WTO Accession and the Labor Market: Estimations for Russia



Adjustment Costs of Trade Liberalization: Estimations for the Russian Labor Market

Akhmed Akhmedov, Evgenia Bessonova, Ivan Cherkashin, Irina Denisova, Elena Grishina, Denis Nekipelov

May 2004

Abstract

The paper investigates adjustment costs of trade liberalization in Russia by estimating various labor market elasticities with respect to indicators of trade liberalization in the 90-ies. In particular, the influence of tariff reduction on demand for labor is estimated, inter-sectoral employment flows in recent years and their determinants are studied, as well as determinants of sectoral wage premiums and of wage differentials between skilled and unskilled labor. The estimated elasticities of labor demand and wages show to be of very moderate size implying a modest adjustment cost in the labor market.

JEL Classification: J31, J62, F16

Keywords: Labor Market, Trade Liberalization, Labor Demand, Wage Premiums, Employment Flows

Introduction

Trade reforms, including the liberalization related to WTO accession, having long-run benefits, have at least short-run costs. In particular, the expected resource reallocation is not costless: some transitional unemployment and loss of output could be experienced when some inefficient enterprises are shut down. Moreover, the costs and benefits are unlikely to be uniformly distributed. Hence, in the short-run, there are going to be winners and losers. In the long-run, however, there is evidence that countries which experienced trade-led growth also experienced income growth of the poor which was in line with the average income growth.

One of the questions of interest for policy makers is how large the adjustment costs of trade liberalization are, i.e. how strong are the potentially disadvantageous short-run outcomes that result from trade liberalization. A significant portion of potential costs are related to the influence of trade reforms on the labor market.

There are several potential channels of influence of trade shocks on the labor market. Free trade expects to change relative prices, and hence redistribution of resources to more efficient use. That would affect output composition, and in turn, demand for labor. Changes in demand for labor transmitted through labor market would shift employment and income distribution between sectors. In addition to this indirect influence, changes in relative prices could affect employment and incomes directly: changes in relative prices of inputs would affect labor demand, while adjustment of relative prices of consumer goods is expected to affect labor supply. Being transmitted trough the labor market this direct effect will also change sectoral distribution of employment and incomes.

The total outcome of the resource reallocation and the magnitude of adjustment costs depend both on the characteristics of external shocks and on degree of rigidity and flexibility of internal markets. The degree of flexibility of labor market reflected, among others, by regional and sectoral mobility determines the speed of transition of workers from unemployment to employment or from old jobs to new jobs, thus shaping the size of adjustment costs.

The paper attempts to estimate responsiveness of Russian labor market with respect to international trade parameters using the experienced of trade liberalization during the 90-ies. The 90-ies are characterized by the increased openness of the Russian economy resulting in a significant increase in import competition experienced by Russian producers on the one hand, and on enlarged opportunities for exporting sector. The level of tariffs was changing significantly during the period aiming at domestic industry protection. Tariff levels increased in all industries during the period of 1994-1998, except for those in building materials. Wood processing and light industries were those with persistently high tariffs, while chemical industries and fuel and energy industries had relatively low tariffs throughout the period (Diagram 1.1).

[pic]Source: CEFIR calculations

Diagram 1.1. Tariff dynamics in Russian industry, 1994-98

In what follows we look at several channels of the influence of trade shocks on the labor market by estimating labor market elasticities with respect to trade liberalization indicators. In particular, the influence of tariff reduction on demand for labor is estimated in Section 2, inter-sectoral employment flows in recent years and their determinants are studied in Section 3, as well as determinants of sectoral wage premiums (Section 4) and of wage differentials between skilled and unskilled labor (Section 5).

We find low magnitudes of responsiveness of the labor demand to trade liberalization, both through the indirect effect of output changes and directly through the influence of tariffs and import penetration. This suggests that the adjustment costs to trade liberalization in the form of changes in industrial labor demand are not high. Moreover, one should take into account the effects of the shift from industrial employment to employment in services which are to dampen the effect of trade shocks.

We also find that trade liberalization does not have a significant effect on wages. It is likely that tariff reduction and trade liberalization would lead to only slight increase in the wage differentials between skilled and unskilled labor. It is obtained that there is no significant effect of tariffs on wages and wage premiums. Therefore, no significant evidence for the claim that “workers in more protected industries earn relatively more” is found. The results of the paper suggest that there is a significant negative effect of import on wage premiums while export orientation positively affects wages.

2. Estimation of Labor Demand Elasticities

In this section we analyze changes in employment due to trade liberalization. Labor demand, and in particular, the elasticity of labor demand with respect to output is the key determinant of employment on the labor market. Trade reform is expected to result in an increased demand for labor by exporting sectors and in a decrease in labor demand in import-competing sectors.

We use balance sheets of Russian large and medium enterprises for 1995-2000 to estimate labor demand equation[1] and to calculate possible changes in employment due to various shocks in output and tariffs.

Table 2.1 reports the obtained labor demand elasticities with respect both to wage and output for the whole economy sample and for each of the nine 2-digit OKONH industry sub-samples.

For the entire sample the wage labor demand elasticity is equal to –0.40 implying that a 10% increase in real wage would diminish labor demand for 4%. The labor demand elasticity with respect to output equals 0.22 which means that a 10% increase or decrease in output would cause a 2.2% increase or decrease in labor demanded. These estimates are higher in the absolute value than those reported by Konings and Lehmann (1999) for the Russian enterprises in 1996-1997, but they are still lower than elasticity in Poland, Hungary and Czech Republic during the transition period and than respective elasticity in developed countries.

The estimated coefficient for the lagged employment equals 0.24 which is lower than reported for earlier Russia and other transitional economies. This is a sign of decreased inertia of the Russian labor market in the second half of the 1990-ies.

To estimate the sensitivity of demand for labor to trade openness indicators, we included (lagged) tariff and import penetration levels in labor demand equations. It turns out that both indicators are statistically significant, with higher import tariffs being associated with higher (lagged) demand for labor and higher import penetration – with lower demand for labor[2]. Hence, a positive impact of trade barriers and a negative impact of trade liberalization on the number of workers demanded by the Russian industry as a whole is obtained. The magnitude of the influence is not high, however.

In addition to the estimates for industry as a whole, labor demands for nine particular industrial sectors were also estimated (Table 2.1). It is clear from the table that coefficients for lagged employment vary across industries. The short-run wage labor demand elasticities are insignificantly different from zero in power, petrochemical, and machinery construction industries, but are as high as –0.58 in wood, woodworking, pulp and paper industry and –0.61 in light industry. The output labor demand elasticities are significantly different from zero in all industries and vary from 0.12 in power and mining industry up to 0.31 in woodworking, pulp and paper industry[3]. The variation is in line with the basic intuition and the Hicks and Marshall’s labor demand rules. The products of light, food, and construction materials industries are likely to face more competitive markets, i.e. markets characterized by higher product price elasticities. This in turn results in higher own price demand elasticities of inputs. It is worth noticing that in the same industries where low wage labor demand elasticities are reported, high coefficients for lagged employment are observed (around 0.55) indicating significant labor demand inertia in these industries.

It is instructive that only weak support to the hypothesis of positive impact of trade barriers, such as higher tariffs rates, on labor demand is found on industry subsamples. In all industries with exception of metallurgy the corresponding coefficients at tariff variable were found to be insignificantly different from zero. The same results hold when using the ratio of import goods to the domestic output as a measure of trade protection. Only in light industry the higher share of imported goods has statistically significant negative impact on the number of employed. The low correlations between tariff level, import penetration rates and labor demand found in our regressions does not mean, however, that trade liberalization does not have impact on the labor demand since trade liberalization affects industrial structure and output in industries, which in turn affects demand for labor.

The estimation of labor demand elasticities show that they very not only across industries, but also across regions. Table 2.2 and Diagrams 2.1, 2.2 show the estimates of labor demand equation for each of the eleven economic regions and separately for Kalingradskaya oblast[4]. All labor demand elasticities with respect to output are significantly different from zero and vary from 0.15 in Povolzhskiy region up to 0.34 in the Northern economic region. The negative and statistically significant impact of real wage changes on labor demand by firms has been found in all 12 analyzed regions, with the Northern, East-Siberian economic regions and Kaliningradskaya oblast experiencing the highest own wage elasticities (around –0.55), while the North-Western economic region having the lowest elasticity of –0.18. Diagram 2.1 makes clear that wage labor demand elasticities are higher in the northeastern parts of the Russian Federation as compared to the western European part, with exception of Kaliningradskaya oblast.

Brown and Earle (2001) explain interregional differences in gross job flows by the differences in concentration of employers. At the least concentrated markets, i.e. at the markets with higher number of potential employers, the employees have more outside opportunities. This restricts firms to destroy job places. However, in our case we obtain the reverse result, i.e. we find higher wage labor demand elasticities in northeastern parts of Russia. We also conclude that the measure of concentration of employers used – Herfindal-Hershman index - is not significant in the most of the regressions. This implies that other sources of interregional differences could come into play, with distinctions in the industrial structure and variations in the degree of paternalism of regional authorities across regions[5] being the candidates. The degree of paternalism in turn could depend on the political orientation of the political leader of the region and on the level of political system development.

Turning to the tariff and import penetration variables included in the model to measure effect of trade openness on labor demand, we found positive impact of higher trade barriers on the number of employed in several regions. In all cases, except one, when these variables are significant, the tariff level coefficient is positive and the import penetration coefficient is negative.

Summing up, it could be concluded that the Russian labor market is characterized by rather low labor demand elasticities with respect to output and wages. Those are higher though than at the beginning of the transition period implying that on the whole Russian enterprises became more sensitive to the changes in output than they were in 1996-1997. The latter is supported by higher labor demand elasticity with respect to output and wages and lower inertia. The estimated labor demand elasticites vary not only across industries, but also across regions. The direct influence of trade openness on employment could be outlined only in some industries and regions, however. In most of the cases higher protection corresponds to higher number of workers demanded by firms, holding other things constant. With exception of some cases higher industry growth rates and bigger size of the regional economy also lead to higher employment.

The found low magnitudes of responsiveness of the labor demand to trade liberalization, both through the indirect effect of output changes and directly through the influence of tariffs and import penetration, suggests that the adjustment costs to trade liberalization in the form of changes in industrial labor demand are not high. Moreover, one should take into account the effects of the shift from industrial employment to employment in services which are to dampen the effect of trade shocks. The next section is to shed some light on the issue of inter-sectoral labor mobility and its determining factors.

3. Analysis of Inter-Sectoral Labor Flows

Trade liberalization, by changing production structure, induces changes in structure of labor demand, and via interaction with labor supply affects sectoral structure of employment. The latter induces inter-sectoral labor flows, with pace of adjustment to shocks of trade liberalization depending on degree of flexibility of economic system. This section studies inter-sectoral labor flows.

A stylized search model of inter-sectoral mobility of labor with a worker choosing one vacancy from several sectors would name the relative wages in different sectors adjusted for costs of retraining and vacancy arrival rates in sectors as the key factors behind the worker’s choice. The model predicts that the probability of movement from one sector into another depends on the magnitude of retraining costs, alternative (non-labor) income, costs of search, job availability (offer arrival rates) and employment opportunities in sectors – mean and variance of wages in comparing sectors. Trade liberalization influences probability to change sector of employment by affecting job arrival rate in various sectors and characteristics of wage distribution across sectors.

The study of direction and intensity of labor flow in Russia using vector auto regression model[6] and tracing the impact of exogenous variables on probabilities to move across sectors is based on Goskomstat publications[7].

Based on the time series data on labor flows between 17 and 35 sectors provided by Goskomstat, we integrate the sectors into three and five sectors: manufacturing sector, services sector and unemployment as the three-sector grouping and raw materials sector, processing sector, financial services and management sector, other services sector and unemployment as the five-sector grouping.

As the first step we estimated a model describing flows of labor between three basic groups of sectors: manufacturing, service and unemployment. These sectors were consolidated in the model of vector autoregression in the form:

[pic]= Р [pic].

Estimation results for components of the matrix of probabilities[8] are given in Tables 3.1 and 3.2. One can notice that obtained matrices of probabilities of transitions of labor between sectors appear diagonal - diagonal elements of the matrices are the only significant coefficients. The result implies that the main role is given to labor flows within the sectors.

To investigate the role of external macroeconomic factors on inter-sectoral flows of labor in Russia vector autoregression with exogenous variables was run[9]. Three parameters were used: total labor demand as measured by the number of vacancies registered with employment service, industrial sector labor demand measured as a volume of industrial output, and the ratio of average incomes to average wages as the indicator for alternative earnings. The results are presented in Table 3.3. It appears that the probability to stay in the manufacturing sector is higher the higher is the overall demand for labor. At the same time, the higher is the output (and hence labor demand) in manufacturing sector the lower is the probability to remain in manufacturing sector and the higher is the probability of unemployment.

Similar analysis could be carried out for five-sectoral division. The appropriate reduced form of the equation of vector auto regression looks like:

[pic]= Р [pic].

Results of an estimated of model of vector autoregression are given in Table 3.4. One may notice that the net flows of labor from processing sectors to raw material sector, and also to sector offering services, are small (the appropriate coefficients are insignificant). However the interesting fact is that the probability at which labor moves from sectors, offering other services (including transport, communications, public health services and education) to the sectors offering services of financial intermediatin is high enough (17.6%). Adding exogenous parameters shows that the probability to keep work in raw sectors is higher the higher is the number of vacancies registered with employment service. Moreover the probability of transition from processing sectors to raw material sector is lower the higher is the volume of industrial output.

The estimates allow generating the so called impulse response functions showing the dynamics of the response of employment in the studied sectors to exogenous shocks. As Diagram 3.1 shows, a positive employment shock in raw materials sector results in a new equilibrium with modest growth of employment in all other sectors except for the processing sector. This implies that an expansion of export creating sector due to trade liberalization generates redistribution of labor from processing industries to services.

[pic]

Diagram 3.1

When there is a positive employment shock in services, however, employment in processing sector declines sharply with employment in raw materials industries slightly increasing (Diagram 3.2). Hence, provided trade liberalization boosts financial services, employment in all the rest of the sectors except for processing industry is likely to increase.

[pic]

Diagram 3.2

Data for regions of the Russian Federation allow further studying changes in matrices of probabilities of inter-sectoral mobility[10]. To estimate responsiveness of the estimated probabilities of movement across sectors to exogenous variables reflecting retraining costs, alternative income, costs of job search, vacancy arrival rate, and characteristics of wage offer distribution (mean and variance), fixed effects model was estimated. The following variables were used as proxies for the aforementioned parameters: the share of employment in small business to reflect alternative income, GRP per capita and profitability of the enterprises in the region to reflect job arrival rate, the ratio of wages in industry to average wage in the region, the ratio of exports in GRP and the fraction of value added created in industry to GRP to characterize attractiveness of jobs in production vs. services, and the decile index of differentiation of accrued wages in regions to reflect uncertainty in regional labor markets.

Results are presented in Table 3.8 and can be summarized by the following table, with columns corresponding to sectors shedding labor and rows - to sectors accepting labor.

| |Production |Services |Unemployment |

|Production |Profitability(-), |Profitability(+), |GRP (+), |

| |Relative wage(-), |Relative wage(+), |Fraction of export (-), Profitability |

| |Fraction of value added (+) |Fraction of value added(-) |(-), |

| | | |Fraction of employed in small bus. (+), |

| | | |Fraction of value added (-) |

| | | | |

|Services |GRP (+), |GRP (-) |Profitability(+), |

| |Decile ratio(+), |Decile ratio(-) |Fraction of value added(-) |

| |Relative wage(-) |Relative wage(+) | |

| | |Fract. of value added(-) | |

| | | | |

|Unemployment |Fraction of export (-), Decile|GRP (-), |Fraction of value added(+) |

| |ratio(-), Relative wage(+) |Decile ratio(+), | |

| | |Fraction of value added(+) | |

One may notice that the main results support economic intuition. Some dependences do not quite correspond to simple economic intuition, however, as, for example, the reduction of probability of employment in industry with growth of profitability of the enterprises of region. It is necessary to emphasize that in the given framework labor supply rather than labor demand is studied. Hence, the increased profitability could cause increase of reservation wage along with the growth of demand for labor and, accordingly, reduction of probability of employment in the appropriate sectors.

To summarize, the main result of time series analysis holding exogenous variables constant is the diagonal property of probability of inter-sectoral labor mobility matrix. The latter is likely to signal about stability of employment distribution across sectors with reallocation occurring within broadly defined sectors mainly.

When variation in exogenous parameters is allowed non-diagonal elements turn out to be significant. This is an indication in favor of changes in inter-sectoral labor mobility reflecting changes in labor market structure. Industrial production and registered demand (vacancies) turn out to be significant factors determining inter-sectoral labor mobility. The higher is the labor demand the higher is the probability to preserve job in the relevant sector. At the same time, the higher is the industrial output the higher is the probability to move to service sector.

The data supports the hypothesis that an expansion of export creating sector due to trade liberalization would generate redistribution of labor from processing industries to services.

4. The influence of changes in sectoral production structure on sectoral wage distribution

Trade liberalization affects not only employment structure of an economy but earning profiles as well. The latter is to influence earnings and income inequality, and hence, poverty. The section presents an empirical study of wage responsiveness to changes in import tariffs of recent period. The analysis is based on Russian Longitudinal Monitoring Survey (RLMS), rounds 5-8, matched with sectoral indicators of trade liberalization.

Several procedures to estimate the responsiveness of wages to the changes in tariffs were used.

The fist approach is, following Goldberg and Pavcnik (2001), a two-step procedure with wage premiums due to industrial affiliation of workers being estimated at the first stage (controlling for observable differences in individual characteristics), and then the premiums being regressed on tariffs in fixed effect panel framework.

First-stage: [pic] ,

where i – worker, j – industry, wij - worker i’s wages, Hij – a vector of worker i’s characteristics (age; age squared; gender; two education type dummies, skill type dummies[11]), region (Moscow region dummy, region unemployment level, gross regional product) and firm type dummies (foreign or Russian, government or private), Iij - industry indicators that reflect worker i’s industry affiliation[12], wpj – industry wage premium.

Second-stage: [pic] ,

where wpj – industry wage premium, Tjt - the vector of tariffs, import, export, import and export ratios, Djt - time indicators.

The results are summarized in Table 4.1, while Table 4.2 reports the transformed[13] wage premiums for having job in the particular sector (machine-building industry is the reference category). The results show that workers in Fuel & Energy industries earned 82.03% more than workers in Machine-building industry with the same observable characteristics. In contrast, workers in Agriculture always earn 50-60% less than workers in Machine-building industry with the same observable characteristics.

Not all estimates are significant and the test for coefficient equality was done for 1994 (Table 4.3) and 1998 (Table 4.4) years. The test for coefficient equality for 1994 year shows that industry wage premiums can be divided into four groups relative to the wage premiums in Machine-building industry. The first group of industries with the largest wage premiums includes Fuel & Energy and Metal industries. The second group consists of Chemical, Building materials, Light and Food industries. Wood processing belongs to the third group. And the last group with the smallest wage premiums is Agriculture.

The results of the second step of the procedure are presented in Tables 4.5 and 4.6.

From the Table 4.5 it may be observed that there is a positive, although insignificant, effect of import tariffs on wage premiums. Therefore, using the procedure, it cannot be concluded that workers in more protected industries had larger wage premiums.

The second approach used is the estimate of whether affiliation with export-oriented industries, import-competing industries, or industries with high inter-industry trade (versus industries with high share of non-tradables) influence wages. The estimated equation is the following form:

,

where wij - worker i’s wages, Hij – a vector of worker i’s characteristics (age; age squared; gender; two education type dummies, skill type dummies), region (Moscow region dummy, region unemployment level, gross regional product) and firm type dummies (foreign or Russian, government or private), IOij – industry orientation dummies: export oriented, import competing, inter-industry trading.

The results are summarized in Table 4.7. It turns out that workers in import competing industries earn less than workers with the same observable characteristics in other industries. There is a positive, although insignificant, effect of being affiliated with export-oriented industries.

Finally, we analyzed how tariffs and volumes of import and export affect wages by applying fixed effect panel to the following equation:

,

where Tj- the vector of tariffs, import, export, Dj – region and time indicators.

The results are summarized in Table 4.8. There is a positive, although insignificant, effect of import tariffs on wages. In another specification lagged tariffs positively affect wages, although this result is again insignificant.

Overall, it has been obtained that wage premiums in export orientated Fuel & Energy and Metal industries are high. The industries are known to have relatively low tariff levels, large share of skilled workers employed. These industries have large profits and have increasing demand for labor. In Russia these industries are situated in the remote regions, where the supply of labor is limited, because of worker’s low mobility. Therefore, in Fuel & Energy and Metal industries employers have opportunities and needs for paying the high wage premiums to the employees.

Wage premiums in Wood processing industry and Agriculture, which have a large proportion of low skilled labor, are low. Firstly, Wood processing industry and Agriculture are not as profitable as Fuel & Energy and Metal industries. Hence, in Wood processing industry and Agriculture employers can not pay large wage premiums. The similar result was obtained for Mexican firms (Revenga A. (1997)): the firms with the large share of low skilled workers decrease the worker’s ability to capture rents.

There is no significant effect of tariffs on wages and wage premiums. This result is in line with the fact that there is no unique relation between wages and trade protection for every country. Some researchers have found a negative relation between wages and trade protection, whereas others have found a positive relation.

5. Wage differentials between skilled and unskilled labor

One of the effects of trade liberalization on economic and social development is its influence on the wage gap between skilled and unskilled labor. The Section studies the dynamics of wage differentials with respect to skills and the factors which determine the dynamics. RLMS database is used.

Wage gap between skilled and unskilled labor[14] is analyzed employing Oaxaca-Ranson decomposition. Wage equations for skilled and unskilled workers separately for each year are estimated, as well as separate regressions for 1994 and 1998 years for each type of worker (skilled and unskilled). This allows us to decompose the wage gaps both “statically” and “dynamically”.

The static wage gap decomposition – between skilled and unskilled for each year - led to the results presented in the Table 5.1. The wage gap between skilled and unskilled workers was 52.99% in 1994 and decreased to 46.32% in 1998. All else being equal, observable differences in education accounted for 55.4-66.5% of the Skilled/Unskilled gap in 1994 and 30.3-66.7% of the gap in 1998. Observable differences in Moscow & St. Petersburg accounted for 5.32-9.19% of the Skilled/Unskilled gap in 1994 and for 3.48-6.12% of the gap in 1998. It was found that work in non-manufacturing sector[15] tended to increase the wage gap between skilled and unskilled workers in 1994, whereas work in manufacturing sector decreased it in 1994 and 1998. Among all industries in manufacturing sector, Light and Food industries decreased Skilled/Unskilled gap most of all in 1994. However, in 1998 the wage differentials between skilled and unskilled labor was the smallest in Machine building industries in the manufacturing.

Overall, the major share of wage differentials is explained not by individual differences in workers’ endowments but by the differences in returns to the individual characteristics. Observable individual characteristics explain 44.77-49.62% of the wage gap between skilled and unskilled labor in 1994 and 20.99-28.39% of the wage gap in 1998, with the rest being explained by the difference in returns to individual characteristics.

Table 5.2 summarizes results of dynamic wage differential decomposition. Firstly, there was decline in wages of skilled and unskilled labor. Skilled workers had 40.69% decline in wages during the period of 1994 to 1998, and unskilled workers suffered more because they had 43.28% decline in wages. It follows that the manufacturing sector affiliation mitigated the decrease in the wages of all workers between 1994 and 1998, whereas work in non-manufacturing sector intensified this decrease, with unskilled workers suffering more: 10.87-14.24% decrease in wages of unskilled workers versus 7.04-7.29% decrease in wages of skilled workers. Gender wage differentials favored males: the wages of males decreased less than the wages of females during the period of observation both for skilled and unskilled workers.

Education turns to help the unskilled: as decomposition follows that if it has not been for education, the wage decrease of unskilled workers would be even more, however, there is no such effect for skilled labor. This result can be explained by the rise in education endowment of unskilled workers in 1998, in comparison with 1994. Education endowment of unskilled labor increased in such a short period due to reallocation of the labor force: there was an outflow of more educated workers from skilled group into unskilled group. For example, share of educated workers in unskilled occupation group was 60% in 1994 and 65% in 1998.

Overall, it has been obtained that the wage gap between skilled and unskilled decreased by 12.59% from 1994 to 1998. Taking into account the increase in tariff levels during the period of 1994-1998, it seems that the increase in tariff levels is likely to be associated with the decrease in wage gap between skilled and unskilled labor. But the evidence for this conclusion is not very strong, because the industry affiliation does not explain much of the wage variation between skilled and unskilled workers. This result coincides with that of obtained for Mexico (Cragg, Epelbaum (1996)): that industry dummies did not explain much of the wage gap between skilled and unskilled labor.

It was also obtained that work in manufacturing sector decreased wage gap between skilled and unskilled workers, whereas work in non-manufacturing sector increased it. It should be noted that the effect on unskilled workers was more harmful: the mitigation of the reduction in wages of manufacturing unskilled workers was less than that of in wages of manufacturing skilled workers, and the reduction in wages of non-manufacturing unskilled workers was sharper. These results can be explained by the following: although on average the wages in manufacturing are lower than the wages in non-manufacturing sector, they are less volatile. For example, many business firms were closed in 1998, because of the crisis. Many workers employed in private firms had large decrease in their wages, whereas in government firms, which adhere the labor law, the workers had a smaller decrease in their wages. Skilled workers are more mobile and have larger opportunities of finding the job; therefore, the wage reduction effect on skilled workers was less than that of on unskilled workers.

Conclusions

We investigate several features of the Russian labor market which play an important role in economic adaptation to trade reforms. We estimate wage and output labor demand elasticities; study the influence of import tariffs and import competition on labor demand; measure the intensity of inter-sectoral labor flows and analyze factors which determine the flows; study factors of inter-sectoral and inter-skill wage differentiation.

We find that own wage and output labor demand elasticities have grown significantly since the transition period but are rather low as compared to industrialized countries. Labor flows analysis shows that intensiveness of inter-sectoral labor flows is small. These findings provide evidence that labor market became more dynamic but we do not observe enough sectoral mobility. Given the high production concentration inherited from the soviet economy, low sectoral mobility could slow down the production structure changes in response to the relative prices changes.

The found low magnitudes of responsiveness of the labor demand to trade liberalization, both through the indirect effect of output changes and directly through the influence of tariffs and import penetration, suggests that the adjustment costs to trade liberalization in the form of changes in industrial labor demand are not high. Moreover, one should take into account the effects of the shift from industrial employment to employment in services which are to dampen the effect of trade shocks.

On the basis of the results obtained the prediction can be made that the future trade liberalization would not have a significant effect on wages. It is likely that tariff reduction and trade liberalization would lead to only slight increase in the wage differentials between skilled and unskilled labor. It is obtained that there is no significant effect of tariffs on wages and wage premiums. Therefore, no significant evidence for the claim that “workers in more protected industries earn relatively more” is found. The results of the paper suggest that there is a significant negative effect of import on wage premiums while export orientation positively affects wages.

References

Некипелов и др. (2002), «Народнохозяйственные последствия присоединения России к ВТО», НИС, АН РФ

Arellano. M., Bond S. (1991) Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies 58: 277-297.

Brown, J. David, Earle John S. (2001) Gross Job Flows in Russian Industry Before and After Reforms: Has Destruction Become More Creative. SITE, Stockholm School of Economics, CEU

Cherkashin Ivan (2003) “Russia’s Accession to WTO: Labor Demand Story” – NES Master Thesis.

Cragg M.I. and M. Epelbaum (1996): “Why has Wage Dispersion Grown in Mexico? Is it Incidence of Reforms or the Growing Demand for skills?”, Journal of Development Economics, Vol. 51, pp. 99-116.

Goldberg, P.K. and N. Pavcnik (2001): “Trade Protection and Wages: Evidence From the Colombian Trade Reforms”, NBER Working Paper No. 8575.

Grishina Elena (2003) “The Effect of Tariff Reduction and Trade Liberalization on Wages in Russia” – NES Master Thesis.

Konings, Jozef, Hartmut Lehmann (1999) Going back to Basics: Marshall and Labour Demand in Russia. LICOS, Centre for Transition Economies, Economics Department Catholic University of Leuven.

Nekipelov Denis (2003) “Analysis of Inter-Sectoral Labor Flows in Russia” – NES Master Thesis.

Revenga, A. (1997): “Employment and Wage Effects of Trade Liberalization: the Case of Mexican Manufacturing”, Journal of labor Economics, Vol.15, pp.S20-43.

|Table 2.1. Estimation of labor demand – sectoral differences. |

| |

|Table 2.2. Estimation of labor demand – regional differences. |

| |

DIAGRAM 2.1 - REGIONAL VIEW (short-run)

The short-run output labor demand elasticities

[pic]

The short-run wage labor demand elasticities.

[pic]

|Table 3.1. VAR estimation. | | |

|  |Production |Unemployment |Binary |F-statistics | | |

| | | |variable | | | |

|Production |0.99 |0.027 |0.002 |104.79 | | |

| |[0.01]*** |[0.20] |[0.80] | | | |

|Unemployment |0.0001 |0.977 |0.0007 |930.45 | | |

| |[0.03] |[0.02]*** |[0.0006] |  | | |

|Note: SE are in parentheses. | | |

| | | | | | | |

|Table 3.2. VAR estimation. | | |

|  |Services |Unemployment |Binary |F-statistics | | |

| | | |variable | | | |

|Services |0.99 |0.1 |-0.001 |123.14 | | |

| |[0.02]*** |[0.13] |[0.003] | | | |

|Unemployment |0.003 |0.955 |0.0001 |949.15 | | |

| |[0.003] |[0.02]*** |[0.0006] |  | | |

|Note: SE are in parentheses. | | |

| | | | | | | |

|Table 3.3. VAR estimation. |

|  |Production |Services |Production∙ |Production ∙ |Services∙ |F-statistic|

| | | |Demand for |Production |Demand for |s |

| | | |labor | |labor | |

|Production |0.511 |0.512 |0.468 |-0.179 |-0.401 |104.79 |

| |[0.20]*** |[0.21]*** |[0.23]** |[0.03]*** |[0.23]* | |

|Services |0.129 |0.869 |-0.07 |0.136 | |930.45 |

| |[0.03]*** |[0.03]*** |[0.02]*** |[0.04]*** |  |  |

|Note: SE are in parentheses. | | |

| | | | | | | |

|Table 3.4. VAR estimation. | |

|  |Raw sectors |Processing |Services |Other Services|F-statistic| |

| | |sectors |financial | |s | |

| | | |intermediation| | | |

| | | |and management| | | |

|Raw sectors |0.975 |0.013 |0.005 |-0.003 |216.84 | |

| |[0.15]*** |[0.06] |[0.05] |[0.03] | | |

|Processing sectors |0.047 |0.912 |0.034 |-0.017 |154.78 | |

| |[0.45] |[0.14]*** |[0.039] |[0.03] | | |

|Services financial intermediation and |-0.072 |0.183 |0.89 |0.176 |42.42 | |

|management | | | | | | |

| |[0.29] |[0.48] |[0.11]*** |[0.07]*** | | |

|Other Services |-0.137 |0.038 |0.109 |0.654 |10.11 | |

| |[0.52] |[0.68] |[0.19] |[0.13]*** |  | |

|Note: SE are in parentheses. | | |

|Table 3.5. VAR estimation. | | | |

|  |Raw sectors |Processing |Other |Unemployment|Binary |F-statistics| | | |

| | |sectors |Services | |variable | | | | |

|Raw sectors |0.975 |0.013 |0.005 |-0.003 |-0.003 |

| | | | | | | | | | |

|Table 3.6. VAR estimation. |

|  |Raw sectors |Processing |Services |Other |Raw sectors∙|Process |Other |Services |F-statistic|

| | |sectors |financial |Services |Demand for |industry∙ |Services ∙ |finance |s |

| | | |intermediati| |labor |Production |Demand for |intermed∙ | |

| | | |on and | | | |labor |Demand for | |

| | | |management | | | | |labor | |

|Raw sectors |0.406 |0.085 |0.102 |0.257 |0.551 |-0.129 |-0.256 |-0.053 |216,84 |

|GRP per capita |-0.302 |0.051 |0.534 |0.521 |-0.567 |-0.138 |0.064 |-0.401 |-0.167 |

| | [1.65] | [0.31]| [4.31]** | [3.27]** | [2.25]*| | | [2.85]** | |

| | | | | | |[1.15] |[0.34] | |[0.87] |

|Fraction of export in GRP |0.306 |-0.074 |-0.309 |0.429 |0.074 |-0.058 |-1.189 |-0.208 |0.401 |

| | | | [1.99]*| | | | [2.84]**| | |

| |[1.16] |[0.37] | |[1.31] |[0.15] |[0.29] | |[0.59] |[1.58] |

|Profitability of enterprises |-55.897 |59.331 |-26.491 |18.487 |-11.039 |24.45 |5.288 |-23.669 |-0.3 |

| | [2.78]** | [4.95]** | [2.12]*| | [0.40] | [2.92]**| | | |

| | | | |[1.40] | | |[0.28] |[1.81] |[0.02] |

|Decile differentiation ratio |-0.003 |0.004 |0.001 |0.013 |-0.014 |-0.002 |-0.01 |0.009 |0 |

| | | | | [2.70]** | [3.03]** | | | [2.27]*| |

| |[0.70] |[1.17] |[0.38] | | |[0.86] |[2.55]* | |[0.12] |

|Fraction of employed at small |-1.49 |0.387 |1.113 |-0.649 |1.134 |-0.337 |1.102 |0.668 |-0.337 |

|enterprises | | | | | | | | | |

| | | | [3.00]** | | | | | | |

| |[1.94] |[0.87] | |[0.76] |[1.57] |[1.04] |[1.50] |[0.80] |[1.30] |

|Fraction of wage in industry |-0.46 |0.306 |0.119 |-0.554 |0.631 |0.072 |0.431 |-0.286 |-0.026 |

|to average wage | | | | | | | | | |

| | [2.68]** | [2.44]*| | [3.04]** | [3.72]** | | [3.66]**| | |

| | | |[1.62] | | |[0.95] | |[1.49] |[0.34] |

|Value added in industry |0.008 |-0.004 |-0.006 |0.003 |-0.008 |-0.006 |-0.004 |0.008 |0.005 |

| | [2.87]** | [2.51]*| [3.82]** | | [2.45]*| [4.14]**| | [3.02]** | |

| | | | |[0.89] | | |[1.50] | |[2.45]* |

|Intercept |1.088 |-0.074 |0.175 |0.689 |0.553 |0.339 |0.028 |0.113 |0.712 |

| | [4.02]** | | | [2.11]*| | [3.13]**| | | [4.88]**|

| | |[0.40] |[1.14] | |[1.78] | |[0.11] |[0.42] | |

|Number of obs. |157 |157 |157 |157 |157 |157 |157 |157 |157 |

|Number of groups |79 |79 |79 |79 |79 |79 |79 |79 |79 |

|Table 4.1. OLS Regression (Machine building industry is the reference group) |

|  |1994 |1995 |1996 |1998 |

|Wage premiums in Fuel & Energy industries |0.497 |0.281 |0.521 | |

| |[2.94]*** |[1.73]* |[2.49]** | |

|Wage premiums in Metal industries |0.421 |0.154 |0.513 |0.157 |

| |[2.46]** |[-0.97] |[2.44]** |[-0.65] |

|Wage premiums in Chemical industries |0.209 |0.211 |0.625 |0.159 |

| |[-0.69] |[-0.98] |[2.61]*** |[-0.25] |

|Wage premiums in Wood processing |-0.243 |-0.14 |0.439 |-0.978 |

| |[-1.41] |[-0.79] |[2.14]** |[3.46]*** |

|Wage premiums in Building materials |0.233 |0.044 |0.004 |-0.109 |

| |[-1.16] |[-0.22] |[-0.01] |[-0.4] |

|Wage premiums in Light industry |0.226 |-0.044 |0.25 |-0.254 |

| |[-1.24] |[-0.23] |[-1.1] |[-0.93] |

|Wage premiums in Food industry |0.233 |0.044 |0.253 |-0.338 |

| |[-1.59] |[-0.29] |[-1.62] |[1.74]* |

|Wage premiums in other industries  |-0.202 |-0.075 |0.417 |-0.056 |

| |[-0.92] |[-0.39] |[1.73]* |[-0.19] |

|Wage premiums in Agriculture  |-0.761 |-0.706 |-0.705 |-0.891 |

| |[7.08]*** |[6.06]*** |[4.99]*** |[6.66]*** |

|Note: Dependent variable log hourly wage, t-statistics in parentheses, *, ** and *** indicate 10%, 5% and |

|1% significance, respectively. |

| |

| | | | | |

| | | | | |

|Table 4.2. OLS Regression (Machine Building industry is the reference group) |

|  |1994 |1995 |1996 |1998 |

|Wage premiums in Fuel & Energy industries |64.38 |32.45 |68.37 | |

| |52.35 |16.65 |67.03 |17 |

|Wage premiums in Chemical industries |23.24 |23.49 |86.82 |17.23 |

| |-21.57 |-13.06 |55.12 |-62.39 |

|Wage premiums in Building materials |26.24 |4.5 |0.4 |-10.33 |

| |25.36 |-4.3 |28.4 |-22.43 |

|Wage premiums in Food industry |26.24 |4.5 |28.79 |-28.68 |

| |-18.29 |-7.23 |51.74 |-5.45 |

|Wage premiums in Agriculture  |-53.28 |-50.64 |-50.59 |-58.98 |

|Dependent variable log hourly wage, the numbers are the percentages of the worker’s wage. |

| |

|Table 4.3. Test for coefficient equality for 1994 year, H0: wpi=wpj |

|  |wp11 |wp12 |wp13 |wp15 |wp16 |wp17 |wp18 |wp19 |wp20 |

|wp11 |1 | | | | | | | | |

|wp12 |0.72 |1 | | | | | | | |

|wp13 |0.38 |0.52 |1 | | | | | | |

|wp15 |0 |0 |0.18 |1 | | | | | |

|wp16 |0.28 |0.44 |0.95 |0.05 |1 | | | | |

|wp17 |0.23 |0.4 |0.96 |0.04 |0.98 |1 | | | |

|wp18 |0.18 |0.34 |0.94 |0.02 |1 |0.98 |1 | | |

|wp19 |0.01 |0.02 |0.25 |0.87 |0.12 |0.11 |0.07 |1 | |

|wp20 |0 |0 |0 |0 |0 |0 |0 |0.01 |1 |

| | | | | | | | | | |

| | | | | | | | | | |

|Table 4.4. Test for coefficient equality for 1998 year, H0: wpi=wpj | |

|  |wp12 |wp13 |wp15 |wp16 |wp17 |wp18 |wp19 |wp20 | |

|wp12 |1 | | | | | | | | |

|wp13 |1 |1 | | | | | | | |

|wp15 |0 |0.1 |1 | | | | | | |

|wp16 |0.43 |0.69 |0.02 |1 | | | | | |

|wp17 |0.23 |0.54 |0.05 |0.69 |1 | | | | |

|wp18 |0.07 |0.44 |0.04 |0.45 |0.78 |1 | | | |

|wp19 |0.55 |0.75 |0.02 |0.89 |0.6 |0.39 |1 | | |

|wp20 |0 |0.1 |0.76 |0 |0.02 |0 |0 |1 | |

|Table 4.5. Panel Regression | |Table 4.6. OLS Regression | | | | |

|with Fixed Effects | | | | | | |

|tariffs |2.149 | |import |-0.201 | | | | |

| |[-0.7] | | |[1.72]* | | | | |

|lagged tariffs |-3.557 | |export |0.485 | | | | |

| |[-1.7] | | |[-0.9] | | | | |

|1994 year |0.255 | |1994 year |0.112 | | | | |

| |[2.10]** | | |[-0.86] | | | | |

|1996 year |0.288 | |1996 year  |0.251 | | | | |

| |[2.59]** | | |[2.07]** | | | | |

|1998 year |0.144 | |1998 year  |-0.069 | | | | |

| |[-0.94] | | |[-0.56] | | | | |

|The dependent variable is estimated wage premium, t-statistics in | | | | |

|parentheses, *, ** indicate 10% and 5% significance, respectively. | | | | |

| | | | | |

| | | | | | | | | |

| | | | | |

|Table 4.7. OLS Regression (nontraded industry is a reference group) | |Table 4.8. Panel regression with fixed |

| | |effects. |

|  |1994 |1995 |1996 |1998 | |  |(i) |(ii) |

|high |0.189 | |-0.149 | | |tariffs |0.028 |-0.967 |

|inter-industry | | | | | | | | |

|trade | | | | | | | | |

| |[-0.433] | |[-0.241] | | | |[-0.04] |[-0.75] |

|export oriented|0.159 |0.299 |0.549 |0.056 | |1994 year |0.224 |0.196 |

| |[-1.3] |[2.209]** |[-3.788]*** |[-0.233] | | |[2.52]** |[2.29]** |

|Import |0 |0.1 |0.026 |-0.085 | |1996 year |-0.035 |0.023 |

|competing | | | | | | | | |

| |[-0.003] |[-0.831] |[-0.2] |[-0.481] | | |[-0.47] |[-0.33] |

|The dependent variable is log hourly wage, t-statistics in parentheses, | |1998 year |-0.361 |-0.324 |

|**, *** indicate 5% and 1% significance, respectively. | | | | |

| | | |[3.93]*** |[2.95]*** |

| | | | | | |GRP |-0.749 | |

| | | | | | | |[-0.4] | |

| | | | | | |tariffs | |0.763 |

| | | | | | |lagged | | |

| | | | | | | |  |[-0.49] |

| | | | | | |The dependent variable is log hourly wage, |

| | | | | | |t-statistics in parentheses, **, *** |

| | | | | | |indicate 5% and 1% significance, |

| | | | | | |respectively. |

| | | | | | | |

| |

|Table 5.1. State Decomposition of Log Hourly Wage Differences |

|  |1994 |1998 |

|  |Unskilled |Skilled |Unskilled |Skilled |

| |weight |weight |weight |weight |

|Total log wage differential |52.99 |52.99 |46.32 |46.32 |

|Age |-1.29 |-0.41 |-6.77 |2.95 |

|Gender |-8.24 |-4.98 |-5.06 |-10.67 |

|Education |66.5 |55.4 |66.7 |30.3 |

|Moscow & St. Petersburg |9.19 |5.32 |6.12 |3.48 |

|Wage premiums in Fuel & Energy |-2.97 |-3.25 |  | |

|industries | | | | |

|Wage premiums in Metal industries |-2.92 |-3.77 |-2.57 |-2.81 |

|Wage premiums in Chemical industries |-1.93 |-1.57 |-0.11 |-0.13 |

|Wage premiums in Machine building |-2.25 |-1.45 |-5.29 |-4.75 |

|Wage premiums in Wood processing |-1.15 |-1.43 |0.09 |0 |

|Wage premiums in Building materials |-0.86 |-1.74 |-1.91 |-5.42 |

|Wage premiums in Light industry |-3.45 |-4.01 |-1.45 | |

|Wage premiums in Food industry |-3.52 |-4.06 |-3.33 |-4.77 |

|Wage premiums in other industries  |0.3 |0.32 |-0.01 |-0.01 |

|Nonmanufactoring |2.16 |10.4 |-18 |12.78 |

|Attributable to difference in |49.62 |44.77 |28.39 |20.99 |

|observable characteristics | | | | |

|Attributable to difference in |50.38 |55.23 |71.61 |79.01 |

|unobservable characteristics | | | | |

|Table 5.2. Dynamic Decomposition of Log Hourly Wage Differences |

|  |Skilled |Unskilled |

|  |1994 year |1998 year |1994 year |1998 year |

|Total log wage differential |-40.69 |-40.69 |-43.28 |-43.28 |

|Age |-0.46 |-0.59 |-1.57 |1.53 |

|Gender |0.75 |0.46 |0.05 |0.1 |

|Education |-3.14 |-3.63 |6.28 |4.13 |

|Moscow & St. Petersburg |-0.09 |-0.11 |1.94 |2.27 |

|Wage premiums in Fuel & Energy |4.82 | |2.43 | |

|industries | | | | |

|Wage premiums in Metal industries |1.14 |1.29 |0.49 |0.47 |

|Wage premiums in Chemical industries |2.66 |2.11 |0.89 |1.03 |

|Wage premiums in Machine building |1.82 |2.21 |1.87 |3.17 |

|Wage premiums in Wood processing |1.31 |0.51 |0.24 |0 |

|Wage premiums in Building materials |-0.63 |-0.44 |1.21 |1.19 |

|Wage premiums in Light industry |0.95 |0.35 |1.03 | |

|Wage premiums in Food industry |1.17 |1 |1.22 |1.3 |

|Wage premiums in other industries  |0.2 |0.31 |0.45 |0.91 |

|Nonmanufactoring |-7.29 |-7.04 |-10.87 |-14.24 |

|To difference in observable |3.22 |-3.58 |5.65 |1.86 |

|characteristics | | | | |

|To difference in unobservable |96.78 |103.58 |94.35 |98.14 |

|characteristics | | | | |

-----------------------

[1] We estimate the following form of labor demand equation:

[pic]

where Li,t – is the number of workers employed at the enterprise at period t, Qi,t – sales of enterprise i during year t, and Wi,t – average wage at enterprise i in year t, X – is a set of other variables, dt – time dummies. In our case X contains such regressors as tariffs, import penetration index, unemployment level, Gross Regional Product over Gross Domestic Product in the Russian Federation, industrial output index, real regional average wage and Herfindal-Hershman concentration Index.

[2] Herfindal-Hershman index, average wage in the region, GRP per capita over the all-Russia GDP per capita and time dummies were used to control for regional and time differences and turned to be significant.

[3] Speaking about differences in coefficients between regions and industries we do not mean that they are not statistically identical. Some kind of poolability test is required here. However, the existing first-order autocorrelation of the residuals, which does not cause inconsistency of Arellano-Bond GMM estimator, makes it quite difficult to construct the formal test. This could be the sphere for further research.

[4] The estimates of the model also show that enterprises, located in the regions with higher unemployment or smaller economy size, are likely to have lower number of employed. The mixed results were obtained for industry growth. The higher rates of industry growth rates correspond to higher employment in power, metallurgy, machinery construction and food industries, but to lower employment in construction materials and light industry.

[5] In this sense our result could be driven by less paternalism of regional authorities in the North-East of Russia as compared with the European part.

[6] It is possible to show that probabilities to move from one sector to another comprise a stochastic matrix NxN with elements P = (pij) . With probabilities being stable over time, the dynamics of distribution of employment over sectors is determined by Markov process: xt+1 = P xt , where x – vector of employment distribution over sectors. As a result the dynamics of employment distribution across sectors is described by vector auto regression of order 1.

[7] “Social and economic position of Russia, 1998 – 2003” and “Short-term economic indicators of the Russian Federation, 2003”.

[8] To check whether the dynamics of inter - sectoral distribution of labor under steady matrix of probabilities of transitions of workers can be described by Markov equation we estimated vector auto regression of the second order. Coefficients for lagged terms of the second order appeared insignificant thus supporting our hypothesis.

[9] Nonlinearity was taken into account by including the products of shares of employment in the appropriate sector and values of macroeconomic factors. It provided linearity of dependence of probability of transition of worker from one sector to another from investigated exogenous parameters. Indeed, since the estimated equation is of the form xt=Pxt-1+Zt xt-1, it can be transformed to expression xt =(P + Zt )xt-1, and the matrix of probabilities of transition in such formulation depends on the matrix of exogenous parameters Zt.

[10] The procedure to estimate components of transition matrix is to search a matrix on a basis of k observations of realization of dsuch formulation depends on the matrix of exogenous parameters Zt.

[11] The procedure to estimate components of transition matrix is to search a matrix Р on a basis of k observations of realization of distribution of workers in sectors x1, …, xk. Data allow to build k-1 equations to calculate components of matrix Р which has the property that xN=PxN-1=P2xN-2=… PN-1x1, and Р∙1=1. Direct calculations of the matrix of transition probabilities between sector result in probabilities greater than one or smaller zero. This is due to a significant share of errors in initial data and because the process of displacement of workers between sectors can deviate from Markov’s. To estimate the components of the probability matrix the following problem was solved in GAUSS:[pic] for all j under condition Р(x1 x2…xk-1)= (x2 x3…xk ) и 0 ................
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