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Regional Convergence in the Russian Federation: Spatial and Temporal Dynamics


Set in the context neoclassical growth theory the discussion of economic convergence is revisited in the context of the Russian Federation. Compared to previous similar studies, here a larger more comprehensive data set is implemented (1994–2013) allowing to check for differences in convergence during different time periods. Using a panel approach more reliable results are achieved. The stability of these results is strengthened by estimating Kernel density to test for the presence of potential groups of regions with different steady states, on the one hand, and Markov transition matrices to test for the temporal stability of the regions on the other. Finally, a quantile regression approach is used to assure overall stability of the convergence speed. All results show that Russia reports absolute convergence up to Vladimir Putin’s second term as president and occurring again during his third term in office and conditional convergence in all time periods. All results remain stable even when including spatial effects or when testing for temporal stability. Quantile regression analysis also reports a more or less stable speed of convergence across the whole time horizon which is significantly higher than comparable results for the US or across regions of the European Union.

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  1. 1.

    See Solow (1956) and Swan (1956).

  2. 2.

    Critic of using the basic Solow model as of Romer (1987), Lucas (1988) or Romer (1989) is however noted and treated accordingly.

  3. 3.

    \(y_t=k_t^{\beta }\)

  4. 4.

    It can be noted that the difference between \(\alpha _1\) and \(\beta \) is almost negligible up to a value of 0.3–0.4 for \(\alpha _1\) while it becomes significant for values larger than 0.5.

  5. 5.

    Note that the approach by Bottazzi and Peri (2003) essentially reduced the spatial problem to a one-dimensional problem. However, a lot of spatial information is lost when referring to this approach and it is based on strict a priori assumptions as regards the spillover reaches.

  6. 6.

    In an extended version position (i,  j) is assigned a weight of \(\frac{1}{s}\) if \(s-1\) countries are lying on the shortest path from country i to country j. Alternatively approaches where a region’s impacts exponentially decreases have been considered in the literature.

  7. 7.

    This approach, however, becomes problematic for highly non-convex regions. Additionally, the collection of output data on a metropolitan level becomes very hard to nigh on impossible making it difficult for this approach to be applied consistently.

  8. 8.

    The spatial error model already accounts for a considerable share of the spatial autocorrelation induced via spatially lagged independent variables and thus using the more common spatial error model renders an inclusion of Durbin-type lags obsolete. Furthermore, running a test for spatial autocorrelation to assure robustness has shown that only marginal spatial effects exist that might justify the use of a Durbin type model.

  9. 9.

    While a focus on labor productivity would have been an interesting insight in the course of this study no comprehensive data set has been available to allow for a study as regionally disaggregated as has been done herein.

  10. 10.

    While data has been available for 1994 to 2014 the last year had to be dropped to calculate growth rates.

  11. 11.

    This compares to a half-life for closing the gap between richest and poorest region of approximately 1.6 years.

  12. 12.

    This compares to a half-life of 58.7 years.

  13. 13.

    Note that in comparison to Solanko (2003) all estimations report at least reasonably high \(R^2\) statistics which could, however, be due to the fact that here a panel estimator has been used.

  14. 14.

    See Mankiw and Weil (1992).

  15. 15.

    Note that Solanko (2003) argues against the inclusion of both the SME share and an education variable (here the number of students). Arguments for both cases separately can be found in the literature, e.g. Fingleton et al. (2003) for the share of SME and De La Fuente (2000) for human capital and even Solanko (2003) includes each variable separately. Considering the results in Table 3, the results by Solanko (2003) seem to hold here as well and the SME variable might be dropped from the regression.

  16. 16.

    As argued in Perret (2013) the government personnel also offers to control for part of the political structure as it is tightly linked to the pervasiveness of corruption.

  17. 17.

    While no data has been available on the savings rate the price level can used as a very weak and noisy proxy as a significant share of the Russian population still lives near the poverty line and a high price level might infer a lower potential for saving money.

  18. 18.

    The implemented kernel density estimator used a Gaussian kernel and the optimal bandwidth is calculated from the standard deviation and the interquartile distance.

  19. 19.

    A use of markov chains in the context of economic convergence can be found i.a. in Fingleton (1997).

  20. 20.

    i.e. cell (i,  j) represents those regions that in 1994 have been in quartile j while in 2013 they are in quartile i.

  21. 21.

    It needs to be noted that applying quantile regression analysis to the different time periods might lead to a loss in predictive quality as in this case for the 4 year periods only eight observation per 5% quantile would be usable leading to too few degrees of freedom for stable results.


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Correspondence to Jens K. Perret.

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Perret, J.K. Regional Convergence in the Russian Federation: Spatial and Temporal Dynamics. J. Quant. Econ. 17, 11–39 (2019).

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  • Russian Federation
  • Quantile regression
  • Regional economics
  • Economic convergence
  • Growth dynamics
  • Panel econometrics
  • Spatial econometrics
  • Kernel density estimation

JEL Classification

  • C21
  • C23
  • P25
  • R11