This paper shows how to remove attenuation bias in regression analyses due to measurement error in historical data for a given variable of interest by using a secondary measure which can be easily generated from digitized newspapers. We provide three methods for using this secondary variable to...

Count data regressions are an important tool for empirical analyses ranging from analyses of patent counts to measures of health and unemployment. Along with negative binomial, Poisson panel regressions are a preferred method of analysis because the Poisson conditional fixed effects maximum...

What are the statistical and computational problems associated with robust nonlinear regression? This paper presents a number of possible approaches to these problems and develops a particular algorithm based on the work of Powell and Dennis

Recent empirical work in several economic fields, particularly environmental and energy economics, has adapted the regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular threshold in time. In this guide for practitioners,...

Conventional estimates of the impact of taxes on investment may be seriously biased by measurement error in the cost of capital. The existence and size of such error, however, has not been documented. Using panel data on different types of capital equipment, this paper provides direct evidence...

We propose a general method of moments technique to identify measurement error in self-reported and transcript-reported schooling using differences in wages, test scores, and other covariates to discern the relative verity of each measure. We also explore the implications of such reporting...

We assess the extent and cost of misallocation in agriculture in less-developed countries comparing the analysis at the plot and farm levels. Using detailed data from Uganda, we show that the plot-level analysis leads to extremely large estimates of reallocation gains, even after adjusting for...

There is growing concern that the increasing use of machine learning and artificial intelligence-based systems may exacerbate health disparities through discrimination. We provide a hierarchical definition of discrimination consisting of algorithmic discrimination arising from predictive scores...

A large body of evidence finds that relative mobility in the US has declined over the past 150 years. However, long-run mobility estimates are usually based on white samples and therefore do not account for the limited opportunities available for non-white families. Moreover, historical data...

Measurement errors are often a large source of bias in survey data. Lack of knowledge of the determinants of such errors makes it difficult for data producers to reduce the extent of errors and for data users to assess the validity of analyses using the data. We study the determinants of...