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 <- "D:/Populism and Democrary/World value survey/WVS 2017/WVS_Cross-National_Wave_7_Rdata_v6_0.rdata"
stopifnot(file.exists(data_file))
load(data_file)
wvs_raw7 <- `WVS_Cross-National_Wave_7_v6_0`
wvs7 <- wvs_raw7
Country statistics
country_lookup_wvs <- tibble::tribble(
~B_COUNTRY, ~country,
8, "Albania",
20, "Andorra",
32, "Argentina",
51, "Armenia",
36, "Australia",
40, "Austria",
31, "Azerbaijan",
50, "Bangladesh",
112, "Belarus",
68, "Bolivia",
70, "Bosnia Herzegovina",
76, "Brazil",
100, "Bulgaria",
124, "Canada",
152, "Chile",
156, "China",
170, "Colombia",
191, "Croatia",
196, "Cyprus",
203, "Czechia",
208, "Denmark",
218, "Ecuador",
818, "Egypt",
233, "Estonia",
231, "Ethiopia",
246, "Finland",
250, "France",
268, "Georgia",
276, "Germany",
826, "Great Britain",
300, "Greece",
320, "Guatemala",
344, "Hong Kong SAR",
348, "Hungary",
352, "Iceland",
356, "India",
360, "Indonesia",
364, "Iran",
368, "Iraq",
380, "Italy",
392, "Japan",
400, "Jordan",
398, "Kazakhstan",
404, "Kenya",
410, "South Korea",
417, "Kyrgyzstan",
422, "Lebanon",
428, "Latvia",
440, "Lithuania",
434, "Libya",
446, "Macao SAR",
458, "Malaysia",
462, "Maldives",
484, "Mexico",
496, "Mongolia",
499, "Montenegro",
504, "Morocco",
104, "Myanmar",
528, "Netherlands",
554, "New Zealand",
558, "Nicaragua",
566, "Nigeria",
807, "North Macedonia",
909, "Northern Ireland",
578, "Norway",
586, "Pakistan",
604, "Peru",
608, "Philippines",
616, "Poland",
620, "Portugal",
630, "Puerto Rico",
642, "Romania",
643, "Russia",
688, "Serbia",
702, "Singapore",
703, "Slovakia",
705, "Slovenia",
724, "Spain",
752, "Sweden",
756, "Switzerland",
158, "Taiwan ROC",
762, "Tajikistan",
764, "Thailand",
788, "Tunisia",
792, "Turkey",
804, "Ukraine",
840, "United States",
858, "Uruguay",
860, "Uzbekistan",
862, "Venezuela",
704, "Vietnam",
716, "Zimbabwe"
)
country_n <- wvs7 |>
dplyr::filter(!is.na(B_COUNTRY)) |>
dplyr::count(B_COUNTRY, name = "n", sort = TRUE) |>
dplyr::left_join(country_lookup_wvs, by = "B_COUNTRY") |>
dplyr::relocate(country, .before = B_COUNTRY)
country_year_n <- wvs7 |>
dplyr::filter(!is.na(B_COUNTRY), !is.na(A_YEAR)) |>
dplyr::count(B_COUNTRY, A_YEAR, name = "n", sort = TRUE) |>
dplyr::left_join(country_lookup_wvs, by = "B_COUNTRY") |>
dplyr::relocate(country, .before = B_COUNTRY)
country_year_n
## country B_COUNTRY A_YEAR n
## 1 Canada 124 2020 4018
## 2 Indonesia 360 2018 3200
## 3 China 156 2018 3036
## 4 Great Britain 826 2022 2609
## 5 United States 840 2017 2596
## 6 Turkey 792 2018 2415
## 7 Netherlands 528 2022 2145
## 8 Hong Kong SAR 344 2018 2075
## 9 Bolivia 68 2017 2067
## 10 Singapore 702 2020 2012
## 11 Pakistan 586 2018 1995
## 12 Australia 36 2018 1813
## 13 Russia 643 2017 1810
## 14 Brazil 76 2018 1762
## 15 Mexico 484 2018 1741
## 16 India 356 2023 1692
## 17 Mongolia 496 2020 1638
## 18 Germany 276 2018 1528
## 19 Colombia 170 2018 1520
## 20 Thailand 764 2018 1500
## 21 Iran 364 2020 1499
## 22 Peru 604 2018 1400
## 23 Japan 392 2019 1353
## 24 Malaysia 458 2018 1313
## 25 Ukraine 804 2020 1289
## 26 Kazakhstan 398 2018 1276
## 27 Kenya 404 2021 1266
## 28 Romania 642 2018 1257
## 29 Uzbekistan 860 2022 1250
## 30 South Korea 410 2018 1245
## 31 Nigeria 566 2018 1237
## 32 Ethiopia 231 2020 1230
## 33 Guatemala 320 2020 1229
## 34 Armenia 51 2021 1223
## 35 Taiwan ROC 158 2019 1223
## 36 Zimbabwe 716 2020 1215
## 37 Tunisia 788 2019 1208
## 38 Jordan 400 2018 1203
## 39 Bangladesh 50 2018 1200
## 40 Myanmar 104 2020 1200
## 41 Czechia 203 2022 1200
## 42 Ecuador 218 2018 1200
## 43 Greece 300 2017 1200
## 44 Iraq 368 2018 1200
## 45 Kyrgyzstan 417 2020 1200
## 46 Lebanon 422 2018 1200
## 47 Morocco 504 2021 1200
## 48 Nicaragua 558 2020 1200
## 49 Philippines 608 2019 1200
## 50 Slovakia 703 2022 1200
## 51 Vietnam 704 2020 1200
## 52 Tajikistan 762 2020 1200
## 53 Egypt 818 2018 1200
## 54 Libya 434 2022 1196
## 55 Venezuela 862 2021 1190
## 56 Puerto Rico 630 2018 1127
## 57 New Zealand 554 2020 1057
## 58 Serbia 688 2017 1046
## 59 Maldives 462 2021 1039
## 60 Macao SAR 446 2019 1023
## 61 Andorra 20 2018 1004
## 62 Argentina 32 2017 1003
## 63 Chile 152 2018 1000
## 64 Cyprus 196 2019 1000
## 65 Uruguay 858 2022 1000
## 66 Northern Ireland 909 2022 447
Macro regions
macro_map_prevstyle <- tibble::tribble(
~B_COUNTRY, ~country, ~region,
# India (own category)
356, "India", "India",
# South Asia
586, "Pakistan", "South Asia",
50, "Bangladesh", "South Asia",
462, "Maldives", "South Asia",
# 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",
702, "Singapore", "East & Southeast Asia",
458, "Malaysia", "East & Southeast Asia",
608, "Philippines", "East & Southeast Asia",
764, "Thailand", "East & Southeast Asia",
360, "Indonesia", "East & Southeast Asia",
496, "Mongolia", "East & Southeast Asia",
446, "Macao SAR", "East & Southeast Asia",
104, "Myanmar", "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)",
434, "Libya", "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)",
# Sub-Saharan Africa
566, "Nigeria", "Sub-Saharan Africa",
716, "Zimbabwe", "Sub-Saharan Africa",
231, "Ethiopia", "Sub-Saharan Africa",
404, "Kenya", "Sub-Saharan Africa",
# Latin America & Caribbean
32, "Argentina", "Latin America & Caribbean",
68, "Bolivia", "Latin America & Caribbean",
76, "Brazil", "Latin America & Caribbean",
152, "Chile", "Latin America & Caribbean",
170, "Colombia", "Latin America & Caribbean",
218, "Ecuador", "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",
558, "Nicaragua", "Latin America & Caribbean",
630, "Puerto Rico","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",
909, "Northern Ireland","Western Europe & Offshoots",
20, "Andorra", "Western Europe & Offshoots",
40, "Austria", "Western Europe & Offshoots",
208, "Denmark", "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",
352, "Iceland", "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
8, "Albania", "Eastern Europe & Post-Soviet",
51, "Armenia", "Eastern Europe & Post-Soviet",
31, "Azerbaijan","Eastern Europe & Post-Soviet",
112, "Belarus", "Eastern Europe & Post-Soviet",
70, "Bosnia Herzegovina", "Eastern Europe & Post-Soviet",
191, "Croatia", "Eastern Europe & Post-Soviet",
203, "Czechia", "Eastern Europe & Post-Soviet",
233, "Estonia", "Eastern Europe & Post-Soviet",
268, "Georgia", "Eastern Europe & Post-Soviet",
398, "Kazakhstan","Eastern Europe & Post-Soviet",
417, "Kyrgyzstan","Eastern Europe & Post-Soviet",
428, "Latvia", "Eastern Europe & Post-Soviet",
440, "Lithuania", "Eastern Europe & Post-Soviet",
499, "Montenegro","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",
703, "Slovakia", "Eastern Europe & Post-Soviet",
705, "Slovenia", "Eastern Europe & Post-Soviet",
762, "Tajikistan","Eastern Europe & Post-Soviet",
804, "Ukraine", "Eastern Europe & Post-Soviet",
860, "Uzbekistan","Eastern Europe & Post-Soviet",
807, "North Macedonia", "Eastern Europe & Post-Soviet"
)
w7_countries <- wvs7 %>%
distinct(B_COUNTRY) %>%
filter(!is.na(B_COUNTRY))
# Keep only mapping rows relevant to Wave 7
macro_map_w7 <- macro_map_prevstyle %>%
semi_join(w7_countries, by = "B_COUNTRY")
# Join macro-region onto the microdata
wvs7 <- wvs7 %>%
left_join(macro_map_w7 %>% select(B_COUNTRY, macro_region = region), by = "B_COUNTRY")
missing_map <- wvs7 %>%
distinct(B_COUNTRY) %>%
left_join(macro_map_w7, by = "B_COUNTRY") %>%
filter(is.na(region))
missing_map
## [1] B_COUNTRY country region
## <0 rows> (or 0-length row.names)
wvs7 %>%
group_by(macro_region) %>%
summarise(
n_respondents = n(),
n_countries = n_distinct(B_COUNTRY),
.groups = "drop"
) %>%
arrange(desc(n_respondents))
## # A tibble: 8 × 3
## macro_region n_respondents n_countries
## <chr> <int> <int>
## 1 East & Southeast Asia 23218 14
## 2 Western Europe & Offshoots 19417 11
## 3 Latin America & Caribbean 17439 13
## 4 Eastern Europe & Post-Soviet 13951 11
## 5 Middle East & North Africa (MENA) 12321 9
## 6 Sub-Saharan Africa 4948 4
## 7 South Asia 4234 3
## 8 India 1692 1
Regional demographics
# Age Cohorts (Q262 = age, X003R = recoded age groups)
wvs7 <- wvs7 %>%
mutate(
age = Q262,
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 <- wvs7 %>%
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,
digits = 3,
caption = "Age-group distribution by macro region (Wave 7)"
)
Age-group distribution by macro region (Wave 7)
| East & Southeast Asia |
18–24 |
2939 |
0.127 |
| East & Southeast Asia |
25–34 |
4670 |
0.201 |
| East & Southeast Asia |
35–44 |
4793 |
0.206 |
| East & Southeast Asia |
45–54 |
4538 |
0.195 |
| East & Southeast Asia |
55–64 |
3742 |
0.161 |
| East & Southeast Asia |
65+ |
2536 |
0.109 |
| Eastern Europe & Post-Soviet |
18–24 |
1622 |
0.116 |
| Eastern Europe & Post-Soviet |
25–34 |
2899 |
0.208 |
| Eastern Europe & Post-Soviet |
35–44 |
2638 |
0.189 |
| Eastern Europe & Post-Soviet |
45–54 |
2430 |
0.174 |
| Eastern Europe & Post-Soviet |
55–64 |
2225 |
0.159 |
| Eastern Europe & Post-Soviet |
65+ |
2137 |
0.153 |
| India |
18–24 |
564 |
0.333 |
| India |
25–34 |
308 |
0.182 |
| India |
35–44 |
293 |
0.173 |
| India |
45–54 |
265 |
0.157 |
| India |
55–64 |
166 |
0.098 |
| India |
65+ |
96 |
0.057 |
| Latin America & Caribbean |
18–24 |
3530 |
0.202 |
| Latin America & Caribbean |
25–34 |
3818 |
0.219 |
| Latin America & Caribbean |
35–44 |
3211 |
0.184 |
| Latin America & Caribbean |
45–54 |
2667 |
0.153 |
| Latin America & Caribbean |
55–64 |
2253 |
0.129 |
| Latin America & Caribbean |
65+ |
1960 |
0.112 |
| Middle East & North Africa (MENA) |
18–24 |
2011 |
0.163 |
| Middle East & North Africa (MENA) |
25–34 |
2927 |
0.238 |
| Middle East & North Africa (MENA) |
35–44 |
2854 |
0.232 |
| Middle East & North Africa (MENA) |
45–54 |
2257 |
0.183 |
| Middle East & North Africa (MENA) |
55–64 |
1628 |
0.132 |
| Middle East & North Africa (MENA) |
65+ |
644 |
0.052 |
| South Asia |
18–24 |
749 |
0.177 |
| South Asia |
25–34 |
1311 |
0.310 |
| South Asia |
35–44 |
1115 |
0.263 |
| South Asia |
45–54 |
592 |
0.140 |
| South Asia |
55–64 |
330 |
0.078 |
| South Asia |
65+ |
137 |
0.032 |
| Sub-Saharan Africa |
18–24 |
1390 |
0.281 |
| Sub-Saharan Africa |
25–34 |
1689 |
0.341 |
| Sub-Saharan Africa |
35–44 |
938 |
0.190 |
| Sub-Saharan Africa |
45–54 |
494 |
0.100 |
| Sub-Saharan Africa |
55–64 |
260 |
0.053 |
| Sub-Saharan Africa |
65+ |
177 |
0.036 |
| Western Europe & Offshoots |
18–24 |
1763 |
0.091 |
| Western Europe & Offshoots |
25–34 |
3215 |
0.166 |
| Western Europe & Offshoots |
35–44 |
3236 |
0.167 |
| Western Europe & Offshoots |
45–54 |
3377 |
0.174 |
| Western Europe & Offshoots |
55–64 |
3360 |
0.173 |
| Western Europe & Offshoots |
65+ |
4466 |
0.230 |
# Gender (Q260)
wvs7 <- wvs7 %>%
mutate(
gender = case_when(
Q260 == 1 ~ "Male",
Q260 == 2 ~ "Female",
TRUE ~ NA_character_
)
)
gender_region <- wvs7 %>%
filter(!is.na(macro_region), !is.na(gender)) %>%
count(macro_region, gender) %>%
group_by(macro_region) %>%
mutate(share = n / sum(n)) %>%
ungroup()
gender_region
## # A tibble: 16 × 4
## macro_region gender n share
## <chr> <chr> <int> <dbl>
## 1 East & Southeast Asia Female 12383 0.534
## 2 East & Southeast Asia Male 10825 0.466
## 3 Eastern Europe & Post-Soviet Female 7933 0.569
## 4 Eastern Europe & Post-Soviet Male 6018 0.431
## 5 India Female 734 0.434
## 6 India Male 958 0.566
## 7 Latin America & Caribbean Female 9249 0.530
## 8 Latin America & Caribbean Male 8188 0.470
## 9 Middle East & North Africa (MENA) Female 6133 0.498
## 10 Middle East & North Africa (MENA) Male 6186 0.502
## 11 South Asia Female 2113 0.500
## 12 South Asia Male 2117 0.500
## 13 Sub-Saharan Africa Female 2449 0.496
## 14 Sub-Saharan Africa Male 2492 0.504
## 15 Western Europe & Offshoots Female 10136 0.524
## 16 Western Europe & Offshoots Male 9211 0.476
# Immigrant status (Q263 = self-born in country)
wvs7 <- wvs7 %>%
mutate(
immigrant_status = case_when(
Q263 == 1 ~ "Native",
Q263 == 2 ~ "Immigrant",
TRUE ~ NA_character_
)
)
immigrant_region <- wvs7 %>%
filter(!is.na(macro_region), !is.na(immigrant_status)) %>%
count(macro_region, immigrant_status) %>%
group_by(macro_region) %>%
mutate(share = n / sum(n)) %>%
ungroup()
immigrant_region
## # A tibble: 15 × 4
## macro_region immigrant_status n share
## <chr> <chr> <int> <dbl>
## 1 East & Southeast Asia Immigrant 1447 0.0624
## 2 East & Southeast Asia Native 21736 0.938
## 3 Eastern Europe & Post-Soviet Immigrant 484 0.0347
## 4 Eastern Europe & Post-Soviet Native 13448 0.965
## 5 India Native 1692 1
## 6 Latin America & Caribbean Immigrant 240 0.0138
## 7 Latin America & Caribbean Native 17173 0.986
## 8 Middle East & North Africa (MENA) Immigrant 212 0.0172
## 9 Middle East & North Africa (MENA) Native 12106 0.983
## 10 South Asia Immigrant 13 0.00307
## 11 South Asia Native 4221 0.997
## 12 Sub-Saharan Africa Immigrant 19 0.00384
## 13 Sub-Saharan Africa Native 4925 0.996
## 14 Western Europe & Offshoots Immigrant 3246 0.170
## 15 Western Europe & Offshoots Native 15874 0.830
# Missingness check
wvs7 %>%
summarise(
missing_age = sum(is.na(Q262)),
missing_gender = sum(is.na(Q260)),
missing_immigrant = sum(is.na(Q263))
)
## missing_age missing_gender missing_immigrant
## 1 0 0 0
table(wvs7$Q263, useNA = "always")
##
## -5 -2 -1 1 2 <NA>
## 186 157 41 91175 5661 0
# Missingness by macro_region
wvs7 %>%
group_by(macro_region) %>%
summarise(
n_obs = n(),
missing_age = sum(is.na(Q262)),
missing_gender = sum(is.na(Q260)),
missing_immigrant = sum(is.na(Q263))
)
## # A tibble: 8 × 5
## macro_region n_obs missing_age missing_gender missing_immigrant
## <chr> <int> <int> <int> <int>
## 1 East & Southeast Asia 23218 0 0 0
## 2 Eastern Europe & Post-Sovi… 13951 0 0 0
## 3 India 1692 0 0 0
## 4 Latin America & Caribbean 17439 0 0 0
## 5 Middle East & North Africa… 12321 0 0 0
## 6 South Asia 4234 0 0 0
## 7 Sub-Saharan Africa 4948 0 0 0
## 8 Western Europe & Offshoots 19417 0 0 0
Macro-region analysis (Economic system)
# Economic values (Wave 7: Q106-Q111)
likert_vars <- paste0("Q", 106:111)
wvs7 <- wvs7 %>%
mutate(across(all_of(likert_vars), ~ifelse(.x < 0, NA, .x)))
# keep valid cases (non-missing macro_region + at least one likert var)
wvs_clean <- wvs7 %>%
filter(!is.na(macro_region))
svy_design <- svydesign(
ids = ~1,
weights = ~W_WEIGHT, # Wave 7 weight (like V258)
data = wvs_clean
)
# Country-level means first
country_means <- lapply(likert_vars, function(v){
svyby(
as.formula(paste0("~", v)),
~B_COUNTRY + macro_region, # B_COUNTRY instead of V2
svy_design,
svymean,
na.rm = TRUE
) %>%
rename(country_mean = 3) %>%
mutate(variable = v)
}) %>% bind_rows()
# Region means (country-weighted)
region_means_country_weighted <- country_means %>%
group_by(macro_region, variable) %>%
summarise(
mean = mean(country_mean, na.rm = TRUE),
n_countries = n(),
.groups = "drop"
)
# Updated labels for Wave 7 economic values
var_labels <- c(
Q106 = "Income equality vs Inequality incentives (1=More equal, 10=Larger differences)",
Q107 = "Private vs government ownership of business (1=Private, 10=Government)",
Q108 = "Government vs individual responsibility (1=Government, 10=Individuals)",
Q109 = "Competition good vs harmful (1=Good, 10=Harmful)",
Q110 = "Success: hard work vs luck (1=Hard work, 10=Luck/connections)",
Q111 = "Environment vs economic growth (1=Protect env, 2=Economic growth)"
)
for(v in likert_vars){
temp_table <- region_means_country_weighted %>%
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[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}
Regional comparison: Income equality vs Inequality incentives (1=More
equal, 10=Larger differences)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
South Asia
|
5.43
|
3
|
|
Western Europe & Offshoots
|
5.61
|
11
|
|
Latin America & Caribbean
|
6.14
|
13
|
|
Middle East & North Africa (MENA)
|
6.33
|
9
|
|
East & Southeast Asia
|
6.43
|
14
|
|
Sub-Saharan Africa
|
6.70
|
4
|
|
India
|
7.14
|
1
|
|
Eastern Europe & Post-Soviet
|
7.23
|
11
|
Regional comparison: Private vs government ownership of business
(1=Private, 10=Government)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
Western Europe & Offshoots
|
4.85
|
11
|
|
South Asia
|
5.26
|
3
|
|
East & Southeast Asia
|
5.59
|
14
|
|
Sub-Saharan Africa
|
5.77
|
4
|
|
Latin America & Caribbean
|
5.77
|
13
|
|
Middle East & North Africa (MENA)
|
5.95
|
9
|
|
Eastern Europe & Post-Soviet
|
6.13
|
11
|
|
India
|
6.43
|
1
|
Regional comparison: Government vs individual responsibility
(1=Government, 10=Individuals)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
Sub-Saharan Africa
|
4.24
|
4
|
|
Middle East & North Africa (MENA)
|
4.45
|
9
|
|
South Asia
|
4.62
|
3
|
|
Western Europe & Offshoots
|
4.90
|
11
|
|
Eastern Europe & Post-Soviet
|
4.97
|
11
|
|
East & Southeast Asia
|
5.30
|
14
|
|
Latin America & Caribbean
|
5.36
|
13
|
|
India
|
5.37
|
1
|
Regional comparison: Competition good vs harmful (1=Good, 10=Harmful)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
Sub-Saharan Africa
|
3.28
|
4
|
|
Western Europe & Offshoots
|
3.73
|
11
|
|
South Asia
|
3.90
|
3
|
|
Middle East & North Africa (MENA)
|
3.90
|
9
|
|
Eastern Europe & Post-Soviet
|
3.92
|
11
|
|
East & Southeast Asia
|
4.17
|
14
|
|
Latin America & Caribbean
|
4.55
|
13
|
|
India
|
5.11
|
1
|
Regional comparison: Success: hard work vs luck (1=Hard work,
10=Luck/connections)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
Sub-Saharan Africa
|
3.22
|
4
|
|
South Asia
|
3.60
|
3
|
|
East & Southeast Asia
|
4.41
|
14
|
|
Latin America & Caribbean
|
4.52
|
13
|
|
Middle East & North Africa (MENA)
|
4.57
|
9
|
|
India
|
4.58
|
1
|
|
Western Europe & Offshoots
|
4.69
|
11
|
|
Eastern Europe & Post-Soviet
|
4.89
|
11
|
Regional comparison: Environment vs economic growth (1=Protect env,
2=Economic growth)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
Western Europe & Offshoots
|
1.39
|
11
|
|
East & Southeast Asia
|
1.43
|
14
|
|
Latin America & Caribbean
|
1.44
|
13
|
|
India
|
1.46
|
1
|
|
Sub-Saharan Africa
|
1.53
|
4
|
|
Eastern Europe & Post-Soviet
|
1.53
|
11
|
|
Middle East & North Africa (MENA)
|
1.55
|
9
|
|
South Asia
|
1.55
|
3
|
And again the results are amazing, pretty much what we discussed.
This also aligns with the results we see in PALS survey. Over the years
from 2012 to 2022 India has seen a complete shift in most of the
questions where India was lowest in terms of regional average in 2012,
it has completely turnaround in 2022, where India has highest regional
averages.
Macro region analysis (democratic system)
# Democratic system variables
demo_vars <- paste0("Q", 240:250)
# remove invalid codes (-1 to -5)
wvs7 <- wvs7 %>%
mutate(across(all_of(demo_vars), ~ifelse(.x < 0, NA, .x)))
# keep valid cases
wvs_clean_demo <- wvs7 %>%
filter(!is.na(macro_region))
# survey design
svy_design_demo <- svydesign(
ids = ~1,
weights = ~W_WEIGHT,
data = wvs_clean_demo
)
# SURVEY-WEIGHTED COUNTRY MEANS
country_means_demo <- lapply(demo_vars, function(v){
svyby(
as.formula(paste0("~", v)),
~B_COUNTRY + macro_region,
svy_design_demo,
svymean,
na.rm = TRUE
) %>%
rename(country_mean = 3) %>%
mutate(variable = v)
}) %>% bind_rows()
# MACRO REGION AVERAGE OF COUNTRY MEANS
region_means_demo <- country_means_demo %>%
group_by(macro_region, variable) %>%
summarise(
mean = mean(country_mean, na.rm = TRUE),
n_countries = n(),
.groups = "drop"
)
demo_labels <- c(
Q240 = "Political self-placement (1=Left, 10=Right)",
Q241 = "Democracy: Taxing rich & subsidizing poor (1=Not essential, 10=Essential)",
Q242 = "Democracy: Religious authorities interpret laws (1=Not essential, 10=Essential)",
Q243 = "Democracy: Free elections for leaders (1=Not essential, 10=Essential)",
Q244 = "Democracy: State aid for unemployment (1=Not essential, 10=Essential)",
Q245 = "Democracy: Army takeover when govt incompetent (1=Not essential, 10=Essential)",
Q246 = "Democracy: Civil rights protect against oppression (1=Not essential, 10=Essential)",
Q247 = "Democracy: State makes incomes equal (1=Not essential, 10=Essential)",
Q248 = "Democracy: People obey rulers (1=Not essential, 10=Essential)",
Q249 = "Democracy: Women equal rights (1=Not essential, 10=Essential)",
Q250 = "Importance of democratic country (1=Not important, 10=Absolutely)"
)
for(v in demo_vars){
temp_table <- region_means_demo %>%
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[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}
Regional comparison: Political self-placement (1=Left, 10=Right)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
India
|
6.38
|
1
|
|
Sub-Saharan Africa
|
5.85
|
4
|
|
Latin America & Caribbean
|
5.67
|
13
|
|
Western Europe & Offshoots
|
5.24
|
11
|
|
East & Southeast Asia
|
4.40
|
14
|
|
South Asia
|
4.38
|
3
|
|
Eastern Europe & Post-Soviet
|
4.26
|
11
|
|
Middle East & North Africa (MENA)
|
2.65
|
9
|
Regional comparison: Democracy: Taxing rich & subsidizing poor
(1=Not essential, 10=Essential)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
South Asia
|
7.46
|
3
|
|
Middle East & North Africa (MENA)
|
7.13
|
9
|
|
India
|
6.92
|
1
|
|
Eastern Europe & Post-Soviet
|
6.52
|
11
|
|
Western Europe & Offshoots
|
6.39
|
11
|
|
East & Southeast Asia
|
6.33
|
14
|
|
Sub-Saharan Africa
|
5.99
|
4
|
|
Latin America & Caribbean
|
5.40
|
13
|
Regional comparison: Democracy: Religious authorities interpret laws
(1=Not essential, 10=Essential)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
South Asia
|
6.63
|
3
|
|
Middle East & North Africa (MENA)
|
5.17
|
9
|
|
Sub-Saharan Africa
|
4.82
|
4
|
|
Latin America & Caribbean
|
4.25
|
13
|
|
Eastern Europe & Post-Soviet
|
4.18
|
11
|
|
East & Southeast Asia
|
4.09
|
14
|
|
India
|
3.72
|
1
|
|
Western Europe & Offshoots
|
2.57
|
11
|
Regional comparison: Democracy: Free elections for leaders (1=Not
essential, 10=Essential)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
Western Europe & Offshoots
|
8.84
|
11
|
|
South Asia
|
8.42
|
3
|
|
Sub-Saharan Africa
|
8.05
|
4
|
|
Eastern Europe & Post-Soviet
|
7.93
|
11
|
|
India
|
7.91
|
1
|
|
Middle East & North Africa (MENA)
|
7.88
|
9
|
|
Latin America & Caribbean
|
7.69
|
13
|
|
East & Southeast Asia
|
7.48
|
14
|
Regional comparison: Democracy: State aid for unemployment (1=Not
essential, 10=Essential)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
Eastern Europe & Post-Soviet
|
7.34
|
11
|
|
Western Europe & Offshoots
|
7.30
|
11
|
|
South Asia
|
7.25
|
3
|
|
Middle East & North Africa (MENA)
|
7.12
|
9
|
|
India
|
7.04
|
1
|
|
Sub-Saharan Africa
|
6.98
|
4
|
|
East & Southeast Asia
|
6.60
|
14
|
|
Latin America & Caribbean
|
6.28
|
13
|
Regional comparison: Democracy: Army takeover when govt incompetent
(1=Not essential, 10=Essential)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
South Asia
|
6.45
|
3
|
|
Sub-Saharan Africa
|
5.55
|
4
|
|
Eastern Europe & Post-Soviet
|
5.10
|
11
|
|
India
|
4.84
|
1
|
|
Latin America & Caribbean
|
4.72
|
13
|
|
East & Southeast Asia
|
4.62
|
14
|
|
Middle East & North Africa (MENA)
|
4.41
|
9
|
|
Western Europe & Offshoots
|
2.95
|
11
|
Regional comparison: Democracy: Civil rights protect against oppression
(1=Not essential, 10=Essential)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
Western Europe & Offshoots
|
8.00
|
11
|
|
South Asia
|
7.69
|
3
|
|
Sub-Saharan Africa
|
7.60
|
4
|
|
Eastern Europe & Post-Soviet
|
7.56
|
11
|
|
Middle East & North Africa (MENA)
|
7.38
|
9
|
|
India
|
7.37
|
1
|
|
East & Southeast Asia
|
7.04
|
14
|
|
Latin America & Caribbean
|
6.69
|
13
|
Regional comparison: Democracy: State makes incomes equal (1=Not
essential, 10=Essential)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
South Asia
|
7.07
|
3
|
|
Middle East & North Africa (MENA)
|
6.80
|
9
|
|
Eastern Europe & Post-Soviet
|
6.39
|
11
|
|
India
|
6.29
|
1
|
|
East & Southeast Asia
|
6.08
|
14
|
|
Sub-Saharan Africa
|
5.69
|
4
|
|
Latin America & Caribbean
|
5.39
|
13
|
|
Western Europe & Offshoots
|
5.03
|
11
|
Regional comparison: Democracy: People obey rulers (1=Not essential,
10=Essential)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
South Asia
|
7.93
|
3
|
|
Sub-Saharan Africa
|
7.63
|
4
|
|
India
|
6.44
|
1
|
|
Middle East & North Africa (MENA)
|
6.16
|
9
|
|
Eastern Europe & Post-Soviet
|
6.13
|
11
|
|
East & Southeast Asia
|
5.57
|
14
|
|
Latin America & Caribbean
|
5.51
|
13
|
|
Western Europe & Offshoots
|
4.94
|
11
|
Regional comparison: Democracy: Women equal rights (1=Not essential,
10=Essential)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
Western Europe & Offshoots
|
8.98
|
11
|
|
Eastern Europe & Post-Soviet
|
8.12
|
11
|
|
India
|
8.04
|
1
|
|
South Asia
|
8.03
|
3
|
|
Latin America & Caribbean
|
7.82
|
13
|
|
East & Southeast Asia
|
7.76
|
14
|
|
Sub-Saharan Africa
|
7.61
|
4
|
|
Middle East & North Africa (MENA)
|
7.24
|
9
|
Regional comparison: Importance of democratic country (1=Not important,
10=Absolutely)
|
Macro Region
|
Mean (country-weighted, 1–10)
|
N countries
|
|
Western Europe & Offshoots
|
8.91
|
11
|
|
Sub-Saharan Africa
|
8.61
|
4
|
|
India
|
8.43
|
1
|
|
Latin America & Caribbean
|
8.37
|
13
|
|
East & Southeast Asia
|
8.19
|
14
|
|
Eastern Europe & Post-Soviet
|
8.17
|
11
|
|
Middle East & North Africa (MENA)
|
8.16
|
9
|
|
South Asia
|
7.96
|
3
|
Macro region analysis (value system)
Neighbours
# Neighbors rejection items (Wave 7: Q27-Q35)
neigh_vars <- paste0("Q", 27:35)
# Clean negative codes
wvs7 <- wvs7 %>%
mutate(across(all_of(neigh_vars), ~ifelse(.x < 0, NA, .x)))
wvs_clean_neigh <- wvs7 %>%
filter(!is.na(macro_region))
svy_design_neigh <- svydesign(
ids = ~1,
weights = ~W_WEIGHT,
data = wvs_clean_neigh
)
# SURVEY-WEIGHTED COUNTRY % rejecting each group
country_prop_neigh <- lapply(neigh_vars, function(v){
svyby(
as.formula(paste0("~I(", v, " == 1)")), # 1 if rejected (mentioned), 0 otherwise
~B_COUNTRY + macro_region, # V2 → B_COUNTRY
svy_design_neigh,
svymean,
na.rm = TRUE
) %>%
rename(country_prop = 3) %>%
mutate(variable = v)
}) %>% bind_rows()
# MACRO REGION AVERAGE OF COUNTRY PROPORTIONS
region_prop_neigh <- country_prop_neigh %>%
group_by(macro_region, variable) %>%
summarise(
mean_prop = mean(country_prop, na.rm = TRUE),
n_countries = n(),
.groups = "drop"
)
# Labels for Wave 7 "Undesirable neighbors" (Q27-Q35)
neigh_labels <- c(
Q27 = "Drug addicts",
Q28 = "Alcoholics",
Q29 = "People with AIDS",
Q30 = "Homosexuals",
Q31 = "Immigrants/foreign workers",
Q32 = "Different race",
Q33 = "Different religion",
Q34 = "Different language",
Q35 = "Other countries"
)
for(v in neigh_vars){
temp_table <- region_prop_neigh %>%
filter(variable == v) %>%
mutate(mean_prop = round(mean_prop * 100, 1)) %>% # Convert to %
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 neighbors:", neigh_labels[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}
Undesirable neighbors: Drug addicts
|
Macro Region
|
% Rejecting (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
72.4
|
11
|
|
East & Southeast Asia
|
61.1
|
14
|
|
Eastern Europe & Post-Soviet
|
49.9
|
11
|
|
Latin America & Caribbean
|
48.4
|
13
|
|
Middle East & North Africa (MENA)
|
24.9
|
9
|
|
Sub-Saharan Africa
|
24.1
|
4
|
|
India
|
19.8
|
1
|
|
South Asia
|
16.0
|
3
|
Undesirable neighbors: Alcoholics
|
Macro Region
|
% Rejecting (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
94.7
|
11
|
|
East & Southeast Asia
|
90.1
|
14
|
|
Eastern Europe & Post-Soviet
|
88.3
|
11
|
|
Sub-Saharan Africa
|
86.0
|
4
|
|
Latin America & Caribbean
|
85.1
|
13
|
|
Middle East & North Africa (MENA)
|
69.2
|
9
|
|
South Asia
|
66.2
|
3
|
|
India
|
65.2
|
1
|
Undesirable neighbors: People with AIDS
|
Macro Region
|
% Rejecting (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
97.2
|
11
|
|
Latin America & Caribbean
|
95.2
|
13
|
|
East & Southeast Asia
|
87.3
|
14
|
|
Eastern Europe & Post-Soviet
|
79.5
|
11
|
|
Sub-Saharan Africa
|
75.6
|
4
|
|
India
|
75.5
|
1
|
|
Middle East & North Africa (MENA)
|
68.2
|
9
|
|
South Asia
|
67.0
|
3
|
Undesirable neighbors: Homosexuals
|
Macro Region
|
% Rejecting (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
98.7
|
11
|
|
Latin America & Caribbean
|
95.4
|
13
|
|
East & Southeast Asia
|
91.7
|
14
|
|
Sub-Saharan Africa
|
88.5
|
4
|
|
Middle East & North Africa (MENA)
|
87.5
|
9
|
|
Eastern Europe & Post-Soviet
|
86.4
|
11
|
|
India
|
83.0
|
1
|
|
South Asia
|
79.0
|
3
|
Undesirable neighbors: Immigrants/foreign workers
|
Macro Region
|
% Rejecting (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
97.8
|
11
|
|
Latin America & Caribbean
|
96.3
|
13
|
|
East & Southeast Asia
|
90.0
|
14
|
|
Sub-Saharan Africa
|
83.1
|
4
|
|
Eastern Europe & Post-Soviet
|
81.8
|
11
|
|
India
|
78.2
|
1
|
|
Middle East & North Africa (MENA)
|
76.2
|
9
|
|
South Asia
|
69.8
|
3
|
Undesirable neighbors: Different race
|
Macro Region
|
% Rejecting (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
82.0
|
11
|
|
Sub-Saharan Africa
|
81.8
|
4
|
|
East & Southeast Asia
|
80.9
|
14
|
|
Eastern Europe & Post-Soviet
|
78.4
|
11
|
|
Latin America & Caribbean
|
77.8
|
13
|
|
India
|
73.2
|
1
|
|
South Asia
|
69.5
|
3
|
|
Middle East & North Africa (MENA)
|
68.6
|
9
|
Undesirable neighbors: Different religion
|
Macro Region
|
% Rejecting (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
96.6
|
11
|
|
Latin America & Caribbean
|
94.6
|
13
|
|
East & Southeast Asia
|
85.3
|
14
|
|
Sub-Saharan Africa
|
84.5
|
4
|
|
Eastern Europe & Post-Soviet
|
77.7
|
11
|
|
India
|
65.5
|
1
|
|
Middle East & North Africa (MENA)
|
55.8
|
9
|
|
South Asia
|
53.0
|
3
|
Undesirable neighbors: Different language
|
Macro Region
|
% Rejecting (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
83.8
|
11
|
|
Latin America & Caribbean
|
79.7
|
13
|
|
Sub-Saharan Africa
|
69.1
|
4
|
|
East & Southeast Asia
|
64.9
|
14
|
|
Eastern Europe & Post-Soviet
|
57.1
|
11
|
|
India
|
47.7
|
1
|
|
Middle East & North Africa (MENA)
|
36.0
|
9
|
|
South Asia
|
35.4
|
3
|
Undesirable neighbors: Other countries
|
Macro Region
|
% Rejecting (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
97.4
|
11
|
|
East & Southeast Asia
|
93.4
|
14
|
|
Latin America & Caribbean
|
90.7
|
13
|
|
Eastern Europe & Post-Soviet
|
85.7
|
11
|
|
Sub-Saharan Africa
|
81.7
|
4
|
|
Middle East & North Africa (MENA)
|
79.2
|
9
|
|
India
|
71.5
|
1
|
|
South Asia
|
70.9
|
3
|
Moral permisiveness
1: Never justifiable and 10: Always justifiable
# Full moral permissiveness (Q177–Q195)
moral_vars <- paste0("Q", 177:195)
wvs7 <- wvs7 %>%
mutate(across(all_of(moral_vars), ~ifelse(.x < 0, NA, .x)))
wvs_clean_moral <- wvs7 %>%
filter(!is.na(macro_region))
svy_design_moral <- svydesign(
ids = ~1,
weights = ~W_WEIGHT,
data = wvs_clean_moral
)
country_moral <- lapply(moral_vars, function(v){
svyby(
as.formula(paste0("~", v)),
~B_COUNTRY + macro_region,
svy_design_moral,
svymean,
na.rm = TRUE
) %>%
rename(country_mean = 3) %>%
mutate(variable = v)
}) %>% bind_rows()
region_moral <- country_moral %>%
group_by(macro_region, variable) %>%
summarise(
mean_score = mean(country_mean, na.rm = TRUE),
n_countries = n(),
.groups = "drop"
)
# Labels (update Q185-Q195)
moral_labels <- c(
Q177 = "Claiming govt benefits not entitled to",
Q178 = "Avoiding fare on public transport",
Q179 = "Stealing property",
Q180 = "Cheating on taxes if you have a chance",
Q181 = "Accepting bribe in course of duties",
Q182 = "Homosexuality",
Q183 = "Prostitution",
Q184 = "Abortion",
Q185 = "Divorce",
Q186 = "Sex before marriage",
Q187 = "Suicide",
Q188 = "Euthanasia (ending life of incurably sick)",
Q189 = "Man beating wife",
Q190 = "Parents beating children",
Q191 = "Violence against other people",
Q192 = "Terrorism (political/ideological/religious)",
Q193 = "Having casual sex",
Q194 = "Political violence",
Q195 = "Death penalty"
)
for(v in moral_vars){
temp_table <- region_moral %>%
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[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}
Moral permissiveness: Claiming govt benefits not entitled to
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Latin America & Caribbean
|
3.9
|
13
|
|
East & Southeast Asia
|
3.3
|
14
|
|
Eastern Europe & Post-Soviet
|
3.2
|
11
|
|
Middle East & North Africa (MENA)
|
2.9
|
9
|
|
India
|
2.7
|
1
|
|
Sub-Saharan Africa
|
2.3
|
4
|
|
South Asia
|
2.2
|
3
|
|
Western Europe & Offshoots
|
2.0
|
11
|
Moral permissiveness: Avoiding fare on public transport
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Eastern Europe & Post-Soviet
|
3.6
|
11
|
|
Latin America & Caribbean
|
3.4
|
13
|
|
East & Southeast Asia
|
2.8
|
14
|
|
Sub-Saharan Africa
|
2.4
|
4
|
|
Western Europe & Offshoots
|
2.4
|
11
|
|
India
|
2.3
|
1
|
|
South Asia
|
2.1
|
3
|
|
Middle East & North Africa (MENA)
|
1.8
|
9
|
Moral permissiveness: Stealing property
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
East & Southeast Asia
|
2.1
|
14
|
|
Eastern Europe & Post-Soviet
|
2.1
|
11
|
|
India
|
1.9
|
1
|
|
Sub-Saharan Africa
|
1.9
|
4
|
|
Latin America & Caribbean
|
1.8
|
13
|
|
Middle East & North Africa (MENA)
|
1.7
|
9
|
|
South Asia
|
1.5
|
3
|
|
Western Europe & Offshoots
|
1.5
|
11
|
Moral permissiveness: Cheating on taxes if you have a chance
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Eastern Europe & Post-Soviet
|
2.8
|
11
|
|
East & Southeast Asia
|
2.3
|
14
|
|
Latin America & Caribbean
|
2.3
|
13
|
|
Sub-Saharan Africa
|
2.2
|
4
|
|
Middle East & North Africa (MENA)
|
2.1
|
9
|
|
India
|
2.0
|
1
|
|
Western Europe & Offshoots
|
1.9
|
11
|
|
South Asia
|
1.7
|
3
|
Moral permissiveness: Accepting bribe in course of duties
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Eastern Europe & Post-Soviet
|
2.3
|
11
|
|
East & Southeast Asia
|
2.2
|
14
|
|
Sub-Saharan Africa
|
2.1
|
4
|
|
Latin America & Caribbean
|
2.0
|
13
|
|
India
|
1.9
|
1
|
|
Middle East & North Africa (MENA)
|
1.9
|
9
|
|
South Asia
|
1.5
|
3
|
|
Western Europe & Offshoots
|
1.5
|
11
|
Moral permissiveness: Homosexuality
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Western Europe & Offshoots
|
7.0
|
11
|
|
Latin America & Caribbean
|
4.4
|
13
|
|
East & Southeast Asia
|
4.0
|
14
|
|
India
|
2.7
|
1
|
|
Eastern Europe & Post-Soviet
|
2.6
|
11
|
|
Sub-Saharan Africa
|
2.0
|
4
|
|
Middle East & North Africa (MENA)
|
1.7
|
9
|
|
South Asia
|
1.5
|
3
|
Moral permissiveness: Prostitution
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Western Europe & Offshoots
|
4.6
|
11
|
|
Latin America & Caribbean
|
3.3
|
13
|
|
East & Southeast Asia
|
2.8
|
14
|
|
Eastern Europe & Post-Soviet
|
2.7
|
11
|
|
India
|
2.2
|
1
|
|
Sub-Saharan Africa
|
2.0
|
4
|
|
South Asia
|
1.5
|
3
|
|
Middle East & North Africa (MENA)
|
0.8
|
9
|
Moral permissiveness: Abortion
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Western Europe & Offshoots
|
5.8
|
11
|
|
Eastern Europe & Post-Soviet
|
3.6
|
11
|
|
East & Southeast Asia
|
3.3
|
14
|
|
Latin America & Caribbean
|
2.9
|
13
|
|
India
|
2.8
|
1
|
|
Middle East & North Africa (MENA)
|
2.5
|
9
|
|
Sub-Saharan Africa
|
2.2
|
4
|
|
South Asia
|
2.1
|
3
|
Moral permissiveness: Divorce
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Western Europe & Offshoots
|
7.3
|
11
|
|
Latin America & Caribbean
|
5.3
|
13
|
|
Eastern Europe & Post-Soviet
|
4.7
|
11
|
|
East & Southeast Asia
|
4.6
|
14
|
|
Middle East & North Africa (MENA)
|
4.2
|
9
|
|
South Asia
|
3.4
|
3
|
|
India
|
3.1
|
1
|
|
Sub-Saharan Africa
|
3.1
|
4
|
Moral permissiveness: Sex before marriage
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Western Europe & Offshoots
|
7.6
|
11
|
|
Latin America & Caribbean
|
5.1
|
13
|
|
Eastern Europe & Post-Soviet
|
4.7
|
11
|
|
East & Southeast Asia
|
4.4
|
14
|
|
Sub-Saharan Africa
|
2.8
|
4
|
|
India
|
2.2
|
1
|
|
South Asia
|
1.6
|
3
|
|
Middle East & North Africa (MENA)
|
1.5
|
9
|
Moral permissiveness: Suicide
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Western Europe & Offshoots
|
3.6
|
11
|
|
East & Southeast Asia
|
2.7
|
14
|
|
Eastern Europe & Post-Soviet
|
2.5
|
11
|
|
Latin America & Caribbean
|
2.4
|
13
|
|
India
|
1.9
|
1
|
|
Sub-Saharan Africa
|
1.9
|
4
|
|
Middle East & North Africa (MENA)
|
1.7
|
9
|
|
South Asia
|
1.6
|
3
|
Moral permissiveness: Euthanasia (ending life of incurably sick)
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Western Europe & Offshoots
|
5.8
|
11
|
|
East & Southeast Asia
|
4.2
|
14
|
|
Latin America & Caribbean
|
3.6
|
13
|
|
Eastern Europe & Post-Soviet
|
3.5
|
11
|
|
India
|
2.9
|
1
|
|
Sub-Saharan Africa
|
2.3
|
4
|
|
Middle East & North Africa (MENA)
|
2.2
|
9
|
|
South Asia
|
2.1
|
3
|
Moral permissiveness: Man beating wife
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Sub-Saharan Africa
|
2.2
|
4
|
|
East & Southeast Asia
|
2.1
|
14
|
|
Eastern Europe & Post-Soviet
|
2.1
|
11
|
|
Middle East & North Africa (MENA)
|
2.1
|
9
|
|
India
|
2.0
|
1
|
|
Latin America & Caribbean
|
1.7
|
13
|
|
South Asia
|
1.7
|
3
|
|
Western Europe & Offshoots
|
1.0
|
11
|
Moral permissiveness: Parents beating children
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Sub-Saharan Africa
|
4.5
|
4
|
|
India
|
3.6
|
1
|
|
East & Southeast Asia
|
3.4
|
14
|
|
Latin America & Caribbean
|
3.1
|
13
|
|
Middle East & North Africa (MENA)
|
3.0
|
9
|
|
Eastern Europe & Post-Soviet
|
2.5
|
11
|
|
South Asia
|
2.3
|
3
|
|
Western Europe & Offshoots
|
1.7
|
11
|
Moral permissiveness: Violence against other people
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
East & Southeast Asia
|
2.2
|
14
|
|
Eastern Europe & Post-Soviet
|
2.0
|
11
|
|
Latin America & Caribbean
|
2.0
|
13
|
|
Sub-Saharan Africa
|
2.0
|
4
|
|
India
|
1.9
|
1
|
|
Middle East & North Africa (MENA)
|
1.9
|
9
|
|
Western Europe & Offshoots
|
1.7
|
11
|
|
South Asia
|
1.6
|
3
|
Moral permissiveness: Terrorism (political/ideological/religious)
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
East & Southeast Asia
|
2.2
|
14
|
|
Sub-Saharan Africa
|
2.0
|
4
|
|
Eastern Europe & Post-Soviet
|
1.9
|
11
|
|
India
|
1.9
|
1
|
|
Latin America & Caribbean
|
1.9
|
13
|
|
South Asia
|
1.6
|
3
|
|
Middle East & North Africa (MENA)
|
1.5
|
9
|
|
Western Europe & Offshoots
|
1.3
|
11
|
Moral permissiveness: Having casual sex
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Western Europe & Offshoots
|
5.7
|
11
|
|
Latin America & Caribbean
|
3.9
|
13
|
|
Eastern Europe & Post-Soviet
|
3.5
|
11
|
|
East & Southeast Asia
|
2.9
|
14
|
|
Sub-Saharan Africa
|
2.4
|
4
|
|
India
|
1.9
|
1
|
|
South Asia
|
1.5
|
3
|
|
Middle East & North Africa (MENA)
|
1.1
|
9
|
Moral permissiveness: Political violence
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
East & Southeast Asia
|
2.3
|
14
|
|
Eastern Europe & Post-Soviet
|
2.1
|
11
|
|
Latin America & Caribbean
|
2.0
|
13
|
|
Sub-Saharan Africa
|
2.0
|
4
|
|
India
|
1.8
|
1
|
|
Western Europe & Offshoots
|
1.7
|
11
|
|
Middle East & North Africa (MENA)
|
1.6
|
9
|
|
South Asia
|
1.5
|
3
|
Moral permissiveness: Death penalty
|
Macro Region
|
Mean score (1–10)
|
N countries
|
|
Middle East & North Africa (MENA)
|
4.8
|
9
|
|
East & Southeast Asia
|
4.7
|
14
|
|
South Asia
|
4.5
|
3
|
|
Western Europe & Offshoots
|
3.9
|
11
|
|
India
|
3.8
|
1
|
|
Latin America & Caribbean
|
3.7
|
13
|
|
Eastern Europe & Post-Soviet
|
3.4
|
11
|
|
Sub-Saharan Africa
|
2.7
|
4
|
Institutional trust
trust_vars_range <- paste0("Q", 64:81)
trust_labels_range <- c(
Q64 = "Churches/ equivalent",
Q65 = "Armed forces",
Q66 = "Press",
Q67 = "Television",
Q68 = "Labor unions",
Q69 = "Police",
Q70 = "Courts",
Q71 = "Government",
Q72 = "Political parties",
Q73 = "Parliament",
Q74 = "Civil service",
Q75 = "Universities",
Q76 = "Elections",
Q77 = "Major companies",
Q78 = "Banks",
Q79 = "Environmental organizations",
Q80 = "Women's organizations",
Q81 = "Charitable or humanitarian organizations"
)
# Clean negative codes + filter
wvs_clean_trust <- wvs7%>%
mutate(across(all_of(trust_vars_range), ~ifelse(.x < 0, NA, .x))) %>%
filter(!is.na(macro_region), !is.na(W_WEIGHT))
for(v in trust_vars_range) {
temp_table <- wvs_clean_trust %>%
filter(!is.na(.data[[v]])) %>%
group_by(macro_region) %>%
summarise(
prop_high = round(sum(W_WEIGHT * (.data[[v]] <= 2)) / sum(W_WEIGHT) * 100, 1), # ← W_WEIGHT
.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_range[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}
High confidence: Churches/ equivalent
|
Macro Region
|
% High confidence (1-2)
|
|
South Asia
|
92.9
|
|
India
|
89.7
|
|
Sub-Saharan Africa
|
82.2
|
|
Eastern Europe & Post-Soviet
|
68.1
|
|
Middle East & North Africa (MENA)
|
66.6
|
|
East & Southeast Asia
|
60.6
|
|
Latin America & Caribbean
|
60.5
|
|
Western Europe & Offshoots
|
41.7
|
High confidence: Armed forces
|
Macro Region
|
% High confidence (1-2)
|
|
South Asia
|
87.8
|
|
India
|
86.9
|
|
Middle East & North Africa (MENA)
|
78.2
|
|
Western Europe & Offshoots
|
75.1
|
|
Eastern Europe & Post-Soviet
|
73.7
|
|
East & Southeast Asia
|
73.5
|
|
Sub-Saharan Africa
|
60.6
|
|
Latin America & Caribbean
|
41.1
|
High confidence: Press
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
65.8
|
|
East & Southeast Asia
|
55.3
|
|
South Asia
|
53.0
|
|
Sub-Saharan Africa
|
47.9
|
|
Eastern Europe & Post-Soviet
|
39.7
|
|
Middle East & North Africa (MENA)
|
31.9
|
|
Western Europe & Offshoots
|
29.4
|
|
Latin America & Caribbean
|
27.8
|
High confidence: Television
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
63.4
|
|
East & Southeast Asia
|
59.3
|
|
South Asia
|
57.6
|
|
Sub-Saharan Africa
|
55.8
|
|
Eastern Europe & Post-Soviet
|
46.4
|
|
Middle East & North Africa (MENA)
|
39.1
|
|
Latin America & Caribbean
|
29.4
|
|
Western Europe & Offshoots
|
28.7
|
High confidence: Labor unions
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
66.2
|
|
South Asia
|
61.9
|
|
East & Southeast Asia
|
56.7
|
|
Sub-Saharan Africa
|
48.3
|
|
Eastern Europe & Post-Soviet
|
41.5
|
|
Western Europe & Offshoots
|
37.2
|
|
Middle East & North Africa (MENA)
|
33.4
|
|
Latin America & Caribbean
|
21.1
|
High confidence: Police
|
Macro Region
|
% High confidence (1-2)
|
|
Western Europe & Offshoots
|
72.1
|
|
East & Southeast Asia
|
71.7
|
|
Middle East & North Africa (MENA)
|
67.8
|
|
India
|
66.8
|
|
Eastern Europe & Post-Soviet
|
57.3
|
|
South Asia
|
48.6
|
|
Sub-Saharan Africa
|
43.0
|
|
Latin America & Caribbean
|
32.5
|
High confidence: Courts
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
74.6
|
|
East & Southeast Asia
|
73.0
|
|
South Asia
|
62.6
|
|
Western Europe & Offshoots
|
62.4
|
|
Middle East & North Africa (MENA)
|
58.5
|
|
Sub-Saharan Africa
|
52.8
|
|
Eastern Europe & Post-Soviet
|
48.8
|
|
Latin America & Caribbean
|
26.2
|
High confidence: Government
|
Macro Region
|
% High confidence (1-2)
|
|
East & Southeast Asia
|
69.2
|
|
India
|
65.1
|
|
South Asia
|
60.5
|
|
Sub-Saharan Africa
|
50.9
|
|
Eastern Europe & Post-Soviet
|
47.2
|
|
Middle East & North Africa (MENA)
|
37.8
|
|
Western Europe & Offshoots
|
35.7
|
|
Latin America & Caribbean
|
22.6
|
High confidence: Political parties
|
Macro Region
|
% High confidence (1-2)
|
|
East & Southeast Asia
|
48.6
|
|
India
|
42.3
|
|
South Asia
|
36.8
|
|
Eastern Europe & Post-Soviet
|
31.5
|
|
Sub-Saharan Africa
|
29.1
|
|
Middle East & North Africa (MENA)
|
23.6
|
|
Western Europe & Offshoots
|
16.6
|
|
Latin America & Caribbean
|
11.5
|
High confidence: Parliament
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
73.8
|
|
East & Southeast Asia
|
56.6
|
|
South Asia
|
51.0
|
|
Sub-Saharan Africa
|
41.8
|
|
Eastern Europe & Post-Soviet
|
39.4
|
|
Western Europe & Offshoots
|
30.5
|
|
Middle East & North Africa (MENA)
|
28.5
|
|
Latin America & Caribbean
|
15.4
|
High confidence: Civil service
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
80.6
|
|
East & Southeast Asia
|
68.7
|
|
South Asia
|
60.0
|
|
Eastern Europe & Post-Soviet
|
54.0
|
|
Sub-Saharan Africa
|
50.8
|
|
Western Europe & Offshoots
|
49.5
|
|
Middle East & North Africa (MENA)
|
40.2
|
|
Latin America & Caribbean
|
20.5
|
High confidence: Universities
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
86.3
|
|
South Asia
|
83.5
|
|
East & Southeast Asia
|
79.6
|
|
Western Europe & Offshoots
|
74.3
|
|
Sub-Saharan Africa
|
73.6
|
|
Eastern Europe & Post-Soviet
|
68.3
|
|
Latin America & Caribbean
|
63.9
|
|
Middle East & North Africa (MENA)
|
51.1
|
High confidence: Elections
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
69.3
|
|
East & Southeast Asia
|
59.1
|
|
Western Europe & Offshoots
|
54.0
|
|
South Asia
|
53.2
|
|
Eastern Europe & Post-Soviet
|
49.4
|
|
Sub-Saharan Africa
|
41.8
|
|
Middle East & North Africa (MENA)
|
35.2
|
|
Latin America & Caribbean
|
27.1
|
High confidence: Major companies
|
Macro Region
|
% High confidence (1-2)
|
|
East & Southeast Asia
|
60.9
|
|
Sub-Saharan Africa
|
57.6
|
|
South Asia
|
56.0
|
|
India
|
55.0
|
|
Eastern Europe & Post-Soviet
|
48.3
|
|
Latin America & Caribbean
|
35.6
|
|
Western Europe & Offshoots
|
35.4
|
|
Middle East & North Africa (MENA)
|
34.3
|
High confidence: Banks
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
88.9
|
|
East & Southeast Asia
|
79.9
|
|
South Asia
|
74.4
|
|
Sub-Saharan Africa
|
73.6
|
|
Eastern Europe & Post-Soviet
|
53.1
|
|
Latin America & Caribbean
|
42.0
|
|
Western Europe & Offshoots
|
41.5
|
|
Middle East & North Africa (MENA)
|
39.7
|
High confidence: Environmental organizations
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
77.0
|
|
East & Southeast Asia
|
72.2
|
|
Sub-Saharan Africa
|
63.4
|
|
South Asia
|
61.6
|
|
Western Europe & Offshoots
|
60.3
|
|
Eastern Europe & Post-Soviet
|
52.1
|
|
Latin America & Caribbean
|
51.8
|
|
Middle East & North Africa (MENA)
|
43.7
|
High confidence: Women’s organizations
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
76.1
|
|
East & Southeast Asia
|
72.8
|
|
Western Europe & Offshoots
|
65.4
|
|
Sub-Saharan Africa
|
64.8
|
|
South Asia
|
62.9
|
|
Eastern Europe & Post-Soviet
|
56.9
|
|
Latin America & Caribbean
|
54.5
|
|
Middle East & North Africa (MENA)
|
45.9
|
High confidence: Charitable or humanitarian organizations
|
Macro Region
|
% High confidence (1-2)
|
|
India
|
74.2
|
|
Sub-Saharan Africa
|
72.0
|
|
East & Southeast Asia
|
70.8
|
|
South Asia
|
66.3
|
|
Western Europe & Offshoots
|
62.1
|
|
Eastern Europe & Post-Soviet
|
58.4
|
|
Latin America & Caribbean
|
54.3
|
|
Middle East & North Africa (MENA)
|
53.0
|
I think we need to first dissect what we mean by values and what is
preferences in our analysis.What I have gathered from the results, and
if we see this in light of our discussion that values are static and
preferences changes over time, then I think for India we do have a
static democratic value system and religious value system, but not a
economic value system - market choices, inequality and other things are
preferred as and when it suits or work well (if not we change our
preference to alternative). Also it aligns with our PALS analysis that
we tend to be more ethical and moral (liberal) until the practical
manifestation of our ethical and moral choices are not presented before
us (for example choice of neighbour).
Child Qualities
# Child quality variables (Wave 7: Q7-Q17)
child_vars_w7 <- paste0("Q", 7:17)
# Wave 7 labels (Q7-Q17, confirmed from codebook)
child_labels_w7 <- c(
Q7 = "Good manners",
Q8 = "Independence",
Q9 = "Hard work",
Q10 = "Feeling of responsibility",
Q11 = "Imagination",
Q12 = "Tolerance & respect",
Q13 = "Thrift",
Q14 = "Determination & perseverance",
Q15 = "Religious faith",
Q16 = "Unselfishness",
Q17 = "Obedience"
)
# Clean: recode negative values as NA
wvs_clean_child_w7 <- wvs7%>%
mutate(across(all_of(child_vars_w7), ~ifelse(.x < 0, NA, .x))) %>%
filter(!is.na(macro_region), !is.na(W_WEIGHT))
# Country-level weighted proportions (1 = Important)
country_prop_child <- lapply(child_vars_w7, function(v){
wvs_clean_child_w7%>%
filter(!is.na(.data[[v]])) %>%
group_by(B_COUNTRY, macro_region) %>%
summarise(
country_prop = sum(W_WEIGHT * (.data[[v]] == 1)) / sum(W_WEIGHT),
variable = v,
.groups = "drop"
)
}) %>% bind_rows()
# Macro-region average of country proportions (country-weighted)
region_child_w7 <- country_prop_child %>%
group_by(macro_region, variable) %>%
summarise(
mean_prop = mean(country_prop, na.rm = TRUE),
n_countries = n(),
.groups = "drop"
)
for(v in child_vars_w7) {
temp_table <- region_child_w7%>%
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_w7[v]),
format = "html"
) %>%
kable_styling(full_width = FALSE)
)
cat("<br><br>")
}
Child quality: Good manners
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
Sub-Saharan Africa
|
88.6
|
4
|
|
Middle East & North Africa (MENA)
|
85.3
|
9
|
|
Latin America & Caribbean
|
84.9
|
13
|
|
South Asia
|
84.6
|
3
|
|
India
|
80.7
|
1
|
|
Western Europe & Offshoots
|
78.2
|
11
|
|
East & Southeast Asia
|
78.1
|
14
|
|
Eastern Europe & Post-Soviet
|
63.0
|
11
|
Child quality: Independence
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
India
|
57.6
|
1
|
|
East & Southeast Asia
|
56.3
|
14
|
|
Western Europe & Offshoots
|
50.9
|
11
|
|
Eastern Europe & Post-Soviet
|
36.2
|
11
|
|
Sub-Saharan Africa
|
34.2
|
4
|
|
Latin America & Caribbean
|
33.4
|
13
|
|
Middle East & North Africa (MENA)
|
32.7
|
9
|
|
South Asia
|
28.4
|
3
|
Child quality: Hard work
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
India
|
74.2
|
1
|
|
Sub-Saharan Africa
|
73.3
|
4
|
|
Eastern Europe & Post-Soviet
|
66.4
|
11
|
|
Middle East & North Africa (MENA)
|
54.3
|
9
|
|
East & Southeast Asia
|
53.0
|
14
|
|
Western Europe & Offshoots
|
51.5
|
11
|
|
South Asia
|
43.9
|
3
|
|
Latin America & Caribbean
|
39.9
|
13
|
Child quality: Feeling of responsibility
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
East & Southeast Asia
|
72.2
|
14
|
|
Latin America & Caribbean
|
70.0
|
13
|
|
Eastern Europe & Post-Soviet
|
66.3
|
11
|
|
India
|
65.3
|
1
|
|
Middle East & North Africa (MENA)
|
63.4
|
9
|
|
Western Europe & Offshoots
|
60.6
|
11
|
|
South Asia
|
50.6
|
3
|
|
Sub-Saharan Africa
|
38.9
|
4
|
Child quality: Imagination
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
28.1
|
11
|
|
East & Southeast Asia
|
24.6
|
14
|
|
India
|
22.3
|
1
|
|
Latin America & Caribbean
|
20.5
|
13
|
|
Middle East & North Africa (MENA)
|
19.6
|
9
|
|
Eastern Europe & Post-Soviet
|
18.6
|
11
|
|
Sub-Saharan Africa
|
12.4
|
4
|
|
South Asia
|
11.1
|
3
|
Child quality: Tolerance & respect
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
74.5
|
11
|
|
Latin America & Caribbean
|
70.3
|
13
|
|
Middle East & North Africa (MENA)
|
65.7
|
9
|
|
Sub-Saharan Africa
|
58.7
|
4
|
|
East & Southeast Asia
|
57.1
|
14
|
|
Eastern Europe & Post-Soviet
|
53.4
|
11
|
|
South Asia
|
49.2
|
3
|
|
India
|
44.1
|
1
|
Child quality: Thrift
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
East & Southeast Asia
|
36.7
|
14
|
|
Eastern Europe & Post-Soviet
|
35.7
|
11
|
|
India
|
32.0
|
1
|
|
South Asia
|
30.4
|
3
|
|
Middle East & North Africa (MENA)
|
28.7
|
9
|
|
Latin America & Caribbean
|
26.6
|
13
|
|
Western Europe & Offshoots
|
26.6
|
11
|
|
Sub-Saharan Africa
|
15.7
|
4
|
Child quality: Determination & perseverance
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
Western Europe & Offshoots
|
39.9
|
11
|
|
East & Southeast Asia
|
37.5
|
14
|
|
Eastern Europe & Post-Soviet
|
37.4
|
11
|
|
Middle East & North Africa (MENA)
|
31.5
|
9
|
|
India
|
30.4
|
1
|
|
Latin America & Caribbean
|
26.3
|
13
|
|
Sub-Saharan Africa
|
24.3
|
4
|
|
South Asia
|
21.0
|
3
|
Child quality: Religious faith
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
South Asia
|
77.8
|
3
|
|
Middle East & North Africa (MENA)
|
63.8
|
9
|
|
Sub-Saharan Africa
|
61.8
|
4
|
|
Latin America & Caribbean
|
37.1
|
13
|
|
India
|
26.8
|
1
|
|
East & Southeast Asia
|
25.1
|
14
|
|
Eastern Europe & Post-Soviet
|
23.8
|
11
|
|
Western Europe & Offshoots
|
18.8
|
11
|
Child quality: Unselfishness
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
Middle East & North Africa (MENA)
|
34.2
|
9
|
|
Western Europe & Offshoots
|
33.9
|
11
|
|
Latin America & Caribbean
|
32.9
|
13
|
|
South Asia
|
27.9
|
3
|
|
East & Southeast Asia
|
23.5
|
14
|
|
Eastern Europe & Post-Soviet
|
23.1
|
11
|
|
India
|
21.3
|
1
|
|
Sub-Saharan Africa
|
16.7
|
4
|
Child quality: Obedience
|
Macro Region
|
% Mentioning (country-weighted)
|
N countries
|
|
Sub-Saharan Africa
|
52.0
|
4
|
|
Latin America & Caribbean
|
50.6
|
13
|
|
Middle East & North Africa (MENA)
|
44.1
|
9
|
|
South Asia
|
39.6
|
3
|
|
Eastern Europe & Post-Soviet
|
23.6
|
11
|
|
India
|
22.1
|
1
|
|
Western Europe & Offshoots
|
21.8
|
11
|
|
East & Southeast Asia
|
21.0
|
14
|