GROUPED-DATA ESTIMATION AND TESTING IN SIMPLE LABOR-SUPPLY MODELS

Inappropriate identifying assumptions and measurement error in hourly wage data may account for the poor performance of some empirical labor-supply models. This paper discusses an efficient generalization of Wald's method of fitting straight lines that is robust to measurement error, imposes mild identifying assumptions, and is useful for the estimation of labor-supply models with panel data. A Two-Stage Least-Squares (TSLS) equivalent of the Efficient Wald estimator is presented and a TSLS overidentification test statistic is shown to be a test statistic for the equality of alternative Wald estimates of the same parameter. In an empirical example, estimated labor-supply elasticities range from 0.6 to 0.8. A test for measurement error based on the difference between Efficient Wald and Analysis-of-Covariance estimators is also proposed. Application of the test indicates that measurement error may be responsible for low or negative estimates of labor-supply elasticities.