The Gender Earnings Gap inside a Russian firm: Evidence from Personnel Data [work in progress]

1. The Gender Earnings Gap inside a Russian firm: Evidence from Personnel Data[work in progress] Thomas Dohmen (Maastricht University, IZA and DIW) Hartmut Lehmann (DARRT, University of Bologna, IZA, CERT, WDI and DIW) Anzelika Zaiceva (DARRT, University of Bologna and IZA) Arbeitstreffen 27. März 2008 - Mannheim

2. Motivation and background • Under communism: gender equality (formally) emphasized, LM participation of women relatively high and GEG relatively low, BUT, segregation of women into traditional “female” occupations (UNICEF, 1999; Brainerd, 2000; World Bank, 2002; Malceva and Roshin, 2006). • The transition from planned to a market economy in Russia has produced economic “winners” and “losers” (Brainerd, 1998). Changes had more pronounced effects for women: collapsing welfare system and child-care facilities, sharp increase in unemployment, decrease of public employment, home production as an outside option. • Transition  enterprise restructuring, adjustment to product demand shocks ranged from labour hoarding to mass lay-offs, adoption of decentralised system of wage bargaining or firm-level negotiations, increasing competition and emergence of hard budget constraints. ⇓ All these phenomena: potentially implications for gender segregation and discrimination in the labor market

3. Theoretical models • Taste-based employers’ discrimination (Becker, 1957): as competition increases discrimination decreases; • Productivity differences between men and women and sorting into firms (Kremer, 1993); • Both strands predict segregation. • Lazear and Rosen (1990): 1) Women with higher expected value of home time  a higher separation probability for them and a higher ability threshold level to be promoted; 2) promotion rates (and thus wages) do not differ by gender at very high levels of ability; 3) female wages are lower because they are underrepresented in high-paying jobs (segregation). • Booth et al. (2003): if market opportunities and outside options different, women may be promoted more or the same as men, but receive lower wage increases upon promotion. Thus, promotion may not automatically mitigate the gender wage gap

4. Related literature 1 • Vast literature on GEG in the West: meta-analysis of gender pay gap studies by Weichselbaumer and Winter-Ebmer (2005): finding gap on average 26%. Kunze (2008) surveys literature on GEG and decompositions methodologies (looks at various biases due to unobserved heterogeneity). • Bayard et al. (2003) point to importance of matched e-e: not controlling for segregation at the level of establishment and jobs overstates the role of occupational and/or industry segregation in the economy. Using matched e-e data also reduces the bias from unobserved heterogeneity by focusing on selected samples of more homogenous groups of workers and provides evidence on the within-establishment within-occupation segregation (Kunze, 2008). • Using personnel data may further reduce unobserved heterogeneity (Kunze, 2008). Such data are not representative, but provide a detailed look at the internal labor market (Ransom and Oaxaca, 2005). Also, it is possible to “more credibly investigate whether wage gaps still exist when job characteristics and rank are controlled for” (Kunze, 2008).

5. Related literature 2: Studies with personnel data • Few studies due to data scarcity. • In general, find gender differences in pay, mobility and promotion opportunities within a firm in Western economies (see, among others, Ransom and Oaxaca, 2005 for the US; Jones and Makepeace, 1996 for the UK). • Barnet-Verzat and Wolff (2008) use personnel data on executives in a French firm, control for hierarchical levels, report GEG of 2-5% and find evidence of the “glass ceiling” effect (i.e. wage gap is higher at the highest quantiles of the wage distribution).

6. Related literature 3: Gender Wage Gap in Russia • Most of the studies analyze the GEG in the early years of transition. All use household survey data. • Brainerd (2000) finds that a dramatic widening of the wage distribution during 1991-1994increases the gap since women are above all located in the lower part of the wage distribution. • Reilly (1999) suggests that the monthly GEG is stable over 1992-1996 at around 37% (hourly – 25%). Also finds an increasing “unexplained” part of the differential. • Ogloblin (1999) suggests that the major determinant of the GWG during 1994-1996 is occupational segregation. • Kazakova (2007) finds that the GWG has decreased from around 35% in 1996 to around 16% in 2002 in panel estimation. • Gerry et al. (2004) and Kazakova (2007) focus on wage arrears as a potential explanation behind the wage gap and show that a temporary increase in the gap in 1998 was related to thefact that women in the lower part of the wage distribution were less affected by wage arrears than men.

7. This paper • It is the first paper that studies the GEG in a transition economy using personnel data. • Documents the evolution of the GEG over 1997-2002 using unique personnel data from a manufacturing firm in Russia. • Analyses potential reasons behind the gender gap: • Bonuses; • Arrears; • Change in composition of the workforce a-la Hunt (2002); • Trade-off between Job security and wages; • Discrimination; • Segregation.

8. Background • A Russian firm operating in one of the Central Russian oblast in the “machine building and metal works” sector producing equipment for gas and oil production and smith-press equipment. • Out of 17 Central Russian oblasts, oblast, wherefirm, is 8th in terms of wages (2006 data). • The ratio of females’ to males’ wages (wf / wm) in this oblast in industry constituted 66% in 1999, 67% in 2001, 62% in 2002 and 64% in 2003. • In 2005, the occupational distribution of the females’ to males’ wages ratio in this oblast was as follows: • Managers: 70% • Specialists: 67% • Other employees: 65% • Workers: 50%.

9. The firm • The firm operates in a product market characterized as follows: • 6.2% export share (CIS); 93.8% for Russian market, of which 1.5% for regional market; • no regional competitor; • more than 5 competitors in the Russian market, among them firms from EU. • It was founded in the 1950s and privatized in 1992: • ownership structure (in 2002): workers/employees/managers (53.1%) former employees (21.5%), Russian entities (25.4%) – caveat: top management seems to have decisive majority (interview with CEO); • Employed in 2007 about 3400 employees; • Formally there is collective wage bargaining at the firm but trade union officials are “in the pocket of the CEO.”

10. Data • We created electronic files based on records from the personnel archive of the firm. • All employees, except for top managers; • Panel data over 1997-2002 with info on wages, bonuses and arrears; • Rich set of demographic and human capital variables; • Financial variables are deflated to 1997 using corresponding CPIs; • Sample selection: drop part-time employees [keep those who are polnaya stavka and work full week], drop if missing explanatory variables (0.1%); • Sample size is around 3,000 observations per year; • Dependent variables: • Log of net average monthly wage; • Log of total monthly compensation (avg. monthly wage + monthly bonuses).

11. Methodology • 1st step: estimate augmented Mincerian regressions for wages and total compensation by gender. • Controls: tenure (squared + cubed), age (squared + cubed), 6 education categories, 3 family status categories, dummies for children (0 vs. 1 vs. 2 and more children), any outside training, on the job training, internal mobility. • 2nd step: perform the following decompositions • For means: Oaxaca-Blinder (1973) and Neumark (1988) /Oaxaca-Ransom (1994) • For quantiles: Machado-Mata (2005) • For changes over 1997-2002 at the means: Juhn, Murphy and Pierce (1991) • For changes over 1997-2002 at quantiles: Machado-Mata (2005) • 3rd step: Explanations of the GEG – in its initial stage.

12. Proportion of females in the firm

13. Earnings by gender, 1997 and 2002:All employees Workers

14. Evolution of the GEG inside the firm

15. OB decomposition, all employees

16. GEG at the means • At best one third of the gap is explained by differences in observed characteristics. • GEG decreased between 1997 and 2002 by approx. 20 points. • GEG is small and for the most part insignificant for managers (in line with the predictions of Lazear and Rosen, 1990) and (in some years) for service staff. • GEG for the entire workforce is driven by the earnings differentials for engineers and production workers. • Workers have by far the highest gaps, little of which is explained by differences in observed characteristics.

17. GEG at the quantiles: raw and adjusted gaps

18. GEG at the quantiles: MM (2005) total gap and gap due to coefficients 1997 2002

19. GEG at the quantiles: MM (2005) • In general, GEG has roughly an inverted U-shape profile across wage distribution, apart from 2002. • In 2002, the GEG is particularly small (but increases at the highest percentiles). • The highest quantile in 1997 (in line with Gerry et al., 2004; Lazear and Rosen, 1990, predictions) and the lowest in 2002 exhibit particularly low gender differentials. However, there is evidence for an increase of a “glass ceiling” effect. • The main portion of the GEG is due to the differences in coefficients: comparison of the actual female wage distribution and the counterfactual one that would be obtained if females kept their characteristics but were paid like males.

20. Potential explanations of the GEG

21. Potential explanations of the GEG: bonuses • NO, since the decomposition and regression results for total compensation are very similar to those of the GEG.

22. Potential explanations of the GEG : trade-off between secure jobs and wages - NO • In the firm, the majority of separations are quits (79% among all separations). • After having controlled for productivity characteristics and occupations, females have on average 3 p.p. higher probability to quit than males. • They have also 1 p.p. higher probability to be laid-off.

23. Potential explanations of the GEG : segregation • Production workers have the highest gender earnings gaps that contribute most to the overall GEG at the firm. • Production workers have jobs that are linked to levels - 8 for “primary workers” and 6 for “auxiliary workers” that are highly correlated with wages. • Descriptive exercise because of the endogeneity of the job levels. • Ransom and Oaxaca (2005): “But this makes the male/female wage difference that we observe all the more startling: among these workers , although wages were set by a collective bargaining that was, ostensibly, gender neutral, a large wage differential arose because women were placed in jobs different from those assigned to similar men”.

24. Distribution of workers by levels

25. Probability to be in the primary levels in 2002

26. Results for workers including levels at the means: Oaxaca-Blinder decomposition

27. Results for workers at the quantiles with and w/o levels:total gap and gap due to coefficients No levels With levels

28. Change in GEG and its potential explanations

29. Change in GEG 1997-2002: JMP (1991)

30. Change in GEG 1997-2002: JMP (1991) • About 29 percent of the decrease can be explained by changes in observed characteristics and prices. Changes in observed characteristics about four times as important as changes in observed prices. • About 6 points of the reduction of the gap is because women improve their position in the male residual earnings distribution, while about 8 points are due to a narrowing of this distribution. • Although contribution of the narrowing of earnings distribution is the largest, the joint contribution of gender-specific effect has the most weight (contrary to the early years of transition, see Brainerd, 2000).

31. Change in GEG 1997 and 2002 at the quantiles: MM (2005)

32. Change in GEG 1997 and 2002 at the quantiles: MM (2005) • Raw gap fell more at the bottom than at the top (see row 3). Is that due to chnages in Xs or changes in βs? WOMEN: • If the distribution of women’s Xs had not changed from 1997, the gap would have decreased at the bottom, but would have stayed almost the same throughout the rest of the distribution (row 6). Thus, women’s characteristics were better in 1997 at the bottom, but not in the rest of the distribution. That does not help to explain the larger fall at the bottom. • If women in 2002 had the returns to their characteristics as in 1997, the gap would have been even negative at the top (benefiting women over men) and would have risen a lot at the bottom. Changes in βs contributed to the large reduction in the gap at the bottom and an increase at the top. Thus, a large increase in the prices of women’s characteristics at the bottom (i.e. decrease in “discrimination”) is an explanation of the larger fall of the GEG at the bottom.

33. Change in GEG 1997 and 2002 at the quantiles: MM (2005) MEN • If men in 2002 had characteristics of 1997, the gap would have been slightly larger at the bottom 10th percentile and almost the same in the rest of the distribution. Thus, at the very bottom men’s Xs were slightly better in 1997 than in 2002, and worsening in men’s Xs contributed to the fall in the gap there (however, to a small extent). The best from the bottom have moved away. • If men in 2002 had 1997 βs, the gap would have been larger everywhere. Men’s βs in 1997 were better than in 2002 and decline in rewards for men contributed to reducing the gap throughout the whole distribution. The reductionin βs, however, is higher at the top than at the bottom. ⇓ IT IS INCREASED REWARDS OF WOMEN AT THE BOTTOM THAT MAINLY GENERATE THE LARGER FALL OF THE GEG AT THE BOTTOM (TOGETHER WITH A SLIGHT WORSENING IN MEN’S CHARACTERISTICS)

34. Potential explanations of the decline in GEG: change in rewards + composition effect • Probit model for separation shows that less-skilled women (fem*pc10, fem*pc20) are LESS likely to separate from the firm (by 18 to 20 p.p.) Not Hunt’s (2002) story. • JMP (1991) decomposition shows that approx. 1/3 of the decrease can be explained by observed characteristics and prices, and some of the reduction is due to the improvement of women’s position in the male residual wage distribution and to a narrowing of this distribution. Joint contribution of gender-specific effect is important • Indeed, MM decomposition shows that the gap has declined more at the bottom than at the top of the distribution: rewards for women increased at the bottom (mainly) + men with better characteristics left the bottom of the distribution (small effect). That seems to explain the fall of the gap.

35. What we have found so far • There exists an intra-firm GEG that declines over 1997-2002, which is driven by the GEG for production workers. • Most of the gap at their means or across the entire wage distributions as well as most of the changes in gender gaps is unexplained by the observables. • Gender gap is the highest for production workers and it is absent for managers. • Bonuses, wage arrears or wages-secure jobs- tradeoff do not seem to be reasons behind the existence of the GEG.

36. What we have found so far (cont’d) • 1/3 of the fall of GEG at the mean is explained by changes in observed characteristics and prices. The decline of GEG is largely due to a decline in the lowest part of the distribution. The reason seems to be the fact that men with better characteristics leave the bottom of the wage distribution that also improves relative position of women in residual men wage distribution. The decreased rewards for men constitute another reason. Most importantly, the rewards to characteristics for women improve disproportionately at the bottom of the distribution. • For production workers the gap is almost completely explained when workers’ levels are included into the regressions. Moreover, job levels explain about 45-59% of all the variation in wages (R2 from the respective regressions as in Ransom and Oaxaca, 2005). • Thus, the potential explanation of the existence of the GEG seems to be existence of segregation in the internal labor market in Russia. • However, the lower job assignment of women could only to a small degree be explained by individual productivity characteristics and deserves further explorations (lower entry-level jobs vs. lower promotion opportunities).