In this paper we use indirect inference to estimate a joint model of earnings, employment, job changes, wage rates, and work hours over a career. Our model incorporates duration dependence in several variables, multiple sources of unobserved heterogeneity, job-specific error components in both wages and hours, and measurement error. We use the model to address a number of important questions in labor economics, including the source of the experience profile of wages, the response of job changes to outside wage offers, and the effects of seniority on job changes. We provide estimates of the dynamic response of wage rates, hours, and earnings to various shocks and measure the relative contributions of the shocks to the variance of earnings in a given year and over a lifetime. We find that human capital accounts for most of the growth of earnings over a career although job seniority and job mobility also play significant roles. Unemployment shocks have a large impact on earnings in the short run as well a substantial long long-term effect that operates through the wage rate. Shocks associated with job changes and unemployment make a large contribution to the variance of career earnings and operate mostly through the job-specific error components in wages and hours.

Earnings dynamics are richer than those typically used in macro models. This paper provides multiple contributions. First, it proposes a non-parametric way to model rich earnings dynamics that is easy to use in structural models. Second, it constructs a large, synthetic, data set that matches the earnings dynamics of the U.S. tax earnings. Third, it estimates our non-parametric earnings processes using two data sets: the Panel Study of Income Dynamics and our synthetic tax data. Fourth, it compares the implications of our earnings processes to those of a standard AR(1) in a life cycle structural model of savings and consumption.

Selected papers from a conference held in honour of Professor Dale T. Mortensen upon the occasion of his 65th birthday. It includes papers on some of Professor Dale T. Mortensen's current research topics, as well as additional theoretical papers, and micro- and macro-econometric papers.

Earnings dynamics are much richer than those typically used in macro models with heterogenous agents. This paper provides multiple contributions. First, it proposes a simple non-parametric method to model rich earnings dynamics that is easy to estimate and introduce in structural models. Second, it applies our method to estimate a nonparametric earnings process using two data sets: the Panel Study of Income Dynamics and a large, synthetic, data set that matches the dynamics of the U.S. tax earnings. Third, it uses a life cycle model of consumption to compare the consumption and saving implications of our two estimated processes to those of a standard AR(1). We find that, unlike the standard AR(1) process, our estimated, richer earnings process generates an increase in consumption inequality over the life cycle that is consistent with the data and better fits the savings of the households at the bottom 60% of the wealth distribution.

The stochastic process for earnings is the key element of incomplete markets models in modern quantitative macroeconomics. We show that a simple modification of the canonical process used in the literature leads to a dramatic improvement in the measurement of earnings dynamics in administrative and survey data alike. Empirically, earnings at the start or end of earnings spells are lower and more volatile than the observations in the interior of earnings histories, reflecting the effects of working less than the full year as well as deviations of wages due to e.g. tenure effects. Ignoring these properties of earnings, as is standard in the literature, leads to a substantial mismeasurement of the variances of permanent and transitory shocks and induces the large and widely documented divergence in the estimates of these variances based on fitting the earnings moments in levels or growth rates. Accounting for these effects enables more accurate analysis using quantitative models with permanent and transitory earnings risk, and improves empirical estimates of consumption insurance against permanent earnings shocks.

In this article, we survey the literature on individual earnings dynamics with a particular focus on allowing for pervasive heterogeneity across individuals. We structure the discussion around ARMA processes with nonlinear trends for each individual. We show that allowing for pervasive and codependent heterogeneity in individual parameters has a major impact on econometric modeling, estimation, and substantive conclusions. We describe an econometric method that is suitable for models with pervasive heterogeneity. We develop a long list of statistics that describe any earnings panel in great detail and provide a demanding set of features of the data for fitting. This list encompasses most moments used in the literature and provides novel statistics based on individual regressions. Finally, we present an empirical illustration using a long Danish panel. Based on this, we provide some conclusions concerning earnings dynamics but emphasize that details will vary according to the sample.