Informal pay gaps in good and bad times: Evidence from Russia
BARGAIN, Olivier
Laboratoire d'analyse et de recherche en économie et finance internationales [Larefi]
Laboratoire d'analyse et de recherche en économie et finance internationales [Larefi]
BARGAIN, Olivier
Laboratoire d'analyse et de recherche en économie et finance internationales [Larefi]
< Réduire
Laboratoire d'analyse et de recherche en économie et finance internationales [Larefi]
Langue
EN
Article de revue
Ce document a été publié dans
Journal of Comparative Economics. 2021-09, vol. 49, n° 3, p. 693-714
Résumé en anglais
Informal work is traditionally large in Russia and has further increased in the recent years. We explore the implications of this shift in terms of wage dynamics. Our characterization is based on the estimation of informal ...Lire la suite >
Informal work is traditionally large in Russia and has further increased in the recent years. We explore the implications of this shift in terms of wage dynamics. Our characterization is based on the estimation of informal pay gaps at the mean and along the wage distribution, relying on the Russian Longitudinal Monitoring Survey for 2003–2017. Our approach comprises three original features: we rely on unconditional quantile effects of informality, we incorporate quantile-specific fixed effects using a tractable approach, and we suggest a treatment of the incidental parameter bias. Over the whole period, informal wage penalties are relatively small and do not suggest heavily segmented labor markets, even at low wage levels. Yet, in the past decade, a substantial negative selection into informal employment and self-employment has taken place, on average and especially at low earnings. Economic downturns and labor market policies have likely contributed to the shakeout of less productive workers in the formal sector, making the low-tier informal sector more of a last resort.< Réduire
Mots clés en anglais
Russia
Informal employment
Wage gap
Unconditional quantile regression
Fixed effects
Incidental parameter bias
Jackknife