by an inversely U-shaped pattern, reaching their maximum of 28 percent in 2003. Within roughly the last 5 years, wage premiums to higher education for women have stabilized at around 12 percent, a couple of percentage points ahead of men. Gender-wise enrollment rates in higher education (not shown) 10 years later appear to match the differences in rates of return, strengthening the hypothesis that market rates of return to education in Russia do indeed influence individual continuing school decisions.

A similar comparative picture is observed with respect to vocational education, albeit with a different kind of variation by gender (see Figure 7): returns for males are almost flat within the time period while returns for females shows a concave pattern. The overall outcome concerning payoffs to schooling isolated by gender has been confirmed in a similar fashion by past studies (see, for example, Cheidvasser and Benítez-Silva 2007).

# <u>Instrumental Variable Specification</u>

A sizeable proportion of the earnings literature holds that returns estimated from Ordinary Least Squares (OLS) may be biased due to the possible presence of an omitted variable bias and resulting heterogeneity in the net benefits of additional schooling across individuals. Instrumental variable (IV) regression is a method used to deal with these issues (Card 1999; Patrinos and Sakellariou 2005).

As instrumental variables, we use indicators of the Parental Socio-Economic Status (SES) of individuals when the individuals were 15 years old. Even though some authors express the opinion that family background related variables may suffer the same problem as an endogenous education variable, variables such as father’s education have been used as instruments in earnings functions (see, for example, Dearden 1998; Harmon and Walker 2000; Hoogerheide, Block, and Thurik 2012; Ichino and Winter-Ebmer 1999; Pons and Gonzalo 2001). Parental education can be said to be related to the schooling level of an individual through genetic or environmental effects when an individual is a dependent child in a parent’s household. However, the direct influence of parental education on adult earnings, independent of the influence on schooling, would be mild. In such a case it has been shown that the findings would not substantially deviate from the benchmark case of a strictly exogenous instrument.

The current paper exploited retrospective RLMS questions, asked in 2006 and 2011, about mother’s and father’s occupation (J216AC08, J216BC08), and their highest achieved education level (J217A, J217B) at a respondent's age of 15. Occupational categories were converted to indices with the help of The Standard Occupational Prestige Scale (SIOPS) (Ganzeboom and Treiman 2019). The final family background measures represented maximum values for the two SES dimensions between two parents. Besides, following the lead of several past studies (Angrist and Krueger 1991; Card 1999; Kim et al. 2019) we make use of dummies for the Russian regions, in which individuals reside at the time of the interview (STATUS), as instruments. The analysis was performed, using 2018 RLMS data to capture the most recent labor market situation.

The general TSLS specification of interest can be written by the following equations.

First stage: