How big are the differences in the informal sector wage gap in Argentina? : evidence using quantile regression on panel data

Abstract

I estimate a quantile regression (QR) to assess informal wage penalties at different points along the conditional wage distribution in Argentina and within the country. I exploit a panel data set from a quarterly household survey from 2016 to 2018 (Encuesta Permanente de Hogares) from the National Institute of Statistics in Argentina, INDEC (Instituto Nacional de Estadistica y Censos de Argentina) and the identifications relays on the cross-sector movers. Results show that the wage gap is bigger for the lower 20% of the distribution than the upper 80% of conditional distribution for the whole country, and this effect is reduced by one third when I consider the individuals' heterogeneity. I find a reversion in the penalty gap appears in Buenos Aires, NOA and NEA, where the lower and upper workers have a higher penalty than the median informal worker. These estimations are robust to the informal definition and highlight the importance of a distribution analysis to account for heterogeneous effects in the labor market.