Oaxaca-Blinder (1973):

Difference in characteristics + Difference in coefficientsAssumptions: Index number problem: we first use males wages as a non-discriminatory benchmark (in line with other studies on Russia), and then use a pooled model (Neumark, 1988, Oaxaca and Ransom, 1994)

It is based on the OLS property that the mean wage conditional on average characteristics is equal to the unconditional mean wage that does not hold in the context of quantile regression:

Machado-Mata (2005)

Denote by the wage of individual i with characteristics X who leaves behind a fraction of individuals () with the same characteristics (Koenker and Basset, 1978).

Then the gender gap can be decomposed using the Machado-Mata (2005) methodology and dropping superscript i as follows:

[in words: total gap = difference in characteristics + difference in coefficients + residual]

Assumptions: we use males wages as a non-discriminatory benchmark

Juhn, Murphy, Pierce (1991)

To decompose changes in the wage gap over 1997-2002 we use Juhn, Murphy, Pierce (1991)Assume that the residual consists of two parts where is the standard deviation of the residual of the male wage equation, and is the standardized residual with a mean of zero and a standard deviation of 1.

1st term (the Observed Xs effect): the effect of changes in observable characteristics over time2nd term (the Observed Prices effect): the contribution of changes in the prices of the observed skills of men3rd term (the Gap effect): the effect of changes in the relative position of women in the male residual wage distribution, 4th term (the Unobserved Prices effect): the contribution of the widening or narrowing of the male residual distribution

Assumptions: first use males coefficients as a benchmark, and then experiment also with the pooled model

Development and Reform Research Team University of Bologna

Proportion of females in the firm

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Earnings by gender, 1997 and 2002:All employees Workers

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Evolution of the GEG inside the firm

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OB decomposition, all employees

Determinants of wages, 1997 and 2002

Determinants of wages, 1997 and 2002 (contd)

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GEG at the meansAt 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.

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GEG at the quantiles: raw and adjusted gaps

Determinants of wages, 1997 and 2002

Determinants of wages, 1997 and 2002 (contd)

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GEG at the quantiles: MM (2005) total gap and gap due to coefficients1997 2002

Machado-Mata: All employees

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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.

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Potential explanations of the GEG

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Potential explanations of the GEG: bonusesNO, since the decomposition and regression results for total compensation are very similar to those of the GEG.

Development and Reform Research Team University of Bologna

Potential explanations of the GEG : trade-off between secure jobs and wages - NOIn 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.

Development and Reform Research Team University of Bologna

Potential explanations of the GEG : segregationProduction 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.

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Distribution of workers by levels

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Probability to be in the primary levels in 2002

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

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Results for workers at the quantiles with and w/o levels:total gap and gap due to coefficients

No levels With levels

Machado-Mata: Workers 2002

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Change in GEG and its potential explanations

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Change in GEG 1997-2002: JMP (1991)

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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).

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

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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 s?

WOMEN:If the distribution of womens 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, womens 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 s 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 womens characteristics at the bottom (i.e. decrease in discrimination) is an explanation of the larger fall of the GEG at the bottom.

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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 mens Xs were slightly better in 1997 than in 2002, and worsening in mens 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 s, the gap would have been larger everywhere. Mens s in 1997 were better than in 2002 and decline in rewards for men contributed to reducing the gap throughout the whole distribution. The reductionin s, 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 MENS CHARACTERISTICS)

Development and Reform Research Team University of Bologna

Potential explanations of the decline in GEG: change in rewards + composition effectProbit 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 Hunts (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 womens 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.

Probability to separate (Pooled): ME from Probit

Probability to separate (contd)

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What we have found so farThere 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.

Development and Reform Research Team University of Bologna

What we have found so far (contd)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).