library(tidyverse)
library(haven)
library(labelled)
library(survey)
library(forcats)
library(openxlsx)
library(Hmisc)
library(ggplot2)
library(kableExtra)
library(dplyr)
library(tidyr)
library(stringr)
library(knitr)
library(cregg)
library(scales)
library(readxl)
data_file_w5 <- "D:/Populism and Democrary/World value survey/WVS 2006/F00007944-WV5_Data_R_v20180912.rds"
stopifnot(file.exists(data_file_w5))
wvs_raw5 <- readRDS(data_file_w5)
wvs5 <- wvs_raw5
dim(wvs5)## [1] 83975 414country_lookup_w5 <- tibble::tribble(
~V2, ~country,
20, "Andorra",
32, "Argentina",
36, "Australia",
50, "Bangladesh",
76, "Brazil",
100, "Bulgaria",
124, "Canada",
152, "Chile",
156, "China",
158, "Taiwan ROC",
170, "Colombia",
191, "Croatia",
196, "Cyprus",
231, "Ethiopia",
233, "Estonia",
246, "Finland",
250, "France",
268, "Georgia",
276, "Germany",
288, "Ghana",
320, "Guatemala",
344, "Hong Kong SAR",
348, "Hungary",
356, "India",
360, "Indonesia",
364, "Iran",
368, "Iraq",
380, "Italy",
392, "Japan",
400, "Jordan",
404, "Kenya",
410, "South Korea",
422, "Lebanon",
440, "Lithuania",
458, "Malaysia",
466, "Mali",
484, "Mexico",
498, "Moldova",
504, "Morocco",
528, "Netherlands",
554, "New Zealand",
566, "Nigeria",
578, "Norway",
586, "Pakistan",
604, "Peru",
608, "Philippines",
616, "Poland",
620, "Portugal",
642, "Romania",
643, "Russia",
646, "Rwanda",
682, "Saudi Arabia",
688, "Serbia",
704, "Vietnam",
705, "Slovenia",
710, "South Africa",
716, "Zimbabwe",
724, "Spain",
752, "Sweden",
756, "Switzerland",
764, "Thailand",
780, "Trinidad & Tobago",
788, "Tunisia",
792, "Turkey",
804, "Ukraine",
818, "Egypt",
826, "Great Britain",
840, "United States",
854, "Burkina Faso",
858, "Uruguay",
862, "Venezuela",
894, "Zambia"
)
wvs5 <- wvs5 %>%
mutate(
V2 = as.numeric(V2),
V260 = as.numeric(V260),
V259 = as.numeric(V259),
V259A = as.numeric(V259A)
)
country_n_w5 <- wvs5 %>%
dplyr::filter(!is.na(V2)) %>%
dplyr::count(V2, name = "n", sort = TRUE) %>%
dplyr::left_join(country_lookup_w5, by = "V2") %>%
dplyr::relocate(country, .before = V2)
country_year_n_w5 <- wvs5 %>%
dplyr::filter(!is.na(V2), !is.na(V260)) %>%
dplyr::count(V2, V260, name = "n", sort = TRUE) %>%
dplyr::left_join(country_lookup_w5, by = "V2") %>%
dplyr::relocate(country, .before = V2)
print(country_year_n_w5, n = Inf)## # A tibble: 58 × 4
## country V2 V260 n
## <chr> <dbl> <dbl> <int>
## 1 Egypt 818 2008 3051
## 2 Colombia 170 2005 3025
## 3 South Africa 710 2006 2988
## 4 Iraq 368 2006 2701
## 5 Iran 364 2007 2667
## 6 Canada 124 2006 2164
## 7 Germany 276 2006 2064
## 8 Russia 643 2006 2033
## 9 Indonesia 360 2006 2015
## 10 India 356 2006 2001
## 11 China 156 2007 1991
## 12 Romania 642 2005 1776
## 13 Mexico 484 2005 1560
## 14 Ghana 288 2007 1534
## 15 Mali 466 2007 1534
## 16 Thailand 764 2007 1534
## 17 Burkina Faso 854 2007 1534
## 18 Rwanda 646 2007 1507
## 19 Brazil 76 2006 1500
## 20 Ethiopia 231 2007 1500
## 21 Georgia 268 2009 1500
## 22 Peru 604 2006 1500
## 23 Zambia 894 2007 1500
## 24 Vietnam 704 2006 1495
## 25 Australia 36 2005 1421
## 26 Turkey 792 2007 1346
## 27 Hong Kong SAR 344 2005 1252
## 28 United States 840 2006 1249
## 29 Switzerland 756 2007 1241
## 30 Taiwan ROC 158 2006 1227
## 31 Serbia 688 2006 1220
## 32 Malaysia 458 2006 1201
## 33 Jordan 400 2007 1200
## 34 South Korea 410 2005 1200
## 35 Morocco 504 2007 1200
## 36 Spain 724 2007 1200
## 37 Japan 392 2005 1096
## 38 Cyprus 196 2006 1050
## 39 Netherlands 528 2006 1050
## 40 Moldova 498 2006 1046
## 41 Great Britain 826 2005 1041
## 42 Slovenia 705 2005 1037
## 43 Norway 578 2007 1025
## 44 Finland 246 2005 1014
## 45 Italy 380 2005 1012
## 46 Hungary 348 2009 1007
## 47 Andorra 20 2005 1003
## 48 Sweden 752 2006 1003
## 49 Argentina 32 2006 1002
## 50 Trinidad & Tobago 780 2006 1002
## 51 Bulgaria 100 2006 1001
## 52 France 250 2006 1001
## 53 Chile 152 2006 1000
## 54 Guatemala 320 2004 1000
## 55 Poland 616 2005 1000
## 56 Ukraine 804 2006 1000
## 57 Uruguay 858 2006 1000
## 58 New Zealand 554 2004 954wvs5 %>%
distinct(V2) %>%
filter(!is.na(V2)) %>%
left_join(country_lookup_w5, by = "V2") %>%
filter(is.na(country))## # A tibble: 0 × 2
## # ℹ 2 variables: V2 <dbl>, country <chr>wvs5 %>%
filter(V2 %in% c(586, 50)) %>%
count(V2)## # A tibble: 0 × 2
## # ℹ 2 variables: V2 <dbl>, n <int>macro_map_w5 <- tibble::tribble(
~V2, ~country, ~region,
# India (standalone)
356, "India", "India",
# East & Southeast Asia
156, "China", "East & Southeast Asia",
344, "Hong Kong SAR", "East & Southeast Asia",
392, "Japan", "East & Southeast Asia",
410, "South Korea", "East & Southeast Asia",
158, "Taiwan ROC", "East & Southeast Asia",
458, "Malaysia", "East & Southeast Asia",
608, "Philippines", "East & Southeast Asia",
764, "Thailand", "East & Southeast Asia",
360, "Indonesia", "East & Southeast Asia",
704, "Vietnam", "East & Southeast Asia",
# Middle East & North Africa (MENA)
818, "Egypt", "Middle East & North Africa (MENA)",
368, "Iraq", "Middle East & North Africa (MENA)",
400, "Jordan", "Middle East & North Africa (MENA)",
422, "Lebanon", "Middle East & North Africa (MENA)",
504, "Morocco", "Middle East & North Africa (MENA)",
788, "Tunisia", "Middle East & North Africa (MENA)",
792, "Turkey", "Middle East & North Africa (MENA)",
364, "Iran", "Middle East & North Africa (MENA)",
682, "Saudi Arabia", "Middle East & North Africa (MENA)",
# Sub-Saharan Africa
566, "Nigeria", "Sub-Saharan Africa",
716, "Zimbabwe", "Sub-Saharan Africa",
404, "Kenya", "Sub-Saharan Africa",
231, "Ethiopia", "Sub-Saharan Africa",
710, "South Africa", "Sub-Saharan Africa",
288, "Ghana", "Sub-Saharan Africa",
646, "Rwanda", "Sub-Saharan Africa",
854, "Burkina Faso", "Sub-Saharan Africa",
894, "Zambia", "Sub-Saharan Africa",
466, "Mali", "Sub-Saharan Africa",
# Latin America & Caribbean
32, "Argentina", "Latin America & Caribbean",
76, "Brazil", "Latin America & Caribbean",
152, "Chile", "Latin America & Caribbean",
170, "Colombia", "Latin America & Caribbean",
484, "Mexico", "Latin America & Caribbean",
604, "Peru", "Latin America & Caribbean",
858, "Uruguay", "Latin America & Caribbean",
862, "Venezuela", "Latin America & Caribbean",
320, "Guatemala", "Latin America & Caribbean",
780, "Trinidad & Tobago", "Latin America & Caribbean",
# Western Europe & Offshoots
36, "Australia", "Western Europe & Offshoots",
124, "Canada", "Western Europe & Offshoots",
840, "United States", "Western Europe & Offshoots",
554, "New Zealand", "Western Europe & Offshoots",
826, "Great Britain", "Western Europe & Offshoots",
20, "Andorra", "Western Europe & Offshoots",
246, "Finland", "Western Europe & Offshoots",
250, "France", "Western Europe & Offshoots",
276, "Germany", "Western Europe & Offshoots",
300, "Greece", "Western Europe & Offshoots",
348, "Hungary", "Western Europe & Offshoots",
380, "Italy", "Western Europe & Offshoots",
528, "Netherlands", "Western Europe & Offshoots",
578, "Norway", "Western Europe & Offshoots",
620, "Portugal", "Western Europe & Offshoots",
724, "Spain", "Western Europe & Offshoots",
752, "Sweden", "Western Europe & Offshoots",
756, "Switzerland", "Western Europe & Offshoots",
196, "Cyprus", "Western Europe & Offshoots",
# Eastern Europe & Post-Soviet
100, "Bulgaria", "Eastern Europe & Post-Soviet",
233, "Estonia", "Eastern Europe & Post-Soviet",
268, "Georgia", "Eastern Europe & Post-Soviet",
440, "Lithuania", "Eastern Europe & Post-Soviet",
498, "Moldova", "Eastern Europe & Post-Soviet",
616, "Poland", "Eastern Europe & Post-Soviet",
642, "Romania", "Eastern Europe & Post-Soviet",
643, "Russia", "Eastern Europe & Post-Soviet",
688, "Serbia", "Eastern Europe & Post-Soviet",
705, "Slovenia", "Eastern Europe & Post-Soviet",
804, "Ukraine", "Eastern Europe & Post-Soviet",
191, "Croatia", "Eastern Europe & Post-Soviet"
)
# ── Attach to wvs5 ────────────────────────────────────────────────────────────
w5_countries <- wvs5 %>%
distinct(V2) %>%
filter(!is.na(V2))
macro_map_w5_filtered <- macro_map_w5 %>%
semi_join(w5_countries, by = "V2")
wvs5 <- wvs5 %>%
left_join(macro_map_w5_filtered %>% select(V2, macro_region = region), by = "V2")
# ── Checks ────────────────────────────────────────────────────────────────────
missing_map_w5 <- wvs5 %>%
distinct(V2) %>%
left_join(macro_map_w5, by = "V2") %>%
filter(is.na(region))
missing_map_w5 # should be 0 rows## # A tibble: 0 × 3
## # ℹ 3 variables: V2 <dbl>, country <chr>, region <chr>wvs5 %>%
group_by(macro_region) %>%
summarise(
n_respondents = n(),
n_countries = n_distinct(V2),
.groups = "drop"
) %>%
arrange(desc(n_respondents))## # A tibble: 7 × 3
## macro_region n_respondents n_countries
## <chr> <int> <int>
## 1 Western Europe & Offshoots 20499 17
## 2 East & Southeast Asia 13011 9
## 3 Latin America & Caribbean 12589 9
## 4 Middle East & North Africa (MENA) 12165 6
## 5 Sub-Saharan Africa 12097 7
## 6 Eastern Europe & Post-Soviet 11613 9
## 7 India 2001 1# Age Cohorts (V237 = age in Wave 5)
wvs5 <- wvs5%>%
mutate(
V237 = as.numeric(V237),
age = V237,
age_group = case_when(
age < 25 ~ "18–24",
age < 35 ~ "25–34",
age < 45 ~ "35–44",
age < 55 ~ "45–54",
age < 65 ~ "55–64",
age >= 65 ~ "65+",
TRUE ~ NA_character_
)
)
age_region_w5 <- wvs5%>%
filter(!is.na(macro_region), !is.na(age_group)) %>%
count(macro_region, age_group) %>%
group_by(macro_region) %>%
mutate(share = n / sum(n)) %>%
ungroup()
kable(
age_region_w5,
digits = 3,
caption = "Age-group distribution by macro region (Wave 5)"
)| macro_region | age_group | n | share |
|---|---|---|---|
| East & Southeast Asia | 18–24 | 2145 | 0.165 |
| East & Southeast Asia | 25–34 | 2631 | 0.202 |
| East & Southeast Asia | 35–44 | 2894 | 0.222 |
| East & Southeast Asia | 45–54 | 2507 | 0.193 |
| East & Southeast Asia | 55–64 | 1664 | 0.128 |
| East & Southeast Asia | 65+ | 1170 | 0.090 |
| Eastern Europe & Post-Soviet | 18–24 | 1674 | 0.144 |
| Eastern Europe & Post-Soviet | 25–34 | 2124 | 0.183 |
| Eastern Europe & Post-Soviet | 35–44 | 2157 | 0.186 |
| Eastern Europe & Post-Soviet | 45–54 | 2203 | 0.190 |
| Eastern Europe & Post-Soviet | 55–64 | 1622 | 0.140 |
| Eastern Europe & Post-Soviet | 65+ | 1833 | 0.158 |
| India | 18–24 | 213 | 0.106 |
| India | 25–34 | 522 | 0.261 |
| India | 35–44 | 501 | 0.250 |
| India | 45–54 | 368 | 0.184 |
| India | 55–64 | 207 | 0.103 |
| India | 65+ | 190 | 0.095 |
| Latin America & Caribbean | 18–24 | 2543 | 0.202 |
| Latin America & Caribbean | 25–34 | 3097 | 0.246 |
| Latin America & Caribbean | 35–44 | 2585 | 0.205 |
| Latin America & Caribbean | 45–54 | 1898 | 0.151 |
| Latin America & Caribbean | 55–64 | 1354 | 0.108 |
| Latin America & Caribbean | 65+ | 1112 | 0.088 |
| Middle East & North Africa (MENA) | 18–24 | 2525 | 0.208 |
| Middle East & North Africa (MENA) | 25–34 | 3561 | 0.293 |
| Middle East & North Africa (MENA) | 35–44 | 2707 | 0.223 |
| Middle East & North Africa (MENA) | 45–54 | 1751 | 0.144 |
| Middle East & North Africa (MENA) | 55–64 | 991 | 0.081 |
| Middle East & North Africa (MENA) | 65+ | 630 | 0.052 |
| Sub-Saharan Africa | 18–24 | 3556 | 0.294 |
| Sub-Saharan Africa | 25–34 | 3612 | 0.299 |
| Sub-Saharan Africa | 35–44 | 2207 | 0.182 |
| Sub-Saharan Africa | 45–54 | 1328 | 0.110 |
| Sub-Saharan Africa | 55–64 | 831 | 0.069 |
| Sub-Saharan Africa | 65+ | 563 | 0.047 |
| Western Europe & Offshoots | 18–24 | 2260 | 0.110 |
| Western Europe & Offshoots | 25–34 | 3254 | 0.159 |
| Western Europe & Offshoots | 35–44 | 4097 | 0.200 |
| Western Europe & Offshoots | 45–54 | 3662 | 0.179 |
| Western Europe & Offshoots | 55–64 | 3371 | 0.164 |
| Western Europe & Offshoots | 65+ | 3855 | 0.188 |
# Gender (V235: 1 = Male, 2 = Female)
wvs5 <- wvs5%>%
mutate(
V235 = as.numeric(V235),
gender = case_when(
V235 == 1 ~ "Male",
V235 == 2 ~ "Female",
TRUE ~ NA_character_
)
)
gender_region_w5 <- wvs5%>%
filter(!is.na(macro_region), !is.na(gender)) %>%
count(macro_region, gender) %>%
group_by(macro_region) %>%
mutate(share = n / sum(n)) %>%
ungroup()
gender_region_w5## # A tibble: 14 × 4
## macro_region gender n share
## <chr> <chr> <int> <dbl>
## 1 East & Southeast Asia Female 6628 0.510
## 2 East & Southeast Asia Male 6377 0.490
## 3 Eastern Europe & Post-Soviet Female 6244 0.538
## 4 Eastern Europe & Post-Soviet Male 5369 0.462
## 5 India Female 861 0.431
## 6 India Male 1137 0.569
## 7 Latin America & Caribbean Female 6651 0.528
## 8 Latin America & Caribbean Male 5938 0.472
## 9 Middle East & North Africa (MENA) Female 6478 0.534
## 10 Middle East & North Africa (MENA) Male 5658 0.466
## 11 Sub-Saharan Africa Female 5986 0.495
## 12 Sub-Saharan Africa Male 6095 0.505
## 13 Western Europe & Offshoots Female 10813 0.529
## 14 Western Europe & Offshoots Male 9644 0.471wvs5%>%
summarise(
missing_age = sum(is.na(V237)),
missing_gender = sum(is.na(V235))
)## # A tibble: 1 × 2
## missing_age missing_gender
## <int> <int>
## 1 0 0# Missing variables by macro_region
wvs5%>%
group_by(macro_region) %>%
summarise(
n_obs = n(),
missing_age = sum(is.na(V237)),
missing_gender = sum(is.na(V235))
)## # A tibble: 7 × 4
## macro_region n_obs missing_age missing_gender
## <chr> <int> <int> <int>
## 1 East & Southeast Asia 13011 0 0
## 2 Eastern Europe & Post-Soviet 11613 0 0
## 3 India 2001 0 0
## 4 Latin America & Caribbean 12589 0 0
## 5 Middle East & North Africa (MENA) 12165 0 0
## 6 Sub-Saharan Africa 12097 0 0
## 7 Western Europe & Offshoots 20499 0 0# Economic values (Wave 5: V116-V121)
likert_vars_w5 <- paste0("V", 116:121)
wvs5 <- wvs5%>%
mutate(across(all_of(likert_vars_w5), ~ifelse(as.numeric(.x) < 0, NA, as.numeric(.x))))
# Keep valid cases
wvs_clean_w5 <- wvs5%>%
filter(!is.na(macro_region))
svy_design_w5 <- svydesign(
ids = ~1,
weights = ~V259,
data = wvs_clean_w5
)
# Country-level means first
country_means_w5 <- lapply(likert_vars_w5, function(v){
svyby(
as.formula(paste0("~", v)),
~V2 + macro_region,
svy_design_w5,
svymean,
na.rm = TRUE
) %>%
rename(country_mean = 3) %>%
mutate(variable = v)
}) %>% bind_rows()
# Region means (country-weighted)
region_means_w5 <- country_means_w5%>%
group_by(macro_region, variable) %>%
summarise(
mean = mean(country_mean, na.rm = TRUE),
n_countries = n(),
.groups = "drop"
)
# Labels for Wave 5 economic values
var_labels_w5 <- c(
V116 = "Income equality vs Inequality incentives (1=More equal, 10=Larger differences)",
V117 = "Private vs government ownership of business (1=Private, 10=Government)",
V118 = "Government vs individual responsibility (1=Government, 10=Individuals)",
V119 = "Competition good vs harmful (1=Good, 10=Harmful)",
V120 = "Success: hard work vs luck (1=Hard work, 10=Luck/connections)",
V121 = "People get rich at expense of others vs Wealth can grow (1=Can get rich at expense, 10=Wealth can grow)"
)for(v in likert_vars_w5){
temp_table <- region_means_w5%>%
filter(variable == v) %>%
select(macro_region, mean, n_countries) %>%
arrange(mean)
print(
kable(
temp_table,
digits = 2,
col.names = c("Macro Region",
"Mean (country-weighted, 1–10)",
"N countries"),
caption = paste("Regional comparison:", var_labels_w5[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| India | 4.79 | 1 |
| Western Europe & Offshoots | 5.35 | 17 |
| Middle East & North Africa (MENA) | 5.51 | 6 |
| Eastern Europe & Post-Soviet | 6.00 | 9 |
| Latin America & Caribbean | 6.04 | 9 |
| East & Southeast Asia | 6.35 | 9 |
| Sub-Saharan Africa | 6.68 | 7 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Western Europe & Offshoots | 4.00 | 17 |
| Eastern Europe & Post-Soviet | 5.00 | 9 |
| Sub-Saharan Africa | 5.54 | 7 |
| East & Southeast Asia | 5.63 | 9 |
| India | 5.85 | 1 |
| Latin America & Caribbean | 6.05 | 9 |
| Middle East & North Africa (MENA) | 6.05 | 6 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Middle East & North Africa (MENA) | 3.50 | 6 |
| Eastern Europe & Post-Soviet | 4.24 | 9 |
| India | 4.47 | 1 |
| East & Southeast Asia | 4.99 | 9 |
| Latin America & Caribbean | 5.02 | 9 |
| Sub-Saharan Africa | 5.07 | 7 |
| Western Europe & Offshoots | 5.45 | 17 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Middle East & North Africa (MENA) | 2.73 | 6 |
| India | 2.78 | 1 |
| Sub-Saharan Africa | 3.66 | 7 |
| East & Southeast Asia | 3.94 | 9 |
| Eastern Europe & Post-Soviet | 3.98 | 9 |
| Latin America & Caribbean | 4.00 | 9 |
| Western Europe & Offshoots | 4.01 | 17 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Middle East & North Africa (MENA) | 3.02 | 6 |
| India | 3.45 | 1 |
| East & Southeast Asia | 3.69 | 9 |
| Latin America & Caribbean | 3.95 | 9 |
| Sub-Saharan Africa | 4.16 | 7 |
| Western Europe & Offshoots | 4.67 | 17 |
| Eastern Europe & Post-Soviet | 4.86 | 9 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Middle East & North Africa (MENA) | 4.63 | 6 |
| India | 5.45 | 1 |
| Eastern Europe & Post-Soviet | 5.94 | 9 |
| Sub-Saharan Africa | 5.96 | 7 |
| East & Southeast Asia | 6.05 | 9 |
| Western Europe & Offshoots | 6.09 | 17 |
| Latin America & Caribbean | 6.28 | 9 |
# Democratic system variables (Wave 5: V152-V162)
demo_vars_w5 <- paste0("V", 152:162)
# Remove invalid codes
wvs5 <- wvs5%>%
mutate(across(all_of(demo_vars_w5), ~ifelse(as.numeric(.x) < 0, NA, as.numeric(.x))))
# Keep valid cases
wvs_clean_demo_w5 <- wvs5%>%
filter(!is.na(macro_region))
# Survey design
svy_design_demo_w5 <- svydesign(
ids = ~1,
weights = ~V259,
data = wvs_clean_demo_w5
)
# Survey-weighted country means
country_means_demo_w5 <- lapply(demo_vars_w5, function(v){
svyby(
as.formula(paste0("~", v)),
~V2 + macro_region,
svy_design_demo_w5,
svymean,
na.rm = TRUE
) %>%
rename(country_mean = 3) %>%
mutate(variable = v)
}) %>% bind_rows()
# Macro-region average of country means
region_means_demo_w5 <- country_means_demo_w5%>%
group_by(macro_region, variable) %>%
summarise(
mean = mean(country_mean, na.rm = TRUE),
n_countries = n(),
.groups = "drop"
)
# Labels confirmed from questionnaire
demo_labels_w5 <- c(
V152 = "Democracy: Governments tax the rich and subsidize the poor (1=Not essential, 10=Essential)",
V153 = "Democracy: Religious authorities interpret the laws (1=Not essential, 10=Essential)",
V154 = "Democracy: People choose leaders in free elections (1=Not essential, 10=Essential)",
V155 = "Democracy: People receive state aid for unemployment (1=Not essential, 10=Essential)",
V156 = "Democracy: Army takes over when government is incompetent (1=Not essential, 10=Essential)",
V157 = "Democracy: Civil rights protect people's liberty against oppression (1=Not essential, 10=Essential)",
V158 = "Democracy: The economy is prospering (1=Not essential, 10=Essential)",
V159 = "Democracy: Criminals are severely punished (1=Not essential, 10=Essential)",
V160 = "Democracy: People can change the laws in referendums (1=Not essential, 10=Essential)",
V161 = "Democracy: Women have the same rights as men (1=Not essential, 10=Essential)",
V162 = "Importance of democracy (1=Not at all important, 10=Absolutely important)"
)for(v in demo_vars_w5){
temp_table <- region_means_demo_w5%>%
filter(variable == v) %>%
select(macro_region, mean, n_countries) %>%
arrange(desc(mean))
print(
kable(
temp_table,
digits = 2,
col.names = c("Macro Region",
"Mean (country-weighted, 1–10)",
"N countries"),
caption = paste("Regional comparison:", demo_labels_w5[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| India | 8.13 | 1 |
| Middle East & North Africa (MENA) | 7.51 | 6 |
| East & Southeast Asia | 6.30 | 9 |
| Eastern Europe & Post-Soviet | 6.28 | 9 |
| Sub-Saharan Africa | 6.12 | 7 |
| Western Europe & Offshoots | 5.70 | 17 |
| Latin America & Caribbean | 4.56 | 9 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Middle East & North Africa (MENA) | 6.22 | 6 |
| Sub-Saharan Africa | 4.96 | 7 |
| East & Southeast Asia | 4.20 | 9 |
| India | 4.17 | 1 |
| Eastern Europe & Post-Soviet | 3.86 | 9 |
| Latin America & Caribbean | 3.25 | 9 |
| Western Europe & Offshoots | 2.36 | 17 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Eastern Europe & Post-Soviet | 8.73 | 9 |
| India | 8.62 | 1 |
| Middle East & North Africa (MENA) | 8.58 | 6 |
| Sub-Saharan Africa | 8.35 | 7 |
| Western Europe & Offshoots | 7.75 | 17 |
| East & Southeast Asia | 7.27 | 9 |
| Latin America & Caribbean | 6.57 | 9 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| India | 8.30 | 1 |
| Eastern Europe & Post-Soviet | 7.97 | 9 |
| Middle East & North Africa (MENA) | 7.70 | 6 |
| Sub-Saharan Africa | 6.99 | 7 |
| Western Europe & Offshoots | 6.45 | 17 |
| East & Southeast Asia | 6.13 | 9 |
| Latin America & Caribbean | 5.30 | 9 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Middle East & North Africa (MENA) | 5.92 | 6 |
| India | 5.47 | 1 |
| Sub-Saharan Africa | 4.24 | 7 |
| Eastern Europe & Post-Soviet | 4.15 | 9 |
| East & Southeast Asia | 4.13 | 9 |
| Latin America & Caribbean | 3.37 | 9 |
| Western Europe & Offshoots | 2.69 | 17 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Eastern Europe & Post-Soviet | 8.40 | 9 |
| Middle East & North Africa (MENA) | 8.13 | 6 |
| India | 7.95 | 1 |
| Sub-Saharan Africa | 7.42 | 7 |
| Western Europe & Offshoots | 7.24 | 17 |
| East & Southeast Asia | 6.96 | 9 |
| Latin America & Caribbean | 5.97 | 9 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Middle East & North Africa (MENA) | 8.50 | 6 |
| Eastern Europe & Post-Soviet | 8.37 | 9 |
| East & Southeast Asia | 7.91 | 9 |
| Sub-Saharan Africa | 7.75 | 7 |
| India | 7.39 | 1 |
| Western Europe & Offshoots | 6.27 | 17 |
| Latin America & Caribbean | 5.89 | 9 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Middle East & North Africa (MENA) | 8.36 | 6 |
| Eastern Europe & Post-Soviet | 7.92 | 9 |
| Sub-Saharan Africa | 7.83 | 7 |
| India | 7.53 | 1 |
| East & Southeast Asia | 7.12 | 9 |
| Western Europe & Offshoots | 6.35 | 17 |
| Latin America & Caribbean | 5.41 | 9 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Eastern Europe & Post-Soviet | 8.31 | 9 |
| Middle East & North Africa (MENA) | 8.22 | 6 |
| Sub-Saharan Africa | 7.88 | 7 |
| India | 7.36 | 1 |
| Western Europe & Offshoots | 7.06 | 17 |
| East & Southeast Asia | 6.77 | 9 |
| Latin America & Caribbean | 5.92 | 9 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Eastern Europe & Post-Soviet | 8.89 | 9 |
| India | 8.21 | 1 |
| Sub-Saharan Africa | 8.18 | 7 |
| Western Europe & Offshoots | 8.06 | 17 |
| Middle East & North Africa (MENA) | 7.60 | 6 |
| East & Southeast Asia | 7.35 | 9 |
| Latin America & Caribbean | 6.79 | 9 |
| Macro Region | Mean (country-weighted, 1–10) | N countries |
|---|---|---|
| Sub-Saharan Africa | 8.64 | 7 |
| Western Europe & Offshoots | 8.40 | 17 |
| Eastern Europe & Post-Soviet | 8.14 | 9 |
| East & Southeast Asia | 7.59 | 9 |
| Latin America & Caribbean | 7.55 | 9 |
| Middle East & North Africa (MENA) | 7.42 | 6 |
| India | 7.08 | 1 |
# Neighbours rejection items (Wave 5: V34-V42, excluding V43 optional)
neigh_vars_w5 <- paste0("V", 34:42)
# Clean negative codes
wvs5 <- wvs5%>%
mutate(across(all_of(neigh_vars_w5), ~ifelse(as.numeric(.x) < 0, NA, as.numeric(.x))))
wvs_clean_neigh_w5 <- wvs5%>%
filter(!is.na(macro_region))
svy_design_neigh_w5 <- svydesign(
ids = ~1,
weights = ~V259,
data = wvs_clean_neigh_w5
)
# Survey-weighted country proportions rejecting each group (1 = mentioned/rejected)
country_prop_neigh_w5 <- lapply(neigh_vars_w5, function(v){
svyby(
as.formula(paste0("~I(", v, "== 1)")),
~V2 + macro_region,
svy_design_neigh_w5,
svymean,
na.rm = TRUE
) %>%
rename(country_prop = 3) %>%
mutate(variable = v)
}) %>% bind_rows()
# Macro-region average of country proportions
region_prop_neigh_w5 <- country_prop_neigh_w5%>%
group_by(macro_region, variable) %>%
summarise(
mean_prop = mean(country_prop, na.rm = TRUE),
n_countries = n(),
.groups = "drop"
)
# Labels confirmed from questionnaire
neigh_labels_w5 <- c(
V34 = "Drug addicts",
V35 = "People of a different race",
V36 = "People who have AIDS",
V37 = "Immigrants/foreign workers",
V38 = "Homosexuals",
V39 = "People of a different religion",
V40 = "Heavy drinkers",
V41 = "Unmarried couples living together",
V42 = "People who speak a different language"
)for(v in neigh_vars_w5){
temp_table <- region_prop_neigh_w5%>%
filter(variable == v) %>%
mutate(mean_prop = round(mean_prop * 100, 1)) %>%
select(macro_region, mean_prop, n_countries) %>%
arrange(desc(mean_prop))
print(
kable(
temp_table,
digits = 1,
col.names = c("Macro Region",
"% Rejecting (country-weighted)",
"N countries"),
caption = paste("Undesirable neighbours:", neigh_labels_w5[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}| Macro Region | % Rejecting (country-weighted) | N countries |
|---|---|---|
| India | 46.2 | 1 |
| Latin America & Caribbean | 29.7 | 9 |
| Western Europe & Offshoots | 24.9 | 17 |
| Sub-Saharan Africa | 19.4 | 7 |
| Eastern Europe & Post-Soviet | 16.0 | 9 |
| East & Southeast Asia | 14.9 | 9 |
| Middle East & North Africa (MENA) | 4.5 | 6 |
| Macro Region | % Rejecting (country-weighted) | N countries |
|---|---|---|
| Latin America & Caribbean | 95.0 | 9 |
| Western Europe & Offshoots | 92.3 | 17 |
| Eastern Europe & Post-Soviet | 82.3 | 9 |
| Sub-Saharan Africa | 79.2 | 7 |
| East & Southeast Asia | 66.9 | 9 |
| India | 56.5 | 1 |
| Middle East & North Africa (MENA) | 44.0 | 6 |
| Macro Region | % Rejecting (country-weighted) | N countries |
|---|---|---|
| Western Europe & Offshoots | 81.8 | 17 |
| Latin America & Caribbean | 80.0 | 9 |
| Sub-Saharan Africa | 72.1 | 7 |
| India | 56.3 | 1 |
| Eastern Europe & Post-Soviet | 47.0 | 9 |
| East & Southeast Asia | 30.6 | 9 |
| Middle East & North Africa (MENA) | 12.4 | 6 |
| Macro Region | % Rejecting (country-weighted) | N countries |
|---|---|---|
| Western Europe & Offshoots | 88.2 | 17 |
| Latin America & Caribbean | 83.5 | 9 |
| Eastern Europe & Post-Soviet | 79.7 | 9 |
| Sub-Saharan Africa | 76.4 | 7 |
| India | 65.0 | 1 |
| East & Southeast Asia | 58.7 | 9 |
| Middle East & North Africa (MENA) | 36.6 | 6 |
| Macro Region | % Rejecting (country-weighted) | N countries |
|---|---|---|
| Western Europe & Offshoots | 82.1 | 17 |
| Latin America & Caribbean | 68.1 | 9 |
| India | 59.7 | 1 |
| Eastern Europe & Post-Soviet | 38.9 | 9 |
| East & Southeast Asia | 37.6 | 9 |
| Sub-Saharan Africa | 30.1 | 7 |
| Middle East & North Africa (MENA) | 4.1 | 6 |
| Macro Region | % Rejecting (country-weighted) | N countries |
|---|---|---|
| Western Europe & Offshoots | 88.3 | 17 |
| Latin America & Caribbean | 83.3 | 9 |
| Eastern Europe & Post-Soviet | 82.5 | 9 |
| Sub-Saharan Africa | 79.7 | 7 |
| East & Southeast Asia | 59.6 | 9 |
| India | 56.0 | 1 |
| Middle East & North Africa (MENA) | 51.0 | 6 |
| Macro Region | % Rejecting (country-weighted) | N countries |
|---|---|---|
| India | 51.6 | 1 |
| Latin America & Caribbean | 49.6 | 9 |
| Western Europe & Offshoots | 38.4 | 17 |
| Sub-Saharan Africa | 36.1 | 7 |
| East & Southeast Asia | 27.0 | 9 |
| Eastern Europe & Post-Soviet | 25.3 | 9 |
| Middle East & North Africa (MENA) | 8.0 | 6 |
| Macro Region | % Rejecting (country-weighted) | N countries |
|---|---|---|
| Western Europe & Offshoots | 89.9 | 17 |
| Eastern Europe & Post-Soviet | 88.2 | 9 |
| Latin America & Caribbean | 84.5 | 9 |
| Sub-Saharan Africa | 70.1 | 7 |
| India | 56.6 | 1 |
| East & Southeast Asia | 47.3 | 9 |
| Middle East & North Africa (MENA) | 15.1 | 6 |
| Macro Region | % Rejecting (country-weighted) | N countries |
|---|---|---|
| Eastern Europe & Post-Soviet | 88.0 | 9 |
| Western Europe & Offshoots | 87.1 | 17 |
| Sub-Saharan Africa | 80.1 | 7 |
| Latin America & Caribbean | 72.9 | 9 |
| East & Southeast Asia | 60.0 | 9 |
| India | 59.9 | 1 |
| Middle East & North Africa (MENA) | 33.5 | 6 |
# Moral permissiveness (Wave 5: V198-V208)
moral_vars_w5 <- paste0("V", 198:208)
# Clean negative codes
wvs5 <- wvs5%>%
mutate(across(all_of(moral_vars_w5), ~ifelse(as.numeric(.x) < 0, NA, as.numeric(.x))))
wvs_clean_moral_w5 <- wvs5%>%
filter(!is.na(macro_region))
svy_design_moral_w5 <- svydesign(
ids = ~1,
weights = ~V259,
data = wvs_clean_moral_w5
)
# Survey-weighted country means
country_moral_w5 <- lapply(moral_vars_w5, function(v){
svyby(
as.formula(paste0("~", v)),
~V2 + macro_region,
svy_design_moral_w5,
svymean,
na.rm = TRUE
) %>%
rename(country_mean = 3) %>%
mutate(variable = v)
}) %>% bind_rows()
# Macro-region average of country means
region_moral_w5 <- country_moral_w5%>%
group_by(macro_region, variable) %>%
summarise(
mean_score = mean(country_mean, na.rm = TRUE),
n_countries = n(),
.groups = "drop"
)
# Labels exactly as in questionnaire
moral_labels_w5 <- c(
V198 = "Claiming government benefits to which you are not entitled",
V199 = "Avoiding a fare on public transport",
V200 = "Cheating on taxes if you have a chance",
V201 = "Someone accepting a bribe in the course of their duties",
V202 = "Homosexuality",
V203 = "Prostitution",
V204 = "Abortion",
V205 = "Divorce",
V206 = "Euthanasia—ending the life of the incurable sick",
V207 = "Suicide",
V208 = "For a man to beat his wife"
)for(v in moral_vars_w5){
temp_table <- region_moral_w5%>%
filter(variable == v) %>%
mutate(mean_score = round(mean_score, 1)) %>%
select(macro_region, mean_score, n_countries) %>%
arrange(desc(mean_score))
print(
kable(
temp_table,
digits = 1,
col.names = c("Macro Region",
"Mean score (1–10)",
"N countries"),
caption = paste("Moral permissiveness:", moral_labels_w5[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| India | 3.1 | 1 |
| East & Southeast Asia | 3.0 | 9 |
| Eastern Europe & Post-Soviet | 2.8 | 9 |
| Latin America & Caribbean | 2.7 | 9 |
| Sub-Saharan Africa | 2.7 | 7 |
| Western Europe & Offshoots | 2.1 | 17 |
| Middle East & North Africa (MENA) | 1.9 | 6 |
| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| India | 3.3 | 1 |
| Eastern Europe & Post-Soviet | 3.1 | 9 |
| Latin America & Caribbean | 2.7 | 9 |
| Sub-Saharan Africa | 2.7 | 7 |
| Western Europe & Offshoots | 2.4 | 17 |
| East & Southeast Asia | 2.3 | 9 |
| Middle East & North Africa (MENA) | 1.7 | 6 |
| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| India | 3.0 | 1 |
| Eastern Europe & Post-Soviet | 2.9 | 9 |
| Sub-Saharan Africa | 2.4 | 7 |
| Western Europe & Offshoots | 2.2 | 17 |
| East & Southeast Asia | 2.1 | 9 |
| Latin America & Caribbean | 2.0 | 9 |
| Middle East & North Africa (MENA) | 1.5 | 6 |
| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| India | 3.0 | 1 |
| Sub-Saharan Africa | 2.5 | 7 |
| Eastern Europe & Post-Soviet | 2.1 | 9 |
| East & Southeast Asia | 1.9 | 9 |
| Latin America & Caribbean | 1.7 | 9 |
| Western Europe & Offshoots | 1.7 | 17 |
| Middle East & North Africa (MENA) | 1.4 | 6 |
| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| Western Europe & Offshoots | 6.1 | 17 |
| Latin America & Caribbean | 3.7 | 9 |
| Eastern Europe & Post-Soviet | 3.1 | 9 |
| India | 3.0 | 1 |
| East & Southeast Asia | 2.9 | 9 |
| Sub-Saharan Africa | 2.1 | 7 |
| Middle East & North Africa (MENA) | 0.7 | 6 |
| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| Western Europe & Offshoots | 3.9 | 17 |
| India | 3.1 | 1 |
| Latin America & Caribbean | 3.0 | 9 |
| Eastern Europe & Post-Soviet | 2.8 | 9 |
| Sub-Saharan Africa | 2.4 | 7 |
| East & Southeast Asia | 2.3 | 9 |
| Middle East & North Africa (MENA) | 0.7 | 6 |
| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| Western Europe & Offshoots | 5.4 | 17 |
| Eastern Europe & Post-Soviet | 4.1 | 9 |
| India | 3.3 | 1 |
| East & Southeast Asia | 3.0 | 9 |
| Sub-Saharan Africa | 2.6 | 7 |
| Latin America & Caribbean | 2.5 | 9 |
| Middle East & North Africa (MENA) | 1.5 | 6 |
| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| Western Europe & Offshoots | 6.7 | 17 |
| Eastern Europe & Post-Soviet | 5.1 | 9 |
| Latin America & Caribbean | 4.8 | 9 |
| East & Southeast Asia | 3.9 | 9 |
| India | 3.9 | 1 |
| Sub-Saharan Africa | 3.6 | 7 |
| Middle East & North Africa (MENA) | 3.2 | 6 |
| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| Western Europe & Offshoots | 5.7 | 17 |
| Eastern Europe & Post-Soviet | 4.3 | 9 |
| East & Southeast Asia | 4.2 | 9 |
| India | 3.9 | 1 |
| Latin America & Caribbean | 3.3 | 9 |
| Sub-Saharan Africa | 2.9 | 7 |
| Middle East & North Africa (MENA) | 1.7 | 6 |
| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| India | 3.4 | 1 |
| Western Europe & Offshoots | 3.3 | 17 |
| Eastern Europe & Post-Soviet | 2.4 | 9 |
| East & Southeast Asia | 2.3 | 9 |
| Sub-Saharan Africa | 2.3 | 7 |
| Latin America & Caribbean | 1.9 | 9 |
| Middle East & North Africa (MENA) | 1.1 | 6 |
| Macro Region | Mean score (1–10) | N countries |
|---|---|---|
| India | 3.1 | 1 |
| Sub-Saharan Africa | 3.1 | 7 |
| Eastern Europe & Post-Soviet | 1.9 | 9 |
| East & Southeast Asia | 1.7 | 9 |
| Western Europe & Offshoots | 1.3 | 17 |
| Latin America & Caribbean | 1.2 | 9 |
| Middle East & North Africa (MENA) | 1.2 | 6 |
1: Never justifiable and 10: Always justifiable
# Trust variables (Wave 5: V131-V145)
trust_vars_w5 <- paste0("V", 131:145)
# Labels exactly as in questionnaire
trust_labels_w5 <- c(
V131 = "Churches/ religious leaders",
V132 = "Armed forces",
V133 = "Press",
V134 = "Television",
V135 = "Labor unions",
V136 = "Police",
V137 = "Courts",
V138 = "Government (in your nation's capital)",
V139 = "Political parties",
V140 = "Parliament",
V141 = "Civil service",
V142 = "Major companies",
V143 = "Environmental organizations",
V144 = "Women's organizations",
V145 = "Charitable or humanitarian organizations"
)
# Clean negative codes, filter valid cases
wvs_clean_trust_w5 <- wvs5%>%
mutate(across(all_of(trust_vars_w5), ~ifelse(as.numeric(.x) < 0, NA, as.numeric(.x)))) %>%
filter(!is.na(macro_region), !is.na(V259))for(v in trust_vars_w5){
temp_table <- wvs_clean_trust_w5%>%
filter(!is.na(.data[[v]])) %>%
group_by(macro_region) %>%
summarise(
prop_high = round(
sum(V259 * (.data[[v]] <= 2)) / sum(V259) * 100, 1
),
.groups = "drop"
) %>%
arrange(desc(prop_high))
print(
kable(
temp_table,
digits = 1,
col.names = c("Macro Region",
"High confidence % (1–2)"),
caption = paste("High confidence:", trust_labels_w5[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}| Macro Region | High confidence % (1–2) |
|---|---|
| India | 83.4 |
| Sub-Saharan Africa | 82.5 |
| Middle East & North Africa (MENA) | 80.8 |
| Eastern Europe & Post-Soviet | 71.0 |
| Latin America & Caribbean | 68.2 |
| East & Southeast Asia | 58.6 |
| Western Europe & Offshoots | 48.4 |
| Macro Region | High confidence % (1–2) |
|---|---|
| India | 83.3 |
| East & Southeast Asia | 70.9 |
| Middle East & North Africa (MENA) | 70.3 |
| Sub-Saharan Africa | 65.0 |
| Western Europe & Offshoots | 64.7 |
| Eastern Europe & Post-Soviet | 62.3 |
| Latin America & Caribbean | 50.0 |
| Macro Region | High confidence % (1–2) |
|---|---|
| India | 75.8 |
| East & Southeast Asia | 59.6 |
| Sub-Saharan Africa | 54.6 |
| Middle East & North Africa (MENA) | 48.3 |
| Latin America & Caribbean | 39.9 |
| Eastern Europe & Post-Soviet | 37.0 |
| Western Europe & Offshoots | 29.8 |
| Macro Region | High confidence % (1–2) |
|---|---|
| India | 74.9 |
| East & Southeast Asia | 64.0 |
| Middle East & North Africa (MENA) | 62.1 |
| Sub-Saharan Africa | 60.5 |
| Eastern Europe & Post-Soviet | 43.9 |
| Latin America & Caribbean | 40.5 |
| Western Europe & Offshoots | 34.3 |
| Macro Region | High confidence % (1–2) |
|---|---|
| India | 54.4 |
| East & Southeast Asia | 51.7 |
| Sub-Saharan Africa | 45.7 |
| Middle East & North Africa (MENA) | 41.2 |
| Western Europe & Offshoots | 37.3 |
| Eastern Europe & Post-Soviet | 29.1 |
| Latin America & Caribbean | 27.8 |
| Macro Region | High confidence % (1–2) |
|---|---|
| Western Europe & Offshoots | 73.9 |
| Middle East & North Africa (MENA) | 67.0 |
| East & Southeast Asia | 64.7 |
| India | 64.1 |
| Sub-Saharan Africa | 57.8 |
| Eastern Europe & Post-Soviet | 39.0 |
| Latin America & Caribbean | 38.0 |
| Macro Region | High confidence % (1–2) |
|---|---|
| India | 68.9 |
| East & Southeast Asia | 68.2 |
| Middle East & North Africa (MENA) | 62.5 |
| Western Europe & Offshoots | 59.8 |
| Sub-Saharan Africa | 58.1 |
| Latin America & Caribbean | 34.1 |
| Eastern Europe & Post-Soviet | 32.0 |
| Macro Region | High confidence % (1–2) |
|---|---|
| East & Southeast Asia | 60.8 |
| Middle East & North Africa (MENA) | 60.3 |
| Sub-Saharan Africa | 57.7 |
| India | 54.9 |
| Latin America & Caribbean | 41.4 |
| Western Europe & Offshoots | 39.3 |
| Eastern Europe & Post-Soviet | 31.2 |
| Macro Region | High confidence % (1–2) |
|---|---|
| India | 46.4 |
| East & Southeast Asia | 43.4 |
| Sub-Saharan Africa | 37.6 |
| Middle East & North Africa (MENA) | 28.8 |
| Western Europe & Offshoots | 20.7 |
| Latin America & Caribbean | 17.3 |
| Eastern Europe & Post-Soviet | 15.8 |
| Macro Region | High confidence % (1–2) |
|---|---|
| India | 62.4 |
| Sub-Saharan Africa | 54.9 |
| East & Southeast Asia | 51.3 |
| Middle East & North Africa (MENA) | 50.2 |
| Western Europe & Offshoots | 38.4 |
| Latin America & Caribbean | 22.2 |
| Eastern Europe & Post-Soviet | 21.8 |
| Macro Region | High confidence % (1–2) |
|---|---|
| East & Southeast Asia | 61.3 |
| India | 54.3 |
| Sub-Saharan Africa | 52.9 |
| Middle East & North Africa (MENA) | 50.8 |
| Western Europe & Offshoots | 47.2 |
| Eastern Europe & Post-Soviet | 33.4 |
| Latin America & Caribbean | 27.0 |
| Macro Region | High confidence % (1–2) |
|---|---|
| Sub-Saharan Africa | 53.8 |
| India | 51.5 |
| East & Southeast Asia | 43.4 |
| Middle East & North Africa (MENA) | 41.2 |
| Latin America & Caribbean | 40.8 |
| Western Europe & Offshoots | 36.6 |
| Eastern Europe & Post-Soviet | 34.4 |
| Macro Region | High confidence % (1–2) |
|---|---|
| East & Southeast Asia | 65.8 |
| Western Europe & Offshoots | 63.0 |
| India | 61.4 |
| Sub-Saharan Africa | 60.3 |
| Latin America & Caribbean | 57.4 |
| Middle East & North Africa (MENA) | 54.0 |
| Eastern Europe & Post-Soviet | 50.1 |
| Macro Region | High confidence % (1–2) |
|---|---|
| East & Southeast Asia | 67.3 |
| India | 64.6 |
| Sub-Saharan Africa | 63.1 |
| Western Europe & Offshoots | 57.3 |
| Latin America & Caribbean | 54.0 |
| Middle East & North Africa (MENA) | 50.4 |
| Eastern Europe & Post-Soviet | 47.8 |
| Macro Region | High confidence % (1–2) |
|---|---|
| Middle East & North Africa (MENA) | 71.5 |
| Western Europe & Offshoots | 66.1 |
| Sub-Saharan Africa | 65.0 |
| East & Southeast Asia | 64.5 |
| Latin America & Caribbean | 60.0 |
| India | 56.9 |
| Eastern Europe & Post-Soviet | 51.7 |
# Child quality variables (Wave 5: V12-V21)
child_vars_w5 <- paste0("V", 12:21)
# Labels exactly as in questionnaire
child_labels_w5 <- c(
V12 = "Independence",
V13 = "Hard work",
V14 = "Feeling of responsibility",
V15 = "Imagination",
V16 = "Tolerance and respect for other people",
V17 = "Thrift, saving money and things",
V18 = "Determination, perseverance",
V19 = "Religious faith",
V20 = "Unselfishness",
V21 = "Obedience"
)
# Clean negative codes
wvs_clean_child_w5 <- wvs5%>%
mutate(across(all_of(child_vars_w5), ~ifelse(as.numeric(.x) < 0, NA, as.numeric(.x)))) %>%
filter(!is.na(macro_region), !is.na(V259))
# Country-level weighted proportions (1 = Mentioned/Important)
country_prop_child_w5 <- lapply(child_vars_w5, function(v){
wvs_clean_child_w5%>%
filter(!is.na(.data[[v]])) %>%
group_by(V2, macro_region) %>%
summarise(
country_prop = sum(V259 * (.data[[v]] == 1)) / sum(V259),
variable = v,
.groups = "drop"
)
}) %>% bind_rows()
# Macro-region average of country proportions (country-weighted)
region_child_w5 <- country_prop_child_w5%>%
group_by(macro_region, variable) %>%
summarise(
mean_prop = mean(country_prop, na.rm = TRUE),
n_countries = n(),
.groups = "drop"
)for(v in child_vars_w5){
temp_table <- region_child_w5%>%
filter(variable == v) %>%
mutate(mean_prop = round(mean_prop * 100, 1)) %>%
select(macro_region, mean_prop, n_countries) %>%
arrange(desc(mean_prop))
print(
kable(
temp_table,
digits = 1,
col.names = c("Macro Region",
"% Mentioning (country-weighted)",
"N countries"),
caption = paste("Child quality:", child_labels_w5[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}| Macro Region | % Mentioning (country-weighted) | N countries |
|---|---|---|
| India | 66.9 | 1 |
| East & Southeast Asia | 65.5 | 9 |
| Western Europe & Offshoots | 61.7 | 17 |
| Eastern Europe & Post-Soviet | 47.9 | 9 |
| Sub-Saharan Africa | 47.0 | 7 |
| Middle East & North Africa (MENA) | 42.6 | 6 |
| Latin America & Caribbean | 38.4 | 9 |
| Macro Region | % Mentioning (country-weighted) | N countries |
|---|---|---|
| India | 81.3 | 1 |
| Sub-Saharan Africa | 74.5 | 7 |
| Eastern Europe & Post-Soviet | 69.7 | 9 |
| Middle East & North Africa (MENA) | 60.3 | 6 |
| East & Southeast Asia | 53.5 | 9 |
| Latin America & Caribbean | 39.9 | 9 |
| Western Europe & Offshoots | 39.6 | 17 |
| Macro Region | % Mentioning (country-weighted) | N countries |
|---|---|---|
| Western Europe & Offshoots | 80.2 | 17 |
| Latin America & Caribbean | 74.2 | 9 |
| Middle East & North Africa (MENA) | 74.2 | 6 |
| East & Southeast Asia | 73.9 | 9 |
| Eastern Europe & Post-Soviet | 73.4 | 9 |
| India | 68.0 | 1 |
| Sub-Saharan Africa | 48.1 | 7 |
| Macro Region | % Mentioning (country-weighted) | N countries |
|---|---|---|
| Western Europe & Offshoots | 34.4 | 17 |
| India | 25.4 | 1 |
| East & Southeast Asia | 22.4 | 9 |
| Sub-Saharan Africa | 21.9 | 7 |
| Latin America & Caribbean | 20.3 | 9 |
| Eastern Europe & Post-Soviet | 18.5 | 9 |
| Middle East & North Africa (MENA) | 17.3 | 6 |
| Macro Region | % Mentioning (country-weighted) | N countries |
|---|---|---|
| Western Europe & Offshoots | 83.0 | 17 |
| Latin America & Caribbean | 74.1 | 9 |
| Middle East & North Africa (MENA) | 70.6 | 6 |
| Eastern Europe & Post-Soviet | 66.5 | 9 |
| Sub-Saharan Africa | 62.2 | 7 |
| East & Southeast Asia | 59.0 | 9 |
| India | 55.8 | 1 |
| Macro Region | % Mentioning (country-weighted) | N countries |
|---|---|---|
| India | 55.4 | 1 |
| East & Southeast Asia | 52.4 | 9 |
| Eastern Europe & Post-Soviet | 44.4 | 9 |
| Sub-Saharan Africa | 35.4 | 7 |
| Middle East & North Africa (MENA) | 34.5 | 6 |
| Western Europe & Offshoots | 32.5 | 17 |
| Latin America & Caribbean | 30.8 | 9 |
| Macro Region | % Mentioning (country-weighted) | N countries |
|---|---|---|
| Western Europe & Offshoots | 48.0 | 17 |
| Eastern Europe & Post-Soviet | 41.8 | 9 |
| India | 40.7 | 1 |
| East & Southeast Asia | 39.9 | 9 |
| Sub-Saharan Africa | 36.5 | 7 |
| Latin America & Caribbean | 29.0 | 9 |
| Middle East & North Africa (MENA) | 28.0 | 6 |
| Macro Region | % Mentioning (country-weighted) | N countries |
|---|---|---|
| Middle East & North Africa (MENA) | 74.3 | 6 |
| Sub-Saharan Africa | 56.6 | 7 |
| Latin America & Caribbean | 44.2 | 9 |
| India | 41.3 | 1 |
| Eastern Europe & Post-Soviet | 33.8 | 9 |
| East & Southeast Asia | 25.0 | 9 |
| Western Europe & Offshoots | 18.4 | 17 |
| Macro Region | % Mentioning (country-weighted) | N countries |
|---|---|---|
| Latin America & Caribbean | 41.0 | 9 |
| Sub-Saharan Africa | 37.3 | 7 |
| Middle East & North Africa (MENA) | 36.1 | 6 |
| Western Europe & Offshoots | 34.7 | 17 |
| India | 34.2 | 1 |
| East & Southeast Asia | 28.0 | 9 |
| Eastern Europe & Post-Soviet | 27.1 | 9 |
| Macro Region | % Mentioning (country-weighted) | N countries |
|---|---|---|
| Sub-Saharan Africa | 62.4 | 7 |
| India | 55.9 | 1 |
| Middle East & North Africa (MENA) | 55.8 | 6 |
| Latin America & Caribbean | 53.6 | 9 |
| Eastern Europe & Post-Soviet | 32.5 | 9 |
| Western Europe & Offshoots | 31.9 | 17 |
| East & Southeast Asia | 24.0 | 9 |