Nonclassical Measurement Error Research Articles

On the Measurement of Job Risk in Hedonic Wage Models

We examine the incidence, form, and research consequences of measurement error in measures of fatal injury risk in U.S. workplaces using both Bureau of Labor Statistics and National Intitute of Occupational Safety and Health data. Of the various measures examined the NIOSH industry risk measure produces implicit value of life estimates most in line with both economic theory and the mode result for the existing literature. Because we find non-classical measurement error that differs across risk measures and is not independent of other regressors, innovative statistical procedures need be applied to obtain statistically improved estimates of wage-fatality risk tradeoffs.

The Labor Supply Response to (Mismeasured but) Predictable Wage Changes

Most panel data studies of intertemporal labor supply assume classical measurement error. Recent validation studies refute this assumption. In this study I address nonclassical measurement error explicitly. I use data on males from the Panel Study of Income Dynamics Validation Study to purge measurement error from the Panel Study of Income Dynamics. I find a large amount of predictable wage variation in the data, even after allowing for measurement error. However, there is almost no labor supply response to these predictable wage changes. Therefore, failure to control for nonclassical measurement error cannot explain the low estimated labor supply elasticities in other papers.

Modeling Earnings Measurement Error: A Multiple Imputation Approach

Recent survey validation studies suggest that measurement error in earnings data is pervasive and violates classical measurement error assumptions, and therefore may bias estimation of crosssection and longitudinal earnings models. We model the structure of earnings measurement error using data from the Panel Study of Income Dynamics Validation Study (PSDIVS). We then use Rubin's (1987) multiple imputation techniques to estimate consistent earnings equations under non-classical earnings measurement error in the PSID. Our technique is readily generalized, and the empirical results demonstrate the potential importance of correcting for measurement error in earnings and related data, particularly during recessions.

The FIGIJ Newsletter, No. 2, June 1993

This study uses a sample of young Australian twins to examine whether the findings reported in [Ashenfelter, Orley and Krueger, Alan, (1994). ‘Estimates of the Economic Return to Schooling from a New Sample of Twins’, American Economic Review, Vol. 84, No. 5, pp.1157–73] and [Miller, P.W., Mulvey, C and Martin, N., (1994). ‘What Do Twins Studies Tell Us About the Economic Returns to Education?: A Comparison of Australian and US Findings’, Western Australian Labour Market Research Centre Discussion Paper 94/4] are robust to choice of sample and dependent variable. The economic return to schooling in Australia is between 5 and 7 percent when account is taken of genetic and family effects using either fixed-effects models or the selection effects model of Ashenfelter and Krueger. Given the similarity of the findings in this and in related studies, it would appear that the models applied by [Ashenfelter, Orley and Krueger, Alan, (1994). ‘Estimates of the Economic Return to Schooling from a New Sample of Twins’, American Economic Review, Vol. 84, No. 5, pp.1157–73] are robust. Moreover, viewing the OLS and IV estimators as lower and upper bounds in the manner of [Black, Dan A., Berger, Mark C., and Scott, Frank C., (2000). ‘Bounding Parameter Estimates with Nonclassical Measurement Error’, Journal of the American Statistical Association, Vol. 95, No.451, pp.739–748], it is shown that the bounds on the return to schooling in Australia are much tighter than in [Ashenfelter, Orley and Krueger, Alan, (1994). ‘Estimates of the Economic Return to Schooling from a New Sample of Twins’, American Economic Review, Vol. 84, No. 5, pp. 1157–73], and the return is bounded at a much lower level than in the US.