Wald estimators


Labor supply research has not yet produced a clear statement of the size of
the labor supply elasticity nor how it should be measured. Measurement error in
hourly wage data and the use of inappropriate identifying assumptions can account
for the poor performance of some empirical labor supply models. I propose here a
generalization of Wald's method of fitting straight lines that is robust to
measurement error, imposes mild testable identifying assumptions, and is useful
for the estimation of life-cycle labor supply models with panel data. A
convenient Two-Stage Least Squares (TSLS) equivalent of the generalized Wald
estimator is presented and a TSLS over-identification test statistic is shown to
be the test statistic for equality of alternative Wald estimates of the same
parameter. These results are applied to labor supply models using a sample of
continuously employed prime-age males. Labor supply elasticities from the two
best-fitting models that pass tests of over-identifying restrictions range from
0.6 to 0.8 . A test for measurement error based on the difference between
generalized Wald and Analysis of Covariance estimators is also proposed.
Application of the test indicates that measurement error can account for low or
negative Analysis of Covariance estimates of labor supply elasticities.

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Journal of Econometrics, Vol. 47, February/March 1991
Angrist, J. (1988). Grouped Data Estimation and Testing in Simple Labor Supply Models. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01v405s9384 (Original work published July 1988)
Working Papers