Advertisement

Advertisement

Gender inequality as a barrier to economic growth: a review of the theoretical literature

  • Open access
  • Published: 15 January 2021
  • Volume 19 , pages 581–614, ( 2021 )

Cite this article

You have full access to this open access article

  • Manuel Santos Silva 1 &
  • Stephan Klasen 1  

49k Accesses

23 Citations

21 Altmetric

Explore all metrics

In this article, we survey the theoretical literature investigating the role of gender inequality in economic development. The vast majority of theories reviewed argue that gender inequality is a barrier to development, particularly over the long run. Among the many plausible mechanisms through which inequality between men and women affects the aggregate economy, the role of women for fertility decisions and human capital investments is particularly emphasized in the literature. Yet, we believe the body of theories could be expanded in several directions.

Similar content being viewed by others

scholarly articles on gender inequality

Gender Inequality and Growth in Europe

Stephan Klasen & Anna Minasyan

The Effect of Gender Inequality on Economic Development: Case of African Countries

Khayria Karoui & Rochdi Feki

scholarly articles on gender inequality

The Feminization U

Avoid common mistakes on your manuscript.

1 Introduction

Theories of long-run economic development have increasingly relied on two central forces: population growth and human capital accumulation. Both forces depend on decisions made primarily within households: population growth is partially determined by households’ fertility choices (e.g., Becker & Barro 1988 ), while human capital accumulation is partially dependent on parental investments in child education and health (e.g., Lucas 1988 ).

In an earlier survey of the literature linking family decisions to economic growth, Grimm ( 2003 ) laments that “[m]ost models ignore the two-sex issue. Parents are modeled as a fictive asexual human being” (p. 154). Footnote 1 Since then, however, economists are increasingly recognizing that gender plays a fundamental role in how households reproduce and care for their children. As a result, many models of economic growth are now populated with men and women. The “fictive asexual human being” is a dying species. In this article, we survey this rich new landscape in theoretical macroeconomics, reviewing, in particular, micro-founded theories where gender inequality affects economic development.

For the purpose of this survey, gender inequality is defined as any exogenously imposed difference between male and female economic agents that, by shaping their behavior, has implications for aggregate economic growth. In practice, gender inequality is typically modeled as differences between men and women in endowments, constraints, or preferences.

Many articles review the literature on gender inequality and economic growth. Footnote 2 Typically, both the theoretical and empirical literature are discussed, but, in almost all cases, the vast empirical literature receives most of the attention. In addition, some of the surveys examine both sides of the two-way relationship between gender inequality and economic growth: gender equality as a cause of economic growth and economic growth as a cause of gender equality. As a result, most surveys end up only scratching the surface of each of these distinct strands of literature.

There is, by now, a large and insightful body of micro-founded theories exploring how gender equality affects economic growth. In our view, these theories merit a separate review. Moreover, they have not received sufficient attention in empirical work, which has largely developed independently (see also Cuberes & Teignier 2014 ). By reviewing the theoretical literature, we hope to motivate empirical researchers in finding new ways of putting these theories to test. In doing so, our work complements several existing surveys. Doepke & Tertilt ( 2016 ) review the theoretical literature that incorporates families in macroeconomic models, without focusing exclusively on models that include gender inequality, as we do. Greenwood, Guner and Vandenbroucke ( 2017 ), in turn, review the theoretical literature from the opposite direction; they study how macroeconomic models can explain changes in family outcomes. Doepke, Tertilt and Voena ( 2012 ) survey the political economy of women’s rights, but without focusing explicitly on their impact on economic development.

To be precise, the scope of this survey consists of micro-founded macroeconomic models where gender inequality (in endowments, constraints, preferences) affects economic growth—either by influencing the economy’s growth rate or shaping the transition paths between multiple income equilibria. As a result, this survey does not cover several upstream fields of partial-equilibrium micro models, where gender inequality affects several intermediate growth-related outcomes, such as labor supply, education, health. Additionally, by focusing on micro-founded macro models, we do not review studies in heterodox macroeconomics, including the feminist economics tradition using structuralist, demand-driven models. For recent overviews of this literature, see Kabeer ( 2016 ) and Seguino ( 2013 , 2020 ). Overall, we find very little dialogue between the neoclassical and feminist heterodox literatures. In this review, we will show that actually these two traditions have several points of contact and reach similar conclusions in many areas, albeit following distinct intellectual routes.

Although the incorporation of gender in macroeconomic models of economic growth is a recent development, the main gendered ingredients of those models are not new. They were developed in at least two strands of literature. First, since the 1960s, “new home economics” has applied the analytical toolbox of rational choice theory to decisions being made within the boundaries of the family (see, e.g., Becker 1960 , 1981 ). Footnote 3 A second literature strand, mostly based on empirical work at the micro level in developing countries, described clear patterns of gender-specific behavior within households that differed across regions of the developing world (see, e.g., Boserup 1970 ). Footnote 4 As we shall see, most of the (micro-founded) macroeconomic models reviewed in this article use several analytical mechanisms from "new home economics”; these mechanisms can typically rationalize several of the gender-specific regularities observed in early studies of developing countries. The growth theorist is then left to explore the aggregate implications for economic development.

The first models we present focus on gender discrimination in (or on access to) the labor market as a distortionary tax on talent. If talent is randomly distributed in the population, men and women are imperfect substitutes in aggregate production, and, as a consequence, gender inequality (as long as determined by non-market processes) will misallocate talent and lower incentives for female human capital formation. These theories do not rely on typical household functions such as reproduction and childrearing. Therefore, in these models, individuals are not organized into households. We review this literature in section 2 .

From there, we proceed to theories where the household is the unit of analysis. In sections 3 and 4 , we cover models that take the household as given and avoid marriage markets or other household formation institutions. This is a world where marriage (or cohabitation) is universal, consensual, and monogamous; families are nuclear, and spouses are matched randomly. The first articles in this tradition model the household as a unitary entity with joint preferences and interests, and with an efficient and centralized decision making process. Footnote 5 These theories posit how men and women specialize into different activities and how parents interact with their children. Section 3 reviews these theories. Over time, the literature has incorporated intra-household dynamics. Now, family members are allowed to have different preferences and interests; they bargain, either cooperatively or not, over family decisions. Now, the theorist recognizes power asymmetries between family members and analyzes how spouses bargain over decisions. Footnote 6 These articles are surveyed in section 4 .

The final set of articles we survey take into account how households are formed. These theories show how gender inequality can influence economic growth and long-run development through marriage market institutions and family formation patterns. Among other topics, this literature has studied ages at first marriage, relative supply of potential partners, monogamy and polygyny, arranged and consensual marriages, and divorce risk. Upon marriage, these models assume different bargaining processes between the spouses, or even unitary households, but they all recognize, in one way or another, that marriage, labor supply, consumption, and investment decisions are interdependent. We review these theories in section 5 .

Table 1 offers a schematic overview of the literature. To improve readability, the table only includes studies that we review in detail, with articles listed in order of appearance in the text. The table also abstracts from models’ extensions and sensitivity checks, and focuses exclusively on the causal pathways leading from gender inequality to economic growth.

The vast majority of theories reviewed argue that gender inequality is a barrier to economic development, particularly over the long run. The focus on long-run supply-side models reflects a recent effort by growth theorists to incorporate two stylized facts of economic development in the last two centuries: (i) a strong positive association between gender equality and income per capita (Fig. 1 ), and (ii) a strong association between the timing of the fertility transition and income per capita (Fig. 2 ). Footnote 7 Models that endogenize a fertility transition are able to generate a transition from a Malthusian regime of stagnation to a modern regime of sustained economic growth, thus replicating the development experience of human societies in the very long run (e.g., Galor 2005a , b ; Guinnane 2011 ). In contrast, demand-driven models in the heterodox and feminist traditions have often argued that gender wage discrimination and gendered sectoral and occupational segregation can be conducive to economic growth in semi-industrialized export-oriented economies. Footnote 8 In these settings—that fit well the experience of East and Southeast Asian economies—gender wage discrimination in female-intensive export industries reduces production costs and boosts exports, profits, and investment (Blecker & Seguino 2002 ; Seguino 2010 ).

figure 1

Income level and gender equality. Income is the natural log of per capita GDP (PPP-adjusted). The Gender Development Index is the ratio of gender-specific Human Development Indexes: female HDI/male HDI. Data are for the year 2000. Sources: UNDP

figure 2

Income level and timing of the fertility transition. Income is the natural log of per capita GDP (PPP-adjusted) in 2000. Years since fertility transition are the number of years between 2000 and the onset year of the fertility decline. See Reher ( 2004 ) for details. Sources: UNDP and Reher ( 2004 )

In most long-run, supply-side models reviewed here, irrespectively of the underlying source of gender differences (e.g., biology, socialization, discrimination), the opportunity cost of women’s time in foregone labor market earnings is lower than that of men. This gender gap in the value of time affects economic growth through two main mechanisms. First, when the labor market value of women’s time is relatively low, women will be in charge of childrearing and domestic work in the family. A low value of female time means that children are cheap. Fertility will be high, and economic growth will be low, both because population growth has a direct negative impact on long-run economic performance and because human capital accumulates at a slower pace (through the quantity-quality trade-off). Second, if parents expect relatively low returns to female education, due to women specializing in domestic activities, they will invest relatively less in the education of girls. In the words of Harriet Martineau, one of the first to describe this mechanism, “as women have none of the objects in life for which an enlarged education is considered requisite, the education is not given” (Martineau 1837 , p. 107). In the long run, lower human capital investments (on girls) lead to slower economic development.

Overall, gender inequality can be conceptualized as a source of inefficiency, to the extent that it results in the misallocation of productive factors, such as talent or labor, and as a source of negative externalities, when it leads to higher fertility, skewed sex ratios, or lower human capital accumulation.

We conclude, in section 6 , by examining the limitations of the current literature and pointing ways forward. Among them, we suggest deeper investigations of the role of (endogenous) technological change on gender inequality, as well as greater attention to the role and interests of men in affecting gender inequality and its impact on growth.

2 Gender discrimination and misallocation of talent

Perhaps the single most intuitive argument for why gender discrimination leads to aggregate inefficiency and hampers economic growth concerns the allocation of talent. Assume that talent is randomly distributed in the population. Then, an economy that curbs women’s access to education, market employment, or certain occupations draws talent from a smaller pool than an economy without such restrictions. Gender inequality can thus be viewed as a distortionary tax on talent. Indeed, occupational choice models with heterogeneous talent (as in Roy 1951 ) show that exogenous barriers to women’s participation in the labor market or access to certain occupations reduce aggregate productivity and per capita output (Cuberes & Teignier 2016 , 2017 ; Esteve-Volart 2009 ; Hsieh, Hurst, Jones and Klenow 2019 ).

Hsieh et al. ( 2019 ) represent the US economy with a model where individuals sort into occupations based on innate ability. Footnote 9 Gender and race identity, however, are a source of discrimination, with three forces preventing women and black men from choosing the occupations best fitting their comparative advantage. First, these groups face labor market discrimination, which is modeled as a tax on wages and can vary by occupation. Second, there is discrimination in human capital formation, with the costs of occupation-specific human capital being higher for certain groups. This cost penalty is a composite term encompassing discrimination or quality differentials in private or public inputs into children’s human capital. The third force are group-specific social norms that generate utility premia or penalties across occupations. Footnote 10

Assuming that the distribution of innate ability across race and gender is constant over time, Hsieh et al. ( 2019 ) investigate and quantify how declines in labor market discrimination, barriers to human capital formation, and changing social norms affect aggregate output and productivity in the United States, between 1960 and 2010. Over that period, their general equilibrium model suggests that around 40 percent of growth in per capita GDP and 90 percent of growth in labor force participation can be attributed to reductions in the misallocation of talent across occupations. Declining in barriers to human capital formation account for most of these effects, followed by declining labor market discrimination. Changing social norms, on the other hand, explain only a residual share of aggregate changes.

Two main mechanisms drive these results. First, falling discrimination improves efficiency through a better match between individual ability and occupation. Second, because discrimination is higher in high-skill occupations, when discrimination decreases, high-ability women and black men invest more in human capital and supply more labor to the market. Overall, better allocation of talent, rising labor supply, and faster human capital accumulation raise aggregate growth and productivity.

Other occupational choice models assuming gender inequality in access to the labor market or certain occupations reach similar conclusions. In addition to the mechanisms in Hsieh et al. ( 2019 ), barriers to women’s work in managerial or entrepreneurial occupations reduce average talent in these positions, resulting in aggregate losses in innovation, technology adoption, and productivity (Cuberes & Teignier 2016 , 2017 ; Esteve-Volart 2009 ). The argument can be readily applied to talent misallocation across sectors (Lee 2020 ). In Lee’s model, female workers face discrimination in the non-agricultural sector. As a result, talented women end up sorting into ill-suited agricultural activities. This distortion reduces aggregate productivity in agriculture. Footnote 11

To sum up, when talent is randomly distributed in the population, barriers to women’s education, employment, or occupational choice effectively reduce the pool of talent in the economy. According to these models, dismantling these gendered barriers can have an immediate positive effect on economic growth.

3 Unitary households: parents and children

In this section, we review models built upon unitary households. A unitary household maximizes a joint utility function subject to pooled household resources. Intra-household decision making is assumed away; the household is effectively a black-box. In this class of models, gender inequality stems from a variety of sources. It is rooted in differences in physical strength (Galor & Weil 1996 ; Hiller 2014 ; Kimura & Yasui 2010 ) or health (Bloom et al. 2015 ); it is embedded in social norms (Hiller 2014 ; Lagerlöf 2003 ), labor market discrimination (Cavalcanti & Tavares 2016 ), or son preference (Zhang, Zhang and Li 1999 ). In all these models, gender inequality is a barrier to long-run economic development.

Galor & Weil ( 1996 ) model an economy with three factors of production: capital, physical labor (“brawn”), and mental labor (“brain”). Men and women are equally endowed with brains, but men have more brawn. In economies starting with very low levels of capital per worker, women fully specialize in childrearing because their opportunity cost in terms of foregone market earnings is lower than men’s. Over time, the stock of capital per worker builds up due to exogenous technological progress. The degree of complementarity between capital and mental labor is higher than that between capital and physical labor; as the economy accumulates capital per worker, the returns to brain rise relative to the returns to brawn. As a result, the relative wages of women rise, increasing the opportunity cost of childrearing. This negative substitution effect dominates the positive income effect on the demand for children and fertility falls. Footnote 12 As fertility falls, capital per worker accumulates faster creating a positive feedback loop that generates a fertility transition and kick starts a process of sustained economic growth.

The model has multiple stable equilibria. An economy starting from a low level of capital per worker is caught in a Malthusian poverty trap of high fertility, low income per capita, and low relative wages for women. In contrast, an economy starting from a sufficiently high level of capital per worker will converge to a virtuous equilibrium of low fertility, high income per capita, and high relative wages for women. Through exogenous technological progress, the economy can move from the low to the high equilibrium.

Gender inequality in labor market access or returns to brain can slow down or even prevent the escape from the Malthusian equilibrium. Wage discrimination or barriers to employment would work against the rise of relative female wages and, therefore, slow down the takeoff to modern economic growth.

The Galor and Weil model predicts how female labor supply and fertility evolve in the course of development. First, (married) women start participating in market work and only afterwards does fertility start declining. Historically, however, in the US and Western Europe, the decline in fertility occurred before women’s participation rates in the labor market started their dramatic increase. In addition, these regions experienced a mid-twentieth century baby boom which seems at odds with Galor and Weil’s theory.

Both these stylized facts can be addressed by adding home production to the modeling, as do Kimura & Yasui ( 2010 ). In their article, as capital per worker accumulates, the market wage for brains rises and the economy moves through four stages of development. In the first stage, with a sufficiently low market wage, both husband and wife are fully dedicated to home production and childrearing. The household does not supply labor to the market; fertility is high and constant. In the second stage, as the wage rate increases, men enter the labor market (supplying both brawn and brain), whereas women remain fully engaged in home production and childrearing. But as men partially withdraw from home production, women have to replace them. As a result, their time cost of childrearing goes up. At this stage of development, the negative substitution effect of rising wages on fertility dominates the positive income effect. Fertility starts declining, even though women have not yet entered the labor market. The third stage arrives when men stop working in home production. There is complete specialization of labor by gender; men only do market work, and women only do home production and childrearing. As the market wage rises for men, the positive income effect becomes dominant and fertility increases; this mimics the baby-boom period of the mid-twentieth century. In the fourth and final stage, once sufficient capital is accumulated, women enter the market sector as wage-earners. The negative substitution effect of rising female opportunity costs dominates once again, and fertility declines. The economy moves from a “breadwinner model” to a “dual-earnings model”.

Another important form of gender inequality is discrimination against women in the form of lower wages, holding male and female productivity constant. Cavalcanti & Tavares ( 2016 ) estimate the aggregate effects of wage discrimination using a model-based general equilibrium representation of the US economy. In their model, women are assumed to be more productive in childrearing than men, so they pay the full time cost of this activity. In the labor market, even though men and women are equally productive, women receive only a fraction of the male wage rate—this is the wage discrimination assumption. Wage discrimination works as a tax on female labor supply. Because women work less than they would without discrimination, there is a negative level effect on per capita output. In addition, there is a second negative effect of wage discrimination operating through endogenous fertility. Since lower wages reduce women’s opportunity costs of childrearing, fertility is relatively high, and output per capita is relatively low. The authors calibrate the model to US steady state parameters and estimate large negative output costs of the gender wage gap. Reducing wage discrimination against women by 50 percent would raise per capita income by 35 percent, in the long run.

Human capital accumulation plays no role in Galor & Weil ( 1996 ), Kimura & Yasui ( 2010 ), and Cavalcanti & Tavares ( 2016 ). Each person is exogenously endowed with a unit of brains. The fundamental trade-off in the these models is between the income and substitution effects of rising wages on the demand for children. When Lagerlöf ( 2003 ) adds education investments to a gender-based model, an additional trade-off emerges: that between the quantity and the quality of children.

Lagerlöf ( 2003 ) models gender inequality as a social norm: on average, men have higher human capital than women. Confronted with this fact, parents play a coordination game in which it is optimal for them to reproduce the inequality in the next generation. The reason is that parents expect the future husbands of their daughters to be, on average, relatively more educated than the future wives of their sons. Because, in the model, parents care for the total income of their children’s future households, they respond by investing relatively less in daughters’ human capital. Here, gender inequality does not arise from some intrinsic difference between men and women. It is instead the result of a coordination failure: “[i]f everyone else behaves in a discriminatory manner, it is optimal for the atomistic player to do the same” (Lagerlöf 2003 , p. 404).

With lower human capital, women earn lower wages than men and are therefore solely responsible for the time cost of childrearing. But if, exogenously, the social norm becomes more gender egalitarian over time, the gender gap in parental educational investment decreases. As better educated girls grow up and become mothers, their opportunity costs of childrearing are higher. Parents trade-off the quantity of children by their quality; fertility falls and human capital accumulates. However, rising wages have an offsetting positive income effect on fertility because parents pay a (fixed) “goods cost” per child. The goods cost is proportionally more important in poor societies than in richer ones. As a result, in poor economies, growth takes off slowly because the positive income effect offsets a large chunk of the negative substitution effect. As economies grow richer, the positive income effect vanishes (as a share of total income), and fertility declines faster. That is, growth accelerates over time even if gender equality increases only linearly.

The natural next step is to model how the social norm on gender roles evolves endogenously during the course of development. Hiller ( 2014 ) develops such a model by combining two main ingredients: a gender gap in the endowments of brawn (as in Galor & Weil 1996 ) generates a social norm, which each parental couple takes as given (as in Lagerlöf 2003 ). The social norm evolves endogenously, but slowly; it tracks the gender ratio of labor supply in the market, but with a small elasticity. When the male-female ratio in labor supply decreases, stereotypes adjust and the norm becomes less discriminatory against women.

The model generates a U-shaped relationship between economic development and female labor force participation. Footnote 13 In the preindustrial stage, there is no education and all labor activities are unskilled, i.e., produced with brawn. Because men have a comparative advantage in brawn, they supply more labor to the market than women, who specialize in home production. This gender gap in labor supply creates a social norm that favors boys over girls. Over time, exogenous skill-biased technological progress raises the relative returns to brains, inducing parents to invest in their children’s education. At the beginning, however, because of the social norm, only boys become educated. The economy accumulates human capital and grows, generating a positive income effect that, in isolation, would eventually drive up parental investments in girls’ education. Footnote 14 But endogenous social norms move in the opposite direction. When only boys receive education, the gender gap in returns to market work increases, and women withdraw to home production. As female relative labor supply in the market drops, the social norm becomes more discriminatory against women. As a result, parents want to invest relatively less in their daughters’ education.

In the end, initial conditions determine which of the forces dominates, thereby shaping long-term outcomes. If, initially, the social norm is very discriminatory, its effect is stronger than the income effect; the economy becomes trapped in an equilibrium with high gender inequality and low per capita income. If, on the other hand, social norms are relatively egalitarian to begin with, then the income effect dominates, and the economy converges to an equilibrium with gender equality and high income per capita.

In the models reviewed so far, human capital or brain endowments can be understood as combining both education and health. Bloom et al. ( 2015 ) explicitly distinguish these two dimensions. Health affects labor market earnings because sick people are out of work more often (participation effect) and are less productive per hour of work (productivity effect). Female health is assumed to be worse than male health, implying that women’s effective wages are lower than men’s. As a result, women are solely responsible for childrearing. Footnote 15

The model produces two growth regimes: a Malthusian trap with high fertility and no educational investments; and a regime of sustained growth, declining fertility, and rising educational investments. Once wages reach a certain threshold, the economy goes through a fertility transition and education expansion, taking off from the Malthusian regime to the sustained growth regime.

Female health promotes growth in both regimes, and it affects the timing of the takeoff. The healthier women are, the earlier the economy takes off. The reason is that a healthier woman earns a higher effective wage and, consequently, faces higher opportunity costs of raising children. When female health improves, the rising opportunity costs of children reduce the wage threshold at which educational investments become attractive; the fertility transition and mass education periods occur earlier.

In contrast, improved male health slows down economic growth and delays the fertility transition. When men become healthier, there is only a income effect on the demand for children, without the negative substitution effect (because male childrearing time is already zero). The policy conclusion would be to redistribute health from men to women. However, the policy would impose a static utility cost on the household. Because women’s time allocation to market work is constrained by childrearing responsibilities (whereas men work full-time), the marginal effect of health on household income is larger for men than for women. From the household’s point of view, reducing the gender gap in health produces a trade-off between short-term income maximization and long-term economic development.

In an extension of the model, the authors endogeneize health investments, while keeping the assumption that women pay the full time cost of childrearing. Because women participate less in the labor market (due to childrearing duties), it is optimal for households to invest more in male health. A health gender gap emerges from rational household behavior that takes into account how time-constraints differ by gender; assuming taste-based discrimination against girls or gender-specific preferences is not necessary.

In the models reviewed so far, parents invest in their children’s human capital for purely altruistic reasons. This is captured in the models by assuming that parents derive utility directly from the quantity and quality of children. This is the classical representation of children as durable consumption goods (e.g., Becker 1960 ). In reality, of course, parents may also have egoistic motivations for investing in child quantity and quality. A typical example is that, when parents get old and retire, they receive support from their children. The quantity and quality of children will affect the size of old-age transfers and parents internalize this in their fertility and childcare behavior. According to this view, children are best understood as investment goods.

Zhang et al. ( 1999 ) build an endogenous growth model that incorporates the old-age support mechanism in parental decisions. Another innovative element of their model is that parents can choose the gender of their children. The implicit assumption is that sex selection technologies are freely available to all parents.

At birth, there is a gender gap in human capital endowment, favoring boys over girls. Footnote 16 In adulthood, a child’s human capital depends on the initial endowment and on the parents’ human capital. In addition, the probability that a child survives to adulthood is exogenous and can differ by gender.

Parents receive old-age support from children that survive until adulthood. The more human capital children have, the more old-age support they provide to their parents. Beyond this egoistic motive, parents also enjoy the quantity and the quality of children (altruistic motive). Son preference is modeled by boys having a higher relative weight in the altruistic-component of the parental utility function. In other words, in their enjoyment of children as consumer goods, parents enjoy “consuming” a son more than “consuming” a girl. Parents who prefer sons want more boys than girls. A larger preference for sons, a higher relative survival probability of boys, and a higher human capital endowment of boys positively affect the sex ratio at birth, because, in the parents’ perspective, all these forces increase the marginal utility of boys relative to girls.

Zhang et al. ( 1999 ) show that, if human capital transmission from parents to children is efficient enough, the economy grows endogenously. When boys have a higher human capital endowment than girls, and the survival probability of sons is not smaller than the survival probability of daughters, then only sons provide old-age support. Anticipating this, parents invest more in the human capital of their sons than on the human capital of their daughters. As a result, the gender gap in human capital at birth widens endogenously.

When only boys provide old-age support, an exogenous increase in son preference harms long-run economic growth. The reason is that, when son preference increases, parents enjoy each son relatively more and demand less old-age support from him. Other things equal, parents want to “consume” more sons now and less old-age support later. Because parents want more sons, the sex ratio at birth increases; but because each son provides less old-age support, human capital investments per son decrease (such that the gender gap in human capital narrows). At the aggregate level, the pace of human capital accumulation slows down and, in the long run, economic growth is lower. Thus, an exogenous increase in son preference increases the sex ratio at birth, and reduces human capital accumulation and long-run growth (although it narrows the gender gap in education).

In summary, in growth models with unitary households, gender inequality is closely linked to the division of labor between family members. If women earn relatively less in market activities, they specialize in childrearing and home production, while men specialize in market work. And precisely due to this division of labor, the returns to female educational investments are relatively low. These household behaviors translate into higher fertility and lower human capital and thus pose a barrier to long-run development.

4 Intra-household bargaining: husbands and wives

In this section, we review models populated with non-unitary households, where decisions are the result of bargaining between the spouses. There are two broad types of bargaining processes: non-cooperative, where spouses act independently or interact in a non-cooperative game that often leads to inefficient outcomes (e.g., Doepke & Tertilt 2019 , Heath & Tan 2020 ); and cooperative, where the spouses are assumed to achieve an efficient outcome (e.g., De la Croix & Vander Donckt 2010 ; Diebolt & Perrin 2013 ). As in the previous section, all of these non-unitary models take the household as given, thereby abstracting from marriage markets or other household formation institutions, which will be discussed separately in section 5 . When preferences differ by gender, bargaining between the spouses matters for economic growth. If women care more about child quality than men do and human capital accumulation is the main engine of growth, then empowering women leads to faster economic growth (Prettner & Strulik 2017 ). If, however, men and women have similar preferences but are imperfect substitutes in the production of household public goods, then empowering women has an ambiguous effect on economic growth (Doepke & Tertilt 2019 ).

A separate channel concerns the intergenerational transmission of human capital and woman’s role as the main caregiver of children. If the education of the mother matters more than the education of the father in the production of children’s human capital, then empowering women will be conducive to growth (Agénor 2017 ; Diebolt & Perrin 2013 ), with the returns to education playing a crucial role in the political economy of female empowerment (Doepke & Tertilt 2009 ).

However, different dimensions of gender inequality have different growth impacts along the development process (De la Croix & Vander Donckt 2010 ). Policies that improve gender equality across many dimensions can be particularly effective for economic growth by reaping complementarities and positive externalities (Agénor 2017 ).

The idea that women might have stronger preferences for child-related expenditures than men can be easily incorporated in a Beckerian model of fertility. The necessary assumption is that women place a higher weight on child quality (relative to child quantity) than men do. Prettner & Strulik ( 2017 ) build a unified growth theory model with collective households. Men and women have different preferences, but they achieve efficient cooperation based on (reduced-form) bargaining parameters. The authors study the effect of two types of preferences: (i) women are assumed to have a relative preference for child quality, while men have a relative preference for child quantity; and (ii) parents are assumed to have a relative preference for the education of sons over the education of daughters. In addition, it is assumed that the time cost of childcare borne by men cannot be above that borne by women (but it could be the same).

When women have a relative preference for child quality, increasing female empowerment speeds up the economy’s escape from a Malthusian trap of high fertility, low education, and low income per capita. When female empowerment increases (exogenously), a woman’s relative preference for child quality has a higher impact on household’s decisions. As a consequence, fertility falls, human capital accumulates, and the economy starts growing. The model also predicts that the more preferences for child quality differ between husband and wife, the more effective is female empowerment in raising long-run per capita income, because the sooner the economy escapes the Malthusian trap. This effect is not affected by whether parents have a preference for the education of boys relative to that of girls. If, however, men and women have similar preferences with respect to the quantity and quality of their children, then female empowerment does not affect the timing of the transition to the sustained growth regime.

Strulik ( 2019 ) goes one step further and endogeneizes why men seem to prefer having more children than women. The reason is a different preference for sexual activity: other things equal, men enjoy having sex more than women. Footnote 17 When cheap and effective contraception is not available, a higher male desire for sexual activity explains why men also prefer to have more children than women. In a traditional economy, where no contraception is available, fertility is high, while human capital and economic growth are low. When female bargaining power increases, couples reduce their sexual activity, fertility declines, and human capital accumulates faster. Faster human capital accumulation increases household income and, as a consequence, the demand for contraception goes up. As contraception use increases, fertility declines further. Eventually, the economy undergoes a fertility transition and moves to a modern regime with low fertility, widespread use of contraception, high human capital, and high economic growth. In the modern regime, because contraception is widely used, men’s desire for sex is decoupled from fertility. Both sex and children cost time and money. When the two are decoupled, men prefer to have more sex at the expense of the number of children. There is a reversal in the gender gap in desired fertility. When contraceptives are not available, men desire more children than women; once contraceptives are widely used, men desire fewer children than women. If women are more empowered, the transition from the traditional equilibrium to the modern equilibrium occurs faster.

Both Prettner & Strulik ( 2017 ) and Strulik ( 2019 ) rely on gender-specific preferences. In contrast, Doepke & Tertilt ( 2019 ) are able to explain gender-specific expenditure patterns without having to assume that men and women have different preferences. They set up a non-cooperative model of household decision making and ask whether more female control of household resources leads to higher child expenditures and, thus, to economic development. Footnote 18

In their model, household public goods are produced with two inputs: time and goods. Instead of a single home-produced good (as in most models), there is a continuum of household public goods whose production technologies differ. Some public goods are more time-intensive to produce, while others are more goods-intensive. Each specific public good can only be produced by one spouse—i.e., time and good inputs are not separable. Women face wage discrimination in the labor market, so their opportunity cost of time is lower than men’s. As a result, women specialize in the production of the most time-intensive household public goods (e.g., childrearing activities), while men specialize in the production of goods-intensive household public goods (e.g., housing infrastructure). Notice that, because the household is non-cooperative, there is not only a division of labor between husband and wife, but also a division of decision making, since ultimately each spouse decides how much to provide of his or her public goods.

When household resources are redistributed from men to women (i.e., from the high-wage spouse to the low-wage spouse), women provide more public goods, in relative terms. It is ambiguous, however, whether the total provision of public goods increases with the re-distributive transfer. In a classic model of gender-specific preferences, a wife increases child expenditures and her own private consumption at the expense of the husband’s private consumption. In Doepke & Tertilt ( 2019 ), however, the rise in child expenditures (and time-intensive public goods in general) comes at the expense of male consumption and male-provided public goods.

Parents contribute to the welfare of the next generation in two ways: via human capital investments (time-intensive, typically done by the mother) and bequests of physical capital (goods-intensive, typically done by the father). Transferring resources to women increases human capital, but reduces the stock of physical capital. The effect of such transfers on economic growth depends on whether the aggregate production function is relatively intensive in human capital or in physical capital. If aggregate production is relatively human capital intensive, then transfers to women boost economic growth; if it is relatively intensive in physical capital, then transfers to women may reduce economic growth.

There is an interesting paradox here. On the one hand, transfers to women will be growth-enhancing in economies where production is intensive in human capital. These would be more developed, knowledge intensive, service economies. On the other hand, the positive growth effect of transfers to women increases with the size of the gender wage gap, that is, decreases with female empowerment. But the more advanced, human capital intensive economies are also the ones with more female empowerment (i.e., lower gender wage gaps). In other words, in settings where human capital investments are relatively beneficial, the contribution of female empowerment to human capital accumulation is reduced. Overall, Doepke and Tertilt’s ( 2019 ) model predicts that female empowerment has at best a limited positive effect and at worst a negative effect on economic growth.

Heath & Tan ( 2020 ) argue that, in a non-cooperative household model, income transfers to women may increase female labor supply. Footnote 19 This result may appear counter-intuitive at first, because in collective household models unearned income unambiguously reduces labor supply through a negative income effect. In Heath and Tan’s model, husband and wife derive utility from leisure, consuming private goods, and consuming a household public good. The spouses decide separately on labor supply and monetary contributions to the household public good. Men and women are identical in preferences and behavior, but women have limited control over resources, with a share of their income being captured by the husband. Female control over resources (i.e., autonomy) depends positively on the wife’s relative contribution to household income. Thus, an income transfer to the wife, keeping husband unearned income constant, raises the fraction of her own income that she privately controls. This autonomy effect unambiguously increases women’s labor supply, because the wife can now reap an additional share of her wage bill. Whenever the autonomy effect dominates the (negative) income effect, female labor supply increases. The net effect will be heterogeneous over the wage distribution, but the authors show that aggregate female labor supply is always weakly larger after the income transfer.

Diebolt & Perrin ( 2013 ) assume cooperative bargaining between husband and wife, but do not rely on sex-specific preferences or differences in ability. Men and women are only distinguished by different uses of their time endowments, with females in charge of all childrearing activities. In line with this labor division, the authors further assume that only the mother’s human capital is inherited by the child at birth. On top of the inherited maternal endowment, individuals can accumulate human capital during adulthood, through schooling. The higher the initial human capital endowment, the more effective is the accumulation of human capital via schooling.

A woman’s bargaining power in marriage determines her share in total household consumption and is a function of the relative female human capital of the previous generation. An increase in the human capital of mothers relative to that of fathers has two effects. First, it raises the incentives for human capital accumulation of the next generation, because inherited maternal human capital makes schooling more effective. Second, it raises the bargaining power of the next generation of women and, because women’s consumption share increases, boosts the returns on women’s education. The second effect is not internalized in women’s time allocation decisions; it is an intergenerational externality. Thus, an exogenous increase in women’s bargaining power would promote economic growth by speeding up the accumulation of human capital across overlapping generations.

De la Croix & Vander Donckt ( 2010 ) contribute to the literature by clearly distinguishing between different gender gaps: a gap in the probability of survival, a wage gap, a social and institutional gap, and a gender education gap. The first three are exogenously given, while the fourth is determined within the model.

By assumption, men and women have identical preferences and ability, but women pay the full time cost of childrearing. As in a typical collective household model, bargaining power is partially determined by the spouses’ earnings potential (i.e., their levels of human capital and their wage rates). But there is also a component of bargaining power that is exogenous and captures social norms that discriminate against women—this is the social and institutional gender gap.

Husbands and wives bargain over fertility and human capital investments for their children. A standard Beckerian result emerges: parents invest relatively less in the education of girls, because girls will be more time-constrained than boys and, therefore, the female returns to education are lower in relative terms.

There are at least two regimes in the economy: a corner regime and an interior regime. The corner regime consists of maximum fertility, full gender specialization (no women in the labor market), and large gender gaps in education (no education for girls). Reducing the wage gap or the social and institutional gap does not help the economy escaping this regime. Women are not in labor force, so the wage gap is meaningless. The social and institutional gap will determine women’s share in household consumption, but does not affect fertility and growth. At this stage, the only effective instruments for escaping the corner regime are reducing the gender survival gap or reducing child mortality. Reducing the gender survival gap increases women’s lifespan, which increases their time budget and attracts them to the labor market. Reducing child mortality decreases the time costs of kids, therefore drawing women into the labor market. In both cases, fertility decreases.

In the interior regime, fertility is below the maximum, women’s labor supply is above zero, and both boys and girls receive education. In this regime, with endogenous bargaining power, reducing all gender gaps will boost economic growth. Footnote 20 Thus, depending on the growth regime, some gender gaps affect economic growth, while others do not. Accordingly, the policy-maker should tackle different dimensions of gender inequality at different stages of the development process.

Agénor ( 2017 ) presents a computable general equilibrium that includes many of the elements of gender inequality reviewed so far. An important contribution of the model is to explicitly add the government as an agent whose policies interact with family decisions and, therefore, will impact women’s time allocation. Workers produce a market good and a home good and are organized in collective households. Bargaining power depends on the spouses’ relative human capital levels. By assumption, there is gender discrimination in market wages against women. On top, mothers are exclusively responsible for home production and childrearing, which takes the form of time spent improving children’s health and education. But public investments in education and health also improve these outcomes during childhood. Likewise, public investment in public infrastructure contributes positively to home production. In particular, the ratio of public infrastructure capital stock to private capital stock is a substitute for women’s time in home production. The underlying idea is that improving sanitation, transportation, and other infrastructure reduces time spent in home production. Health status in adulthood depends on health status in childhood, which, in turn, relates positively to mother’s health, her time inputs into childrearing, and government spending. Children’s human capital depends on similar factors, except that mother’s human capital replaces her health as an input. Additionally, women are assumed to derive less utility from current consumption and more utility from children’s health relative to men. Wives are also assumed to live longer than their husbands, which further down-weights female’s emphasis on current consumption. The final gendered assumption is that mother’s time use is biased towards boys. This bias alone creates a gender gap in education and health. As adults, women’s relative lower health and human capital are translated into relative lower bargaining power in household decisions.

Agénor ( 2017 ) calibrates this rich setup for Benin, a low income country, and runs a series of policy experiments on different dimensions of gender inequality: a fall in childrearing costs, a fall in gender pay discrimination, a fall in son bias in mother’s time allocation, and an exogenous increase in female bargaining power. Footnote 21 Interestingly, despite all policies improving gender equality in separate dimensions, not all unambiguously stimulate economic growth. For example, falling childrearing costs raise savings and private investments, which are growth-enhancing, but increase fertility (as children become ‘cheaper’) and reduce maternal time investment per child, thus reducing growth. In contrast, a fall in gender pay discrimination always leads to higher growth, through higher household income that, in turn, boosts savings, tax revenues, and public spending. Higher public spending further contributes to improved health and education of the next generation. Lastly, Agénor ( 2017 ) simulates the effect of a combined policy that improves gender equality in all domains simultaneously. Due to complementarities and positive externalities across dimensions, the combined policy generates more economic growth than the sum of the individual policies. Footnote 22

In the models reviewed so far, men are passive observers of women’s empowerment. Doepke & Tertilt ( 2009 ) set up an interesting political economy model of women’s rights, where men make the decisive choice. Their model is motivated by the fact that, historically, the economic rights of women were expanded before their political rights. Because the granting of economic rights empowers women in the household, and this was done before women were allowed to participate in the political process, the relevant question is why did men willingly share their power with their wives?

Doepke & Tertilt ( 2009 ) answer this question by arguing that men face a fundamental trade-off. On the one hand, husbands would vote for their wives to have no rights whatsoever, because husbands prefer as much intra-household bargaining power as possible. But, on the other hand, fathers would vote for their daughters to have economic rights in their future households. In addition, fathers want their children to marry highly educated spouses, and grandfathers want their grandchildren to be highly educated. By assumption, men and women have different preferences, with women having a relative preference for child quality over quantity. Accordingly, men internalize that, when women become empowered, human capital investments increase, making their children and grandchildren better-off.

Skill-biased (exogenous) technological progress that raises the returns to education over time can shift male incentives along this trade-off. When the returns to education are low, men prefer to make all decisions on their own and deny all rights to women. But once the returns to education are sufficiently high, men voluntarily share their power with women by granting them economic rights. As a result, human capital investments increase and the economy grows faster.

In summary, gender inequality in labor market earnings often implies power asymmetries within the household, with men having more bargaining power than women. If preferences differ by gender and female preferences are more conducive to development, then empowering women is beneficial for growth. When preferences are the same and the bargaining process is non-cooperative, the implications are less clear-cut, and more context-specific. If, in addition, women’s empowerment is curtailed by law (e.g., restrictions on women’s economic rights), then it is important to understand the political economy of women’s rights, in which men are crucial actors.

5 Marriage markets and household formation

Two-sex models of economic growth have largely ignored how households are formed. The marriage market is not explicitly modeled: spouses are matched randomly, marriage is universal and monogamous, and families are nuclear. In reality, however, household formation patterns vary substantially across societies, with some of these differences extending far back in history. For example, Hajnal ( 1965 , 1982 ) described a distinct household formation pattern in preindustrial Northwestern Europe (often referred to as the “European Marriage Pattern”) characterized by: (i) late ages at first marriage for women, (ii) most marriages done under individual consent, and (iii) neolocality (i.e., upon marriage, the bride and the groom leave their parental households to form a new household). In contrast, marriage systems in China and India consisted of: (i) very early female ages at first marriage, (ii) arranged marriages, and (iii) patrilocality (i.e., the bride joins the parental household of the groom).

Economic historians argue that the “European Marriage Pattern” empowered women, encouraging their participation in market activities and reducing fertility levels. While some view this as one of the deep-rooted factors explaining Northwestern Europe’s earlier takeoff to sustained economic growth (e.g., Carmichael, de Pleijt, van Zanden and De Moor 2016 ; De Moor & Van Zanden 2010 ; Hartman 2004 ), others have downplayed the long-run significance of this marriage pattern (e.g., Dennison & Ogilvie 2014 ; Ruggles 2009 ). Despite this lively debate, the topic has been largely ignored by growth theorists. The few exceptions are Voigtländer and Voth ( 2013 ), Edlund and Lagerlöf ( 2006 ), and Tertilt ( 2005 , 2006 ).

After exploring different marriage institutions, we zoom in on contemporary monogamous and consensual marriage and review models where gender inequality affects economic growth through marriage markets that facilitate household formation (Du & Wei 2013 ; Grossbard & Pereira 2015 ; Grossbard-Shechtman 1984 ; Guvenen & Rendall 2015 ). In contrast with the previous two sections, where the household is the starting point of the analysis, the literature on marriage markets and household formation recognizes that marriage, labor supply, and investment decisions are interlinked. The analysis of these interlinkages is sometimes done with unitary households (upon marriage) (Du & Wei 2013 ; Guvenen & Rendall 2015 ), or with non-cooperative models of individual decision-making within households (Grossbard & Pereira 2015 ; Grossbard-Shechtman 1984 ).

Voigtländer and Voth ( 2013 ) argue that the emergence of the “European Marriage Pattern” is a direct consequence of the mid-fourteen century Black Death. They set up a two-sector agricultural economy consisting of physically demanding cereal farming, and less physically demanding pastoral production. The economy is populated by many male and female peasants and by a class of idle, rent-maximizing landlords. Female peasants are heterogeneous with respect to physical strength, but, on average, are assumed to have less brawn relative to male peasants and, thus, have a comparative advantage in the pastoral sector. Both sectors use land as a production input, although the pastoral sector is more land-intensive than cereal production. All land is owned by the landlords, who can rent it out for peasant cereal farming, or use it for large-scale livestock farming, for which they hire female workers. Crucially, women can only work and earn wages in the pastoral sector as long as they are unmarried. Footnote 23 Peasant women decide when to marry and, upon marriage, a peasant couple forms a new household, where husband and wife both work on cereal farming, and have children at a given time frequency. Thus, the only contraceptive method available is delaying marriage. Because women derive utility from consumption and children, they face a trade-off between earned income and marriage.

Initially, the economy rests in a Malthusian regime, where land-labor ratios are relatively low, making the land-intensive pastoral sector unattractive and depressing relative female wages. As a result, women marry early and fertility is high. The initial regime ends in 1348–1350, when the Black Death kills between one third and half of Europe’s population, exogenously generating land abundance and, therefore, raising the relative wages of female labor in pastoral production. Women postpone marriage to reap higher wages, and fertility decreases—moving the economy to a regime of late marriages and low fertility.

In addition to late marital ages and reduced fertility, another important feature of the “European Marriage Pattern” was individual consent for marriage. Edlund and Lagerlöf ( 2006 ) study how rules of consent for marriage influence long-run economic development. In their model, marriages can be formed according to two types of consent rules: individual consent or parental consent. Under individual consent, young people are free to marry whomever they wish, while, under parental consent, their parents are in charge of arranging the marriage. Depending on the prevailing rule, the recipient of the bride-price differs. Under individual consent, a woman receives the bride-price from her husband, whereas, under parental consent, her father receives the bride-price from the father of the groom. Footnote 24 In both situations, the father of the groom owns the labor income of his son and, therefore, pays the bride-price, either directly, under parental consent, or indirectly, under individual consent. Under individual consent, the father needs to transfer resources to his son to nudge him into marrying. Thus, individual consent implies a transfer of resources from the old to the young and from men to women, relative to the rule of parental consent. Redistributing resources from the old to the young boosts long-run economic growth. Because the young have a longer timespan to extract income from their children’s labor, they invest relatively more in the human capital of the next generation. In addition, under individual consent, the reallocation of resources from men to women can have additional positive effects on growth, by increasing women’s bargaining power (see section 4 ), although this channel is not explicitly modeled in Edlund and Lagerlöf ( 2006 ).

Tertilt ( 2005 ) explores the effects of polygyny on long-run development through its impact on savings and fertility. In her model, parental consent applies to women, while individual consent applies to men. There is a competitive marriage market where fathers sell their daughters and men buy their wives. As each man is allowed (and wants) to marry several wives, a positive bride-price emerges in equilibrium. Footnote 25 Upon marriage, the reproductive rights of the bride are transferred from her father to her husband, who makes all fertility decisions on his own and, in turn, owns the reproductive rights of his daughters. From a father’s perspective, daughters are investments goods; they can be sold in the marriage market, at any time. This feature generates additional demand for daughters, which increases overall fertility, and reduces the incentives to save, which decreases the stock of physical capital. Under monogamy, in contrast, the equilibrium bride-price is negative (i.e., a dowry). The reason is that maintaining unmarried daughters is costly for their fathers, so they are better-off paying a (small enough) dowry to their future husbands. In this setting, the economic returns to daughters are lower and, consequently, so is the demand for children. Fertility decreases and savings increase. Thus, moving from polygny to monogamy lowers population growth and raises the capital stock in the long run, which translates into higher output per capita in the steady state.

Instead of enforcing monogamy in a traditionally polygynous setting, an alternative policy is to transfer marriage consent from fathers to daughters. Tertilt ( 2006 ) shows that when individual consent is extended to daughters, such that fathers do not receive the bride-price anymore, the consequences are qualitatively similar to a ban on polygyny. If fathers stop receiving the bride-price, they save more physical capital. In the long run, per capita output is higher when consent is transferred to daughters.

Grossbard-Shechtman ( 1984 ) develops the first non-cooperative model where (monogamous) marriage, home production, and labor supply decisions are interdependent. Footnote 26 Spouses are modeled as separate agents deciding over production and consumption. Marriage becomes an implicit contract for ‘work-in-household’ (WiHo), defined as “an activity that benefits another household member [typically a spouse] who could potentially compensate the individual for these efforts” (Grossbard 2015 , p. 21). Footnote 27 In particular, each spouse decides how much labor to supply to market work and WiHo, and how much labor to demand from the other spouse for WiHo. Through this lens, spousal decisions over the intra-marriage distribution of consumption and WiHo are akin to well-known principal-agent problems faced between firms and workers. In the marriage market equilibrium, a spouse benefiting from WiHo (the principal) must compensate the spouse producing it (the agent) via intra-household transfers (of goods or leisure). Footnote 28 Grossbard-Shechtman ( 1984 ) and Grossbard ( 2015 ) show that, under these conditions, the ratio of men to women (i.e., the sex ratio) in the marriage market is inversely related to female labor supply to the market. The reason is that, as the pool of potential wives shrinks, prospective husbands have to increase compensation for female WiHo. From the potential wife’s point of view, as the equilibrium price for her WiHo increases, market work becomes less attractive. Conversely, when sex ratios are lower, female labor supply outside the home increases. Although the model does not explicit derive growth implications, the relative increase in female labor supply is expected to be beneficial for economic growth, as argued by many of the theories reviewed so far.

In an extension of this framework, Grossbard & Pereira ( 2015 ) analyze how sex ratios affect gendered savings over the marital life-cycle. Assuming that women supply a disproportionate amount of labor for WiHo (due, for example, to traditional gender norms), the authors show that men and women will have very distinct saving trajectories. A higher sex ratio increases savings by single men, who anticipate higher compensation transfers for their wives’ WiHo, whereas it decreases savings by single women, who anticipate receiving those transfers upon marriage. But the pattern flips after marriage: precautionary savings raise among married women, because the possibility of marriage dissolution entails a loss of income from WiHo. The opposite effect happens for married men: marriage dissolution would imply less expenditures in the future. The higher the sex ratio, the higher will be the equilibrium compensation paid by husbands for their wives’ WiHo. Therefore, the sex ratio will positively affect savings among single men and married women, but negatively affect savings among single women and married men. The net effect on the aggregate savings rate and on economic growth will depend on the relative size of these demographic groups.

In a related article, Du & Wei ( 2013 ) propose a model where higher sex ratios worsen marriage markets prospects for young men and their families, who react by increasing savings. Women in turn reduce savings. However, because sex ratios shift the composition of the population in favor of men (high saving type) relative to women (low saving type) and men save additionally to compensate for women’s dis-saving, aggregate savings increase unambiguously with sex ratios.

In Guvenen & Rendall ( 2015 ), female education is, in part, demanded as insurance against divorce risk. The reason is that divorce laws often protect spouses’ future labor market earnings (i.e., returns to human capital), but force them to share their physical assets. Because, in the model, women are more likely to gain custody of their children after divorce, they face higher costs from divorce relative to their husbands. Therefore, the higher the risk of divorce, the more women invest in human capital, as insurance against a future vulnerable economic position. Guvenen & Rendall ( 2015 ) shows that, over time, divorce risk has increased (for example, consensual divorce became replaced by unilateral divorce in most US states in the 1970s). In the aggregate, higher divorce risk boosted female education and female labor supply.

In summary, the rules regulating marriage and household formation carry relevant theoretical consequences for economic development. While the few studies on this topic have focused on age at marriage, consent rules and polygyny, and the interaction between sex ratios, marriage, and labor supply, other features of the marriage market remain largely unexplored (Borella, De Nardi and Yang 2018 ). Growth theorists would benefit from further incorporating theories of household formation in gendered macro models. Footnote 29

6 Conclusion

In this article, we surveyed micro-founded theories linking gender inequality to economic development. This literature offers many plausible mechanisms through which inequality between men and women affects the aggregate economy (see Table 1 ). Yet, we believe the body of theories could be expanded in several directions. We discuss them below and highlight lessons for policy.

The first direction for future research concerns control over fertility. In models where fertility is endogenous, households are always able to achieve their preferred number of children (see Strulik 2019 , for an exception). The implicit assumption is that there is a free and infallible method of fertility control available for all households—a view rejected by most demographers. The gap between desired fertility and achieved fertility can be endogeneized at three levels. First, at the societal level, the diffusion of particular contraceptive methods may be influenced by cultural and religious norms. Second, at the household level, fertility control may be object of non-cooperative bargaining between the spouses, in particular, for contraceptive methods that only women perfectly observe (Ashraf, Field and Lee 2014 ; Doepke & Kindermann 2019 ). More generally, the role of asymmetric information within the household is not yet explored (Walther 2017 ). Third, if parents have preferences over the gender composition of their offspring, fertility is better modeled as a sequential and uncertain process, where household size is likely endogenous to the sex of the last born child (Hazan & Zoabi 2015 ).

A second direction worth exploring concerns gender inequality in a historical perspective. In models with multiple equilibria, an economy’s path is often determined by its initial level of gender equality. Therefore, it would be useful to develop theories explaining why initial conditions varied across societies. In particular, there is a large literature on economic and demographic history documenting how systems of marriage and household formation differed substantially across preindustrial societies (e.g., De Moor & Van Zanden 2010 ; Hajnal 1965 , 1982 ; Hartman 2004 ; Ruggles 2009 ). In our view, more theoretical work is needed to explain both the origins and the consequences of these historical systems.

A third avenue for future research concerns the role of technological change. In several models, technological change is the exogenous force that ultimately erodes gender gaps in education or labor supply (e.g., Bloom et al. 2015 ; Doepke & Tertilt 2009 ; Galor & Weil 1996 ). For that to happen, technological progress is assumed to be skill-biased, thus raising the returns to education—or, in other words, favoring brain over brawn. As such, new technologies make male advantage in physical strength ever more irrelevant, while making female time spent on childrearing and housework ever more expensive. Moreover, recent technological progress increased the efficiency of domestic activities, thereby relaxing women’s time constraints (e.g., Cavalcanti & Tavares 2008 ; Greenwood, Seshadri and Yorukoglu 2005 ). These mechanisms are plausible, but other aspects of technological change need not be equally favorable for women. In many countries, for example, the booming science, technology, and engineering sectors tend to be particularly male-intensive. And Tejani & Milberg ( 2016 ) provide evidence for developing countries that as manufacturing industries become more capital intensive, their female employment share decreases.

Even if current technological progress is assumed to weaken gender gaps, historically, technology may have played exactly the opposite role. If technology today is more complementary to brain, in the past it could have been more complementary to brawn. An example is the plow that, relative to alternative technologies for field preparation (e.g., hoe, digging stick), requires upper body strength, on which men have a comparative advantage over women (Alesina, Giuliano and Nunn 2013 ; Boserup 1970 ). Another, even more striking example, is the invention of agriculture itself—the Neolithic Revolution. The transition from a hunter-gatherer lifestyle to sedentary agriculture involved a relative loss of status for women (Dyble et al. 2015 ; Hansen, Jensen and Skovsgaard 2015 ). One explanation is that property rights on land were captured by men, who had an advantage on physical strength and, consequently, on physical violence. Thus, in the long view of human history, technological change appears to have shifted from being male-biased towards being female-biased. Endogeneizing technological progress and its interaction with gender inequality is a promising avenue for future research.

Fourth, open economy issues are still almost entirely absent. An exception is Rees & Riezman ( 2012 ), who model the effect of globalization on economic growth. Whether global capital flows generate jobs primarily in female or male intensive sectors matters for long-run growth. If globalization creates job opportunities for women, their bargaining power increases and households trade off child quantity by child quality. Fertility falls, human capital accumulates, and long-run per capita output is high. If, on the other hand, globalization creates jobs for men, their intra-household power increases; fertility increases, human capital decreases, and steady-state income per capita is low. The literature would benefit from engaging with open economy demand-driven models of the feminist tradition, such as Blecker & Seguino ( 2002 ), Seguino ( 2010 ). Other fruitful avenues for future research on open economy macro concern gender analysis of global value chains (Barrientos 2019 ), gendered patterns of international migration (Cortes 2015 ; Cortes & Tessada 2011 ), and the diffusion of gender norms through globalization (Beine, Docquier and Schiff 2013 ; Klasen 2020 ; Tuccio & Wahba 2018 ).

A final point concerns the role of men in this literature. In most theoretical models, gender inequality is not the result of an active male project that seeks the domination of women. Instead, inequality emerges as a rational best response to some underlying gender gap in endowments or constraints. Then, as the underlying gap becomes less relevant—for example, due to skill-biased technological change—, men passively relinquish their power (see Doepke & Tertilt 2009 , for an exception). There is never a male backlash against the short-term power loss that necessarily comes with female empowerment. In reality, it is more likely that men actively oppose losing power and resources towards women (Folbre 2020 ; Kabeer 2016 ; Klasen 2020 ). This possibility has not yet been explored in formal models, even though it could threaten the typical virtuous cycle between gender equality and growth. If men are forward-looking, and the short-run losses outweigh the dynamic gains from higher growth, they might ensure that women never get empowered to begin with. Power asymmetries tend to be sticky, because “any group that is able to claim a disproportionate share of the gains from cooperation can develop social institutions to fortify their position” (Folbre 2020 , p. 199). For example, Eswaran & Malhotra ( 2011 ) set up a household decision model where men use domestic violence against their wives as a tool to enhance male bargaining power. Thus, future theories should recognize more often that men have a vested interest on the process of female empowerment.

More generally, policymakers should pay attention to the possibility of a male backlash as an unintended consequence of female empowerment policies (Erten & Keskin 2018 ; Eswaran & Malhotra 2011 ). Likewise, whereas most theories reviewed here link lower fertility to higher economic growth, the relationship is non-monotonic. Fertility levels below the replacement rate will eventually generate aggregate social costs in the form of smaller future workforces, rapidly ageing societies, and increased pressure on welfare systems, to name a few.

Many theories presented in this survey make another important practical point: public policies should recognize that gender gaps in separate dimensions complement and reinforce one another and, therefore, have to be dealt with simultaneously. A naïve policy targeting a single gap in isolation is unlikely to have substantial growth effects in the short run. Typically, inequalities in separate dimensions are not independent from each other (Agénor 2017 ; Bandiera & Does 2013 ; Duflo 2012 ; Kabeer 2016 ). For example, if credit-constrained women face weak property rights, are unable to access certain markets, and have mobility and time constraints, then the marginal return to capital may nevertheless be larger for men. Similarly, the return to male education may well be above the female return if demand for female labor is low or concentrated in sectors with low productivity. In sum, “the fact that women face multiple constraints means that relaxing just one may not improve outcomes” (Duflo 2012 , p. 1076).

Promising policy directions that would benefit from further macroeconomic research are the role of public investments in physical infrastructure and care provision (Agénor 2017 ; Braunstein, Bouhia and Seguino 2020 ), gender-based taxation (Guner, Kaygusuz and Ventura 2012 ; Meier & Rainer 2015 ), and linkages between gender equality and pro-environmental agendas (Matsumoto 2014 ).

See Echevarria & Moe ( 2000 ) for a similar complaint that “theories of economic growth and development have consistently neglected to include gender as a variable” (p. 77).

A non-exhaustive list includes Bandiera & Does ( 2013 ), Braunstein ( 2013 ), Cuberes & Teignier ( 2014 ), Duflo ( 2012 ), Kabeer ( 2016 ), Kabeer & Natali ( 2013 ), Klasen ( 2018 ), Seguino ( 2013 , 2020 ), Sinha et al. ( 2007 ), Stotsky ( 2006 ), World Bank ( 2001 , 2011 ).

For an in-depth history of “new home economics” see Grossbard-Shechtman ( 2001 ) and Grossbard ( 2010 , 2011 ).

For recent empirical reviews see Duflo ( 2012 ) and Doss ( 2013 ).

Although the unitary approach has being rejected on theoretical (e.g., Echevarria & Moe 2000 ; Folbre 1986 ; Knowles 2013 ; Sen 1989 ) and empirical grounds (e.g., Doss 2013 ; Duflo 2003 ; Lundberg et al. 1997 ), these early models are foundational to the subsequent literature. As it turns out, some of the key mechanisms survive in non-unitary theories of the household.

For nice conceptual perspectives on conflict and cooperation in households see Sen ( 1989 ), Grossbard ( 2011 ), and Folbre ( 2020 ).

The relationship depicted in Fig. 1 is robust to using other composite measures of gender equality (e.g., UNDP’s Gender Inequality Index or OECD’s Social Institutions and Gender Index (SIGI) (see Branisa, Klasen and Ziegler 2013 )), and other years besides 2000. In Fig. 2 , the linear prediction explains 56 percent of the cross-country variation in per capita income.

See Seguino ( 2013 , 2020 ) for a review of this literature.

The model allows for sorting on ability (“some people are better teachers”) or sorting on occupation-specific preferences (“others derive more utility from working as a teacher”) (Hsieh et al. 2019 , p. 1441). Here, we restrict our presentation to the case where sorting occurs primarily on ability. The authors find little empirical support for sorting on preferences.

Because the home sector is treated as any other occupation, the model can capture, in a reduced-form fashion, social norms on women’s labor force participation. For example, a social norm on traditional gender roles can be represented as a utility premium obtained by all women working on the home sector.

Note, however, that discrimination against women raises productivity in the non-agricultural sector. The reason is that the few women who end up working outside agriculture are positively selected on talent. Lee ( 2020 ) shows that this countervailing effect is modest and dominated by the loss of productivity in agriculture.

This is not the classic Beckerian quantity-quality trade-off because parents cannot invest in the quality of their children. Instead, the mechanism is built by assumption in the household’s utility function. When women’s wages increase relative to male wages, the substitution effect dominates the income effect.

The hypothesis that female labor force participation and economic development have a U-shaped relationship—known as the feminization-U hypothesis—goes back to Boserup ( 1970 ). See also Goldin ( 1995 ). Recently, Gaddis & Klasen ( 2014 ) find only limited empirical support for the feminization-U.

The model does not consider fertility decisions. Parents derive utility from their children’s human capital (social status utility). When household income increases, parents want to “consume” more social status by investing in their children’s education—this is the positive income effect.

Bloom et al. ( 2015 ) build their main model with unitary households, but show that the key conclusions are robust to a collective representation of the household.

This assumption does not necessarily mean that boys are more talented than girls. It can be also interpreted as a reduced-form way of capturing labor market discrimination against women.

Many empirical studies are in line with this assumption, which is rooted in evolutionary psychology. See Strulik ( 2019 ) for references. There are several other evolutionary arguments for men wanting more children (including with different women). See, among others, Mulder & Rauch ( 2009 ), Penn & Smith ( 2007 ), von Rueden & Jaeggi ( 2016 ). However, for a different view, see Fine ( 2017 ).

They do not model fertility decisions. So there is no quantity-quality trade-off.

In their empirical application, Heath & Tan ( 2020 ) study the Hindu Succession Act, which, through improved female inheritance rights, increased the lifetime unearned income of Indian women. Other policies consistent with the model are, for example, unconditional cash transfers to women.

De la Croix & Vander Donckt ( 2010 ) show this with numerical simulations, because the interior regime becomes analytically intractable.

We focus on gender-related policies in our presentation, but the article simulates additional public policies.

Agénor and Agénor ( 2014 ) develop a similar model, but with unitary households, and Agénor and Canuto ( 2015 ) have a similar model of collective households for Brazil, where adult women can also invest time in human capital formation. Since public infrastructure substitutes for women’s time in home production, more (or better) infrastructure can free up time for female human capital accumulation and, thus, endogenously increase wives’ bargaining power.

Voigtländer and Voth ( 2013 ) justify this assumption arguing that, in England, employment contracts for farm servants working in animal husbandry were conditional on celibacy. However, see Edwards & Ogilvie ( 2018 ) for a critique of this assumption.

The bride-price under individual consent need not be paid explicitly as a lump-sum transfer. It could, instead, be paid to the bride implicitly in the form of higher lifetime consumption.

In Tertilt ( 2005 ), all men are similar (except in age). Widespread polygyny is possible because older men marry younger women and population growth is high. This setup reflects stylized facts for Sub-Saharan Africa. It differs from models that assume male heterogeneity in endowments, where polygyny emerges because a rich male elite owns several wives, while poor men remain single (e.g., Gould, Moav and Simhon 2008 ; Lagerlöf 2005 , 2010 ).

See Grossbard ( 2015 ) for more details and extensions of this model and Grossbard ( 2018 ) for a non-technical overview of the related literature. For an earlier application, see Grossbard ( 1976 ).

The concept of WiHo is closely related but not equivalent to the ‘black-box’ term home production used by much of the literature. It also relates to feminist perspectives on care and social reproduction labor (c.f. Folbre 1994 ).

In the general setup, the model need not lead to a corner solution where only one spouse specializes in WiHo.

For promising approaches, see, among others, Cubeddu and Ríos-Rull ( 2003 ), Goussé, Jacquemet and Robin ( 2017 ), Greenwood, Guner, Kocharkov and Santos ( 2016 ), Guler, Guvenen and Violante ( 2012 ), Walther ( 2017 ), Wong ( 2016 ).

Agénor, P.-R. (2017). A computable overlapping generations model for gender and growth policy analysis. Macroeconomic Dynamics , 21 (1), 11–54.

Article   Google Scholar  

Agénor, P.-R., & Agénor, M. (2014). Infrastructure, women’s time allocation, and economic development. Journal of Economics , 113 (1), 1–30.

Agénor, P.-R., & Canuto, O. (2015). Gender equality and economic growth in Brazil: A long-run analysis. Journal of Macroeconomics , 43 , 155–172.

Alesina, A., Giuliano, P., & Nunn, N. (2013). On the origins of gender roles: women and the plough. Quarterly Journal of Economics , 128 (2), 469–530.

Ashraf, N., Field, E., & Lee, J. (2014). Household bargaining and excess fertility: an experimental study in Zambia. American Economic Review , 104 (7), 2210–2237.

Bandiera, O., & Does, A. N. (2013). Does gender inequality hinder development and economic growth? evidence and policy implications. World Bank Research Observer , 28 (1), 2–21.

Barrientos, S. (2019). Gender and work in global value chains: Capturing the gains? Cambridge: Cambridge University Press.

Becker, G. S. (1960). An economic analysis of fertility. In Demographic and Economic Change in Developed Countries . Princeton: Princeton University Press, pp. 209–240.

Becker, G. S. (1981). A treatise on the family . Cambridge, Massachusetts: Harvard University Press.

Google Scholar  

Becker, G. S., & Barro, R. J. (1988). A reformulation of the economic theory of fertility. Quarterly Journal of Economics , 103 (1), 1–26.

Beine, M., Docquier, F., & Schiff, M. (2013). International migration, transfer of norms and home country fertility. Canadian Journal of Economics , 46 (4), 1406–1430.

Blecker, R. A., & Seguino, S. (2002). Macroeconomic effects of reducing gender wage inequality in an export-oriented, semi-industrialized economy. Review of Development Economics , 6 (1), 103–119.

Bloom, D. E., Kuhn, M., & Prettner, K. (2015). The Contribution of Female Health to Economic Development . NBER Working Paper 21411, National Bureau of Economic Research, Cambridge, MA.

Borella, M., De Nardi, M., & Yang, F. (2018). The aggregate implications of gender and marriage. The Journal of the Economics of Ageing , 11 , 6–26.

Boserup, E. (1970). Woman’s role in economic development . London: George Allen and Unwin Ltd.

Branisa, B., Klasen, S., & Ziegler, M. (2013). Gender inequality in social institutions and gendered development outcomes. World Development , 45 , 252–268.

Braunstein, E. (2013). Gender, growth and employment. Development , 56 (1), 103–113.

Braunstein, E., Bouhia, R., & Seguino, S. (2020). Social reproduction, gender equality and economic growth. Cambridge Journal of Economics , 44 (1), 129–156.

Carmichael, S. G., de Pleijt, A., van Zanden, J. L., & De Moor, T. (2016). The European marriage pattern and its measurement. Journal of Economic History , 76 (01), 196–204.

Cavalcanti, T., & Tavares, J. (2016). The output cost of gender discrimination: a model-based macroeconomics estimate. Economic Journal , 126 (590), 109–134.

Cavalcanti, T. Vd. V., & Tavares, J. (2008). Assessing the "Engines of Liberation”: Home Appliances and Female Labor Force Participation. The Review of Economics and Statistics , 90 (1), 81–88.

Cortes, P. (2015). The feminization of international migration and its effects on the children left behind: evidence from the Philippines. World Development , 65 , 62–78.

Cortes, P., & Tessada, J. (2011). Low-skilled immigration and the labor supply of highly skilled women. American Economic Journal: Applied Economics , 3 (3), 88–123.

Cubeddu, L., & Ríos-Rull, J.-V. (2003). Families as shocks. Journal of the European Economic Association , 1 (2–3), 671–682.

Cuberes, D., & Teignier, M. (2014). Gender inequality and economic growth: a critical review. Journal of International Development , 26 (2), 260–276.

Cuberes, D., & Teignier, M. (2016). Aggregate effects of gender gaps in the labor market: a quantitative estimate. Journal of Human Capital , 10 (1), 1–32.

Cuberes, D., & Teignier, M. (2017). Macroeconomic costs of gender gaps in a model with entrepreneurship and household production. The B.E. Journal of Macroeconomics , 18 (1), 20170031.

De la Croix, D., & VanderDonckt, M. (2010). Would empowering women initiate the demographic transition in least developed countries? Journal of Human Capital , 4 (2), 85–129.

De Moor, T., & Van Zanden, J. L. (2010). Girl power: The European marriage pattern and labour markets in the north sea region in the late medieval and early modern period. Economic History Review , 63 (1), 1–33.

Dennison, T., & Ogilvie, S. (2014). Does the European marriage pattern explain economic growth? Journal of Economic History , 74 (3), 651–693.

Diebolt, C., & Perrin, F. (2013). From stagnation to sustained growth: the role of female empowerment. American Economic Review , 103 (3), 545–549.

Doepke, M., & Kindermann, F. (2019). Bargaining over babies: Theory, evidence, and policy implications. American Economic Review , 109 (9), 3264–3306.

Doepke, M., & Tertilt, M. (2009). Women’s Liberation: What’s in It for Men? Quarterly Journal of Economics , 124 (4), 1541–1591.

Doepke, M., & Tertilt, M. (2016). Families in macroeconomics. In J. B. Taylor and H. Uhlig (eds.), Handbook of Macroeconomics , vol. 2, Amsterdam: Elsevier, pp. 1789–1891.

Doepke, M., & Tertilt, M. (2019). Does female empowerment promote economic development? Journal of Economic Growth , 24 (4), 309–343.

Doepke, M., Tertilt, M., & Voena, A. (2012). The economics and politics of women’s rights. Annual Review of Economics , 4 (1), 339–372.

Doss, C. (2013). Intrahousehold bargaining and resource allocation in developing countries. The World Bank Research Observer , 28 (1), 52–78.

Du, Q., & Wei, S.-J. (2013). A theory of the competitive saving motive. Journal of International Economics , 91 (2), 275–289.

Duflo, E. (2003). Grandmothers and granddaughters: old-age pensions and intrahousehold allocation in South Africa. The World Bank Economic Review , 17 (1), 1–25.

Duflo, E. (2012). Women empowerment and economic development. Journal of Economic Literature , 50 (4), 1051–1079.

Dyble, M., Salali, G. D., Chaudhary, N., Page, A., Smith, D., Thompson, J., Vinicius, L., Mace, R., & Migliano, A. B. (2015). Sex equality can explain the unique social structure of hunter-gatherer bands. Science , 348 (6236), 796–798.

Echevarria, C., & Moe, K. S. (2000). On the need for gender in dynamic models. Feminist Economics , 6 (2), 77–96.

Edlund, L., & Lagerlöf, N.-P. (2006). Individual versus parental consent in marriage: implications for intra-household resource allocation and growth. American Economic Review , 96 (2), 304–307.

Edwards, J., & Ogilvie, S. (2018). Did the Black Death cause economic development by “inventing” fertility restriction? CESifo Working Papers 7016, Munich.

Erten, B., & Keskin, P. (2018). For better or for worse? Education and the prevalence of domestic violence in Turkey. American Economic Journal: Applied Economics , 10 (1), 64–105.

Esteve-Volart, B. (2009). Gender discrimination and growth: theory and evidence from India . Mimeo: York University.

Eswaran, M., & Malhotra, N. (2011). Domestic violence and women’s autonomy in developing countries: theory and evidence. Canadian Journal of Economics , 44 (4), 1222–1263.

Fine, C. (2017). Testosterone rex: Myths of sex, science, and society . New York, NY: WW Norton & Company.

Folbre, N. (1986). Hearts and spades: paradigms of household economics. World Development , 14 (2), 245–255.

Folbre, N. (1994). Who pays for the kids: gender and the structures of constraint . New York: Routledge.

Book   Google Scholar  

Folbre, N. (2020). Cooperation & conflict in the patriarchal labyrinth. Daedalus , 149 (1), 198–212.

Gaddis, I., & Klasen, S. (2014). Economic development, structural change, and women’s labor force participation. Journal of Population Economics , 27 (3), 639–681.

Galor, O. (2005a). From stagnation to growth: unified growth theory. Handbook of Economic Growth , vol. 1, North-Holland: Elsevier, pp. 171–293.

Galor, O. (2005b). The demographic transition and the emergence of sustained economic growth. Journal of the European Economic Association , 3 (2-3), 494–504.

Galor, O., & Weil, D. N. (1996). The gender gap, fertility, and growth. American Economic Review , 86 (3), 374–387.

Goldin, C. (1995). The U-shaped female labor force function in economic development and economic history. In T. P. Schultz (ed.), Investment in Women’s Human Capital and Economic Development . Chicago, IL: University of Chicago Press, pp. 61–90.

Gould, E. D., Moav, O., & Simhon, A. (2008). The mystery of monogamy. American Economic Review , 98 (1), 333–57.

Goussé, M., Jacquemet, N., & Robin, J.-M. (2017). Household labour supply and the marriage market in the UK, 1991-2008. Labour Economics , 46 , 131–149.

Greenwood, J., Guner, N., Kocharkov, G., & Santos, C. (2016). Technology and the changing family: a unified model of marriage, divorce, educational attainment, and married female labor-force participation. American Economic Journal: Macroeconomics , 8 (1), 1–41.

Greenwood, J., Guner, N., & Vandenbroucke, G. (2017). Family economics writ large. Journal of Economic Literature , 55 (4), 1346–1434.

Greenwood, J., Seshadri, A., & Yorukoglu, M. (2005). Engines of liberation. Review of Economic Studies , 72 (1), 109–133.

Grimm, M. (2003). Family and economic growth: a review. Mathematical Population Studies , 10 (3), 145–173.

Grossbard, A. (1976). An economic analysis of polygyny: The case of Maiduguri. Current Anthropology , 17 (4), 701–707.

Grossbard, S. (2010). How “Chicagoan” are Gary Becker’s Economic Models of Marriage? Journal of the History of Economic Thought , 32 (3), 377–395.

Grossbard, S. (2011). Independent individual decision-makers in household models and the New Home Economics. In J. A. Molina (ed.), Household Economic Behaviors . New York, NY: Springer, pp. 41–56.

Grossbard, S. (2015). The Marriage Motive: A Price Theory of Marriage. How Marriage Markets Affect Employment, Consumption, and Savings . New York, NY: Springer.

Grossbard, S. (2018). Marriage and Marriage Markets. In S. L. Averett, L. M. Argys and S. D. Hoffman (eds.), The Oxford Handbook of Women and the Economy . New York, NY: Oxford University Press, pp. 55–74.

Grossbard, S., & Pereira, A. M. (2015). Savings, Marriage, and Work-in-Household. In S. Grossbard, The Marriage Motive . New York, NY: Springer New York, pp. 191–209.

Grossbard-Shechtman, A. (1984). A theory of allocation of time in markets for labour and marriage. The Economic Journal , 94 (376), 863–882.

Grossbard-Shechtman, S. (2001). The new home economics at Colombia and Chicago. Feminist Economics , 7 (3), 103–130.

Guinnane, T. W. (2011). The historical fertility transition: a guide for economists. Journal of Economic Literature , 49 (3), 589–614.

Guler, B., Guvenen, F., & Violante, G. L. (2012). Joint-search theory: new opportunities and new frictions. Journal of Monetary Economics , 59 (4), 352–369.

Guner, N., Kaygusuz, R., & Ventura, G. (2012). Taxation and household labour supply. The Review of Economic Studies , 79 (3), 1113–1149.

Guvenen, F., & Rendall, M. (2015). Women’s emancipation through education: a macroeconomic analysis. Review of Economic Dynamics , 18 (4), 931–956.

Hajnal, J. (1965). European Marriage Patterns in Perspective. In D. V. Glass and D. E. C. Eversley (eds.), Population in History: Essays in Historical Demography , 6 . London: Edward Arnold Ltd, pp. 101–143.

Hajnal, J. (1982). Two kinds of preindustrial household formation system. Population and Development Review , 8 (3), 449–494.

Hansen, C. W., Jensen, P. S., & Skovsgaard, C. V. (2015). Modern gender roles and agricultural history: the neolithic inheritance. Journal of Economic Growth , 20 (4), 365–404.

Hartman, M. S. (2004). The Household and the Making of History: A Subversive View of the Western Past . Cambridge: Cambridge University Press.

Hazan, M., & Zoabi, H. (2015). Sons or daughters? Sex preferences and the reversal of the gender educational gap. Journal of Demographic Economics , 81 (2), 179–201.

Heath, R., & Tan, X. (2020). Intrahousehold bargaining, female autonomy, and labor supply: theory and evidence from India. Journal of the European Economic Association , 18 (4), 1928–1968.

Hiller, V. (2014). Gender inequality, endogenous cultural norms, and economic development. Scandinavian Journal of Economics , 116 (2), 455–481.

Hsieh, C.-T., Hurst, E., Jones, C. I., & Klenow, P. J. (2019). The allocation of talent and US economic growth. Econometrica , 87 (5), 1439–1474.

Kabeer, N. (2016). Gender equality, economic growth, and women’s agency: the “endless variety” and “monotonous similarity” of patriarchal constraints. Feminist Economics , 22 (1), 295–321.

Kabeer, N., & Natali, L. (2013). Gender Equality and Economic Growth: Is there a Win-Win? IDS Working Papers 417. Brighton: Institute of Development Studies.

Kimura, M., & Yasui, D. (2010). The Galor-Weil gender-gap model revisited: from home to market. Journal of Economic Growth , 15 , 323–351.

Klasen, S. (2018). The impact of gender inequality on economic performance in developing countries. Annual Review of Resource Economics , 10 , 279–298.

Klasen, S. (2020). From ‘MeToo’ to Boko Haram: a survey of levels and trends of gender inequality in the world. World Development , 128 , 104862.

Knowles, J. A. (2013). Why are married men working so much? An aggregate analysis of intra-household bargaining and labour supply. Review of Economic Studies , 80 (3), 1055–1085.

Lagerlöf, N.-P. (2003). Gender equality and long-run growth. Journal of Economic Growth , 8 , 403–426.

Lagerlöf, N.-P. (2005). Sex, equality, and growth. Canadian Journal of Economics , 38 (3), 807–831.

Lagerlöf, N.-P. (2010). Pacifying monogamy. Journal of Economic Growth , 15 (3), 235–262.

Lee, M. (2020). Allocation of Female Talent and Cross-Country Productivity Differences . Mimeo: UC San Diego.

Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics , 22 (1), 3–42.

Lundberg, S. J., Pollak, R. A., & Wales, T. J. (1997). Do husbands and wives pool their resources? Evidence from the United Kingdom child benefit. Journal of Human Resources , 32 (3), 463–480.

Martineau, H. (1837). Society in America , vol. 3. London: Saunders & Otley.

Matsumoto, S. (2014). Spouses’ time allocation to pro-environmental activities: Who is saving the environment at home? Review of Economics of the Household , 12 (1), 159–176.

Meier, V., & Rainer, H. (2015). Pigou meets Ramsey: gender-based taxation with non-cooperative couples. European Economic Review , 77 , 28–46.

Mulder, M. B., & Rauch, K. L. (2009). Sexual conflict in humans: variations and solutions. Evolutionary Anthropology: Issues, News, and Reviews , 18 (5), 201–214.

Penn, D. J., & Smith, K. R. (2007). Differential fitness costs of reproduction between the sexes. Proceedings of the National Academy of Sciences , 104 (2), 553–558.

Prettner, K., & Strulik, H. (2017). Gender equity and the escape from poverty. Oxford Economic Papers , 69 (1), 55–74.

Rees, R., & Riezman, R. (2012). Globalization, gender, and growth. Review of Income and Wealth , 58 (1), 107–117.

Reher, D. S. (2004). The demographic transition revisited as a global process. Population, Space and Place , 10 (1), 19–41.

Roy, A. D. (1951). Some thoughts on the distribution of earnings. Oxford Economic Papers , 3 (2), 135–146.

Ruggles, S. (2009). Reconsidering the Northwest European Family System: Living Arrangements of the Aged in Comparative Historical Perspective. Population and Development Review , 35 (2), 249–273.

Seguino, S. (2010). Gender, distribution, and balance of payments constrained growth in developing countries. Review of Political Economy , 22 (3), 373–404.

Seguino, S. (2013). From micro-level gender relations to the macro economy and back again. In D. M. Figart and T. L. Warnecke (eds.), Handbook of Research on Gender and Economic Life . Cheltenham: Edward Elgar Publishing, pp. 325–344.

Seguino, S. (2020). Engendering macroeconomic theory and policy. Feminist Economics , 26 , 27–61.

Sen, A. (1989). Cooperation, inequality, and the family. Population and Development Review , 15 , 61–76.

Sinha, N., Raju, D., & Morrison, A. (2007). Gender equality, poverty and economic growth . World Bank Policy Research Paper 4349. Washington, DC: The World Bank.

Stotsky, J. G. (2006). Gender and its relevance to macroeconomic policy: a survey . IMF Working Paper 06/233. Washington, DC: International Monetary Fund.

Strulik, H. (2019). Desire and development. Macroeconomic Dynamics , 23 (7), 2717–2747.

Tejani, S., & Milberg, W. (2016). Global defeminization? Industrial upgrading and manufacturing employment in developing countries. Feminist Economics , 22 (2), 24–54.

Tertilt, M. (2005). Polygyny, fertility, and savings. Journal of Political Economy , 113 (6), 1341–1371.

Tertilt, M. (2006). Polygyny, women’s rights, and development. Journal of the European Economic Association , 4 , 523–530.

Tuccio, M., & Wahba, J. (2018). Return migration and the transfer of gender norms: evidence from the Middle East. Journal of Comparative Economics , 46 (4), 1006–1029.

Voigtländer, N., & Voth, H.-J. (2013). How the West “invented” fertility restriction. American Economic Review , 103 (6), 2227–2264.

von Rueden, C. R., & Jaeggi, A. V. (2016). Men’s status and reproductive success in 33 nonindustrial societies: effects of subsistence, marriage system and reproductive strategy. Proceedings of the National Academy of Sciences , 113 (39), 10824–10829.

Walther, S. (2017). Moral hazard in marriage: the use of domestic labor as an incentive device. Review of Economics of the Household , 15 (2), 357–382.

Wong, H.-P. C. (2016). Credible commitments and marriage: when the homemaker gets her share at divorce. Journal of Demographic Economics , 82 (3), 241–279.

World Bank (2001). Engendering Development Through Gender Equality in Rights, Resources, and Voice . New York, NY: Oxford University Press.

World Bank (2011). World Development Report 2012: Gender Equality and Development . Washington, DC: The World Bank.

Zhang, J., Zhang, J., & Li, T. (1999). Gender bias and economic development in an endogenous growth model. Journal of Development Economics , 59 (2), 497–525.

Download references

Acknowledgements

We thank the Editor, Shoshana Grossbard, and three anonymous reviewers for helpful comments. We gratefully acknowledge funding from the Growth and Economic Opportunities for Women (GrOW) initiative, a multi-funder partnership between the UK’s Department for International Development, the Hewlett Foundation and the International Development Research Centre. All views expressed here and remaining errors are our own. Manuel dedicates this article to Stephan Klasen, in loving memory.

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and affiliations.

Department of Economics, University of Goettingen, Platz der Goettinger Sieben 3, 37073, Goettingen, Germany

Manuel Santos Silva & Stephan Klasen

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Manuel Santos Silva .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Santos Silva, M., Klasen, S. Gender inequality as a barrier to economic growth: a review of the theoretical literature. Rev Econ Household 19 , 581–614 (2021). https://doi.org/10.1007/s11150-020-09535-6

Download citation

Received : 27 May 2019

Accepted : 07 December 2020

Published : 15 January 2021

Issue Date : September 2021

DOI : https://doi.org/10.1007/s11150-020-09535-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Gender equality
  • Economic growth
  • Human capital
  • Comparative development

JEL classification

  • Find a journal
  • Publish with us
  • Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Published: 10 February 2022

The influence of pay transparency on (gender) inequity, inequality and the performance basis of pay

  • Tomasz Obloj   ORCID: orcid.org/0000-0003-2089-0074 1 &
  • Todd Zenger   ORCID: orcid.org/0000-0002-9830-4066 2  

Nature Human Behaviour volume  6 ,  pages 646–655 ( 2022 ) Cite this article

3583 Accesses

20 Citations

162 Altmetric

Metrics details

  • Business and management
  • Scientific community

Recent decades have witnessed a growing focus on two distinct income patterns: persistent pay inequity, particularly a gender pay gap, and growing pay inequality. Pay transparency is widely advanced as a remedy for both. Yet we know little about the systemic influence of this policy on the evolution of pay practices within organizations. To address this void, we assemble a dataset combining detailed performance, demographic and salary data for approximately 100,000 US academics between 1997 and 2017. We then exploit staggered shocks to wage transparency to explore how this change reshapes pay practices. We find evidence that pay transparency causes significant increases in both the equity and equality of pay, and significant and sizeable reductions in the link between pay and individually measured performance.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

scholarly articles on gender inequality

Data availability

All data that support the findings of this study have been deposited in the Open Inter-university Consortium for Political and Social Research Repository under project number 155541 and are available at https://doi.org/10.3886/E155541V1 . The names of individual academics and institutions have been blinded and are represented in the data with author-generated unique identifiers.

Code availability

The statistical code generating all results reported in the manuscript has been deposited in the Open Inter-university Consortium for Political and Social Research Repository under project number 155541 and is available at https://doi.org/10.3886/E155541V1 .

Shen, H. Inequality quantified: mind the gender gap. Nature 495 , 22–24 (2013).

Article   CAS   Google Scholar  

Piketty, T. & Saez, E. Inequality in the long run. Science 344 , 838–843 (2014).

Eckhoff, T. Justice: Its Determinants in Social Interaction (Rotterdam Univ. Press, 1974).

Blau, F. D. & Kahn, L. M. in The New Palgrave Dictionary of Economics (eds Vernengo, M. et al.) 7118–7128 (Palgrave Macmillan, 2008).

Blau, F. D. & Kahn, L. M. The gender wage gap: extent, trends, and explanations. J. Econ. Lit. 55 , 789–865 (2017).

Article   Google Scholar  

Cobb, A. J. How firms shape income inequality: stakeholder power, executive decision making, and the structuring of employment relationships. Acad. Manage. Rev. 41 , 324–348 (2015).

Alvaredo, F., Chancel, L., Piketty, T., Saez, E. & Zucman, G. World Inequality Report 2018 (World Inequality Lab, 2018).

Ramachandran, G. Pay transparency. Penn St. L. Rev. 4 , 1043–1080 (2011).

Google Scholar  

Private Sector Workers Lack Pay Transparency: Pay Secrecy May Reduce Women’s Bargaining Power and Contribute to Gender Wage Gap (Institute for Women’s Policy Research, 2017); https://iwpr.org/wp-content/uploads/2020/09/Q068-Pay-Secrecy.pdf

Mas, A. Does transparency lead to pay compression? J. Polit. Econ. 125 , 1683–1721 (2017).

Baker, M., Halberstam, Y., Kroft, K., Mas, A. & Messacar, D. Pay transparency and the gender gap. Preprint at NBER https://www.nber.org/papers/w25834 (2019).

Bennedsen, M., Simintzi, E., Tsoutsoura, M. & Wolfenzon, D. Do firms respond to gender pay gap transparency? Preprint at NBER https://www.nber.org/papers/w25435 (2020).

Cullen, Z. B. & Pakzad-Hurson, B. Equilibrium effects of pay transparency. Preprint at NBER https://www.nber.org/papers/w28903 (2021).

Gartenberg, C. & Wulf, J. Pay harmony? Social comparison and performance compensation in multibusiness firms. Organ. Sci. 28 , 39–55 (2017).

Ma, Y., Oliveira, D. F., Woodruff, T. K. & Uzzi, B. Women who win prizes get less money and prestige. Nature 565 , 287–288 (2019).

Blau, F. D. & DeVaro, J. New evidence on gender differences in promotion rates: an empirical analysis of a sample of new hires. Ind. Relat. 46 , 511–550 (2007).

Babcock, L., Recalde, M. P., Vesterlund, L. & Weingart, L. Gender differences in accepting and receiving requests for tasks with low promotability. Am. Econ. Rev. 107 , 714–747 (2017).

Sarsons, H. Recognition for group work: gender differences in academia. Am. Econ. Rev. Pap. Proc. 2017 107 , 141–145 (2017).

2014/124/EU: Commission Recommendation of 7 March 2014 on Strengthening the Principle of Equal Pay Between Men and Women Through Transparency (Text with EEA Relevance) (European Commission, 2014); https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32014H0124&from=EN

Correia, S. Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator. Working Paper http://scorreia.com/research/hdfe.pdf (2017).

Deshpande, M. & Li, Y. Who is screened out? Application costs and the targeting of disability programs. Am. Econ. J. Econ. Policy 11 , 213–248 (2019).

Freyaldenhoven, S., Hansen, C. & Shapiro, J. M. Pre-event trends in the panel event-study design. Am. Econ. Rev. 109 , 3307–3338 (2019).

Cook, K. S. & Hegtvedt, K. A. Distributive justice, equity, and equality. Annu. Rev. Sociol. 9 , 217–241 (1983).

Godechot, O. & Senik, C. Wage comparisons in and out of the firm: evidence from a matched employer–employee French database. J. Econ. Behav. Organ. 117 , 395–410 (2015).

Card, D., Mas, A., Moretti, E. & Saez, E. Inequality at work: the effect of peer salaries on job satisfaction. Am. Econ. Rev. 102 , 2981–3003 (2012).

Luttmer, E. F. P. Neighbors as negatives: relative earnings and well-being. Q. J. Econ. 120 , 963–1002 (2005).

Camerer, C. F. & Fehr, E. When does “economic man” dominate social behavior? Science 311 , 47–52 (2006).

Brosnan, S. F. & Waal de, F. M. B. Evolution of responses to (un)fairness. Science 346 , 1251776 (2014).

Nickerson, J. A. & Zenger, T. Envy, comparison costs, and the economic theory of the firm. Strateg. Manage. J. 29 , 1429–1449 (2008).

Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O. & Ng, K. Y. Justice at the millennium: a meta-analytic review of 25 years of organizational justice research. J. Appl. Psychol. 86 , 425–445 (2001).

Obloj, T. & Zenger, T. Organization design, proximity, and productivity responses to upward social comparison. Organ. Sci. 28 , 1–18 (2017).

Basurto, X., Blanco, E., Nenadovic, M. & Vollan, B. Integrating simultaneous prosocial and antisocial behavior into theories of collective action. Sci. Adv. 2 , e1501220 (2016).

Breza, E., Kaur, S. & Shamdasani, Y. The morale effects of pay inequality. Q. J. Econ. 133 , 611–663 (2018).

Auspurg, K., Hinz, T. & Sauer, C. Why should women get less? Evidence on the gender pay gap from multifactorial survey experiments. Am. Sociol. Rev. 82 , 179–210 (2017).

Perez-Truglia, R. The effects of income transparency on well-being: evidence from a natural experiment. Am. Econ. Rev. 110 , 1019–1054 (2020).

Malani, A. & Reif, J. Interpreting pre-trends as anticipation: impact on estimated treatment effects from tort reform. J. Public Econ. 124 , 1–17 (2015).

Download references

Acknowledgements

We thank O. Shelef, J. Snyder, Z. Cullen and M. Higgins for helpful comments and suggestions, as well as seminar participants at Brigham Young University, Carnegie Mellon University, Dartmouth College, Harvard University, LMU, Ohio State University, Purdue University, the University of Indiana, the University of Maryland, the University of Michigan, the University of Minnesota and the University of Utah. We particularly thank A. Olejniczak and his team at Academic Analytics; various data providers with government agencies and universities in the states of California, Connecticut, Florida, New York, Pennsylvania, Texas, Virginia and West Virginia; and A. Eichholtzer, J. Cox and H. Yang for research assistantship. This research has been partially funded by the French National Research Agency Grant No. ANR-16-TERC-0020-01 (T.O.). Academic Analytics and this funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and affiliations.

HEC Paris, Jouy-en-Josas, France

Tomasz Obloj

University of Utah, Salt Lake City, UT, USA

Todd Zenger

You can also search for this author in PubMed   Google Scholar

Contributions

T.O. and T.Z. jointly conceived the project and supervised the data collection. T.O. conducted the data analyses with input from T.Z. T.O. and T.Z. jointly drafted the manuscript.

Corresponding author

Correspondence to Tomasz Obloj .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Human Behaviour thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended data fig. 1 the unconditional and conditional gender wage gap..

Notes: The figure presents OLS regression estimates explaining (ln) salaries. Plotted coefficients of year dummies interacted with Female indicator, with 95% confidence intervals. Levels are scaled by the value on un-interacted Female indicator. Unconditional gap is based on a model with year dummies only. Conditional gap is based on models with year, academic domain, and institution fixed effects as well as controls for academic tenure (ln), number of academic articles, number of published books, number of awards, number of grants, and number of patents. Regression results used to generate this plot are reported in Supplementary Table 3.1 .

Extended Data Fig. 2 Equity in organizations: The effect of pay transparency on gender wage gap.

Notes: The figure presents regression coefficients from an OLS regression model explaining (ln) wages. Reference category is 1 year prior to transparency shock. Plotted coefficients: dummy variable for Female interacted with years from (to) transparency shock with 95% CIs. Standard errors clustered on institution. Controls include academic tenure (ln), number of published academic articles, number of published books, number of awards, number of grants, and number of patents, and institution, individual, and year fixed effects. Regression results used to generate this plot are reported in Supplementary Table 3.3 .

Extended Data Fig. 3 Equity in organizations: The effect of pay transparency on gender wage gap: additional specifications.

Notes: The figure presents regression coefficients from an OLS regression model explaining (ln) wages. Reference category is 1 year prior to transparency shock (1 and 2 years for 2SLS results, panel C). Plotted coefficients: dummy variable for Female interacted with years from (to) transparency shock with 95% CIs. Standard errors clustered on institution. Controls include academic tenure (ln), number of published academic articles, number of published books, number of awards, number of grants, and number of patents, and institution, individual, and year fixed effects. Panel A – population restricted to 2004-2013 inclusive; Panel B – stacked difference in differences model (see text for more details). Panel C – 2SLS results, instrumented covariate: women’s mean earnings as a % of men’s in the private sector (see below for more details), excluded instrument: lead of transparency shock. Panel D and E – population restricted to CT, FL, PA, TX, VA (omitted states: California, New York, West Virginia). Panel F – full population. Regression results used to generate these plots are reported in Supplementary Tables 3.3 , 3.4 , and 3.5 .

Extended Data Fig. 4 Equity and equality in organizations: The effect of wage transparency on variance in market wage residuals and wage variance.

Notes: The figures present regression coefficients from an OLS regression model explaining variance in market wage residuals (Left panel) and variance in (ln) wages (Right panel). Reference category is 1 year prior to transparency shock. Both variables are calculated within Institution-Academic Field (11 categories). Plotted coefficients: years from (to) transparency shock with 95% CIs. Standard errors clustered on institution. Controls include reference group mean productivity levels and reference group productivity variances as well as year and academic field-institution fixed effects. Regression results used to generate these plots are reported in Supplementary Table 4.3 .

Extended Data Fig. 5 Equality in organizations: Distribution of market wage residuals, by transparency shock.

Notes: The figure presents kernel density estimates of wage regression residuals by transparency shocks. Controls include institution, academic domain, and year fixed effects. Means and standard deviations are calculated averaging across all time periods. Residuals trimmed at 1% and 99%. Two-sample combined Kolmogorov-Smirnov tests for equality of distribution functions: 0.041, p-value<0.001.

Supplementary information

Supplementary information.

Supplementary Sections 1–6 and Tables 1.1–6.3.

Reporting Summary.

Peer review information., rights and permissions.

Reprints and permissions

About this article

Cite this article.

Obloj, T., Zenger, T. The influence of pay transparency on (gender) inequity, inequality and the performance basis of pay. Nat Hum Behav 6 , 646–655 (2022). https://doi.org/10.1038/s41562-022-01288-9

Download citation

Received : 05 May 2020

Accepted : 04 January 2022

Published : 10 February 2022

Issue Date : May 2022

DOI : https://doi.org/10.1038/s41562-022-01288-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

A bibliometric analysis of the gender gap in the authorship of leading medical journals.

  • Oscar Brück

Communications Medicine (2023)

A strategic approach to managerial compliance with equal pay policies

  • Julien Picault

SN Business & Economics (2023)

Performance-based incentives and innovative activity in small firms: evidence from German manufacturing

  • Karl Aschenbrücker
  • Tobias Kretschmer

Journal of Organization Design (2022)

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

scholarly articles on gender inequality

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Twenty years of gender equality research: A scoping review based on a new semantic indicator

Contributed equally to this work with: Paola Belingheri, Filippo Chiarello, Andrea Fronzetti Colladon, Paola Rovelli

Roles Conceptualization, Formal analysis, Funding acquisition, Visualization, Writing – original draft, Writing – review & editing

Affiliation Dipartimento di Ingegneria dell’Energia, dei Sistemi, del Territorio e delle Costruzioni, Università degli Studi di Pisa, Largo L. Lazzarino, Pisa, Italy

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Visualization, Writing – original draft, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Engineering, University of Perugia, Perugia, Italy, Department of Management, Kozminski University, Warsaw, Poland

ORCID logo

Roles Conceptualization, Formal analysis, Funding acquisition, Writing – original draft, Writing – review & editing

Affiliation Faculty of Economics and Management, Centre for Family Business Management, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

  • Paola Belingheri, 
  • Filippo Chiarello, 
  • Andrea Fronzetti Colladon, 
  • Paola Rovelli

PLOS

  • Published: September 21, 2021
  • https://doi.org/10.1371/journal.pone.0256474
  • Reader Comments

9 Nov 2021: The PLOS ONE Staff (2021) Correction: Twenty years of gender equality research: A scoping review based on a new semantic indicator. PLOS ONE 16(11): e0259930. https://doi.org/10.1371/journal.pone.0259930 View correction

Table 1

Gender equality is a major problem that places women at a disadvantage thereby stymieing economic growth and societal advancement. In the last two decades, extensive research has been conducted on gender related issues, studying both their antecedents and consequences. However, existing literature reviews fail to provide a comprehensive and clear picture of what has been studied so far, which could guide scholars in their future research. Our paper offers a scoping review of a large portion of the research that has been published over the last 22 years, on gender equality and related issues, with a specific focus on business and economics studies. Combining innovative methods drawn from both network analysis and text mining, we provide a synthesis of 15,465 scientific articles. We identify 27 main research topics, we measure their relevance from a semantic point of view and the relationships among them, highlighting the importance of each topic in the overall gender discourse. We find that prominent research topics mostly relate to women in the workforce–e.g., concerning compensation, role, education, decision-making and career progression. However, some of them are losing momentum, and some other research trends–for example related to female entrepreneurship, leadership and participation in the board of directors–are on the rise. Besides introducing a novel methodology to review broad literature streams, our paper offers a map of the main gender-research trends and presents the most popular and the emerging themes, as well as their intersections, outlining important avenues for future research.

Citation: Belingheri P, Chiarello F, Fronzetti Colladon A, Rovelli P (2021) Twenty years of gender equality research: A scoping review based on a new semantic indicator. PLoS ONE 16(9): e0256474. https://doi.org/10.1371/journal.pone.0256474

Editor: Elisa Ughetto, Politecnico di Torino, ITALY

Received: June 25, 2021; Accepted: August 6, 2021; Published: September 21, 2021

Copyright: © 2021 Belingheri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its supporting information files. The only exception is the text of the abstracts (over 15,000) that we have downloaded from Scopus. These abstracts can be retrieved from Scopus, but we do not have permission to redistribute them.

Funding: P.B and F.C.: Grant of the Department of Energy, Systems, Territory and Construction of the University of Pisa (DESTEC) for the project “Measuring Gender Bias with Semantic Analysis: The Development of an Assessment Tool and its Application in the European Space Industry. P.B., F.C., A.F.C., P.R.: Grant of the Italian Association of Management Engineering (AiIG), “Misure di sostegno ai soci giovani AiIG” 2020, for the project “Gender Equality Through Data Intelligence (GEDI)”. F.C.: EU project ASSETs+ Project (Alliance for Strategic Skills addressing Emerging Technologies in Defence) EAC/A03/2018 - Erasmus+ programme, Sector Skills Alliances, Lot 3: Sector Skills Alliance for implementing a new strategic approach (Blueprint) to sectoral cooperation on skills G.A. NUMBER: 612678-EPP-1-2019-1-IT-EPPKA2-SSA-B.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The persistent gender inequalities that currently exist across the developed and developing world are receiving increasing attention from economists, policymakers, and the general public [e.g., 1 – 3 ]. Economic studies have indicated that women’s education and entry into the workforce contributes to social and economic well-being [e.g., 4 , 5 ], while their exclusion from the labor market and from managerial positions has an impact on overall labor productivity and income per capita [ 6 , 7 ]. The United Nations selected gender equality, with an emphasis on female education, as part of the Millennium Development Goals [ 8 ], and gender equality at-large as one of the 17 Sustainable Development Goals (SDGs) to be achieved by 2030 [ 9 ]. These latter objectives involve not only developing nations, but rather all countries, to achieve economic, social and environmental well-being.

As is the case with many SDGs, gender equality is still far from being achieved and persists across education, access to opportunities, or presence in decision-making positions [ 7 , 10 , 11 ]. As we enter the last decade for the SDGs’ implementation, and while we are battling a global health pandemic, effective and efficient action becomes paramount to reach this ambitious goal.

Scholars have dedicated a massive effort towards understanding gender equality, its determinants, its consequences for women and society, and the appropriate actions and policies to advance women’s equality. Many topics have been covered, ranging from women’s education and human capital [ 12 , 13 ] and their role in society [e.g., 14 , 15 ], to their appointment in firms’ top ranked positions [e.g., 16 , 17 ] and performance implications [e.g., 18 , 19 ]. Despite some attempts, extant literature reviews provide a narrow view on these issues, restricted to specific topics–e.g., female students’ presence in STEM fields [ 20 ], educational gender inequality [ 5 ], the gender pay gap [ 21 ], the glass ceiling effect [ 22 ], leadership [ 23 ], entrepreneurship [ 24 ], women’s presence on the board of directors [ 25 , 26 ], diversity management [ 27 ], gender stereotypes in advertisement [ 28 ], or specific professions [ 29 ]. A comprehensive view on gender-related research, taking stock of key findings and under-studied topics is thus lacking.

Extant literature has also highlighted that gender issues, and their economic and social ramifications, are complex topics that involve a large number of possible antecedents and outcomes [ 7 ]. Indeed, gender equality actions are most effective when implemented in unison with other SDGs (e.g., with SDG 8, see [ 30 ]) in a synergetic perspective [ 10 ]. Many bodies of literature (e.g., business, economics, development studies, sociology and psychology) approach the problem of achieving gender equality from different perspectives–often addressing specific and narrow aspects. This sometimes leads to a lack of clarity about how different issues, circumstances, and solutions may be related in precipitating or mitigating gender inequality or its effects. As the number of papers grows at an increasing pace, this issue is exacerbated and there is a need to step back and survey the body of gender equality literature as a whole. There is also a need to examine synergies between different topics and approaches, as well as gaps in our understanding of how different problems and solutions work together. Considering the important topic of women’s economic and social empowerment, this paper aims to fill this gap by answering the following research question: what are the most relevant findings in the literature on gender equality and how do they relate to each other ?

To do so, we conduct a scoping review [ 31 ], providing a synthesis of 15,465 articles dealing with gender equity related issues published in the last twenty-two years, covering both the periods of the MDGs and the SDGs (i.e., 2000 to mid 2021) in all the journals indexed in the Academic Journal Guide’s 2018 ranking of business and economics journals. Given the huge amount of research conducted on the topic, we adopt an innovative methodology, which relies on social network analysis and text mining. These techniques are increasingly adopted when surveying large bodies of text. Recently, they were applied to perform analysis of online gender communication differences [ 32 ] and gender behaviors in online technology communities [ 33 ], to identify and classify sexual harassment instances in academia [ 34 ], and to evaluate the gender inclusivity of disaster management policies [ 35 ].

Applied to the title, abstracts and keywords of the articles in our sample, this methodology allows us to identify a set of 27 recurrent topics within which we automatically classify the papers. Introducing additional novelty, by means of the Semantic Brand Score (SBS) indicator [ 36 ] and the SBS BI app [ 37 ], we assess the importance of each topic in the overall gender equality discourse and its relationships with the other topics, as well as trends over time, with a more accurate description than that offered by traditional literature reviews relying solely on the number of papers presented in each topic.

This methodology, applied to gender equality research spanning the past twenty-two years, enables two key contributions. First, we extract the main message that each document is conveying and how this is connected to other themes in literature, providing a rich picture of the topics that are at the center of the discourse, as well as of the emerging topics. Second, by examining the semantic relationship between topics and how tightly their discourses are linked, we can identify the key relationships and connections between different topics. This semi-automatic methodology is also highly reproducible with minimum effort.

This literature review is organized as follows. In the next section, we present how we selected relevant papers and how we analyzed them through text mining and social network analysis. We then illustrate the importance of 27 selected research topics, measured by means of the SBS indicator. In the results section, we present an overview of the literature based on the SBS results–followed by an in-depth narrative analysis of the top 10 topics (i.e., those with the highest SBS) and their connections. Subsequently, we highlight a series of under-studied connections between the topics where there is potential for future research. Through this analysis, we build a map of the main gender-research trends in the last twenty-two years–presenting the most popular themes. We conclude by highlighting key areas on which research should focused in the future.

Our aim is to map a broad topic, gender equality research, that has been approached through a host of different angles and through different disciplines. Scoping reviews are the most appropriate as they provide the freedom to map different themes and identify literature gaps, thereby guiding the recommendation of new research agendas [ 38 ].

Several practical approaches have been proposed to identify and assess the underlying topics of a specific field using big data [ 39 – 41 ], but many of them fail without proper paper retrieval and text preprocessing. This is specifically true for a research field such as the gender-related one, which comprises the work of scholars from different backgrounds. In this section, we illustrate a novel approach for the analysis of scientific (gender-related) papers that relies on methods and tools of social network analysis and text mining. Our procedure has four main steps: (1) data collection, (2) text preprocessing, (3) keywords extraction and classification, and (4) evaluation of semantic importance and image.

Data collection

In this study, we analyze 22 years of literature on gender-related research. Following established practice for scoping reviews [ 42 ], our data collection consisted of two main steps, which we summarize here below.

Firstly, we retrieved from the Scopus database all the articles written in English that contained the term “gender” in their title, abstract or keywords and were published in a journal listed in the Academic Journal Guide 2018 ranking of the Chartered Association of Business Schools (CABS) ( https://charteredabs.org/wp-content/uploads/2018/03/AJG2018-Methodology.pdf ), considering the time period from Jan 2000 to May 2021. We used this information considering that abstracts, titles and keywords represent the most informative part of a paper, while using the full-text would increase the signal-to-noise ratio for information extraction. Indeed, these textual elements already demonstrated to be reliable sources of information for the task of domain lexicon extraction [ 43 , 44 ]. We chose Scopus as source of literature because of its popularity, its update rate, and because it offers an API to ease the querying process. Indeed, while it does not allow to retrieve the full text of scientific articles, the Scopus API offers access to titles, abstracts, citation information and metadata for all its indexed scholarly journals. Moreover, we decided to focus on the journals listed in the AJG 2018 ranking because we were interested in reviewing business and economics related gender studies only. The AJG is indeed widely used by universities and business schools as a reference point for journal and research rigor and quality. This first step, executed in June 2021, returned more than 55,000 papers.

In the second step–because a look at the papers showed very sparse results, many of which were not in line with the topic of this literature review (e.g., papers dealing with health care or medical issues, where the word gender indicates the gender of the patients)–we applied further inclusion criteria to make the sample more focused on the topic of this literature review (i.e., women’s gender equality issues). Specifically, we only retained those papers mentioning, in their title and/or abstract, both gender-related keywords (e.g., daughter, female, mother) and keywords referring to bias and equality issues (e.g., equality, bias, diversity, inclusion). After text pre-processing (see next section), keywords were first identified from a frequency-weighted list of words found in the titles, abstracts and keywords in the initial list of papers, extracted through text mining (following the same approach as [ 43 ]). They were selected by two of the co-authors independently, following respectively a bottom up and a top-down approach. The bottom-up approach consisted of examining the words found in the frequency-weighted list and classifying those related to gender and equality. The top-down approach consisted in searching in the word list for notable gender and equality-related words. Table 1 reports the sets of keywords we considered, together with some examples of words that were used to search for their presence in the dataset (a full list is provided in the S1 Text ). At end of this second step, we obtained a final sample of 15,465 relevant papers.

thumbnail

  • PPT PowerPoint slide
  • PNG larger image
  • TIFF original image

https://doi.org/10.1371/journal.pone.0256474.t001

Text processing and keyword extraction

Text preprocessing aims at structuring text into a form that can be analyzed by statistical models. In the present section, we describe the preprocessing steps we applied to paper titles and abstracts, which, as explained below, partially follow a standard text preprocessing pipeline [ 45 ]. These activities have been performed using the R package udpipe [ 46 ].

The first step is n-gram extraction (i.e., a sequence of words from a given text sample) to identify which n-grams are important in the analysis, since domain-specific lexicons are often composed by bi-grams and tri-grams [ 47 ]. Multi-word extraction is usually implemented with statistics and linguistic rules, thus using the statistical properties of n-grams or machine learning approaches [ 48 ]. However, for the present paper, we used Scopus metadata in order to have a more effective and efficient n-grams collection approach [ 49 ]. We used the keywords of each paper in order to tag n-grams with their associated keywords automatically. Using this greedy approach, it was possible to collect all the keywords listed by the authors of the papers. From this list, we extracted only keywords composed by two, three and four words, we removed all the acronyms and rare keywords (i.e., appearing in less than 1% of papers), and we clustered keywords showing a high orthographic similarity–measured using a Levenshtein distance [ 50 ] lower than 2, considering these groups of keywords as representing same concepts, but expressed with different spelling. After tagging the n-grams in the abstracts, we followed a common data preparation pipeline that consists of the following steps: (i) tokenization, that splits the text into tokens (i.e., single words and previously tagged multi-words); (ii) removal of stop-words (i.e. those words that add little meaning to the text, usually being very common and short functional words–such as “and”, “or”, or “of”); (iii) parts-of-speech tagging, that is providing information concerning the morphological role of a word and its morphosyntactic context (e.g., if the token is a determiner, the next token is a noun or an adjective with very high confidence, [ 51 ]); and (iv) lemmatization, which consists in substituting each word with its dictionary form (or lemma). The output of the latter step allows grouping together the inflected forms of a word. For example, the verbs “am”, “are”, and “is” have the shared lemma “be”, or the nouns “cat” and “cats” both share the lemma “cat”. We preferred lemmatization over stemming [ 52 ] in order to obtain more interpretable results.

In addition, we identified a further set of keywords (with respect to those listed in the “keywords” field) by applying a series of automatic words unification and removal steps, as suggested in past research [ 53 , 54 ]. We removed: sparse terms (i.e., occurring in less than 0.1% of all documents), common terms (i.e., occurring in more than 10% of all documents) and retained only nouns and adjectives. It is relevant to notice that no document was lost due to these steps. We then used the TF-IDF function [ 55 ] to produce a new list of keywords. We additionally tested other approaches for the identification and clustering of keywords–such as TextRank [ 56 ] or Latent Dirichlet Allocation [ 57 ]–without obtaining more informative results.

Classification of research topics

To guide the literature analysis, two experts met regularly to examine the sample of collected papers and to identify the main topics and trends in gender research. Initially, they conducted brainstorming sessions on the topics they expected to find, due to their knowledge of the literature. This led to an initial list of topics. Subsequently, the experts worked independently, also supported by the keywords in paper titles and abstracts extracted with the procedure described above.

Considering all this information, each expert identified and clustered relevant keywords into topics. At the end of the process, the two assignments were compared and exhibited a 92% agreement. Another meeting was held to discuss discordant cases and reach a consensus. This resulted in a list of 27 topics, briefly introduced in Table 2 and subsequently detailed in the following sections.

thumbnail

https://doi.org/10.1371/journal.pone.0256474.t002

Evaluation of semantic importance

Working on the lemmatized corpus of the 15,465 papers included in our sample, we proceeded with the evaluation of semantic importance trends for each topic and with the analysis of their connections and prevalent textual associations. To this aim, we used the Semantic Brand Score indicator [ 36 ], calculated through the SBS BI webapp [ 37 ] that also produced a brand image report for each topic. For this study we relied on the computing resources of the ENEA/CRESCO infrastructure [ 58 ].

The Semantic Brand Score (SBS) is a measure of semantic importance that combines methods of social network analysis and text mining. It is usually applied for the analysis of (big) textual data to evaluate the importance of one or more brands, names, words, or sets of keywords [ 36 ]. Indeed, the concept of “brand” is intended in a flexible way and goes beyond products or commercial brands. In this study, we evaluate the SBS time-trends of the keywords defining the research topics discussed in the previous section. Semantic importance comprises the three dimensions of topic prevalence, diversity and connectivity. Prevalence measures how frequently a research topic is used in the discourse. The more a topic is mentioned by scientific articles, the more the research community will be aware of it, with possible increase of future studies; this construct is partly related to that of brand awareness [ 59 ]. This effect is even stronger, considering that we are analyzing the title, abstract and keywords of the papers, i.e. the parts that have the highest visibility. A very important characteristic of the SBS is that it considers the relationships among words in a text. Topic importance is not just a matter of how frequently a topic is mentioned, but also of the associations a topic has in the text. Specifically, texts are transformed into networks of co-occurring words, and relationships are studied through social network analysis [ 60 ]. This step is necessary to calculate the other two dimensions of our semantic importance indicator. Accordingly, a social network of words is generated for each time period considered in the analysis–i.e., a graph made of n nodes (words) and E edges weighted by co-occurrence frequency, with W being the set of edge weights. The keywords representing each topic were clustered into single nodes.

The construct of diversity relates to that of brand image [ 59 ], in the sense that it considers the richness and distinctiveness of textual (topic) associations. Considering the above-mentioned networks, we calculated diversity using the distinctiveness centrality metric–as in the formula presented by Fronzetti Colladon and Naldi [ 61 ].

Lastly, connectivity was measured as the weighted betweenness centrality [ 62 , 63 ] of each research topic node. We used the formula presented by Wasserman and Faust [ 60 ]. The dimension of connectivity represents the “brokerage power” of each research topic–i.e., how much it can serve as a bridge to connect other terms (and ultimately topics) in the discourse [ 36 ].

The SBS is the final composite indicator obtained by summing the standardized scores of prevalence, diversity and connectivity. Standardization was carried out considering all the words in the corpus, for each specific timeframe.

This methodology, applied to a large and heterogeneous body of text, enables to automatically identify two important sets of information that add value to the literature review. Firstly, the relevance of each topic in literature is measured through a composite indicator of semantic importance, rather than simply looking at word frequencies. This provides a much richer picture of the topics that are at the center of the discourse, as well as of the topics that are emerging in the literature. Secondly, it enables to examine the extent of the semantic relationship between topics, looking at how tightly their discourses are linked. In a field such as gender equality, where many topics are closely linked to each other and present overlaps in issues and solutions, this methodology offers a novel perspective with respect to traditional literature reviews. In addition, it ensures reproducibility over time and the possibility to semi-automatically update the analysis, as new papers become available.

Overview of main topics

In terms of descriptive textual statistics, our corpus is made of 15,465 text documents, consisting of a total of 2,685,893 lemmatized tokens (words) and 32,279 types. As a result, the type-token ratio is 1.2%. The number of hapaxes is 12,141, with a hapax-token ratio of 37.61%.

Fig 1 shows the list of 27 topics by decreasing SBS. The most researched topic is compensation , exceeding all others in prevalence, diversity, and connectivity. This means it is not only mentioned more often than other topics, but it is also connected to a greater number of other topics and is central to the discourse on gender equality. The next four topics are, in order of SBS, role , education , decision-making , and career progression . These topics, except for education , all concern women in the workforce. Between these first five topics and the following ones there is a clear drop in SBS scores. In particular, the topics that follow have a lower connectivity than the first five. They are hiring , performance , behavior , organization , and human capital . Again, except for behavior and human capital , the other three topics are purely related to women in the workforce. After another drop-off, the following topics deal prevalently with women in society. This trend highlights that research on gender in business journals has so far mainly paid attention to the conditions that women experience in business contexts, while also devoting some attention to women in society.

thumbnail

https://doi.org/10.1371/journal.pone.0256474.g001

Fig 2 shows the SBS time series of the top 10 topics. While there has been a general increase in the number of Scopus-indexed publications in the last decade, we notice that some SBS trends remain steady, or even decrease. In particular, we observe that the main topic of the last twenty-two years, compensation , is losing momentum. Since 2016, it has been surpassed by decision-making , education and role , which may indicate that literature is increasingly attempting to identify root causes of compensation inequalities. Moreover, in the last two years, the topics of hiring , performance , and organization are experiencing the largest importance increase.

thumbnail

https://doi.org/10.1371/journal.pone.0256474.g002

Fig 3 shows the SBS time trends of the remaining 17 topics (i.e., those not in the top 10). As we can see from the graph, there are some that maintain a steady trend–such as reputation , management , networks and governance , which also seem to have little importance. More relevant topics with average stationary trends (except for the last two years) are culture , family , and parenting . The feminine topic is among the most important here, and one of those that exhibit the larger variations over time (similarly to leadership ). On the other hand, the are some topics that, even if not among the most important, show increasing SBS trends; therefore, they could be considered as emerging topics and could become popular in the near future. These are entrepreneurship , leadership , board of directors , and sustainability . These emerging topics are also interesting to anticipate future trends in gender equality research that are conducive to overall equality in society.

thumbnail

https://doi.org/10.1371/journal.pone.0256474.g003

In addition to the SBS score of the different topics, the network of terms they are associated to enables to gauge the extent to which their images (textual associations) overlap or differ ( Fig 4 ).

thumbnail

https://doi.org/10.1371/journal.pone.0256474.g004

There is a central cluster of topics with high similarity, which are all connected with women in the workforce. The cluster includes topics such as organization , decision-making , performance , hiring , human capital , education and compensation . In addition, the topic of well-being is found within this cluster, suggesting that women’s equality in the workforce is associated to well-being considerations. The emerging topics of entrepreneurship and leadership are also closely connected with each other, possibly implying that leadership is a much-researched quality in female entrepreneurship. Topics that are relatively more distant include personality , politics , feminine , empowerment , management , board of directors , reputation , governance , parenting , masculine and network .

The following sections describe the top 10 topics and their main associations in literature (see Table 3 ), while providing a brief overview of the emerging topics.

thumbnail

https://doi.org/10.1371/journal.pone.0256474.t003

Compensation.

The topic of compensation is related to the topics of role , hiring , education and career progression , however, also sees a very high association with the words gap and inequality . Indeed, a well-known debate in degrowth economics centers around whether and how to adequately compensate women for their childbearing, childrearing, caregiver and household work [e.g., 30 ].

Even in paid work, women continue being offered lower compensations than their male counterparts who have the same job or cover the same role [ 64 – 67 ]. This severe inequality has been widely studied by scholars over the last twenty-two years. Dealing with this topic, some specific roles have been addressed. Specifically, research highlighted differences in compensation between female and male CEOs [e.g., 68 ], top executives [e.g., 69 ], and boards’ directors [e.g., 70 ]. Scholars investigated the determinants of these gaps, such as the gender composition of the board [e.g., 71 – 73 ] or women’s individual characteristics [e.g., 71 , 74 ].

Among these individual characteristics, education plays a relevant role [ 75 ]. Education is indeed presented as the solution for women, not only to achieve top executive roles, but also to reduce wage inequality [e.g., 76 , 77 ]. Past research has highlighted education influences on gender wage gaps, specifically referring to gender differences in skills [e.g., 78 ], college majors [e.g., 79 ], and college selectivity [e.g., 80 ].

Finally, the wage gap issue is strictly interrelated with hiring –e.g., looking at whether being a mother affects hiring and compensation [e.g., 65 , 81 ] or relating compensation to unemployment [e.g., 82 ]–and career progression –for instance looking at meritocracy [ 83 , 84 ] or the characteristics of the boss for whom women work [e.g., 85 ].

The roles covered by women have been deeply investigated. Scholars have focused on the role of women in their families and the society as a whole [e.g., 14 , 15 ], and, more widely, in business contexts [e.g., 18 , 81 ]. Indeed, despite still lagging behind their male counterparts [e.g., 86 , 87 ], in the last decade there has been an increase in top ranked positions achieved by women [e.g., 88 , 89 ]. Following this phenomenon, scholars have posed greater attention towards the presence of women in the board of directors [e.g., 16 , 18 , 90 , 91 ], given the increasing pressure to appoint female directors that firms, especially listed ones, have experienced. Other scholars have focused on the presence of women covering the role of CEO [e.g., 17 , 92 ] or being part of the top management team [e.g., 93 ]. Irrespectively of the level of analysis, all these studies tried to uncover the antecedents of women’s presence among top managers [e.g., 92 , 94 ] and the consequences of having a them involved in the firm’s decision-making –e.g., on performance [e.g., 19 , 95 , 96 ], risk [e.g., 97 , 98 ], and corporate social responsibility [e.g., 99 , 100 ].

Besides studying the difficulties and discriminations faced by women in getting a job [ 81 , 101 ], and, more specifically in the hiring , appointment, or career progression to these apical roles [e.g., 70 , 83 ], the majority of research of women’s roles dealt with compensation issues. Specifically, scholars highlight the pay-gap that still exists between women and men, both in general [e.g., 64 , 65 ], as well as referring to boards’ directors [e.g., 70 , 102 ], CEOs and executives [e.g., 69 , 103 , 104 ].

Finally, other scholars focused on the behavior of women when dealing with business. In this sense, particular attention has been paid to leadership and entrepreneurial behaviors. The former quite overlaps with dealing with the roles mentioned above, but also includes aspects such as leaders being stereotyped as masculine [e.g., 105 ], the need for greater exposure to female leaders to reduce biases [e.g., 106 ], or female leaders acting as queen bees [e.g., 107 ]. Regarding entrepreneurship , scholars mainly investigated women’s entrepreneurial entry [e.g., 108 , 109 ], differences between female and male entrepreneurs in the evaluations and funding received from investors [e.g., 110 , 111 ], and their performance gap [e.g., 112 , 113 ].

Education has long been recognized as key to social advancement and economic stability [ 114 ], for job progression and also a barrier to gender equality, especially in STEM-related fields. Research on education and gender equality is mostly linked with the topics of compensation , human capital , career progression , hiring , parenting and decision-making .

Education contributes to a higher human capital [ 115 ] and constitutes an investment on the part of women towards their future. In this context, literature points to the gender gap in educational attainment, and the consequences for women from a social, economic, personal and professional standpoint. Women are found to have less access to formal education and information, especially in emerging countries, which in turn may cause them to lose social and economic opportunities [e.g., 12 , 116 – 119 ]. Education in local and rural communities is also paramount to communicate the benefits of female empowerment , contributing to overall societal well-being [e.g., 120 ].

Once women access education, the image they have of the world and their place in society (i.e., habitus) affects their education performance [ 13 ] and is passed on to their children. These situations reinforce gender stereotypes, which become self-fulfilling prophecies that may negatively affect female students’ performance by lowering their confidence and heightening their anxiety [ 121 , 122 ]. Besides formal education, also the information that women are exposed to on a daily basis contributes to their human capital . Digital inequalities, for instance, stems from men spending more time online and acquiring higher digital skills than women [ 123 ].

Education is also a factor that should boost employability of candidates and thus hiring , career progression and compensation , however the relationship between these factors is not straightforward [ 115 ]. First, educational choices ( decision-making ) are influenced by variables such as self-efficacy and the presence of barriers, irrespectively of the career opportunities they offer, especially in STEM [ 124 ]. This brings additional difficulties to women’s enrollment and persistence in scientific and technical fields of study due to stereotypes and biases [ 125 , 126 ]. Moreover, access to education does not automatically translate into job opportunities for women and minority groups [ 127 , 128 ] or into female access to managerial positions [ 129 ].

Finally, parenting is reported as an antecedent of education [e.g., 130 ], with much of the literature focusing on the role of parents’ education on the opportunities afforded to children to enroll in education [ 131 – 134 ] and the role of parenting in their offspring’s perception of study fields and attitudes towards learning [ 135 – 138 ]. Parental education is also a predictor of the other related topics, namely human capital and compensation [ 139 ].

Decision-making.

This literature mainly points to the fact that women are thought to make decisions differently than men. Women have indeed different priorities, such as they care more about people’s well-being, working with people or helping others, rather than maximizing their personal (or their firm’s) gain [ 140 ]. In other words, women typically present more communal than agentic behaviors, which are instead more frequent among men [ 141 ]. These different attitude, behavior and preferences in turn affect the decisions they make [e.g., 142 ] and the decision-making of the firm in which they work [e.g., 143 ].

At the individual level, gender affects, for instance, career aspirations [e.g., 144 ] and choices [e.g., 142 , 145 ], or the decision of creating a venture [e.g., 108 , 109 , 146 ]. Moreover, in everyday life, women and men make different decisions regarding partners [e.g., 147 ], childcare [e.g., 148 ], education [e.g., 149 ], attention to the environment [e.g., 150 ] and politics [e.g., 151 ].

At the firm level, scholars highlighted, for example, how the presence of women in the board affects corporate decisions [e.g., 152 , 153 ], that female CEOs are more conservative in accounting decisions [e.g., 154 ], or that female CFOs tend to make more conservative decisions regarding the firm’s financial reporting [e.g., 155 ]. Nevertheless, firm level research also investigated decisions that, influenced by gender bias, affect women, such as those pertaining hiring [e.g., 156 , 157 ], compensation [e.g., 73 , 158 ], or the empowerment of women once appointed [ 159 ].

Career progression.

Once women have entered the workforce, the key aspect to achieve gender equality becomes career progression , including efforts toward overcoming the glass ceiling. Indeed, according to the SBS analysis, career progression is highly related to words such as work, social issues and equality. The topic with which it has the highest semantic overlap is role , followed by decision-making , hiring , education , compensation , leadership , human capital , and family .

Career progression implies an advancement in the hierarchical ladder of the firm, assigning managerial roles to women. Coherently, much of the literature has focused on identifying rationales for a greater female participation in the top management team and board of directors [e.g., 95 ] as well as the best criteria to ensure that the decision-makers promote the most valuable employees irrespectively of their individual characteristics, such as gender [e.g., 84 ]. The link between career progression , role and compensation is often provided in practice by performance appraisal exercises, frequently rooted in a culture of meritocracy that guides bonuses, salary increases and promotions. However, performance appraisals can actually mask gender-biased decisions where women are held to higher standards than their male colleagues [e.g., 83 , 84 , 95 , 160 , 161 ]. Women often have less opportunities to gain leadership experience and are less visible than their male colleagues, which constitute barriers to career advancement [e.g., 162 ]. Therefore, transparency and accountability, together with procedures that discourage discretionary choices, are paramount to achieve a fair career progression [e.g., 84 ], together with the relaxation of strict job boundaries in favor of cross-functional and self-directed tasks [e.g., 163 ].

In addition, a series of stereotypes about the type of leadership characteristics that are required for top management positions, which fit better with typical male and agentic attributes, are another key barrier to career advancement for women [e.g., 92 , 160 ].

Hiring is the entrance gateway for women into the workforce. Therefore, it is related to other workforce topics such as compensation , role , career progression , decision-making , human capital , performance , organization and education .

A first stream of literature focuses on the process leading up to candidates’ job applications, demonstrating that bias exists before positions are even opened, and it is perpetuated both by men and women through networking and gatekeeping practices [e.g., 164 , 165 ].

The hiring process itself is also subject to biases [ 166 ], for example gender-congruity bias that leads to men being preferred candidates in male-dominated sectors [e.g., 167 ], women being hired in positions with higher risk of failure [e.g., 168 ] and limited transparency and accountability afforded by written processes and procedures [e.g., 164 ] that all contribute to ascriptive inequality. In addition, providing incentives for evaluators to hire women may actually work to this end; however, this is not the case when supporting female candidates endangers higher-ranking male ones [ 169 ].

Another interesting perspective, instead, looks at top management teams’ composition and the effects on hiring practices, indicating that firms with more women in top management are less likely to lay off staff [e.g., 152 ].

Performance.

Several scholars posed their attention towards women’s performance, its consequences [e.g., 170 , 171 ] and the implications of having women in decision-making positions [e.g., 18 , 19 ].

At the individual level, research focused on differences in educational and academic performance between women and men, especially referring to the gender gap in STEM fields [e.g., 171 ]. The presence of stereotype threats–that is the expectation that the members of a social group (e.g., women) “must deal with the possibility of being judged or treated stereotypically, or of doing something that would confirm the stereotype” [ 172 ]–affects women’s interested in STEM [e.g., 173 ], as well as their cognitive ability tests, penalizing them [e.g., 174 ]. A stronger gender identification enhances this gap [e.g., 175 ], whereas mentoring and role models can be used as solutions to this problem [e.g., 121 ]. Despite the negative effect of stereotype threats on girls’ performance [ 176 ], female and male students perform equally in mathematics and related subjects [e.g., 177 ]. Moreover, while individuals’ performance at school and university generally affects their achievements and the field in which they end up working, evidence reveals that performance in math or other scientific subjects does not explain why fewer women enter STEM working fields; rather this gap depends on other aspects, such as culture, past working experiences, or self-efficacy [e.g., 170 ]. Finally, scholars have highlighted the penalization that women face for their positive performance, for instance when they succeed in traditionally male areas [e.g., 178 ]. This penalization is explained by the violation of gender-stereotypic prescriptions [e.g., 179 , 180 ], that is having women well performing in agentic areas, which are typical associated to men. Performance penalization can thus be overcome by clearly conveying communal characteristics and behaviors [ 178 ].

Evidence has been provided on how the involvement of women in boards of directors and decision-making positions affects firms’ performance. Nevertheless, results are mixed, with some studies showing positive effects on financial [ 19 , 181 , 182 ] and corporate social performance [ 99 , 182 , 183 ]. Other studies maintain a negative association [e.g., 18 ], and other again mixed [e.g., 184 ] or non-significant association [e.g., 185 ]. Also with respect to the presence of a female CEO, mixed results emerged so far, with some researches demonstrating a positive effect on firm’s performance [e.g., 96 , 186 ], while other obtaining only a limited evidence of this relationship [e.g., 103 ] or a negative one [e.g., 187 ].

Finally, some studies have investigated whether and how women’s performance affects their hiring [e.g., 101 ] and career progression [e.g., 83 , 160 ]. For instance, academic performance leads to different returns in hiring for women and men. Specifically, high-achieving men are called back significantly more often than high-achieving women, which are penalized when they have a major in mathematics; this result depends on employers’ gendered standards for applicants [e.g., 101 ]. Once appointed, performance ratings are more strongly related to promotions for women than men, and promoted women typically show higher past performance ratings than those of promoted men. This suggesting that women are subject to stricter standards for promotion [e.g., 160 ].

Behavioral aspects related to gender follow two main streams of literature. The first examines female personality and behavior in the workplace, and their alignment with cultural expectations or stereotypes [e.g., 188 ] as well as their impacts on equality. There is a common bias that depicts women as less agentic than males. Certain characteristics, such as those more congruent with male behaviors–e.g., self-promotion [e.g., 189 ], negotiation skills [e.g., 190 ] and general agentic behavior [e.g., 191 ]–, are less accepted in women. However, characteristics such as individualism in women have been found to promote greater gender equality in society [ 192 ]. In addition, behaviors such as display of emotions [e.g., 193 ], which are stereotypically female, work against women’s acceptance in the workplace, requiring women to carefully moderate their behavior to avoid exclusion. A counter-intuitive result is that women and minorities, which are more marginalized in the workplace, tend to be better problem-solvers in innovation competitions due to their different knowledge bases [ 194 ].

The other side of the coin is examined in a parallel literature stream on behavior towards women in the workplace. As a result of biases, prejudices and stereotypes, women may experience adverse behavior from their colleagues, such as incivility and harassment, which undermine their well-being [e.g., 195 , 196 ]. Biases that go beyond gender, such as for overweight people, are also more strongly applied to women [ 197 ].

Organization.

The role of women and gender bias in organizations has been studied from different perspectives, which mirror those presented in detail in the following sections. Specifically, most research highlighted the stereotypical view of leaders [e.g., 105 ] and the roles played by women within firms, for instance referring to presence in the board of directors [e.g., 18 , 90 , 91 ], appointment as CEOs [e.g., 16 ], or top executives [e.g., 93 ].

Scholars have investigated antecedents and consequences of the presence of women in these apical roles. On the one side they looked at hiring and career progression [e.g., 83 , 92 , 160 , 168 , 198 ], finding women typically disadvantaged with respect to their male counterparts. On the other side, they studied women’s leadership styles and influence on the firm’s decision-making [e.g., 152 , 154 , 155 , 199 ], with implications for performance [e.g., 18 , 19 , 96 ].

Human capital.

Human capital is a transverse topic that touches upon many different aspects of female gender equality. As such, it has the most associations with other topics, starting with education as mentioned above, with career-related topics such as role , decision-making , hiring , career progression , performance , compensation , leadership and organization . Another topic with which there is a close connection is behavior . In general, human capital is approached both from the education standpoint but also from the perspective of social capital.

The behavioral aspect in human capital comprises research related to gender differences for example in cultural and religious beliefs that influence women’s attitudes and perceptions towards STEM subjects [ 142 , 200 – 202 ], towards employment [ 203 ] or towards environmental issues [ 150 , 204 ]. These cultural differences also emerge in the context of globalization which may accelerate gender equality in the workforce [ 205 , 206 ]. Gender differences also appear in behaviors such as motivation [ 207 ], and in negotiation [ 190 ], and have repercussions on women’s decision-making related to their careers. The so-called gender equality paradox sees women in countries with lower gender equality more likely to pursue studies and careers in STEM fields, whereas the gap in STEM enrollment widens as countries achieve greater equality in society [ 171 ].

Career progression is modeled by literature as a choice-process where personal preferences, culture and decision-making affect the chosen path and the outcomes. Some literature highlights how women tend to self-select into different professions than men, often due to stereotypes rather than actual ability to perform in these professions [ 142 , 144 ]. These stereotypes also affect the perceptions of female performance or the amount of human capital required to equal male performance [ 110 , 193 , 208 ], particularly for mothers [ 81 ]. It is therefore often assumed that women are better suited to less visible and less leadership -oriented roles [ 209 ]. Women also express differing preferences towards work-family balance, which affect whether and how they pursue human capital gains [ 210 ], and ultimately their career progression and salary .

On the other hand, men are often unaware of gendered processes and behaviors that they carry forward in their interactions and decision-making [ 211 , 212 ]. Therefore, initiatives aimed at increasing managers’ human capital –by raising awareness of gender disparities in their organizations and engaging them in diversity promotion–are essential steps to counter gender bias and segregation [ 213 ].

Emerging topics: Leadership and entrepreneurship

Among the emerging topics, the most pervasive one is women reaching leadership positions in the workforce and in society. This is still a rare occurrence for two main types of factors, on the one hand, bias and discrimination make it harder for women to access leadership positions [e.g., 214 – 216 ], on the other hand, the competitive nature and high pressure associated with leadership positions, coupled with the lack of women currently represented, reduce women’s desire to achieve them [e.g., 209 , 217 ]. Women are more effective leaders when they have access to education, resources and a diverse environment with representation [e.g., 218 , 219 ].

One sector where there is potential for women to carve out a leadership role is entrepreneurship . Although at the start of the millennium the discourse on entrepreneurship was found to be “discriminatory, gender-biased, ethnocentrically determined and ideologically controlled” [ 220 ], an increasing body of literature is studying how to stimulate female entrepreneurship as an alternative pathway to wealth, leadership and empowerment [e.g., 221 ]. Many barriers exist for women to access entrepreneurship, including the institutional and legal environment, social and cultural factors, access to knowledge and resources, and individual behavior [e.g., 222 , 223 ]. Education has been found to raise women’s entrepreneurial intentions [e.g., 224 ], although this effect is smaller than for men [e.g., 109 ]. In addition, increasing self-efficacy and risk-taking behavior constitute important success factors [e.g., 225 ].

Finally, the topic of sustainability is worth mentioning, as it is the primary objective of the SDGs and is closely associated with societal well-being. As society grapples with the effects of climate change and increasing depletion of natural resources, a narrative has emerged on women and their greater link to the environment [ 226 ]. Studies in developed countries have found some support for women leaders’ attention to sustainability issues in firms [e.g., 227 – 229 ], and smaller resource consumption by women [ 230 ]. At the same time, women will likely be more affected by the consequences of climate change [e.g., 230 ] but often lack the decision-making power to influence local decision-making on resource management and environmental policies [e.g., 231 ].

Research gaps and conclusions

Research on gender equality has advanced rapidly in the past decades, with a steady increase in publications, both in mainstream topics related to women in education and the workforce, and in emerging topics. Through a novel approach combining methods of text mining and social network analysis, we examined a comprehensive body of literature comprising 15,465 papers published between 2000 and mid 2021 on topics related to gender equality. We identified a set of 27 topics addressed by the literature and examined their connections.

At the highest level of abstraction, it is worth noting that papers abound on the identification of issues related to gender inequalities and imbalances in the workforce and in society. Literature has thoroughly examined the (unconscious) biases, barriers, stereotypes, and discriminatory behaviors that women are facing as a result of their gender. Instead, there are much fewer papers that discuss or demonstrate effective solutions to overcome gender bias [e.g., 121 , 143 , 145 , 163 , 194 , 213 , 232 ]. This is partly due to the relative ease in studying the status quo, as opposed to studying changes in the status quo. However, we observed a shift in the more recent years towards solution seeking in this domain, which we strongly encourage future researchers to focus on. In the future, we may focus on collecting and mapping pro-active contributions to gender studies, using additional Natural Language Processing techniques, able to measure the sentiment of scientific papers [ 43 ].

All of the mainstream topics identified in our literature review are closely related, and there is a wealth of insights looking at the intersection between issues such as education and career progression or human capital and role . However, emerging topics are worthy of being furtherly explored. It would be interesting to see more work on the topic of female entrepreneurship , exploring aspects such as education , personality , governance , management and leadership . For instance, how can education support female entrepreneurship? How can self-efficacy and risk-taking behaviors be taught or enhanced? What are the differences in managerial and governance styles of female entrepreneurs? Which personality traits are associated with successful entrepreneurs? Which traits are preferred by venture capitalists and funding bodies?

The emerging topic of sustainability also deserves further attention, as our society struggles with climate change and its consequences. It would be interesting to see more research on the intersection between sustainability and entrepreneurship , looking at how female entrepreneurs are tackling sustainability issues, examining both their business models and their company governance . In addition, scholars are suggested to dig deeper into the relationship between family values and behaviors.

Moreover, it would be relevant to understand how women’s networks (social capital), or the composition and structure of social networks involving both women and men, enable them to increase their remuneration and reach top corporate positions, participate in key decision-making bodies, and have a voice in communities. Furthermore, the achievement of gender equality might significantly change firm networks and ecosystems, with important implications for their performance and survival.

Similarly, research at the nexus of (corporate) governance , career progression , compensation and female empowerment could yield useful insights–for example discussing how enterprises, institutions and countries are managed and the impact for women and other minorities. Are there specific governance structures that favor diversity and inclusion?

Lastly, we foresee an emerging stream of research pertaining how the spread of the COVID-19 pandemic challenged women, especially in the workforce, by making gender biases more evident.

For our analysis, we considered a set of 15,465 articles downloaded from the Scopus database (which is the largest abstract and citation database of peer-reviewed literature). As we were interested in reviewing business and economics related gender studies, we only considered those papers published in journals listed in the Academic Journal Guide (AJG) 2018 ranking of the Chartered Association of Business Schools (CABS). All the journals listed in this ranking are also indexed by Scopus. Therefore, looking at a single database (i.e., Scopus) should not be considered a limitation of our study. However, future research could consider different databases and inclusion criteria.

With our literature review, we offer researchers a comprehensive map of major gender-related research trends over the past twenty-two years. This can serve as a lens to look to the future, contributing to the achievement of SDG5. Researchers may use our study as a starting point to identify key themes addressed in the literature. In addition, our methodological approach–based on the use of the Semantic Brand Score and its webapp–could support scholars interested in reviewing other areas of research.

Supporting information

S1 text. keywords used for paper selection..

https://doi.org/10.1371/journal.pone.0256474.s001

Acknowledgments

The computing resources and the related technical support used for this work have been provided by CRESCO/ENEAGRID High Performance Computing infrastructure and its staff. CRESCO/ENEAGRID High Performance Computing infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes (see http://www.cresco.enea.it/english for information).

  • View Article
  • PubMed/NCBI
  • Google Scholar
  • 9. UN. Transforming our world: The 2030 Agenda for Sustainable Development. General Assembley 70 Session; 2015.
  • 11. Nature. Get the Sustainable Development Goals back on track. Nature. 2020;577(January 2):7–8
  • 37. Fronzetti Colladon A, Grippa F. Brand intelligence analytics. In: Przegalinska A, Grippa F, Gloor PA, editors. Digital Transformation of Collaboration. Cham, Switzerland: Springer Nature Switzerland; 2020. p. 125–41. https://doi.org/10.1371/journal.pone.0233276 pmid:32442196
  • 39. Griffiths TL, Steyvers M, editors. Finding scientific topics. National academy of Sciences; 2004.
  • 40. Mimno D, Wallach H, Talley E, Leenders M, McCallum A, editors. Optimizing semantic coherence in topic models. 2011 Conference on Empirical Methods in Natural Language Processing; 2011.
  • 41. Wang C, Blei DM, editors. Collaborative topic modeling for recommending scientific articles. 17th ACM SIGKDD international conference on Knowledge discovery and data mining 2011.
  • 46. Straka M, Straková J, editors. Tokenizing, pos tagging, lemmatizing and parsing ud 2.0 with udpipe. CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies; 2017.
  • 49. Lu Y, Li, R., Wen K, Lu Z, editors. Automatic keyword extraction for scientific literatures using references. 2014 IEEE International Conference on Innovative Design and Manufacturing (ICIDM); 2014.
  • 55. Roelleke T, Wang J, editors. TF-IDF uncovered. 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval—SIGIR ‘08; 2008.
  • 56. Mihalcea R, Tarau P, editors. TextRank: Bringing order into text. 2004 Conference on Empirical Methods in Natural Language Processing; 2004.
  • 58. Iannone F, Ambrosino F, Bracco G, De Rosa M, Funel A, Guarnieri G, et al., editors. CRESCO ENEA HPC clusters: A working example of a multifabric GPFS Spectrum Scale layout. 2019 International Conference on High Performance Computing & Simulation (HPCS); 2019.
  • 60. Wasserman S, Faust K. Social network analysis: Methods and applications: Cambridge University Press; 1994.
  • 141. Williams JE, Best DL. Measuring sex stereotypes: A multination study, Rev: Sage Publications, Inc; 1990.
  • 172. Steele CM, Aronson J. Stereotype threat and the test performance of academically successful African Americans. In: Jencks C, Phillips M, editors. The Black–White test score gap. Washington, DC: Brookings; 1998. p. 401–27

scholarly articles on gender inequality

  • Previous Article

This paper identifies five key issues that are important for the continued efforts to tackle gender inequality: (i) gender inequality needs to be distinguished from gender gaps. Not all gender gaps necessarily reflect gender inequality as some gender gaps are not driven by the lack of equal rights, responsibilities and opportunities bywomen and girls, and this has important implications on policy designs to address gender inequity. However, the literature has paid little attention to this issue, often using gender inequality and gender gaps interchangeably; (ii) the evolving focus of gender inequality suggests there is still a long way to go to fully address gender inequality. Particularly gender inequality is taking more subtle and implicit forms, though the social and economic benefits from addressing the remaininggender inequality is still likely to be substantial; (iii) addressing gender inequality benefits everyone, not just women. Thus, the entire society should work together, even for each individual’s own interest; (iv) both general policies and targeted gender policies can help address gender inequality.However, as gender inequality becomes more subtle and implicit, targeted gender policies will likely need to play an increasing role, which also makes separating gender inequality from gender gaps all that more important; and (v) addressing gender inequality does not need to start with policies targeted at its root causes, but needs to end with eliminating the root causes. Only then, any remaining gender gaps would only reflect preference and comparative advantage between men and women. The paper concludes by discussing gaps in the literature and policy challenges going forward.

I. Introduction

Gender gaps have been observed in a broad range of social and economic dimensions and well-documented in the literature. Here gender gaps refer to the observed differences between men and women or between boys and girls in the relevant indicators. For example:

Gender gaps in nutritional intake have been often reported as a result of intra household allocation of resources in South Asia, with also evidence in sub-Sahara Africa ( Pal, 1999 ; World Bank, 2006 ; Hadley and others, 2007 ; Dasgupta, 2016 ; Hafeez and Quintana-Domeque, 2018 ).

In developing countries, while gender gaps in school enrollment have been narrowing rapidly over the recent decades, particularly for preprimary, primary and secondary education, considerable gaps still remain for tertiary education and there are large variations across countries ( Demery and Gaddis, 2009 ; Duflo, 2012 ; Austen and others, 2013 ; Evans and others, 2021 ). Furthermore, significant differences exist in the field of study between male and female students, likely in nearly all countries but with most evidence from advanced economies ( OECD, 2017 ; Cook and others, 2021 ).

Empirical studies, based on subjective self-reporting of unmet healthcare needs, find that women are more likely to report healthcare access related issues (Socías and others, 2016; Daher and others, 2021 ).

Access to formal financial services is generally lower for women than for men. Over time, access to financial services has increased worldwide, but significant gaps remain by gender, and both saving and borrowing services are more accessible to men than to women ( Demirgüç-Kunt and others, 2015 ; Sahay and others, 2020 ).

Differences between male and female labor force participation rates have narrowed, but the gaps remain high in most of the world, with large variations across regions and countries ( Field and others, 2010 ; Alesina and others, 2013 ; Bernhardt and others, 2018 ; Jayachandran, 2021 ). Even when women participate in the labor market, they tend to be overrepresented in certain sectors, often characterized by low status and low pay ( OECD, 2012 ; ILO, 2012 ). Particularly, women are strongly under-represented in corporate managerial positions and political leadership ( Profeta and others, 2014 ; OECD, 2017 ). Even for the same jobs and with similar qualifications, women tend to be paid less ( OECD, 2012 ; OECD, 2017 ; NSF, 2021 ).

Women are subject to more violence at home, in commuting, and at work ( Jayachandran, 2021 ). In addition, legal barriers to women’s rights and opportunities remain pervasive. Women on average have only three-quarters of the legal protections given to men during their working life, ranging from bans on entering some jobs to a lack of equal pay or freedom from sexual harassment ( World Bank, 2021 ).

Many research and policy work often equates gender gaps with gender inequality without clearly defining them. According to UN Women ,

“ Equality between women and men (gender equality) refers to the equal rights, responsibilities and opportunities of women and men and girls and boys. Equality does not mean that women and men will become the same but that women’s and men’s rights, responsibilities and opportunities will not depend on whether they are born male or female. Gender equality implies that the interests, needs and priorities of both women and men are taken into consideration, recognizing the diversity of different groups of women and men. ”

This suggests that not all gender gaps necessarily reflect gender inequality, as defined above. This has important policy implications, that is, policies should focus on eliminating gender inequality, not on achieving an equal gender share or fully closing all gender gaps.

The urgency to address gender inequality stems from its substantial social and economic consequences. First and foremost, gender inequality is a matter of fairness and concerning the wellbeing of women. 1 For example, some gender inequality reflects direct harmful actions against women —such as violence, harassment, and the resulting fear—or restrictions on women’s behaviors, legal or social. More generally, as gender inequality is the result of gender bias and social norms that restrict women’s rights and opportunities, it leads to lower welfare for women. Furthermore, as women account for half of the population, gender inequality means potentially a substantial misallocation of human capital, including both investment in women and utilization of women talent. A growing body of literature shows that reducing gender inequality can help foster better household decision-making, improve firm/institution performance, and generate substantial macroeconomic benefits, through boosting productivity and economic growth, strengthening macroeconomic and financial stability, and lowering income inequality ( Kochhar and others, 2017 ; Sahay and others, 2018 ; Cihák and Sahay, 2020 ; Gonzales and others, 2015 b).

There is clear evidence that gender inequality narrows as countries develop and new technologies, such as labor-saving household appliances, are being developed and widely adopted ( Jayachandran, 2015 ; Tewari and Wang, 2021 ). However, the interrelationships between women empowerment and economic development are probably too weak to be self-sustaining, and because of the social and economic significance of gender inequality, policy actions are needed to speed up the process ( Duflo, 2012 ). For example, around 82 percent of 40-year-old inventors are men, and while this gender gap in innovation is shrinking gradually, at the current rate of convergence, it will take another 118 years to reach gender parity ( Bell and others, 2019 ).

One of the United Nation’s Sustainable Development Goals (SDGs) is to achieve gender equality and empower all women and girls. 2 Many efforts have been taken over the past decades, particularly after the establishment of the SDGs in 2015, to tackle gender inequality. For example, public investment in education has nearly erased the gender gaps in primary and secondary school enrollment; legislative reforms have led to reductions in discrimination; countries have enacted reforms to boost women’s economic opportunities; countries have enacted laws or introduced policies to end child and early marriage, provide paternity and parental leave, reduce the gender wage gap, address violence against women including sexual harassment, and promote women in leadership ( World Bank, 2021 ; OECD, 2014 ; OECD, 2017 ). 3

While globally important progress has been made in some areas (e.g., enrollment in primary and secondary education), substantial gender inequality still remains in many other areas (e.g., enrollment in tertiary education, labor force participation, wages, and leadership positions). Furthermore, the COVID-19 pandemic has disproportionately affected women, further exacerbating pre-existing gender inequality, for example, as women shouldered more burden in taking care of young children when schools were closed ( Albanesi and Kim, 2021 ; Bluedorn and others 2021 ; Fabrizio and others, 2021 ; WEF, 2021 ).

Thus, much work still lies ahead to achieve gender equality, with some forms of gender inequality still existing in nearly all countries and often in relation to the SDGs. As countries seek to step up their efforts to address gender inequality, many questions remain for policymakers. This includes: (i) what are the main forms of gender inequality for countries at different stages of development? (ii) what are the economic benefits from continued efforts to reduce gender inequality, are the benefits diminishing as some gender inequality is being eliminated, and who would benefit from lower gender inequality? (iii) What policies are most effective in addressing gender inequality, what are the tradeoffs of adopting different types of policies, and are some of the policies more about ticking a box rather than making a real difference? And (iv) what are the roles of different types of policies at eliminating gender inequality, given the root causes of gender inequality is often social and cultural?

The literature on the economic impacts of gender inequality and the policies to address gender inequality has been growing rapidly over the recent decades. In addition, many countries have adopted policies to tackle gender inequality for many years, and there is a lot to learn from their experiences. This paper intends to draw on the vast literature—which tends to focus on specific aspects of gender inequality and policies —and the diverse country experiences to provide a holistic view of gender inequality and shed light on some of the key policy questions that can help countries approach gender issues in a more systematic manner. More specifically, the paper identifies five key lessons:

Gender inequality versus gender gaps . Gender inequality differs from gender gaps in important ways, and this has important policy implications. However, the literature often equates gender inequality with gender gaps and use them interchangeably. This paper defines gender gaps as the observed differences between men and women or between boys and girls in the various social and economic indicators, and gender inequality refers to the part of gender gaps that are driven by gender bias and unequal gender rights and opportunities. The rest of the gaps are driven by preference/comparative advantage between men and women. Therefore, policies should be targeted at reducing gender inequality, which does not necessarily mean to fully close all gender gaps.

The evolving focus of gender inequality . Gender inequality extends to nearly every dimension of social and economic activities. The policy focus often varies by country, depending on their circumstances and level of development. There appears to be a shift toward more subtle and implicit forms of gender inequality, as gender reforms deepen, for example, from school enrollment to quality of education and field of study and from labor force participation to distribution of employment across sector s and pay. However, this does not mean that the social and economic impacts of the remaining gender inequality are smaller. In fact, the literature has shown that they could have substantial economic consequences. Furthermore, for countries that are still at the early stage of addressing gender inequality, this suggests that they should learn from the experiences of other countries, and it may be more effective and efficient to tackle different forms of gender inequality simultaneously. For example, countries could consider policy measures to simultaneously address gender inequality in tertiary enrollment and field of study, rather than tackling gender imbalances in field of study only after gender inequality in enrollment is eliminated.

The benefits of reducing gender inequality go beyond women . Gender equality may be seen by some as a zero-sum game, from an economic point of view. Less unpaid work at home and higher labor force participation by women would mean more unpaid work at home and lower labor force participation for men. Better representation at leadership positions by women would mean less for men. It is, however, important to recognize that better gender equality benefits not just women, but it enlarges the economic pie and benefits everyone, through several potential channels: (i) women tend to make better decisions regarding children; (ii) gender-mixed teams are more productive; and (iii) lower gender inequality can bring important macroeconomic benefits to everyone, with stronger economic growth and financial stability, more jobs, and less income inequality.

Policies and their designs matter . Large variations in gender gaps among countries with a similar level of development and in the same region suggest that policy interventions and their designs can make a difference, and this is further illustrated with an econometric analysis of gender laws and regulations and selected gender gaps. In addition, the literature provides strong evidence that a broad range of policy reforms can help reduce gender inequality and ultimately improve social and economic outcomes. However, not all policy interventions work under all circumstances, and policy tradeoffs are often involved. The paper compares general policies and targeted gender policies and discusses some considerations in their designs and implementation.

Policy actions do not have to start with those targeted at the root causes . While gender inequality shows many symptoms, the root causes are typically traced to gender bias and social norms. Ideally, reforms should be directly targeted at the root causes. However, this appears difficult with limited policy options (e.g., educational programs, information campaign, and legal reforms to ensure women’s rights and opportunities), and it takes time to change people’s views and beliefs. Instead, policies have focused on reducing gender inequality in different areas such as education, labor market, and financial access. Not only do these policies have immediate impacts on gender inequality, but they could also help change social norms. While policies may not need to start with the root causes of gender inequality, fully eliminating gender inequality requires eventually addressing the root causes.

The rest of the paper is organized as follows. Section II to VI in turn take on the five key lessons discussed above. Section VII concludes with a discussion on the gaps in the literature and on some considerations in addressing gender inequality going forward.

II. Gender gaps and gender inequality: definitions and drivers

Gender gaps are defined here as the observed differences between men and women or between boys and girls in the various social and economic indicators. Gender gaps can be considered to consist of two components, one that is caused by unequal rights, responsibilities, and opportunities for women and girls 4 and the other that is driven by women’s preference 5 or comparative advantage between men and women. 6 The former is what is defined in this paper as gender inequality, and the latter is the result of efficient allocation of human capital. For example, for school enrollment in primary and secondary education, it would be expected that most, if not all, of the gender gaps reflect gender inequality. For tertiary education in advanced economies, female enrollment rate is about 25 percent higher than that of male ( Figure 1 ). This gap, however, would not be expected to reflect gender inequality, that is, boys are facing less rights and opportunities. Instead, this likely reflects preference and choices (e.g., girls have comparative advantage in brain-based sectors and the returns to education are higher in such sectors ( Pitt and others, 2012 )). On the other hand, males represent a very small share of employment in registered nurses, some of which may indeed reflect social norms that hinder male’s entry into this profession. 7

Distinguishing between gender gaps and gender inequality has important policy implications. For the part of gender gaps that reflect preference/comparative advantage between men and women, there would be no need for policy intervention as there is no welfare loss from such gaps. For example, in many advanced economies where female tertiary enrollment rate is higher than that of male, there appears no need for policy interventions to further increase male tertiary enrollment rate to close the gap. On the other hand, there is a clear need to address gender inequality as it hurts women’s wellbeing, leads to distortions, and reduces overall social welfare. In many developing economies where female tertiary enrollment rate is lower than that of male due to gender bias, if it is left unaddressed, there would be an underutilization of women’s talent. Recognizing the difficulties often in separating gender inequality from gender gaps, Section V discusses some implications on policy designs.

Better understanding the drivers of gender inequality and gender gaps helps formulate effective policies. Both the theoretical and empirical literature offers evidence on the main drivers of gender gaps and gender inequality, particularly in the context of economic development:

Comparative advantage improves for women as countries develop. Women have a comparative advantage in mentally intensive tasks while men in physical intensive tasks; the process of development entails a growing capital stock and thus reduces the female-male wage gap, which in turn causes female labor force participation to increase ( Galor and Weil, 1996 ). 8 As brain-based sectors grow, if the returns to education are higher in brain-based than in brawn-based occupations, girls’ schooling could overtake that of boys ( Pitt and others, 2012 ). Gender differences in labor productivity as a driver of gender gaps are also supported by empirical evidence ( Qian, 2008 ; Alesina and others, 2013 ; Carranza; 2014 ). This strand of literature highlights the mechanism through which gender gaps narrow as countries develop, by largely reducing the part of gender gaps that reflect preference/comparative advantage between men and women.

Economic development is associated with better physical infrastructure and more advanced technology, making home production more efficient and less labor intensive. Because women perform the lion’s share of household chores, advances in home production technologies mainly free up women’s time and lead to an increase in female labor force participation ( Greenwood and others, 2005 ; Dinkelman, 2011 ; Tewari and Wang, 2021 ). As women performing much home production likely reflects both preference/comparative advantage between men and women and gender bias/social norms, better physical infrastructure and more advanced technology help reduce both components of gender gaps. 9

Gender bias and cultural barriers to women’s rights and opportunities are major drivers of gender gaps ( Jayachandran, 2015 ; Jayachandran, 2021 ; Alesina and others, 2013 ; Bernhardt and others, 2018 ). For example, Fernandez and Fogli (2009) show that whether a female second-generation immigrant in the United States works is strongly influenced by the female employment and fertility norms in her ancestral homeland. One form of barriers is the lack of basic legal rights, preventing women from joining the formal labor market or becoming entrepreneurs in many countries. Women are sometimes legally restricted from heading a household, pursuing a profession, or owning or inheriting assets. Such legal restrictions significantly hamper female labor force participation and pose a drag on female entrepreneurship (World Bank, 2015). Limiting gender bias and cultural barriers helps close gender gaps through reducing gender inequality.

Figure 1 illustrates conceptually the interrelationship between gender gaps, gender inequality, their drivers, and policy interventions to address them:

The root causes of gender inequality are gender bias and social norms that restrict women’s rights and opportunities, which, together with preference /comparative advantage between men and women, are the root drivers of gender gaps.

Gender bias/social norms and preference/comparative advantage between men and women interact with other factors (e.g., development, technological advances, and public policies) in determining gender gaps and gender inequality in different areas such as education, labor market and financial access. In other words, the root causes of gender inequality are gender bias and social norms; gender inequality in different areas are just symptoms of the root causes. This means that while some policies can help reduce gender inequality in some of these areas, fully addressing gender inequality would require the elimination of the root causes, gender bias/social norms.

As discussed above, development, technological advances, and public policies can affect gender bias/social norms and preference/comparative advantage between men and women.

Furthermore, interventions to lower gender inequality in different areas could also in turn alter gender bias/social norms. For example, as women become more educated and more women participate in the labor market, attitude toward women’s education and work may start to shift (see section VI for additional discussions).

Figure 1.

Gender inequality, gender gaps and their causes

Citation: IMF Working Papers 2022, 232; 10.5089/9798400224843.001.A001

  • Download Figure
  • Download figure as PowerPoint slide

III. The evolving focus of gender inequality: still a long way to go

Progress on gender equality has continually been made and differs substantially by country, particularly in relation to their stage of development. Consequently, the focus of gender inequality also varies by country and continue to evolve as some gender gaps are closed while others emerge and attract the attention of the public and policymakers. In general, the focus of gender inequality is shifting from gender gaps that are more explicit and visible to the public and policymakers to those that are more subtle and implicit. Given that gender inequality exists in broad areas, this section focuses on education, labor market, financial access, and legal barriers, as examples.

A. Education

The focus of gender inequality in education appears to be shifting from access to education (e.g., school enrollment) to quality of education and field of study.

For emerging and developing economies as a group, the gender gaps in access to preprimary, primary and secondary education are being closed, though some countries are still lagging behind; however, there are still gaps for tertiary education ( Appendix Figure 1a-1d ). As a result, many emerging and developing economies are still trying to achieve gender equality in access to education, particularly for tertiary education ( Demery and Gaddis, 2009 ; Duflo, 2012 ; Austen and others, 2013 ; Evans and others, 2021 ).

Advanced economies, instead, have been focusing on gender equality in quality of education, including gender distribution by field of study, as they have largely achieved gender equality in access to preprimary, primary and secondary education decades ago and to tertiary education since mid-2000s. For example, across the OECD, boys outperformed girls in mathematics by an average of eight points in 2015 —equivalent to around one-fifth of a year of schooling—and by 5 points in 2018; on the other hand, girls significantly outperform boys in reading in all countries and economies that participated in PISA 2018 ( OECD, 2017 ; OECD, 2019 ).

One area that has received increasing attention is the large differences in field of study between boys and girls, with girls particularly underrepresented in the fields of science and engineering and overrepresented in social science related fields ( Appendix Figure 2 ). The distributions are remarkably similar between more developed economies and the rest of the world, indicating that this is an issue common for all economies ( Appendix Figure 2a shows the global distribution and Appendix Figure 2b shows the distribution for OECD countries during a similar period). For example, college-educated women in the United States have sorted into majors that systematically lower their potential wages relative to men; to what extent women choose a major in anticipation of future family demands, based on individual preferences, under the burden of restrictive social norms, or for any other reason remains an unanswered question ( Sloane and others, 2021 ).

Women appear to be particularly under-represented in science, technology, engineering, and math (STEM). In the United State, in 1970, only 9 percent of all doctorates in the science and engineering fields, including social sciences, were awarded to women; by 2018, that share was nearly 47 percent. A closer look indicates that a large part of this is driven by high shares of women in psychology and social sciences. Despite the progress, persistent barriers to women pursuing degrees in STEM fields abound (Cook and others, 20 21).

B. Labor market

When it comes to the labor market, while efforts are continued to reduce gender inequality in labor force participation, narrow the gender wage gap, and boost the representation of women in political leadership, increasing attention is given to the large gaps in the sectoral distribution of female and male employment, women’s role in innovation, and women’s share in corporate management positions.

The differences between male and female labor force participation rates remain high, although the gaps have been narrowing over the past decades ( Appendix Figure 3a ). The gaps are smaller and also closing more rapidly in advanced economies. The gender gaps in emerging economies, in fact, have widened over the past two decade, and this is almost entirely driven by the declining female labor force participation in China and India. In China, the likely underlying factors include structural changes in the Chinese economy where households can afford to have only one wage earner, reduction in state childcare support, and rising gender-biased hiring practices; in India, the decline may reflect the declining employment in agriculture, safety concerns and the lack of transportation infrastructure for women to join the urban labor force , and the U-shaped relationship between education and labor force participation as education level improves for women ( Li, 2019 ; Zhang and Huang, 2020 ; Gupta and Bhamoriya, 2020 ; Hare, 2016 ). Excluding China and India, the gender gaps in labor force participation rates in emerging economies are still larger than those in developing economies, which partially reflects the large gaps in emerging MENAP countries. Low labor force participation, particularly for women, has been a major policy concern for many advanced economies and some emerging economies, as they face an aging population. As women in these economies tend to be well educated, it would be a considerable waste if they do not fully engage in economic activities.

There are also large gaps in the sectoral distribution of female and male employment, likely reflecting the differences in field of study. In advanced economies, women are less likely to work in the agriculture and industry sectors and more likely to work in the service sector; but there is a shift in the trend around 2018 from the service sector to the industry sector. Emerging and developing economies share broadly similarly trends over the past decade or so: relatively larger shares of women work in the agriculture sector; and women are moving rapidly from the agriculture and industry sectors to the service sector ( Appendix Figure 3b-3d ). In OECD countries, female employment in the service sector accounts for 80 percent of employed women, compared with 60 percent for men. Within this sector, women fill a disproportionately high share of occupations in health and community services, followed by education ( OECD, 2012 ). ILO (2012) finds that women are overrepresented in sectors characterized by low status and low pay.

Gender gaps in occupations within the science and engineering (S&E) field have been a particular concern. In the United States, by 2019 women made up 29 percent of the S&E workers, but female scientists and engineers are more likely to work in non-S&E occupations than in S&E occupations ( Cook and others, 2021 ). In 2019, 70 percent of psychologists were women, but just 14 percent of engineers and 29 percent of the workforce in computer and mathematical sciences were women. Women often start their careers working in the innovation economy, but then leave for various reasons, including the need to provide childcare, the lack of family-leave policies, and poor workplace climate ( Cook and others, 2021 ).

Increasing attention is also paid to women’s role in innovation, widely viewed as a central driver of productivity and economic growth. Gender inequality persists at every state of innovation, from education and training, to the practice of invention, and to the commercialization of those inventions ( Cook, 2019 ; Cook and others, 2021 ). Women hold only 5.5 percent of commercialized patents and represent just 10 percent of US patent inventors and only 15 percent of inventors in the life sciences. This in part reflects women’s underrepresentation in jobs involving development and design ( Hunt and others, 2013 ). In addition, discriminatory practice leads to inequality in patenting outcomes, even without discriminatory laws. Patent applications by women inventors were found to be more likely to be rejected than those of men, and those rejections were less likely to be appealed by the applicant teams. Conditional on being granted, patent applications by women inventors had a smaller fraction of their claims allowed, on average, than did applications by men. Further, those claims allowed had more words added during prosecution, thus reducing their scope and value. The granted patents of women inventors also received fewer citations than those of men and were less likely to be maintained by their assignees ( Cook and Kongcharoen, 2010 ; Jensen and others, 2018 ).

What has received particular attention is the underrepresentation of women in politics and corporate management positions. Representation of women in politics has improved substantially across all economies, with the proportion of seats held by women in national parliaments about doubled over the past two decades, likely due to the high public visibility; the gender gap, however, remains large ( Appendix Figure 4a ). For senior and middle management positions, there has been, however, little progress over the past two decades ( Appendix Figure 4b ). It appears that the success in political leadership has not been trickled down to the corporate world, highlighting the challenges to make changes in less visible areas. Across the 27 EU countries, only 25 percent of business owners with employees are women, and the low share of women had only marginally grown over 2000-2010 in EU27, Canada and United States ( OECD, 2014 ). A number of countries have enacted legislation requiring a set quota of female representation on corporate boards , the effectiveness and efficacy of such policy, however, has been intensely debated ( Kuzmina and Melentyeva, 2021 ; Greene and others, 2020 ; Lleras-Muney and others, 2019 ; Levi and others, 2014 ; Gregory-Smith and others, 2014 ; Strøm and others, 2014 ).

The gender wage gap has declined in most countries where data are available over the past two decades. Significant gap, however, still persist, averaging around 11 percent, and the gap varies substantially across countries ( Appendix Figure 5 ). While a large part of the gender gap in earnings can be explained by women working fewer hours in the labor market than men, women’s work force interruptions, gender differences in occupations and industries, a significant part of the gender pay gap remain unexplained, suggesting that factors such as discrimination and gender differences in psychological attributes and noncognitive skills are also important contributors to the gender pay gap ( OECD, 2017 ; Blau and Kahn, 2017 ). For example, using a personnel dataset from a large Chinese company, Chen and others (2021a) find that the gender wage gap is small in the early stages of careers and becomes increasingly evident when female employees get married and have children. Whereas the short-term peak around childbirth can be explained by women’ reduced working hours, the long-term trend is caused by women’s concentration in lower-level jobs.

C. Financial access and legal barriers

More attention is gradually drawing to access to credit by female entrepreneurs, as to financial access by females as individuals.

On account ownership at a financial institution/with a mobile-money-service provider, advanced economies have largely closed the gender gap; emerging economies have been making steady progress, with the gap narrowing from 23 percent in 2011 to 7 percent in 2021; little progress, however, has been seen in low-income developing countries over the last decades, with the gap staying at around 27 percent ( Appendix Figure 6a ).

The evidence on whether fintech can help close gender gaps in financial access, particularly in developing and emerging economies, still appears limited. Sahay and others (2020) find that gender gaps are lower on average in digital financial inclusion than in traditional financial inclusion, but there are significant variations across and within geographical regions. Chen and others (2021b) find a large fintech gender gap: while 29 percent of men use fintech products and services, only 21 percent of women do. Various factors contribute to the gender gap in fintech, including financial and digital literacy and socio-culture factors, suggesting that fintech by itself may only have limited impacts in reducing gender inequality in financial access, and policies to address more fundamental drivers of gender inequality are essential ( Khera and others, 2022 ; Chen and others, 2021b ).

On entrepreneurship financing, a significant gender gap still exists, even in advanced economies. Women are less likely than men to report that they can access the financing needed to start a business in all countries except for Mexico and the United States, with an average gap of eight percentage points in OECD countries ( Appendix Figure 6c ).

On legal barriers to gender equality, substantial progress has been made in all country groups, but effective implementation of the enacted laws and regulations remain a challenge in some countries. According to the Women, Business and Law Index, advanced economies have removed almost all the legal barriers to gender equality; significant gaps, however, still exist in emerging and developing economies ( Appendix Figure 6b ). 10 The impact of adopting gender equality legislation, however, would be limited if they are not fully implemented and enforced. For example, there is evidence from Ghana that reforms to inheritance laws led to few positive changes in terms of women’s inheritance ( Gedzi, 2012 ); a positive legal change in Pakistan has not allowed women to claim their entitled inheritances because of factors such as lack of education, patriarchal behaviors, and forced marriages ( Ahmad and others, 2016 ). Furthermore, cultural and economic factors may pose challenges to women exerting their rights, as in the case of reforming gendered land ownership laws in Kenya, Rwanda, and Uganda ( Djurfeldt, 2020 ).

D. Policy considerations

The literature suggests that there is still a long way to go to achieve gender equality for most economies:

Gender inequality remains large. While advanced economies have largely closed gender gaps in access to education and individual access to financial services, and removed legal barriers to gender equality, gender gaps in leadership positions, labor force participation, and pay remain sizable. Furthermore, more subtle gender gaps still persistent, such as in quality of education including field of study, sectoral distribution of employment, entrepreneurship financing, and innovation. 11 Emerging and developing economies faces additional challenges to achieve equality in access to tertiary education, individual access to financial services, and legal rights.

Closing the remaining gender inequality will likely be more challenging, as countries move to address gender inequality that is more implicit and subtle. This is because such inequality may be less visible to the public and thus may face less social pressures; with the difficulties in distinguishing preference/comparative advantage between men and women from gender bias/cultural barriers for such inequality, effective and efficient policies may be lacking; and addressing such inequality may require changing people’s mindset, which tends to be more difficult.

The social and economic impact of further reducing gender inequality is likely substantial. The more implicit and subtle nature of gender inequality does not necessarily mean less social and economic benefits from removing such forms of inequality. For example, in the case of United States, 94 percent of doctors and lawyers were white men in 1960; by 2010, the fraction was just 62 percent; similar changes in other high-skilled occupations have occurred throughout the U.S. economy during the last 50 years; given that the innate talent for these professions is unlikely to have changed differently across groups, the change in the occupational distribution since 1960 suggests that a substantial pool of innately talented women and black men in 1960 were not pursuing their comparative advantage ; it is estimated that between 20 and 40 percent of growth in aggregate market output per person during the period can be explained by the improved allocation of talent ( Hsieh and others, 2019 ). In a study of PhDs, GDP per capita could be 0.6 to 4.4 percent higher if women and African Americans were able to participate more fully in the innovation economy ( Cook and Yang, 2018 ).

One potential lesson, particular for emerging and developing economies, is that in addressing gender inequality, it may be more effective and efficient for policy designs to consider the whole spectrum of gender inequality, including both the highly visible ones and the more implicit and subtle ones. For example, in closing the gender gap in access to tertiary education, countries should also be mindful about the gender differences in field of study and actively help remove any barriers that may hinder the ability of female students in pursuing STEM fields. Another example would be to pay full attention to both the adoption and the implementation of gender equality laws.

IV. The benefits of reducing gender inequality go beyond women

The literature has documented broad social and economic benefits from lowering gender inequality, including the increasing emphasis on its macroeconomic effects ( Kolovich and others, 2020 ). Reducing gender inequality affects not only women, but everyone.

First and foremost, women benefit from lower gender inequality. This includes, for example, better career development, higher pay, and less violence, less discrimination and more equal rights, through improvements in human capital development, job opportunities including in leadership positions and as entrepreneurs, access to finance, and legal and regulatory environment.

Second, children benefit from lower gender inequality and women’s empowerment. Women’s choices appear to emphasize child welfare more than those of men, and children seem to be better off when their mothers control relatively more of their family’s resources. For example, Miller (2008) presents evidence on how suffrage rights for American women helped children to benefit from the scientific breakthroughs of the bacteriological revolution, with child mortality declining by 8–15 percent (or 20,000 annual child deaths nationwide), through large increases in local public health spending. Leight and Liu (2020) document that more-educated mothers appear to compensate for differences between their children, investing more in children who exhibit greater noncognitive deficits, while no such effect is found for men. Pitt and others (2003) find that women’s access to credit has a large and statistically significant impact on two of three measures of the child health, but no such effect is found for men.

Third, reducing gender inequality could potentially help increase the productivity of teams and improve the performance of firms and other institutions. This is primarily through the diversity channel, in the sense that mixed-gender teams are more productive and creative and tend to make better decisions ( Rock and Grant, 2016 ; Ozgen, 2021 ). Cook and Kongcharoen (2010) find that all-male and all-female patent teams commercialize their patents less than mixed-gender patent teams, with a similar finding from Østergaard and others (2011 ). Herring (2009) finds that gender diversity is associated with increased sales revenue, more customers, and greater relative profits. A number of studies find that gender quotas at corporate board are associated with improvements in firm performances, though there is still no consensus in the literature ( Strøm and others, 2014 ; Levi and others, 2014 ; Kuzmina and Melentyeva, 2021 ; Owen and Temesvary, 2018 ; Green and others, 2020 ). 12

Fourth, lower gender inequality can bring important macroeconomic benefits to everyone, with stronger economic growth and financial stability, more jobs, and less income inequality ( Kochhar and others, 2017 ; Sahay and others, 2015; Sahay and others, 2018 ; Cihak and Sahay, 2020 ).

Better matching female talent to human capital development and employment, including as corporate and political leaders and entrepreneurs, can substantially boost economic growth and strengthen economic and financial stability. For example, higher female labor force participation can substantially boost economic growth ( Kochhar and others, 2017 ; Kolovich and others, 2020 ). As discussed earlier, between 20 to 40 percent of growth in aggregate market output per person between 1960 and 2010 in the United States can be explained by improved allocation of talent ( Hsieh and others, 2019 ). Innovation is widely viewed as a central driver of productivity growth and output, and gender inequality hinders innovation at every state of the process, particularly as a growing literature is showing better outcomes of more diverse and mixed-gender teams ( Rock and Grant 2016 ; Cook, 2019 ; Cook and others, 2021 ). The literature also finds positive association between financial inclusion and economic growth, and reducing gender inequality in financial access, including through fintech, can thus help increase economic growth, particularly in countries with low levels of financial inclusion (Sahay and others, 2015; Sahay and others, 2020 ). There is also evidence that female leadership, including as financial regulators, is associated with financial and political stability ( Sahay and others, 2018 ; Caprioli, 2005 ).

Reducing gender inequality could also help lower income inequality and, in turn, improve social stability and economic growth ( Gonzales and others, 2015b ). Gender wage gaps directly contribute to income inequality. Conversely, policies to address gender inequality benefit females in low-income households the most, also reducing income inequality. For example, reducing gender gaps in school enrollment means that girls from poor households are more likely to receive education , thereby increasing their lifetime earnings potential ( Demery and Gaddis, 2009 ). In addition, financial inclusion of women is found to have a strong link to lower income inequality; this is because, while financial inclusion benefits everybody, the gains for women are quantitatively larger (Aslan and others, 2017; Cihák and Sahay, 2020 ).

V. Policies and their designs matter: general versus targeted policies

There is strong evidence from the literature that government policies and their designs matter for gender gaps and gender inequality. The key question, however, is how government policies can be designed to achieve gender equality while minimizing their efficiency cost (or maximizing the efficiency benefit).

A. The role of policies in closing gender gaps

A broad range of government policies and programs can affect gender gaps, such as public investment to improve access to education and healthcare, childcare subsides, paid parental leave, eliminating tax penalties for secondary earners, and laws and regulations to ensure women’s rights and opportunities ( Rim, 2021 ; Ruhm, 1998 ; Dustmann and Schönberg, 2012 ; Heath and Jayachandran, 2018 ; Evans and Yuan, 2022 ; Bick and Fuchs-Schündeln, 2017 ; Olivetti and Petrongolo, 2017 ; Gonzales and others, 2015 a; Hyland and others, 2020 ). For example, Rim (2021) finds that banning gender discrimination in admissions was successful in reducing gender disparity in graduate education. Sometimes, the policy interventions involve tradeoffs between different gender gaps. For example, Ruhm (1998) finds that parental leave is associated with increases in women’s employment, but with reductions in their relative wages at extended durations. Lalive and others (2014) find that, for parental leave, a system that combines cash benefits with job protection dominates other designs in generating time for care immediately after birth while maintaining mothers’ medium-term labor market attachment.

In addition to the large variations in gender gaps by level of development as shown in Section III, gender gaps also vary substantially among countries at a similar level of development and in the same region, for several selected gender gap measures ( Appendix Figure 7 ). Assuming countries in the same region have similar gender social norms, this suggests that government policies potentially play an important role in explaining cross-country variations in gender gaps.

As an illustration, here we estimate the effects of laws and regulations that ensure equal opportunities for women (measured by Women, Business and the Law Index) on five gender gaps (these gaps are selected as they are key measures of women’s economic opportunities, tend to present in many countries, and are widely reported). 13 The estimates are based on a fixed effects specification with a time trend and lagged key independent variable. The model uses per capita GDP in purchasing power parity (PPP) terms to control for level of development, country fixed effects to control for time-invariant factors (e.g., social norms), and a time trend to control for global trends. 14 The results suggest that gender laws and regulations are associated with lower gender gaps in some areas (e.g., account ownership at a financial institution /with a mobile-money-service provider and proportion of seats held by women in national parliaments). The estimates on gender gaps in labor force participation, female share of senor and middle management, and pay are not statistically significant ( Appendix Table 1 ). One likely explanation is that the introduction of gender equality laws and regulations helps raise awareness and can lead to changes that face relatively less barriers (e.g., financial access) or are highly visible by the public (parliament seats). More fundamental changes, however, may take time (e.g., labor force participation, senior and middle management, and pay).

B. General versus targeted policies

The effects of government policies on gender inequality and economic efficiency would depend on their specific designs and country-specific social and economic structures and conditions, and thus should be assessed on a policy-by-policy basis. There are, however, also commonalities among government policies, and it would be useful to understand their advantages and disadvantages. For example, gender-sensitive government policies can be broadly classified into two groups: general policies that apply to all genders indiscriminately but affect one gender more than the other and targeted policies at a specific gender.

By definition, nearly all macro policies—including fiscal policies, monetary policies, and exchange rate policies as well as macro-financial and macro-structural policies—belong to general policies, as they are primarily intended to boost economic growth and employment and achieve macro and financial stability. This, however, does not necessarily mean that macro policies are gender neutral. In fact, many of these policies have implications on gender gaps and gender inequality, because they affect different segments of the economy differently, and the distributions of female and male population also differ across these segments of the economy. For example, on fiscal policies, family-based income taxation implicitly raises the marginal tax rate for the income of secondary earners—who tend to be women—and contributes to the gender inequality in labor force participation ( Bick and Fuchs-Schündeln, 2017 ); while public education and health spending on average may still favor boys, the benefits from additional spending tend to be captured more by poor girls, as they are more likely to be still lacking access to education and healthcare ( Demery and Gaddis, 2009 ). On financial sector policies, while financial inclusion benefits everyone, the gains for women are quantitatively larger ( Cihák and Sahay, 2020 ). Monetary, exchange rate policies and macro structural policies have also been found to have gender implications. 15

Micro policies refer to government programs that target specific entities such as firms and households, and thus gender-sensitive micro policies can be either general or targeted policies. This includes a variety of programs such as (un)conditional cash transfers, hygiene promotion and water treatment, educational programs on gender equality for students, legal reforms to enhance women’s rights, conditional cash transfers for dropped out girls, reservation of subway cars exclusively for women, and gender quotas on corporate boards or political seats. Many of these programs have been shown to improve outcomes for women or girls ( Hahn and others, 2018 ; Harari, 2019 ; Beaman and others, 2012 ).

In general, targeted gender policies conceptually are less efficient as they exclude males who may be better suited for the opportunities. However, with the presentence of gender inequality (e.g., gender bias and social norms that hinder women’s rights and opportunities), general programs can also be inefficient in the sense that preference may be given to less qualified males. Because gender gaps can be driven by gender inequality or preference/comparative advantage between men and women or most likely both, and empirically it is difficult to separate the two effects, the key challenge for targeted gender policies is how to set the policy target s, as fully closing gender gaps may not be appropriate. Below are a few considerations:

It is not even clear that targeted gender policies are more effective in closing gender gaps. For example, from 267 educational interventions in 54 low- and middle-income countries, general interventions deliver average gains for girls that are comparable to girl-targeted interventions in improving access and learning ( Evans and Yuan, 2022 ). However, the most effective programs may not be the most cost-effective. Due to the lack of cost data, the cost-effectiveness of the programs could not be assessed.

There is evidence that some gender targeted policies may have unintended consequences or lead to inefficiencies. For example, the findings from a program that reserves subway cars exclusively for women in Mexico City suggest that while the program seems to be successful at reducing sexual harassment toward women, it also increases aggression incidents among men ( Aguilar and others, 2021 ). While the policy of setting gender quotas on corporate boards is still intensely debated, some studies find that the policy is associated with negative returns, and the negative effect is less severe for firms with a greater supply of female candidates, and for those that can more easily replace male directors or attract female directors ( Green and others, 2020 ). This appears to indicate that this policy may indeed lead to less qualified women being selected in some circumstances. Furthermore, there is also evidence that the policy has very little discernible impact on women in business beyond its direct effect on the women who made it into boardrooms ( Bertrand and others, 2019 ). This suggests that the policy may be more of ticking a box exercise. Afridi and others (2017) find short-term costs of gender-affirmative action policies for political leadership positions, but that once initial disadvantages recede, women leaders are neither more nor less effective local politicians than men. 16 While this does not mean that these policies should not be pursued, it does raise the need for careful designing such programs, particularly as its long-run or economy-wide impact may be difficult to identify in the studies. 17

For some policies, there is less ambiguity on their efficiency implications. For example, legal reforms to provide equal rights to women, by definition, is addressing gender inequality directly. This may be one potential reason for the rapid progress in removing legal gender barriers. Another example is educational programs on gender inequality, it is in fact more effective to be targeted to both genders , as reducing gender inequality requires the active participation by men as well ( Dhar and others, 2022 ). In some instances, preference/comparative advantage between men and women are expected to play a limited role, such as access to basic education (e.g., preprimary, primary and secondary) and healthcare. In such cases, fully closing the gender gaps would unlikely introduce any major distortions.

General policies tend to introduce less gender-specific distortions, although they can only address gender inequality, often in a more gradual manner. For example, conditional cash transfer programs can help improve school attendance of both boys and girls and benefit girls more than boys because more girls lack access to education in the absence of the programs. However, on the margin, boys are likely still less qualified than girls, even if the programs have helped narrow the gap. With this in mind, general policies may be particularly useful in circumstances where it is difficult to assess to what extent that the gender gaps are due to gender inequality. One potential area is formal labor force participation for which it is unclear how much of the lower labor force participation for women is due to gender inequality and how much is due to preference. In such a case, targeted policies such as wage subsidies for women may not be advisable, while general policies such as childcare subsidies may be more appropriate. 18

In areas where only targeted gender policies may be effective (e.g., in situations where men and women compete with each other), it would make sense to be conservative, by setting the gender quotas low initially and gradually increase them as more evidence becomes available. For example, only targeted gender policies are likely effective in promoting female leadership (e.g., gender quotas on corporate boards), as the number of leadership positions is fixed, and more female leaders mean fewer male leaders. This appears to be the case in some countries that have adopted policies to set gender quotas on corporate boards, through it is unclear if the design is indeed driven by such a consideration. For example, Malaysia’s publicly traded firms must have at least one-woman director on their boards from September 1, 2022; and California requires public companies headquartered in California to have at least one female director by the end of 2019 and at least two (three) female directors on five (six or more) member boards by the end of 2021.

VI. Policy actions do not have to start with those targeted at the root causes

As discussed in Section II, the root causes of gender inequality are gender bias/social norms that restrict women’s rights and opportunities. Only until the root causes are eliminated, gender equality can be fully achieved; some gender gaps may still remain but are driven by preference /comparative advantage between men and women. Before that, it is unlikely that gender inequality in different areas such as education, labor market, and financial access can be fully removed. With the difficulties in separating gender inequality from efficient allocation, general policies may have difficulties in fully eliminating gender inequality, while targeted gender policies run the risk of either not fully addressing gender inequality or introducing additional gender distortions. With these constraints, how should policies be designed? Should policies only focus on those that are directly targeted at gender inequality (e.g., removing legal barriers) and its root causes (e.g., educational programs and information campaigns)?

This paper argues that addressing gender inequality does not have to solely rely on policies that are targeted at gender inequality and its root causes, and other general and targeted policies can still play a key role in addressing gender inequality, for several reasons:

First, while social norms evolve as countries develop (e.g., higher income, better education, and technological advances), this is often slow, almost by definition. There is evidence that some interventions can help change social norms. This includes educational programs on gender inequality and exposure to (female) role models. For example, an intervention in India that engaged adolescent girls and boys in classroom discussions about gender equality for two years, aiming to reduce their support for societal norms that restrict women ’s and girls’ opportunities, is shown to have persistent effects and leads to shifts in behavior, more so for boys than girls ( Dhar and others, 2022 ). The findings from Bell and others (2019 ) suggest that if girls were as exposed to female inventors as boys are to male inventors in their childhood commuting zones, the current gender gap in innovation would shrink by half. 19 The scope for policies directly targeting gender inequality (e.g., removing legal barriers) also appears limited.

Second, policies to reduce gender inequality in different areas such as education and labor market can be effective, with substantial immediate benefits for women and for the entire society, as discussed throughout the paper and particularly in Section IV. Examples include general policies and targeted gen der policies to improve access to education (e.g., public investment in education and conditional cash transfers for girls) and boost labor force participation (e.g., childcare subsidies and eliminating tax penalties for secondary earners).

Third, policies to address gender inequality in different areas can also indirectly influence gender bias and social norms, the root causes of gender inequality. For example, policies that help narrow the gender inequality in education in turn also help shape gender attitude, as it increases women’s income and bargaining power at home ( Le and Nguyen, 2021 ). Gender quotas in political leadership can help influence adolescent girls’ career aspirations and educational attainment—reflecting primarily a role model effect of female leadership—and reduce gender discrimination in the long-term ( Beaman and others, 2012 ; Pande and Ford, 2012 ). A program to enhance financial inclusion of women—under which rural Indian women received bank accounts, training in account use, and direct deposit of public sector wages into their own (versus husbands ’) accounts— incentivizes women to work and helps liberalize women’s own work-related norms and shift perceptions of community norms ( Field and others, 2021 ).

While addressing gender inequality does not have to start with and solely focus on policies that are targeted at the root causes of gender inequality, it would need to end there, as fully eliminating gender inequality would require addressing the root causes of gender inequality, and policies aiming at reducing gender inequality in different areas can only go so far. Only then, while some gender gaps may still exist, the allocation of human capital would be fully efficient, reflecting preference /comparative advantages between men and women.

VII. Discussions

This paper identifies five key issues that are particularly important for the continued efforts to tackle gender inequality:

It is critical to clearly define gender inequality and distinguish it from gender gaps. This has important implications on the policy designs to address gender inequity. However, the literature has paid little attention to this issue, often using gender inequality and gender gaps interchangeably. This paper defines gender gaps as the observed differences between men and women or between boys and girls in the various social and economic indicators, and gender inequality refers to the part that is driven by gender bias and unequal gender rights and opportunities. However, empirically estimating the corresponding gender inequality for each gender gap remains a challenge and requires more efforts on data collection and methodological developments.

The focus of gender inequality has been evolving over time. As some gender gaps are closed, other gender gaps are emerging (not necessarily new, but attracting the attentions of the public and policymakers). This suggests that there is still a long way to go to fully addressing gender inequality. Particularly, gender inequality is getting more subtle and implicit, though the social and economic benefits from addressing the remain gender inequality is still likely to be substantial. This highlights the need to apply a gender lens to a broad range of policies and practices to understand their potential implications on gender inequality. Such efforts help develop a comprehensive strategy, instead of a piece-meal approach with which only some gender inequality is addressed at a time.

Addressing gender inequality benefits everyone, not just women. Thus, the entire society should work together, even for each individual’s own interest. Lower gender inequality not only benefits women, but also benefits children—as women trend to emphasize child welfare more than men—and the entire economy through the positive productivity externality from more balanced gender roles, and improved economic growth, financial stability, and income inequality. In addition to further strengthening the empirical evidence in these areas, there is an urgent need for the findings to be incorporated into policy designs and decision-making.

Policies and their designs can help accelerate the decline of gender inequality from economic development and technological advances. Both general policies and targeted gender policies can play a role, and the pros and cons of such policies should be carefully assessed. As gender inequality becomes more subtle and implicit (e.g., in field of study, the distribution of employment across sectors, and mid-level management positions), general policies will typically not work, unlike for school enrollments and labor force participation. Thus, targeted gender policies will need to play a bigger role. More analytical work is needed on what programs work and under what conditions. Also, this means that analytical work geared at separating gender inequality from gender gaps is all that more important.

While fully addressing gender inequality requires the elimination of the root cause s of gender inequality (e.g., gender bias and social norms), this does not mean that policies are not targeted at the root causes of gender inequality do not have a role. In fact, they can still be effective, as they can generate immediate social and economic benefits and indirectly affect gender bias and social norms. Policies directly targeted at the root causes of gender inequality would be generally preferred but appear limited, and research to expand the policy toolkit would be particularly useful.

One general issue in the efforts to address gender inequality is the lack of gender disaggregated data. Great progress has been made. For example, The IMF’s Financial Access Survey (FAS) is a unique source of annual supply-side data on access to and use of basic financial services by gender. The World Development Indicators (WDI) from the World Bank now present many statistics by male and female separately. Missing data, however, are still widespread, particularly in low-income countries. Therefore, continued efforts are still needed to further expand data availability in terms of both coverage and quality.

Another important issue the paper only marginally touches upon is the challenge of turning policy designs into practices. The analysis of Women, Business, and the Law index on several gender gaps suggests that it is not automatic that laws and regulations to promote gender equality will lead to immediate improvements in gender outcomes. Implementation remains a challenge for many countries, particularly developing economies with limited administrative capacity. For example, as reported in Evans and Yuan (2022) , many similar policy interventions have substantially different impacts across countries. Conditional cash transfer in South Africa is the best intervention among the 267 educational interventions in 54 low- and middle-income countries, while conditional cash transfer in the Philippines is one of the ten worst interventions. Thus, the importance of effective implementation cannot be overstated.

Appendix Figure 1.

Gender Gaps in Education

Appendix Figure 2.

Gender Gaps in Field of Study

Appendix Figure 3.

Gender Gaps in Labor Force Participation and Employment by Sector

Appendix Figure 4.

Gender Gaps in Leadership Positions

Appendix Figure 5.

Gender Wage Gap in Selected Economies

Appendix Figure 6.

Gender Gaps in Financial Access and Legal Barriers to Gender Equality

Appendix Figure 7.

Large Variations in Gender Gaps across Countries

Alternative Specifications on the Effects of Laws and Regulations on Selected Gender Gaps

Afridi Farzana , Vegard Iversen , and M. R. Sharan , 2017 , “ Women Political Leaders, Corruption, and Learning: Evidence from a Large Public Program in India ,” Economic Development and Cultural Change, Volume 66 , Number 1 : 1 – 30 .

  • Search Google Scholar
  • Export Citation

Aguilar , Arturo , Emilio Gutiérrez , and Paula Soto Villagrá n, 2021 , “ Benefits and Unintended Consequences of Gender Segregation in Public Transportation: Evidence from Mexico City’s Subway System ,” Economic Development and Cultural Change , Volume 69 , Number 4 : 1379 –1410.

Ahmad , Mahtab , Moazma Batool , and Sophia Dziegielewski , 2016 , “ State of Inheritance Rights: Women in a Rural District in Pakistan ,” Journal of Social Service Research 42 ( 5 ): 622 – 29 .

Albanesi , Stefania , and Jiyeon Kim , 2021 , “ Effects of the COVID-19 Recession on the US Labor Market: Occupation, Family, and Gender ,” The Journal of Economic Perspectives, Vol. 35 , No. 3 : 3 - 24 .

Alesina , Alberto , Paola Giuliano , and Nathan Nunn , 2013 , “ On the Origins of Gender Roles: Women and the Plough ,” Quarterly Journal of Economics 128 : 469 – 530 .

Alonso , Cristian , Mariya Brussevich , Era Dabla-Norris , Yuko Kinoshita , and Kalpana Kochhar , 2019 , “ Reducing and Redistributing Unpaid Work: Stronger Policies to Support Gender Equality ,” IMF Working Paper WP/19/225 , International Monetary Fund , Washington DC .

Alonso-Albarran , Virginia , Teresa Curristine , Gemma Preston , Alberto Soler , Nino Tchelishvili , and Sureni Weerathunga , 2021 , “ Gender Budgeting in G20 Countries ,” IMF Working Paper WP/21/269 , International Monetary Fund , Washington DC .

Austen , Siobhan , Monica Costa , Rhonda Sharp , and Diane Elson , 2013 , “ Expenditure Incidence Analysis: A Gender-Responsive Budgeting Tool for Educational Expenditure in Timor-Leste? ” Feminist Economics 19 : 1 – 24 .

Beaman , Lori , Esther Duflo , Rohini Pande , and Petia Topalova , 2012 , “ Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India ,” Science , 335 ( 6068 ): 582 - 586 .

Becker , Charles , Cecilia Elena Rouse , Mingyu Chen , 2016 , “ Can a Summer Make a Difference? The Impact of the American Economic Association Summer Program on Minority Student Outcomes ,” Economics of Education Review , Volume 53 : 46 - 71 .

Bell , Alex , Raj Chetty , Xavier Jaravel , Neviana Petkova , and John Van Reenen , 2019 , “ Who Becomes an Inventor in America? The Importance of Exposure to Innovation ,” The Quarterly Journal of Economics , 134 ( 2 ): 647 - 713 .

Bergman , Nittai , David Matsa , and Michael Weber , 2022 , “ How Tight Labor Markets Facilitate Broad-Based Employment Growth ”, NBER Working Paper 29651, National Bureau of Economic Research .

Bernhardt , Arielle , Erica Field , Rohini Pande , Natalia Rigol , Simone Schaner , and Charity Troyer-Moore , 2018 , “ Male Social Status and Women’s Work ,” AEA Papers and Proceedings , 108 : 363 - 67 .

Bharadwaj , Prashant , Giacomo De Giorgi , David Hansen , and Christopher A. Neilson , 2016 , “ The Gender Gap in Mathematics: Evidence from Chile ,” Economic Development and Cultural Change , Volume 65 , Number 1 : 141 – 166 .

Bick , Alexander , and Nicola Fuchs-Schündeln , 2017 , “ Quantifying the Disincentive Effects of Joint Taxation on Married Women’s Labor Supply ,” American Economic Review , 107 ( 5 ): 100 - 104 .

Blau , Francine , and Lawrence Kahn , 2017 , “ The Gender Wage Gap: Extent, Trends, and Explanations ,” Journal of Economic Literature , 55 ( 3 ): 789 - 865 .

Bluedorn , John , Francesca Caselli , Niels-Jakob Hansen , Ippei Shibata , and Marina Tavares , 2021 , “ Gender and Employment in the Covid-19 Recession: Evidence on She-cessions ,” IMF Working Paper WP/21/95 , International Monetary Fund , Washington DC .

Buehren , Niklas , Markus Goldstein , Kenneth Leonard , Joao Montalvao , and Kathryn Vasilaky , 2022 , “ Spillover Effects of Girls’ Empowerment on Brothers’ Competitiveness: Evidence from a Lab-in-the-Field Experiment in Uganda ,” Economic Development and Cultural Change , Volume 70 , Number 2 : 653 – 670 .

Cabral , Marika , and Marcus Dillender , 2021a , “ Disparities in Health Care and Medical Evaluations by Gender: A Review of Evidence and Mechanisms .” AEA Papers and Proceedings , 111 : 159 - 63 .

Cabral , Marika and Marcus Dillender , 2021 b, “ Gender Differences in Medical Evaluations: Evidence from Randomly Assigned Doctors ,” NBER Working Paper 29541 , National Bureau of Economic Research .

Caprioli , Mary , 2005 , “ Primed for Violence: The Role of Gender Inequality in Predicting Internal Conflict ,” International Studies Quarterly , Vol. 49 , No. 2 : 161 - 178 .

Carranza , Eliana , 2014 , “ Soil Endowments, Female Labor Force Participation, and the Demographic Deficit of Women in India ,” American Economic Journal: Applied Economics , 6 ( 4 ): 197 - 225 .

Chen , Yi , Hong Zhang , and Li-An Zhou , 2021a , “ Motherhood and Gender Wage Differentials within a Chinese Firm ,” Economic Development and Cultural Change , Volume 70 , Number 1 : 283 –320.

Chen , Sharon , Sebastian Doerr , Jon Frost , Leonardo Gambacorta , and Hyun Song Shin , 2021b , “ The Fintech Gender Gap” , BIS Working Paper No. 931 , Bank for International Settlements (Basel) .

Cihák , Martin and Ratna Sahay , 2020 , “ Finance and Inequality ,” IMF Staff Discussion Note, SDN/20/01 , International Monetary Fund , Washington, DC .

Cook , Lisa , Janet Gerson , and Jennifer Kuan , 2021 , “ Closing the Innovation Gap in Pink and Black ,” NBER Working Paper 29354 , National Bureau of Economic Research .

Cook , Lisa , 2019 , “ The Innovation Gap in Pink and Black ,” in Wisnioski , Hintz , and Stettler Kleine , eds. Does America Need More Innovators? Cambridge, MA : The MIT Press .

Cook , Lisa , and Yanyan Yang , 2018 , “ Missing Women and Minorities: Implications for Innovation and Growth ,” Presentation ( http://www.yanyanyang.com/uploads/5/6/5/2/56523543/aeapinkblack_cookyang.pdf ).

Cook , Lisa , and Chaleampong Kongcharoen , 2010 , “ The Idea Gap in Pink and Black ,” NBER Working Paper 16331 , National Bureau of Economic Research .

Cordoba , Juan , Anni Isojarvi , and Haoran Li , 2021 , “ Equilibrium Unemployment: The Role of Discrimination ,” Finance and Economics Discussion Series 2021-080 . Washington : Board of Governors of the Federal Reserve System .

Dhar , Diva , Tarun Jain , and Seema Jayachandran , 2022 , “ Reshaping Adolescents’ Gender Attitudes: Evidence from a School-Based Experiment in India ,” American Economic Review , 112 ( 3 ): 899 - 927 .

Daher , Marilyne , Mahmoud Rifai , Riyad Kherallah , Fatima , Rodriguez , Dhruv Mahtta , Erin Michos , Safi Khan , Laura Petersen , and Salim Virani , 2021 , “ Gender Disparities in Difficulty Accessing Healthcare and Cost-related Medication Non-adherence: The CDC Behavioral Risk Factor Surveillance System (BRFSS) Survey ,” Preventive Medicine , Volume 153 .

Dang , Hai-Anh , Trung Hoang , and Ha Nguyen , 2021 , “ The Long-Run and Gender-Equalizing Impacts of School Access: Evidence from the First Indochina War ,” Economic Development and Cultural Change , Volume 70 , Number 1 : 453 - 484 .

Dasgupta , Shatanjaya , 2016 , “ Son Preference and Gender Gaps in Child Nutrition: Does the Level of Female Autonomy Matter? ” Review of Development Economics , Volume 20 , Issue 2 : 375 - 386 .

Demery , Lionel , and Isis Gaddis , 2009 , “ Social Spending, Poverty, and Gender Equality in Kenya: A Benefit Incidence Analysis ,” Deutsche Gesellschaft for Technische Zusammenarbeit , Nairobi .

Demirgüç-Kunt , Asli , Leora Klapper , Dorothe Singer , and Peter Van Oudheusden , 2015 , “ The Global Findex Database 2014: Measuring Financial Inclusion around the World ,” Policy Research Working Paper No. WPS 7255 , World Bank , Washington DC .

Dinkelman , Taryn , 2011 , “ The Effects of Rural Electrification on Employment: New Evidence from South Africa ,” American Economic Review , 101 ( 7 ): 3078 - 3108 .

Djurfeldt , Agnes Andersson , 2020 , “ Gendered Land Rights, Legal Reform and Social Norms in the Context of Land Fragmentation—A Review of the Literature for Kenya, Rwanda and Uganda ,” Land Use Policy 90 : 104305 .

Downes , Ronnie , and Scherie Nicol , 2020 , “ Designing and Implementing Gender Budgeting – a Path to Action ,” OECD Journal on Budgeting , Volume 2020 Issue 2 : 67 - 96 .

Duflo , Esther , 2012 , “Women Empowerment and Economic Development”, Journal of Economic Literature , 50 ( 4 ): 1051 – 79 .

Dustmann , Christian , and Uta Schönberg , 2012 , “ Expansions in Maternity Leave Coverage and Children’s Long-Term Outcomes .” American Economic Journal: Applied Economics , 4 ( 3 ): 190 - 224 .

Erten , Bilge , and Martina Metzger , 2019 , “ The Real Exchange Rate, Structural Change, and Female Labor Force Participation ,” World Development , Vol. 117© : 296 - 312 .

Evans , David , Akmal Maryam , and Jakiela Pamela , 2021 , “ Gender Gaps in Education: The Long View ,” IZA Journal of Development and Migration, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA) , vol. 12 ( 1 ): 1 - 27 .

Evans , David , and Fei Yuan , 2022 , “ What We Learn about Girls’ Education from Interventions That Do Not Focus on Girls ,” The World Bank Economic Review , 36 ( 1 ), 2022, 244 – 267 .

Fabrizio , Stefania , Diego Gomes , and Marina Mendes Tavares , 2021 , “ COVID-19 She-Cession: The Employment Penalty of Taking Care of Young Children ,” IMF Working Paper WP/21/58 , International Monetary Fund , Washington DC .

Fernandez , Raquel , and Alessandra Fogli , 2009 , “ Culture: An Empirical Investigation of Beliefs, Work, and Fertility ,” American Economic Journal: Macroeconomics 1 : 146 – 77 .

Field , Erica , Seema Jayachandran , Rohini Pande , 2010 , “ Do Traditional Institutions Constrain Female Entrepreneurship? A Field Experiment on Business Training in India ,” American Economic Review 100 ( 2 ): 125 - 29 .

Field , Erica , Rohini Pande , Natalia Rigol , Simone Schaner , and Charity Troyer Moore , 2021 , “ On Her Own Account: How Strengthening Women’s Financial Control Impacts Labor Supply and Gender Norms .” American Economic Review , 111 ( 7 ): 2342 - 75 .

Galor , Oded , and David Weil , 1996 , “The Gender Gap, Fertility and Growth, American Economic Review, 86 ( 3 ): 374 – 387 .

Gedzi , Victor Selorme , 2012 , “ Women’s Property Relations after Intestate Succession PNDC Law 111 in Ghana ,” Research on Humanities and Social Sciences 2 ( 9 ): 211 – 219 .

Gonzales , Christian , Sonali Jain-Chandra , Kalpana Kochhar , and Monique Newiak , 2015 a, “ Fair Play: More Equal Laws Boost Female Labor Force Participation ,” IMF Staff Discussion Note, SDN/15/02 .

Gonzales , Christian , Sonali Jain-Chandra , Kalpana Kochhar , Monique Newiak , and Tlek Zeinullayev , 2015b , “ Catalyst for Change: Empowering Women and Tackling Income Inequality ,” IMF Staff Discussion Note 15/20 , International Monetary Fund , Washington, DC .

Greene , Daniel , Vincent Intintoli , and Kathleen Kahle , 2020 , “ Do Board Gender Quotas Affect Firm Value? Evidence from California Senate Bill No. 826 ,” Journal of Corporate Finance 60 ( 101526 ).

Greenwood Jeremy , Ananth Seshadri , and Mehmet Yorukoglu , 2005 , “ Engines of Liberation ,” Review of Economic Studies , 72 : 109 – 33 .

Gregory-Smith , Ian , Brian Main , Charles O’Reilly III , 2014 , “ Appointments, Pay and Performance in UK Boardrooms by Gender ,” The Economic Journal . 124 ( 574 ): F109 –F128.

Gupta , Rajesh , and Vaibhav Bhamoriya , 2020 , “ ’Give Me Some Rail’: An Enquiry into Puzzle of Declining Female Labour Force Participation Rate ,” Management and Labour Studies , Volume 46 , issue 1 : 7 - 23 .

Hadley , Craig , David Lindstrom , Fasil Tessema , and Tefara Belachew , 2007 , “ Gender Bias in the Food Insecurity Experience of Ethiopian Adolescents ,” Social Science and Medicine 66 ( 2 ): 427 – 438 .

Hafeez , Naima , and Climent Quintana-Domeque , 2018 , “ Son Preference and Gender-Biased Breastfeeding in Pakistan ,” Economic Development and Cultural Change , Volume 66 , Number 2 : 179 - 215 .

Hahn , Youjin , Asadul Islam , Kanti Nuzhat , Russell Smyth , and Hee-Seung Yang , 2018 , “ Education, Marriage, and Fertility: Long-Term Evidence from a Female Stipend Program in Bangladesh ,” Economic Development and Cultural Change , Volume 66 , Number 2 : 383 - 415 .

Harari , Mariaflavia , 2019 , “ Women’s Inheritance Rights and Bargaining Power: Evidence from Kenya ,” Economic Development and Cultural Change , Volume 68 , Number 1 : 189 - 138 .

Hare , Denise , 2016 , “ What Accounts for the Decline in Labor Force Participation among Married Women in Urban China, 1991–2011? ” China Economic Review , Volume 38© : 251 - 266 .

Heath , Rachal , and Seema Jayachandran , 2018 , “ The Causes and Consequences of Increased Female Education and Labor Force Participation in Developing Countries ,” In: Susan Averett , Laura Argys , and Saul Hoffman (eds), The Oxford handbook of women and the economy, Oxford University Press , New York , page: 395 – 424 .

Herring , Cedric , 2009 , “ Does Diversity Pay?: Race, Gender, and the Business Case for Diversity ,” American Sociological Review , Vol. 74 , No. 2 : 208 - 224 .

Holden , Livia , and Azam Chaudhary , 2013 , “ Daughters’ Inheritance, Legal Pluralism, and Governance in Pakistan ,” Journal of Legal Pluralism and Unofficial Law 45 ( 1 ): 104 –23

Hsieh , Chang-Tai , Erik Hurst , Charles Jones , and Peter Klenow , 2019 , “ The Allocation of Talent and U.S. Economic Growth ,” Econometrica , Vol. 87 , No. 5 : 1439 – 1474 .

Hunt , Jennifer , Jean-Philippe Garant , and Hannah Herman , and David Munroe , 2013 , “ Why Are Women Underrepresented amongst Patentees? ” Research Policy , Elsevier , vol. 42 ( 4 ): 831 - 843 .

Hyland , Marie , Simeon Djankov , and Pinelopi Koujianou Goldberg , 2020 , “ Gendered Laws and Women in the Workforce ,” American Economic Review: Insights , 2 ( 4 ): 475 – 490 .

Hyland , Marie , Simeon Djankov , and Pinelopi Koujianou Goldberg , 2021 , “ Do Gendered Laws Matter for Women’s Economic Empowerment? ” PIIE Working Paper .

International Labour Organization (ILO) , 2012 , “ Employment and Gender Differences in the Informal Economy .” PowerPoint presentation , Geneva .

Jensen , Kyle , Balazs Kovacs , and Olav Sorenson , 2018 , “ Gender Differences in Obtaining and Maintaining Patent Rights ,” Nature Biotechnology 36 : 307 - 309 .

Jayachandran , Seema , 2015 , “ The Roots of Gender Inequality in Developing Countries ,” Annual Review of Economics , Vol. 7 : 63 - 88 .

Jayachandran , Seema , 2021 , “ Social Norms as a Barrier to Women’s Employment in Developing Countries ,” IMF Economic Review , 69 ( 3 ): 576 - 595 .

Khera , Purva , Sumiko Ogawa , Ratna Sahay , and Mahima Vasishth , 2022 , “ Women in Fintech: As Leaders and Users ,” IMF Working Paper WP/22/140 , International Monetary Fund , Washington, DC .

Kim , Jin Ho and Benjamin Williams , 2021 , “ Minimum Wage and Women’s Decision-Making Power within Households: Evidence from Indonesia ,” Economic Development and Cultural Change , Volume 70 , Number 1 : 359 - 414 .

Kochhar , Kochhar , Sonali Jain-Chandra , and Monique Newiak , 2017 , Women, Work, and Economic Growth: Leveling the Playing Field , International Monetary Fund , Washington, DC .

Kolovich , Lisa , Vivian Malta , Monique Newiak , and David Robinson , 2020 , “ Gender Equality and Macroeconomic Outcomes: Evidence and Policy Implications ,” Oxford Review of Economic Policy , Volume 36 , Number 4 : 743 – 759 .

Kuzmina , Olga , and Valentina Melentyeva , 2021 , “ Gender Diversity in Corporate Boards: Evidence from Quota-Implied Discontinuities ,” ZEW – Centre for European Economic Research Discussion Paper No. 21-023 .

Lalive , Rafael , Analía Schlosser , Andreas Steinhauer , Josef Zweimüller , 2014 , “ Parental Leave and Mothers’ Careers: The Relative Importance of Job Protection and Cash Benefits ,” The Review of Economic Studies , Volume 81 , Issue 1 : 219 – 265 .

Le , Kien , and My Nguyen , 2021 , “ How Education Empowers Women in Developing Countries ,” The B.E. Journal of Economic Analysis & Policy , Vol. 21 , No. 2 : 511 - 536 .

Leight , Jessica and Elaine Liu , 2020 , “ Maternal Education, Parental Investment, and Noncognitive Characteristics in Rural China ,” Economic Development and Cultural Change , Volume 69 , Number 1 : 213 - 251 .

Levi , Maurice , Kai Li , and Feng Zhang , 2014 , “ Director Genders and Mergers and Acquisitions ,” Journal of Corporate Finance . 28 : 185 – 200 .

Li , Cindy , 2019 , “ Falling Female Labor Force Participation in China and India ,” Pacific Exchang e Blog, Federal Reserve Bank of San Francisco ( https://www.frbsf.org/banking/asia-program/pacific-exchange-blog/falling-flpr-china-and-india/ ).

List , John , 2004 , “ The Nature and Extent of Discrimination in the Marketplace: Evidence from the Field ,” The Quarterly Journal of Economics , Vol. 119 , No. 1 : 49 - 89 .

Lleras-Muney , Adriana , Sissel Jensen , Sandra Black , and Marianne Bertrand , 2019 , “ Breaking the Glass Ceiling? The Effect of Board Quotas on Female Labour Market Outcomes in Norway ,” The Review of Economic Studies . 86 ( 1 ): 191 – 239 .

Low , Hamish , and Luigi Pistaferri , 2019 , “ Disability Insurance: Error Rates and Gender Differences ,” NBER Working Paper 26513 , National Bureau of Economic Research .

Maity , Bipasha , 2020 , “ Consumption and Time-Use Effects of India’s Employment Guarantee and Women’s Participation ,” Economic Development and Cultural Change , Volume 68 , Number 4 : 1185 - 1231 .

Miller , Grant , 2008 , “Women’s Suffrage, Political Responsiveness, and Child Survival in American History”, The Quarterly Journal of Economics , Volume 123 , Issue 3 : 1287 –1327.

NSF , 2021 . \ Women, Minorities, and Persons with Disabilities in Science and Engineering .” Arlington VA : National Science Foundation .

Olivetti , Claudia , and Barbara Petrongolo , 2017 , “ The Economic Consequences of Family Policies: Lessons from a Century of Legislation in High-Income Countries ,” Journal of Economic Perspectives , 31 ( 1 ): 205 - 30 .

Organisation for Economic Co-operation and Development (OECD) , 2012 , Closing the Gender Gap: Act Now , OECD , Paris .

Organisation for Economic Co-operation and Development (OECD) , 2014 , “ Gender Equality: Gender Equality in Entrepreneurship ,” OECD Social and Welfare Statistics database , Paris .

Organisation for Economic Co-operation and Development (OECD) , 2017 , “ The Pursuit of Gender Equality: An Uphill Battle ,” OECD , Paris .

Organisation for Economic Co-operation and Development (OECD) , 2019 , “ PISA 2018 Results (Volume II): Where All Students Can Succeed ,” OECD , Paris .

Organisation for Economic Co-operation and Development (OECD) , 2020 , “ All Hands In? Making Diversity Work for All ,” OECD , Paris .

Østergaard, Christian, Bram Timmermans, and Kari Kristinsson, 2011 , “ Does a Different View Create Something New? The Effect of Employee Diversity on Innovation ,” Research Policy , 40 , 500 – 509 .

Owen , Ann , and Judit Temesvary , 2018 , “ The Performance Effects of Gender Diversity on Bank Boards ,” Journal of Banking and Finance , 90 : 50 - 63 .

Ozgen , Ceren , 2021 , “ The Economics of Diversity: Innovation, Productivity and the Labour Market ,” Journal of Economic Surveys , Volume 35 , Issue 4 : 1168 - 1216 .

Pacheco , Jorge , Francisca Crispi , Tania Alfaro , María Soledad Martínez , and Cristóbal Cuadrado , 2021 , “ Gender Disparities in Access to Care for Time-sensitive Conditions during COVID-19 Pandemic in Chile ,” BMC Public Health , 21 ( 1 ): 1802 .

Pathak , Yuvraj and Karen Macours , 2017 , “ Women’s Political Reservation, Early Childhood Development, and Learning in India ,” Economic Development and Cultural Change , Volume 65 , Number 4 : 741 - 766 .

Pitt , Mark , Shahidur Khandker , and Jennifer Cartwright , 2006 , “ Empowering Women with Micro Finance: Evidence from Bangladesh ,” Economic Development and Cultural Change 54 ( 4 ): 791 –831.

Pitt , Mark , Shahidur Khandker , Omar Haider Chowdhury , and Daniel Millimet , 2003 , “ Credit Programs for the Poor and the Health Status of Children in Rural Bangladesh ,” International Economic Review , Volume 44 , Issue 1 : 87 - 118 .

Pitt , Mark , Mark Rosenzweig , and Mohammad Nazmul Hassan , 2012 , “ Human Capital Investment and the Gender Division of Labor in a Brawn-Based Economy ,” American Economic Review , 102 ( 7 ): 3531 - 60 .

Profeta , Paola , Livia Amidani Aliberti , Alessandra Casarico , Marilisa D’Amico , and Anna Puccio , 2014 , “ Quotas on Boards: Evidence from the Literature ,” In Women Directors . Palgrave Macmillan , London .

Pal , Sarmistha , 1999 , “ An Analysis of Childhood Malnutrition in Rural India: Role of Gender, Income and Other Household Characteristics ,” World Development 27 ( 7 ): 1151 – 1171 .

Pande , Rohini , and Deanna Ford , 2012 , “ Gender Quotas and Female Leadership ,” World Bank , Washington, DC .

Qian Nancy , 2008 , “ Missing Women and the Price of Tea in China: The Effect of Sex-Specific Earnings on Sex Imbalance ,” Quarterly Journal of Economics , 123 : 1251 – 85 .

Rendall , Michelle , 2017 , “ Brain versus Brawn: The Realization of Women’s Comparative Advantage ,” Working Paper No. 491 , Institute for Empirical Research in Economics , University of Zurich .

Rim , Nayoung , 2021 , “ The Effect of Title IX on Gender Disparity in Graduate Education ,” Journal of Policy Analysis and Management , Vol. 40 , No. 2 : 521 – 552 .

Rock , David , and Heidi Grant , 2016 , “ Why Diverse Teams Are Smarter ,” Harvard Business Review, November 4: https://hbr.org/2016/11/why-diverse-teams-are-smarter , last accessed October 27 , 2021 .

Rubolino , Enrico , 2022 , “ Taxing the Gender Gap: Labor Market Effects of a Payroll Tax Cut for Women in Italy ,” CESifo Working Paper No. 9671 .

Ruhm , Christopher , 1998 , “ The Economic Consequences of Parental Leave Mandates: Lessons from Europe ,” Quarterly Journal of Economics , 113 ( 1 ): 285 – 317 .

Sahay , Ratna , Martin Cihák , and other IMF Staff, 2018 , “ Women in Finance: A Case for Closing Gaps ,” IMF Staff Discussion Note, SDN/18/05 , International Monetary Fund , Washington DC .

Sahay , Ratna , Ulric Eriksson von Allmen , Amina Lahreche , Purva Khera , Sumiko Ogawa , Majid Bazarbash , and Kimberly Beaton , 2020 , “ The Promise of Fintech; Financial Inclusion in the Post COVID-19 Era ,” IMF Working Paper WP/20/09 , International Monetary Fund , Washington DC .

Sloane , Carolyn , Erik Hurst , and Dan Black , 2021 , “ College Majors, Occupations, and the Gender Wage Gap ,” Journal of Economic Perspectives , Volume 35 , Number 4 : 223 – 248 .

Socías , Eugenia , Mieke Koehoorn , and Jean Shoveller , 2015 , “ Gender Inequalities in Access to Health Care among Adults Living in British Columbia, Canada ,” Women’s Health Issues , Volume 26 , Issue 1 : 74 - 79 .

Strøm , Reidar , Bert D’Espallier , and Roy Mersland , 2014 , “ Female Leadership, Performance, and Governance in Microfinance Institutions .” Journal of Banking and Finance 42 : 60 –75.

Tewari , Ishani , and Yabin Wang , 2021 , “ Durable Ownership, Time Allocation, and Female Labor Force Participation: Evidence from China’s “Home Appliances to the Countryside” Rebate ,” Economic Development and Cultural Change , Volume 70 , Number 1 : 87 - 127 .

World Economic Forum (WEF) , 2021 , “ Global Gender Gap Report 2021 ,” World Economic Forum , Geneva .

World Bank , 2006 , “ Repositioning Nutrition as Central to Development: A Strategy for Large Scale Action ,” World Bank , Washington DC .

World Bank , 2021 , Women, Business and the Law 2021 , World Bank , Washington DC .

Xiao , Pengpeng , 2021 , “ Wage and Employment Discrimination by Gender in Labor Market Equilibrium ,” Working Paper 144 , VATT Institute for Economic Research .

Zhang , Eva , and Tianlei Huang , 2020 , “ The Gender Gap in Labor Force Participation Is Widening in China While Narrowing in Other Parts of the World ,” PIIE Charts , Peterson Institute for International Economics ( https://www.piie.com/research/piie-charts/gender-gap-labor-force-participation-widening-china-while-narrowing-other-parts ).

In the rest of the paper, the discussions typically center around gender inequality against women, but the same arguments can be made for gender inequality against men when applicable.

The global commitment to achieving gender equality an d accelerating efforts to end gender inequality is reflected in the 2030 Sustainable Development Goal 5 , which includes nine targets covering discrimination and violence against women, child marriage, unpaid care and domestic work, leadership role, access to reproductive health, rights to economic resources, and technology use to promote women empowerment. In addition, achieving other SDGs could also have important implications for gender equality, for example, under Sustainable Development Goal 4 on quality education .

For example, a number of countries have mandated gender diversity on corporate boards of directors, including Austria, Belgium, Finland, France, Germany, Iceland, India, Israel, Italy, Kenya, Netherlands, Norway, Pakistan, Portugal, Spain, Quebec of Canada, and California of United States. Malaysia is one recent case and mandates its publicly traded firms to have at least one-woman director on their boards from September 1, 2022.

This includes both taste-based and statistical discrimination; taste-based discrimination refers to less favorable attitudes and prejudice towards women, while statistical discrimination refers to the use of perception or statistics on women as a group in decision-making when information on a specific woman is lacking; for example, firms may make employment and pay decisions, based on average leave days taken and average job turnover rates for women and men; studies have found that statistical discrimination plays an important role in gender gaps , such as in wages and employment ( List, 2004 ; Xiao, 2020; Cordoba and others, 2021 ).

It should be noted that preference here refers to choices made in the absence of gender inequality. This is important as gender inequality and the associated social norms often operate through affecting the willingness of men and women in making certain choices.

For example, the comparative advantage of women often refers to the innate advantage of women in brain versus brawn jobs in the literature.

According to the Bureau of Labor Statistics, around 13 percent of registered nurses in the United States are male in 2021.

The empirical observation of U-shaped female labor force participation over the course of economic development reflects other factors that also influence the decision of women entering the labor market ( Jayachandran, 2021 ). This includes the less need for a second income earner in a household, women’s comparative advantage in rearing children, the need to balance employment with household responsibilities, and social/cultural norms on “suitable” jobs for women, for example, between manufacturing jobs and service sector jobs.

While there is little empirical evidence on to what extent unpaid work is driven by preference and social norms, it is generally recognized that both play a role ( Alonso and others, 2019 ).

The index measures laws and regulations that affect women’s economic opportunities, based on eight indicators structured around women’s interactions with the law as they move through their careers: mobility, workplace, pay, marriage, parenthood, entrepreneurship, assets, and pension. Although it is critical to ensuring women’s economic inclusion, implementation of laws is not currently measured. Instead, Women, Business and the Law identifies legal differences between men and women as one step toward a better understanding of where women’s economic rights may be restricted in practice ( World Bank, 2021 ).

While the paper focuses on education, labor market, financial access and legal barriers, similar patterns are also observed in other areas. For example, in advanced economies, while there are little gender differences in health insurance and the ability to seek healthcare, a growing body of evidence suggests that female patients—relative to male patients—receive less healthcare for similar medical conditions and are more likely to be told by providers that their symptoms are emotionally driven rather than arising from a physical impairment; recent evidence also shows that there are large gender gaps in receiving benefits from social insurance programs that rely on medical evaluations ( Cabral and Dillender, 2021a ). For example, Low and Pistaferri (2019) show that female applicants for Social Security Disability Insurance are 20 percentage points more likely to be rejected than similar male applicants. The gender imbalance in the physician workforce can explain a large part of the gap ( Cabral and Dillender, 2021b ).

The literature of broad diversity (e.g., gender, race, and age) on firm productivity and team performance also yields mixed effects (see OECD (2020) for a review).

The study sample covers all countries between 1990 and 2019, when data are available.

Please see Appendix Table 1 for alternative specifications. Without including a time trend, the estimates are larger, more statistically significant, and have the expected signs for all five gender gaps, including labor force participation. Gonzales and others (2015a) and Hyland and others (2020 ; 2021) do not include a time trend and show similar results. The results from a random effects specification often lie somewhere in between.

See, for example, Bergman and others (2022) on the gender employment implication of the Federal Reserve’s recent move from a strict to an average inflation targeting framework; Erten and Metzger (2019) on currency undervaluation and female labor force participation ; and Kim and Williams (2021) on the effects of the minimum wage on women’s intrahousehold bargaining power.

Beaman and other (2012) , however, find that quota policies for female leadership helps improve adolescent girls’ career aspirations and educational attainment.

For example, the studies typically do not consider the impact of gender quotas on reducing gender bias in the broad society.

One example of targeted policies at gender inequality in employment is a payroll tax cut for female hires, introduced in 2012 in Italy to spur female employment and to stimulate business activity by reducing labor costs. The preferential tax rate is only available in occupations with large gender employment gap and has requirement for length in unemployment, which varies by age, whether in economically disadvantaged areas, and occupation. In addition, the preferential payroll tax scheme is valid for up to 12 months for temporary jobs and 18 months for permanent jobs. Firms can use the payroll tax cut only if overall employment would not decrease with respect to past employment. The complex eligibility criteria highlight the challenges in designing targeted gender policies while limiting their efficiency cost. Rubolino (2022) finds that payroll tax cut generates long-lasting growth in female employment with little effect on net wages and without crowding out male employment. However, the efficiency implication of the reform is not fully analyzed, as it is unclear what would have happened had the tax cut not been gender targeted,

See also Cook and others (2021) and Becker and others (2016) , which show that targeted mentoring programs can have significant and long-lasting effects on inclusion in STEM careers, where income, race, and gender gaps in acquiring education have been due to a lack of mentoring and exposure to science and innovation careers rather than differences in ability.

Cover IMF Working Papers

How Gender Inequality Persists in the Modern World

  • Gender & Sexuality
  • Media & Public Opinion

Connect with the author

Ridgeway

In the United States as in many other societies, gender relationships are changing and inequalities between men and women are questioned in virtually every sphere – at work, in the home, and in public affairs. Yet the cold, hard facts show that gender gaps and inequalities persist, even in the face of startling social and economic transformations and concerted movements to challenge women’s subordination.

How can this be? Especially in advanced industrial nations, why are gender inequalities proving so difficult to surpass? My research shows that the answers lie, above all, in how people think about gender as they relate to one another. Day by day people use gender as taken-for-granted common sense to manage their relationships with others. Interpersonal negotiations are constantly influenced by gender stereotypes – and that, in turn, causes older ways of thinking about men and women and their relationships to be carried into all spheres of life and even into very new kinds of tasks and social settings.

Continuing Gender Inequalities in the United States

There can be little doubt that gender inequality does still persist in the United States, as some striking facts make clear:

  • Women still make only about 80% of what men earn for full time work.   
  • Women are less likely to hold managerial or supervisory positions, and when they do, their positions carry less authority.   
  • “Housewives” are perceived as in the lower half of all groups in social status, below “blue collar workers.”   
  • Even when both partners earn wages, women do twice as much housework and child care.   
  • To be sure, American women have made substantial gains since 1970. But gains have leveled off since the 1990s, suggesting that the gender revolution may be stalling – or at least slowing down. 

Making Sense of Persistent Gender Inequality

The persistence of gender inequality in the face of modern legal, economic, and political processes that work against it suggests that there must also be on-going social processes that continually recreate gender inequality. I have pulled together evidence from sociology, psychology, and the study of social cognition – how people perceive the social world – to develop an explanation of how gender differences and hierarchies function and end up being recreated again and again.

Research shows that widely shared gender stereotypes act as a “common knowledge” cultural frame that people use to begin the process of relating to one another and coordinating their interaction. That might seem obvious – and harmless. So what if people start out by classifying each other by gender and shaping their mutual contacts accordingly? As it turns out, the use of gender as an initial framing device in personal interactions has many unintended consequences, because gendered meanings get carried far beyond areas of life having directly to do with sex or reproduction. Social scientists and other observers have amassed lots of evidence showing that stereotypes and assumptions about men and women shape everyday personal interactions and shape gender inequalities in jobs, wages, authority, and family responsibilities.

Men, for example, tend to be seen as more authoritative and women more communal in orientation. In workplaces, this can readily lead people to expect and defer to men in charge – and to look to women to carry on routine group maintenance efforts. Studies show that in job interviews where men and women have the same qualifications, one gender gets more offers according to traditional assumptions about gender proclivities.

How People Approach New Situations

Similar social-psychological and interpersonal processes help explain how past gender relationships live on into the future, as older ideas and assumptions about men, women, and their relationships end up being used by everyone to shape new economic and social arrangements as they emerge. As research shows, “common knowledge” gender stereotypes change more slowly than do material arrangements between men and women, even though social beliefs do eventually respond to material changes. As a result of this cultural lag, people confront new, uncertain circumstances with traditional gender beliefs, especially because sites of innovation tend to be small and located outside established organizations. Ironically, both the uncertainty and the personal closeness of such innovative settings increase the likelihood that participants will draw on the convenient cultural frames to organize new ways of doing things.

In high-tech startups in Silicon Valley, for example, female scientists and engineers have been shown to be at a special disadvantage in small firms launched with a non-bureaucratic organizational culture. Small groups of men working flexibly and collegially often launch such ventures, which end up prospering in the marketplace. Without anyone consciously intending it, even very highly qualified women can be left to the side – and they are unlikely to make up ground as long as its original “boys’ club” atmosphere and assumptions about the kinds of people most likely to be innovative and high-achieving persist in the organization’s outlook and ways of attracting and promoting people. High-tech is not only innovative but male-dominated.

Are Gender Inequities Impossible to Overcome?

Once we understand how powerful everyday gender assumptions can be in shaping ongoing social relationships in all spheres, we better understand why gender inequalities are so difficult to overcome. Gender equality is not impossible to attain – but the struggle is constant and is sure to have ups and downs. My research also suggests that the fight for gender equality will have to be waged at the level of how people think, even as laws and institutional policies open new doors. Our assumptions about what women and men can and should do have a long way to catch up with the new possibilities created by education, economic innovation, and equal legal rights.

Read more in Cecilia L. Ridgeway, Framed by Gender: How Gender Inequality Persists in the Modern World (Oxford University Press, 2011).

Related Content

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List

Logo of plosone

The persistence of pay inequality: The gender pay gap in an anonymous online labor market

Leib litman.

1 Department of Psychology, Lander College, Flushing, New York, United States of America

Jonathan Robinson

2 Department of Computer Science, Lander College, Flushing, New York, United States of America

3 Department of Health Policy & Management, Mailman School of Public Health, Columbia University, New York, New York, United States of America

Cheskie Rosenzweig

4 Department of Clinical Psychology, Columbia University, New York, New York, United States of America

Joshua Waxman

5 Department of Computer Science, Stern College for Women, New York, New York, United States of America

Lisa M. Bates

6 Department of Epidemiology, Mailman School of Public Health, Columbia University New York, New York, United States of America

Associated Data

Due to the sensitive nature of some of the data, and the terms of service of the websites used during data collection (including CloudResearch and MTurk), CloudResearch cannot release the full data set to make it publically available. The data are on CloudResearch's Sequel servers located at Queens College in the city of New York. CloudResearch makes data available to be accessed by researchers for replication purposes, on the CloudResearch premises, in the same way the data were accessed and analysed by the authors of this manuscript. The contact person at CloudResearch who can help researchers access the data set is Tzvi Abberbock, who can be reached at [email protected] .

Studies of the gender pay gap are seldom able to simultaneously account for the range of alternative putative mechanisms underlying it. Using CloudResearch, an online microtask platform connecting employers to workers who perform research-related tasks, we examine whether gender pay discrepancies are still evident in a labor market characterized by anonymity, relatively homogeneous work, and flexibility. For 22,271 Mechanical Turk workers who participated in nearly 5 million tasks, we analyze hourly earnings by gender, controlling for key covariates which have been shown previously to lead to differential pay for men and women. On average, women’s hourly earnings were 10.5% lower than men’s. Several factors contributed to the gender pay gap, including the tendency for women to select tasks that have a lower advertised hourly pay. This study provides evidence that gender pay gaps can arise despite the absence of overt discrimination, labor segregation, and inflexible work arrangements, even after experience, education, and other human capital factors are controlled for. Findings highlight the need to examine other possible causes of the gender pay gap. Potential strategies for reducing the pay gap on online labor markets are also discussed.

Introduction

The gender pay gap, the disparity in earnings between male and female workers, has been the focus of empirical research in the US for decades, as well as legislative and executive action under the Obama administration [ 1 , 2 ]. Trends dating back to the 1960s show a long period in which women’s earnings were approximately 60% of their male counterparts, followed by increases in women’s earnings starting in the 1980s, which began to narrow, but not close, the gap which persists today [ 3 ]. More recent data from 2014 show that overall, the median weekly earnings of women working full time were 79–83% of what men earned [ 4 – 9 ].

The extensive literature seeking to explain the gender pay gap and its trajectory over time in traditional labor markets suggests it is a function of multiple structural and individual-level processes that reflect both the near-term and cumulative effects of gender relations and roles over the life course. Broadly speaking, the drivers of the gender pay gap can be categorized as: 1) human capital or productivity factors such as education, skills, and workforce experience; 2) industry or occupational segregation, which some estimates suggest accounts for approximately half of the pay gap; 3) gender-specific temporal flexibility constraints which can affect promotions and remuneration; and finally, 4) gender discrimination operating in hiring, promotion, task assignment, and/or compensation. The latter mechanism is often estimated by inference as a function of unexplained residual effects of gender on payment after accounting for other factors, an approach which is most persuasive in studies of narrowly restricted populations of workers such as lawyers [ 10 ] and academics of specific disciplines [ 11 ]. A recent estimate suggests this unexplained gender difference in earnings can account for approximately 40% of the pay gap [ 3 ]. However, more direct estimations of discriminatory processes are also available from experimental evidence, including field audit and lab-based studies [ 12 – 14 ]. Finally, gender pay gaps have also been attributed to differential discrimination encountered by men and women on the basis of parental status, often known as the ‘motherhood penalty’ [ 15 ].

Non-traditional ‘gig economy’ labor markets and the gender pay gap

In recent years there has been a dramatic rise in nontraditional ‘gig economy’ labor markets, which entail independent workers hired for single projects or tasks often on a short-term basis with minimal contractual engagement. “Microtask” platforms such as Amazon Mechanical Turk (MTurk) and Crowdflower have become a major sector of the gig economy, offering a source of easily accessible supplementary income through performance of small tasks online at a time and place convenient to the worker. Available tasks can range from categorizing receipts to transcription and proofreading services, and are posted online by the prospective employer. Workers registered with the platform then elect to perform the advertised tasks and receive compensation upon completion of satisfactory work [ 16 ]. An estimated 0.4% of US adults are currently receiving income from such platforms each month [ 17 ], and microtask work is a growing sector of the service economy in the United States [ 18 ]. Although still relatively small, these emerging labor market environments provide a unique opportunity to investigate the gender pay gap in ways not possible within traditional labor markets, due to features (described below) that allow researchers to simultaneously account for multiple putative mechanisms thought to underlie the pay gap.

The present study utilizes the Amazon Mechanical Turk (MTurk) platform as a case study to examine whether a gender pay gap remains evident when the main causes of the pay gap identified in the literature do not apply or can be accounted for in a single investigation. MTurk is an online microtask platform that connects employers (‘requesters’) to employees (‘workers’) who perform jobs called “Human Intelligence Tasks” (HITs). The platform allows requesters to post tasks on a dashboard with a short description of the HIT, the compensation being offered, and the time the HIT is expected to take. When complete, the requester either approves or rejects the work based on quality. If approved, payment is quickly accessible to workers. The gender of workers who complete these HITs is not known to the requesters, but was accessible to researchers for the present study (along with other sociodemographic information and pay rates) based on metadata collected through CloudResearch (formerly TurkPrime), a platform commonly used to conduct social and behavioral research on MTurk [ 19 ].

Evaluating pay rates of workers on MTurk requires estimating the pay per hour of each task that a worker accepts which can then be averaged together. All HITs posted on MTurk through CloudResearch display how much a HIT pays and an estimated time that it takes for that HIT to be completed. Workers use this information to determine what the corresponding hourly pay rate of a task is likely to be, and much of our analysis of the gender pay gap is based on this advertised pay rate of all completed surveys. We also calculate an estimate of the gender pay gap based on actual completion times to examine potential differences in task completion speed, which we refer to as estimated actual wages (see Methods section for details).

Previous studies have found that both task completion time and the selection of tasks influences the gender pay gap in at least some gig economy markets. For example, a gender pay gap was observed among Uber drivers, with men consistently earning higher pay than women [ 20 ]. Some of the contributing factors to this pay gap include that male Uber drivers selected different tasks than female drivers, including being more willing to work at night and to work in neighborhoods that were perceived to be more dangerous. Male drivers were also likely to drive faster than their female counterparts. These findings show that person-level factors like task selection, and speed can influence the gender pay gap within gig economy markets.

MTurk is uniquely suited to examine the gender pay gap because it is possible to account simultaneously for multiple structural and individual-level factors that have been shown to produce pay gaps. These include discrimination, work heterogeneity (leading to occupational segregation), and job flexibility, as well as human capital factors such as experience and education.

Discrimination

When employers post their HITs on MTurk they have no way of knowing the demographic characteristics of the workers who accept those tasks, including their gender. While MTurk allows for selective recruitment of specific demographic groups, the MTurk tasks examined in this study are exclusively open to all workers, independent of their gender or other demographic characteristics. Therefore, features of the worker’s identity that might be the basis for discrimination cannot factor into an employer’s decision-making regarding hiring or pay.

Task heterogeneity

Another factor making MTurk uniquely suited for the examination of the gender pay gap is the relative homogeneity of tasks performed by the workers, minimizing the potential influence of gender differences in the type of work pursued on earnings and the pay gap. Work on the MTurk platform consists mostly of short tasks such as 10–15 minute surveys and categorization tasks. In addition, the only information that workers have available to them to choose tasks, other than pay, is the tasks’ titles and descriptions. We additionally classified tasks based on similarity and accounted for possible task heterogeneity effects in our analyses.

Job flexibility

MTurk is not characterized by the same inflexibilities as are often encountered in traditional labor markets. Workers can work at any time of the day or day of the week. This increased flexibility may be expected to provide more opportunities for participation in this labor market for those who are otherwise constrained by family or other obligations.

Human capital factors

It is possible that the more experienced workers could learn over time how to identify higher paying tasks by virtue of, for example, identifying qualities of tasks that can be completed more quickly than the advertised required time estimate. Further, if experience is correlated with gender, it could contribute to a gender pay gap and thus needs to be controlled for. Using CloudResearch metadata, we are able to account for experience on the platform. Additionally, we account for multiple sociodemographic variables, including age, marital status, parental status, education, income (from all sources), and race using the sociodemographic data available through CloudResearch.

Expected gender pay gap findings on MTurk

Due to the aforementioned factors that are unique to the MTurk marketplace–e.g., anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect a gender pay gap to be evident on the platform to the same extent as in traditional labor markets. However, potential gender differences in task selection and completion speed, which have implications for earnings, merit further consideration. For example, though we expect the relative homogeneity of the MTurk tasks to minimize gender differences in task selection that could mimic occupational segregation, we do account for potential subtle residual differences in tasks that could differentially attract male and female workers and indirectly lead to pay differentials if those tasks that are preferentially selected by men pay a higher rate. To do this we categorize all tasks based on their descriptions using K-clustering and add the clusters as covariates to our models. In addition, we separately examine the gender pay gap within each topic-cluster.

In addition, if workers who are experienced on the platform are better able to find higher paying HITs, and if experience is correlated with gender, it may lead to gender differences in earnings. Theoretically, other factors that may vary with gender could also influence task selection. Previous studies of the pay gap in traditional markets indicate that reservation wages, defined as the pay threshold at which a person is willing to accept work, may be lower among women with children compared to women without, and to that of men as well [ 21 ]. Thus, if women on MTurk are more likely to have young children than men, they may be more willing to accept available work even if it pays relatively poorly. Other factors such as income, education level, and age may similarly influence reservation wages if they are associated with opportunities to find work outside of microtask platforms. To the extent that these demographics correlate with gender they may give rise to a gender pay gap. Therefore we consider age, experience on MTurk, education, income, marital status, and parental status as covariates in our models.

Task completion speed may vary by gender for several reasons, including potential gender differences in past experience on the platform. We examine the estimated actual pay gap per hour based on HIT payment and estimated actual completion time to examine the effects of completion speed on the wage gap. We also examine the gender pay gap based on advertised pay rates, which are not dependent on completion speed and more directly measure how gender differences in task selection can lead to a pay gap. Below, we explain how these were calculated based on meta-data from CloudResearch.

To summarize, the overall goal of the present study was to explore whether gender pay differentials arise within a unique, non-traditional and anonymous online labor market, where known drivers of the gender pay gap either do not apply or can be accounted for statistically.

Materials and methods

Amazon mechanical turk and cloudresearch.

Started in 2005, the original purpose of the Amazon Mechanical Turk (MTurk) platform was to allow requesters to crowdsource tasks that could not easily be handled by existing technological solutions such as receipt copying, image categorization, and website testing. As of 2010, researchers increasingly began using MTurk for a wide variety of research tasks in the social, behavioral, and medical sciences, and it is currently used by thousands of academic researchers across hundreds of academic departments [ 22 ]. These research-related HITs are typically listed on the platform in generic terms such as, “Ten-minute social science study,” or “A study about public opinion attitudes.”

Because MTurk was not originally designed solely for research purposes, its interface is not optimized for some scientific applications. For this reason, third party add-on toolkits have been created that offer critical research tools for scientific use. One such platform, CloudResearch (formerly TurkPrime), allows requesters to manage multiple research functions, such as applying sampling criteria and facilitating longitudinal studies, through a link to their MTurk account. CloudResearch’s functionality has been described extensively elsewhere [ 19 ]. While the demographic characteristics of workers are not available to MTurk requesters, we were able to retroactively identify the gender and other demographic characteristics of workers through the CloudResearch platform. CloudResearch also facilitates access to data for each HIT, including pay, estimated length, and title.

The study was an analysis of previously collected metadata, which were analyzed anonymously. We complied with the terms of service for all data collected from CloudResearch, and MTurk. The approving institutional review board for this study was IntegReview.

Analytic sample

We analyzed the nearly 5 million tasks completed during an 18-month period between January 2016 and June 2017 by 12,312 female and 9,959 male workers who had complete data on key demographic characteristics. To be included in the analysis a HIT had to be fully completed, not just accepted, by the worker, and had to be accepted (paid for) by the requester. Although the vast majority of HITs were open to both males and females, a small percentage of HITs are intended for a specific gender. Because our goal was to exclusively analyze HITs for which the requesters did not know the gender of workers, we excluded any HITs using gender-specific inclusion or exclusion criteria from the analyses. In addition, we removed from the analysis any HITs that were part of follow-up studies in which it would be possible for the requester to know the gender of the worker from the prior data collection. Finally, where possible, CloudResearch tracks demographic information on workers across multiple HITs over time. To minimize misclassification of gender, we excluded the 0.3% of assignments for which gender was unknown with at least 95% consistency across HITs.

The main exposure variable is worker gender and the outcome variables are estimated actual hourly pay accrued through completing HITs, and advertised hourly pay for completed HITs. Estimated actual hourly wages are based on the estimated length in minutes and compensation in dollars per HIT as posted on the dashboard by the requester. We refer to actual pay as estimated because sometimes people work multiple assignments at the same time (which is allowed on the platform), or may simultaneously perform other unrelated activities and therefore not work on the HIT the entire time the task is open. We also considered several covariates to approximate human capital factors that could potentially influence earnings on this platform, including marital status, education, household income, number of children, race/ethnicity, age, and experience (number of HITs previously completed). Additional covariates included task length, task cluster (see below), and the serial order with which workers accepted the HIT in order to account for potential differences in HIT acceptance speed that may relate to the pay gap.

Database and analytic approach

Data were exported from CloudResearch’s database into Stata in long-form format to represent each task on a single row. For the purposes of this paper, we use “HIT” and “study” interchangeably to refer to a study put up on the MTurk dashboard which aims to collect data from multiple participants. A HIT or study consist of multiple “assignments” which is a single task completed by a single participant. Columns represented variables such as demographic information, payment, and estimated HIT length. Column variables also included unique IDs for workers, HITs (a single study posted by a requester), and requesters, allowing for a multi-level modeling analytic approach with assignments nested within workers. Individual assignments (a single task completed by a single worker) were the unit of analysis for all models.

Linear regression models were used to calculate the gender pay gap using two dependent variables 1) women’s estimated actual earnings relative to men’s and 2) women’s selection of tasks based on advertised earnings relative to men’s. We first examined the actual pay model, to see the gender pay gap when including an estimate of task completion speed, and then adjusted this model for advertised hourly pay to determine if and to what extent a propensity for men to select more remunerative tasks was evident and driving any observed gender pay gap. We additionally ran separate models using women’s advertised earnings relative to men’s as the dependent variable to examine task selection effects more directly. The fully adjusted models controlled for the human capital-related covariates, excluding household income and education which were balanced across genders. These models also tested for interactions between gender and each of the covariates by adding individual interaction terms to the adjusted model. To control for within-worker clustering, Huber-White standard error corrections were used in all models.

Cluster analysis

To explore the potential influence of any residual task heterogeneity and gender preference for specific task type as the cause of the gender pay gap, we use K-means clustering analysis (seed = 0) to categorize the types of tasks into clusters based on the descriptions that workers use to choose the tasks they perform. We excluded from this clustering any tasks which contained certain gendered words (such as “male”, “female”, etc.) and any tasks which had fewer than 30 respondents. We stripped out all punctuation, symbols and digits from the titles, so as to remove any reference to estimated compensation or duration. The features we clustered on were the presence or absence of 5,140 distinct words that appeared across all titles. We then present the distribution of tasks across these clusters as well as average pay by gender and the gender pay gap within each cluster.

The demographics of the analytic sample are presented in Table 1 . Men and women completed comparable numbers of tasks during the study period; 2,396,978 (48.6%) for men and 2,539,229 (51.4%) for women.

In Table 2 we measure the differences in remuneration between genders, and then decompose any observed pay gap into task completion speed, task selection, and then demographic and structural factors. Model 1 shows the unadjusted regression model of gender differences in estimated actual pay, and indicates that, on average, tasks completed by women paid 60 (10.5%) cents less per hour compared to tasks completed by men (t = 17.4, p < .0001), with the mean estimated actual pay across genders being $5.70 per hour.

*Model adjusted for race, marital status, number of children and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.

In Model 2, adjusting for advertised hourly pay, the gender pay gap dropped to 46 cents indicating that 14 cents of the pay gap is attributable to gender differences in the selection of tasks (t = 8.6, p < .0001). Finally, after the inclusion of covariates and their interactions in Model 3, the gender pay differential was further attenuated to 32 cents (t = 6.7, p < .0001). The remaining 32 cent difference (56.6%) in earnings is inferred to be attributable to gender differences in HIT completion speed.

Task selection analyses

Although completion speed appears to account for a significant portion of the pay gap, of particular interest are gender differences in task selection. Beyond structural factors such as education, household composition and completion speed, task selection accounts for a meaningful portion of the gender pay gap. As a reminder, the pay rate and expected completion time are posted for every HIT, so why women would select less remunerative tasks on average than men do is an important question to explore. In the next section of the paper we perform a set of analyses to examine factors that could account for this observed gender difference in task selection.

Advertised hourly pay

To examine gender differences in task selection, we used linear regression to directly examine whether the advertised hourly pay differed for tasks accepted by male and female workers. We first ran a simple model ( Table 3 ; Model 3A) on the full dataset of 4.93 million HITs, with gender as the predictor and advertised hourly pay as the outcome including no other covariates. The unadjusted regression results (Model 4) shown in Table 3 , indicates that, summed across all clusters and demographic groups, tasks completed by women were advertised as paying 28 cents (95% CI: $0.25-$0.31) less per hour (5.8%) compared to tasks completed by men (t = 21.8, p < .0001).

*Models adjusted for race, marital status, number of children, and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.

Model 5 examines whether the remuneration differences for tasks selected by men and women remains significant in the presence of multiple covariates included in the previous model and their interactions. The advertised pay differential for tasks selected by women compared to men was attenuated to 21 cents (4.3%), and remained statistically significant (t = 9.9, p < .0001). This estimate closely corresponded to the inferred influence of task selection reported in Table 2 . Tests of gender by covariate interactions were significant only in the cases of age and marital status; the pay differential in tasks selected by men and women decreased with age and was more pronounced among single versus currently or previously married women.

To further examine what factors may account for the observed gender differences in task selection we plotted the observed pay gap within demographic and other covariate groups. Table 4 shows the distribution of tasks completed by men and women, as well as mean earnings and the pay gap across all demographic groups, based on the advertised (not actual) hourly pay for HITs selected (hereafter referred to as “advertised hourly pay” and the “advertised pay gap”). The average task was advertised to pay $4.88 per hour (95% CI $4.69, $5.10).

The pattern across demographic characteristics shows that the advertised hourly pay gap between genders is pervasive. Notably, a significant advertised gender pay gap is evident in every level of each covariate considered in Table 4 , but more pronounced among some subgroups of workers. For example, the advertised pay gap was highest among the youngest workers ($0.31 per hour for workers age 18–29), and decreased linearly with age, declining to $0.13 per hour among workers age 60+. Advertised houry gender pay gaps were evident across all levels of education and income considered.

To further examine the potential influence of human capital factors on the advertised hourly pay gap, Table 5 presents the average advertised pay for selected tasks by level of experience on the CloudResearch platform. Workers were grouped into 4 experience levels, based on the number of prior HITs completed: Those who completed fewer than 100 HITs, between 100 and 500 HITs, between 500 and 1,000 HITs, and more than 1,000 HITs. A significant gender difference in advertised hourly pay was observed within each of these four experience groups. The advertised hourly pay for tasks selected by both male and female workers increased with experience, while the gender pay gap decreases. There was some evidence that male workers have more cumulative experience with the platform: 43% of male workers had the highest level of experience (previously completing 1,001–10,000 HITs) compared to only 33% of women.

Table 5 also explores the influence of task heterogeneity upon HIT selection and the gender gap in advertised hourly pay. K-means clustering was used to group HITs into 20 clusters initially based on the presence or absence of 5,140 distinct words appearing in HIT titles. Clusters with fewer than 50,000 completed tasks were then excluded from analysis. This resulted in 13 clusters which accounted for 94.3% of submitted work assignments (HITs).

The themes of all clusters as well as the average hourly advertised pay for men and women within each cluster are presented in the second panel of Table 5 . The clusters included categories such as Games, Decision making, Product evaluation, Psychology studies, and Short Surveys. We did not observe a gender preference for any of the clusters. Specifically, for every cluster, the proportion of males was no smaller than 46.6% (consistent with the slightly lower proportion of males on the platform, see Table 1 ) and no larger than 50.2%. As shown in Table 5 , the gender pay gap was observed within each of the clusters. These results suggest that residual task heterogeneity, a proxy for occupational segregation, is not likely to contribute to a gender pay gap in this market.

Task length was defined as the advertised estimated duration of a HIT. Table 6 presents the advertised hourly gender pay gaps for five categories of HIT length, which ranged from a few minutes to over 1 hour. Again, a significant advertised hourly gender pay gap was observed in each category.

Finally, we conducted additional supplementary analyses to determine if other plausible factors such as HIT timing could account for the gender pay gap. We explored temporal factors including hour of the day and day of the week. Each completed task was grouped based on the hour and day in which it was completed. A significant advertised gender pay gap was observed within each of the 24 hours of the day and for every day of the week demonstrating that HIT timing could not account for the observed gender gap (results available in Supplementary Materials).

In this study we examined the gender pay gap on an anonymous online platform across an 18-month period, during which close to five million tasks were completed by over 20,000 unique workers. Due to factors that are unique to the Mechanical Turk online marketplace–such as anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect earnings to differ by gender on this platform. However, contrary to our expectations, a robust and persistent gender pay gap was observed.

The average estimated actual pay on MTurk over the course of the examined time period was $5.70 per hour, with the gender pay differential being 10.5%. Importantly, gig economy platforms differ from more traditional labor markets in that hourly pay largely depends on the speed with which tasks are completed. For this reason, an analysis of gender differences in actual earned pay will be affected by gender differences in task completion speed. Unfortunately, we were not able to directly measure the speed with which workers complete tasks and account for this factor in our analysis. This is because workers have the ability to accept multiple HITs at the same time and multiple HITs can sit dormant in a queue, waiting for workers to begin to work on them. Therefore, the actual time that many workers spend working on tasks is likely less than what is indicated in the metadata available. For this reason, the estimated average actual hourly rate of $5.70 is likely an underestimate and the gender gap in actual pay cannot be precisely measured. We infer however, by the residual gender pay gap after accounting for other factors, that as much as 57% (or $.32) of the pay differential may be attributable to task completion speed. There are multiple plausible explanations for gender differences in task completion speed. For example, women may be more meticulous at performing tasks and, thus, may take longer at completing them. There may also be a skill factor related to men’s greater experience on the platform (see Table 5 ), such that men may be faster on average at completing tasks than women.

However, our findings also revealed another component of a gender pay gap on this platform–gender differences in the selection of tasks based on their advertised pay. Because the speed with which workers complete tasks does not impact these estimates, we conducted extensive analyses to try to explain this gender gap and the reasons why women appear on average to be selecting tasks that pay less compared to men. These results pertaining to the advertised gender pay gap constitute the main focus of this study and the discussion that follows.

The overall advertised hourly pay was $4.88. The gender pay gap in the advertised hourly pay was $0.28, or 5.8% of the advertised pay. Once a gender earnings differential was observed based on advertised pay, we expected to fully explain it by controlling for key structural and individual-level covariates. The covariates that we examined included experience, age, income, education, family composition, race, number of children, task length, the speed of accepting a task, and thirteen types of subtasks. We additionally examined the time of day and day of the week as potential explanatory factors. Again, contrary to our expectations, we observed that the pay gap persisted even after these potential confounders were controlled for. Indeed, separate analyses that examined the advertised pay gap within each subcategory of the covariates showed that the pay gap is ubiquitous, and persisted within each of the ninety sub-groups examined. These findings allows us to rule out multiple mechanisms that are known drivers of the pay gap in traditional labor markets and other gig economy marketplaces. To our knowledge this is the only study that has observed a pay gap across such diverse categories of workers and conditions, in an anonymous marketplace, while simultaneously controlling for virtually all variables that are traditionally implicated as causes of the gender pay gap.

Individual-level factors

Individual-level factors such as parental status and family composition are a common source of the gender pay gap in traditional labor markets [ 15 ] . Single mothers have previously been shown to have lower reservation wages compared to other men and women [ 21 ]. In traditional labor markets lower reservation wages lead single mothers to be willing to accept lower-paying work, contributing to a larger gender pay gap in this group. This pattern may extend to gig economy markets, in which single mothers may look to online labor markets as a source of supplementary income to help take care of their children, potentially leading them to become less discriminating in their choice of tasks and more willing to work for lower pay. Since female MTurk workers are 20% more likely than men to have children (see Table 1 ), it was critical to examine whether the gender pay gap may be driven by factors associated with family composition.

An examination of the advertised gender pay gap among individuals who differed in their marital and parental status showed that while married workers and those with children are indeed willing to work for lower pay (suggesting that family circumstances do affect reservation wages and may thus affect the willingness of online workers to accept lower-paying online tasks), women’s hourly pay is consistently lower than men’s within both single and married subgroups of workers, and among workers who do and do not have children. Indeed, contrary to expectations, the advertised gender pay gap was highest among those workers who are single, and among those who do not have any children. This observation shows that it is not possible for parental and family status to account for the observed pay gap in the present study, since it is precisely among unmarried individuals and those without children that the largest pay gap is observed.

Age was another factor that we considered to potentially explain the gender pay gap. In the present sample, the hourly pay of older individuals is substantially lower than that of younger workers; and women on the platform are five years older on average compared to men (see Table 1 ). However, having examined the gender pay gap separately within five different age cohorts we found that the largest pay gap occurs in the two youngest cohort groups: those between 18 and 29, and between 30 and 39 years of age. These are also the largest cohorts, responsible for 64% of completed work in total.

Younger workers are also most likely to have never been married or to not have any children. Thus, taken together, the results of the subgroup analyses are consistent in showing that the largest pay gap does not emerge from factors relating to parental, family, or age-related person-level factors. Similar patterns were found for race, education, and income. Specifically, a significant gender pay gap was observed within each subgroup of every one of these variables, showing that person-level factors relating to demographics are not driving the pay gap on this platform.

Experience is a factor that has an influence on the pay gap in both traditional and gig economy labor markets [ 20 ] . As noted above, experienced workers may be faster and more efficient at completing tasks in this platform, but also potentially more savvy at selecting more remunerative tasks compared to less experienced workers if, for example, they are better at selecting tasks that will take less time to complete than estimated on the dashboard [ 20 ]. On MTurk, men are overall more experienced than women. However, experience does not account for the gender gap in advertised pay in the present study. Inexperienced workers comprise the vast majority of the Mechanical Turk workforce, accounting for 67% of all completed tasks (see Table 5 ). Yet within this inexperienced group, there is a consistent male earning advantage based on the advertised pay for tasks performed. Further, controlling for the effect of experience in our models has a minimal effect on attenuating the gender pay gap.

Another important source of the gender pay gap in both traditional and gig economy labor markets is task heterogeneity. In traditional labor markets men are disproportionately represented in lucrative fields, such as those in the tech sector [ 23 ]. While the workspace within MTurk is relatively homogeneous compared to the traditional labor market, there is still some variety in the kinds of tasks that are available, and men and women may have been expected to have preferences that influence choices among these.

To examine whether there is a gender preference for specific tasks, we systematically analyzed the textual descriptions of all tasks included in this study. These textual descriptions were available for all workers to examine on their dashboards, along with information about pay. The clustering algorithm revealed thirteen categories of tasks such as games, decision making, several different kinds of survey tasks, and psychology studies.We did not observe any evidence of gender preference for any of the task types. Within each of the thirteen clusters the distribution of tasks was approximately equally split between men and women. Thus, there is no evidence that women as a group have an overall preference for specific tasks compared to men. Critically, the gender pay gap was also observed within each one of these thirteen clusters.

Another potential source of heterogeneity is task length. Based on traditional labor markets, one plausible hypothesis about what may drive women’s preferences for specific tasks is that women may select tasks that differ in their duration. For example, women may be more likely to use the platform for supplemental income, while men may be more likely to work on HITs as their primary income source. Women may thus select shorter tasks relative to their male counterparts. If the shorter tasks pay less money, this would result in what appears to be a gender pay gap.

However, we did not observe gender differences in task selection based on task duration. For example, having divided tasks into their advertised length, the tasks are preferred equally by men and women. Furthermore, the shorter tasks’ hourly pay is substantially higher on average compared to longer tasks.

Additional evidence that scheduling factors do not drive the gender pay gap is that it was observed within all hourly and daily intervals (See S1 and S2 Tables in Appendix). These data are consistent with the results presented above regarding personal level factors, showing that the majority of male and female Mechanical Turk workers are single, young, and have no children. Thus, while in traditional labor markets task heterogeneity and labor segmentation is often driven by family and other life circumstances, the cohort examined in this study does not appear to be affected by these factors.

Practical implications of a gender pay gap on online platforms for social and behavioral science research

The present findings have important implications for online participant recruitment in the social and behavioral sciences, and also have theoretical implications for understanding the mechanisms that give rise to the gender pay gap. The last ten years have seen a revolution in data collection practices in the social and behavioral sciences, as laboratory-based data collection has slowly and steadily been moving online [ 16 , 24 ]. Mechanical Turk is by far the most widely used source of human participants online, with thousands of published peer-reviewed papers utilizing Mechanical Turk to recruit at least some of their human participants [ 25 ]. The present findings suggest both a challenge and an opportunity for researchers utilizing online platforms for participant recruitment. Our findings clearly reveal for the first time that sampling research participants on anonymous online platforms tends to produce gender pay inequities, and that this happens independent of demographics or type of task. While it is not clear from our findings what the exact cause of this inequity is, what is clear is that the online sampling environment produces similar gender pay inequities as those observed in other more traditional labor markets, after controlling for relevant covariates.

This finding is inherently surprising since many mechanisms that are known to produce the gender pay gap in traditional labor markets are not at play in online microtasks environments. Regardless of what the generative mechanisms of the gender pay gap on online microtask platforms might be, researchers may wish to consider whether changes in their sampling practices may produce more equitable pay outcomes. Unlike traditional labor markets, online data collection platforms have built-in tools that can allow researchers to easily fix gender pay inequities. Researchers can simply utilize gender quotas, for example, to fix the ratio of male and female participants that they recruit. These simple fixes in sampling practices will not only produce more equitable pay outcomes but are also most likely advantageous for reducing sampling bias due to gender being correlated with pay. Thus, while our results point to a ubiquitous discrepancy in pay between men and women on online microtask platforms, such inequities have relatively easy fixes on online gig economy marketplaces such as MTurk, compared to traditional labor markets where gender-based pay inequities have often remained intractable.

Other gig economy markets

As discussed in the introduction, a gender wage gap has been demonstrated on Uber, a gig economy transportation marketplace [ 20 ], where men earn approximately 7% more than women. However, unlike in the present study, the gender wage gap on Uber was fully explained by three factors; a) driving speed predicted higher wages, with men driving faster than women, b) men were more likely than women to drive in congested locations which resulted in better pay, c) experience working for Uber predicted higher wages, with men being more experienced. Thus, contrary to our findings, the gender wage gap in gig economy markets studied thus far are fully explained by task heterogeneity, experience, and task completion speed. To our knowledge, the results presented in the present study are the first to show that the gender wage gap can emerge independent of these factors.

Generalizability

Every labor market is characterized by a unique population of workers that are almost by definition not a representation of the general population outside of that labor market. Likewise, Mechanical Turk is characterized by a unique population of workers that is known to differ from the general population in several ways. Mechanical Turk workers are younger, better educated, less likely to be married or have children, less likely to be religious, and more likely to have a lower income compared to the general United States population [ 24 ]. The goal of the present study was not to uncover universal mechanisms that generate the gender pay gap across all labor markets and demographic groups. Rather, the goal was to examine a highly unique labor environment, characterized by factors that should make this labor market immune to the emergence of a gender pay gap.

Previous theories accounting for the pay gap have identified specific generating mechanisms relating to structural and personal factors, in addition to discrimination, as playing a role in the emergence of the gender pay gap. This study examined the work of over 20,000 individuals completing over 5 million tasks, under conditions where standard mechanisms that generate the gender pay gap have been controlled for. Nevertheless, a gender pay gap emerged in this environment, which cannot be accounted for by structural factors, demographic background, task preferences, or discrimination. Thus, these results reveal that the gender pay gap can emerge—in at least some labor markets—in which discrimination is absent and other key factors are accounted for. These results show that factors which have been identified to date as giving rise to the gender pay gap are not sufficient to explain the pay gap in at least some labor markets.

Potential mechanisms

While we cannot know from the results of this study what the actual mechanism is that generates the gender pay gap on online platforms, we suggest that it may be coming from outside of the platform. The particular characteristics of this labor market—such as anonymity, relative task homogeneity, and flexibility—suggest that, everything else being equal, women working in this platform have a greater propensity to choose less remunerative opportunities relative to men. It may be that these choices are driven by women having a lower reservation wage compared to men [ 21 , 26 ]. Previous research among student populations and in traditional labor markets has shown that women report lower pay or reward expectations than men [ 27 – 29 ]. Lower pay expectations among women are attributed to justifiable anticipation of differential returns to labor due to factors such as gender discrimination and/or a systematic psychological bias toward pessimism relative to an overly optimistic propensity among men [ 30 ].

Our results show that even if the bias of employers is removed by hiding the gender of workers as happens on MTurk, it seems that women may select lower paying opportunities themselves because their lower reservation wage influences the types of tasks they are willing to work on. It may be that women do this because cumulative experiences of pervasive discrimination lead women to undervalue their labor. In turn, women’s experiences with earning lower pay compared to men on traditional labor markets may lower women’s pay expectations on gig economy markets. Thus, consistent with these lowered expectations, women lower their reservation wages and may thus be more likely than men to settle for lower paying tasks.

More broadly, gender norms, psychological attributes, and non-cognitive skills, have recently become the subject of investigation as a potential source for the gender pay gap [ 3 ], and the present findings indicate the importance of such mechanisms being further explored, particularly in the context of task selection. More research will be required to explore the potential psychological and antecedent structural mechanisms underlying differential task selection and expectations of compensation for time spent on microtask platforms, with potential relevance to the gender pay gap in traditional labor markets as well. What these results do show is that pay discrepancies can emerge despite the absence of discrimination in at least some circumstances. These results should be of particular interest for researchers who may wish to see a more equitable online labor market for academic research, and also suggest that novel and heretofore unexplored mechanisms may be at play in generating these pay discrepancies.

A final note about framing: we are aware that explanations of the gender pay gap that invoke elements of women’s agency and, more specifically, “choices” risk both; a) diminishing or distracting from important structural factors, and b) “naturalizing” the status quo of gender inequality [ 30 ] . As Connor and Fiske (2019) argue, causal attributions for the gender pay gap to “unconstrained choices” by women, common as part of human capital explanations, may have the effect, intended or otherwise, of reinforcing system-justifying ideologies that serve to perpetuate inequality. By explicitly locating women’s economic decision making on the MTurk platform in the broader context of inegalitarian gender norms and labor market experiences outside of it (as above), we seek to distance our interpretation of our findings from implicit endorsement of traditional gender roles and economic arrangements and to promote further investigation of how the observed gender pay gap in this niche of the gig economy may reflect both broader gender inequalities and opportunities for structural remedies.

Supporting information

Funding statement.

The authors received no specific funding for this work.

Data Availability

IMAGES

  1. Tackling Gender-Inequality Through the Law

    scholarly articles on gender inequality

  2. ⭐ Speech on gender equality in india. Speech on Gender. 2022-10-12

    scholarly articles on gender inequality

  3. Transgenderin India Gender Inequality Point Of View

    scholarly articles on gender inequality

  4. (PDF) Gender Inequality in Education among Adolescents

    scholarly articles on gender inequality

  5. Case Study Of Gender Inequality In The Workplace

    scholarly articles on gender inequality

  6. Newspaper Articles On Gender Inequality In India / Milestones For

    scholarly articles on gender inequality

COMMENTS

  1. Twenty years of gender equality research: A scoping review based on a new semantic indicator

    Introduction The persistent gender inequalities that currently exist across the developed and developing world are receiving increasing attention from economists, policymakers, and the general public [e.g., 1 - 3 ].

  2. Full article: Gender and sex inequalities: Implications and resistance

    Introduction. Although the world has seen great strides toward gender/sex equality, a wide gap still remains and unfortunately may be widening. The World Economic Forum (WEF, Citation 2017) annually evaluates the world's progress toward gender inequality in economic participation and opportunity, educational attainment, health and survival, and political empowerment.

  3. Gendered stereotypes and norms: A systematic review of interventions

    This article systematically reviews interventions aiming to address gendered stereotypes and norms across several outcomes of gender inequality such as violence against women and sexual and reproductive health, to draw out common theory and practice and identify success factors.

  4. Gender inequalities in the workplace: the effects of organizational

    Gender inequalities in HR policy are a form of institutional discrimination. We review evidence of institutional discrimination against women within HR policies set out to determine employee selection, performance evaluations, and promotions.

  5. Full article: Gender and Intersecting Inequalities in Education

    Articles Gender and Intersecting Inequalities in Education: Reflections on a Framework for Measurement Elaine Unterhalter , Helen Longlands & Rosie Peppin Vaughan Pages 509-538 | Published online: 23 Jul 2022 Cite this article https://doi.org/10.1080/19452829.2022.2090523 In this article Full Article Figures & data References Citations Metrics

  6. Gender equality: the route to a better world

    nature editorials article EDITORIAL 06 September 2023 Gender equality: the route to a better world Health outcomes, ending poverty and greening the environment are boosted when power is shared...

  7. Gender inequality as a barrier to economic growth: a review of the

    The vast majority of theories reviewed argue that gender inequality is a barrier to economic development, particularly over the long run. The focus on long-run supply-side models reflects a recent effort by growth theorists to incorporate two stylized facts of economic development in the last two centuries: (i) a strong positive association between gender equality and income per capita (Fig. 1 ...

  8. Gender equality in science, medicine, and global health: where are we

    Gender inequality is transformed into health risk through the following: discriminatory values, norms, beliefs, and practices; differential exposures and susceptibilities to disease, disability, and injuries; biases in health systems; and biases in health research. 4

  9. Full article: Gender equality in higher education and research

    Gender equality in higher education and .... Journal of Gender Studies Volume 31, 2022 - Issue 1: Gender Equality in Higher Education and Research; Guest editors: Rodrigo Rosa, Sara Clavero and Giacomo Viggiani Free access 29,629 Views 10 CrossRef citations to date 0 Altmetric Listen Editorial Gender equality in higher education and research

  10. Gender equality in the workplace: An introduction.

    ABSTRACT This year 2020 marks the 100th anniversary of American women winning the right to vote. This right was a great symbol of democracy and an essential step toward gender equity not only in voting but also in society as a whole. Unfortunately, the tide over the last 100 years has not been as swift as the suffragists might have imagined.

  11. The impact of gender discrimination on a Woman's Mental Health

    The study by Stepanikova et al. [ [5] ] published in this issue of EClinicalMedicine expands on previous research around gender inequality and health to investigate the impact of the broad construct of "perceived gender discrimination" in relation to a woman's mental health.

  12. Gender inequality and self-publication are common among academic

    Article Open access Published: 16 January 2023 Gender inequality and self-publication are common among academic editors Fengyuan Liu, Petter Holme, Matteo Chiesa, Bedoor AlShebli & Talal...

  13. Women's Assessments of Gender Equality

    Olympe de Gouges (1971:6-7), a French revolutionary, published The Rights of Women as a companion piece to the Declaration of the Rights of Man, with equally universal aspirations: to recognize "the natural, inalienable, and sacred rights of the woman," who "is born free and lives equal to man in rights." Laws must apply equally to women and men.

  14. Justifying gender discrimination in the workplace: The mediating role

    Introduction. The latest release from the World Economic Forum—the Gender Gap Report 2016 []-indicates that in the past 10 years, the global gender gap across education and economic opportunity and politics has closed by 4%, while the economic gap has closed by 3%.Extrapolating this trajectory, the report underlines that it will take the world another 118 years—or until 2133 -to close ...

  15. The influence of pay transparency on (gender) inequity, inequality and

    Recent decades have witnessed a growing global focus on two distinct income patterns: persistent pay inequity, particularly a gender pay gap, and growing pay inequality 1,2.Though these terms are ...

  16. Workplace gender equality in the post-pandemic era: Where to next

    Conclusion. The COVID-19 pandemic revealed and accentuated many longstanding gendered inequalities in the labour market. In this introduction to the special issue, we highlight five key areas that will be crucial to achieving workplace gender equality in the post-pandemic era. These themes, which echo the findings of the extant research on the ...

  17. Gendered and feminist inequalities: A review and framing notes

    The article opts for a social inequality framework in order to understand the complex and contested spaces and zones within which the feminisation of inequalities are located, positioned and interpreted. ... She is a Senior lecturer in Gender Studies and an Academic leader of the Development Cluster in the School of Social Sciences at the ...

  18. Twenty years of gender equality research: A scoping review based on a

    Abstract Gender equality is a major problem that places women at a disadvantage thereby stymieing economic growth and societal advancement. In the last two decades, extensive research has been conducted on gender related issues, studying both their antecedents and consequences.

  19. Gender Inequalities in Education

    The terrain of gender inequalities in education has seen much change in recent decades. This article reviews the empirical research and theoretical perspectives on gender inequalities in educational performance and attainment from early childhood to young adulthood. Much of the literature on children and adolescents attends to performance differences between girls and boys. Of course ...

  20. Tackling Gender Inequality: Definitions, Trends, and Policy Designs

    This paper identifies five key issues that are important for the continued efforts to tackle gender inequality: (i) gender inequality needs to be distinguished from gender gaps. Not all gender gaps necessarily reflect gender inequality as some gender gaps are not driven by the lack of equal rights, responsibilities and opportunities bywomen and girls, and this has important implications on ...

  21. Gender discrimination in the United States: Experiences of women

    In adjusted models, Native American, black, and Latina women had higher odds than white women of reporting gender discrimination in several domains, including health care. Latinas' odds of health care avoidance versus whites was (OR [95% CI]) 3.69 (1.59, 8.58), while blacks' odds of discrimination in health care visits versus whites was 2. ...

  22. How Gender Inequality Persists in the Modern World

    June 1, 2013 Gender & Sexuality Media & Public Opinion Labor Share Connect with the author Cecilia L. Ridgeway Stanford University In the United States as in many other societies, gender relationships are changing and inequalities between men and women are questioned in virtually every sphere - at work, in the home, and in public affairs.

  23. The persistence of pay inequality: The gender pay gap in an anonymous

    Introduction. The gender pay gap, the disparity in earnings between male and female workers, has been the focus of empirical research in the US for decades, as well as legislative and executive action under the Obama administration [1, 2].Trends dating back to the 1960s show a long period in which women's earnings were approximately 60% of their male counterparts, followed by increases in ...