Jacob Mincer and Labor Supply–Before and Aftermath

This paper discusses the impact that Jacob Mincer's 1962 paper Labor-Force Participation of Married Women... had on the analysis and empirical estimation of the labor supply of married women, and the supply of labor in general. It is argued that this paper has revolutionized the analysis of labor supply. The sharp increase in married women's labor supply still constitutes a challenge to labor economists who try to explain the phenomenon in terms of income and price effects derived from cross-section studies. It constituted a puzzle to labor economists in the 1950s and 1960s, still captives of the notion of a backwards-bending supply of labor. Mincer combined a theoretical model distinguishing between three uses of time (leisure, work at home, and work in the market) and Friedman's distinction between permanent and transitory earning. He showed that the wage has a positive effect on married women's labor supply, and that this supply is more affected by transitory than by permanent income changes. The new theory serves as the scaffold on which Mincer builds the empirical estimation. The interplay between theory, data, and empirical estimation, and the ingenuity of the empirical research using scant data sources, made this paper the object of emulation. The ideas first discussed in this paper generated many of the developments in the analysis of labor supply witnessed over the last four decades.     

Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models

Bayesian analysis often concerns an evaluation of models with different dimensionality as is necessary in, for example, model selection or mixture models. To facilitate this evaluation, transdimensional Markov chain Monte Carlo (MCMC) relies on sampling a discrete indexing variable to estimate the posterior model probabilities. However, little attention has been paid to the precision of these estimates. If only few switches occur between the models in the transdimensional MCMC output, precision may be low and assessment based on the assumption of independent samples misleading. Here, we propose a new method to estimate the precision based on the observed transition matrix of the model-indexing variable. Assuming a first-order Markov model, the method samples from the posterior of the stationary distribution. This allows assessment of the uncertainty in the estimated posterior model probabilities, model ranks, and Bayes factors. Moreover, the method provides an estimate for the effective sample size of the MCMC output. In two model selection examples, we show that the proposed approach provides a good assessment of the uncertainty associated with the estimated posterior model probabilities.

     

Multiple Perspectives on Inference for Two Simple Statistical Scenarios

ABSTRACT

When data analysts operate within different statistical frameworks (e.g., frequentist versus Bayesian, emphasis on estimation versus emphasis on testing), how does this impact the qualitative conclusions that are drawn for real data? To study this question empirically we selected from the literature two simple scenarios—involving a comparison of two proportions and a Pearson correlation—and asked four teams of statisticians to provide a concise analysis and a qualitative interpretation of the outcome. The results showed considerable overall agreement; nevertheless, this agreement did not appear to diminish the intensity of the subsequent debate over which statistical framework is more appropriate to address the questions at hand.     

Willentliches Vergessen – Voraussetzung für Flexibilität und Veränderungsfähigkeit in einer sich permanent verändernden Welt

Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO) - Dieser Beitrag im Journal Gruppe. Interaktion. Organisation. stellt dar, wie willentliches...