Estimating spillovers using panel data, with an application to the classroom
By P. Arcidiacono, G. Foster, Natalie Goodpaster, Josh Kinsler. 2012
150 citations recorded by the source corpus.
Obtaining consistent estimates of spillovers in an educational context is hampered by at least two issues: selection into peer groups and peer effects emanating from unobservable characteristics. We develop an algorithm for estimating spillovers using panel data that addresses both of these problems. The key innovation is to allow the spillover to operate through the fixed effects of a student’s peers. The only data requirements are multiple outcomes per student and heterogeneity in the peer group over time. We first show that the non-linear least squares estimate of the spillover parameter is consistent and asymptotically normal for a fixed T. We then provide an iterative estimation algorithm that is easy to implement and converges to the non-linear least squares solution. Using University of Maryland transcript data, we find statistically significant peer effects on course grades, particularly in courses of a collaborative nature. We compare our method with traditional approaches to the estimation of peer effects, and quantify separately the biases associated with selection and spillovers through peer unobservables.
Subfields: Education
View Paper: https://www.semanticscholar.org/paper/77b83b9ff9d6a6804222816b45d5eaed070461cd
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In Education
- Esteban M. Aucejo
- Teresa Romano
- Patrick M. Kline
- P. Arcidiacono
- K. Spenner
- Michael P. Keane
- Christopher R. Walters
- Paul B. Ellickson
Citations in the corpus
- Trade Liberalization and Regional Dynamics (2017, unknown): “We estimate (6) using the iterative algorithm described by Arcidiacono et al. (2012) and calculate standard errors using the wild bootstrap method suggested by Davidson and MacKinnon (2006), with 500 iterations.”
- Assessing the Effect of School Days and Absences on Test Score Performance. CEP Discussion Paper No. 1302. (2014, methodology): “We employ an iterative fixed-effects estimator introduced by Arcidiacono et al. (2012) to reduce the computational cost of estimating the multi-level fixed effects model of student achievement.”
- What happens after enrollment? An analysis of the time path of racial differences in GPA and major choice (2012, background): “Speci cation (1) (2) (3) (4) No Initial Major 0.”
- Leave‐Out Estimation of Variance Components (2018, background): “, 2016; Silver, 2016) and the economics of education (Arcidiacono et al., 2012).”