Chapter 1 adapts models of public and private employer learning to the market for teachers. It then use statewide, micro-level, administrative data from North Carolina to formulate value-added measures (VAMs) of teacher productivity. It exploits the adoption of VAMs of teacher performance by two of the largest school districts in the state, a shock to the available information for some, but not all, employers, to provide an initial direct test of asymmetric employer learning. Consistent with a shock to public information, for job moves within the district, this work finds that the adoption of value-added measures increases the probability that high-VAM teachers move to higher-performing schools. For moves out of the district, the impacts of policy are mitigated and even reversed by teachers with lower value-added measures becoming more likely to move to higher-performing schools. This adverse selection to plausibly less informed principals is consistent with asymmetric employer learning. Further, this chapter provides evidence that these moves lead to an increase in the sorting of teachers across schools within district, exacerbating the inequality in access to high quality teaching.Chapter 2 examines worker mobility, and empirically tests whether all firms learn about workers' abilities at the same rate (symmetric learning) or whether current employers accumulate and use private information about their workers (asymmetric learning). The employer learning model allows for both public and private learning, and thus, nests symmetric learning as a special case. The model predicts that conditional on employees' easily observable reference groups, workers are adversely selected into job switches and layoffs on the basis of difficult to observe characteristics, such as intellectual ability. Inversely, conditional on ability, the model predicts that as the mean ability of a worker's reference group increases, the likelihood of job separation increases. Under asymmetric private learning, these effects should become more pronounced over the length of continuous working spells. The same effects should diminish with experience, in the presence of public learning. In keeping with earlier examinations of employer learning hypotheses, this study examines evidence from the 1979 cohort of the National Longitudinal Survey of Youth, using AFQT as the difficult to observe measure of ability. Conditional on AFQT score, workers with higher education from more selective institutions are are positively selected into job switches and moves from employment to unemployment during recessions. The dynamics of these effects largely play out as predicted. While this works presents evidence adverse selection on the basis of AFQT, for job-to-unemployment transitions, the same is not true for job-to-job moves. The dynamic effects for AFQT are likewise inconsistent. Accordingly, the evidence largely rejects symmetric learning in favor asymmetric learning.Chapter 3 discusses estimation of multilevel/hierarchical linear models that include cluster-level random intercepts and random slopes. Viewing the models as structural, the random intercepts and slopes represent the effects of omitted cluster-level covariates that may be correlated with included covariates. The resulting correlations between random effects (intercepts and slopes) and included covariates lead to bias when using standard random-effects (RE) estimators such as (restricted) maximum likelihood. While the problem of correlations between unit-level covariates and random intercepts is well-known and can be handled by fixed-effects (FE) estimators, the problem of correlations between unit-level covariates and random slopes is rarely considered. When applied to models with random slopes, the standard FE estimator does not rely on standard cluster-level exogeneity assumptions, but requires an "uncorrelated variance assumption" that the variances of unit-level covariates are uncorrelated with their random slopes. This work proposes a "per-cluster regression" (PC) estimator that is straightforward to implement in standard software, and shows analytically that it is unbiased for all regression coefficients under cluster-level endogeneity and violation of the uncorrelated variance assumption. The PC, RE, and an augmented FE estimator are applied to a real dataset and evaluated in a simulation study that demonstrates that the PC estimator performs well in practice.
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