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It is very common in empirical research to estimate several regression models to check the robustness of the results or to evaluate how the estimate of the coefficient of interest changes as we add a set of covariates to a baseline model. For example, in explaining the sources of the gender wage gap, very often researchers estimate multiple wage equations in order to evaluate how the gender-dummy coefficient changes as individual and job characteristics are added to the model and then attribute this difference to the new set of variables included in the model. This approach is not exempt of criticism as the order in which covariates are added is not irrelevant.
In this seminar, I will present a decomposition technique, proposed by Gelbach (2016), that appeals to the omitted variable bias formula to unambiguously disentangle the contribution of each covariate to the change in the estimate of the coefficient of the variable under scrutiny. This procedure was applied to matched employer-employee data in Portugal to decompose the sources of the wage losses of displaced workers (Raposo, Portugal & Carneiro, 2021).
Anabela Carneiro is Professor of Economics at the University of Porto, a Research Economist at the Center for Economics and Finance at UPorto, and Director of the Master in Economics at FEP. She received her PhD at the University of Porto, in 2003.
Her research interests are in Labour Economics and the Economics of Entrepreneurship, with an emphasis on wage formation and the role of human capital on firm survival. She has also participated in the elaboration of several technical reports on the labour market for entities such as the Norte Portugal Regional Coordination and Development Commission, Portuguese Ministry of Economy and Ministry of Labour of Mozambique.