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Measuring Social Interaction Effects when Instruments are Weak

Listed author(s):
  • Stephen L. Ross

    (University of Connecticut)

  • Zhentao Shi

    (Chinese University of Hong Kong)

Studies that can distinguish between exogenous and endogenous peer effects of social interactions are relatively rare. One recent identification strategy exploits partial overlapping groups of peers. If a student has two groups of separated peers, the peer choices are correlated through that specific student's choice, but one group's attributes are assumed to directly influence neither the other peer group's attributes nor the choices. In the context of academic performance in higher education, however, the evidence of peer effects on academic outcomes has been mixed, creating a potential for weak instruments. We utilize a period of transition when students were being reassigned to dormitories from a new campus to an old campus. Many groups of roommates were broken up at the end of freshman year, and then combined with other groups of students from the same school in the sophomore year. We find reduced-form evidence that information about a student's previous year roommates can explain the current test scores of their new roommates. However, due to weak instruments, the estimated endogenous effects appear unreasonably large. We draw on weak-IV robust tests, namely the Anderson-Rubin-type S-test (Stock and Wright, 2000) and Kleibergen's Lagrangian multiplier test (Kleibergen, 2005), to provide properly-sized tests for the endogenous effects between the test scores of current roommates and to calculate lower bounds of such effects. These tests strongly reject the null hypothesis of no endogenous effects. JEL Classification: C26, C51, I23, J00 Key words: academic performance, hypothesis testing, endogenous peer effects, random assignment, weak instruments

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Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2016-37.

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Length: 40 pages
Date of creation: Dec 2016
Handle: RePEc:uct:uconnp:2016-37
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Web page: http://www.econ.uconn.edu/

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  2. Victor Lavy & Analia Schlosser, 2011. "Mechanisms and Impacts of Gender Peer Effects at School," American Economic Journal: Applied Economics, American Economic Association, vol. 3(2), pages 1-33, April.
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  4. Patrick Bayer & Stephen L. Ross & Giorgio Topa, 2008. "Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes," Journal of Political Economy, University of Chicago Press, vol. 116(6), pages 1150-1196, December.
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  7. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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  9. Giacomo De Giorgi & Michele Pellizzari & William Gui Woolston, 2012. "Class Size And Class Heterogeneity," Journal of the European Economic Association, European Economic Association, vol. 10(4), pages 795-830, 08.
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  11. James H. Stock & Jonathan Wright, 2000. "GMM with Weak Identification," Econometrica, Econometric Society, vol. 68(5), pages 1055-1096, September.
  12. Aizer, Anna & Currie, Janet, 2004. "Networks or neighborhoods? Correlations in the use of publicly-funded maternity care in California," Journal of Public Economics, Elsevier, vol. 88(12), pages 2573-2585, December.
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  14. José Luis Montiel Olea & Carolin Pflueger, 2013. "A Robust Test for Weak Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 358-369, July.
  15. Robert Bifulco & Jason M. Fletcher & Stephen L. Ross, 2011. "The Effect of Classmate Characteristics on Post-secondary Outcomes: Evidence from the Add Health," American Economic Journal: Economic Policy, American Economic Association, vol. 3(1), pages 25-53, February.
  16. Friesen, Jane & Krauth, Brian, 2011. "Ethnic enclaves in the classroom," Labour Economics, Elsevier, vol. 18(5), pages 656-663, October.
  17. Jean-Marie Dufour, 1997. "Some Impossibility Theorems in Econometrics with Applications to Structural and Dynamic Models," Econometrica, Econometric Society, vol. 65(6), pages 1365-1388, November.
  18. Victor Lavy & Analía Schlosser, 2011. "Corrigendum: Mechanisms and Impacts of Gender Peer Effects at School," American Economic Journal: Applied Economics, American Economic Association, vol. 3(3), pages 268-268, July.
  19. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
  20. Foster, Gigi, 2006. "It's not your peers, and it's not your friends: Some progress toward understanding the educational peer effect mechanism," Journal of Public Economics, Elsevier, vol. 90(8-9), pages 1455-1475, September.
  21. Frank Kleibergen, 2005. "Testing Parameters in GMM Without Assuming that They Are Identified," Econometrica, Econometric Society, vol. 73(4), pages 1103-1123, 07.
  22. Bet Caeyers & Marcel Fafchamps, 2016. "Exclusion Bias in the Estimation of Peer Effects," NBER Working Papers 22565, National Bureau of Economic Research, Inc.
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