Equation (1) estimates the average treatment effect. However, looking only at the mean effect likely misses important heterogeneity in effect size across the ability distribution. Specifically, exposure to same-gender role models is likely to incentivize students at the right tail of the ability distribution who satisfy, or are close to satisfying, the requirements for choosing STEMM programs, while it may not be sufficient for incentivizing students at the left tail. To explore this, table 4 show results from estimating unconditional quantile regressions, using the methodology of Firpo, Fortin, and Lemieux (2009). With respect to compulsory school STEMM GPA, the results in panel A of table 4 suggest that the effect of same-gender GP match on educational performance loads on individuals in the right tail of the ability distribution. While the results for high school STEMM GPA are slightly noisier with respect to quantiles 7 and 8 (panel B of table 4), the general pattern of results is similar to that for compulsory school STEMM GPA.

Table 4.

Effect of Same-Gender GP on STEMM GPA: Quantile Effects

QuantileQuantileQuantileQuantileQuantileQuantileQuantileQuantileQuantile
123456789
A: Compulsory school STEMM GPA
Same-gender GP 0.006 −0.000 0.011 0.011 0.082* 0.082* 0.067* 0.067* 0.149*
(0.030) (0.044) (0.032) (0.032) (0.043) (0.043) (0.035) (0.035) (0.070)
B: High school STEMM GPA
Same-gender GP −0.027 0.060 0.076 0.145** 0.135** 0.156** 0.096 0.045 0.140**
(0.084) (0.066) (0.056) (0.062) (0.052) (0.063) (0.058) (0.067) (0.068)
QuantileQuantileQuantileQuantileQuantileQuantileQuantileQuantileQuantile
123456789
A: Compulsory school STEMM GPA
Same-gender GP 0.006 −0.000 0.011 0.011 0.082* 0.082* 0.067* 0.067* 0.149*
(0.030) (0.044) (0.032) (0.032) (0.043) (0.043) (0.035) (0.035) (0.070)
B: High school STEMM GPA
Same-gender GP −0.027 0.060 0.076 0.145** 0.135** 0.156** 0.096 0.045 0.140**
(0.084) (0.066) (0.056) (0.062) (0.052) (0.063) (0.058) (0.067) (0.068)

Authors' estimation of equation (1) as described in the text using the unconditional quantile regression method discussed in Firpo, Fortin, and Lemieux (2009). Regressions include municipality, year of swap, birth year, and previous GP fixed effects. Standard errors are clustered at the level of the exogenously assigned GP. Sample includes all girls born between 1988 and 1996 who were subject to at least one exogenous GP swap prior to age 15. Significant at $*$10%, $**$5%, $***$1%.

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