quickly. The change in the wage structure is documented in Calvo, López-Calva, and Posadas (2015). We apply a new decomposition methodology that allows us to compute the wage structure and the composition effects at different percentiles of the earnings distribution. The methodology, which was developed by Fortin, Lemieux, and Firpo (2011), was used to analyze the increase in wage inequality in the United States during the last decade. This methodology was applied to analyze gender wage gaps only in two studies, but with countries that do not comply with the assumptions that make it valid, namely nonrandom selections of women into the labor force. These two studies are Chi and Li (2008) and Carrillo, Gandelman, and Robano (2014), for urban China and for 12 Latin American countries, respectively, where there is evidence of selection effects in women’s employment (World Bank, 2011).

Firpo, Fortin, and Lemieux’s decomposition methodology (FFL hereafter) builds on econometric methods used in the program evaluation literature, and presents several advantages with respect to other decomposition methodologies as discussed in Fortin, Lemieux, and Firpo (2011). The methodology is based on the estimation of recentered influence functions (RIF) as opposed to other estimates of the earnings equations. The most important advantage is that it can be used to compute several statistics (not only the mean) without losing the ability to identify the contribution of each covariate to the wage structure and the composition effects. Previous methodologies designed to decompose the gender wage gap at different percentiles—such as Machado and Mata (2005), which was based on conditional quantile estimations—could only disentangle the composition and the wage structure effect. Understanding the contribution of covariates is of particular importance, especially in the case of Russia, to analyze the links between the gender gap in pay, occupational segregation, the distribution of employment across economic sectors, and other factors.

However, the RIF decomposition relies on two assumptions for identification: ignorability and common support. The first assumption simply states that unobservables are equally distributed in the two groups used for the decomposition; in this case, men and women. Thus, with nonrandom selection of women into the labor force, this decomposition cannot be applied since it will violate the ignorability assumption. This assumption limits the applicability of the RIF decomposition to analyze the gender gap in pay in many countries. However, given the high female labor force participation in Russia, and as we test below, this is not a concern for our study. The second assumption requires that there is at least one observation for men and women for each combination of observable characteristics. In this way, a counterfactual can be computed for each observation in the sample.

This paper contributes to the understanding of the gender wage gap along the earnings distribution by describing the changes in the covariates associated with this gap. In this way, we can understand if there is either a “sticky-floor” or “glass-ceiling” effect in Russia. We observe that the largest gender 3