Overall, we find that the inequality levels of wage earnings are relatively low in all samples studied. The mean log deviation (i.e. GE(0)) is estimated to be 0.189. Men’s wages seem to be more unequal than women’s. The results for the share of inequality of opportunity show that for the full sample, at least 24% of this observed inequality is due to circumstances of the individual that should be orthogonal to wages. Once we account for the fact that the observed set of circumstances is by no means the complete list of all the relevant circumstances and use individual fixed effects as the superset of all circumstances that characterize an individual using the methodology described in Section 2, the share explained by inequality of opportunity jumps up to 63%. Furthermore, recognizing the fact that circumstances may have an indirect effect of effort itself and accounting for that bumps up the total share of inequality of opportunity in total earnings to 64%.

Breaking down the sample and treating males and females separately reduces the overall inequality – as it should because gender no longer remains a discriminant – as well as the shares explained by circumstances but the broad pattern is roughly similar. Total inequality for males and females are 0.18 and 0.16 respectively and the bounds on inequality of opportunity range from 17% to 51% for males and 17% to 68% for females. This suggests that even if the lower bounds are identical, the upper bound for wage inequality that is attributable to circumstances in Russia is higher for female workers than their male counterparts. In the second panel of Figure 3, we present the same results using GE(1) instead of GE(0) and confirm that the results are very similar.

What does all this mean? Any attempt to calculate inequality of opportunity is in some ways an attempt to parse out the component of inequality that an individual faces due to the circumstances endowed to him/her by the accident of birth versus the inequality that may emerge as a result of differential choices such as effort. The empirical distinction of these two is of paramount importance for policy because the scope and indeed even public appetite for policy action that attempts to redress such inequality is likely to be higher if a larger share of the inequality is due to components that are universally acknowledged to be unfair. Thus, from this point of view, all the attempts that have been made to make such an empirical distinction have been constrained in their ability to fully account for the unfair inequality because of the lack of data sets that allow all possible set of circumstances to be observed. What we show here is that, if one is talking about wage inequality in Russia and one were to work with any of the cross-sections of the RLMS surveys and use the same set of circumstances we use, the estimated inequality of opportunity would be severely underestimated: the upper bound may be higher by a factor of three to four.

A methodological note is also is in order on what exactly individual fixed effects capture in this context and whether all of what is captured can be regarded as being outside of the realm of individual responsibility. In this methodology, individual fixed effects capture all of the time invariant attributes of individuals which society agrees should not influence wages, and more. They also capture other aspects of the individual such as unobserved ability, aptitude or intelligence. Should ability be something an individual is held accountable for? Or is it the society that should be faulted for inequalities that are attributed to such traits? This is indeed a hotly debated question that touches upon, among other things, genetics: for example, what is the exact extent to which cognitive abilities are transmitted through generations? The original Barros et al. (2009) formulation is predicated on the argument that inequality emanating from differences in talent is fair: the argument goes that “success in life should depend on people’s choices, effort and talents, not on their circumstances at birth”. So by the Barros (2009) framework, the upper bound we have estimated by treating unobserved ability, talent and aptitude etc. to 11