Working Papers

Ability Peer Effects in Small Study Teams (link)

Job Market Paper

While team homework assignments are a widely used teaching tool, evidence on the relationship between a student’s knowledge obtained from teamwork and her team’s ability composition remains scarce. My identification strategy relies on within-student variation in achievement across two similar courses, of which only one has team assignments. This variation allows me to establish a causal effect of a team’s ability composition on individual achievement. I establish this link in a setting where students can self-select their peers potentially based on social ties, which have been found to often profoundly affect peer interactions. Based on their performance in the previous year, I classify students to be either regular or very high ability. I find that the share of very high ability peers has a statistically significant and sizable negative effect on regular students, whereas very high ability students are seemingly (but not statistically significantly) affected positively. The result suggests that encouraging students to form homogeneous ability teams might increase their individual performance.

Keywords
peer achievement spillovers, social networks, post-secondary education, homework assignments, free-riding

JEL Codes
I21, I23, J24, L23

Estimation of Spatial Sample Selection Models: A Partial Maximum Likelihood Approach (link)

Renata Rabovič & Pavel Čížek
Revise & Resubmit at Journal of Econometrics 

To analyze data obtained by non-random sampling in the presence of cross-sectional dependence, estimation of a sample selection model with a spatial lag of a latent dependent variable or a spatial error in both the selection and outcome equations is considered. Since there is no estimation framework for the spatial lag model and the existing estimators for the spatial error model are either computationally demanding or have poor small sample properties, we suggest to estimate these models by the partial maximum likelihood estimator, following Wang, et al. (2013)'s framework for a spatial error probit model. We show that the estimator is consistent and asymptotically normally distributed. To facilitate easy and precise estimation of the variance matrix without requiring the spatial stationarity of errors, we propose the parametric bootstrap method. Monte Carlo simulations demonstrate the advantages of the estimators.

Keywords
asymptotic distribution, maximum likelihood, near epoch dependence, sample selection model

JEL Codes
C13, C31, C34


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