Random Effects Endogeneity: A Variable Pretest

The appealing but complex Hausman and Taylor (1981) random effects (instrumental variable) estimator requires prior knowledge that certain explanatory variables in a panel are uncorrelated with the latent group effects. The purpose of this examination is to outline a tractable variable pretest that facilitates the initial sorting of regressors as likely exogenous or endogenous. The variable pretest proposed herein builds on the pretest estimator suggested by Baltagi et al (2003) by providing the necessary foundation for regressor identification. Extensions are suggested for the two-way error components construct. Keywords: Panel data, Random effects, Variable pretest, Hausman-Taylor. JEL Classification: C12, C13, C23.

How Elastic is the Labour Supply of Female Migrants Relative to the Labour Supply of Female Natives?

This study estimates the wage elasticities of migrants and natives by using data from the German Socio-Economic Panel from 1984 to 2015 and a grouping instrumental variable estimator. Female migrants who live with a partner have lower own- and cross-wage elasticities than respective female natives, and the elasticities of non-Western female migrants are insignificant. The relationship between participation and elasticity is not in all cases positive, but parallel to labour market integration, the time since migration increases the elasticities of women. Elasticities indicate the potential to increase participation; therefore, it is especially important for non-Western female migrants to remove barriers to flexible wage responses.

A Shrinkage Instrumental Variable Estimator for Large Datasets

This paper proposes and discusses an instrumental variable estimator that can be of particular relevance when many instruments are available and/or the number of instruments is large relative to the total number of observations. Intuition and recent work (see, e.g., Hahn, 2002) suggest that parsimonious devices used in the construction of the final instruments may provide effective estimation strategies. Shrinkage is a well known approach that promotes parsimony. We consider a new shrinkage 2SLS estimator. We derive a consistency result for this estimator under general conditions, and via Monte Carlo simulation show that this estimator has good potential for inference in small samples.