The first chapter tests a recently proposed hypothesis regarding rates of social mobility. Recent work by Gregory Clark and coauthors uses a new surnames approach to examine intergenerational mobility, finding much higher persistence rates than traditionally estimated. Clark proposes a model of social mobility to explain the diverging estimates, including the crucial but untested assumption that traditional estimates of intergenerational persistence are biased downward because they use only one measure (e.g., earnings) of underlying status. I test for evidence of this using an approach from Lubotsky and Wittenberg (2006), incorporating information from multiple measures into an estimate of intergenerational persistence with the least attenuation bias. Contrary to Clark's prediction, I do not find evidence of substantial bias in prior estimates.The second chapter, coauthored with Martin Nybom, further examines this hypothesis using rich administrative data for Sweden. We exploit detailed proxy measures to test the proposition regarding attenuation bias in prior estimates for Sweden, and also conduct a Sweden-U.S. comparison. We find no evidence of substantial bias in prior estimates, or that the Sweden-U.S. difference in persistence is smaller than found in previous research. We further explore the concept of family status by incorporating mothers, thereby also contributing to the literature on intergenerational transmission for women. We find that while mothers’ income is a poor proxy for status, incorporating information on mothers’ occupation improves the ability to capture transmission from mothers to both sons and daughters.The third chapter, coauthored with Cassandra Guarino and Jeffrey Wooldridge, examines the SAS® EVAAS® models for estimating teacher effectiveness, which are used by several states and districts in teacher evaluation programs despite little attention in the evaluation literature. The EVAAS approach involves using one of two distinct models, the Multivariate Response Model (MRM) or the Univariate Response Model (URM). The MRM jointly models scores from multiple subjects, grades, and cohorts in a 5-year period; it is generally limited to within-district purposes due to the large computational burden and is sometimes not feasible if data requirements cannot be met. Hence, the URM was developed for these situations. The URM models a single subject, and thus is less intensive computationally and more flexible with respect to data requirements. The method involves the computation of a composite score on several lagged scores in multiple subjects, and then using this composite score as the only regressor in empirical Bayes’ estimation of the teacher effects. In this paper, we discuss and illustrate advantages and disadvantages of the EVAAS approach relative to the other widely used and studied value-added methods. We perform simulations to evaluate their ability to uncover true teacher effects under various teacher assignment scenarios. We also use administrative data to illustrate the extent of agreement between the URM and other common value-added approaches. Although the differences are small in our administrative data, we show with theory and simulations that standard linear regression using OLS performs at least as well as—and sometimes better than—the more complicated EVAAS URM.
Read
