Peer Effects in Academic Cheating

Carrell, Scott E.; Malmstrom, Frederick V.; West, James E.

doi:10.1353/jhr.2008.0013

Cited by 108 publications

(43 citation statements)

References 45 publications

(40 reference statements)

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“…Table 1 indicates the 11 percent of the players in the sample played with Canseco at some point in their careers, while 1.7 percent were currently playing with him in a given year. 8 The bottom panel of Table 1 , and Canseco played in less than half of the seasons in our sample. 9 This measure takes the number of runs that a pitcher allows the opposing team to obtain, and scales it by the number of innings played, so that it represents the average number of runs which would have been scored o¤ the pitcher in a full game.…”

Section: The Data and Backgroundmentioning

confidence: 92%

Learning unethical practices from a co-worker: The peer effect of Jose Canseco

Gould

Kaplan

2011

Labour Economics

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This paper examines the issue of whether workers learn productive skills from their co-workers, even if those skills are unethical. Speci…cally, we estimate whether Jose Canseco, a star baseball player in the late 1980's and 1990's, a¤ected the performance of his teammates by introducing them to steroids. Using panel data, we show that a player's performance increases signi…cantly after they played with Jose Canseco. After checking 30 comparable players from the same era, we …nd that no other baseball player produced a similar e¤ect. Furthermore, the positive e¤ect of Canseco disappears after 2003, the year that drug testing was implemented. These results suggest that workers not only learn productive skills from their co-workers, but sometimes those skills may derive from unethical practices. These …ndings may be relevant to many workplaces where competitive pressures create incentives to adopt unethical means to boost productivity and pro…ts. Our analysis leads to several potential policy implications designed to reduce the spread of unethical behavior among workers.

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Section: The Data and Backgroundmentioning

confidence: 92%

Learning unethical practices from a co-worker: The peer effect of Jose Canseco

Gould

Kaplan

2011

Labour Economics

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“…4 One set of papers uses proxy variables to break the link between unobserved and peer ability (Arcidiacono and Nicholson (2005), Hanushek, Kain, Markman, and Rivkin (2003), and Betts and Morell (1999)). Another set of papers relies on some form of random assignment (Sacerdote (2001), Zimmerman (2003), Winston and Zimmerman (2003), Foster (2006), Lehrer and Ding (2007), Carrell and Hoekstra (2010), Carrell, Fullerton, andWest (2009), Carrell, West, andMalmstrom (2008), and Hoxby (2001)). Finally, researchers have tried to circumvent the endogeneity problem with instrumental variables (Evans, Oates, and Schwab (1992)).…”

Section: Introductionmentioning

confidence: 99%

Estimating spillovers using panel data, with an application to the classroom

Arcidiacono

Foster

Goodpaster

et al. 2012

Quantitative Economics

115

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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 nonlinear 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 nonlinear 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.

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“…Many education researchers address this challenge using randomized or controlled variation in peer group composition. Peer effects have been documented on standardized test scores (Hoxby, 2000), college GPAs (Sacerdote, 2001), college entrance examination scores (Ding and Lehrer, 2007), cheating (Carrell, Malmstrom, and West, 2008), job search (Marmaros and Sacerdote, 2002), and major choices (Di Giorgi, Pellizzari, and Redaelli, 2010). Estimated peer effects may be sensitive to the definition of peer groups (Foster, 2006) and the measurement of peer characteristics (Stinebrickner and Stinebrickner, 2006).…”

mentioning

confidence: 99%

Academic Peer Effects with Different Group Assignment Policies: Residential Tracking versus Random Assignment

Garlick¹

2014

Policy Research Working Papers

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I study the relative academic performance of students tracked or randomly assigned to South African university dormitories. Tracking reduces low-scoring students' GPAs but has little effect on high-scoring students. This lowers mean GPA and raises GPA dispersion. I also directly estimate peer effects using random variation in peer groups across dormitories. Living with higher-scoring peers raises students' GPAs and this effect is larger for low-scoring students. Peer effects operate largely within race groups but operate both within and across programs of study. This suggests that spatial proximity alone does not generate peer effects. Interaction of some sort is required, but direct academic collaboration is not the relevant form of interaction. I integrate the results from variation in group assignment policies and variation in group composition by drawing on the matching and sorting literatures. Both sets of results imply that own and peer academic performance are substitutes in GPA production and that GPA may be a concave function of peer group performance. The cross-dormitory results correctly predict a negative effect of tracking on low-scoring students but understate the magnitude of the observed effect. I show that this understatement reflects both policy-sensitive parameter estimates and problems with extrapolation outside the support of the data observed under random assignment. This underlines the value of using both cross-policy and cross-group variation to study peer effects.

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Peer Effects in Academic Cheating

Cited by 108 publications

References 45 publications

Learning unethical practices from a co-worker: The peer effect of Jose Canseco

Learning unethical practices from a co-worker: The peer effect of Jose Canseco

Estimating spillovers using panel data, with an application to the classroom

Academic Peer Effects with Different Group Assignment Policies: Residential Tracking versus Random Assignment

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