We examine economic mobility in India while rigorously accounting for measurement error. Such an analysis is imperative to fully understand the welfare effects of the rise in inequality that has occurred in India over the past few decades. To proceed, we extend recently developed methods on the...

While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, non-classical measurement error can lead to estimates which are biased and inference which lacks power. Here, we consider a particular type of non-classical...

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the...

Understanding the relationship between disability and employment is critical and has long been the subject of study. However, estimating this relationship is difficult, particularly with survey data, since both disability and employment status are known to be misreported. Here, we use a partial...

We study the identification and estimation of covariate-conditioned average marginal effects of endogenous regressors in nonseparable structural systems when the regressors are mismeasured. We control for the endogeneity by making use of covariates as control variables; this ensures conditional...

Estimation of the causal effect of a binary treatment on outcomes often requires conditioning on covariates to address selection on observed variables. This is not straightforward when one or more of the covariates are measured with error. Here, we present a new semi-parametric estimator that...

We examine economic mobility in India while accounting for misclassification to better understand the welfare e§ects of the rise in inequality. To proceed, we extend recently developed methods on the partial identification of transition matrices. Allowing for modest misclassification, we find...