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Ethnic and parental effects on schooling outcomes before and during the transition: evidence from the Baltic countries

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Abstract

This paper examines human capital gap between titular ethnicities and Russian-speaking minorities, which has emerged in Estonia, Latvia, and Lithuania during the transition and remains significant after controlling for parental education. For recent cohorts, unexplained gap is declining in Lithuania (despite absence of Russian language tertiary education) and in Estonia. Furthermore, we investigate intergenerational mobility in the Baltic countries. Parental education has a strong positive effect on propensity to obtain tertiary education, both in the Soviet era and post-Soviet period. Transition to the market has weakened mother’s education effect for titular ethnicities, while the opposite is true for minorities.

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Notes

  1. On ethnic effects in the Baltic labor markets, see Kroncke and Smith (1999) and Hazans (2003, 2005, 2007).

  2. Hereafter, “higher education” and “tertiary education” are used as synonyms. “University” is used (loosely) instead of “institution of higher education.” “Students” are “tertiary students” if not stated otherwise.

  3. For instance, Chevalier et al. (2005) use instrumental variable methods to simultaneously account for the endogeneity of parental education and paternal income and find that the strong effects of parental education become insignificant. On the other hand, Bjorklund et al. (2004) find that, after accounting for genetics, the causal effects of parental education remains highly significant.

  4. Only some students received scholarships; moreover, the typical scholarship was about 30% of a young worker’s salary.

  5. Starting in the 1970s, for instance, young men had an additional incentive to pursue full-time higher education, as many universities have established their own military departments through which students could avoid the draft.

  6. Degree of competition varied across fields of study. For instance, in the University of Latvia, applicants-admission ratio for full-time studies in the 1980s ranged from 1–1.5 in mathematics and physics to 3–4 in economics to 7–8 in foreign languages.

  7. A university could turn down the applicant’s documents based on the grades in secondary school certificate or based on “personal characteristic” signed by school administration and the komsomol secretary. Some top higher education institutes in Moscow and Leningrad were famous by not accepting documents from Jewish applicants. Above all, formal and informal quotas (conditional on not failing in the entry exams) existed for some special categories (men after military service; orphans; applicants from the countryside, etc.) One of the documents required from an applicant was an autobiography with full details on the applicant’s parents. Thus, social background could, in principle, be used as a screening device. According to the prevailing ideology, one would suppose that policy would favor applicants from working class backgrounds.

  8. The composition of the pool of admitted students according to the way of getting in differed across regions of the Soviet Union, across universities in the same city, and even across departments of the same university. In the Baltic countries, the third channel (bribing) did exist but was, on average, of relatively small importance.

  9. There were some asymmetries in terms of fields though; for example, studies in titular languages offered a wider choice in humanities, while some programs in technical sciences were available only in Russian.

  10. A model of tertiary attainment conditional on secondary education was estimated as well; results did not give much extra insight compared to model 1 and are not reported.

  11. Colding (2006) is an example of a recent study where missing mother’s (respectively, father’s) education is replaced by a special dummy for 65% (respectively, 58%) of immigrants’ children.

  12. The attainment models have been estimated also with sample selection into living with at least one parent (see Section 6 for details).

  13. This has been done perfectly for the NORBALT data as well as for Estonian LFS data. For Latvian and Lithuanian LFS data, it was possible to exclude only those who have immigrated during the 10 years before observation. This is enough for the models of secondary and tertiary enrollment. For the models or secondary and tertiary attainment of the 18/21–45 year olds, Latvian and Lithuanian samples include some respondents who have immigrated at age 18 or older. However, using estimates based on the NORBALT survey, the proportion of such respondents is negligible in the pooled samples and is below 10% in the minority subsamples.

  14. Latvian and Lithuanian statistical offices do not possess data of the 1989 Census.

  15. Note also that the ethnic effects in columns 1–4 are virtually unchanged if parental controls are omitted.

  16. Regarding the validity of the instruments, it seems reasonable to believe that the method of participating in the survey is uncorrelated with errors in the tertiary attainment equation. Regarding the other instrument, early marriage can, in theory, affect consequent schooling outcomes; however, two critical schooling decisions are made at the age of 15 and 17–18, while mean year of the first marriage in the Baltic countries is about 25/23 for men/women. More than 85% of population aged 18 to 24 have never been married. The instrument seems beyond suspicion as long as secondary attainment is concerned. Furthermore, our definition of “single” excludes cohabiting, which makes association between “single” status and marriage weaker. So it is plausible that being “single” should not have a direct effect on tertiary attainment. Correlation between “single” and error term in linear probability model is close to zero. We thus consider this as a valid instrument as well. Given that that the two instruments are virtually uncorrelated (the correlation is −0.05 in Estonia, 0.13 in Latvia, and −0.16 in Lithuania), and nevertheless, they give very similar results, we believe the results are credible.

  17. Formally, suppose that every (adult) member of current generation (t) can have either high (y t  = 1) or low (y t  = 0) education level, which is determined by the following probit model: \( y^{ * }_{t} = \beta _{t} y_{{t - 1}} + \mu _{t} z + \gamma ^{\prime }_{t} X_{t} + \varepsilon ,{\text{ }}y_{t} = 1 \) if \( y^{ * }_{t} > 0 \), y t  = 0 if \( y^{ * }_{t} \leqslant 0 \), ɛ ∼ N(0, 1), where y t−1 is parents’ education level; z is a binary variable defining two demographic groups, and X is a vector of other relevant demographic characteristics. Assume that impact of parental education, demographics other than z, and unobservables does not change over time: β t+1=β t =β, γ t+1=γ t =γ. Assume also that μ t  > 0 without loss of generality. Then human capital gap between demographic groups, conditional on parental education and other demographic characteristics, δ t+1(y, X)=E(y t+1|y t =y, z=1, X)−E(y t+1|y t =y, z = 0, X)=Φ(βy+μ t+1+γ′X)− Φ(βy+γ′X) is larger (respectively, smaller) in generation t + 1 than in generation t if μ t+1 > μ t (respectively, μ t+1 < μ t ).

  18. In Latvia, the reason was a sharp drop in secondary completion rate of minorities (from 80.4% for those born in 1972–1978 to 74.4% for those born in 1979–1983). In Estonia, a similar cohort effect was accompanied by intensive completion of secondary education by individuals of titular ethnicity aged 25 and older between 1999 and 2004.

  19. Controls include gender, region and rural dummies, birth year dummies, as well as observation year dummies. See next subsection for a formal definition of explained and unexplained gap. Note that cohort and age effects cannot be estimated separately. However, assuming that age effects on propensity to obtain higher education are independent of ethnicity, the trend in the unexplained ethnic gap should be attributed to the cohort effects.

  20. We have made similar comparison also for LFS-based models estimated (by country) separately for population born in 1957–1971 and in 1972–1983 (results are available on request). The minority coefficients are larger in size for the latest cohort (which could receive tertiary education only in the post-transition period) for Latvia and Estonia: −0.443 (0.067) vs −0.320 (0.046) and −0.398 (0.117) vs −0.335 (0.090), respectively; while it goes the other way in Lithuania: −0.360 (0.088) vs −0.441 (0.070).

  21. This decomposition has been used, e.g., by Gang et al. (2002); see also Yun (2005a,b).

  22. The definition of unexplained difference given in 3 and 4 is equivalent to the one applied in Fig. 5 (see discussion above), except for the fact that, here, means are taken over the whole sample rather than by cohort.

  23. In these cases, the results are based on pooled rather than year-by-year samples because the differences between the years were not substantial.

  24. For comparison, similar estimates (for own birth children) are 0.20 and 0.29 in the USA (Plug 2004) and 0.15 and 0.21 in Sweden (Bjorklund et al. 2004).

  25. LFS-based country-specific estimates for tertiary enrollment in 2001–2004 not conditional on completion of secondary school give very similar (0.27 to 0.30) maternal effects; paternal effects range from 0.16 to 0.24. Similar effects for 17 year olds in Switzerland (Bauer and Riphahn 2006) are 0.28 (mother) and 0.32 (father). Note that parental education effects in Switzerland are stronger than elsewhere in Western Europe (Woessman 2004).

  26. In Latvia, for example, the number of state-financed students declined by roughly one third between 1989 and 1994 and remained stable thereafter, while the number of students paying tuition fees increased more than 20-fold between 1992 and 2002 and accounted for 73% of all students in 2002.

  27. See estimated earnings functions in Kroncke and Smith (1999) and Hazans (2003, 2005).

  28. This is also true for those born before 1923 in one of the Baltic countries (which were independent market economies between 1918 and 1940). This group, however, makes up less than 5% of the relevant NORBALT samples, while the LFS-based samples do not include such respondents at all.

  29. Available evidence from the LFS-based models is consistent with these effects being smaller in the 2000s compared to the previous 5 yeas in Latvia and Lithuania; these results are not reported.

  30. According to Latvian LFS (other sources do not provide the field of study), 65% of non-Latvian men with higher education born between 1938 and 1957 belong to these categories.

  31. Price levels in the three countries differ somewhat but not strongly; different sources disagree on purchasing power parity adjusted exchange rates. However, adjusting for price diferences between the countries would change only the values of country-specific dummies, which are not the parameters of interest in this study.

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Acknowledgements

The authors gratefully acknowledge support by a grant from the CERGE-EI Foundation under a program of the Global Development Network. All opinions expressed are those of the authors and have not been endorsed by CERGE-EI or the GDN. NORBALT datasets were generously provided by the Fafo Institute for Applied Social Science in Oslo, Norway. Raul Eamets provided crucial help with Estonian LFS data. We thank Steven Rivkin, Randall Filer, Michael Spagat, Libor Dusek, two anonymous referees, and the editor (Christian Dustmann), who have read the previous versions of the paper and made very helpful comments. We also thank Jeffrey Smith, Raul Eamets, Tiiu Paas, Ott Toomet, as well as participants of GDN global conference “Research for Results in Education” (Prague, April 2005), and EALE 2006 conference (Prague, September 2006) for useful comments during presentations of the previous versions.

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  1. Faculty of Economics, University of Latvia, Aspazijas Blvd. 5, LV-1050, Riga, Latvia

    Mihails Hazans

  2. Department of Economics, Northwestern University, 2001 Sheridan Road Evanston, IL, 60208, USA

    Ija Trapeznikova

  3. BICEPS, Strelnieku 4a, LV-1010, Riga, Latvia

    Olga Rastrigina

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  1. Mihails Hazans
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Correspondence to Mihails Hazans.

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Hazans, M., Trapeznikova, I. & Rastrigina, O. Ethnic and parental effects on schooling outcomes before and during the transition: evidence from the Baltic countries. J Popul Econ 21, 719–749 (2008). https://doi.org/10.1007/s00148-007-0134-y

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