WPS7803 Policy Research Working Paper 7803 Unequal before the Law Measuring Legal Gender Disparities across the World Sarah Iqbal Asif Islam Rita Ramalho Alena Sakhonchik Development Economics Global Indicators Group August 2016 Policy Research Working Paper 7803 Abstract Several economies have laws that treat women differently preexisting measures of gender inequality. A high degree from men. This study explores the degree of such legal of legal gender disparities is found to be negatively asso- gender disparities across 167 economies around the world. ciated with a wide range of outcomes, including years This is achieved by constructing a simple measure of legal of education of women relative to men, labor force par- gender disparities to evaluate how countries perform. The ticipation rates of women relative to men, proportion of average number of overall legal gender disparities across women top managers, proportion of women in parlia- 167 economies is 17, ranging from a minimum of 2 to a ment, percentage of women that borrowed from a financial maximum of 44. The maximum possible legal gender dis- institution relative to men, and child mortality rates. Sub- parities is 71. The measure is found to be correlated with categories within the legal disparities measure help to other measures of gender inequality, implying the measure uncover specific types of legal disparities across economies. does capture gender inequality while also differing from This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at siqbal4@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Unequal before the Law: Measuring Legal Gender Disparities across the World Sarah Iqbala, Asif Islamb, Rita Ramalhoc, Alena Sakhonchikd (DECIG, World Bank, Washington DC, USA) JEL: D63, I39 J16 K10 Keywords: Gender inequality, legal institutions, Women, Business and the Law a Women, Business and the Law, DECIG, World Bank Washington DC, USA. Email: siqbal4@worldbank.org b Enterprise Analysis Unit, DECIG, World Bank, Washington DC. Email: aislam@worldbank.org c Doing Business, DECIG, World Bank Washington DC, USA. Email: rramalho@ifc.org d Women, Business and the Law, DECIG, World Bank Washington DC, USA. Email: asakhonchik@worldbank.org Unequal before the Law: Measuring Legal Gender Disparities across the World 1. Introduction Gender equality has come to the forefront of policy debates, not only because it deprives a basic human right, but also due to a surge in evidence that illuminates the extensive costs it incurs on society. Gender inequality, generally approximated by inequalities in employment and education opportunities, can result in low human capital, low productivity, and low economic growth (Abu- Ghaida and Klasen, 2004; Bandara, 2015; Baliamoune-Lutz and McGillivray, 2015; Dollar and Gatti, 1999; Gaddis and Klasen, 2014; Goldin, 1995; Klasen, 2002; Klasen and Lammana, 2009; Knowles et al., 2002; Lagerlof, 2003; World Bank, 2011). After establishing gender equality as a crucial goal, the natural question that follows is how does one achieve it? There is some indication that gender-based policies are necessary and that development and gender equality may not provide a virtuous cycle by themselves (Duflo, 2012). There are also multiple causes of gender inequality, and there is merit in understanding each of them to attach relative importance and prioritize accordingly. At the same time there has been wide acceptance that institutions play a crucial role in dictating the paths that economies take. Defined by North (1990) as “rules of the game in a society or, more formally, are the humanly devised constraints that shape human interaction,” institutions have been found to be a fundamental explanation of long run economic growth (Acemoglu et al., 2005). Therefore it would come as no surprise that if institutions are shaped to discriminate on the basis of gender, gender inequalities would permeate throughout society. Accordingly, gender inequalities in social institutions have been found to have negative gender-related development 2    outcomes in areas such as female education, child mortality, fertility, and governance (Branisa et al., 2013). In this study, the focus is magnified even further by exploring legal institutions that discriminate on the basis of gender. Such institutions promote gender inequality before the law and are easily identifiable with concrete implications for gender inequality outcomes. This has some support in the literature. The presence of non-discrimination clauses in hiring has been found to be related to positive women’s labor force participation in the formal private sector (Amin and Islam, 2015). Similarly restrictions to women’s rights to inheritance and property as well as impediments to opening a bank account or freely pursuing a profession are found to be strongly associated with large gender gaps in female labor force participation (Gonzales et al., 2015). The primary goal of the paper is to construct a simple composite measure to illuminate the legal disparities faced by women and how it is linked to their wellbeing.1 By and large the composite measure is a summation of the number of legal disparities faced by married and unmarried women. The components of this measure are based on 7 areas – Accessing institutions, Using property, Getting a job, Providing incentives to work, Going to court, Building credit, and Protecting women from violence. This measure is then used to elucidate the degree of legal gender disparities as well as the composition of these disparities across the world, including both developed and developing economies. The study then explores how well the measure correlates with other gender inequality indices and gender inequality outcomes.                                                         1 This composite measure builds on a measure used by World Bank’s Women, Business and the Law (WBL) as a tool to capture overall legal disparities faced by women, and is entirely based on the data collected by WBL. 3    The data used to construct the legal gender disparities measure are provided by the World Bank's Women, Business and the Law database. Data are collected through several rounds of interaction with practitioners with expertise in family, labor and criminal law, including lawyers, judges, academics and members of civil society organizations working on gender issues. The data are constructed based on the responses to questionnaires, conference calls, written correspondence and visits by the Women, Business and the Law team. The respondents also provide references to the relevant laws and regulations. The Women, Business and the Law team collects the texts of relevant laws and regulations and checks questionnaire responses for accuracy. Questionnaire responses are verified against codified sources of national law including constitutions, codes, laws, statutes, rules, regulations and procedures, in areas such as labor, social security, civil procedure, tax, violence against women, marriage and family, inheritance, nationality and land. The users of Women, Business and the Law data are of a wide variety. Institutions such as the World Bank, OECD, United Nations, IMF, and the Millennium Challenge Corporation have all used the data. Academic studies have also been conducted based primarily on Women, Business and the Law data (Amin and Islam, 2015; Gonzales et al., 2015). A number of composite measures of gender inequality do exist, and they typically try to capture gender inequality as a whole or different aspects of it. Several indices have been constructed to capture gender inequality in outcomes such as gender differences in education, employment, health and political involvement. These include three measures from the UNDP: Gender Inequality Index (UNDP, 2010), Gender Empowerment Measure and the Gender-Related Development Index (UNDP, 1995). Other measures also based on outcomes include the Global Gender Gap Index (Lopez-Claros and Zahidi, 2005), the Gender Equity Index (Social Watch, 4    2005) and the African Gender Status Index (Economic Commission for Africa, 2004). Details of the indices are summarized in table A3. There are several issues with using outcome-based measures. First, typically income is included in these measures, which tends to have the highest degree of variation. This is problematic as the variable with the most variation gets the highest weight (Dijksra, 2001). Second, several of the outcome data are sparse, measured differently across economies, and mix different aspects such as empowerment and well-being (Klasen and Schueler, 2011). For instance the Gender-Related Development Index relies on income measures which are derived from a gender breakdown of labor force participation data and non- agricultural earnings data. Labor force participation data are unreliable and difficult to compare across countries. Furthermore earnings data are sparse and may come from sectors that do not represent the whole working population. Thus these measures can be problematic. Measures that are more in line in with this study include the Social Institutions and Gender Index (SIGI; Branisa et al., 2014) and two indices from the CIRI Human Rights Data project – Women’s Economic Rights Index (WECON), and the Women’s Social Rights Index (WOSOC). WECON concentrates on women’s equal rights in the labor market while WOSOC focuses on women’s rights in the social sphere including education, marriage, travel etc. The information on laws for these indices is based on the data collected by the Women, Business and the Law (WBL) project of the World Bank Group. There are certain differences between this measures and what this study proposes. Both WECON and WOSOC capture a subset of laws and provide scores in the range of 0 to 3 based on the premise of whether internationally recognized human rights for women are included in the laws. 5    The narrow range of the scores from 0 to 3 limits the possibility of capturing the heterogeneity of laws across countries given that there will be a lot of clustering around certain scores. The SIGI index goes beyond legal institutions and attempts to capture social institutions in general by combining data on laws from WBL with other measures from the OECD Gender, Institutions and Development (GID) database. In contrast the WBL measure of legal gender disparities proposed in this study relies only on data on laws and provides a wide range of scores given the measure is based on disparities in a wide number of laws. There are several advantages of the WBL measure of legal gender disparities. First by narrowing down the focus to legal institutions, many conceptual and empirical issues are cast aside. For instance the problems of using earnings measures that include inconsistent methodologies of data collection across countries and patchy data are entirely avoided as laws are more precisely measureable. Conceptually what this measure tries to capture is well defined, so there is no combination of measures of different elements that may lead to some loss in clarity of interpretation. Furthermore, the measure presented is not an index. It is a simple summation of legal disparities, which on one hand does away with a lot of nuances, but on the other hand presents a measure that is easily replicable, and malleable to any particular need. Complexity and difficulty in replication are common problems with established indices, which then become inaccessible for various institutions including NGOs (Charmes and Wieringa, 2003). It is important to note that the measure is maintained by WBL, who are the primary data generators of legal disparities that are used by other indices. This is a unique advantage as it ensures the measure will be at the cutting edge of uncovering new laws and updating existing laws that may have important implications for gender equality. 6    To summarize, the contribution of the study is as follows (i) constructs a measure of legal gender disparities including subcategories, (ii) elucidates the patterns of legal gender disparities across the world, (iii) validates the measure by exploring correlations with other gender inequality measures, and (iv) analyzes the association with gender legal disparities and several gender- related outcomes. Section 2 provides the methodology, section 3 unveils the patterns of legal gender disparities, section 4 shows the validity of the measure with other similar measures, section 5 shows the association of the measure with various outcomes, and finally section 6 concludes. 2. Methodology The construction of the WBL measure of legal gender disparities is based on three main principles. These principles are as follows. Women are considered to face unequal treatment before the law if (i) laws exist with explicit gender-based differences, or (ii) there is an absence of laws that protect the status of women e.g. absence of a non-discrimination clause in the constitution, or (iii) general laws exist that may imply gender-based legal differences or undermine existing laws that protect the status of women e.g. personal or customary law. Laws that satisfy any of these principles are marked as legal disparities for married and unmarried women. The measure of legal gender disparities can be presented by the equation below for economy j: wblrestrj   iU1 , M1 AccInstij   iU 2 , M 2 Propertyij   iU 3 , M 3 GetJobij   iU 4 , M 4 IncWrkij   iU 5 , M 5 Courtij (1) +  iU 6 , M 6 Violenceij   iU 7 , M 7 Creditij 7    Where U is the total number of legal disparities for unmarried women, M is the total number of legal disparities for married women, and i represents a legal disparity. As shown above there are 7 major subcategories for which laws are considered. These subcategories are Accessing institutions (AccInst), Using property (Property), Getting a job (GetJob), Providing incentives to work (IncWrk), Going to court (Court), Building credit (credit) and Protecting women from violence (Violence). Accessing institutions encompasses the legal ability of women to interact with public authorities and the private sector in the same way as men. Examples include whether married women can pursue a trade or profession in the same way as married men, and whether married women can obtain a national ID card the same was as married men. Using property explores the ability of women to own, manage, control, and inherit property. Getting a job examines restrictions on women to work, for example are women restricted from working in certain professions, tasks or even night hours. Providing incentives to work assesses differences in tax credits and deductions available for women versus men. Going to court considers whether women’s testimony in court is given the same weight as men’s. Building credit covers legal disparities in access to credit on the basis of gender or marital status. Finally, Protecting women from violence explores whether laws that limit domestic violence exist and the scope of such legislation. One important implication of equation (1) is that if a law applies to both married and unmarried women, it is counted twice. For example if an unmarried woman cannot apply for a passport the same way as an unmarried man, this is counted as one legal disparity. If a married women cannot apply for a passport the same way as a married man, this is counted as another legal disparity. If the law does not prohibit discrimination by creditors on the basis of gender in access to credit, 8    then this is counted as two legal disparities as it applies to both married and unmarried women. Thus the value of a disparity is either 1 if it only applies to married, and 2 if it applies to both married and unmarried women. There is only one specific situation where the value of the disparity is not assigned a score of 1 or 2 – differences in paternity and maternity leave. In this case the disparity is measured as (1-(P/M)), where P is length of paternity leave and M is maternity leave. A value of 0 would indicate parity in paternity and maternity leave. A value of 1 would indicate the highest degree of disparity as length of paternity leave would essentially be zero. Thus the greater the length of maternity leave relative to paternity leave, the greater the gender disparity (paternity leave does not tend to be greater than maternity leave). Since the disparity applies to both married men and women, it is multiplied by 2. The rationale behind having a continuous measure for the disparity in paternity and maternity leave is to capture the heterogeneity in the amount of leave allowed across economies. A full listing of legal gender disparities and the respective score assignment are presented in table A1in the appendix by subcategory. Data on legal disparities are available for 167 economies based on data collected for the 2016 Women, Business and Law report. 3. Economy Rankings and Regional Patterns The ordering of economies from the lowest to the highest number of legal gender disparities is presented in the appendix in table A2. Overall the top 5 in decreasing order are as follows, with the number of legal gender disparities indicated in brackets: Slovak Republic (2), Portugal (3), Australia (4) , Spain (5.77) and Mexico (5.88); all are high income OECD economies with the exception of Mexico.2 At the other end, the bottom five (in order of lowest to highest) ranked                                                         2 The details of the legal disparities in the top 5 economies are as follows. For the Slovak Republic, there is a legal disparity in the amount of paternity and maternity leave in the Getting a Job subcategory. For Portugal, in the 9    include Saudi Arabia (49.97), the Islamic Republic of Iran (43.9), the Republic of Yemen (41), Jordan (40), and Iraq (40). The United Kingdom is ranked 16th with 7 legal gender disparities, while the United States is ranked 32nd, tied with four other economies with 9 legal gender disparities. To provide a regional overview, figure A1 presents the average number of legal gender disparities per country by region. The MENA region fares the worst with the largest number of legal gender disparities per country, while the high income OECD economies have the lowest number of legal gender disparities per country. The Middle East and North Africa (MENA), South Asia (SA), Sub-Saharan Africa (SSA) and East Asia and the Pacific (EAP) are above the world average estimates of the total number of legal gender disparities per country. Latin America and the Caribbean (LAC), Eastern Europe and Central Asia (ECA) and the high income OECD economies fall below the world average estimates of the total number of legal gender disparities per country. Composition of legal gender disparities There is a substantial variation of the composition or type of legal gender disparities by region. This is uncovered by exploring the proportion of legal gender disparities by each subcategory of the composite measure. The proportion of legal gender disparities can be interpreted as follows.                                                         Accessing Institutions subcategory non-discrimination clause in the Constitution does not explicitly mention gender, and the law does not prohibit discrimination by creditors on the basis of marital status in the Building Credit category. For Australia, legal disparities come from absence of non-discrimination clause based on gender in the Constitution in the Accessing Institutions subcategory and absence of legal obligation for employers to provide break time for nursing mothers in the Getting a Job subcategory. For Spain, legal disparities in the Getting a Job subcategory come from a difference in the amount of paternity and maternity leave and absence of legal prohibition for employers to ask about family status during interview; in the Building Credit subcategory - from absence of prohibition of discrimination on the basis of marital status; from Protection of Women against Violence subcategory – from absence of legal criminalization of marital rape in the legislation. For Mexico, legal disparities come from a difference in the amount of paternity and maternity leave, absence of legal prohibition for employers to ask about family status during interview and absence of legal provision mandating equal remuneration for work of equal value in the Getting a Job subcategory.  10    Take for example the Middle East and North Africa (MENA) where the subcategory of Accessing institutions attains a value of 28 percent. This indicates that of all the legal gender disparities in Accessing institutions, the MENA region has 28 percent of them. Accordingly, a region with 100% values for all subcategories implies that all the countries in the region have all the possible legal gender disparities considered for the composite measure. The rationale of looking at proportions of disparities instead of the absolute score of legal gender disparities is twofold. For one, the total number of legal gender disparities for a subcategory would be biased by the number of laws explored within the subcategory since the distribution of the number of legal disparities across subcategories is unequal. Second, using total legal gender disparities by region would bias the estimates as some regions have more economies than others. Thus, taking proportions negates both distortions and provides a picture of the composition of the legal gender disparities. Figure A2, presents the proportions of legal gender disparities by region. The MENA region has the highest proportions of legal gender disparities, while the high income OECD economies have the lowest proportions of legal gender disparities. Latin America and the Caribbean (LAC), Eastern Europe and Central Asia (ECA) and the high income OECD economies fall below the world average estimates of the total proportions of legal gender disparities. Middle East and North Africa (MENA), South Asia (SA), Sub-Saharan Africa (SSA) and East Asia and the Pacific (EAP) are above the world average estimates of the total proportions of legal gender disparities. The types of legal gender disparities vary across the regions. For instance MENA has the highest proportions of legal gender disparities in every single subcategory but for the Building credit subcategory (where South Asia and Sub-Saharan Africa fare worse) and 11    Providing incentives to work subcategory where only EAP fares worse. While the high income OECD economies generally have the lowest proportions of legal gender disparities across all the regions, they still lag behind the ECA and LAC regions in Accessing institutions. There is substantial heterogeneity in legal gender disparities within regions as well. Figure A3 presents the proportions of legal gender disparities in Sub-Saharan Africa by subcategory. Mauritania has the highest proportions of legal gender disparities while South Africa has the lowest. All economies in the region except South Africa have both the gender legal disparities that fall under the Building credit subcategory. Only Mauritania and Sudan have legal gender disparities in the Going to court subcategory in the region. Figure A4 shows the source of gender legal disparities for EAP. Hong Kong SAR, China has the lowest proportions of disparities while Brunei Darussalam has the highest. Brunei Darussalam attains this status due to its high proportion of disparities in Accessing institutions, which is the highest in the region, tied with Malaysia. Gender legal disparities in Building credit are the most prevalent in the region with only the Philippines, Mongolia, and Hong Kong SAR, China having no disparities. Figure A5 shows the source of gender legal disparities for LAC. Haiti has the highest proportions of legal gender disparities while Mexico has the lowest. Haiti attains this status due to the lack of domestic violence legislation. Figure A6 shows the source of gender legal disparities in ECA. The Russian Federation has the highest proportions of legal gender disparities followed by Uzbekistan, mostly due to disparities in Violence. Croatia has the lowest proportions of legal gender disparities in the region. 12    For high income OECD economies, Chile, Japan, and Israel have the highest proportions of legal gender disparities while the Slovak Republic, Australia and Ireland have the lowest (figure A7). While Chile and Japan have the top two highest proportions of legal gender disparities, the composition of legal disparities differs. For Chile it is mostly due to legal disparities in Getting a job, while in Japan it is due to lack of domestic violence legislation. In South Asia, Afghanistan and Pakistan have the highest proportions of legal disparities while Bhutan and India have the lowest. Afghanistan is the only economy in the region with 100 percent legal disparities in three subcategories - Building credit, Going to court, and Violence (figure A8). In MENA, the Islamic Republic of Iran has the highest proportions of legal gender disparities followed by Saudi Arabia, while Malta and Morocco have the lowest in the region (figure A9). 4. Correlations with Other Gender Indices The WBL composite measure of legal disparities provides a clean measure of differential treatment of men and women before the law. There are two potential critiques of this measure. On one hand it may be unrelated to gender equality where the law on the books does not reflect the realities on the ground. Under this scenario, the WBL composite measure of legal disparities would be uncorrelated with other measures of gender inequality. On the other hand, the measure could be perfectly correlated with other gender indices, thereby making the WBL measure of legal disparities redundant. To address both critiques, the correlations between the WBL measure of legal disparities and other gender-related measures are explored. The following gender-related measures are considered: CIRI Women's Social Rights, CIRI Women's Economic Rights, CIRI Women's Political Rights, WEF Gender Gap Index, Social Watch Gender Equity Index, Ratio of 13    female to male human development index, UN Gender Inequality Index (GII), and the OECD Social Institutions and Gender Index (SIGI). The findings are provided in table 1. The directions of the correlations are as expected – greater legal gender disparities are correlated with greater gender inequality. All the measures, apart from the SIGI and GII, higher values imply greater gender equality, and thus they are negatively correlated with the WBL measure of legal gender disparities. Accordingly SIGI and GII are positively correlated with the WBL measure of legal gender disparities as higher values of SIGI and GII imply greater gender inequality. All correlations are statistically significant at the 1 percent level. The SIGI index has the highest correlation with the WBL measure of legal gender disparities (0.70), which is not surprising given that the SIGI index covers social institutions that encompass certain legal institutions. The correlation between the WBL measure of legal gender disparities and other measures of gender inequality does provide some support of the view that the law in the books may influence realities in the ground, addressing the first critique of the measure. Furthermore, none of the gender-related indices are perfectly correlated with the WBL measure of legal gender disparities. This implies that the measure developed captures a different and more specific nature of legal institutions unlike the other gender-related measures. Thus the WBL measure of legal gender disparities provides additional empirical information to the existing plethora of gender-related indices, addressing the second critique of being redundant. 5. Relationship with Gender-Specific Outcomes 14    In this section simple regression analysis is conducted to explore the relationship between the WBL measure of legal gender disparities and gender-specific outcomes. Furthermore, for each outcome, subcategories of the WBL measure are explored. The theoretical underpinning of how gender inequality before the law affects an assortment of gender-specific outcomes is straightforward. The literature has empirically shown that household bargaining models are more reflective of reality than unitary household models (Thomas, 1997). Household bargaining models are characterized by household decisions dictated by the distribution of bargaining power within the household. In contrast, unitary models assume households make decisions as a single unit. The main implication is that the greater the bargaining power of women, the more influence they will have on decisions in the household. Such bargaining power is determined by the threat point which is influenced by a number of factors including “extrahousehold environmental parameters” (McElroy, 1990).3 Social and legal institutions can be considered salient determinants of such “extrahousehold environmental parameters” (Branisa et al., 2013). Thus, unequal gender laws reduce the bargaining power of women that then reduces their influence in household decisions. This results in worse outcomes for women such as low labor force participation, low education as well as lower access to financial resources. Furthermore if mothers’ are the primary caregivers of children, lowering their bargaining power could lead to lower health outcomes for children (Maitra, 2004; Thomas, 1997). Accordingly, the following gender-specific outcomes are considered in this study: women’s education as measured by years of education of women relative to men, economic empowerment as measured by women’s labor force participation relative to men as well as the proportion of                                                         3 Threat point is defined as the level of utility the member gets in case the bargaining process ends in disagreement. 15    women top managers, political empowerment as measured by the proportion of women in parliament, percentage of women that borrowed from a financial institution relative to men, and child mortality rates. All regressions presented are cross-country in nature, using OLS. The estimations account for the level of development using real GDP per capita, and regional factors using region-specific fixed effects. There is a possibility that legal gender disparities may be capturing the quality of institutions in general, and thus all estimations control for the Rule of Law indicator from the World Governance Indicators. Huber/white standard errors are used in all estimations. The summary statistics of all variables used are presented in table 2. Note that sample composition varies with different outcome variables. Table 2 also presents the maximum possible number of legal gender disparities. For instance, on average the economies of the sample have 17 legal gender disparities. This ranges from a low of 2 to a maximum of 44. However, the maximum possible number of legal disparities is 71, which no economy in the sample comes close to attaining. Data descriptions and sources for all variables excluding legal variables are in the appendix in table A4. The findings presented below should be taken with caution as they do not imply causality given the possibilities of omitted variable bias and reverse causality. Education Results for women’s years of education relative to men are presented in table 3. Overall legal gender disparities have a negative association with women’s years of education relative to men, statistically significant at the 5 percent level. Thus an increase in the laws that treat men and women differently is associated with lower years of education for women relative to men. Using the measure of women relative to men years of education ensures that factors that affect both 16    men and women’s education levels in general are accounted for. For the subcategories, legal disparities in Accessing institutions, Going to court, Providing incentives to work, and Violence protection have a statistically significant negative association with women-to-men years of education. The rest of the subcategories have negative but statistically insignificant coefficients. Thus laws that restrict women’s ability to interact with the private sector, reduce work incentives, limit domestic violence protection and have disparity of weight of testimony of women vs men are correlated with lower education of women relative to men. Economic empowerment Of course, improvements in women’s education may not necessarily translate into improvements in economic empowerment. Therefore we explore the relationship between legal gender disparities and labor force participation of women relative to men as presented in table 4. Overall legal gender disparities have a negative association with women’s labor force participation rates relative to men, statistically significant at the 1 percent level. Thus an increase in the laws that treat men and women differently is associated with a lower participation of women in the labor force relative to men. Taking the women-to-men ratio in labor force participation rates ensures that factors that affect both men and women’s labor force participation rates in general are accounted for. For the subcategories, legal disparities in Accessing institutions, Using property, Getting a job, and Going to court have a statistically significant negative association with women-to-men labor force participation rates. The rest of the subcategories have negative but statistically insignificant coefficients. Thus laws that restrict women’s ability to interact with the private sector, ability to own, manage, and inherit property, work restrictions, and disparity of 17    weight of testimony of women vs men are correlated with lower labor force participation of women relative to men. Increases in labor force participation for women may not entail economic empowerment, especially if women enter into low paying jobs that may not improve their welfare. We therefore consider an alternate measure of economic empowerment – the proportion of women top managers in formal firms. Table 5 shows that an increase in the overall legal gender disparities is negatively correlated with the proportion of women top managers. The finding is statistically significant at the 5 percent level. Regarding subcategories, legal disparities in Accessing institutions, Using property, and Going to court have statistically significant negative associations with the proportion of women top managers. The implication may be that such gender disparities in laws disempower women and thus they are less likely to advance in their careers into top managerial positions.4 One important caveat is that data on women top managers are mainly available for developing economies from the World Bank Enterprise Surveys. Thus the findings in table 5 only apply to developing economies. However given the consistency of results between women’s labor force participation and proportion of women top managers, the findings are suggestive of a negative relationship between high legal gender disparities and women’s economic empowerment. Political empowerment Economic empowerment does not necessarily imply political empowerment, and thus we explore the relationship between legal gender disparities and the proportion of women in parliament. The                                                         4 Amin and Islam (2016) explore this relationship at the firm-level and uncover a negative relationship between legal gender disparities and the probability of a firm having a top woman manager. 18    findings in table 6 show that an increase in the overall legal gender disparities measure is associated with fewer seats held by women in national parliaments. The finding is statistically significant at the 1 percent level. Regarding subcategories, legal disparities in Getting a job, Building credit, and Violence protection have statistically significant negative associations with the proportion of women in parliament. The implication may be that such gender disparities in laws disempower women and thus they are less likely to be represented in parliament. Of course, reverse causality may be true as well whereby more women in parliament results in better laws for women. The data at hand are insufficient to disentangle the effects. Access to finance Results for women who borrowed from a financial institutions relative to men are presented in table 7. Overall legal gender disparities have a negative association with women borrowing from a financial institution relative to men. The finding is statistically significant at the 5 percent level. Regarding subcategories, legal disparities in Accessing institutions, Using property, and Going to court have a statistically significant negative association with the percentage of women who borrowed from a financial institution relative to men. This is as expected given that disparities in in the Accessing institutions subcategory, such as whether a married women can obtain a national ID the same way as a man, could encumber women’s ability to access formal financial institutions. Similarly the ability of women to own, manage, and inherit property may determine whether they have savings at all in the first place. Child mortality 19    With regards to outcomes for children, mortality of children under 5 is explored. The findings are reported in table 8. Overall gender legal disparities have a positive association with child mortality, statistically significant at the 5 percent level. Thus the greater the legal disparities women face relative to men, the higher the child mortality rate. Only the Going to court and Violence protection subcategories have a statistically significant association with child mortality. Thus, similar weights in testimonies by men and women as well as domestic violence legislation may improve women’s empowerment, which in turn results in greater allocation of resources towards the children, increasing their chances of survival. It is also worth noting that unlike the previous indicators, there is no statistically significant association between legal disparities and mortality rates of boys relative to girls (results not presented). The findings, as indicated above, are only for children. The implication may be that women’s empowerment improves mortality outcomes for both boys and girls equally. The results presented provide some important findings regarding legal gender disparities and various outcomes. The subcategories of the legal gender disparities have heterogeneous effects across the outcomes, implying different elements of legal gender disparities may matter more or less for different outcomes. It is beyond the scope of this study to explain why certain forms of legal empowerment may matter more than others. One can speculate on a myriad of factors that may be influential including variation in enforcement of different types of laws, as well as institutions that may complement the effectiveness of certain laws as opposed to others. 6. Conclusion 20    In this study legal gender disparities across the world are explored with the construction of a composite measure, generally created as a summation of gender legal disparities. Subcategories of this measure are used to elucidate the heterogeneity of the type of legal gender disparities across several regions and economies. The composite measure is then shown to be correlated with other gender-related measures, albeit not perfectly correlated thereby indicating the composite measure does capture a different dimension than other gender-related measures. Finally the composite measure is found to be correlated with important gender outcomes. A larger number of legal gender disparities is found to be negatively associated with years of education of women relative to men, labor force participation rates of women relative to men, proportion of women top managers, proportion of women in parliament, percentage of women that borrowed from a financial institution relative to men, and child mortality rates. These findings stand after accounting for the level of development, the rule of law, and region-specific factors. The benefit of the legal gender disparities composite measure is that it offers a clean focus on the legal dimension of gender equalities that is precisely measured and emanates from the same data source. This ensures consistency across countries and accuracy. Furthermore, the measure is easily replicable, and through its subcategories can assist governments and policy makers to focus on specific gender legal disparities that exist in their economies. There are certain limitations of the proposed measure. First, given the nature of the focus, the measure cannot provide any indication of the degree of enforcement of the laws. However the 21    positive association uncovered with various gender outcomes and other composite measures may indicate there is some degree of enforcement otherwise none of these results is likely to hold. Second, certain laws typically apply to the formal economy but not the informal economy, and therefore the implications of legal gender disparities in the informal economy is beyond the scope of this measure. Third, while the underlying data are rich and collected through the rigorous efforts of the Women, Business and the Law project of the World Bank, the laws explored are not the universe of all possible laws. Some laws may be omitted due to difficulty in obtaining a consistent indicator across economies, or simply due to the fact that they may be covered in future reports. Finally the data are cross-sectional in nature, however a panel dimension can eventually be developed. Thus in time, it can be expected that the proposed measure will be more comprehensive. This will be a boon for future research, as well as policy makers who wish to measure progress of gender equality in relation to the Sustainable Development Goals (SDG). 22    References Abu-Ghaida, Dina and Stephan Klasen (2004), “The Costs of Missing the Millennium Development Goal on Gender Equity” World Development 32(7):1075-1107 Acemoglu, Daron, Simon Johnson, and James A. Robinson (2005) "Institutions as a Fundamental Cause of Long-Run Growth." In Handbook of Economic Growth, Volume 1A, ed. Philippe Aghion and Steven N. Durlauf, 385-472. Amsterdam and San Diego: Elsevier, North- Holland. Amin, Mohammad and Asif Islam (2016). "Can Gender-Based differences in the Law Disempower Women? Evidence from Firm-level Data on Female Managers in Developing Economies" Mimeograph (2016) http://works.bepress.com/mohammad_amin/65/ (Accessed 3/26/2016) Amin, Mohammad and Asif Islam (2015) “Does Mandating Nondiscrimination in Hiring Practices Influence Women's Employment? Evidence Using Firm-Level Data” Feminist Economics 21(4): 28-60 Baliamoune-Lutz, Mina and Mark McGillivray (2015), “The Impact of Gender Inequality in Education on Income in Africa and the Middle East” Economic Modelling 47(2015):1-11 Bandara, Amarakoon (2015), “The Economic Cost of Gender Gaps in Effective Labor: Africa's Missing Growth Reserve” Feminist Economics 21(2): 162-186 Branisa, Boris, Stephan Klasen, and Maria Ziegler (2013) “Gender Inequality in Social Institutions and Gendered Development Outcomes.” World Development 45:252–68. Branisa, Boris, Stephan Klasen, Maria Ziegler, Denis Drechsler, and Johannes Jutting (2013) “The Institutional Basis of Gender Inequality: The Social Institutions and Gender Index (SIGI).” Feminist Economics 20(2):29-64. Dijkstra, A. Geske (2006) “Towards a Fresh Start in Measuring Gender Equality: A Contribution to the Debate.” Journal of Human Development 7(2): 275–83. Dulfo, Esther (2012), “Women Empowerment and Economic Development,” Journal of Economic Literature 50(4): 1051-1079 Dollar, David and Roberta Gatti (1999), “Gender Inequality, Income, and Growth: Are Good Times Good for Women?” Working Paper 20771, World Bank. Economic Commission for Africa. 2004. The African Gender and Development Index. Addis Ababa: Economic Commission for Africa 23    Gaddis, Isis and Stephan Klasen (2014) “Economic Development, Structural Change, and Women’s Labor Force Participation: A Reexamination of the Feminization U Hypothesis.” Journal of Population Economics 27(3): 639–81. Goldin, Claudia (1995), “The U-Shaped Female Labor Force Function in Economic Development and Economic History.” In: Schultz TP Investment in Women’s Human Capital and Economic Development. University of Chicago Press; 1995. pp. 61-90. Gonzales, Christian, Sonali Jain-Chandra, Kalpana Kochhar, and Monique Newiak (2015) “Fair Play: More Equal Laws Boost Female Labor Force Participation” IMF Staff Discussion Note SDN/15/02 Klasen, Stephan (2002), “Low Schooling for Girls, Slower Growth for All? Cross-Country Evidence on the Effect of Gender Inequality in Education on Economic Development.” World Bank Economic Review 16(3): 345–73 Klasen, Stephan and Dana Schüler (2011) “Reforming the Gender-Related Development Index and the Gender Empowerment Measure: Implementing Some Specific Proposals.” Feminist Economics 17(1): 1–30 Klasen, Stephan and Francesca Lamanna (2009) “The Impact of Gender Inequality in Education and Employment on Economic Growth in Developing Countries: New Evidence for a Panel of Countries.” Feminist Economics 15(3): 91–132. Knowles, Stephen, Paula K. Lorgelly, and P. Dorian Owen (2002) “Are Educational Gender Gaps a Brake on Economic Development? Some Cross-Country Empirical Evidence.” Oxford Economic Papers 54(1): 118–49. Lagerlof, Nils-Petter (2003), “Gender Equality and Long-Run Growth” Journal of Economic Growth 8:403-426 Lopez-Claros, Augusto and Saadia Zahidi (2005), Women’s Empowerment: Measuring the Global Gender Gap. Geneva: World Economic Forum. Maitra, Pushkar (2004) “Parental bargaining, health inputs and child mortality in India.” Journal of Health Economics, 23(2), 259–291. McElroy, Majorie B. (1990) “The empirical content of Nash-bargained household behavior.” Journal of Human Resources, 25, 559–583. North, Douglass C. (1990) Institutions, Institutional Change, and Economic Performance. New York: Cambridge University Press. Social Watch (2005) Roars and Whispers Gender and Poverty: Promises vs. Action. Montevideo: Social Watch. 24    Thomas, Duncan (1997) Incomes, expenditures and health outcomes: Evidence on intra- household resource allocation. In Lawrence James Haddad, John Hoddinott, & Harold Alderman (Eds.), Intra-household resource allocation in developing countries: Models, methods and policy. Baltimore, MD: The Johns Hopkins Press. United Nations Development Programme UNDP (1995) Human Development Report 1995. Gender and Human Development. New York: Oxford University Press. United Nations Development Programme UNDP (2010) Human Development Report 2010. The Real Wealth of Nations: Pathways to Human Development. New York: Palgrave Macmillan. World Bank (2011) World Development Report 2012: Gender Equality and Development. Washington, DC: World Bank. 25    Table 1: Correlations between Legal Disparities and Gender Indices Legal disparities: Overall measure (Higher values, greater gender disparity)       Correlation coefficient Significance Number of observations CIRI Women's Social Rights 2004 (Higher values greater gender equality) -0.50 *** 147 CIRI Women's Economic Rights 2011 (Higher value greater gender equality) -0.56 *** 164 CIRI Women's Political Rights 2011 (Higher values greater gender equality) -0.41 *** 164 WEF Gender Gap Index 2015 (Higher values greater gender equality) -0.66 *** 136 Social Watch Gender equity index 2012 (Higher values greater gender equality) -0.66 *** 141 Ratio of female to male human development index 2013 (Higher values greater gender equality) -0.57 *** 136 UN gender inequality index 2013 (Higher values worse gender equality) 0.53 *** 142 OECD Social Institutions and Gender Index 2014 (Higher values worse gender equality) 0.70 *** 98 Table 2: Summary Statistics for Regressions Analysis Maximum possible Obs Mean Std. Dev. Min Max    disparities Legal disparities: Overall measure 167 17.4 9.1 2 43.9 71 Legal disparities: Accessing Institutions 167 2.7 3.3 0 18 30 Legal disparities: Using Property 167 1.0 1.6 0 7 7 Legal disparities: Getting a Job 167 9.6 3.9 0 19.9 22 Legal disparities: Going to Court 167 0.2 0.5 0 2 2 Legal disparities: Providing Incentives to Work 167 0.2 0.5 0 2 2 Legal disparities: Building Credit 167 2.3 1.2 0 3 3 Legal disparities: Violence Protection 167 1.5 1.6 0 5 5 Log of GDP per capita (constant 2005 US$) 157 11,175 15,727 155.25 79,509 Rule of law 167 0.0 1.0 -1.79 1.97 Years of education, women relative to men ages 25 plus 163 0.8 0.2 0.2 1.1 Labor force participation rate, women relative to men 159 0.7 0.2 0.2 1.0 Proportion of seats held by women in national parliaments (%) 157 20.6 11.3 0 63.8 Proportion of women top managers (%) 113 17.3 8.7 1.2 38.6 Borrowed from a financial institution, women relative to men (% age 15+) 136 0.8 0.3 0.1 2.2 Mortality rate, under-5 (per 1,000 live births) 162 30.9 33.1 2 167.4 26    Table 3: Legal Gender Disparities and Years of Education of Women Relative to Men OLS Years of education, women over men ages 25 plus coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se Legal disparities: Overall measure -0.004** (0.002) Legal disparities: Accessing -0.012*** Institutions (0.004) Legal disparities: Using Property -0.015 (0.013) Legal disparities: Getting a Job -0.002 (0.003) Legal disparities: Going to Court -0.054* (0.030) Legal disparities: Providing Incentives -0.036** to Work (0.018) Legal disparities: Building Credit -0.001 (0.011) Legal disparities: Violence Protection -0.015** (0.008) Log of GDP per capita (constant 2005 0.073*** 0.075*** 0.069*** 0.070*** 0.070*** 0.070*** 0.070*** 0.073*** US$) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) Rule of law -0.027 -0.018 -0.013 -0.015 -0.014 -0.015 -0.011 -0.020 (0.019) (0.019) (0.018) (0.019) (0.018) (0.018) (0.018) (0.018) region==East Asia & Pacific 0.028 0.034 0.024 0.012 0.009 0.033 0.013 -0.002 (0.035) (0.034) (0.036) (0.034) (0.034) (0.036) (0.040) (0.036) region==Europe & Central Asia 0.049** 0.059*** 0.052*** 0.052** 0.052*** 0.051** 0.056*** 0.042* (0.021) (0.017) (0.019) (0.022) (0.019) (0.020) (0.022) (0.023) region==Latin America & Caribbean 0.115*** 0.116*** 0.113*** 0.114*** 0.110*** 0.108*** 0.116*** 0.096*** (0.029) (0.025) (0.027) (0.028) (0.027) (0.028) (0.035) (0.030) region==Middle East & North Africa -0.094* -0.085 -0.121** -0.163*** -0.136*** -0.163*** -0.168*** -0.159*** (0.054) (0.053) (0.061) (0.048) (0.047) (0.047) (0.052) (0.047) region==South Asia -0.215** -0.211** -0.216** -0.246*** -0.225*** -0.255*** -0.245*** -0.258*** (0.085) (0.084) (0.092) (0.089) (0.085) (0.089) (0.091) (0.089) region==Sub-Saharan Africa -0.090* -0.079 -0.097* -0.113** -0.111** -0.111** -0.110** -0.113** (0.050) (0.050) (0.051) (0.050) (0.049) (0.049) (0.054) (0.050) Constant 0.323** 0.252* 0.298** 0.304** 0.289** 0.291** 0.287* 0.293** (0.145) (0.148) (0.147) (0.149) (0.146) (0.144) (0.147) (0.145) Number of observations 153 153 153 153 153 153 153 153 Adjusted R2 0.674 0.679 0.665 0.658 0.671 0.665 0.658 0.668 note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported Omitted region: North America 27    Table 4: Legal Gender Disparities and Labor Force Participation of Women Relative to Men    OLS Dependent Variable Women over men labor force participation rate (% of population ages 15-64), 2013 coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se Legal disparities: Overall measure -0.008*** (0.002) Legal disparities: Accessing Institutions -0.017*** (0.005) Legal disparities: Using Property -0.039*** (0.012) Legal disparities: Getting a Job -0.008** (0.004) Legal disparities: Going to Court -0.132*** (0.030) Legal disparities: Providing Incentives -0.013 to Work (0.020) Legal disparities: Building Credit -0.005 (0.009) Legal disparities: Violence Protection -0.009 (0.010) Log of GDP per capita (constant 2005 -0.031** -0.029* -0.038** -0.036** -0.036** -0.037** -0.037** -0.035** US$) (0.015) (0.015) (0.015) (0.015) (0.014) (0.016) (0.016) (0.016) Rule of law 0.058*** 0.079*** 0.082*** 0.071*** 0.081*** 0.087*** 0.087*** 0.082*** (0.020) (0.020) (0.021) (0.022) (0.019) (0.023) (0.023) (0.022) region==East Asia & Pacific -0.037 -0.032 -0.032 -0.065* -0.069** -0.055* -0.054* -0.071** (0.034) (0.030) (0.033) (0.034) (0.031) (0.030) (0.031) (0.033) region==Europe & Central Asia -0.018 -0.001 -0.014 -0.021 -0.013 -0.008 -0.000 -0.014 (0.024) (0.017) (0.020) (0.026) (0.021) (0.021) (0.020) (0.023) region==Latin America & Caribbean -0.115*** -0.104*** -0.114*** -0.113*** -0.117*** -0.108*** -0.096*** -0.119*** (0.035) (0.032) (0.032) (0.036) (0.034) (0.035) (0.036) (0.037) region==Middle East & North Africa -0.280*** -0.291*** -0.289*** -0.385*** -0.328*** -0.408*** -0.400*** -0.406*** (0.068) (0.067) (0.068) (0.057) (0.055) (0.053) (0.052) (0.055) region==South Asia -0.197** -0.200** -0.172* -0.247*** -0.195** -0.252*** -0.238** -0.256*** (0.087) (0.085) (0.098) (0.093) (0.082) (0.095) (0.096) (0.093) region==Sub-Saharan Africa 0.050 0.061 0.051 0.004 0.017 0.014 0.024 0.013 (0.050) (0.046) (0.049) (0.050) (0.045) (0.047) (0.048) (0.049) Constant 1.191*** 1.079*** 1.156*** 1.206*** 1.131*** 1.133*** 1.137*** 1.131*** (0.138) (0.135) (0.135) (0.141) (0.130) (0.140) (0.142) (0.142) Number of observations 151 151 151 151 151 151 151 151 Adjusted R2 0.593 0.581 0.585 0.552 0.621 0.535 0.535 0.537 note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported Omitted region: North America 28    Table 5: Legal Gender Disparities and Proportion of Women Top Managers    OLS Dependent Variable Proportion of women top managers coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se Legal disparities: Overall measure -0.266** (0.131) Legal disparities: Accessing -0.614** Institutions (0.288) Legal disparities: Using Property -2.262*** (0.633) Legal disparities: Getting a Job -0.228 (0.240) Legal disparities: Going to Court -4.280*** (0.753) Legal disparities: Providing 0.741 Incentives to Work (1.091) Legal disparities: Building Credit -0.104 (0.667) Legal disparities: Violence Protection -0.143 (0.537) Log of GDP per capita (constant 2005 -0.989 -0.867 -1.387 -1.060 -1.201 -1.062 -1.040 -0.990 US$) (1.028) (1.026) (1.040) (1.037) (1.034) (1.055) (1.058) (1.091) Rule of law 0.978 1.543 1.694 1.421 1.609 1.898 1.827 1.740 (1.764) (1.620) (1.538) (1.836) (1.620) (1.682) (1.670) (1.757) region==Europe & Central Asia -8.318** -8.300** -9.085*** -7.881** -7.424** -7.090* -7.636** -7.640** (3.316) (3.489) (3.472) (3.499) (3.451) (3.655) (3.416) (3.445) region==Latin America & Caribbean -5.495* -6.065* -6.178* -5.240 -5.095 -4.719 -5.189 -5.327 (3.288) (3.508) (3.406) (3.465) (3.428) (3.671) (3.449) (3.437) region==Middle East & North Africa -17.287*** -17.834*** -14.353*** -20.397*** -18.195*** -20.682*** -20.702*** -20.571*** (3.512) (3.591) (3.556) (3.352) (3.240) (3.337) (3.384) (3.412) region==South Asia -14.430*** -15.068*** -12.318*** -16.021*** -14.081*** -15.790*** -16.212*** -16.285*** (4.019) (4.119) (4.036) (4.149) (4.071) (4.250) (4.142) (4.094) region==Sub-Saharan Africa -10.794*** -10.863*** -10.685*** -11.999*** -11.423*** -11.458*** -11.670*** -11.612*** (3.078) (3.277) (3.128) (3.258) (3.189) (3.341) (3.342) (3.377) Constant 39.009*** 35.288*** 39.838*** 37.805*** 36.595*** 35.089*** 35.634*** 35.185*** (8.258) (8.535) (8.690) (8.111) (8.599) (8.739) (8.922) (8.940) Number of observations 108 108 108 108 108 108 108 108 Adjusted R2 0.322 0.317 0.358 0.297 0.342 0.292 0.290 0.291 note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported Omitted region: Eastern Europe and Central Asia (sample consists of only developing economies) 29    Table 6: Legal Gender Disparities and Proportion of Seats Held by Women in National Parliaments    OLS Dependent Variable Proportion of seats held by women in national parliaments (%), 2013 coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se Legal disparities: Overall measure -0.355*** (0.137) Legal disparities: Accessing Institutions -0.468 (0.321) Legal disparities: Using Property -0.278 (0.886) Legal disparities: Getting a Job -0.418* (0.249) Legal disparities: Going to Court -0.983 (1.823) Legal disparities: Providing Incentives to 0.017 Work (1.774) Legal disparities: Building Credit -2.103*** (0.763) Legal disparities: Violence Protection -1.855*** (0.643) Log of GDP per capita (constant 2005 -0.029 -0.008 -0.192 -0.167 -0.156 -0.171 -0.307 0.204 US$) (1.438) (1.492) (1.514) (1.510) (1.506) (1.524) (1.470) (1.368) Rule of law 1.464 2.436 2.625 1.876 2.597 2.660 2.256 1.569 (1.962) (1.998) (1.995) (2.040) (2.012) (2.001) (1.986) (1.874) region==East Asia & Pacific 0.785 0.472 0.011 -0.325 -0.212 -0.165 3.345 -1.802 (3.496) (3.357) (3.469) (3.684) (3.503) (3.574) (3.449) (3.946) region==Europe & Central Asia 5.955* 6.586** 6.407** 5.743* 6.419** 6.469** 9.038*** 4.966 (3.104) (2.670) (2.907) (3.215) (2.907) (2.906) (3.065) (3.647) region==Latin America & Caribbean 2.490 2.425 2.409 2.254 2.360 2.445 6.900* 0.384 (3.669) (3.434) (3.634) (3.812) (3.643) (3.647) (3.751) (4.093) region==Middle East & North Africa 3.631 0.736 -1.754 -1.107 -2.044 -2.625 2.034 -1.383 (4.582) (4.320) (4.540) (3.942) (3.777) (3.746) (3.971) (4.299) region==South Asia 2.824 1.721 0.999 0.560 0.863 0.460 5.361 -0.869 (5.808) (5.370) (5.533) (5.503) (5.351) (5.259) (5.426) (5.838) region==Sub-Saharan Africa 7.977* 7.561* 6.644 5.916 6.425 6.394 11.100** 6.358 (4.498) (4.377) (4.484) (4.640) (4.405) (4.412) (4.593) (4.711) Constant 22.579* 17.852 19.040 22.921* 18.702 18.756 20.866 19.336 (13.055) (13.209) (13.548) (13.927) (13.431) (13.635) (13.117) (12.566) Number of observations 150 150 150 150 150 150 150 150 Adjusted R2 0.117 0.090 0.079 0.093 0.080 0.078 0.111 0.134 note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported Omitted region: North America 30    Table 7: Legal Gender Disparities and Women Who Borrowed from a Financial Institution Relative to Men    OLS Dependent Variable Borrowed from a financial institution, women relative to men (% age 15+), 2013 coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se Legal disparities: Overall measure -0.009** (0.004) Legal disparities: Accessing Institutions -0.026** (0.011) Legal disparities: Using Property -0.044** (0.020) Legal disparities: Getting a Job -0.009 (0.010) Legal disparities: Going to Court -0.137** (0.067) Legal disparities: Providing Incentives 0.062 to Work (0.072) Legal disparities: Building Credit -0.025 (0.025) Legal disparities: Violence Protection 0.003 (0.021) Log of GDP per capita (constant 2005 -0.038 -0.034 -0.048 -0.045 -0.048 -0.045 -0.047 -0.048 US$) (0.041) (0.042) (0.040) (0.041) (0.040) (0.040) (0.040) (0.040) Rule of law 0.000 0.022 0.032 0.022 0.032 0.046 0.031 0.043 (0.057) (0.058) (0.056) (0.057) (0.057) (0.057) (0.058) (0.054) region==East Asia & Pacific 0.120* 0.141** 0.118* 0.101 0.093 0.080 0.140* 0.110 (0.063) (0.069) (0.063) (0.065) (0.067) (0.070) (0.072) (0.067) region==Europe & Central Asia 0.019 0.042 0.027 0.022 0.026 0.046 0.064 0.041 (0.051) (0.048) (0.048) (0.055) (0.050) (0.050) (0.051) (0.053) region==Latin America & Caribbean -0.024 -0.014 -0.017 -0.011 -0.025 0.011 0.037 0.002 (0.073) (0.074) (0.073) (0.075) (0.078) (0.079) (0.079) (0.079) region==Middle East & North Africa 0.103 0.140 0.078 -0.022 0.026 -0.058 0.001 -0.055 (0.134) (0.171) (0.115) (0.111) (0.143) (0.128) (0.133) (0.126) region==South Asia -0.195 -0.180 -0.173 -0.259* -0.214* -0.249 -0.213 -0.264* (0.138) (0.136) (0.142) (0.156) (0.123) (0.157) (0.164) (0.160) region==Sub-Saharan Africa -0.000 0.035 -0.001 -0.052 -0.045 -0.038 0.015 -0.039 (0.087) (0.091) (0.092) (0.087) (0.088) (0.084) (0.094) (0.087) Constant 1.300*** 1.156*** 1.266*** 1.313*** 1.267*** 1.212*** 1.251*** 1.237*** (0.368) (0.360) (0.355) (0.382) (0.353) (0.349) (0.354) (0.349) Number of observations 131 131 131 131 131 131 131 131 Adjusted R2 0.028 0.047 0.014 0.001 0.038 0.004 -0.001 -0.007 note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported 31    Omitted region: North America Table 8: Legal Gender Disparities and Infant Mortality Rate    OLS Dependent Variable Mortality rate, under-5 (per 1,000 live births), 2013 coef/se coef/se coef/se coef/se coef/se coef/se coef/se coef/se Legal disparities: Overall measure 0.453** (0.194) Legal disparities: Accessing 0.756 Institutions (0.654) Legal disparities: Using Property 0.620 (1.231) Legal disparities: Getting a Job 0.634 (0.512) Legal disparities: Going to Court 5.386* (3.151) Legal disparities: Providing Incentives -0.726 to Work (2.200) Legal disparities: Building Credit -1.027 (0.838) Legal disparities: Violence Protection 2.677** (1.197) Log of GDP per capita (constant 2005 -5.800* -5.848* -5.472* -5.563* -5.527* -5.498* -5.549* -6.010* US$) (3.158) (3.364) (3.148) (3.094) (3.165) (3.166) (3.167) (3.087) Rule of law -4.808 -6.004 -6.333 -5.196 -6.139 -6.494 -6.637 -4.891 (4.192) (4.373) (4.207) (3.721) (4.129) (4.179) (4.109) (4.036) region==East Asia & Pacific -9.559*** -9.272*** -8.390** -7.731** -7.569** -7.504** -6.088* -5.429 (3.458) (3.428) (3.300) (3.273) (2.992) (3.341) (3.145) (3.393) region==Europe & Central Asia -10.717*** -11.546*** -11.183*** -10.240*** -11.030*** -11.383*** -10.048*** -9.142*** (2.171) (2.063) (2.155) (2.386) (2.124) (2.234) (2.244) (2.663) region==Latin America & Caribbean -10.185*** -10.070*** -9.965*** -9.789*** -9.555*** -10.152*** -7.841** -7.058** (2.976) (2.834) (2.912) (3.032) (2.884) (3.063) (3.328) (3.344) region==Middle East & North Africa -15.349*** -12.888** -9.464* -9.562** -10.845** -7.358** -5.195 -9.358** (5.861) (6.033) (5.024) (4.699) (4.384) (3.591) (3.890) (3.899) region==South Asia 2.658 3.705 4.670 5.645 3.765 5.762 8.334 7.853 (9.209) (9.528) (10.226) (9.800) (8.644) (9.998) (10.173) (9.693) region==Sub-Saharan Africa 33.447*** 33.654*** 35.127*** 36.324*** 35.642*** 35.737*** 38.048*** 35.807*** (7.808) (8.549) (8.349) (7.828) (7.647) (7.662) (7.793) (7.497) Constant 71.295*** 77.310*** 74.611*** 69.271** 74.876*** 75.152*** 75.928*** 73.949*** (26.948) (28.727) (27.154) (28.825) (27.444) (27.453) (27.539) (26.854) Number of observations 154 154 154 154 154 154 154 154 Adjusted R2 0.733 0.729 0.726 0.729 0.730 0.725 0.726 0.738 note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported Omitted region: North America 32    33    APPENDIX Table A1: Components of the WBL Legal Gender Disparity Measure WBL Legal Gender Disparity Composite: Score A “no” response to questions below is considered a disparity and therefore assigned a score Assignment Accessing Institutions If there is a nondiscrimination clause in the constitution, does it mention gender? 2 If customary law is recognized as a valid source of law under the constitution, is it invalid if it violates constitutional 2 provisions on nondiscrimination or equality? If personal law is recognized as a valid source of law under the constitution, is it invalid if it violates constitutional 2 provisions on nondiscrimination or equality? Can an unmarried woman apply for a passport in the same way as an unmarried man? 1 Can a married woman apply for a passport in the same way as a married man? 1 Can an unmarried woman obtain a national ID card in the same way as an unmarried man? 1 Can a married woman obtain a national ID card in the same way as a married man? 1 Can an unmarried woman travel outside the country in the same way as an unmarried man? 1 Can a married woman travel outside the country in the same way as a married man? 1 Can an unmarried woman travel outside her home in the same way as an unmarried man? 1 Can a married woman travel outside her home in the same way as a married man? 1 Can an unmarried woman get a job or pursue a trade or profession in the same way as an unmarried man? 1 Can a married woman get a job or pursue a trade or profession in the same way as a married man? 1 Can an unmarried woman sign a contract in the same way as an unmarried man? 1 Can a married woman sign a contract in the same way as a married man? 1 Can an unmarried woman register a business in the same way as an unmarried man? 1 Can a married woman register a business in the same way as a married man? 1 Can an unmarried woman open a bank account in the same way as an unmarried man? 1 Can a married woman open a bank account in the same way as a married man? 1 Can an unmarried woman choose where to live in the same way as an unmarried man? 1 Can a married woman choose where to live in the same way as a married man? 1 Can an unmarried woman confer citizenship on her children in the same way as an unmarried man? 1 Can a married woman confer citizenship on her children in the same way as a married man? 1 Can an unmarried woman be head of household or head of family in the same way as an unmarried man? 1 Can a married woman be head of household or head of family in the same way as a married man? 1 Can a married woman confer citizenship to a non-national spouse in the same way as a man? 1 Are married women required by law to obey their husbands? 1 Using Property Who legally administers marital property? 1 Does the law provide for the valuation of nonmonetary contributions? 1 Do unmarried men and unmarried women have equal ownership rights to property? 1 Do married men and married women have equal ownership rights to property? 1 Do sons and daughters have equal rights to inherit assets from their parents? 2 Do female and male surviving spouses have equal rights to inherit assets? 1 Going to Court Does a woman's testimony carry the same evidentiary weight in court as a man's? 2 Providing Incentives to Work 34    Are there tax deductions or credits specific to men? 2 Building Credit Does the law prohibit discrimination by creditors on the basis of gender in access to credit? 2 Does the law prohibit discrimination by creditors on the basis of marital status in access to credit? 1 Getting a Job Is there a difference in the age at which a man and a women can retire and receive full benefits? 2 Can non-pregnant and non-nursing women work the same night hours as men? 2 Does the law mandate equal remuneration for work of equal value? 2 Does the law mandate nondiscrimination based on gender in hiring? 2 Is it prohibited for prospective employers to ask about family status? 2 Is dismissal of pregnant workers prohibited? 2 Are employers required to provide break time for nursing mothers? 2 Is there a difference in the age at which a man and a woman can retire and receive partial benefits? 2 Is there a difference in the mandatory retirement age for men and women? 2 Can non-pregnant and non-nursing women do the same jobs as men? 2 Is there a difference in the length of paid maternity and paternity leave?* 2*(M-P)/M Protecting Women from Violence Is there domestic violence legislation? 2 Is there legislation that specifically addresses sexual harassment? 2 Does legislation explicitly criminalize marital rape? 1 *Where M is length of maternity leave and P is length of paternity leave 35    Table A2: Legal Gender Disparities across the World (by increasing overall gender disparities) Providing WBL Measure Accessing Using Getting Going to Building Violence Economy Incentives to of Legal Gender Institutions Property a Job Court Credit Protection Work Disparities Slovak Republic 0.00 0.00 2.00 0.00 0.00 0.00 0.00 2.00 Portugal 2.00 0.00 0.00 0.00 0.00 1.00 0.00 3.00 Australia 2.00 0.00 2.00 0.00 0.00 0.00 0.00 4.00 Spain 0.00 0.00 3.77 0.00 0.00 1.00 1.00 5.77 Mexico 0.00 0.00 5.88 0.00 0.00 0.00 0.00 5.88 Hungary 0.00 0.00 3.94 0.00 0.00 0.00 2.00 5.94 Ireland 2.00 0.00 4.00 0.00 0.00 0.00 0.00 6.00 New Zealand 0.00 0.00 6.00 0.00 0.00 0.00 0.00 6.00 Norway 2.00 0.00 0.00 0.00 0.00 3.00 1.00 6.00 Puerto Rico (U.S.) 0.00 0.00 6.00 0.00 0.00 0.00 0.00 6.00 Latvia 2.00 0.00 3.82 0.00 0.00 1.00 0.00 6.82 Taiwan, China 0.00 0.00 3.82 0.00 0.00 3.00 0.00 6.82 Peru 0.00 0.00 5.91 0.00 0.00 1.00 0.00 6.91 Netherlands 0.00 0.00 3.96 0.00 0.00 0.00 3.00 6.96 Malta 0.00 0.00 5.98 0.00 0.00 1.00 0.00 6.98 Sweden 2.00 0.00 4.00 0.00 0.00 1.00 0.00 7.00 United Kingdom 2.00 0.00 4.00 0.00 0.00 0.00 1.00 7.00 France 2.00 0.00 5.80 0.00 0.00 0.00 0.00 7.80 South Africa 0.00 0.00 7.95 0.00 0.00 0.00 0.00 7.95 Kosovo 0.00 0.00 5.99 0.00 0.00 1.00 1.00 7.99 Czech Republic 2.00 0.00 4.00 0.00 0.00 1.00 1.00 8.00 St. Lucia 1.00 0.00 4.00 0.00 0.00 3.00 0.00 8.00 Slovenia 2.00 0.00 5.43 0.00 0.00 1.00 0.00 8.43 Estonia 0.00 0.00 3.86 0.00 0.00 3.00 2.00 8.86 Bosnia and Herzegovina 0.00 0.00 7.96 0.00 0.00 0.00 1.00 8.96 Luxembourg 2.00 0.00 5.96 0.00 0.00 1.00 0.00 8.96 Italy 2.00 0.00 5.99 0.00 0.00 1.00 0.00 8.99 Austria 2.00 0.00 6.00 0.00 0.00 0.00 1.00 9.00 Montenegro 2.00 0.00 4.00 0.00 0.00 3.00 0.00 9.00 Namibia 0.00 0.00 6.00 0.00 0.00 3.00 0.00 9.00 Serbia 0.00 0.00 6.00 0.00 0.00 3.00 0.00 9.00 United States 2.00 0.00 6.00 0.00 0.00 0.00 1.00 9.00 Lithuania 2.00 0.00 5.52 0.00 0.00 1.00 1.00 9.52 Finland 2.00 0.00 5.67 0.00 0.00 1.00 1.00 9.67 Belgium 2.00 0.00 3.81 0.00 0.00 1.00 3.00 9.81 Timor-Leste 0.00 1.00 5.88 0.00 0.00 3.00 0.00 9.88 Greece 2.00 1.00 5.97 0.00 0.00 1.00 0.00 9.97 Cambodia 0.00 0.00 4.00 0.00 2.00 3.00 1.00 10.00 Canada 0.00 0.00 8.00 0.00 0.00 0.00 2.00 10.00 Croatia 2.00 0.00 8.00 0.00 0.00 0.00 0.00 10.00 36    Table A2 (cont…) Providing WBL Measure Accessing Using Getting Going to Building Violence Economy Incentives to of Legal Gender Institutions Property a Job Court Credit Protection Work Disparities Guyana 0.00 1.00 6.00 0.00 0.00 3.00 0.00 10.00 Hong Kong SAR, China 2.00 1.00 7.91 0.00 0.00 0.00 0.00 10.91 Bulgaria 2.00 0.00 7.93 0.00 0.00 0.00 1.00 10.93 Korea, Rep. 0.00 0.00 7.93 0.00 0.00 3.00 0.00 10.93 Dominican Republic 0.00 0.00 7.95 0.00 0.00 3.00 0.00 10.95 Iceland 2.00 0.00 4.00 0.00 0.00 3.00 2.00 11.00 Zimbabwe 0.00 0.00 8.00 0.00 0.00 3.00 0.00 11.00 Ecuador 0.00 1.00 7.71 0.00 0.00 3.00 0.00 11.71 Denmark 2.00 0.00 7.78 0.00 0.00 1.00 1.00 11.78 Uruguay 2.00 0.00 5.86 0.00 0.00 3.00 1.00 11.86 Maldives 0.00 1.00 5.90 0.00 0.00 3.00 2.00 11.90 Paraguay 2.00 0.00 5.94 0.00 0.00 3.00 1.00 11.94 Armenia 0.00 0.00 6.00 0.00 0.00 3.00 3.00 12.00 Germany 2.00 0.00 8.00 0.00 0.00 1.00 1.00 12.00 Romania 2.00 0.00 9.76 0.00 0.00 1.00 0.00 12.76 Venezuela, RB 0.00 0.00 9.85 0.00 0.00 3.00 0.00 12.85 Mauritius 5.00 0.00 3.88 0.00 0.00 3.00 1.00 12.88 Nicaragua 0.00 0.00 9.88 0.00 0.00 3.00 0.00 12.88 Rwanda 2.00 0.00 7.90 0.00 0.00 3.00 0.00 12.90 Moldova 0.00 0.00 12.00 0.00 0.00 1.00 0.00 13.00 Trinidad and Tobago 1.00 0.00 10.00 0.00 0.00 0.00 2.00 13.00 Vietnam 0.00 0.00 12.00 0.00 0.00 1.00 0.00 13.00 Philippines 5.00 1.00 5.77 0.00 2.00 0.00 0.00 13.77 Poland 2.00 0.00 9.85 0.00 0.00 1.00 1.00 13.85 Burundi 2.00 3.00 5.90 0.00 0.00 3.00 0.00 13.90 Tanzania 1.00 3.00 3.93 0.00 0.00 3.00 3.00 13.93 Bolivia 0.00 0.00 13.93 0.00 0.00 0.00 0.00 13.93 Japan 0.00 0.00 8.00 0.00 0.00 3.00 3.00 14.00 Kazakhstan 0.00 0.00 8.00 0.00 0.00 3.00 3.00 14.00 Kyrgyz Republic 0.00 0.00 12.00 0.00 0.00 1.00 1.00 14.00 Switzerland 0.00 0.00 10.00 0.00 0.00 3.00 1.00 14.00 Colombia 0.00 0.00 11.80 0.00 0.00 3.00 0.00 14.80 Bhutan 1.00 1.00 9.82 0.00 0.00 3.00 0.00 14.82 Argentina 2.00 0.00 9.96 0.00 0.00 3.00 0.00 14.96 Albania 0.00 0.00 12.00 0.00 0.00 3.00 0.00 15.00 Grenada 1.00 1.00 8.00 0.00 0.00 3.00 2.00 15.00 India 2.00 1.00 8.00 0.00 0.00 3.00 1.00 15.00 Macedonia, FYR 0.00 0.00 12.00 0.00 0.00 3.00 0.00 15.00 Malawi 2.00 1.00 8.00 0.00 0.00 3.00 1.00 15.00 Mongolia 0.00 0.00 14.00 0.00 0.00 0.00 1.00 15.00 Nigeria 1.00 1.00 10.00 0.00 0.00 3.00 0.00 15.00 37    Table A2 (cont…) Providing WBL Measure Accessing Using Getting Going to Building Violence Economy Incentives to of Legal Gender Institutions Property a Job Court Credit Protection Work Disparities Côte d'Ivoire 2.00 2.00 5.96 0.00 0.00 3.00 3.00 15.96 Azerbaijan 2.00 0.00 12.00 0.00 0.00 1.00 1.00 16.00 Bahamas, The 4.00 0.00 8.00 0.00 0.00 3.00 1.00 16.00 Ethiopia 0.00 0.00 12.00 0.00 0.00 3.00 1.00 16.00 Honduras 1.00 0.00 12.00 0.00 0.00 3.00 0.00 16.00 Israel 4.00 0.00 8.00 0.00 0.00 3.00 1.00 16.00 Ukraine 2.00 0.00 10.00 0.00 0.00 3.00 1.00 16.00 Zambia 2.00 0.00 10.00 0.00 0.00 3.00 1.00 16.00 Uganda 1.00 4.00 7.90 0.00 0.00 3.00 1.00 16.90 Brazil 0.00 0.00 13.92 0.00 0.00 3.00 0.00 16.92 Guatemala 2.00 0.00 9.95 0.00 0.00 3.00 2.00 16.95 Antigua and Barbuda 0.00 1.00 10.00 0.00 0.00 3.00 3.00 17.00 Fiji 1.00 0.00 10.00 0.00 2.00 3.00 1.00 17.00 Panama 0.00 0.00 14.00 0.00 0.00 3.00 0.00 17.00 St. Kitts and Nevis 0.00 1.00 10.00 0.00 0.00 3.00 3.00 17.00 Turkey 0.00 0.00 14.00 0.00 0.00 3.00 0.00 17.00 El Salvador 2.00 0.00 11.93 0.00 0.00 3.00 1.00 17.93 China 2.00 0.00 11.95 0.00 0.00 3.00 1.00 17.95 Botswana 3.00 1.00 10.00 0.00 0.00 3.00 1.00 18.00 Ghana 3.00 1.00 8.00 0.00 0.00 3.00 3.00 18.00 Jamaica 0.00 0.00 12.00 0.00 0.00 3.00 3.00 18.00 Seychelles 3.00 1.00 10.00 0.00 0.00 3.00 1.00 18.00 Suriname 1.00 0.00 12.00 0.00 0.00 3.00 2.00 18.00 Thailand 3.00 0.00 12.00 0.00 0.00 3.00 0.00 18.00 Kenya 2.00 1.00 9.69 0.00 0.00 3.00 3.00 18.69 Singapore 5.00 0.00 9.87 0.00 0.00 3.00 1.00 18.87 Burkina Faso 1.00 0.00 9.94 0.00 2.00 3.00 3.00 18.94 Morocco 4.00 4.00 5.94 0.00 2.00 0.00 3.00 18.94 Belize 1.00 0.00 14.00 0.00 0.00 3.00 1.00 19.00 Costa Rica 2.00 0.00 14.00 0.00 0.00 3.00 0.00 19.00 Georgia 2.00 0.00 14.00 0.00 0.00 3.00 0.00 19.00 São Tomé and Príncipe 2.00 0.00 14.00 0.00 0.00 3.00 0.00 19.00 Togo 3.00 1.00 7.96 0.00 2.00 3.00 3.00 19.96 Belarus 2.00 0.00 12.00 0.00 0.00 3.00 3.00 20.00 St. Vincent and the Grenadines 2.00 0.00 12.00 0.00 0.00 3.00 3.00 20.00 Tajikistan 2.00 0.00 12.00 0.00 0.00 3.00 3.00 20.00 Myanmar 1.00 0.00 13.69 0.00 0.00 3.00 3.00 20.69 Benin 7.00 1.00 7.94 0.00 2.00 3.00 0.00 20.94 Indonesia 3.00 3.00 7.96 0.00 2.00 3.00 2.00 20.96 Mozambique 2.00 0.00 15.97 0.00 0.00 3.00 0.00 20.97 38    Table A2 (cont…) Providing WBL Measure Accessing Using Getting Going to Building Violence Economy Incentives to of Legal Gender Institutions Property a Job Court Credit Protection Work Disparities Bangladesh 1.00 4.00 12.00 0.00 0.00 3.00 1.00 21.00 Dominica 1.00 0.00 14.00 0.00 0.00 3.00 3.00 21.00 Liberia 2.00 1.00 10.00 0.00 0.00 3.00 5.00 21.00 Madagascar 3.00 0.00 14.00 0.00 0.00 3.00 1.00 21.00 Papua New Guinea 2.00 1.00 12.00 0.00 0.00 3.00 3.00 21.00 South Sudan 0.00 1.00 12.00 0.00 0.00 3.00 5.00 21.00 Chile 3.00 2.00 13.92 0.00 0.00 3.00 0.00 21.92 Angola 0.00 0.00 16.00 0.00 0.00 3.00 3.00 22.00 Haiti 5.00 1.00 8.00 0.00 0.00 3.00 5.00 22.00 Nepal 3.00 4.00 12.00 0.00 0.00 3.00 0.00 22.00 Mali 4.00 1.00 9.94 0.00 0.00 3.00 5.00 22.94 Senegal 5.00 4.00 9.98 0.00 0.00 3.00 1.00 22.98 Barbados 5.00 0.00 12.00 0.00 0.00 3.00 3.00 23.00 Sri Lanka 4.00 1.00 14.00 0.00 0.00 3.00 1.00 23.00 Uzbekistan 0.00 0.00 16.00 0.00 0.00 3.00 4.00 23.00 Russian Federation 2.00 0.00 14.00 0.00 0.00 3.00 5.00 24.00 Sierra Leone 5.00 1.00 16.00 0.00 0.00 3.00 0.00 25.00 Tonga 2.00 6.00 12.00 0.00 0.00 3.00 2.00 25.00 Tunisia 2.00 4.00 11.93 0.00 2.00 3.00 3.00 25.93 Djibouti 4.00 4.00 9.94 0.00 0.00 3.00 5.00 25.94 Egypt, Arab Rep. 7.00 4.00 10.00 0.00 0.00 3.00 3.00 27.00 Gabon 7.00 2.00 10.00 0.00 0.00 3.00 5.00 27.00 Cameroon 8.00 2.00 9.94 0.00 0.00 3.00 5.00 27.94 Congo, Rep. 4.00 2.00 12.00 0.00 2.00 3.00 5.00 28.00 Swaziland 3.00 7.00 10.00 0.00 0.00 3.00 5.00 28.00 Lebanon 7.00 4.00 12.00 0.00 0.00 3.00 3.00 29.00 Brunei Darussalam 10.00 4.00 10.00 0.00 2.00 3.00 1.00 30.00 Congo, Dem. Rep. 7.00 2.00 15.96 0.00 0.00 3.00 3.00 30.96 Malaysia 10.00 3.00 12.00 0.00 2.00 3.00 1.00 31.00 West Bank and Gaza 7.00 4.00 12.00 2.00 0.00 3.00 3.00 31.00 Kuwait 6.00 4.00 14.00 2.00 0.00 3.00 3.00 32.00 Pakistan 6.00 4.00 16.00 2.00 0.00 3.00 1.00 32.00 Algeria 4.00 4.00 19.94 0.00 0.00 3.00 3.00 33.94 Syrian Arab Republic 9.00 4.00 14.00 2.00 0.00 3.00 3.00 35.00 Qatar 10.00 4.00 14.00 2.00 0.00 3.00 3.00 36.00 Sudan 12.00 4.00 12.00 2.00 0.00 3.00 3.00 36.00 Bahrain 11.00 4.00 15.97 0.00 0.00 3.00 3.00 36.97 Mauritania 9.00 5.00 14.00 2.00 0.00 3.00 5.00 38.00 United Arab Emirates 12.00 4.00 16.00 0.00 0.00 3.00 3.00 38.00 Afghanistan 11.00 4.00 13.78 2.00 0.00 3.00 5.00 38.78 Oman 9.00 4.00 16.00 2.00 0.00 3.00 5.00 39.00 39    Table A2 (cont…) Providing WBL Measure Accessing Using Getting Going to Building Violence Economy Incentives to of Legal Gender Institutions Property a Job Court Credit Protection Work Disparities Iraq 10.00 4.00 18.00 0.00 2.00 3.00 3.00 40.00 Jordan 14.00 4.00 16.00 2.00 0.00 3.00 1.00 40.00 Yemen, Rep. 11.00 4.00 18.00 2.00 0.00 3.00 3.00 41.00 Iran, Islamic Rep. 14.00 4.00 15.90 2.00 0.00 3.00 5.00 43.90 Saudi Arabia 18.00 4.00 19.97 2.00 0.00 3.00 3.00 49.97 40    Table A3: Gender Indices Gender Index Description Country coverage Years Social Institutions and Gender Measures long-lasting social institutions defined 160 economies for 2009, Index (SIGI) as societal practices and legal norms. 5 subindices SIGI 2014 2012, - Family code 2014 - Civil liberties - Physical integrity - Son preference - Ownership rights Gender-related Development Measures gender gap in human development 142 countries introduced Index (GDI) - UNDP achievements in three basic dimensions of in 1995 human development: health, measured by female and male life expectancy at birth; education, measured by female and male expected years of schooling for children and female and male mean years of schooling for adults ages 25 and older; and command over economic resources, measured by female and male estimated earned income. Part of HDI. Gender Empowerment Measure Designed to measure "whether women and men 142 countries introduced (GEM) - UNDP are able to actively participate in economic and in 1995 political life and take part in decision-making" The GEM is determined using three basic indicators: Proportion of seats held by women in national parliaments, percentage of women in economic decision making positions (incl. administrative, managerial, professional and technical occupations) and female share of income (earned incomes of males vs. females) Gender Inequality Index (GII) - The GII measures gender inequalities in three 142 countries introduced UNDP important aspects of human development— in 2010 reproductive health measured by maternal mortality ratio and adolescent birth rates; empowerment, measured by proportion of parliamentary seats occupied by females and proportion of adult females and males aged 25 years and older with at least some secondary education; and economic status expressed as labour market participation and measured by labour force participation rate of female and male populations aged 15 years and older. Global Gender Gap Index - WEF The Global Gender Gap Index examines the gap 111 countries 2006-2014 between men and women in four fundamental categories (subindexes): Economic Participation and Opportunity, Educational Attainment, Health and Survival and Political Empowerment. Gender Equity Index - Social The Gender Equity Index (GEI) measures the 168 countries 2007, Watch gap between women and men in education, the 2008, economy and political empowerment. 2009, 2012 41    Women's Political Rights Index Right of women to vote (whether included in 202 countries 1981-2011 (WOPOL) - CIRI Human Rights laws) Data Project Women's Economic Rights Index Women's equal rights in the labor market 202 countries 1981-2011 (WECON) - CIRI Human Rights (whether included in laws) Data Project Women's Social Rights Index Women's equal rights in social sphere (marriage, 202 countries 1981-2005 (WOSOC) - CIRI Human Rights inheritance, travel, education, etc.) (whether (retired in Data Project (retired in 2005) included in laws) - retired in 2005 2005)     Table A4: Data Description and Source Variable Definition Source Gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not Log of GDP per capita (constant 2005 US$) included in the value of the products. It is World Development Indicators calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2005 U.S. dollars Captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of Rule of law World Governance Indicators contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Mean number of years of education by age and Years of education, women relative to men Institute for Health Metrics and sex estimated from censuses and nationally ages 25 plus Evaluation (IHME), 2015. representative surveys. Proportion of the population ages 15 and older Labor force participation rate, women that is economically active: all people who World Development Indicators, relative to men supply labor for the production of goods and services during a specified period. Women in parliaments are the percentage of World Development Indicators,  Proportion of seats held by women in parliamentary seats in a single or lower chamber obtained from Inter-Parliamentary national parliaments (%) held by women. Union (IPU) (www.ipu.org) Proportion of women top managers (%) Self-explanatory World Bank Enterprise Surveys Borrowed from a financial institution, Self-explanatory Global Findex Database women relative to men (% age 15+) Probability per 1,000 that a newborn baby will Mortality rate, under-5 (per 1,000 live die before reaching age five, if subject to age- World Development Indicators births) specific mortality rates of the specified year 42    Figure A1: Average Number of Legal Gender Disparities per Country by Region AVERAGE NUMBER OF DISPARITIES PER COUNTRY BY  SUBCATEGORY AND REGION  MENA 8 3 14 10 4 3 33 SA 4 3 11 1 0 3 1 22 SSA 3 3 10 0 01 2 20 EAP 3 2 10 011 1 18 Accessing Institutions Bulding Credit LAC 1 3 10 01 15 Getting a Job Going to Court Providing Incentives to Work ECA 1 2 10 01 14 Using Property Violence disparities High income: OECD 1 1 6 01 9 WORLD 3 2 10 0 01 2 17 0 5 10 15 20 25 30 35 40 45 *Figures at the end of the bars are the total number of gender disparities per country by region 43    Figure A2: Proportions of Legal Gender Disparities by Region PROPORTION OF DISPARITIES BY SUBCATEGORY AND  REGION (%)  MENA 28 91 64 47 18 54 61 SA 12 100 52 25 0 36 28 SSA 10 97 46 6 11 21 42 EAP 9 74 44 0 35 16 20 Accessing Institutions Building Credit LAC 4 85 45 0 3 23 Getting a Job Going to Court Providing Incentives to Work ECA 3 67 44 0 24 Using Property Violence High income: 5 39 25 0 1 18 OECD World 9 77 44 8 8 15 31 0 50 100 150 200 250 300 350 400 44    Figure A3: Proportions of Legal Gender Disparities in Sub-Saharan Africa (SSA) PROPORTION OF DISPARITIES BY SUBCATEGORY (%)  ‐ SUB‐SAHARAN AFRICA Mauritania 30 100 64 100 71 100 Sudan 40 100 55 100 57 60 Congo, Rep. 13 100 55 0 29 100 Swaziland 10 100 45 0 100 100 Togo 10 100 36 0 14 60 Burkina Faso 3 100 45 0 0 60 Cameroon 27 100 45 0 29 100 Gabon 23 100 45 0 29 100 Congo, Dem. Rep. 23 100 73 0 29 60 Benin 23 100 36 0 140 Mali 13 100 45 014 100 South Sudan 0 100 55 014 100 Liberia 7 100 45 014 100 Senegal 17 100 45 0 57 20 Angola 0 100 73 0 60 Kenya 7 100 44 014 60 Tanzania 3 100 180 43 60 Côte d'Ivoire 7 100 27 0 29 60 Ghana 10 100 36 014 60 Uganda 3 100 36 0 57 20 Sierra Leone 17 100 73 0140 Accessing Institutions Madagascar 10 100 64 0 20 Building Credit Seychelles 10 100 45 014 20 Getting a Job Botswana 10 100 45 014 20 Mozambique 7 100 73 0 Going to Court Malawi 7 100 36 014 20 Providing Incentives to Work Burundi 7 100 27 0 43 0 Using Property Ethiopia 0 100 55 0 20 Violence Zambia 7 100 45 0 20 São Tomé and Príncipe 7 100 64 0 Nigeria 3 100 45 0140 Mauritius 17 100 180 20 Rwanda 7 100 36 0 Zimbabwe 0 100 36 0 Namibia 0 100 27 0 South Africa 0 36 0 0 50 100 150 200 250 300 350 400 450 500 45    Figure A4: Proportions of Legal Gender Disparities in East Asia and the Pacific (EAP) PROPORTION OF DISPARITIES BY SUBCATEGORY (%) ‐ EAST ASIA & PACIFIC Brunei Darussalam 33 100 45 100 57 20 Malaysia 33 100 55 100 43 20 Indonesia 10 100 36 100 43 40 Tonga 7 100 55 0 86 40 Fiji 3 100 45 100 0 20 Cambodia 0 100 18 100 0 20 Papua New Guinea 7 100 55 014 60 Myanmar 3 100 62 0 60 China 7 100 54 0 20 Singapore 17 100 45 0 20 Accessing Institutions Thailand 10 100 55 0 Building Credit Philippines 17 0 26 100 140 Getting a Job Going to Court Timor‐Leste 0 100 27 0140 Providing Incentives to Work Taiwan, China 0 100 17 0 Using Property Violence Vietnam 0 33 55 0 Mongolia 0 64 0 20 Hong Kong SAR, China 70 36 0140 0 50 100 150 200 250 300 350 400 46    Figure A5: Proportions of Legal Gender Disparities in Latin America and the Caribbean (LAC) PROPORTION OF DISPARITIES BY SUBCATEGORY (%)  ‐ LATIN AMERICA AND THE CARIBBEAN Haiti 17 100 36 100 Barbados 17 100 55 60 Dominica 3 100 64 60 St. Kitts and Nevis 0 100 45 60 Antigua and Barbuda 0 100 45 60 St. Vincent and the Grenadines 7 100 55 60 Jamaica 0 100 55 60 Suriname 3 100 55 40 Grenada 3 100 36 40 Guatemala 7 100 45 40 Belize 3 100 64 20 El Salvador 7 100 54 20 Costa Rica 7 100 64 0 Bahamas, The 13 100 36 20 Panama 0 100 64 0 Brazil 0 100 63 0 Honduras 3 100 55 0 Paraguay 7 100 27 20 Colombia 0 100 54 0 Accessing Institutions Uruguay 7 100 27 20 Building Credit Argentina 7 100 45 0 Getting a Job Ecuador 0 100 35 0 Going to Court Nicaragua 0 100 45 0 Venezuela, RB 0 100 45 0 Providing Incentives to Work Guyana 0 100 27 0 Using Property Dominican Republic 0 100 36 0 Violence St. Lucia 3 100 18 0 Trinidad and Tobago 3 0 45 40 Bolivia 0 63 0 Peru 0 33 27 0 Puerto Rico (U.S.) 0 27 0 Mexico 0 27 0 0 50 100 150 200 250 300 47    Figure A6: Proportions of Legal Gender Disparities by in Eastern and Central Europe (ECA) PROPORTION OF DISPARITIES BY SUBCATEGORY (%) ‐ EASTERN EUROPE AND CENTRAL ASIA Russian Federation 7 100 64 100 Uzbekistan 0 100 73 80 Tajikistan 7 100 55 60 Belarus 7 100 55 60 Kazakhstan 0 100 36 60 Armenia 0 100 27 60 Ukraine 7 100 45 20 Georgia 7 100 64 0 Turkey 0 100 64 0 Macedonia, FYR 0 100 55 0 Albania 0 100 55 0 Serbia 0 100 27 0 Montenegro 7 100 18 0 Azerbaijan 7 33 55 20 Accessing Institutions Kyrgyz Republic 0 33 55 20 Building Credit Getting a Job Moldova 0 33 55 0 Going to Court Lithuania 7 33 25 20 Providing Incentives to Work Romania 7 33 44 0 Using Property Kosovo 0 33 27 20 Violence Bulgaria 70 36 20 Latvia 7 33 17 0 Bosnia and Herzegovina 0 36 20 Croatia 70 36 0 0 50 100 150 200 250 300 48    Figure A7: Proportions of Legal Gender Disparities in High Income OECD economies PROPORTION OF DISPARITIES BY SUBCATEGORY (%)  ‐ High Income OECD Chile 10 100 63 29 0 Japan 0 100 36 0 60 Israel 13 100 36 0 20 Switzerland 0 100 45 0 20 Iceland 7 100 18 0 40 Estonia 0 100 18 0 40 Korea, Rep. 0 100 36 0 Norway 7 100 0 20 Belgium 7 33 17 0 60 Poland 7 33 45 0 20 Germany 7 33 36 0 20 Denmark 7 33 35 0 20 Finland 7 33 26 0 20 Greece 7 33 27 14 0 Czech Republic 7 33 18 0 20 Netherlands 0 18 0 60 Accessing Institutions Canada 0 36 0 40 Spain 0 33 17 0 20 Building Credit Italy 7 33 27 0 Getting a Job Luxembourg 7 33 27 0 Going to Court Slovenia 7 33 25 0 Providing Incentives to Work Sweden 7 33 18 0 Using Property Hungary 0 18 0 40 Violence United States 70 27 0 20 Austria 70 27 0 20 United Kingdom 7 0 18 0 20 Portugal 7 33 0 France 70 26 0 New Zealand 0 27 0 Ireland 7 0 18 0 Australia 70 9 0 Slovak Republic 0 9 0 0 50 100 150 200 250 49    Figure A8: Proportions of Legal Gender Disparities in South Asia PROPORTION OF DISPARITIES BY SUBCATEGORY  (%) ‐ SOUTH ASIA Afghanistan 37 100 63 100 57 100 Pakistan 20 100 73 100 57 20 Bangladesh 3 100 55 0 57 20 Nepal 10 100 55 0 57 0 Sri Lanka 13 Accessing Institutions 100 64 014 20 Building Credit Getting a Job Going to Court Maldives 0 100 27 014 40 Providing Incentives to Work Using Property Violence India 7 100 36 014 20 Bhutan 3 100 45 0140 0 100 200 300 400 500 50    Figure A9: Proportions of Legal Gender Disparities in the Middle East and North Africa (MENA) PROPORTION OF DISPARITIES BY SUBCATEGORY (%) ‐ MIDDLE EAST & NORTH AFRICA Iran, Islamic Rep. 47 100 72 100 0 57 100 Saudi Arabia 60 100 91 100 0 57 60 Oman 30 100 73 100 0 57 100 Yemen, Rep. 37 100 82 100 0 57 60 Iraq 33 100 82 0 100 57 60 Qatar 33 100 64 100 0 57 60 Syrian Arab Republic 30 100 64 100 0 57 60 Kuwait 20 100 64 100 0 57 60 Jordan 47 100 73 100 0 57 20 West Bank and Gaza 23 100 55 100 0 57 60 Tunisia 7 100 54 0 100 57 60 United Arab Emirates 40 100 73 0 57 60 Bahrain 37 100 73 0 57 60 Accessing Institutions Algeria 13 100 91 0 57 60 Building Credit Djibouti 13 100 45 0 57 100 Getting a Job Lebanon 23 100 55 0 57 60 Going to Court Egypt, Arab Rep. 23 100 45 0 57 60 Providing Incentives to Work Using Property Morocco 130 27 0 100 57 60 Violence Malta 0 33 27 0 0 50 100 150 200 250 300 350 400 450 500 51