Many school and college admission systems use centralized mechanisms to allocate seats based on applicant preferences and school priorities. When tie-breaking uses non-randomly assigned criteria like distance or a test score, applicants with the same preferences and priorities are not directly comparable. The non-lottery setting does generate a kind of local random assignment that opens the door to regression discontinuity designs. This paper introduces a hybrid RD/propensity score empirical strategy that exploits quasi-experiments embedded in serial dictatorship, a mechanism widely used for college and selective K-12 school admissions. We use our approach to estimate achievement effects of Chicago's exam schools.

We examine the evolution of economics research using a machine-learning-based classification of publications into fields and styles. The changing field distribution of publications would not seem to favor empirical papers. But economics' empirical shift is a within-field phenomenon; even fields that traditionally emphasize theory have gotten more empirical. Empirical work has also come to be more cited than theoretical work. The citation shift is sharpened when citations are weighted by journal importance. Regression analyses of citations per paper show empirical publications reaching citation parity with theoretical publications around 2000. Within fields and journals, however, empirical work is now cited more.