Grouped Data Estimation and Testing in Simple Labor Supply Models
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.
|Date of creation:||Jul 1988|
|Contact details of provider:|| Postal: Firestone Library, Princeton, New Jersey 08544-2098|
Phone: 609 258-4041
Fax: 609 258-2907
Web page: http://www.irs.princeton.edu/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:pri:indrel:234. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Bobray Bordelon)
If references are entirely missing, you can add them using this form.