Abstract
Inequality of Opportunity (IO) refers to that inequality stemming from factors, called circumstances, beyond the scope of individual responsibility, such as gender, race, place of birth or socioeconomic background. In general, circumstances do not directly convert into future individual’s income. Indeed, different circumstances in childhood lead to different levels of education and different occupational categories which, in turn, contribute to generate divergent levels of income during adulthood. Using the Intergenerational Transmission modules in 2005 and 2011 from the EU-SILC, we estimate the importance of attained education and occupational category as mediating channels in the generation of IO in 26 European countries. We find that the attained level of education channels up to 30% of total IO, with important differences across Europe. Once attained education is taken into account, occupation explains less than 5% of IO in most countries. Moreover, the importance of education as a channel for IO is negatively correlated both with the share of the population that attains tertiary levels of education and with the importance of government expenditure in education relative to GDP.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
As we discuss in the results section, using income instead of social class as the socioeconomic ‘destination’ variable seems to find a relevant but somewhat smaller role of education mediating the O-D connection.
Note that a particular education decision could be partly dependent on a current occupation, which allows the individual to afford that education, for example. But the subsequent final occupation achieved—and the eventual income generated—will be conditioned to the educational level previously acquired.
As a robustness check, we also carry out the analysis using the Intergenerational Transmission of Poverty module from EU-SILC 2005, finding that our main results are comparable, and that there is no clear trend of change in the mediating role of education or occupation between the two waves analyzed.
The broadly used Gini coefficient is not additively decomposable. In the case that type income ranges overlap, which occurs in our case, this measure is decomposable in three terms: a between-group component, a within-group component and a residual. The problem here is how to assign the last term to the between-group and within-group components.
Additionally, the parametric approach allows us to take full advantage of the high number of circumstances in the EU-SILC database, since non-parametric estimates are inaccurate when observations in some of the types are scarce.
Note that \(\tilde{\phi }^{i} = \exp [\hat{\psi }^{\top }\overline{C} + \hat{\varepsilon }_{i}]\) is equivalent to: \(\exp [\hat{\psi }^{\top }\overline{C}] \cdot \exp [\hat{\varepsilon }_{i}]\). Applying MLD to this last expression, and given that \(\exp [\hat{\psi }^{\top }\overline{C}]\) is constant, it is true—recall that the MLD index is scale invariant—that \(I_{MLD}(\tilde{\phi }) = I_{MLD}(\exp {[\hat{\varepsilon }_{i}}])\). Thus, in a parametric framework using the MLD inequality measure, the within inequality component boils down to the MLD of the distribution of the residual term from the parametric regression.
Analogously, it could be argued that effort, or at least part of it, is transformed into income through a set of mediating factors H. Thus, we could express the component of income not explained by observed circumstances (from Eq. 4) as \(y^{\overline{C}}_{i} = \exp {[\varepsilon _{i}]} = f(H_{i}, \Omega _{i})\), where \(H_{i}\) collects observed mediators between effort and income, and \(\Omega _{i}\) includes the effect on income of unobserved mediators and a random component. Unfortunately, \(\varepsilon _{i}\) includes the effect of not only effort but also unobserved circumstances. We are then unable to isolate the effect of effort, unless effort variables are explicitly considered in Eq. (4).
Note that this expression is precisely equivalent to a standardised distribution obtained by applying \(\hat{\eta }\) to a constant average level of attained education \(\overline{E}\) and adding the residual term. Because both distributions differ only in a change of scale, it is true that \(I_{MLD} y_{i}^{C, \overline{EDU}} = I_{MLD}(\exp {[\hat{\eta }\overline{E} + \hat{\nu }_{i}]}) = I_{MLD}(\exp {[\hat{\nu }_{i}}])\). Hence, \(I_{MLD} y_{i}^{C,{\,} \overline{EDU}}\) can be interpreted as the part of IO not explained by individual differences in the education level.
For the sake of robustness, we also use the analogous “Intergenerational Transmission of Poverty” module from the EU-SILC 2005 wave. See Online Appendix.
Categories include: managerial, professional, technician, clerical, sales, skilled agricultural, craft trade, machine operation, elementary occupation and armed/military occupation. Note that we have also included ‘unemployed’ as occupational category for those individuals who were unemployed, not disabled to work nor retired, and for which the occupational category was not coded.
See Online Appendix for more details on the choices made to make the data from both waves homogeneous.
The equivalence scale used by Eurostat is \(1 + 0.5 * (HM_{14} -1) + 0.3 * HM_{13}\), where \(HM_{14}\) refers to the individuals in the house who are fourteen or older, while \(HM_{13}\) refers to the individuals in the house who are thirteen or younger. Although we considered using individual labour income as the proxy variable for the economic advantage—and not just to determine the household head—we found impossible to obtain that variable homogeneously among countries —some countries provide only gross income while others provide only the net measure—and therefore discarded that option. Again for the sake of comparability, income from 2010 (2011 wave) has been converted to 2004 (2005 wave) terms using the Harmonised Consumer Price Index published by Eurostat. Note that in the EU-SILC waves respondents report data from the previous year.
We calculated the adjusted box-plot for each country and wave, accounting for skewness and using the parameter 3 to exclude observations placed more than three quartiles below or above the adjusted interquartile range (see Hubert and Vandervieren 2008).
The coefficients from regressing income on circumstances for each country and wave in order to obtain the \(y{_i}^{C}\) ‘smoothed distribution’ (Eq. 4) are available in the Online Appendix. This Appendix also displays the estimated coefficients in the IO channels regressions for each educational level and each occupational category of the individual (for all countries in both waves) and the descriptive statistics for the 2005 wave.
Recall that our result for the UK in 2011 (individuals born between 1950 and 1980) is that education mediates around 10% of inequality of opportunity.
The discrepancy in the mediating role of individual education in class versus income mobility surely deserves further research. Possible explanations could lie in the relatively low contribution of education inequality to income inequality (about 20% in the U.S. according to Breen and Chung 2015), the different return to skills that individuals obtain (Rothstein 2017), or the increase in between-firms inequality in workers income (Song et al. 2015)
Note that, in the absence of any objective measure of effort (as it is usually the case) estimates are obtained under the assumption that all individuals in the same income quantile at different types belong to the same tranche and thus exert the same level of effort.
References
Angrist, J. D., & Krueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics, 106(4), 979–1014.
Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education (2009th ed.). Chicago: University of Chicago Press.
Björklund, A., Jäntti, M., & Roemer, J. E. (2012). Equality of opportunity and the distribution of long-run income in Sweden. Social Choice and Welfare, 39(2), 675–696.
Blanden, J., Haveman, R., Smeeding, T., & Wilson, K. (2014). Intergenerational mobility in the United States and Great Britain: A comparative study of parent–child pathways. Review of Income and Wealth, 60(3), 425–449.
Bourguignon, F. (1979). Decomposable income inequality measures. Econometrica: Journal of the Econometric Society, 47(4), 901–920.
Bradbury, K., & Triest, R. K. (2016). Inequality of opportunity and aggregate economic performance. RSF: The Russell Sage Foundation Journal of the Social Sciences, 2(2), 178–201.
Breen, R., & Chung, I. (2015). Income inequality and education. Sociological. Science, 2, 454–477.
Breen, R., & Karlson, K. B. (2013). Education and social mobility: New analytical approaches. European Sociological Review, 30(1), 107–118.
Brunori, P., Peragine, V., & Serlenga, L. (2016). Upward and downward bias when measuring inequality of opportunity. ECINEQ WP 2016-406.
Brzezinski, M. (2015). Inequality of opportunity in Europe before and after the Great Recession. University of Warsaw-working papers, No. 2/2015 (150).
Bukodi, E., & Goldthorpe, J. H. (2016). Educational attainment-relative or absolute-as a mediator of intergenerational class mobility in Britain. Research in Social Stratification and Mobility, 43, 5–15.
Card, D. (1999). The causal effect of education on earnings. Handbook of Labor Economics, 3, 1801–1863.
Checchi, D., & Peragine, V. (2010). Inequality of opportunity in Italy. The Journal of Economic Inequality, 8(4), 429–450.
Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the land of opportunity? The geography of intergenerational mobility in the United States. The Quarterly Journal of Economics, 129(4), 1553–1623.
Deutsch, J., Alperin, M. N. P., & Silber, J. (2018). Using the Shapley decomposition to disentangle the impact of circumstances and efforts on health inequality. Social Indicators Research, 138(2), 523–543.
Eide, E. R., & Showalter, M. H. (1999). Factors affecting the transmission of earnings across generations: A quantile regression approach. Journal of Human Resources, 34(1), 253–267.
Ferreira, F. H., & Gignoux, J. (2011). The measurement of inequality of opportunity: Theory and an application to Latin America. Review of Income and Wealth, 57(4), 622–657.
Ferreira, F. H., & Gignoux, J. (2014). The measurement of educational inequality: Achievement and opportunity. The World Bank Economic Review, 28(2), 210–246.
Fleurbaey, M. (2008). Fairness, responsibility, and welfare. Oxford: Oxford University Press.
Foster, J. E., & Shneyerov, A. A. (2000). Path independent inequality measures. Journal of Economic Theory, 91(2), 199–222.
Fuller, S., & Vosko, L. F. (2008). Temporary employment and social inequality in Canada: Exploring intersections of gender, race and immigration status. Social Indicators Research, 88(1), 31–50.
Gamboa, L. F., & Waltenberg, F. D. (2012). Inequality of opportunity for educational achievement in Latin America: Evidence from PISA 2006–2009. Economics of Education Review, 31(5), 694–708.
Hubert, M., & Vandervieren, E. (2008). An adjusted boxplot for skewed distributions. Computational Statistics and Data Analysis, 52(12), 5186–5201.
Jusot, F., Tubeuf, S., & Trannoy, A. (2013). Circumstances and efforts: How important is their correlation for the measurement of inequality of opportunity in health? Health Economics, 22(12), 1470–1495.
Krueger, A. B., & Lindahl, M. (2001). Education for growth: Why and for whom? Journal of Economic Literature, 39(4), 1101–1136.
Lefranc, A., Pistolesi, N., & Trannoy, A. (2008). Inequality of opportunities vs. inequality of outcomes: Are western societies all alike? Review of Income and Wealth, 54(4), 513–546.
Lemieux, T. (2006). The “Mincer equation” thirty years after schooling, experience, and earnings. Berlin: Springer.
Li Donni, P., Rodríguez, J. G., & Rosa Dias, P. (2015). Empirical definition of social types in the analysis of inequality of opportunity: A latent classes approach. Social Choice and Welfare, 44(3), 673–701.
Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42.
Marrero, G. A., & Rodríguez, J. G. (2011). Inequality of opportunity in the United States: Trends and decomposition. Inequality of opportunity: Theory and measurement, chapter 9 (pp. 217–246). Bingley: Emerald Group Publishing Limited.
Marrero, G. A., & Rodríguez, J. G. (2012). Inequality of opportunity in Europe. Review of Income and Wealth, 58(4), 597–621.
Marrero, G. A., & Rodríguez, J. G. (2013). Inequality of opportunity and growth. Journal of Development Economics, 104, 107–122.
Marrero, G. A., & Rodríguez, J. G. (2016). Inequality ... of opportunity and economic performance. Chapter in the Special Issue Economic Mobility, edited by the Federal Reserve Bank of St. Louis and the Board of Governors of the Federal Reserve System (pp. 385–419). https://www.stlouisfed.org/community-development/publications/economic-mobility
Marrero, G. A., Rodríguez, J. G., & Van der Weide, R. (2016). Unequal opportunity, unequal growth. World bank policy research working paper (No.7853).
Palomino, J. C., Marrero, G. A., & Rodríguez, J. G. (2018). One size doesn’t fit all: A quantile analysis of intergenerational income mobility in the US (1980–2010). The Journal of Economic Inequality, 16(3), 347–367. https://doi.org/10.1007/s10888-017-9372-8.
Pérez-González, F. (2006). Inherited control and firm performance. The American Economic Review, 96(5), 1559–1588.
Psacharopoulos, G. (1994). Returns to investment in education: A global update. World Development, 22(9), 1325–1343.
Rawls, J. (1971). A Theory of Justice. Cambridge, MA: Harvard University Press.
Rodríguez, J. G. (2008). Partial equality-of-opportunity orderings. Social Choice and Welfare, 31(3), 435–456.
Roemer, J. E. (1993). A pragmatic approach to responsibility for the egalitarian planner. Philosophy and Public Affairs, 20, 146–166.
Roemer, J. E. (1998). Equality of opportunity. Cambridge: Harvard University Press.
Roemer, J. E., Aaberge, R., Colombino, U., Fritzell, J., Jenkins, S. P., Lefranc, A., et al. (2003). To what extent do fiscal regimes equalize opportunities for income acquisition among citizens? Journal of Public Economics, 87(3), 539–565.
Rosa Dias, P. (2009). Inequality of opportunity in health: Evidence from a UK cohort study. Health Economics, 18(9), 1057–1074.
Rothstein, J. (2017). Inequality of educational opportunity? schools as mediators of the intergenerational transmission of income. Washington Center for Equitable Growth, Working papers series.
Sen, A. (1980). Equality of what? In S. McMurrin (Ed.), Tanner lectures on human values (Vol. 1). Cambridge: Cambridge University Press.
Shorrocks, A. F. (1980). The class of additively decomposable inequality measures. Econometrica: Journal of the Econometric Society, 48(3), 613–625.
Song, J., Price, D. J., Guvenen, F., Bloom, N., & Von Wachter, T. (2015). Firming up inequality. National Bureau of Economic Research: Technical report.
Sullivan, P. (2010). Empirical evidence on occupation and industry specific human capital. Labour Economics, 17(3), 567–580.
Teyssier, G. (2017). Inequality of opportunity: New measurement methodology and impact on growth. Working Paper presented at the 7th ECINEQ Meeting (NYC).
Trostel, P., Walker, I., & Woolley, P. (2002). Estimates of the economic return to schooling for 28 countries. Labour Economics, 9(1), 1–16.
Acknowledgements
We would like to thank the editor and two anonymous referees, as well as Eduardo Gonzalo-Almorox, Gabriela Sicilia, and the participants at the 34th IARIW conference in Dresden and at the 28th EALE conference in Ghent for valuable comments and suggestions. The authors acknowledge the financial support of the Ministerio de Economía y Competitividad of Spain (Palomino and Rodríguez through project ECO2016-76506-C4-1-R and Marrero through project ECO2016-76818- C3-2-P), from Comunidad de Madrid (Spain) under project S2015/HUM-3416-DEPOPORCM. Marrero also thanks the support provided by the María Rosa Alonso Research Program, financed by the Cabildo Insular de Tenerife (Spain). The authors are also grateful for microdata access provided by the European Commission (Eurostat) through the research project 221/2014-EU-SILC. The usual disclaimer applies.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Appendix I: Decomposing Ex-post IO Channels
Appendix I: Decomposing Ex-post IO Channels
1.1 The Hybrid Ex Post-Ex Ante Method
Our strategy could potentially be applied to the version of the ex-post approach proposed by Checchi and Peragine (2010). This approach partitions the population in p tranches (groups of people belonging to different types but exerting the same level of effort), and then re-scales each tranche distribution in such a way that all tranches have the same mean as the overall distribution (see Checchi and Peragine 2010, p. 436):
where \(y_i^{W}\) is the re-scaled income of individual i belonging to tranche p. \({\overline{Y}}\) and \(\overline{y}^{p}\) are the overall mean and the p tranche mean, respectively. The complete re-scaled distribution \(y^{W}\)thus eliminates all differences between tranches (effort) and retains only differences due to circumstances, which makes it equivalent to our smoothed distribution and \(y^{W} = y^{C}\).Footnote 20
Based on that distribution, the second step of our methodology could be applied, and the role of the channeling variables could be measured just as described in section 2.2. This would formally be a hybrid method in which \(y^{C} = y \mid C\) is estimated ex-post (i.e. assuming that people with the same level of effort belonging to different types should have the same mean income), but the channeling role of the education (or other mediating factors) is estimated ex-ante, ie., assuming groups of people with different levels of education should have the same mean ‘circumstance conditioned income’ and measuring the educational IO channel as the deviation from that assumption.
1.2 The Ex-post Decomposition
Alternatively, the above mentioned ex-post method could be adapted and used again to partition \(y^{W}\) in tranches using the individual education level information. A tranche f would in this case be a group of people having different levels of education but exerting the same level of effort (proxied again by the division in deciles, percentiles, etc.). Each tranche distribution would then be re-scaled again so all tranches have the same overall mean (implying that all effort differences have been equalised and that differences can only be attributed to the different level of education).
where \(y_i^{W-EDU}\) is the re-scaled circumstance condition income \(y{_i}^{C} = y{_i}^{W}\) of individual i belonging to tranche f. \(\overline{Y^{W}}\) and \(\overline{{y^W}}^{f}\) are the overall mean and the f tranche mean, respectively. Thus, the inequality of this twice re-scaled distribution would be the part of IO channelled by education.
If we intend to analyse the channeling role of a second variable once education has been taken into account, we will use the re-scaled distribution \(y^{W}\) that retained only differences due to circumstances, and will transform it in such a way that all types (made according to the levels of the first channel considered) belonging to the same tranche have the same mean. This way we eliminate the differences attributable to the first channel (e.g. education). Secondly, we would proceed to re-scale once again this very new distribution in the way described in the paragraph above, using in this case the new channeling variable (eg. occupation). The inequality of this last distribution would be the component of IO channelled by occupation once education has been accounted for.
See Table 3.
Rights and permissions
About this article
Cite this article
Palomino, J.C., Marrero, G.A. & Rodríguez, J.G. Channels of Inequality of Opportunity: The Role of Education and Occupation in Europe. Soc Indic Res 143, 1045–1074 (2019). https://doi.org/10.1007/s11205-018-2008-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-018-2008-y