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Diagnosing the ‘Russian Disease’: Growth and Structure of the Russian Economy

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Abstract

The Russian economy is experiencing ‘Russian Disease’ (R-D) whose major symptom is a strong positive relationship between the real output growth and oil prices from 1995 through 2010. The symptoms differ significantly from those characterizing Dutch Disease. We also show that there is no negative impact of the real exchange rate on output growth in Russia. Second, we find a long-run positive relationship between oil prices and the real exchange rate. Third, we show that the effect of oil prices can be captured by terms of trade and trading gains in the System of National Accounts. Fourth, we show that the increase in imports due to real appreciation of rubles, in turn, contributed to GDP growth in the trade sector, which is a major source of overall Russian growth and a symptom of R-D.

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Notes

  1. Rapid increases in imports of capital equipments and intermediate inputs must have contributed to improvements in productivity and commodity quality. This role of imports in Russia will not be considered here in an explicit manner.

  2. We have the following regression (OLS) for 1999M01-2010M12 with all coefficients at the 1% significance level (adj. R2=0.981):

    where manu and ind are the logarithms of real manufacturing output and industrial output, respectively. The values in brackets show the absolute values of t-statistics.

  3. Here, we use the so-called Nicholson method, as in BEA's national accounts prior to 2010. See Nicholson (1960), OECD (2006), and Kuboniwa (2007).

  4. Our computations were made by using Eviews 7, PcGive 13.2, STATA 11, and JMULTI.

  5. Using OLS, we have the following result:

    This shows that the elasticity is lower than that in Equation 7a whereas the underlying trend rate is higher than that in Equation 7a.

  6. However, it is noteworthy that the Johansen rank test does not support this cointegration for the whole period of 1995Q3–2010Q4, while it does for the period of 1995Q3–2008Q4.

  7. Rautava (2009) employs the same type of Equation 7a for 1995Q3–2006Q4 without reporting the significance levels of coefficients and the results of the Johansen rank test.

  8. The difference between our database and that of Rautava (2004) should be marginal.

  9. Using the VEC estimator (lag=0) for {gdp*, oil*, reer, gov}, with dummies of dm1, dm2, and dm3, we have the followings:

    where ECT1: the error correction term for {gdp*, oil*, reer}. Error corrections for {gdp*, oil*, reer} are as follows:

    where ECT2: the error correction term for {gov, gdp*, oil*}. D(reer) is entirely governed by three dummies. That is to say, the long-run relation (ECT1) in the VEC is largely affected by the short-run movements of this inexplicable variable of reer. Exclusion of dm1 makes the sign of ECT1 positive despite the satisfactory rank test results. Exclusion of dm2 produces an outlier (the coefficient of reer of −1.9). Exclusion of dm3 yields smaller values of coefficients of oil (0.09) and reer (−0.09). The Johansen rank tests for {gdp, oil, reer, gov} indicate two cointegrations at 5% level (lag=1) and we have without any dummy variables:

    When we introduce dummies, we have

  10. Beck et al. (2007) seem to have faithfully updated Rautava (2004) for 1995Q1–2006Q1. Surprisingly, without showing any estimation result corresponding to equation 7a″, which might have been their analytical base, they also emphasized the negative impact of the real exchange rate on the Russian growth. However, in our VEC estimations for {gdp, oil, reer, gov} with two dummies for 1998Q3 and 1998Q4, we never had a negative coefficient of reer for the cointegrating equation of {gdp, oil, reer}. The value of the coefficient of reer in a usual form (not in a cointegration vector form) was 0.18 [4.9] (lag=0), 0.22 [5.2] (lag=1), 0.01 [3.7] (lag=2), 0.19 [6.9] (lag=3), 0.20 [6.9] (lag=4) and 0.11 [7.0] (lag=5) for 1995Q1–2006Q1 including pre-samples. Here the value in bracket is the absolute value of t-statistic.

  11. The Johansen system also yields a similar value of the elasticity of 0.309 [4.918]. From OLS and DOLS (lag=7, lead=0), we have the elasticity of 0.256 [14.443] and 0.288 [8.368], respectively.

  12. The growth of manufacturing output can also be captured by the changes in the terms of trade for 1995Q3–2010Q4:

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Acknowledgements

I would like to thank two anonymous referees and the Editor for their valuable comments and editorial suggestions on earlier drafts of this paper. I am also grateful to Jouko Rautava and Iikka Korhonen for their useful comments on my work during my stay at the Bank of Finland for August-September 2011.

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  1. Institute of Economic Research, Hitotsubashi University, 2-1 Naka, Kunitachi, 186-8603, Tokyo, Japan

    Masaaki Kuboniwa

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Appendix

Appendix

The order of integration of the series is important for the selected regressions. We tested for unit roots by the commonly used Augmented Dickey-Fuller (ADF) tests. Table A1 shows results using the lag length selected by the Schwarz information criterion (maximum lag length=10). For all series of variables in levels, we cannot reject the null hypothesis of nonstationarity. In other words, all variables are nonstationary. Performing the tests for the first differences of variables, we reject the null hypothesis of nonstationarity. Since all variables have to be differenced once to obtain stationarity, they are integrated of order 1, I(1).

Table A1 Results of ADF tests for variables
Full size table

To test whether the nonstationary I(1) variables in our regressions are cointegrated or spuriously related, we examined the properties of the regression (1995Q3–2010Q4 or 1995M03–2010M12) by the ADF test and the Hansen Parameter Instability test (Hansen, 1992). Table A2 reports our results in the cases with none of the exogenous terms for regressions in this paper for the ADF test. For all regressions, we can reject the null hypothesis of no cointegration by the ADF tests and cannot reject the null hypothesis of cointegration by the Hansen test. In other words, the nonstationary variables in all of our regressions are cointegrated. We also tested the null hypothesis of no cointegration by the Johansen rank test, applying it to each of our regressions. Table A3 reports that the Johansen test rejects the null hypothesis of no cointegration for some of our regressions without restricting sample periods and introducing dummy variables.

Table A2 ADF and Hansen tests for cointegrations
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Table A3 Johansen rank tests
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Kuboniwa, M. Diagnosing the ‘Russian Disease’: Growth and Structure of the Russian Economy. Comp Econ Stud 54, 121–148 (2012). https://doi.org/10.1057/ces.2012.1

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