The Council of Economic Advisers

February 2020

September 29, 2017

The W-GDP Index: Empowering 

Women’s Economic Activity through 

Addressing Legal Barriers
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 1

Executive Summary

Many developing countries place legal restrictions on women that inhibit their ability to make 

economic decisions in the same way as men, or fail to have in place basic protections to 

prohibit discrimination against women on the basis of sex. These legal barriers to women’s 

full and free participation in the economy impede economic success. Similar to other forms of 

government intervention, regulating women’s participation in the labor market, ownership of 

property, access to credit, ability to travel freely, and access to institutions distorts economic 

activity and leads to the underutilization of productive inputs in an economy, lowering

economic output. The Trump Administration’s Women’s Global Development and Prosperity 

(W-GDP) Initiative, launched in February 2019, focuses on addressing these barriers 

worldwide. Tackling legal barriers to women’s full and free participation in the economy is 

smart economic policy.

In this report, we develop country-specific measures of women’s legal and economic freedom 

for each of the five foundational factors that are targeted by the W-GDP Initiative. These 

measures combine to form the single W-GDP Women’s Economic Freedom Index, which we 

refer to as the W-GDP Index. This index quantifies prior legal reforms that affect women’s 

economic freedom in developing countries and can be used to track the W-GDP Initiative’s 

progress in removing barriers to equal economic opportunity for women.

Deregulating women’s economic activity by removing legal restrictions on them and 

providing them with the same legal protections as men can yield large increases in economic 

output and promote overall economic development. Our research indicates that fully

removing the legal barriers to women’s economic activity could increase annual global gross 

domestic product (GDP) by $7.7 trillion, or 8.3 percent. Most of this estimated increase in 

global GDP comes from gains in South Asia, East Asia, and the Pacific, areas with large 

populations where sex-specific regulations are fairly prevalent. In the most restrictive

regions, fully eliminating these legal barriers could increase per capita GDP in South Asia and 

Sub-Saharan Africa by over 60 percent. Separate estimates focusing on employment 

restrictions predict that fully eliminating restrictions on women’s employment would 

generate annual gains to global GDP of $1.5 trillion.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 2

Introduction

In this report, we review the economic evidence on the relationship between women’s 

economic freedom and economic development, particularly as it relates to the core areas of 

legal reform identified by the Trump Administration’s W-GDP Initiative through the 

“Presidential Memorandum on Addressing Legal and Societal Barriers to Women’s Global 

Development and Prosperity.” Substantial economic research suggests that removing legal 

barriers to women’s participation in the economy and society has large benefits not only for 

women and children but also for overall economic development and the development of a 

modern, advanced economy. The W-GDP Initiative focuses on five foundational areas of legal 

reforms to provide women with equal opportunities to access institutions, access credit, own 

and manage property, travel freely, and work in occupations and jobs of their choosing.

To measure legal restrictions that women face in different areas of economic activity, we

construct empirical measures of women’s economic freedom by country by using the World 

Bank’s (2020) data from its “Women, Business, and the Law Report 2020.” Our analysis uses 

these measures to quantify the possible near-term economic benefits if all countries changed 

their laws to allow women to participate in the economy in the same way as men. These

short-term benefits are the result of expanded female labor market participation, as labor 

markets can adjust relatively quickly. We are unable to quantify the exact timing of these 

short-term benefits, and the timing could depend other features of the economy. Longerterm benefits resulting from increasing human capital investment in women and children

would be even larger, but would take longer to be fully realized. We reach our estimates using 

simple regression models, and our main finding is that there is a statistically significant 

negative relationship between restrictive regulations on women’s economic activity and real 

per capita GDP. We also generate estimates of the benefits of removing these restrictions 

using calibrated macroeconomic models. 

Removing all restrictions would be predicted to increase annual global GDP by up to $7.7

trillion in 2018 dollars, or 8.3 percent using the simple regression models. Most of this

increase is estimated to occur in South Asia and East Asia and the Pacific, regions with large 

populations where restrictive sex-specific regulations are fairly prevalent. We estimate that 

fully deregulating women’s economic activity in the most restrictive regions would increase 

real per capita GDP by over 60 percent. Removing legal restrictions on jobs and labor force 

participation, along with on owning and managing property, are predicted to generate the 

largest increases to GDP. Given data limitations, our estimates are not able to isolate the 

effect of legal restrictions on their own, and these predicted gains could reflect unaccountedfor differences between countries. Separate estimates focusing on employment restrictions, 

where there is sufficient variation in the data to control for permanent cross-country 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 3

differences, predict that fully eliminating restrictions on women’s employment would 

generate annual gains to global GDP of $1.5 trillion. 

In order to support our regression analysis, we also conduct macroeconomic model-based 

estimates that suggest gains of a similar magnitude, in the range of $4.9 trillion to $9.2 

trillion. Importantly, the macroeconomic models suggest that the GDP gains would not just 

be a one-time level shift, but that long-run global economic growth rates would increase by 

0.41 percentage point per year (e.g., 3.91 percent vs. the current forecast of 3.5 percent). 

Although our estimates do not establish a causal relationship, our results suggest that there 

are large potential economic gains associated with deregulating women’s economic activity.

Supporting our estimates is a wealth of theoretical and empirical economics literature finding

that expanded economic freedom for women is a critical component of overall economic 

development. Furthermore, the specific areas of legal reform in the developing world

targeted by the W-GDP Initiative have been shown to have positive benefits in microeconomic 

studies.

The economic case for equal economic freedom for women can be made by using the costbenefit analysis framework commonly used when evaluating regulations. When the cost 

imposed by a restrictive law or regulation exceeds its benefit, there is a net benefit to 

repealing the law or regulation. In a previous report, we find large benefits from the 

Administration’s deregulatory agenda, which follow a similar cost-benefit framework when 

evaluating restrictive regulations (CEA 2019). In another earlier report, we also highlighted 

the benefits of free market systems and the large costs of socialist policies (CEA 2018). 

The remainder of the report is organized as follows. In the second section, we describe the WGDP Initiative in detail. In the third section, we discuss the relationship between economic 

development and the expansion of women’s economic freedom. In the fourth section, we 

present an overview of the economic literature as it relates to the types of restrictive laws 

targeted by the W-GDP Initiative. In the fifth section, we discuss the data used in our analysis 

and develop an empirical measure of women’s relative economic freedom, which we refer to 

in this report as the W-GDP Women’s Economic Freedom Index, or W-GDP Index. In the sixth 

section, we propose a simple model to estimate the relationship between economic output 

and the W-GDP Index, and we present our main findings for the benefits of expanding 

women’s economic freedom. The seventh section concludes the report.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 4

The W-GDP Initiative

President Trump established the W-GDP Initiative in February 2019 to advance women’s 

global economic empowerment. The initiative has three pillars: women prospering in the 

workforce, women succeeding as entrepreneurs, and women enabled in the economy. The 

first pillar’s goal is increasing female labor force participation through vocational education 

and skills training. The second pillar’s goal is increasing women entrepreneurs’ and business 

owners’ access to capital, markets, and networks. The third pillar’s goal is reducing barriers 

and enhancing protections in policies, laws, regulations, and practices to facilitate women’s 

full and free participation in the economy. This report focuses specifically on the economic 

evidence on the effects of legal and regulatory barriers on women’s economic empowerment, 

an important component of the W-GDP Initiative’s third pillar.

In many developing countries, women face some relative disadvantage compared with men 

due to laws, regulations, and social norms that either restrict their economic activity or fail to 

offer them the same legal protections as men. The third pillar of the W-GDP Initiative focuses

on five foundational areas of legal reform:

1. Accessing institutions: Lifting restrictions on women’s authority to sign legal 

documents, such as contracts and court documents, and addressing unequal access 

to courts and administrative bodies for women, whether officially or through a lack of 

proper enforcement.

2. Building credit: Ensuring women’s equal access to credit and capital to start and grow 

their businesses, and prohibiting discrimination in access to credit on the basis of sex

or marital status.

3. Owning and managing property: Lifting restrictions on women’s possessing and 

managing property, including limitations on inheritance and the ability to transfer, 

purchase, or lease property.

4. Traveling freely: Addressing constraints on women’s freedom of movement, including 

restrictions on obtaining passports on the basis of sex.

5. Removing restrictions on employment: Eliminating barriers that limit working hours, 

occupations, or tasks on the basis of sex.

The Relationship between Women’s Economic Rights and 

Economic Development 

This section outlines the overall patterns of women’s participation in the economy and role in 

driving economic development. As economies industrialize and develop, women tend to gain 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 5

more economic rights. Economic development also changes female labor force participation, 

education, and fertility, along with human capital investments in children. We first discuss the 

empirical relationship between economic development and female labor force participation. 

We then discuss the causal relationship between women’s economic empowerment and 

economic development.

There is a well-established, “U-shape” relationship between female labor force participation 

and economic development when looking at the cross section of countries at a given point in 

time (see figure 1). Female labor force participation rates (LFPRs) are highest in low-income 

countries where women engage in subsistence activities and work in informal sectors. But as 

GDP rises, the female LFPR falls as the economy transitions to industrial employment that 

consists of mainly men. As education rises, fertility rates tend to decrease, so higher-income 

countries experience increases in the female LFPR in response to the expanding services 

sector. 

Heath and Jayachandran (2017) note that the U-shaped curve has shifted upward over the 

past few decades, as fertility has fallen more quickly than expected. This upward shift means 

that individual countries might not follow the U-shaped pattern of decreasing female labor 

force participation in their initial stages of development. 

0

20

40

60

80

100

5 6 7 8 9 10 11 12

Log GDP per capita

Figure 1. Female Labor Force Participation Rate by GDP per Capita, 

2017

Female LFPR (percent)

Sources: World Bank; CEA calculations.

Note: LFPR = labor force participation rate. Bubble size reflects country population. The dashed red line is the 

second-order polynominal best-fit line to the unweighted country level data. Labor force participation rates are 

among all women age 15 and older. 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 6

As economies industrialize, they experience a demographic transition. Economic 

development leads to rising incomes, falling mortality, declining fertility, and, often, 

expanding rights for women. Galor and Weil (1996) develop a macroeconomic model and 

explain this demographic transition. Before the demographic transition, economies are 

characterized by slow technological progress and slow growth in income per capita. 

Advanced economies are characterized by faster growth in per capita income and lower 

fertility rates. 

There is a strong positive relationship between economic development and women’s 

economic empowerment. Economic development tends to be associated with an increase in 

women’s economic rights. However, the exact causal relationship is not clear. Though 

expanding women’s economic rights could cause economic growth, the process of economic 

development could also naturally lead to greater women’s empowerment. Doepke and Tertilt 

(2009) argue that economic development naturally leads to an increase in women’s rights. In 

the process of economic development, human capital becomes more important, which leads

men to voluntarily extend rights to women for the future benefit of their children.

The other possibility is that women’s empowerment causes economic development. For 

example, if women have a greater preference for health and human capital investments in 

children, greater women’s empowerment could promote economic growth. Duflo (2003) 

studies the expansion of South Africa’s pension system at the end of apartheid and finds that 

girls who lived with a grandmother who received the pension when the girls were young were 

taller, which likely reflects better childhood nutrition. 

There can also be production inefficiencies in agriculture when men make decisions for 

agricultural production on plots of land owned by different members of the household. Udry 

(1996) and Goldstein and Udry (2008) find evidence of production inefficiencies in Africa, 

where, in many countries, women maintain separate ownership of property and production 

decisions are made collectively in the household (where the woman might have little 

decision-making power).

1 Expanding women’s legal protections can also promote economic 

growth through credit and household portfolio choice (Hazan, Weiss, and Zoabi 2019).2

 

1

In Burkina Faso, Udry (1996) finds that more fertilizer was used on male owned plots than on women-owned 

plots, which led to a decrease in household production of 6 percent compared with a situation where the plots 

were fertilized equally. In Ghana, Goldstein and Udry (2008) find that weak property rights led to an 

overutilization of the land. Women farmers fallowed their land less often, which is necessary to restore 

nutrients, as their property rights are less secure and fallowed land is more likely to be claimed by another.

2 Under the British common law system, women had an incentive to hold real estate that husbands could not 

bequeath or sell without the wife’s permission. Giving married women property rights over non-real assets 

increased holdings of financial assets and led to the development of non-agriculture and capital-intensive 

industries in the economy.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 7

In a review of the literature, Duflo (2012) argues that there is evidence to suggest that 

causality runs in both directions. Thus, economic growth leads to women’s empowerment,

and women’s empowerment can promote economic growth. However, the causal 

interrelationship is likely to be too weak for the process to be self-sustaining in a virtuous 

cycle. Moreover, economic growth by itself is likely not sufficient to generate equal economic 

opportunity for women without a separate push for legal reforms.

An Overview of the Literature on Specific Laws, Regulations, and 

Restrictions Targeted by the W-GDP Initiative

In this section, we examine the theoretical and empirical economics literature on the effects 

of removing barriers to equal economic rights for women for each of the five categories of 

laws targeted by the W-GDP Initiative. Although many of the studies described in this section 

do study the effect of legal reform on economic development directly, the legal reforms 

generate positive outcomes along a number of dimensions in ways that are likely to support

current and future economic growth. These outcomes include improvements in women’s

health, schooling, labor force participation, job outcomes, and entrepreneurship. In some 

areas, the effects of removing legal (de jure) restrictions has not been widely studied, so we 

also include studies of de facto barriers. 

Accessing Institutions

A 2015 report from the International Monetary Fund found that removing legal restrictions on 

access to legal institutions and property reduces the gap in men and women’s labor force 

participation by 2 to 3 percentage points (Gonzales et al. 2015). The report uses an earlier 

version of the World Bank’s Women, Business, and the Law (WBL) report and database that 

measure access to legal institutions and property protections for a set of 100 countries from

1960 through 2010.

3 The labor force gap is estimated using simple regression models with 

country fixed effects. There is also evidence that greater women’s participation in the 

political system can lead to improved outcomes for women. Iyer and others (2012) find that 

greater women’s representation in local government in India led to increased reporting of 

crimes against women. Additionally, Ghani, Mani, and O’Connell (2013) find that greater 

women’s political representation in India is associated with increased female labor force 

participation.

Building Credit

3 According to the World Bank, “Women, Business, and the Law (WBL) measures gender inequality in the law. The 

dataset identifies barriers to women’s economic participation and encourages the reform of discriminatory 

laws.” For more information and the database, see https://wbl.worldbank.org/.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 8

Ensuring women’s equal access to credit and capital to start and grow their businesses is 

critical to promoting inclusive economic growth. Empowering women entrepreneurs can also 

benefit the overall economy. Cuberes and Teignier (2016) estimate that excluding women 

from entrepreneurship reduces the average output per worker in the economy by 12 percent, 

and a decrease in the average quality of entrepreneurs drives this reduction. Research also

shows that increasing women’s financial inclusion strengthens their economic security, with 

positive spillover effects for their children.

Giving women access and control over a bank account has been found to have positive 

effects in a number of studies. Field and others (2019) use a field experiment in India to 

determine that depositing women’s earnings into women-owned bank accounts rather than 

those owned by male heads of households increased the share of women classified as 

workers by 40 percent. This increase was particularly strong for women who had not 

previously worked and for women whose husbands disapprove of women working. A study in 

Chile finds that individuals (91 percent of whom were women) randomly assigned to receive 

access to no-fee savings accounts reduced their short-term debt by 20 percent and reduced 

consumption cutbacks associated with negative income shocks (Kast and Pomeranz 2014). 

Finally, in a field experiment in Nepal, women in impoverished communities who randomly 

received access to no-fee bank accounts increased their spending on education and nutrition

(Prina 2015). Follow-up research on the study participants suggests that financial access 

among these women also led to an increase in schooling for their daughters (Chiapa, Prina, 

and Parker 2015).

The overall evidence on access to credit for women entrepreneurs is more mixed, with many 

studies showing little to no effect, though the literature tends to focus on the effects of 

microcredit programs.4 Many microcredit programs target women because women are less 

likely to have access to formal credit. De Mel, McKenzie, and Woodruff (2009) study cash 

grants to businesses in Sri Lanka. They find that the grants led to profit increases in maleowned businesses but not for women-owned businesses. Based on a microcredit field 

experiment in India, Banerjee and others (2015) find a small effect on profits of existing 

businesses but no significant effect on health, education, or women’s empowerment.

Attanasio and others (2015) find modest positive effects from a microcredit program in 

Mongolia on the creation (but not profits) of women-owned businesses.

Owning and Managing Property

A large body of research shows that women having legal authority to own and manage their 

property plays an important role in overall economic development. The studies by Udry 

4 Although the research on microcredit programs is mixed, it is important to note that the W-GDP Initiative 

supports credit for women-owned enterprises more broadly, including small and medium-sized businesses.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 9

(1996) and Goldstein and Udry (2008) mentioned above show that agricultural output can 

suffer as the result of weak property rights for women. Women have also been found to make 

different decisions when deciding how to allocate household resources. Giving women 

greater control over household spending decisions has been found to promote overall 

economic growth—if women are more likely to invest in the human capital of their children. 

Using data on Mexico’s Progresa, a conditional cash transfer program, Doepke and Tertilt 

(2009) find that an increase in women’s share of household incomes leads to increased

expenditures on children.

Geddes and Lueck (2002) find an association between the expansion of women’s selfownership rights and property rights and increases in the wealth and growth of cities in 

historical U.S. data. Geddes, Lueck, and Tennyson (2012) use historical U.S. data and find that 

extending property rights to women increases school attendance for girls, particularly those 

age 15 to 19 years. Deininger, Goyal, and Nagarajan (2013) find that allowing women to inherit 

property in India led to increased women’s education.

Traveling Freely

Among the five categories of laws addressed by the W-GDP Initiative, there is the least 

economic research covering the effects of removing legal restrictions on women’s travel.5

However, there is some evidence that improving access to transportation improves women’s

outcomes. Removing legal restrictions on travel may also be a necessary condition for 

women to fully benefit from expanded legal protections in the other four categories of laws. 

Martinez and others (2019) find that women who received access to public transit as the 

result of the expansion of the transit system in Lima had an increase in employment of 8 to 16 

percent and an increase in hourly earnings of 12 to 23 percent. Muralidharan and Prakash 

(2017) find that giving bicycles to girls who remained in secondary school increased women’s

secondary school enrollment by 32 percent in a state in India. 

Removing Restrictions on Employment

Many research papers find negative effects on the overall economy from the employment gap 

between men and women and legal restrictions on women participating in the labor force.

Woetzel and others (2015) estimate that fully closing the employment gap could increase 

global GDP by up to $28 trillion. Under the more conservative estimated scenario, where each 

country is assumed to match the improvement of the best-performing country in the region, 

global GDP would increase by $12 trillion. Esteve-Volart (2004) finds that a 10 percent 

 

5 Note that the W-GDP Initiative focuses in particular on women’s ability to travel freely from their homes and 

across international borders, including women’s ability to obtain passports. However, to the best of our 

knowledge, the effects of these restrictions have not been studied in the economics literature.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 10

increase in the female-to-male worker ratio would increase total output per capita by 8 

percent in India, and a 10 percent increase in the female-to-male managers ratio would 

increase this measure by 2 percent. Klasen and Lamanna (2009) use a panel regression of 

countries from 1960 through 2000 to estimate the lost output due to gaps between men and 

women in employment and education. They find that the Middle East and North Africa have 

lost 0.9 to 1.7 percentage points and 0.1 to 1.6 percentage points of growth, respectively, 

compared with East Asia due to their gaps between men and women. Cavalcanti and Tavares 

(2016) find that barriers to female labor force participation can have large macroeconomic 

effects. They estimate that a 50 percent increase in the wage gap between men and women 

reduces steady-state per capita GDP by 35 percent.

Restrictions on the types of occupations women can enter can lead to a misallocation of 

talent. Hsieh and others (2019) argue that discrimination causes misallocation of talent to 

different occupations. They find that the reduction in discrimination against African 

Americans and women from 1960 to 2010 was responsible for between 20 and 40 percent of 

the increase in aggregate market output per person in the United States. Mulligan and 

Rubinstein (2005) estimate that a large fraction of the GDP growth in the United States since 

1973 has been due to the increase in the human capital supply of women.

Country-Specific Data Measures

In this section, we develop country-specific data measures of women’s legal economic 

freedom for each of the five categories that are targeted by the W-GDP Initiative. These 

measures are combined to form the single W-GDP Index, which measures women’s economic 

freedom. The W-GDP Index quantifies prior legal reforms affecting women’s economic 

freedom and can be used to track the progress of the W-GDP Initiative in removing barriers to 

equal economic opportunity for women.

The World Bank’s Women, Business, and the Law Report

The following data are from the World Bank’s Women, Business, and the Law report, which

report measures legal barriers to women’s participation in the economy relative to men’s in 

eight different categories. The 2020 WBL report included 12 years of country-level data for 

2009 to 2020.

6 The questions ask about legal conditions in the year preceding the report year

in the largest business city in each economy, so the data years are 2008 to 2019. The WBL

constructs indices for each of the eight categories; these indices are the percentage of the 

measures within each category where women have the same legal rights and protections as 

men. The WBL only considers de jure legal rights and restrictions and does not include de 

 

6 The 2020 report includes 50 years of data, but the full 50 year data set has not yet been released.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 11

facto restrictions such as cultural practices. The overall WBL Index is the average of the eight

category indices.

We use 11 years of the WBL data through 2018, because economic data like GDP are not yet 

available for 2019. The remaining data used in this report come from the World Bank’s World 

Development Indicators database.

7

W-GDP measures and the W-GDP Index

The WBL’s eight categories differ from the five W-GDP Initiative categories, so a crosswalk is 

needed to map the WBL measures to the W-GDP Initiative’s categories. The WBL is broader 

than the five foundational categories in the W-GDP Initiative, so some of the WBL measures do 

not map to any W-GDP category. The W-GDP measures provide an empirical framework for 

measuring progress in removing legal barriers to women’s participation in the economy.

Table 1 presents the WBL measures that are used to calculate the W-GDP measures. The WGDP category indices are defined as the percentage of “yes” answers to the WBL measures 

that are contained within the category. These form the five W-GDP sub-indices: Travel Index, 

Employment Index, Institutions Index, Credit Index, and Property Index. The overall W-GDP 

Index is the average of these five sub-Indices.

8 All the indices can range from 0 to 100, where 

100 represents women being fully empowered as it relates to the law. The Travel and 

Property indices correspond to World Bank indices (“Mobility” and “Assets”). The 

Employment, Institutions, and Credit indices do not correspond to a single World Bank index 

but are constructed from components of one or multiple indices.

 

7 For the World Bank’s World Development Indicators database, see http://datatopics.worldbank.org/worlddevelopment-indicators/.

8 We follow the WBL by using equal weighting of the measures within category, and equally weighting the 

category indices when forming the overall index. This could create bias in the index if certain measures are more 

important than others. 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 12

Table 1. Crosswalk from the World Bank’s Women, Business, and the 

Law Measures to the W-GDP Measures

W-GDP measure WBL measure

Travel Can a woman apply for a passport in the same way as a man?

Can a woman travel outside the country in the same way as a man?

Can a woman travel outside her home in the same way as a man?

Can a woman choose where to live in the same way as a man?

Employment Can a woman get a job or pursue a trade or profession in the same way as a man?

Does the law prohibit discrimination based on gender in employment?

Can women work the same night hours as men?

Can women work in jobs deemed dangerous in the same way as men?

Are women able to work in the same industries as men?

Is dismissal of pregnant workers prohibited?

Institutions Can a woman be head of household in the same way as a man?

Can a woman sign a contract in the same way as a man?

Credit Can a woman register a business in the same way as a man?

Can a woman open a bank account in the same way as a man?

Does the law prohibit discrimination in access to credit based on gender?

Property Do men and married women have equal ownership rights to immovable property?

Do sons and daughters have equal rights to inherit assets from their parents?

Do female and male surviving spouses have equal rights to inherit assets?

Does the law grant spouses equal administrative authority over assets during 

marriage?

Does the law provide for the valuation of nonmonetary contributions?

Sources: White House; World Bank.

Summary Statistics and Changes over Time

We omit from the sample any country with missing W-GDP Index data or per capita GDP data 

in any of the 11 years. This leaves a sample of 174 countries, with 11 observations per country. 

Figure 2 shows the change in the distribution of the overall W-GDP Index from the start of the 

sample in 2008 to its end in 2018. The W-GDP Index scores tend to be bunched toward the top 

of the range, and this feature has become more pronounced over time. In 2008, there were 25 

countries with a W-GDP Index of 100; by 2018, the number of countries with a W-GDP Index of 

100 had nearly doubled, to 47.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 13

Table 2 shows the number of countries that had an increase, decrease, or no change in each 

of these indices from 2008 to 2018. The majority of countries have no change for each of the 

indices over this period, with the Property, Travel, and Institution indices having an 

overwhelming majority of countries with no change (greater than 90 percent). Changes in the 

indices are almost always positive, as only one country had a decrease in the W-GDP Index 

during these 11 years. There were 62 countries with a positive change in their W-GDP Index, 

and the majority of the countries (about 64 percent) had no change in the W-GDP Index.

Table 2. Number of Countries That Experienced a Change or No 

Change in Various Indices from 2008 to 2018

Change (2008–18) W-GDP Travel Employment Institutions Credit Property

Positive 62 10 37 7 26 8

No change 111 163 137 167 148 166

Negative 1 1 0 0 0 0

Sources: World Bank; CEA calculations.

3.3 2.8

8.3

5.5 5.6 5.6 7.2 7.9

18.2

12.9

30.4

28.1 27.1

37.1

0

10

20

30

40

2008 2018

31 to 40 41 to 50 51 to 60 61 to 70 71 to 80 81 to 90 91 to 100

Figure 2. Distribution of W-GDP Indices, 2008–18

Percentage of countries

Sources: World Bank; CEA calculations.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 14

Table 3 shows the average W-GDP Index across regions, weighted by country population in 

2008 and 2018. The Middle East and North Africa region has the lowest average W-GDP Index,

and the high-income countries that belong to the Organization for Economic Cooperation 

and Development (OECD) have the highest average W-GDP Index.

9 The change in the average 

W-GDP Index for all countries over this period is 1.03. Low-income countries generally saw the 

largest average increase and high-income countries saw the smallest (many high-income 

countries started at or close to the maximum). The Latin America and the Caribbean region

had the largest increase in the average W-GDP Index over this period, though all regions 

showed some improvement in their average W-GDP Index.

Table 3. Average W-GDP Index by Region in 2008 and 2018

Region Number of countries 2008 2018

High-income: OECD 32 96.54 (5.87) 97.58 (5.25)

East Asia and Pacific 23 84.86 (5.55) 85.15 (5.73)

Europe and Central Asia 22 87.87 (3.03) 89.55 (3.66)

Latin America and Caribbean 29 89.82 (5.30) 94.10 (5.09)

Middle East and North Africa 16 52.03 (15.08) 52.37 (14.43)

South Asia 8 75.18 (8.80) 77.60 (10.20)

Sub-Saharan Africa 44 73.47 (16.77) 75.65 (16.22)

All countries 174 82.27 (13.22) 83.31 (13.64)

Sources: World Bank; CEA calculations.

Note: OECD = Organization for Economic Cooperation and Development. Average values are 

weighted by population. Standard deviations are in parentheses.

 

9 The high-income OECD region is composed of OECD member countries that are classified as high-income 

countries by the World Bank. These countries do not appear in any of the other regions. 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 15

Figure 3 shows the change relative to the 2008 Index values by region. The average W-GDP 

Index for all the regions experienced very little change until 2013. In 2013 and 2014, there 

were relatively large increases in the average W-GDP Index for Latin America and the 

Caribbean. Sub-Saharan Africa saw a steady increase in the average W-GDP Index over the 

sample period. South Asia experienced a relatively large increases in 2018. Because the 

averages are weighted by population, changes in countries with larger populations will have 

a greater impact on the regional W-GDP Index.

Figure 4 shows the change in the average component W-GDP indices relative to the 2008 

global average values. The Employment and Credit indices experienced the largest relative 

increases over the period. The Institutions Index was largely unchanged over the period. The

Property and Travel indices experienced slight declines over the period. The decline in these 

indices was generally not due to countries becoming more restrictive, but were the result of 

population growth in countries with lower-than-average index values. Similar to figure 3, 

changes in the indexes of high-population countries can have a relatively large effect on the 

overall index.10

 

10 A change in the Employment Index in India is largely responsible for the 2018 increase in the Employment 

Index and the 2018 increase in the South Asia regional index.

98

100

102

104

106

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

East Asia and Pacific Europe and Central Asia High income: OECD

Latin America & Caribbean Middle East & North Africa South Asia

Sub-Saharan Africa Global

Figure 3. Change in WGDP Index, 2008–18

Index (2008=100)

Sources: World Bank; CEA calculations

Note: Data are weighted by population.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 16

Table 4 presents the summary statistics for the key variables over the period, weighted by 

each country’s population. The average W-GDP Index during the period is 82.54. Of the 

component indices, the Institutions Index is the highest, at an average of 94.88, followed by 

the Travel Index, at an average of 92.57, reflecting that many countries have no restrictions on 

access to institutions or travel for women. The average ratio of the female LFPR to the male 

LFPR is 63.7, indicating that the female LFPR is on average 63.7 percent as much as the male 

LFPR. The average per capita GDP across countries for the period is $11,754 in 2018 dollars.

Table 4. Summary Statistics, 2008–19

Variable

N 

(country years) Mean

Standard 

deviation Minimum Maximum

W-GDP Index 1914 82.54 13.51 31.33 100

Travel Index 1914 92.57 19.13 0 100

Employment Index 1914 68.78 22.79 0 100

Institutions Index 1914 94.88 15.19 0 100

Credit Index 1914 71.86 15.68 0 100

Property Index 1914 84.61 20.84 0 100

LFPR ratio 1815 63.71 23.01 8.45 103.8

Per capita GDP (2018 dollars) 1914 11,754 17,439 242.1 124702

Sources: World Bank; CEA calculations.

Note: LFPR = labor force participation rate. Average values are weighted by population. 

Standard deviations are in parentheses.

Travel

Employment

Institutions

Credit

Property

98

100

102

104

106

108

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Figure 4. Change in the W-GDP Component Indices, 2008–18

Index (2008 = 100)

Sources: World Bank; CEA calculations.

Note: Data are weighted by population.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 17

Estimating the Relationship between the W-GDP Index and 

Economic Output 

In this section, we quantify the relationship between women’s economic freedom in a 

country and real GDP per capita using a simple regression framework that provides us with 

plausible estimates. These estimates are used to predict the effect of removing all legal 

barriers that are targeted by the W-GDP Initiative. We also estimate the relationship between 

the W-GDP indices and other social and economic outcomes. Finally, we present results from 

calibrated macroeconomic models and find benefits of a similar magnitude as the regression 

model. Although these results are not causal, they are consistent with the extensive literature 

reviewed above in showing that expanding women’s economic freedom generally and 

removing legal barriers in these specific areas have significant economic benefits.

Simple Regression Models of Country per Capita GDP

We estimate the correlational relationship between per capita GDP and the W-GDP indices

using simple regression models. The basic model takes the form of

per capita GDPi,t = β ∗ Ii,t

WGDP + α ∗ Regioni + γ ∗ yeart + εi,t.

Per capita GDP for country i in year t is a function of the W-GDP Index in that year, as well as 

an indicator of a country’s region and an indicator of the year. The results tend to be similar 

using gross national income instead of GDP (not reported). The advantage of GDP is that it is 

available for more countries, while gross national income may be a better measure for 

smaller countries with a large foreign sector. We also present results using the LFPR ratio as 

the outcome variable.

Challenges to Causal Identification

Estimating the effect of legal restrictions (W-GDP Index) on economic output is challenging,

for a number of reasons. First, the W-GDP Index may be endogenous due to reverse causality 

(economic development leading to greater women’s economic rights). Even without 

addressing the endogeneity issue, the simple regression model would ideally estimate the 

within-country effect of changes in the W-GDP Index over time. As presented above, there is 

little within-country variation in the W-GDP Index during the period. This makes panel data 

estimation methods or even a model with country fixed effects unfeasible. Additionally, there 

is very little variation across countries for some of the component indices, which could 

generate misleading coefficient estimates.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 18

The basic model that we estimate recovers the correlations observed in the data and should 

be interpreted accordingly. We use these correlations to predict what would happen if the 

legal barriers to women’s economic participation were removed. Because the correlations 

primarily measure cross-country variation, this exercise should be interpreted as what would 

happen if highly restrictive countries were made to resemble less restrictive countries. In 

addition to the legal barriers, this could involve other differences between the countries that 

we are unable to control for in the estimation.

The Employment Index is the only index with sufficient within country variation during the 

sample period to estimate a model with country fixed effects. In addition to the main model, 

we also estimate a version of the model with country fixed effects using the Employment 

Index for the sample of countries that have a change in the Employment Index over the 

sample period.

Correlation between the W-GDP Index and GDP

Table 5 presents the regression result of the W-GDP Index on per capita GDP. An increase in 

the index by 1 is associated with an increase in per capita GDP of $63.48. This represents an 

increase of a little over 1 percent for the population-weighted median country. Some of the 

remaining correlation—after controlling for year and region between per capita GDP and the 

W-GDP Index—could be due to permanent, unobserved differences across countries within 

the same region.

Table 5. Results from Regressing Real Per Capita 

GDP on the W-GDP Index, 2008–18

Indicator Result

W-GDP Index 63.48***

(16.48)

Region indicators Yes

Year indicators Yes

Sources: World Bank; CEA calculations.

Note: Data are weighted by population. Robust standard errors are in parentheses. *p

< 0.1, **p < 0.05, ***p < 0.01.

Instead of estimating the model with the aggregate W-GDP Index, the individual component 

indices can be used. Given that the various indices likely interact in ways that are not 

captured by this simple regression model, along with the limited variation within some of the 

component indices, the results using the individual component indices should be interpreted 

with some caution. These results are presented in table 6. In terms of the component indices, 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 19

the Employment, Institutions, and Property indices have positive and significant coefficients. 

The Travel and Credit indices have negative and significant coefficients. The Travel Index’s

coefficient should be interpreted with some caution, given that most countries start with a 

perfect or near-perfect Travel Index. The benefits of freedom of movement also may not be 

readily apparent in the data because empowerment, along the other dimensions, may be a 

necessary condition for realizing the economic benefits of greater freedom of travel. Finally, 

the harm of travel restrictions may be most intense at low values of the index, and there were

very few countries with highly restrictive travel for women during this period.

Table 6. Results from Regressing Per Capita GDP on the W-GDP 

Component Indices, 2008–18

Variable Estimated coefficient

Travel Index

–93.21***

(11.69)

Employment Index

83.82***

(14.09)

Institutions Index

49.24***

(8.74)

Credit Index

–39.73***

(14.38)

Property Index

72.24***

(9.69)

Region indicators Yes

Year indicators Yes

Sources: World Bank; CEA calculations.

Note: Data are weighted by population. Robust standard errors are in parentheses. *p < 0.1, **p < 0.05, ***p <

0.01.

The Correlation between the W-GDP Index and other Economic Variables

Table 7 presents the results for different outcome variables. Looking at the relationship 

between the W-GDP Index and other social and economic outcomes shows that a higher WGDP Index is associated with a higher female LFPR relative to the male LFPR. An increase in 

the W-GDP Index is also associated with a higher female school enrollment relative to male 

enrollment.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 20

Table 7. Results from Regressing the Labor Force 

Participation Rate Ratio and School Attendance Ratio on 

the W-GDP Index in Separate Regressions, 2008–18

LFPR ratio (female/male) School attendance ratio (female/male)

W-GDP 

Index

0.332*** 0.343***

(0.039) (0.047)

Sources: World Bank; CEA calculations.

Note: LFPR = labor force participation rate. All regressions include region, year, and income group 

indicators. Data are weighted by population. Robust standard errors are in parentheses. *p < 0.1, 

**p < 0.05, ***p < 0.01.

The Economic Effect of Improving Women’s Empowerment

In this subsection, the simple regression models are used to predict the effect of eliminating 

legal restrictions for women. It is important to note that the results are not causal. Countries 

that have higher W-GDP indices and higher per capita GDP or female LFPRs could differ from 

countries with a lower W-GDP Index and lower per capita GDP or female LFPRs for reasons 

that are not captured in the regression. It could be due to country-specific factors that are 

constant over time, or to country-specific factors that tend to change along with the legal 

rights. 

One example of a country-specific factor that is likely to vary with women’s legal rights is

cultural attitudes toward women, and this factor could cause both the legal changes and the 

changes in economic outcomes. Changes in any unobserved factors would need to 

accompany the hypothetical legal changes in order for these predicted economic gains to be 

accurate. 

Because the correlations primarily capture correlations across countries, this numerical 

exercise does not isolate the effect of legal barriers. The results presented in this subsection 

should be interpreted as the predicted gains from making more restrictive countries more 

like less restrictive countries. Therefore, the estimates are likely an upper bound of the effect 

of the legal reforms themselves.

Simulating the female LFPR effect. Table 8 presents the predicted changes to the LFPR ratio

(the ratio of female LFPR to the male LFPR, times 100) from removing all legal barriers 

reflected in the W-GDP Index to women’s full participation in the economy implied by the 

correlations in the data. If all countries had a W-GDP Index of 100, female labor force 

participation relative to male participation could have been 5.55 percentage points higher in 

2018. If the male participation rate and female population remained unchanged, this implies 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 21

that there would be 5.55 additional female workers for every 100 male workers in the 

economy in 2018. The regions with the largest increase of female participation relative to 

male participation would be the Middle East and North Africa (+15.8 percentage points) 

followed by Sub-Saharan Africa (+8.1 percentage points) and South Asia (+7.4 percentage 

points).

11

Table 8. Predicted Changes to the LFPR Ratio from Removing 

Legal Barriers 

Region Average LFPR ratio

Predicted change in LFPR ratio if 

W-GDP Index = 100

High-income: OECD 79.94 0.80

East Asia and Pacific 77.22 4.94

Europe and Central Asia 69.80 3.47

Latin America and

Caribbean 67.42 1.96

Middle East and North 

Africa 27.72 15.83

South Asia 33.09 7.44

Sub-Saharan Africa 83.96 8.09

All Countries 64.00 5.55

Sources: World Bank; CEA calculations.

Note: LFPR = labor force participation rate. Average values are weighted by population. 

Simulating the GDP effect. In this subsection, we use the basic correlational estimates to 

evaluate the effect on per capita GDP of increasing women’s empowerment. These estimates

will likely capture the short term gains from deregulating women’s economic activity, but 

there could also be a longer term impact. For example, if women tend to invest more in 

health and human capital, that should lead to faster long-term growth; but the simple 

regression cannot capture this dynamic effect. Table 9 compares the average per capita GDP 

in 2018 by region with the predicted average per capita GDP if all countries had a W-GDP

Index of 100. Because countries can differ in ways other than their W-GDP Index for which the 

simple correlations are not able to control, these results do not isolate the effects of removing 

legal restrictions. Therefore, this effect should be interpreted as the change in per capita GDP 

that would occur if countries with restrictions on female economic activity more closely 

 

11 Sub-Saharan Africa has a relatively high level of relative female labor force participation because many 

countries are on the left of the U-shaped women’s relative LFPR observed in figure 1. In this case, economic 

development may lower relative female LFPR. A more flexible, functional-form specification in the regression 

model would be needed to capture nonlinearities in the relationship between relative women’s labor force 

participation and the W-GDP Index. 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 22

resembled countries without any of these restrictions. The per capita gain times the total

population gives the total benefit.

Table 9. Average per Capita GDP in 2018 by Region versus Predicted 

Average if the W-GDP Index = 100

Region

Mean per 

capita GDP 

(in 2018 

dollars)

Predicted change 

in per capita GDP 

if W-GDP Index = 

100

Total 

population 

in 2017 

(in billions)

Total benefit 

(in billions of 

2018 dollars)

High-income: OECD 52,060 153.32 1.090 167

East Asia and Pacific 7,813 943.00 2.068 1,950

Europe and Central Asia 11,068 663.53 0.401 266

Latin America and

Caribbean 10,471 374.80 0.581 218

Middle East and North Africa 8,715 3,023.76 0.336 1,015

South Asia 2,171 1,422.09 1.814 2,580

Sub-Saharan Africa 2,073 1,545.93 0.965 1,492

All countries 12,722 1,059.67 7.255 7,688

Sources: World Bank; CEA calculations.

Note: Average values are weighted by population. 

Based on this model, increasing the W-GDP Index to 100 for all countries would increase

global per capita GDP by $1,060 in 2018 dollars, or by about 8.3 percent. This represents an 

increase in global GDP of about $7.7 trillion. South Asia and East Asia and the Pacific would 

see the majority of these gains, due in part to their large populations. Sub-Saharan Africa and 

the Middle East and North Africa would also see large gains. The predicted increase in per 

capita GDP in Sub-Saharan Africa and South Asia represents an increase of more than 50 

percent from current levels.

Table 10 uses the regression results with the individual W-GDP Indices to estimate the effect 

of removing all legal barriers within a given category. Given the limited variation within some 

of the component indices, these estimates should be interpreted with caution. The largest 

potential gains come from the Employment Index, at nearly $17 trillion dollars, followed by 

the Property Index, at $8.3 trillion. The cumulative predicted effect of setting all the 

component indices to 100 would be an increase in global per capita GDP of $1,901 (2018

dollars), or 14.9 percent. This represents an increase in global GDP of about $13.8 trillion.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 23

Table 10. Simulated Change of Removing All Legal Barriers within Specific 

Categories

Effect of removing all restrictions on:

Predicted change in global per 

capita GDP 

(in 2018 dollars)

Total benefit 

(in billions of 2018 dollars)

Travel –702.5 –5,097

Employment 2,308.3 16,748

Accessing institutions 251.4 1,824

Accessing credit –1,094.0 –7,937

Owning and managing property 1,137.4 8,252

All 1,900.7 13,790

Sources: World Bank; CEA calculations.

Note: The total benefit is calculated as the predicted change in global per capita GDP times the total population in 2018 (7.255 

billion).

Model-based GDP effects. In addition to the GDP effects estimated using simple regression 

models, we also used a variety of macroeconomic models to predict the effect of removing all 

legal barriers to women’s participation in the economy. The models and methodology behind

these estimates are described in appendix A. By removing barriers that prevent women from 

becoming full participants in economic life, global economic output would increase as a 

result of improved productivity, a larger stock of productive capital, enhanced labor market 

participation, and improved educational outcomes. We estimate that fully removing these 

barriers would result in $4.9 trillion to $9.2 trillion in additional global economic output per 

year, depending on the time horizon. Long-run global economic growth rates would also 

increase by 0.41 percentage point per year (e.g., 3.91 percent vs. the current forecast of 3.5

percent).

The Effect of the Employment Index on the LFPR and GDP

As shown in the previous subsection, isolating the contribution of the individual component 

indices is challenging, due to the limited variation across countries and over time in some of 

the component indices. The component index with the greatest variation over the sample 

period is the Employment Index. In this subsection, we estimate the effect of changes in the 

Employment Index on the LFPR and GDP ratios for the set of countries where the 

Employment Index changed over the sample period. The within-country variation present in 

this index allows for the estimation of the model with country fixed effects. Unlike the results 

in the previous section, these results are identified by the within-country variation over time 

rather than the variation across countries. Therefore, these estimates more closely capture

the causal effect of changes in legal restrictions surrounding women’s employment (though 

the issue of potential reverse causality remains).
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 24

Regression results. Table 11 presents the regression results for the 37 countries that have a 

change in the Employment Index over the sample period. There is a strong, statistically 

significant relationship between the Employment Index and the LFPR ratio. The relationship 

between the Employment Index and per capita GDP is only marginally significant.

Table 11. Country Fixed-Effect Regression 

Results for the Employment Index

LFPR ratio Per capita GDP

Employment Index

0.101*** 

(0.019)

7.514* 

(4.467)

Country indicators Yes Yes

Year indicators Yes Yes

Sources: World Bank; CEA calculations.

Note: LFPR = labor force participation rate. Regressions are weighted by population. 

The sample is restricted to countries with a change in their Employment Index over 

the sample period. Robust standard errors are in parentheses. *p < 0.1, **p < 0.05, 

***p < 0.01.

The effect of removing legal barriers to employment. In Table 12 we use the coefficient 

estimates for the Employment Index to predict what would happen to the LFPR ratio and per 

capita GDP if all countries removed all legal barriers for women’s employment. We predict 

that removing all legal barriers to women’s employment would increase the global LFPR ratio 

by 2.77 percentage points and global per capita GDP by about $207. The overall increase in 

GDP would be about $1.5 trillion annually.

Table 12. Predicted Change to the LFPR Ratio and Per Capita GDP If All 

Countries Removed Legal Barriers to Women’s Employment

Region

Predicted change in 

LFPR ratio

Predicted change in per 

capita GDP Change in GDP (billions)

High-income: OECD 0.38 28.68 31.27

East Asia and Pacific 2.71 202.52 418.88

Europe and Central Asia 2.79 208.22 83.40

Latin American and Caribbean 0.70 52.63 30.59

Middle East and North Africa 6.16 460.20 154.53

South Asia 4.14 309.13 560.89

Sub-Saharan Africa 3.08 229.93 221.84

All countries 2.77 206.94 1,501.43

Sources: World Bank; CEA calculations.

Note: LFPR = labor force participation rate. Average values are weighted by population. 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 25

Conclusion

Our review of existing economic research indicates that removing legal barriers to women’s 

participation in the economy will generate substantial economic benefits. These barriers are 

targeted by the Trump Administration’s W-GDP Initiative. Some of these benefits will be 

relatively quick to appear as labor resources are more efficiently allocated in the economy. 

But perhaps more important, women’s economic empowerment increases human capital 

investment. Over the long term, greater human capital can help to drive the process of 

economic development and the transition from traditional to modern economies.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 26

Appendix A:

Macroeconomic Model-Based Estimate Methodology

This appendix describes the methodology behind the model-based estimates of the GDP 

effects of all legal barriers to women’s participation in the economy.

Methodology 1: Growth Accounting

Consider the following production function:

Yt = ZtKt

a

(htLt)

b

,

where Zt is total factor productivity (which implicitly includes other factor inputs), Kt is 

physical capital, ht is human capital per worker, Lt is labor, and a (b) is the capital (labor) 

share of aggregate income. The impact on output from changes to productivity, physical 

capital, human capital, and labor is given by

%∆Y

GDP = %∆ZGDP + a ∗ %∆KGDP + b ∗ %∆hGDP + b ∗ %∆LGDP

All that is required is to plug in country-specific values for a and b along with empirical 

estimates from the literature for the impact of the W-GDP Initiative on each of the ratios on 

the right. Unfortunately, reliable estimates are difficult to come by for the impact of W-GDP 

on physical capital, so this method only assesses the contribution of W-GDP to higher output 

through total factor productivity, human capital, and labor.

Methodology 2: Growth Models

Methodology 1 is atheoretical, in that it makes no claims about equilibrium relationships 

between At, Kt, ht, and Lt. In reality, a wide range of macroeconomic models predict that an 

increase in one variable often leads to a long-run positive response of the other variables. As 

a result, even if the data provide empirical estimates for the response of labor to the W-GDP 

Initiative but not the response of capital, it is possible to derive the equilibrium response of 

the other variables to arrive at the total impact on economic output.

A basic Solow model. Assume that Yt = ZKt

a

(AtLt)

1−a

, where At is labor-augmenting 

productivity and Lt = ENNtis the product of the employment-to-population ratio EN and the 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 27

working age population Nt. In this model, Z is constant, the population grows at rate n, and 

productivity grows at rate g, both of which are policy-insensitive parameters. Assume that the 

savings rate s is constant and the depreciation rate of capital is d. The model says that the

long-run growth rate is g + n and is therefore independent of policy. However, the W-GDP 

Initiative alters the level of output through changes to the savings rate s and the 

employment-to-population ratio:

Yt

WGDP

Yt

= (

Z

WGDP

Z

)

1

1−a

(

EN

WGDP

EN

) (

s

WGDP

s

)

a

1−a

The required inputs to this calculation are the country-specific value of a as well as the 

impact of W-GDP on total factor productivity, the employment-to-population ratio, and the 

savings rate.

Endogenous growth. Consider a simple version of the Romer model. Output is given by Yt =

AthLy, where At is technological progress, h is human capital, and LY is labor in the production 

sector. Technological progress is generated in the research-and-development (R&D) sector 

according to ∆At+1 = ZAthLa. Suppose labor Ly + La = ENN is divided between the 

production and R&D sectors according to La = θaENN and Ly = (1 − θa

)ENN. The resulting 

economic growth rate is gY = ZθahENN. Thus, the impact of the W-GDP Initiative on the 

growth rate through changes to Z, h, and E is given by

gY

WGDP

gY

= (

Z

WGDP

Z

) (

h

WGDP

h

) (

EN

WGDP

EN

)

Data sources and calculations.

Labor share, a:

Data come from the Penn World Table.

Human capital, h:
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 28

Our measure of human capital is average years of schooling from the Barro-Lee data set. To 

assess the impact of the W-GDP Initiative, we do the following:

1. Calculate the average schooling gap in countries with a current W-GDP Index 

above 80.

2. For each country with a W-GDP Index below 80, we rescale the average years of 

schooling for females such that the male/female gap is brought into line with the 

average for countries with a W-GDP above 80.

3. We then calculate the overall average years of schooling under the W-GDP 

Initiative’s reforms by taking a population-weighted average of the raw male years 

of schooling from the Barro-Lee data and the counterfactual female years of 

schooling constructed in the previous step.

4. The ratio of human capital h

WGDP/h is then the ratio of this newly constructed hGDP

and the overall average years of schooling in each country from the Barro-Lee 

data.

Labor, L:

Data on the labor force participation and employment-to-population rates come from the 

International Labor Organization’s database. To assess the impact of the W-GDP Initiative, we 

follow an analogous procedure to the one for human capital to arrive at counterfactual values 

of L in each country.

Total factor productivity, Z:

Using data from Sub-Saharan Africa, Udry (1996) finds that 6 percent of output is lost because 

of inefficient factor allocation within the household. By empowering women, the W-GDP 

Initiative can lead to efficient within-household bargaining and therefore eliminate this lost 

productivity. Thus, we model a 6 percent increase in Z from the W-GDP Initiative.

Savings rate, s:

Baseline savings rates come from the Penn World Table’s data on the share of gross capital 

formation. To assess the effect of women’s empowerment from the W-GDP Initiative on the 

savings rate, we look to two relevant papers from the academic literature. Hazan, Weiss, and 

Zoabi (2019) find that improving women’s property rights (by eliminating coverture) 
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 29

increases financial saving by anywhere from 1.0 to 6.3 percentage points (for a mean of 3.65).

Tertilt (2006) finds a larger increase, of 8 percentage points, in the savings rate from 

empowering women. Thus, the analysis here assumes that the W-GDP Initiative increases the 

savings rate by 5.83 percentage points (the average between 3.65 and 8) in countries with a 

current W-GDP Index score below 80.

Appendix B:

2019 W-GDP Index Values

Table A-1 presents the population-weighted W-GDP indices for 2019. These data are not used 

in the analysis, because 2019 economic data are not yet available.

Table A-1. Population-Weighted W-GDP Indices, 2019 

Measure Average Standard deviation

W-GDP Index 83.61 13.10

Travel Index 93.08 17.49

Employment Index 72.75 21.11

Institutions Index 95.19 14.81

Credit Index 72.63 16.43

Property Index 84.39 21.04

Sources: World Bank; CEA calculations.

Note: Values are weighted by population.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 30

Table A-2 presents the W-GDP indices by country for 2019.

Table A-2. W-GDP Indices by Country, 2019

Economy Region W-GDP

Index

Travel 

Index

Property

Index

Employment

Index

Institutions 

Index

Credit 

Index

Afghanistan South Asia 54.7 50.0 40.0 16.7 100.0 66.7

Angola Sub-Saharan Africa 93.3 100.0 100.0 66.7 100.0 100.0

Albania Europe and Central Asia 100.0 100.0 100.0 100.0 100.0 100.0

United Arab 

Emirates

Middle East and North 

Africa 63.0 25.0 40.0 83.3 100.0 66.7

Argentina Latin America & 

Caribbean 86.7 100.0 100.0 66.7 100.0 66.7

Armenia Europe and Central Asia 93.3 100.0 100.0 100.0 100.0 66.7

Antigua and 

Barbuda

Latin America and

Caribbean 81.0 75.0 80.0 83.3 100.0 66.7

Australia High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Austria High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Azerbaijan Europe and Central Asia 90.0 100.0 100.0 50.0 100.0 100.0

Burundi Sub-Saharan Africa 75.3 100.0 60.0 100.0 50.0 66.7

Belgium High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Benin Sub-Saharan Africa 72.7 50.0 80.0 66.7 100.0 66.7

Burkina Faso Sub-Saharan Africa 81.7 75.0 100.0 66.7 100.0 66.7

Bangladesh South Asia 68.0 100.0 40.0 33.3 100.0 66.7

Bulgaria Europe and Central Asia 100.0 100.0 100.0 100.0 100.0 100.0

Bahrain Middle East and North 

Africa 58.0 50.0 40.0 33.3 100.0 66.7

Bosnia and 

Herzegovina Europe and Central Asia 93.3 100.0 100.0 66.7 100.0 100.0

Belarus Europe and Central Asia 86.7 100.0 100.0 66.7 100.0 66.7

Belize Latin America and

Caribbean 81.7 75.0 100.0 66.7 100.0 66.7

Bolivia Latin America and

Caribbean 90.0 100.0 100.0 50.0 100.0 100.0

Brazil Latin America and

Caribbean 93.3 100.0 100.0 100.0 100.0 66.7

Brunei 

Darussalam East Asia and Pacific 68.7 50.0 60.0 66.7 100.0 66.7
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 31

Economy Region W-GDP

Index

Travel 

Index

Property 

Index

Employment

Index

Institutions 

Index

Credit 

Index

Bhutan South Asia 82.7 100.0 80.0 100.0 100.0 33.3

Botswana Sub-Saharan Africa 73.7 75.0 60.0 66.7 100.0 66.7

Central African 

Republic Sub-Saharan Africa 65.0 75.0 100.0 33.3 50.0 66.7

Canada High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Switzerland High-income: OECD 93.3 100.0 100.0 100.0 100.0 66.7

Chile High-income: OECD 75.3 100.0 60.0 100.0 50.0 66.7

China East Asia and Pacific 86.7 100.0 100.0 66.7 100.0 66.7

Côte d’Ivoire Sub-Saharan Africa 86.7 100.0 100.0 66.7 100.0 66.7

Cameroon Sub-Saharan Africa 45.3 50.0 60.0 33.3 50.0 33.3

Congo, Rep. Sub-Saharan Africa 52.0 50.0 60.0 33.3 50.0 66.7

Colombia Latin America and

Caribbean 90.0 100.0 100.0 83.3 100.0 66.7

Comoros Sub-Saharan Africa 63.0 75.0 40.0 83.3 50.0 66.7

Cabo Verde Sub-Saharan Africa 100.0 100.0 100.0 100.0 100.0 100.0

Costa Rica Latin America and

Caribbean 86.7 100.0 100.0 66.7 100.0 66.7

Cyprus Europe and Central Asia 91.7 75.0 100.0 83.3 100.0 100.0

Czech Republic High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Germany High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Dominica Latin America and

Caribbean 78.3 75.0 100.0 50.0 100.0 66.7

Denmark High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Dominican 

Republic

Latin America and

Caribbean 100.0 100.0 100.0 100.0 100.0 100.0

Algeria Middle East and North 

Africa 63.0 75.0 40.0 33.3 100.0 66.7

Ecuador Latin America and

Caribbean 93.3 100.0 100.0 100.0 100.0 66.7

Egypt, Arab Rep. Middle East and North 

Africa 48.0 50.0 40.0 33.3 50.0 66.7

Spain High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Estonia High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Ethiopia Sub-Saharan Africa 86.7 100.0 100.0 66.7 100.0 66.7
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 32

Economy Region W-GDP

Index

Travel 

Index

Property 

Index

Employment

Index

Institutions 

Index

Credit 

Index

Finland High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Fiji East Asia and Pacific 85.0 75.0 100.0 83.3 100.0 66.7

France High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Micronesia, Fed. 

Sts. East Asia and Pacific 78.7 100.0 60.0 66.7 100.0 66.7

Gabon Sub-Saharan Africa 48.7 50.0 60.0 50.0 50.0 33.3

United Kingdom High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Georgia Europe and Central Asia 100.0 100.0 100.0 100.0 100.0 100.0

Ghana Sub-Saharan Africa 86.0 100.0 80.0 83.3 100.0 66.7

Guinea Sub-Saharan Africa 67.0 75.0 60.0 50.0 50.0 100.0

Gambia, The Sub-Saharan Africa 85.3 100.0 60.0 100.0 100.0 66.7

Guinea-Bissau Sub-Saharan Africa 40.3 75.0 60.0 16.7 50.0 0.0

Equatorial Guinea Sub-Saharan Africa 53.7 75.0 60.0 83.3 50.0 0.0

Greece High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Grenada Latin America and

Caribbean 93.3 100.0 100.0 100.0 100.0 66.7

Guatemala Latin America and

Caribbean 86.7 100.0 100.0 66.7 100.0 66.7

Guyana Latin America and

Caribbean 95.0 75.0 100.0 100.0 100.0 100.0

Hong Kong SAR, 

China East Asia & Pacific 100.0 100.0 100.0 100.0 100.0 100.0

Honduras Latin America and

Caribbean 96.7 100.0 100.0 83.3 100.0 100.0

Croatia Europe and Central Asia 100.0 100.0 100.0 100.0 100.0 100.0

Haiti Latin America and

Caribbean 79.3 50.0 80.0 100.0 100.0 66.7

Hungary High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Indonesia East Asia and Pacific 75.3 100.0 60.0 100.0 50.0 66.7

India South Asia 82.7 100.0 80.0 66.7 100.0 66.7

Ireland High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Iraq Middle East and North 

Africa 46.3 25.0 40.0 50.0 50.0 66.7

Iceland High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 33

Economy Region W-GDP

Index

Travel 

Index

Property 

Index

Employment

Index

Institutions 

Index

Credit 

Index

Israel High-income: OECD 86.7 100.0 100.0 66.7 100.0 66.7

Italy High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Jamaica Latin America and

Caribbean 86.7 100.0 100.0 66.7 100.0 66.7

Jordan Middle East and North 

Africa 38.0 0.0 40.0 33.3 50.0 66.7

Japan High-income: OECD 90.0 100.0 100.0 83.3 100.0 66.7

Kazakhstan Europe and Central Asia 86.7 100.0 100.0 66.7 100.0 66.7

Kenya Sub-Saharan Africa 82.7 100.0 80.0 100.0 100.0 33.3

Kyrgyz Republic Europe and Central Asia 93.3 100.0 100.0 66.7 100.0 100.0

Cambodia East Asia and Pacific 100.0 100.0 100.0 100.0 100.0 100.0

Kiribati East Asia & Pacific 85.3 100.0 60.0 100.0 100.0 66.7

St. Kitts and Nevis Latin America and

Caribbean 82.7 100.0 80.0 66.7 100.0 66.7

Korea, Rep. High-income: OECD 86.7 100.0 100.0 66.7 100.0 66.7

Kuwait Middle East and North 

Africa 51.3 50.0 40.0 0.0 100.0 66.7

Lao PDR East Asia and Pacific 100.0 100.0 100.0 100.0 100.0 100.0

Lebanon Middle East and& North 

Africa 78.0 100.0 40.0 83.3 100.0 66.7

Liberia Sub-Saharan Africa 89.3 100.0 80.0 100.0 100.0 66.7

Libya Middle East & North Africa 63.0 75.0 40.0 83.3 50.0 66.7

St. Lucia Latin America &

Caribbean 88.3 75.0 100.0 100.0 100.0 66.7

Sri Lanka South Asia 79.3 100.0 80.0 50.0 100.0 66.7

Lesotho Sub-Saharan Africa 86.0 100.0 80.0 83.3 100.0 66.7

Lithuania Europe & Central Asia 100.0 100.0 100.0 100.0 100.0 100.0

Luxembourg High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Latvia Europe & Central Asia 100.0 100.0 100.0 100.0 100.0 100.0

Morocco Middle East & North Africa 81.3 100.0 40.0 66.7 100.0 100.0

Moldova Europe & Central Asia 100.0 100.0 100.0 100.0 100.0 100.0

Madagascar Sub-Saharan Africa 71.7 75.0 100.0 66.7 50.0 66.7

Maldives South Asia 88.0 100.0 40.0 100.0 100.0 100.0
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 34

Economy Region W-GDP

Index

Travel 

Index

Property 

Index

Employment

Index

Institutions 

Index

Credit 

Index

Mexico Latin America &

Caribbean 100.0 100.0 100.0 100.0 100.0 100.0

Marshall Islands East Asia & Pacific 70.7 100.0 20.0 66.7 100.0 66.7

North Macedonia Europe & Central Asia 96.7 100.0 100.0 83.3 100.0 100.0

Mali Sub-Saharan Africa 59.3 50.0 80.0 50.0 50.0 66.7

Malta Middle East & North Africa 96.7 100.0 100.0 83.3 100.0 100.0

Myanmar East Asia & Pacific 74.3 75.0 80.0 50.0 100.0 66.7

Montenegro Europe & Central Asia 90.0 100.0 100.0 50.0 100.0 100.0

Mongolia East Asia & Pacific 100.0 100.0 100.0 100.0 100.0 100.0

Mozambique Sub-Saharan Africa 90.0 100.0 100.0 83.3 100.0 66.7

Mauritania Sub-Saharan Africa 50.0 100.0 0.0 33.3 50.0 66.7

Mauritius Sub-Saharan Africa 100.0 100.0 100.0 100.0 100.0 100.0

Malawi Sub-Saharan Africa 83.3 50.0 100.0 100.0 100.0 66.7

Malaysia East Asia & Pacific 65.3 50.0 60.0 50.0 100.0 66.7

Namibia Sub-Saharan Africa 88.3 75.0 100.0 100.0 100.0 66.7

Niger Sub-Saharan Africa 49.0 75.0 20.0 66.7 50.0 33.3

Nigeria Sub-Saharan Africa 69.3 50.0 80.0 50.0 100.0 66.7

Nicaragua Latin America &

Caribbean 100.0 100.0 100.0 100.0 100.0 100.0

Netherlands High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Norway High-income: OECD 93.3 100.0 100.0 100.0 100.0 66.7

Nepal South Asia 78.0 100.0 40.0 83.3 100.0 66.7

New Zealand High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Oman Middle East & North Africa 41.3 0.0 40.0 50.0 50.0 66.7

Pakistan South Asia 56.3 75.0 40.0 33.3 100.0 33.3

Panama Latin America &

Caribbean 90.0 100.0 100.0 83.3 100.0 66.7

Peru Latin America &

Caribbean 100.0 100.0 100.0 100.0 100.0 100.0

Philippines East Asia & Pacific 87.0 75.0 60.0 100.0 100.0 100.0

Palau East Asia & Pacific 70.7 100.0 20.0 66.7 100.0 66.7
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 35

Economy Region W-GDP

Index

Travel 

Index

Property 

Index

Employment

Index

Institutions 

Index

Credit 

Index

Papua New 

Guinea East Asia & Pacific 74.3 75.0 80.0 50.0 100.0 66.7

Poland High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Puerto Rico 

(United States)

Latin America &

Caribbean 100.0 100.0 100.0 100.0 100.0 100.0

Portugal High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Paraguay Latin America &

Caribbean 100.0 100.0 100.0 100.0 100.0 100.0

Qatar Middle East & North Africa 53.0 25.0 40.0 33.3 100.0 66.7

Russian 

Federation Europe & Central Asia 86.7 100.0 100.0 66.7 100.0 66.7

Rwanda Sub-Saharan Africa 90.0 100.0 100.0 83.3 100.0 66.7

Saudi Arabia Middle East & North Africa 78.0 100.0 40.0 50.0 100.0 100.0

Sudan Sub-Saharan Africa 34.7 0.0 40.0 16.7 50.0 66.7

Senegal Sub-Saharan Africa 53.0 75.0 40.0 33.3 50.0 66.7

Singapore East Asia & Pacific 86.7 100.0 100.0 66.7 100.0 66.7

Solomon Islands East Asia & Pacific 71.0 75.0 80.0 33.3 100.0 66.7

Sierra Leone Sub-Saharan Africa 79.3 100.0 80.0 50.0 100.0 66.7

El Salvador Latin America &

Caribbean 100.0 100.0 100.0 100.0 100.0 100.0

Serbia Europe & Central Asia 100.0 100.0 100.0 100.0 100.0 100.0

São Tomé and 

Príncipe Sub-Saharan Africa 93.3 100.0 100.0 100.0 100.0 66.7

Suriname Latin America &

Caribbean 80.0 100.0 100.0 66.7 100.0 33.3

Slovak Republic High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Slovenia High-income: OECD 96.7 100.0 100.0 83.3 100.0 100.0

Sweden High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Eswatini Sub-Saharan Africa 45.3 100.0 60.0 66.7 0.0 0.0

Seychelles Sub-Saharan Africa 84.3 75.0 80.0 100.0 100.0 66.7

Chad Sub-Saharan Africa 53.7 75.0 60.0 50.0 50.0 33.3

Togo Sub-Saharan Africa 89.3 100.0 80.0 100.0 100.0 66.7

Thailand East Asia & Pacific 90.0 100.0 100.0 83.3 100.0 66.7

Tajikistan Europe & Central Asia 93.3 100.0 100.0 66.7 100.0 100.0
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 36

Economy Region W-GDP

Index

Travel 

Index

Property 

Index

Employment

Index

Institutions 

Index

Credit 

Index

Tonga East Asia & Pacific 70.7 100.0 20.0 66.7 100.0 66.7

Trinidad and 

Tobago

Latin America &

Caribbean 91.7 75.0 100.0 83.3 100.0 100.0

Tunisia Middle East & North Africa 64.7 100.0 40.0 66.7 50.0 66.7

Turkey Europe Central Asia 90.0 100.0 100.0 83.3 100.0 66.7

Tanzania Sub-Saharan Africa 85.3 100.0 60.0 100.0 100.0 66.7

Uganda Sub-Saharan Africa 76.3 75.0 40.0 100.0 100.0 66.7

Ukraine Europe and Central Asia 90.0 100.0 100.0 50.0 100.0 100.0

Uruguay Latin America and

Caribbean 90.0 100.0 100.0 83.3 100.0 66.7

United States High-income: OECD 100.0 100.0 100.0 100.0 100.0 100.0

Uzbekistan Europe and Central Asia 90.0 100.0 100.0 83.3 100.0 66.7

St. Vincent and 

the Grenadines

Latin America and

Caribbean 81.7 75.0 100.0 66.7 100.0 66.7

Vietnam East Asia and Pacific 93.3 100.0 100.0 66.7 100.0 100.0

Vanuatu East Asia and Pacific 67.0 75.0 60.0 50.0 50.0 100.0

Samoa East Asia and Pacific 88.3 75.0 100.0 100.0 100.0 66.7

Yemen, Rep. Middle East and North 

Africa 43.0 25.0 40.0 33.3 50.0 66.7

South Africa Sub-Saharan Africa 100.0 100.0 100.0 100.0 100.0 100.0

Zambia Sub-Saharan Africa 91.0 75.0 80.0 100.0 100.0 100.0

Zimbabwe Sub-Saharan Africa 100.0 100.0 100.0 100.0 100.0 100.0

Sources: World Bank; CEA calculations.

Note: OECD = Organization for Economic Cooperation and Development.
CEA • The W-GDP Index: Empowering Women’s Economic Activity through Addressing Legal Barriers 37

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4.pdf.
ABOUT THE COUNCIL OF ECONOMIC ADVISERS

The Council of Economic Advisers, an agency within the Executive Office of 

the President, is charged with offering the President objective economic 

advice on the formulation of both domestic and international economic 

policy. The Council bases its recommendations and analysis on economic 

research and empirical evidence, using the best data available to support 

the President in setting our nation's economic policy.

www.whitehouse.gov/cea 

February 2020