Journal Issue

Euro Area Policies: Selected Issues

International Monetary Fund. European Dept.
Published Date:
July 2017
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Inequality of Opportunity, Inequality of Income, and Long-Term Growth1

Income inequality has increased in several euro area countries over the last few decades. We explore whether the relationship between income inequality and growth depends on equality of opportunity. This question is critical in the euro area where several countries exhibit higher levels of inequality of opportunities. Our econometric results confirm a robust negative effect of widening income disparities on growth in presence of high inequality of opportunity. Reducing income inequality can therefore accelerate growth in the euro area. Over the long-run, addressing the root causes of inequality of opportunity can make growth less sensitive to shifts in income distribution.

A. Income Inequality and Risks to Growth

1. Income inequality has increased in a number of euro area countries over the last decades. The rise in market income inequality (measured by the Gini coefficient) explains almost all the increase in inequality in the region. Redistribution has offset only a fraction of this increase, putting a dent on public finances in a number of countries. The increase has been remarkable in several southern European countries which already face important macroeconomic challenges (Figure 1).

Figure 1.Rising Inequality and Economic Challenges

2. The rise in market income inequality is often cited as an important contributor to rising populism, societal stress and demands for protection (Alesina and Rodrik, 1994, Persson and Tabellini, 1994, and Alesina et al., 2017). There is a long literature in the social sciences on the tendency for economic insecurity to beget authoritarian and nativist political parties (see Ingleheart and Norris, 2016, for a survey). For example, this has underpinned classic accounts of the rise of fascism in Weimar Germany and Poujadism in France (Lipset, 1960, Bell, 1963, Sauer, 1967). The modern version of this politico-economic argument typically focuses on the growing gaps between winners and losers from global trade or on rising skill-premia due to the march of technology, both of which could drive demand for protectionism. Moreover, stagnant middle class wages and limited job mobility have been advanced as powerful motives for resentment of “outsiders” seen as competing for jobs and benefits (Inglehart, 2016), especially in an era where growing social fragmentation and secularization have eroded traditional collective structures (Inglehart and Norris, 2011). For these and many other reasons, high and persistent income inequality is intrinsically undesirable.

3. However, assessing whether higher income inequality retards economic growth is much more challenging. The effect of income inequality on growth is ambiguous and much disputed in the literature. Theoretically, the effect can go either way. An increase in income inequality arising, say, from substantial rewards to risky entrepreneurship and innovation, could boost economic growth. By contrast, higher inequality could impair growth if low-income households are persistently less productive because of slower human capital accumulation and greater financial exclusion. Empirically, too, there is little consensus. Some studies have found a significant and negative effect of inequality on growth and its duration (Berg and Ostry, 2011, Berg et al., 2014, and Cingano, 2014). But others have found no systematic negative effect of inequality on growth (Forbes, 2000; Kraay, 2015) or a negative effect only at very high levels of inequality (Banerjee and Duflo, 2003).

4. A recent strand of the microeconomic literature has emphasized the influence of inequality of opportunity in driving bottom and top income growth. These studies exploit the variability of U.S. states data to demonstrate that inequality of opportunity affects negatively the future income growth of the poor and positively that of the rich (Marrero and Rodriguez, 2013; Hsieh et al., 2013; Bradbury and Triest, 2016; Marrero et al., 2016). The rationale is that inequality of opportunity may harm economic growth because it favors human capital accumulation by well-off individuals.2 Moreover, perceptions of unequal opportunities, which affect individual aspirations, may also reduce investments in human capital. In a nutshell, it is mostly inequality of opportunity that is holding back the growth prospects at the bottom end of the income distribution in the United States.

5. We explore whether the relationship between income inequality and growth depends on equality of opportunity, using comparable cross-country data. Unequal opportunity represents inefficiency because barriers prevent the most productive use of human and other resources. This dimension has received less attention in the cross-country literature, in part due to the difficulty in measuring equality of opportunity in a comparable manner across countries.3 At first, equality of opportunity can be measured using cross-country data on various indices of intergenerational mobility, such as the elasticity of son’s income (or education) to father’s income (or education). There are also broader measures of equality of opportunity defined as the inequality that arises due to circumstances outside the person’s control such as parental education, race and country of origin.4

6. Our central hypothesis is that in economies characterized by low equality of opportunity, income inequality acts as a drag on growth. An increase in income inequality becomes entrenched across generations due to various market failures connected with social stratification. This retards growth, for example by holding back human capital development or causing talent misallocation. On the other hand, in countries with high equality of opportunity, an increase in income inequality is easily reversed precisely because low-income people have access to the same opportunities as others. In such societies, therefore, an increase in income inequality is less harmful to growth.

B. Inequality of Opportunity in the Euro Area

7. Intergenerational mobility is low in the euro area. Comparable cross-country data on intergenerational earnings and education mobility (Corak, 2013, 2016; Hertz, et al., 2007) show lower levels of intergenerational mobility in a number of euro area in countries, including large ones such as Italy, Ireland, France, Spain, and Germany.5 In contrast, and consistently across various measures, Finland stands out as one of the most mobile society in the sample. Furthermore, there is a broad positive association between intergenerational mobility of earnings and of education in the sample, though some outliers exist (e.g., the United States).

8. Euro area countries also stand out in broader measures of inequality of opportunity. Inequality of opportunity (defined as the inequality that is due to circumstances outside the person’s control such as parental education, race and country of origin) is particularly high in a number of euro area countries compared with the rest of EU countries with comparable data (Checchi et al., 2016 and Brzezinski, 2015). A number of large euro area countries fall above the EU sample average and this includes Greece, Spain, Italy and to a certain extent Portugal (Figure 2). Higher inequality of opportunity is also observed in new member states (Lithuania, Estonia, and Latvia).

Figure 2.Intergenerational Mobility and Inequality of Opportunity

9. Within the euro area, countries with low intergenerational mobility have experienced the largest increases in market income inequality. Three high-debt countries—Spain, Italy and Portugal—stand out in terms of registering particularly high increases in market inequality. In contrast, Finland, with a high degree of intergenerational mobility has not seen income inequality rise. While these are simply associations, they do suggest that low inter-generational mobility provides favorable conditions for rapid rises in income inequality, which are then difficult to reverse. Moreover, given that the sample period includes the global financial crisis and subsequent recession, the data suggest that growth collapses in low-mobility countries can disproportionally worsen the income distribution compared to other economies. The next section investigates the effect of income inequality and inequality of opportunities on long-term growth.

Change in Market Gini and Intergenerational Immobility

(Change in Gini expressed in percentage points; 2004–2013)

(*) Portugal’s elasticity comes from OECD (2010).

Sources: OECD statistics and Corak (2015).

C. Baseline Specification and Results

Baseline specification

10. No systematic relationship is evident between inequality and growth in our sample if we leave out inequality of opportunity. We first take a look at the raw data in an attempt to isolate basic correlations between income inequality and per capita GDP growth in our sample. We take a long-term view by averaging the disposable income Gini (from the SWIID database) and real per capita growth over the period 1970 to 2015. To ensure the homogeneity of the sample for this exercise, we focus on advanced OECD countries. We find no discernible relationship between long-term income inequality and growth. However, after accounting for differences in inequality of opportunity (here approximated by the intergenerational earnings elasticity provided by Corak (2016)), a definite pattern emerges: that of a strong negative correlation among the sub-sample of countries with low intergenerational earnings mobility (Figure 3).

Figure 3.Income Inequality, Intergenerational Mobility, and Growth in the OECD

11. The chief innovation of our study is to model growth as a function of both income inequality and its interaction with measures of inequality of opportunity A number of studies linking growth to income inequality have imposed a linear relationship between the two variables. However, some studies have recognized that the effect of inequality may not be linear, as the marginal effect of inequality can be conditional on the level of economic development (Brueckner and Lederman, 2015) or on the level of income inequality itself (Banerjee and Duflo, 2003). Other studies focusing on U.S. states have decomposed the inequality variable into a component approximating inequality of opportunity and a residual component measuring inequality due to effort, and have assessed their respective effects on growth (Marrero and Rodriguez, 2013; Marrero et al., 2016). These studies concluded that inequality of opportunity is the component which is negatively associated with growth. Our baseline specification takes the following form:

where GROWTH denotes the 5-year nonoverlapping average of real per capita GDP growth in each country i observed at each sub-period τ with raw data starting from 1960 and y denotes (log) real GDP per capita ui and γτ denote country-fixed effects and period-specific dummies that account for both time-invariant unobservable factors at the country level and common shocks to countries, respectively. Income inequality is measured by the Gini coefficient of net disposable income using data from Solt (2016). IMi refers to the indicator of intergenerational immobility proxied by cross-country comparable estimates of intergenerational earnings (Corak, 2016) and intergenerational education (Hertz et al., 2007) elasticities. These indicators are time-invariant within countries.6 X is a matrix of covariates which includes investment and trade openness (measured by exports) expressed in percent of GDP. We also control for the initial (lagged) per capita income level to capture beta convergence. Real per capita GDP, investment and trade data come from Penn World Tables. The sample comprises all countries for which we have data on intergenerational earnings or intergenerational education elasticities. Given extensive data requirements needed to estimate indicators, the sample is dominated by OECD countries.7

12. The widening of income disparities is expected to worsen per capita growth mainly in countries exhibiting a high degree of intergenerational immobility. That is, we expect that θ2 < 0. If, in addition, θ1 ≥ 0, so that the direct impact of income inequality on growth is positive, then a threshold arises for intergenerational immobility:

where IM* measures the threshold of intergenerational earnings or education elasticities beyond which income inequality (GINI) unambiguously retards growth.

13. Estimating model 1 requires a number of adjustments. First, within country changes in income inequality (g) are not necessarily independent of growth shocks. Higher growth could lower income inequality if it benefits the poor more than the rich; and it could raise income inequality if it does the opposite. By lagging the Gini variable in the model, we have reduced somewhat the likelihood of such reverse causality. However, endogeneity issues driven by measurement error and/or omitted variables could still bias the results. Moreover, the OLS estimator is inconsistent because the lagged per capita income variable is correlated with the error term in the presence of fixed effects (Nickell bias). We therefore implement an instrumental variables strategy. The equation in levels and the equation in first differences are combined in a system and estimated with an extended System-GMM estimator that allows for the use of lagged differences and lagged levels of the explanatory variables as instruments (Blundell and Bond, 1998). The number of lags of the endogenous variable has been limited to avoid the overfitting bias due to instrument proliferation (Roodman, 2009). Two specification tests are used to check the validity of the instruments. The first is the standard Sargan/Hansen test for overidentifying restrictions. The second test examines the hypothesis that there is no second-order serial correlation in the first-differenced residuals.


14. The results show that income inequality reduces growth when intergenerational mobility is low. Regardless of the type of intergenerational elasticity (earnings or education), the coefficient associated with the additive term of income inequality is positive while the interaction term with intergenerational elasticity is negative. Thus, the marginal effect of income inequality on growth becomes negative at high levels of intergenerational immobility (Table 1). Threshold levels of intergenerational earnings and education immobility are computed as when corresponding intergenerational elasticities reach 0.3 and 0.9.

15. Several euro area countries fall above the earnings elasticity threshold, implying that income inequality has an unambiguously negative effect on growth. This includes countries such as Spain, France, Italy and to some extent Germany, which is very close to the threshold. Other countries also fall well above this threshold (United States, United Kingdom, Switzerland). The results suggest that an increase in income inequality by one standard deviation in the European sub-sample (corresponding to 2.7 units of Gini expressed in percentage points) will knock 0.2 percentage points off average growth in the next 5-year period for a level of intergenerational elasticity set at the level of Italy (0.5).

D. Robustness Checks

Endogeneity of income inequality

16. The baseline model is re-estimated by augmenting the system GMM with external instruments for income inequality. So far, the identification strategy was based on the use of the lagged Gini in the growth regression with the view that the 5-year lag of income inequality will not be directly affected by current growth realizations. To assess the robustness of our results, we resort to instrumental variables. We use the 10-year lagged level of the adolescent fertility rate to instrument the income inequality variable. The identification strategy is that a high fertility rate among adolescents is likely to weigh on their human capital accumulation and on their prospects in the labor market when they become adult. This would worsen income distribution under the assumption that higher fertility rates are likely to be more prevalent for adolescents in low-income households. Conditional on controlling for other determinants of growth such as lagged per capita income, investment, trade and overall fertility rate, lagged adolescent fertility rate is less likely to affect growth directly. We further instrument income inequality following the approach of a number of recent studies (Brueckner, 2013 and Brueckner and Lederman, 2015). The approach consists in constructing an income inequality variable that is adjusted for the impact that GDP per capita growth has on income inequality. This second instrument is “by construction” uncorrelated with the dependent variable, real per capita GDP growth.8

17. These alternative approaches to controlling for the endogeneity of income inequality yield similar results to the baseline regressions. The negative effect of income inequality on growth is confirmed at higher levels of intergenerational immobility. In Table 2, the estimates show that only the interaction term of income inequality crossed with the indicator of intergenerational immobility exhibits a negative sign. The key difference with previous results lies in the magnitude of the point estimates. The thresholds of intergenerational earnings immobility beyond which the marginal effect of income inequality on growth is negative are now relatively lower (0.26 for the intergenerational earnings elasticity and stable for the intergenerational education elasticity).

Alternative measures of income distribution

18. As a second robustness test, we investigate the growth consequences of inequality in different parts of the income distribution. Following previous empirical studies (Cingano, 2014), we replace the income Gini coefficient by a measure of inequality taking into account only “top” and “bottom” inequality. More precisely, we compute the ratio of mean disposable income in the top income quintile divided by the mean disposable income in the bottom quintile.9 Unlike the Gini coefficient, which takes into account the full distribution of incomes, this measure focuses only on the gap between the richest and the poorest. An increase in the ratio of Q5/Q1 will indicate a worsening of the income distribution at the tails of the distribution.

19. The results are again consistent with baseline estimates. Regardless of the measure of intergenerational elasticity (Table 3), there is a strong and statistically significant effect of the worsening of the income distribution on growth, mainly in countries characterized by low levels of intergenerational earnings (column 1) or education mobility (column 2). Thus, the mechanism at work here can be driven by changes at the extreme ends of the income distribution alone. This is consistent with the class of theories positing that the impact on growth arises from sub-optimal investment decisions by constrained people at the bottom of the income distribution, as described earlier.

Controlling for non-linearity in the level of the Gini

20. Our baseline results could be biased by the positive correlation between income inequality and measures of intergenerational immobility. Several papers have documented a positive association between intergenerational immobility and income inequality (Andrews and Leigh, 2009; Corak, 2013). A bias could arise if the interaction term (income inequality crossed with our measures of intergenerational immobility) captures instead the effect of inequality on growth at higher levels of income inequality. In other words, the bias is strong if one were to assume that intergenerational immobility measures are confounded with income inequality measures given the positive and strong correlation between the two. We could have ruled out this bias by controlling additively for measures of intergenerational immobility in the models, but this is clearly not possible with country-fixed effects. The strategy we adopt consists in controlling for income inequality in a quadratic fashion and assessing whether the coefficient associated with the interaction term of income inequality crossed with intergenerational immobility remains significant. This would be consistent with other studies that find pronounced negative effects of income inequality on growth when income inequality reaches high levels (Banerjee and Duflo, 2003).

21. The results are robust to controlling for additional non-linearities. More specifically, allowing income inequality to enter the estimating equation in quadratic form makes no qualitative difference to the baseline result of a negative effect of inequality on growth in the presence of high intergenerational immobility (Table 4). The results remain significant regardless of the measure of intergenerational immobility (earnings or education). Interestingly, the quadratic term of income inequality is not significant in the presence of the interaction term. This could suggest that the non-linearities observed by previous studies could be capturing underlying differences in inequality of opportunity.

E. Policies to Level the Playing Field

22. Our results suggest that reducing inequality can accelerate growth in the euro area. As several euro area countries exhibit relatively low levels of intergenerational mobility, policies that reduce income inequality can accelerate growth by reducing the burden on the most vulnerable and helping the disadvantaged to maximize their full potential. In particular, reducing high levels of unemployment is crucial in several European countries; our results suggest that the resulting fall in income dispersion could act as a powerful motor of growth.

23. Over the long run, addressing the root causes of inequality of opportunity is crucial. Our paper shows that equalizing individual opportunity may promote not only equity but also ensure stable growth even in periods of large swings in income inequality. International evidence suggests that leveling the playing field requires structural reforms. More precisely, reforms that encourage human capital investment, reduce barriers to labor markets and spur innovation are likely to be critical.

24. Investing in human capital, including at the early age is key. Previous studies have emphasized the key role played by lower constraints to human capital accumulation. For example, Corak (2016) recognizes the need to invest into high quality early childhood, primary and secondary schooling as it is likely to be of relatively more benefit to families lower in the socio-economic scale than if it was directed to the subsidization of tertiary education. Marrero and Rodríguez (2012) document a positive association between lower school dropout rates and higher equality of opportunities. The chart below shows the existence of a negative correlation between the amount of public expenditures in primary and secondary education and social immobility in the OECD. This implies that euro area countries will need to do more to level the playing field by keeping students in the education system for a longer period of time: recent data on the number of 15–29 year-old not in employment, education or training (NEET) remains very large in a number of euro area countries. As documented extensively in OECD (2016), this situation has significant social, political, and economic consequences, including social exclusion with adverse implications for intergenerational mobility. In sum, there is a strong case for public intervention to ensure equal access to high quality education across income strata, and to provide incentives to stay in the education system for longer.

Education Expenditure and Intergenerational Income Elasticity

Source: Corak (2015) and OECD statistics.

Young People Not in Employment, Education, or Training (NEET)

(In percent of all young people; 15–29 years; 2015 estimates)

Sources: OECD statistics.

25. Labor market inequality should be addressed. The study by Marrero and Rodríguez (2012) has highlighted a positive association between long-term unemployment and inequality of opportunity in Europe. Hysteresis effects—such as skills-depreciation—related to protracted unemployment exacerbate the risk of social exclusion and social immobility. Hence labor market rigidities that keep structural unemployment high should be eliminated to ensure better equality of opportunity. Interestingly, the chart below shows a strong and positive correlation between long-term unemployment and social immobility in the OECD. Labor market inequality is also associated with a high prevalence of temporary contracts in total employment, which can weigh on social mobility through several channels. Precarious “outsiders” in the labor market cycle between temporary jobs and unemployment more frequently, which makes them more vulnerable to income shocks. Furthermore, higher reliance on temporary contracts can hurt productivity, as there is less investment in temporary workers and long spells in unemployment reduce human capital accumulation. In the euro area, long-term unemployment and labor market duality remain significant in a number of countries, thereby weighing on social mobility.

Long-term Unemployment and Intergenerational Income Elasticity

Source: Corak (2015) and OECD statistics.

Share of Temporary Employment in 2015

(In percent of total employment)

Source: OECD statistics.

26. Promoting innovation can foster social mobility. A recent study by Aghion et al. (2015) finds a positive and significant relationship between innovativeness and social mobility in the United States. The authors argue that the two are connected by the nature of creative destruction, which arises when there is scope for having new innovators (entrants) replace current firm owners (incumbents). With the limited cross-country data at our disposal, we confirm the results of Aghion et al. (2015) using a sample of European countries: there is a positive association between innovation (measured here by patents applications per capita) and social mobility in advanced Europe. Innovation in part requires investment in research and development (R&D) which in the private sector is supported by financial widening, including non-bank financing alternatives such as venture capital. In the euro area, however, both R&D spending and venture capital investment ratios remain low compared to best practice in the OECD.

R&D Expenditures, 2014

(In percent of GDP)

Sources: Eurostat and WEO.

European Patent Application and Intergenerational Income Elasticity

Source: Corak (2015) and OECD statistics.

Venture Capital Investments, 2015

(In percent of GDP)

Venture Capital Investments in Europe, 2015

(In percent of GDP)

F. Conclusion

27. Although income inequality is widely recognized as undesirable, its relationship to economic growth has been difficult to establish. We provide evidence that the relationship is mediated by equality of opportunity. Income inequality has a negative impact on growth in those economies characterized by low equality of opportunity, as measured by intergenerational mobility. Since most euro area countries in fact exhibit low intergenerational mobility, our results suggest that reducing income inequality could boost growth in the short run. Over the long run, it is crucial to level the playing field by equalizing opportunities. Policies such as creating equal access to high quality education, removing labor market rigidities and spurring innovation could help.

Table 1.Effect of Income Inequality on Per Capita Growth: System-GMM Estimates. Non-Overlapping 5-Year Periods
Dependent variable:(1)(2)
Real GDP per capita growth (in percent)


Gini × Intergenerational elasticity (earnings)−0.322***

Gini × Intergenerational elasticity (education)−0.109***

Lagged real per capita GDP, log−5.620***


Lagged investment-to-GDP5.499


Trade openness1.872




Threshold of IE:0.290.9
Period dummiesYesYes
AR[1]: p-value0.0220.086
AR[2]: p-value0.8690.173
Hansen OID: p-value0.4300.404
No of instruments1618
Number of countries2127
z-statistics in parentheses

*** p<0.01, ** p<0.05, * p<0.1
Table 2.Effect of Income Inequality on Per Capita Growth: System-GMM-IV Estimates. Non-Overlapping 5-Year Periods
Dependent variable:(1)(2)
Real GDP per capita growth (in percent)


Gini × Intergenerational elasticity (earnings)−0.282**

Gini × Intergenerational elasticity (education)−0.169***

Lagged real per capita GDP, log−4.665***


Lagged investment-to-GDP2.868


Trade openness1.784




External instruments for inequalityYesYes
Period dummiesYesYes
Threshold of IE:
AR[1]: p-value0.1000.123
AR[2]: p-value0.3010.250
Hansen OID: p-value0.5190.339
No of instruments1717
Number of countries2127
z-statistics in parentheses

*** p<0.01, ** p<0.05, * p<0.1
Table 3.Effect of Income Inequality on Per Capita Growth: System-GMM Estimates. Alternative Measures of Inequality. Non-Overlapping 5-Year Periods
Dependent variable:(1)(2)
Real GDP per capita growth (in percent)


(Q5/Q1) × Intergenerational elasticity (earnings)−1.027***

(Q5/Q1) × Intergenerational elasticity (education)−0.497**

Lagged real per capita GDP, log−3.342***


Lagged investment-to-GDP−3.337


Trade openness2.100***




Period dummiesNoNo
AR[1]: p-value0.0260.065
AR[2]: p-value0.2760.247
Hansen OID: p-value0.3560.251
No of instruments77
Number of countries1927
z-statistics in parentheses

*** p<0.01, ** p<0.05, * p<0.1
Table 4.Effect of Income Inequality on Per Capita Growth: System-GMM Estimates. Controlling for Squared Gini. Non-Overlapping 5-Year Periods
Dependent variable:(1)(2)
Real GDP per capita growth (in percent)


Gini × Intergenerational elasticity (earnings)−0.325***

Gini × Intergenerational elasticity (education)−0.0931**

Gini squared−0.000235


Lagged real per capita GDP, log−4.572***


Lagged investment-to-GDP7.104


Trade openness1.231




Period dummiesYesYes
AR[1]: p-value0.0100.085
AR[2]: p-value0.4990.168
Hansen OID: p-value0.6820.299
No of instruments1718
Number of countries2127
z-statistics in parentheses

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

Prepared by Shekhar Aiyar and Christian Ebeke (both EUR). We are indebted to Vito Peragine and Michal Brzezinski for kindly sharing their data on inequality of opportunity.

The theory behind this idea has been explored at length in the literature, dating back to the classic paper by Galor and Zeira (1988), where income inequality in the presence of financial constraints prevents poor families from investing optimally in schooling, thereby harming growth.

As noted by Corak (2016), to accurately measure the intergenerational earnings elasticity requires estimates of the lifetime earnings prospects of both parents and their children in their adulthood. Good estimates of lifetime earnings require having several years of earnings data during a period in the life cycle when individuals are established in their career jobs (when they are 40 to 50 or so years of age), and these estimates must be available for both the parent and the child. As such the members of a family have to be followed and connected to each other over a period that easily spans several decades. Moreover, these estimates of the intergenerational earnings elasticity which are derived from published studies, must be adjusted for methodological comparability following the methodology described in Corak (2006). Our paper therefore takes advantage of recent estimates published by Corak (2016) which are comparable across a large number of countries.

The procedure of decomposing total inequality into inequality of opportunity and inequality of effort components has gained great popularity in recent years. Using an ex-ante criterion, population is partitioned according to individuals’ circumstances and inequality of opportunity is evaluated in terms of differences between individuals endowed with the same circumstances, so that inequality of opportunity is represented by the between-group component of the overall inequality.

Intergenerational earnings or education elasticity measures the elasticity of individuals’ income and education levels with respect to their parent’s income or education level.

We will later explore in the paper the robustness of our results to alternative measures of inequality of opportunity.

For the intergenerational earnings elasticity, the regression sample includes the following 21 advanced and emerging market countries: Argentina, Australia, Brazil, Canada, Switzerland, Chile, China, Germany, Denmark, Spain, Finland, France, United Kingdom, Italy, Japan, Norway, New Zealand, Peru, Singapore, Sweden, and United States. The sample increases to 27 countries when we use data on intergenerational education elasticity.

The auxiliary equation used to “extract” the residual component of income inequality which does not depend on growth is itself estimated using a panel instrumental variable approach in which growth is instrumented by its two-and three-year lags. The residuals derived from this estimation are then used as instruments for income inequality in the growth regressions we are interested in.

Data on income by quintiles are drawn from Brueckner et al. (2015).

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