Chapter

Chapter 4. Tackling Income Inequality

Editor(s):
Kalpana Kochhar, Sonali Jain-Chandra, and Monique Newiak
Published Date:
February 2017
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Author(s)
Christian Gonzales, Sonali Jain-Chandra, Kalpana Kochhar, Monique Newiak and Tlek Zeinullayev 

Attaining a more equitable society and narrowing gender differences are desirable not just from a social equity perspective but also because doing so will benefit the macroeconomy. The previous chapters discuss various channels through which higher gender inequality can impede growth, productivity, and development outcomes. This chapter highlights a new channel through which gender inequality can interact with the economy—through its effect on income inequality.

Income inequality and gender-related inequality interact in various ways. First, gender wage gaps directly contribute to income inequality. Furthermore, large gaps in labor force participation rates between men and women are likely to result in unequal earnings between the sexes, thereby exacerbating income inequality. These economic outcomes may be a consequence of unequal opportunities and enabling conditions for men and women and for boys and girls.

Several dimensions of gender inequality are associated strongly with income inequality across time and across countries at all income levels. This chapter shows that:

  • Gender inequality is strongly associated with income inequality. This intuitive hypothesis was verified in an empirical analysis that controls for the standard drivers of income inequality previously highlighted in the literature and extends the United Nations’ Gender Inequality Index (GII) to cover two decades for almost 140 countries. An increase in the multidimensional GII from zero (perfect gender equality) to 1 (perfect gender inequality) is associated with an increase in net income inequality, as measured by the Gini coefficient, which ranges from zero (full income equality) to 100 (full income inequality). This increase in net inequality is almost 10 points.

  • Gender inequality exists everywhere, but it varies. These empirical results hold for countries across all levels of development, but the relevant dimensions of gender inequality are different. For advanced economies—where gender gaps in education are largely closed and opportunity across the sexes is more equal—income inequality arises mainly because of differences in economic participation by men and women. In emerging market and low-income economies, unequal opportunity (in particular gender gaps in education and health) appears to pose the main obstacle to more equal income distribution.

Improving equality of opportunity and removing legal and other obstacles that prevent women from reaching their full economic potential would give women the option to become economically active, should they so choose.

Macroeconomic Implications of Income and Gender Inequality

Income inequality can impede economic growth in various ways. Higher inequality in income and wealth can lead to underinvestment in physical and human capital (Galor and Zeira 1993; Galor and Moav 2004; Aghion, Caroli, and Garcia-Penalosa 1999). Income inequality has been associated with lower levels of mobility across generations (Corak 2013) and can dampen aggregate demand (Carvalho and Rezai 2014). On the other hand, inequality can stimulate growth by spurring innovation and entrepreneurship and, in developing economies, by allowing at least a few individuals to accumulate the minimum resources to start a business (Lazear and Rosen 1981; Barro 2000).

While the effect of income inequality on growth is ambiguous in principle, recent IMF studies show empirically that, in fact, a less equal income distribution hurts growth. In particular, lower net income inequality has been robustly associated with faster growth and longer growth episodes (Ostry, Berg, and Tsangarides 2014). Moreover, the distribution of income also matters in its own right. An increase in the income share of the top 20 percent is associated with lower GDP growth over the medium term, whereas an increase in the income share of the bottom 20 percent is associated with higher GDP growth (Dabla-Norris and others 2015). Using U.S. microcensus data, van der Weide and Milanovic (2014) show that income inequality decreases income growth for the poor but not for the rich.

Likewise, the various dimensions of gender-based inequality also have major macroeconomic and development-related implications. Gender inequality can influence economic outcomes through several channels (Elborgh-Woytek and others 2013):

  • Development—There is a positive association between gender equality and GDP per capita, competitiveness levels, and human development indicators (WEF 2014; Duflo 2012; Figure 4.1). Women are more likely than men to invest a large proportion of their household income in the education of their children; higher economic participation and earnings by women could therefore translate into higher expenditure on school enrollment for children (Aguirre and others 2012; Miller 2008; Rubalcava, Teruel, and Thomas 2004; Thomas 1990).

  • Economic growth—Gender gaps in economic participation restrict the pool of talent in the labor market and can thus yield a less efficient allocation of resources and total factor productivity losses and lower GDP growth (Cuberes and Teignier 2016; Esteve-Volart 2004). In a cross-country study, Klasen (1999) shows that 0.4 to 0.9 percentage points of the difference in growth rates between east Asia, sub-Saharan Africa, south Asia, and the Middle East can be explained by differences in gender gaps in education. Figure 4.2 and Box 4.1 highlight that higher gender inequality (as measured by the multidimensional GII) is associated with lower economic growth. This finding is consistent with Hakura and others 2016, which shows that gender inequality is negatively associated with growth, in particular in low-income countries, broadly confirming the findings by Amin, Kuntchev, and Schmidt (2015), which are based on a cross section of countries.

  • Macroeconomic stability—In countries facing a shrinking workforce, raising economic participation, including by women, can directly yield growth and stability gains by mitigating the impact of a decline in the labor force on growth potential and ensuring stability of pension systems (Steinberg and Nakane 2012).

Figure 4.1.Gender Inequality and GDP per Capita

Sources: United Nations Development Program (UNDP), Human Development Report; World Bank, World Development Indicators database; and IMF staff estimates.

Figure 4.2.Gender Inequality and GDP Growth

Sources: United Nations Development Program, Human Development Report; World Bank, World Development Indicators database; and IMF staff estimates.

Note: Growth of GDP per capita was regressed on initial income to control for convergence.

Box 4.1.Gender Inequality and Economic Growth

Previous studies highlight that gender gaps in labor force participation, entrepreneurial activity, and education impede economic growth (Cuberes and Teignier 2012; Esteve-Volart 2004; Klasen and Lamanna 2009). Cuberes and Teigner (2016) simulate an occupational choice model that imposes several frictions on economic participation and wages of women and show that gender gaps in entrepreneurship and labor force participation significantly reduce income per capita. IMF 2015 finds that legal equality is robustly related to real GDP growth per capita in all countries.

We use the United Nations’ Gender Inequality Index (GII), which captures three dimensions of gender inequality, including labor market participation, reproductive health, and empowerment (see Box 4.2 for details on the construction of the index). This multidimensional index is then included in cross-country growth regressions. The results in Table 4.1.1 highlight that higher gender inequality is associated with lower economic growth even when controlling for a number of determinants of growth such as investment, population growth, institutional quality, and education. The results indicate that an amelioration of gender inequality that corresponds to a 0.1 reduction in the GII is associated with almost 1 percentage point higher economic growth. In a similar exercise, IMF 2015 finds that increases in the GII are associated with a decrease in growth in low-income countries, on top of the effect of initial income inequality, as measured by the ratio of the top 20 to the bottom 40 percent of the income distribution.

Table 4.1.1.Gender Inequality and Economic Growth
Dependent Variable: Growth in GDP per Capita
Fixed Effects1System GMM2
VARIABLES(1)(2)(3)(4)
Log (Initial income per capita)−0.1068***

(0.0116)
−0.0975***

(0.0137)
−0.0539***

(0.0165)
−0.0202***

(0.0049)
UNDP Gender Inequality Index (GII)− 0.1120**

(0.0431)
− 0.1131**

(0.0430)
− 0.3818***

(0.1099)
− 0.0885*

(0.0452)
Log (Investment)0.0225***

(0.0084)
0.0205**

(0.0100)
Log (Population growth)0.0046

(0.0048)
0.0118

(0.0109)
Log (Total education)− 0.0013

(0.0176)
0.0238

(0.0179)
Large negative terms of trade shock− 0.0004

(0.0049)
− 0.0069

(0.0124)
Political institutions0.0002

(0.0004)
0.0002

(0.0006)
Openness0.0238***

(0.0079)
0.0113

(0.0121)
Debt liabilities− 0.0081***

(0.0024)
− 0.0140***

(0.0040)
Observations (five-year averages)508405508405
Countries1289712897
Sources: Barro and Lee 2013; IMF, World Economic Outlook database; Lane and Milesi-Ferretti 2012; Ostry, Berg, and Tsangarides 2014; Penn World Tables; Polity IV; United Nations Development Program, Human Development Report; World Bank, World Development Indicators database; and IMF staff estimates.

Estimated using country and year fixed-effects panel regressions with robust standard errors clustered at the country level shown in parentheses, *p < 0.10; **p < 0.05; ***p < 0.01.

GMM = generalized method of moments estimation. Estimated using two-step system GMM. Standard errors in parentheses, *p < 0.10; **p < 0.05; ***p < 0.01. Standard tests for the joint validity of instruments, as well as AR tests were satisfied. The Windmeijer (2005) finite sample correction for standard errors was used.

Two Sides of the Same Story: How Gender and Income Inequality are Linked

Gender and income inequality have mostly been treated as separate topics in the literature, but they can and do interact through the following channels:

  • Inequality of economic outcomes—Gender wage gaps directly contribute to income inequality. Moreover, high gaps in labor force participation rates between men and women are likely to result in unequal earnings between the sexes, thereby creating and exacerbating income inequality. Also, women are more likely to work in the informal sector, in which earnings are lower, which widens the gender earnings gap and exacerbates income inequality.

  • Inequality of opportunity—Inequality of opportunity (such as unequal access to education, health services, financial markets, and resources, as well as differences in political empowerment) is strongly associated with income inequality (Mincer 1958; Becker and Chiswick 1966; Galor and Zeira 1993; Brunori, Ferreira, and Peragine 2013; Murray, Lopez, and Alvarado 2013; Castello-Climent and Domenech 2014). Inequality of opportunity is also strongly associated with gender gaps in opportunity. These unequal enabling conditions for men and women, and for boys and girls, may result in unequal economic outcomes. Specifically,

    • Education—Gender gaps in education persist, leading to higher inequality in opportunity (when both boys and girls go to school, opportunities are more equal than they are when only boys go to school). If one segment of the population is excluded from educational opportunities, future income for this segment will be lower than for the other, resulting in higher income inequality.

    • Financial access and inclusion—On average, women still have lower access to financial services than men, which makes it more difficult for them to start businesses or invest in education, exacerbating inequality of opportunity and thereby lowering wage and other income for women and worsening income inequality.

We find that several dimensions of gender inequality are associated with income inequality across time and across countries at all income levels. Following the arguments described previously, we empirically examine the effect of differences in outcomes and opportunities for men and women on income inequality.1 Controlling for the drivers of inequality highlighted in the literature, the results indicate that gender inequality is strongly associated with income inequality. These results hold for countries across all levels of development, but the relevant dimensions vary. For advanced economies—with largely closed gender gaps in education and more equal economic opportunities for both sexes—income inequality arises mainly through gender gaps in economic participation. In emerging market and low-income economies, inequality of opportunity (in particular, gender gaps in education, political empowerment, and health) appears to pose the main obstacle to a more equal income distribution.

The analysis uses existing cross-country data on inequality, which have certain drawbacks. Empirical analysis of the drivers and consequences of income inequality has been impeded by the limitations of existing data sets. The Standardized World Income Inequality database draws from a number of sources with a view to maximizing comparability while ensuring the widest possible coverage across countries and over time. Nevertheless, these data have drawbacks, as missing observations are generated through model-based multiple imputation estimates. Using higher-quality data from the Luxembourg Income Study yields very similar results but restricts the sample to a smaller set of countries.

Gender and Income Inequality: a Worldwide Connection

Gender inequality in outcomes and opportunity is strongly related to income inequality worldwide. The GII combines the following dimensions of outcome-and opportunity-based gender inequality: the labor market (gap between male and female labor force participation rates), education (difference between secondary and higher education rates for men and women), empowerment (female shares in parliament), and health (maternal mortality ratio and adolescent fertility). Box 4.2 contains details of the GII and its construction.2

Box 4.2.Measuring Gender Inequality

There is no universally accepted compound measure of gender inequality, and so most studies focus on specific elements of gender inequality such as gaps in education, health, labor force participation, and political representation. In 1995, the United Nations Development Program created the related Gender Development Index (GDI) and the Gender Empowerment Measure as first attempts to develop a comprehensive measure of gender inequality. Several improvements to both indices eventually resulted in the creation of the Gender Inequality Index (GII) in 2010.

What the GII Measures

The GII is a composite measure of gender inequality in the areas of reproductive health (maternal mortality ratios and adolescent fertility rates), empowerment (share of parliamentary seats and education attainment at the secondary level for both males and females), and economic opportunity (labor force participation rates by sex). While not directly mapped to the GDI, the higher values of the GII can be interpreted to be a loss in human development. While the GII has drawbacks (such as a complicated functional form and combining indicators that compare men and women with indicators that pertain only to women), it is preferable to alternatives such as the GDI (in which one of the main components is not observed and is imputed). The regressions in this chapter are also run on subcomponents of the GII, with the findings being robust to the inclusion of these subcomponents.

Extending the GII

Previously, the GII index was available for 2008 and from 2011 to 2013. As the underlying data used for the construction of the index are available from 1990 onward, a major innovation of the paper underlying this chapter was to extend the GII from 1990 to 2010 (Gonzales and others 2015b). Data that are available only every five years were linearly interpolated. Because this analysis uses five-year panel regressions, this interpolation is not a major concern, but the nature of the data could be limiting in other types of analysis. There is a close relationship between the actual and constructed GII (correlation of 0.97) (Table 4.2.1).

Table 4.2.1.Correlation between Gender Inequality Index and Other Indices
GII (Constructed)GII (Original)
SIGI(0.89)(0.88)
WEOI(0.66)(0.67)
Gender CPIA0.500.60
GII (constructed)1.000.97
UNDP GII (original)1.00
Note: Negative signs reflect the fact that higher values for some indices represent higher inequality, whereas others represent higher equality. Time Coverage by Index: SIGI (2009, 2012, 2014), WEOI (2010 and 2012), CPIA (2005–14), GII Constructed (1990–2010), GII Original (2008, 2011–13).

A large number of gender-related indices have been developed, including the Economist Intelligence Unit’s Women’s Economic Opportunity Index, the Organisation for Economic Co-operation and Development’s Social Institution and Gender Index, the World Bank’s Country Policy and Institutional Assessments Gender Equality Rating, and the World Economic Forum’s Global Gender Gap Index. However, most of these indices were created recently, which limits time coverage for empirical work. For years for which data overlap, the extended GII is highly correlated with other gender-related indices.

This analysis uses the extended index because it provides a long time series that enables empirical analysis. Figure 4.3, panel 1 shows that the GII varies significantly across countries, with more gender inequality prevalent in south Asia and in the Middle East and north Africa. Encouragingly, gender inequality as measured by the GII has been declining in the majority of countries (panel 2).

Figure 4.3.Gender Inequality across Countries

Source: United Nations Development Program, Human Development Report; and IMF staff estimates.

The GII is highly correlated with income inequality, with the share of the top 10 percent earners of the income distribution across countries, and with poverty (Figure 4.4). This highlights that gender inequality in outcomes and in opportunity both interact closely with the level of income inequality across countries.

Figure 4.4.Gender Inequality, Income Inequality, and Poverty

Note: HICs = high-income countries; LICs = low-income countries; MICs = middle-income countries; PPP = purchasing power parity.

Income Inequality and Gender Gaps in Labor Force Participation

Large gaps in labor force participation rates between men and women are likely to result in inequality of earnings between the sexes, thereby increasing income inequality (Figure 4.5; Box 4.3). The correlation between gender gaps in labor force participation and income inequality is strongest in high-income countries.

Figure 4.5.Gender Gaps in Labor Force Participation and Income Inequality

Sources: Solt 2016; World Bank, World Development Indicators database.

Notes: HICs = high-income countries; LICs = low-income countries; MICs = middle-income countries.

Box 4.3.Employment and Income Gaps in Advanced Economies

The gaps between men and women in employment and earnings have been shrinking over the past 20 years in advanced economies (Figure 4.3.1; OECD 2015). Between 1992 and 2013, the gender employment gap decreased by 8 percentage points in the Organisation for Economic Co-operation and Development (OECD) on average, with Spain and Ireland experiencing the highest decline of almost 20 percentage points. However, the increase in men’s unemployment as a result of the global financial crisis has been driving these results to a large extent (OECD 2012). The earnings gap between men and women has also declined by 4 percentage points compared with 2000, but men’s median incomes remain higher than women’s in all OECD countries. Women take home on average 15 percent less than men; they have higher chances of ending up in lower-paying jobs and face a lower probability of being promoted in their careers than men.

A higher proportion of working women has been associated with lower income inequality in the OECD. In particular, an increase in the proportion of households with working women (from 52 percent in the mid-1980s/early 1990s to 61 percent in the late 2000s), on average, decreased income inequality by 1 Gini point. The increasing work intensity of women was also associated with lower income inequality. Overall, the study finds that having more households with women in paid work, especially full-time work, means less income inequality by about 2 Gini points.

Figure 4.3.1.OECD Employment and Income Gaps

(Male minus female employment and income, percent)

Source: OECD 2015.

Note: Countries are listed using International Organization for Standardization (ISO) three-letter country codes.

This may be because there are fewer differences in the levels of education and working conditions between men and women in these countries. Also, there tends to be less legal and other discrimination between men and women in employment. In these circumstances, gender gaps in labor force participation would translate directly into differences in earnings for men and women, and thus to increased income inequality. In particular, in Organisation for Economic Co-operation and Development (OECD) countries, an increase in the proportion of households with working women decreased income inequality by 1 Gini point on average, and OECD countries with large gender pay gaps tend to have larger employment gaps as well (OECD 2015; see Box 4.3). In lower-income countries, the correlation between income inequality and gender gaps in labor force participation tends to be lower, as other gender gaps (in education and health) are significant and are key drivers of income inequality.

Inequality of Opportunity: Education, Financial Access, and Health

Considerable gender gaps in education persist and are closely linked with more unequal access to education and to inequality in outcomes, particularly income inequality.

Gender gaps in education (measured by the difference in years of schooling between men and women) are highly correlated with overall inequality in educational attainment across countries, as measured by the education Gini coefficient (Figure 4.6). There appears to be a clear gender dimension in access to education. Progress has been made, and gender gaps in education and the education Gini have been declining steadily over the past decades. Sub-Saharan Africa and the Middle East and north Africa exhibit the highest education-related inequality. Gender gaps in literacy rates among adults remain substantial in low- and middle-income countries, likely reflecting a lag that will exist until narrower gender gaps in primary and secondary education translate into higher literacy rates.3

Figure 4.6.Education

Source: World Bank, World Development Indicators database, 2015.

Notes: AFR = Africa; AP = Asia and Pacific; EUR = Europe; MC = Middle East and central Asia; WH = western hemisphere; LICs = low-income countries; LMCs = lower middle-income countries; MICs = middle-income countries.

Empirical studies have found that a more equal distribution of education is associated with a more equal income distribution. However, the large decline in education inequality has not coincided with a similar decrease in income inequality over time. This is likely due to growing returns to education, skill-biased technological change, and globalization as offsetting factors (Thomas, Wan, and Fan 2001; de Gregorio and Lee 2002; Castello-Climent and Domenech 2014; Dabla-Norris and others 2015).

Limited financial access can increase inequality, and financial access by income and gender are closely related. Figure 4.7 shows that financial access for women is lower than it is for men, while higher-income households have greater access to financial services. Access to financial services has increased worldwide, but it remains fragmented across gender and income, with women and the poorest 40 percent of the income distribution having a smaller probability of access to financial services in each region of the world. In three regions (the Middle East and north Africa, south Asia, and sub-Saharan Africa), the gender gap in financial access actually increased between 2011 and 2014.

Figure 4.7.Financial Inclusion

Countries where access to financial services is unequal across income groups also tend to have large gender gaps in access to financial services. This could be because weaker financial access among income groups distorts the allocation of resources, which results in underinvestment in human and physical capital and can thereby exacerbate income inequality (Galor and Zeira 1993). Better access to financial services has been empirically associated with lower Gini coefficients (Honohan 2007). However, theoretically, the effect may be nonlinear. In a micro-founded general equilibrium model, Dabla-Norris and others (2015) find that lowering the cost to financial access may decrease inequality only after a critical share of the population uses these services and that lowering collateral constraints may increase inequality because it favors economies of scale for the most productive businesses.

Inequality in access to health services is widespread in some countries and is associated with higher income inequality. Specifically, maternal health and adolescent fertility are closely related to income inequality and the incidence of poverty. High fertility rates have been associated with less economic activity by women. In particular, high adolescent fertility prevents girls from going to school and subsequently entering the labor market. It also means that women enter the labor market with limited skills, which increases inequality in education, economic participation, and pay between men and women. This association is reflected in higher degrees of inequality and higher poverty rates for countries in which adolescent fertility is high (Figure 4.8).

Figure 4.8.Health

Sources: Solt 2016; World Bank, World Development Indicators database.

Notes: HICs = high-income countries; LICs = low-income countries; MICs = middle-income countries; PPP = purchasing power parity.

Linking Gender and Income Inequality Empirically

The literature posits a number of standard determinants that drive income inequality. Recent analysis by the IMF (Dabla-Norris and others 2015) finds that the drivers of income inequality include technological progress and the resulting increase in skill premiums, globalization, the decline of some labor market institutions, and financial openness and deepening. The relevant drivers differ depending on the level of a country’s development: the rise in the skill premium is a key driver in advanced economies, whereas financial deepening (absent commensurate increases in financial inclusion) has driven inequality in emerging market and developing economies.

The main contribution of this analysis is to examine the importance of gender inequality as a source of income inequality. We find that gender inequality drives income inequality above and beyond determinants previously identified in the literature.

The importance of gender inequality is examined as a source of income inequality. The GII, which captures both gender inequality in outcomes (labor force participation gap and share of female seats in parliament) and gender inequality in opportunity (education gaps, maternal mortality, and adolescent fertility), is significantly related to income inequality. An increase in the GII from zero (perfect gender equality) to 1 (perfect gender inequality) is associated with an increase in net inequality by almost 10 points. Alternatively, if the GII falls from the highest level of 0.7 (highest level in the sample, seen in Yemen) to the median level of 0.4 (seen in Peru), the net Gini decreases by 3.4 points, which is similar to the difference in net Gini between Mali and Switzerland.

The analysis also finds that gender inequality has a strong association with the actual distribution of income in an economy (Table 4.1). Higher gender inequality is strongly associated with higher income shares in the top 10 percent income group, possibly because being a woman may undermine earning possibilities disproportionately at the higher end of the income distribution. If the GII increases from the median to the highest levels, the income share of the top 10 percent increases by 5.8 percentage points, which is the difference between Norway and Greece. Gender inequality also goes hand in hand with lower income shares at the bottom of the income distribution. If the GII increases from median to highest levels, the income share of the bottom 20 percent declines by 2 percentage points (which is similar to the difference between Estonia and Uganda).

Table 4.1.Gender Inequality and Income Distribution
Dependent Variable: Net Gini and Income Shares
(1)(2)(3)(4)(5)
VARIABLESNet GiniTop 10Top 60Bottom 40Bottom 20
United Nations Gender9.761*16.81*10.09**−9.367**−5.934**
Inequality Index (GII)(5.589)(8.431)(4.444)(4.385)(2.390)
Trade Openness−0.0109− 0.00942− 0.01460.01320.00588
(0.0140)(0.0121)(0.0101)(0.0102)(0.00550)
Financial Openness0.0422***0.0310***0.0347***− 0.0291***− 0.0141**
(0.0113)(0.0115)(0.00967)(0.0100)(0.00544)
Technology−1.56725.3022.83*− 22.24*−14.59**
(18.53)(20.74)(12.21)(12.45)(6.187)
Financial Deepening0.0233**0.0230***0.0208**− 0.0200**− 0.00876**
(0.00916)(0.00785)(0.00809)(0.00800)(0.00385)
Financial Deepening χ− 0.0286***− 0.0208**− 0.0315***0.0296***0.0132***
AM Interaction(0.0101)(0.00952)(0.00847)(0.00841)(0.00408)
Educational Attainment−0.793**− 0.504− 0.481**0.546***0.292***
(0.334)(0.318)(0.194)(0.203)(0.109)
Labor Market Institutions0.688***0.2680.331**− 0.249*− 0.133*
(0.197)(0.172)(0.133)(0.140)(0.0733)
Government Spending− 0.320***− 0.356***−0.112**0.132**0.0660**
(0.102)(0.105)(0.0501)(0.0533)(0.0256)
Population over the0.361**0.2060.251*− 0.292**− 0.140*
Age of 65(0.150)(0.175)(0.136)(0.134)(0.0709)
Observations (five-year averages)338208244244244
Countries9766898989
Adjusted R-squared0.2360.4210.3590.3450.305
Sources: Barro-Lee Education Attainment data set; Fraser Institute; IMF, World Economic Outlook database; Solt Database; UNU-WIDER World Income Inequality Database; World Bank, World Development Indicators database; World Economic Forum; and IMF staff estimates.Notes: AM = advanced market. Estimated using country and year fixed effects panel regressions with robust standard errors clustered at the country level shown in parentheses, *p < 0.10; **p < 0.05; ***p < 0.01.

The key results of the analysis also support previous findings in the literature that financial openness, labor market institutions, and government spending are significantly associated with income inequality.

  • In particular, greater financial openness is associated with rising income inequality. One explanation is that higher capital flows, including foreign direct investment, are destined for high-skill and capital-intensive sectors, which also lowers the income share of unskilled workers, thereby exacerbating income inequality.

  • Consistent with Dabla-Norris and others 2015, technological progress is associated with a decline in the income share of the bottom 10 percent, though unlike in previous findings, the association with the income decline at the top is not statistically significant. Technological advances have driven enhanced productivity and growth but have also been accompanied by a rising skill premium, leading to higher income inequality.

  • In line with previous findings, financial deepening is associated with higher income inequality, as credit is often concentrated and financial inclusion does not keep pace with deepening.

  • Higher government spending (a proxy for redistribution-related spending) is associated with a decline in income inequality.

  • The easing of labor market regulations in favor of business4 is associated with greater income inequality and rising income share of the top 10 percent. It also has a dampening effect on the income share of the bottom 10 percent. This result is consistent with Dabla-Norris and others 2015 and Jaumotte and Osorio Buitron 2015, which find that changes in labor market regulations that reduce labor’s bargaining power are associated with the rise of income inequality in advanced economies. Specifically, the decline in unionization is related to the rise of top income shares, whereas the reduction in minimum wages is correlated with considerable increases in overall income inequality.

Finally, while there are some common drivers, different aspects of gender inequality matter for different country groups. Figure 4.9 depicts the different drivers across country groups. In all countries, gender gaps in labor force participation and education are the main drivers of income inequality, in addition to standard determinants of income inequality.5 For advanced economies, with gaps in the access to health and education largely closed, the gender gap in labor force participation is the key aspect of gender inequality that affects income inequality. For emerging markets and low-income countries, gender gaps in opportunities (education and health) are also found to be important drivers of income inequality. In addition, in low-income countries, women’s health is an important driver of income inequality, as inequality in opportunities translates sharply into income gaps.

Figure 4.9.Marginal Effect of Gender Inequality on Net Gini, 2010

(Gini points)

Note: AMs = advanced markets; EMs = emerging markets; EMDEs = emerging and developing economies.

Conclusions

This analysis documents the strong association between gender-based economic inequalities and a more unequal overall income distribution. Improving equality of opportunity and removing legal and other obstacles that prevent women from reaching their full economic potential would give women the option to become economically active, should they so choose. Increased gender equity and female economic participation are associated with higher growth, more favorable development outcomes, and lower income inequality.

Redistribution complements but is not a substitute for gender-specific policies geared toward reducing gender and income inequality. Previous IMF work shows that redistribution generally has a benign effect on growth and is only negatively related to growth in the most strongly redistributive countries (Ostry, Berg, and Tsangarides 2014). Therefore, redistributive policies can help lower income inequality directly and, if not excessive, can promote growth. However, in order to alleviate deeper inequality of opportunity—such as unequal access to the labor force, health, education, and financial access between men and women—more targeted policy interventions are needed as a complement to redistribution.

A significant decrease in gender gaps will require work on many fronts. Providing women with equal economic opportunities will require an integrated set of policies, including antidiscrimination laws (Elborgh-Woytek and others 2013) and the revision of tax policies. Some of these policies fall outside the IMF’s core area of expertise and require close collaboration with other organizations, such as the World Bank. Moreover, policy changes are, at most, necessary conditions for leveling the playing field, but may not be sufficient. In addition, cultural, societal, and religious norms are also relevant, but on these, this chapter takes no position.

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Annex 4.1. Econometric Methodology

For a wider global sample, including advanced economies, emerging markets, and developing countries, a fixed-effects regression model was created by estimating the following relationship over the period from 1980 to 2012:

Country fixed effects control for country-specific drivers of income inequality that are not explicitly controlled for in the regressions and if omitted could result in misleading results. The Hausman test indicates that a fixed effects model is appropriate for both sets of panel regressions. We also conduct a number of robustness checks in which we run regressions with a number of different specifications:

  • We use alternatives to net Gini, including the Gini from the Luxembourg Income Study and market Gini. The results still hold using this higher quality income inequality data.

  • We use human capital from the Penn World Tables as an alternative to total years of education to capture the skill premium.

  • As an alternative to government spending (which is intended to capture redistribution), we use education spending for all countries and social spending for OECD countries.

  • We also control for female mortality as an indicator of health provision.

  • We control for education Gini.

  • To capture the direct effect of gender wage inequality on income inequality, we include the gender wage gap in the regressions on top of inequality in a sample of OECD countries.

While some control variables from the baseline regression were not significant in some of the alternative specifications, the Gender Inequality Index and some subcomponents were always significant, indicating that the results are robust to changes in econometric specification. To address concerns about the direction of causality between gender and income inequality, we employ instrumental variables regressions. We use a novel set of instruments drawing on previous analysis contained in Gonzales and others 2015a. We use various legal restrictions on women’s economic participation as instruments for the gender gap in labor force participation as this link has been established in previous IMF work (Gonzales and others 2015a). The legal restrictions related to guaranteed equality under the law and daughters’ inheritance rights are the strongest instruments as seen in the first-stage regression. Using these variables to instrument for gender gap in labor force participation, the second-stage regression highlights that a widening of the gender gap in labor force participation leads to greater income inequality. Tests for the validity of the instrument and for overidentification suggest that these are valid instruments. We also use instruments other than the legal restrictions from the World Bank’s World, Business and the Law data set. We include (1) the lag of the share of female tertiary teachers, which has been previously used in the literature as an instrument (the rationale being that girls feel encouraged by a female role model); and (2) the lag of the labor force participation gap (as the income distribution is only affected by how many women are on the market right now compared with men and not by the labor force participation gap from five years ago). Both instruments are individually highly significant in the first stage; the Hansen test indicates that they are valid instruments; and finally, both education gap and labor force participation gap are significant in the second stage.

A version of this chapter was previously published as Gonzales and others 2015b.

These theoretical arguments could be most clearly tested if income inequality were measured at the individual level. However, available data measure income inequality at the household level. Smaller gender gaps could potentially lead to higher income inequality across households if husbands and wives had the same (potential) income. However, in our empirical exercise, we find a strong association between gender inequality and income inequality even at the household level.

The GII ranges between zero (equal) and 1 (unequal), with a higher value of the index indicating more gender disparity in health, empowerment, and labor market outcomes. The world average score on the GII is 0.451. Regional averages range from 0.13 percent among European Union countries to nearly 0.58 percent in sub-Saharan Africa. Sub-Saharan Africa, south Asia, and the Middle East and north Africa exhibit the highest gender inequality (with average GII values of 0.58, 0.54, and 0.55, respectively).

There is no significant difference in the completion of primary and secondary school rates between boys and girls worldwide. Women’s postsecondary school enrollment rates have recently surpassed those of men, on average.

The indicator pertaining to labor market regulations is drawn from the Fraser Institute. It is a composite index that captures the extent to which regulations govern issues such as minimum wages, hiring and firing, collective bargaining, mandated cost of hiring, and mandated cost of worker dismissal.

For details on the variable sources and on econometric results, including estimation tables, see Gonzales and others 2015b. For a summary of the empirical strategy and the nature of robustness checks, see Annex 4.1.

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