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Applied Econometrics and International Development. AEID.Vol. 5-3 (2005)

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THE GENDER EARNINGS DIFFERENTIAL IN RUSSIA AFTER A DECADE OF ECONOMIC TRANSITION

OGLOBLIN, Constantin* Abstract The gender earnings differential in Russia 2000-02 is examined using a nationally representative household survey. Adjusted for hours worked, women’s monthly earnings are 62% of men’s, and women’s long-run effective wage is 69% of men’s. While women’s higher human capital endowments reduce the gender earnings differential, job segregation by gender accounts for about three quarters of it. Wage arrears compress earnings actually received and slightly reduce the gender pay gap. The unexplained part of the differential is largely attributed to discrimination against women. JEL classification: J1, J3, P2 Key words: gender, Russia, transition, wages. 1. Introduction Russia’s current system of wages has evolved from the legacy of the Soviet era through a decade of radical economic reforms. In Soviet Russia the principle of equal pay for equal work, institutionalized through the Constitution and Labor Codes, and the highly centralized wage system seemed to minimize the possibility for a systematic gender pay gap to exist. Nevertheless, researches have found that a gender wage differential of about 30 percentage points in favor of men existed in the Soviet economy during the 1970s and 1980s (McAuley (1981), Ofer and Vinokur (1992) Gorbachev’s reforms during the perestroyka period (1987-1991) relaxed the rigidities of the centralized wage system and let economic forces play a more important role in determining wages. * Dr. Constantin Ogloblin, School of Economic Development, Georgia Southern University, P.O. Box 8152, Statesboro, GA 30460, USA; e-mail: [email protected]. Acknowledgement: The author thanks Lyudmila Leontyeva for her useful comments and her help with industry coding.

Applied Econometrics and International Development AEID.Vol. 5-3 (2005)

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Katz (2001) examined the gender wage differential using household data collected in 1989. She calculated female/male wage-ratios of 66% for monthly and 73% for hourly wages and attributed most of the gender pay gap to discrimination against women.1 The first years of Russia’s economic “shock therapy,” which started in 1992 with the price and wage liberalization, resulted in a wage system that Standing (1996) characterizes as the most flexible conceivable. The centralized system of wages virtually ceased to exist. In the unstable economic environment of the first years of transition, available resources and adjustment for inflation became key determinants of wages. The managers’ influence on wage decisions increased greatly with virtually no workers’ interference (see Commander et al. (1995 and 1996)). Newell and Reilly (1996) estimated the gender wage differential in 1992 at about 30 log percentage points (a female/male ratio of 74%), and suggested that it was largely a result of gender differences in returns to worker characteristics. 2 Glinskaya and Mroz (2000) calculated gender wage ratios for Russian urban workers in 1992-95. They found that during this period, the female/male ratio of mean log hourly wages remained relatively stable at about 70%, with some slight fluctuations coming from changes in the wages received by a minority of men in the tails of the wage distribution. While differences in hours worked appeared to explain about half the gender differential in monthly wages, the worker characteristics included in the model explained almost none of the differential in hourly wages. Brainerd (1998, 2000) found that the female/male ratio of mean monthly wages fell from 80% in 1991 to 68% in 1994 and argued that this increase in the

1 For a criticism of this interpretation see Ogloblin (2002). 2 Given the relatively short time period between the introduction of the “shock therapy” measures (the beginning of 1992) and the time when the data were collected (Summer and Fall of 1992), the observed wage structure only partly reflected the impact of these measures on the gender pay gap.

Ogloblin, C. The gender earnings differential after a decade of economic transition

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gender gap was due almost entirely to the increase in overall wage inequality, not to gender-specific factors. 3 The period of 1994-96 may be viewed as the second stage of Russia’s economic transition. On this stage, the mass privatization program had been completed, the economic decline coupled with high inflation continued, but the official unemployment rates remained surprisingly low. This is also the period when wage arrears became rampant, affecting more than 100,000 enterprises and organizations in all industries and regions and about half of all workers. Ogloblin (1999) examined the gender earnings differential during this period. He calculated the female/male ratio of mean log monthly wages for 1994-96 at 66.4%. Equalization of hours worked by assuming a standard 41-hour workweek raised the gender wage ratio to 68.4%, and after the correction of the fully observed earnings for selectivity bias—which existed due to the high incidence of wage arrears—the ratio rose to 71.7%. An Oaxaca-Blinder-Neumark decomposition explained 92.6% of the gender pay gap by occupational, industrial, and firm-type (private versus state) segregation by gender. In 2000-02, Russia experienced a new phase of transition, with the economic environment and labor-market conditions being quite different from those in 1994-96. After recovering from the 1998 financial crisis, the economy started to grow, and labor market responded quickly with rising real wages, falling unemployment, and declining wage arrears. The growing understanding of the need to adjust Russia’s labor relations to the new economic conditions resulted in the passage in 2001 of a new Labor Code, which took a modest step in making labor-market legislation more consistent with a market economy. On this new stage of economic transition, how big is the gender earnings differential and what factors determine it? The present

3 Given that all previous Soviet-period evidence suggests a female/male monthly wage ratio of 65-70%, the 80% figure for 1991 looks anomalous, so the increase in the gender pay gap may be overstated.


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study attempts to answer these questions. Building on the methodology used in Ogloblin (1999), I adjust workers’ monthly earnings for hours worked and correct the reported received earnings for the selection bias resulting from wage arrears. Then, I apply the Oaxaca-Blinder-Neumark method to decompose the gender earnings differential and quantify the contributions of different factors to the pay gap. Using similar methodology makes the results of this study directly comparable with those for 1994-96 in Ogloblin (1999). I also suggest some methodological improvements. First, the selectivity correction procedure is implemented by estimating the Heckman model by maximum likelihood, which is a more efficient way to obtain unbiased wage equation estimates than the previously used two-step method. Second, in addition to the gender differential in selectivity corrected monthly earnings, I estimate and decompose the gender differential in effective long-run wages. 2. Data and Statistical Procedures The study uses individual-level data from Rounds 9-11 of the Russia Longitudinal Monitoring Survey (RLMS), a household-based nationally representative survey designed and implemented by an interdisciplinary partnership of leading Russian and American experts.4 The survey draws a multi-stage probability sample. First, 1,850 consolidated rayons (administrative subdivisions similar to counties in the United States) serve as primary sampling units (PSUs). The number of households drawn into the sample is 4,718. Then, within each selected PSU the population is stratified into urban and rural substrata, and the target sample size is allocated proportionately to the two substrata. The data for Rounds 9, 10, and 11 were collected in September–December 2000, 2001, and 2002 respectively. In both urban and rural substrata, interviewers were

4 The survey has been coordinated by the Carolina Population Center (CPC) at the University of North Carolina at Chapel Hill in collaboration with Paragon Research International and Russian Academy of Sciences. Detailed project descriptions, including the sampling techniques and the RLMS datasets, are available from the RLMS Web site (http://www.cpc.unc.edu/projects/rlms/rlms_home.html).


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required to visit each selected dwelling up to three times to secure the interviews. They were not allowed to make substitutions of any kind. The household response rate exceeded 80%, and individual questionnaires were obtained from over 97% of the individuals listed on the household rosters. The sample in the present study includes working individuals, women aged 18-54 years old and men aged 18-59 years old, which are considered the normal working ages for women and men in Russia. A preliminary analysis of the data has shown that the differences and trends in the gender patterns of employment and wages within the period of 2000-02 are not well-defined and much less significant than those between this period as a whole and the previous periods. Hence, pooled data from the three rounds of the RLMS are analyzed as a homogeneous dataset representing the new phase of Russia’s transition. The Stata/SE 8.2 software package and statistical procedures were used to process data and generate econometric results.5 3. The Gender Pay Gap in Russia 2000-02 Two separate variables are used to measure wages and the gender pay differential. The first measure is the amount of after-tax earnings—including wages, bonuses, and benefits—received by the respondents at their primary place of employment in the last month before the interview.6 One disadvantage of this variable is that, in the presence of the widespread wage arrears, the reported amounts of earnings received last month do not always represent workers’ regular monthly earnings, but may also reflect non-payments or underpayments of wages, as well as back wages paid in the reference month. This wage measure, however, is the only one available in the

5 The detailed descriptions of the statistical methods and the Stata program files are available from the author on request. 6 According to the RLMS, in 2000-02, only 4.0% of wage employed women and 2.7% of men had a second place of employment where they worked for a wage. Hence, secondary employment is not likely to significantly affect wages and the gender pay gap.


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RLMS Rounds 57. I use it to ensure proper comparisons of the present study’s findings with the previous stage of transition. To make this measure of monthly earnings more accurate, Ogloblin (1999) suggested to limit the sample to only those workers who are not owed back wages—that is, whose reported wages are not likely to be affected by wage arrears—and then correct the estimated wages and the gender differential for possible selectivity bias. The present study adopts this approach. In the RLMS Rounds 9-11, respondents also report their after-tax average monthly wages at the primary place of employment in the last 12 months before the interview, regardless of whether they were paid on time or not. That is, this variable reflects workers’ effective monthly wages smoothed over time. Table 1 shows the geometric -mean last-month real earnings calculated for those workers who received them in full, the geometric-mean wages averaged over 12 months for 2000-02, and the female/male earnings ratios. Table 1. Real Wages by Gender (in December 2000 rubles).a

1994-96 2000-02 Earnings received last month b

Women 1634 1377 Men 2438 2178

Female/male ratio 0.670 0.632 Average monthly wage c

Women — 1223 Men — 1854

Female/male ratio — 0.660 a Geomemetric mean wages, calculated from the RLMS data, Rounds 5-7 and 9-11, for wage employed workers, women aged 18-54 and men aged 18-59. Real wages are calculated using the official monthly CPIs reported in Goskomstat of Russia (2003). b After-tax wages, bonuses, and benefits received from the primary place of employment in the last 30 days before the interview; calculated for those who worked at least 10 hours per week in the last 30 days before the interview and were not owed back wages. c After-tax average monthly wages received from the primary place of employment in the last 12 month before the interview; calculated for those who worked at the current place of employment for at least one month.


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Real monthly earnings of women and men after the recovery from the economic crisis are still somewhat lower than in the pre-crisis economy. The gender earnings ratios, however, show little change. After a decade of Russia’s economic reforms, they remain remarkably close to the estimates of the gender pay gap at earlier stages of Russia’s transition and to those in the Soviet economy. 4. The Methodology of the Gender Earnings Differential

Decomposition What shaped the gender earnings differential in 2000-02? To answer this question, I decompose the differential using the method originally proposed by Oaxaca (1973) and Blinder (1973) with modifications suggested by Neumark (1988). The decomposition may be written as follows: ( ) ( ) ( )m f p m f m p m p f fW W− = − + − + −ß X X ß ß X ß ß X (1) where Wm and Wf are log mean earnings of men and women, Xm and Xf are vectors of mean characteristics of men and women, ßm and ßf are vectors of coefficients estimated from the male and female earning equations, and ßp is the vector of the earning-equation estimates obtained from the pooled sample of women and men, which according to Oaxaca and Ransom (1994) may be viewed as nondiscriminatory earning structure. On the right-hand side of Equation 1, the first term is the component of the earnings differential that is explained by the variables in the equation. The second and the third terms account for the unexplained part of the differential. These terms (sometimes referred to as the male wage advantage and the female wage disadvantage respectively) are often viewed as a measure of gender discrimination, but as explained in Ogloblin (2002), may also reflect unobserved worker endowments and preferences. I first estimate the earnings equation where the dependent variable is the log of the last-month earnings (Model A). The sample here is limited to only those workers who were not owed back wages. To


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account for possible selectivity bias, the following selection model is applied: V *

i = AZi + ui ; Vi = 1 if V *i > 0; Vi = 0 if V *

i ≤ 0 (2)

Pr( 1) ( )i iV = = Φ AZ (3) where Zi is the vector of characteristics that are instrumental in selecting individual i into the sample with no arrears, A is the vector of coefficients to estimate, u ∼ N(0, 1) is the error term, Vi = 1 if individual i is owed no back wages and Vi = 0 otherwise, and Φ (•) is the cumulative probability function for the standard normal variable. In the regression model s

i i iW e= +ßX (4)

the earnings, siW , are observed only if Vi = 1, and β is the vector of

parameters to be estimated. If e ∼ N(0, s) and corr(e, u) = ρ, then E [W s

i Vi = 1] = βXi + βλλi (5)

where ßλ = σρ and

λi = )f(AZ)(AZ

i

iϕ (6)

where ϕ (•) is the probability density function for the standard normal variable. I estimate the parameters of this selection model by maximum likelihood, separately for women and men, and correct the last-month earnings variable for selectivity as follows: Wi = W s

i - βλ λi (7)

where Wi is individual i’s log earnings corrected for selectivity bias. I also estimate wage equations where the dependent variable is the average monthly wage received in the last 12 month (Model B). Before computing and decomposing the gender pay differentials, I adjust earnings reported by the RLMS respondents for inflation and time trend by including a set of dummy variables (t) that represent the interview month in vector X and calculating time adjusted log wages ( Wt ) as follows:

ti i t iW W= − ß t . (8)


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To account for the effects of gender differences in hours worked and worker status, I apply the technique proposed by Blau and Kahn (1996) with modifications suggested by Ogloblin (1999). The regression equation then may be written as follows: (ß ß ß )i t i p ph fh xW p ph fh= + + + +ß t ß X (9) where p is the dummy for part-time work (defined as less than 35 hours per week), and ph and fh are the interactions of log weekly work hours with part- and full-time status.7 The inclusion of the status variable and its interaction with hours terms is justified by the fact that the monthly wages of part-time workers may differ from those of full-time workers due not only to reduced hours, but also to different wage systems and wage-setting practices for part-time and full-time workers. The estimates of parameter ßp, ßph, and ßfp, are used to adjust each person’s earnings by assuming a standard 41-hour workweek, as follows:

ß ß ß ( ln41)h ti i p ph fhW W p ph fh= − − − − (10)

where Wh is the adjusted monthly earnings, and the difference (Wt – Wh) shows the effect of hours worked and worker status, which in terms of gender earnings differential may be expressed as

( ) ( )t t h hh m f m fD W W W W= − − − (11)

where the subscripts m and f indicate male and female workers’ earnings. Log earnings adjusted for hours and status (Wh) are used in Equation 1 to decompose the gender differential. The first subset of independent variables included in the wage equations reflects conventional human capital characteristics—formal education and work experience. The gender differences in Russian workers’ human capital endowments are reflected in Table

7 The worker part-time or fulltime status is best defined by usual weekly hours worked, the data on which are not available in the RLMS Rounds 5-7. For the purposes of comparison, in Model A hours actually worked last month are used as an indicator of the worker’s status.


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2. As follows from the table, Russian women are better educated than are men.8 About 57% of women have a university or specialized secondary degree, while the respective figure for men is only 35%. Men are more inclined toward vocational education, which is consistent with the traditional gender job stereotyping and occupational patterns. Table 2. Human Capital Endowments by Gender, 2000-02 (%).a

Variable Women Men Education b

Incomplete secondary 7.6 14.8 General secondary 17.3 22.5 Secondary with vocational training 17.9 27.4 Specialized secondary 33.2 16.8 University 24.0 18.5

Professional training in the last 2 years 10.1 9.0 Labor-market experience (years)

Mean 16.9 17.9 Standard deviation 10.4 11.2

Tenure (years) Mean 7.7 6.6 Standard deviation 8.2 8.1

a Calculated from the RLMS data, Rounds 9-11, for wage employed workers, women aged 18-54 and men aged 18-59b The highest degree obtained.

Education is represented by dummy variables for four levels of

educational attainment—incomplete secondary, secondary with vocational, specialized secondary, and university—with general 8 General secondary education in Russia is 10-11 years of a general school. Secondary education with vocational training can be obtained at vocational schools, which offer vocational training either along with complete general secondary education (for those who enter them with only 7-8 years of general schooling) or in addition to general secondary education. Students of specialized secondary schools acquire some special (technical, medical, pedagogical, art) knowledge along with or in addition to general secondary education.


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secondary education as the baseline. General labor-market experience is reflected by the total number of years in the individual’s employment record, and its square term is included to model typical experience-earnings profiles. Specific human capital is reflected by tenure with the current firm and a dummy variable that equals one if the worker took courses for the improvement of professional skills during the last two years. The marital status dummy (equals one if the respondent is married or lives together with her partner) is expected to capture the influence of family responsibilities on earnings. Three categorical variables account for industrial, occupational, and sectoral distribution of employment. The industry dummies are defined by the classification based on the tabulation categories in International Standard Industrial Classification (ISIC) with the specific features of the Russian transition economy taken into account9 Four firm-type dummies represent budgetary organizations, private firms, firms with mixed ownership, and organizations whose ownership is indeterminate, with state enterprises as the baseline category.10 To account for the gender earnings differential resulting from occupational segregation by gender I adopt the approach suggested by Ogloblin (1999). That is, the four-digit occupations within each one-digit category are grouped according to the following rule: an occupation is “female” or “male” if more than 70% of those in the occupation are women or men, respectively.11 The occupation dummies included in Equation 1 reflect this categorization.12

9 The industries are coded by the author from the original textual responses. 10 By state enterprises I mean for-profit businesses owned by the state or municipal governments, while budgetary organizations are non-profit organizations directly financed from the state or municipal budgets. 11 Occupations were coded by the RLMS team, generally following the International Standard Classification of Occupations (ISCO-88) but taking into account the idiosyncrasies of some occupations in Russia. 12 Some very small gender-specific categories are aggregated into bigger one-digit classes.


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To capture regional economic conditions, the decomposition equation includes a dummy for rural areas and a set of controls for the two largest metropolises—Moscow and St. Petersburg—and six macro regions: Caucasus, North, Ural, Volga, West Siberia, and East Siberia, with the Central region as the baseline.13 The specification of vector Z in the selection Equation 3 reflects the understanding that wage arrears in Russia depend on firm- and wage-system-specific characteristics, region, and time, rather than individual-specific characteristics (Earle and Sabirianova (2002), Lehman et al. (1999)). Thus, vector Z includes time-trend variables, a dummy for part-time status, and categorical variables for firm type, industry, occupation, and region. The earnings equation with selectivity correction (Equation 2) is estimated by maximum likelihood, separately for women and men. 5. Explaining the Gender Wage Differential The earnings equation estimates are reported in Table 3, and the gender wage differential is decomposed in Table 4. 14 The gender differential in time-detrended earnings is 0.454 log points for the earnings received last month and 0.418 log points for the 12-month average monthly wages, which corresponds to the female/male earnings ratios of 63.5% and 65.9% respectively15 The maximum likelihood estimation of Equation 2 has revealed significant negative selection of both women and men into the no-

13 Since the RLMS data are not regionally representative, the macro region variables are used only to control for different regional conditions and not to draw conclusions about the effect of a particular macro region on wages and the gender differential. 14 The coefficient estimates from Equation 3, the time-trend, hours, and status coefficient estimates from Equation 9, all computations of adjusted earnings and the details of gender differential decomposition are available from the author on request. 15 Notice that these figures are very close to those in Table 1, i.e. the calculated gender earnings ratios are robust to the method used to adjust wages for inflation.


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arrear sector (ρ is negative at the 0.01 level), i.e. workers with lower contractual wages are more likely to receive their wages in full than those with higher contractual wages. This suggests that wage non-payments compress earnings actually received by workers and, since men are more highly paid, mitigate the gender differential measured by the wages received compared to that in contractual wages. This reasoning is supported by the fact that the incidence of wage arrears is higher among men than among women (see Ogloblin (2005)). The mitigating effect of wage arrears on the gender earnings differential, however, is rather small: the differential in the selectivity-corrected earnings is only by 3.5 log percentage points greater than that calculated for workers who received their wages in full. Table 3. Earnings Equation Estimates by Gender. a

Model A b Model B c Variable

Women Men Women Men Education (general secondary)

Incomplete secondary

-0.046 (0.049)

-0.092** (0.045)

-0.104*** (0.036)

-0.111*** (0.031)

Secondary with vocational

0.107*** (0.037)

0.038 (0.037)

0.045 (0.028)

-0.031 (0.026)

Specialized secondary

0.128*** (0.034)

0.097** (0.041)

0.091*** (0.025)

0.056* (0.030)

University 0.403*** (0.040)

0.391*** (0.048)

0.392*** (0.030)

0.285*** (0.034)

On-the-job training 0.178*** (0.036)

0.060 (0.044)

0.130*** (0.027)

0.128*** (0.032)

Years of experience 0.016*** (0.004)

0.025*** (0.004)

0.021*** (0.003)

0.016*** (0.003)

Years of experience2/100

-0.036*** (0.011)

-0.063*** (0.011)

-0.042*** (0.008)

-0.042*** (0.008)

Tenure 0.003** (0.002)

0.001 (0.002)

0.002* (0.001)

-0.003** (0.001)

Married -0.026 (0.024)

0.133*** (0.038)

-0.054*** (0.018)

0.145*** (0.027)

Industry (manufacturing) Agriculture -0.042

(0.076) -0.235*** (0.075)

-0.388*** (0.044)

-0.655*** (0.038)

Extractive industries 0.624*** 0.523*** 0.673*** 0.595***


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(0.104) (0.071) (0.070) (0.048) Construction 0.363***

(0.087) 0.262***

(0.054) 0.166***

(0.054) 0.176***

(0.035) Transportation 0.357***

(0.074) 0.100*

(0.055) 0.220***

(0.049) 0.103***

(0.036) Trade, consumer services

-0.136*** (0.045)

-0.043 (0.054)

-0.051 (0.032)

0.034 (0.039)

Housing, utilities, municipal services

0.178* (0.095)

0.131 (0.093)

0.053 (0.063)

0.134** (0.060)

Health care -0.413*** (0.094)

-0.485*** (0.137)

-0.194*** (0.064)

-0.144 (0.088)

Education -0.461*** (0.091)

-0.510*** (0.121)

-0.286*** (0.062)

-0.214*** (0.079)

Professional services -0.058 (0.057)

-0.084 (0.072)

0.075* (0.040)

0.011 (0.050)

Public administration 0.033 (0.105)

–0.065 (0.144)

0.257*** (0.073)

0.247*** (0.093)

Protective services –0.057 (0.116)

0.035 (0.104)

0.080 (0.078)

0.141** (0.070)

Others –0.448*** (0.083)

–0.360*** (0.110)

–0.406*** (0.056)

–0.403*** (0.079)

Type of firm (state enterprise) State organization –0.041

(0.089) –0.141 (0.100)

–0.131** (0.060)

–0.255*** (0.063)

Mixed ownership 0.147*** (0.046)

0.057 (0.045)

0.122*** (0.031)

0.135*** (0.029)

Private ownership 0.205*** (0.039)

0.064 (0.040)

0.186*** (0.027)

0.098*** (0.026)

Occupation (operator, other) Manager (all categories)

0.299*** (0.071)

0.064 (0.076)

0.220*** (0.051)

0.129** (0.054)

Professional, female 0.282*** (0.061)

–0.074 (0.115)

0.256*** (0.044)

0.134 (0.083)

Professional, other 0.111* (0.065)

0.006 (0.078)

0.046 (0.049)

0.102* (0.054)

Technician, female 0.066 (0.055)

–0.038 (0.146)

0.013 (0.040)

–0.084 (0.115)

Technician, other 0.239*** (0.063)

–0.006 (0.069)

0.176*** (0.048)

0.108** (0.050)

Clerk, female –0.056 –0.085 –0.096** 0.071


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(0.055) (0.112) (0.042) (0.083) Service/market, female

–0.197*** (0.056)

–0.003 (0.106)

–0.227*** (0.043)

–0.010 (0.083)

Service/market, male 0.144 (0.180)

0.038 (0.091)

0.128 (0.142)

0.055 (0.066)

Craft, male 0.057 (0.066)

–0.104** (0.052)

0.017 (0.049)

–0.002 (0.038)

Operator, male 0.225** (0.106)

0.032 (0.053)

0.098 (0.077)

0.037 (0.038)

Unskilled, female –0.275*** (0.058)

–0.407** (0.165)

–0.250*** (0.045)

–0.403*** (0.124)

Unskilled, male –0.124 (0.114)

–0.396*** (0.065)

–0.499*** (0.073)

–0.321*** (0.047)

Unskilled, other –0.209** (0.097)

–0.468*** (0.110)

–0.220*** (0.060)

–0.327*** (0.065)

Rural –0.138*** (0.036)

–0.265*** (0.046)

–0.302*** (0.023)

–0.437*** (0.026)

ρ –0.767*** –0.713*** — — σ 0.743 0.789 — — βλ –0.570 –0.562 — — N 3549 3010 5251 4552 Wald χ2 2337*** 1382*** — — F — — 97.8*** 82.5*** R2 — — 0.539 0.533

a Standard errors are in parentheses; baseline categories are in italics, regressions also include dummies for missing/indeterminate industry and type of firm. b The dependent variable is log earnings received last month. c The dependent variable is log average monthly wage received during the last 12 months. * Statistically significant at the 0.1 level; ** at the 0.05 level; *** at the 0.01 level (two-tailed tests). This also partly explains why the differential in the selectivity-corrected earnings (Model A) is greater than that in the effective long-run wages (Model B). Another explanation of a smaller gender differential in Model B compared to Model A is that bonuses (which are included in the earnings measure in Model A, but not in Model B) favor men.


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Table 4. Decomposition of the Gender Earnings Differential. Model A Model B

Gross differential a 0.454 0.418 Gross differential corrected for selectivity b 0.490 — Differential adjusted for hours and status c 0.481 0.375

Differences in characteristics d 0.332 0.225 Human capital –0.042 –0.044 Job segregation 0.367 0.274

Industry 0.176 0.082 Type of firm 0.026 0.052 Occupation 0.165 0.140

Other characteristics 0.007 –0.004 Unexplained differential e 0.149 0.150

a The gender differential in mean log wages adjusted for time trend computed as; the figure for Model A is computed from Equation (8) where

Wi is not corrected for selectivity. b Computed as t tm fW W− from Equation

(8) where W i is corrected for selectivity. c Computed as h hm fW W− .

d Computed as ( )m f p−X X ß . e Computed as ( ) ( )m m p f p f− + −X ß ß X ß ß . Adjusting wages for hours worked and worker status leaves the gender differential in Model A virtually unchanged and slightly lowers the differential in Model B. As may be calculated from the figures in Table 4, assuming a standard workweek for all workers, women’s monthly earnings are 61.8% of men’s, and women’s long-run effective monthly wage is 68.7% of men’s. For comparison, the gender earnings ratios calculated by Blau and Kahn (1996) with adjustment for hours are 61.4% for the U.K., 64.6% for Switzerland, 64.9% for Hungary, 65.4% for the U.S., 70.2% for Germany, 72.7 for Austria, and 77.3% for Sweden. The coefficient estimates in the earnings equations do not essentially differ between the two models, with Model B’s estimates being more efficient. Compared to general secondary education, a university degree brings substantial wage premiums to both male and female workers. The returns to specialized secondary education are


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clearly smaller, but also statistically significant. Professional training brings significant positive returns to women and men (in Model B). The experience-earnings profiles for both genders are typical, concave, reaching their maximums at 23-25 years of experience for women and 19-20 years of experience for men. Remarkably, women’s higher human capital endowments—mainly educational attainment—result in a more than 4 log percentage points wage advantage. That is, the female/male earnings ratios would have been lower (59.3% for the hours-adjusted monthly earnings and 65.7% for the hours-adjusted long-run effective wages) had women’s human capital endowments been the same as men’s. The two models are also consistent in showing that gender job segregation is responsible for most of the gender earnings differential. With the offsetting effect of human capital endowments, occupational, industrial, and firm-type segregation by gender accounts for 76.3% of the gross differential in monthly earnings and 73.0% of the gross differential in the effective long-run wages. As the estimates in Table 3 show, the most highly paid industries are extractive industries, construction, and transportation, which are heavily dominated by men. And the lowest paid industries, such as education and health care, are by large “female” industries. Also, the earnings in the men-dominated private sector are higher than those in the state sector, where most women work. The main contributors of occupational segregation to the gender pay gap are highly paid “male” operator and craft occupations and lower-valued “female” technician, clerical, service, and unskilled, occupations. These women’s occupational disadvantages are party offset by relatively high-paying “female” professional occupations and low-paying “male” unskilled occupations, which reduces the gender pay differential, but only by about 3 log percentage points.16 The unexplained residual of the gender pay differential is of almost the same size in both models (see Table 4). It accounts for 31% of the selectivity adjusted gender earnings differential in Model A and 16 The patterns of job segregation by gender in Russia 2000-02 are described and analyzed in Ogloblin (2005).


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40% of the gross gender wage differential in Model B.17 With the models’ relatively high explanatory power and much of occupational gender segregation captured at the four-digit level, unobserved worker endowments and preferences are nor likely to account for much of this residual and hence most of it is likely to reflect discrimination against women, i.e. unequal pay for equal work and equal productivity-related characteristics.18 The present study’s results of the 2000-02 gender earnings differential decompositions are remarkably similar to those obtained in Ogloblin (1999) for 1994-96. In both cases women have an advantage in human capital endowments, and job segregation by gender explains most of the gender pay gap, with virtually the same industries and occupations being the main contributors. Some differences, however, are worthy of note. First, the female/male ratio calculated for the selectivity-corrected monthly earnings is noticeably lower in 2000-02 (61.8%) than in 1994-96 (71.7%).19 However, the 2000-02 ratio of female to male effective long-run wages (68.7%), is very close to the 1994-96 ratio calculated for the last-month earnings without selectivity correction (68.4%).

17 The greater unexplained percentage of the gross differential in Model B is due mainly to the fact that in Model A job segregation by gender explains a greater proportion of a greater gross differential. 18 Jones (1983) shows that the residual terms in Equation 1 cannot be further decomposed in a meaningful way. 19 To some extent, this is because in 1994-96 the selectivity bias was found to be negative for women and positive for men, which led to a smaller gender differential after correction for selectivity, while in 2000-02 the selection bias was negative for both genders, increasing the selectivity adjusted differential compared to the uncorrected one. The 2000-02 female/male ratio calculated for last-month wages without selectivity correction is 64.2%. Note also that Ogloblin (1999) used Heckman’s (1979) two-step selectivity correction procedure, which did not yield statistically significant ßλ. Applying this technique in the present study also did not produce statistically significant selection correction terms. That is, the one-step maximum likelihood estimation appears to be more efficient, producing statistically significant selection correction coefficients.


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Further, the unexplained differential in the last-month earnings increased by almost 10 log percentage points in 2000-02 compared to 1994-96, and its share in the gross differential doubled. This suggests that the increase in the gross differential in monthly earnings is largely due to increased discrimination. One explanation is that since the wage arrears are lower in 2000-02, their mitigating effect on the gender pay gap is smaller. More generally, it appears that as the Soviet past is becoming more distant, the old labor-market institutions and centralized wage-setting practices are fading away, and the new labor legislation is relaxing strict regulations, employers enjoy more discretion to set wages and use it to discriminate against women.

6. Conclusions After a decade of radical economic reforms, the gender pay gap in Russia remains fairly close to its levels during the late Soviet era and the earlier stages of the transition to market. Assuming a standard workweek for all workers, women’s mean monthly earnings are 61.8% of men’s, and women’s long-run average effective wage is 68.7% of that of men. Wage non-payments, which at the new stage of transition became less widespread, compress earnings actually received by workers and slightly reduce the gender differential. The two models used in this study are consistent in showing that in Russia 2000-02, industrial, occupational, and firm-type segregation by gender accounts for a large part of the gender pay gap. With the offsetting effect of women’s higher human capital endowments, job segregation by gender is responsible for 76.3% of the gross differential in monthly earnings and 73.0% of the gross differential in the effective long-run wages. The unexplained part of the gender earnings differential, which accounts for 31% of the gross differential in monthly earnings and 40% of the gross differential in the long-run effective wages, is likely to be largely due to discrimination against women. This unexplained fraction of the gender pay gap increased by almost 10 log percentage points in 2000-02 compared to 1994-96, and its share in the gross


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differential doubled. Although the lower incidence of wage arrears and hence their smaller mitigating effect on the gender pay gap may play some role in this, the main suggested explanation is that employers’ greater discretion in wage setting led to more discrimination. That is, with the further wage system decentralization, employers’ behavior, less restricted by the government, tends to be more influenced by the patriarchal stereotypes and the perception of women as less productive labor force (see Ogloblin (2005)). Finally, as Ogloblin (2002) points out, no discussion of the gender pay gap in Russia can be complete without taking into account the lower legal retirement age for women. State pensions, which are still received by most workers, in proportion to their wages, may be viewed as deferred labor earnings. The legal retirement age for women in Russia is 55, while for men it is 60. The life expectancy for women is much longer than for men, 73 years and 62 years respectively. Hence, an average woman is expected to be on a pension for18 years, while an average man is expected to receive it only for 2 years. I estimate that taking this into account rises the ratio of female to male lifetime earnings per month of work to 78%.20 References Blau, Francine D. and Lawrence M. Kahn. 1996. “Wage Structure and Gender Earnings Differentials: an International Comparison.” Economica Vol. 63(S), pp. 29-62.

20 The data on life expectancy are 2003 estimates. The assumptions used to calculate the lifetime earnings differential are (1) an average monthly pension is about 31% of the average monthly wage (calculated from Goskomstat of Russia (2003)); (2) real wage growth and pension indexation offset present-value discounting; (3) the female/male long-run effective wage ratio for working individuals remains constant at 68.7%; (4) women work continuously from 18 to 55 years old, and men work from 18 to 60 years old. The details of the calculations are available on request.


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Blinder, Alan S. 1973. “Wage Discrimination: Reduced Form and Structural Estimates.” Journal of Human Resources, Vol. 8, No. 4, pp. 436-55. Brainerd, Elizabeth. 1998. “Winners and Losers in Russia's Economic Transition.” The American Economic Review Vol. 88 , No. 5, pp.1094-116. Brainerd, Elizabeth. 2000. “Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union.” Industrial and Labor Relations Review Vol. 54, No. 1, pp. 138-62. Commander, Simon, John McHale, and Ruslan Yemtsov. 1995. “Russia.” In Unemployment, Restructuring, and the Labor Market in Eastern Europe and Russia , ed. Simon Commander and Fabrizio Coricelli, 147-92. Washington, D. C. The World Bank. Commander, Simon, et al. 1996. “How Russian Firms Make Their Wage and Employment Decisions.” In Enterprise Restructuring and Economic policy in Russia, ed. Simon Commander, Qimiao Fan, and Mark E. Schaffer, 15-51. Washington, D. C.: The World Bank. Earle John S. and Klara Z. Sabirianova. 2002. “How late to pay? Understanding wage arrears in Russia.” Journal of Labor Economics Vol. 20, No. 3, pp. 661-707. Glinskaya, Elena and Thomas A. Mroz. 2000. “The Gender Gap in Wages in Russia from 1992 to 1995.” Journal of Population Economics Vol. 13 , No. 2, pp. 353-86. Goskomstat of Russia. 2003. Rossiyskiy Statisticheskiy Yezhegodnik (Russian Statistical Yearbook). Moscow. Heckman, James. 1979. “Sample Selection Bias as Specification Error.” Econometrica Vol. 47, No. 1, pp. 153-61. Jones, F. L. 1983. “On Decomposing the Wage Gap: A Critical Comment on Blinder’s Method.” Journal of Human Resources Vol. 18, No. 1, pp. 127-30. Katz, Katarina. 2001. Gender, Work and Wages in the Soviet Union: A legacy of Discrimination. New York: Palgrave. Lehmann, Hartmut, Jonathan Wadsworth, and Alessandro Acquisti. 1999. “Grime and Punishment: Job Insecurity and Wage Arrears in the Russian Federation.” Journal of Comparative Economics Vol. 27, No. 4, pp. 595-617.


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McAuley, Alastair. 1981. Women’s Work and Wages in the Soviet Union. London: George Allen & Unwin. Neumark, David. 1988. “Employers’ Discriminatory Behavior and the Estimation of Wage Discrimination.” Journal of Human Resources Vol. 23, No. 3, pp. 279-95. Newell, Andrew and Barry Reilly. 1996. “The Gender Wage Gap in Russia: Some Empirical Evidence.” Labour Economics Vol. 3, No. 3, pp. 337-56. Oaxaca, Ronald. 1973. “Male-Female Wage Differentials in Urban Labor Markets.” International Economic Review Vol. 14, No. 3, pp. 693-709. Oaxaca, Ronald L. and Michael R. Ransom. 1994. “On Discrimination and the Decomposition of Wage Differentials.” Journal of Econometrics Vol. 61: 5-21. Ofer G. and A. Vinokur. 1992. The Soviet Household under the Old Regime: Economic Conditions and Behavior in the 1970s. Cambridge: Cambridge University Press. Ogloblin, Constantin G. 1999. “The Gender Earnings Differential in the Russian Transition Economy.” Industrial and Labor Relations Review Vol. 52, No. 4, pp. 602-27. Ogloblin, Constantin G. 2002. “Gender, Work and Wages in the Soviet Union: A Legacy of Discrimination. By Katarina Katz.” Book review. Industrial and Labor Relations Review. Vol. 56, No. 1, pp. 188-90. Ogloblin, Constantin. 2005. “The Sectoral Distribution of Employment and Job Segregation by Gender in Russia.” Regional and Sectoral Economic Studies. Vol. 5, No. 2. Standing, Guy. 1996. Russian Unemployment and Enterprise Restructuring: Reviving Dead Souls. New York: St. Martin’s Press. ________________________ Journal published by the Euro-American Association of Economic Development. http://www.usc.es/economet/eaa.htm

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