This paper presents an independent large-scale experimental evaluation of two online goal-setting interventions. Both interventions are based on promising findings from the field of social psychology. Approximately 1,400 first-year undergraduate students at a large Canadian university were randomly assigned to complete one of two online goal-setting treatments or a control task. Additionally, half of treated participants also were offered the opportunity to receive follow-up goal-oriented reminders through e-mail or text messages in an attempt to test a cost-effective method for increasing the saliency of treatment. Across all treatment groups, we observe no evidence of an effect on GPA, course credits, or second year persistence. Our estimates are precise enough to discern a seven percent standardized performance effect at a five percent significance level. Our results hold by subsample, for various outcome variables, and across a number of specifications.

This paper presents an independent large-scale experimental evaluation of two online goal-setting interventions. Both interventions are based on promising findings from the field of social psychology. Approximately 1,400 first-year undergraduate students at a large Canadian university were randomly assigned to complete one of two online goal-setting treatments or a control task. Additionally, half of treated participants also were offered the opportunity to receive follow-up goal-oriented reminders through e-mail or text messages in an attempt to test a cost-effective method for increasing the saliency of treatment. Across all treatment groups, we observe no evidence of an effect on GPA, course credits, or second year persistence. Our estimates are precise enough to discern a seven percent standardized performance effect at a five percent significance level. Our results hold by subsample, for various outcome variables, and across a number of specifications.

This paper revisits the identification and estimation of a class of semiparametric (distributionfree) panel data binary choice models with lagged dependent variables, exogenous covariates, and entity fixed effects. Using an "identification at infinity" argument, we show that the model is point identified in the presence of a free-varying continuous covariate. In contrast with the celebrated Honoré and Kyriazidou (2000), our method permits time trends of any form and does not suffer from the "curse of dimensionality". We propose an easily implementable conditional maximum score estimator. The asymptotic properties of the proposed estimator are fully characterized. A small-scale Monte Carlo study demonstrates that our approach performs satisfactorily in finite samples. We illustrate the usefulness of our method by presenting an empirical application to enrollment in private hospital insurance using the HILDA survey data.