Models
M1
lm1 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_pct ~ female * fac_female, data = ., fixed_effects = ~ course_fe, se_type = "stata", clusters = facid)
lm2 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_z ~ female * fac_female, data = ., fixed_effects = ~ course_fe, se_type = "stata", clusters = facid)
knitreg(list(lm1, lm2), custom.note = "%stars", stars = c(0.01, 0.05, 0.1), include.ci = F, dcolumn = T, booktabs = T, float.pos = "H", caption = "", digits = 3, include.nclust = F, include.rsquared = F, include.adjrs = F, include.rmse = F, custom.model.names = c("Course grade (pct)", "Course grade (z)"), custom.coef.map = list("female:fac_female" = "female_stu x female_fac", "female" = "female_stu", "fac_female" = "female_fac"))
|
|
Course grade (pct)
|
Course grade (z)
|
|
female_stu x female_fac
|
-0.400
|
-0.018
|
|
|
(1.225)
|
(0.043)
|
|
female_stu
|
12.487***
|
0.436***
|
|
|
(0.796)
|
(0.027)
|
|
female_fac
|
1.229
|
0.044
|
|
|
(0.999)
|
(0.034)
|
|
Num. obs.
|
28878
|
28878
|
|
***p < 0.01; **p < 0.05; *p <
0.1
|
M2
lm1 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_pct ~ female * fac_female + reservation_stu + age + b_i_jeemain_score + b_math_g1_score + b_physics_g1_score + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac + miss_reservation_stu + miss_age + miss_b_i_jeemain_score + miss_b_math_g1_score + miss_b_physics_g1_score + miss_fac_associate_professor + miss_fac_professor + miss_fac_yearsinhighed + miss_fac_highest_degree_phd + miss_fac_highest_degree_phd_in_prog + miss_fac_degree_college_elite + miss_reservation_fac + b_ses_1 + b_ses_2 + b_ses_3 + b_ses_4 + b_ses_5 + b_ses_6 + b_i_ses_1 + b_i_ses_2 + b_i_ses_3 + b_ct_score + b_ql_score + miss_b_ct_score + miss_b_ql_score + b_i_enter_engineering_exam_2 + b_i_enter_engineering_exam_3 + b_i_enter_engineering_exam_NA + area_2 + area_3 + area_NA + hstype_2 + hstype_3 + hstype_4 + hstype_5 + hstype_6 + hstype_7 + hstype_NA + father_ed_ind_2 + father_ed_ind_3 + father_ed_ind_4 + father_ed_ind_5 + father_ed_ind_6 + father_ed_ind_7 + father_ed_ind_8 + father_ed_ind_NA + mother_ed_ind_2 + mother_ed_ind_3 + mother_ed_ind_4 + mother_ed_ind_5 + mother_ed_ind_6 + mother_ed_ind_7 + mother_ed_ind_8 + mother_ed_ind_NA + old_sib_factor_0 + old_sib_factor_2 + old_sib_factor_3 + old_sib_factor_4 + old_sib_factor_more + old_sib_factor_NA + young_sib_factor_0 + young_sib_factor_2 + young_sib_factor_3 + young_sib_factor_4 + young_sib_factor_more + young_sib_factor_NA + `school_years_english_factor_1-9` + `school_years_english_factor_10-12` + `school_years_english_factor_13-16` + school_years_english_factor_NA + district_clean, data = ., fixed_effects = ~ course_fe, se_type = "stata", clusters = facid)
lm2 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_z ~ female * fac_female + reservation_stu + age + b_i_jeemain_score + b_math_g1_score + b_physics_g1_score + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac + miss_reservation_stu + miss_age + miss_b_i_jeemain_score + miss_b_math_g1_score + miss_b_physics_g1_score + miss_fac_associate_professor + miss_fac_professor + miss_fac_yearsinhighed + miss_fac_highest_degree_phd + miss_fac_highest_degree_phd_in_prog + miss_fac_degree_college_elite + miss_reservation_fac + b_ses_1 + b_ses_2 + b_ses_3 + b_ses_4 + b_ses_5 + b_ses_6 + b_i_ses_1 + b_i_ses_2 + b_i_ses_3 + b_ct_score + b_ql_score + miss_b_ct_score + miss_b_ql_score + b_i_enter_engineering_exam_2 + b_i_enter_engineering_exam_3 + b_i_enter_engineering_exam_NA + area_2 + area_3 + area_NA + hstype_2 + hstype_3 + hstype_4 + hstype_5 + hstype_6 + hstype_7 + hstype_NA + father_ed_ind_2 + father_ed_ind_3 + father_ed_ind_4 + father_ed_ind_5 + father_ed_ind_6 + father_ed_ind_7 + father_ed_ind_8 + father_ed_ind_NA + mother_ed_ind_2 + mother_ed_ind_3 + mother_ed_ind_4 + mother_ed_ind_5 + mother_ed_ind_6 + mother_ed_ind_7 + mother_ed_ind_8 + mother_ed_ind_NA + old_sib_factor_0 + old_sib_factor_2 + old_sib_factor_3 + old_sib_factor_4 + old_sib_factor_more + old_sib_factor_NA + young_sib_factor_0 + young_sib_factor_2 + young_sib_factor_3 + young_sib_factor_4 + young_sib_factor_more + young_sib_factor_NA + `school_years_english_factor_1-9` + `school_years_english_factor_10-12` + `school_years_english_factor_13-16` + school_years_english_factor_NA + district_clean, data = ., fixed_effects = ~ course_fe, se_type = "stata", clusters = facid)
knitreg(list(lm1, lm2), custom.note = "%stars", stars = c(0.01, 0.05, 0.1), include.ci = F, dcolumn = T, booktabs = T, float.pos = "H", caption = "", digits = 3, include.nclust = F, include.rsquared = F, include.adjrs = F, include.rmse = F, custom.model.names = c("Course grade (pct)", "Course grade (z)"), custom.coef.map = list("female:fac_female" = "female_stu x female_fac", "female" = "female_stu", "fac_female" = "female_fac"))
|
|
Course grade (pct)
|
Course grade (z)
|
|
female_stu x female_fac
|
0.528
|
0.013
|
|
|
(1.137)
|
(0.039)
|
|
female_stu
|
12.747***
|
0.447***
|
|
|
(0.805)
|
(0.028)
|
|
female_fac
|
1.195
|
0.042
|
|
|
(0.826)
|
(0.028)
|
|
Num. obs.
|
28469
|
28469
|
|
***p < 0.01; **p < 0.05; *p <
0.1
|
M3
lm1 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_pct ~ female * fac_female + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac, data = ., fixed_effects = ~ course_fe, se_type = "stata", clusters = facid)
lm2 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_z ~ female * fac_female + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac, data = ., fixed_effects = ~ course_fe, se_type = "stata", clusters = facid)
knitreg(list(lm1, lm2), custom.note = "%stars", stars = c(0.01, 0.05, 0.1), include.ci = F, dcolumn = T, booktabs = T, float.pos = "H", caption = "", digits = 3, include.nclust = F, include.rsquared = F, include.adjrs = F, include.rmse = F, custom.model.names = c("Course grade (pct)", "Course grade (z)"), custom.coef.map = list("female:fac_female" = "female_stu x female_fac", "female" = "female_stu", "fac_female" = "female_fac"))
|
|
Course grade (pct)
|
Course grade (z)
|
|
female_stu x female_fac
|
-0.424
|
-0.019
|
|
|
(1.232)
|
(0.043)
|
|
female_stu
|
12.512***
|
0.436***
|
|
|
(0.797)
|
(0.028)
|
|
female_fac
|
1.279
|
0.044
|
|
|
(0.908)
|
(0.031)
|
|
Num. obs.
|
28878
|
28878
|
|
***p < 0.01; **p < 0.05; *p <
0.1
|
M4
lm1 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_pct ~ female * fac_female + reservation_stu + age + b_i_jeemain_score + b_math_g1_score + b_physics_g1_score + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac + miss_reservation_stu + miss_age + miss_b_i_jeemain_score + miss_b_math_g1_score + miss_b_physics_g1_score + miss_fac_associate_professor + miss_fac_professor + miss_fac_yearsinhighed + miss_fac_highest_degree_phd + miss_fac_highest_degree_phd_in_prog + miss_fac_degree_college_elite + miss_reservation_fac + b_ses_1 + b_ses_2 + b_ses_3 + b_ses_4 + b_ses_5 + b_ses_6 + b_i_ses_1 + b_i_ses_2 + b_i_ses_3 + b_ct_score + b_ql_score + miss_b_ct_score + miss_b_ql_score + b_i_enter_engineering_exam_2 + b_i_enter_engineering_exam_3 + b_i_enter_engineering_exam_NA + area_2 + area_3 + area_NA + hstype_2 + hstype_3 + hstype_4 + hstype_5 + hstype_6 + hstype_7 + hstype_NA + father_ed_ind_2 + father_ed_ind_3 + father_ed_ind_4 + father_ed_ind_5 + father_ed_ind_6 + father_ed_ind_7 + father_ed_ind_8 + father_ed_ind_NA + mother_ed_ind_2 + mother_ed_ind_3 + mother_ed_ind_4 + mother_ed_ind_5 + mother_ed_ind_6 + mother_ed_ind_7 + mother_ed_ind_8 + mother_ed_ind_NA + old_sib_factor_0 + old_sib_factor_2 + old_sib_factor_3 + old_sib_factor_4 + old_sib_factor_more + old_sib_factor_NA + young_sib_factor_0 + young_sib_factor_2 + young_sib_factor_3 + young_sib_factor_4 + young_sib_factor_more + young_sib_factor_NA + `school_years_english_factor_1-9` + `school_years_english_factor_10-12` + `school_years_english_factor_13-16` + school_years_english_factor_NA + district_clean + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac, data = ., fixed_effects = ~ course_fe, se_type = "stata", clusters = facid)
lm2 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_z ~ female * fac_female + reservation_stu + age + b_i_jeemain_score + b_math_g1_score + b_physics_g1_score + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac + miss_reservation_stu + miss_age + miss_b_i_jeemain_score + miss_b_math_g1_score + miss_b_physics_g1_score + miss_fac_associate_professor + miss_fac_professor + miss_fac_yearsinhighed + miss_fac_highest_degree_phd + miss_fac_highest_degree_phd_in_prog + miss_fac_degree_college_elite + miss_reservation_fac + b_ses_1 + b_ses_2 + b_ses_3 + b_ses_4 + b_ses_5 + b_ses_6 + b_i_ses_1 + b_i_ses_2 + b_i_ses_3 + b_ct_score + b_ql_score + miss_b_ct_score + miss_b_ql_score + b_i_enter_engineering_exam_2 + b_i_enter_engineering_exam_3 + b_i_enter_engineering_exam_NA + area_2 + area_3 + area_NA + hstype_2 + hstype_3 + hstype_4 + hstype_5 + hstype_6 + hstype_7 + hstype_NA + father_ed_ind_2 + father_ed_ind_3 + father_ed_ind_4 + father_ed_ind_5 + father_ed_ind_6 + father_ed_ind_7 + father_ed_ind_8 + father_ed_ind_NA + mother_ed_ind_2 + mother_ed_ind_3 + mother_ed_ind_4 + mother_ed_ind_5 + mother_ed_ind_6 + mother_ed_ind_7 + mother_ed_ind_8 + mother_ed_ind_NA + old_sib_factor_0 + old_sib_factor_2 + old_sib_factor_3 + old_sib_factor_4 + old_sib_factor_more + old_sib_factor_NA + young_sib_factor_0 + young_sib_factor_2 + young_sib_factor_3 + young_sib_factor_4 + young_sib_factor_more + young_sib_factor_NA + `school_years_english_factor_1-9` + `school_years_english_factor_10-12` + `school_years_english_factor_13-16` + school_years_english_factor_NA + district_clean + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac, data = ., fixed_effects = ~ course_fe, se_type = "stata", clusters = facid)
knitreg(list(lm1, lm2), custom.note = "%stars", stars = c(0.01, 0.05, 0.1), include.ci = F, dcolumn = T, booktabs = T, float.pos = "H", caption = "", digits = 3, include.nclust = F, include.rsquared = F, include.adjrs = F, include.rmse = F, custom.model.names = c("Course grade (pct)", "Course grade (z)"), custom.coef.map = list("female:fac_female" = "female_stu x female_fac", "female" = "female_stu", "fac_female" = "female_fac"))
|
|
Course grade (pct)
|
Course grade (z)
|
|
female_stu x female_fac
|
0.528
|
0.013
|
|
|
(1.137)
|
(0.039)
|
|
female_stu
|
12.747***
|
0.447***
|
|
|
(0.805)
|
(0.028)
|
|
female_fac
|
1.195
|
0.042
|
|
|
(0.826)
|
(0.028)
|
|
Num. obs.
|
28469
|
28469
|
|
***p < 0.01; **p < 0.05; *p <
0.1
|
M5
lm1 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_pct ~ female * fac_female + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac, data = ., fixed_effects = ~ course_fe + stdid, se_type = "stata", clusters = facid)
lm2 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_z ~ female * fac_female + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac, data = ., fixed_effects = ~ course_fe + stdid, se_type = "stata", clusters = facid)
knitreg(list(lm1, lm2), custom.note = "%stars", stars = c(0.01, 0.05, 0.1), include.ci = F, dcolumn = T, booktabs = T, float.pos = "H", caption = "", digits = 3, include.nclust = F, include.rsquared = F, include.adjrs = F, include.rmse = F, custom.model.names = c("Course grade (pct)", "Course grade (z)"), custom.coef.map = list("female:fac_female" = "female_stu x female_fac", "fac_female" = "female_fac"))
|
|
Course grade (pct)
|
Course grade (z)
|
|
female_stu x female_fac
|
2.227**
|
0.075**
|
|
|
(0.890)
|
(0.030)
|
|
female_fac
|
0.088
|
0.006
|
|
|
(0.747)
|
(0.025)
|
|
Num. obs.
|
28878
|
28878
|
|
***p < 0.01; **p < 0.05; *p <
0.1
|
M6
lm1 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_pct ~ female * fac_female + reservation_stu + age + b_i_jeemain_score + b_math_g1_score + b_physics_g1_score + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac + miss_reservation_stu + miss_age + miss_b_i_jeemain_score + miss_b_math_g1_score + miss_b_physics_g1_score + miss_fac_associate_professor + miss_fac_professor + miss_fac_yearsinhighed + miss_fac_highest_degree_phd + miss_fac_highest_degree_phd_in_prog + miss_fac_degree_college_elite + miss_reservation_fac + b_ses_1 + b_ses_2 + b_ses_3 + b_ses_4 + b_ses_5 + b_ses_6 + b_i_ses_1 + b_i_ses_2 + b_i_ses_3 + b_ct_score + b_ql_score + miss_b_ct_score + miss_b_ql_score + b_i_enter_engineering_exam_2 + b_i_enter_engineering_exam_3 + b_i_enter_engineering_exam_NA + area_2 + area_3 + area_NA + hstype_2 + hstype_3 + hstype_4 + hstype_5 + hstype_6 + hstype_7 + hstype_NA + father_ed_ind_2 + father_ed_ind_3 + father_ed_ind_4 + father_ed_ind_5 + father_ed_ind_6 + father_ed_ind_7 + father_ed_ind_8 + father_ed_ind_NA + mother_ed_ind_2 + mother_ed_ind_3 + mother_ed_ind_4 + mother_ed_ind_5 + mother_ed_ind_6 + mother_ed_ind_7 + mother_ed_ind_8 + mother_ed_ind_NA + old_sib_factor_0 + old_sib_factor_2 + old_sib_factor_3 + old_sib_factor_4 + old_sib_factor_more + old_sib_factor_NA + young_sib_factor_0 + young_sib_factor_2 + young_sib_factor_3 + young_sib_factor_4 + young_sib_factor_more + young_sib_factor_NA + `school_years_english_factor_1-9` + `school_years_english_factor_10-12` + `school_years_english_factor_13-16` + school_years_english_factor_NA + district_clean, data = ., fixed_effects = ~ course_fe + facid, se_type = "stata", clusters = facid)
lm2 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_z ~ female * fac_female + reservation_stu + age + b_i_jeemain_score + b_math_g1_score + b_physics_g1_score + fac_associate_professor + fac_professor + fac_yearsinhighed + fac_highest_degree_phd + fac_highest_degree_phd_in_prog + fac_degree_college_elite + reservation_fac + miss_reservation_stu + miss_age + miss_b_i_jeemain_score + miss_b_math_g1_score + miss_b_physics_g1_score + miss_fac_associate_professor + miss_fac_professor + miss_fac_yearsinhighed + miss_fac_highest_degree_phd + miss_fac_highest_degree_phd_in_prog + miss_fac_degree_college_elite + miss_reservation_fac + b_ses_1 + b_ses_2 + b_ses_3 + b_ses_4 + b_ses_5 + b_ses_6 + b_i_ses_1 + b_i_ses_2 + b_i_ses_3 + b_ct_score + b_ql_score + miss_b_ct_score + miss_b_ql_score + b_i_enter_engineering_exam_2 + b_i_enter_engineering_exam_3 + b_i_enter_engineering_exam_NA + area_2 + area_3 + area_NA + hstype_2 + hstype_3 + hstype_4 + hstype_5 + hstype_6 + hstype_7 + hstype_NA + father_ed_ind_2 + father_ed_ind_3 + father_ed_ind_4 + father_ed_ind_5 + father_ed_ind_6 + father_ed_ind_7 + father_ed_ind_8 + father_ed_ind_NA + mother_ed_ind_2 + mother_ed_ind_3 + mother_ed_ind_4 + mother_ed_ind_5 + mother_ed_ind_6 + mother_ed_ind_7 + mother_ed_ind_8 + mother_ed_ind_NA + old_sib_factor_0 + old_sib_factor_2 + old_sib_factor_3 + old_sib_factor_4 + old_sib_factor_more + old_sib_factor_NA + young_sib_factor_0 + young_sib_factor_2 + young_sib_factor_3 + young_sib_factor_4 + young_sib_factor_more + young_sib_factor_NA + `school_years_english_factor_1-9` + `school_years_english_factor_10-12` + `school_years_english_factor_13-16` + school_years_english_factor_NA + district_clean, data = ., fixed_effects = ~ course_fe + facid, se_type = "stata", clusters = facid)
knitreg(list(lm1, lm2), custom.note = "%stars", stars = c(0.01, 0.05, 0.1), include.ci = F, dcolumn = T, booktabs = T, float.pos = "H", caption = "", digits = 3, include.nclust = F, include.rsquared = F, include.adjrs = F, include.rmse = F, custom.model.names = c("Course grade (pct)", "Course grade (z)"), custom.coef.map = list("female:fac_female" = "female_stu x female_fac", "female" = "female_stu"))
|
|
Course grade (pct)
|
Course grade (z)
|
|
female_stu x female_fac
|
0.925
|
0.026
|
|
|
(1.171)
|
(0.040)
|
|
female_stu
|
12.618***
|
0.442***
|
|
|
(0.820)
|
(0.028)
|
|
Num. obs.
|
28469
|
28469
|
|
***p < 0.01; **p < 0.05; *p <
0.1
|
M7
lm1 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_pct ~ female * fac_female, data = ., fixed_effects = ~ course_fe + stdid + facid, se_type = "stata", clusters = facid)
lm2 <-
df_grades_sfix %>%
mutate(course_fe = str_c(department_id, course_name, sep = "_")) %>%
lm_robust(course_rank_z ~ female * fac_female, data = ., fixed_effects = ~ course_fe + stdid + facid, se_type = "stata", clusters = facid)
knitreg(list(lm1, lm2), custom.note = "%stars", stars = c(0.01, 0.05, 0.1), include.ci = F, dcolumn = T, booktabs = T, float.pos = "H", caption = "", digits = 3, include.nclust = F, include.rsquared = F, include.adjrs = F, include.rmse = F, custom.model.names = c("Course grade (pct)", "Course grade (z)"), custom.coef.map = list("female:fac_female" = "female_stu x female_fac"))
|
|
Course grade (pct)
|
Course grade (z)
|
|
female_stu x female_fac
|
2.601***
|
0.087***
|
|
|
(0.926)
|
(0.031)
|
|
Num. obs.
|
28878
|
28878
|
|
***p < 0.01; **p < 0.05; *p <
0.1
|
M8
lm1 <-
df_grades_sfix %>%
mutate(
course_fe = str_c(department_id, course_name, sep = "_"),
classroom_fe = str_c(course_fe, facid, sep = "_")
) %>%
lm_robust(course_rank_pct ~ female * fac_female, data = ., fixed_effects = ~ course_fe + stdid + classroom_fe, se_type = "stata", clusters = facid)
lm2 <-
df_grades_sfix %>%
mutate(
course_fe = str_c(department_id, course_name, sep = "_"),
classroom_fe = str_c(course_fe, facid, sep = "_")
) %>%
lm_robust(course_rank_z ~ female * fac_female, data = ., fixed_effects = ~ course_fe + stdid + classroom_fe, se_type = "stata", clusters = facid)
knitreg(list(lm1, lm2), custom.note = "%stars", stars = c(0.01, 0.05, 0.1), include.ci = F, dcolumn = T, booktabs = T, float.pos = "H", caption = "", digits = 3, include.nclust = F, include.rsquared = F, include.adjrs = F, include.rmse = F, custom.model.names = c("Course grade (pct)", "Course grade (z)"), custom.coef.map = list("female:fac_female" = "female_stu x female_fac"))
|
|
Course grade (pct)
|
Course grade (z)
|
|
female_stu x female_fac
|
2.638***
|
0.087***
|
|
|
(0.953)
|
(0.032)
|
|
Num. obs.
|
28878
|
28878
|
|
***p < 0.01; **p < 0.05; *p <
0.1
|