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How important is the family? Evidence from sibling correlations in permanent earnings in the USA, Germany, and Denmark

Abstract

This paper is the first to analyze the impact of family background on permanent earnings based on sibling correlations in Germany and to provide a cross-country comparison of Germany, Denmark, and USA. The main findings are that family and community background has a stronger influence on permanent earnings in Germany than in Denmark, and a comparable influence is found in USA. This holds true for both male and female siblings. A deeper analysis of Germany shows that family background also plays an important role in explaining variations in family income, wages, education, and risk attitudes.

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Notes

  1. See Solon (1999), Black and Devereux (2011), and Björklund and Jäntti (2009) for an extensive overview of the literature on intergenerational mobility.

  2. In addition to parental income, these include parental education, parents’ social networks, but also parental attitudes and parenting styles.

  3. Two examples would be the neighborhood and the quality of the available schools.

  4. In contrast to the economic literature, the sociological literature on sibling correlations or sibling resemblance has focused primarily on educational outcomes or prestige score measures. See, for example, Hauser and Wong (1989) for the USA and Sieben et al. (2001) for Germany.

  5. For details, see next section.

  6. Solon et al. (1991) showed that not controlling for transitory fluctuations leads to serious underestimation of sibling correlations. See Solon (1992) for a similar result for IGEs.

  7. There is a discussion in the literature on whether the model should be estimated allowing for serial correlation of the transitory individual component. As gaps of different lengths in the series of yearly earnings observations are common especially in the survey data, I did not incorporate a serial correlations model. If serial correlation was a problem, the correlations presented in this paper would be downwardly biased. Björklund et al. (2002) showed that accounting for autocorrelated errors in the Danish data changed the brother correlation only slightly from 0.25 to 0.29. Mazumder (2008) argued that estimating the model allowing for serial correlation has no effect on his estimates for the USA. Nevertheless, if there were a problem with serial correlations, the corrected German estimates would be even higher than those presented in this paper. This would leave the main results unaffected.

  8. See, for example, Mazumder (2008) for economic and related noneconomic outcomes and Mazumder (2011) for health outcomes.

  9. Most authors have focused on brother pairs and sister pairs. Given the differences in labor market attachment between brothers and sisters, allowing for mixed sibling pairs would lead to estimates that depend heavily on how many brother–sister pairs are observed in each family.

  10. Comi (2010) calculated sibling correlations in early career earnings for seven European countries including Germany. The results are not listed in Table 1 as they focus exclusively on early career outcomes and therefore cannot be seen as a proxy for equality of opportunity. Schnitzlein (2012) presented brother correlations in permanent earnings for different ethnic groups in Denmark. As the results in Table 1 do not distinguish between ethnic groups, these results are also not included. Björklund and Jäntti (2012) presented brother and sister correlations in earnings in Fig. 1 in their article. The results are very similar to the results in Björklund et al. (2002, 2004).

  11. A few studies were published before Solon et al. (1991), but as they suffer from various sources of bias as described in Solon et al. (1991), I did not include them in Table 1. See Solon (1999) for a survey.

  12. This is very similar to the findings in Solon (1989, 1992) for IGEs.

  13. The corresponding brother correlation in income was 0.34.

  14. See, for example, Corak (2006).

  15. Further estimates of intergenerational mobility in Germany can be found, for example, in Grawe (2004) and Wiegand (1997). For a detailed overview, see Table 1 in Schnitzlein (2009).

  16. I used SOEP version SOEPv25.

  17. See http://www.human.cornell.edu/pam/research/centers-programs/german-panel/cnef.cfm for an overview of the available data and Frick et al. (2007) for additional information.

  18. In an earlier version of this article, I presented robustness tests including different alternative definitions on whom to count as siblings (Schnitzlein 2011). As the results remained virtually unchanged, I focus on this strict definition here.

  19. Unfortunately, there is no English documentation available. An English description of the database can be found in Timmermans (2010).

  20. I will provide a robustness test to show that the established international ranking is robust to variations in this restriction.

  21. These numbers include siblings as well as singletons. In the estimation, I follow the existing literature and estimate the model including singletons. For a discussion, see Solon et al. (1991) and Mazumder (2008).

  22. Note that women’s labor market participation rates clearly differ across countries. In 2010, 71.1 % of Danish women aged between 15 and 64 were employed. The corresponding rates were 66.1 % in Germany and 62.4 % in USA (OECD 2012). The women reporting positive earnings in my sample, therefore, might be a more homogeneous group in USA and Germany than in Denmark. To test the reliability of the results, estimates were made using family income instead of labor earnings for Germany (presented in Tables 5 and A.3). The observed pattern of estimates for women being lower than the estimates for brothers remains stable.

  23. As stated in the note to Table 3, the average number of yearly individual earnings observations varies among the countries. In Denmark and Germany, the average numbers are very similar as follows: 4.5/4.8 years for brothers and 4.6/4.2 years for sisters. Due to the biannual rhythm of the PSID, the corresponding numbers for USA are 3.2 years. This difference can lead to downwardly biased estimates for USA (for a discussion, see Solon et al. 1991). While this supports the finding that there is a significant difference between Denmark and USA, it is unclear what would happen to the difference between Germany and the US. As a robustness check, I reestimated the model for the US using additional years from the CNEF data back to 1994. This specification contains 4.5 individual yearly earnings observations for US brothers and sisters. The sibling correlations in this specification remain largely unaffected (brothers 0.44/sisters 0.29). As I wanted to ensure that the data cover a comparable period in time in all three countries, I did not include the additional years in the main analysis. The full results from this specification are available from the author upon request.

  24. Table A2 in the electronic appendix contains the associated number of observations, individuals, and families.

  25. In an earlier version of this article, I presented additional robustness tests including different age restrictions. These can be found in Schnitzlein (2011).

  26. The main difference between the samples in Tables 3 and 5 is not the inclusion or the exclusion of individuals with missing information for one of the outcomes, but the restriction that for each individual, only years with full information on all three outcomes were considered. Thus the biggest difference is found in the number of observations and not in the number of families or individuals. The results from an unbalanced panel can be found in Table A3 in the electronic appendix.

  27. Included were all individuals that are at least 25 years of age. For each individual, the most recent level of education achieved was included. As there is no yearly variation on the level of the individual, the model was only estimated with two levels. Therefore, the number of observations and individuals is identical in Table 6 for education.

  28. Comparable estimates for Sweden can be found in Björklund and Jäntti (2012), and for USA, in Mazumder (2008, 2011). All estimates are very close to the ones presented in this paper for Germany.

  29. A similar result for USA is found by Mazumder (2008).

  30. An overview of the literature on education and family background can be found in Björklund and Salvanes (2010).

  31. Respondents are asked to answer the question Are you generally a person who is fully prepared to take risks or do you try to avoid taking risks? On an 11-point scale ranging from 0 (risk averse) to 10 (f ully prepared to take risks). This question has been included in the SOEP questionnaire in 2004, 2006, and 2008. See also discussion in Dohmen et al. (2011).

  32. See Table 1 specification 3 in Dohmen et al. (2012).

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Acknowledgments

I thank Regina T. Riphahn, Anders Björklund, Guido Heineck, Olaf Groh-Samberg, the editor, and two anonymous referees as well as conference and seminar participants in Perth (GB), Philadelphia, Nuremberg, Limerick, Borkop, Delmenhorst, Hangzhou, and Berlin for their helpful comments and suggestions. Part of this research was carried out during a research visit to the Aarhus School of Business. I am particularly grateful to Tor Eriksson for his helpful comments and valuable support during my stay. This project was part of a dissertation funded by the Institute for Employment Research (IAB) in Nuremberg, Germany

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Correspondence to Daniel D. Schnitzlein.

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Schnitzlein, D.D. How important is the family? Evidence from sibling correlations in permanent earnings in the USA, Germany, and Denmark. J Popul Econ 27, 69–89 (2014). https://doi.org/10.1007/s00148-013-0468-6

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Keywords

  • Sibling correlations
  • Intergenerational mobility
  • REML
  • Germany
  • SOEP

JEL Classification

  • D1
  • D3
  • J62