Building on a recently developed methodology for sensitivity analysis that parametrizes omitted variable bias in terms of partial R2 measures, I propose a simple statistic to capture the severity of omitted variable bias in any observational study: the probability of omitted variable bias...

A regression model is considered where earnings are explained by schooling and ability. It is assumed that schooling is measured with error and that there are no data on ability. Regressing earnings on observed schooling then yields an estimate of the return to schooling that is subject to...

This paper decomposes differences in poverty incidence (head count ratio) using estimates from a regression equation, synthesizing the approaches proposed in World Bank (2003) and Yun (2004). A significance test is developed for characteristics and coefficients effects when decomposing...

This paper examines the fertility transition through a new lens: the extensive margin. Parents with high levels of children might substitute quality for quantity as the constraints on quality relax or those on quantity tighten. However, along the extensive margin, the quantity-quality trade-off...

We provide an axiomatization-based justification for applying the Owen value to decompose R2 in OLS models if prior knowledge can be used to form groups of regressor variables. The assumptions made by the axioms are not only plausible with respect to the variables but also clarify the meaning of...

First order conditions from the dynamic optimization problems of consumers and firms are important tools in empirical macroeconomics. When estimated on micro-data these equations are typically linearized so standard IV or GMM methods can be employed to deal with the measurement error that is...

In contrast to his contribution to other areas, Shubhashis Gangopadhyay's contributions to our understanding of poverty are often thought of as indirect consequences of the main themes of his work. Yet in more than 15 published papers Gangopadhyay directly takes on poverty, including its...

Omitted variable bias (OVB) of OLS estimators is a serious and ubiquitous problem in social science research. Often researchers use the direction of the bias in substantive arguments or to motivate estimation methods to deal with the bias. This paper offers a geometric interpretation of OVB that...

In this paper I discuss three issues related to bias of OLS estimators in a general multivariate setting. First, I discuss the bias that arises from omitting relevant variables. I offer a geometric interpretation of such bias and derive sufficient conditions in terms of sign restrictions that...