Abstract
In this chapter, we provide evidence on compensating differentials in the labor market from the largest transition economy, Russia. Using the NOBUS micro-data and a methodology based on the estimation of the wage equation augmented by aggregate regional characteristics, we show that wage differentials across Russian regions have a compensative nature. Russian workers receive wage compensations for living in regions with a higher price level and worse nonpecuniary characteristics, such as a relatively low life expectancy, a high level of air pollution, poor medical services, a colder climate, and a higher unemployment level. These compensations are not associated with the existing government system of compensating wage coefficients. After adjusting for regional amenities and disamenities, regional wages become positively correlated with interregional migration flows. According to our estimates, wage compensations along with differences in employment composition are able to account for about three-fourths of the observed variation in wages across Russian regions.
JEL Classification J3, J6, P2, R2
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Notes
- 1.
Although since 2007 the minimum wage level in Russia substantially increased, the Kaitz index (which is the ratio of minimum wage to the average wage) is still low compared with the OECD countries (Muravyev and Oshchepkov 2012) and most of the CEE countries.
- 2.
- 3.
As the magnitude of differentiation depends on the country’s particular administrative division, we used several variants of divisions. All results are presented in Table 4.1.
- 4.
Standard decompositions of the total wage inequality show that the effect of the regional factor on the wage inequality is the largest in comparison with other factors such as human capital characteristics, industries and occupations (e.g., Lukiyanova 2008; Oshchepkov 2009). At the same time, the impact of the regional factor on the total wage inequality in Russia is much higher than that in OECD countries (see Oshchepkov 2007).
- 5.
This result contrasts to conclusions of Combes et al. (2008) for France that spatial wage differences in France are mainly explained by differences in employment composition. However, this may be easily explained by much greater spatial variation in amenities and disamenities in Russia than in France.
- 6.
At the background of rich literature on the CEE countries, papers focusing on Russia are rare. The only study known to us is that of Bornhorst and Commander (2006). They examined regional unemployment in five transition countries: Chezh Republic, Hungary, Poland, Romania, and Russia. Most of the results show that correlations between regional wages, unemployment rates, and migration flows in Russia clearly differ from those in the other four countries (see, e.g., Figs. 3 and 4 in Bornhorst and Commander 2006).
- 7.
The wage curve assumes the opposite relation between individual wages and unemployment.
- 8.
Possible deviations from the perfect competition conditions in the Russian context are discussed in Oshchepkov (2009).
- 9.
The concentration of highly productive employees may also explain why firms operate in regions with a relatively high wage level. In this chapter, we control for the regional employment composition, and therefore this possibility is accounted for.
- 10.
A possible explanation for this fact is that such employees face a smaller depreciation of accumulated human capital. At the same time, younger employees are on average less constrained by family and social ties.
- 11.
- 12.
- 13.
A similar methodology was used earlier by Krueger and Summers (1988).
- 14.
It should be noted that it is impossible to adjust interregional wage differentials for regional characteristics with the use of regional dummy variables because of the problem of total multicollinearity. Papers that used regional dummies adjusted only for the regional employment structure (see, for example, Haisken-DeNew and Schwarze 1997; Azzoni and Servo 2002; Garcia and Molina 2002; Viera et al. 2005).
- 15.
These data were used in a number of studies on Russia; see Gustafsson and Nivorozhkina (2011) and Staneva et al. (2010) among recent examples. More information on NOBUS is available on the site of World Bank (see http://go.worldbank.org/VWPUL3S9F0).
- 16.
- 17.
We do not need control for different tax and transfer systems in regions (as, e.g., Johnson 1983), because the personal income tax rate in Russia is equal to 13 % in all regions and social security payments are rather low comparing to wage levels.
- 18.
Only three regions, the Republics of Dagestan and Ingushetiya and the Stavropol Region, border Chechnya.
- 19.
Peculiarities of the system of labor compensation in the Northern territories are described in the article No. 50 of the Russian Labor Code. The magnitude of regional coefficients and the order of their implementation are set by the Russian government. A current list of areas and the magnitude of corresponding wage coefficients are presented in the joint information letter by the Pension Department of the Ministry of Labor (dated by 09.06.2003, No. 1199–1116), the Department of Incomes and Welfare of Population of the Ministry of Labor (dated by 19.05.2003, No. 670–679), and Russian Pension Fund (dated by 09.06.2003, No. 25–23/5995).
- 20.
A higher wage level in a region may push up regional prices in the short run. However, in our theoretical framework, we consider the long-run period, when an interregional wage structure is close to the state of equilibrium. In the long run, relatively high prices in a region will attract producers (sellers) to the market. They will increase their supply of goods up until the benefit (that is the difference between prices in two regions) is less than transportation costs. Therefore, an interregional structure of prices for tradable goods in long run is determined by transportation costs and does not depend on regional wages.
- 21.
These services are public conveyances, communication, and public utilities. For more information about the composition of the price index see in “Ceni v Rossii” (“Prices in Russia”), Rosstat (2004).
- 22.
We should note that the price index for a common set of goods and services is a Laysperes price index. However, the optimal consumption structure may differ across regions, and so differences in regional price levels may either overestimate or underestimate the differences in the levels of utility that were brought about by these price differentials. In our study we do not control for this possibility.
- 23.
In earlier versions of our paper we included regional housing prices to the regressions along with the price index. There are two reasons why we excluded housing prices from this version. Firstly, housing is a very specific good comparing with other goods and services included in the fixed set. The prices for housing are much higher than for “standard” goods, and people purchase houses much more rarely than such good as, for example, food and clothes. Although people may also rent housing, these expenditures are also higher than expenditures on “standard” goods and services, and only about 2.7 % individuals from the regression sample rented housing. Secondly, unfortunately we did not manage to find an appropriate instrument for housing prices, which would be correlated with them, but not correlated with regional incomes.
- 24.
A similar method to solve the problem of endogeneity in the wage regression framework was used by Moretti (2004). He instrumented the percentage of college educated workers in the labor force of a city by the presence of land-grant colleges (which were founded in 1862 in the context of the federal program). For Russia, a similar method was used in Muravyev (2008). The author argued that the educational structure of cities under the central planning was determined by the federal government rather than by the market and instrumented the 1994 share of people with higher education with the respective share in 1989.
- 25.
According to Rosstat, during the period from 2000 to 2005, the net migration coefficient was the highest in Moscow and the Moscow region (if data on the Republic of Ingushetia are not considered). In 2002, Moscow and the Moscow region had a positive exchange of migrants with 47 regions; the Tumenskaya Region had the next largest positive migration balance, with seven regions.
- 26.
Rosstat, “Demographic yearbook”, 2001–2007 issues.
- 27.
See, for example, Gimpelson and Lukiyanova (2009).
- 28.
This may be due to different agglomeration effects: input–output linkages, thicker markets with better employer–employee matching and higher specialization of workers, knowledge accumulation, or the localization of HC externalities, etc. (Fujita and Tisse 2002).
- 29.
The intra-class correlation coefficient estimated for residuals received from Specification 1 is positive and significant. This indicates that standard errors of coefficients for regional characteristics in Specification 1 are underestimated.
- 30.
Following recommendations presented in the Chap. 8 of Baum (2006), we employed a series of tests for the relevance of the instruments and the Durbin–Wu–Hausman test of the endogeneity of our infrastructure variables. We note that, unfortunately, these tests are not technically executable in the survey regression framework; therefore, we employed them estimating Specification 3 by simple OLS. The results of the tests are presented in Tables 4.5 and 4.6. The tests fully support the relevance of our instruments and the endogeneity of the infrastructure variables.
Table 4.5 Tests for the relevance of the instruments Table 4.6 Tests for endogeneity of the infrastructure variables - 31.
We cannot exclude also the possibility that with the use of some other (unknown to us) instruments, the infrastructure variables in our regression will be significant. However, one can expect that the coefficient of a variable reflecting medical services will remain negative, because it is biased upward due to the “welfare effect.”
- 32.
- 33.
We note that all regional characteristics are significant at the 10 % level, and they are jointly significant at the 5 % level. As an additional robustness check, we reestimated specification 9 excluding from the sample two Russian capitals—Moscow and Saint Petersburg— which are two regions that are outliers on most of the regional characteristics used. Though this reduced the size of coefficients at medical staff and life expectancy, all the coefficients remained significant at the 10 % level.
- 34.
We note that high life expectancy in Russia’s southern regions might be a consequence of a high proportion of people with specific religious, cultural and ethnic traditions. Therefore, it could be difficult to receive this amenity by moving to these regions. However, firstly, living in a neighborhood where people live longer might be a self-dependent amenity for migrants (for instance, from the point of view of gaining experience). Secondly, high life expectancy is not possible without favorable natural and environmental conditions.
- 35.
The positive relationship between wages and regional unemployment level may be also interpreted in a dynamic perspective. For instance, recent studies suggest that high unemployment regions have a higher rate of reallocation (e.g., Pastore 2012). If we assume that in regions with more intense worker reallocation and industrial restructuring wages are higher than in other regions, then we receive a positive correlation between regional wages and unemployment. However, we are not aware of studies which examine the relationship between restructuring and unemployment in Russia.
- 36.
The estimated coefficient of the regional price index became lower, but it still does not significantly differ from one (the p-value of the F(1,78)-statistics of the corresponding F-test is equal to 0.379). Changes in other coefficients are not significant either.
- 37.
Among the elements of the employment composition, the industrial mix plays the most important role contributing about 8 % of IWD (for more details see Oshchepkov 2009).
- 38.
Within the framework used in our paper, we implicitly treat employment composition as fixed. However, at each moment differences in the employment composition across regions are subject to the ongoing and uneven process of industrial restructuring. Therefore, in several years the impact of employment composition may rise (or fall). In order to investigate our results in such a dynamic perspective, we need regionally representative panel micro-data, which are not available.
- 39.
Although we control the size of settlement in our regressions, Moscow (as well as St. Petersburg) may demand a special treatment, because they differ from other cities in the top category (one million people or more).
- 40.
It may be also the case that relatively highly paid workers underrepresented more in the Moscow subsample of the database used than in the subsamples for other regions.
- 41.
Adjusted and unadjusted wage premiums tend to be relatively stable in time. Therefore, wage premiums estimated for 2003 may influence migration decisions in other years. We present correlations between adjusted and unadjusted wage premiums for the period 2000–2005.
- 42.
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Acknowledgements
I am grateful to Tilman Brück, Irina Denisova, Vladimir Gimpelson, Rostislav Kapelushnikov, Hartmut Lehmann, Anna Lukiyanova, Alexander Muravyev, Sergei Roschin, and two anonymous referees for valuable comments and suggestions. The support from the Study Foundation of the Berlin House of Representatives and the Basic Research Program of the National Research University Higher School of Economics is gratefully acknowledged.
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Oshchepkov, A. (2015). Compensating Wage Differentials Across Russian Regions. In: Mussida, C., Pastore, F. (eds) Geographical Labor Market Imbalances. AIEL Series in Labour Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55203-8_4
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