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Some new insights on economic convergence and growth in Central, Eastern, and Southeastern Europe

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Abstract

The existing empirical literature on economic convergence and growth emphasizes the importance of foreign capital inflows for the Central, Eastern, and Southeastern European (CESEE) countries. This paper challenges such arguments by stating that not all forms of foreign capital inflows are beneficial for the economic growth of CESEE countries. Our results suggest that remittances (as an alternative foreign capital inflow) tend to slow down economic growth. Moreover, apart from the prevailing trends to investigate the economic convergence of CESEE towards Western European countries, this paper focuses on economic convergence within the CESEE region, that is, on economic convergence of the non-EU CESEE countries towards EU CESEE countries. We found that, in the last two decades, the living standard in the CESEE region has become increasingly equal. There is a tendency for poorer non-EU CESEE countries to grow faster than richer EU CESEE countries, which confirms the existence of absolute \(\beta\)-convergence. We have also found that each CESEE country converges 2.8% closer to its own steady state, in the sense of conditional \(\beta\), every year.

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Fig. 1

Source: World Development Indicators

Fig. 2

Source: World Development Indicators, own calculations

Fig. 3

Source: World Development Indicators, own calculations

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Notes

  1. Note that according to neoclassical growth theory, poor countries with a low level of initial GDP per capita have a lower capital per worker ratio and, therefore, a higher marginal product to capital, which, for a given investment rate, translates into a faster economic growth.

  2. It is worth noting that it is possible (mainly at the theoretical level) for poor countries to grow faster than rich ones but without their standards of living converging over time. This implies that, although necessary, absolute β-convergence is not a sufficient condition for σ-convergence (see Sala-i-Martin 1996).

  3. Albania, Belarus, Macedonia, Moldova, Montenegro, Russian Federation, Serbia, Turkey and Ukraine.

  4. Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and Slovenia.

  5. We define foreign capital inflows as an increase in the amount of money available from foreign sources for investment in human and physical capital.

  6. Note that if there are no decreasing returns to all capital, then there is no steady state in this model.

  7. Note that in Eq. 2, there is no period-specific effect, \(g(t - e^{ - \lambda \tau } (t - 1))\), since we work with demeaned data.

  8. The A(0) term also refers to resource endowments, climate, institutions, and so on.

  9. Mankiw et al. (1992) assume that the same production function applies to human capital, physical capital, and consumption, and that human capital depreciates at the same rate as physical capital.

  10. Note that if \(\alpha = \beta = 1/3\) and \(n + g + d = 0.06\), than the convergence rate is 0.02. Also note that, in that case, the country moves halfway to steady state in approximately 35 years and needs 115 years to eliminate 90% of the distance to its steady state.

  11. Note that permanent measurement errors are a constituent part of the time-invariant country-specific effects.

  12. The instruments for the subsequent first-differences (the lagged levels of the variables) are weak, which results with undesirable finite sample properties in terms of bias and imprecision.

  13. Unlike the first-differenced GMM estimator that treats the model as a system of equations in differences for each period, the system GMM estimator creates a system of two equations for each period.

  14. In this respect, Blundell et al. (2000) have reported desirable finite sample properties of the system GMM estimator.

  15. In this regard, to remain consistent with our second analytical framework (determinants-of-growth equations), we differ from the original version of the M-R-W’s growth model where the human capital variable is measured during the period. Our approach is in line with Caselli et al. (1996) and Bond et al. (2001) where the human capital variable is also measured at the start of the period (when using M-R-W’s growth model). We estimated the M-R-W’s growth model using the human capital variable that is measured during the period (as in the original version of the model) as well. Interestingly, the results were incredibly similar to those when the human capital variable was measured at the start of the period. These results are available upon request.

  16. Following Barro (1991) and Caselli et al. (1996) we use the secondary-school enrollment rate variable as a proxy for the initial stock of human capital. In this respect, we are aware (a) that this variable is a far-from-perfect proxy for a country’s human capital since it restricts the human capital stock in the form of education only, and (b) that using human capital per person variable (such as the average educational attainment or the index of human capital) rather than the variable that relates to the flow of investment in human capital might be a better solution. However, such data is not available for all CESEE countries.

  17. Following Barro and Sala-i-Martin (1995), we treat the democracy variable as strictly exogenous, although the exogeneity of this variable with respect to economic growth can be questioned (see Ch.11: 438–439).

  18. In this respect, it is well known that there is no precise guidance on what is a relatively safe number of instruments. Keeping the instrument count below N (number of groups) does not safeguard the Hansen test (see Roodman 2009).

  19. As noted in Barro and Sala-i-Martin (2004), neoclassical growth models (Solow-Swan and Remsey models) predict that, for given control variables, an equiproportionate increase in the initial stocks of physical and human capital would reduce the rate of economic growth (as a result of diminishing returns to all capital). However, imbalances between physical and human capital tend to raise the growth rate, which means that although the influence of physical capital (K) on growth would be negative, the effect of human capital (H) tends to be positive. More concretely, given the level of real GDP, a higher initial stock of human capital translates into a higher ratio of human to physical capital (H/K), which creates higher growth rates. As noted in Barro (2001, 2013) there are two main channels through which the higher (H/K) generates higher growth rates: (1) more human capital facilitates the absorption of superior technologies from leading countries, and (2) human capital tends to be more difficult to adjust than physical capital, which means that a country that starts with a high (H/K) (such as CESEE countries at the start of transition) tends to grow rapidly by adjusting the quantity of physical capital upward.

  20. Note that, empirically, the “iron low of convergence” takes the form of unconditional convergence law (see Barro 2015).

  21. Note that if most of the CESEE countries had achieved a moderate amount of democracy, then the squared term of the democracy variable would have been negative and statistically significant.

  22. These findings are in line with those reported by Chami et al. (2005) and Barajas et al. (2009) where remittances also exhibit a negative effect on economic growth.

  23. We define altruism as an immigrant’s concern over the living standard of family members in the domicile country.

  24. There is another plausible explanation for the negative impact of remittances on economic growth in CESEE region that is related with the inability of remittances to compensate human capital losses stemming from the massive labour migration to Western Europe, United States of America, Canada, and Australia, in the last two decades. However, this assumption requires more extensive investigation.

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Correspondence to Dimitar Eftimoski.

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Appendix

Appendix

See Tables 4 and 5.

Table 4 Definition and sources of the variables
Table 5 Descriptive statistics, 4-year averages for the period 1997–2016

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Eftimoski, D. Some new insights on economic convergence and growth in Central, Eastern, and Southeastern Europe. Empirica 47, 863–884 (2020). https://doi.org/10.1007/s10663-019-09458-1

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