The Experience-Earnings Profile: Productivity-Augmenting or Purely Contractual? Evidence from the UK

We test the human capital interpretation of the experience-earnings profile. Does the upward sloping portion of the experience-earnings profile reflect on-the-job training which in turn causes the experience-productivity profile to slope upwards, or do purely contractual factors determine the nature of life-cycle earnings. Herein, we provide additional evidence on the relationship between productivity and earnings by examining earnings differentials in the UK academic labor market for economists. Using a test first suggested by Mincer, we find that the empirical results are consistent with human capital theory. We find that, although the positive relationship between earnings and experience persists when individual productivity measures are included in the salary equations for lecturers and senior lecturers, the positive relationship becomes statistically insignificant when the same productivity measures are included in the salary equations for professors. For lecturers and senior lecturers, the experience-salary profile properly reflects the structure of the national pay scale rather than variations in individual research productivity. At the professor level, where individual salaries are not determined by a pay scale, the data support the human capital explanation of the positive experience-earnings profile.

Quasi-binomial zero-inflated regression model suitable for variables with bounded support

In recent years, a variety of regression models, including zero-inflated and hurdle versions, have been proposed to explain the case of a dependent variable with respect to exogenous covariates. Apart from the classical Poisson, negative binomial and generalised Poisson distributions, many proposals have appeared in the statistical literature, perhaps in response to the new possibilities offered by advanced software that now enables researchers to implement numerous special functions in a relatively simple way. However, we believe that a significant research gap remains, since very little attention has been paid to the quasi-binomial distribution, which was first proposed over fifty years ago. We believe this distribution might constitute a valid alternative to existing regression models, in situations in which the variable has bounded support. Therefore, in this paper we present a zero-inflated regression model based on the quasi-binomial distribution, taking into account the moments and maximum likelihood estimators, and perform a score test to compare the zero-inflated quasi-binomial distribution with the zero-inflated binomial distribution, and the zero-inflated model with the homogeneous model (the model in which covariates are not considered). This analysis is illustrated with two data sets that are well known in the statistical literature and which contain a large number of zeros.