Chapter

Chapter 3. Gender Inequality and Macroeconomic Performance

Editor(s):
Kalpana Kochhar, Sonali Jain-Chandra, and Monique Newiak
Published Date:
February 2017
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Author(s)
David Cuberes, Monique Newiak and Marc Teignier 

Economists have extensively studied and discussed the existence, origins, and importance of several types of gender gaps, including in wages, labor force participation, presence in certain occupations, access to inputs, education, and power within the household. This chapter analyzes the effects of gender inequality from a macroeconomic perspective. The argument is that gender gaps in pay and in access to resources, occupations, and credit—among other things—not only have negative microeconomic effects on women but also imply large costs for the aggregate economy. To make this argument both qualitatively and quantitatively, the chapter focuses on the labor market and examines two gender gaps—participation in the labor force and participation in entrepreneurial occupations.

An economy-wide model, based on Cuberes and Teignier 2016, was simulated to describe the occupational choices of individuals. The chapter presents the benchmark setup, which is used to examine the effects of these gender gaps in advanced Organisation for Economic Co-operation and Development (OECD) countries. The model is extended to capture the different realities of the labor market in developing economies. These models are then simulated to predict the potential market costs associated with these two gaps. Aggregate data are used to estimate the country-specific gender gaps and to quantify the income loss relative to a situation without gender gaps. Dynamic income gains are computed under different scenarios for closing the gender gap in the labor market for a subsample of countries, taking into account the expected evolution of fertility. This is especially important for high-income countries, where fertility rates have been steadily declining for several decades and where the working-age population is projected to shrink in the decades ahead. In the absence of significant immigration in many of these countries, a more efficient use of the female labor force seems a promising strategy to increase labor force participation and mitigate the economic impact of aging.

Finally, the chapter empirically analyzes the relationship between estimated gender gaps in the labor market and the survey-based assessment of societies’ values regarding gender issues as captured by the World Values Survey (WVS). The WVS collects detailed information from nationally representative surveys conducted in almost 100 countries that together comprise almost 90 percent of the world’s population. It has been widely used by scientists and policymakers to examine changes in global beliefs, values, and motivations. The topics covered in the questionnaires include economic development, democratization, religion, gender equality, social capital, and subjective well-being. On gender, the WVS measures ask whether interviewees agree with a range of statements such as “when a mother works for pay, the children suffer” or “when jobs are scarce, men should have more right to a job than women.” These indicators are shown to correlate significantly with labor force participation gender gaps but not much with gender gaps in entrepreneurship.

Evidence Linking Gender Inequality and Growth

Some important empirical and theoretical papers explore the two-directional link between gender inequality in the labor market and economic growth or aggregate productivity.1

In the empirical arena, several studies document that economic growth has a positive effect on gender equality in the labor market (Dollar and Gatti 1999; Tzannatos 1999; Stotsky 2006), and even more show through different measures that gender inequality is detrimental to economic growth (Tzannatos 1999; Klasen 2002; Abu-Ghaida and Klasen 2004; Stotsky 2006; Klasen and Lamanna 2009).

Theoretical papers identify several channels through which gender inequality may decrease as countries develop. First, as countries develop, fertility rates fall, and as a result, female labor force participation rises. Becker and Lewis (1973) assume that the income effect on a household’s fertility—which leads to a desire to have more children—is smaller than the substitution effect—which motivates households to have fewer children. This implies that there exists a threshold of income per capita above which a country’s fertility starts to decrease. This decline in fertility facilitates the incorporation of women into the labor market and therefore helps reduce the gender gap in labor force participation (Becker 1985).

Another explanation emphasizes the technological progress that almost always accompanies the process of economic growth (Greenwood, Seshadri, and Yorukoglu 2005; Olivetti 2006; Attanasio, Low, and Sanchez-Marcos 2008). In particular, as countries experience technological progress in household production, women—who tend to specialize in the production of household goods—can produce the same amount of goods and services much more efficiently and are able to spend more hours working in the formal labor market, thereby diminishing the gender gap in labor force participation. Medical advances such as the birth control pill (Goldin and Katz 2002; Bailey 2006) and the reduction in postpartum disabilities (Albanesi and Olivetti, 2016) have also helped increase women’s participation in the labor market.2

A third popular explanation for the role of economic growth in decreasing gender inequality is that as countries become richer, women enjoy more rights, perhaps because they derive greater benefits from their education. When education results in better jobs and salaries, parents are motivated to educate sons and daughters equally as an investment in future generations (Doepke and Tertilt 2009) or perhaps because that is what other parents are doing (Lagerlöf 2003).

Alternatively, some studies emphasize the effect of labor demand forces that favor women. Many argue that, as countries develop, there is expansion in the services sector, as well as in occupations where physical force is less important, which leads women’s employment to increase faster than men’s—see Goldin (1990, 2006); Galor and Weil (1996); Weinberg (2000); Rendall (2010); Akbulut (2011); Ngai and Petrongolo (2015); and Buera, Kaboski, and Zhao (2013). Other demand-driven explanations are discussed in Olivetti (2006); Heathcote, Storesletten, and Violante (2010); Black and Spitz-Oener (2010); Gayle and Golan (2012); Beaudry and Lewis (2014); and Goldin (2014b).

Finally, a society’s values about women could also explain the rise in female labor force participation. This includes views on married women working (Fernández, Fogli, and Olivetti 2004), how women feel about the effect of their labor market choices on their children (Fogli and Veldkamp 2011), and women’s own sense of self (Fernández 2013). A society’s views often translate into changes in regulations. For instance, Fernández (2014) suggests that economic development and its associated decline in fertility lead to reforms of property rights in favor of women. Fortin (2005) uses data from the 1990, 1995, and 1999 WVS to study how religious beliefs and gender-role attitudes affect female labor supply and the gender wage gap in 25 OECD countries. She finds that antiegalitarian views display a strong negative association with female employment rates and the gender pay gap, although her results must be taken with caution given the low number of observations. More recently, the World Bank (2013), using later waves of the WVS, shows that for a 10 percent increase in the proportion of people who agree with the statement “scarce jobs should go to men first” there is a reduction in women’s employment rate of 5 to 9 percent.

Other articles study theoretically the two-directional link between gender inequality and economic growth. For example, in Galor and Weil (1996), women are found to have a comparative advantage in intellectual activities, while men have the edge in physical tasks. As a result, as an economy develops and the demand for skilled labor increases, wages rise faster for women than for men. This, in turn, reduces women’s fertility, as they make the optimal choice to increase their participation in the labor market. Over the long term, the drop in population increases capital per worker and, hence, induces faster growth. In this scenario, if a limit is introduced on the rise in the relative women’s wage, perhaps due to discrimination, the optimal choices of women are distorted, and potential output growth is reduced.

Very few papers quantify the efficiency losses associated with specific gender gaps using a theoretical framework. One exception is Cavalcanti and Tavares (2016), who calibrate the Galor and Weil (1996) model assuming the presence of wage discrimination against women. Their results suggest that the aggregate effects of such discrimination are very large and can explain a significant fraction of differences in output per capita across countries.3 Using a similar methodology, Hsieh and others (2013) compute the growth benefits of removing the occupational friction between races and genders in the United States between 1960 and 2008.

A Theoretical Model to Quantify the Costs of Occupational Gender Gaps

The benchmark theoretical model used in this chapter is a simple extension of the model by Lucas (1978), in which individuals with different innate managerial abilities choose to become workers, self-employed, or employers. In the model, goods are produced using a span-of-control technology that combines managerial talent and other inputs to produce goods. Figure 3.1 displays the occupational choice map predicted by this model.

Figure 3.1.Occupational Choice Map in the Theoretical Framework

Individuals with the highest level of entrepreneurial talent prefer to be employers, whereas those with the least prefer to work for someone else’s firm and those with intermediate levels of talent gravitate toward self-employment. At the same time, those with more talent run larger firms: the more talented among those who become employers (managers who hire workers to produce goods) manage firms with more workers and capital than the less talented, and those who are self-employed run firms with more or less capital depending on their level of talent.

There are an equal number of men and women in the model, all of whom draw their entrepreneurial talent from the exact same distribution function. This does not imply that every man and woman has the same talent, but instead that they have the same probability of being born with a given level of talent. The only difference between men and women in this model is that the latter are subject to several exogenous constraints on their choices. The model takes these constraints as given—that is, the focus is on explaining their effects instead of their origins. It is entirely possible that women choose not to participate in the labor market and, even if this diminishes the economy’s productivity, it may enhance welfare.

The first constraint is that only a fraction of women participate in the labor market. The implicit assumption is that women cannot produce goods outside the labor market; thus, if a woman is excluded from the market her productivity is zero.4 Second, only a fraction of those women who do participate in the labor market are “allowed” to freely choose their occupation. More specifically, some women are barred from being employers and others are barred from self-employment. All of these gaps are assumed to be random and unrelated to women’s talent. Therefore, it is possible in the model to have a very talented woman who, in a world without frictions or constraints, would become an employer with a large firm but ends up being a worker. As a consequence, an individual with less talent will likely become an entrepreneur, making the average firm’s productivity fall due to the corresponding misallocation of resources.

The main objective of the exercise is to quantify how large these costs are in the model and in each of the countries in the sample. To do this, some additional structure is imposed. Following the existing literature, the initial assumption is that an individual’s talent is drawn from a Pareto distribution. Reasonable values for several crucial parameters of the model were then assumed. In particular, the parameter that captures the span-of-control element of the production technologies is set at 0.790, as in Buera and Shin 2011. This allows the capital share in the production function to be pinned down, at a value of 0.114. Finally, data on the share of employers and self-employed people in the sample of OECD countries are used to infer the values of two additional parameters of the production function and the distribution of talent.5

Table 3.1 shows the results of the most extreme scenario, in which women are entirely excluded from each occupation. In particular, the model shows that the exclusion of all women from becoming employers or self-employed generates an income loss of 10 percent in the short term (when the capital stock is kept constant) and 11 percent in the long term (when the capital stock is adjusted to its new steady-state value). The income loss, which can be interpreted as overall GDP, would be 7.1 percent in the short term and 8.6 percent in the long term if all women were excluded from being employers but not from self-employment; it would be 47.0 percent in the short term and 50.0 percent in the long term if all women were excluded from any occupation in the labor market.

Table 3.1.Potential Income Losses from Gender Gaps (Benchmark Model)(Percent)
Short TermLong Term
Largest possible employership gap7.18.6
Largest possible entrepreneurship gap10.111
Largest possible labor force participation gap46.850

Actual cross-country data are used for 2010 for the male-female ratio of labor force participation, share of employers, and share of self-employed people for the sample of 33 OECD countries.6 The average labor force participation ratio is 0.78, which means that only 78 women participate in the labor market for every 100 men in the OECD sample; the average ratio of employers was 0.38; and the average self-employment ratio was 0.65. Therefore, the model indicates an average entrepreneurship gender gap of 0.43 (the fraction of women excluded from becoming employers and self-employed) and an employership gender gap of 0.18 (the fraction of women excluded from employership but not from self-employment).

On average, the income lost because of the entrepreneurship gaps amounts to 5.7 percent, whereas the total cost—the sum of the entrepreneurship and labor force participation costs—is 15.4 percent. Given that the mean GDP per capita in 2010 for OECD countries was $35,672, eliminating all the labor market gender gaps studied in this chapter would imply an average income increase of about $6,500 per capita in that year. Eliminating the entrepreneurship gender gaps, on the other hand, would translate to an average income rise of about $2,200 for each OECD inhabitant.

Turkey has the largest income loss due to the labor force participation gender gap (Figure 3.2, panel 1), and Israel has the largest income loss due to gender gaps in entrepreneurship (panel 2). In Turkey, where GDP per capita in 2010 was $10,111, income per capita would have increased by about $5,000 if all gender gaps had been eliminated and by about $800 without the entrepreneurship gender gap. In Israel, on the other hand, GDP per capita in 2010 was $30,736, which would have meant an income gain of about $4,900 barring all labor market gender gaps and about $2,400 without the entrepreneurship gap.

Figure 3.2.Costs to Advanced Economies of Occupational Gender Gaps, 2010

Source: derived from Cuberes and Teignier (2016)

Note: Sample of 33 Organisation for Economic Co-operation and Development (OECD) countries for 2010.

Extended Model

The benchmark model has clear-cut implications for occupational choices: people with the most entrepreneurial talent become employers, the least talented ones end up being workers, and those with intermediate talent choose self-employment. However, there is some evidence that, in developing economies, low-skilled workers tend to be self-employed rather than employees (Poschke 2013). To capture this situation, a new friction is introduced into the model: only a (random) fraction of both men and women are allowed to become workers. As before, there is no speculation about the causes of this constraint. The share of this necessity self-employed is then calibrated using data for non-OECD countries from the International Labour Organization for the most recent year available.

Table 3.2 summarizes the potential costs associated with gender gaps in this extended model, assuming that only 25 percent of men and women who want to be workers are allowed to do so. The costs associated with entrepreneurship gender gaps are much larger than in the benchmark model, because most of the women excluded from entrepreneurship are barred from becoming employees. Interestingly, however, the costs associated with gender gaps in employers are now significantly smaller, because the average talent of employers is not as negatively affected—the effective labor supply is now lower and, as a result, the equilibrium wage rate falls by less, which leads to a smaller number of low-talent agents choosing to become entrepreneurs.

Table 3.2.Potential Income Losses from Gender Gaps (Extended Model)(Percent)
Short TermLong Term
Largest possible employership gap33.7
Largest possible entrepreneurship gap33.536.1

Table 3.3 presents the long-term effects of the gender gap in developing economies, grouped in seven regions. The Middle East and north Africa, with a total income loss of 38 percent, is the region with the largest loss, followed by south Asia and Latin America and the Caribbean. South Asia, on the other hand, has the largest income loss due to gender gaps in entrepreneurship, followed by east Asia and the Pacific, the Middle East and north Africa, and central Asia. For some low-income countries, the income losses estimated in this chapter may be regarded as low when compared with the average losses for the OECD sample but they are significant in absolute terms. It is important to point out that the model may not capture all of the constraints women face in their labor market choices. More detailed data on employment by industry and type of job would be necessary to quantitatively estimate these restrictions. However, to the extent that they distort the efficient allocation of labor, the actual aggregate losses from gender inequality in the labor market would be larger, and the estimates in this chapter can be interpreted as a lower bound.

Table 3.3.Income Losses from Gender Gaps in Developing Economies(Percent)
Entrepreneurship GapsAll Gaps
Central Asia7.110.1
East Asia and the Pacific7.816.0
Europe5.410.8
Latin America and the Caribbean5.317.3
Middle East and Northern Africa7.737.8
South Asia9.824.9
Sub-Saharan Africa6.012.0
Source: Cuberes and Teignier 2016.Note: Includes non-Organisation for Economic Co-operation and Development countries using data from the International Labour Organization.

The Role of Demographics

Population growth has stalled in several countries, and the United Nations Population Fund projects that, under an assumption of medium fertility,7 in high-and middle-income countries, there will be a rise in dependency ratios, defined as the size of the non-working-age population to the working-age population (ages 15–64) (Figure 3.3). In high-income countries, dependency ratios could increase from slightly above 50 percent in 2015 to almost 75 percent in 2060 and above 80 percent in 2100. In middle-income countries, the dependency ratio could rise from almost 50 percent in 2015 to more than 61 percent in 2060 and almost 70 percent in 2100. With a lower share of the population in the labor force, real GDP per capita growth in these countries could decline. However, as highlighted elsewhere in this chapter, many of the affected countries possess pools of highly educated women, many of whom do not participate in the labor force or are underrepresented in self-employment and among employers.

Figure 3.3.Population Dependency Ratios, 2015–2100

(Population younger than age 15 or older than age 64 as percent of population ages 15–64)

Source: United Nations Population Division.

Note: Assumes medium fertility.

To model the implications of a (relative) decline in the labor force, the model is augmented by a restriction on both the male and female workforce to capture the increase in the dependency ratio for men and women over time. The effects of these declines are then explored under four scenarios: (1) no change in gender gaps in the labor market; (2) a constant decrease in gender gaps over time, with their elimination in 50 years; (3) a constant decrease in gender gaps over time, with their elimination in 100 years; and (4) a constant decrease in gender gaps over time, with their elimination in 150 years.

The results from this simulation imply that decreasing gender gaps in the labor market could substantially mitigate the economic cost of population aging. Table 3.4 outlines the scenarios in countries for which a change in the dependency ratio, all else equal, would result in GDP per capita losses of at least 5 percent by 2035. The results suggest that even relatively slow decreases in gender gaps in the labor force could significantly reduce the negative effect on GDP from population aging. In several countries (Chile, Czech Republic, Japan, Lebanon, FYR Macedonia, Malta, Mauritius), continuous steps to eliminate gender gaps in 50 years could overcompensate for the negative effects from an overall declining labor force by 2035, leading to overall GDP gains. In the vast majority of other countries, the effect of rising dependency ratios could be decreased by more than 50 percent if gender gaps were eliminated in continuous steps over 50 years. Policies to speed up gender gap declines would, of course, yield higher gains.

Table 3.4.Income Losses Due to Dependency Ratio Increases under Different Gender Gap Scenarios, 2035(Percent of GDP; negative numbers = income gain)
Gender GapGender GapGender Gap
No Change inDisappearsDisappears inDisappears
Gender Gapsin 150 Years100 Yearsin 50 Years
Australia5.63.93.10.5
Austria12.210.7107.9
Belgium8.16.25.22.5
Barbados8.86.85.93.5
Chile5.72.71.3−3.3
Croatia6.75.54.93.1
Cyprus5.53.72.80.3
Czech Republic63.52.3−1.3
Germany13.712.111.49.1
Denmark7.45.74.92.7
Estonia5.14.44.13.6
Finland6.95.85.34
France5.74.23.51.5
Hong Kong SAR1614.413.611.4
Iceland6.24.94.22.4
Italy10.67.96.52.5
Japan6.33.92.7−0.9
Republic of Korea15.713.512.49.1
Lebanon5.6−1.8−5.8−18.1
Lithuania6.15.85.75.7
Luxembourg9.47.26.12.8
Macao SAR15.313.913.311.6
Macedonia, Former Yugoslav7.54.12.4−2.7
Republic
Malta9.24.72.3−4.8
Mauritius6.42.1−0.1−6.9
Netherlands10.89.18.25.7
New Zealand6.54.94.11.8
Norway7.35.64.82.5
Poland6.85.24.42.1
Portugal7.165.64.2
Romania6430.5
Singapore13.911.710.77.5
Slovakia7.65.74.82.2
Slovenia11.910.49.77.5
Sweden5.73.82.90.4
Thailand8.976.13.8
Switzerland11.29.58.76.2
United Kingdom64.23.41
United States7.665.22.9
Source: Authors’ calculations.

The Role of Attitudes Toward Women

One plausible explanation for the adverse labor outcomes of women in a country’s labor market is the value that a society places on women. A society’s values regarding gender issues, as measured by the World Values Survey, are compared using scatter plots against the gender gaps in employers calculated in Cuberes and Teignier 2016.

This analysis, similar to Fortin 2005 and World Bank 2013, points to a strong negative correlation between gender gaps in labor force participation and attitudes toward women. However, as in these two works, this exercise reflects only correlation and not causation. In particular, this correlation does not prove the existence of discrimination against women; it does, however, suggest that discrimination is a good candidate to explain why it is difficult for women to participate in the labor market in some countries.8

As noted, the WVS data set collects detailed information from nationally representative surveys conducted in almost 100 countries that together comprise almost 90 percent of the world’s population. The WVS includes the following statements about gender equality, with which respondents are asked if they agree or disagree:

  • “When jobs are scarce, men should have more right to a job than women.”

  • “If a woman earns more than her husband, it’s almost certain to cause problems.”

  • “Having a job is the best way for a woman to be an independent person.”

  • “When a mother works for pay, the children suffer.”

  • “On the whole, men make better political leaders than women do.”

  • “A university education is more important for a boy than for a girl.”

  • “On the whole, men make better business executives than women do.”

  • “Being a housewife is just as fulfilling as working for pay.”

  • “It is justifiable for a man to beat his wife.”

Table 3.5 shows how much the extent of agreement with each statement in a given country correlates with that country’s gender gap in the labor market, as calculated in Cuberes and Teignier 2016.

Table 3.5.Correlation between Labor Gender Gaps and Views on Women’s Rights
Indicator of Values about Women’s RightsLabor Force Participation

Gender Gaps
Employership

Gender Gaps
When jobs are scarce, men should have more right to a−0.60***−0.07
job than women
If a woman earns more than her husband, it’s almost−0.48***−0.32**
certain to cause problems
Having a job is the best way for a woman to be an0.25*0.02
independent person
When a mother works for pay, the children suffer−0.72***−0.07
On the whole, men make better political leaders than−0.54***−0.06
women
A university education is more important for a boy−0.55***−0.08
than for a girl
On the whole, men make better business executives−0.54***−0.12
than women
Being a housewife is just as fulfilling as working for pay−0.28*−0.01
It is justifiable for a man to beat his wife0.220.23
Source: Statements from World Values Survey; values reflect authors’ calculations.Note: *p < 0.10; **p < 0.05; ***p < 0.01.

It is apparent that the labor force participation gender gaps correlate with the expected sign in all cases. However, somewhat surprisingly, the estimated gender gaps in entrepreneurship show no significant correlation with how women are valued in a society.

Figure 3.4 plots the negative relationship across countries between four of the indicators from the WVS and the labor force participation gender gaps. Clearly, the negative relationship is not driven by any specific outlier. The second striking observation is that there is a tremendous amount of clustering of countries by region. In particular, all of the plots show Middle Eastern and north African countries heavily concentrated in the area with low value placed on women and large gender gaps in labor force participation. Finally, although the relationship between values and gender gaps in the labor market is strong, there is quite a bit of variation around the trend line, implying that other factors, such as policies, may contribute to explaining gender gaps in the labor market.

Figure 3.4.Views on Women’s Rights and Gender Gaps in Labor Market Participation

Source: Statements from World Values Survey; values reflect authors’ calculations.

The way a society values women seems to have a lot to do with low female participation in the labor market, but it is less relevant when it comes to the gap in the share of women in the labor market that are employers. Figure 3.5 shows very low correlation and highlights the possible role of other omitted explanatory variables, such as parental leave policies, labor market arrangements, market competition, and education policies.

Figure 3.5.Views on Women’s Rights and the Employer Gender Gap

Source: Statements from World Values Survey; values reflect authors’ calculations.

Conclusions

There are clear macroeconomic effects of gender inequality in the labor market. The quantitative framework provided in Cuberes and Teignier 2016 predicts significant macroeconomic losses from gender inequality; this analysis finds an average income loss due to the estimated labor market gender gaps of 15.4 percent of GDP in the OECD sample of advanced economies and 17.5 percent in the non-OECD sample of developing economies. There are important differences across countries and geographical regions: The Middle East and north Africa had the largest income losses, averaging 38 percent, followed by south Asia and Latin America and the Caribbean, with long-term income losses of 25 and 17.3 percent, respectively.

In terms of demographics, the simulation results suggest that even relatively slow decreases in gender gaps in the labor market could significantly reduce the negative effects on GDP of population aging. In the majority of countries, more than 50 percent of the effect of rising dependency ratios (that is, proportional decreases in the size of the workforce) could be eliminated in 50 years by a gradual removal of gender gaps, and in several countries this scenario could more than offset the negative effects from a declining labor force by 2035, leading to overall GDP gains.

The analysis indicates that differences in social values can only partially explain the heterogeneity in the labor market gender gaps observed across countries. There is a significant negative correlation between how much a country respects women’s rights and female labor force participation, but almost no correlation with gender gaps when it comes to the percentage of employers in the labor force.

In any case, the strong negative association between how women are viewed and their labor force participation suggests that other factors likely play a role, and public policies aimed at reducing these gaps may have an effect. These are some examples of policies that could promote gender equality in the labor market:

  • Paid maternity leave and child support

  • A gender-neutral legal framework for business

  • Reduced administrative burdens on firms and fewer excessive regulatory restrictions

  • Equal access to financing for female and male entrepreneurs

  • Financing programs paired with support measures such as financial literacy training, mentoring, coaching, and consulting services

  • Increased access to support networks, including professional advice on legal and financial matters

This analysis focuses on observed gender disparities in access to the labor force and entrepreneurship; it does not take into account other types of gender gaps that exist in many countries’ labor markets, such as women’s employment in firms of various sizes, by job type, or by sector.9 To the extent that gaps represent additional hurdles faced by women and imply a distortion of efficient labor allocation, actual aggregate losses from gender inequality in the labor market would be larger. In that sense, this chapter’s estimates of the costs of labor gender gaps can be regarded as a lower bound, especially in developing economies. On the other hand, the fact that we assume away the possibility of household production may be overstating the costs we calculate.

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Annex 3.1. List of Countries by Region

Central Asia: Armenia, Kazakhstan, Kyrgyz Republic

East Asia and the Pacific: Australia, Hong Kong SAR, Japan, Malaysia, Philippines, Singapore, Korea, Thailand

Europe: Belarus, Cyprus, Estonia, Germany, Netherlands, Poland, Romania, Russia, Slovenia, Spain, Sweden, Ukraine

Latin America and the Caribbean: Brazil, Chile, Colombia, Ecuador, Mexico, Peru, Trinidad and Tobago, Uruguay

Middle East and northern Africa: Bahrain, Egypt, Kuwait, Lebanon, Morocco, Qatar, Tunisia, Turkey, Yemen

North America: United States

South Asia: India, Pakistan

Sub-Saharan Africa: Ghana, Rwanda, South Africa, Zimbabwe

A version of this chapter was previously published as Cuberes and Teignier 2016.

For a more detailed review, see Cuberes and Teignier 2014.

A related factor that has contributed to the incorporation of mothers into the labor force is the increasing availability of childcare (Attanasio, Low, and Sanchez-Marcos 2008).

A related paper is Esteve-Volart 2004, which developed a dynamic model with gender gaps in employment and various distortions in the allocation of talent.

This omits the possibility of women producing some type of good in the household sector or in the informal economy. This topic is discussed at the end of the chapter.

For more details on how these parameters are chosen, see Cuberes and Teignier 2016.

The OECD countries included in the analysis are Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Mexico, the Netherlands, Norway, Poland, Portugal, the Slovak Republic, Slovenia, South Korea, Spain, Sweden, Switzerland, Turkey, the United Kingdom, and the United States.

The medium projection assumes a decline of fertility for countries where large families are still prevalent as well as a slight increase of fertility in several countries with fewer than two children per woman on average.

See Bertrand 2011 for a detailed discussion of causality.

A note of caution: It is tempting to interpret gender gaps in female labor force participation as evidence of discrimination against women, but this conclusion can be highly misleading. As is clear from the literature review, supply-driven factors may explain why women participate less actively in the market and would remain even in the absence of discriminatory behavior by firms.

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