Working Papers

Achievement Peer Effects in Small Study Teams

(new version is coming soon)

Although teamwork has become an important determinant of labor market success, many university graduates are lacking teamwork skills. To close this skill gap, many universities make use of homework assignments that students have to complete in small study teams as a teaching tool. Yet, we know surprisingly little about the effect of team composition on a student's knowledge obtained from teamwork. I study the effect of ability composition of a student's study team on her academic achievement. Peer effects are identified using within-student variation in achievement across two similar courses, of which only one has team homework assignments. I classify students to be either very high ability or regular and find that the share of very high ability peers has a statistically significant and sizable negative effect on regular students. The effect on very high ability students is statistically indistinguishable from zero. These results are consistent with a model where regular students are mainly affected by a negative free-riding effect, whereas for very high ability students, the negative free-riding effect is offset by positive effects stemming from peer pressure and mutual learning. The results suggest that forming homogeneous ability teams might increase students' individual performance.

Keywords
peer achievement spillovers, social networks, post-secondary education, 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