Paper Reading 1: Analysis

Concepts: Clustered Standard Errors
Methods: Hendonic Regression, Test for Randomization

Gender Peer Effects on Students’ Academic and Noncognitive Outcomes: Evidence and Mechanisms

Jie Gong, Yi Lu and Hong Song, 2019, Journal of Human Resources

Empirical analysis:

$Y_{ics}=\alpha+\beta_{1}Peerfem_{-ics}+\beta_{2}Female_{ics}+\phi X_{ics}+ \tau W_{cs}+\lambda_{sg}+\epsilon_{ics}$

• $Y_{ics}$: Measures of academic and noncognitive outcomes for student i in class c of school s
• $Peerfem_{ics}$: Proportion of females in i’s class, excluding i;
• $Female_{ics}$: Binary. whether i is female;
• $X_{ics}$: i’s predetermined characteristics and teacher controls;
• $W_{cs}$: Peers’ ability controls, including baseline academic ability for male and female peer.
• $\lambda_{sg}$: school-grade fixed effect;

We cluster standard errors at the class level, accounting for correlation in outcomes for students in the same class.

• Clustered standard errors: Estimate the standard error of a regression parameter in settings where observations may be subdivided into smaller-sized groups (“clusters”) and where the treatment assignment is correlated within each group. It’s useful when treatment is assigned at the level of a cluster instead of at the individual level.
  • E.g., we want to discover whether a teaching technique improves student test scores. We assign teachers in “treated” classrooms to try new technique, while leaving “control” classrooms unaffected. When analyzing results, we want to keep data at the student level (However, classical SE are inappropriate because student test scores within each class are not independently distributed. Instead, students in classes with better teachers have high test scores regardless of whether they receive the experimental treatment). Thus we cluster SE at classroom level to account for this aspect of the experiment.

Main Results

A. Gender Peer Effects on Academic Performance

1. Examine the gender peer effect on students’ academic outcomes using regression model
   Interpretation: All regressions include subject and school-grade fixed effects. The coefficient for the proportion of female peers is positive and statistically significant, which suggests that on average, when a student has more female peers in the class, he or she tends to achieve higher grades. After controlling for predetermined characteristics of the focal student, the teachers, and the academic ability of female and male peers, we find that the effect is consistently positive and statistically significant at the 1% level. The coefficient, 1.019, suggests that a 10-percentage-point (approximately 1.25 standard deviation) increase in the proportion of female classmates raises a student’s test score by 10.19% of a standard deviation.

B. Gender Peer Effects on Noncognitive Outcomes
Interpretation: the estimated impact on mental stress is small in magnitude and statistically insignificant, which suggests that having more female peers does not appear to influence students’ mental stress levels. Table 4, columns 4 to 6, report the estimated effects on students’ social acclimation and general satisfaction in school. Overall, we find a positive effect of having more female classmates on students’ outcomes along this dimension. The effect remains robust after controlling for student and teacher characteristics, as well as for peers’ ability.

Robustness Checks

1.Effects from female students’ ability spillover.
Concern: Effects may come from the spillover of female students’ academic ability and performance, given that the literature has established girls’ advantage in test scores during primary and middle school.

• There are some gender differences between female and male characteristics and baseline academic ability, but the magnitudes are small. The pattern of academic performance before middle school is mixed: while male students are more likely to repeat grades, they are also more likely to skip grades.
• when we control for the academic ability of female and male peers, the main results remain similar.

2.Teacher assignment, differential teaching and grading.
Concern: it may not reflect better academic achievement, but rather differential teaching and grading by teachers.

• Conduct a balancing test on teachers characteristics(i.e., regressing teacher pre-determined characteristics (gender, education, etc.) on female peer proportion and controlling for school-grade fixed effect.)
• Results: Most estimates are statistically insignificant, suggesting no strong correlation between teachers characteristics and the percentage of female students.