Parental Investment and School Quality: Evidence from Ghana

2025-12-01

2026-07-01

Letter sound knowledge measured by teacher-led oral reading fluency assessments.

I am only using letter sound knowledge rather than other literacy components because it is one of the most basic literacy skills and thus closest on the causal chain.

For the government schools, I will explore using attendance as an outcome, but not all schools are reliably recording this. Conditional on securing grant funding, I will also measure parental engagement using a survye.

I will use a 2 by 2 factorial design where the parent meetings are randomized to half the schools, and school leader training is cross randomized on top of that. So there will be 4 total groups: 1 getting nothing, 1 getting only parent meetings, 1 getting only school leader training, and 1 getting both.

Not available

Done using R in office using the following randomization code and seed.

# Randomize government schools

here::i_am("code/randomize_baseline.R")

pacman::p_load(tidyverse, randomizr)

gov_schools_orf_df = haven::read_dta(here::here(
"data",
"clean_baseline_gov_schools.dta"
))
orf_df = haven::read_dta(here::here("data", "clean_baseline_gov_students.dta"))

set.seed(612873201)


# Create stratification cells: 4 schools per cell
# There are three schools that ran no assessments: they are in a strata of three together
# There are 62 remaining after that, so 2 over 4 per cell. Those are added to cells roughly in the middle
gov_schools_orf_df = gov_schools_orf_df |>
arrange(letter_sound_knowledge_eb) |>
mutate(
stratification_cell = c(
rep(1:6, each = 4), # First 24 schools: 6 strata of 4
rep(7, 4), # Next 4 schools: 1 stratum of 4 (was 5)
rep(8, 4), # Next 4 schools: 1 stratum of 4 (was 5)
rep(9:15, each = 4), # Next 28 schools: 7 strata of 4
rep(16, 3) # Last 3 schools: 1 stratum of 3
)
) |>
mutate(
treat = block_ra(
blocks = stratification_cell
)
) |>
relocate(treat, .after = school) |>
relocate(stratification_cell, .after = treat)

haven::write_dta(
gov_schools_orf_df,
here::here("data", "clean_baseline_gov_schools_with_treatment.dta")
)


# Merge with student level
tmp_schools = gov_schools_orf_df |> select(school, treat, stratification_cell)

orf_df = orf_df |>
left_join(tmp_schools, by = join_by(school)) |>
relocate(treat, .after = student_name) |>
relocate(stratification_cell, .after = treat)

orf_df |>
haven::write_dta(here::here(
"data",
"clean_baseline_gov_students_with_treatment.dta"
))


#### Randomize private schools

private_schools_df = haven::read_dta(here::here(
"data",
"clean_baseline_private_schools.dta"
))

private_students_df = haven::read_dta(here::here(
"data",
"clean_basleine_private_students.dta"
))


set.seed(12398173)

# Firsts randomize 24 private schools that have an observation for beginning of the year assessments
# Create stratification cells: 4 schools per cell

private_schools = private_schools_df |>
filter(!is.na(letter_sound_knowledge_eb)) |>
arrange(letter_sound_knowledge_eb) |>
mutate(
stratification_cell = c(
rep(1:6, each = 4) + 16 # There are 16 strat cells from gov schools
)
) |>
mutate(
treat = block_ra(
blocks = stratification_cell
)
) |>
relocate(treat, .after = school) |>
relocate(stratification_cell, .after = treat)

# Now randomize 16 schools that didn't have an assessment
private_schools_egra = private_schools_df |>
filter(is.na(letter_sound_knowledge_eb)) |>
arrange(letter_sound_knowledge_eb) |>
mutate(
stratification_cell = c(
rep(1:4, each = 4) + 22 # There are 16 strat cells from gov schools and 6 from first batch of private schools
)
) |>
mutate(
treat = block_ra(
blocks = stratification_cell
)
) |>
relocate(treat, .after = school) |>
relocate(stratification_cell, .after = treat)


# Combine
private_schools_combined_df = private_schools |>
bind_rows(private_schools_egra)


haven::write_dta(
private_schools_combined_df,
here::here("data", "clean_baseline_private_schools_with_treatment.dta")
)


# Write treated schools list for Simon
gov_schools_orf_df |>
filter(treat == 1) |>
select(school) |>
writexl::write_xlsx(here::here("data", "treated_gov_schools.xlsx"))

private_schools_combined_df |>
filter(treat == 1) |>
select(school) |>
writexl::write_xlsx(here::here("data", "treated_private_schools.xlsx"))


# Combine into one data set
all_schools_df = gov_schools_orf_df |>
bind_rows(private_schools_combined_df) |>
rename(treat_parent = treat)


### Now cross-randomize school leader training

# This is done using the same stratification cels
# So basically within strat cell 1, the two treat schools are split and the two control schools are split
set.seed(312323814)

cross_randomized_df = map(
seq_along(1:max(all_schools_df$stratification_cell)),
function(i) {
all_schools_df |>
filter(stratification_cell == {{ i }}) |>
mutate(treat_school_leader = block_ra(blocks = treat_parent)) |>
relocate(treat_school_leader, .after = treat_parent)
}
) |>
bind_rows()

# Write to excel
cross_randomized_df |>
haven::write_dta(here::here(
"data",
"full_treatment_list.dta"
))

Schools.

Yes

103 schools. 63 government and 40 private.

Approximately 25 children per school, so 2,575 students.

51 treatment schools and 52 control.