Showing papers by "Daniel L. Millimet published in 2021"
TL;DR: In this paper, the authors consider a particular type of non-classical measurement error: skewed errors and assess potential solutions to this problem, focusing on the stochastic frontier model and nonlinear least squares.
Abstract: 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 measurement error: skewed errors. Unfortunately, skewed measurement error is likely to be a relatively common feature of many outcomes of interest in political science research. This study highlights the bias that can result even from relatively "small" amounts of skewed measurement error, particularly if the measurement error is heteroskedastic. We also assess potential solutions to this problem, focusing on the stochastic frontier model and nonlinear least squares. Simulations and three replications highlight the importance of thinking carefully about skewed measurement error, as well as appropriate solutions.
21 citations
TL;DR: In this article, the authors describe the methodology of partial identification and its applicability to empirical research in preventive medicine and public health, and demonstrate the applicability of the partial identification methods using three empirical examples drawn from published literature.
3 citations
TL;DR: The authors used a partial identification approach to bound the joint distribution of disability and employment status in the presence of misclassification, finding that the employment gap is at least 15.2% before the Great Recession and 22.0% afterward.
Abstract: 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 identification approach to bound the joint distribution of disability and employment status in the presence of misclassification. Allowing for a modest amount of misclassification leads to bounds on the labor market status of the disabled that are not overly informative given the relative size of the disabled population. Thus, absent further assumptions, even a modest amount of misclassification creates much uncertainty about the employment gap between the non-disabled and disabled. However, additional assumptions considered are shown to have some identifying power. For example, under our most stringent assumptions, we find that the employment gap is at least 15.2% before the Great Recession and 22.0% afterward.