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Secondary versus higher education for growth: the case of three countries with different human capital’s structure and quality

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Abstract

The study estimates the impact of human capital on growth by focusing on three countries which differ in the human capital’s structure and quality: the Czech Republic, Estonia and Bulgaria. Human capital is measured by the labor force having completed at least upper secondary education. The co-integrating regressions based on the neoclassical production function are solved by the DOLS method with structural breaks. The results do not support the hypothesis that higher educational attainment per se accelerates aggregate output since the countries differ in both the sign and magnitude of the regression coefficients. However, the findings justify the view that the role of tertiary education increases with the level of economic development. One plausible explanation of this outcome is that the quality of human capital is more important for growth than its quantity. With regard to that, the paper provides evidence that in the European economies the foreign language learning used as a measure of the quality of schooling is more strongly linked to the increments of real GDP per capita than educational attainment. The impact of higher education might be explained also in light of the labor market channel, specifically the deepening qualification mismatch in countries with rising tertiary education graduation rates.

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Notes

  1. The abbreviation NMS-10 (New Member States) represents the group of post-communist EU members comprising Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia. Croatia is excluded from the analysis due to incomplete data sets. The data on educational attainment presented in this paragraph refer to the average values over the investigated period (2000–2013).

  2. The papers on CEE focus primarily on the role of FDI (see, for example, the meta-analysis of Iwasaki and Tokunaga 2014).

  3. Structural Eq. (4) is similar to that received by Nonneman and Vanhoudt (1996, p. 945) who estimate an augmented Solow model with physical capital, human capital and technological know-how.

  4. The unit root test with structural break suggested by Saikkonen and Lyutkepohl (2002) and Lanne et al. (2002) has been alternatively applied. It supports the conclusion that all regression variables contain a unit root. The results are available upon request.

  5. The marginal productivity of capital (MPH) is linked to its elasticity (β) and its share in aggregate output as follows:

  6. The source of data is Eurostat.

  7. The additional indicators related to pupils learning French or those who don’t learn any foreign languages do not produce robust results.

  8. It should be noted that the composition of the two groups is not exactly the same. For example, some people leave school earlier while others, usually the most educated population in the NMS, go to study or work abroad. However, these differences are not so big as to change the cross-country regression estimates significantly.

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Acknowledgments

I would like to to express my gratitude to the Editor as well as two anonymous Reviewers for their helpful comments.

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  1. Burgas Free University, 62 San Stefano St., 8000, Burgas, Bulgaria

    Mariya Neycheva

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  1. Mariya Neycheva
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Correspondence to Mariya Neycheva.

Appendix

Appendix

See Table 9.

Table 9 Unit root test for the first differences of the variables in the models
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Neycheva, M. Secondary versus higher education for growth: the case of three countries with different human capital’s structure and quality. Qual Quant 50, 2367–2393 (2016). https://doi.org/10.1007/s11135-015-0267-0

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