1 + 1[1] 2asmt33
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file_path <- file.choose()
uct <- read.csv(
file_path,
stringsAsFactors = FALSE
)
dim(uct)[1] 250 116names(uct) [1] "gender" "payment_type"
[3] "payment_size" "primary_income0"
[5] "primary_income1" "village"
[7] "b_age" "b_married"
[9] "b_hhsize" "b_edu"
[11] "asset_total_ppp0" "asset_total_ppp1"
[13] "cons_nondurable_ppp0" "cons_nondurable_ppp1"
[15] "ent_total_rev_ppp0" "ent_total_rev_ppp1"
[17] "fs_hhfoodindexnew0" "fs_hhfoodindexnew1"
[19] "med_hh_healthindex0" "med_hh_healthindex1"
[21] "ed_index0" "ed_index1"
[23] "psy_index_z0" "psy_index_z1"
[25] "ih_overall_index_z0" "ih_overall_index_z1"
[27] "asset_livestock_ppp0" "asset_livestock_ppp1"
[29] "asset_cows_ppp0" "asset_cows_ppp1"
[31] "asset_smalllivestock_ppp0" "asset_smalllivestock_ppp1"
[33] "asset_birds_ppp0" "asset_birds_ppp1"
[35] "asset_furniture_ppp0" "asset_furniture_ppp1"
[37] "asset_ag_ppp0" "asset_ag_ppp1"
[39] "asset_radiotv_ppp0" "asset_radiotv_ppp1"
[41] "asset_trans_ppp0" "asset_trans_ppp1"
[43] "asset_phone_ppp0" "asset_phone_ppp1"
[45] "asset_savings_ppp0" "asset_savings_ppp1"
[47] "asset_land_owned_total0" "asset_land_owned_total1"
[49] "cons_ownfood_ppp_m0" "cons_ownfood_ppp_m1"
[51] "cons_boughtfood_ppp_m0" "cons_boughtfood_ppp_m1"
[53] "cons_cereals_ppp_m0" "cons_cereals_ppp_m1"
[55] "cons_meatfish_ppp_m0" "cons_meatfish_ppp_m1"
[57] "cons_fruitveg_ppp_m0" "cons_fruitveg_ppp_m1"
[59] "cons_dairy_ppp_m0" "cons_dairy_ppp_m1"
[61] "cons_fats_ppp_m0" "cons_fats_ppp_m1"
[63] "cons_sugars_ppp_m0" "cons_sugars_ppp_m1"
[65] "cons_otherfood_ppp_m0" "cons_otherfood_ppp_m1"
[67] "cons_alcohol_ppp_m0" "cons_alcohol_ppp_m1"
[69] "cons_tobacco_ppp_m0" "cons_tobacco_ppp_m1"
[71] "cons_med_total_ppp_m0" "cons_med_total_ppp_m1"
[73] "cons_ed_ppp_m0" "cons_ed_ppp_m1"
[75] "cons_social_ppp_m0" "cons_social_ppp_m1"
[77] "cons_total_ppp0" "cons_total_ppp1"
[79] "psy_cesdscore0" "psy_cesdscore1"
[81] "psy_worries_z0" "psy_worries_z1"
[83] "psy_stressscore_z0" "psy_stressscore_z1"
[85] "psy_hap_z0" "psy_hap_z1"
[87] "psy_sat_z0" "psy_sat_z1"
[89] "psy_trust_z0" "psy_trust_z1"
[91] "psy_locus_z0" "psy_locus_z1"
[93] "psy_scheierscore_z0" "psy_scheierscore_z1"
[95] "psy_rosenbergscore_z0" "psy_rosenbergscore_z1"
[97] "ent_farmprofit_ppp0" "ent_farmprofit_ppp1"
[99] "fs_foodcred_often0" "fs_foodcred_often1"
[101] "heighttoage_HH_z0" "heighttoage_HH_z1"
[103] "weighttoage_HH_z0" "weighttoage_HH_z1"
[105] "armcirctoage_HH_z0" "armcirctoage_HH_z1"
[107] "med_child_healthindex0" "med_child_healthindex1"
[109] "ed_schoolattend0" "ed_schoolattend1"
[111] "fin_loansout_ppp0" "fin_loansout_ppp1"
[113] "durable_investment0" "durable_investment1"
[115] "nondurable_investment0" "nondurable_investment1" table(uct$payment_type)
control lump sum monthly
157 46 47 uct$treatment <- ifelse(
uct$payment_type == "control",
"Control",
"UCT"
)
uct$treatment <- factor(
uct$treatment,
levels = c("Control", "UCT")
)
table(uct$treatment)
Control UCT
157 93 uct$asset_change <-
uct$asset_total_ppp1 -
uct$asset_total_ppp0colSums(
is.na(
uct[c(
"payment_type",
"asset_total_ppp0",
"asset_total_ppp1",
"asset_change"
)]
)
) payment_type asset_total_ppp0 asset_total_ppp1 asset_change
0 0 0 0 aggregate(
asset_change ~ treatment,
data = uct,
FUN = function(x) {
c(
n = length(x),
mean = mean(x),
sd = sd(x),
median = median(x),
minimum = min(x),
maximum = max(x)
)
}
) treatment asset_change.n asset_change.mean asset_change.sd
1 Control 157.0000 215.4057 370.9055
2 UCT 93.0000 409.9797 537.7460
asset_change.median asset_change.minimum asset_change.maximum
1 145.0000 -732.1048 1633.1736
2 362.3630 -1751.6474 1477.1260tapply(uct$asset_change, uct$treatment, length)Control UCT
157 93 tapply(uct$asset_change, uct$treatment, mean) Control UCT
215.4057 409.9797 tapply(uct$asset_change, uct$treatment, sd) Control UCT
370.9055 537.7460 tapply(uct$asset_change, uct$treatment, median)Control UCT
145.000 362.363 par(mfrow = c(2, 2))
hist(
uct$asset_change[uct$treatment == "Control"],
main = "Control group",
xlab = "Change in assets"
)
qqnorm(
uct$asset_change[uct$treatment == "Control"],
main = "Control Q-Q plot"
)
qqline(
uct$asset_change[uct$treatment == "Control"],
col = "red"
)
hist(
uct$asset_change[uct$treatment == "UCT"],
main = "UCT group",
xlab = "Change in assets"
)
qqnorm(
uct$asset_change[uct$treatment == "UCT"],
main = "UCT Q-Q plot"
)
qqline(
uct$asset_change[uct$treatment == "UCT"],
col = "red"
)
par(mfrow = c(1, 1))aggregate(
asset_total_ppp0 ~ treatment,
data = uct,
FUN = mean
) treatment asset_total_ppp0
1 Control 194.9054
2 UCT 420.0682t.test(
asset_total_ppp0 ~ treatment,
data = uct,
var.equal = FALSE
)
Welch Two Sample t-test
data: asset_total_ppp0 by treatment
t = -4.2316, df = 127.14, p-value = 4.407e-05
alternative hypothesis: true difference in means between group Control and group UCT is not equal to 0
95 percent confidence interval:
-330.4546 -119.8711
sample estimates:
mean in group Control mean in group UCT
194.9054 420.0682 welch_test <- t.test(
asset_change ~ treatment,
data = uct,
alternative = "two.sided",
var.equal = FALSE,
conf.level = 0.95
)
welch_test
Welch Two Sample t-test
data: asset_change by treatment
t = -3.082, df = 144.4, p-value = 0.002463
alternative hypothesis: true difference in means between group Control and group UCT is not equal to 0
95 percent confidence interval:
-319.35558 -69.79233
sample estimates:
mean in group Control mean in group UCT
215.4057 409.9797 mean_control <- mean(
uct$asset_change[uct$treatment == "Control"]
)
mean_uct <- mean(
uct$asset_change[uct$treatment == "UCT"]
)
difference <- mean_uct - mean_control
ci_uct_minus_control <-
-rev(welch_test$conf.int)
mean_control[1] 215.4057mean_uct[1] 409.9797difference[1] 194.574ci_uct_minus_control[1] 69.79233 319.35558boxplot(
asset_change ~ treatment,
data = uct,
col = c("lightblue", "wheat"),
outline = FALSE,
main = "Change in Total Household Assets by Transfer Group",
xlab = "Transfer group",
ylab = "Change in total household assets (PPP)"
)
set.seed(2026)
stripchart(
asset_change ~ treatment,
data = uct,
method = "jitter",
vertical = TRUE,
add = TRUE,
pch = 16,
cex = 0.6,
col = rgb(0, 0, 0, 0.35)
)
points(
x = c(1, 2),
y = c(mean_control, mean_uct),
pch = 18,
cex = 1.5,
col = "darkred"
)
png(
"UCT_asset_change.png",
width = 1800,
height = 1200,
res = 200
)
boxplot(
asset_change ~ treatment,
data = uct,
col = c("lightblue", "wheat"),
outline = FALSE,
main = "Change in Total Household Assets by Transfer Group",
xlab = "Transfer group",
ylab = "Change in total household assets (PPP)"
)
set.seed(2026)
stripchart(
asset_change ~ treatment,
data = uct,
method = "jitter",
vertical = TRUE,
add = TRUE,
pch = 16,
cex = 0.6,
col = rgb(0, 0, 0, 0.35)
)
points(
c(1, 2),
c(mean_control, mean_uct),
pch = 18,
cex = 1.5,
col = "darkred"
)
dev.off()png
2 getwd()[1] "C:/AssumptionsResource"