The transmission of educational inequality across generations: A global view

Tom Hertz, Tamara Jayasundera, Patrizio Piraino, Sibel Selcuk, Nicole Smith, Alina Verashchagina 26 July 2008

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The degree to which economic status is transmitted across generations characterises, in a broad sense, the degree of equality of life chances in a society. Societies may tolerate higher levels of inequality of economic outcomes if such differences result from what is perceived to be a fair and meritocratic process. In the United States, academic studies showing that earnings persist across generations at a higher rate than most other Western nations have challenged the popular notion of the “American Dream”. This has brought about an increasing awareness among the public and policy makers of the importance of the intergenerational transmission of inequality, as witnessed by a growing number of newspapers articles and analytical reports by foundations and commissions (see the series on economic mobility printed in the New York Times and the Wall Street Journal in the spring of 2005, or the reports sponsored by the Pew Charitable Trusts in 2008).

In labour economics, intergenerational status persistence is generally estimated by simple linear measures of statistical association between parents’ and children’s economic status. During the last fifteen years, a large number of studies have measured and compared these social statistics across nations with different economic systems and values. This literature – surveyed by, among others, Solon (1999, 2002), Corak (2006), and Bjorklund and Jantti (2008) – has identified and addressed a variety of estimation issues and has paved the way for studies attempting to discern the determinants of intergenerational inequality (e.g. Bjorklund et al., 2006; Oreopolous, 2003).1

International comparisons and fifty-year trends

Comparisons among countries and over time can contribute to an understanding of the mechanisms underlying the intergenerational transmission of economic status. How are countries with different institutional settings in the labour market, different educational systems, and different levels of cross-sectional inequality doing in terms of intergenerational mobility?

While many studies of status persistence are available for the United States, Europe, and a few less-industrialised countries, our understanding of international differences and trends in this social statistic is far from complete, especially for developing countries. Our recent work seeks to fill this gap by providing estimates of 50-year trends in two simple measures of status persistence for a sample of 29 developing and former communist economies (Hertz et al. 2007). Comparable estimates are also presented for twelve Western European countries and the United States. For reasons of data availability, we use educational attainment as the measure of socio-economic status. Education per se is a marker of status, and it is a key correlate of income, occupational status, and prestige.

Figure 1 shows global patterns in the inheritance of educational attainment. It presents the trends over time of two simple measures of status persistence: the coefficient from a regression of children’s schooling against that of their parents, and the correlation between the two. The first general conclusion we can draw from Figure 1 is that the intergenerational educational regression coefficients and correlations are positive and significant, in both the statistical and practical senses, in virtually every society included in the sample. In other words, the inheritance of inequality is a global phenomenon.

Over 50 years, the regression coefficient of parents’ education as a predictor of schooling in the next generation fell substantially for the sample as a whole, while the correlation between parent and child schooling shows no such trend: it has risen in as many countries as it has fallen. It thus appears that a one-year difference in parents’ education now corresponds to a smaller difference, on average, in the expected value of their children’s schooling than it previously did – this being a statement about a weakened statistical association, not a diminished causal connection. Yet because the variance of parents’ education has also increased relative to the variance of schooling in the second generation (see Figure 2), the intergenerational correlation has not fallen. On average, for the countries in our sample, a one-standard-deviation difference in parental education corresponds to a schooling difference of about 0.4 standard deviations in the next generation, and this figure has held steady for a half-century. In other words, around the world, parents’ schooling alone explains as much of the variance of children’s schooling as ever.

Figure 1 Intergenerational educational regression coefficient and correlations for 42 countries, by birth cohort

Notes: The two graphs present 403 country-cohort-specific estimates of the regression coefficients (top), and the correlations (bottom), plotted against the second generation’s year of birth, along with their trend lines.

Figure 2 explains the difference in the trends in grade persistence (the regression coefficients) and standardised persistence (the correlations). Average education has grown steadily over time in our sample, first at an increasing rate and then at a decreasing rate. The standard deviation of schooling, however, at first increased, then decreased, confirming Ram’s (1990) finding of a Kuznets-type relationship between the level and dispersion of education. This finding is intuitive: if initially nearly everyone is uneducated, and then a minority gain access to schooling, the variance of education will increase. That this rate of increase should slow and eventually turn negative is also intuitive, unless education grows without bound. If the share of people with no, or very little, schooling falls sufficiently as the mean and mode rise, this will reduce the mass in the left tail of the distribution enough to reduce its variance.

Figure 2 Trends in means and standard deviations of schooling, by children's year of birth cohort

Notes: the figure plots the predicted values from regressions of the variable indicated against year of birth and its square, from our panel of 42 countries over a maximum of 10 cohorts. Country fixed effects are included, meaning that the trends represent within-country changes over time.

Table 1 compares long-run average persistence across the globe. Forty-two countries are ranked by their parent-child schooling correlations.2 Noticeably, the seven Latin American nations in the sample occupy the top seven positions. These results are consistent with those derived in an earlier study of Latin America (Behrman et al., 2001), and suggest a link between the region’s well-known high levels of cross-sectional inequality and the degree of economic mobility. The seven Latin American countries had an average parental schooling correlation of 0.60 compared to 0.41 for eight Eastern Bloc nations, 0.39 for ten Asian nations, 0.39 again for 13 Western nations, and 0.36 for a small sample of four African countries (Hertz et al., 2007). The European Nordic nations stand out in featuring less persistence, on average, than the non-Nordic high-income nations. This finding, which echoes the results from research on income, suggests a role for government policy, given these countries’ levels of political commitment to social welfare provision.

Table 1 Countries ranked by average parent-child schooling correlation, ages 20-69

Country
Coefficient
Rank
Correlation
Rank
Peru
0.88
6
0.66
1
Ecuador
0.72
12
0.61
2
Panama
0.73
11
0.61
3
Chile
0.64
18
0.60
4
Brazil
0.95
4
0.59
5
Colombia
0.80
8
0.59
6
Nicaragua
0.82
7
0.55
7
Indonesia
0.78
9
0.55
8
Italy†
0.67
17
0.54
9
Slovenia†
0.54
27
0.52
10
Egypt
1.03
2
0.50
11
Hungary†
0.61
20
0.49
12
Sri Lanka
0.61
19
0.48
13
Pakistan
1.00
3
0.46
14
USA
0.46
33
0.46
15
Switzerland†
0.49
30
0.46
16
Ireland†
0.70
15
0.46
17
South Africa (KwaZulu-Natal)
0.69
16
0.44
18
Poland†
0.48
31
0.43
19
Vietnam
0.58
23
0.40
20
Philippines
0.41
36
0.40
21
Belgium (Flanders)
0.41
35
0.40
22
Estonia
0.54
28
0.40
23
Sweden
0.58
26
0.40
24
Ghana
0.71
13
0.39
25
Ukraine
0.37
40
0.39
26
East Timor
1.27
1
0.39
27
Bangladesh (Matlab)
0.58
25
0.38
28
Slovakia
0.61
21
0.37
29
Czech Republic†
0.44
34
0.37
30
Netherlands
0.58
24
0.36
31
Norway
0.40
38
0.35
32
Nepal
0.94
5
0.35
33
New Zealand†
0.40
37
0.33
34
Finland
0.48
32
0.33
35
Northern Ireland
0.59
22
0.32
36
Great Britain†
0.71
14
0.31
37
Malaysia
0.38
39
0.31
38
Denmark
0.49
29
0.30
39
Kyrgyzstan
0.20
42
0.28
40
China (Rural)
0.34
41
0.20
41
Ethiopia (Rural)
0.75
10
0.10
42

Remarks

Evidence on the transmission of social status is highly relevant for the debate concerning the appropriate role of government in reducing economic inequality. In modern societies, disagreement over redistribution is partially accounted for by conflicting ideas about what determines economic success. A highly mobile society, where individuals succeed or fail independently of their family background suggests a different set of measures to support the poor compared to a more rigid society, where segments of the population experience intergenerational cycles of poverty.

The empirical evidence on the intergenerational transmission of economic status, as a global phenomenon, is likely to stimulate more research. While our comparative analysis between regions/countries and historical periods offer some understanding of basic institutional factors, there is much to investigate regarding the actual transmission mechanisms underlying the inheritance of inequality. This is the most pressing challenge for all researchers interested in the long-term dynamics of inequality, and it is of most importance for public policy. What mechanisms of transmission are deemed unfair? What are the optimal areas for government interventions? What is the most likely effect of policy intervention on any specific mechanism?

References

Behrman, Jere, Alejandro Gaviria, and Miguel Székely (2001). "Intergenerational mobility in Latin America." Economia, 2(1):1-44.
Björklund, Anders and Markus Jäntti (2008). “Intergenerational income mobility and the role of family background.” In Wiemer Salverda, Brian Nolan, and Tim Smeeding (editors). Handbook of Economic Inequality. Oxford: Oxford University Press.
Björklund, Anders, Mikael Lindahl, and Erik Plug (2006). “The Origins of Intergenerational Associations: Lessons from Swedish Adoption Data.” Quarterly Journal of Economics. Vol. 121, pp. 999-1028.
Corak, Miles (2006). "Do poor children become poor adults? Lessons from a cross country comparison of generational earnings mobility" in Dynamics of Inequality and Poverty, edited by John Creedy and Guyonne Kalb. Research on Economic Inequality, Vol. 13. Amsterdam: Elsevier.
Oreopoulos, Philip (2003). “The Long-Run Consequences of Growing Up in a Poor Neighbourhood.” Quarterly Journal of Economics. Vol. 118, pp. 1533-75.
Hertz Tom, Tamara Jayasundera, Patrizio Piraino, Sibel Selcuk, Nicole Smith, and Alina Verashchagina (2007). “The Inheritance of Educational Inequality: International Comparisons and Fifty-Year Trends,The B.E. Journal of Economic Analysis & Policy: Vol. 7: Iss. 2 (Advances), Article 10.
Ram, Rati (1990). "Educational expansion and schooling inequality: International evidence and some implications." Review of Economics and Statistics, 72(2):266-274.
Solon, Gary (1999). "Intergenerational mobility in the labor market" in Handbook of Labor Economics, Vol. 3A, edited by Orley Ashenfelter and David Card. Amsterdam: Elsevier.
Solon, Gary (2002). “Cross-Country Differences in Intergenerational Earnings Mobility.” Journal of Economic Perspectives. Vol. 16, 59-66.

 


 

Footnotes

1 A few recent Vox articles examine different specific mechanisms that can help explain the intergenerational correlation in economic outcomes (Carneiro et al. on 22 November 2007; Dohmen et al. and Guryan et al. on 5 July 2008).
2 The regression coefficients are more volatile than are the correlations, but the two rankings are clearly related.

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Topics:  Education

Tags:  intergenerational transmission, educational inequality

Assistant professor of economics at American University

Consultant at International Food Policy Research Institute

Research economist at Statistics Canada

Ph.D. candidate in Economics at the American University

Research Professor at the Center on Education and the Workforce, Georgetown University

PhD Candidate in Economics, University of Siena, Italy