Abstract
Many writers have expressed a concern that growing educational assortative mating will lead to greater inequality between households in their earnings or income. In this article, we examine the relationship between educational assortative mating and income inequality in Denmark between 1987 and 2006. Denmark is widely known for its low level of income inequality, but the Danish case provides a good test of the relationship between educational assortative mating and inequality because although income inequality increased over the period we consider, educational homogamy declined. Using register data on the exact incomes of the whole population, we find that change in assortative mating increased income inequality but that these changes were driven by changes in the educational distributions of men and women rather than in the propensity for people to choose a partner with a given level of education.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Of course, changing the distribution of types will not change the income gaps between any pair of types, but it may change the average gap by making some types more numerous and others less so.
Details are available online (http://en.iu.dk/education-in-denmark/the-danish-education-system).
Just over 2% of men and women are missing information on education. These are immigrants who were educated in their home country before they came to Denmark and whose educational activities were not recorded in the registers. Statistics Denmark conducts regular surveys of this group to collect the missing information, but the data are not yet complete.
This growth of single-person households could be an indication of the reduced utility of forming partnerships and/or of sex differences in life expectancy and an aging population. But over this period, widows and widowers declined, respectively, from 20% to 10% and from 5% to 3% of the population, while unmarried women and men increased from 32% to 43% and from 56% to 62%, respectively, suggesting that the increase in single households is the result of changed mating behavior rather than population aging.
Obviously, employers may fail to report employer’s earnings, just as self-employed persons may report only part of their earnings. However, in Denmark, the tax authorities are extremely vigilant, and tax fraud is punishable by heavy fines and possible imprisonment.
The reason for the high share of Danish citizens with zero earnings is quite complex. Generous welfare benefits increase reservation wages and may reduce benefit recipients’ incentives to work, while the rules often prevent recipients taking a low-income job if they wish to maintain their entitlement to benefits. Furthermore, in Denmark there is not as much stigma attached to receiving income replacement benefits as there is in the United Kingdom or the United States.
For deflating, we use the official consumer price index published by Statistics Denmark (http://www.dst.dk/da/Statistik/emner/prisindeks/forbrugerprisindeks.aspx).
For a report in Danish on this, see Økonomi- og Erhvervsministeriet (2002).
To understand how we constructed these counterfactual distributions, consider the case in which we allowed only the distribution of couple households to change and kept single-person households fixed at their 1987 distribution. We first constructed the square 64-cell table of household types (where the 64,64 cell contains a structural zero representing the “Absent,” “Absent” category) in which the last row and last column contains the proportion in the single household types in 1987 and the remainder of the table contains the proportions of couple households in 2006. The entries of this table will not necessarily sum to 1, nor will their row or column totals match those of the 1987 or 2006 observed distributions of household types. So we used the Deming-Stephan (1940) algorithm (also called iterative proportional fitting) to adjust the entries in the table so that their marginal distributions matched that for 2006 (Deming and Stephan 1940). This was then our counterfactual table. An analogous method was used to form the counterfactual table in which only the pattern of educational homogamy changes. Further details of the method are available on request from the first author.
The lower age limit for this analysis is now 28 rather than 30, but analyses using 30 as the lower limit yield identical results.
As shown in Fig. 6, inequality tends to be quite high for couples in which one or both partners have missing information on education.
The detailed results are available from the authors on request.
Holding \( \overline x_j \) at its 1987 value and allowing both p j and T j to take their 2006 values yields a counterfactual Theil value of 0.1916, an increase of 20% compared with the observed increase of 21%.
References
Blossfeld, H.-P. (2009). Educational assortative marriage in comparative perspective. Annual Review of Sociology, 35, 513–530.
Blossfeld, H.-P., & Timm, A. (2003). Who marries whom? Educational systems as marriage markets in modern societies. Dordrecht, The Netherlands: Kluwer.
Breen, R., & Salazar, L. (2010). Has increased women’s educational attainment led to greater earnings inequality in the UK? A multivariate decomposition analysis. European Sociological Review, 26, 143–158.
Breen, R., & Salazar, L. (2011). Educational assortative mating and earnings inequality in the United States. American Journal of Sociology, 117, 808–843.
Burtless, G. (1999). Effects of growing wage disparities and changing family composition on the U.S. income distribution. European Economic Review, 43, 853–865.
Cancian, M., & Reed, D. (1999). The impact of wives’ earnings on income inequality: Issues and estimates. Demography, 36, 173–184.
Cancian, M., & Schoeni, R. F. (1998). Wives’ earnings and the level and distribution of married couples’ earnings in developed countries. Journal of Income Distribution, 8(1).
Deming, W. E., & Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal total are known. The Annals of Mathematical Statistics, 11, 427–444.
Esping-Andersen, G. (2007). Sociological explanations of changing income distributions. American Behavioral Scientist, 50, 639–658.
Jespersen, P. M. (2008). Vi forelsker os i en kopi af os selv [We fall in love with copies of ourselves]. Ugebrevet A4, 2008(13), 8–10.
Karoly, L. A., & Burtless, G. (1995). Demographic change, rising earnings inequality, and the distribution of personal well-being, 1959–1989. Demography, 32, 379–405.
Lerman, R. I. (1996). The impact of the changing US family structure on child poverty and income inequality. Economica, 63, S119–S139.
Leth-Sørensen, S. (2003). Who moves together with whom in Denmark? In H.-P. Blossfeld & A. Timm (Eds.), Who marries whom? Educational systems as marriage markets in modern societies (pp. 213–234). Dordrecht, The Netherlands: Kluwer.
Martin, M. A. (2006). Family structure and income inequality in families with children, 1976 to 2000. Demography, 43, 421–445.
McCall, L., & Percheski, C. (2010). Income inequality: New trends and research directions. Annual Review of Sociology, 36, 329–347.
McLanahan, S., & Percheski, C. (2008). Family structure and the reproduction of inequalities. Annual Review of Sociology, 34, 257–276.
Nielsen, H. S., & Svarer, M. (2009). Educational homogamy: How much is opportunities? Journal of Human Resources, 44, 1066–1086.
Økonomi- og Erhvervsministeriet. (2002). Vækstvilkår i Danmark [Growing conditions in Denmark]. Retrieved from http://www.evm.dk/resources/oem/static/publikationer/html/vvilkaar/ren.htm#kap06
Oppenheimer, V. K. (1988). A theory of marriage timing. American Journal of Sociology, 94, 563–591.
Oppenheimer, V. K. (1994). Women’s rising employment and the future of the family in industrial societies. Population and Development Review, 20, 293–342.
Pencavel, J. (2006). A life cycle perspective on changes in earnings inequality among married men and women. The Review of Economics and Statistics, 88, 232–242.
Piketty, T., & Saez, E. (2003). Income inequality in the United States, 1913–1998. Quarterly Journal of Economics, 118, 1–39.
Reed, D., & Cancian, M. (2009). Rising family income inequality: The importance of sorting. Unpublished paper, La Follette School of Public Affairs, University of Wisconsin–Madison.
Schwartz, C. R. (2010). Earnings inequality and the changing association between spouses’ earnings. American Journal of Sociology, 115, 1524–1557.
Schwartz, C. R., & Mare, R. D. (2005). Trends in educational assortative marriage from 1940 to 2003. Demography, 42, 621–646.
Sweeney, M. M. (2002). Two decades of family change: The shifting economic foundations of marriage. American Sociological Review, 67, 132–147.
Western, B., Bloome, D., & Percheski, C. (2008). Inequality among American families with children, 1975 to 2005. American Sociological Review, 73, 903–920.
Acknowledgements
We thank Jens Bonke, Vida Maralani, and the Editor and reviewers of Demography for their very helpful comments.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Breen, R., Andersen, S.H. Educational Assortative Mating and Income Inequality in Denmark. Demography 49, 867–887 (2012). https://doi.org/10.1007/s13524-012-0111-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13524-012-0111-2