Identifying Peer Effects in Student Academic Achievement by Spatial Autoregressive Models with Group Unobservables

By Xu Lin. Journal of Labor Economics, 2010

304 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Education, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b3700592845a6c5c4a158db83c87a9eaf536f00d

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Citations in the corpus

Random Coefficients on Endogenous Variables in Simultaneous Equations Models (2014, methodology): “These friendship data have been widely used to study the impact of social interactions on many different outcomes of interest (e.g., Bramoullé et al. 2009 and Lin 2010).”
R&D Networks: Theory, Empirics, and Policy Implications (2019, background): “Liu et al. [2012] test the key player policy for juvenile crime in the United States, while Lindquist and Zenou [2013] identify key players for co-offending networks in Sweden.”
Does Co‐authorship Lead to Higher Academic Productivity? (2015, unknown): “Recently, Bramoullé, Djebbari and Fortin (2009) and Lee, Liu and Lin (2010) provide the necessary and sufficient conditions under which peer effects can be identified using network data.”
Do Peers Affect Student Achievement? Evidence from Canada Using Group Size Variation (2010, methodology): “3 dataset is an exception, see e.g. Calvo-Armengol et al. (2009) and Lin (2010).4 For this reason, many studies focus on the grade-within-school level (e.g., Hanushek et al. 2003, Angrist and Lang 2004).”
Criminal Networks: Who is the Key Player? (2012, methodology): “One can get rid of the 27These results are formally derived in Bramoullé et al. (2009) (see, in particular, their Proposition 3) and used in Calvó-Armengol et al. (2009) and Lin (2010).”
Heterogeneous peer effects in education (2014, unknown): “A number of papers have dealt with the identification and estimation of peer effects with network data (e.g. Bramoullé et al., 2009; Liu and Lee, 2010, Calvó-Armengol et al., 2009; Lin, 2010; Lee et al., 2010; Liu et al., 2012).”
Disentangling the Roles of Institutional and Individual Poverty in the Identification of Gifted Students (2018, unknown): “These include lower teacher expectations (Jussim & Eccles, 1992; Jussim, Eccles, & Madon, 1996), peer effects (Lin, 2010; Mashburn, Justice, Downer, & Pianta, 2009), and mundane curriculum (Schmidt, Cogan, Houang, & McKnight, 2009).”