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Childhood family structure and schooling outcomes: evidence for Germany

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An Erratum to this article was published on 10 July 2010

Abstract

We analyze the impact on schooling outcomes of growing up in a non-intact family in Germany. We find that this experience is associated with worse outcomes according to estimates from models that do not control for possible correlations between common unobserved determinants of family structure and educational performance. Evidence of adverse effects emerges also when endogeneity is accounted for. In such cases, however, the point estimates are typically smaller, and their confidence intervals are large enough to include zero, particularly for individuals who grew up in Western Germany.

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Notes

  1. Two recent examples of a similar multimethod approach are Levine and Zimmerman (2005) and Currie and Tekin (2006). They use a variety of statistical methods to measure the effects of a child’s exposure to welfare benefit receipt on developmental outcomes, and child maltreatment on crime, respectively.

  2. A technical Appendix, where we discuss in detail the identification problems common to all studies of the relationship between childhood family structure and child outcomes and where we outline the empirical strategies underlying each of the econometric methods used in our analysis, is available at: http://www.diw-berlin.de/soep/26652.html?uid=tsiedler.

  3. Arguably, death is not a completely exogenous event and that some of the factors that determine it may also play a role in determining the adult outcomes of children. For example, parents’ educational attainment and socioeconomic status are likely to be powerful drivers of mortality (e.g., Marmot et al. 1984; Elo and Preston 1996). These, in turn, might be correlated with parental smoking, alcohol consumption, and physical activity levels, which may significantly raise the probability of death. Lantz et al. (1998), however, find that such health behaviours explain no more than a modest 12% of the predictive effect of income on mortality.

  4. Most of the evidence discussed above is for the USA. Exceptions include Jonsson and Gähler (1997) and Björklund et al. (2007) for Sweden, Cherlin et al. (1995) and Ermisch and Francesconi (2001) for Britain, Corak (2001) for Canada, and Piketty (2003) for France.

  5. In addition, we also computed Manski bounds using a subset of variables in X ij (such as age, sex, and mother’s education) to create subgroups of respondents (Manski 1990). All the point estimates presented in Section 4 fell within the bounds, and so, for brevity, the bounds are not shown.

  6. See Behrman et al. (1994), Rosenzweig and Wolpin (1995), and Ermisch et al. (2004).

  7. Further details of the empirical specification of Eq. 2 are provided in Section 4.

  8. The SOEP is documented at http://www.diw.de/english/sop/service/index.html.

  9. Sample membership refers to the location when the household was originally sampled, and not current location, because of subsequent mobility between the former East Germany and West Germany. Foreign children, other than those from Guestworker families, were excluded from the analysis due to small sample sizes: nine children from the West German sample and one from the East German sample were dropped.

  10. Father-only families were excluded from the sample: only 75 children (or 2% of individuals in our final sample) were dropped.

  11. See Dustmann (2004) for further details.

  12. This discussion refers to West Germany. After reunification, East Germany adopted the educational system of West Germany (Jeschek 2000). Before 1990, the GDR had a similar school system, albeit with some differences in the length of the various secondary school tracks (e.g., completion of the Gymnasium track required 8 rather than 9 years). Such differences are inconsequential for the measurement of our dependent variables. They only marginally affect our measures of maternal education, but this does not drive any of the differences in results for the East German and West German samples. They are irrelevant for the estimation of mother FE models.

  13. We also analyzed the probability that a child repeated a grade (i.e., whether he/she was ever held back in school) during primary school years. The results of this analysis were similar to those found for test scores and are therefore not reported.

  14. Information on secondary school track at 14 was obtained from parents. For this outcome, we restricted our analysis to children who were enrolled at one of the three main types of secondary school (Hauptschule, Realschule, and Gymnasium).

  15. Ginther and Pollak (2004) distinguished children reared in “blended” families—stepchildren and their half-siblings who are the biological children of both parents—from children reared in traditional intact families as well as from children reared in other family structures. Because of small sample sizes, however, our measures cannot make this distinction.

  16. For children born outside of a partnership before 1983 and for the mother’s marital histories prior to 1983, we cannot know exactly whether the mother cohabited with or married the biological father. For the 255 children (less than 9% of the individuals in the three samples pooled) whose mother partnered within 1 year, we assumed that she moved in with the biological father. Similarly, divorces before 1983 refer to breakdowns of legal marriages only, whereas during the panel years, they cover both legal marriage and cohabitation disruptions.

  17. In earlier work (Francesconi et al. 2005), however, we defined childhood as spanning years 0–16 rather than 0–10 as here. Changing the definition did not change any of our substantive conclusions.

  18. We also experimented with another measure that further distinguished mothers who repartnered after divorce or husband’s death from mothers who did not. We do not report the estimates for such a measure because of the small size of the samples on which this analysis was performed, especially for the East German and Guestworker samples. But the results are qualitatively similar to those shown below.

  19. The greater proportion of highly educated mothers in the East German sample reflects the different educational systems operating in the two Germanies before unification (Frick 2007). This, in turn, is captured by the categorization that we use in the empirical analysis. In particular, the maternal education variable has four categories, in ascending order: general secondary school qualification or less, intermediate school qualification, Abitur, technical college or university degree. To simplify cross-sample comparisons, we used the same broad categories for each sample, though qualifications in the former FRG were different from those in the former GDR, and qualifications in Germany differ from those obtained abroad by mothers in the Guestworker sample. Using an alternative categorization of educational qualifications for mothers, i.e., distinguishing between mothers with engineering and technical college degrees from mothers with university degrees, did not change our key results presented in the next section.

  20. We also investigated the extent to which members of the sibling subsamples and main samples had the same characteristics. Considering the control variables reported in Table 3 (with the obvious exception of only children) and the dependent variables, we found no significant difference between the sibling sample and the main sample at conventional significance levels for all variables except for number of brothers and sisters.

  21. We also performed this analysis using local linear regression matching models and Chamberlain conditional logit models. Because results obtained were similar to those shown in Table 4, they are not reported.

  22. We performed a number of tests to check how the matching on the estimated propensity score successfully balanced the distribution of covariates between treatment and control group. First, we computed standard two-sample mean t tests between treatment and comparison group for all covariates used after matching. Second, we calculated the Pseudo-R 2 from probit estimation of the conditional probability of growing up in a non-intact family both before and after matching (Sianesi 2004). Third, we calculated standardized biases between the treated and control groups before and after matching (Rosenbaum and Rubin 1985). The various test statistics indicated that matching was successful.

  23. In other regressions (not reported for brevity), we also controlled for childhood family income and maternal employment in the estimation of the FE model. Their inclusion did not change our main results.

  24. An argument that differential benefit treatments after parental divorce and after death of a parent dilute identification power is weak. In Germany, if a father dies during childhood, his widow is entitled to receive up to 40% of his earnings. After parental divorce, there is little scope for divorcing parents to agree on bilateral financial transfers in the shadow of the law. In general, the courts establish the exact level of child maintenance, which is typically determined on the basis of the child’s age and the noncustodial parent’s income. There are also guidelines that guarantee a minimum child transfer, independently of the economic circumstances of the noncustodial parent. Overall, the two types of transfers are of comparable levels.

  25. Estimates for “Father died” from the FE model are not presented because of the small number of households with within-family variation (see discussion in Section 3.4 and Table 2 notes). Additional analysis in which we distinguish this treatment reveals that having experienced a parental divorce reduces the chances of achieving Abitur or higher qualifications by almost 19 percentage points, but this effect is not statistically significant at conventional levels (p-value = 0.107). Similar considerations hold when the comparison is performed with respect to individuals who were born to unmarried mothers.

  26. As a robustness check, we reestimated the model for this sample also including a set of dummy variables for mothers’ and fathers’ nationality. The estimates on the family structure variables were very similar to those reported in Table 5, while the nationality dummies were jointly statistically insignificant.

  27. Before 1990, migration between the former GDR and FRG was virtually inexistent. Since then, migration is allowed, but there is one uniform legal family code applied to the whole of Germany. Hence, our results are unlikely to suffer from selective migration bias whereby migration decisions are related to divorce regimes.

  28. Excluding parents who divorced in 1976 and 1977 from the West German sample meant dropping 13 observations, i.e., 4% of all divorced mothers in the sample (or 0.5% of all mothers). Importantly, for the estimation of Gymnasium attendance, 1976 and 1977 are included as pre-reform years for individuals from the former GDR; otherwise the control group would not have information on the pre-reform period.

  29. The different timing of the region variables is because the SOEP does not ask respondents about housing and residential location prior to their joining the panel.

  30. For brevity, the estimates are not shown, but are available from the authors.

  31. The smaller size of these subsamples reduced the precision of some of such estimates, however.

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Acknowledgements

Financial support from the Anglo-German Foundation, the UK Economic and Social Research Council, DIW Berlin, and the University of Essex is gratefully acknowledged. We wish to thank the Editor (Christian Dustmann) and two anonymous referees for guidance and helpful suggestions. We are also grateful to Anders Björklund, Miles Corak, Monica Costa Diaz, John Ermisch, Steve Machin, Anna Vignoles, and seminar participants at HM Treasury (London), the Departments for Work and Pension and for Education and Skills (London), Institute of Education, Institute for Fiscal Studies, University of Essex, the 2006 Royal Economic Society and ESPE Conferences, and the CEPR Conference on Economics of Education and Education Policy in Europe (Uppsala) for helpful comments.

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Authors and Affiliations

  1. Department of Economics, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

    Marco Francesconi

  2. Institute for Social and Economic Research, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

    Stephen P. Jenkins

  3. German Socio-Economic Panel Study, DIW Berlin, Mohrenstr. 58, 10117, Berlin, Germany

    Thomas Siedler

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  1. Marco Francesconi
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  2. Stephen P. Jenkins
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  3. Thomas Siedler
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Correspondence to Thomas Siedler.

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Responsible editor: Christian Dustmann

An erratum to this article can be found at http://dx.doi.org/10.1007/s00148-010-0327-7

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Francesconi, M., Jenkins, S.P. & Siedler, T. Childhood family structure and schooling outcomes: evidence for Germany. J Popul Econ 23, 1073–1103 (2010). https://doi.org/10.1007/s00148-009-0242-y

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