metadata: authors: 0: author: Hyland,Marie Caitriona
1: author: Djankov,Simeon
2: author: Goldberg,Pinelopi Koujianou
count: World
docna: 0: docna: Gendered Laws
docty: Policy Research Working Paper
subtopic: Gender and Development,Anthropology,Gender and Social Development,Human Rights,Labor Markets
teratopic: Culture and Development,Gender,Law and Development,Social Protections and Labor
abstracts: cdata!: This paper offers for the first time a
            global picture of gender discrimination by the law as it
            affects women's economic opportunity and charts the
            evolution of legal inequalities over five decades. Using the
            World Bank's newly extended Women, Business and the Law
            database, the paper documents large and persistent gender
            inequalities, especially with regard to equal pay and
            treatment of parenthood. The paper finds positive
            associations between improvements in the law and several
            labor market outcomes, and establishes a small, but over
            time increasing, causal impact of more equal laws on higher
            female labor force participation.
alt_title: 0: alt_title: القوانين المبنية على التمييز بين الجنسيْن
repnme: repnme: Gendered Laws
display_title: Gendered Laws
listing_relative_url: /research/2019/12/31626506/gendered-laws
topicv3: Gender and Human Development m1326908 919,Gender m1327621 615,Labor Force Participation m1504050 2746,Labor Supply m1504055 2751,Discrimination m1505707 3379
publishtoextweb_dt: 2019-12-17T00:00:00Z
historic_topic: Culture and Development,Gender,Law and Development,Social Protections and Labor
origu: DEC Chief Economist (DECCE)
no_of_pages: 32
author_keys: 0: author_key: 1506658
1: author_key: 124574
2: author_key: 1513476
fullavailablein: node_id: 31626506
model_extractions: Policy Research Working Paper 9080

# Gendered Laws

_Marie Hyland Simeon Djankov Pinelopi Koujianou Goldberg_ Development Economics Vice Presidency (DEC) & Office of the Chief Economist December 2019
Policy Research Working Paper 9080

# **Abstract**

This paper offers for the first time a global picture of gender discrimination by the law as it affects women’s economic opportunity and charts the evolution of legal inequalities over five decades. Using the World Bank’s newly extended _Women, Business and the Law_ database, the paper documents large and persistent gender inequalities, especially with regard to equal pay and treatment of parenthood. The paper finds positive associations between improvements in the law and several labor market outcomes, and establishes a small, but over time increasing, causal impact of more equal laws on higher female labor force participation.

This paper is a product of the Development Economics Vice Presidency (DEC) and the Office of the Chief Economist. 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:// www.worldbank.org/prwp. The authors may be contacted at mhyland@worldbank.org, sdjankov@worldbank.org, and penny.goldberg@yale.edu.

_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
Gendered Laws MARIE HYLAND, SIMEON DJANKOV, PINELOPI KOUJIANOU GOLDBERG<sup>1</sup>

**Key words:** Law; gender; employment **JEL codes:** J16; J21; J82; K38; N40

> 1 Hyland: World Bank, 1818 H Street NW, Washington, DC 20433 (e‐mail: mhyland@worldbank.org); Djankov: London School of Economics, and World Bank, 1818 H Street NW, Washington, DC 20433 (e‐mail: sdjankov@worldbank.org); Goldberg: Yale University, and World Bank, 1818 H Street NW, Washington, DC 20433 (e‐mail: penny.goldberg@yale.edu); The views expressed here are those of the authors and should not be attributed to the World Bank.
# **1. Introduction**

One of the most salient and pervasive forms of gender discrimination is the unequal treatment of women and men by the law. Until recently, women did not have the right to vote in most countries. In many parts of the world, women are still not allowed to participate in certain professions. In England, until 2003, the law of rape postulated, in effect, that a man who had sex with a woman believing that it was consensual had to be acquitted, even if there were no reasonable grounds for this belief. While examples of gendered laws abound, what has been missing is a complete picture of the relative severity, evolution, and impact of legal gender discrimination around the world. Most of the existing evidence on gendered laws and reforms comes from a limited number of (usually, developed) economies, but it is largely unknown to what extent the findings hold when measured on a much wider scale. The World Bank’s newly compiled _Women, Business and the Law_ (WBL) database aims to fill this knowledge gap. The database measures equality of economic opportunity under the law between men and women in 190 economies, for 50 years, from 1970 until today.

The purpose of this paper is twofold. First, we provide a description of the newly constructed WBL database, which will be updated annually and made available to the public, and use it to document a series of stylized facts regarding legal gender discrimination across the world. Second, we examine whether improvements in the legal treatment of women have contributed to more equal labor market outcomes, specifically to higher female labor force participation, closing of the wage gap between men and women, and lower occupational segregation. To establish a causal effect of legal reforms, we exploit a novel methodology suggested by a recent paper by Freyaldenhoven, Hansen and Shapiro (AER, 2019) – FHS henceforth.

We document the existence of large gaps between the legal treatment of men and women despite the tremendous progress that has been made in the past five decades. The gaps are most pronounced in the areas of _Getting Paid_ and _Having Children_ – that is, we find that women are most disadvantaged by the law when it comes to the compensation they receive and their treatment once they have had children. We find positive associations between improvements in the law and higher female labor force participation, a lower wage gap between men and women, and less occupational segregation. We establish that the first of these correlations allows for a causal interpretation. Specifically, application of the FHS methodology suggests that more equal laws have led to higher female labor force participation. The effect is small, suggesting that factors other than laws are also responsible for women’s higher labor 2
force participation, but it increases with time, as more years from the initial enactment of legal reforms pass.

Overall, our results suggest that gendered laws matter, but they matter more for some labor market outcomes than others. The effect of legal reforms is relatively small, but this is to be expected, given that legal discrimination is only one among many sources of gender imbalances and that the passing of better laws does not guarantee that these laws will be enforced, especially in countries with social norms disadvantaging women’s participation in the formal economy. But laws are actionable in the short run – in contrast to norms and attitudes, which may take longer time to change. In future research, it would be interesting to link the measures of women’s legal treatment in the WBL database to measures that capture women’s actual economic agency and investigate the relationship between _de jure_ and _de facto_ female empowerment.

The remainder of the paper is structured as follows. Section 2 presents a brief description of the WBL database. Section 3 provides on overview of the literature on the impacts of gender discrimination under the law. Section 4 documents a series of stylized facts for the 190 countries and 50 years in our sample. Section 5 examines the causal impact of legal reforms on labor market outcomes. Section 6 concludes.

# **2. WBL Database: A Short Description**

The WBL data set focuses on legislation that may impact a woman’s access to employment and entrepreneurial activity; it does not cover legal gender discrimination across all aspects of a woman’s life. The information on the legal environment in each economy is collected through collaboration of legal experts based in the World Bank with local experts, including lawyers, judges, civil society representatives and public officials. In total, a network of more than 10,000 legal experts contribute to the WBL project.

The data set attempts to capture inequality in legislation throughout the duration of a woman’s working life, from the time she can enter the labor force through to retirement. Thirty‐five individual legislative issues are aggregated into the following eight indicators, with four or five binary questions in each: _Going Places_ examines constraints on freedom of movement; _Starting a Job_ analyzes laws affects a woman’s decision to work; _Getting Paid_ measures laws and regulations affecting women’s pay; _Getting Married_ assesses legal constraints related to marriage; _Having Children_ examines laws that affect women’s work after having children; _Running a Business_ analyzes constraints to women starting and running a business; _Managing Assets_ considers gender differences in property and inheritance; and, finally, _Getting a Pension_ 3
assesses laws that affect a woman’s pension. A complete list of the eight indicators and 35 topics is presented in Table A1.

Indicator‐level scores are obtained by calculating the unweighted average of the four or five binary questions within that indicator and scaling the result to 100. Overall scores are then calculated by taking an unweighted average of the eight indicators, with 100 representing the highest possible score. Take, for example, the legal environment in Afghanistan in 2019: this economy receives a score of 50 out of 100 for the _Going Places_ indicator because women face legal restrictions in two of the four legislative issues covered (women in Afghanistan cannot travel outside their home, nor can they choose where to live, in the same way as a man). In the _Getting Paid_ indicator, women face legal restrictions in only one of the four issues covered and, so, Afghanistan receives a score of 75 for this indicator. Overall, based on an unweighted average of the eight indicators, the WBL index score for Afghanistan is 38.13. A WBL index score of 100 would indicate that there are no legal inequalities between men and women in the areas covered by the database.<sup>2</sup>

# **3. Existing Literature**

There is a growing body of evidence that suggests that the types of legal inequalities measured under the WBL index matter for women’s economic outcomes.<sup>3</sup> Take, for instance, laws that impact women’s mobility. Demirguc‐Kunt, Klapper and Singer (2013) document a relationship between legal mobility restrictions and women’s access to finance; Islam, Muzi and Amin (2019) find that travel restrictions placed on women are associated with lower levels of female business ownership; and Htun, Jensenius and Nelson‐Nuñez (2019) find that such restrictions may reduce women’s labor supply. Laws that impact women’s decisions to work and their salaries, should they do so, have also been shown to be impactful. According to research by Zabalza and Tzannatos (1985), UK legislation forbidding discrimination based on gender is associated with increased employment and earnings of women. Hallward‐Driemeier and Gajigo (2015) show that, in Ethiopia, lifting constraints on women’s right to work outside the home is associated with increased engagement of women in the formal labor market. Amin and Islam (2015) document a link between laws mandating nondiscrimination based on gender and women’s employment. While laws mandating nondiscrimination in employment have generally been associated with positive outcomes for women, evidence from the United Kingdom links equal pay legislation with a decrease in female

> 2 A full description of the laws covered under the WBL and the methodology used to calculate the index can be found in World Bank (2019b).

> 3 A summary of this evidence is provided in World Bank (2019a) and a more complete overview in Roy (2019).

4
employment in the manufacturing sector (Pike, 1985), while some evidence from the United States shows no impact (Gunderson, 1975). On the other hand, Zveglich and van der Meulen Rodgers (2003) and Islam, Muzi and Amin (2019) show that lifting job restrictions on female workers is associated with improved employment outcomes.

The impact of unilateral divorce legislation on female labor supply appears to hinge on legislation on the division of property (Gray, 1998; Voena, 2015). Evidence on the impact of maternity, paternity and parental leave legislation on female labor market outcomes is equivocal. Early evidence from Europe (Ruhm, 1998) finds that short periods of paid parental leave are associated with increases in women’s employment with no impact on earnings, but that longer leave durations, while also related to increased female employment, are associated with reductions in women’s wages. For the United States, Baum and Charles (2003) find that maternity leave legislation does not have a statistically significant impact on maternal leave taking, but that it does make women who take maternity leave more likely to return to their pre‐birth jobs afterwards (the latter finding is similar to the results of Baker and Milligan (2008) who study the impact of Canadian maternity leave entitlements). Schönberg and Ludsteck (2014) find that expansion in maternity leave coverage in Germany is associated with reductions in mothers’ labor supply in the short run, but that the long‐run impacts are small. Olivetti and Petrongolo (2017) highlight that when examining the effects of such family leave policies, determining causal relationships is extremely challenging. Recent research on the long‐run effects of paid parental leave in California (Bailey et al., 2019) finds no increase in female employment, earnings or attachment to employers as a result of the policy, but, on the contrary, finds that, for first‐time mothers, paid family leave is associated with lower employment and earnings in the long run.

Research has found that laws that enable women to sign a contract or open a bank account are associated with higher female labor participation (Gonzales et al., 2015). Similarly, experimental evidence from India finds that when women’s wages are deposited into their own bank accounts, their labor supply increases (Field et al., 2016). There is quite a substantial body of evidence that links improved property rights for women (the _Managing Assets_ indicator) to female labor supply. For example, cross‐country studies by Hallward‐Driemeier, Hasan and Rusu (2013) and Gonzales et al. (2015) show that equalization of property and inheritance rights between men and women is associated with increased female labor supply. Heath and Tan (2019) find that reforms in inheritance laws in India increase women’s labor supply and that the effect is particularly strong for high‐paying jobs. Finally, in terms of laws that are related to pensions and retirement, several country‐specific studies, such as for Austria (Staubli and Zweimüller, 2013), Australia 5
(Atalay and Barrett, 2015) and the United Kingdom (Cribb, Emmerson and Tetlow, 2016), show that increases in retirement ages are associated with increased female labor supply.

The impact of reforming the types of laws covered under the WBL index extends beyond women’s economic empowerment. Giving more rights to women is associated with improved educational outcomes for women (Branisa, Klasen and Ziegler, 2013; Deininger, Goyal and Nagarajan, 2013; Roy, 2015; Deininger et al., 2019; Harari, 2019), reductions in fertility rates (Branisa, Klasen and Ziegler, 2013), increased household expenditure on education, health and nutrition (Mishra and Sam, 2016), improved educational outcomes for the next generation (Menon, Van Der Meulen Rodgers and Nguyen, 2014), and improved health outcomes for women (Anderson, 2018; Harari, 2019). The types of laws that have been most commonly associated with these outcomes are those that give greater property and inheritance rights to women, which are believed, in turn, to increase their bargaining power within the household.

From a macroeconomic perspective, reductions in gender inequality may be associated with higher growth through several channels. For example, Goldin (1986) discusses the relationship between increased female labor force participation and earnings and economic growth in the United States. Klasen (2002) notes that inequalities in education between men and women negatively impact growth by lowering the average economywide level of human capital. Lagerlöf (2003) suggests that gender equality equalizes the human capital of husbands and wives, which increases women’s opportunity cost of having children and causes the fertility rate to fall. Cavalcanti and Tavares (2016) suggest that gender discrimination impacts output through two channels – it decreases women’s participation in economic activity, which has a direct impact on output, and it is associated with higher levels of fertility and lower levels of investment in human capital, which have long‐run implications for economic growth.

While the studies presented above have illustrated some of the ways in which gender equality under the law is related to economic outcomes, what has been missing from the literature is a global picture of the extent of these legal inequalities – contrasting the results between countries where women are given the same rights under the law as men with those where the legal gender gap is large, and examining the evolution of gender equality over time. What has also been lacking is an analysis of whether the relationships documented above are always causal, and whether they hold when measured on a much wider scale. These are the gaps in the literature that we attempt to address in this paper.

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# **Stylized Fact 2:  Women are most severely penalized when it comes to laws that are related to having children and getting paid.**

Looking at the aggregate WBL index masks some interesting differences between the eight topics covered. Breaking the aggregate index down into the eight indicators, the data—displayed in Panel B in Figure 1— show that, in 2019, women faced the greatest inequality in laws related to having children, where the global average was 53.89 out of 100 possible points (or 60.39 when each economy is weighted by its share in global population). On the other hand, laws placing constraints on freedom of movement ( _Going Places_ ) show the lowest levels of gender inequality (average of 87.24 points when unweighted and 92.36 when population weighted). When comparing weighted and unweighted scores, we can see that the relative rankings of the indicators change. For example, when country scores are weighted by population, the averages show that, across the world, women are most severely penalized when it comes to laws that are related to getting paid (population‐weighted average score of 47.81 in 2019). This reflects the fact that in some highly‐populated countries, such as China and India, women are severely discriminated against in this domain. In 2019, China and India scored 25 out of a possible 100 points in this indicator.

The regional averages for each topic—summarized in Table 1—show some interesting patterns. In high‐ income OECD economies, the average score is high for each topic; laws related to getting paid show the greatest degree of inequality but, nonetheless, receive a score of 89.84. There is more variation in other regions, take Europe and Central Asia for example; based on the aggregate WBL score, this region had the second highest average score (84.23 out of 100 points – see Figure 1). However, economies in this region clearly have significant room for improvement in terms of laws that affect the size of a woman’s pension, where their average score is 54 points.

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**Table 1: Average 2019 score by region for each WBL indicator**

||Going<br>Places|Starting a<br>Job|Getting<br>Paid|Getting<br>Married|Having<br>Children|Running a<br>Business|Managing<br>Assets|Getting a<br>Pension|
|---|---|---|---|---|---|---|---|---|
|East Asia &|89.00|68.00|67.00|86.40|35.20|82.00|76.80|66.00|
|Pacific|(16.27)|(34.25)|(21.31)|(20.59)|(32.29)|(11.46)|(27.50)|(25.90)|
|Europe &|99.00|85.00|66.00|96.80|80.00|93.00|100.00|54.00|
|Central Asia|(5.00)|(22.82)|(31.36)|(7.48)|(16.33)|(11.46)|(0.00)|(23.58)|
|High‐income|100.00|97.66|89.84|95.00|90.00|95.31|98.75|90.63|
|OECD|(0.00)|(9.75)|(18.90)|(11.36)|(12.44)|(9.91)|(7.07)|(16.50)|
|Latin|92.19|76.56|69.53|88.75|45.00|82.81|98.13|80.47|
|America &|(13.38)|(30.41)|(20.80)|(14.31)|(26.88)|(13.38)|(5.92)|(20.80)|
|Caribbean|||||||||
|Middle East|52.50|58.75|37.50|33.00|33.00|80.00|43.00|58.75|
|& North<br>Africa|(37.96)|(39.96)|(27.51)|(26.97)|(25.36)|(10.26)|(13.42)|(28.42)|
|South Asia|90.63|84.38|46.88|70.00|30.00|71.88|55.00|50.00|
||(18.60)|(18.60)|(38.82)|(26.19)|(10.69)|(16.02)|(20.70)|(23.15)|
|Sub‐Saharan|82.29|76.04|62.50|67.50|44.58|72.40|73.33|80.21|
|Africa|(21.85)|(30.05)|(30.07)|(30.49)|(22.69)|(21.41)|(24.87)|(24.71)|

# **Stylized Fact 3:  Religious laws do not explain gendered laws.**

Looking at the results for MENA, the poorest performing region in terms of gender equality, Table 1 shows that women face the lowest levels of legal discrimination when it comes to starting and running a business (an average score of 80 out of 100), and the highest number of constraints in laws related to marriage and laws that impact their economic rights upon having children. Apart from Malta (which is an outlier with a WBL score of 88.75 in 2019), all countries in the region are majority Muslim population; thus, the high number of legal gender restrictions in the region may relate to countries’ compliance with conservative versions of Sharia (Islamic law). As discussed by Bowen (2018), during periods of colonization across Muslim Asia, Africa and the Middle East, colonial rulers applied their own Western law to criminal or commercial disputes, but were less inclined to reshape family law, which retained much of its Islamic influence.

While respecting nations’ religious beliefs and values, it is worth considering to what extent laws that discriminate by gender could be reformed in the MENA region within the confines of Islamic law. Several authors have argued that it is patriarchal cultures, and not Islamic law, that is behind gender inequality in many countries. Braunstein (2014) argues that people too readily assume that Islam is a suitable proxy for 9
patriarchal preferences, and Ross (2008) maintains that in the Middle East the presence of substantial oil rents (that protect patriarchal norms), and not Islam, is the reason that women have made so little progress in terms of asserting their rights. Another reason to question the absolute link between Islamic law and legal gender restrictions is that, as Noland (2005), Bowen (2013) and Barlas (2019) note, laws derived from readings of the Quran are subject to many interpretations – as is the case with the reading of many sacred texts – and there are wide divergences in terms of practice. This may be why, as noted in King and Mason (2001), there is such a large variation in the autonomy of Muslim women in different contexts.

Mashhour (2005) argues that the restrictions placed on women’s rights in many Muslim countries are not the result of Islamic law, but rather the result of patriarchal norms in these societies. Indeed, the author notes that the main aim of Sharia is to maintain justice and promote public welfare. By comparing the cases of Tunisia and the Arab Republic of Egypt (which, it is worth noting, had WBL scores in 2019 of 70 and 45, respectively), Mashhour (2005) argues that the liberal case of Tunisia should be considered as a model for how to incorporate gender equality in the family sphere within the bounds of Sharia. He argues that, as Sharia is partly divine (coming from the Quran and Sunna) and partly human‐derived, it is not static but, rather, is constantly evolving. He concludes that common ground can indeed be found between Islamic law and gender equality.

# **Stylized Fact 4:  The past five decades have seen tremendous progress, but the pace of reform has differed across regions.**

While the current state of legal gender equality is what matters to women now, it is also interesting to chart the progress of reform over the five decades covered by the WBL data. The unweighted global average WBL score increased from 46.48 to 75.23 points between 1970 and 2019, but different regions have made progress in terms of legal gender equality at different paces. As Figure 2 illustrates, while OECD economies are currently at the top of the ranking, in the 1970s, women in the Europe and Central Asia region faced fewer legal gender restrictions than their OECD counterparts. On the other hand, while regional average scores in the Latin America and Caribbean (LAC) and East Asia and Pacific (EAP) regions were very close in 1970, at 49.34 and 48.75 points respectively, economies in LAC have made greater progress towards legal gender equality over time, achieving a score of 79.18 by 2019, compared to 71.30 in EAP. Another interesting comparison is between the Sub‐Saharan Africa and South Asia regions; while legal gender restrictions in 1970 were fewer in South Asia (WBL score of 43.67) compared to Sub‐Saharan 10
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<!-- Start of picture text --> c 50<br>2<br>&<br>=Qa|<br>8<br>HH>,& | 40<br>mo}<br>2<br><=<br>2o 30<br>=<br>no<br>-<br>fe}<br>‘@ 20<br>ie)<br>a}<br>a<br>5 10<br>|<br>o<br>.<br>ot ddl WLLI LD dulERLE bdPLLilabd bli lll lk dial wll<br>TNMOMTNOOR-DHDOTKAYNTOOKR-DHOTAMYMNTOOKRDHOTAYMNTOOKR-DHDOTKNMATO<br>BMEREREREERER OD DDDDDDDADHWHWDHWHIAIRWHAWARHDODODOOOVOOCOCOTTOR OD<br>fopFopopkoploploploploplop lop loplerlopio lepler lep ler lop leplerler ler lop le 1er1e le le) 1S 1 ole olelelololololol@lolololololololo)rr rrr crrT<br>SOTHOTN NNN NNNN NNN NNN ANN NNN<br>G9) East Asia & Pacific (—) Europe& Central Asia [49 High income: OECD<br>(+) Latin America& Caribbean [77) Middle East& North Africa [7] South Asia<br>Sub-Saharan Africa<br><!-- End of picture text -->
**Stylized Fact 5:  The pace of reform varies not only across countries, but also across the individual indicators.**

As Figure 5 illustrates, _Starting a Job_ displays the fastest pace of reform, when reforms are unweighted (Panel A) and when they are weighted by population (Panel B). The _Going Places_ and _Managing Assets_ indicators, both of which start from a relatively high base, display the slowest pace of reforms. The _Getting Paid_ indicator also displays a slow pace of reform, despite starting from a relatively low base (this is particularly notable when average scores are weighted by population). Legal reform also progressed at a relatively slow pace in the _Getting a Pension_ indicator. In fact, when the scores are weighted by population share, we see some regression of the laws that impact the size of a woman’s pension in the latter half of the 2000s (Panel B in Figure 5). This drop in _Getting a Pension_ , which is only evident when global averages are weighted by population, reflects a negative reform that was made in China in 2008. In that year, a labor contract law was enacted, which introduced mandatory retirement ages that differed by gender. Before this law was enacted, there was no mandatory retirement age.<sup>4</sup> The differing pace of reforms across the indicators suggests that countries may display differing degrees of resistance to granting women equal rights under the law in different areas, and that economic factors may motivate reforms. For example, the fast pace of reforms in _Starting a Job_ indicates that the progress in this area may have been motivated by the desire to better integrate women into the labor force during a time of rising demand for labor. We now turn to a more systematic investigation of the effects of reforms on labor market outcomes, using a framework that takes the potential economic motivation of reforms into account.

> 4 ‐ Labor Contract Law of the People's Republic of China of 29 June 2007, Art. 44(2) <u>http://www.gov.cn/flfg/2007 06/29/content_669394.htm [accessed November 11, 2019].</u>

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<!-- Start of picture text --> Panel A: Unweighted scores Panel B: Population-weighted scores<br>100 - 100-<br>or aie Ta 7 Bi /<br>Ell —_—_— 4e _ —_—> I<br>- ot oe Tsope ) we = 7 =4<br>—_— SFr. og 7 Sy ean<br>60 - Vig / 60 - ame ri 5<br>7 s / F taal v= 2 f cane:<br>— ae / 3 ae 4 or<br>ry oe / Sa<br>VY hes 17 rs<br>40 - Lose/| ¢ Ra “ 40 7 a | G 'r<br>ee Wig = one’<br>Dee == ><br>ee 20 - as 2 seniu<br>20- «a ueerrrn?<br>a ez<br>| | | | i] | | | i | | |<br>1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020<br>Year Year<br>“Going places —-— Starting ajob —— Getting paid — = Getting married<br>===» Having children == Runningabusiness -'= Managing assets -— Gettinga pension<br><!-- End of picture text -->
national estimates only for the 32 high‐income OECD economies in our data and use modeled ILO estimates for the remaining 158 economies. While this introduces a source of heterogeneity into the measurement of our outcome variable, even if we were to use ILO estimates exclusively, there would be differences between countries in terms of the data, as ILO sources for labor force statistics vary between countries depending on data availability. Furthermore, the inclusion of country fixed effects in model estimation should address this issue. Data on the gender wage gap are also from two sources – the ILO and OECD; ILO gender wage gap data are the difference between the mean earnings of men and the mean earnings of women, expressed as a percentage of the earnings of men; OECD data are similarly calculated but use median as opposed to mean earnings. As the OECD figures should be less sensitive to outliers, we use the OECD data whenever possible, and only use ILO figures in countries and years where OECD data are not available. For the third outcome, occupational segregation, we use occupational data compiled by the ILO to compute an Occupational Segregation Index by sex.<sup>6</sup> We model the relationship between the WBL index and labor force outcomes as follows: Y is the outcome for country i at time _t_ ; WBL represents the WBL score in _t‐1_ ; and Z�,� represents a set of potential control variables, including fertility rate, female education, income and male population. The terms γ� and ∅� represent country and year fixed effects; u�,� is the error term. The WBL index is measured at a one‐year lag, as we would not expect to see an impact of legal reform on women’s labor market outcomes in the same year that reforms were implemented.

The coefficient β represents the relationship between gender equality and women’s labor force outcomes across 190 countries, accounting for unobservable differences between countries and time periods (captured by the fixed effects). However, within this framework we cannot attribute a causal effect of legal gender equality to female labor force participation, as there may be some time‐varying unobservable η�,�, which is correlated with both the WBL score and female labor supply. A likely candidate for this

> 6 As discussed by Blau, Brummund and Liu (2013), differences in the distribution of men and women across a range of occupations can be described in a simple index. The most commonly used measure of occupational segregation was developed by Duncan and Duncan (1955), and can be calculated as: OSI� ��0.5�∑|m� �� �f��| , where m�� and f�� are the proportion of males and females, respectively, that are employed in occupation _i_ at time _t._ ILO data on employment by occupation are available for 143 economies from 1991 to 2017; however, the series has several gaps, as coverage varies from year to year and between economies. The data are, in general, much more complete for high‐income OECD economies.

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unobservable factor is labor demand – when the demand for labor is increasing, more women are likely to join the labor force; at the same time, economies may be more likely to remove restrictions that prevent women from participating in the workforce to facilitate the provision of additional workers. To deal with this issue, we adopt the novel approach of Freyaldenhoven, Hansen and Shapiro (2019), FHS henceforth, who outline a methodology to identify causal effects in a panel data study when a time‐ varying unobservable confound η�,� is present, through the use of a covariate which is only related to the policy in question through the unobservable confound. To implement their approach, it is necessary to identify a covariate x�,� that is affected by the confounding variable η�,� but unaffected by the WBL index. Once a covariate has been identified, the authors show that β , the causal effect of the policy in question, can be estimated via two‐stage‐least squares (2SLS) regression of the outcome Y�,� on the policy variable of interest (in our case, WBL�,��� ) and the covariate x�,� using leads of the policy variable as an instrument for x�,� . As FHS explain, the covariate x�,� is a noisy proxy for the confound η�,� . If x�,� were a perfect proxy for the confound, simply adding it is a control variable would allow for causal identification of the parameter β .

Thus, the first model we estimate is a modified version of equation (1) in which we directly control for the covariate x in an OLS model: �,� Next, we proceed to implement the FHS procedure by estimating the following equation via 2SLS, using the lead of the policy variable (i.e., WBL�,��� ) as the instrument for x�,� : FHS note that the choice of the covariate x�,� should be based on economic reasoning; we employ as our covariate regional non‐agricultural value added, interacted with country i ’s oil rents as a percentage of GDP.<sup>7</sup> We hypothesize that this variable is an imperfect proxy for labor demand because, as economies in a region move away from agricultural production and towards manufacturing and services industries, this should cause the demand for labor to increase. We interact regional non‐agricultural value added with oil rents for several reasons – oil production has been tied to slower economic growth (for a discussion of

> 7 Data on oil rents as a percentage of GDP are from the World Bank’s WDI data set. Per unit oil rents are the difference between the world price of the commodity and the costs of producing it (the estimated extraction and harvesting costs). Per unit rents are multiplied by the physical quantities that countries extract and are presented as a share of GDP.

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the “natural resource curse” refer to Sachs and Warner, 2001) and violent conflict (Bannon and Collier, 2003), which may dampen the demand for increased female labor despite regional industrialization. A stronger argument for its inclusion comes from the work of Ross (2008) who shows that a rise in the value of oil production may reduce female labor force participation because natural resource booms result in a decline of the traded sector where, in developing economies in particular, many women tend to be employed (for example, in export‐oriented manufacturing plants) and growth in the nontraded sector (for example, sectors such as construction, from which women tend to be excluded). An important assumption of the FHS model is that the policy variable of interest should not have a direct impact on the covariate. It is for this reason that we use a measure of non‐agricultural value added at the regional level that excludes the observation of country i . For oil rents, we assume these are exogenously determined and do not anticipate that equalization of gender laws would impact them.

The application of the FHS methodology relies on a number of assumptions. According to FHS, a necessary condition for the application of their methodology is that the policy variable of interest should not have a direct impact on the covariate. We test this by regressing x�,� on the WBL index; the regression results (presented in Appendix Table A3) confirm no impact (the p‐value on the WBL index in this regression is 0.69). A second condition of the FHS approach is evidence of a pre‐trend in the covariate x�,� . We test this by regressing our covariate x�,� on the lead of the WBL index ( WBL�,���� . If there is a pre‐trend present, the coefficient on the lead of the policy variable should be statistically significant. The results from our baseline model (regression of female labor supply on the WBL index and the covariate) show that, according to the first stage of the 2SLS model, the lead of the WBL index is a significant predictor of x�,� (results presented in Table A4 in the Appendix). The results also show that the p‐value from the under‐ identification test is 0.01, suggesting that we can reject the null hypothesis of under‐identification.

In our baseline model, we do not include any other covariates; this is our preferred specification, as the only endogenous variable contained in the model is the WBL index, which we explicitly account for through the use of the FHS methodology. However, we test the robustness of our results to the inclusion of several covariates that may also be associated with women’s labor market outcomes; specifically, the fertility rate, female education, income (GDP per capita and its square term), and the male working‐age population; these are comprised in the vector Z�,� in equations (1) to (3).

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# **5.2 Results**

The results of the panel data regressions, using the OLS and FHS models (i.e., equations (2) and (3)) are presented in Table 2.

Columns (1) to (4) in Table 2 consider the relationship between gender equality under the law and the female labor force participation rate. Columns (1) and (2) present the OLS results, under a parsimonious model and after the inclusion of several, potentially endogenous, covariates. In both cases, the relationship is positive and significant; the inclusion of the additional covariates has very little impact on the coefficient on the WBL index. The FHS results, presented in columns (3) and (4), imply that the relationship between gender equality and female labor supply is slightly stronger than the OLS results would suggest, although the OLS and FHS models do not yield statistically different results. Regardless of the model used, the magnitude of the effect is modest – a one‐point increase in the WBL index is associated with an increase in female labor force participation of between 0.047 and 0.053 percent. On average, implementing one WBL reform increases the index by 2.9 points; thus, the reform of a single one of the 35 WBL data points would be associated with an increase in female labor supply of between 0.14 and 0.15 percent.

Columns (5) to (6) in Table 2 show that, according to OLS estimation, improvements in gender equality under the law are associated with a reduction in the wage gap between men and women. Again, while the relationship between legal gender equality and women’s economic outcomes is positive (in that it is associated with a lower wage gap), it is modest; a one‐point increase in the WBL index is associated with a 0.09 to 0.1‐percentage point reduction in the wage gap (the median gender wage gap in our sample is 15.64 percent). However, according to the FHS model results (columns (7) and (8)), there is no statistically significant relationship between the WBL index and the pay gap. As the FHS model is designed to identify the causal impact of a policy variable, we can infer from these results that there is no causal effect of the WBL index on the gender wage gap. Similar results hold for occupational segregation between genders (columns (9) through (12) in Table 2). The OLS results with other covariates included (column (10)) suggest that increases in the WBL index are associated with lower levels of occupational segregation between genders, but the results under the FHS model show no significant effect (columns (11) and (12)).

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**Table 2: The relationship between legal gender equality and women’s labor market outcomes**

|||**Female labor f**|**orce partici**|**pation**||**Gender**|**wage gap**|||**Occupational**|**segregatio**|**n**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||**OLS**||**FHS**||**OLS**||**FHS**||**OLS**||**FHS**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|**(11)**|**(12)**|
|**WBL index (t‐1)**|0.047***|0.047***|0.053***|0.048**|‐0.088***|‐0.101***|‐0.063|‐0.260|‐0.026|‐0.056**|0.004|‐0.048|
||(0.009)|(0.009)|(0.016)|(0.020)|(0.030)|(0.034)|(0.067)|(1.012)|(0.023)|(0.024)|(0.100)|<br>(0.037)|
|**Regional non‐ag**|0.000**|0.000*|0.002|0.001|0.003**|0.003**|‐0.029|‐0.173|‐0.003***|‐0.003***|0.078|0.015|
|**VA * Oil rents**|(0.000)|(0.000)|(0.004)|(0.004)|(0.001)|(0.001)|(0.070)|(1.103)|(0.001)|(0.001)|(0.210)|<br>(0.057)|
|**GDP per capita**||0.000***||0.000***||0.000||‐0.003||0.000***||0.001**|
|**(constant 2010**||(0.000)||(0.000)||(0.000)||(0.021)||(0.000)||(0.000)|
|**US$)**|||||||||||||
|**GDP per capita**||‐0.000||‐0.000||‐0.000||0.000||‐0.000***||‐0.000***|
|**squared**||(0.000)||(0.000)||(0.000)||(0.000)||(0.000)||(0.000)|
|**Fertility rate**||‐2.084***||‐2.085***||1.705**||0.263||‐1.959**||‐2.019**|
|||(0.179)||(0.179)||(0.770)||(9.640)||(0.796)||(0.919)|
|**Male working**||0.000||0.000||0.000**||0.000||‐0.000*||‐0.000|
|**age population**||(0.000)||(0.000)||(0.000)||(0.000)||(0.000)||(0.000)|
|**Average years of**||1.258***||1.260***||5.359***||27.129||0.931||0.721|
|**education,**||(0.175)||(0.177)||(1.303)||(136.696)||(1.484)||(1.811)|
|**female ages 25**|||||||||||||
|**plus**|||||||||||||
|**_cons**|36.684***|<br>29.628***|||17.436***|‐54.180***|||33.599***|26.548*|||
||(0.722)|(1.461)|||(2.853)|(15.391)|||(2.023)|(15.819)|||
|**_N**|4,927|4,459|4,927|4,458|999|949|990|943|1,395|1,215|1,370|1,194|

**Notes: Standard errors are presented in parentheses; .01 ‐ ***; .05 ‐ **; .1 ‐ *. All regressions include country and year fixed effects**

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While the impacts of WBL‐type reforms on female labor supply are modest, the data suggest that the impacts do appear to be increasing over time. Panel A in Table 3 shows that, if the WBL index is measured at a five‐year lag, a one‐point increase in the index is associated with an increase in the female labor force participation rate of approximately 0.06 percentage points, which increases to approximately 0.07 after 10 years. The data show no impact of the WBL index on the wage gap or occupational segregation when longer lags are used.

Turning next to the eight individual indicators that make up the WBL index, Panel B in Table 3 shows that the indicators that are significantly associated with increased female labor supply are _Starting a Job_ , _Getting Married_ and _Running a Business_ . The fact that _Starting a Job_ and _Running a Business_ show evidence of a causal relationship with female labor supply provides support for our choice of methodology, as one would expect these laws to most directly impact women’s decisions to work (either by seeking employment or by starting their own business). Additionally, several other studies have documented a link between the removal of discrimination against married women (captured in the _Getting Married_ indicator) and female labor supply.<sup>8</sup> While we find no robust evidence of a link between the individual indicators and the gender wage gap,<sup>9</sup> we do find that some of the indicators underlying the aggregate index are related to rates of occupational segregation between genders. Panel C in Table 3 shows that reforming constraints related to marriage ( _Getting Married_ ) and the size of a woman’s pension ( _Getting a Pension_ ) are associated with lower levels of occupational segregation between genders. On the other hand, Panel C in Table 3 also shows that laws and regulations affecting women’s pay and women’s work after having children ( _Getting Paid_ and _Having Children_ ) are associated with higher levels of occupational segregation between men and women. These negative impacts are not entirely surprising – early analysis of the impacts of maternity leave on female labor market outcomes shows that while short periods of maternity leave are associated with increased female employment, longer periods of leave are associated with negative wage effects (Ruhm, 1998). Olivetti and Petrongolo (2017) highlight the nuanced relationship between “family‐friendly” leave policy and female labor market outcomes that have been uncovered in the literature. Furthermore, recent evidence on the long‐term impacts of California’s Paid Family Leave Act finds it is associated with declines in female employment and wages for first‐time mothers (Bailey et al., 2019). On the increased occupational segregation associated with positive reforms in the _Getting Paid_ indicator, it is plausible that

> 8 Goldin and Olivetti (2013); Hallward‐Driemeier, Hasn and Rusu (2013); Gonzales et al. (2015).

> 9 The results are not presented here for brevity but are shown in Table A5 in the Appendix.

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legal reform in this area may result in women working in more family‐friendly jobs that offer more flexibility but are associated with lower pay. Early evidence of unintended consequences of equal‐pay legislation is presented by Pike (1985), who finds that equal pay legislation in the United Kingdom was associated with a decline in female employment in the manufacturing sector.

# **6. Conclusions**

While several individual studies have illustrated how legal gender discrimination is related to economic outcomes, the literature has been missing a global picture of legal inequalities – contrasting the experience of countries where women are given the same rights under the law as men with that of countries where the legal gender gap is large, and examining the evolution of gender equality over time. This paper fills these gaps. First, we present an overview of the WBL database, highlighting the differences between countries and regions, and chart the development of gender equality over time. We find that, across the world, women still face by legal gender discrimination across multiple domains. The level of legal discrimination is particularly onerous in some highly‐populated countries and, therefore, restricts economic opportunities for a very large number of women. Having documented the extent of and trends in legal discrimination between men and women, we then study the relationship between these legal restrictions and women’s labor market outcomes. Focusing on female labor force participation, panel estimations of data from 190 economies over 50 years reveals that a more level legal playing field between men and women is associated with more women participating in the workforce; there is also some indication from the data that better rights for women are associated with a lower gender pay gap.

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**Table 3: Testing the results along additional dimensions – FHS results only**

**_Panel A: The relationship between labor force outcomes and lagged WBL reform_**

||**Y = Fem**<br>**par**|**ale labor force**<br>**ticipation**|**Y = Gend**|**er wage gap**|**Y = Occupat**|**ional segregation**|||
|---|---|---|---|---|---|---|---|---|
||**WBL in t‐5**|**WBL in t‐10**|**WBL in t‐5**|**WBL in t‐10**|**WBL in t‐5**|**WBL in t‐10**|||
|WBL index|0.059***|0.070***|0.061|0.016|‐0.006|0.019|||
||(0.009)|(0.010)|(0.199)|(0.084)|(0.110)|(0.362)|||
|Regional non‐ag VA<br>*|‐0.001|‐0.004*|‐0.112|‐0.086|‐0.012|‐0.047|||
|Oil rents|(0.002)|(0.002)|(0.177)|(0.076)|(0.047)|(0.223)|||
|_N|4,910|4,859|984|959|1,370|1,370|||
|**_Panel B: The relation_**<br>|**_ship between i_**<br>**Going**<br>**Places**|**_ndividual indicator_**<br>**Starting a Job**|**_s and female labo_**<br>**Getting Paid**|**_r force participati_**<br>**Getting**<br>**Married**|**_on_**<br>**Having**<br>**Children**|**Running a**<br>**Business**|**Managing**<br>**Assets**|**Getting a**<br>**Pension**|
|WBL index t‐1|0.048|0.010*|0.014|0.032**|0.011|0.014**|0.011|0.022|
||(0.097)|(0.005)|(0.013)|(0.013)|(0.015)|(0.006)|(0.013)|(0.066)|
|Regional non‐ag VA<br>*|0.016|‐0.006|0.017|0.007|‐0.018|‐0.002|0.004|0.045|
|Oil rents|(0.108)|(0.007)|(0.021)|(0.006)|(0.018)|(0.006)|(0.011)|(0.106)|
|_N<br>**_Panel C: The relation_**|4,927<br>**_ship between i_**|4,927<br>**_ndividual indicator_**|4,927<br>**_s and occupation_**|4,927<br>**_al segregation_**|4,927|4,927|4,927|4,927|
||**Going**<br>**Places**|**Starting a Job**|**Getting Paid**|**Getting**<br>**Married**|**Having**<br>**Children**|**Running a**<br>**Business**|**Managing**<br>**Assets**|**Getting a**<br>**Pension**|
|WBL index t‐1|‐0.453|‐0.006|0.014*|‐0.048***|0.060***|‐0.107|‐0.088|‐0.037**|
||(0.933)|(0.019)|(0.008)|(0.017)|(0.021)|(0.105)|(0.147)|(0.016)|
|Regional non‐ag VA<br>*|0.085|‐0.027|0.009|0.015|0.041|0.067|‐0.181|0.010|
|Oil rents|(0.544)|(0.040)|(0.040)|(0.012)|(0.036)|(0.096)|(0.214)|(0.025)|
|_N|1,370|1,370|1,370|1,370|1,370|1,370|1,370|1,370|

**Notes: Standard errors are presented in parentheses; .01 ‐ ***; .05 ‐ **; .1 ‐ *. All regressions include country and year fixed effects**

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