metadata: authors: 0: author: Iqbal,Sarah
1: author: Islam,Asif Mohammed
2: author: Ramalho,Rita
3: author: Sakhonchik,Alena
count: World
docna: 0: docna: Unequal before the law : measuring legal
            gender disparities across the world
docty: Policy Research Working Paper
subtopic: Anthropology,Labor Markets,Educational Sciences,Gender and Development,Gender and Social Development,Inequality
teratopic: Macroeconomics and Economic Growth,Gender,Law and Development
abstracts: cdata!: Several economies have laws that treat
            women differently from men. This study explores the degree
            of such legal gender disparities across 167 economies around
            the world. This is achieved by constructing a simple measure
            of legal gender disparities to evaluate how countries
            perform. The average number of overall legal gender
            disparities across 167 economies is 17, ranging from a
            minimum of 2 to a maximum of 44. The maximum possible legal
            gender disparities is 71. The measure is found to be
            correlated with other measures of gender inequality,
            implying the measure does capture gender inequality while
            also differing from preexisting measures of gender
            inequality. A high degree of legal gender disparities is
            found to be negatively associated with a wide range of
            outcomes, including 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. Subcategories within the legal disparities measure
            help to uncover specific types of legal disparities across economies.
repnme: repnme: Unequal before the law : measuring legal
            gender disparities across the world
display_title: Unequal before the law : measuring
            legal gender disparities across the world
listing_relative_url: /research/2016/08/26722425/unequal-before-law-measuring-legal-gender-disparities-across-world
topicv3: Inequality under Law m1326002 925,Gender and Human Development m1326908 919,Education m1327048 470,Gender m1327621 615,Labor Force Participation m1504050 2746
publishtoextweb_dt: 2016-08-23T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Gender,Law and Development
no_of_pages: 53
author_keys: 0: author_key: 1003861
1: author_key: 950662
2: author_key: 683601
3: author_key: 1297789
model_extractions: 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 from men. This study explores the degree of such legal gender disparities across 167 economies around the world. This is achieved by constructing a simple measure of legal gender disparities to evaluate how countries perform. The average number of overall legal gender disparities across 167 economies is 17, ranging from a minimum of 2 to a maximum of 44. The maximum possible legal gender disparities is 71. The measure is found to be correlated with other measures of gender inequality, implying the measure does capture gender inequality while also differing from preexisting measures of gender inequality. A high degree of legal gender disparities is found to be negatively associated with a wide range of outcomes, including 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. Subcategories within the legal disparities measure help to uncover specific types of legal disparities across economies.

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 Iqbal<sup>a</sup> , Asif Islam<sup>b</sup> , Rita Ramalho<sup>c</sup> , Alena Sakhonchik<sup>d</sup> (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 (AbuGhaida 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.<sup>1</sup> 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.

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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 nonagricultural 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.

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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.

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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 genderrelated 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: 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.<sup>2</sup> 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 16<sup>th</sup> with 7 legal gender disparities, while the United States is ranked 32<sup>nd</sup> , 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.

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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.

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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**

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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).<sup>3</sup> 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.

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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.<sup>4</sup> 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.

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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**

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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 intrahousehold 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.

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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 m**|**easure (Higher value**|**s, 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 worsegender equality)|0.70|***|98|

# Table 2: <u>Summary Statistics for Regressions Analysis</u>

||Obs|Mean|Std. Dev.|Min|Max|Maximum possible<br>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||
|Mortalityrate, under-5(per 1,000 live births)|162|30.9|33.1|2|167.4||

26
Table <u>3: Legal Gender Disparities and Years of Education of Women Relative to Men</u>

|||||**O**|**LS**||||
|---|---|---|---|---|---|---|---|---|
||||**Years of ed**|**ucation, wom**|**en 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<br>Institutions||-0.012***|||||||
|||(0.004)|||||||
|Legal disparities: Using Property|||-0.015||||||
||||(0.013)||||||
|Legal disparities: Getting a Job||||-0.002<br>(0.003)|||||
|Legal disparities: Going to Court|||||-0.054*||||
||||||(0.030)||||
|Legal disparities: Providing Incentives<br>to Work||||||-0.036**|||
|||||||(0.018)|||
|Legal disparities: Building Credit|||||||-0.001<br>(0.011)||
|Legal disparities: Violence Protection||||||||-0.015**|
|||||||||(0.008)|
|Log of GDP per capita (constant 2005<br>US$)|0.073***|0.075***|0.069***|0.070***|0.070***|0.070***|0.070***|0.073***|
||(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 <u>and Labor Force Participation of Women Relative to Men</u>

|||||**O**|**LS**||||
|---|---|---|---|---|---|---|---|---|
|**Dependent Variable**|**Wo**|**men over me**|**n labor force**|**participatio**|**n 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***<br>(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**<br>(0.004)|||||
|Legal disparities: Going to Court|||||-0.132***||||
||||||(0.030)||||
|Legal disparities: Providing Incentives<br>to Work||||||-0.013|||
|||||||(0.020)|||
|Legal disparities: Building Credit|||||||-0.005<br>(0.009)||
|Legal disparities: Violence Protection||||||||-0.009|
|||||||||(0.010)|
|Log of GDP per capita (constant 2005<br>US$)|-0.031**|-0.029*|-0.038**|-0.036**|-0.036**|-0.037**|-0.037**|-0.035**|
||(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 <u>5: Legal Gender Disparities and Proportion of Women Top Managers</u>

|||||**O**|**LS**||||
|---|---|---|---|---|---|---|---|---|
|**Dependent Variable**|||**Pro**|**portion of wo**|**men top mana**|**gers**|||
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Legal disparities: Overall measure|-0.266**<br>(0.131)||||||||
|Legal disparities: Accessing<br>Institutions||-0.614**<br>(0.288)|||||||
|Legal disparities: Using Property|||-2.262***<br>(0.633)||||||
|Legal disparities: Getting a Job||||-0.228<br>(0.240)|||||
|Legal disparities: Going to Court|||||-4.280***<br>(0.753)||||
|Legal disparities: Providing<br>Incentives to Work||||||0.741<br>(1.091)|||
|Legal disparities: Building Credit|||||||-0.104<br>(0.667)||
|Legal disparities: Violence Protection||||||||-0.143|
|||||||||(0.537)|
|Log of GDP per capita (constant 2005<br>US$)|-0.989|-0.867|-1.387|-1.060|-1.201|-1.062|-1.040|-0.990|
||(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

|||||**O**|**LS**||||
|---|---|---|---|---|---|---|---|---|
|**Dependent Variable**||**Proportio**|**n of seats he**|**ld 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<br>(0.886)||||||
|Legal disparities: Getting a Job||||-0.418*<br>(0.249)|||||
|Legal disparities: Going to Court|||||-0.983||||
||||||(1.823)||||
|Legal disparities: Providing Incentives to<br>Work||||||0.017|||
|||||||(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<br>US$)|-0.029|-0.008|-0.192|-0.167|-0.156|-0.171|-0.307|0.204|
||(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 <u>to Men</u>

|||||**O**|**LS**||||
|---|---|---|---|---|---|---|---|---|
|**Dependent Variable**|**Bo**|**rrowed from**|**a financial i**|**nstitution, w**|**omen relati**|**ve to men (%**|**age 15+), 2**|**013**|
||**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<br>|||||
|||||(0.010)|||||
|Legal disparities: Going to Court|||||-0.137**||||
||||||(0.067)||||
|Legal disparities: Providing Incentives<br>to Work||||||0.062|||
|||||||(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<br>US$)|-0.038|-0.034|-0.048|-0.045|-0.048|-0.045|-0.047|-0.048|
||(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 <u>8: Legal Gender Disparities and Infant Mortality Rate</u>

|||||**OL**|**S**||||
|---|---|---|---|---|---|---|---|---|
|**Dependent Variable**|||**Mortality ra**|**te, under-5 (p**|**er 1,000 live b**|**irths), 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<br>Institutions||0.756|||||||
|||(0.654)|||||||
|Legal disparities: Using Property|||0.620||||||
||||(1.231)||||||
|Legal disparities: Getting a Job||||0.634<br>(0.512)|||||
|Legal disparities: Going to Court|||||5.386*||||
||||||(3.151)||||
|Legal disparities: Providing Incentives<br>to Work||||||-0.726|||
|||||||(2.200)|||
|Legal disparities: Building Credit|||||||-1.027<br>(0.838)||
|Legal disparities: Violence Protection||||||||2.677**|
|||||||||(1.197)|
|Log of GDP per capita (constant 2005<br>US$)|-5.800*|-5.848*|-5.472*|-5.563*|-5.527*|-5.498*|-5.549*|-6.010*|
||(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