metadata: authors: 0: author: Chiplunkar,Gaurav Anand
1: author: Kleineberg,Tatjana Karina
count: World
docna: 0: docna: Gender Barriers, Structural Transformation,
            and Economic Development
docty: Policy Research Working Paper
abstracts: cdata!: The representation and significance of
            women in the labor force have grown significantly over the
            past five decades around the globe. Using nationally
            representative data from more than 90 countries, this paper
            documents distinct gender patterns in employment transitions
            across both sectors and occupations during this period.
            Using a model of occupational and sectoral choice and
            focusing on six major economies, the paper finds that
            declining gender barriers &#x2014; defined as
            gender-specific distortions in employment and wages
            &#x2014; were a key driver of the observed rise in
            female labor force participation, expansion of the service
            sector, and increases in real GDP per capita from 1970 to
            2018, but with substantial variation across countries.
display_title: Gender Barriers, Structural
            Transformation, and Economic Development
listing_relative_url: /research/2025/03/34465194/gender-barriers-structural-transformation-economic-development
topicv3: Economic Growth m1326241 471,Structural Transformation for Growth
            m1326245 1089,Labor Force Participation m1504050 2746,Gender and Growth m1504464 3105
publishtoextweb_dt: 2025-03-10T00:00:00Z
dois: 10.1596/1813-9450-11083
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 67
author_keys: 0: author_key: 1837502
1: author_key: 1232204
model_extractions: Policy Research Working Paper 11083 Gender Barriers, Structural Transformation, and Economic Development _Gaurav Chiplunkar Tatjana Kleineberg_ Development Economics Development Research Group March 2025
Policy Research Working Paper 11083

# **Abstract**

The representation and significance of women in the labor force have grown significantly over the past five decades around the globe. Using nationally representative data from more than 90 countries, this paper documents distinct gender patterns in employment transitions across both sectors and occupations during this period. Using a model of occupational and sectoral choice and focusing on six major economies, the paper finds that declining gender barriers—defined as gender-specific distortions in employment and wages—were a key driver of the observed rise in female labor force participation, expansion of the service sector, and increases in real GDP per capita from 1970 to 2018, but with substantial variation across countries.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted attkleineberg@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Gender Barriers, Structural Transformation, and Economic Development<sup>∗</sup>

Gaurav Chiplunkar Tatjana Kleineberg University of Virginia World Bank

## **Keywords:** Economic Growth, Structural Transformation, Gender, Misallocation

> ∗We thank Claudia Olivetti for a very insightful discussion, as well as Julieta Caunedo, Cheryl Doss, Kevin Donovan, Doug Gollin, Charles Gottlieb, Michael Peters, Barbara Petrongolo, Markus Poschke, Tommaso Porzio, Todd Schoellman, and participants in conferences and seminars at the American University, Boston University, Columbia University, Cornell University, CREI, Dartmouth University, Northwestern University, the Richmond Federal Reserve, University of Virginia, the World Bank, the ASSA in San Antonio, the CEPR STEG Annual Workshop, the EFEG session of the NBER Summer Institute, NEUDC, SED, SEA Annual Conference, and WADES for helpful suggestions and comments. Facundo Ulivarri provided excellent research assistance. This project benefited from funding from the World Bank Research Support Budget (RSB). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Chiplunkar: Darden School of Business, University of Virginia, 100, Darden Blvd., Charlottesville, VA 22905. Email: ChiplunkarG@darden.virginia.edu. Kleineberg: Development Research Group, World Bank, 1818 H Street NW, Washington, DC 20433. Email: tkleineberg@worldbank.org.
# **1 Introduction**

The role of women in the labor force has undergone a profound transformation over the past century, evolving from a largely marginalized position (Boserup, 1975) to one of increasing representation, influence, and leadership in the global workforce (Goldin, 2024). In the 1970s, for instance, there were, on average, fewer than four women for every ten men in professional and managerial positions across a large cross-section of countries. By the 2010s, this number increased to more than eight women for every ten men. This growing representation of women in the labor market not only reflects progress toward gender equality, but also has significant implications for countries’ aggregate employment structures, productivity, and economic growth.<sup>1</sup> Over the same period, economies around the world have undergone fundamental structural changes, with employment transitioning from agriculture to manufacturing and services (Kuznets, 1973).

In this paper, we study the evolution of gendered labor market opportunities, economic growth, and structural change empirically and theoretically. First, we use micro-data from 91 countries at different stages of development and over five decades to document new evidence on the link between economic development and gendered labor market outcomes. We then propose a model to study these patterns. Our framework integrates two broad explanations for the observed evolution of gendered labor market outcomes and economic growth: first, economic mechanisms such as sector- or occupation-specific technological change, income effects in consumption, and improved education; and second, gender-specific distortions arising from discrimination and social norms. We structurally estimate our model to six major economies, which offer high-quality gender-disaggregated data on _both_ wages and employment over five decades. We then use the estimated model to study the effects of changing gender distortions on structural change and aggregate growth in these countries. We find that gender distortions are important and have declined in most countries. On average, the decline in gender distortions in our sample countries accounts for a substantial share of overall economic growth and reallocation of employment in the labor market.

We begin by providing new evidence on gendered employment patterns across occupations and sectors. Our analysis draws on censuses and nationally representative household and labor force surveys from 91 countries, spanning five decades (1970-2018) and covering over 300 country-years. Using these data, we first show that the canonical process of

> 1See Cuberes and Teignier (2014); Hsieh et al. (2019); Ranasinghe (2024a,b); Chiplunkar and Goldberg (2024).

2
– structural transformation employment transitions from agriculture to manufacturing – and services with economic development is primarily driven by men. Women follow a different pattern: at low levels of development, they exit agriculture and primarily transition out of the labor force. At higher levels of development, women re-enter the labor force, primarily in the service sector. Unlike men, women’s representation in manufacturing remains low across all development levels. We further document gender differences in occupational choices _within_ sectors, showing that men increasingly work in professional and industrial jobs in richer countries, while women’s employment (relative to men) expands faster in clerical jobs but slower in professional and industrial jobs.

In addition to gender employment gaps, we also analyze the evolution of gender wage gaps for a select set of countries which provide high-quality micro-data on hourly earnings. We find that wage gaps have narrowed over time across all occupation-sector pairs. However, women in the 2010s still earned, on average, only 70 cents for every dollar earned by men. Furthermore, we observe a weak correlation between gender wage – gaps and countries’ development levels in contrast to gender employment gaps that tend to decrease in richer countries. Additionally, gender employment and wage gaps do not necessarily correlate across occupation-sector pairs.

We then propose a model of occupational and sectoral choice that can speak to these empirical findings and that builds on Hsieh et al. (2019). The documented changes in gendered employment and wage patterns – both across countries and within countries over time – can be partly driven by previously studied economic channels, such as income effects in consumption, sector- or factor-specific technological change, or shifts in the supply of, or returns to human capital.<sup>2</sup> Specifically, technological change and income effects can increase labor demand in the “female-gendered” service sector, disproportionately benefiting women (Ngai and Petrongolo, 2017; Fan et al., 2023). Additionally, the narrowing of gender education gaps has expanded the supply of women’s human capital (Evans et al., 2021; Feng et al., 2023). On the other hand, the documented changes in gendered labor market outcomes can also be driven by a decline in barriers that women face in certain occupations and sectors, which would increase women’s _labor supply_ in them.<sup>3</sup> Our model enables us to disentangle the effects of economic mechanisms from those

> 2See for example Ngai and Pissarides (2007); Boppart (2014); Herrendorf et al. (2013, 2014); Comin et al. (2021); Porzio et al. (2022); Huneeus and Rogerson (2024).

> 3See Jayachandran (2021) for a review of social norms around women’s labor force participation and Hyland et al. (2020) for a review of legislative changes over time and across countries that improve women’s rights for fair remuneration and for accessing job opportunities.

3
of changing gender barriers. In the model, individuals differ by gender and ability, while occupation-sector pairs differ in wage rates per unit of human capital, returns to ability, and costs or barriers that men and women face when working in them. Individuals receive idiosyncratic preference shocks across occupation-sector pairs and then choose their occupation and sector. We model the option of not participating in the labor force as an additional choice, which we call the “home sector” (Hsieh et al., 2019; Ngai and Petrongolo, 2017). On the demand side, consumers have non-homothetic preferences, allowing them to shift consumption away from agriculture and toward services as their – income rises a key driver of structural transformation emphasized in the literature.<sup>4</sup> Wage rates per human capital unit in each occupation-sector and prices in each sector are endogenously determined in equilibrium.

Following Hsieh et al. (2019), we define two types of gender barriers: first, “gender norms” which capture the excess utility costs that women incur relative to men when working in a given occupation-sector pair; and second, female “wage discrimination”, which captures differences in remuneration that women receive _per human capital unit_ relative to men. In the absence of distortions, an efficient allocation of talent ensures that high-ability workers sort into sectors and occupations that offer the highest returns to their ability. However, gender barriers distort these choices, shifting workers away from the jobs where they have a comparative advantage. For instance, high-ability women may opt out of the labor market or work in clerical jobs with low returns to their ability rather than pursuing professional or managerial jobs with higher returns. Such distortions therefore lead to a misallocation of talent, reducing aggregate productivity and output.

– We estimate the model to a core sample of six large economies India, Indonesia, Brazil, Mexico, Canada and the United States – for which we have employment and hourly wage data spanning four to five decades. Gender barriers in each occupation-sector and country-year are quantified as “wedges” which ensure that our model equations precisely match the data on gender employment and wage gaps after accounting for economic fundamentals and individuals’ employment and consumption choices in equilibrium. For example, the model expresses the observed gender wage gap in each occupation-sector as a function of a model-implied gender gap in human capital and the wage discrimination women face relative to men. To measure gender barriers, we assume that men and women have the same productivity within each occupation-sector, conditional on their model-implied human capital levels.<sup>5</sup>

> 4See Boppart (2014); Herrendorf et al. (2014); Comin et al. (2021).

> 5In robustness checks, we relax this assumption and allow for gender-specific comparative advantage

4
Our estimates of gender barriers reveal three main findings. First, gender norms have declined significantly over time, particularly in the service sector and in professional, managerial, and clerical jobs. Second, wage discrimination has also declined across all sectors and occupations, but relatively less in professional jobs. Third, trends in gender barriers vary considerably across countries: they worsened in India, showed little improvement in Indonesia, and declined substantially in Brazil, Mexico, Canada, and the United States. As a validation, we show that our estimated gender barriers strongly correlate across countries and over time with empirically measured gender norms and labor market constraints.

Using our estimated model, we then quantify the contribution of changing gender barriers to countries’ structural transformation and economic growth over the past five decades, in the spirit of a growth accounting exercise. To do so, we simulate counterfactual employment and growth paths for each country while holding gender barriers in each occupation-sector fixed at their calibrated values from the 1970s but allowing all other variables, such as technologies and skill distributions, to evolve over time. We find that declining gender barriers had large effects on female labor force participation, structural transformation, and economic growth in all countries of our sample, except India. Averaging across countries, we find that changes in gender barriers between 1970 and the 2010s accounted for 40%-45% of employment growth and 20%-35% of output growth in the manufacturing and service sectors. Declining gender barriers particularly contributed to the rapid rise of the service sector, revealing a novel channel for explaining the premature deindustrialization observed in developing countries in recent decades (Rodrik, 2016; Huneeus and Rogerson, 2024). On average, changes in gender barriers accounted for 28% of observed real GDP per capita growth in our sample, with significant – heterogeneity across countries ranging from 10% in Indonesia and 28% in the United States to 40% in Canada and Mexico, and over 50% in Brazil. In contrast, worsening gender barriers in India imply that economic growth would have been higher if these barriers had not changed.

In a series of additional analyses, we examine the mechanisms and robustness of our findings. First, we show in our baseline model that declining gender norms had a larger effect on economic growth than reductions in wage discrimination. Second, we show that our aggregate results are similar in a model where workers sort into occupations and sectors based on idiosyncratic _talent_ shocks rather than _preference_ shocks, as in the baseline across occupation-sector pairs.

5
model. Despite similar aggregate effects, the relative importance of wage discrimination and gender norms reverses in a model with talent shocks, which aligns with the findings from Hsieh et al. (2019) and is further discussed in Section 7.2. Third, we show that nonhomothetic preferences _amplify_ the effects of declining gender barriers on aggregate and sectoral outcomes because falling barriers make economies richer, shifting consumption and labor demand away from agriculture and toward manufacturing and services. Lastly, we perform a robustness check that accounts for gender-specific comparative advantage across occupation-sector pairs, which we quantify from a benchmark economy – the – United States in 2015 which we assume to have no wage discrimination, following Lee (2024). After adjusting for these estimated gender productivity differences in all country-years, changes in gender barriers remain relevant, accounting for 21% of observed growth in GDP per capita, compared to 28% in the baseline.

**Related Literature.** A large literature documents gender disparities in the labor market, but only a few recent studies provide cross-country comparisons and examine the macroeconomic implications of gender roles. Cuberes and Teignier (2014), Albanesi et al. (2023), and Olivetti et al. (2024) provide excellent reviews. Several papers study the link between gender roles and structural transformation in the United States, including Ngai and Petrongolo (2017), Ngai et al. (2024), Moro et al. (2017), and Rendall (2018). Across countries, Gottlieb et al. (2023), Bridgman et al. (2018) and Bick et al. (2022) use rich time-use surveys to examine the implications of gender differences in hours worked. Our – quantitative approach which measures gender barriers as wedges and quantifies their – importance for aggregate outcomes aligns most closely with Hsieh et al. (2019), Lee (2024), and Chiplunkar and Goldberg (2024). Hsieh et al. (2019) study the aggregate implications of talent misallocation due to gender and racial barriers in the United States. They find, similar to us, that a reduction in barriers explains 20-40% of US economic growth over the last decades. Lee (2024) documents that gender barriers in poor countries are higher in non-agriculture sectors, also aligning with our findings.

While the literature on structural transformation emphasizes sectoral employment transitions, we further document transitions across occupations within sectors, with a focus on gender. We find that gender differences across sectors are more important in poorer countries, while occupational differences matter more in richer countries, in line with recent work by Caunedo et al. (2023) and Bandiera et al. (2022). We therefore develop one consistent framework to _jointly_ study the extensive margin of entering the labor force and the intensive margin of choosing a particular sector and 6
occupation. The model incorporates the drivers of employment choices that have been emphasized in the macro-labor and structural transformation literature, which include heterogeneity in workers’ ability and hence their comparative advantage, gender-specific barriers, differential returns to ability across occupation-sectors (Hsieh et al., 2019; Cassan et al., 2024), as well as non-homothetic demand, sector-specific technological change, and changing human capital supply (Ngai and Pissarides, 2007; Herrendorf et al., 2014; Boppart, 2014; Ngai and Petrongolo, 2017; Comin et al., 2021; Porzio et al., 2022; Feng et al., 2023).

The paper is organized as follows: Section 2 describes the data and provides new evidence on the link between economic development and gendered labor market outcomes. Section 3 presents the theoretical model and Section 4 describes the identification and model quantification. Sections 5 and 6 present the estimation results and the counterfactual simulations. Section 7 examines mechanisms and tests the robustness of our analysis. Section 8 concludes.

# **2 Empirical Facts**

## **2.1 Data**

**Data Sources and Sample Description.** We use data from the Integrated Public Use Microdata Series (IPUMS International, 2020), which provides harmonized individuallevel data on demographic and employment variables from nationally representative censuses, and household and labor force surveys for many countries and years. We extract employment information by occupation, sector, and gender for 91 countries and 305 country-years. The time coverage ranges from 1960 to 2020 and includes, on average, 3-4 rounds of data for each country. Online Appendix C shows that the data has good coverage over the time period and the entire development spectrum. We complement the IPUMS data with labor force surveys from the World Bank Global Labor Database (GLD) and the World Bank i2d2 database.<sup>6</sup>

– For our quantitative exercise, we use a core sample of six large economies India, – Indonesia, Mexico, Brazil, Canada, and the United States which additionally offer high-quality micro data on hours worked and compensation, enabling us to calculate workers’ hourly wages. In addition, these countries provide data for more than four

> 6A description of the database can be found at https://github.com/worldbank/gld.

7
decades, allowing us to study the process of structural transformation and the evolution of gendered labor market outcomes over a long period of time. The six economies span a wide income spectrum (Figure OA.7 in the Online Appendix) and cover around 25-30% of the world population. For Indonesia, we complement the IPUMS census data with the SAKERNAS labor force survey to extend the time coverage of wage data to 2018. For India, we use survey data from the National Sample Surveys (NSS), the Employment-Unemployment Survey (EUS), and the Periodic Labor Force Survey (PLFS) up to 2018.

**Sector and Occupation Classification.** We define three market sectors – agriculture, manufacturing, and services – by assigning the more detailed industry codes from IPUMS and the labor force surveys to these three categories. The categorization follows the literature (Herrendorf et al. (2013); Herrendorf and Schoellman (2018)) and is outlined in Online Appendix C, Table OA.6. We define a fourth sector as the “home sector” to which we attribute unemployed and inactive individuals, following Hsieh et al. (2019).

At the occupation level, we use 1-digit ISCO codes. We aggregate the top three ISCO codes, which results in the following seven occupation categories: (1) professionals, (2) clerks, (3) service workers, (4) skilled agricultural workers, (5) crafts and trades workers, (6) plant and machine operators, and (7) elementary occupation workers. To minimize measurement errors arising from small sample sizes in specific occupation-sector-gender cells, we impose two restrictions. First, we consider only two occupations within the agriculture sector: skilled agricultural workers and elementary occupations.<sup>7</sup> Second, we model the occupation of “skilled agricultural workers” only in the agriculture sector and we assign all workers in that occupation to the agriculture sector. More information is provided in Online Appendix C.

**Construction of the Data Set.** First, we calculate employment shares of men and women in each occupation-sector and country-year. We allocate part-time workers proportionally between the reported occupation-sector and the home sector based on their hours worked. This adjustment is particularly important because women are more likely to work part time. Second, we compute hourly earnings for men and women in each occupation-sector for the limited sample of countries that provide data on hours worked and earnings. Further details on the data cleaning and dataset construction are available in Online Appendix C.

> 7We attribute the small percentage of workers who work in different occupations in the agriculture sector proportionally to the same occupation in the other two sectors.

8
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shares in agriculture, while richer countries have lower agricultural employment shares and higher shares in manufacturing and market services. The share of men in the home sector (i.e., not in the labor force) is small and relatively stable across different levels of economic development.

Figure 1b shows different employment patterns for women. Similar to men, women’s employment shares in agriculture are high in poor countries and low in richer ones. However, in low- and middle-income countries, lower agricultural employment shares among women coincide with higher shares in the home sector, i.e., with low female labor force participation. In richer countries, female labor force participation is higher again, with most women working in the service sector.<sup>8</sup> Female employment shares in manufacturing remain consistently low across all levels of economic development.

These gender differences in sectoral employment suggest that higher female labor force participation might increase employment in services relative to manufacturing. Hence, changes in gender roles and rising female labor force participation over recent decades may have contributed to the premature deindustrialization and service-led growth observed in many developing and emerging countries (Rodrik, 2016; Fan et al., 2023; Huneeus and Rogerson, 2024). To further examine this possibility, Figures 1c and 1d present how changes in agricultural employment shares correlate with corresponding changes in manufacturing and services between the 1970s and 2010s, separately for men and women, and restricting the sample to low- and middle-income countries. For each country, we calculate changes in sectoral employment shares over time, conditional on labor force participation, using the first (1970-1990) and last (2000-2019) years of available data. Figure 1c shows that a 10 percentage-point (p.p.) decline in men’s agricultural employment share is, on average, associated with an increase of 4.4 p.p. in manufacturing and 5.7 p.p. in services. For women, the shift toward services is stronger with employment shares increasing by 2.4 p.p. in manufacturing and by 7.7 p.p. in services (Figure 1d).<sup>9</sup>

> 8Figure 1b confirms the U-shaped relationship between female labor force participation and economic development, which has been extensively studied in the literature (Goldin, 1995, 2024; Mammen and Paxson, 2000).

> 9Figure OA.1 in the Online Appendix shows that gender differences are more pronounced when including changes in labor force participation. For men, a 10 p.p. decline in the combined share of agricultural and home sector employment is, on average, associated with a 5 p.p. increase in manufacturing and services. For women, these numbers are 1.7 p.p. for manufacturing and 8.4 p.p. for services. Figure OA.2 in the Online Appendix additionally shows that the flattening of total manufacturing employment shares in Brazil and Mexico during the past decades is driven by women, while men’s employment shares in manufacturing continue to rise.

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More generally, sectors employ occupations in different proportions, creating a direct link between sectoral and occupational employment transitions. Figure OA.3 in the Online Appendix shows this link by plotting occupational employment shares separately _within_ the manufacturing and service sectors against countries’ GDP per capita. Overall, the service sector uses professional and clerical occupations more intensely at all development levels. The manufacturing sector is highly concentrated in trade and service occupations in poorer countries but diversifies across occupations in richer ones. Gender differences across occupations within these sectors are relatively small in poorer countries but increase in richer ones. The largest gender gap emerges in clerical occupations, where women account for the entire employment increase with development. Men and women both contribute to the rise in professional and managerial jobs in rich countries, but men remain 5-10 p.p. more likely to work in these occupations than women.

# **2.4 Gender Segregation across Sectors and Occupations**

Having documented gender differences in employment choices across sectors and occupations, we now use the Theil Information H-Index to systematically measure gender segregation across occupation-sector pairs in each country-year. Similar to the Gini coefficient, the H-Index measures segregation on a normalized scale between 0 and 1 by using the entropic “distance” between the observed empirical distribution and an ideal gender-equal distribution. A higher value indicates greater segregation, while zero indicates perfect gender equality. The index allows decomposing total gender segregation across occupation-sector pairs into two additive components, which separately measure gender differences _across_ sectors and across occupations _within_ sectors. Appendix A.1 formally defines the index and decomposition.

Figure 3a plots the segregation across occupation-sector pairs against countries’ real GDP per capita, pooling data across all countries and years. The figure shows an inverted U-shape relationship, indicating that gender segregation is lower in poor and rich countries but high in middle-income countries. Figure 3b plots the share of total gender segregation that is explained by differences across occupations _within_ sectors. This component is significantly higher in richer countries, where it accounts for up to 50% of overall segregation, compared to 20% in poorer countries.<sup>10</sup> This finding is consistent

> 10Changes in labor force participation are attributed to across-sector variation, as inactive individuals are assigned to the home sector, which consists of a single occupation. To assess the importance of the extensive employment margin, we also compute the H-index conditional on labor force participation. The results remain similar and the importance of the within-sector variation in richer countries becomes

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Table 1: Gender Employment and Wage Ratios

||Employm|ent Ratio|Wage|Ratio|
|---|---|---|---|---|
||1970-75|2010-18|1970-75|2010-18|
||(1)|(2)|(3)|(4)|
|**_Panel A. Sectors_**|||||
|Home|8.07|3.63|||
|Agriculture|0.27|0.27|0.56|0.73|
|Manufacturing|0.28|0.28|0.50|0.69|
|Services|0.65|0.93|0.54|0.71|
|**_Panel B. Occupations_**|||||
|Professional|0.33|0.82|0.58|0.70|
|Clerks|1.08|1.41|0.80|0.86|
|Craft, Trade, Service|0.39|0.57|0.46|0.67|
|Agricultural|0.25|0.24|0.56|0.72|
|Machine Op., Elementary|0.39|0.50|0.51|0.73|
|**_Panel C. Countries_**|||||
|India|||0.56|0.60|
|Indonesia|||0.54|0.73|
|Brazil|||0.57|0.72|
|Mexico|||0.65|0.75|
|Canada|||0.44|0.71|
|United States|||0.47|0.70|

_<u>Notes:</u>_ This table reports female-to-male employment ratios (Columns 1-2) and wage ratios (Columns 3-4) in the 1970s and 2010s. The ratios divide employment shares (average wages) of women in each occupation sector by those of men. Panels A-C report average ratios for sectors, occupations, and countries. Averages of employment (wage) ratios are weighted by the employment (income) of each occupation-sector.

countries, which decreased to 3.6 in the 2010s (Table 1, Panel A). Women remain most underrepresented in the agriculture and manufacturing sectors, which employ only 2.7 women for every 10 men, with little change over time. In contrast, women’s employment expanded rapidly in the service sector, increasing from 6.5 to 9.3 women for every 10 men over the past decades. Turning to occupations, Panel B of Table 1 shows that women were underrepresented in all occupations except clerical jobs in the 1970s. The largest catch-up occurred in professional and managerial jobs, where women’s employment increased from 3.3 to 8.2 for every 10 men. Women’s overrepresentation in clerical jobs grew further over time, rising from 11 to 14 women for every 10 men. Employment gaps in other 14
occupations improved over time, but remain large in the 2010s.

**Gender Wage Gaps over Time.** Table 1 further documents changes in gender wage gaps over time (columns 3-4). In the 1970s, women earned, on average, 50-56 cents for every dollar earned by men across all sectors. Wage gaps exhibited more variation across occupations in the 1970s with women earning 46 cents per dollar earned by men in craft, trade, and service occupations but 80 cents in clerical jobs. Gender wage gaps have narrowed significantly over time, but remain persistent in the 2010s, with women earning, on average, around 70 cents for every dollar earned by men in most sectors and occupations. Comparing wage gaps across countries shows little correlation with economic development (Panel C). In the 1970s, gender wage gaps across countries range from 0.44 to 0.65, which improves to 0.6-0.75 in the 2010s. Moreover, we find that the levels and trends of wage and employment ratios are not necessarily correlated across occupations-sectors. For instance, in the 2010s, women were better represented in the service sector than in agriculture or manufacturing, yet wage ratios remained similar across sectors (Panel A, Columns 2 and 4). Likewise, gender employment gaps in professional and clerical occupations narrow rapidly over time and with economic development, but their wage gaps remain significant at all development levels.<sup>11</sup>

# **2.6 Discussion and Model Implications**

Overall, our findings emphasize distinct gender patterns in labor force participation, occupational and sectoral choices, and wage gaps along the development spectrum and within countries over time. The documented narrowing of gender employment and wage gaps over time can be partly explained by well-studied economic drivers of structural transformation and economic growth, such as income effects in consumption, sectorspecific technological change, or rising educational attainment. For instance, technological change or income effects have increased the _labor demand_ in the predominantly female service sector, disproportionately benefiting women (Ngai and Petrongolo, 2017). However, gendered labor market outcomes also respond to changes in the barriers that women face in the labor market, such as gender norms or wage discrimination.

To disentangle the effects of economic forces from gender-specific barriers, and to assess their relative importance, we develop a quantitative model of occupational and

> 11Figure OA.4 in the Online Appendix plots each country’s employment and wage ratios by sector and occupation against GDP per capita. These figures thus illustrate changes in each gender gap both _within_ countries over time and _across_ countries with economic development.

15
sectoral choice that incorporates the above-mentioned mechanisms and can be taken to the data. The fact that gender differences in occupational choices are particularly important at higher levels of economic development underscores the need for a unified framework that incorporates labor force participation as well as occupational and sectoral choices to study the effects of gender roles on macroeconomic outcomes over long time periods and across countries with varying levels of economic development. Our model framework allows us: (i) to measure gender barriers for each occupation-sector and country-year by matching model equations to observed gender employment and wage gaps; and (ii) to implement counterfactual simulations which quantify the effects of changes in gender barriers on countries’ paths of structural transformation and economic development, in the spirit of a growth accounting exercise.

# **3 Model**

We now present the set-up of the occupational and sectoral choice model (Roy, 1951), solve individuals’ employment and consumption choices and firms’ production decisions, and define the equilibrium of the model.

## **3.1 Setup, Gender Barriers, and Preferences**

The economy consists of a mass _Ng_ of individuals of gender _g_ , who are either male _m_ or female _f_ . Each individual has an ability _z_ that is drawn from a gender specific ability distribution _Fg_ ( _z_ ). There are _O_ occupations and _J_ sectors. Occupation-sector pairs _oj_ differ in their returns to ability _z_ , denoted by _κojg_ , which can potentially vary across gender. Workers receive idiosyncratic preference shocks _νoj_<sup>_i_acrossalloccupation-sector</sup> pairs _oj_ .

**Gender Barriers.** Two gender-specific barriers can distort the efficient allocation of workers across occupation-sector pairs. First, each worker of gender _g_ incurs a utility cost _Aojg_ when working in a given occupation-sector _oj_ . This utility cost can capture general amenities that influence the desirability of working in the occupation-sector pair. However, systematic differences in utility costs between men and women likely indicate underlying gender norms or barriers. We therefore define the _excess_ utility cost that women incur relative to men when working in the same occupation-sector as a gender-specific barrier. Throughout the paper, we refer to this distortion as “gender

16
norms” and we formally define it as: _λoj_ = 1 _− Aojf /Aojm_ .<sup>12</sup> The second gender distortion arises if men and women earn a different wage rate _per human capital unit_ in the same occupation-sector pair. We refer to this wage differential as female “wage discrimination”, which we formally define as the percentage difference between gender-specific wage rates _wojg_ in each occupation-sector pair: _τoj_ = 1 _− wojf /wojm_ .<sup>13</sup>

**Consumer Preferences.** Individuals have non-homothetic CES preferences across agriculture, manufacturing, and service goods, allowing sectoral expenditure shares to change with income levels (Comin et al. (2021)).<sup>14</sup> Preferences for a consumption bundle _C_ over sectors _k_ = _{A, M, S}_ are defined by the following constraint:

where _σ >_ 0 is the standard CES elasticity of substitution across sectors, _εk_ is the non-homothetic elasticity of substitution, and _θk_ are sectoral preference shifters. Setting _εk_ = 1 _∀k_ corresponds to the standard homothetic CES preferences. Individuals maximize their utility subject to the budget constraint � _k_<sup>_p_</sup> _k_<sup>_C_</sup> _k_<sup>=</sup><sup>_I_</sup> _ojg_<sup>(</sup><sup>_z_).Theincomeearned</sup> by an individual of gender _g_ and ability _z_ who works in occupation-sector pair _oj_ is equal to _Iojg_ ( _z_ ) = _wojg_ exp( _zκojg_ ), where _wojg_ is the wage rate per human capital unit in _oj_ , which can vary across genders due to gender wage discrimination, and _κojg_ are occupation-sector-specific returns to ability.

Services are a CES composite of home and market services _S_ = _{hs, ms}_ , following Ngai and Petrongolo (2017) and Ngai et al. (2024) so that:

> 12Our model and employment data cannot distinguish whether gender differences in utility costs, _λoj_ , stem from intrinsic gender-specific preferences or from restrictive and coercive gender norms. For example, the underrepresentation of women relative to men in engineering jobs could reflect either a genuine preference differential (in the absence of constraints) or societal factors such as stigma, discouragement, or discrimination of women. Given this limitation, we restrict our analysis to a growth accounting exercise and we refrain from any normative welfare analysis, in line with the literature (Hsieh et al., 2019; Chiplunkar and Goldberg, 2024; Ranasinghe, 2024a; Cuberes and Teignier, 2014; Lee, 2024).

> 13In the baseline model, we assume that men and women with the same ability draw _z_ have the same productivity in each occupation-sector pair. Hence, we do not allow for any gender-specific comparative advantage across occupation-sector pairs. We relax this assumption in Section 7.4.

> 14Such income effects have been emphasized in the literature as an important driver of structural transformation, e.g., Herrendorf et al. (2013); Alder et al. (2022); Comin et al. (2021); Conte (2022); Fan et al. (2023).

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where _σs >_ 0 is the elasticity of substitution between home and market services, and _αs_ are the preference weights with<sup>�</sup> _s_<sup>_α_</sup> _s_<sup>= 1.</sup>

# **3.2 Consumption and Employment Choices**

**Consumption Choice across Sectoral Goods.** With non-homothetic preferences, individuals of gender _g_ and ability _z_ who work in occupation _o_ and sector _j_ spend the following share of their income on goods from sector _k_ :<sup>15</sup>

where the price index _Pojg_ ( _z_ ) for an individual with income _Iojg_ ( _z_ ) is implicitly defined: Appendix A.2 provides the proof. From Equation (2), the ratio of expenditure shares across sectors _k_ and _m_ is given by: Similar to homothetic CES preferences, the ratio of sectoral expenditure shares depends on preference shifters ( _θk/θm_ ), and a substitution effect that arises from relative sectoral prices ( _pk/pm_ ). With non-homothetic preferences, sectoral expenditure shares further depend on an income effect, whose importance is governed by the relative difference between the elasticities _εk −εm_ . Within the service composite, the respective consumption shares of home and market services are given by the standard CES expression: where _φS_ is the expenditure share on the entire service composite and _PS_ is the CES price index of the service composite (cf. Online Appendix B.1).

> 15 We denote the consumption sectors by _k_ and the sector of individuals’ employment by _j_ . Workers’ occupation-sector pair of employment _oj_ matters for workers’ income which affects their sectoral expenditure shares due to non-homothetic preferences.

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**Employment Choice Across Occupation-Sector Pairs.** The utility of a worker _i_ of gender _g_ and ability _z_ who works in an occupation-sector pair _oj_ is given by:

where _Aojg_ are the gender-specific utility costs that an individual of gender _g_ incurs when working in occupation-sector _oj_ , _νoj_<sup>_i_areidiosyncraticpreferenceshocksacross</sup> occupation-sector pairs, and _V_ ( _Iojg_ ( _z_ ) _, Pojg_ ( _z_ )) is the indirect utility from consumption, which equals real income _Iojg_ ( _z_ ) _/Pojg_ ( _z_ ). We assume that preference shocks _νoj_<sup>_i_arei.i.d.</sup> and follow a Fréchet distribution with dispersion parameter _σν_ . Under this assumption, the share of workers of gender _g_ and ability type _z_ working in occupation-sector pair _oj_ is equal to: Equation (6) shows that workers’ employment choices depend on the gender-specific utility cost _Aojg_ and the real income that they receive when working in a given occupation-sector.

# **3.3 Production and Equilibrium**

**Human Capital Supply:** We then aggregate across individuals’ employment choices to solve for the aggregate supply of human capital in each occupation-sector pair _oj_ :

which integrates over the human capital units of each ability _z_ and gender _g_ type, weighted by the probability that such a type chooses a given occupation-sector _oj_ .

**Production:** A representative firm in each sector _j_ produces output _Yj_ with a Cobb Douglas production function that differs in productivity _Bj_ and uses human capital from each occupation as inputs with expenditure shares _γoj_ . Firms’ profit maximization is then given by:

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where _pj_ are sectoral prices and _woj_ are the wage rates per human capital unit that equal workers’ marginal product. Following Hsieh et al. (2019), we assume that men are paid the undistorted wage rate _woj_ while women face wage discrimination _τoj_ that introduces a wedge between their effective wage rate and their marginal product so that _wojf_ = (1 _− τoj_ ) _woj_ .<sup>16</sup>

**Equilibrium:** The exogenous parameters of the model characterize preferences over sectoral consumption _{σ, σs, αs, {εj, θj}∀j}_ , production functions _{{Bj}∀j, {γoj, κojg}∀oj}_ , the dispersion of preference shocks across occupation-sector pairs { _σν_ }, the mean and standard deviation of gender-specific ability distributions { _μgz, σgz_ }, and gender barriers _{{τoj}∀oj, {Aojg}∀ojg}_ . Given these parameters, the equilibrium is defined in each country-year by a vector of sectoral prices and occupation-and-sector-specific wage rates _{{pj}∀j, {woj}∀oj}_ so that: (i) workers make optimal consumption and employment choices; (ii) firms in each sector hire human capital from each occupation to maximize profits; (iii) labor markets clear in each occupation-sector pair; and (iv) good markets clear in each sector.

# **4 Model Quantification**

To quantify our model parameters, we determine a set of parameters externally, either by fitting them directly to observed data moments, or by taking estimates from the literature. The remaining parameters are then obtained by fitting the model’s equilibrium conditions to selected data moments using a simulated method of moments approach in an iterative algorithm that is described in Appendix B. Table 2 defines all model parameters and summarizes how they are determined.

> 16This wage discrimination can be micro-founded, as in Hsieh et al. (2019), by assuming that entrepreneurs have a disutility _δoj_ for hiring women. In equilibrium, this disutility is then exactly compensated by the profits that entrepreneurs earn from paying women below their marginal product, so that entrepreneurs are on the margin indifferent between hiring men or women.

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Table 2: Model Parameters and Baseline Values

|**Sym. Defnition**|**Determination**|**Values**|
|---|---|---|
|**_Panel A. Preference Parameters_**|||
|_σ_<br>Elasticity of substitution across _j_|Comin et al. (2021)|0.25|
|_εj_<br>Income efect non-homoth. CES|Comin et al. (2021)|_{_0_._01_,_1_,_1_._3_}_|
|_θj_<br>Sectoral preference shifters|Calibrated across countries||
|_σν_<br>Dispersion of pref. shocks over _oj_|Hsieh et al. (2019)|2|
|_σs_<br>Elasticity of substitution b/w home<br>and market services|Ngai and Petrongolo (2017)|2.3|
|_αs_<br>Preference weight on home services|Ngai and Petrongolo (2017)|0.2|
|**_Panel B. Ability Distribution and Re_**|**_turns to Ability_**||
|_μgz_<br>Avg. ability _z_ by gender<br>_σgz_<br>S.D. of ability _z_ by gender<br>_κoj_<br>Returns to ability in _oj_|Avg. Years of Schooling<br>S.D. Schooling Yrs<br>Mincer Regressions||
|**_Panel C. Production Technology_**|||
|_γoj_<br>Expenditure shares on _Hoj_<br>_Bj_<br>Sectoral productivity of sector _j_|Wage Expenditure by _oj_<br>Zero proft conditions||
|**_Panel D. Gender Barriers_**|||
|_λoj_<br>Gender norms/excess utility cost|Gender Emp. Ratios||
|_τoj_<br>Female wage discrimination in _oj_|Gender Wage Ratios||

_<u>Notes:</u>_ This table defines all model parameters and describes how we determine them in our quantification exercise. The preference parameters listed in Panel A are constant across countries and time, but sectoral preference shifters _θj_ can vary across countries. All other parameters can vary across countries and years.

# **4.1 Externally Determined Parameters**

**Preference Parameters:** We set the dispersion of workers’ preference shocks across occupation-sector pairs to _σν_ = 2, following Hsieh et al. (2019) and Cassan et al. (2024). For the CES preferences over home and market services, we set the elasticity of substitution to _σs_ = 2 _._ 3, and the preference weight on home services to _αs_ = 0 _._ 2, following Ngai and Petrongolo (2017). For the non-homothetic CES preferences, we use estimates from Comin et al. (2021), setting the elasticity of substitution to _σ_ = 0 _._ 25 and the parameters that discipline income effects to _εj_ = _{_ 0 _._ 01 _,_ 1 _,_ 1 _._ 3 _}_ for _j_ = _{A, M, S}_ .<sup>17</sup> We assume preference parameters to be the same across countries and over time. Sectoral preference shifters _θj_

> 17As in Comin et al. (2021), we can only identify _εj_ relative to a base sector, so we normalize _εj_ for manufacturing to 1.

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can vary across countries and are estimated jointly with the remaining parameters as described in Section 4.2.

**Ability Distributions:** We assume that ability _z_ follows a normal distribution with a mean and variance that can vary across gender, countries, and over time, so that _z ∼ N_ ( _μgz, σgz_ ). We calibrate the mean and variance of each distribution to the observed distribution of years of schooling for each gender, country, and year.<sup>18</sup>

**Returns to Ability:** We estimate the returns to ability for each occupation-sector _κoj_ by estimating a Mincer regression, similar to Fan et al. (2023).<sup>19</sup> Recall that a worker of gender _g_ and ability _z_ who works in an occupation-sector pair _oj_ earns income equal to: _Iojg_ ( _z_ ) = _wojg ×_ exp( _zκoj_ ). To estimate _κoj_ for the six countries in our core sample, we take the log of this structural equation and use our individual-level data to estimate the following Mincerian wage regression separately for each country-year:

where Wage _i_ is individual _i_ ’s hourly income, YrsSchool _i_ are years of schooling, Exp _i_ is experience, and _αojg_ are occupation-sector-gender fixed effects that capture, among other things, the average wage rates in a given occupation-sector-gender cell. The coefficients ˆ _κoj_ estimates the returns to schooling for each occupation-sector pair. We assume no returns to education in the home sector, setting _κhome_ = 0.

# **4.2 Parameters Estimated within the Model**

The remaining parameters to be calibrated are sectoral preference shifters _{{θj}∀j}_ , sectoral productivity and occupational expenditure shares _{{Bj}∀j, {γoj}∀oj}_ , and gender barriers _{{τoj}∀oj, {Aojg}∀ojg}_ . We now describe the intuition of our quantification strategy, while Appendix B describes the numerical procedure and iterative algorithm.

> 18 We assume for now that the distribution of ability (or education) is exogenously determined and constant in our counterfactuals. This assumption can be relaxed by modeling education choices endogenously as a function of education costs and education returns. Holding the distribution constant in our counterfactuals provides a lower-bound for the importance of changes in gender barriers because declining gender barriers over time increased women’s return to education and therefore would have increased their incentives to invest in human capital.

> 19In the baseline calibration, we restrict the returns to education _κoj_ to be the same for men and women. We experimented with a model version in which the returns can vary by gender, estimating _κojg_ . Overall, we find little variation across gender in these parameters and our qualitative and quantitative results remain mostly unchanged in both versions.

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**Gender Norms (** _λoj_ **):** We infer the gender-specific utility cost of working in each occupation-sector _Aojg_ from the occupational and sectoral choices shown in Equation (6):

This equation shows that the share of individuals of gender _g_ who chooses to work in a given occupation-sector _oj_ depends on the real income (or indirect utility) that they receive and the utility cost they incur when working in _oj_ . Using Equation (6) and computing the real income term within our model, we can then infer _Aojg_ as a residual that exactly matches the observed employment shares by occupation, sector, and gender in each country-year. _Aojg_ are only identified _relative_ to other occupation-sector pairs for each gender _g_ , so we normalize _Ahome,g_ = 1 for men and women. For each gender, working in any other occupation-sector can incur either a utility cost or premium relative to working at home. Gender norms are then given by _λoj_ = 1 _− Aojf /Aojm_ , capturing the _excess_ costs that women incur relative to men when working in occupation-sector _oj_ relative to staying at home. For example, a value of _λoj_ = 0 _._ 2 indicates that women face an _additional_ cost equivalent to 20% of their real income in occupation-sector pair _oj_ compared to men. A value of _λoj_ = 0 implies gender parity in utility costs, while larger values indicate higher barriers for women relative to men.

**Wage Discrimination (** _τoj_ **):** For each _oj_ , the model expresses the average gender wage ratio as a function of a model-implied human capital ratio and wage discrimination:

where we use _wojf_ = (1 _− τoj_ ) _wojm_ and where _H ojg_ is the average human capital of workers with gender _g_ , who choose to work in the occupation-sector pair _oj_ . _H ojg_ is endogenously determined in equilibrium and depends on workers’ ability distributions _Fg_ ( _z_ ), occupation-sector-specific returns to human capital _κoj_ , and the extent to which workers sort across occupation-sector pairs based on their ability.<sup>20</sup> Using Equation (10) and computing the gender human capital gap within our model, we can then infer female

> 20As previously mentioned, we assume in the baseline calibration that men and women have the same productivity in each occupation-sector pair conditional on their ability _z_ , abstracting from any gender-specific comparative advantage across occupation-sector pairs. We relax this assumption in a robustness check in Section 7.4.

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wage discrimination _τoj_ as a wedge to exactly match the observed female-to-male wage ratios in each occupation, sector, country, and year.

**Inferring Gender Barriers:** Since average human capital by gender, occupation, and sector _H ojg_ and workers’ real income _Iojg/Pojg_ are jointly determined in equilibrium, we solve for them in an iterative algorithm, which simultaneously solves for equilibrium wage rates in each occupation-sector and sectoral prices. We impose no restrictions on the values of gender barriers across countries, time, or genders, allowing women, relative to men, to receive either a penalty or a premium in wages and utility when working in a given occupation-sector.

**Sectoral Preference Shifters, Prices, and Productivity:** In the joint estimation, we further quantify sectoral preference shifters _θj_ , productivities _Bj_ , and prices _pj_ to ensure that zero-profit conditions and goods market clearing conditions hold in each sector. These conditions require that consumers’ expenditure and wage bills equal firms’ revenue in each sector, so that _pjYj_ = _pjCj_ = _WageBillj_ =<sup>�</sup> _o_<sup>_w_</sup> _oj_<sup>_H_</sup> _oj_<sup>.</sup>

With non-homothetic CES preferences, sectoral expenditure shares for _j_ = _{A, M, S}_ depend on the product between sectoral preference shifters and sectoral prices, as shown in Equation (2). We therefore first solve for the joint term, which we denote as _ψj ≡ θjpj_<sup>1</sup><sup>_−σ_</sup> , by matching the model-implied sectoral expenditure to the empirically observed sectoral wage bill.<sup>21</sup> In a second step, we then separate preference shifters and prices by making the identifying assumption that preference shifters can vary across countries but not over time. Under this assumption, _changes_ in _ψj_ over time pin down the growth in sectoral prices. Without loss of generality, we normalize prices for sectors _j_ = _{A, M, S}_ to 1 in the first period of each country, which infers the values of preference shifters _θj_ for each country. To solve for the separate prices of home and market services within the service composite, we use Equation (5) which is the standard CES expression for sectoral expenditure shares.

In the estimation procedure, we further match the data on real GDP per capita growth rates for each country. To compute real income or GDP growth in our model, we compute a Fisher price index between the first and last year for each country and use it to deflate the model-implied income measure. In a final step, we infer the production parameters _Bj_ and _γoj_ from firms’ profit functions and first order conditions (cf. Equation 8). Appendix B provides more details on the numerical procedure and iterative algorithm.

> 21The calibration exactly matches sectoral wage bills in the data, because it exactly matches empirical employment shares and average wages in each occupation-sector _oj_ when inferring _Aojg_ and _wojg_ .

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# **5 Gender Barrier Estimates and Model Validation**

We now present our estimates of gender barriers, and show that the estimates correlate with empirical measures of gender norms and women’s legal rights across countries and over time.

**Gender Norms (** _λoj_ **).** Table 3 shows that gender norms have, on average, decreased across all sectors and occupations during the past decades (columns 1-2), especially in the service sector and in professional and clerical occupations. A comparison across countries in Panel C shows that gender norms have worsened in India, changed little in Indonesia, but improved substantially in the other countries in our sample. Brazil and Mexico exhibited very high gender norms in the 1970s, but have seen the largest improvements – over time consistent with rising female labor force participation in these countries. In the 2010s, gender norms show a clear negative correlation with economic development: they are highest in India, lower in Mexico, Brazil, and Indonesia, and significantly lower in Canada and the United States.

**Wage Discrimination (** _τoj_ **).** Table 3 shows that average wage discrimination has declined across all sectors, from around 45% in the 1970s to around 30% in the 2010s (Columns 3-4, Panel A). Levels and changes of wage discrimination vary substantially across occupations but are relatively similar across sectors (Panel A-B), highlighting the importance of occupations to study gender barriers in the labor market. In the 1970s, wage discrimination ranged from 52% in crafts and trade occupations, to 36% in professional jobs, and 15% in clerical jobs. Unlike gender norms, wage discrimination has shown only modest improvements in professional and clerical occupations. A comparison across countries in Panel C shows that wage discrimination increased slightly in India, but declined substantially in all other countries, reaching average levels of 25-30% in the 2010s.<sup>22</sup> Unlike gender norms, wage discrimination does not exhibit a clear correlation with economic development. Additionally, improvements in one gender barrier do not necessarily imply progress in the other one. For example, while Indonesia reduced wage discrimination by 14 p.p., gender norms remained largely unchanged.<sup>23</sup>

> 22The worsening of gender barriers in India is consistent with a large literature that studies India’s low and declining female labor force participation, along with the continued persistence of restrictive social norms, despite strong and rapid economic growth over the last decades. See recent work by Fletcher et al. (2017); Deshpande and Singh (2021); Deshpande and Kabeer (2024); Deshpande and Singh (2024).

> 23Figure OA.5 in the Online Appendix shows the evolution of gender barriers across countries and over time by plotting each country’s estimated gender norms and wage discrimination by sector and

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Table 3: Measures of Gender Norms and Female Wage Discrimination

||Gender|Norms|Wage Dis|crimination|
|---|---|---|---|---|
||1970-75|2010-18|1970-75|2010-18|
||(1)|(2)|(3)|(4)|
|**_Panel A. Sectors_**|||||
|Agriculture|0.73|0.67|0.42|0.29|
|Manufacturing|0.73|0.66|0.48|0.32|
|Services|0.63|0.38|0.43|0.29|
|**_Panel B. Occupations_**|||||
|Professional|0.73|0.40|0.36|0.31|
|Clerks|0.59|0.28|0.15|0.14|
|Craft, Trade, Service|0.66|0.54|0.52|0.34|
|Agricultural|0.73|0.69|0.42|0.29|
|Machine Op., Elementary|0.73|0.61|0.48|0.28|
|**_Panel C. Countries_**|||||
|India|0.70|0.77|0.34|0.37|
|Indonesia|0.50|0.48|0.41|0.27|
|Brazil|0.87|0.47|0.46|0.33|
|Mexico|0.81|0.57|0.34|0.25|
|Canada|0.64|0.26|0.56|0.30|
|United States|0.50|0.16|0.53|0.32|

_<u>Notes:</u>_ This table reports the gender norm _λoj_ and wage discrimination estimates in the 1970s and 2010s for the countries in our core sample. Panels A-C present average barriers by sector, occupation, and country. Averages are weighted by the total income in each occupation-sector and country-year.

**Model Validation.** As a validation, we examine whether our estimates of gender – – barriers which are inferred as structural residuals relate to measurable changes in social norms and women rights. To do so, we use the World Bank’s “Women, Business, and the Law” (WBL) database, which evaluates 35 aspects of countries’ legal code to construct indicators that measure gender equality in the labor market, at the workplace, and in the legal system over the past five decades and across 190 countries (World Bank, 2019; Hyland et al., 2020).

We merge the data sets at the country-year level and regress our estimates of wage ˆ discrimination _τojct_ or gender norms _λ_<sup>ˆ</sup> _ojct_ on the different indicators from the WBL data for each country-year, controlling for the log of GDP per capita.

occupation against GDP per capita.

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Table OA.1 in the Online Appendix shows a negative correlation between our wage discrimination estimates and measures of gender equality at the workplace, and between our gender norm estimates and WBL indicators related to household norms, which measure, for example, gender parity in heading a household and laws against domestic violence. Estimates of both gender barriers correlate negatively and significantly with the overall WBL index. These results indicate that our estimates capture meaningful information about measurable changes in underlying social and gender norms across countries and over time.

In terms of model fit, we find a strong correlation between our estimated gender barriers and their empirical targets in the data across occupations, sectors, countries and years, i.e., gender norms _λojct_ correlate with observed gender employment ratios; and wage discrimination _τojct_ with observed gender wage gaps (cf. Figure OA.6 in the Online Appendix).<sup>24</sup>

# **6 Effects of Changes in Gender Barriers**

We now use our estimated model to quantify the share of countries’ overall growth and structural transformation that can be attributed to changes in gender barriers over the past five decades. To do so, we simulate counterfactuals in which gender barriers _{τoj, λoj}_ are held fixed at their calibrated values from each country’s initial year, while – other parameters such as sector- and occupation-specific technological change { _γoj_ , _Bj}_ , gender-specific education distributions _{μzg_ , _σzg}_ , and returns to human capital _κoj_ – evolve according to their calibrated trajectories over time. We first fix both gender barriers at their initial levels and then gender norms and wage discrimination separately. We simulate counterfactual equilibrium paths for each country’s employment structure, sectoral output, wages, and real GDP. To quantify the share of observed growth explained by changes in gender barriers, we compute 1 _− g_ ˆ _x/gx_ , where _gx_ is the observed growth rate in the data, and _g_ ˆ _x_ is the counterfactual growth rate that we obtain when fixing gender barriers at their 1970s levels.

> 24 Since our quantification approach directly targets gender employment and wage gaps, any difference between these targets and our estimates stems from additional terms in the estimating equations, namely: real income in the case of gender norms, and human capital gaps in the case of wage discrimination.

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# **6.1 Effects on Sectoral Employment and Output**

**Employment Transitions across Sectors.** Figure 4 shows the average effects of changes in gender barriers on sectoral employment shares across the six countries of our core sample. The black bars show the average annual percentage point (p.p.) change in sectoral employment shares that we observe between the 1970s and 2010s in the data. Population share in the home and agriculture sectors declined respectively by an average of 0.14 p.p. and 0.36 p.p. per year (Figure 4a). In contrast, population shares in the manufacturing and service sectors increased respectively by 0.05 p.p. and 0.45 p.p. per year. Figure 4b shows that the reallocation from the home sector to market services was primarily driven by women, while changes in the agriculture and manufacturing sectors were smaller for women relative to men.

These transitions would have looked significantly different if gender barriers had not changed since the 1970s. Without changes in wage discrimination and gender norms (lightest gray bar on the right), the population share in the home sector would have _increased_ by 0.09 p.p. per year, which would have resulted in slower employment growth in manufacturing (0.03 p.p.) and services (0.24 p.p.), with minimal effects on the agriculture sector. It follows that changes in both gender barriers together account, on average, for approximately 40% of the observed changes in employment in manufacturing (1-0.03/0.05) and 45% in services. Hence, declining gender barriers significantly accelerated structural transformation in our sample countries by enabling women to transition from the home sector primarily into market services, and, to a lesser extent, into manufacturing, while leaving agricultural employment trends largely unchanged. These effects are primarily driven by changes in gender norms rather than changes in wage discrimination, as shown by the two darker gray bars in the middle.

The measures in Figure 4a represent the shares of the total working-age _population_ in each sector, including the share that is inactive and works in the home sector. Instead, the literature typically focuses on sectoral employment shares _conditional_ on labor force participation, which does not account for changes in labor force participation. Using this measure, Figure 4c shows that keeping gender barriers at their 1970s levels more than _doubles_ the growth in manufacturing employment shares, while slowing the growth in services by 16%. These findings imply that changes in gender barriers and women’s entry into the labor market over the past decades increased the absolute _number_ of workers in manufacturing but reduced its _employment share_ among active workers because the women who joined the labor market disproportionally entered the service sector. Hence, 28
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**Employment Transitions across Occupations.** Figure 4d documents how changes in gender barriers affected occupational employment shares over the past decades. In the data (black bars), rising female labor force participation and declining agricultural employment have coincided with employment growth in professional and managerial occupations (0.31 p.p. per year), machine-operating and elementary occupations (0.10 p.p.), and crafts, trade, and service occupations (0.07 p.p.). Overall, declining gender barriers account for a third of the observed growth in professional occupations, half for elementary occupations, and around 70% for craft, trade, and service occupations – – (lightest gray bars on the left). Clerical occupations where women are overrepresented would have experienced a steeper decrease if barriers had not changed. Effects are again primarily driven by changes in gender norms rather than changes in wage discrimination, as shown by the two darker gray bars in the middle.

Table 4: Change in Sectoral Output Explained by Gender Barriers

||Agriculture|Manufacturing|Services|
|---|---|---|---|
||(1)|(2)|(3)|
|India|-5.90|-3.59|-2.94|
|Indonesia|1.81|4.45|14.98|
|Brazil|34.45|44.80|61.64|
|Mexico|12.59|23.59|53.46|
|Canada|11.41|32.52|44.72|
|USA|16.50|16.85|32.75|
|Average|11.81|19.77|34.10|

_<u>Notes:</u>_ This table reports the share of sectoral output growth, which is explained by changes in ˆ gender barriers between the 1970s and 2010s. To calculate this share, we compute (1 _− g/g_ ), where _g_ represents the sectoral output growth from our baseline calibration, and _g_ ˆ is the counterfactual output growth obtained when both gender barriers ( _τojct_ and _λojct_ ) are fixed at their calibrated values from the 1970s. The last row presents a simple average across the six countries in our core sample.

**Effects on Sectoral Output.** In addition to the employment transitions, changing gender barriers also affected sectoral output growth.<sup>25</sup> Table 4 shows that changes in gender norms and wage discrimination account, on average, for 12% of real output growth in agriculture, 20% in manufacturing, and 34% in services, with substantial variation

> 25Unlike employment shares, our calibration does not exactly match sectoral output in each countryyear. Instead, the calibration targets nominal wage shares within each sector and adjusts sectoral prices to ensure market clearing in each sector. The reported effects on sectoral output therefore compare the values between our baseline calibration and counterfactuals.

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across our sample countries. A notable outlier is India, where sectoral output would – have been 3-6% higher had gender barriers not changed which is consistent with the worsening of gender barriers shown in Table 3. Among the other countries, we find the smallest impact on sectoral output growth in Indonesia and the largest in Brazil. Across sectors, reductions in gender barriers have contributed the most to output growth in the service sector (15-60% across countries), followed by manufacturing (5-45%), and the least in agriculture (2-34%).

# **6.2 Effects on Aggregate Income and Productivity**

Given the importance of changes in gender barriers for employment transitions and sectoral output, we now examine their contribution to aggregate earnings and GDP.

**Effects on Output and Productivity.** Table 5 shows that changes in gender barriers over the past decades explain, on average, 28% of the observed real GDP per capita growth in our sample countries. The effects vary considerably across countries, ranging from -5% in India to over 50% in Brazil.

Falling gender barriers increase GDP per capita through two channels: first, by increasing women’s labor supply (extensive margin), and second, by improving the allocation of talent within the labor market as workers improve their sorting into occupations and sectors based on their comparative advantage. The combined effect of these channels on – – workers’ productivity i.e., output or GDP _per market worker_ is theoretically ambiguous because an improved talent allocation raises average human capital per worker, but higher labor force participation lowers it because the marginal worker who enters the labor market is negatively selected on ability. Quantitatively, we find that changes in gender barriers account for 3% of real GDP growth _per worker_ (Column (2) of Table 5), indicating that the productivity gains from improved sorting marginally outweigh the negative selection effect from the extensive margin.

The effects are primarily driven by changes in gender norms, which account for an average of 24% of the observed real GDP per capita growth, while reductions in wage discrimination explain only 4% (see Table OA.2 in the Online Appendix). Changes in gender norms are more important because they declined most in occupations that offer high returns to ability, such as professional and clerical jobs, while wage discrimination has decreased more in occupations with lower returns to ability, as shown in Table 3.

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Table 5: Share of GDP and Earnings Growth Due to Changes in Gender Barriers

||Real <br>Per Capita|GDP<br>Per Worker|Real E<br>Men|arnings<br>Women|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|India|-4.93|-0.35|-2.91|-27.51|
|Indonesia|9.93|1.00|-5.07|56.47|
|Brazil|55.98|5.68|-15.36|96.81|
|Mexico|39.43|2.50|-20.96|85.14|
|Canada|41.45|2.39|-14.01|83.68|
|USA|27.81|6.00|-11.76|79.34|
|Average|28.28|2.87|-11.68|62.32|

_<u>Notes:</u>_ This table reports the share of real GDP and real earnings growth, which is explained by changes in gender barriers between the 1970s and 2010s. The shares are calculated as described in the notes of Table 4. The last row presents a simple average across the six countries of our core sample.

**Effects on Earnings for Men and Women.** Table 5 further shows that changes in gender barriers over the past decades reduced real wage growth by an average of 12% for men while increasing it by 62% for women (Columns 3-4). These results are driven by changes in the workforce composition and equilibrium effects: declining gender barriers increase women’s labor force participation and enable them to enter the occupations and sectors where they were most productive, including high-return jobs. In equilibrium, these changes increase the competition for men in high-return jobs, causing some lower-ability men to shift to lower-return occupation-sectors or to exit the labor force altogether. Table OA.2 in the Online Appendix shows that changes in gender norms alone account for nearly the entire effect on real earnings growth for men. For women, both gender barriers matter because declining wage discrimination directly raises women’s wage rates per human capital unit, in addition to affecting their employment choices.<sup>26</sup>

**Summary.** Our counterfactual analysis shows that changes in gender barriers have played an important role in driving structural transformation and GDP growth over the past decades in our sample countries, primarily by increasing female labor force participation and expanding employment in the service sector and in professional, trade, and service occupations. Comparing countries at different development levels, we find – – that changes in gender barriers had the smallest or even negative effects in the poorer

> 26The direct effects of lower wage discrimination matter for earnings but not for aggregate GDP because we attribute all discriminatory taxes on women to firms’ profits. Hence, effects on aggregate GDP are only driven by changes in employment choices.

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countries of our sample (India and Indonesia), and the largest effects in the middle-income countries (Brazil and Mexico). The richest countries (Canada and the United States) fall in between but still exhibit large effects.

# **7 Mechanisms and Robustness of Results**

We next perform a series of analyses and robustness checks to study specific mechanisms of our findings and to examine the sensitivity of our quantitative results.

## **7.1 Non-Homothetic Preferences and Income**

We first assess the importance of non-homothetic preferences for our findings by simplifying our baseline model to homothetic CES preferences, setting _εj_ = 1 _∀j_ in Equation (1). With homothetic CES preferences, the expenditure shares in sectors _j_ = _{A, M, S}_ simplify to _φj_ = _θj ×_ ( _pj/P_ )<sup>1</sup><sup>_−σ_</sup> , where _P_ is the standard CES price index and sectoral expenditure shares no longer depend on individuals’ income levels. We re-estimate all parameters and gender barriers in the homothetic model and implement the same counterfactuals.

Table 6 compares the results between the non-homothetic benchmark (column 1) and the homothetic model (column 2). We find that non-homothetic preferences amplify the effects of changing gender barriers, accounting for 28% of observed real GDP per capita growth, compared to 23% with homothetic preferences. In the non-homothetic model, changing gender barriers also explain a larger share of output growth in manufacturing (20% vs 16%) and services (34% vs 28%), while accounting for a similar share of agricultural output growth. Intuitively, declining gender barriers increase individuals’ income levels. Under non-homothetic preferences, these income gains shift expenditure shares away from agriculture and toward manufacturing and services, which amplifies the employment and output gains in these sectors.

## **7.2 Preference versus Talent Shocks**

We next analyze how our quantitative results change when workers receive idiosyncratic _talent_ shocks _νoj_<sup>_ti_acrossoccupation-sectorpairs(Roy,1951),insteadofthe</sup><sup>_preference_</sup> shocks _νoj_<sup>_i_usedinthebaselinemodel.Withpreferenceshocksaninefficientallocationof</sup> talent can emerge because workers differ in absolute ability _z_ and occupation-sector pairs differ in their returns to ability _κoj_ . This model setup captures a _vertical_ dimension of 33
Table 6: Importance of Non-Homothetic Preferences

|B|enchmark Model|Homothetic Demand|
|---|---|---|
||(1)|(2)|
|Real GDP per capita|28.28|22.73|
|Real GDP per worker|2.87|1.54|
|Agricultural Output|11.81|10.77|
|Manufacturing Output|19.77|15.92|
|Service Output|34.10|27.75|

_<u>Notes:</u>_ This table reports the share of real GDP and sectoral output growth, which is explained by changes in gender barriers between the 1970s and 2010s for the benchmark model with non-homothetic preferences (Column (1)) and a model with homothetic CES preferences (Colum (2)). The shares are calculated as described in the notes of Table 4. We average across the six countries in our core sample.

comparative advantage: high-ability _z_ types have an absolute advantage in all occupationsectors but a comparative advantage in occupation-sectors with higher returns to ability. Gender barriers can then distort the sorting of high-ability _z_ workers into high-return occupation-sectors. I.i.d. talent shocks across occupation-sector pairs instead represents a _horizontal_ dimension of comparative advantage. When combining absolute ability _z_ and i.i.d. talent shocks, workers’ comparative advantage in a given occupation-sector pair depends on the triplet _{νoj_<sup>_ti, zi, κoj}_.</sup> Using talent shocks, instead of preference shocks, adds complexity to the model with non-homothetic preferences because the talent shocks directly affect workers’ income in each occupation-sector pair. Under non-homothetic preferences, income levels further affect individuals’ sectoral expenditure shares, and therefore the relative weights that they assign to nominal earnings, sectoral prices, and occupational disutility _Aojg_ . These additional equilibrium interactions complicate the employment choice problem, which no longer admits the tractable, closed-form solution proposed in Equation (6). To avoid this complexity, we use a model with homothetic preferences to examine the effects of gender barriers when shocks across occupation-sector pairs are specified either as preference or talent shocks. For both models, we re-estimate all parameters and implement all counterfactuals. Section B.2 in the Online Appendix solves the model with talent shocks.

Table 7 shows that changes in gender barriers account for a similar share of the observed GDP per-capita growth with preference or talent shocks (Columns 1-2). The talent shock model, however, explains a larger share of GDP growth per worker (7.2% vs. 1.5%) because it provides two levers to improve the talent allocation of workers: first, by 34
Table 7: Impact of Preference vs Talent Shocks

||Both Barriers|Gender|Norms|Wage Di|scrimination|
|---|---|---|---|---|---|
||PreferenceTalent|Preference|Talent|Preferenc|eTalent|
||(1)<br>(2)|(3)|(4)|(5)|(6)|
|Real GDP per capita|22.73<br>21.78|14.10|-6.89|7.48|29.13|
|Real GDP per worker|1.54<br>7.17|2.82|-2.93|-1.20|9.39|

_<u>Notes:</u>_ This table reports the share of real GDP growth, which is explained by changes in gender barriers between the 1970s and 2010s, averaging across the six countries in our core sample. The shares are calculated as described in the notes of Table 4. The columns presents the growth contributions in models where workers receive either idiosyncratic _preference_ or _talent_ shocks across occupation-sector pairs. Columns (1) and (2) report contributions for both gender barriers, columns (3) and (4) for gender norms only, and columns (5) and (6) for wage discrimination only. Both models use homothetic CES preferences on the demand side.

improving the sorting of high-ability _z_ types into high-return occupation-sectors; and second, by improving the sorting of workers into occupation-sectors based on their talent shocks _ν_<sup>_ti_Thesecondchannelisabsentinthemodelwithpreferenceshocks.</sup> _oj_<sup>.</sup> Table 7 further shows that changes in _gender norms_ have larger effects on aggregate growth in the model with preference shocks (Columns 3-4), whereas changes in _wage discrimination_ matter more in the model with talent shocks (Columns 5-6). To understand this result, it is useful to examine how each model links workers’ occupation-sector choices to groups’ average human capital across occupation-sector pairs. In the model with preference shocks, a group’s average ability _z_ ̄ _oj_<sup>_g_isdecreasinginthegroup’semployment</sup> share _πoj|gz_ only in _high-return_ occupation-sectors. This relation arises because the first entrants of a group into the high-return occupation have the highest ability _z_ , leading to a decline in the group’s average ability as additional workers enter. The opposite holds for low-return occupations. In the model with talent shocks, a group’s average occupation-sector-specific talent _ν_ ̄ _oj_<sup>_tg_decreasesinthegroup’semploymentshare</sup><sup>_πoj|g_</sup> which holds for all occupation-sectors.

As a result, a model with talent shocks infers a large decline in women’s average human capital in _high-return_ occupations where women’s employment shares have increased over time (e.g., professional occupations), as it predicts reductions in both average ability _z_ ̄ _oj_<sup>_g_</sup> and average occupation-sector-specific talent _ν_ ̄ _oj_<sup>_tg_.Amodelwithpreferenceshocksinstead</sup> captures only the decline in average ability _z_ ̄ _oj_<sup>_g_.Tomatchtheobservedchangesingender</sup> employment and wage gaps, the model with talent shocks therefore infers a larger decline in female wage discrimination _τoj_ in these occupations, while the model with preference 35
Table 8: Robustness to the Dispersion of Preference Shocks _σν_

||_σν_ = 1_._5|_σν_ = 2 (Benchmark)|_σν_ = 4|
|---|---|---|---|
||(1)|(2)|(3)|
|All Gender Barriers|30.20|28.28|22.53|
|Gender Norms Only|26.67|23.68|14.25|
|Wage Discrimination Only|3.49|4.39|7.12|

_<u>Notes:</u>_ This table reports the share of real GDP growth, which is explained by changes in gender barriers between the 1970s and 2010s, for different levels of dispersion in workers’ occupation-sectorspecific preference shocks _σε_ . The shares are calculated as described in the notes of Table 4. We average across the six countries in our core sample. A smaller value of _σε_ indicates higher dispersion.

shocks infers a larger decline in gender norms _λoj_ over time.

# **7.3 Dispersion of Preference Shocks**

We next examine how our results change with the dispersion of workers’ preference shocks across occupation-sector pairs _σν_ . Smaller values of _σν_ indicate higher dispersion. Our baseline model uses _σν_ = 2, following Hsieh et al. (2019) and Cassan et al. (2024). We now consider alternative values, ranging from 1.5 (high dispersion) to 4 (low dispersion).

Table 8 shows that a higher dispersion of preference shocks increases the share of economic growth that is explained by changes in gender barriers. Intuitively, the model infers higher levels of gender norms to explain observed occupational choices when idiosyncratic preferences are more dispersed across occupation-sectors, and vice-versa for less dispersion. The estimates of wage discrimination are more stable because they are calibrated to observed wage gaps, which are less affected by the dispersion of preference shocks than employment choices. Hence, Table 8 shows that the importance of changes in gender norms decreases when preference shocks are less dispersed, while the importance of wage discrimination increases.

# **7.4 Gender-Specific Comparative Advantage**

In our benchmark model, we assume that men and women with the same ability _z_ are equally productive in each occupation-sector. Under this assumption, we infer female wage discrimination _τoj_ to match observed gender wage gaps in each occupation-sector using Equation 10. However, if men or women had a systematic comparative advantage in specific occupation-sector pairs, then our estimates of _τoj_ would also reflect these 36
Table 9: Robustness to Adjustments for Gender-Specific Comparative Advantage

|Model:|Benchmark|With Comparative Adv.|
|---|---|---|
||(1)|(2)|
|All Gender Barriers|28.28|20.77|
|Gender Norms Only|23.68|17.28|
|Wage Discrimination Only|4.39|3.12|

_<u>Notes:</u>_ This table reports the share of real GDP growth, which is explained by changes in gender barriers between the 1970s and 2010s. The shares are calculated as described in the notes of Table 4. We average across the six countries in our core sample. The columns compare the results from our benchmark model (Column (1)) and from a model that adjusts for a gender-specific comparative advantage across occupations-sector pairs (Column (2)). We measure such comparative advantage from a benchmark economy – the United States in 2015 – which we assume to have no wage discrimination.

productivity effects. Measuring true productivity differences across genders in each occupation-sector pair and disentangling them from gender barriers is challenging (if not impossible), as it would require detailed data on labor input and output, separately for men and women. Such granular data does not exist for a large set of occupations, sectors, years, and countries. Instead, we implement two analyses to examine the potential role of gender-specific comparative advantage and its implications for our results.

First, we examine whether our estimates of _τojct_ show greater variation across occupation-sector pairs, or across country-years. This information provides insights into the underlying factors that _τojct_ reflects, as gender-specific productivity differences (unlike gender barriers) are expected to vary more across occupation-sector pairs and less across country-years.<sup>27</sup> Regressing our estimates of _τojct_ on occupation, sector, or occupation-sector fixed effects explains at most 20% of the variation in _τojct_ with _R_<sup>2</sup> values ranging from 0.01 to 0.2. This finding indicates that a substantial part of the variation in our _τojct_ estimates is driven by variation across country-years, making them more likely to reflect gender barriers than systematic gender productivity differences within occupation-sector pairs.

In a second exercise, we repeat our counterfactual analysis while explicitly accounting for a gender-specific comparative advantage in each occupation-sector pair. To do so, we select a benchmark economy, the United States in 2015, where we assume no wage discrimination (Lee, 2024). Under this assumption, any observed gender wage

> 27More specifically, it is reasonable to assume that the relative ranking of occupation-sectors in terms of a potential male (or female) comparative advantage is consistent across countries and over time. For example, agricultural jobs are likely to be consistently more brawn-intensive, which can benefit men relative to women, than office jobs.

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– gap in a given occupation-sector pair after adjusting for differences in model-implied – human capital reflects actual differences in productivity between men and women. Hence, we estimate these gender-specific comparative advantages for each occupationsector pair for our benchmark economy, _δoj,US_ 2015, and apply the estimates to all other countries and years. Under this adjustment, the effective human capital units of a woman who works in occupation-sector _oj_ in any country-year is then given by: _h_<sup>_i_</sup> _ojf_<sup>(</sup><sup>_z_)=</sup> (1 _− δoj,US_ 2015) _×_ exp( _z_<sup>_i_</sup> _κoj_ ). Table 9 shows that changes in gender barriers account for 21% of observed real GDP per capita growth under this adjustment, compared to 28% in the benchmark model. The moderate reduction also holds when we fix either gender norms (17% vs 24%) or wage discrimination (3% vs 4%). Hence, changes in gender barriers over the past decades remain an important channel for explaining economic growth in our sample, even when adjusting for a gender-specific comparative advantage.

# **8 Conclusion**

This paper provides new evidence on the gender dimension of structural transformation across a large cross-section of countries over the past five decades. We find significant gender gaps in employment and wages that have narrowed on average over time, but continue to persist even today, and even in the most developed countries. We develop a general equilibrium occupational and sectoral choice model to quantify the effects of changes in gender barriers on macroeconomic outcomes, after accounting for the economic channels emphasized in the literature. We find that declining gender barriers have made substantial contributions to the observed increase in female labor force participation, the rise of the service sector, and real GDP per capita growth, but with substantial variation across countries.

By developing a unified framework to study _both_ occupational and sectoral choices, our model not only extends the approaches in the literature (Hsieh et al., 2019), but is particularly useful to study employment transitions over long time horizons and across countries that span a wide spectrum of economic development, where employment structures vary significantly across sectors and occupations. Our analysis cannot identify the factors that led to a rapid decline in gender barriers in some countries but not in others. Future comparative research is therefore needed to examine the underlying drivers of these changes and to propose specific policies that can reduce gender barriers and promote gender parity in the labor market.

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