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Differential Peer Effects, Student Achievement, and Student Absenteeism: Evidence From a Large-Scale Randomized Experiment


Using data from a well-executed randomized experiment, I examine the effects of gender composition and peer achievement on high school students’ outcomes in disadvantaged neighborhoods. Results show that having a higher proportion of female peers in the classroom improves girls’ math test scores only in less-advanced courses. For male students, the estimated gender peer effects are positive but less precisely estimated. I also find no effect of average classroom achievement on female math test scores. Males, on the other hand, seem to benefit from a higher-achieving classroom. I propose mechanisms relating to lower gender stereotype influences and gender-specific attitudes toward competition as potential explanations for peer effects findings. Finally, having a higher proportion of female students in the classroom decreases student absenteeism among male students but has no impact on female attendance.

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  1. The number of public schools providing same-sex classrooms increased to approximately 400 schools in 2011 from 12 schools in 2002. There were also more than 110 same-sex schools nationwide in 2011 (Chandler 2011).

  2. A large literature has also examined the effect of peers on student outcomes in college (see, e.g., Carrell et al. 2009; Foster 2006; Lyle 2007; Sacerdote 2001; Stinebrickner and Stinebrickner 2006; Zimmerman 2003). Moreover, recent studies have examined peer effects in labor markets (see, e,g,., Arcidiacono and Nicholson 2005; Black et al. 2013; Bifulco et al. 2011, 2014; Mas and Moretti 2009).

  3. A few studies have also examined the impact of single-sex schooling on student outcomes (see, e.g., Doris et al. 2013; Jackson 2012; Park et al. 2013).

  4. The other major identification issue—referred to as the endogeneity or the reflection problem (Manski 1993; Moffitt 2001; Sacerdote 2001)—occurs because it is often difficult to separate the effect that the peer group has on the student from the effect the student has on the peer group. Suggesting a regression of own achievement on contemporaneous average peer achievement is problematic because these outcomes are jointly determined, and peer achievement is likely to be endogenous to the model. Apart from this threat to identification, peers usually share an environment and thus are potentially subject to similar shocks. Ignoring these correlated effects may also contaminate the estimated peer effects.

  5. Peer aggregation at the classroom level has been limited. See, for example, Antecol et al. (2016), Betts and Zau (2004), Burke and Sass (2013), and Hoxby and Weingarth (2005) for notable exceptions.

  6. The mission of the programs is not limited to secondary schools. Both TFA and TNTP recruit teachers to serve in primary schools in disadvantaged communities.

  7. The control teachers could have entered teaching through either a traditional route or a less-selective alternative certification program.

  8. Because the data are confidential, the sample sizes are rounded to the nearest multiple of 10.

  9. There are 320 teachers in the data (70 TFA, 80 TF, and 170 control group teachers), indicating that some teachers in the study taught in more than one block in different periods during the school day and in both survey years.

  10. The high school study sample includes 110 classroom matches: 120 TFA/TF and 130 control group classrooms from a total of 40 schools. There are also 160 teachers (20 TFA, 50 TF, and 90 control group teachers).

  11. Baseline test scores for all students in the same classroom match come from the same grade level. To ensure this, MPR administrators calculated the tenth percentile of the current grade level within the math classroom matches, identified the highest grade level that was less than the tenth percentile grade level and was a grade level in which end-of-year state assessments were administered, and then used that particular grade level to obtain the baseline test scores.

  12. I also examine whether the baseline characteristics of the attrites are correlated with beginning-of-year classroom measures (e.g., average classroom math achievement) but do not find evidence for any correlation. These results are available upon request.

  13. The average proportion of the compliers in the end-of-year classrooms is approximately 85 %.

  14. Information on student and teacher characteristics is not available for all student observations. I use dummy variables to control for the missing values in student and teacher characteristics.

  15. More than 300 (330) late enrollees had nonmissing baseline math test scores.

  16. An increase in the number of tests increase the likelihood of falsely rejecting the null hypothesis, the so-called multiplicity problem (Anderson 2008). Specifically, of 19 hypotheses, the probability of falsely rejecting at least one of the 19 null hypotheses at the 10 % level is 1 – 0.919 = 0.865. Therefore, rejection of one or two hypotheses among many does not necessarily pose a threat to randomization.

  17. Only 47 noncomplier students switched to a different block. For ease of notation, I use λ b in the equations throughout this article, but I control for the end-of-year block fixed effects in the estimations of peer effects.

  18. As an alternative, I use the grade-level baseline achievement distribution to specify the achievement terciles. The results from this exercise are virtually identical to those presented herein and are available upon request.

  19. The mean of within-block difference in the proportion of female students across classroom matches is .1, with a standard deviation of .09. This difference also ranges between 0 and .55.

  20. I also examine the variance decomposition in the average math achievement of the classrooms. The within-block variation accounts for approximately 4 % of the total variation in the average math achievement.

  21. The coefficient estimates on the instrument from the first stage are 0.718 (SE = 0.075) and 0.750 (SE = 0.073) for female and male subsamples, respectively, and the first-stage F-statistics are 89.92 and 102.96, respectively.

  22. I interpret the coefficient magnitudes using the NWEA nationwide normative sample.

  23. I also examine the effects of peer achievement by excluding classroom gender composition measures. The coefficient estimates on peer achievement effects remain intact. I also add interaction terms between average peer math achievement and gender composition in the classroom to look at any interactive impact. These additional terms are all statistically indifferent from zero.

  24. Using the Angrist-Pischke first-stage statistic, I reject the null hypothesis of weak identification for all five endogenous variables.

  25. Compared with IV regressions, OLS estimates reported in columns 1 and 3 of Table 8 are less significant and are smaller in magnitude.

  26. Apart from the main focus of this study, I examine the effects of student-teacher gender match on math achievement in high schools and fail to find any significant effect of gender match on either female or male students’ achievement.

  27. Following McCrary and Royer (2011), power calculations for a two-sided test are based on the following inequality:

    \( \frac{N- n}{N}>\frac{1.96^2}{\left({\hat{\uptheta}}_N-{\uptheta}_0\right)} S\hat{E}-1, \)

    where θ0 is the point hypothesis to be tested (e.g., 0 in this case), \( {\hat{\uptheta}}_N \) is the point estimate that I expect to obtain in the large sample size N (e.g., the actual point estimates from the randomized sample n), and \( S\hat{E} \) is the estimated standard error using this sample.

  28. I also estimate gender composition and average effects of peer achievement for the subsample of students with nonmissing absentee rates (1,250 females and 1,010 males). The findings for peer effects are very similar, and these additional results are available upon request.

  29. I also estimate a model similar to Eq. (6) where I construct achievement indicators denoting student’s own math baseline achievement at each decile of the course-level baseline achievement distribution. Viewing the complete set of results, it appears that male students in the first five deciles of the baseline achievement distribution seem to benefit the most from an improvement in average math achievement.

  30. I also experiment with the same analysis using fraction of gender-specific peers in the classroom. The results reinforce findings from Table 7 that it is the average of the entire classroom—not just same-sex peers’ achievement—that matters for male students.

  31. The sample weights reflect the probability of assignment of a student to a TFA/TF classroom.

  32. In addition to these robustness checks, I first estimate the gender peer effects separately for TFA and TNTP subsamples. Next, I interact student and teacher controls with indicators for student’s position in the math baseline achievement distribution. The coefficient estimates are similar to those reported throughout this article.

  33. Comparing the mean, median, as well as the range of the proportion of female students in the classrooms, I do not observe any discernible patterns across math subjects.

  34. Whitmore (2005) also reported the pooled sample results from a specification in which the share of female peers and average achievement of the classroom were simultaneously controlled for. The estimated effect of the share of female students falls from 2 percentile points to 1.3 when achievement of the classroom is included to the gender peer effects specification.

  35. Psychology literature offers ample evidence regarding the relationship between peer influences and age. Conformity to peer pressure follows an inverted U-shaped age pattern, with peer effects peaking during mid-adolescence (see, e.g., Blakemore and Choudhury 2006; Brown et al. 1986).


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Correspondence to Ozkan Eren.

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Eren, O. Differential Peer Effects, Student Achievement, and Student Absenteeism: Evidence From a Large-Scale Randomized Experiment. Demography 54, 745–773 (2017).

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  • Peer effects
  • Instrumental variables
  • Randomized experiment
  • Teach for America
  • Power calculations