilities. In line with most of the economic literature on this topic, my findings suggest that labour opportunities outside home increase the woman’s relative income that positively shifts bargaining power and reservation utility of the wife. In Mayan communities, this may lead to an increase of domestic violence. Whenever the women’s economic empowerment is accompanied by a rejection of the rule-setting decided by the man, the increase of relative income may lead to higher domestic violence, as supported by backlash theorists. The violent man, in fact, knows that divorce is a credible threat in this context, especially if women become economically independent from their husbands or they are supported by their families. However, when women are more sustained by their parents but do not work, they are expected to suffer less domestic violence. In this case, the economic empowerment does not threaten the traditional role of the husband, although it may empower women’s decision making. The paper contributes to the existing literature in several ways. First of all, it employs ethnographic information on women’s economic status related to domestic violence in Mexico that, collected into narratives, inform the development of a non-cooperative model of bargaining. Secondly, it specifically involves indigenous people, Mexican-Mayan women in the State of Quintana Roo, and an original dataset set up from a field work. Thirdly, predictions from the model are then econometrically tested with survey data collected from the same population that is the focus of the ethnographic interviews. Results show that, in line with non- cooperative models that supports male backlash, relative income has a positive significant effect on domestic violence. Conversely, results stress the importance of networks and relationships with parents in decreasing domestic violence. Moreover, Mayan women do not curtail their power itself but they prefer to reduce the exercise of their power in order to decrease the amount of abuse as showed by Eswaran and Malhotra (2011).

2. LITERATURE REVIEW

The debate on the correlation between economic status of women and domestic violence has been extensively investigated in sociology, anthropology and economic literature. Regarding to the sociological approach, Johnson (1995) is the pioneering work on the topic. Taking into consideration the incidence of domestic violence in the United States and in other Western countries, Johnson argues that there are two forms of couple violence to be taken into consideration for implementing public policies. On one hand, there is domestic violence as common couple violence which is less patriarchal and more a product of nowadays economic stress beyond any specific gender discrimination. Although domestic violence occurs in all social classes, poverty may be treated as a strong predictor of violence everywhere (Ellsberg et al., 1999) or in some geographic locations but not in others (Johnson, 2003; Martin et al., 1999). Moreover, economic stress may be dramatically exasperated in case of health problems (Bertocchi et al. 2012, Bloch et al. 2002, Gonzalez-Brenes 2004, Koenig et al. 2014, Oduro et al. 2012, Okasha et al. 2014, Rao 1998, Rapp et al. 2012). In these conditions, domestic violence is occasionally perpetrated either by husbands and wives and usually, national surveys serve to capture these isolated reactions to conflicts (Kalil & Danziger, 2000; Lloyd & Taluc, 1999; Rodriguez et al., 2001; Tolman & Rosen, 2001). This approach is also supported by recent studies on women’s violence in homosexual relationships. Results showed that lesbian relationships are usually more violent than gay male (Bologna et al. 1987) and heterosexual couples (Lie et al. 1991). In particular, Renzetti (1992) argues that in lesbian relationships there are high levels of dependency that can be associated with domestic violence. Further evidence demonstrates that men are not more violent in heterosexual than homosexual relationships and thus, domestic violence cannot be considered as a function of patriarchal dominance towards women (Tjaden et al. 2000). On the other hand, domestic violence may be considered as a matter of male power rooted deeply in the patriarchal traditions of the family, as many feminist theorists have conceptualized (Dobash et al. 1979, 2004; Dutton 2006; McHugh et al. 2005; Felson 2002). In patriarchal societies, in fact, women are expected to be assertive and other-advocating, that means being competent and supportive of male decisions and career (Amanatullah 2012). Both assertive and self-advocating women and not-assertive and other-advocating women suffer social judgments because in the first case, ambitious and focused women can challenge male dominance by competing for male roles, while in the second case supportive and anxious women can be discriminated due to their lower presumed competency (Amanatullah 2012).

Whenever social judgments turn to domestic violence, Johnson (1995) defines violence as patriarchal terrorism that is perpetrated by the husband in order to exercise control over the wife who belongs to the man. Patriarchal terrorism is mainly physical violence, but it also includes economic subordination, threats, isolation and further psychological abuses. Differently from the common couple violence where the abuse is motivated by a need to control in the specific situation, in patriarchal terrorism the escalation of violence is related to male power and it can occur for two dynamics. First, if the wife resists to the husband control, the level of violence perpetrated by the husband can escalate until she subdues. In this case, the domestic violence can be either defined as instrument used by the husband in order to obtain something, such as extorting money or forcing the victim to behave as desired; or as male violent backlash against women who do not behave in line with traditional roles (Rudman et al. 1998, 2004). The latter definition has been proposed by backlash theorists who have also argued that women in fear of backlash from husbands tend to avoid to exercise their talents (or capabilities, Sen 1985) and their bargaining power within the household. Second, even if the wife submits, the husband may decide to use violence motivated by a need of control or of displaying that control (Dobash & Dobash 1979). In this case, expressive violence provides an assailant with direct gratification; the gratification may result simply from pleasure in inflicting harm. The existing literature strongly indicates that domestic violence occurs for both instrumental and expressive reasons and usually data on it are obtained from shelters and other public agencies that reach primarily victims of violence. On the economic perspective, the household in developing countries is usually modelled as conﬂictual where violent relationships tend to be unstable, the dominant person does not necessarily assure the other person the minimum utility level required for the individual to stay, and divorce may be the ultimate threat when bargaining fails (Folbre 1986, Sen 1990). Economic theories focus on non-cooperative bargaining models where both partners join non- cooperative process where the tacit division of responsibilities follows traditional gender roles. Each partner accepts to be responsible for gender-specific set of household activities as long as the his (or her) reservation utility is assured. For instance, the higher is the female bargaining power the more credible is her threat against the husband. Bargaining power can be shifted by several intra and extra-household factors, such as wage, employment, education, unearned income, assets ownership, external support from relatives and social services, laws and social norms that regulate marriage, divorce and inheritance (Anderson et al. 2009, Lundberg and Pollak 1993, Mabsout et al. 2010, Pollak 2005, Aizer 2010).

Usually, bargaining models assume that woman’s reservation utility is binding in equilibrium and predict that intra and extra-household socio-economic factors increase the female bargaining power, rising the credibility of the woman’s threat point and reducing the level of domestic violence in equilibrium. In particular, Straus and colleagues argue that employed women are less economically dependent on their partners and are therefore less likely to tolerate abuse (Straus et al. 1980). This standard prediction is inconsistent with backlash theory and instrumental violence. In their pioneering theoretical work on the economics of domestic violence and labour income, Tauchen et al. (1991) finds that when woman’s reservation utility is binding in equilibrium, an increase in woman’s income leads to a reduction of domestic violence in order to ensure her reservation utility. In this case, opportunities outside the family affect the distribution of resources within the family and domestic violence has only an expressive purpose. Conversely, when reservation utility is not binding in equilibrium, the increase of male relative income or the income gap in favor of the wife may serve to increase domestic violence as backlash or instrument of extortion. The employment effect, instead, leads to opposite results: increases in the proportion of the year that the husband is employed serve to decrease the level of domestic violence. Thus, not relative labour income per se that matters, but rather the woman’s economic position relative to her partner’s that is important. Extending this work, Farmer and Tiefenthaler (1997) incorporates the effects of family, welfare, shelters, divorce settlements and show that women's incomes and other financial support received from outside the marriage decrease the level of violence within the household because they increase the woman's threat point. This relation can also be positive if women’s reservation utility is not binding. In a more recent paper, taking into consideration wage discrimination in the United States, Aizer (2010) stresses that is not the actual wage that matters but the potential one, since the woman’s relative income at the equilibrium does not necessarily equals the threat point. This implies that a reduction in the wage gap may increase female bargaining power and decrease the risk of domestic violence, even if the woman is not currently working. A limitation of these models is that they assume divorce as a credible option regardless traditions and laws enforced in the context and Pareto efficient resource allocation as a result of the perfect enforceability of marital power. In fact, equilibria are not necessarily Pareto optimal, since distribution depend on history and culture that may affect whether resources are controlled by the husband or by the wife (Lundberg and Pollak 1993). Moreover, divorce is generally possible in high-income countries where divorce rights are legally ensured, but it is

6 not always applicable in middle-low-income countries where traditional norms are usually against divorce. In the latter case, instead of divorce option, employment can be considered as internal option that increases the woman’s relative bargaining power in the relationship, providing her with independent means of support if the relationship ends (Lundberg and Pollak 1993, Woolley 1988, Chen and Woolley 2001, Anderson and Eswaran 2009). For instance, using data of rural Bangladesh, Anderson and Eswaran (2009) shows that employment outside husbands’ farms contributes to female bargaining power, while non-labor income and working for family businesses do not. It can also serve to increase her self-confidence and support by her social network (London, Scott, Edin, & Hunter, 2004; Raver, 2003). Moreover, a difficulty of bringing these theoretical models into the empirical domain is that the relative income is observed in equilibrium and not at the threat point. Thus, since these models consider internal threat points, in case of breakdown the prediction on female bargaining power depends on employment if she is working and on the wife’s management of the household income if she is not working. In fact, if a woman was working before the breakdown, then she is supposed to keep earning a similar income also after the breakdown, otherwise if she was not working, she continues not working but increases her power over household income management. For instance, she can pretend to allocate the husband’s income more in line to her preferences (Lundberg and Pollak 1993). Alternative mechanisms model domestic violence as backlash and instrumental purpose. Assuming not binding woman’s reservation utility, the standard prediction does not hold and as the female bargaining power rises, domestic violence increases too because the internal threat is not credible and the woman’s reservation utility may not be assured. In this case, it should be assumed that intra-household resource allocation decisions can also be Pareto inefficient as a result of the imperfect enforceability of marital power. Using data in rural India, Bloch and Rao (2002) describes a non-cooperative bargaining model that includes domestic violence as an instrument of extortion in case of asymmetric information. The spousal abuse increases as woman’s reservation utility improves showing that, when divorce is costly and not an option for women, a dissatisfied husband can exercise more violence on a woman from a richer caste in order to extract more dowry from her family. The same mechanism can be applied to working women who have more resources to be extracted than non-working women. Bobonis, Castro and Gonzalez-Brenes (2009) expands Bloch and Rao (2002)’s model in Mexico and it is the first study that examines whether the husband strategically uses domestic violence as an extortion instrument against the wife in order to obtain money from cash flow. Results show that female beneficiaries of the conditional cash transfer by Oportunidades are less likely

7 to be victims of physical violence and more likely victims of other violent threats. Finally, Eswaran and Malhotra (2011) argues that an improvement in the wife’s reservation utility would increase female autonomy in the non-cooperative equilibrium and this may be accompanied by an increase, and not decrease, of the domestic violence. In fact, increases in woman’s education level, outside options, and the support groups available to women may incite more spousal violence both as backlash and as a mean of extortion for ensuring that the wife allocates resources more in line with the husband’s preferences. A further implementation of this model was done by Heath (2012) in urban areas of Bangladesh. Results ensure the positive correlation between work and domestic violence, but only among women with less education or a young age at first marriage. In the empirical domain, disagreement still persists regarding the sign, direction and magnitude of the association between female bargaining power and domestic violence. It can be due to measurement errors, such as different definitions of domestic abuse or difficult-to-measures ways for female employment; or to reverse causality problems in the estimation strategy used. In the latter case, generally, not accounting for endogeneity leads to a positive association (Eswaran and Malhotra 2011, Macmillan and Gartner 1999). For instance, Macmillan and Gartner (1999) shows that domestic violence depends on woman’s economic position relative to her partner’s: when both partners are employed, the risk of domestic violence lowers; otherwise if the wife is working and the husband is not employed, there are higher risks of spousal abuse. Not accounting for endogeneity may improperly lead to backlash interpretation. The only exception is Panda and Agarwal (2005) that does not employ any instrumental variable for women’s ownership of property and shows a negative association between female bargaining power and domestic violence. Conversely, accounting for endogeneity entails a negative association. For instance, Tauchen, Witte and Long (1991) and Farmer and Tiefenthaler (1997) use panel data on victims of domestic violence to examine the impact of changes in a woman’s income over time on violence, while Aizer (2010) uses an index of female hospitalizations for assault as instrument of labour income. Rao (1998) employs calorie intake of children as instrument of female control over economic resource and shows that women who have less control over resource allocation within the household, suffer higher domestic violence. Same violence-reducing effects of assets are also confirmed in Bhattacharya (2009) who employs caste indicators, number of male and female children and type of family as instruments of woman’s work status. Finally, Chin (2007) shows that by using the interaction between rainfall shocks and rice-wheat dichotomy as an instrument of women’s working status, the correlation between female bargaining power and

8 domestic violence is negative, otherwise if not accounting for endogeneity, the correlation is positive. The only exception is Bloch and Rao (2002) that shows a positive association between female bargaining power and domestic violence, although they use the ratio of the number of women to the number men at marriageable ages as instrumental variable. To my knowledge, the majority of the research on domestic violence in Mexico has been done by sociologists and anthropologists. For instance, using ethnographic data of working-class families in Guadalajara, Gonza’lez de la Rocha (1994) argues that domestic violence is used by Mexican men to maintain their dominant position and to reaffirm their asymmetric power. Economic research on domestic violence has flourished since the recent nationally- representative surveys in 2003, 2006 and 2011 (the National Survey on Relationships within the Household - ENDIREH, and the National Survey of Violence Against Women - ENVIM), which include detailed information on the prevalence of male-to-female spousal abuse and threats of violence against women. These studies have usually analyzed the potential correlation between traditional norms, female employment and domestic violence. Villarreal (2007) finds that domestic violence is closely associated with the level of control that Mexican men have within the household: higher level of coercive control by men over their partners lowers the probability of female employment and increase the risk of violence. Moreover, indigenous women are at lower risk of experiencing partner violence than non- indigenous women. Frias and Angel (2012) shows that the risk of domestic violence mainly results from engaging non-traditional gender-role attitudes and social stressors associated with exogamy and social marginalization rather than indigeneity issues. Same results are also confirmed by Avila Burgos et al. (2014). Empirical estimates show that domestic violence is positively correlated with male unemployment, urban areas and alcohol consumption, while it decreases if both partners have high education and believe in non-patriarchal values. The effects of alcohol abuse, employment and domestic violence are also confirmed by Angelucci (2007). Results show that the increase in wife’s relative income reduces alcohol abuse and drunken aggressive behavior in the long run, while short-term fluctuations in husband’s relative income have no significant effect on domestic abuse. Recently, using the last wave of ENDIREH (2011), Castro et al. (2011) finds that the risk of domestic violence is negatively correlated with men’s engagement in household activities and positively correlated with women’s employment outside the home, marriage at young age, more than three children in the household, high decision-making, both partners suffered domestic violence at their infancy, urban areas and low educational level. Moreover, results show that cash flow programs (such as Oportunidades) rises the risk of physical and sexual

9 violence, but not receiving it increases the risk of psychological and economic violence. A limitation of Avila Burgos et al. (2014), Castro et al. (2011) and Frias and Angel (2012) is that they do not take into account potential endogeneity of women’s economic status, while both Villarreal (2007) and Angelucci (2007) use instrumental variable estimations. In particular, Villarreal uses woman’s occupation, number of children and economic help from relatives as instruments of employment, while Angelucci employs temporary health shocks of household members as instrument for wife’s income, and natural disasters and village agricultural wage as instruments for husband’s income.

3. ETHNOGRAPHIC NARRATIVES

The bargaining process in the Mayan household is described by non-cooperative models, as any other low-middle income context. Ethnographic narratives would help in understanding the economic model that best fits traditional family by investigating whether divorce is a credible threat or not (Tauchen et al. 1991, Farmer and Tiefenthaler 1997) and whether domestic violence is a matter of common couple violence (economically explained by Lundberg and Pollak 1993, Woolley 1988, Chen and Woolley 2001, Anderson and Eswaran 2009) or a matter of patriarchal supremacy that leads to instrumental violence or male backlash (Bloch and Rao 2002, Bobonis, Castro and Gonzalez-Brenes 2009, Eswaran and Malhotra 2011). In Mexico, 40.6% of the women has suffered psychological violence by their partners at least ones in their lives, 23.8% economic violence, 13.5% physical violence and, lastly, 7.3% sexual violence (ENDIREH 2011). The majority of the women victim of violence are divorced and it shows either a correlation between abuses and divorce or an over-reporting of past abuses by divorced women. Looking at the State of Quintana Roo, there was an increase of domestic violence denounced at the Attorney General's office: from 1.360 in 2008 up to 2.548 in 2013. Only a few cases have occurred in Mayan municipalities of Felipe Carrillo Puerto, Othón P. Blanco, José Maria Morelos, Lazaro Cardenés and Tulum (respectively 189 in 2008 and 536 in 2013). In particular, according to the official statistics done by the Attorney General's office of Quintana Roo (2014), Othón P. Blanco is the most violent Mayan community while Lazaro Cardenés and Tulum are the least ones. In this case, conditions of poverty may exaggerate violent attitudes against women. In fact, in Othón P. Blanco only 20.27% of the employed population earns a minimum wage, while the situation is better in Felipe Carrillo Puerto, José María Morelos and Lázaro Cárdenas where the percentage rises to 36%. The Mayan Region is generally the poorest area of the State

10 with the only exception of the municipality of Tulum, whose economy is based on massive tourism of the Caribbean coast (INEGI 2014). Moreover, any health problem generates further imbalances in the household economy, increasing health expenses, economic stresses and cases of domestic abuse. This is especially true for diabetes of type 2 which has one of the highest rates in Mexico, with 12% of the population suffering from this condition and the majority of them lives in the Mayan Region. Domestic violence has a multidimensional nature and to understand why this phenomenon is widespread in Mayan society, it would help to outline the factors that may influence the bargaining process within traditional households. It is not only a matter of weighting the power of each member in taking decisions, but understanding domestic violence mainly concerns to contextualize the female condition in line with cultural, social and personal expectations. Today, Mayan society is characterized by several forms of sexism, but the first Spanish colonists who reached Mexico in XIV century described an indigenous patriarchal society where there was a strong respect for women and for female religious, economic and social roles (Proskouriakoff 1964; Marcus 2001; Coggins 1975; Gailey 1987; Hendon 1997). So, initially, the patriarchal system itself was not characterized by any gender inequality nor domestic violence. By the time, the powerful Spanish domination over Mayan people brought industrialization, capitalism and Catholicism favoring the development of cultural, religious and economic syncretism that have deeply de-powered female roles in the society (Patel 2012). Thus, the Mayans that we know today, went through a long history of colonization that, together with the most recent globalization and the national Mexican acculturalization, has deprived the female condition from its original Mayan dignity. The several forms of gender inequalities and domestic violence may come from this cultural, religious and economic syncretism, but they may be typical of any other rural society as well. The main issue here is to identify the most common factors that in Mayan communities are still depriving the female condition and understand how women construct their bargaining power within the households. The interviews conducted in the Mayan Region revealed that the wife mainly suffers a lack of economic autonomy from the husband who is usually reluctant to contribute to the household’s expenditure. The husband, in fact, seems to spend most of his income in buying personal goods (such as alcohol, tobacco, etc.) rather than satisfying primary family needs. This may lead to a further issue, which is the husband’s alcohol addiction that usually makes him aggressive against the wife. Alcoholism is a quite diffused problem in the Mayan Region and it may be explained by an increase of male frustration in life due to economic uncertainties and the related crisis of gender roles within the society (Lozano-Cortes 2013).

The increase of poverty conditions and cultural change that currently undergoing in Mexican- Mayan society have produced a partial redistribution of gender roles with respect to household activities, leading to a growing number of women entering the labor force and the slight increment of men’s participation in household productive (Schmuker 1998; García and de Oliveira 2006). However, sociological research show that women still experience very low levels of autonomy and limited participation in household decision-making processes (Casique 2001). The division of traditional roles is still very common. To the husband belongs everything concerning public and productive activities while the wife is usually responsible for productive issues, and only by imposing a domination over the wife, the man can ensure his social honor and dignity (Lundberg 1993). The male supremacy is imposed by abusing of the wife, especially in the case of jealousy. The women responders have often complained that their husbands limit them in working, going out, having friends and visiting the parents out of the village, making them isolated from relative and friends and increasing their economic dependency. This jealousy may have its roots in the concept of woman as a property of the man who has to limit and educate her to respect and keep forward with social roles. Roles of women, thus, may be changing, but they are doing so rather slowly. Evidence from the recent national surveys on domestic violence (ENDIREH 2003, 2006, 2011) also shows that female wage earners face a 30% higher risk of suffering any form of intimate partner violence (physical, sexual or emotional abuse), as compared to non-working women. Female employment might constitute a threat to many men, either because those women have an income of their own, or because having a job requires diverting time and attention outside the household, or a combination of factors of this nature. These findings seem to confirm sociological theories: domestic violence in Mexican-Mayan communities is thus, either common couple violence and patriarchal terrorism (Bobonis 2009, Castro 2006, 2008, 2011). Moreover, Mayan women reported a common frustration for the educational level achieved at the marriage. The majority of them would have loved to study longer in order to have access to more job’s opportunities, but since they were not economically supported by their parents they could not study as long as wished. Instead, they were raised up in line with traditional values and mostly educated to stay at home and to take care of the children and the husband. Their frustration for not having studied as much as they had wished may explain their intent of spending most of the family budget in children’s education, particularly in the daughter’s ones. Education, in fact, has been often defined by the women as the most important inheritance that parents can ever leave to their children. On the contrary, according to them, husbands consider

12 the investment in education as a waste of money and they usually prefer to engage sons in agricultural works and to leave daughters at homes with their wives. The lack or the low level of education reduces the employment opportunities of women affecting their economic status too. However, the problem with the low women’s employment is also due to other reasons. First of all, women usually accept gender discrimination in manual and in touristic jobs because they believe that, especially manual jobs cannot be equally done by women as well as men. This means a huge reduction in job’s opportunities for women especially in villages. Secondly, several women report to fear potential aggressive reactions from the husbands if employed outside home. As a result of it, they either do not work at all or decide to work without husbands’ knowledge. The latter seems to be a solution adopted by almost every married woman that started working after the marriage. In this way, they can earn money and have a complete control over the budget without suffering any pressure or abuse by the husband. Over 225 women, 169 do not have own income and a small number (50 women) is involved in self-employment activities. At the same time, economic independency seems to have increased their self-esteem and life satisfaction. According to narratives, the increase of female income and education leads to male backlash because any option outside the marriage is credible in Mayan villages. On one hand, women suffer from isolation which is a form of de facto male domination. As said previously, when Mayan women move to the house of their parents-in-laws often they are forced to cut relations with the original family. This means that they are not emotionally nor economically supported by their parents anymore. Moreover, the male jealousy for what the wife may be doing outside the household discourages women to build any new network of friends. This attitude is even more dramatic if she earns her own income. On the other hand, women suffer cultural and legislative limitations to divorce. Although the state of Quintana Roo has recently introduced unilateral and no-fault divorce in the legislation (2013), divorce is now easier but not very implemented in Mayan villages for two reasons. First of all, it is not well-seen by the community, and particularly women would be rarely supported by their parents in case of divorce, because women are expected to get over problems and stand whatever husband’s decisions. Moreover, during the interviews several women have claimed that the legal system does not really protect them because abuses are often very difficult to be proved and male judges are often abusers of their wives too (only 15 divorced women of 225 interviewees). Secondly, the lack of control on personal economic resources would make difficult to women to leave their husbands when problems occur, especially because they will never get the custody of the children.

Taking into consideration these ethnographic narratives, I expect that woman’s relative income and control of personal economic resources may increase female bargaining power within the household and it may lead to higher domestic violence as male backlash.

4. DATA AND BASIC DESCRIPTIVE ANALYSIS

4.1. The survey

This case study of domestic violence in 21 Mayan municipalities of Quintana Roo (Mexico) is part of a larger investigation on eco-tourism that was financed by the World Bank from April to June 2014. The structure of the survey was designed taking into consideration recent national surveys on domestic violence (ENDIREH 2003, 2006, 2011) and the field work on domestic violence against women in Mayan region done by the Women’s Institute of Quintana Roo (Instituto Quintanarroense de la Mujer) and the University of Quintana Roo (Lozano Cortes 2009). I have also added questions on life satisfaction in order to cross self-reported levels of happiness with other socio-economic and domestic violence data and understand what may influence women’s well-being. Finally, meeting with academics of the University of Quintana Roo and with Mayan women during the pilot survey I could benefit from further suggestions that were included in the last version of the questionnaire. Special attention was given to intra- household data that would allow to compare female and male socio-economic conditions in order to test which factors may be credible threats and reduce domestic violence within Mayan communities.

4.2. Dependent Variable: Domestic Violence

According to the survey, out of 225 women, 89 has suffered at least one type of domestic violence in their lives and the majority of them has reported more than one violence, in particular economic violence has occurred 224 times, psychological violence 454 times and physical violence 138 times (see Table1 in Appendix). By taking into consideration the severity over the incidence of domestic violence, results show that physical violence has the highest weighted severity (1.1) compared to economic (0.71) and psychological violence (0.6), although the latter one is the most frequent. Since the responders were only women who has ever experienced violence in their lives, data shows either that women perceive physical violence generally more dangerous than the other violence and that several women have often suffered what they consider the most serious physical abuses, such as being grabbed by the husband (1.2).

Regarding to economic violence, the most serious and rare abuse is the husband who is reluctant to spend money for the household (1). On the contrary, the most common and the least important economic violence (0.47) is the husband that does not allow the wife to work. Finally, the most serious psychological abuse (0.88) although very rare is the husband who threatens the wife to leave the house and bring the children away with him. Instead, one of the most diffused and least serious (0.28) is the husband who gets angry with the wife if the housework is not well-done (such as meals, cleaning, etc.). Capturing the incidence and the severity of domestic violence allows to make a specific distinction between whether the abuse happens or not and how dangerous it is considered by the women. As showed above, the most common and the least serious abuses are those related to gender roles, such as being prevented from working outside the house or being blamed for not doing housework. Looking at the data, it resulted that there is a small number of Mayan women who earn own income and those who work are either single and do not actually suffer domestic violence or are still in a violent relation and work in family business without any fix monthly wage. This may confirm the nature of domestic violence as patriarchal terrorism. Psychological violence is reported by women above 45 years old regardless their employment (Figures 2 and 4 in Appendix). Instead, economic violence is mainly suffered by women in their productive age (25 to 45 years old) and employed in housework or in-house economic activities (Figures 2 and 4 in Appendix). By adopting the violence option, the husband strengthens the male regime even when he is not threatened by his wife. Domestic violence may be also correlated with social status and the widespread poverty in the Mayan region. According to data collected, the monthly income of Mayan households is about 4.000MXN which means 2.4 USD per capital per day (closed to the poverty line) if we consider a family composed by four people on average. Usually, the poorest families are those in which the husband is the head of the household and lives outside the house (1850MXS month, 1USD per day per capita) or those families composed by a single woman (3053MXS per month, less than 2USD per day per capita). Economic pressure may favor couple dissatisfactions increasing the risk of domestic abuse (see Figure 5 in Appendix). Domestic violence, thus, seems to be related to economic pressure, gender roles and power issues. In a context where female bargaining power cannot be strenghtened by options outside the marriage (such as parent’s network and divorce), as the female income increases or household economic conditions worsen-off, domestic violence is expected to rise due to the male backlash (see Figure 3 in Appendix).

Female education is also associated with domestic violence. Data show two different trends: when there is no or low education, psychological and physical violence prevail over economic violence; with the increase of years of schooling the percentage of economic violence rises while the incidence of other abuses decreases. According to these correlations, it seems that women suffer domestic violence anyway regardless their educational levels. The only thing that may change is the type and perhaps, the intensity of the violence. High female educated would enlarge women’s opportunities to get a job outside the home, earn own money and have potential sexual relationship with other men. Of course, the incidence of domestic violence should also take into consideration the husband’s education as well. The average level of education of both partners is the middle school (not completed: 8 years) and the head of the household is usually more educated than his (her) partner apart from his (her) sex. The only exception are single divorced women who are the oldest and the lowest educated of the sample, mostly confined to unskilled and under-paid jobs in Mayan communities. Psychological and economic abuses may be tolerated till they explode into an escalation of violence by both partners. However, social isolation, economic dependency and low education of women may reduce their opportunities to react to domestic abuse. In order to better test this mechanism, I construct two dependent indexes of domestic violence: one capturing the incidence of the domestic violence and the other one capturing the severity of abuses. Given the economic, physical and psychological dimension of the domestic violence, the categorical variables chosen to measure it are Ignored Wife, Put Children Against Wife, Jealous, Husband Not Housework, Cannot Meet Friends, Angry Uncomplished Housework, Threatened Leave House With Children, Insulted, Beated, Thrown Objects, Kicked, Grabbed Hair, Not Allow Work, Steal Money Asset, Threatened Not Allowance, Miser Husband and Complained Wife Money Management. In order to construct the indexes, I first need to reduce this complexity into a smaller dimension unit by using the principal-component factor method retaining factors with eigenvalues greater than one (Castro 2011, 2006). Starting with the Incidence-Domestic-Violence-Index, I recode as 1 if the woman has ever experienced domestic violence and 0, otherwise. The consistency of the items is very high (the Cronbach’s alpha is 0.8789). Then I conduct the exploratory analysis and I obtain four factors with eigenvalues greater than one that explain 56% variance of the total variance. Rotating the four factors with the orthogonal method (varimax), the correlated variables are converted in four linearly uncorrelated household assets dimensions. The higher the factor loading (high variance), the more relevant is the item for explaining the factor.

Based on the outputs shown in the Annex, the seventeen items are thus classified in four dimensions, such as the Incidence of Psychological and Physical Domestic Violence (Factor I explains 17.4%, 0.1743, of the total variance); the Incidence of Economic and Physical Domestic Violence (Factor II explains 15%, 0.1514); the Incidence of Psychological and Economic 1 Domestic Violence (Factor III explains 13%, 0.1280); and the Incidence of Psychological and Economic 2 Domestic Violence (Factor IV explains 10%, 0.1041). Once I classify the items in four dimensions, the Incidence-Domestic-Violence-Index, then, comes from the sum of the four and it has a very high sample adequacy (Kaiser-Meyer-Olkin, KMO, is meritorious, 0.8826). The same procedure has been replicated for constructing the Severity-Domestic-Violence- Index, but in this case I recode women’s responses as a categorical variable equal to 0 if there is not any abuse reported, to 1 if the abuse suffered is not serious, to 2 if the abuse is serious and to 3 if it isvery serious. The consistency of the items is slightly higher than for the Incidence Index, meaning that the severity-variables better explain the same concept of violence (the Cronbach’s alpha is 0.9067). Then I conduct the exploratory analysis and I obtain three factors with eigenvalues greater than one that explain 55,37% variance of the total variance. Based on the outputs shown in the Annex, after varimax rotation the seventeen items are classified in three dimensions, such as the Severity of the Psychological and Physical Domestic Violence (Factor I explains 41%, 0.4095 of the total variance); the Severity of the Economic and Physical Domestic Violence (Factor II explains 7.4%, 0.0740); and the Severity of the Psychological Domestic Violence (Factor III explains 7%, 0.0702). Once I classify the items in these dimensions, the Severity-Domestic-Violence-Index, is calculated as the sum of the three and it has a very high sample adequacy (KMO is meritorious, 0.8842). Both Indexes are scaled between zero and one and employed as a continuous dependent variable.

4.3. Explanatory variable

The explanatory variables of interest are the set of intra and extra-household factors that affects the woman’s reservation utility which is the female bargaining power in equilibrium. Those factors are: woman’s relative income contribution to the household income (Income Sharing Rule), female education (Education Mayan Women), female asset at marriage (House Ownership), economic support by the Government (Oportunidades and Land Ownership), and woman’s satisfaction with her purchasing power (Income), closest network of friends and relatives (Parents and Friends), people from the community and local infrastructures (Neighbours, Health Services and Public Services).

The level of education is calculated in years of schooling according to the national education system in Mexico: six years to complete the primary school (or first level of technical school), nine years to complete the middle school (or second years of technical school), twelve years to complete the High school (or third level of technical school), sixteen years to complete the Bachelor degree, eighteen years to complete the Master’s degree and twenty-one years to complete the Doctoral degree. The economic support by the Government (Oportunidades and Land Ownership) and assets at marriage (House Ownership) variables are dichotomous and equal 1 if women own (or she is beneficiary of) them and 0 otherwise. Women’s satisfaction variables are categorical and equal to 2 if women are very unsatisfied, to 3 if unsatisfied, to 4 if normal, to 5 if satisfied, to 6 if very satisfied, to 7 if super-satisfied. On one hand, the reservation utility is a matter of access to goods. Since the economic empowerment may be influenced by the domestic violence itself, I only include assets that woman may have inherited and brought to the marriage, such as the house ownership and education. The land issue in Mexico, instead, deserves special attention here. Land is communal (ejido) and community members individually farm designate parcels and collectively maintain communal holdings. Ejidatarios do not actually own the land, but are allowed to use their allotted parcels indefinitely as long as they do not fail to use the land for more than two years. They can pass their rights on to their children and, since the 90’s, they can sell it as well. On the other hand, the reservation utility concerns personal satisfaction with own income, network of friends and relatives, people from the community and access to local infrastructures, no matter what is the net income or the quality of the relationships or the public services. According to Mayan tradition (but it Is typical of every farmer culture), after the marriage woman is expected to leave her family and move to the husband’s house. This may lead to change village and community and be part of an extended household where the husband’s father is usually the head of the household. In the new context, woman’s exclusion is exasperated by the jealousy of the husband who discourages any opportunities to have friends in the neighbourhood and in the scare economic resources that do not allow the woman to frequently visit her parents. By reducing social relationships, the woman becomes emotionally and economic fragile, exclusively depending on the husband. The vicious circles may be avoided and interrupted by the intervention of the Government. Together with the development of local infrastructures or social services (such as health centres, public transportations, lightening in the main roads, etc.), the Government economically supports Mexican citizens with social assisted programs (such as Oportunidades, rebranded as Prospera) that are designed to target poverty by providing cash payments to poor

18 families in exchange for regular school attendance, health clinic visits, and nutrition support. Cash payments are made from the government directly to the saving accounts of the women who are supposed to manage the money. According to ENDIREH (2011), the majority of the women beneficiaries of the programme are married (17.4%) or widows (16.8%). Single and divorced women also benefit from it but are less numerous (10.6% and 5.5% respectively). In the Mayan sample (2014), more than 50% of the households benefits from the cash payments that compose almost all the women’s income (66%). The immediate effect of cash payments and transfers from relatives is to enlarge the woman’s satisfaction with her purchasing power, especially if, with zero personal income, they are the main economic sources. Of course, the purchasing power also depends on needs and expectations of expenses, but for sure it is not strictly positive correlated with income. All these factors are exogenous variables apart from the Income Sharing Rule, which is the wife’s contribution to the household income and may be endogenously determined with Domestic Violence. In fact, women’s decisions to work and earn money may be negatively affected by the husband in case he is inflicting abuse on her. The only exception would be if the woman was working at the time of the marriage and kept doing the same job along the marriage, but there are not data on that. Thus, in order to use the Income Sharing Rule as one of the explanatory variables I need to instrument it as explained in the procedure in the Measurement section.

4.4. Covariates

I include a set of control variables at the intra-household and household level that may be correlated with the explanatory variables and explain domestic violence as well. Regarding to the intra-household level, I use age (Age Mayan Women and Age Mayan Men), education of the husband (Education of Mayan Men) and labor variables (Women Occupation, Male Occupation, Male Black Labour, Male Labour, Female Black Labour and Female Labour). The age is measured in years and male education in years of schooling as for female education. Age and education are both non-linear variables. Employment variables concern occupation, contractual jobs and self-employment. Occupation is a categorical variable ordered in an upward scale from the less to the higher paying jobs according to INEGI (2014), in particular it equals 0 if unemployment, 1 if farms animals, 2 if service forestry, 3 if forestry management, 4 if service industry, 5 if agriculture, 6 if construction, 7 if trade, 8 if manufactory, 9 if transportation sector and 10 if touristic industry. Labour is a binary variable that equals to 1 if the woman (or the man) has a fix-term contract and to 0 if it is a temporal job. Black Labour is

19 also a binary variable that equals to 1 the woman (or the man) is self-employed in own activities and to 0 if in family business. According to ENDIREH (2011), the majority of the women is employed in fix-term jobs (63.1%) and 80% of them is single or divorced, while in Mayan sample (2014) only 16% of the women is employed in fix-term jobs and almost everyone is married. Special attention deserves the empowerment indexes: Women Decision Making Index, Women Freedom Index, Women Household Engagement Index and Cultural Role Index. Within the household, the decision-making seems to be higher for women than for men (28.6% of cases versus 19.9% for men) and it is gender-based. Usually the man decides whether going out, buying furniture, moving house, having sex and using contraceptives, that are family decisions linked with the male social power. Female decision-making, instead, is limited to whatever implies managing the house although household’s expenditure, food consumption and children care are done under the husband’s control (Pahl 1995). The Decision Making Index is constructed based on thirteen questions that include decisions over Work, Buy Food, Children Education, Children Freedom, Children Health, Hanging Out, Money Management, Moving House Town, Buying Furniture, Sexual Relations, Contraception Methods, Who Uses Contraception Methods and Number Children. I order the responses in an upward scale from 0 (patriarchal attitudes) to 2 (egalitarian power), in particular, as 0 if the husband makes decision alone, as 1 if the couple makes decision together, as 2 the wife makes decision alone. The consistency of the items is high (the Cronbach’s alpha is 0.6543). However, it is not possible to capture to which extent the wife is free to make decisions alone or, otherwise is influenced by her husband. The same issue concerns the participation of the wife in couple’s decision. In both cases, the woman has the power in the decision process as long as the relationships between the two partners is based on equal values. In order to get a single index, I conduct the exploratory analysis by using principal-component factor method retaining factors with eigenvalues greater than one as it was done previously. As a result, thirteen factors have eigenvalues greater than one and explain 72.53% variance of the total variance. Rotating the thirteen factors with the orthogonal method (varimax), the correlated variables are converted in six linearly uncorrelated decision making dimensions. The higher the factor loading (high variance), the more relevant is the item for explaining the factor. Based on the outputs shown in the Annex, the thirteen items are thus classified in six dimensions, such as the Everyday Decision (Factor I explains 21%, 0.2082), the Children Decision (Factor II explains 13%, 0.1321), the Sexual Decision (Factor III explains 11%, 0.1139),

20 the Special Decision (Factor VI explains 10%, 0.0973), the Reproductive Decision (Factor V explains 9%, 0.0925), and the Family Planning (Factor VI explains 8%, 0.0813). Once we classified the items in six dimensions, the Decision-Making-Index, then, comes from the sum of the six and it has a miserable sample adequacy (KMO is miserable, 0.5426). The woman’s freedom concerns permissions to perform several actions, such as are working for a pay, visiting her relatives and friends, going for shopping and enjoying her free time. Data shows that the wife frequently needs the husband’s permission for critical decisions considered out of the traditional gender role, such as accepting a job’s opportunity or going out with friends for a party. I construct the Freedom Index based on four questions referring to Freedom Party, Freedom Visit Parents, Freedom Shopping and Freedom Work. I recode the responses from 0 (low freedom) to 3 (high freedom), in particular, as 0 if the wife does not go alone or cannot do the activity, as 1 if the wife must ask permission to her husband and depending on his answer the activity can be done or not, as 2 if the wife must inform her husband but she has the freedom to do the activity, and as 3 if the wife does not need to do anything related to her husband. The consistency of the items is very mediocre (the Cronbach alpha is 0.5023). In order to get a single index, I conduct the exploratory analysis by using principal-component factor method retaining factors with eigenvalues greater than one as it was done previously. As a result, one factor has eigenvalues greater than one, the Freedom Index, that explain 40.47% variance of the total variance and whose KMO is mediocre (0.6141). Gender roles are captured by the Women Household Engagement Index and Men Household Engagement Index. The latter one is used as instrumental variable for the Income Sharing and it is better explained in the measurement part. Data show a fairly well-defined intra-household division of roles between the husband and the wife and confirms the genderdization of tasks in Mayan society. The main "female activities" are the housework (93% women versus 57% men) and the care of children (77% women versus 46% men). Husbands, instead, are mostly involved in reparation of broken domestic objects, fixtures or artifacts (60%), and a few of them is in charge of wood collection, agricultural works in fruit garden and bill payments. Regarding to the collection of water, the majority of the families (59%) buys drinkable water from street sellers (private service of trucks). Looking at the data by location, there might be some differences between urban and rural areas. In particular, in the municipalities of Tulum and Othòn P. Blanco, data show the highest percentages of husbands who are never involved in housework. In the two urban areas of Lazaro Cardenas and Felipe Carrillo Puerto there are high levels of gender equality in the division of roles within the household, and complete equality between wives and husbands is only reached in Jose Maria Morelos.

I construct the Women Household Engagement Index based on four questions referring to activities done exclusively by women (Housework and Child Caring) and by men (Reparation and Payment Responsibility) and measure the level of engagement of each member in activities at home. I create categorical variables that capture the frequency of women's engagement in activities at home: I score as 0 if women never do it, as 2 if they do it twice a week, as 4 if they do it four days a week and as 7 if it is a daily action. Then I conduct the exploratory analysis and I obtain two factors with eigenvalues greater than one that explain 56% variance of the total variance. Rotating the two factors with the orthogonal method (varimax), the correlated variables are converted in two linearly uncorrelated household assets dimensions. The higher the factor loading (high variance), the more relevant is the item for explaining the factor. Based on the outputs shown in the Annex, the four items are thus classified in two dimensions, such as the Caring Activities (Factor I explains 30%, 0.2953) and Maintenance Activities (Factor II explains 27%, 0.2699). Once we classified the items in two dimensions, the Women Household Engagement Index, then, comes from the sum of the two and it has a miserable sample adequacy (KMO is 0.4964). However, I did not proceed to aggregate the variables in a single index because the Cronbach’s alpha is very low (0.1533) and I checked that all the results do not hold if I included each variable separately instead of a single index. Finally, woman’s opinions on gender roles reflect upon whether she is more or less conservative. Data show mainly traditional women who respect gender roles. Almost half of the female population seems to accept that the husband’s jealousy may limit woman’s freedom. The majority of the responders, in fact, believes that a so-called good wife should obey the husband who is the main responsible for the household income. At the same time, they believe that women should be free to decide whether to work or not (78.67%) because they have the same capability of the men to earn money (84.89%) although less opportunities to be involved in heavy manual works (such as workmen, etc.) (51.56%) and children’s education should be a shared responsibility between partners (97.33%). The Gender Role Index can be captured by the following eight variables: Husband Right Beat, Sexual Obligations, Only Husband Have To Work, Obey To Husband, Share Responsibility Children, Free To Work, See Friends Against Husband Will and Equal Capabilities. Since some variables claim patriarchal attitudes (Husband Right Beat, Sexual Obligations, Only Husband Have To Work and Obey To Husband) and others more egalitarian values (Share Responsibility Children, Free To Work, See Friends Against Husband Will and Equal Capabilities). I score as 1 to more egalitarian opinions (such as women against Husband Right Beat, Sexual Obligations, Only Husband Have To Work and Obey To Husband and supportive of Share Responsibility

Children, Free To Work, See Friends Against Husband Will and Equal Capabilities) and 0 to more patriarchal responses (otherwise) (Castro 2011). The consistency of the items is mediocre (The Cronbach’s alpha is 0.5034) and the ultimate goal is to reduce this complexity into a unique Cultural Role Index with values between 1 (egalitarian roles) and 0 (otherwise). Then I conduct the exploratory analysis and I obtained two factors with eigenvalues greater than one that explain 46.76% variance of the total variance. Rotating the three factors with the orthogonal method (varimax), the correlated variables are converted in three linearly uncorrelated decision making dimensions. The higher the factor loading (high variance), the more relevant is the item for explaining the factor. Based on the outputs shown in the Annex, the eight items are thus classified in three dimensions, such as Equal Capabilities (Factor I explains 21%, 0.2068), Patriarchal Values (Factor II explains 13.4%, 0.1340), and Women Right (Factor III explains 12.7%, 0.1268). Once I classify the items in three dimensions, the Gender Role Index, then, comes from the sum of the six and it has a mediocre sample adequacy (KMO is 0.6226). Regarding to the household level, I have included the following household variables: Length of Relationship, Family Dimension, Maya City, Alcohol Consumption, Tobacco Consumption, Household Income, Household Economic Status Index and Health Status Index. The Length of Relationship is the number of years the couple has been living together. Family dimension is the number of people composing the household and it is measured by exponential variable that may capture its non-linear trend. Location is measured as a binary variable equal to 1 if the household is located in urban areas and 0 if in a rural. Alcohol and tobacco consumption are binary variables equal to 1 if the household consumes the good and 0 otherwise. Household income is the sum of both partner’s incomes expressed in Mexican pesos. Since the sample also includes incomes from self or black employment, I here assume that income does not correspond to wages. Socio-economic and health problems may exaggerate domestic violence, as it is the case for numerous families where at least one member suffers diabetes. In a large household there may be more socio-economic stress due to a reduction of the household income per capita and it may rise the risk of domestic abuses. However, when the number of children is very high the wife may be better protected by them from potential domestic violence (Bertocchi et al. 2012, Bloch et al. 2002, Gonzalez-Brenes 2004, Koenig et al. 2014, Oduro et al. 2012, Okasha et al. 2014, Rao 1998, Rapp et al.2012). Household assets and health status are measured in indexes. The Household Socio-economic Index is composed by household’s assets, such as The Household Socio-economic Index is composed by household’s assets, such as Sewing Machine

Ownership, Water Availability Inside House, Wood Energy, Television Ownership, Heater Ownership, Fridge Ownership, Vehicle Ownership, Animal Domestic Consumption, Cement Wall, Saving Account, Mobile Ownership and Radio Ownership. Each asset is a binary variable that I recode as 1 if the family owns the asset and 0, otherwise. The Cronbach’s alpha quite high (0.6838). Then I conduct the exploratory analysis and I obtain two factors with eigenvalues greater than one that explain 55.15% variance of the total variance. Rotating the two factors with the orthogonal method (varimax), the correlated variables are converted in four linearly uncorrelated household assets dimensions. The higher the factor loading (high variance), the more relevant is the item for explaining the factor. Based on the outputs shown in the Annex, the eight items are thus classified in four dimensions, such as Main Household Goods Index (20%, 0.2024), Telecommunication Index (13%, 0.1264), Women Goods Index (11.4%, 0.1140) and Secondary Household Goods Index (10.8%, 0.1087). Once I classify the items in four dimensions, the Household Socio-economic Index, then, comes from the sum of the four and it has a very high sample adequacy (KMO is middling 0.7072). In vulnerable socioeconomic strata, any disease generates imbalances in the household economy, increasing health expenses and cases of domestic abuse. This is especially true for diabetes of type 2 which has one of the highest rates in Mexico, with 12% of the population suffering from this condition and the majority of them lives in the Mayan Region. The Health Status Index is composed by the following variables: Hypertension Problems, Overweight Problems, Diabetes Problems and Respiratory Problems. Each health problem is a binary variable that I recode as 1 if at least one member of the family has the problem and 0, otherwise. Then I conduct the exploratory analysis and I obtain two factors with eigenvalues greater than one that explain 55.87% variance of the total variance. Rotating the two factors with the orthogonal method (varimax), the correlated variables are converted in two linearly uncorrelated household assets dimensions. The higher the factor loading (high variance), the more relevant is the item for explaining the factor. Based on the outputs shown in the Annex, the eight items are thus classified in two dimensions, such as Respiratory Problems (Factor I explains 30%, 0.3014) and Nutrition Problems (Factor II explains 26%, 0.2572). Once we classified the items in two dimensions, the Health-Status-Index, then, comes from the sum of the two and the KMO is miserable (0.5154). However, I do not proceed to aggregate the variables in a single index because the Cronbach’s alpha is very low (0.2241) and I check that all the results do not hold if I included each variable separately instead of a single index.

5. EMPIRICAL STRATEGY

The regression equation of interest is given by the following expression:

퐷푉푖 = 훽0 + 훿푋푖 + 훽1푍푖 + 휀푖 (1) where i refers to woman, 퐷푉푖 is the variable capturing either the incidence and the severity of domestic violence, 푋푖 includes the set of explanatory variables and, therefore, 훿 is the parameter of interest, 푍푖 includes the set of covariates and 휀푖 is the error term. The main concern in estimating the effect of women's economic status on the incidence of domestic violence is the potential endogeneity of the women's share income from unobserved heterogeneity, measurement error and reverse causality (Wooldrige 2010). Unobserved heterogeneity arises if unobserved variables that explain domestic violence are correlated with explanatory variables. For instance, according to the literature, women's working decisions may be influenced by cultural factors, alcohol abuse, health conditions, etc. The problem was faced by controlling for these variables. Measurement error, instead, arises when domestic violence is correlated with the amount of measurement error. However, it is not expected to be a serious problem for two reasons. First of all, data were collected by trained women, following the standard procedure. Secondly, measurement error in the dependent variable causes the variance to be large but, as checked with ANOVA Test, it was not the case. The main endogeneity problem is the potential reverse causality of domestic violence on the Income Sharing Rule. Whenever the traditional division of gender roles is maintained with the use of violence, the woman’s decision to work and to earn money may be conditioned by the abuse itself. In order to avoid reverse causality, I employed an instrumental variable approach and estimate the model using IV regression. I chose the Men Household Engagement Index as a valid instrumental variable because consistent with three conditions: exogenous and sufficiently correlated with the endogenous variable Income Sharing Rule; uncorrelated with unobservable factors included in the error term; and associated with domestic violence only through their effects on the Income Sharing Rule. The first condition is tested by running the first-stage regression of IV model. Results are reported in Table 21 in Annex. Table 21 shows that there are several variables significantly correlated to the women’s relative income. In line with the literature, working outside home increases the women’s relative income, while being employed in family business does not. Moreover, good quality of social relationships may protect women from economic fragility that comes from isolation and increase her relative income. Older women are usually more powerful than younger ones and higher consumption of alcohol within the household may be associated with lower women’s power in deciding how to allocate resources. Finally, an interesting result comes from the negative correlation between diabetes and the women’s relative income, 25 meaning that women who suffer diabetes may face more problems in getting economical independent. This result may need special attention when considering Mayan region since it has the highest rate of diseases of the country. Amongst many, the Men Household Engagement Index is the most correlated to Income Sharing Rule and it is the only exogenous variable that is not associated with domestic violence. Regarding the two other conditions, they are called exclusion restrictions and cannot be tested. Thus, my identification assumption is that Men Household Engagement is uncorrelated with unobserved factors included in the error term and it only affects domestic violence through its effect on the Income Sharing Rule. However, although the instrument seems to be exogenous, there can be still problems in model identification. Men Household Engagement, in fact, can be correlated with women’s labour opportunities, which means that if the husband reduces his working hours in order to help at home, the wife may need to compensate the loss of wage by working outside the house. Differently, here I assume that if the husband employs part of his free time to child care and cleaning, women would benefit from this support and domestic violence may be reduced, as showed by Castro (2011). This is furthermore, linked to the fact that the Husband Not Housework is the most diffused psychological violence. Finally, since the large number of covariates included in the analysis may increase the risk of over-identification of the model, I select the best combination of independent variables by testing two different things simultaneously: the exclusion of one or more exogenous variables that should be included in the structural equation; and the uncorrelation of the instruments with the error term. Avoiding multicollinearity, I include All specifications include control variables at intra-household level (Cultural Role Index, Female Labour, Female Black Labour, Age Mayan Women, Satisfaction with Income, Parents, Neighbours and Friends, Education Mayan Women, State Support and Land Ownership) and at household level (Household Economic Status Index, Tobacco Consumption, Alcohol Consumption, Respiratory Problems, Diabetes Problems, Hypertension Problems, Maya City, Length Relationship and Family Dimension). I drop Men Age, Male Education and House Ownership due to missing values, male labour variables (Male Black Labour and Male Labour) because of their correlation with the instrumental variable, and Women Occupation because of its correlation with Female Labour and Female Black Labour. The variance due to unnecessary terms estimation is then reduced and the prediction performance of the model is improved. Further problems with over-identified model may occur when an exogenous variable is instrumented thinking of its endogeneity. In this case, I test for endogeneity with both domestic violence indexes and I reject the null hypothesis that the

Income Sharing Rule is exogenous (at 1%). Thus, I am correct in treating the Income Sharing Rule as an endogenous variable and in instrumenting it by using the Men Household Engagement Index. In order to construct the instrumental variable, I consider four questions that refer to activities done exclusively by women (Housework and Child Caring) and by men (Reparation and Payment Responsibility). I create categorical variables that capture the frequency of men's engagement in activities at home: I score as 0 if men never do it, as 2 if they do it twice a week, as 4 if they do it four days a week and as 7 if it is a daily action. The internal consistency is good (Cronbach’s alpha is 0.4762). Then I conduct the exploratory analysis and I obtained two factors with eigenvalues greater than one that explain 41.56% variance of the total variance. Rotating the two factors with the orthogonal method (varimax), the correlated variables are converted in two linearly uncorrelated household assets dimensions. The higher the factor loading (high variance), the more relevant is the item for explaining the factor. Based on the outputs shown in the Annex, the four items are thus classified into one dimension, the Household Engagement (Factor I explains 42%, 0.4156) that is the Men Household Engagement Index, whose KMO is mediocre (0.6229). The level of male engagement in home activities (mean value is 0.39) is way lower than the female one (0.62) and it may influence the women’s employment in productive activities, explaining the correlation with the instrumented variable.

6. RESULTS

5.1. Incidence of domestic violence

Tables 25 and 26 in Appendix report results from OLS and IV regressions for Income Sharing Rule, Women Decision Index, Female Labour, Female Black Labour, Education of Mayan Women, State Support, Satisfaction with Parents, Satisfaction with Income, Urban Areas and Consumption of Alcohol. Regarding to the Incidence of Domestic Violence, regressions estimates show that in both models the Income Sharing Rule is statistically significant and positively correlated to Psychological-Physical Domestic Violence (0.073 in A and 0.553 in B), Economic-Physical Domestic Violence (0.020 in A and 0.063 in B), Psychological-Economic Domestic Violence 1 (0.017 in A and 0.042 in B) and Incidence of Domestic Violence Index (0.117 in A and 0.680 in B). Differently, Psychological-Economic Domestic Violence 2, which includes Put Children Against Wife and Complained Wife Money Management, is not significant both in OLS and in IV estimates.

The magnitude of the effect and the significance level are usually larger in IV estimates than in OLS ones. An example is the Psychological-Physical Index that includes Ignored Wife, Husband Not Housework, Angry Unaccomplished Housework, Threaten Leave House With Children, Beat and Grabbed Hair. One-point increase in relative wage is estimated to increase the probability of Psychological-Physical abuses by 0.553 in IV (while by 0.073 in OLS) and it is significant at 1% (while at 5% in OLS). The 95% confidence interval for the IV estimate is between 0.021 and 0.890, which is a very wide range compared to the coefficient interval in OLS (0.005 and 0.117). The standard error is large too (0.041 in A and 0.178 in B for the Incidence Index), but not excessively large otherwise the instruments would have been weak. The presence of larger confidence intervals and standard errors are a price to pay to get a consistent indicator of the woman’s share income assuming that the Income Sharing Rule is endogenous (Wooldridge 2010). By extending the analysis to the other variables, results reported in Table 25 show that female education may reduce the risk of domestic violence (0.015 in OLS and 0.112 in IV), while in urban areas domestic violence may be higher than in rural communities (0.095 in OLS and 0.132 in IV). Controlling for endogeneity of the woman’s relative income in IV regressions may lead to ambiguous results for Women Decision Making, Female Labour, Satisfaction With Income and Alcohol Consumption. One-point increase in alcohol consumption is estimated to increase the probability of the incidence of abuses by 0.094 in IV (while it is not significant in OLS) and one-point increase in women’s decision making in domestic sphere may reduce domestic violence by 0.301 in IV (while it is not significant in OLS). Moreover, Female Labour and Satisfaction With Income are significantly associated to the Incidence of Domestic Violence in OLS estimates, but not in IV regressions. Finally, State Support and Satisfaction With Parents are non-significantly associated to the Incidence of Domestic Violence.

5.2. Severity of domestic violence

Now I restrict the analysis to the Severity of Domestic Violence. Table 26 shows similar results to the Incidence Index. Regressions estimates show that in both models the Income Sharing Rule is statistically and positively associated to Economic-Physical Domestic Violence (0.036 in A and 0.141 in B) and Severity of Domestic Violence (0.089 in A and 0.543 in B). The 95% confidence interval for the IV estimate is between 0.018 and 0.542, which is a very wide range compared to the coefficient interval in OLS (-0.001 and 0.089). I get more ambiguous results when regressing for Psychological-Physical Domestic Violence (-0.001 in A and 0.684 in B) and

Psychological Domestic Violence (0.007 in A and 0.008 in B). The latter violence is never significantly associated to relative income, while Psychological-Physical Domestic Violence is only significantly associated to Income Sharing Rule in IV estimates. The standard error is large too (0.017 in A and 0.270 in B for the Severity Index), but not excessively large otherwise the instruments would have been weak. Regarding to the other variables, in keeping with the incidence of domestic violence, Table 26 shows that one-point increase of the Women Decision Making Index may decrease the risk of the severity of domestic violence by 0.256 in IV (while in OLS it is not significant) and in urban areas the risk of abuses is higher than in rural communities (0.068 in OLS and 0.100 in IV, both significant at 5%). Moreover, Satisfaction with Income is significant in OLS but not in IV estimates. The main difference in results between the two indexes is that Female Education and Alcohol Consumption are here not significant associated to women’s relative income in IV estimates, while Satisfaction with Parents and State Support do. In particular, satisfaction with parents is significantly and negatively associated with the severity of domestic violence, and the state support (which mainly includes Oportunidades Program) has the opposite effect. The positive association between state support and domestic violence may follows the income sharing analysis: the increase of women’s budget does not necessarily lead to a decrease of domestic violence as long as the relative income is low and divorce is not fully accepted by the society. Receiving money from the Government, thus, would contribute to move women away from their traditional reproductive roles without making them economically empowered and domestic violence becomes an instrument to extract the income transfer (Bobonis et al. 2013). Alternatively, female education and satisfaction with parents’ support are negatively associated to domestic violence. With more socio-economic support, divorce would be a credible threat and, as a consequence of it, domestic violence may be curtailed. In particular, there is a deep literature that supports the promotion of female education in order to increase jobs’ opportunities and woman’s economic autonomy versus domestic violence (Bowlus and Seitz 2006, Eswaran et al. 2009, 2011, Koenig et al. 2014, Toufique et al.2007).

7. ROBUSTNESS CHECKS

I check the validity of results for potential multicollinearity, sample heterogeneity and instrument weakness. Regarding to multicollinearity, I estimate equation 1 including several new control variables and the results hold for different model specifications. In order to account for sample heterogeneity, I run equation 1 restricting the sample to women aged 20-60, which 29 is usually considered the working age population; to urban areas, where traditional gender roles may be less followed than in rural areas; to married women, that usually report to be less abused than divorced ones; to the 10th and 90th percentile of Men Household Engagement Index, since 60% of the husbands is not involved in house cleaning and child care; and to the 10th and 90th percentile of Income Sharing Rule, since 80% of the wives does not earn any relative income. Panel (a) of Table 27 shows results obtained in running the model only for women aged 20-60 sub-sample and results hold: Income Sharing Rule exerts a positive and significant effect on both domestic violence. OLS and IV estimations confirm what obtained in the baseline model, although the significance level and the magnitude of IV estimates are much larger (0.676 for incidence violence significant at 1% and 0.649 for severity violence significant at 1%) with respect to the OLS results (0.129 and 0.094 significant at 5% respectively). In Panel (b) of Table 27 I restrict the analysis to the urban areas sub-sample and also in this case results hold: relative income is positively correlated with incidence and severity domestic violence and the magnitude and the significance level of its effect is very high in both models. In OLS estimates, one-point increase of relative wage boosts incidence violence by 0.162 and severity violence by 0.107, while in IV estimates the increase is by 0.462 and 0.221 respectively. Also in married women sub-sample results hold and they are also better in 2SLS than in OLS. Panel (c) of Table 27 shows that the magnitude of the effect of income sharing on both domestic violence is larger in IV (1.444 for incidence and 1.203 for severity) than in OLS (0.100 and 0.100 respectively). Moreover, relative income is not significantly associated with the incidence violence in OLS, while it is significant in IV at 5%. Finally, results hold also if I drop percentiles of Men Household Engagement Index lower that 10% and higher than 90%. Dropping values lower than 10th percentiles means that I do not consider when the Men Household Engagement Index is very low and close to patriarchal values. In this case, Panel (d) shows that IV estimates are not significant while OLS do. On the contrary, by dropping values higher than 90th percentiles I mainly keep patriarchal values of the Men Household Engagement Index. In this case, Panel (e) shows that IV estimates of Income Sharing Rule are significantly associated to domestic violence while OLS’s do not. This result may suggest that the instrument better explains income sharing rule the lower men are engaged in cleaning and child caring, which is in line with the traditional division of gender roles. Replicating this analysis to the Income Sharing Rule I get specular results. Panel (f) shows that when dropping values lower than 10th percentiles, which are mainly zero-income values, results hold: as the relative income increases, the domestic violence increases too. Differently it happens in Panel (g) without income values higher than 90th percentiles. In this case, the

30 remaining income values are almost all zero-income values and this may explain why in both models Income Sharing Rule is not significant. I also conduct robustness check for examining the strength of the instrument. Two main approaches are used to decide whether reject the null hypothesis that the instrument is weak. First, the rule of thumb based on Staiger and Stock (1997) suggest that the F statistic values of the Income Sharing Rule should not exceed 10 for inference based on the 2SLS estimator to be reliable when there is one endogenous regressor. There is an extensive debate on the appropriateness of the rule. Table 21 shows that F-statistic of the Income Sharing Rule associated with the Incidence Domestic Violence Index (and the Severity-Incidence Domestic Violence Index) is 14.71, with the Psychological-Physical Domestic Violence is 8.66, with the Economic-Physical of Domestic Violence Index is 4.03 and with the Psychological Domestic Violence is 8.13. By applying this rule, the instrument may be strong for explaining both the Incidence Domestic Violence Index and the Severity-Incidence Domestic Violence Index, and it may be weak in any other case because F-statistic is lower than 10. Second, the approach proposed by Stock, Wright and Yogo (2002) suggest to look at the critical values for 2SLS relative biases of 5%, 10%, 20%, and 30% for models with 1, 2, or 3 endogenous regressors and between 3 and 30 excluded exogenous variables (instruments). They also provide critical values for worst-case rejection rates of 5%, 10%, 20%, and 25% for nominal 5% Wald tests of the endogenous regressors with 1 or 2 endogenous regressors and between 1 and 30 instruments. In my case, since I have one endogenous regressor and one instrument, I can only look at 2SLSL Size of nominal 5% Wald Test and LIML Size of nominal 5% Wald Test. Supposing that I accept a rejection rate of 10%, I cannot reject the null hypothesis of weak instrument for any domestic violence index. However, if I increase the rejection rate up to 15%, I can reject the null hypothesis of weak instrument for the Incidence of Domestic Violence Index (as well as for the Severity-Incidence of Domestic Violence Index), but not for any severity- incidence sub-indexes. These results arise a question on whether or not including the weak instrument into the analysis of the Psychological-Physical Domestic Violence Index, the Economic-Physical Domestic Violence Index and the Psychological Domestic Violence Index. There is an open debate on it. Some authors suggest that estimation based on Fuller’s modification of the limited- information maximum likelihood (LIML) provides more robust estimates. Running equation 1 with Fuller’s modification, Table 28 shows that results are exactly equal to 2SLS estimates (see Table 26) and this means that the equation is exactly identified. Thus, I can include the weak

31 instrument although it is weak for severity-incidence sub-indices according to Stock, Wright and Yogo (2002).

8. MECHANISM

In this section I examine the mechanism predicted by standard bargaining models which shows that an increase of the relative income may improve the female bargaining power. The female bargaining power is a synonymous of woman’s autonomy and is usually defined as the exercise of decision-making of the wife within the household. The empirical literature has often used female assets at marriage as the explanatory variable of the female bargaining power and it may include physical (car, house, land, etc.), financial (saving account), individual (education, labor, age, civil status, family size, residence, etc.) and social goods (network of friends and parents) (Doss 1996; Schultz 1999; Thomas 1999; Mendoza et al. 2002; Friedmann-Sanchez 2006). Further authors, instead, used other variables, such as female employment outside of the home (Anderson et al. 2005), sex of the child (Li et al. 2011), cultural factors, institutions and norms (Lundberg & Pollak 1997, Mabsout et al. 2010), migration status (McPeak et al. 2006), agricultural technology development (Quisumbing et al. 2004), earnings (Golan et al. 2008), wage (Pollak 2005), shared income (Sow et al. 2002; Yusof 2010; Attanasio et al. 2013) and personal assets (Quisumbing et al. 2000). Others have also combined income, wage and assets and used them together as explanatory variables of female bargaining power (Bertocchi et al. 2012, Frankenberg et al. 2001, Friedberg 2006 and Razzaque et al. 2009). Referring to the literature, I test the hypothesis that an increase of the woman’s income may improve the female bargaining power by assuming two scenarios: one considering the Income Sharing Rule an exogenous variable (OLS regression) and the other one taking into account for the potential endogeneity of Income Sharing Rule (IV regression). I estimate the following specification that captures the effects of explanatory variables on female bargaining power:

퐵푃푖 = 훽0 + 훿푋푖 + 훽1푍푖 + 휀푖 (2)

where i refers to woman, 퐵푃푖 is the female bargaining power measured by the Decision Making

Index, 푋푖 includes the set of explanatory variables and, therefore, 훿 is the parameter of interest,

푍푖 includes the same set of covariates and domestic violence indexes, and 휀푖 is the error term. As mentioned previously, the Decision Making Index corresponds to whom has the final say over Work, Buy Food, Children Education, Children Freedom, Children Health, Hanging Out, Money Management, Moving House Town, Buying Furniture, Sexual Relations, Contraception 32

Methods, Who Uses Contraception Methods and Number of Children. The index is internally consistent (the Cronbach’s alpha is 0.6543) and it is scaled up from 0 if the husband makes decisions alone to 1 if the wife makes decisions alone. Moreover, since Factor I and Factor II of the index explain 34% of the total variance of the index and they mainly include decisions over child caring, food consumption, ect., the higher is the Decision Making Index the more traditional is the division of gender roles and the exercise of power within the household. Tables 29 reports OLS and IV estimates including the Incidence of Domestic Violence Index as one of the covariates while Table 30 including the Severity-Incidence of Domestic Violence Index. Both tables lead to similar findings in terms of sign and magnitude of the coefficients. First of all, when not accounting for endogeneity, the Income Sharing Rule is significantly positive associated to the Decision Making Index at 5% significance level in both cases, as showed by Table 29 (0.071 in OLS) and by Table 30 (0.072 in OLS). Otherwise, in IV estimates the relative income is not significant. Secondly, the Women Freedom Index is positively associated to the Decision Making Index at 1% significance level in both cases, as showed by Table 29 (0.173 in OLS and 0.191 in IV) and by Table 30 (0.168 in OLS and 0.188 in IV). Thirdly, the age of the wife is negatively associated to the Decision Making Index and results are identical in both tables 29 and 30 (-0.003 in OLS and -0.003 in IV). Finally, the impact of urban areas on decision making may change whether accounting or not for endogeneity of the relative income. In fact, in OLS estimates Urban Areas is not significant in both tables, while in IV it is significant at 10% (0.047 in Table 29 and 0.050 in Table 30). The main difference concerns results reported on domestic violence. The Incidence of Domestic Violence Index is never significant, while the Severity of Domestic Violence Index is negatively significant in IV (-0.135) and insignificant in OLS. These findings do not provide support for the prediction of standard household bargaining models. In fact, the increase of the relative income does not affect the exercise of the female power within the household. In Mayan communities earning money does not make women more powerful for several reasons. First of all, jobs are usually under-paid and the increase of the relative income does not make women economically independent. Secondly, the woman’s income may be managed by the husband or being allocated according to his preferences. Female bargaining power, instead, is highly positive correlated to the Women Freedom Index. This shows that the more the woman is free to take personal decisions the higher would be her capacity to exercise the power within the family. In this case, conditions of freedom depend on personal capability, ability, desire and on social constrains that influence what women think to desire and to be able and capable to do. This may explain why in urban areas women usually

33 exercise more power than in rural areas where social judgments are very intense. The exercise of power is then linked to the exercise of freedom that eventually is particularly restricted in conditions of deprivation and lack of opportunities, but it seems more related to traditions and politics than to economic issues. A final comment is reserved to the negative association between domestic violence and decision making. The risk of suffering more frequent and intense abuses may lead women to curtail their exercise of power. This leads to couple of considerations. On one hand, women may decide to work for boosting their economic power and eventually having the opportunity to divorce, and at the same time, accept to not exercise a higher bargaining power in order to avoid domestic violence. On the other hand, the husband may decide to be more violent and use the domestic violence as an instrument to force women to reduce their exercise of power or to extort their money.

9. DISCUSSION AND CONCLUSION

In this paper I identify the effect of the woman’s relative income (Income Sharing Rule) on domestic violence and on female decision making (or autonomy). The woman’s relative income is just one of several socio-economic factors that may influence the woman’s reservation utility function, such as female education (Education Mayan Women), female asset at marriage (House Ownership), economic support by the Government (Oportunidades and Land Ownership), and woman’s satisfaction with her purchasing power (Income), closest network of friends and relatives (Parents and Friends), people from the community and local infrastructures (Neighbours, Health Services and Public Services). Accounting for potential endogeneity of the woman’s relative income, I use the Men Household Engagement Index as instrumental variable. In the estimation strategy, all specifications include control variables at intra-household level (Cultural Role Index, Female Labour, Female Black Labour, Age Mayan Women, Satisfaction with Income, Parents, Neighbours and Friends, Education Mayan Women, State Support and Land Ownership) and at household level (Household Economic Status Index, Tobacco Consumption, Alcohol Consumption, Respiratory Problems, Diabetes Problems, Hypertension Problems, Maya City, Length Relationship and Family Dimension). Findings show a positively significant association between the woman’s relative income and domestic abuse. The increase of woman’s income, thus, does not necessarily lead to a decrease of domestic violence, as demonstrated by the standard prediction. In particular, there is a deep literature that supports the promotion of female education in order to increase jobs’

34 opportunities and woman’s economic autonomy versus domestic violence (Bowlus and Seitz 2006, Eswaran et al. 2009, 2011, Koenig et al. 2014, Toufique et al.2007). Alternatively, the husband may feel to be threatened by the increase of the relative income because high economic resources may allow women to leave their relationships, making divorce a more credible threat. This approach is sustained by backlash theorists and it may explain domestic violence in Mayan communities. In this case, husbands would use domestic violence as a reaction or instrument of extortion against their wives. These results hold when I reduce the sample to 20-60 age women and to urban areas, and I drop values higher than 90th percentile in the Men Household Engagement Index and values lower than 10th percentile in the Income Sharing Rule. Instead, since 80% of women have zero income, results do not hold if dropping values lower than 10th percentile in the Men Household Engagement Index and values higher than 90th percentile in the Income Sharing Rule. Moreover, findings show insignificant association between the woman’s relative income and female decision making. Economic resources may increase women’s opportunities but do not necessarily influence the exercise of power. Female bargaining power, instead, is high positively correlated to the Women Freedom Index and negatively associated to the Severity- Incidence of Domestic Violence. This shows that the more the woman is free to take personal decisions, the higher would be her capacity to exercise power within the family. On the opposite side, the risk that domestic violence may increase when women exercise more power, makes women to prefer curtailing their decision-making in order to avoid potential abuses. These findings open to further considerations. First of all, the economic status of the woman might be measured as a multidimensional set of explanatory variables that, apart from that relative income, affect the female bargaining power. This is a particularly essential concept in low-income countries, where by tradition women do not have own income and their economic status is composed by several assets, including social networks. Secondly, domestic violence may be used as an instrument of extortion by the husband especially if the wife does not have any options to leave to relationship. Differently, being supported by parents would ensure concrete opportunities out of the marriage without being in conflict with the traditional role of the woman. The increase of relative income and family support to women may make divorce a more credible threat. Thirdly, the fact that the relative income increases domestic violence without influencing the exercise of power, may help to decide the most efficient intervention for reducing domestic violence in Mayan context. For instance, conditional cash transfer programs may contribute in poverty-alleviation, as programs providing transfers to mothers are doing in Brazil, Colombia, Honduras, Jamaica, and Nicaragua, among many (Rawlings and

Rubio 2003; Maluccio and Flores 2004). However, although women’s empowerment is one of the programs’ objectives, domestic violence may be an unintended consequence.

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11. APPENDIX

Technical Note on Field Work Methodology

Sample Size and Sample Distribution. The survey is a subsample of a larger World Bank sample that includes all the municipalities of Quintana Roo. First of all, I measure the World Bank sample size. Many different formulas can be used depending on what is known about the population (standard deviation, mean, variance, etc.). In this case, since the population is almost unknown I use the Slovin’s formula. 푁 푛 = 1 + 푁푒2 where n is the sample size, N is the total population of inference and 푒2 is squared error tolerance. I hypothesized 96% of confidence interval that means: 100% - 96% = 4% or 0.04 of error tolerance. Then I find the total population of inference (80.222) and I substitute it into the formula. Finally, I get the sample size (620) reduced up to 614 due to budget and time constraints. Secondly, I distribute the sample according to the total population of Quintana Roo including Maya and non-Maya people (“Diseño de la muestra censal 2010”, INEGI 2010; “Catalogo de las localidades”, SEDESOL 2015) (Table 1). Dividing the local population of each place by the total population of inference I obtain the proportion of the local population. Then multiplying this proportion by the total sample size (614) I calculate the distribution of the number of interviews per place. For instance, in 2014 the population of Felipe Carrillo Puerto was 25.744 that corresponded to 32% of the total inference population of the Municipalities selected for the survey. Thus, since the number of interviews per place should be proportional either to the local population over the total one (32%) and to the sample size (614), by multiplying 614 by 0,32 I obtain that 197 is the total number of interviews to be applied in Felipe Carrillo Puerto. Thirdly, I replicate this calculation only for Mayan subsample. In keeping with SEDESOL (2015), Mayan Municipalities were selected on the language basis. The total population of inference fell from 80.222 to 57.166. Confidence interval and error tolerance are adjusted according to budget and time constraints. Thus, I hypothesize 94% of confidence interval that means 6% of

42 error term. By substituting these data into the Slovin’s formula, I obtain the new sample size for Mayan communities that is 228. The finally sample size is reduced to 225 due to missing items. Table 1 shows the distribution of the interviews along five municipalities and twenty-one Mayan communities. For instance, in Felipe Carrillo Puerto the total number of interviews applied was 101, proportional to the subsample size (225) and the urban population (25.774) over the total Mayan population of inference (57.268).

Table 1. Sample distribution by communities Municipality Village/City Tot Popul Indigen Popul Percentage Observations Felipe Carrillo Puerto FELIPE CARRILLO PUERTO 25744 19275 0,449535517 101,1454914 Felipe Carrillo Puerto CHUNYAXCHÉ 191 191 0,003335196 0,750419082 Felipe Carrillo Puerto SEÑOR 3095 3073 0,054044143 12,15993225 Felipe Carrillo Puerto TIHOSUCO 4994 4931 0,087204023 19,62090522 sub-tot #1 Inference population 34024 27470 134 sub-tot #2 Municipality 75026 65041 Othón P. Blanco CHACCHOBEN 728 408 0,01271216 2,860236083 Othón P. Blanco HUAY-PIX 1649 574 0,02879444 6,47874904 Othón P. Blanco HUAY-PIX 3 2 5,23853E-05 0,011786687 sub-tot #1 Inference population 2380 984 9 sub-tot #2 Municipality 244553 52519 José María Morelos DZIUCHÉ 2870 2248 0,050115248 11,27593071 José María Morelos KANTEMÓ 229 193 0,003998743 0,89971712 José María Morelos SABÁN 2167 2150 0,037839631 8,513917022 José María Morelos X-CABIL 1087 1087 0,018980932 4,270709646 José María Morelos X-QUEROL 102 102 0,001781099 0,400747363 sub-tot #1 Inference population 6455 5780 25 sub-tot #2 Municipality 36179 32110 Lázaro Cárdenas IGNACIO ZARAGOZA 2213 1990 0,038642872 8,694646225 Lázaro Cárdenas KANTUNILKÍN 7150 5265 0,124851575 28,09160439 Lázaro Cárdenas NUEVO DURANGO 225 225 0,003928896 0,884001537 Lázaro Cárdenas PACCHEN 131 131 0,00228749 0,514685339 Lázaro Cárdenas SAN ÁNGEL 1041 788 0,018177691 4,089980443 Lázaro Cárdenas SOLFERINO 799 328 0,013951945 3,139187679 sub-tot #1 Inference population 11559 8727 46 sub-tot #2 Municipality 25333 19573 Tulum COBÁ 1278 1214 0,022316128 5,021128728 Tulum FRANCISCO UH MAY 655 545 0,011437452 2,573426696 Tulum MACARIO GÓMEZ 510 465 0,008905497 2,003736816 Tulum MANUEL ANTONIO AY 407 365 0,007106936 1,599060557 sub-tot #1 Inference population 2850 2589 11 sub-tot #2 Municipality 28263 15474

Source: Author’s computation (World Bank Survey 2014)

Sampling method. The choice of the sampling method depends on the type and the accuracy of statistical analysis, the information available and the operational costs at the researchers’ disposal. Regarding to the World Bank sample, I apply the Simple random sampling (SRS) that best suits situations where not much information is available about the population and data collection can be efficiently conducted on randomly distributed items ensuring their normal distribution. In SRS of a given size, all such subsets of the frame are given an equal probability. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results. However, SRS can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population (over- representing sex for instance). The Mayan sub-sample, instead, is considered as a part of the population sampled in Quintana Roo by the World Bank and I use the Clustering Sampling in order to capture intra-household and domestic violence data in Mayan Region (a specific geographic and cultural area of Quintana Roo). The total population of Quintana Roo is divided into Mayan and non-Mayan groups (or clusters) and only a simple random sample of Mayan group is selected and analyzed in this study. The cluster analysis allowed me to expand the World Bank survey on intra- household characteristics with additional questions on domestic violence and I only apply it to the Mayan cluster. The main difference between cluster sampling and stratified sampling is that in stratified sampling a random sample is drawn from each of the strata, while in cluster sampling only the selected clusters are studied. Technical consideration about interviews. The enumerators are mainly women over 20 years old with at least a Bachelor degree. They are trained by researchers and PhD students of the University of Quintana Roo and the University of Rome Tor Vergata under the supervision of Dr. Rene Lozano Gonzales. Mayan anthropologists are involved during the whole field work and it affected the quality of the answers in order to reduce potential measurement errors. Interviews are done in Spanish, they last between one hour up to two hours and are conducted at home. Due to the sensitivity of the subject, women’s confidence is gained by ensuring the confidentiality of the information shared with the enumerators and undertaking interviews only in absence of the husband in the house. Software for data analysis. Results are, firstly, collected in a dataset by using the statistic program SPSS (13.0 version). Then, basic statistical analysis and regressions models are done in Stata (2011).

Full sample and Mayan subsample: main statistical differences

The World Bank sample and the Mayan subsample are two independent samples and I applied the two-sample t test with unequal variance in order to check whether the differences in sample distributions are random or statistically significant (Ruxton 2006). Implementing t-test for the main covariates of interest, results show that the group means are significantly different as the p-value in the Pr(|T| > |t|) row (under Ha: diff != 0) is less than 0.10 in Education Women,

Education Men, Education Head Household, Education Partner, Income Head Household, Women Income and Men Income. Looking at the Mean column, people from Mayan sample have higher education and income means level, except for women’s income, which is higher in the full sample than in Mayan one. Family Dimension and Income Household are the only variables that are not significant: this may show that they are not influenced by the cultural context. However, in Mayan sample mean values suffer of larger standard deviations that measure data dispersion. I conclude that difference in means between samples are not random and may be due to the clustering selection by region and Mayan culture.

Table 2. The two-sample t test with unequal variance

Full Sample Mayan Sample Mean Difference T-Statistic [p-value] mean mean mean t-stat Family dimension 4.41 4.37 0.36 0.25 (1.90) (1.81) [0.802]

Education Women 4.79 7.38 -2.58 -7.63 (3.86) (4.47) [0.000]

Education Men 5.2 7.97 -2.77 -7.15 (4.19) (4.84) [0.000]

Education Head Household 4.97 7.75 -2.05 -7.51 (4.13) (4.87) [0.000]

Education Partner 4.99 7.56 -2.56 -7.25 (3.90) (4.42) [0.000]

Income Household 3860.97 4039.5 -178.53 -0.87 (2735.62 (2549.34) [0.384]

Income Women 1075.46 809.826 265.64 1.72 (1978.96) (1889.54) [0.085]

Income Men 2841.12 3360.88 -519.76 -2.51 (2656.22) (2537.76) [0.022]

Income Head Household 3234.94 3879.58 -644.64 -3.29 (2578.48) (2413.09) [0.001] Income is in Mexican pesos (value change of the year 2014). Standard deviations in parentheses. A t-test of the diﬀerence in means is provided in the last column with associated level of signiﬁcance in squared brackets. Source: Author’s computation (World Bank Survey 2014) Women’s Narratives

Narratives are essential to understand domestic violence and the decision process within the household. I have collected narratives by applying in-depth-interviews together with structural interviews. Table 3 shows that the main reasons of abuses are the lack of the wife’s economic power, the husband’s jealousy and his addiction to alcohol.

Table 3. Reasons Domestic Violence Reasons of Domestic Violence in Maya Region 1. The husband’s jealousy and his intention to limit the wife’s freedom to move (Psychological violence) 2. The wife economic dependency from the husband’s income (Economic Violence) 3. The husband reluctance to contribute to the households’ expenditures (Economic Violence) 4. Husband’s alcohol addiction and his frustrations in life (Physical Violence) Source: Author’s computation (World Bank Survey 2014)

Women seem to accept a certain level of domestic violence within the household, but when physical abuses increase above this level, they may decide to leave the relationship. Divorce is a credible threat in Mayan communities, especially if women work or benefit from the economic support of their parents. Women also believe that a good education may help them in finding a job outside home and becoming more autonomous. Table 4 shows the main socio-economic factors in Mayan communities that increase women’s reservation utility and would eventually make the wife’s threat costlier for the husband.

Table 4. Socio-economic factors Factors Specific narratives Education Unsatisfaction with the educational level achieved Importance of investing in children’s education (particularly to daughter’s one) Employment Gender discrimination in manual and touristic jobs Female black labor without the husband’s knowledge Network Migration to the husband’s house Few relationships with friends, neighbors and parents Source: Author’s computation (World Bank Survey 2014)

Finally, I design an organogram chart of the narratives that clarifies the process of women disempowerment that affect the risk of domestic violence in Mayan communities. At the first level I insert the factors that, according to women’s opinions, are the initial conditions of gender discrimination and I call them “patriarchal pre-conditions”. In a unique set I gather Gender

46 discrimination at work and Low female education because they are interrelated to each other, while I keep Transfer to the husband’s house in a separate set because the patriarchal extensive family is a traditional habit that has nothing to do with education and employment. They all lead the wife to be isolated and socio-economically dependent from the husband’s income.

Figure 1. Organogram chart of the narratives

Level I Patriarchal background Gender Gender Low female Migration to discrimination inequality education the husband’s at work at home house

Level II Socio-economic & A few job’s Unsatisfaction with self-esteem own education Social exclusion: consequences opportunities loss of relations with parents

Level III Women’s disempowerment

Socio-economic dependency of the wife to the husband’s income and

poor social network

Source: Author’s computation

Prevalence Rate of domestic violence in Mayan communities

Table 5. Description of Domestic Violence questions Type of Domestic Violence Variable Name Description violeconom_161 Did not allow her to manage money violeconom_162 Did not want to spend money for the household expences Economic Violence violeconom_163 Threatened not to give money violeconom_164 Took money or property (animals, etc) violeconom_165 Not allowed to work violpsic_231 Insulted violpsic_232 Threatened to leave the house and bring the children violpsic_233 Got angry housework are not done (meals, cleaning) Psychological Violence violpsic_234 Not allowed to hang out and/or to invite people at home violpsic_235 Did not help with the housework on propose violpsic_236 Jealous violpsic_237 Put parents and children against his wife violpsic_238 Ignored his wife violfis_251 Grabbed by the hear violfis_252 Fastened to something Physical Violence violfis_253 Kicked violfis_254 Thrown objects violfis_255 Beat Source: partially retrieved from Lozano-Cortes (2009) and Castro (2008)

Table 6. Types of Domestic Violence (the sample includes married, single and divorced women) Type of Domestic Violence Sample Weight Did not allow her to manage money 64 0.5 Did not want to spend money for the household expenditure 37 1 Threatened not to give money 36 0.75 Took money or property (animals, etc.) 13 0.84 Not allowed to work 74 0.47 Subtotal Economic Violence 224 0.71 Insulted 42 0.78 Threatened to leave the house and bring the children 35 0.88 Got angry housework are not done (meals, cleaning) 71 0.28 Not allowed to hang out and/or to invite people at home 54 0.55 Did not help with the housework although he had time to do it 99 0.41 Jealous 68 0.56 Put parents and children against his wife 16 0.69 Ignored his wife 69 0.62 Subtotal Psychological Violence 454 0.6 Grabbed by the hair 39 1.2 Fastened 0 0 Kicked 18 1 Thrown objects 32 0.97 Beat 49 1.18 Subtotal Physical Violence 138 1.1 Total 816 0.8 Note: Importance is measured as 2=very serious; 1=serious; 0=not very important. The column total exceeded the number of women interviewed (225) because of multiple responses.

Figure 2. Women subject to Domestic Violence by Age Women Psychologically Violated Women Physically Violated Women Economically Violated Tot Women Interviewed Above 45

From 25 to 45

Up To 25

Figure 3. Women subject to Domestic Violence by Education

Women Psychological Violated Women Physically Violated 18 Women Economically Violated Tot Women Interviewed 16

Figure 4. Women subject to Domestic Violence by Employment Women with and without violence Psychological Violence Physical Violence Economic Violence Agriculture

Commerce

Housework

Livestock farming Manifacture

Services (not…

Transporte

Figure 5. Women subject to Domestic Violence by Income

Women with Psychological Violence Above 9300 Women with Physical Violence Women with Economic Violence 4500-8000 Women with and without violence

3001-4500

1501-3000

Up to 1500 Income (messican pesos) (messican Income 0

0 20 40 60 80 100 120 140 160 180 Female population Source: Author’s computation (World Bank Survey 2014). Please consider multiple choices and multiple violence presence

Construction of the Domestic Violence Indexes: Factor Analysis

Severity-Incidence Domestic Violence Index

In order to construct the Severity-Incidence Domestic Violence Index, I first select the categorical variables that measure the severity of the abuse according to women's opinions (0=no abuse; 1=not important; 2=important; 3=very important): Ignored Wife, Put Children Against Wife, Jealous, Husband Not Housework, Cannot Meet Friends, Angry Unaccomplished Housework, Threatened Leave House With Children, Insulted, Beat, Thrown Objects, Kicked, Grabbed Hair, Not Allow Work, Steal Money Asset, Threatened Not Allowance, Miser Husband and Complained Wife Money Management. These variables are strongly correlated and their internal consistency (joint Cronbach’s alpha) is very good (0.9067 on 0-1 scale), meaning that they capture the same concept of domestic violence. In addition to it, I compute the Kaiser- Meyer-Olkin (KMO) measure of sampling adequacy. KMO takes values between 0 and 1, with small values meaning that overall the variables have too little in common (no correlation) to warrant a factor analysis. Heuristically, the following labels are given to values of KMO: 0.00 to 0.49 is unacceptable; 0.50 to 0.59 is miserable; 0.60 to 0.69 is mediocre; 0.70 to 0.79 is middling; 0.80 to 0.89 is meritorious; 0.90 to 1.00 is marvelous. Generally, all values greater than 0.5 are acceptable and in this case, KMO is meritorious (0.8842). Secondly, I drop observations with missing values in any of the violent events and I conduct the exploratory analysis by using principal-component factor method because of correlated variables and the small dimension of the sample (221 observations). Before rotation, I decide how many factors to keep by adopting the Kaiser's rule: the common factors that have the highest correlation with all factors are those with eigenvalues greater than one. By applying this rule, the factors retained are three and they explain 55.37% of the total variance. Since the interest is to reduce the number of variables in the correlation matrix, I choose to rotate the three factors using orthogonal method (varimax) which converts the correlated variables in linearly uncorrelated domestic violence dimensions: 1. Psychological and Physical Domestic Violence (Factor I explains 41%, 0.4095) includes Jealous, Cannot Meet Friends, Angry Unaccomplished Housework, Threatened Leave House With Children, Beat, Thrown Objects and Grabbed Hair; 2. Economic and Physical Domestic Violence (Factor II explains 7.4%, 0.0740) includes Kicked, Not Allow Work, Steal Money Asset, Threatened Not Allowance, Miser Husband and Complained Wife Money Management;

3. Psychological Domestic Violence (Factor III explains 7%, 0.0702) includes Ignored Wife, Put Children Against Wife, Husband Not Housework and Insulted.

Table 7. Factor loadings of variables related to severity of domestic violence

Variable Factor 1 Factor 2 Factor 3 Uniqueness Ignored Wife 0.4485 0.1167 0.6945 0.3029 Put Children Against Wife -0.0420 0.3146 0.6733 0.4459 Jealous Husband 0.5703 0.2022 0.2584 0.5671 Husband Not Housework 0.2369 0.1808 0.5661 0.5908 Cannot Meet Friends 0.5152 0.3793 0.2135 0.5451 Angry Unaccomplished Housework 0.4722 0.0348 0.4406 0.5817 Threatened Leave House With Children 0.6195 0.1374 0.4552 0.3901 Insulted 0.2479 0.3762 0.5771 0.4640 Beat 0.7969 0.2619 0.1465 0.2749 Thrown Objects 0.5789 0.4321 0.0099 0.4781 Kicked 0.4391 0.6130 0.0444 0.4294 Grabbed Hair 0.7442 0.2164 0.2394 0.3420 Not Allowed To Work 0.4440 0.4457 -0.1189 0.5901 Steal Money Asset 0.0785 0.7170 0.2048 0.4378 Threatened Not Allowance 0.2925 0.7638 0.1567 0.3064 Miser Husband 0.2104 0.7328 0.3210 0.3156 Complained Wife Money Management 0.2008 0.5159 0.4104 0.5251 Note: I use the principal-component factor method. I rotate the factor loadings using orthogonal method (varimax)

Most of psychological and severe physical abuse have higher loadings in Factor I and economic and “non-severe” physical abuse in Factor II, which corresponds to the diffusion of the domestic violence within the Mayan population. It is interesting to notice that severe physical violence is conceptually associated with psychological abuse that may be linked with patriarchal values and backlash theory. This factorial analysis confirms the content of narratives. Jealousy, husband punishment if the wife does not accomplish with household cleaning and child caring, isolation from friends and family and beating are the most common abuses, as shown in Factor I, and they are also the most denounced by women’s narratives. A question arose whether to keep items with very large uniqueness. The uniqueness refers to the proportion of variance not explained by the common factors and usually above 0.6 it is considered a very large uniqueness. In this case, all items are below 0.6 but, in general I decide to keep all items and look at their uniqueness for better understanding the single index. For instance, it shows that Beating is the best variable explained by the Index and it further confirms that the escalation of physical violence is considered the most severe abuse.

Finally, I standardize and multiply each factor by its proportion of variation explained from the total of variation explained (meaning 0.4095/0.5537=0.7396; 0.0740/0.5537=0.1336; 0.0702/0.5537=0.1268). By doing so, I create three sub-indexes (Psychological and Physical Domestic Violence Index, Economic and Physical Domestic Violence Index and Psychological Domestic Violence Index), one for each dimension of domestic violence, and the Severity- Incidence Domestic Violence Index is the weighted sum of the three:

Severity-Incidence Domestic Violence Index = Psychological and Physical Domestic Violence Index + Economic and Physical Domestic Violence Index + Psychological Domestic Violence Index

The individual Cronbach’s alpha for Psychological and Physical Domestic Violence Index is 0.8454, for Economic and Physical Domestic Violence Index is 0.8027, for Psychological Domestic Violence Index is 0.7219. Finally, Table 8 shows that the abuses included in the Psychological and Physical Domestic Violence Index are considered way more severe than any other one (74%).

Table 8. Severity-Incidence Domestic Violence: descriptive statistics

Violent Category N mean sd min max Psychological-Physical Index 225 0.099 0.147 0 0.7396 Economic-Physical Index 225 0.017 0.028 0 0.1336 Psychological Index 225 0.017 0.024 0 0.1268 Note: “N” stands for number of observations, “sd” for standard deviations, “min” minimum value and “max” for maximum value

Incidence-Domestic Violence Index

The Incidence-Domestic Violence Index is composed by the same variables used for the previous domestic index, such as Ignored Wife, Put Children Against Wife, Jealous, Husband Not Housework, Cannot Meet Friends, Angry Unaccomplished Housework, Threatened Leave House With Children, Insulted, Beat, Thrown Objects, Kicked, Grabbed Hair, Not Allow Work, Steal Money Asset, Threatened Not Allowance, Miser Husband and Complained Wife Money Management. However, since I am now interested in the incidence of each item, I record as 1 if the woman has experienced domestic violence at least ones in her life and 0, otherwise. In order to get a single index, I conduct the exploratory analysis by using principal-component factor method retaining factors with eigenvalues greater than one as done for the Severity Index. As a

52 result, four factors have eigenvalues greater than one and explain 56% variance of the total variance. I rotate the four factors using orthogonal method (varimax) which converts the correlated variables in linearly uncorrelated domestic violence dimensions: 1. Incidence Psychological and Physical Domestic Violence (Factor I explains 36.5%, 0.3655) includes Ignored Wife, Husband Not Housework, Angry Unaccomplished Housework, Threaten Leave House With Children, Beat and Grabbed Hair; 2. Economic and Physical Domestic Violence (Factor II explains 7%, 0.0685) includes Thrown Objects, Kicked, Steal Money Asset and Threatened Not Allowance; 3. Psychological and Economic 1 Domestic Violence (Factor III explains 6.2%, 0.0622) includes Jealous, Cannot Meet Friends, Insulted, Not Allow Work and Miser Husband; 4. Psychological and Economic 2 Domestic Violence (Factor IV explains 6%, 0.0616) includes Put Children Against Wife and Complained Wife Money Management. Also in the incidence violence the most common abuses are psychological and physical domestic violence, as showed by Factor 1. This confirms the content of narratives and is in line with results of the Severity Index.

Table 9. Factor loadings of variables related to incidence of domestic violence

Variable Factor 1 Factor 2 Factor 3 Factor 4 Uniqueness Ignored Wife 0.6711 0.1357 0.2616 0.3107 0.3662 Put Children Against Wife 0.1188 0.0686 0.1348 0.7564 0.3908 Jealous Husband 0.3542 0.0741 0.5710 0.1523 0.5198 Husband Not Housework 0.5621 0.1968 0.2269 -0.1751 0.5632 Cannot Meet Friends 0.3336 0.1460 0.6623 -0.0207 0.4283 Angry Unaccomplished Housework 0.5994 0.0689 -0.1998 0.3811 0.4509 Threatened Leave House With Children 0.5623 0.2345 0.1178 0.4346 0.4262 Insulted 0.3369 0.1723 0.4736 0.3037 0.5403 Beat 0.6701 0.3305 0.2984 0.0575 0.3493 Thrown Objects 0.2702 0.5643 0.1709 0.0317 0.5783 Kicked 0.3214 0.7663 0.1342 -0.0357 0.2901 Grabbed Hair 0.6634 0.3594 0.2473 0.0996 0.3596 Not Allowed To Work 0.0089 0.1079 0.5614 0.1672 0.6451 Steal Money Asset 0.1108 0.8317 0.0405 0.2012 0.2538 Threatened Not Allowance 0.0866 0.5101 0.4283 0.3032 0.4569 Miser Husband 0.1306 0.4443 0.5243 0.3099 0.4146 Complained Wife Money Management 0.2333 0.2999 0.1485 0.5907 0.4846 Note: I use the principal-component factor method. I rotate the factor loadings using orthogonal method (varimax)

The only variable whose uniqueness is greater than 0.6 is Not Allowed to Work, meaning that it is not very captured by the index, while Stile Money and Assets variable is almost all explained by the index, as showed by its uniqueness value (0.2538). I standardize and multiply each factor by its proportion of variation explained from the total of variation explained, and I sum them:

Incidence-Domestic Violence Index = Incidence Psychological and Physical Domestic Violence + Economic and Physical Domestic Violence + Psychological and Economic 2 Domestic Violence + Psychological and Economic 2 Domestic Violence

The Cronbach’s alpha is 0.8789 and the KMO is meritorious (0.8826). The individual Cronbach’s alpha for Incidence Psychological and Physical Domestic Violence is 0.7909, for Economic and Physical Domestic Violence is 0.7190, for Psychological and Economic 2 Domestic Violence is 0.6882, for Psychological and Economic 2 Domestic Violence is 0.3946. Finally, Table 10 shows that the abuses of Psychological and Physical Domestic Violence Index are the most frequent ones. It is interesting to notice that first of all, the Incidence Psychological and Physical Domestic Violence Index and the Severity-Incidence Psychological and Physical Domestic Violence Index include almost the same types of abuses; secondly, by comparing the max values of the two indexes, the Severity-Incidence Psychological and Physical Domestic Violence Index has the highest values. This shows that the most diffused abuses are also considered the most severe ones.

Table 10. Incidence Domestic Violence: descriptive statistics

Violent Category N mean sd min max Psychological-Physical Index 225 0.175 0.197 0 0.6552 Economic-Physical Index 225 0.013 0.028 0 0.1228 Psychological-Economic_1 Index 225 0.027 0.031 0 0.1115 Psychological-Economic_2 Index 225 0.019 0.032 0 0.1104 Note: “N” stands for number of observations, “sd” for standard deviations, “min” minimum value and “max” for maximum value

Covariates of domestic violence in the literature

Table 11. Covariates of domestic violence

Independent Name of the Variable Description Sources Variable Age AgeMayanWomen Age of Mayan wife Ahmed 2005 AgeMayanMen Age of Mayan husband Bertocchi et al. 2012 Bloch et al. 2002 Coker et al. 1998 Eng et al. 2009 Fageeh 2014 Koenig et al. 2014 Oduro et al. 2012 Okasha et al. 2014 Rapp et al.2012 Semahegn 2013 Education EduMayanWomen Education of Mayan wife Ahmed 2005 EduMayanMen Education of Mayan Bertocchi et al. 2012 husband Bloch et al. 2002 Eng et al. 2009 Erten et al. 2015 Eswaran et al. 2009 Fageeh 2014 Gonzalez-Brenes 2004 Koenig et al. 2014 Mbugua 2014 Oduro et al. 2012 Okasha et al. 2014 Rapp et al.2012 Semahegn 2013 Toufique et al.2007

Total Household WomenTotIncome Income of the Wife Ahmed 2005 Income MenTotIncome Income of the husband Bertocchi et al. 2012 Bloch et al. 2002 Eng et al. 2009 Farmer 1997 Mbugua 2014 Oduro et al. 2012 Rao 1998 Rapp et al.2012 Thafvelin 2014 Toufique et al.2007 Oportunidades EconomicSupport Receiving money from Angelucci et al. 2007 the Government Household FamilyDimension Number of family Bertocchi et al. 2012 Dimension members Bloch et al. 2002 Gonzalez-Brenes 2004 Koenig et al. 2014 Oduro et al. 2012 Okasha et al. 2014 Rao 1998 Rapp et al.2012 Water Availability WaterAvailabilityInsideHouse Water and sanitation Castro 2003 inside the house Urban Location MayaCity Residence in urban area Eng et al. 2009 55

Gonzalez-Brenes 2004 Mbugua 2014 Okasha et al. 2014 Rapp et al.2012 Semahegn 2013 Lenght of LongStay Length of residence in Bloch et al. 2002 relationship the location of interview Koenig et al. 2014 Okasha et al. 2014 Rao 1998 Female Assets AnimalsForFamilyConsumption Ownership of animals Bhattacharya et al. 2009 AnimalsForSelling for family consumption Erten et al. 2015 AnimalforWorking Ownership of animals to Farmer et al. 2000, 1997 LandOwnership sell Gracia et al. 2006 EquipmentOwnership Ownership of animals Lonzano-Cortes et al. CarOwnership for agricultural works 2009 HouseOwnership Ownership of land Panda et al. 2005 Ownership of equipment Rapp et al. 2012 and machinery Rao 1998 Ownership of car/motorbike Ownership of house Household Assets MotoOwnership Ownership of the house Barrientos et al. 2013 SewingMachineOwnership Ownership of the sewing Mbugua 2014 RadioOwnership machine Okasha et al. 2014 FridgeOwnership Ownership of the radio PhoneOwnership Ownership of the fridge MobileOwnership Ownership of the phone TelevisionOwnership Ownership of the mobile Ownership of the television Head of Household MaleHeadHousehold Sex of whom take Rapp et al.2012 decisions in the household Health Conditions RespiratoryProblems Respiratory problems Semahegn 2013 (household level) DiabetesProblems Diabetes problems OverwightProblems Overweight problems AlcoholTobacco AlcoholConsumption Consumption of alcohol Eng et al. 2009 Consumption TobaccoConsumption Consumption of tobacco Fageeh 2014 (household level) Koenig et al. 2014 Mbugua 2014 Thafvelin 2014 Occupation WomenOccupation Women in Housework Eswaran et al. 2009 MaleOccupation Men in Agriculture Kishor et al. 2004 Koenig et al. 2014 Okasha et al. 2014 Semahegn 2013 Toufique et al.2007 Labour FemaleLabour Any contractual jobs Coker et al. 1998 MaleLabour apart from housewife Eswaran et al. 2009 Any contractual jobs Oduro et al. 2012 apart from agriculture Okasha et al. 2014 Panda et al. 2005 Rapp et al.2012 Semahegn 2013 Black Labour FemaleBlackLabour Temporal jobs with no Panda et al. 2005 MaleBlackLabour contacts Temporal jobs with no contacts

Self-esteem SatisfiedWithBody Satisfaction with her Williams et al. 2004 SatisfiedWithEduLevel personal aspect SatisfiedWithFreeTime Satisfaction with her level of education Satisfaction with her leisure time Network Satisfaction SatisfactionWithFriends Satisfaction with her Nouhjah et al. 2014 SatisfactionWithParents friendships SatisfactionWithNeighbors Satisfaction with her parents Satisfaction with her neighbors Source: Author’s computation

Construction of Household Indexes: Factor Analysis

Household Socio-Economic Index

The Household Socio-Economic Index is composed by household’s assets, such as Sewing Machine Ownership, Water Availability Inside House, Wood Energy, Television Ownership, Heater Ownership, Fridge Ownership, Vehicle Ownership, Animal Domestic Consumption, Cement Wall, Saving Account, Mobile Ownership and Radio Ownership. I drop Credit Account and Phone Ownership variables because those assets are mainly diffused in urban areas and they would distort the results since the sample includes either urban and rural areas. Saving Account, instead, is almost exclusively owned by women that receive money from the state program Oportunidades. Each asset is a binary variable that I recode as 1 if the family owns the asset and 0, otherwise. In order to get a single index, I conduct the exploratory analysis by using principal-component factor method retaining factors with eigenvalues greater than one as it was done previously. As a result, four factors have eigenvalues greater than one and explain 55.15% variance of the total variance. I rotate the four factors using orthogonal method (varimax) which converted the correlated variables in linearly uncorrelated household assets dimensions: 1. Main Household Goods (Factor I explains 24%, 0.2396) includes apparatus for heating, refrigerating food, sewing and water supply and sanitary purposes such as Water Availability Inside House, Heater Ownership, Fridge Ownership and Cement Wall; 2. Telecommunication (Factor II explains 12%, 0.1233) includes any long-distance communication that usually involves electrical and electromagnetic technologies, such as Mobile Ownership, Radio Ownership and Television Ownership; 3. Female Goods (Factor III explains 10%, 0.0968) includes any good that is under the wife’s management, such as Sewing Machine Ownership, Animal Domestic Consumption and Wood Energy; 4. Secondary Household Goods (Factor IV explains 9%, 0.0918) includes either financial and economic resources, such as Saving Account and Vehicle Ownership. This classification is in line with the International Classification of Goods and Services (NICE). The uniqueness of Television Ownership, Vehicle Ownership and Sewing Machine Ownership is above 0.6, meaning that the variance of these variables is not well explained by the common factor, while the variance of Saving Account (0.3508) and Fridge Ownership (0.3515) is captured by the index although Saving Account is not a very common financial resource.

Table 12. Factor loadings of variables related to incidence of household economic resources

Variable Factor 1 Factor 2 Factor 3 Factor 4 Uniqueness Heater Ownership 0.7157 0.1587 -0.0972 0.0446 0.4512 Wood Energy -0.6989 -0.1281 0.1506 -0.1195 0.4582 Water Availability Inside House 0.5114 0.0265 -0.0220 0.5143 0.4728 Fridge Ownership 0.6942 0.0595 0.3799 0.1368 0.3515 Vehicle Ownership 0.2370 0.3905 -0.0991 0.4190 0.6060 Sewing Machine Ownership 0.1236 0.2205 0.6750 0.1097 0.4685 Radio Ownership 0.0481 0.6833 -0.0023 0.2901 0.4467 Mobile Ownership 0.0309 0.7401 -0.0300 -0.1558 0.4261 Television Ownership 0.3856 0.4507 0.2024 -0.1777 0.5757 Animal Domestic Consumption -0.2109 -0.1956 0.7392 -0.0748 0.3653 Cement Wall 0.6453 -0.1175 -0.3675 -0.1597 0.4092 Saving Account -0.0044 -0.0171 0.0480 0.8041 0.3508 Note: I use the principal-component factor method. I rotate the factor loadings using orthogonal method (varimax)

Finally, I standardize and multiply each factor by its proportion of variation explained from the total of variation explained, and I sum them:

Household Socio-Economic Index = Apparatus Index + Telecommunication Index

The joint Cronbach’s alpha is quite high (0.6270) and the KMO is middling (0.6997). The individual Cronbach’s alpha for Apparatus Index is 0.5621 and for Telecommunication Index is 0.4108.

Health Status Index

The Health Status Index is composed by the following variables: Hypertension Problems, Overweight Problems, Diabetes Problems and Respiratory Problems. Each health problem is a binary variable that I recode as 1 if at least one member of the family has the problem and 0, otherwise. In order to get a single index, I conduct the exploratory analysis by using principal- component factor method retaining factors with eigenvalues greater than one as done previously. As a result, two factors have eigenvalues greater than one and explain 55.87% variance of the total variance. I rotate the two factors using orthogonal method (varimax) which converted the correlated variables in linearly uncorrelated household assets dimensions: 1. Respiratory Problems (Factor I explains 30%, 0.3014) includes Respiratory Problems;

2. Nutrition Problems (Factor II explains 26%, 0.2572) includes Hypertension Problems and Overweight Problems. The factorial analysis shows that the main health problem is respiratory and related apparatus.

Table 13. Factor loadings of variables related to incidence of household economic resources

Variable Factor 1 Factor 2 Uniqueness Hypertension 0.0027 0.7548 0.4302 Overweight 0.0579 0.7348 0.4516 Diabetes -0.7427 -0.0232 0.4478 Respiratory 0.7504 0.0362 0.4367

Note: I use the principal-component factor method. I rotate the factor loadings using orthogonal method (varimax)

I exclude Diabetes Problems because it has a loading below 0.3 in both factors. The uniqueness of all variables is below 0.6, meaning that their variance is well-captured by the common factors. I standardized and multiplied each factor by its proportion of variation explained from the total of variation explained, and I summed them:

Health Status Index = Nutrition Problems Index + Respiratory Problems Index

The Cronbach’s alpha is 0.2241 and the KMO is miserable (0.5154). Nutrition Problem dimension is 0.2066.

Construction of Women's Empowerment Indexes: Factor Analysis

Decision Making Index

The Decision Making Index investigates over decisions that concern both family and women's life. In keeping with Castro (2011), I order the responses in an upward scale as 0 if the husband makes decision alone, as 1 if the couple makes decision together, as 2 the wife makes decision alone. The variables included are the following ones: Work, Buy Food, Children Education, Children Freedom, Children Health, Hanging Out, Money Management, Moving House Town, Buying Furniture, Sexual Relations, Contraception Methods, Who Uses Contraception Methods and Number Children. It is not possible to capture to which extent the wife is free to make decisions alone or if she is influenced by her husband. The same issue concerns the participation of the wife in couple’s decision. In both cases, I assume that women’s opinions become power in the decision process as long as the relationships between the two partners are based on equal values. In order to get a single index, I conduce the exploratory analysis by using principal-component factor method retaining factors with eigenvalues greater than one as it was done previously. As a result, six factors have eigenvalues greater than one and explain 72.53% variance of the total variance. I rotate the six factors using orthogonal method (varimax) which converted the correlated variables in linearly uncorrelated decision-making dimensions: 1. Everyday Decision (Factor I explains 21%, 0.2082) includes Buy Food and Money Management; 2. Children Decision (Factor II explains 13%, 0.1321) includes Children Education, Children Freedom and Children Health; 3. Sexual Decision (Factor III explains 11%, 0.1139) includes Sexual Relations; 4. Special Decision (Factor VI explains 10%, 0.0973) includes Hanging Out, Moving House Town, Buying Furniture. 5. Reproductive Decision (Factor V explains 9%, 0.0925) includes Contraception Methods and Who Uses Contraception Methods; 6. Family Planning (Factor VI explains 8%, 0.0813) includes Number Children. The factorial analysis shows that the exercise of the women’s bargaining power mainly concerns everyday decisions within the domestic sphere, such as buying food or child care.

Table 14. Factor loadings of variables related to women decision making

Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Uniqueness Work 0.2215 0.0215 0.2288 0.1342 0.3883 -0.6723 0.2775 Buy Food 0.8453 -0.0094 -0.1295 -0.0363 -0.0186 -0.0096 0.2669 Children Education -0.0735 0.8978 -0.0715 0.1178 -0.0468 0.0662 0.1631 Children Freedom 0.1727 0.8648 0.0515 -0.1523 0.0934 -0.1003 0.1776 Children Health 0.1704 0.2252 -0.8229 0.0527 0.1279 0.1494 0.2017 Hanging Out -0.0422 -0.0232 -0.0570 0.9006 -0.0428 -0.0786 0.1753 Money Management 0.7556 0.1612 0.1087 0.1437 0.0475 0.0304 0.3675 Moving House Town 0.3269 0.1023 0.1968 0.5114 0.3867 0.2634 0.3635 Buying Furniture 0.4661 -0.0392 0.2181 0.5818 0.1535 0.2027 0.3305 Sexual Relations 0.1775 0.2213 0.7800 0.0950 0.0530 0.2277 0.2475 Contraception Methods -0.2220 0.1102 0.0941 0.1800 0.6994 0.0947 0.3992 Who Use Contraception 0.1746 -0.0480 -0.2753 -0.1288 0.7346 -0.0388 0.3337 Number Children 0.1621 -0.0259 0.0782 0.0782 0.2219 0.7901 0.2674

Note: I use the principal-component factor method. I rotate the factor loadings using orthogonal method (varimax)

The uniqueness of all variables is below 0.6, meaning that the variance of all variables well explained by the index. I standardize and multiply each factor by its proportion of variation explained from the total of variation explained, and I sum them:

Decision Making Index = Everyday Decision Index + Children Decision Index + Sexual Decision Index + Special Decision Index + Reproductive Decision Index + Family Planning Index

The Cronbach’s alpha is 0.6543 and the KMO is mediocre (0.5426). The individual Cronbach’s alpha for Everyday Decision Index is 0.6276, for Children Decision Index is 0.5995, for Special Decision Index is 0.5334 and for Reproductive Decision Index is 0.3438.

Freedom Index

The Freedom Index estimates the power of married women over their choices and their freedom of movement and it includes: Freedom Party, Freedom Visit Parents, Freedom Shopping and Freedom Work). In keeping with Castro (2011), I recode the responses from 0 (low freedom) to 3 (high freedom), in particular, as 0 if the wife does not go alone or cannot do the activity, as 1 if the wife must ask permission to the husband and depending on his answer the activity can be done, as 2 if the wife must inform the husband but she has the freedom to do the activity anyway, and as 3 if the wife does not need to do anything related to her husband. In

62 order to get a single index, I conduct the exploratory analysis by using principal-component factor method retaining factors with eigenvalues greater than one as it was done previously. As a result, one factor has eigenvalues greater than one and explain 40.47% variance of the total variance. I rotate the factor using orthogonal method (varimax) which converted the correlated variables in linearly uncorrelated freedom dimension: 1. Freedom (Factor I explains 41%, 0.4047) includes Freedom Party, Freedom Visit Parents, Freedom Shopping and Freedom Work.

Table 15. Factor loadings of variables related to female freedom at home

Variable Factor 1 Uniqueness Freedom to Party 0.6701 0.5509 Freedom to Visit Parents and Friends 0.7552 0.4297 Freedom to Go Shopping 0.6527 0.5740 Freedom to Work 0.4166 0.8264

Note: I use the principal-component factor method. I rotate the factor loadings using orthogonal method (varimax)

Only the uniqueness of Freedom To Work is above 0.6, meaning that the variance of this variable is not explained by the common factor. I standardize and multiply the factor by its proportion of variation explained from the total of variation explained, and I obtain the Women Freedom Index. The Cronbach’s alpha is 0.5023 and the KMO is mediocre (0.6141). It means that the four variables are strongly correlated to each other and explain the same concept of freedom.

Women Household Engagement Index

The Women Household Engagement Index captures the women's engagement in activities at home. In order to get a single index, I conduct the exploratory analysis by using principal- component factor method retaining factors with eigenvalues greater than one done previously. As a result, two factors have eigenvalues greater than one and explain 56.52% variance of the total variance. I rotate the two factors using orthogonal method (varimax) which converted the correlated variables in linearly uncorrelated women contribution to home works dimensions: 1. Caring Activities (Factor I explains 30%, 0.2953) includes Women Housework and Women Child Caring; 2. Maintenance Activities (Factor II explains 27%, 0.2699) includes Women Reparation 63

and Women Payment Responsibility. The factorial analysis confirms that women are mainly involved in household activities related to cleaning and child caring.

Table 16. Factor loadings of variables related to women engagement to activities at home

Variable Factor 1 Factor 2 Uniqueness Housework 0.7739 -0.0578 0.3978 Child Caring 0.7463 0.0759 0.4373 Reparations 0.1038 0.7078 0.4882 Payments -0.0735 0.7607 0.4159

Note: I use the principal-component factor method. I rotate the factor loadings using orthogonal method (varimax)

The uniqueness of all variables is below 0.6. I standardize and multiply each factor by its proportion of variation explained from the total of variation explained, and I summed them:

Women Household Engagement Index = Caring Activities Index +

Maintenance Activities Index

The Cronbach’s alpha is very low (0.1533) and the KMO is miserable (0.4964). The individual Cronbach’s alpha for Caring Activities Index is 0.2415 and for Maintenance Activities Index is 0.1516. These low numbers suggest that variables are not capturing the same concept of Women's Assets at Marriage. Moreover, I check if results hold when I include each variable separately instead of a single index. Since they do not hold, I do not proceed to aggregate them in a single index.

Cultural Role Index

The Cultural Role Index captures women's opinions about gender roles and it includes Husband Right Beat, Sexual Obligations, Only Husband Have To Work, Obey To Husband, Share Responsibility Children, Free To Work, See Friends Against Husband Will and Equal Capabilities. Since some variables claim patriarchal attitudes (Husband Right Beat, Sexual Obligations, Only Husband Have To Work and Obey To Husband) and others support more egalitarian values (Share Responsibility Children, Free To Work, See Friends Against Husband Will, Equal Opportunities and Equal Capabilities) I score as 1 to more egalitarian opinions (such as women against Husband Right Beat, Sexual Obligations, Only Husband Have To Work and Obey To Husband and supportive of Share Responsibility Children, Free To Work, See Friends

Against Husband Will and Equal Capabilities) and as 0 to patriarchal responses (otherwise) (Castro 2011). The ultimate goal is to reduce this complexity into a unique Cultural Role Index with values between 1 (egalitarian roles) and 0 (patriarchal roles). In order to get a single index, I conduct the exploratory analysis by using principal-component factor method retaining factors with eigenvalues greater than one as done previously. As a result, three factors have eigenvalues greater than one and explain 46.76% variance of the total variance. I rotate the three factors using orthogonal method (varimax) which converted the correlated variables in linearly uncorrelated gender role dimensions: 1. Equal Capabilities (Factor I explains 21%, 0.2068) includes Equal Capabilities and Equal Opportunities; 2. Patriarchal Values (Factor II explains 13.4%, 0.1340) includes Husband Right Beat, Sexual Obligations, Only Husband Have To Work and Obey To Husband; 3. Women Right (Factor III explains 12.7%, 0.1268) includes Share Responsibility Children, Free To Work and See Friends Against Husband Will. The factorial analysis shows that the majority of women supports opinions against patriarchal values, although de facto they may suffer discrimination. This may lead to women's unsatisfaction and conflicts within traditional households.

Table 17. Factor loadings of variables related to women’s opinions on traditional gender roles

Variable Factor 1 Factor 2 Factor 3 Uniqueness Husband Right Beat 0.2296 0.3750 -0.4523 0.6021 Sexual Obligations -0.1933 0.6724 -0.0249 0.5099 Only Husband Has To Work 0.0945 0.6578 -0.0088 0.5584 Obey To Husband 0.1738 0.5506 0.3173 0.5659 Shared Responsibility Children -0.1299 -0.0390 0.6659 0.5381 Free To Work 0.3071 0.2870 0.4842 0.5888 See Friends Against Husband Power 0.3473 0.1709 0.5293 0.5700 Equal Opportunities 0.7755 0.0623 0.0809 0.3882 Equal Capabilities 0.7185 -0.0778 -0.0865 0.4702 Note: I use the principal-component factor method. I rotate the factor loadings using orthogonal method (varimax)

The uniqueness of all variables is below 0.6. I standardize and multiply each factor by its proportion of variation explained from the total of variation explained, and I sum them:

Cultural Role Index = Equal Capability Index + Patriarchal Index + Women Right Index

The Cronbach’s alpha is 0.5034 and the KMO is mediocre (0.6226). The individual Cronbach’s alpha for Equal Capability Index is 0.4141, for Patriarchal Index is 0.3561 and for Women Right Index is 0.3488.

Women’s Relative Income: Descriptive Statistics

Table 18. Women’s relative income

Value N Percent

0 169 79.34

0-1860 13 6.11

1861-5580 22 10.33

5581-9300 9 4.24

Observations 213 100

Note: Income is expressed in Mexican Pesos (MXN). The exchange value in 2014 was 1EURO=17 MXN. Source: Author’s computation

Table 19. Women’s relative income by civil status

Variable N mean sd min max Divorced Women 19 3102.63 2033.281 450 7400 Married Women 194 585.27 1722.814 17 9300 Full sample 213 809.82 1889.54 17 9300 Source: Author’s computation

Table 20. Women’s relative contribution to the household income

Value N Percent Cumulative

0 169 79.34 79.34

0-0.5 5 2.35 81.69

0.5 8 3.76 85.45

0.5-1 4 1.88 87.33

1 27 12.68 100

Observations 213 100

Source: Author’s computation

Women’s Relative Wage: Instrumental Variable

Men Household Engagement Index

The Men Household Engagement Index is composed by activities done exclusively by women (Housework and Child Caring) and by others done mainly by men (Reparation and Payment Responsibility) and measure the level of engagement of the husband in activities at home. I drop Animal Caring, Garden Cleaner and Wood Collector because those activities are mainly done in countryside and they would distort the results since the sample includes either urban and countryside areas. I further drop Elderly Caring because generally absent in Mayan communities. I create categorical variables that capture the frequency of men's engagement in activities at home: I score as 0 if men never do it, as 2 if they do it twice a week, as 4 if they do it four days a week and as 7 if it is a daily action. In order to get a single index, I conduct the exploratory analysis by using principal-component factor method retaining factors with eigenvalues greater than one as it was done previously. As a result, one factor has eigenvalues greater than one and explain 41.56% variance of the total variance. I rotate the factor using orthogonal method (varimax) which converted the correlated variables in linearly uncorrelated dimension of men engagement in activities at home: 1. Household Engagement (Factor I explains 42%, 0.4156) includes Men Housework, Men Child Caring, Men Reparation and Men Payment Responsibility.

Table 21. Factor loadings of variables related to men engagement to activities at home

Variable Factor 1 Uniqueness Housework 0.6779 0.5405 Child Caring 0.6776 0.5409 Reparations 0.6838 0.5324 Payments 0.5253 0.7241

Note: I use the principal-component factor method. I rotate the factor loadings using orthogonal method (varimax)

Only the uniqueness of Men Payment Responsibility is slightly above 0.6, meaning that it is the variable least explained by the common factor. I standardize and multiply the factor by its proportion of variation explained from the total of variation explained, and I obtain Men Household Engagement Index. The Cronbach’s alpha is 0.4762 and the KMO is mediocre (0.6229).

Table 22. Men Household Engagement Index

Value N Percent Cumulative

0 26 11.56 11.56

0-0.5 108 48 59.56

0.5 19 8.44 68.00

0.5-1 71 31.56 99.56

1 1 0.44 100

Observations 225 100

Source: Author’s computation

Table 23. First stage regression of 2SLS, Men Household Engagement Index as instrument for Income Sharing Rule Incidence Severity-Incidence Domestic Violence Domestic Violence Index Psych Econ Psych Phys Phys Female Labour 0.150** 0.278*** 0.269** 0.219** (0.077) (0.096) (0.123) (0.111) Female Black Labour - 0.114** -0.097 -0.066 -0.082 (0.052) (0.083) (0.086) (0.093) Age of Women 0.005** 0.003 0.004 0.003 (0.002) (0.002) (0.003) (0.003) Diabetes Problems -0.138** -0.179** -0.139 -0.165* (0.056) (0.090) (0.091) (0.094) Alcohol Consumption -0.107** -0.100 -0.133 -0.155** (0.053) (0.077) (0.081) (0.073) Satisfaction of Friendship 0.064* 0.086 0.102 0.136** (0.035) (0.056) (0.077) (0.068) Women Decision Making 0.287** 0.380* 0.268 0.288 Index (0.147) (0.195) (0.201) (0.204) Women Freedom Index - 0.271* -0.629*** -0.370** -0.456* (0.149) (0.213) (0.176) (0.245) Men Household -0.448*** -0.423*** -0.314** -0.460*** Engagement Index (0.115) (0.140) (0.153) (0.158)

Observations 211 117 103 115 F statistic 17.78 8.79 3.53 8.14 First stage regression estimates reported for 2014. Coefficients significant at the 1% (***p<0.01), 5% (**p<0.05) and 10% (*p<0.1) significance level. Standard errors in parentheses. Dependent variable is the Income Sharing Rule. All specifications include control variables at intra-household level (Cultural Role Index, Female Labour, Female Black Labour, Age Mayan Women, Satisfaction with Income, Parents, Neighbours and Friends, Education Mayan Women, State Support and Land Ownership) and at household level (Household Economic Status Index, Tobacco Consumption, Alcohol Consumption, Respiratory Problems, Diabetes Problems, Hypertension Problems, Maya City, Length Relationship and Family Dimension). I drop Men Age, Male Education and House Ownership due to missing values, male labour variables (Male Black Labour and Male Labour) because of their correlation with the instrumental variable, and Women Occupation because of its correlation with Female Labour and Female Black Labour.

Descriptive Statistics

Table 24. Descriptive statistics

Variable N mean sd min max Severity-Domestic Index 225 0.134 0.184 0 0.849 Incidence-Domestic Index 225 0.235 0.256 0 0.999 Income Sharing Rule 213 0.176 0.362 0.001 2 Household Total Income 220 4039.5 2549.342 450 9300 Age Woman 225 39.98 13.62 16 76 Age Man 201 45.19 14.02 16 99 Education Woman 225 7.38 4.47 0 21 Education Man 203 7.97 4.84 0 21 Women Occupation 221 2.199 2.612 0 9 Female Black Labour 223 0.251 0.434 0 1 Female Labour 223 0.161 0.368 0 1 Male Occupation 203 5.014 2.508 0 10 Male Black Labour 203 0.029 0.169 0 1 Male Labour 203 0.532 0.500 0 1 House Ownership 197 0.197 0.399 0 1 Land Ownership 225 0.337 0.474 0 1 State Program (Oportunidades) 225 0.537 0.499 0 1 Hypertension Problems 225 0.128 0.335 0 1 Respiratory Problems 225 0.528 0.500 0 1 Diabetes Problems 225 0.204 0.404 0 1 Alcohol Consumption 225 0.257 0.438 0 1 Tobacco Consumption 225 0.097 0.297 0 1 Maya City 225 0.444 0.498 0 1 Length Relationship 225 0.702 0.458 0 1 Family Dimension 225 4.368 1.815 0 12 Satisfaction with Parents 225 4.804 0.783 3 7 Satisfaction with Neighbors 225 4.546 0.712 2 7 Satisfaction with Friends 225 4.564 0.659 3 7 Household Economic Resources Index 225 0.555 0.173 0.058 0.9413 Women Freedom Index 225 0.53 0.191 0 1 Women Decision Making 225 0.514 0.170 0 0.999 Cultural Role Index 225 0.719 0.201 0.162 1 Man Household Engagement Index 225 0.397 0.223 0 1 Note: “N” stands for number of observations, “sd” for standard deviations, “min” minimum value and “max” for maximum value Source: Author’s computation (World Bank Survey 2014)

Incidence Domestic Violence

Table 25. Incidence of Domestic Violence

Model A. OLS Model B. IV/2SLS Psych Econ Psych Psych Index Psych Econ Psych Psych Index Phys Phys Eco1 Eco2 Phys Phys Eco1 Eco2 Income Sharing 0.073* 0.020*** 0.017*** 0.005 0.117** 0.553*** 0.063*** 0.042** 0.021 0.680*** Rule (0.041) (0.007) (0.006) (0.006) (0.055) (0.178) (0.021) (0.021) (0.020) (0.210) Women Decision -0.091 -0.010 -0.040*** 0.011 -0.131 -0.236* -0.023 -0.047*** 0.006 -0.301* Making Index (0.103) (0.014) (0.013) (0.012) (0.128) (0.128) (0.015) (0.014) (0.013) (0.156) Female Labour 0.086* 0.008 0.016** 0.009 0.120** -0.000 0.000 0.012 0.006 0.018 (0.499) (0.007) (0.007) (0.007) (0.065) (0.065) (0.008) (0.008) (0.008) (0.081) Female Education -0.011*** -0.000 -0.001** -0.001*** -0.015*** -0.008* -0.000 -0.001** -0.002*** -0.112** (0.003) (0.000) (0.000) (0.000) (0.005) (0.005) (0.000) (0.000) (0.000) (0.006) State Support 0.003 0.005 0.004 0.005 0.019 0.056 0.010* 0.007 0.007 0.081 (0.031) (0.004) (0.004) (0.004) (0.041) (0.043) (0.005) (0.005) (0.005) (0.053) Satisfaction With -0.040** -0.002 -0.006** -0.006** -0.055** -0.021 -0.000 -0.005** -0.005** -0.033 Income (0.018) (0.002) (0.003) (0.002) (0.022) (0.021) (0.002) (0.002) (0.002) (0.025) Satisfaction With -0.013 -0.006** -0.007** -0.006** -0.033 -0.021 -0.007** -0.007*** -0.007** -0.043 Parents (0.018) (0.003) (0.003) (0.003) (0.024) (0.022) (0.003) (0.003) (0.003) (0.028) Alcohol 0.422 0.000 0.004 -0.006 0.040 0.088** 0.005 0.006 -0.005 0.094* Consumption (0.035) (0.004) (0.005) (0.005) (0.043) (0.043) (0.005) (0.005) (0.005) (0.055) Urban Areas 0.073** 0.009** 0.004 0.005 0.095** 0.107*** 0.013** 0.006 0.006 0.132*** (0.032) (0.004) (0.004) (0.005) (0.041) (0.043) (0.005) (0.005) (0.005) (0.053)

Observations 211 211 211 211 211 211 211 211 211 211 OLS and 2SLS estimates reported for 2014. Coefficients significant at the 1% (***p<0.01), 5% (**p<0.05) and 10% (*p<0.1) significance level. Standard errors in parentheses. Dependent variable is the Income Sharing Rule. All specifications include control variables at intra-household level (Cultural Role Index, Female Labour, Female Black Labour, Age Mayan Women, Satisfaction with Income, Parents, Neighbours and Friends, Education Mayan Women, State Support and Land Ownership) and at household level (Household Economic Status Index, Tobacco Consumption, Alcohol Consumption, Respiratory Problems, Diabetes Problems, Hypertension Problems, Maya City, Length Relationship and Family Dimension). I drop Men Age, Male Education and House Ownership due to missing values, male labour variables (Male Black Labour and Male Labour) because of their correlation with the instrumental variable, and Women Occupation because of its correlation with Female Labour and Female Black Labour.

Severity Domestic Violence

Table 26. Severity of domestic violence

Model A. OLS Model B. IV/2SLS Psych Phys Econ Phys Psych Index Psych Phys Econ Phys Psych Index Income Sharing -0.001 0.036*** 0.007 0.089** 0.744*** 0.167** 0.019 0.542*** Rule (0.043) (0.012) (0.006) (0.038) (0.297) (0.069) (0.018) (0.171) Women Decision -0.065 -0.025 -0.006 -0.119 -0.396* -0.140** -0.009 -0.256** Making Index (0.115) (0.021) (0.012) (0.100) (0.224) (0.059) (0.014) (0.129) Female Labour 0.091* 0.015 0.021*** 0.077 -0.122 -0.019 0.018** -0.010 (0.052) (0.012) (0.008) (0.047) (0.114) (0.023) (0.008) (0.068) Female -0.004 0.001 -0.000 -0.010*** 0.001 0.003* -0.000 -0.007 Education (0.005) (0.001) (0.000) (0.004) (0.008) (0.002) (0.000) (0.004) State Support -0.010 0.011 0.006 0.026 0.112* 0.026** 0.008 0.076* (0.036) (0.007) (0.005) (0.028) (0.066) (0.012) (0.005) (0.041) Satisfaction With -0.017 -0.004 -0.006** -0.038*** 0.000 -0.004 -0.005** -0.021 Income (0.019) (0.003) (0.003) (0.014) (0.031) (0.005) (0.003) (0.018) Satisfaction With -0.039** -0.005 -0.004 -0.035** -0.062** -0.010 -0.004 -0.046** Parents (0.019) (0.004) (0.004) (0.018) (0.029) (0.007) (0.003) (0.021) Alcohol -0.003 -0.002 0.002 0.011 0.057 0.009 0.004 0.054 Consumption (0.034) (0.007) (0.005) (0.030) (0.064) (0.012) (0.005) (0.038) Urban Areas 0.051 -0.000 -0.004 0.068** 0.135** 0.010 -0.003 0.100** (0.033) (0.007) (0.005) (0.029) (0.069) (0.012) (0.005) (0.041)

Observations 117 103 115 211 117 103 115 211 OLS and 2SLS estimates reported for 2014. Coefficients significant at the 1% (***p<0.01), 5% (**p<0.05) and 10% (*p<0.1) significance level. Standard errors in parentheses. Dependent variable is the Income Sharing Rule. All specifications include control variables at intra-household level (Cultural Role Index, Female Labour, Female Black Labour, Age Mayan Women, Satisfaction with Income, Parents, Neighbours and Friends, Education Mayan Women, State Support and Land Ownership) and at household level (Household Economic Status Index, Tobacco Consumption, Alcohol Consumption, Respiratory Problems, Diabetes Problems, Hypertension Problems, Maya City, Length Relationship and Family Dimension). I drop Men Age, Male Education and House Ownership due to missing values, male labour variables (Male Black Labour and Male Labour) because of their correlation with the instrumental variable, and Women Occupation because of its correlation with Female Labour and Female Black Labour.

Robustness checks

Table 27. Robustness checks: sample check

Model A. OLS Model B. IV/2SLS Incidence Severity Incidence Severity Panel (a): Sample of Women between 20 and 60 years-old Income Sharing Rule 0.123** 0.089** 0.890*** 0.689*** (0.060) (0.043) (0.338) (0.268) Observations 183 183 183 183 Panel (b): Sample of Urban Areas Income Sharing Rule 0.162** 0.107** 0.462*** 0.221** (0.068) (0.042) (0.181) (0.105) Observations 115 115 115 115 Panel (c): Sample of Married Women Income Sharing Rule 0.100 0.100* 1.444** 1.203** (0.078) (0.055) (0.641) (0.528) Observations 192 192 192 192 Panel (d): Without Men Household Engagement Index lower than 10th percentile Income Sharing Rule 0.124* 0.092** 2.345 1.539 (0.066) (0.045) (1.899) (1.261) Observations 188 188 188 188 Panel (e): Without Men Household Engagement Index higher than 90th percentile Income Sharing Rule 0.078 0.071 0.661* 0.652** (0.064) (0.047) (0.359) (0.315) Observations 166 166 166 166 Panel (f): Without Income Sharing Rule lower than 10th percentile Income Sharing Rule 0.088 0.059 0.662*** 0.561*** (0.057) (0.041) (0.253) (0.211) Observations 180 180 180 180 Panel (g): Without Income Sharing Rule higher than 90th percentile Income Sharing Rule 0.143 0.117 -10.426 -8.644 (0.125) (0.090) (19.292) (15.845) Observations 184 184 184 184 OLS and 2SLS estimates reported for 2014. Coefficients significant at the 1% (***p<0.01), 5% (**p<0.05) and 10% (*p<0.1) significance level. Standard errors in parentheses. Dependent variable is the Income Sharing Rule. All specifications include control variables at intra-household level (Cultural Role Index, Female Labour, Female Black Labour, Age Mayan Women, Satisfaction with Income, Parents, Neighbours and Friends, Education Mayan Women, State Support and Land Ownership) and at household level (Household Economic Status Index, Tobacco Consumption, Alcohol Consumption, Respiratory Problems, Diabetes Problems, Hypertension Problems, Maya City, Length Relationship and Family Dimension). I drop Men Age, Male Education and House Ownership due to missing values, male labour variables (Male Black Labour and Male Labour) because of their correlation with the instrumental variable, and Women Occupation because of its correlation with Female Labour and Female Black Labour.

Table 28. Robustness checks: instrument check

Severity-Incidence of Domestic Violence Psych Phys Econ Phys Psych Income Sharing Rule 0.744*** 0.167** 0.019 (0.280) (0.072) (0.023)

Observations 117 103 115 Estimation based on Fuller’s modification of the limited-information maximum likelihood (LIML). The LIML method results in consistent estimates that are exactly equal to 2SLS estimates when an equation is exactly identified. Coefficients significant at the 1% (***p<0.01), 5% (**p<0.05) and 10% (*p<0.1) significance level. Standard errors in parentheses. Dependent variable is the Income Sharing Rule. All specifications include control variables at intra-household level (Cultural Role Index, Female Labour, Female Black Labour, Age Mayan Women, Satisfaction with Income, Parents, Neighbours and Friends, Education Mayan Women, State Support and Land Ownership) and at household level (Household Economic Status Index, Tobacco Consumption, Alcohol Consumption, Respiratory Problems, Diabetes Problems, Hypertension Problems, Maya City, Length Relationship and Family Dimension). I drop Men Age, Male Education and House Ownership due to missing values, male labour variables (Male Black Labour and Male Labour) because of their correlation with the instrumental variable, and Women Occupation because of its correlation with Female Labour and Female Black Labour.

Mechanism

Table 29. Decision Making (Incidence of Domestic Violence as one of explanatory variables)

Model A. OLS Model B. IV/2SLS Income Sharing Rule 0.071** 0.156 (0.034) (0.137) Incidence Domestic Violence -0.060 -0.078 (0.047) (0.051) Women Freedom Index 0.173*** 0.191*** (0.063) (0.070) Cultural Role Index 0.111* 0.096 (0.067) (0.066) Age of Women -0.003*** -0.003*** (0.001) (0.001) Urban Areas 0.040 0.047* (0.027) (0.026) Respiratory Problems -0.040* -0.035 (0.024) (0.026)

Observations 211 211 OLS and 2SLS estimates reported for 2014. Coefficients significant at the 1% (***p<0.01), 5% (**p<0.05) and 10% (*p<0.1) significance level. Standard errors in parentheses. Dependent variable is the Income Sharing Rule. All specifications include control variables at intra-household level (Cultural Role Index, Female Labour, Female Black Labour, Age Mayan Women, Satisfaction with Income, Parents, Neighbours and Friends, Education Mayan Women, State Support and Land Ownership) and at household level (Household Economic Status Index, Tobacco Consumption, Alcohol Consumption, Respiratory Problems, Diabetes Problems, Hypertension Problems, Maya City, Length Relationship and Family Dimension). I drop Men Age, Male Education and House Ownership due to missing values, male labour variables (Male Black Labour and Male Labour) because of their correlation with the instrumental variable, and Women Occupation because of its correlation with Female Labour and Female Black Labour.

Table 30. Decision Making (Severity of Domestic Violence as one of explanatory variables)

Model A. OLS Model B. IV/2SLS Income Sharing Rule 0.072** 0.175 (0.034) (0.138) Severity Domestic Violence -0.104 -0.135* (0.066) (0.074) Women Freedom Index 0.168*** 0.188*** (0.063) (0.069) Cultural Role Index 0.114* 0.098 (0.067) (0.067) Age of Women -0.003*** -0.003*** (0.001) (0.001) Urban Areas 0.042 0.050* (0.027) (0.026) Length Relationship 0.038 0.040* (0.026) (0.024)