Principal Component Regression in Spatial Lag Model: Teen Employment in the City of Rosario, Argentina

Abstract

Teen employment is a very important socioeconomic phenomenon because of its consequences on human capital formation. We assess the relation between teen employment and poverty, education, and unemployment in the city of Rosario, using information from the 2010 Argentina Census disaggregated at census block level. We use two different spatial models: The spatial lag model (SLM) and a linear regression model with the spatial component filtered (filtering model, FM). Given the nature of the variables employed, multicollinearity is an issue. One of the techniques proposed in the literature to deal with multicollinearity problems is principal component regression (PCR). We develop an adaptation of such methodology to be used in the SLM. Both models are estimated using their traditional methodologies (instrumental variables for the SLM and OLS for the FM) and using PCR. Although results are similar between the two models, depending on the methodology used in the estimations they differ greatly. Under traditional methodologies estimations show high variability, instability, and contradictory outcomes, but under PCR, results behave according to the literature.