-
Notifications
You must be signed in to change notification settings - Fork 5
/
04_r_alt_control_years.R
256 lines (197 loc) · 8.71 KB
/
04_r_alt_control_years.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
# ---
# ROBUSTNESS: USING ALL COMBINATION OF PREVIOUS YEARS AS ALTERNATIVE CONTROL GROUPS
# ---
library(tidyverse)
library(broom)
library(estimatr)
library(texreg)
theme_set(theme_bw())
#SPECIFY PATHS
tables_path <- "tables/supp/r_alt_control_years/"
plots_path <- "plots/supp/r_alt_control_years/"
models_path <- "data/final/supp/r_alt_control_years/"
#SPECIFY INTERACTION
int_var <- c("female", "ses", "ability")
# OVERALL DEPENDENT VARIABLES
dvs <- c("ALL", "RW", "TBL", "SP", "DMT")
# SPECIFY LABELS
var_names <- list("treat" = "Treatment",
"treat:female" = "Treat x Female",
"treat:seslow" = "Treat x Par. Educ. (low)",
"treat:seslowest" = "Treat x Par. Educ. (lowest)",
"treat:seslow:abilitymiddle" = "Treat x Par. Educ. (low) x Prior Perf. (middle)",
"treat:seslowest:abilitymiddle" = "Treat x Par. Educ. (lowest) x Prior Perf. (middle)",
"treat:seslow:abilitybottom" = "Treat x Par. Educ. (low) x Prior Perf. (bottom)",
"treat:seslowest:abilitybottom" = "Treat x Par. Educ. (lowest) x Prior Perf. (bottom)",
"treat:seslow:female" = "Treat x Par. Educ. (low) x Female",
"treat:seslowest:female" = "Treat x Par. Educ. (lowest) x Female",
"treat:abilitymiddle" = "Treat x Prior Perf. (middle)",
"treat:abilitybottom" = "Treat x Prior Perf. (bottom)",
"female" = "Female",
"seslow" = "Parental Educ. (low)", "seslowest" = "Parental Educ. (lowest)",
"abilitymiddle" = "Prior Perf. (middle)", "abilitybottom" = "Prior Perf. (bottom)",
"year_s" = "Year (std.)",
"days_between_all_s" = "Days between tests (std.)",
"as.factor(class_year)4" = "Age 8",
"as.factor(class_year)5" = "Age 9",
"as.factor(class_year)6" = "Age 10",
"as.factor(class_year)7" = "Age 11",
"(Intercept)" = "(Intercept)")
# LOAD DATA
total_df <- readRDS(paste0("data/edit/analysis.rds")) %>%
mutate(year_s = scale(as.numeric(as.character(substr(year, 5, 8)))),
days_between_all_s = scale(days_between_all)) %>%
mutate(school_id = factor(school_id))
# LIMIT DATA TO NON-MISSING ON SES, ABILITY, AND GENDER
total_df <- total_df %>%
filter(!is.na(female) & !is.na(ses) & !is.na(ability) & !is.na(days_between_all_s))
# SPECIFY TREATMENT AND CONTROL PERIODS
control_periods <- list(c("20162017", "20172018"),
c("20162017", "20182019"),
c("20172018", "20182019"),
c("20162017"), c("20172018"), c("20182019"))
treatment_period <- "20192020"
for(control_period in control_periods) {
total_df <- total_df %>%
mutate(treat = ifelse(year==treatment_period, 1,
ifelse(year %in% control_period, 0, NA)))
# SPECIFY MODELS
if(length(control_period) > 1) {
fun <- " ~ 1 + treat + year_s + days_between_all_s"
} else {
fun <- " ~ 1 + treat + days_between_all_s"
}
# MAIN ANALYSIS -------------------------------------------------------------------------------
fit <- lapply(dvs,
FUN = function(x) lm_robust(formula(paste(x, fun)),
clusters = school_id, se_type = "stata",
data = total_df))
names(fit) <- dvs
# SAVE OUTPUT
screenreg(fit, include.ci = F)
texreg(list(fit$ALL, fit$RW, fit$TBL, fit$SP),
caption = "Overall learning loss, by subject",
custom.model.names = c("Composite", "Maths", "Reading", "Spelling"),
custom.coef.map = var_names,
center = TRUE,
include.ci = FALSE,
label = "table:overall",
file = paste0(tables_path, "ll_overall_",
paste(control_period, collapse = ""),
"_", treatment_period, ".tex"))
# TIDY MODELS
full_models <- c()
for(i in names(fit)) {
tidy_model <- tidy(fit[[i]]) %>%
mutate(model = i,
nobs = fit[[i]]$nobs,
nclusters = fit[[i]]$nclusters)
full_models <- rbind(full_models, tidy_model)
}
# SAVE MODELS
saveRDS(full_models,
file= paste0(models_path, "models_overall_",
paste(control_period, collapse = ""), "_",
treatment_period, ".rds"))
# TOTAL LEARNING LOSS BY GRADE AND SUBJECT ----------------------------------------------------
# SPECIFY CLASS YEARS
class_years <- c(4:7)
# RUN MODELS BY GRADE
fit <- c()
for(i in class_years) {
fit_temp <- lapply(dvs,
FUN = function(x) lm_robust(formula(paste(x, fun)),
clusters = school_id, se_type = "stata",
data = total_df[total_df$class_year==i, ]))
names(fit_temp) <- paste0(dvs, i)
fit <- c(fit_temp, fit)
}
# SAVE OUTPUT
screenreg(fit, include.ci = FALSE)
texreg(fit,
caption = "Overall learning loss by grade",
custom.coef.map = var_names,
center = TRUE,
include.ci = FALSE,
label = "table:grade",
file = paste0(tables_path, "ll_grade_",
paste(control_period, collapse = ""), "_",
treatment_period, ".tex"))
# TIDY MODELS
full_models <- c()
for(i in names(fit)) {
tidy_model <- tidy(fit[[i]]) %>%
mutate(model = i,
nobs = fit[[i]]$nobs,
nclusters = fit[[i]]$nclusters)
full_models <- rbind(full_models, tidy_model)
}
# save models
saveRDS(full_models,
file= paste0(models_path, "models_grade_",
paste(control_period, collapse = ""), "_",
treatment_period, ".rds"))
# LEARNING LOSS BY INTERACTION AND SUBJECT ------------------------------------------------------------
# Interacting variable
for(var in int_var) {
# SPECIFY MODELS
# SPECIFY MODELS
if(length(control_period) > 1) {
fun <- paste0(" ~ 1 + treat * ", var, " + year_s + days_between_all_s")
} else {
fun <- paste0(" ~ 1 + treat * ", var, " + days_between_all_s")
}
# RUN MODEL
fit <- lapply(dvs,
FUN = function(x) lm_robust(formula(paste(x, fun)),
clusters = school_id, se_type = "stata",
data = total_df))
names(fit) <- dvs
# SAVE OUTPUT
print(screenreg(fit, include.ci = FALSE, custom.coef.map = var_names))
texreg(list(fit$ALL, fit$RW, fit$TBL, fit$SP),
caption = paste0("Learning loss by ", var),
custom.model.names = c("Composite", "Maths", "Reading", "Spelling"),
custom.coef.map = var_names,
center = TRUE,
include.ci = FALSE,
label = paste0("table", var),
file = paste0(tables_path, "ll_", var, "_",
paste(control_period, collapse = ""), "_",
treatment_period, ".tex"))
# TIDY MODELS
full_models <- c()
for(i in dvs) {
tidy_model <- tidy(fit[[i]]) %>%
mutate(model = i,
nobs = fit[[i]]$nobs,
nclusters = fit[[i]]$nclusters)
var_cov <- as.data.frame(vcov(fit[[i]]))
# add full terms
for (j in tidy_model$term) {
tidy_model <- tidy_model %>%
mutate(std.error = as.numeric(std.error),
estimate = as.numeric(estimate))
if (grepl("treat:", j)) {
coeff_name <- paste0("full_", j)
estimate <- tidy_model$estimate[tidy_model$term == "treat"] +
tidy_model$estimate[tidy_model$term == j]
treat_loc <- which(names(var_cov) == "treat")
j_loc <- which(names(var_cov) == j)
se <- sqrt(sum(var_cov[c(treat_loc, j_loc), c(treat_loc, j_loc)]))
new_row <- c(coeff_name, estimate, se, NA, NA, NA, NA, NA, i, i, fit[[i]]$nobs, fit[[i]]$nclusters)
tidy_model <- rbind(tidy_model, new_row)
}
}
full_models <- rbind(full_models, tidy_model) %>%
mutate_at(vars(estimate, std.error, statistic, p.value, conf.low, conf.high, df, nobs, nclusters),
function(x) as.numeric(x))
}
sapply(full_models, class)
# save models
saveRDS(full_models,
file= paste0(models_path,
"models_", var, "_",
paste(control_period, collapse = ""), "_", treatment_period, ".rds"))
}
}