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The author is grateful to Richard Barth, Stanley Black, Eric Clifton, Oleh Havrylyshyn, Mohsin Khan, Vincent Koen, Caryl McNeilly, Mark Schaffer, Abdelhak Senhadji, Sunil Sharma, participants of the CEPR Transition Economics Workshop in Budapest in May 1999, and especially to Willem Buiter for valuable comments.
Based on the recession immediately following the launch of reforms and not including the effects of the Russian crisis of 1998.
Note that the log approximation may not be appropriate in this case, since it requires that the growth rates in question be sufficiently small, which is not always the case in Russian data.
Note that in this case the real wage and the real rental rate of capital reflect not only marginal factor products, but also respective utilization rates.
Beside differences across economic sectors, transition process causes differences within sectors, most importantly between state, privatized and de novo private enterprises. However, due to data limitations these phenomena are yet harder to capture empirically, so the present work focuses exclusively on sectoral variations.
The service sector data is taken as a residual and therefore includes all economic activity apart from industry, agriculture and construction.
The unemployment data cover all enterprises, while the shortened day/compulsory leave data cover large and medium enterprises.
The actual length of the shortened working day varies greatly across enterprises, so while the half-day assumption is admittedly arbitrary, it is hard to improve given the data at hand. It is also unclear whether there has been a trend in the number of working hours per day or working days per week. The results, however, appear robust to certain perturbations of this condition.
In the Russian capital accounting (Poletayev, 1997) the rate of growth of the gross fixed capital stock at constant prices equals the difference between “the coefficient of renewal” (the ratio of the value of new facilities created during the year to the capital stock) and “the coefficient of depletion” (the ratio of fixed assets that are depleted during the year to the capital stock). The coefficient of renewal is reported as a share of capital at the end of the year, while the coefficient of depletion is reported as a share of capital at the beginning of the year. The coefficients therefore need to be recalculated uniformly as shares of the capital stock at the end of the previous year.
Note that if the constant returns assumption is inaccurate, the estimated contribution of the TFP drop to the output contraction may be biased.
In other words, the share of capital in the sectoral output is fixed, while the share of capital in the total output is proportional to the sectoral output share. A more accurate account of differences in capital shares by sector is not feasible for lack of information.
These results are very similar to those obtained by De Broeck and Koen, 2000, who found that the TFP drop accounted for 80 percent of the output decline in Russia between 1991 and 1997.
It may be argued that in fact (at least at most state and formerly state enterprises, still comprising the most significant part of the economy) returns to scale are likely to be decreasing, and while returns are probably increasing in the newly emerging private sector, it still constitutes the smaller part of the economy.
The 90 percent confidence interval for the regression coefficient in the capital utilization equation (8) is 0.75±1.94•0.26, or [0.25;1.25].
This paradox was also obtained by De Broeck and Koen, 2000. Note, however, that this result may be sensitive to the assumptions on factor shares by sector.
The 90 percent confidence interval for the regression coefficient in the labor utilization equation (7) is 0.12±1.94•0.04, or [0.05;0.20]. Note that capital utilization in industry was given, while all the other sectoral utilization rates were estimated from equation (7) for labor and from equation (8) for capital.
The regions of Chechnya and Ingushetiya are omitted due to lack of data. 11 AOs (ethnic units contained within other regions) are not distinguished from their host regions for the same reason.
Regional GDP data had not been compiled prior to 1994.
Maintaining the neo-classical production function assumption both at the aggregate and at the regional level, yi/Y=(ai/A)(li/L)α(ki/K)1-α, where upper case letters represent cross-regional averages (i.e., the aggregates divided by the number of regions) for output, labor, and capital, and the aggregate value for productivity, while lower case letters represent regional values for region i.
While early in transition inflation rates differed across regions, the variation has gone down substantially and became largely insignificant by the time period considered here.
The following equation was estimated by OLS: ln(yi/Y)=C+αln(li/L) + βln(ki/K) + εi, where C = ln(ai/A), subject to the following constraint α+β=1. This approach is admittedly simplistic – the same technology is assumed in all regions, any potential endogeneity is ignored, and so on (see Senhadji, 1999) – but sufficient for the needs of this exercise, provided robustness checks for alternative factor shares are performed.