My current research focuses on the detection and measurement of individual effects.
Teacher Effects on Student Behavior (working paper)
I estimate the covariance structure of teacher effects on several outcomes: present and future test scores, present and future attendance, and high school graduation. Studying the covariance matrix of teacher effects reveals the magnitude of teacher effects on each outcome and the relationship between teacher effects on different outcomes while sidestepping the need to estimate individual teacher effects.
Although teachers have substantial effects on test scores four years in the future, these effects are not highly correlated with teacher effects on same-year test scores, implying that the effects of having a teacher who raises same-year test scores fades out quickly, and short-term teacher effects on test scores do not predict long-term effects well. Teacher effects on same-year attendance, by contrast, are highly predictive of longer-term attendance. Teacher effects on test scores are only weakly correlated with teacher effects on attendance. Teachers who are one standard deviation above average at improving high school graduation increase graduation rates by 2% to 8%, although graduation data is limited.
Which Value-Added Estimator Works Best and When? (working paper)
Although a large volume of research has investigated whether the identification assumptions of value-added models hold, the statistical properties of these estimators are less studied, especially in finite samples. In the second chapter, I survey several popular value-added estimation procedures. I discuss conditions under which models are identified, clarify whether estimators are consistent or unbiased, and derive standard errors. I also develop a maximum (quasi-)likelihood estimator. I investigate the bias and precision of different estimators in Monte Carlo data and check whether estimators give similar answers in real data. My focus is on the portion of variance that is due to variation in teacher quality, but I also discuss individual-specific estimates, and ask whether different procedures give highly correlated estimates of teacher effects, and whether a procedure can reliably identify teachers in the bottom 2%. Although I use the language of teachers, classrooms, and students for clarity, these results extend readily to different settings.
Bureaucrat Value Added: The Effect of Individual Bureucrats on Local Economic Outcomes in India, with Jonas Hjort and Gautam Rao. Working paper coming soon!
We use several value-added estimators to study a question relevant to political economy: How much agency do individual bureaucrats have to impact local economic performance? We study high-ranking bureaucrats in the Indian Administrative Service, India's national bureaucracy. These bureaucrats, District Collectors, District Collectors, who are quasi-randomly assigned to manage the bureaucracy of an Indian district and often transfer to different districts in the same state. This setting presents econometric challenges, since we have relatively few observations and high-dimensional covariates. By randomly permuting bureaucrat names, we show that value-added estimators have strong finite-sample biases in this setting. Point estimates suggest that variance in District Collector quality accounts for substantial variance in project completion and night light intensity. However, randomization inference shows that our estimates are in fact insignificant.