The Informal Sector Wage Gap: New Evidence using Quantile Estimations on Panel Data

Olivier Bargain and Prudence Kwenda
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Prudence Kwenda: WITS - University of the Witwatersrand [Johannesburg]

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Abstract: We estimate the informal-formal sector pay gap throughout the conditional wage distribution using panel data from Brazil, Mexico and South Africa. We control for time-invariant unobservables and identification is stemming from inter-sector movers. We control for observables in a non-linear way using propensity score reweighting and carefully check for potential measurement errors. Using similar definitions of informality, we obtain consistent results for all three countries: Informally employed workers earn much less than formal workers primarily because of lower observable and unobservable skills. Estimates of the conditional wage gap show that they are also underpaid compared to their formal sector counterparts. In all three countries, the informal wage penalty is larger in the lower part of the conditional distribution and tends to disappear at the top, i.e., the informal sector increases wage dispersion. The magnitudes of these effects vary across countries, with the largest penalties in lower conditional quantiles of South Africa and more modest wage gaps in Latin America. We suggest explanations in line with different legal and labor market conditions.

Keywords: wage gap; fixed effects; propensity score; conditional random effects; informal sector; quantile regression (search for similar items in EconPapers)
Date: 2013-12
New Economics Papers: this item is included in nep-afr, nep-dev, nep-ger and nep-iue
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00967324
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Related works:
Working Paper: The Informal Sector Wage Gap: New Evidence Using Quantile Estimations on Panel Data (2014)
Working Paper: The Informal Sector Wage Gap: New Evidence using Quantile Estimations on Panel Data (2013)
Working Paper: The Informal Sector Wage Gap: New Evidence Using Quantile Estimations on Panel Data (2009)
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