This appendix contains eight sections. Section 1 gives technical details of how we calculate standard errors in the CPS data. Section 2 discusses changes in the ACS procedures before 2005. Section 3 examines demographic and economic patterns in migration over the past two decades, in more detail than in the main paper. Section 4 examines the cross-sectional variance of location-occupation interactions in earnings when we define locations by MSAs instead of states. Section 5 describes alternative methods to estimate the variance of location-occupation interactions in income. Section 6 measures the segregation of industries across states and of occupations and industries across MSAs. Section 7 gives technical details on the use of SIPP and census data to calculate repeat and return migration rates. Section 8 discusses transition dynamics in the model.

We analyze the secular decline in interstate migration in the United States between 1991 and 2011. Gross flows of people across states are about 10 times larger than net flows, yet have declined by around 50 percent over the past 20 years. We argue that the fall in migration is due to a decline in the geographic specificity of returns to occupations, together with an increase in workers’ ability to learn about other locations before moving there, through information technology and inexpensive travel. These explanations find support in micro data on the distribution of earnings and occupations across space and on rates of repeat migration. Other explanations, including compositional changes, regional changes, and the rise in real incomes, do not fit the data. We develop a model to formalize the geographic-specificity and information mechanisms and show that a calibrated version is consistent with cross-sectional and time-series patterns of migration, occupations, and incomes. Our mechanisms can explain at least one-third and possibly all of the decline in gross migration since 1991.

In this appendix I present details of the model and the empirical analysis, and results of counterfactual experiments omitted from the paper. In Section 1 I describe a simple example that illustrates how, even in the absence of human capital acquisition, productivity shocks, or separation shocks, the learning component of the model can naturally generate mobility between jobs within a firm and turnover between firms. I also include the proofs of Propositions 1 and 2 in the paper. In Section 2 I discuss model identification in detail, where, in particular, I prove that information in my data on the performance ratings of managers allows me to identify the learning process separately from the human capital process. In Section 3 I describe the original U.S. firm dataset of Baker, Gibbs, and Holmström (1994a,b), on which my work is based. In Section 4 I provide details about the estimation of the model, including the derivation of the likelihood function, a description of the numerical solution of the model, and a discussion of the results from a Monte Carlo exercise showing the identifiability of the model’s parameters in practice. There I also derive bounds on the informativeness of the jobs of the competitors of the firm in my data, based on the estimates of the parameters reported in the paper. Finally, in Section 5 I present estimation results based on a larger sample that includes entrants into the firm at levels higher than Level 1. Results of counterfactual experiments omitted from the paper are contained in Tables A.12–A.14.

This paper develops and structurally estimates a labor market model that integrates job assignment, learning, and human capital acquisition to account for the main patterns of careers in firms. A key innovation is that the model incorporates workers’ job mobility within and between firms, and the possibility that, through job assignment, firms affect the rate at which they acquire information about workers. The model is estimated using longitudinal administrative data on managers from one U.S. firm in a service industry (the data of Baker, Gibbs, and Holmström (1994a,b)) and fits the data remarkably well. The estimated model is used to assess both the direct effect of learning on wages and its indirect effect through its impact on the dynamics of job assignment. Consistent with the evidence in the literature on comparative advantage and learning, the estimated direct effect of learning on wages is found to be small. Unlike in previous work, by jointly estimating the dynamics of beliefs, jobs, and wages imposing all of the model restrictions, the impact of learning on job assignment can be uncovered and the indirect effect of learning on wages explicitly assessed. The key finding of the paper is that the indirect effect of learning on wages is substantial: overall learning accounts for one quarter of the cumulative wage growth on the job during the first seven years of tenure. Nearly all of the remaining growth is from human capital acquisition. A related novel finding is that the experimentation component of learning is a primary determinant of the timing of promotions and wage increases. Along with persistent uncertainty about ability, experimentation is responsible for substantially compressing wage growth at low tenures.