UC Santa Barbara

This dissertation consists of three essays that use and develop econometric methods to causally investigate topics in education and development economics. In the first chapter, I develop an econometric framework to correctly and efficiently draw inference in models where estimated value-added (VA) is an explanatory variable (and models where it appears as the dependent variable). Estimated VA measures have become increasingly popular metrics of worker and institutional quality, and they are now widely used in regressions by researchers seeking to establish links between worker quality and a broad range of outcomes. Although consistent standard errors are crucial to obtain correct confidence intervals and assess the validity of conclusions drawn by studies using VA measures in regressions, the literature has not yet tackled this issue. I contribute to this literature by setting up an econometric framework that allows me to show why naïve standard error estimators are inconsistent in such models, derive consistent standard error estimators, propose a more efficient estimator for models using VA measures as explanatory variables, and propose a testable condition under which naïve standard errors are consistent for models that use VA measures as dependent variables. Then, in an application using data from North Carolina public schools, I find that the increase in standard errors resulting from the required correction that I propose is larger than the impact of clustering standard errors. In the second chapter, based on joint work with Serena Canaan and Pierre Mouganie, we use VA measures to provide the first causal evidence on the impact of college advisor quality on student outcomes. To do so, we exploit a unique setting where students are randomly assigned to faculty advisors during their first year of college. We find that having a higher grade VA advisor reduces time to complete freshman year and increases four-year graduation rates by 2.5 percentage points. It also raises high-ability students' likelihood of enrolling and graduating with a STEM degree by 4 percentage points. The magnitudes of our estimated effects are comparable to those from successful financial aid programs and proactive coaching interventions. We also show that non-grade measures of advisor VA predict student success. In particular, advisors who are effective at improving students' persistence and major choice also boost other college outcomes. Our results indicate that allocating resources towards improving the quality of academic advising may play a key role in promoting college success. In the third chapter, based on joint work with Clement de Chaisemartin, we consider the case of a randomized controlled trial with individual-level treatment assignment, and we allow for individual-level and cluster-level (e.g. village-level) shocks to affect the units' potential outcomes. We show that one can draw inference on two estimands: the ATE conditional on the realizations of the cluster-level shocks, using heteroskedasticity-robust standard errors; the ATE netted out of those shocks, using cluster-robust standard errors. We show that by clustering, researchers can test if the treatment would still have had an effect, had the stochastic shocks that occurred during the experiment been different. Then, the decision to cluster or not depends on the level of external validity one would like to achieve.