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...

We estimate the trend in the transitory variance of male earnings in the U.S. using the Michigan Panel Study of Income Dynamics from 1970 to 2004. Using both an error components model as well as simpler but only approximate methods, we find that the transitory variance started to increase in the...

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...

In this paper we investigate the incidence of measurement errors in two independent estimates of long-term price change, within the framework of "multiple indicators" models of price measurement. We develop estimates of the measurement-error variances associated with both the Producer Price...

Seasonal adjustment procedures attempt to estimate the sample realizations of an unobservable economic time series in the presence of both seasonal factors and irregular factors. In this paper we consider a factor which has not been considered explicitly in previous treatments of seasonal...

In three sets of experiments involving over 4,200 subjects, we show that agents motivated to be selfish make systematic decision errors of the kind generally attributed to cognitive limitations or behavioral biases. We show that these decision errors are eliminated (or dramatically reduced) when...

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...

We use Monte Carlo simulations and real data to assess the performance of alternative methods that deal with measurement error in investment equations. Our experiments show that individual-fixed effects, error heteroscedasticity, and data skewness severely affect the performance and reliability...