Intergenerational Earnings Mobility in Post‐Soviet Russia

Abstract

We make use of longitudinal data for the Russian economy over 1994-2016 to obtain earnings information about parents and children. We show that rank correlations give reliable estimates, and find intergenerational correlations for sons and daughters that are comparable to those found in the USA and the UK. Sons earn more than daughters conditional on education and parents' rank. Observable household characteristics and unobservables contribute about the same to the correlations, both more than education.

Poverty in Russia: A Bird's-Eye View of Trends and Dynamics in the Past Quarter of Century

“…19 Borisov and Pissarides, 2020RLMS-HSE 1994 Intergenerational correlations between the parents' and children's income is higher than in Nordic countries, but at the same level as in US, UK and France. Education explained about 20% of the overall correlation, while living area and unobservable charactristics contributed equal amounts of 40% each.…”

Credit and income mobility in Russia

“…As the survey was redesigned in 1994 and consistency with the previous two waves was lost, we use the longitudinal data from 1994 to 2018, the latest survey year available at the time of writing. The database has been used extensively to analyse inequality, poverty dynamics, and inter and intragenerational income mobility in Russia (see Bogomolova and Tapilina 1999;Lokshin and Ravallion 2004;EBRD 2016;Nissanov 2017;Dang et al 2020;Borisov and Pissarides 2020). As most longitudinal surveys, the RLMS suffers from issues related to non-random attrition and measurement error.…”

Child Penalty in Russia: Evidence from an Event Study

“…RLMS is a unique nationally representative panel survey of Russian households, coordinated by Higher School of Economics from Moscow (HSE) which provides detailed information on the health and economic status in the Russian Federation at both household and individual levels. The data has been used extensively to analyse income and wages in Russia (see, among others, Bogomolova and Tapilina, 1999;Jovanovic, 2001;Nissanov, 2017;Borisov and Pissarides, 2020), as well as the gender inequalities (see e.g. Giménez-Nadal et al, 2019).…”

“…The RLMS shares with other longitudinal datasets issues of non-random attrition, due in the specific case to natural causes, refusal to continue participation and moving to another area, as no effort is made to trace respondents who have left the original residence (see Kozyreva et al, 2016). Previous research has indicated that in RLMS data these aspects do not pose issues significantly different from other data sources (Lukiyanova and Oshchepkov, 2012;Perugini, 2020;Borisov and Pissarides, 2020).…”