knitr::opts_chunk$set(message = FALSE, warning = FALSE)
options(knitr.table.format = "html") # needed for the <br>/<small> cell markup
library(dplyr)
library(tidyr)
library(purrr)
library(tibble)
library(ggplot2)
library(ggrepel)
library(knitr)
library(kableExtra)
library(survey)
library(forcats)
library(ggiraph)
# All cleaning and categorisation happens in R/00_wvs_clean_harmonise.R
wvs_all <- readRDS("D:/Populism and Democrary/World value survey/data/clean/wvs_harmonised.rds")
# Comparison set: India vs Western Europe & Offshoots and Middle East & North Africa.
# Adults only: W7 India deliberately interviewed 16-17 year olds (321 of 1,692).
focus_regions <- c("India", "Western Europe & Offshoots", "Middle East & North Africa")
# W3 (1994-99, India 1995) is dropped from this report
wvs <- wvs_all |>
filter(is.na(age) | age >= 18, macro_region %in% focus_regions, wave != "W3") |>
mutate(macro_region = droplevels(macro_region))
india_years <- c(W4 = "2001", W5 = "2006", W6 = "2012", W7 = "2023")
wave_labels <- c(W4 = "W4 (1999–2004)", W5 = "W5 (2004–09)", W6 = "W6 (2010–16)", W7 = "W7 (2017–23)")
common_states <- c("Bihar", "Delhi", "Haryana", "Maharashtra", "Punjab", "Uttar Pradesh", "West Bengal")
styled <- function(k) kable_styling(k, bootstrap_options = c("striped", "condensed"), full_width = FALSE)
# ---- Chart style ------------------------------------------------------------------
# Colour follows the group, in a fixed order (validated for colour-blind separation):
# Western Europe = blue, India = orange, MENA = green.
region_cols <- c(`Western Europe & Offshoots` = "#2a78d6", India = "#eb6834",
`Middle East & North Africa` = "#1baf7a")
year_cols <- c(`2001` = "#86b6ef", `2006` = "#5598e7",
`2012` = "#256abf", `2023` = "#0d366b") # one-hue ramp: later = darker
india_year_num <- c(W4 = 2001, W5 = 2006, W6 = 2012, W7 = 2023)
ink <- "#0b0b0b"; ink2 <- "#52514e"; grid_col <- "#e6e5e1"
theme_wvs <- function(base = 11) {
theme_minimal(base_size = base) +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_line(colour = grid_col, linewidth = 0.3),
axis.text = element_text(colour = ink2), axis.title = element_text(colour = ink2),
plot.title = element_text(face = "bold", size = base + 1, colour = ink),
plot.subtitle = element_text(colour = ink2),
plot.caption = element_text(colour = ink2, hjust = 0, size = base - 2),
legend.position = "top", legend.title = element_blank(), legend.justification = "left",
strip.text = element_text(face = "bold", hjust = 0, colour = ink))
}
knitr::opts_chunk$set(dpi = 110)
# Every chart is rendered with ggiraph: hover a dot, bar or point to see its value
# ggiraph tooltips / ids cannot contain a straight apostrophe
safe_txt <- function(x) gsub("'", "’", x, fixed = TRUE)
show_plot <- function(p, w = 9, h = 5) {
if (isTRUE(getOption("wvs.static"))) return(p) # for checking layout as images
girafe(ggobj = p, width_svg = w, height_svg = h,
options = list(
opts_tooltip(css = paste0("background:#ffffff;color:#0b0b0b;border:1px solid #d9d8d4;",
"border-radius:4px;padding:6px 8px;font-size:12px;")),
opts_hover(css = "stroke:#0b0b0b;stroke-width:1.5px;"),
opts_hover_inv(css = "opacity:0.4;"),
opts_sizing(rescale = TRUE),
opts_toolbar(saveaspng = TRUE)))
}
agree4 <- function(x) case_when(x %in% 1:2 ~ 1, x %in% 3:4 ~ 0)
agree5 <- function(x) case_when(x %in% 1:2 ~ 1, x %in% 3:5 ~ 0)
high10 <- function(x) case_when(x %in% 7:10 ~ 1, x %in% 1:6 ~ 0) # top 4 points of a 1-10 scale
# mean of 0/1 items when at least `k` are answered
row_share <- function(d, vars, k) {
n_ok <- rowSums(!is.na(d[vars]))
ifelse(n_ok >= k, rowMeans(d[vars], na.rm = TRUE), NA_real_)
}
# Institutions that USE power (the state) vs institutions that CHECK power
state_inst <- c("conf_civilservice_hi", "conf_police_hi", "conf_army_hi", "conf_govt_hi")
check_inst <- c("conf_parliament_hi", "conf_parties_hi", "conf_press_hi", "conf_courts_hi")
conc_levels <- c("Liberal (maximalist)", "Minimalist (electoral)", "Illiberal")
add_recodes <- function(d) {
d |>
mutate(
# 1. trust in the two sides of the state (share of 4 institutions trusted, 0-1)
state_trust = row_share(pick(everything()), state_inst, 3),
check_trust = row_share(pick(everything()), check_inst, 3),
trust_gap = state_trust - check_trust,
# 2. demand for governance (delivery) and for accountability: yes/no on each question (combined below)
# governance = what people want government to deliver (ranked aims: picked 1st or 2nd)
gov_growth = case_when(pm_aims1 == 1 | pm_aims2 == 1 ~ 1, pm_aims1 %in% 1:4 ~ 0),
gov_prices = case_when(pm_goals1 == 3 | pm_goals2 == 3 ~ 1, pm_goals1 %in% 1:4 ~ 0),
gov_economy = case_when(pm_society1 == 1 | pm_society2 == 1 ~ 1, pm_society1 %in% 1:4 ~ 0),
gov_crime = case_when(pm_society1 == 4 | pm_society2 == 4 ~ 1, pm_society1 %in% 1:4 ~ 0),
acc_say_govt = case_when(pm_goals1 == 2 | pm_goals2 == 2 ~ 1, pm_goals1 %in% 1:4 ~ 0),
acc_say_work = case_when(pm_aims1 == 3 | pm_aims2 == 3 ~ 1, pm_aims1 %in% 1:4 ~ 0),
acc_petition = case_when(act_petition %in% 1:2 ~ 1, act_petition == 3 ~ 0),
acc_demo = case_when(act_demonstration %in% 1:2 ~ 1, act_demonstration == 3 ~ 0),
acc_boycott = case_when(act_boycott %in% 1:2 ~ 1, act_boycott == 3 ~ 0),
# 3. conception of democracy: yes/no on each liberal and each authoritarian question
lib_elections = case_when(dess_elections %in% 7:10 ~ 1, dess_elections %in% 1:6 ~ 0),
lib_civil = case_when(dess_civilrights %in% 7:10 ~ 1, dess_civilrights %in% 1:6 ~ 0),
lib_women = case_when(dess_women %in% 7:10 ~ 1, dess_women %in% 1:6 ~ 0),
lib_demgood = case_when(reg_democracy %in% 1:2 ~ 1, reg_democracy %in% 3:4 ~ 0),
lib_demimp = case_when(dem_importance %in% 7:10 ~ 1, dem_importance %in% 1:6 ~ 0),
aut_army_ess = case_when(dess_army %in% 7:10 ~ 1, dess_army %in% 1:6 ~ 0),
aut_relig_ess = case_when(dess_religious %in% 7:10 ~ 1, dess_religious %in% 1:6 ~ 0),
aut_army_rule = case_when(reg_army %in% 1:2 ~ 1, reg_army %in% 3:4 ~ 0),
aut_strong = case_when(reg_strongleader %in% 1:2 ~ 1, reg_strongleader %in% 3:4 ~ 0),
aut_experts = case_when(reg_experts %in% 1:2 ~ 1, reg_experts %in% 3:4 ~ 0),
aut_obey = case_when(dess_obey %in% 7:10 ~ 1, dess_obey %in% 1:6 ~ 0),
aut_authority = case_when(fut_authority == 1 ~ 1, fut_authority %in% 2:3 ~ 0),
# liberal score and authoritarian score = share of answered questions with a "yes" (0-1);
# democracy score = liberal score minus authoritarian score (-1 to +1)
aut_share = rowMeans(pick(aut_army_ess, aut_relig_ess, aut_army_rule, aut_strong,
aut_experts, aut_obey, aut_authority), na.rm = TRUE),
lib_share = rowMeans(pick(lib_elections, lib_civil, lib_women, lib_demgood, lib_demimp), na.rm = TRUE),
dem_score = lib_share - aut_share,
# Classified if free elections was answered (asked from 2006) and at least 3 questions in
# each set were answered.
aut_n = rowSums(!is.na(pick(aut_army_ess, aut_relig_ess, aut_army_rule, aut_strong,
aut_experts, aut_obey, aut_authority))),
lib_n = rowSums(!is.na(pick(lib_elections, lib_civil, lib_women, lib_demgood, lib_demimp))),
ess_ok = !is.na(dess_elections) & aut_n >= 3 & lib_n >= 3,
# Types are bands of the democracy score:
# Illiberal: score <= 0, or free elections rated 1-6
# Minimalist: free elections 7-10 and score above 0 but below 0.5
# Liberal: free elections 7-10 and score 0.5 or more
conception = case_when(
!ess_ok ~ NA_character_,
lib_elections == 0 | dem_score <= 0 ~ "Illiberal",
lib_elections == 1 & dem_score >= 0.5 ~ "Liberal (maximalist)",
lib_elections == 1 & dem_score > 0 & dem_score < 0.5 ~ "Minimalist (electoral)"),
conception = factor(conception, conc_levels),
con_liberal = as.numeric(conception == "Liberal (maximalist)"),
con_minimal = as.numeric(conception == "Minimalist (electoral)"),
con_illiberal = as.numeric(conception == "Illiberal"),
# state support (standalone dimension, II.3): yes on each item
ss_govresp_yes = case_when(econ_govresp %in% 1:4 ~ 1, econ_govresp %in% 5:10 ~ 0),
ss_income_yes = case_when(econ_income %in% 1:4 ~ 1, econ_income %in% 5:10 ~ 0),
ss_taxrich_yes = high10(dess_taxrich),
ss_unemp_yes = high10(dess_unemp),
ss_incomeeq_yes = high10(dess_incomeequal),
ss_policy_n = ss_govresp_yes + ss_income_yes,
ss_essential_n = ss_taxrich_yes + ss_unemp_yes + if_else(wave == "W5", 0, ss_incomeeq_yes)
)
}
# State supporter = yes on at least 3 of the state-support items. Classified only where at least
# 4 of the 5 were asked in that country-wave (so from 2006) and the respondent answered all asked.
ss_items <- c("ss_govresp_yes", "ss_income_yes", "ss_taxrich_yes", "ss_unemp_yes", "ss_incomeeq_yes")
# Demand for governance / accountability = yes on at least half of the questions asked in that
# country-wave. Classified if the respondent answered all but at most one of those asked.
gov_items <- c("gov_growth", "gov_prices", "gov_economy", "gov_crime")
acc_items <- c("acc_say_govt", "acc_say_work", "acc_petition", "acc_demo", "acc_boycott")
demand <- function(d, items) {
asked <- d |>
group_by(wave, country_code) |>
summarise(k = sum(map_lgl(pick(all_of(items)), ~ any(!is.na(.x)))), .groups = "drop")
k <- left_join(select(d, wave, country_code), asked, by = c("wave", "country_code"))$k
ans <- rowSums(!is.na(d[items]))
yes <- rowSums(d[items], na.rm = TRUE)
ifelse(k >= 2 & ans >= k - 1, as.numeric(yes / ans >= 0.5), NA_real_)
}
gtype_of <- function(acc, gov) {
factor(case_when(acc == 1 & gov == 0 ~ "Accountability-first",
acc == 1 & gov == 1 ~ "Want both",
acc == 0 & gov == 1 ~ "Governance-first",
acc == 0 & gov == 0 ~ "Neither"),
c("Accountability-first", "Want both", "Governance-first", "Neither"))
}
# All measures of Part II, built the same way for any set of countries
build_measures <- function(d) {
d <- add_recodes(d) |>
group_by(wave, country_code) |>
mutate(ss_asked = sum(map_lgl(pick(all_of(ss_items)), ~ any(!is.na(.x))))) |>
ungroup() |>
mutate(ss_answered = rowSums(!is.na(pick(all_of(ss_items)))),
ss_yes = rowSums(pick(all_of(ss_items)), na.rm = TRUE),
state_support = case_when(ss_asked < 4 | ss_answered < ss_asked ~ NA_real_,
ss_yes >= 3 ~ 1, TRUE ~ 0))
d |>
mutate(gov_demand = demand(d, gov_items),
acc_demand = demand(d, acc_items),
gtype = gtype_of(acc_demand, gov_demand),
g_accfirst = as.numeric(gtype == "Accountability-first"),
g_both = as.numeric(gtype == "Want both"),
g_govfirst = as.numeric(gtype == "Governance-first"),
g_neither = as.numeric(gtype == "Neither"),
# robustness: accountability from stated preferences only (no demonstrations / boycotts)
acc_demand_pref = demand(d, setdiff(acc_items, c("acc_demo", "acc_boycott"))),
gtype_pref = as.character(gtype_of(acc_demand_pref, gov_demand)))
}
wvs <- build_measures(wvs)
# Part II: India vs the UN geoscheme regions. Countries surveyed in at least 3 of W4-W7;
# India on its own. (Internal column name wb_region is kept for the Part III helpers.)
wb_regions <- c("India", "South-eastern Asia", "Western Asia & Northern Africa", "Sub-Saharan Africa",
"Latin America & Caribbean", "Eastern Europe", "Western Europe & Offshoots")
wvs_wb <- wvs_all |>
filter(is.na(age) | age >= 18, wave != "W3", wb_3waves, un_region %in% wb_regions) |>
mutate(wb_region = factor(as.character(un_region), wb_regions)) |>
build_measures()
india <- wvs |> filter(is_india)
# ---- Weighted estimate with 95% CI ------------------------------------------
# SEs use the Kish effective sample size (weights only). The WVS does not release
# sampling clusters, so intervals are too narrow; do not over-read small gaps.
wstat <- function(x, w) {
ok <- !is.na(x) & !is.na(w); x <- x[ok]; w <- w[ok]
if (!length(x)) return(tibble(est = NA_real_, se = NA_real_, n = 0L))
m <- sum(w * x) / sum(w)
neff <- sum(w)^2 / sum(w^2)
tibble(est = m, se = sqrt(sum(w * (x - m)^2) / sum(w) / neff), n = length(x))
}
wdk <- function(x, w) if (all(is.na(x))) NA_real_ else 100 * sum(w[is.na(x)]) / sum(w)
# ---- India over time ------------------------------------------------------------
india_trend <- function(items, mult = 100, data = india) {
imap_dfr(items, function(lab, v) {
data |>
group_by(wave) |>
group_modify(~ bind_cols(wstat(.x[[v]], .x$weight), dk = wdk(.x[[v]], .x$weight))) |>
ungroup() |>
mutate(item = lab, var = v)
}) |>
mutate(lo = est - 1.96 * se, hi = est + 1.96 * se)
}
show_trend <- function(tr, mult = 100, caption = NULL, digits = 1) {
f <- paste0("%.", digits, "f")
tr |>
mutate(cell = ifelse(is.na(est), "—",
sprintf(paste0(f, " [", f, ", ", f, "]<br><small>no answer %.0f%%</small>"),
est * mult, lo * mult, hi * mult, dk)),
year = india_years[wave]) |>
select(Item = item, year, cell) |>
pivot_wider(names_from = year, values_from = cell) |>
(\(x) kable(x, escape = FALSE, caption = caption, align = c("l", rep("c", ncol(x) - 1))))() |>
styled()
}
# ---- Country estimates and India vs regions -------------------------------------
# Region value = unweighted mean of country estimates (each country counts once)
country_est <- function(v, mult = 100, data = wvs) {
data |>
group_by(wave, country_code, country_name, macro_region) |>
filter(any(!is.na(.data[[v]]))) |>
summarise(est = weighted.mean(.data[[v]], weight, na.rm = TRUE) * mult, .groups = "drop")
}
region_compare <- function(v, mult = 100, caption = NULL, digits = 1) {
ce <- country_est(v, mult)
f <- paste0("%.", digits, "f")
reg <- ce |>
group_by(wave, macro_region) |>
summarise(cell = sprintf(paste0(f, " <small>(%d)</small>"), mean(est), n()), .groups = "drop") |>
mutate(macro_region = as.character(macro_region))
rnk <- ce |>
group_by(wave) |>
mutate(r = rank(-est, ties.method = "min"), N = n()) |>
filter(country_code == 356) |>
transmute(wave, macro_region = "India's rank among countries shown (1 = highest)",
cell = sprintf("%d of %d", r, N))
bind_rows(reg, rnk) |>
mutate(wave = wave_labels[wave]) |>
pivot_wider(names_from = wave, values_from = cell, values_fill = "—") |>
arrange(factor(macro_region, levels = c(focus_regions, "India's rank among countries shown (1 = highest)"))) |>
rename(Region = macro_region) |>
(\(x) kable(x, escape = FALSE, caption = caption, align = c("l", rep("c", ncol(x) - 1))))() |>
styled() |>
footnote(general = paste0("Cells: mean of country estimates (number of countries).",
if (grepl("trust|check", v)) " W4 has no courts item, so its checking-trust index uses 3 institutions." else ""),
general_title = "")
}
# ---- Charts used across sections --------------------------------------------------
# India over time: one or more items, India only (orange), items told apart by line type + labels
plot_india_lines <- function(tr, title, ylab = "%", mult = 100, caption = NULL, w = 9, h = 4.6) {
d <- tr |> filter(!is.na(est)) |> mutate(year = india_year_num[wave])
p <- ggplot(d, aes(year, est * mult, group = item)) +
geom_ribbon(aes(ymin = lo * mult, ymax = hi * mult), fill = region_cols["India"], alpha = 0.10) +
geom_line(aes(linetype = item), colour = region_cols["India"], linewidth = 0.8) +
geom_point_interactive(aes(tooltip = sprintf("%s<br>%d: %.1f (95%% CI %.1f to %.1f)", item, year,
est * mult, lo * mult, hi * mult),
data_id = paste(item, year)),
shape = 21, fill = region_cols["India"], colour = "white", size = 3, stroke = 0.8) +
geom_text(data = d |> group_by(item) |> slice_max(year, n = 1),
aes(label = item), hjust = -0.08, size = 3.1, colour = ink) +
scale_x_continuous(breaks = unname(india_year_num), limits = c(2000, 2033)) +
scale_linetype_manual(values = c("solid", "22", "42", "12")) +
labs(title = title, x = NULL, y = ylab, caption = caption) +
theme_wvs() + theme(legend.position = "none")
show_plot(p, w, h)
}
# India vs the two regions over waves: region = mean of countries; India = its own estimate
plot_region_lines <- function(v, title, ylab = "%", mult = 100, caption = NULL, w = 9, h = 4.6) {
d <- country_est(v, mult) |>
group_by(wave, macro_region) |>
summarise(est = mean(est), k = n(), .groups = "drop") |>
mutate(wave_lab = factor(wave_labels[wave], wave_labels), macro_region = factor(macro_region, focus_regions))
p <- ggplot(d, aes(wave_lab, est, colour = macro_region, group = macro_region)) +
geom_line(linewidth = 0.8) +
geom_point_interactive(aes(fill = macro_region, data_id = paste(macro_region, wave),
tooltip = sprintf("%s<br>%s: %.1f<br>%d %s", macro_region, wave_lab, est, k,
ifelse(k == 1, "country", "countries"))),
shape = 21, colour = "white", size = 3.2, stroke = 0.8) +
geom_text(data = d |> group_by(macro_region) |> slice_max(as.integer(wave_lab), n = 1),
aes(label = sprintf("%.0f", est)), hjust = -0.6, size = 3.1, colour = ink, show.legend = FALSE) +
scale_colour_manual(values = region_cols, breaks = focus_regions) +
scale_fill_manual(values = region_cols, guide = "none") +
labs(title = title, x = NULL, y = ylab,
caption = caption %||% "Regions: mean of country estimates. W. Europe in W4 is 4 countries.") +
theme_wvs()
show_plot(p, w, h)
}
# Every country as a dot, ranked within each wave (one panel per wave); India highlighted.
# Dashed lines = region averages (mean of countries).
plot_country_dots <- function(v, title, xlab, mult = 100, waves = NULL, caption = NULL, w = 11, h = NULL) {
ce <- country_est(v, mult)
if (!is.null(waves)) ce <- filter(ce, wave %in% waves)
ce <- ce |>
mutate(wave_lab = factor(wave_labels[wave], wave_labels),
india = country_code == 356,
name = safe_txt(as.character(country_name)),
key = paste(name, wave, sep = "___")) |>
arrange(wave, est) |>
mutate(key = factor(key, unique(key)))
rm <- ce |> filter(!india) |> group_by(wave_lab, macro_region) |> summarise(m = mean(est), .groups = "drop")
n_max <- max(table(ce$wave))
p <- ggplot(ce, aes(est, key)) +
geom_vline(data = rm, aes(xintercept = m, colour = macro_region), linetype = "22", linewidth = 0.5) +
geom_point_interactive(aes(colour = macro_region, size = india, data_id = paste(name, wave),
tooltip = sprintf("%s (%s)<br>%s: %.1f", name, macro_region, wave_lab, est))) +
facet_wrap(~ wave_lab, nrow = 1, scales = "free_y") +
scale_y_discrete(labels = function(x) sub("___.*", "", x)) +
scale_colour_manual(values = region_cols, breaks = focus_regions) +
scale_size_manual(values = c(`FALSE` = 2.2, `TRUE` = 3.8), guide = "none") +
labs(title = title, x = xlab, y = NULL,
caption = caption %||% "Countries ranked within each wave. Dashed lines: region averages (mean of countries).") +
theme_wvs(10) + theme(panel.spacing = unit(1.4, "lines"), panel.grid.major.y = element_blank(),
axis.text.y = element_text(size = 7.5))
show_plot(p, w, h %||% max(4.5, 1.8 + 0.18 * n_max))
}
# ---- Groups within India ------------------------------------------------------------
india_groups <- function(v, mult = 100, min_n = 50, caption = NULL, digits = 1,
groups = c(Sex = "sex_f", Age = "age_cat3", Education = "educ3",
Party = "in_party_vote", Religion = "in_religion", Caste = "in_caste")) {
keep_levels <- list(in_party_vote = c("BJP", "INC", "Left", "Other party"),
in_religion = c("Hindu", "Muslim", "Christian", "Other (Sikh, Buddhist, Jain, ...)"))
f <- paste0("%.", digits, "f")
long <- imap_dfr(groups, function(g, gl) {
d <- india |> filter(!is.na(.data[[g]]))
if (g %in% names(keep_levels)) d <- d |> filter(.data[[g]] %in% keep_levels[[g]])
d |>
mutate(level = factor(as.character(.data[[g]]), levels = levels(droplevels(factor(.data[[g]]))))) |>
group_by(wave, level) |>
group_modify(~ wstat(.x[[v]], .x$weight)) |>
ungroup() |>
mutate(group = factor(gl, levels = names(groups)))
}) |>
filter(n > 0)
wide <- long |>
mutate(cell = ifelse(n < min_n, sprintf("<small>n=%d</small>", n),
sprintf(paste0(f, " <small>(%d)</small>"), est * mult, n)),
year = factor(india_years[wave], levels = india_years)) |>
arrange(group, level, year) |>
select(group, level, year, cell) |>
pivot_wider(names_from = year, values_from = cell, values_fill = "—")
wide |>
select(-group) |>
rename(` ` = level) |>
kable(escape = FALSE, caption = caption, align = c("l", rep("c", ncol(wide) - 2))) |>
styled() |>
pack_rows(index = table(droplevels(wide$group))) |>
footnote(general = sprintf("Cells: estimate (unweighted n). Cells with n < %d are not estimated.", min_n),
general_title = "")
}
# Hindu vs Muslim over time, one panel per item
relig_cols <- c(Hindu = "#4a3aa7", Muslim = "#eda100")
plot_relig_lines <- function(items, title, ylab = "%", ncol = 2, w = 10, h = 6.5, caption = NULL) {
d <- imap_dfr(items, function(lab, v) {
india |>
filter(in_religion %in% names(relig_cols)) |>
group_by(wave, in_religion) |>
group_modify(~ wstat(.x[[v]], .x$weight)) |>
ungroup() |>
filter(n >= 50) |>
mutate(item = lab)
}) |>
mutate(year = india_year_num[wave], item = factor(item, items),
in_religion = factor(as.character(in_religion), names(relig_cols)))
p <- ggplot(d, aes(year, est * 100, colour = in_religion, group = in_religion)) +
geom_line(linewidth = 0.8) +
geom_point_interactive(aes(fill = in_religion, data_id = safe_txt(paste(item, in_religion, year)),
tooltip = safe_txt(sprintf("%s<br>%s, %d: %.1f%%<br>n = %d", item, in_religion, year, est * 100, n))),
shape = 21, colour = "white", size = 2.8, stroke = 0.7) +
geom_text_repel(data = d |> group_by(item, in_religion) |> slice_max(year, n = 1),
aes(label = in_religion), size = 2.9, colour = ink, show.legend = FALSE,
direction = "y", hjust = 0, nudge_x = 1.2, segment.colour = NA, seed = 1) +
facet_wrap(~ item, ncol = ncol) +
scale_colour_manual(values = relig_cols) +
scale_fill_manual(values = relig_cols, guide = "none") +
scale_x_continuous(breaks = unname(india_year_num), limits = c(2000, 2028)) +
scale_y_continuous(limits = c(0, 100)) +
labs(title = title, x = NULL, y = ylab,
caption = caption %||% "India only. Groups with fewer than 50 respondents are not shown.") +
theme_wvs(10) + theme(panel.spacing = unit(1.2, "lines"))
show_plot(p, w, h)
}
# Several items in one wave: India and the two regions side by side (Cleveland dot plot)
plot_items_regions <- function(items, wave_sel, title, xlab = "%", w = 9.5, h = NULL, caption = NULL) {
d <- imap_dfr(items, function(lab, v) {
country_est(v) |>
filter(wave == wave_sel) |>
group_by(macro_region) |>
summarise(est = mean(est), k = n(), .groups = "drop") |>
mutate(item = lab)
}) |>
mutate(item = factor(item, rev(items)), macro_region = factor(macro_region, focus_regions))
p <- ggplot(d, aes(est, item)) +
geom_line(aes(group = item), colour = "#c3c2b7", linewidth = 0.9) +
geom_point_interactive(aes(colour = macro_region, data_id = safe_txt(paste(item, macro_region)),
tooltip = safe_txt(sprintf("%s<br>%s: %.0f%%<br>%d %s", item, macro_region, est, k,
ifelse(k == 1, "country", "countries")))),
size = 3.6) +
scale_colour_manual(values = region_cols, breaks = focus_regions) +
scale_x_continuous(limits = c(0, 100)) +
labs(title = title, x = xlab, y = NULL,
caption = caption %||% paste0(wave_labels[wave_sel], ". Regions: mean of countries.")) +
theme_wvs()
show_plot(p, w, h %||% (1.6 + 0.55 * length(items)))
}
# long version of the group estimates, for charts
group_long <- function(v, groups = c(Sex = "sex_f", Age = "age_cat3", Education = "educ3",
Party = "in_party_vote", Religion = "in_religion", Caste = "in_caste")) {
keep_levels <- list(in_party_vote = c("BJP", "INC", "Left", "Other party"),
in_religion = c("Hindu", "Muslim", "Christian", "Other (Sikh, Buddhist, Jain, ...)"))
imap_dfr(groups, function(g, gl) {
d <- india |> filter(!is.na(.data[[g]]))
if (g %in% names(keep_levels)) d <- d |> filter(.data[[g]] %in% keep_levels[[g]])
d |>
mutate(level = factor(as.character(.data[[g]]), levels = levels(droplevels(factor(.data[[g]]))))) |>
group_by(wave, level) |>
group_modify(~ wstat(.x[[v]], .x$weight)) |>
ungroup() |>
mutate(group = factor(gl, levels = names(groups)))
})
}
# ---- Question table at the start of each section ------------------------------------
fmt_conf <- "4-point: 1 a great deal, 2 quite a lot, 3 not very much, 4 none at all"
fmt_reg <- "4-point: 1 very good, 2 fairly good, 3 fairly bad, 4 very bad"
fmt_ess <- "10-point: 1 not an essential characteristic of democracy ... 10 an essential characteristic"
fmt_agr4 <- "4-point: 1 strongly agree, 2 agree, 3 disagree, 4 strongly disagree"
fmt_agr5 <- "5-point: 1 agree strongly ... 3 neither ... 5 disagree strongly"
fmt_just <- "10-point: 1 never justifiable ... 10 always justifiable"
fmt_surv <- "4-point: 1 definitely should ... 4 definitely should not"
fmt_nb <- "Yes/no: mentioned or not mentioned on a list"
codebook_vars <- readr::read_csv("D:/Populism and Democrary/World value survey/data/clean/wvs_codebook.csv",
show_col_types = FALSE) |>
select(var, W4, W5, W6, W7)
# rows: var (several joined with "+", e.g. "pm_aims1+pm_aims2"), question, format, mapped,
# and optionally `side` to group rows under sub-headings
q_table <- function(rows, caption = "Questions used in this section") {
first <- sub("\\+.*", "", rows$var)
in_india <- vapply(first, function(v) {
yrs <- india |> filter(!is.na(.data[[v]])) |> distinct(wave) |> pull(wave) |> sort()
if (length(yrs)) paste(india_years[yrs], collapse = ", ") else "—"
}, character(1))
wave_ids <- function(v, w) {
ids <- strsplit(v, "+", fixed = TRUE)[[1]]
x <- codebook_vars[[w]][match(ids, codebook_vars$var)]
x <- x[!is.na(x) & x != ""]
if (length(x)) paste(x, collapse = " & ") else "—"
}
tab <- rows |> mutate(india_yrs = unname(in_india))
for (w in c("W4", "W5", "W6", "W7")) tab[[w]] <- vapply(rows$var, wave_ids, character(1), w = w)
k <- tab |>
select(Question = question, `Response format` = format, `How it is counted / what it maps to` = mapped,
`Asked in India` = india_yrs, W4, W5, W6, W7) |>
kable(caption = caption) |>
styled() |>
add_header_above(c(" " = 4, "Question number in the WVS file" = 4)) |>
column_spec(1, width = "17em") |>
column_spec(3, width = "15em")
if ("side" %in% names(rows)) k <- pack_rows(k, index = table(factor(rows$side, unique(rows$side))))
k
}
# India over time, one number per cell (no intervals)
simple_trend <- function(tr, caption, mult = 100, digits = 0, footnote_text = NULL) {
k <- tr |>
mutate(cell = ifelse(is.na(est), "—", formatC(est * mult, format = "f", digits = digits)),
year = india_years[wave]) |>
select(item, year, cell) |>
pivot_wider(names_from = year, values_from = cell) |>
rename(` ` = item) |>
(\(x) kable(x, caption = caption, align = c("l", rep("c", ncol(x) - 1))))() |>
styled()
if (!is.null(footnote_text)) k <- footnote(k, general = footnote_text, general_title = "")
k
}
# ---- Part III helpers: India vs the UN geoscheme regions --------------------------------
wb_country_est <- function(v, mult = 100, data = wvs_wb) {
data |>
group_by(wave, country_code, country_name, wb_region) |>
filter(any(!is.na(.data[[v]]))) |>
summarise(est = weighted.mean(.data[[v]], weight, na.rm = TRUE) * mult, .groups = "drop")
}
wb_rank_label <- "India's rank among all countries (1 = highest)"
# sort key: the row's value in the latest wave that has one
latest_value <- function(tab) {
wcols <- intersect(unname(wave_labels), names(tab))
apply(tab[wcols], 1, function(r) {
v <- suppressWarnings(as.numeric(rev(r)))
v <- v[!is.na(v)]
if (length(v)) v[1] else -Inf
})
}
# one or more measures: rows = India + regions (+ India's rank), columns = waves
wb_compare <- function(vars, caption, mult = 100, digits = 1, rank = TRUE) {
f <- paste0("%.", digits, "f")
# regions resting on a single country in a wave (named under the table)
single <- imap_dfr(vars, function(lab, v) {
wb_country_est(v, mult) |>
filter(wb_region != "India") |>
group_by(wave, wb_region) |>
filter(n() == 1) |>
ungroup() |>
distinct(wave, wb_region, country_name)
}) |>
distinct() |>
arrange(factor(wb_region, wb_regions), wave)
single_note <- if (nrow(single)) {
paste0("Single country: ",
paste(sprintf("%s in %s = %s", single$wb_region, single$wave, single$country_name), collapse = "; "), ".")
} else NULL
tab <- imap_dfr(vars, function(lab, v) {
ce <- wb_country_est(v, mult)
reg <- ce |>
group_by(wave, wb_region) |>
summarise(cell = sprintf(f, mean(est)), .groups = "drop") |>
mutate(row = as.character(wb_region))
out <- reg
if (rank) {
out <- bind_rows(reg, ce |>
group_by(wave) |>
mutate(r = rank(-est, ties.method = "min"), N = n()) |>
filter(country_code == 356) |>
transmute(wave, row = wb_rank_label, cell = sprintf("%d of %d", r, N)))
}
out |> mutate(measure = lab)
}) |>
mutate(col = wave_labels[wave], measure = factor(measure, vars),
row = factor(row, c(wb_regions, wb_rank_label))) |>
arrange(measure, row, wave) |>
select(measure, row, col, cell) |>
pivot_wider(names_from = col, values_from = cell, values_fill = "—")
tab <- tab |>
mutate(.key = latest_value(tab), .rank = row == wb_rank_label) |>
arrange(measure, .rank, desc(.key)) |>
select(-.key, -.rank)
k <- tab |>
select(-measure) |>
rename(` ` = row) |>
(\(x) kable(x, escape = FALSE, caption = caption, align = c("l", rep("c", ncol(x) - 1))))() |>
styled() |>
footnote(general = c("Regions: mean of the country estimates. India: its own estimate. — = not asked.", single_note),
general_title = "")
if (length(vars) > 1) k <- pack_rows(k, index = table(tab$measure))
k
}
# small multiples: one panel per region, India (orange) vs the region (blue)
wb_lines <- function(v, title, ylab = "%", mult = 100, w = 10, h = 8, ylim = NULL) {
ce <- wb_country_est(v, mult)
reg <- ce |> filter(wb_region != "India") |> group_by(wave, wb_region) |>
summarise(est = mean(est), k = n(), .groups = "drop") |> mutate(series = "Region (mean of countries)")
ind <- ce |> filter(wb_region == "India") |> select(wave, est)
panel_ord <- reg |> group_by(wb_region) |> slice_max(as.integer(factor(wave, names(india_years))), n = 1) |>
ungroup() |> arrange(desc(est)) |> pull(wb_region) |> as.character()
d <- bind_rows(mutate(reg, wb_region = as.character(wb_region)),
tidyr::crossing(ind, wb_region = unique(as.character(reg$wb_region))) |> mutate(k = 1L, series = "India")) |>
mutate(wave_short = factor(wave, c("W4", "W5", "W6", "W7")), wb_region = factor(wb_region, panel_ord),
tip = ifelse(series == "India", sprintf("India, %s: %.1f", india_years[wave], est),
sprintf("%s, %s: %.1f<br>%d %s", wb_region, wave_labels[wave], est, k, ifelse(k == 1, "country", "countries"))))
p <- ggplot(d, aes(wave_short, est, colour = series, group = series)) +
geom_line(linewidth = 0.8) +
geom_point_interactive(aes(fill = series, tooltip = safe_txt(tip), data_id = safe_txt(paste(series, wb_region, wave))),
shape = 21, colour = "white", size = 2.6, stroke = 0.7) +
facet_wrap(~ wb_region, ncol = 4, labeller = label_wrap_gen(24)) +
scale_y_continuous(limits = ylim) +
scale_colour_manual(values = c(India = "#eb6834", `Region (mean of countries)` = "#2a78d6")) +
scale_fill_manual(values = c(India = "#eb6834", `Region (mean of countries)` = "#2a78d6"), guide = "none") +
labs(title = title, x = NULL, y = ylab,
caption = "India: W4 2001, W5 2006, W6 2012, W7 2023. Regions: mean of countries surveyed in that wave.") +
theme_wvs(10) + theme(panel.spacing = unit(1.2, "lines"))
show_plot(p, w, h)
}
# one measure by conception type: India and each region, one block per type, columns = waves.
# A country counts for a type only if it has at least min_n respondents of that type in that wave.
wb_by_type <- function(v, caption, min_n = 30, digits = 1) {
f <- paste0("%.", digits, "f")
ce <- wvs_wb |>
filter(!is.na(conception), !is.na(.data[[v]])) |>
group_by(wave, conception, country_code, country_name, wb_region) |>
filter(n() >= min_n) |>
summarise(est = 100 * weighted.mean(.data[[v]], weight), .groups = "drop")
single <- ce |> filter(wb_region != "India") |> group_by(wave, conception, wb_region) |>
filter(n() == 1) |> ungroup() |> distinct(wave, wb_region, country_name) |>
arrange(factor(wb_region, wb_regions), wave)
tab <- ce |>
group_by(wave, conception, wb_region) |>
summarise(cell = sprintf(f, mean(est)), .groups = "drop") |>
mutate(col = wave_labels[wave], wb_region = factor(wb_region, wb_regions)) |>
arrange(conception, wb_region, wave) |>
select(conception, wb_region, col, cell) |>
pivot_wider(names_from = col, values_from = cell, values_fill = "—") |>
complete(conception, wb_region, fill = list()) |>
mutate(across(-c(conception, wb_region), ~ ifelse(is.na(.x), "—", .x)))
tab <- tab |>
mutate(.key = latest_value(tab)) |>
arrange(conception, desc(.key)) |>
select(-.key)
notes <- c(sprintf("Cells: %% among respondents of that type. Regions: mean of country estimates; a country counts only with at least %d respondents of that type. — = too few respondents or not asked.", min_n),
if (nrow(single)) paste0("Single country: ", paste(sprintf("%s in %s = %s", single$wb_region, single$wave,
single$country_name) |> unique(), collapse = "; "), ".") else NULL)
tab |>
select(-conception) |>
rename(` ` = wb_region) |>
(\(x) kable(x, caption = caption, align = c("l", rep("c", ncol(x) - 1))))() |>
styled() |>
pack_rows(index = table(tab$conception)) |>
footnote(general = notes, general_title = "")
}
# one chart for one measure: India and every region as rows, one dot per wave (light = 2001, dark = 2023)
wb_dots <- function(v, title, xlab = "%", mult = 100, w = 9.5, h = 5.6,
caption = "Regions: mean of countries in each wave.
India surveyed in 2001 (W4), 2006 (W5), 2012 (W6) and 2023 (W7).") {
ce <- wb_country_est(v, mult)
d <- ce |>
group_by(wave, wb_region) |>
summarise(est = mean(est), k = n(), cn = paste(sort(country_name), collapse = ", "), .groups = "drop") |>
mutate(wb_region = factor(wb_region, rev(wb_regions)),
wave_lab = factor(wave, names(india_years)),
tip = ifelse(wb_region == "India", sprintf("India %s: %.0f%%", india_years[wave], est),
sprintf("%s, %s: %.0f%%<br>%s", wb_region, wave_labels[wave], est, cn)))
wave_cols <- setNames(unname(year_cols), levels(d$wave_lab))
ord <- d |> group_by(wb_region) |> slice_max(as.integer(wave_lab), n = 1) |> ungroup() |>
arrange(est) |> pull(wb_region) |> as.character()
d <- d |> mutate(wb_region = factor(as.character(wb_region), ord))
p <- ggplot(d, aes(est, wb_region)) +
geom_line(aes(group = wb_region), colour = "#c3c2b7", linewidth = 0.9) +
geom_point_interactive(aes(fill = wave_lab, tooltip = safe_txt(tip), data_id = safe_txt(paste(wb_region, wave))),
shape = 21, colour = "white", size = 3.6, stroke = 0.8) +
scale_fill_manual(values = wave_cols) +
scale_x_continuous(limits = c(0, 100), breaks = seq(0, 100, 25)) +
scale_y_discrete(labels = function(x) ifelse(x == "India", "INDIA", x)) +
labs(title = title, x = xlab, y = NULL,
caption = caption) +
theme_wvs() +
theme(axis.text.y = element_text(colour = ink, size = 10), panel.grid.major.y = element_blank())
show_plot(p, w, h)
}
# stacked shares for the latest wave: India and each region
wb_stack <- function(vars, cols, title, wave_sel = "W7") {
d <- imap_dfr(vars, function(lab, v) {
wb_country_est(v) |> filter(wave == wave_sel) |> group_by(wb_region) |>
summarise(pct = mean(est), k = n(), .groups = "drop") |> mutate(type = lab)
}) |>
mutate(type = factor(type, vars))
ord <- d |> filter(type == vars[[1]]) |> arrange(pct) |> pull(wb_region) |> as.character()
d <- d |> mutate(wb_region = factor(as.character(wb_region), ord))
ggplot(d, aes(pct, wb_region, fill = type)) +
geom_col(width = 0.62, colour = "white", linewidth = 0.6, position = position_stack(reverse = TRUE)) +
geom_text(aes(label = ifelse(pct >= 6, sprintf("%.0f", pct), ""), group = type,
colour = cols[as.character(type)] %in% c("#86b6ef", "#5598e7", "#8a8983")),
position = position_stack(vjust = 0.5, reverse = TRUE), size = 3, show.legend = FALSE) +
scale_fill_manual(values = cols) +
scale_colour_manual(values = c(`TRUE` = ink, `FALSE` = "white"), guide = "none") +
scale_x_continuous(expand = expansion(mult = c(0, 0.03)), labels = function(x) paste0(x, "%")) +
labs(title = title, x = NULL, y = NULL,
caption = paste0(wave_labels[wave_sel], " (India 2023). % of those classified. Regions: mean of countries.")) +
theme_wvs() + theme(panel.grid.major.y = element_blank())
}
# change within India by group: one dot per survey year, joined by a line
plot_dumbbell <- function(v, waves, title, xlab, min_n = 50, mult = 100, w = 9, h = 7.5) {
gl <- group_long(v) |>
filter(wave %in% waves, n >= min_n) |>
mutate(year = factor(india_years[wave], india_years[waves]), level = fct_rev(level))
p <- ggplot(gl, aes(est * mult, level)) +
geom_line(aes(group = level), colour = "#c3c2b7", linewidth = 0.9) +
geom_point_interactive(aes(fill = year, data_id = paste(group, level, year),
tooltip = sprintf("%s: %s<br>%s: %.1f%% (n = %d)", group, level, year, est * mult, n)),
shape = 21, colour = "white", size = 3.6, stroke = 0.8) +
facet_grid(group ~ ., scales = "free_y", space = "free_y", switch = "y") +
scale_fill_manual(values = year_cols) +
labs(title = title, x = xlab, y = NULL, caption = sprintf("Groups with fewer than %d respondents are not shown.", min_n)) +
theme_wvs() +
theme(strip.placement = "outside", strip.text.y.left = element_text(angle = 0, face = "bold"))
show_plot(p, w, h)
}
What this report does
The report asks whether Indians separate governance (a state
that gets things done) from democratic accountability (checks
on that state), what they think democracy is, and whether they want the
state to support the less well-off, and how India compares with every
world region.
| I. Data, samples and coverage |
Which countries and waves, the world regions, how region averages
are formed, who was interviewed, and how India was sampled |
| II. India and the world regions |
II.1 Governance vs accountability (four groups); II.2 Liberal,
minimalist or illiberal conceptions of democracy (democracy score); II.3
State support |
| III. Question by question |
One table per question (trust in 8 institutions, and every question
behind Part II): India and each region, wave by wave |
| IV. PALS 2022 |
A second survey (Public Attitudes towards the Liberal Script,
2021–22): countries and samples, and its questions on the same three
themes, India vs the same regions |
| V. India across both surveys |
Questions asked in both WVS 2023 and PALS 2022, grouped by the
report’s measures: India’s rank among the 15 countries in both surveys,
and India’s nearest countries |
| VI. How far is India from the others? |
The same bridge questions on their full scales: India’s rank,
z-score and effect size in each survey, how well each pair agrees across
countries, and nearest countries by statistical distance |
Comparison set: India against 6 world regions (UN
geoscheme, adjusted), keeping countries surveyed in at least 3 of the 4
waves from W4 (India 2001) to W7 (India 2023). The earlier 1995 wave is
not used.
Two caveats on India’s samples, set out in Part
I:
- The 2001, 2006 and 2012 samples carry no weights: they are
male-skewed and, in 2006 and 2012, mostly rural.
- The 2023 sample is a quota sample in 8 states.
Part I. Data, samples and coverage
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
options(knitr.table.format = "html")
library(dplyr)
library(tidyr)
library(purrr)
library(tibble)
library(knitr)
library(kableExtra)
# All cleaning and categorisation happens in R/00_wvs_clean_harmonise.R
wvs_all <- readRDS("D:/Populism and Democrary/World value survey/data/clean/wvs_harmonised.rds")
# Comparison set: India vs the world regions (UN geoscheme, adjusted; see R/00_wvs_clean_harmonise.R),
# countries surveyed in at least 3 of W4-W7. W3 (1994-99, India 1995) is dropped from this report.
# (The column is called macro_region here so the tables below work unchanged.)
focus_regions <- c("India", "South-eastern Asia", "Western Asia & Northern Africa", "Sub-Saharan Africa",
"Latin America & Caribbean", "Eastern Europe", "Western Europe & Offshoots")
wvs <- wvs_all |>
filter(wave != "W3", wb_3waves, un_region %in% focus_regions) |>
mutate(macro_region = factor(as.character(un_region), focus_regions))
wave_labels <- c(W4 = "W4", W5 = "W5", W6 = "W6", W7 = "W7")
india_years <- c(W4 = "2001", W5 = "2006", W6 = "2012", W7 = "2023")
regions <- levels(wvs$macro_region)
wmean <- function(x, w) {
ok <- !is.na(x) & !is.na(w)
if (!any(ok)) NA_real_ else sum(x[ok] * w[ok]) / sum(w[ok])
}
styled <- function(k) kable_styling(k, bootstrap_options = c("striped", "condensed"), full_width = FALSE)
The comparison set is India against 6 world regions,
built from the countries surveyed in at least 3 of the 4 waves
(2001–2023). Every table in this part is restricted to these
countries.
Before testing any claim, this part sets out:
- what each wave covers
- how countries are grouped into regions, and how region averages are
weighted
- the demographic make-up of each region and of India
- how the India samples were drawn and weighted
Parts II and III use the same data
(data/clean/wvs_harmonised.rds), the same countries and the
same regions.
The four waves
per_country <- wvs |> count(wave, country_code)
wvs |>
group_by(wave) |>
summarise(Fieldwork = paste(min(survey_year, na.rm = TRUE), max(survey_year, na.rm = TRUE), sep = "–"),
Countries = n_distinct(country_code),
Respondents = n(),
`Under 18` = sum(age < 18, na.rm = TRUE),
.groups = "drop") |>
left_join(per_country |> group_by(wave) |>
summarise(`Median N per country` = median(n), `Smallest` = min(n), `Largest` = max(n)),
by = "wave") |>
mutate(`India survey` = india_years[wave]) |>
rename(Wave = wave) |>
kable(caption = "Coverage of the four waves (India and the 6 regions)", format.args = list(big.mark = ",")) |>
styled() |>
footnote(general = "W3 (1994–99) is not used. 'Under 18': respondents aged 15–17 (a few countries sample from 15 or 16); Parts II and III use adults only.",
general_title = "")
Coverage of the four waves (India and the 6 regions)
|
Wave
|
Fieldwork
|
Countries
|
Respondents
|
Under 18
|
Median N per country
|
Smallest
|
Largest
|
India survey
|
|
W4
|
1999–2004
|
23
|
37,052
|
490
|
1,251
|
1,000
|
3,401
|
2001
|
|
W5
|
2004–2008
|
31
|
49,503
|
518
|
1,421
|
954
|
3,051
|
2006
|
|
W6
|
2011–2014
|
32
|
51,402
|
91
|
1,493
|
841
|
4,078
|
2012
|
|
W7
|
2017–2023
|
33
|
51,972
|
387
|
1,289
|
1,000
|
4,018
|
2023
|
|
W3 (1994–99) is not used. ‘Under 18’: respondents aged 15–17
(a few countries sample from 15 or 16); Parts II and III use adults
only.
|
Countries and regions
Region scheme
The regions follow the UN geoscheme (the UN
Statistics Division’s M49 standard), the same grouping PALS uses for its
“your region” questions. The WVS itself has no world-region variable for
all waves. The UN sub-regions are used with these adjustments:
- India is its own group.
- Western Asia and Northern Africa
are combined (Turkey, Iraq, Jordan, Cyprus, Egypt, Morocco).
- Western Europe & Offshoots: Western, Northern
and Southern Europe together with the United States, Canada, Australia
and New Zealand.
- Kyrgyzstan (Central Asia) and
Serbia are placed in Eastern
Europe.
Only countries surveyed in at least 3 of the 4 waves
are kept, so that each region is made of roughly the same countries over
time.
How to read this: even with the 3-wave rule, the
countries in a region change somewhat from wave to wave, so a change in
a region’s average can reflect which countries were
surveyed, not only changing attitudes. Three cases need
care:
- Thin regions: Sub-Saharan Africa has three
countries, and only one (South Africa) in W5. Where a region has a
single country in a wave, it is named under each table in Parts II and
III.
- W4 (2001) gaps: Western Europe & Offshoots is
thin in W4 (Australia and New Zealand were not surveyed, and most
European countries were covered that round by the separate European
Values Study).
- Israel (surveyed in W4 only) is removed from the
data altogether.
cl <- wvs |>
count(macro_region, country_name, wave) |>
mutate(n = format(n, big.mark = ",")) |>
pivot_wider(names_from = wave, values_from = n, values_fill = "—") |>
select(macro_region, Country = country_name, any_of(c("W4", "W5", "W6", "W7"))) |>
arrange(macro_region, Country)
cl |>
select(-macro_region) |>
kable(caption = "Every country in the comparison, with respondents per wave (all ages)", align = c("l", rep("r", 4))) |>
styled() |>
pack_rows(index = table(cl$macro_region)) |>
scroll_box(height = "500px")
Every country in the comparison, with respondents per wave (all ages)
|
Country
|
W4
|
W5
|
W6
|
W7
|
|
India
|
|
India
|
2,002
|
2,001
|
4,078
|
1,692
|
|
South-eastern Asia
|
|
Indonesia
|
1,000
|
2,015
|
—
|
3,200
|
|
Malaysia
|
—
|
1,201
|
1,300
|
1,313
|
|
Philippines
|
1,200
|
—
|
1,200
|
1,200
|
|
Singapore
|
1,512
|
—
|
1,972
|
2,012
|
|
Thailand
|
—
|
1,534
|
1,200
|
1,500
|
|
Vietnam
|
1,000
|
1,495
|
—
|
1,200
|
|
Western Asia & Northern Africa
|
|
Cyprus
|
—
|
1,050
|
1,000
|
1,000
|
|
Egypt
|
3,000
|
3,051
|
1,523
|
1,200
|
|
Iraq
|
2,325
|
2,701
|
1,200
|
1,200
|
|
Jordan
|
1,223
|
1,200
|
1,200
|
1,203
|
|
Morocco
|
1,251
|
1,200
|
1,200
|
1,200
|
|
Turkey
|
3,401
|
1,346
|
1,605
|
2,415
|
|
Sub-Saharan Africa
|
|
Nigeria
|
2,022
|
—
|
1,759
|
1,237
|
|
South Africa
|
3,000
|
2,988
|
3,531
|
—
|
|
Zimbabwe
|
1,002
|
—
|
1,500
|
1,215
|
|
Latin America & Caribbean
|
|
Argentina
|
1,280
|
1,002
|
1,030
|
1,003
|
|
Brazil
|
—
|
1,500
|
1,486
|
1,762
|
|
Chile
|
1,200
|
1,000
|
1,000
|
1,000
|
|
Colombia
|
—
|
3,025
|
1,512
|
1,520
|
|
Mexico
|
1,535
|
1,560
|
2,000
|
1,741
|
|
Peru
|
1,501
|
1,500
|
1,210
|
1,400
|
|
Uruguay
|
—
|
1,000
|
1,000
|
1,000
|
|
Eastern Europe
|
|
Kyrgyzstan
|
1,043
|
—
|
1,500
|
1,200
|
|
Romania
|
—
|
1,776
|
1,503
|
1,257
|
|
Russia
|
—
|
2,033
|
2,500
|
1,810
|
|
Serbia
|
1,200
|
1,220
|
—
|
1,046
|
|
Ukraine
|
—
|
1,000
|
1,500
|
1,289
|
|
Western Europe & Offshoots
|
|
Australia
|
—
|
1,421
|
1,477
|
1,813
|
|
Canada
|
1,931
|
2,164
|
—
|
4,018
|
|
Germany
|
—
|
2,064
|
2,046
|
1,528
|
|
Netherlands
|
—
|
1,050
|
1,902
|
2,145
|
|
New Zealand
|
—
|
954
|
841
|
1,057
|
|
Spain
|
1,209
|
1,200
|
1,189
|
—
|
|
Sweden
|
1,015
|
1,003
|
1,206
|
—
|
|
United States
|
1,200
|
1,249
|
2,232
|
2,596
|
How region averages are weighted
Within a country: every estimate uses that country’s
own survey weight (W4 V245, W5 V259, W6
V258, W7 W_WEIGHT). These weights correct each
national sample towards the national population, mainly on sex, age and
education.
Across countries (a region): the region value is the
simple mean of its country estimates. Every country
counts once, whatever its population or sample size:
- The United States counts the same as New Zealand in Western Europe
& Offshoots.
- Egypt counts the same as Cyprus in Western Asia & Northern
Africa.
This equals pooling respondents with the WVS equilibrated
weight (S018), which rescales every country to N =
1,000.
India vs a region: India is always shown on its own.
The comparison is therefore India vs the average country in each
region, not India vs the region’s population. That is why every
comparison table also gives India’s rank among the countries in
the comparison (India plus every country surveyed in that
wave). The rank does not depend on averaging.
Why not weight by population? Population-weighting
would let the United States dominate Western Europe & Offshoots,
Indonesia South-eastern Asia and Brazil Latin America. The WVS samples
national publics, so the unit of comparison is the country.
The table below checks how much region averages move when only the
countries surveyed in W5, W6 and W7 are kept (a balanced panel). The
example indicator is “a strong leader who does not bother with
parliament and elections is good”.
country_est <- wvs |>
filter(is.na(age) | age >= 18) |>
group_by(wave, country_code, macro_region) |>
filter(any(!is.na(reg_strongleader_good))) |>
summarise(est = 100 * wmean(reg_strongleader_good, weight), .groups = "drop")
panel_ids <- wvs |>
distinct(country_code, wave) |>
group_by(country_code) |>
filter(all(c("W5", "W6", "W7") %in% wave)) |>
distinct(country_code) |>
pull()
bind_rows(
country_est |> mutate(sample = "All countries"),
country_est |> filter(country_code %in% panel_ids) |> mutate(sample = "Balanced panel (W5–W7)")
) |>
filter(wave %in% c("W5", "W6", "W7")) |>
group_by(macro_region, sample, wave) |>
summarise(cell = sprintf("%.1f <small>(%d)</small>", mean(est), n()), .groups = "drop") |>
pivot_wider(names_from = wave, values_from = cell, values_fill = "—") |>
rename(Region = macro_region, Sample = sample) |>
kable(escape = FALSE, caption = "Strong leader is good (%): all countries vs balanced panel") |>
styled() |>
collapse_rows(columns = 1, valign = "top") |>
footnote(general = "Cells: mean of country estimates (number of countries). Adults only.",
general_title = "")
Strong leader is good (%): all countries vs balanced panel
|
Region
|
Sample
|
W5
|
W6
|
W7
|
|
India
|
All countries
|
63.9 (1)
|
64.9 (1)
|
69.1 (1)
|
|
Balanced panel (W5–W7)
|
63.9 (1)
|
64.9 (1)
|
69.1 (1)
|
|
South-eastern Asia
|
All countries
|
40.8 (4)
|
47.9 (4)
|
70.2 (6)
|
|
Balanced panel (W5–W7)
|
65.2 (2)
|
40.6 (2)
|
80.5 (2)
|
|
Western Asia & Northern Africa
|
All countries
|
30.5 (6)
|
47.7 (6)
|
53.3 (6)
|
|
Balanced panel (W5–W7)
|
30.5 (6)
|
47.7 (6)
|
53.3 (6)
|
|
Sub-Saharan Africa
|
All countries
|
43.1 (1)
|
39.6 (3)
|
49.7 (2)
|
|
Latin America & Caribbean
|
All countries
|
44.7 (7)
|
52.9 (7)
|
61.1 (7)
|
|
Balanced panel (W5–W7)
|
44.7 (7)
|
52.9 (7)
|
61.1 (7)
|
|
Eastern Europe
|
All countries
|
57.8 (4)
|
76.0 (4)
|
65.8 (5)
|
|
Balanced panel (W5–W7)
|
66.5 (3)
|
73.9 (3)
|
69.0 (3)
|
|
Western Europe & Offshoots
|
All countries
|
25.8 (8)
|
30.5 (7)
|
28.8 (6)
|
|
Balanced panel (W5–W7)
|
26.8 (5)
|
27.9 (5)
|
29.7 (5)
|
|
Cells: mean of country estimates (number of countries).
Adults only.
|
Demographic profile by region
Each cell is the mean of country-level weighted values, so it
describes the average country in the region, as in
Parts II and III. All respondents are included.
Profile by wave
demo_country <- wvs |>
group_by(wave, country_code, macro_region) |>
summarise(
`Female %` = 100 * wmean(sex == 2, weight),
`Mean age` = wmean(age, weight),
`18–34 %` = 100 * wmean(age_cat3 == "18-34", weight),
`55+ %` = 100 * wmean(age_cat3 == "55+", weight),
`Lower education %` = 100 * wmean(educ3 == "Lower", weight),
`Higher education %`= 100 * wmean(educ3 == "Higher", weight),
`Upper / upper-middle class %` = 100 * wmean(class3 == "Upper / upper middle", weight),
`Lower class %` = 100 * wmean(class3 == "Lower", weight),
.groups = "drop")
for (w in c("W4", "W5", "W6", "W7")) {
cat("\n\n#### ", w, " (India ", india_years[w], ")\n\n", sep = "")
tab <- demo_country |>
filter(wave == w) |>
group_by(macro_region) |>
summarise(Countries = n(), across(`Female %`:`Lower class %`, ~ mean(.x, na.rm = TRUE))) |>
rename(Region = macro_region)
print(tab |>
kable(digits = 1, caption = paste("Demographic profile by region,", w)) |>
styled() |>
row_spec(which(tab$Region == "India"), bold = TRUE, background = "#FFF3D6"))
}
W4 (India 2001)
Demographic profile by region, W4
|
Region
|
Countries
|
Female %
|
Mean age
|
18–34 %
|
55+ %
|
Lower education %
|
Higher education %
|
Upper / upper-middle class %
|
Lower class %
|
|
India
|
1
|
43.2
|
40.2
|
41.5
|
18.1
|
52.2
|
23.4
|
20.0
|
19.8
|
|
South-eastern Asia
|
4
|
50.4
|
39.8
|
37.6
|
20.5
|
28.8
|
20.2
|
18.1
|
11.5
|
|
Western Asia & Northern Africa
|
5
|
50.3
|
36.9
|
50.1
|
13.1
|
54.5
|
15.2
|
19.2
|
18.0
|
|
Sub-Saharan Africa
|
3
|
49.6
|
33.9
|
60.2
|
9.1
|
30.6
|
9.3
|
17.2
|
38.7
|
|
Latin America & Caribbean
|
4
|
52.4
|
39.4
|
43.9
|
19.1
|
30.8
|
17.1
|
15.3
|
15.4
|
|
Eastern Europe
|
2
|
53.8
|
41.7
|
39.4
|
23.4
|
8.6
|
37.5
|
25.7
|
7.5
|
|
Western Europe & Offshoots
|
4
|
50.7
|
44.4
|
33.2
|
28.9
|
23.4
|
31.0
|
30.6
|
4.9
|
W5 (India 2006)
Demographic profile by region, W5
|
Region
|
Countries
|
Female %
|
Mean age
|
18–34 %
|
55+ %
|
Lower education %
|
Higher education %
|
Upper / upper-middle class %
|
Lower class %
|
|
India
|
1
|
43.1
|
41.4
|
36.7
|
19.9
|
50.4
|
22.0
|
20.1
|
27.2
|
|
South-eastern Asia
|
4
|
49.4
|
38.5
|
43.7
|
16.2
|
29.6
|
17.0
|
19.9
|
6.6
|
|
Western Asia & Northern Africa
|
6
|
50.9
|
38.5
|
45.9
|
16.1
|
45.1
|
19.3
|
21.5
|
10.2
|
|
Sub-Saharan Africa
|
1
|
50.0
|
37.3
|
47.9
|
18.5
|
28.6
|
5.0
|
17.6
|
43.9
|
|
Latin America & Caribbean
|
7
|
52.1
|
41.4
|
41.2
|
23.7
|
37.7
|
17.3
|
9.7
|
16.3
|
|
Eastern Europe
|
4
|
53.3
|
44.9
|
31.5
|
30.8
|
15.8
|
28.0
|
16.4
|
14.6
|
|
Western Europe & Offshoots
|
8
|
52.2
|
47.4
|
25.7
|
35.4
|
18.7
|
30.4
|
27.2
|
4.4
|
W6 (India 2012)
Demographic profile by region, W6
|
Region
|
Countries
|
Female %
|
Mean age
|
18–34 %
|
55+ %
|
Lower education %
|
Higher education %
|
Upper / upper-middle class %
|
Lower class %
|
|
India
|
1
|
43.8
|
41.2
|
38.1
|
19.0
|
46.1
|
13.3
|
18.8
|
14.2
|
|
South-eastern Asia
|
4
|
50.8
|
43.3
|
30.7
|
24.1
|
32.7
|
20.4
|
19.9
|
9.7
|
|
Western Asia & Northern Africa
|
6
|
49.6
|
39.1
|
45.3
|
17.7
|
39.8
|
20.9
|
19.4
|
14.2
|
|
Sub-Saharan Africa
|
3
|
51.6
|
35.1
|
59.1
|
13.3
|
18.1
|
9.4
|
13.7
|
41.0
|
|
Latin America & Caribbean
|
7
|
51.4
|
41.7
|
40.3
|
24.1
|
28.1
|
18.8
|
13.0
|
15.6
|
|
Eastern Europe
|
4
|
53.2
|
44.1
|
35.2
|
28.8
|
5.6
|
31.7
|
23.2
|
8.8
|
|
Western Europe & Offshoots
|
7
|
52.0
|
48.7
|
25.3
|
38.6
|
17.4
|
34.2
|
27.6
|
4.1
|
W7 (India 2023)
Demographic profile by region, W7
|
Region
|
Countries
|
Female %
|
Mean age
|
18–34 %
|
55+ %
|
Lower education %
|
Higher education %
|
Upper / upper-middle class %
|
Lower class %
|
|
India
|
1
|
48.9
|
37.8
|
46.2
|
18.8
|
33.9
|
27.2
|
25.7
|
10.1
|
|
South-eastern Asia
|
6
|
51.6
|
41.9
|
36.7
|
22.8
|
51.1
|
24.1
|
20.3
|
12.2
|
|
Western Asia & Northern Africa
|
6
|
50.2
|
40.3
|
40.2
|
20.6
|
44.5
|
25.1
|
17.5
|
11.8
|
|
Sub-Saharan Africa
|
2
|
50.0
|
35.4
|
58.8
|
11.8
|
47.8
|
8.6
|
15.6
|
37.6
|
|
Latin America & Caribbean
|
7
|
51.7
|
42.8
|
37.2
|
26.9
|
37.4
|
20.8
|
12.3
|
17.2
|
|
Eastern Europe
|
5
|
54.0
|
45.2
|
32.9
|
32.0
|
14.5
|
46.6
|
26.0
|
8.3
|
|
Western Europe & Offshoots
|
6
|
52.9
|
50.6
|
23.4
|
44.1
|
10.4
|
41.8
|
37.2
|
4.5
|
Religion by region
rel_country <- wvs |>
filter(!is.na(religion_major)) |>
group_by(wave, country_code, macro_region, religion_major) |>
summarise(w = sum(weight), .groups = "drop_last") |>
mutate(pct = 100 * w / sum(w)) |>
ungroup() |>
complete(nesting(wave, country_code, macro_region), religion_major, fill = list(pct = 0))
rel_country |>
filter(wave == "W7") |>
group_by(macro_region, religion_major) |>
summarise(pct = mean(pct), .groups = "drop") |>
pivot_wider(names_from = religion_major, values_from = pct) |>
rename(Region = macro_region) |>
kable(digits = 1, caption = "Religious denomination by region, W7 (% of respondents, mean of countries)") |>
styled() |>
scroll_box(width = "100%")
Religious denomination by region, W7 (% of respondents, mean of
countries)
|
Region
|
None
|
Catholic
|
Protestant
|
Orthodox
|
Jewish
|
Muslim
|
Hindu
|
Buddhist
|
Other Christian
|
Other
|
|
India
|
0.0
|
1.2
|
0.0
|
0.0
|
0.0
|
9.5
|
83.0
|
1.4
|
0.0
|
4.8
|
|
South-eastern Asia
|
16.8
|
15.4
|
5.0
|
0.3
|
0.0
|
29.0
|
2.0
|
26.1
|
3.5
|
1.9
|
|
Western Asia & Northern Africa
|
0.7
|
0.1
|
0.0
|
12.0
|
0.0
|
86.4
|
0.0
|
0.0
|
0.8
|
0.0
|
|
Sub-Saharan Africa
|
6.0
|
39.7
|
19.9
|
2.6
|
1.8
|
29.5
|
0.0
|
0.0
|
0.3
|
0.3
|
|
Latin America & Caribbean
|
28.8
|
54.5
|
10.0
|
0.1
|
0.1
|
0.0
|
0.2
|
0.1
|
2.5
|
3.7
|
|
Eastern Europe
|
10.0
|
3.7
|
2.3
|
63.1
|
0.1
|
19.7
|
0.1
|
0.1
|
0.5
|
0.4
|
|
Western Europe & Offshoots
|
48.2
|
21.4
|
13.1
|
1.1
|
0.8
|
2.0
|
0.9
|
1.0
|
9.3
|
2.2
|
The India samples
How each sample was drawn
tribble(
~` `, ~`2001 (W4)`, ~`2006 (W5)`, ~`2012 (W6)`, ~`2023 (W7)`,
"Fieldwork", "2001", "Dec 2006 – Jan 2007", "2012", "5 Jun – 2 Jul 2023",
"Organisation", "Not documented in docs/", "Lokniti Network (PI Sandeep Shastri)", "Sandeep Shastri (Jain University CERRSE); W7 report credits Lokniti-CSDS", "Lokniti-CSDS",
"Design", "Not documented in docs/", "Multistage stratified random sample from electoral rolls: 40 Lok Sabha seats → 80 assembly seats → 160 polling stations → voters", "Four-stage stratified random sample from electoral rolls (Lok Sabha seats → assembly seats → polling stations → voters). Plan: 320 seats, 19,444 target", "8 states chosen purposively; 15 assembly seats (PPS); 60 polling stations; random-walk households; age and gender quotas",
"States", "18", "18 of 28 (97% of population)", "17", "8: Bihar, Delhi, Haryana, Maharashtra, Punjab, Telangana, UP, West Bengal",
"Achieved N", "2,002", "2,001", "4,078", "1,692 (1,371 aged 18+)",
"Age range", "18+", "18+", "18+", "16+ (321 aged 16–17 by design)",
"Languages", "—", "10", "—", "5",
"Comparable as a national trend point?", "Yes", "Yes", "Yes", "Partly: 8 states, quota, 60 clusters"
) |>
kable(caption = "India sample designs (from docs/ in each wave folder)") |>
styled() |>
column_spec(1, bold = TRUE) |>
footnote(general = "Sources: W5 study description; W6 India sample design proposal; W7 India technical report (Lokniti-CSDS, July 2023). No India methodology document for W4 is in the project folder.",
general_title = "")
India sample designs (from docs/ in each wave folder)
|
|
2001 (W4)
|
2006 (W5)
|
2012 (W6)
|
2023 (W7)
|
|
Fieldwork
|
2001
|
Dec 2006 – Jan 2007
|
2012
|
5 Jun – 2 Jul 2023
|
|
Organisation
|
Not documented in docs/
|
Lokniti Network (PI Sandeep Shastri)
|
Sandeep Shastri (Jain University CERRSE); W7 report credits Lokniti-CSDS
|
Lokniti-CSDS
|
|
Design
|
Not documented in docs/
|
Multistage stratified random sample from electoral rolls: 40 Lok Sabha
seats → 80 assembly seats → 160 polling stations → voters
|
Four-stage stratified random sample from electoral rolls (Lok Sabha
seats → assembly seats → polling stations → voters). Plan: 320 seats,
19,444 target
|
8 states chosen purposively; 15 assembly seats (PPS); 60 polling
stations; random-walk households; age and gender quotas
|
|
States
|
18
|
18 of 28 (97% of population)
|
17
|
8: Bihar, Delhi, Haryana, Maharashtra, Punjab, Telangana, UP, West
Bengal
|
|
Achieved N
|
2,002
|
2,001
|
4,078
|
1,692 (1,371 aged 18+)
|
|
Age range
|
18+
|
18+
|
18+
|
16+ (321 aged 16–17 by design)
|
|
Languages
|
—
|
10
|
—
|
5
|
|
Comparable as a national trend point?
|
Yes
|
Yes
|
Yes
|
Partly: 8 states, quota, 60 clusters
|
|
Sources: W5 study description; W6 India sample design
proposal; W7 India technical report (Lokniti-CSDS, July 2023). No India
methodology document for W4 is in the project folder.
|
Where the interviews were done
wvs |>
filter(is_india) |>
count(in_state = tidyr::replace_na(in_state, "Not recorded"), wave) |>
mutate(n = as.character(n)) |>
pivot_wider(names_from = wave, values_from = n, values_fill = "—") |>
rename(State = in_state, `2001` = W4, `2006` = W5, `2012` = W6, `2023` = W7) |>
arrange(State) |>
kable(caption = "India: interviews per state", align = c("l", rep("r", 4))) |>
styled() |>
footnote(general = "Bihar, Delhi, Haryana, Maharashtra, Punjab, Uttar Pradesh and West Bengal are the only states in all four waves.",
general_title = "")
India: interviews per state
|
State
|
2001
|
2006
|
2012
|
2023
|
|
Andhra Pradesh
|
152
|
143
|
307
|
—
|
|
Assam
|
61
|
52
|
—
|
—
|
|
Bihar
|
147
|
197
|
332
|
241
|
|
Chhattisgarh
|
46
|
44
|
99
|
—
|
|
Delhi
|
43
|
38
|
94
|
229
|
|
Gujarat
|
96
|
102
|
259
|
—
|
|
Haryana
|
50
|
44
|
122
|
115
|
|
Jharkhand
|
65
|
52
|
160
|
—
|
|
Karnataka
|
112
|
103
|
239
|
—
|
|
Kerala
|
71
|
59
|
192
|
—
|
|
Madhya Pradesh
|
115
|
104
|
256
|
—
|
|
Maharashtra
|
194
|
201
|
280
|
202
|
|
Not recorded
|
—
|
1
|
—
|
—
|
|
Odisha
|
68
|
75
|
355
|
—
|
|
Punjab
|
49
|
53
|
151
|
122
|
|
Rajasthan
|
102
|
112
|
278
|
—
|
|
Tamil Nadu
|
129
|
165
|
—
|
—
|
|
Telangana
|
—
|
—
|
—
|
210
|
|
Unlabelled (W6 code 356019)
|
—
|
—
|
64
|
—
|
|
Uttar Pradesh
|
328
|
301
|
596
|
360
|
|
West Bengal
|
174
|
155
|
294
|
213
|
|
Bihar, Delhi, Haryana, Maharashtra, Punjab, Uttar Pradesh
and West Bengal are the only states in all four waves.
|
Who was interviewed: unweighted and weighted
Comparing the raw sample with the weighted one shows what the weight
corrects.
india <- wvs |>
filter(is_india) |>
mutate(age_band = ifelse(!is.na(age) & age < 18, "16–17", as.character(age_cat3)))
profile_vars <- c(Sex = "sex_f", Age = "age_band", Education = "educ3", Religion = "in_religion",
Caste = "in_caste", `Subjective class` = "class3", `Town size` = "urban_size",
`Party would vote for` = "in_party_vote")
prof <- imap_dfr(profile_vars, function(v, lab) {
india |>
filter(!is.na(.data[[v]])) |>
group_by(wave, level = as.character(.data[[v]])) |>
summarise(n = n(), w = sum(weight), .groups = "drop_last") |>
mutate(Unweighted = 100 * n / sum(n), Weighted = 100 * w / sum(w)) |>
ungroup() |>
mutate(group = lab)
})
prof_wide <- prof |>
mutate(cell = sprintf("%.1f / %.1f", Unweighted, Weighted),
group = factor(group, names(profile_vars)),
year = india_years[wave]) |>
select(group, level, year, cell) |>
pivot_wider(names_from = year, values_from = cell, values_fill = "—") |>
arrange(group) |>
select(group, level, any_of(unname(india_years)))
prof_wide |>
select(-group) |>
rename(` ` = level) |>
kable(caption = "India sample profile: unweighted % / weighted %", align = c("l", rep("c", 4))) |>
styled() |>
pack_rows(index = table(prof_wide$group)) |>
footnote(general = "Percentages are among respondents with a valid answer on that variable. Caste was not asked in 2006.",
general_title = "")
India sample profile: unweighted % / weighted %
|
|
2001
|
2006
|
2012
|
2023
|
|
Sex
|
|
Female
|
43.2 / 43.2
|
43.1 / 43.1
|
43.8 / 43.8
|
43.4 / 48.9
|
|
Male
|
56.8 / 56.8
|
56.9 / 56.9
|
56.2 / 56.2
|
56.6 / 51.1
|
|
Age
|
|
18-34
|
41.5 / 41.5
|
36.7 / 36.7
|
38.1 / 38.1
|
32.6 / 43.6
|
|
35-54
|
40.4 / 40.4
|
43.5 / 43.5
|
42.9 / 42.9
|
33.0 / 33.0
|
|
55+
|
18.1 / 18.1
|
19.9 / 19.9
|
19.0 / 19.0
|
15.5 / 17.7
|
|
16–17
|
—
|
—
|
—
|
19.0 / 5.7
|
|
Education
|
|
Higher
|
23.4 / 23.4
|
22.0 / 22.0
|
13.3 / 13.3
|
23.1 / 27.2
|
|
Lower
|
52.2 / 52.2
|
50.4 / 50.4
|
46.1 / 46.1
|
32.2 / 33.9
|
|
Middle
|
24.4 / 24.4
|
27.6 / 27.6
|
40.6 / 40.6
|
44.6 / 38.9
|
|
Religion
|
|
Christian
|
3.0 / 3.0
|
3.2 / 3.2
|
1.6 / 1.6
|
1.0 / 1.2
|
|
Hindu
|
73.3 / 73.3
|
77.8 / 77.8
|
81.9 / 81.9
|
82.0 / 83.0
|
|
Muslim
|
11.0 / 11.0
|
8.3 / 8.3
|
11.0 / 11.0
|
10.4 / 9.5
|
|
None
|
6.7 / 6.7
|
3.0 / 3.0
|
0.4 / 0.4
|
—
|
|
Other (Sikh, Buddhist, Jain, …)
|
6.1 / 6.1
|
7.6 / 7.6
|
5.1 / 5.1
|
6.6 / 6.2
|
|
Caste
|
|
General / other
|
43.5 / 43.5
|
—
|
32.6 / 32.6
|
31.6 / 32.0
|
|
OBC
|
38.5 / 38.5
|
—
|
40.6 / 40.6
|
44.3 / 44.0
|
|
SC
|
12.9 / 12.9
|
—
|
20.1 / 20.1
|
19.1 / 19.2
|
|
ST
|
5.2 / 5.2
|
—
|
6.7 / 6.7
|
5.0 / 4.8
|
|
Subjective class
|
|
Lower
|
19.8 / 19.8
|
27.2 / 27.2
|
14.2 / 14.2
|
10.1 / 10.1
|
|
Lower middle / working
|
60.2 / 60.2
|
52.7 / 52.7
|
67.0 / 67.0
|
63.9 / 64.1
|
|
Upper / upper middle
|
20.0 / 20.0
|
20.1 / 20.1
|
18.8 / 18.8
|
26.0 / 25.7
|
|
Town size
|
|
100k+
|
11.2 / 11.2
|
7.9 / 7.9
|
3.1 / 3.1
|
5.9 / 5.7
|
|
20k-100k
|
19.0 / 19.0
|
2.8 / 2.8
|
6.4 / 6.4
|
10.5 / 9.9
|
|
Under 20k
|
69.8 / 69.8
|
89.3 / 89.3
|
90.6 / 90.6
|
83.6 / 84.3
|
|
Party would vote for
|
|
BJP
|
28.1 / 28.1
|
21.5 / 21.5
|
28.2 / 28.2
|
46.0 / 45.1
|
|
INC
|
33.1 / 33.1
|
32.4 / 32.4
|
28.0 / 28.0
|
21.4 / 21.9
|
|
Left
|
7.1 / 7.1
|
8.9 / 8.9
|
5.5 / 5.5
|
3.9 / 4.0
|
|
No party / blank / other
|
0.3 / 0.3
|
8.2 / 8.2
|
2.2 / 2.2
|
9.2 / 9.3
|
|
Other party
|
31.5 / 31.5
|
28.9 / 28.9
|
36.2 / 36.2
|
19.6 / 19.7
|
|
Percentages are among respondents with a valid answer on
that variable. Caste was not asked in 2006.
|
The India weights
india |>
group_by(wave) |>
summarise(N = n(),
Min = min(weight), Max = max(weight), Mean = mean(weight),
`CV of weights` = sd(weight) / mean(weight),
`Design effect (1 + CV²)` = 1 + (sd(weight) / mean(weight))^2,
`Effective N` = sum(weight)^2 / sum(weight^2),
.groups = "drop") |>
mutate(wave = india_years[wave]) |>
rename(`Survey year` = wave) |>
kable(digits = 2, caption = "India: survey weights", format.args = list(big.mark = ",")) |>
styled() |>
footnote(general = "Design effect here is from weighting only. Clustering (e.g. 60 polling stations in 2023) adds more, which the public data do not let us estimate.",
general_title = "")
India: survey weights
|
Survey year
|
N
|
Min
|
Max
|
Mean
|
CV of weights
|
Design effect (1 + CV²)
|
Effective N
|
|
2001
|
2,002
|
1.00
|
1.00
|
1
|
0.00
|
1.00
|
2,002.0
|
|
2006
|
2,001
|
1.00
|
1.00
|
1
|
0.00
|
1.00
|
2,001.0
|
|
2012
|
4,078
|
1.00
|
1.00
|
1
|
0.00
|
1.00
|
4,078.0
|
|
2023
|
1,692
|
0.19
|
1.94
|
1
|
0.44
|
1.19
|
1,419.8
|
|
Design effect here is from weighting only. Clustering
(e.g. 60 polling stations in 2023) adds more, which the public data do
not let us estimate.
|
The 2001, 2006 and 2012 India samples are
unweighted. Every weight is 1, so “weighted” India estimates
for those years are raw sample percentages. The profile table above
shows what that means:
- Men are 55–57% of respondents.
- In 2006 and 2012 about 90% live in places under 20,000 people. The
2011 Census puts India’s urban share at about 31%.
- Only 13% have higher education in 2012, against 22–23% in 2001 and
2006.
Only 2023 is weighted: 16–17 year olds are brought down from 19% to
6% of the sample, and the sex split is balanced. For India trends this
means:
- Changes between 2012 and 2023 partly reflect a change from
unweighted to weighted samples.
- Group breakdowns (by sex, education or town size) are more
trustworthy than India totals, because they do not depend on the
sample’s overall mix.
- A useful robustness check is to reweight 2001–2012 to Census sex ×
education × urban margins. This needs Census tables that are not in the
project yet.
Part II. India and the world regions
This part compares India with every world region on three measures:
governance vs accountability, the conception of democracy, and state
support. The regions, countries and samples are described in Part I.
- Regions: six world regions based on the UN
geoscheme (UN Statistics Division, M49 standard): South-eastern Asia;
Western Asia & Northern Africa; Sub-Saharan Africa; Latin America
& Caribbean; Eastern Europe; and Western Europe & Offshoots
(Western, Northern and Southern Europe with the United States, Canada,
Australia and New Zealand). India is shown on its own. Part I gives the
details.
- Countries: only those surveyed in at least 3 of the
4 waves (2001–2023), so that each region is made of roughly the same
countries over time.
- Region averages: the mean of the country estimates,
so each country counts once whatever its sample size. India is its own
estimate, and is also ranked among all the countries shown. Where a
region has a single country in a wave, it is named under the table.
II.1 Governance, accountability, or both?
The question. The passage argued that Indians want
both democracy and a strong, effective government. Do people want a
state that delivers, a state that answers to them, or
both?
What is calculated. Two yes/no measures for each
respondent:
- Demand for governance (delivery), four questions:
economic growth, fighting rising prices, a stable economy and the fight
against crime, each counted as yes when picked among the top two
aims.
- Demand for accountability, including willingness to act,
five questions: more say in government decisions and more say
at work and in communities (picked among the top two aims); has signed
or would sign a petition, attend a peaceful demonstration, join a
boycott.
A respondent demands governance (or accountability) if they say yes
to at least half of the questions they answered (2 of 4; 3 of 5).
Crossing the two gives four groups:
Accountability-first, Want both,
Governance-first and Neither, shown as
a % of those classified. Three questions on each side come from “pick
your top two” lists, so choosing more delivery aims leaves less room for
accountability aims.
fmt_rank <- function(list) paste0("Ranked: 1st and 2nd choice from 4 options (", list, ")")
aims_list <- "economic growth / strong defence / more say at work and in communities / beautiful cities"
goals_list <- "maintaining order / more say in government / fighting rising prices / free speech"
society_list <- "stable economy / more humane society / ideas over money / fight against crime"
fmt_act <- "3-point: 1 have done, 2 might do, 3 would never do"
gov_side <- "Governance (delivery): yes on at least half of 4"
acc_side <- "Accountability, including willingness to act: yes on at least half of 5"
q_table(tribble(
~side, ~var, ~question, ~format, ~mapped,
gov_side, "pm_aims1+pm_aims2", "Aims of the country: a high level of economic growth", fmt_rank(aims_list), "Picked 1st or 2nd = yes",
gov_side, "pm_goals1+pm_goals2", "Most important for the country: fighting rising prices", fmt_rank(goals_list), "Picked 1st or 2nd = yes",
gov_side, "pm_society1+pm_society2", "Most important: a stable economy", fmt_rank(society_list), "Picked 1st or 2nd = yes",
gov_side, "pm_society1+pm_society2", "Most important: the fight against crime", fmt_rank(society_list), "Picked 1st or 2nd = yes",
acc_side, "pm_goals1+pm_goals2", "Most important for the country: giving people more say in important government decisions", fmt_rank(goals_list), "Picked 1st or 2nd = yes",
acc_side, "pm_aims1+pm_aims2", "Aims of the country: people have more say about how things are done at their jobs and in their communities", fmt_rank(aims_list), "Picked 1st or 2nd = yes",
acc_side, "act_petition", "Signing a petition", fmt_act, "1–2 (done or might do) = yes",
acc_side, "act_demonstration", "Attending peaceful demonstrations", fmt_act, "1–2 (done or might do) = yes",
acc_side, "act_boycott", "Joining in boycotts", fmt_act, "1–2 (done or might do) = yes"),
caption = "Questions used in this section")
Questions used in this section
|
|
Question number in the WVS file
|
|
Question
|
Response format
|
How it is counted / what it maps to
|
Asked in India
|
W4
|
W5
|
W6
|
W7
|
|
Governance (delivery): yes on at least half of 4
|
|
Aims of the country: a high level of economic growth
|
Ranked: 1st and 2nd choice from 4 options (economic growth / strong
defence / more say at work and in communities / beautiful cities)
|
Picked 1st or 2nd = yes
|
2001, 2006, 2012, 2023
|
V120 & V121
|
V69 & V70
|
V60 & V61
|
Q152 & Q153
|
|
Most important for the country: fighting rising prices
|
Ranked: 1st and 2nd choice from 4 options (maintaining order / more say
in government / fighting rising prices / free speech)
|
Picked 1st or 2nd = yes
|
2001, 2006, 2012, 2023
|
V122 & V123
|
V71 & V72
|
V62 & V63
|
Q154 & Q155
|
|
Most important: a stable economy
|
Ranked: 1st and 2nd choice from 4 options (stable economy / more humane
society / ideas over money / fight against crime)
|
Picked 1st or 2nd = yes
|
2001, 2006, 2012, 2023
|
V124 & V125
|
V73 & V74
|
V64 & V65
|
Q156 & Q157
|
|
Most important: the fight against crime
|
Ranked: 1st and 2nd choice from 4 options (stable economy / more humane
society / ideas over money / fight against crime)
|
Picked 1st or 2nd = yes
|
2001, 2006, 2012, 2023
|
V124 & V125
|
V73 & V74
|
V64 & V65
|
Q156 & Q157
|
|
Accountability, including willingness to act: yes on at least
half of 5
|
|
Most important for the country: giving people more say in important
government decisions
|
Ranked: 1st and 2nd choice from 4 options (maintaining order / more say
in government / fighting rising prices / free speech)
|
Picked 1st or 2nd = yes
|
2001, 2006, 2012, 2023
|
V122 & V123
|
V71 & V72
|
V62 & V63
|
Q154 & Q155
|
|
Aims of the country: people have more say about how things are done at
their jobs and in their communities
|
Ranked: 1st and 2nd choice from 4 options (economic growth / strong
defence / more say at work and in communities / beautiful cities)
|
Picked 1st or 2nd = yes
|
2001, 2006, 2012, 2023
|
V120 & V121
|
V69 & V70
|
V60 & V61
|
Q152 & Q153
|
|
Signing a petition
|
3-point: 1 have done, 2 might do, 3 would never do
|
1–2 (done or might do) = yes
|
2001, 2006, 2012, 2023
|
V134
|
V96
|
V85
|
Q209
|
|
Attending peaceful demonstrations
|
3-point: 1 have done, 2 might do, 3 would never do
|
1–2 (done or might do) = yes
|
2001, 2006, 2012, 2023
|
V136
|
V98
|
V87
|
Q211
|
|
Joining in boycotts
|
3-point: 1 have done, 2 might do, 3 would never do
|
1–2 (done or might do) = yes
|
2001, 2006, 2012, 2023
|
V135
|
V97
|
V86
|
Q210
|
wb_compare(c(g_accfirst = "Accountability-first", g_both = "Want both",
g_govfirst = "Governance-first", g_neither = "Neither"),
caption = "Governance and accountability groups (% of those classified): India vs the regions")
Governance and accountability groups (% of those classified): India vs
the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Accountability-first
|
|
Western Europe & Offshoots
|
17.2
|
14.3
|
11.9
|
20.5
|
|
India
|
4.8
|
6.1
|
6.0
|
14.3
|
|
Latin America & Caribbean
|
7.8
|
7.4
|
7.5
|
9.7
|
|
Western Asia & Northern Africa
|
6.6
|
5.1
|
3.6
|
7.3
|
|
South-eastern Asia
|
4.4
|
2.7
|
6.4
|
6.6
|
|
Eastern Europe
|
3.1
|
2.1
|
1.8
|
5.9
|
|
Sub-Saharan Africa
|
3.4
|
3.8
|
5.2
|
5.3
|
|
India’s rank among all countries (1 = highest)
|
13 of 22
|
18 of 29
|
17 of 32
|
8 of 33
|
|
Want both
|
|
Western Europe & Offshoots
|
62.2
|
63.0
|
61.8
|
59.3
|
|
Latin America & Caribbean
|
40.4
|
44.6
|
43.4
|
42.2
|
|
India
|
43.0
|
43.7
|
35.0
|
32.7
|
|
Eastern Europe
|
41.1
|
35.2
|
23.9
|
31.5
|
|
Sub-Saharan Africa
|
43.1
|
41.9
|
38.0
|
28.2
|
|
South-eastern Asia
|
25.9
|
20.0
|
30.4
|
26.9
|
|
Western Asia & Northern Africa
|
24.0
|
25.8
|
20.7
|
26.0
|
|
India’s rank among all countries (1 = highest)
|
8 of 22
|
15 of 29
|
18 of 32
|
19 of 33
|
|
Governance-first
|
|
Sub-Saharan Africa
|
50.5
|
49.7
|
52.0
|
60.2
|
|
Western Asia & Northern Africa
|
62.9
|
64.2
|
70.4
|
59.8
|
|
South-eastern Asia
|
64.9
|
70.7
|
56.3
|
57.1
|
|
Eastern Europe
|
53.8
|
59.9
|
70.5
|
55.9
|
|
India
|
47.3
|
44.7
|
53.4
|
44.0
|
|
Latin America & Caribbean
|
47.6
|
43.9
|
45.0
|
42.8
|
|
Western Europe & Offshoots
|
18.9
|
21.0
|
24.6
|
18.2
|
|
India’s rank among all countries (1 = highest)
|
15 of 22
|
15 of 29
|
15 of 32
|
19 of 33
|
|
Neither
|
|
South-eastern Asia
|
4.8
|
6.5
|
6.9
|
9.4
|
|
India
|
4.9
|
5.5
|
5.6
|
9.0
|
|
Western Asia & Northern Africa
|
6.5
|
4.9
|
5.3
|
6.9
|
|
Eastern Europe
|
2.0
|
2.7
|
3.8
|
6.7
|
|
Sub-Saharan Africa
|
3.0
|
4.5
|
4.8
|
6.2
|
|
Latin America & Caribbean
|
4.2
|
4.1
|
4.1
|
5.4
|
|
Western Europe & Offshoots
|
1.7
|
1.7
|
1.6
|
2.0
|
|
India’s rank among all countries (1 = highest)
|
8 of 22
|
6 of 29
|
8 of 32
|
8 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
wb_stack(c(g_accfirst = "Accountability-first", g_both = "Want both",
g_govfirst = "Governance-first", g_neither = "Neither"),
cols = c(`Accountability-first` = "#256abf", `Want both` = "#5598e7",
`Governance-first` = "#e34948", Neither = "#8a8983"),
title = "Governance and accountability groups, latest wave")
Each chart: India and every region as rows, one dot per wave (light =
W4, dark = W7).
gt_plots <- imap(c(g_accfirst = "Accountability-first", g_both = "Want both", g_govfirst = "Governance-first"),
~ wb_dots(.y, title = .x, xlab = "% of those classified"))
if (isTRUE(getOption("wvs.static"))) invisible(lapply(gt_plots, print)) else htmltools::tagList(gt_plots)
II.2 Liberal, minimalist or illiberal?
The question. A minimalist conception of
democracy asks for free elections and rejects authoritarian
alternatives, but does not insist on civil rights, equal rights and a
firm commitment to democracy. A maximalist liberal conception
insists on all of these. An illiberal conception either does
not treat free elections as essential, or accepts authoritarian ways of
governing. Which do people hold?
What is calculated. The liberal
score is the share of 5 liberal questions answered yes; the
authoritarian score is the share of 7 authoritarian
questions answered yes; the democracy score is the
first minus the second (−1 to +1), following Kirsch and Welzel’s
liberal-minus-authoritarian notions of democracy. The three types are
bands of this score:
- Liberal (maximalist): 0.5 or more, with free
elections rated 7–10.
- Minimalist (electoral): above 0 and below 0.5, with
free elections rated 7–10.
- Illiberal: 0 or less, or free elections rated
1–6.
Classified from 2006 (the essentials-of-democracy questions start in
W5), if free elections and at least three questions in each set were
answered. “People obey their rulers” was asked only from 2012, so in
2006 the authoritarian score uses six questions.
lib_side <- "Liberal questions: liberal score = share answered yes"
aut_side <- "Authoritarian questions: authoritarian score = share answered yes"
show_side <- "Shown for each type (not used in the rules)"
q_table(tribble(
~side, ~var, ~question, ~format, ~mapped,
lib_side, "dess_elections", "Essential to democracy: people choose their leaders in free elections", fmt_ess, "7–10 = yes. 1–6 = Illiberal whatever the score",
lib_side, "dess_civilrights", "Essential to democracy: civil rights protect people from state oppression", fmt_ess, "7–10 = yes",
lib_side, "dess_women", "Essential to democracy: women have the same rights as men", fmt_ess, "7–10 = yes",
lib_side, "reg_democracy", "Having a democratic political system", fmt_reg, "1–2 (good) = yes",
lib_side, "dem_importance", "How important is it for you to live in a country that is governed democratically?", "10-point: 1 not at all important ... 10 absolutely important", "7–10 = yes",
aut_side, "dess_army", "Essential to democracy: the army takes over when government is incompetent", fmt_ess, "7–10 = yes",
aut_side, "dess_religious", "Essential to democracy: religious authorities ultimately interpret the laws", fmt_ess, "7–10 = yes",
aut_side, "dess_obey", "Essential to democracy: people obey their rulers", fmt_ess, "7–10 = yes (from 2012)",
aut_side, "reg_army", "Having the army rule", fmt_reg, "1–2 (good) = yes",
aut_side, "reg_strongleader", "Having a strong leader who does not have to bother with parliament and elections", fmt_reg, "1–2 (good) = yes",
aut_side, "reg_experts", "Having experts, not government, make decisions according to what they think is best for the country", fmt_reg, "1–2 (good) = yes",
aut_side, "fut_authority", "In the near future: greater respect for authority", "3-point: 1 good thing, 2 don't mind, 3 bad thing", "1 (good thing) = yes",
show_side, "act_demonstration", "Attending peaceful demonstrations", "3-point: 1 have done, 2 might do, 3 would never do", "1–2 = done or might do",
show_side, "act_boycott", "Joining in boycotts", "3-point: 1 have done, 2 might do, 3 would never do", "1–2 = done or might do"),
caption = "Questions used in this section")
Questions used in this section
|
|
Question number in the WVS file
|
|
Question
|
Response format
|
How it is counted / what it maps to
|
Asked in India
|
W4
|
W5
|
W6
|
W7
|
|
Liberal questions: liberal score = share answered yes
|
|
Essential to democracy: people choose their leaders in free elections
|
10-point: 1 not an essential characteristic of democracy … 10 an
essential characteristic
|
7–10 = yes. 1–6 = Illiberal whatever the score
|
2006, 2012, 2023
|
—
|
V154
|
V133
|
Q243
|
|
Essential to democracy: civil rights protect people from state
oppression
|
10-point: 1 not an essential characteristic of democracy … 10 an
essential characteristic
|
7–10 = yes
|
2006, 2012, 2023
|
—
|
V157
|
V136
|
Q246
|
|
Essential to democracy: women have the same rights as men
|
10-point: 1 not an essential characteristic of democracy … 10 an
essential characteristic
|
7–10 = yes
|
2006, 2012, 2023
|
—
|
V161
|
V139
|
Q249
|
|
Having a democratic political system
|
4-point: 1 very good, 2 fairly good, 3 fairly bad, 4 very bad
|
1–2 (good) = yes
|
2001, 2006, 2012, 2023
|
V167
|
V151
|
V130
|
Q238
|
|
How important is it for you to live in a country that is governed
democratically?
|
10-point: 1 not at all important … 10 absolutely important
|
7–10 = yes
|
2006, 2012, 2023
|
—
|
V162
|
V140
|
Q250
|
|
Authoritarian questions: authoritarian score = share answered
yes
|
|
Essential to democracy: the army takes over when government is
incompetent
|
10-point: 1 not an essential characteristic of democracy … 10 an
essential characteristic
|
7–10 = yes
|
2006, 2012, 2023
|
—
|
V156
|
V135
|
Q245
|
|
Essential to democracy: religious authorities ultimately interpret the
laws
|
10-point: 1 not an essential characteristic of democracy … 10 an
essential characteristic
|
7–10 = yes
|
2006, 2012, 2023
|
—
|
V153
|
V132
|
Q242
|
|
Essential to democracy: people obey their rulers
|
10-point: 1 not an essential characteristic of democracy … 10 an
essential characteristic
|
7–10 = yes (from 2012)
|
2012, 2023
|
—
|
—
|
V138
|
Q248
|
|
Having the army rule
|
4-point: 1 very good, 2 fairly good, 3 fairly bad, 4 very bad
|
1–2 (good) = yes
|
2001, 2006, 2012, 2023
|
V166
|
V150
|
V129
|
Q237
|
|
Having a strong leader who does not have to bother with parliament and
elections
|
4-point: 1 very good, 2 fairly good, 3 fairly bad, 4 very bad
|
1–2 (good) = yes
|
2001, 2006, 2012, 2023
|
V164
|
V148
|
V127
|
Q235
|
|
Having experts, not government, make decisions according to what they
think is best for the country
|
4-point: 1 very good, 2 fairly good, 3 fairly bad, 4 very bad
|
1–2 (good) = yes
|
2001, 2006, 2012, 2023
|
V165
|
V149
|
V128
|
Q236
|
|
In the near future: greater respect for authority
|
3-point: 1 good thing, 2 don’t mind, 3 bad thing
|
1 (good thing) = yes
|
2001, 2006, 2012, 2023
|
V130
|
V78
|
V69
|
Q45
|
|
Shown for each type (not used in the rules)
|
|
Attending peaceful demonstrations
|
3-point: 1 have done, 2 might do, 3 would never do
|
1–2 = done or might do
|
2001, 2006, 2012, 2023
|
V136
|
V98
|
V87
|
Q211
|
|
Joining in boycotts
|
3-point: 1 have done, 2 might do, 3 would never do
|
1–2 = done or might do
|
2001, 2006, 2012, 2023
|
V135
|
V97
|
V86
|
Q210
|
wb_compare(c(con_liberal = "Liberal (maximalist)", con_minimal = "Minimalist (electoral)",
con_illiberal = "Illiberal"),
caption = "Conceptions of democracy (% of those classified): India vs the regions")
Conceptions of democracy (% of those classified): India vs the regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Liberal (maximalist)
|
|
Western Europe & Offshoots
|
72.8
|
67.9
|
70.3
|
|
Eastern Europe
|
48.2
|
31.7
|
31.0
|
|
Latin America & Caribbean
|
45.5
|
36.3
|
28.5
|
|
Western Asia & Northern Africa
|
36.0
|
28.4
|
26.9
|
|
India
|
25.0
|
29.5
|
26.4
|
|
Sub-Saharan Africa
|
37.5
|
22.6
|
20.7
|
|
South-eastern Asia
|
38.0
|
29.9
|
17.3
|
|
India’s rank among all countries (1 = highest)
|
24 of 29
|
19 of 32
|
21 of 33
|
|
Minimalist (electoral)
|
|
Sub-Saharan Africa
|
32.7
|
40.0
|
46.7
|
|
India
|
35.8
|
34.7
|
40.7
|
|
Latin America & Caribbean
|
29.2
|
36.0
|
36.7
|
|
Western Asia & Northern Africa
|
39.6
|
37.2
|
35.1
|
|
Eastern Europe
|
30.6
|
36.5
|
35.1
|
|
South-eastern Asia
|
32.1
|
33.5
|
33.3
|
|
Western Europe & Offshoots
|
13.3
|
15.8
|
13.7
|
|
India’s rank among all countries (1 = highest)
|
6 of 29
|
16 of 32
|
7 of 33
|
|
Illiberal
|
|
South-eastern Asia
|
29.9
|
36.6
|
49.4
|
|
Western Asia & Northern Africa
|
24.5
|
34.4
|
38.0
|
|
Latin America & Caribbean
|
25.4
|
27.7
|
34.8
|
|
Eastern Europe
|
21.2
|
31.8
|
33.9
|
|
India
|
39.2
|
35.9
|
32.9
|
|
Sub-Saharan Africa
|
29.8
|
37.4
|
32.6
|
|
Western Europe & Offshoots
|
13.9
|
16.3
|
16.0
|
|
India’s rank among all countries (1 = highest)
|
4 of 29
|
10 of 32
|
17 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
wb_stack(c(con_liberal = "Liberal (maximalist)", con_minimal = "Minimalist (electoral)", con_illiberal = "Illiberal"),
cols = c(`Liberal (maximalist)` = "#0d366b", `Minimalist (electoral)` = "#256abf", Illiberal = "#86b6ef"),
title = "Conceptions of democracy, latest wave")
Each chart: India and every region as rows, one dot per wave (light =
W5, dark = W7; the types are classified from 2006).
conc_plots <- imap(c(con_liberal = "Liberal (maximalist)", con_minimal = "Minimalist (electoral)", con_illiberal = "Illiberal"),
~ wb_dots(.y, title = .x, xlab = "% of those classified"))
if (isTRUE(getOption("wvs.static"))) invisible(lapply(conc_plots, print)) else htmltools::tagList(conc_plots)
Do liberals act on their views?
What is shown. For each conception type: the % who
have attended or might attend a peaceful demonstration, and the % who
have joined or might join a boycott. If liberals act on their views,
they should be more willing to protest than minimalists and
illiberals.
wb_by_type("acc_demo", caption = "Has attended or might attend a peaceful demonstration (%), by conception of democracy")
Has attended or might attend a peaceful demonstration (%), by conception
of democracy
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Liberal (maximalist)
|
|
Western Europe & Offshoots
|
73.4
|
74.6
|
77.5
|
|
Latin America & Caribbean
|
61.7
|
62.0
|
63.4
|
|
India
|
53.6
|
55.2
|
57.0
|
|
Eastern Europe
|
51.5
|
39.0
|
52.2
|
|
Sub-Saharan Africa
|
58.5
|
52.6
|
48.7
|
|
Western Asia & Northern Africa
|
43.3
|
34.3
|
45.9
|
|
South-eastern Asia
|
26.8
|
23.2
|
33.4
|
|
Minimalist (electoral)
|
|
Western Europe & Offshoots
|
63.5
|
58.7
|
59.7
|
|
Latin America & Caribbean
|
48.4
|
52.2
|
55.0
|
|
India
|
49.9
|
44.8
|
53.2
|
|
Eastern Europe
|
43.9
|
33.6
|
46.4
|
|
Sub-Saharan Africa
|
46.9
|
50.3
|
42.2
|
|
Western Asia & Northern Africa
|
34.1
|
30.2
|
35.5
|
|
South-eastern Asia
|
29.1
|
27.6
|
31.0
|
|
Illiberal
|
|
India
|
46.7
|
43.7
|
63.7
|
|
Western Europe & Offshoots
|
56.3
|
52.4
|
55.7
|
|
Latin America & Caribbean
|
45.2
|
46.1
|
47.8
|
|
Sub-Saharan Africa
|
38.9
|
42.7
|
40.7
|
|
Eastern Europe
|
44.4
|
31.1
|
38.5
|
|
Western Asia & Northern Africa
|
32.0
|
24.9
|
36.8
|
|
South-eastern Asia
|
28.4
|
25.4
|
34.0
|
|
Cells: % among respondents of that type. Regions: mean of
country estimates; a country counts only with at least 30 respondents of
that type. — = too few respondents or not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
wb_by_type("acc_boycott", caption = "Has joined or might join a boycott (%), by conception of democracy")
Has joined or might join a boycott (%), by conception of democracy
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Liberal (maximalist)
|
|
Western Europe & Offshoots
|
65.8
|
67.1
|
75.8
|
|
India
|
55.2
|
47.7
|
40.6
|
|
Western Asia & Northern Africa
|
34.3
|
25.2
|
34.9
|
|
Latin America & Caribbean
|
24.7
|
28.4
|
32.3
|
|
Sub-Saharan Africa
|
42.5
|
39.2
|
27.9
|
|
Eastern Europe
|
30.3
|
16.7
|
27.3
|
|
South-eastern Asia
|
19.0
|
16.2
|
22.0
|
|
Minimalist (electoral)
|
|
Western Europe & Offshoots
|
48.1
|
49.6
|
55.5
|
|
India
|
46.0
|
35.8
|
40.5
|
|
Western Asia & Northern Africa
|
27.4
|
22.0
|
27.5
|
|
Sub-Saharan Africa
|
32.6
|
31.4
|
26.8
|
|
Eastern Europe
|
27.3
|
14.1
|
26.1
|
|
Latin America & Caribbean
|
18.4
|
22.6
|
23.7
|
|
South-eastern Asia
|
17.9
|
20.9
|
20.8
|
|
Illiberal
|
|
Western Europe & Offshoots
|
42.8
|
43.8
|
50.1
|
|
India
|
45.9
|
34.4
|
48.7
|
|
Western Asia & Northern Africa
|
25.7
|
18.9
|
32.0
|
|
Sub-Saharan Africa
|
29.0
|
28.1
|
30.0
|
|
Latin America & Caribbean
|
18.3
|
21.9
|
29.1
|
|
Eastern Europe
|
27.5
|
16.4
|
28.6
|
|
South-eastern Asia
|
18.9
|
19.4
|
24.6
|
|
Cells: % among respondents of that type. Regions: mean of
country estimates; a country counts only with at least 30 respondents of
that type. — = too few respondents or not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
What this shows.
- In most regions, liberals are the most willing to
protest. In the latest wave, 78% of liberals in Western Europe
& Offshoots would attend a peaceful demonstration against 56% of
illiberals; Latin America 63% vs 48%; Eastern Europe 52% vs 39%; Western
Asia & Northern Africa 46% vs 37%. Boycotts show the same order.
South-eastern Asia shows little difference.
- India is the exception in 2023. Indian liberals led
in 2006 and 2012 (55% vs 44% for demonstrations in 2012), but in 2023
illiberals are the most willing: 64% would demonstrate, against 57% of
liberals and 53% of minimalists; for boycotts 49% against 41% and 41%.
In India, holding a liberal idea of democracy no longer goes with a
greater readiness to act on it.
- India’s 2023 liberal group is small (about 300 respondents), so read
the gap with some care.
II.3 State support
The question. Do people want the state to look after
those who cannot look after themselves? This is kept apart from the
conception types: a liberal, a minimalist or an illiberal can each be
for or against state support.
What is calculated. A state
supporter says yes to at least 3 of five questions: government
should take more responsibility (1–4 on the 10-point scale), incomes
should be made more equal (1–4), and taxing the rich, unemployment aid
and the state making incomes equal rated 7–10 as essential to democracy.
Classified from 2006; in 2006 only four were asked (3 of 4).
q_table(tribble(
~var, ~question, ~format, ~mapped,
"econ_govresp", "Government should take more responsibility to ensure that everyone is provided for (1) ... people should take more responsibility to provide for themselves (10)", "10-point scale between the two statements", "1–4 = yes to state support",
"econ_income", "Incomes should be made more equal (1) ... we need larger income differences as incentives (10)", "10-point scale between the two statements", "1–4 = yes to state support",
"dess_taxrich", "Essential to democracy: governments tax the rich and subsidise the poor", fmt_ess, "7–10 = yes to state support",
"dess_unemp", "Essential to democracy: people receive state aid for unemployment", fmt_ess, "7–10 = yes to state support",
"dess_incomeequal", "Essential to democracy: the state makes people's incomes equal", fmt_ess, "7–10 = yes to state support"),
caption = "Questions used in this section. State supporter = yes to at least 3")
Questions used in this section. State supporter = yes to at least 3
|
|
Question number in the WVS file
|
|
Question
|
Response format
|
How it is counted / what it maps to
|
Asked in India
|
W4
|
W5
|
W6
|
W7
|
|
Government should take more responsibility to ensure that everyone is
provided for (1) … people should take more responsibility to provide for
themselves (10)
|
10-point scale between the two statements
|
1–4 = yes to state support
|
2001, 2006, 2012, 2023
|
V143
|
V118
|
V98
|
Q108
|
|
Incomes should be made more equal (1) … we need larger income
differences as incentives (10)
|
10-point scale between the two statements
|
1–4 = yes to state support
|
2001, 2006, 2012, 2023
|
V141
|
V116
|
V96
|
Q106
|
|
Essential to democracy: governments tax the rich and subsidise the poor
|
10-point: 1 not an essential characteristic of democracy … 10 an
essential characteristic
|
7–10 = yes to state support
|
2006, 2012, 2023
|
—
|
V152
|
V131
|
Q241
|
|
Essential to democracy: people receive state aid for unemployment
|
10-point: 1 not an essential characteristic of democracy … 10 an
essential characteristic
|
7–10 = yes to state support
|
2006, 2012, 2023
|
—
|
V155
|
V134
|
Q244
|
|
Essential to democracy: the state makes people’s incomes equal
|
10-point: 1 not an essential characteristic of democracy … 10 an
essential characteristic
|
7–10 = yes to state support
|
2012, 2023
|
—
|
—
|
V137
|
Q247
|
wb_compare(c(state_support = "State supporters (%)"),
caption = "State supporters: India vs the regions")
State supporters: India vs the regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Asia & Northern Africa
|
51.0
|
67.7
|
57.7
|
|
Sub-Saharan Africa
|
39.1
|
40.7
|
53.5
|
|
Eastern Europe
|
44.5
|
61.6
|
52.9
|
|
India
|
46.3
|
71.6
|
52.4
|
|
South-eastern Asia
|
18.3
|
42.0
|
39.3
|
|
Latin America & Caribbean
|
27.3
|
45.4
|
38.7
|
|
Western Europe & Offshoots
|
29.0
|
39.4
|
38.6
|
|
India’s rank among all countries (1 = highest)
|
9 of 29
|
6 of 32
|
10 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
wb_lines("state_support", title = "State supporters: India vs each region", ylab = "% state supporters")
State support and the conception of democracy
What is shown. For each conception type: the % who
are state supporters. This shows whether wanting a supportive state goes
with a liberal, a minimalist or an illiberal idea of democracy.
wb_by_type("state_support", caption = "State supporters (%), by conception of democracy")
State supporters (%), by conception of democracy
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Liberal (maximalist)
|
|
Western Asia & Northern Africa
|
52.3
|
71.3
|
64.7
|
|
Eastern Europe
|
44.7
|
68.4
|
58.5
|
|
India
|
43.7
|
85.6
|
55.1
|
|
Sub-Saharan Africa
|
43.3
|
39.2
|
53.1
|
|
Latin America & Caribbean
|
29.7
|
49.8
|
47.5
|
|
South-eastern Asia
|
17.9
|
42.7
|
45.8
|
|
Western Europe & Offshoots
|
32.4
|
42.1
|
41.2
|
|
Minimalist (electoral)
|
|
Western Asia & Northern Africa
|
54.8
|
73.2
|
67.3
|
|
Sub-Saharan Africa
|
44.9
|
56.4
|
64.5
|
|
Eastern Europe
|
51.8
|
72.8
|
63.5
|
|
India
|
49.7
|
77.6
|
60.6
|
|
South-eastern Asia
|
22.8
|
54.7
|
53.2
|
|
Latin America & Caribbean
|
28.5
|
50.0
|
45.4
|
|
Western Europe & Offshoots
|
25.6
|
40.7
|
42.0
|
|
Illiberal
|
|
Western Asia & Northern Africa
|
35.9
|
52.4
|
41.9
|
|
India
|
38.4
|
53.9
|
39.2
|
|
Sub-Saharan Africa
|
26.8
|
25.7
|
39.1
|
|
Eastern Europe
|
35.8
|
41.3
|
37.6
|
|
South-eastern Asia
|
13.5
|
28.3
|
29.8
|
|
Latin America & Caribbean
|
17.6
|
29.0
|
25.4
|
|
Western Europe & Offshoots
|
14.0
|
25.4
|
23.9
|
|
Cells: % among respondents of that type. Regions: mean of
country estimates; a country counts only with at least 30 respondents of
that type. — = too few respondents or not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
What this shows.
- Everywhere, illiberals are the least state-supportive;
liberals and minimalists are alike. In the latest wave, Western
Europe & Offshoots: 41% of liberals and 42% of minimalists are state
supporters, against 24% of illiberals; Eastern Europe 59%, 64% and 38%;
Latin America 48%, 45% and 25%.
- India follows the same pattern. In 2023, 55% of
Indian liberals and 61% of minimalists are state supporters, against 39%
of illiberals (2012: 86%, 78% and 54%). Wanting democracy and wanting a
supportive state go together, in India as elsewhere.
What this part shows.
All figures are for the latest wave (India 2023) unless stated.
- Governance vs accountability: India sits between Western
Europe & Offshoots and the rest. 44% of Indians are
governance-first, like Latin America (43%); below South-eastern Asia
(57%), Eastern Europe (56%), Western Asia & Northern Africa and
Sub-Saharan Africa (60%); and far above Western Europe & Offshoots
(18%). India’s accountability-first share (14%) is higher than in every
region except Western Europe & Offshoots (21%). “Want both” (33%) is
close to Eastern Europe (32%), below Latin America (42%) and far below
Western Europe & Offshoots (59%).
- Conceptions: few liberals, many minimalists.
India’s liberal share (26%) is like Western Asia & Northern Africa
(27%), Latin America (29%) and Eastern Europe (31%), above South-eastern
Asia (17%) and Sub-Saharan Africa (21%), and far below Western Europe
& Offshoots (70%). Its minimalist share (41%) is among the highest
(7th of 33 countries), with Sub-Saharan Africa (47%); Western Europe
& Offshoots has 14%. Its illiberal share (33%) is mid-range:
South-eastern Asia is highest (49%), Western Europe & Offshoots
lowest (16%).
- State support: India is in the upper middle. 52% of
Indians are state supporters, like Eastern Europe (53%) and Sub-Saharan
Africa (54%), below Western Asia & Northern Africa (58%), and above
South-eastern Asia, Latin America and Western Europe & Offshoots
(39% each).
- Caution: Sub-Saharan Africa has only three
countries (one in W5), so its figures rest on very few surveys.
Part III. Question by question: India and the regions
Part II combines questions into measures (groups, types, state
supporters). This part shows each question on its own, so that every
building block can be checked, and adds trust in institutions.
- One table per question: rows are India and the
regions of Part II (same countries: those surveyed in at least 3 of the
4 waves); columns are the waves.
- What each cell shows: the % giving the answer that
the report counts as “yes” for that question (set out in each section’s
question table). India’s cell is its own estimate; a region’s cell is
the mean of its countries. The last row gives India’s rank among all
countries shown.
- “—” means the question was not asked in that wave
(or not in any of the region’s countries).
- Click a tab to see each question.
# one table per question, printed under its own tab
p4_tabs <- function(items) {
for (v in names(items)) {
cat("\n\n#### ", items[[v]], "\n\n", sep = "")
print(wb_compare(setNames(items[[v]], v),
caption = paste0(items[[v]], ": % counted as yes, India and the regions")))
}
cat("\n\n")
}
III.1 Trust in institutions
What is shown. For each of the 8 institutions: the %
with a great deal or quite a lot of confidence (answers 1–2 on the
4-point scale). The courts question was not asked in W4.
q_table(tribble(
~var, ~question, ~format, ~mapped,
"conf_civilservice", "Confidence in the civil service", fmt_conf, "1–2 = trusts. State (uses power)",
"conf_police", "Confidence in the police", fmt_conf, "1–2 = trusts. State (uses power)",
"conf_army", "Confidence in the armed forces", fmt_conf, "1–2 = trusts. State (uses power)",
"conf_govt", "Confidence in the government", fmt_conf, "1–2 = trusts. State (uses power)",
"conf_parliament", "Confidence in parliament", fmt_conf, "1–2 = trusts. Checks power",
"conf_parties", "Confidence in political parties", fmt_conf, "1–2 = trusts. Checks power",
"conf_press", "Confidence in the press", fmt_conf, "1–2 = trusts. Checks power",
"conf_courts", "Confidence in the courts", fmt_conf, "1–2 = trusts. Checks power"))
Questions used in this section
|
|
Question number in the WVS file
|
|
Question
|
Response format
|
How it is counted / what it maps to
|
Asked in India
|
W4
|
W5
|
W6
|
W7
|
|
Confidence in the civil service
|
4-point: 1 a great deal, 2 quite a lot, 3 not very much, 4 none at all
|
1–2 = trusts. State (uses power)
|
2001, 2006, 2012, 2023
|
V156
|
V141
|
V118
|
Q74
|
|
Confidence in the police
|
4-point: 1 a great deal, 2 quite a lot, 3 not very much, 4 none at all
|
1–2 = trusts. State (uses power)
|
2001, 2006, 2012, 2023
|
V152
|
V136
|
V113
|
Q69
|
|
Confidence in the armed forces
|
4-point: 1 a great deal, 2 quite a lot, 3 not very much, 4 none at all
|
1–2 = trusts. State (uses power)
|
2001, 2006, 2012, 2023
|
V148
|
V132
|
V109
|
Q65
|
|
Confidence in the government
|
4-point: 1 a great deal, 2 quite a lot, 3 not very much, 4 none at all
|
1–2 = trusts. State (uses power)
|
2001, 2006, 2012, 2023
|
V153
|
V138
|
V115
|
Q71
|
|
Confidence in parliament
|
4-point: 1 a great deal, 2 quite a lot, 3 not very much, 4 none at all
|
1–2 = trusts. Checks power
|
2001, 2006, 2012, 2023
|
V155
|
V140
|
V117
|
Q73
|
|
Confidence in political parties
|
4-point: 1 a great deal, 2 quite a lot, 3 not very much, 4 none at all
|
1–2 = trusts. Checks power
|
2001, 2006, 2012, 2023
|
V154
|
V139
|
V116
|
Q72
|
|
Confidence in the press
|
4-point: 1 a great deal, 2 quite a lot, 3 not very much, 4 none at all
|
1–2 = trusts. Checks power
|
2001, 2006, 2012, 2023
|
V149
|
V133
|
V110
|
Q66
|
|
Confidence in the courts
|
4-point: 1 a great deal, 2 quite a lot, 3 not very much, 4 none at all
|
1–2 = trusts. Checks power
|
2006, 2012, 2023
|
—
|
V137
|
V114
|
Q70
|
Each institution
p4_tabs(c(conf_civilservice_hi = "Civil service", conf_police_hi = "Police",
conf_army_hi = "Armed forces", conf_govt_hi = "Government",
conf_parliament_hi = "Parliament", conf_parties_hi = "Political parties",
conf_press_hi = "Press", conf_courts_hi = "Courts"))
Civil service
Civil service: % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
India
|
48.6
|
54.3
|
61.0
|
80.7
|
|
South-eastern Asia
|
69.8
|
64.7
|
70.0
|
73.0
|
|
Sub-Saharan Africa
|
62.5
|
55.4
|
52.1
|
49.6
|
|
Western Europe & Offshoots
|
48.9
|
43.7
|
47.6
|
49.6
|
|
Eastern Europe
|
36.7
|
38.6
|
46.0
|
41.9
|
|
Western Asia & Northern Africa
|
58.1
|
57.3
|
43.1
|
39.4
|
|
Latin America & Caribbean
|
19.6
|
26.0
|
26.7
|
27.0
|
|
India’s rank among all countries (1 = highest)
|
13 of 21
|
10 of 30
|
6 of 32
|
2 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Police
Police: % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Europe & Offshoots
|
72.0
|
73.3
|
75.0
|
77.5
|
|
South-eastern Asia
|
68.7
|
64.9
|
67.1
|
73.1
|
|
Western Asia & Northern Africa
|
76.1
|
72.7
|
64.8
|
66.9
|
|
India
|
38.0
|
64.1
|
51.2
|
66.7
|
|
Eastern Europe
|
35.2
|
35.4
|
40.7
|
46.1
|
|
Sub-Saharan Africa
|
51.6
|
61.1
|
44.3
|
38.8
|
|
Latin America & Caribbean
|
31.4
|
39.3
|
42.0
|
38.8
|
|
India’s rank among all countries (1 = highest)
|
16 of 21
|
12 of 29
|
20 of 32
|
14 of 32
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Armed forces
Armed forces: % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
India
|
92.0
|
83.3
|
87.1
|
86.8
|
|
South-eastern Asia
|
81.5
|
76.9
|
74.9
|
79.7
|
|
Western Asia & Northern Africa
|
73.6
|
76.7
|
74.5
|
74.8
|
|
Western Europe & Offshoots
|
57.6
|
63.2
|
68.7
|
74.1
|
|
Eastern Europe
|
65.7
|
64.9
|
64.2
|
71.5
|
|
Sub-Saharan Africa
|
54.7
|
63.4
|
58.1
|
53.4
|
|
Latin America & Caribbean
|
37.6
|
49.5
|
47.9
|
46.7
|
|
India’s rank among all countries (1 = highest)
|
3 of 22
|
6 of 30
|
3 of 31
|
4 of 32
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Government
Government: % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
South-eastern Asia
|
67.1
|
67.1
|
66.6
|
73.8
|
|
India
|
56.2
|
54.9
|
50.4
|
65.2
|
|
Sub-Saharan Africa
|
53.1
|
70.4
|
45.4
|
46.1
|
|
Western Europe & Offshoots
|
41.4
|
37.0
|
39.2
|
40.0
|
|
Western Asia & Northern Africa
|
58.2
|
64.2
|
47.1
|
37.8
|
|
Eastern Europe
|
33.2
|
31.9
|
38.1
|
35.1
|
|
Latin America & Caribbean
|
33.4
|
42.9
|
38.5
|
26.3
|
|
India’s rank among all countries (1 = highest)
|
7 of 21
|
9 of 30
|
11 of 32
|
6 of 32
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Parliament
Parliament: % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
India
|
55.2
|
62.4
|
58.4
|
73.7
|
|
South-eastern Asia
|
67.0
|
59.1
|
62.9
|
64.4
|
|
Sub-Saharan Africa
|
51.7
|
65.2
|
44.4
|
39.0
|
|
Western Europe & Offshoots
|
44.9
|
35.7
|
37.9
|
34.0
|
|
Eastern Europe
|
29.6
|
21.8
|
31.4
|
25.5
|
|
Western Asia & Northern Africa
|
51.0
|
54.9
|
34.6
|
23.4
|
|
Latin America & Caribbean
|
19.7
|
24.1
|
24.8
|
18.5
|
|
India’s rank among all countries (1 = highest)
|
6 of 21
|
5 of 29
|
5 of 32
|
2 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Political parties
Political parties: % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
South-eastern Asia
|
56.7
|
51.4
|
54.4
|
53.3
|
|
India
|
33.5
|
46.4
|
37.4
|
42.6
|
|
Sub-Saharan Africa
|
39.3
|
43.5
|
36.0
|
31.0
|
|
Eastern Europe
|
20.1
|
16.3
|
28.3
|
20.9
|
|
Western Asia & Northern Africa
|
30.9
|
34.2
|
21.8
|
18.3
|
|
Western Europe & Offshoots
|
24.3
|
20.6
|
20.7
|
17.8
|
|
Latin America & Caribbean
|
16.9
|
18.7
|
18.9
|
13.8
|
|
India’s rank among all countries (1 = highest)
|
6 of 20
|
3 of 29
|
9 of 32
|
5 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Press
Press: % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
India
|
70.3
|
75.8
|
71.8
|
65.6
|
|
South-eastern Asia
|
68.8
|
62.2
|
59.9
|
58.5
|
|
Sub-Saharan Africa
|
61.2
|
60.9
|
52.0
|
43.2
|
|
Western Europe & Offshoots
|
37.1
|
29.1
|
30.8
|
31.7
|
|
Eastern Europe
|
36.3
|
37.0
|
43.0
|
31.4
|
|
Western Asia & Northern Africa
|
50.4
|
49.9
|
39.2
|
30.8
|
|
Latin America & Caribbean
|
37.7
|
41.1
|
40.3
|
30.2
|
|
India’s rank among all countries (1 = highest)
|
2 of 21
|
2 of 30
|
1 of 32
|
3 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Courts
Courts: % counted as yes, India and the regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
South-eastern Asia
|
72.7
|
76.3
|
76.3
|
|
India
|
68.9
|
64.5
|
74.3
|
|
Western Europe & Offshoots
|
56.6
|
62.1
|
64.9
|
|
Western Asia & Northern Africa
|
74.8
|
64.4
|
57.5
|
|
Sub-Saharan Africa
|
65.9
|
52.3
|
48.0
|
|
Eastern Europe
|
32.1
|
36.3
|
35.6
|
|
Latin America & Caribbean
|
33.9
|
33.2
|
29.6
|
|
India’s rank among all countries (1 = highest)
|
8 of 29
|
12 of 32
|
7 of 32
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
III.2 Governance (delivery) questions
What is shown. The four delivery questions of II.1:
the % who pick each aim as the country’s 1st or 2nd most important.
The questions are listed in the table in II.1.
Each question
p4_tabs(c(gov_growth = "Economic growth (top two aims)",
gov_prices = "Fighting rising prices (top two aims)",
gov_economy = "A stable economy (top two aims)",
gov_crime = "The fight against crime (top two aims)"))
Economic growth (top two aims)
Economic growth (top two aims): % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Sub-Saharan Africa
|
84.4
|
78.5
|
80.2
|
78.7
|
|
Western Asia & Northern Africa
|
80.7
|
83.4
|
82.7
|
78.2
|
|
Latin America & Caribbean
|
82.0
|
82.9
|
83.8
|
76.9
|
|
Eastern Europe
|
89.9
|
87.8
|
88.9
|
76.4
|
|
South-eastern Asia
|
86.9
|
86.1
|
78.2
|
73.2
|
|
Western Europe & Offshoots
|
79.6
|
79.8
|
83.1
|
71.5
|
|
India
|
72.3
|
69.8
|
73.0
|
62.7
|
|
India’s rank among all countries (1 = highest)
|
21 of 22
|
29 of 29
|
28 of 32
|
30 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Fighting rising prices (top two aims)
Fighting rising prices (top two aims): % counted as yes, India and the
regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Asia & Northern Africa
|
50.8
|
65.7
|
67.3
|
65.1
|
|
India
|
71.0
|
69.9
|
68.4
|
64.5
|
|
Eastern Europe
|
57.0
|
69.3
|
65.9
|
62.0
|
|
Sub-Saharan Africa
|
62.3
|
64.2
|
65.0
|
56.4
|
|
Latin America & Caribbean
|
48.3
|
48.4
|
53.0
|
52.8
|
|
South-eastern Asia
|
42.0
|
46.7
|
59.5
|
47.6
|
|
Western Europe & Offshoots
|
34.6
|
42.0
|
50.8
|
40.4
|
|
India’s rank among all countries (1 = highest)
|
2 of 23
|
6 of 31
|
9 of 32
|
9 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
A stable economy (top two aims)
A stable economy (top two aims): % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Sub-Saharan Africa
|
82.5
|
73.9
|
78.4
|
81.2
|
|
South-eastern Asia
|
87.6
|
88.8
|
75.2
|
79.9
|
|
Eastern Europe
|
83.3
|
85.3
|
85.4
|
76.4
|
|
Western Europe & Offshoots
|
66.6
|
70.9
|
78.7
|
72.7
|
|
Latin America & Caribbean
|
75.5
|
70.4
|
71.1
|
71.9
|
|
Western Asia & Northern Africa
|
61.6
|
74.4
|
74.9
|
71.5
|
|
India
|
53.9
|
47.0
|
63.1
|
52.0
|
|
India’s rank among all countries (1 = highest)
|
21 of 22
|
29 of 29
|
29 of 32
|
33 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
The fight against crime (top two aims)
The fight against crime (top two aims): % counted as yes, India and the
regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Latin America & Caribbean
|
56.1
|
62.3
|
61.7
|
59.9
|
|
Eastern Europe
|
64.9
|
59.2
|
52.5
|
50.5
|
|
Sub-Saharan Africa
|
67.2
|
79.4
|
46.3
|
49.1
|
|
India
|
60.1
|
67.6
|
65.6
|
48.3
|
|
Western Europe & Offshoots
|
55.5
|
56.6
|
49.0
|
47.7
|
|
Western Asia & Northern Africa
|
56.3
|
55.6
|
57.4
|
45.2
|
|
South-eastern Asia
|
39.8
|
33.9
|
45.0
|
40.1
|
|
India’s rank among all countries (1 = highest)
|
10 of 22
|
6 of 29
|
5 of 32
|
20 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
III.3 Accountability questions
What is shown. The five accountability questions of
II.1: the % who pick more say as one of the top two aims, and the % who
have done or might do each political action. The questions are listed in
the table in II.1.
Each question
p4_tabs(c(acc_say_govt = "More say in important government decisions (top two aims)",
acc_say_work = "More say at work and in communities (top two aims)",
acc_petition = "Signing a petition (done or might do)",
acc_demo = "Attending peaceful demonstrations (done or might do)",
acc_boycott = "Joining in boycotts (done or might do)"))
More say in important government decisions (top two aims)
More say in important government decisions (top two aims): % counted as
yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Europe & Offshoots
|
58.9
|
53.7
|
47.1
|
54.7
|
|
South-eastern Asia
|
47.8
|
49.6
|
45.4
|
54.1
|
|
Latin America & Caribbean
|
49.8
|
50.7
|
49.3
|
47.1
|
|
Sub-Saharan Africa
|
40.0
|
47.4
|
50.2
|
45.6
|
|
Eastern Europe
|
36.1
|
34.8
|
41.1
|
42.3
|
|
India
|
33.3
|
35.8
|
35.9
|
41.2
|
|
Western Asia & Northern Africa
|
35.3
|
39.9
|
30.5
|
39.3
|
|
India’s rank among all countries (1 = highest)
|
19 of 23
|
24 of 31
|
26 of 32
|
26 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
More say at work and in communities (top two aims)
More say at work and in communities (top two aims): % counted as yes,
India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Latin America & Caribbean
|
69.0
|
67.3
|
63.5
|
63.0
|
|
Western Europe & Offshoots
|
70.2
|
67.8
|
66.4
|
60.9
|
|
South-eastern Asia
|
39.5
|
40.0
|
54.9
|
48.1
|
|
Eastern Europe
|
46.9
|
46.7
|
51.1
|
42.6
|
|
Sub-Saharan Africa
|
60.6
|
63.2
|
59.4
|
41.1
|
|
India
|
30.7
|
35.7
|
38.5
|
34.7
|
|
Western Asia & Northern Africa
|
33.7
|
36.5
|
30.6
|
34.6
|
|
India’s rank among all countries (1 = highest)
|
20 of 22
|
26 of 29
|
27 of 32
|
29 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Signing a petition (done or might do)
Signing a petition (done or might do): % counted as yes, India and the
regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Europe & Offshoots
|
87.1
|
88.4
|
86.8
|
93.3
|
|
Latin America & Caribbean
|
59.3
|
62.1
|
59.2
|
60.5
|
|
India
|
57.5
|
54.7
|
38.1
|
47.8
|
|
Eastern Europe
|
49.2
|
40.8
|
28.4
|
45.7
|
|
South-eastern Asia
|
38.6
|
27.0
|
27.5
|
40.1
|
|
Western Asia & Northern Africa
|
44.2
|
38.0
|
28.6
|
37.8
|
|
Sub-Saharan Africa
|
46.1
|
46.9
|
41.9
|
34.7
|
|
India’s rank among all countries (1 = highest)
|
8 of 22
|
17 of 31
|
21 of 31
|
20 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Attending peaceful demonstrations (done or might do)
Attending peaceful demonstrations (done or might do): % counted as yes,
India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Europe & Offshoots
|
72.9
|
69.6
|
67.6
|
70.9
|
|
India
|
52.3
|
46.4
|
47.6
|
57.1
|
|
Latin America & Caribbean
|
39.4
|
52.5
|
52.7
|
54.1
|
|
Eastern Europe
|
52.2
|
45.5
|
34.6
|
42.9
|
|
Sub-Saharan Africa
|
50.2
|
47.9
|
48.0
|
42.7
|
|
Western Asia & Northern Africa
|
26.5
|
36.4
|
30.1
|
38.4
|
|
South-eastern Asia
|
29.5
|
27.7
|
25.7
|
33.8
|
|
India’s rank among all countries (1 = highest)
|
9 of 22
|
20 of 31
|
16 of 31
|
12 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Joining in boycotts (done or might do)
Joining in boycotts (done or might do): % counted as yes, India and the
regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Europe & Offshoots
|
63.7
|
61.9
|
59.6
|
68.0
|
|
India
|
40.4
|
44.5
|
39.0
|
42.6
|
|
Western Asia & Northern Africa
|
35.7
|
28.8
|
21.5
|
30.5
|
|
Sub-Saharan Africa
|
38.4
|
34.7
|
31.8
|
28.0
|
|
Latin America & Caribbean
|
24.1
|
20.8
|
23.8
|
27.4
|
|
Eastern Europe
|
35.6
|
27.8
|
15.5
|
25.8
|
|
South-eastern Asia
|
18.6
|
18.3
|
19.1
|
24.1
|
|
India’s rank among all countries (1 = highest)
|
8 of 22
|
9 of 31
|
10 of 31
|
11 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
III.4 Conception of democracy: liberal questions
What is shown. The five liberal questions of II.2:
the % rating each 7–10 as essential to democracy, saying a democratic
system is good, or rating living in a democracy as important (7–10).
Asked from W5 (W4 for “democratic system”).
The questions are listed in the table in II.2.
Each question
p4_tabs(c(lib_elections = "Essential: free elections (7–10)",
lib_civil = "Essential: civil rights protect people from the state (7–10)",
lib_women = "Essential: women have the same rights as men (7–10)",
lib_demgood = "Having a democratic political system is good",
lib_demimp = "Living in a democracy is important (7–10)"))
Essential: free elections (7–10)
Essential: free elections (7–10): % counted as yes, India and the
regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Europe & Offshoots
|
87.9
|
85.6
|
85.9
|
|
India
|
77.8
|
74.9
|
78.9
|
|
Sub-Saharan Africa
|
82.6
|
72.9
|
78.9
|
|
Eastern Europe
|
87.2
|
75.2
|
76.4
|
|
Western Asia & Northern Africa
|
86.3
|
82.1
|
76.2
|
|
Latin America & Caribbean
|
82.5
|
79.2
|
72.3
|
|
South-eastern Asia
|
76.2
|
70.8
|
62.9
|
|
India’s rank among all countries (1 = highest)
|
25 of 29
|
24 of 32
|
14 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Essential: civil rights protect people from the state (7–10)
Essential: civil rights protect people from the state (7–10): % counted
as yes, India and the regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Europe & Offshoots
|
82.3
|
74.6
|
75.6
|
|
India
|
67.7
|
66.9
|
73.3
|
|
Sub-Saharan Africa
|
70.8
|
61.9
|
72.7
|
|
Eastern Europe
|
85.5
|
72.8
|
72.5
|
|
Western Asia & Northern Africa
|
80.3
|
70.3
|
66.7
|
|
Latin America & Caribbean
|
71.7
|
60.2
|
56.5
|
|
South-eastern Asia
|
69.3
|
59.1
|
54.6
|
|
India’s rank among all countries (1 = highest)
|
25 of 29
|
17 of 32
|
11 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Essential: women have the same rights as men (7–10)
Essential: women have the same rights as men (7–10): % counted as yes,
India and the regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Europe & Offshoots
|
90.9
|
87.5
|
87.1
|
|
Eastern Europe
|
87.9
|
78.1
|
79.5
|
|
India
|
71.0
|
79.0
|
77.4
|
|
Latin America & Caribbean
|
85.3
|
79.7
|
71.5
|
|
Western Asia & Northern Africa
|
72.2
|
67.7
|
69.1
|
|
Sub-Saharan Africa
|
76.1
|
59.9
|
66.1
|
|
South-eastern Asia
|
75.3
|
66.3
|
65.0
|
|
India’s rank among all countries (1 = highest)
|
24 of 29
|
16 of 32
|
17 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Having a democratic political system is good
Having a democratic political system is good: % counted as yes, India
and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Europe & Offshoots
|
92.5
|
92.7
|
91.9
|
91.8
|
|
Sub-Saharan Africa
|
91.0
|
90.3
|
86.8
|
90.0
|
|
South-eastern Asia
|
91.9
|
93.7
|
87.8
|
87.3
|
|
Latin America & Caribbean
|
88.6
|
90.2
|
90.3
|
85.4
|
|
India
|
93.0
|
91.7
|
90.0
|
84.2
|
|
Western Asia & Northern Africa
|
94.3
|
94.6
|
93.7
|
82.6
|
|
Eastern Europe
|
87.8
|
83.6
|
81.0
|
81.5
|
|
India’s rank among all countries (1 = highest)
|
12 of 23
|
19 of 31
|
20 of 32
|
22 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Living in a democracy is important (7–10)
Living in a democracy is important (7–10): % counted as yes, India and
the regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Europe & Offshoots
|
90.7
|
88.4
|
87.8
|
|
India
|
46.4
|
73.7
|
83.0
|
|
Sub-Saharan Africa
|
89.9
|
77.8
|
81.7
|
|
Latin America & Caribbean
|
83.5
|
83.6
|
81.7
|
|
Western Asia & Northern Africa
|
92.6
|
86.0
|
80.5
|
|
South-eastern Asia
|
86.8
|
79.9
|
79.2
|
|
Eastern Europe
|
76.6
|
75.2
|
75.4
|
|
India’s rank among all countries (1 = highest)
|
29 of 29
|
30 of 32
|
14 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
III.5 Conception of democracy: authoritarian questions
What is shown. The seven authoritarian questions of
II.2: the % rating each 7–10 as essential to democracy, calling each way
of governing good, or saying greater respect for authority would be a
good thing. The questions are listed in the table in II.2.
Each question
p4_tabs(c(aut_army_ess = "Essential: the army takes over when government is incompetent (7–10)",
aut_relig_ess = "Essential: religious authorities interpret the laws (7–10)",
aut_obey = "Essential: people obey their rulers (7–10)",
aut_army_rule = "Having the army rule is good",
aut_strong = "Having a strong leader who does not bother with parliament and elections is good",
aut_experts = "Having experts, not government, make decisions is good",
aut_authority = "Greater respect for authority would be a good thing"))
Essential: the army takes over when government is incompetent
(7–10)
Essential: the army takes over when government is incompetent (7–10): %
counted as yes, India and the regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Sub-Saharan Africa
|
36.1
|
37.1
|
42.2
|
|
India
|
38.3
|
45.0
|
40.4
|
|
South-eastern Asia
|
35.8
|
30.5
|
39.2
|
|
Eastern Europe
|
30.0
|
30.0
|
37.7
|
|
Western Asia & Northern Africa
|
44.5
|
35.4
|
35.5
|
|
Latin America & Caribbean
|
28.5
|
26.5
|
30.1
|
|
Western Europe & Offshoots
|
14.9
|
12.1
|
12.1
|
|
India’s rank among all countries (1 = highest)
|
9 of 29
|
4 of 31
|
9 of 31
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Essential: religious authorities interpret the laws (7–10)
Essential: religious authorities interpret the laws (7–10): % counted as
yes, India and the regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Asia & Northern Africa
|
46.8
|
42.9
|
34.1
|
|
India
|
21.7
|
23.3
|
33.2
|
|
South-eastern Asia
|
43.5
|
30.8
|
33.0
|
|
Sub-Saharan Africa
|
41.2
|
37.3
|
33.0
|
|
Eastern Europe
|
22.7
|
19.0
|
24.2
|
|
Latin America & Caribbean
|
23.1
|
20.3
|
20.2
|
|
Western Europe & Offshoots
|
7.2
|
7.6
|
5.5
|
|
India’s rank among all countries (1 = highest)
|
15 of 29
|
13 of 32
|
10 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Essential: people obey their rulers (7–10)
Essential: people obey their rulers (7–10): % counted as yes, India and
the regions
|
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Sub-Saharan Africa
|
60.9
|
74.0
|
|
India
|
53.8
|
58.4
|
|
Eastern Europe
|
47.4
|
50.3
|
|
South-eastern Asia
|
56.9
|
49.9
|
|
Western Asia & Northern Africa
|
53.9
|
48.6
|
|
Latin America & Caribbean
|
48.0
|
40.5
|
|
Western Europe & Offshoots
|
26.7
|
26.5
|
|
India’s rank among all countries (1 = highest)
|
12 of 32
|
8 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
Having the army rule is good
Having the army rule is good: % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
South-eastern Asia
|
52.2
|
59.1
|
38.3
|
54.9
|
|
India
|
20.0
|
36.0
|
45.2
|
39.0
|
|
Sub-Saharan Africa
|
19.8
|
32.7
|
36.1
|
33.3
|
|
Eastern Europe
|
22.4
|
19.9
|
25.2
|
32.3
|
|
Western Asia & Northern Africa
|
30.2
|
38.0
|
23.1
|
30.2
|
|
Latin America & Caribbean
|
22.9
|
26.0
|
26.8
|
29.5
|
|
Western Europe & Offshoots
|
7.0
|
7.4
|
7.6
|
8.6
|
|
India’s rank among all countries (1 = highest)
|
10 of 21
|
7 of 31
|
4 of 30
|
11 of 31
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Having a strong leader who does not bother with parliament and
elections is good
Having a strong leader who does not bother with parliament and elections
is good: % counted as yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
South-eastern Asia
|
51.1
|
40.8
|
47.9
|
70.2
|
|
India
|
59.1
|
63.9
|
64.9
|
69.1
|
|
Eastern Europe
|
40.0
|
57.8
|
76.0
|
65.8
|
|
Latin America & Caribbean
|
44.8
|
44.7
|
52.9
|
61.1
|
|
Western Asia & Northern Africa
|
32.4
|
30.5
|
47.7
|
53.3
|
|
Sub-Saharan Africa
|
34.4
|
43.1
|
39.6
|
49.7
|
|
Western Europe & Offshoots
|
23.3
|
25.8
|
30.5
|
28.8
|
|
India’s rank among all countries (1 = highest)
|
5 of 23
|
4 of 31
|
7 of 32
|
9 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Having experts, not government, make decisions is good
Having experts, not government, make decisions is good: % counted as
yes, India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
India
|
68.0
|
70.5
|
71.4
|
72.5
|
|
South-eastern Asia
|
61.8
|
54.2
|
52.1
|
69.4
|
|
Latin America & Caribbean
|
60.5
|
58.0
|
62.8
|
68.1
|
|
Sub-Saharan Africa
|
64.6
|
59.9
|
65.2
|
65.4
|
|
Eastern Europe
|
77.1
|
65.3
|
66.9
|
64.7
|
|
Western Asia & Northern Africa
|
76.6
|
73.0
|
66.2
|
59.1
|
|
Western Europe & Offshoots
|
40.9
|
45.3
|
51.0
|
51.0
|
|
India’s rank among all countries (1 = highest)
|
9 of 23
|
10 of 31
|
5 of 32
|
8 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Greater respect for authority would be a good thing
Greater respect for authority would be a good thing: % counted as yes,
India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Sub-Saharan Africa
|
81.7
|
78.6
|
66.0
|
88.9
|
|
Latin America & Caribbean
|
71.1
|
78.9
|
73.2
|
71.3
|
|
Western Asia & Northern Africa
|
84.0
|
76.3
|
71.2
|
65.0
|
|
Western Europe & Offshoots
|
55.1
|
59.1
|
59.5
|
56.4
|
|
Eastern Europe
|
52.9
|
65.1
|
59.6
|
54.1
|
|
South-eastern Asia
|
59.8
|
45.3
|
52.2
|
53.8
|
|
India
|
43.4
|
38.0
|
43.3
|
50.2
|
|
India’s rank among all countries (1 = highest)
|
20 of 22
|
28 of 31
|
29 of 32
|
25 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
III.6 State support questions
What is shown. The five state-support questions of
II.3: the % on the government-responsibility and equal-incomes side of
the 10-point scales (1–4), and the % rating each of the three economic
items 7–10 as essential to democracy.
The questions are listed in the table in II.3.
Each question
p4_tabs(c(ss_govresp_yes = "Government should take more responsibility (1–4)",
ss_income_yes = "Incomes should be made more equal (1–4)",
ss_taxrich_yes = "Essential: governments tax the rich and subsidise the poor (7–10)",
ss_unemp_yes = "Essential: state aid for unemployment (7–10)",
ss_incomeeq_yes = "Essential: the state makes people's incomes equal (7–10)"))
Government should take more responsibility (1–4)
Government should take more responsibility (1–4): % counted as yes,
India and the regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Sub-Saharan Africa
|
59.4
|
47.4
|
49.6
|
64.9
|
|
Western Asia & Northern Africa
|
56.7
|
64.0
|
64.8
|
50.6
|
|
Eastern Europe
|
58.4
|
54.2
|
58.4
|
46.8
|
|
Latin America & Caribbean
|
47.8
|
45.0
|
48.7
|
44.4
|
|
India
|
51.5
|
48.4
|
66.7
|
39.5
|
|
Western Europe & Offshoots
|
39.0
|
32.7
|
34.1
|
36.7
|
|
South-eastern Asia
|
32.5
|
30.5
|
34.0
|
35.6
|
|
India’s rank among all countries (1 = highest)
|
13 of 23
|
12 of 31
|
5 of 32
|
20 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Incomes should be made more equal (1–4)
Incomes should be made more equal (1–4): % counted as yes, India and the
regions
|
|
W4 (1999–2004)
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Latin America & Caribbean
|
40.9
|
35.3
|
45.1
|
36.3
|
|
Western Asia & Northern Africa
|
27.3
|
35.9
|
44.6
|
33.8
|
|
Western Europe & Offshoots
|
35.4
|
31.4
|
41.3
|
33.1
|
|
Sub-Saharan Africa
|
31.6
|
37.8
|
21.8
|
24.9
|
|
India
|
54.4
|
48.0
|
71.1
|
20.6
|
|
Eastern Europe
|
36.3
|
31.8
|
49.5
|
19.3
|
|
South-eastern Asia
|
16.9
|
15.1
|
27.9
|
19.1
|
|
India’s rank among all countries (1 = highest)
|
3 of 22
|
4 of 31
|
1 of 32
|
25 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Essential: governments tax the rich and subsidise the poor
(7–10)
Essential: governments tax the rich and subsidise the poor (7–10): %
counted as yes, India and the regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
India
|
72.7
|
65.0
|
69.4
|
|
Western Asia & Northern Africa
|
70.3
|
66.3
|
60.6
|
|
Eastern Europe
|
63.5
|
55.5
|
58.6
|
|
Sub-Saharan Africa
|
60.1
|
44.7
|
54.0
|
|
South-eastern Asia
|
59.9
|
55.6
|
52.5
|
|
Western Europe & Offshoots
|
51.1
|
49.3
|
52.1
|
|
Latin America & Caribbean
|
44.3
|
41.6
|
38.5
|
|
India’s rank among all countries (1 = highest)
|
5 of 29
|
7 of 32
|
6 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Essential: state aid for unemployment (7–10)
Essential: state aid for unemployment (7–10): % counted as yes, India
and the regions
|
|
W5 (2004–09)
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Eastern Europe
|
81.5
|
70.6
|
68.9
|
|
Sub-Saharan Africa
|
74.7
|
57.2
|
67.1
|
|
Western Asia & Northern Africa
|
74.9
|
72.7
|
66.1
|
|
India
|
73.5
|
69.0
|
66.0
|
|
Western Europe & Offshoots
|
66.7
|
61.0
|
58.6
|
|
Latin America & Caribbean
|
59.4
|
57.7
|
55.1
|
|
South-eastern Asia
|
50.1
|
56.2
|
48.7
|
|
India’s rank among all countries (1 = highest)
|
15 of 29
|
15 of 32
|
13 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
|
Single country: Sub-Saharan Africa in W5 = South Africa.
|
Essential: the state makes people’s incomes equal (7–10)
Essential: the state makes people’s incomes equal (7–10): % counted as
yes, India and the regions
|
|
W6 (2010–16)
|
W7 (2017–23)
|
|
Western Asia & Northern Africa
|
59.8
|
58.2
|
|
Eastern Europe
|
59.2
|
57.6
|
|
India
|
64.3
|
54.7
|
|
South-eastern Asia
|
46.8
|
47.2
|
|
Sub-Saharan Africa
|
38.7
|
46.6
|
|
Latin America & Caribbean
|
45.7
|
40.5
|
|
Western Europe & Offshoots
|
27.6
|
27.7
|
|
India’s rank among all countries (1 = highest)
|
6 of 32
|
11 of 33
|
|
Regions: mean of the country estimates. India: its own
estimate. — = not asked.
|
Part IV. PALS 2022: India and the regions
The PALS survey (Public Attitudes towards the
Liberal Script, Wave 1) was carried out in 26 countries between December
2021 and July 2022. It asks several questions close to the themes of
this report, often more directly than the WVS. It is used here as a
second, independent look at India against the same regions.
- One survey, one time point: there is no trend;
India was interviewed in February–March 2022.
- Mode: India and six other countries were
interviewed face to face; the rest online. Online samples
under-represent people without internet access, so differences between
India and the online countries partly reflect mode.
- Scales: most questions are 1–6 scales between two
statements (or from “fully disagree” to “fully agree”). Each cell shows
the % on the 4–6 side: leaning towards the right-hand
statement, or agreeing. Answers “prefer not to say” and “don’t know” are
left out.
- Weights: each country uses PALS’s
post-stratification weight (w2); a region is the mean of its countries,
as in Parts II and III. India is ranked among all countries shown.
- Regions: the same six regions as the WVS parts.
Japan and South Korea (Eastern Asia) are left out; Latvia sits in
Western Europe & Offshoots, as the UN places it in Northern
Europe.
library(haven)
pals_raw <- read_dta("D:/Populism and Democrary/World value survey/PALS/PALS dataset.dta")
pals_region_of <- c(
"India" = "India",
"Indonesia" = "South-eastern Asia", "Singapore" = "South-eastern Asia",
"Türkiye" = "Western Asia & Northern Africa", "Tunisia" = "Western Asia & Northern Africa",
"Ghana" = "Sub-Saharan Africa", "Nigeria" = "Sub-Saharan Africa", "Senegal" = "Sub-Saharan Africa",
"South Africa" = "Sub-Saharan Africa",
"Brazil" = "Latin America & Caribbean", "Chile" = "Latin America & Caribbean",
"Mexico" = "Latin America & Caribbean", "Peru" = "Latin America & Caribbean",
"Poland" = "Eastern Europe", "Russian Federation" = "Eastern Europe",
"France" = "Western Europe & Offshoots", "Germany" = "Western Europe & Offshoots",
"Italy" = "Western Europe & Offshoots", "Spain" = "Western Europe & Offshoots",
"Sweden" = "Western Europe & Offshoots", "Latvia" = "Western Europe & Offshoots",
"United Kingdom of Great Britain and Northern Ireland" = "Western Europe & Offshoots",
"United States of America" = "Western Europe & Offshoots", "Australia" = "Western Europe & Offshoots")
pals_capi <- c("Ghana", "India", "Nigeria", "Peru", "Senegal", "South Africa", "Tunisia")
pals <- pals_raw |>
mutate(country_name = as.character(as_factor(country)),
region = factor(unname(pals_region_of[country_name]), wb_regions)) |>
filter(!is.na(region)) |>
mutate(country_name = recode(country_name,
"United Kingdom of Great Britain and Northern Ireland" = "United Kingdom",
"United States of America" = "United States",
"Russian Federation" = "Russia"))
# one table per question, under tabs: India and the regions in descending order, with India's rank
pals_table <- function(items, caption, side = 4:6) {
cat("\n\n### ", caption, " {.tabset}\n\n", sep = "")
for (v in names(items)) {
est <- pals |>
transmute(region, country_name, w = w2, x = as.numeric(zap_labels(.data[[v]]))) |>
filter(!is.na(x), x %in% c(0:6)) |>
group_by(region, country_name) |>
summarise(est = 100 * weighted.mean(if (all(x %in% 0:1)) x else as.numeric(x %in% side), w), .groups = "drop")
rk <- est |> mutate(r = rank(-est, ties.method = "min")) |> filter(country_name == "India")
tab <- est |> group_by(Region = as.character(region)) |> summarise(v = mean(est), .groups = "drop") |>
arrange(desc(v)) |> mutate(`%` = sprintf("%.0f", v)) |> select(Region, `%`)
cat("\n\n#### ", items[[v]], "\n\n", sep = "")
print(tab |>
kable(caption = paste0(items[[v]], " (%)"), align = c("l", "c")) |>
styled() |>
footnote(general = c(sprintf("India's rank among the %d PALS countries shown: %d (1 = highest).", nrow(est), rk$r),
"Cells: % of respondents (weighted, w2). India: its own estimate. Regions: mean of country estimates."),
general_title = ""))
}
cat("\n\n")
}
# plain question table for PALS items
pals_qtable <- function(rows, caption = "PALS questions used in this section") {
rows |>
kable(caption = caption) |>
styled() |>
column_spec(2, width = "26em")
}
IV.1 Countries and samples
What is shown. The PALS countries in each region,
the number of respondents, and the interview mode. Japan and South Korea
are not used.
pals |>
group_by(region, country_name) |>
summarise(n = n(), .groups = "drop") |>
mutate(mode = ifelse(country_name %in% pals_capi, "face to face", "online")) |>
group_by(Region = region) |>
summarise(Countries = paste0(country_name, " (", format(n, big.mark = ","), "; ", mode, ")", collapse = "; "),
Respondents = sum(n), .groups = "drop") |>
kable(caption = "PALS countries by region (respondents; interview mode)", format.args = list(big.mark = ",")) |>
styled() |>
column_spec(2, width = "40em")
PALS countries by region (respondents; interview mode)
|
Region
|
Countries
|
Respondents
|
|
India
|
India (2,822; face to face)
|
2,822
|
|
South-eastern Asia
|
Indonesia (2,001; online); Singapore (2,010; online)
|
4,011
|
|
Western Asia & Northern Africa
|
Tunisia (2,012; face to face); Türkiye (2,016; online)
|
4,028
|
|
Sub-Saharan Africa
|
Ghana (2,000; face to face); Nigeria (2,000; face to face); Senegal
(1,996; face to face); South Africa (2,030; face to face)
|
8,026
|
|
Latin America & Caribbean
|
Brazil (2,110; online); Chile (2,005; online); Mexico (2,160; online);
Peru (2,018; face to face)
|
8,293
|
|
Eastern Europe
|
Poland (2,037; online); Russia (2,143; online)
|
4,180
|
|
Western Europe & Offshoots
|
Australia (2,032; online); France (2,001; online); Germany (2,020;
online); Italy (2,119; online); Latvia (2,100; online); Spain (2,114;
online); Sweden (2,090; online); United Kingdom (2,007; online); United
States (2,033; online)
|
18,516
|
IV.2 Governance and accountability
What is shown. Two sets of PALS questions close to
section II.1. First, the same “most important aims” list as the WVS:
respondents pick two of four, and each cell is the % who picked that
aim. Second, five questions on who should decide policy
when citizens disagree with another actor: each cell is the % on the 4–6
side, leaning towards that actor rather than citizens.
pals_qtable(tribble(
~Code, ~Question, ~`Response format`, ~`Cell shows`,
"D06_a–d", "Goals for the next ten years: maintaining order / more say in government decisions / fighting rising prices / protecting freedom of speech", "Pick the two most important", "% who picked that goal",
"B01_a", "If citizens and strong political leaders disagree, whose opinion should be most decisive for policy-making?", "1 citizens ... 6 strong political leaders", "% 4–6 (leaders)",
"B01_b", "... citizens and elected politicians", "1 citizens ... 6 elected politicians", "% 4–6 (politicians)",
"B01_c", "... citizens and established experts", "1 citizens ... 6 experts", "% 4–6 (experts)",
"B01_d", "... citizens and religious leaders", "1 citizens ... 6 religious leaders", "% 4–6 (religious leaders)",
"B01_e", "... citizens and the military", "1 citizens ... 6 the military", "% 4–6 (the military)"))
PALS questions used in this section
|
Code
|
Question
|
Response format
|
Cell shows
|
|
D06_a–d
|
Goals for the next ten years: maintaining order / more say in government
decisions / fighting rising prices / protecting freedom of speech
|
Pick the two most important
|
% who picked that goal
|
|
B01_a
|
If citizens and strong political leaders disagree, whose opinion should
be most decisive for policy-making?
|
1 citizens … 6 strong political leaders
|
% 4–6 (leaders)
|
|
B01_b
|
… citizens and elected politicians
|
1 citizens … 6 elected politicians
|
% 4–6 (politicians)
|
|
B01_c
|
… citizens and established experts
|
1 citizens … 6 experts
|
% 4–6 (experts)
|
|
B01_d
|
… citizens and religious leaders
|
1 citizens … 6 religious leaders
|
% 4–6 (religious leaders)
|
|
B01_e
|
… citizens and the military
|
1 citizens … 6 the military
|
% 4–6 (the military)
|
pals_table(c(D06_a = "Goal: maintaining order (picked)", D06_b = "Goal: more say in government decisions (picked)",
D06_c = "Goal: fighting rising prices (picked)", D06_d = "Goal: protecting freedom of speech (picked)"),
caption = "Most important goals")
Most important goals
Goal: maintaining order (picked)
Goal: maintaining order (picked) (%)
|
Region
|
%
|
|
South-eastern Asia
|
55
|
|
Sub-Saharan Africa
|
50
|
|
India
|
46
|
|
Western Europe & Offshoots
|
43
|
|
Western Asia & Northern Africa
|
40
|
|
Latin America & Caribbean
|
38
|
|
Eastern Europe
|
35
|
|
India’s rank among the 24 PALS countries shown: 10 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Goal: more say in government decisions (picked)
Goal: more say in government decisions (picked) (%)
|
Region
|
%
|
|
Eastern Europe
|
48
|
|
Latin America & Caribbean
|
42
|
|
Western Europe & Offshoots
|
41
|
|
South-eastern Asia
|
40
|
|
India
|
37
|
|
Sub-Saharan Africa
|
37
|
|
Western Asia & Northern Africa
|
32
|
|
India’s rank among the 24 PALS countries shown: 17 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Goal: fighting rising prices (picked)
Goal: fighting rising prices (picked) (%)
|
Region
|
%
|
|
Western Asia & Northern Africa
|
72
|
|
Eastern Europe
|
69
|
|
India
|
67
|
|
Sub-Saharan Africa
|
64
|
|
Latin America & Caribbean
|
59
|
|
Western Europe & Offshoots
|
52
|
|
South-eastern Asia
|
49
|
|
India’s rank among the 24 PALS countries shown: 4 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Goal: protecting freedom of speech (picked)
Goal: protecting freedom of speech (picked) (%)
|
Region
|
%
|
|
Western Europe & Offshoots
|
44
|
|
Latin America & Caribbean
|
42
|
|
Sub-Saharan Africa
|
41
|
|
South-eastern Asia
|
39
|
|
Western Asia & Northern Africa
|
37
|
|
Eastern Europe
|
37
|
|
India
|
30
|
|
India’s rank among the 24 PALS countries shown: 21 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
pals_table(c(B01_a = "Strong political leaders should decide (not citizens)",
B01_b = "Elected politicians should decide (not citizens)",
B01_c = "Established experts should decide (not citizens)",
B01_d = "Religious leaders should decide (not citizens)",
B01_e = "The military should decide (not citizens)"),
caption = "Who should decide policy when citizens disagree")
Who should decide policy when citizens disagree
Strong political leaders should decide (not citizens)
Strong political leaders should decide (not citizens) (%)
|
Region
|
%
|
|
South-eastern Asia
|
41
|
|
India
|
35
|
|
Western Europe & Offshoots
|
31
|
|
Sub-Saharan Africa
|
27
|
|
Latin America & Caribbean
|
22
|
|
Eastern Europe
|
21
|
|
Western Asia & Northern Africa
|
21
|
|
India’s rank among the 24 PALS countries shown: 6 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Elected politicians should decide (not citizens)
Elected politicians should decide (not citizens) (%)
|
Region
|
%
|
|
South-eastern Asia
|
39
|
|
India
|
36
|
|
Western Europe & Offshoots
|
33
|
|
Sub-Saharan Africa
|
25
|
|
Latin America & Caribbean
|
23
|
|
Eastern Europe
|
21
|
|
Western Asia & Northern Africa
|
21
|
|
India’s rank among the 24 PALS countries shown: 6 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Established experts should decide (not citizens)
Established experts should decide (not citizens) (%)
|
Region
|
%
|
|
South-eastern Asia
|
52
|
|
Western Europe & Offshoots
|
50
|
|
Latin America & Caribbean
|
43
|
|
India
|
40
|
|
Western Asia & Northern Africa
|
39
|
|
Sub-Saharan Africa
|
37
|
|
Eastern Europe
|
33
|
|
India’s rank among the 24 PALS countries shown: 18 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Religious leaders should decide (not citizens)
Religious leaders should decide (not citizens) (%)
|
Region
|
%
|
|
Sub-Saharan Africa
|
42
|
|
South-eastern Asia
|
41
|
|
India
|
35
|
|
Western Asia & Northern Africa
|
24
|
|
Latin America & Caribbean
|
18
|
|
Western Europe & Offshoots
|
18
|
|
Eastern Europe
|
12
|
|
India’s rank among the 24 PALS countries shown: 5 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
The military should decide (not citizens)
The military should decide (not citizens) (%)
|
Region
|
%
|
|
India
|
39
|
|
South-eastern Asia
|
35
|
|
Western Asia & Northern Africa
|
35
|
|
Sub-Saharan Africa
|
30
|
|
Western Europe & Offshoots
|
29
|
|
Latin America & Caribbean
|
26
|
|
Eastern Europe
|
23
|
|
India’s rank among the 24 PALS countries shown: 3 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
IV.3 Rule of law, tolerance and authoritarian values
What is shown. PALS questions close to the liberal
and authoritarian questions of II.2. For rule of law and tolerance, each
cell is the % leaning to the right-hand statement (the less liberal
side). For the six authoritarianism statements, each cell is the % who
agree (4–6); three statements are worded the other way round, so
agreeing is the less authoritarian answer (marked
“reversed”).
pals_qtable(tribble(
~Code, ~Question, ~`Response format`, ~`Cell shows`,
"B02_a", "Should the government always obey the laws and court decisions, or not be bound by them so it can work unhindered?", "1 always obey ... 6 not bound at all", "% 4–6 (not bound)",
"B02_b", "Should laws be enforced equally for everyone, or differently for different people under certain circumstances?", "1 equally ... 6 differently", "% 4–6 (differently)",
"B02_c", "Should every human have the same basic rights in all countries, or should each society decide?", "1 same rights everywhere ... 6 each society decides", "% 4–6 (society decides)",
"B06", "Should society accept all people equally, or decide whom to accept?", "1 accept all equally ... 6 society decides", "% 4–6 (society decides)",
"D07_a", "It's great that many young people today are prepared to defy authority (reversed)", "1 fully disagree ... 6 fully agree", "% agree (4–6)",
"D07_b", "What our country needs most is discipline, with everyone following our leaders in unity", "1 fully disagree ... 6 fully agree", "% agree (4–6)",
"D07_c", "The old-fashioned ways and values still show the best way to live", "1 fully disagree ... 6 fully agree", "% agree (4–6)",
"D07_d", "There is nothing wrong with premarital sexual intercourse (reversed)", "1 fully disagree ... 6 fully agree", "% agree (4–6)",
"D07_e", "Our society does not need tougher government and stricter laws (reversed)", "1 fully disagree ... 6 fully agree", "% agree (4–6)",
"D07_f", "We have to crack down harder on troublemakers to preserve law and order", "1 fully disagree ... 6 fully agree", "% agree (4–6)"))
PALS questions used in this section
|
Code
|
Question
|
Response format
|
Cell shows
|
|
B02_a
|
Should the government always obey the laws and court decisions, or not
be bound by them so it can work unhindered?
|
1 always obey … 6 not bound at all
|
% 4–6 (not bound)
|
|
B02_b
|
Should laws be enforced equally for everyone, or differently for
different people under certain circumstances?
|
1 equally … 6 differently
|
% 4–6 (differently)
|
|
B02_c
|
Should every human have the same basic rights in all countries, or
should each society decide?
|
1 same rights everywhere … 6 each society decides
|
% 4–6 (society decides)
|
|
B06
|
Should society accept all people equally, or decide whom to accept?
|
1 accept all equally … 6 society decides
|
% 4–6 (society decides)
|
|
D07_a
|
It’s great that many young people today are prepared to defy authority
(reversed)
|
1 fully disagree … 6 fully agree
|
% agree (4–6)
|
|
D07_b
|
What our country needs most is discipline, with everyone following our
leaders in unity
|
1 fully disagree … 6 fully agree
|
% agree (4–6)
|
|
D07_c
|
The old-fashioned ways and values still show the best way to live
|
1 fully disagree … 6 fully agree
|
% agree (4–6)
|
|
D07_d
|
There is nothing wrong with premarital sexual intercourse (reversed)
|
1 fully disagree … 6 fully agree
|
% agree (4–6)
|
|
D07_e
|
Our society does not need tougher government and stricter laws
(reversed)
|
1 fully disagree … 6 fully agree
|
% agree (4–6)
|
|
D07_f
|
We have to crack down harder on troublemakers to preserve law and order
|
1 fully disagree … 6 fully agree
|
% agree (4–6)
|
pals_table(c(B02_a = "Government should not be bound by laws and courts",
B02_b = "Laws can be enforced differently for different people",
B02_c = "Each society should decide which rights people have",
B06 = "Society should decide whom to accept"),
caption = "Rule of law and tolerance")
Rule of law and tolerance
Government should not be bound by laws and courts
Government should not be bound by laws and courts (%)
|
Region
|
%
|
|
India
|
34
|
|
South-eastern Asia
|
27
|
|
Western Asia & Northern Africa
|
22
|
|
Latin America & Caribbean
|
22
|
|
Western Europe & Offshoots
|
19
|
|
Sub-Saharan Africa
|
19
|
|
Eastern Europe
|
10
|
|
India’s rank among the 24 PALS countries shown: 1 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Laws can be enforced differently for different people
Laws can be enforced differently for different people (%)
|
Region
|
%
|
|
India
|
37
|
|
South-eastern Asia
|
30
|
|
Latin America & Caribbean
|
22
|
|
Sub-Saharan Africa
|
22
|
|
Western Europe & Offshoots
|
21
|
|
Western Asia & Northern Africa
|
16
|
|
Eastern Europe
|
14
|
|
India’s rank among the 24 PALS countries shown: 1 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Each society should decide which rights people have
Each society should decide which rights people have (%)
|
Region
|
%
|
|
India
|
41
|
|
Sub-Saharan Africa
|
40
|
|
Eastern Europe
|
37
|
|
South-eastern Asia
|
32
|
|
Western Europe & Offshoots
|
27
|
|
Western Asia & Northern Africa
|
25
|
|
Latin America & Caribbean
|
21
|
|
India’s rank among the 24 PALS countries shown: 3 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Society should decide whom to accept
Society should decide whom to accept (%)
|
Region
|
%
|
|
Sub-Saharan Africa
|
32
|
|
India
|
29
|
|
South-eastern Asia
|
27
|
|
Western Europe & Offshoots
|
24
|
|
Eastern Europe
|
22
|
|
Western Asia & Northern Africa
|
20
|
|
Latin America & Caribbean
|
16
|
|
India’s rank among the 24 PALS countries shown: 6 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
pals_table(c(D07_a = "Young people defying authority is great (reversed)",
D07_b = "Discipline, everyone following our leaders in unity",
D07_c = "Old-fashioned ways and values are best",
D07_d = "Nothing wrong with premarital sex (reversed)",
D07_e = "No need for tougher government and stricter laws (reversed)",
D07_f = "Crack down harder on troublemakers"),
caption = "Authoritarian values")
Authoritarian values
Young people defying authority is great (reversed)
Young people defying authority is great (reversed) (%)
|
Region
|
%
|
|
Eastern Europe
|
70
|
|
India
|
70
|
|
South-eastern Asia
|
63
|
|
Western Asia & Northern Africa
|
60
|
|
Sub-Saharan Africa
|
57
|
|
Latin America & Caribbean
|
54
|
|
Western Europe & Offshoots
|
51
|
|
India’s rank among the 24 PALS countries shown: 4 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Discipline, everyone following our leaders in unity
Discipline, everyone following our leaders in unity (%)
|
Region
|
%
|
|
Sub-Saharan Africa
|
78
|
|
South-eastern Asia
|
75
|
|
India
|
73
|
|
Western Europe & Offshoots
|
54
|
|
Latin America & Caribbean
|
52
|
|
Western Asia & Northern Africa
|
52
|
|
Eastern Europe
|
41
|
|
India’s rank among the 24 PALS countries shown: 4 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Old-fashioned ways and values are best
Old-fashioned ways and values are best (%)
|
Region
|
%
|
|
India
|
72
|
|
Sub-Saharan Africa
|
69
|
|
South-eastern Asia
|
62
|
|
Western Europe & Offshoots
|
60
|
|
Eastern Europe
|
60
|
|
Western Asia & Northern Africa
|
56
|
|
Latin America & Caribbean
|
55
|
|
India’s rank among the 24 PALS countries shown: 3 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Nothing wrong with premarital sex (reversed)
Nothing wrong with premarital sex (reversed) (%)
|
Region
|
%
|
|
Western Europe & Offshoots
|
86
|
|
Eastern Europe
|
77
|
|
Latin America & Caribbean
|
71
|
|
South-eastern Asia
|
50
|
|
India
|
43
|
|
Sub-Saharan Africa
|
34
|
|
Western Asia & Northern Africa
|
29
|
|
India’s rank among the 24 PALS countries shown: 19 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
No need for tougher government and stricter laws (reversed)
No need for tougher government and stricter laws (reversed) (%)
|
Region
|
%
|
|
India
|
60
|
|
Eastern Europe
|
59
|
|
Western Europe & Offshoots
|
58
|
|
Sub-Saharan Africa
|
58
|
|
South-eastern Asia
|
56
|
|
Western Asia & Northern Africa
|
52
|
|
Latin America & Caribbean
|
49
|
|
India’s rank among the 24 PALS countries shown: 10 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Crack down harder on troublemakers
Crack down harder on troublemakers (%)
|
Region
|
%
|
|
South-eastern Asia
|
84
|
|
Western Asia & Northern Africa
|
79
|
|
India
|
77
|
|
Sub-Saharan Africa
|
76
|
|
Western Europe & Offshoots
|
76
|
|
Latin America & Caribbean
|
73
|
|
Eastern Europe
|
64
|
|
India’s rank among the 24 PALS countries shown: 10 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
IV.4 State support
What is shown. PALS questions close to the
state-support questions of II.3: whether society should provide public
goods for everyone, and whether state ownership should grow. Each cell
is the % who agree (4–6) or lean to state ownership (4–6).
pals_qtable(tribble(
~Code, ~Question, ~`Response format`, ~`Cell shows`,
"C04_a–e", "Society should provide, even if this costs everyone: school education without tuition fees / free basic healthcare / welfare benefits for everyone in need / support for disadvantaged groups such as minorities or the poor / support for women to foster gender equality", "1 fully disagree ... 6 fully agree", "% agree (4–6)",
"B03_a", "What should be increased: private or state ownership of businesses and industry?", "1 private ... 6 state", "% 4–6 (state)"))
PALS questions used in this section
|
Code
|
Question
|
Response format
|
Cell shows
|
|
C04_a–e
|
Society should provide, even if this costs everyone: school education
without tuition fees / free basic healthcare / welfare benefits for
everyone in need / support for disadvantaged groups such as minorities
or the poor / support for women to foster gender equality
|
1 fully disagree … 6 fully agree
|
% agree (4–6)
|
|
B03_a
|
What should be increased: private or state ownership of businesses and
industry?
|
1 private … 6 state
|
% 4–6 (state)
|
pals_table(c(C04_a = "Free education", C04_b = "Free healthcare", C04_c = "Welfare benefits",
C04_d = "Support for disadvantaged groups", C04_e = "Support for women",
B03_a = "State ownership should be increased"),
caption = "State support questions")
State support questions
Free education
Free education (%)
|
Region
|
%
|
|
Eastern Europe
|
93
|
|
Western Europe & Offshoots
|
90
|
|
Western Asia & Northern Africa
|
88
|
|
Latin America & Caribbean
|
85
|
|
South-eastern Asia
|
82
|
|
Sub-Saharan Africa
|
80
|
|
India
|
78
|
|
India’s rank among the 24 PALS countries shown: 22 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Free healthcare
Free healthcare (%)
|
Region
|
%
|
|
Eastern Europe
|
92
|
|
Western Europe & Offshoots
|
89
|
|
Western Asia & Northern Africa
|
89
|
|
Latin America & Caribbean
|
86
|
|
South-eastern Asia
|
85
|
|
Sub-Saharan Africa
|
84
|
|
India
|
79
|
|
India’s rank among the 24 PALS countries shown: 22 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Welfare benefits
Welfare benefits (%)
|
Region
|
%
|
|
Western Asia & Northern Africa
|
90
|
|
South-eastern Asia
|
88
|
|
Sub-Saharan Africa
|
85
|
|
Western Europe & Offshoots
|
83
|
|
Latin America & Caribbean
|
83
|
|
Eastern Europe
|
82
|
|
India
|
79
|
|
India’s rank among the 24 PALS countries shown: 19 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Support for disadvantaged groups
Support for disadvantaged groups (%)
|
Region
|
%
|
|
South-eastern Asia
|
90
|
|
Western Asia & Northern Africa
|
86
|
|
Sub-Saharan Africa
|
85
|
|
Western Europe & Offshoots
|
85
|
|
Latin America & Caribbean
|
84
|
|
Eastern Europe
|
83
|
|
India
|
75
|
|
India’s rank among the 24 PALS countries shown: 24 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Support for women
Support for women (%)
|
Region
|
%
|
|
South-eastern Asia
|
84
|
|
Western Europe & Offshoots
|
82
|
|
Latin America & Caribbean
|
82
|
|
Sub-Saharan Africa
|
78
|
|
Eastern Europe
|
78
|
|
India
|
77
|
|
Western Asia & Northern Africa
|
70
|
|
India’s rank among the 24 PALS countries shown: 18 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
State ownership should be increased
State ownership should be increased (%)
|
Region
|
%
|
|
Western Asia & Northern Africa
|
55
|
|
Sub-Saharan Africa
|
49
|
|
India
|
47
|
|
Eastern Europe
|
46
|
|
South-eastern Asia
|
45
|
|
Western Europe & Offshoots
|
44
|
|
Latin America & Caribbean
|
43
|
|
India’s rank among the 24 PALS countries shown: 9 (1 =
highest).
|
|
Cells: % of respondents (weighted, w2). India: its own
estimate. Regions: mean of country estimates.
|
Part V. India across both surveys
This part puts the two surveys side by side. It uses bridge
questions: questions asked in both the WVS (Wave 7) and PALS
(2022) that measure the same thing, sometimes with identical wording.
Each is grouped under the measures used in Part II.
- Countries: only the 15 countries surveyed in both
(Japan and South Korea are left out, as in the rest of the report).
India is ranked against the other 14 in each survey, so the two ranks
can be compared directly.
- Ranks, not levels: the surveys use different scales
(4- and 10-point in the WVS, 6-point in PALS), so the percentages are
not directly comparable across surveys. India’s rank
is. Rank 1 = the highest share among the 15 countries.
- Timing and mode: WVS Wave 7 was fielded in
2017–2023 (India 2023), PALS in 2022. All WVS interviews here are face
to face; in PALS India, Nigeria, Peru and Tunisia were face to face and
the rest online.
- Weights: each survey’s own weight within a country
(WVS survey weight; PALS w2). Adults only.
shared <- c("India", "Australia", "Brazil", "Chile", "Germany", "Indonesia", "Mexico", "Nigeria", "Peru",
"Russia", "Singapore", "Tunisia", "Turkey", "United Kingdom", "United States")
# WVS Wave 7: one yes/no per bridge question
wvs7 <- wvs_all |>
filter(wave == "W7", country_name %in% shared, is.na(age) | age >= 18) |>
mutate(
b_prices = case_when(pm_goals1 == 3 | pm_goals2 == 3 ~ 1, pm_goals1 %in% 1:4 ~ 0),
b_say = case_when(pm_goals1 == 2 | pm_goals2 == 2 ~ 1, pm_goals1 %in% 1:4 ~ 0),
b_speech = case_when(pm_goals1 == 4 | pm_goals2 == 4 ~ 1, pm_goals1 %in% 1:4 ~ 0),
b_order = case_when(pm_goals1 == 1 | pm_goals2 == 1 ~ 1, pm_goals1 %in% 1:4 ~ 0),
b_strong = case_when(reg_strongleader %in% 1:2 ~ 1, reg_strongleader %in% 3:4 ~ 0),
b_experts = case_when(reg_experts %in% 1:2 ~ 1, reg_experts %in% 3:4 ~ 0),
b_army = case_when(reg_army %in% 1:2 ~ 1, reg_army %in% 3:4 ~ 0),
b_religious = case_when(dess_religious %in% 7:10 ~ 1, dess_religious %in% 1:6 ~ 0),
b_authority = case_when(fut_authority == 1 ~ 1, fut_authority %in% 2:3 ~ 0),
b_rulelaw = case_when(dess_civilrights %in% 7:10 ~ 1, dess_civilrights %in% 1:6 ~ 0),
b_menjobs = case_when(jobs_men == 3 ~ 1, jobs_men %in% 1:2 ~ 0), # disagrees: men first
b_stateown = case_when(econ_ownership %in% 6:10 ~ 1, econ_ownership %in% 1:5 ~ 0),
b_welfare = case_when(econ_govresp %in% 1:4 ~ 1, econ_govresp %in% 5:10 ~ 0),
b_luck = case_when(econ_hardwork %in% 6:10 ~ 1, econ_hardwork %in% 1:5 ~ 0),
b_equalinc = case_when(econ_income %in% 1:4 ~ 1, econ_income %in% 5:10 ~ 0),
b_compet = case_when(econ_competition %in% 6:10 ~ 1, econ_competition %in% 1:5 ~ 0),
b_immig = case_when(immig_policy %in% 3:4 ~ 1, immig_policy %in% 1:2 ~ 0),
b_satis = case_when(sat_polsystem %in% 6:10 ~ 1, sat_polsystem %in% 1:5 ~ 0),
b_video = case_when(surv_video %in% 1:2 ~ 1, surv_video %in% 3:4 ~ 0),
b_internet = case_when(surv_email %in% 1:2 ~ 1, surv_email %in% 3:4 ~ 0),
b_vote = case_when(vote_national %in% 1:2 ~ 1, vote_national == 3 ~ 0),
b_saypol = case_when(dess_elections %in% 7:10 ~ 1, dess_elections %in% 1:6 ~ 0),
b_fairlaw = case_when(dess_civilrights %in% 7:10 ~ 1, dess_civilrights %in% 1:6 ~ 0),
b_womensay = case_when(dess_women %in% 7:10 ~ 1, dess_women %in% 1:6 ~ 0),
b_womenlead = case_when(gen_menleaders %in% 3:4 ~ 1, gen_menleaders %in% 1:2 ~ 0), # disagrees: men better leaders
b_bigint = case_when(ei_richbuy %in% 1:2 ~ 1, ei_richbuy %in% 3:4 ~ 0), # rich buy elections often
b_offnot = case_when(conf_govt %in% 3:4 ~ 1, conf_govt %in% 1:2 ~ 0), # little confidence in government
b_taxrich = case_when(dess_taxrich %in% 7:10 ~ 1, dess_taxrich %in% 1:6 ~ 0),
b_nationals = case_when(jobs_nationals == 1 ~ 1, jobs_nationals %in% 2:3 ~ 0))
# PALS 2022: the matching yes/no
p_side <- function(x, side) as.numeric(zap_labels(x)) |> (\(v) case_when(v %in% side ~ 1, v %in% 1:6 ~ 0))()
pals5 <- pals |>
mutate(country_name = recode(country_name, "Türkiye" = "Turkey")) |>
filter(country_name %in% shared) |>
mutate(
b_prices = as.numeric(zap_labels(D06_c)), b_say = as.numeric(zap_labels(D06_b)),
b_speech = as.numeric(zap_labels(D06_d)), b_order = as.numeric(zap_labels(D06_a)),
across(c(b_prices, b_say, b_speech, b_order), ~ ifelse(.x %in% 0:1, .x, NA)),
b_strong = p_side(B01_a, 4:6), b_experts = p_side(B01_c, 4:6), b_army = p_side(B01_e, 4:6),
b_religious = p_side(B01_d, 4:6), b_authority = p_side(D07_b, 4:6),
b_rulelaw = p_side(B02_a, 1:3), b_menjobs = p_side(C05_a, 1:3),
b_stateown = p_side(B03_a, 4:6), b_welfare = p_side(C04_c, 4:6),
b_luck = p_side(B03_c, 4:6), b_nationals = p_side(C05_b, 4:6),
b_equalinc = ifelse(as.numeric(zap_labels(D01_a3)) %in% 0:1, as.numeric(zap_labels(D01_a3)), NA),
b_taxrich = p_side(C04_d, 4:6),
b_compet = p_side(B03_b, 4:6),
b_immig = p_side(C01_d, 4:6),
b_satis = p_side(D02_a, 4:6),
# D09_a is a split-sample experiment: half asked about cameras, half about phone/internet monitoring
b_video = ifelse(as.numeric(zap_labels(D09_a1)) == 1, p_side(D09_a, 4:6), NA),
b_internet = ifelse(as.numeric(zap_labels(D09_a1)) == 2, p_side(D09_a, 4:6), NA),
b_vote = case_when(as.numeric(zap_labels(E01)) == 1 ~ 1, as.numeric(zap_labels(E01)) == 2 ~ 0),
# A03 items were each shown to a random part of the sample (96 = not shown)
across(c(A03_a3, A03_c2, A03_b5), ~ ifelse(as.numeric(zap_labels(.x)) %in% 0:1, as.numeric(zap_labels(.x)), NA), .names = "{.col}_01"),
b_saypol = A03_a3_01, b_fairlaw = A03_c2_01, b_womensay = A03_b5_01,
b_womenlead = p_side(C06_a, 4:6),
b_bigint = p_side(D03_d, 4:6),
b_offnot = p_side(D03_e, 1:3))
bridges <- tribble(
~measure, ~var, ~wvs_q, ~pals_q,
"Governance (delivery)", "b_prices", "Fighting rising prices among the top two aims (Q154–155)", "Same question (D06_c)",
"Accountability", "b_say", "More say in important government decisions among the top two aims (Q154–155)", "Same question (D06_b)",
"Accountability", "b_speech", "Protecting freedom of speech among the top two aims (Q154–155)", "Same question (D06_d)",
"Accountability", "b_vote", "Votes in national elections always or usually (Q222)", "Voted in the last national election (E01; those eligible)",
"Accountability: perceived capture", "b_bigint", "Rich people buy elections, very or fairly often (Q230)", "The government is run by a few big interests looking out for themselves (D03_d, agree 4–6)",
"Accountability: perceived capture", "b_offnot", "Not very much or no confidence in the government (Q71)", "Disagrees that government officials use their power to improve people's lives (D03_e, 1–3)",
"Satisfaction with the political system", "b_satis", "Satisfied with how the political system is functioning (Q252, 6–10)", "Satisfied with the political system (D02_a, 4–6)",
"Authoritarian side", "b_order", "Maintaining order among the top two aims (Q154–155)", "Same question (D06_a)",
"Authoritarian side", "b_strong", "A strong leader who does not bother with parliament and elections is good (Q235)", "Strong political leaders, not citizens, should decide (B01_a, 4–6)",
"Authoritarian side", "b_experts", "Experts, not government, making decisions is good (Q236)", "Established experts, not citizens, should decide (B01_c, 4–6)",
"Authoritarian side", "b_army", "Army rule is good (Q237)", "The military, not citizens, should decide (B01_e, 4–6)",
"Authoritarian side", "b_religious", "Religious authorities interpreting the laws is essential to democracy (Q242, 7–10)", "Religious leaders, not citizens, should decide (B01_d, 4–6)",
"Authoritarian side", "b_authority", "Greater respect for authority would be good (Q45)", "Discipline, everyone following our leaders in unity (D07_b, agree 4–6)",
"Security over freedom", "b_video", "Government should have the right to keep people under video surveillance in public areas (Q196, should 1–2)", "Accepts more camera monitoring of public places against terrorism (D09_a, half the sample, 4–6)",
"Security over freedom", "b_internet", "Government should have the right to monitor e-mails and information on the internet (Q197, should 1–2)", "Accepts monitoring of everyone's phone calls and internet activity without a warrant against terrorism (D09_a, other half, 4–6)",
"Liberal side", "b_saypol", "Free elections are essential to democracy (Q243, 7–10)", "Having a say in politics is necessary to live freely (A03_a3, picked)",
"Liberal side", "b_fairlaw", "Civil rights protecting people from the state are essential to democracy (Q246, 7–10)", "A fair legal system is necessary to live freely (A03_c2, picked; part of the sample)",
"Liberal side", "b_womensay", "Women having the same rights as men is essential to democracy (Q249, 7–10)", "More say for women is necessary to live freely (A03_b5, picked; part of the sample)",
"Liberal side", "b_womenlead", "Disagrees: men make better political leaders than women (Q29)", "Women should be preferred for leadership positions until equal representation (C06_a, agree 4–6)",
"Liberal side", "b_rulelaw", "Civil rights protecting people from the state are essential to democracy (Q246, 7–10)", "Government should obey the laws and courts (B02_a, 1–3)",
"Liberal side", "b_menjobs", "Disagrees: when jobs are scarce, men have more right to a job than women (Q33)", "Disagrees: when jobs are scarce, men should be preferred (C05_a, 1–3)",
"State support", "b_stateown", "State ownership of business should be increased (Q107, 6–10)", "Same question (B03_a, 4–6)",
"State support", "b_compet", "Competition is harmful (Q109, 6–10)", "Same question (B03_b, 4–6)",
"State support", "b_welfare", "Government should take more responsibility to provide for everyone (Q108, 1–4)", "Society should provide welfare benefits for everyone in need (C04_c, agree 4–6)",
"Inequality: redistribution", "b_equalinc", "Incomes should be made more equal (Q106, 1–4)", "The gap between the rich and the poor is a major threat (D01_a3, picked)",
"Inequality: redistribution", "b_taxrich", "Governments taxing the rich and subsidising the poor is essential to democracy (Q241, 7–10)", "Society should support disadvantaged groups, like minorities or the poor (C04_d, agree 4–6)",
"Inequality: exclusion", "b_luck", "Success is more a matter of luck and connections than hard work (Q110, 6–10)", "Wealth and status should be based on ancestry and contacts (B03_c, 4–6)",
"Inequality: exclusion", "b_immig", "Immigration policy: strict limits or prohibit people coming to work (Q130)", "The country should have the right to reject immigrants (C01_d, agree 4–6)",
"Inequality: exclusion", "b_nationals", "When jobs are scarce, employers should prefer nationals over immigrants (Q34, agree)", "When jobs are scarce, citizens should be preferred over migrants (C05_b, agree 4–6)")
# country % for each bridge question, in each survey
bridge_est <- function(d, wcol) {
imap_dfr(setNames(bridges$var, bridges$var), function(v, nm) {
d |>
filter(!is.na(.data[[v]])) |>
group_by(country_name) |>
summarise(est = 100 * weighted.mean(.data[[v]], .data[[wcol]]), .groups = "drop") |>
mutate(var = v)
})
}
b_wvs <- bridge_est(wvs7, "weight") |> mutate(survey = "WVS")
b_pals <- bridge_est(pals5, "w2") |> mutate(survey = "PALS")
b_all <- bind_rows(b_wvs, b_pals) |>
group_by(survey, var) |>
mutate(rank = rank(-est, ties.method = "min"), N = n()) |>
ungroup()
# ---- full-scale versions and bridge correlations (used in Parts V and VI) ----
pf <- function(x, rev = FALSE) { # PALS 1-6, drop "prefer not to say" / "don't know"
v <- as.numeric(zap_labels(x)); v[!v %in% 1:6] <- NA
if (rev) 7 - v else v
}
p01 <- function(x) { v <- as.numeric(zap_labels(x)); ifelse(v %in% 0:1, v, NA) }
wvs_full <- wvs7 |>
transmute(country_name, w = weight,
b_prices = b_prices, b_say = b_say, b_speech = b_speech, b_order = b_order,
b_vote = 4 - vote_national, b_satis = sat_polsystem,
b_bigint = 5 - ei_richbuy, b_offnot = conf_govt,
b_strong = 5 - reg_strongleader, b_experts = 5 - reg_experts, b_army = 5 - reg_army,
b_religious = dess_religious, b_authority = 4 - fut_authority,
b_video = 5 - surv_video, b_internet = 5 - surv_email,
b_saypol = dess_elections, b_fairlaw = dess_civilrights, b_womensay = dess_women,
b_womenlead = gen_menleaders, b_rulelaw = dess_civilrights, b_menjobs = jobs_men,
b_stateown = econ_ownership, b_compet = econ_competition, b_welfare = 11 - econ_govresp,
b_equalinc = 11 - econ_income, b_taxrich = dess_taxrich,
b_luck = econ_hardwork, b_immig = immig_policy, b_nationals = 4 - jobs_nationals)
pals_full <- pals5 |>
transmute(country_name, w = w2,
b_prices = b_prices, b_say = b_say, b_speech = b_speech, b_order = b_order,
b_vote = b_vote, b_satis = pf(D02_a),
b_bigint = pf(D03_d), b_offnot = pf(D03_e, rev = TRUE),
b_strong = pf(B01_a), b_experts = pf(B01_c), b_army = pf(B01_e),
b_religious = pf(B01_d), b_authority = pf(D07_b),
b_video = ifelse(as.numeric(zap_labels(D09_a1)) == 1, pf(D09_a), NA),
b_internet = ifelse(as.numeric(zap_labels(D09_a1)) == 2, pf(D09_a), NA),
b_saypol = p01(A03_a3), b_fairlaw = p01(A03_c2), b_womensay = p01(A03_b5),
b_womenlead = pf(C06_a), b_rulelaw = pf(B02_a, rev = TRUE), b_menjobs = pf(C05_a, rev = TRUE),
b_stateown = pf(B03_a), b_compet = pf(B03_b), b_welfare = pf(C04_c),
b_equalinc = p01(D01_a3), b_taxrich = pf(C04_d),
b_luck = pf(B03_c), b_immig = pf(C01_d), b_nationals = pf(C05_b))
wmean <- function(x, w) { ok <- !is.na(x) & !is.na(w); sum(x[ok] * w[ok]) / sum(w[ok]) }
wvar <- function(x, w) { ok <- !is.na(x) & !is.na(w); m <- wmean(x, w); sum(w[ok] * (x[ok] - m)^2) / sum(w[ok]) }
dist_stats <- function(d, survey) {
map_dfr(bridges$var, function(v) {
x <- d |> select(country_name, w, val = all_of(v)) |> filter(!is.na(val))
cm <- x |> group_by(country_name) |> summarise(m = wmean(val, w), .groups = "drop")
if (!"India" %in% cm$country_name) return(NULL)
oth <- cm |> filter(country_name != "India")
ind <- cm$m[cm$country_name == "India"]
# individual level: India vs the other countries, each country counting equally
xi <- x |> filter(country_name == "India")
xo <- x |> filter(country_name != "India") |> group_by(country_name) |> mutate(w = w / sum(w)) |> ungroup()
dd <- (wmean(xi$val, xi$w) - wmean(xo$val, xo$w)) / sqrt((wvar(xi$val, xi$w) + wvar(xo$val, xo$w)) / 2)
tibble(var = v, survey = survey,
rank = rank(-cm$m, ties.method = "min")[cm$country_name == "India"], N = nrow(cm),
z = (ind - mean(oth$m)) / sd(oth$m), d = dd)
})
}
country_means <- function(d) {
d |> pivot_longer(-c(country_name, w), names_to = "var", values_to = "val") |>
filter(!is.na(val)) |> group_by(var, country_name) |>
summarise(m = wmean(val, w), .groups = "drop")
}
cm_wvs <- country_means(wvs_full)
cm_pals <- country_means(pals_full)
dist <- bind_rows(dist_stats(wvs_full, "WVS"), dist_stats(pals_full, "PALS"))
rho <- inner_join(cm_wvs, cm_pals, by = c("var", "country_name"), suffix = c("_w", "_p")) |>
group_by(var) |>
summarise(rho = cor(m_w, m_p, method = "spearman"), n = n(), .groups = "drop")
short <- c(b_prices = "Fighting rising prices (aim)", b_say = "More say in government (aim)",
b_speech = "Free speech (aim)", b_vote = "Voting", b_bigint = "Rich buy elections / big interests run government",
b_offnot = "Little confidence in government / officials don't help", b_satis = "Satisfied with the political system",
b_order = "Maintaining order (aim)", b_strong = "Strong leader", b_experts = "Experts", b_army = "Army / military",
b_religious = "Religious authority", b_authority = "Respect for authority / discipline",
b_video = "Video / camera surveillance", b_internet = "Internet / phone monitoring",
b_saypol = "Free elections / say in politics", b_fairlaw = "Civil rights / fair legal system",
b_womensay = "Women's equal rights / more say for women", b_womenlead = "Women as leaders",
b_rulelaw = "Civil rights / government bound by courts", b_menjobs = "Rejects men first for jobs",
b_stateown = "State ownership", b_compet = "Competition is harmful", b_welfare = "Government responsibility / welfare",
b_equalinc = "Equal incomes / rich-poor gap a threat", b_taxrich = "Tax the rich / support the disadvantaged",
b_luck = "Luck and connections / ancestry", b_immig = "Restrict / reject immigrants", b_nationals = "Nationals first for jobs")
bridge_strength <- function(r) case_when(r >= 0.6 ~ "Strong", r >= 0.4 ~ "Moderate", TRUE ~ "—")
V.1 The 15 countries
wvs7 |>
group_by(country_name) |>
summarise(`WVS year` = paste(sort(unique(survey_year)), collapse = "/"), `WVS respondents` = n(), .groups = "drop") |>
left_join(pals5 |> count(country_name, name = "PALS respondents"), by = "country_name") |>
mutate(`PALS mode` = ifelse(country_name %in% pals_capi, "face to face", "online"),
Region = unname(pals_region_of[recode(country_name, "Turkey" = "Türkiye", "Russia" = "Russian Federation",
"United Kingdom" = "United Kingdom of Great Britain and Northern Ireland",
"United States" = "United States of America")])) |>
arrange(factor(Region, wb_regions), country_name) |>
select(Country = country_name, Region, `WVS year`, `WVS respondents`, `PALS respondents`, `PALS mode`) |>
kable(caption = "Countries in both surveys (adults)", format.args = list(big.mark = ",")) |>
styled()
Countries in both surveys (adults)
|
Country
|
Region
|
WVS year
|
WVS respondents
|
PALS respondents
|
PALS mode
|
|
India
|
India
|
2023
|
1,371
|
2,822
|
face to face
|
|
Indonesia
|
South-eastern Asia
|
2018
|
3,200
|
2,001
|
online
|
|
Singapore
|
South-eastern Asia
|
2020
|
2,012
|
2,010
|
online
|
|
Tunisia
|
Western Asia & Northern Africa
|
2019
|
1,208
|
2,012
|
face to face
|
|
Turkey
|
Western Asia & Northern Africa
|
2018
|
2,415
|
2,016
|
online
|
|
Nigeria
|
Sub-Saharan Africa
|
2018
|
1,237
|
2,000
|
face to face
|
|
Brazil
|
Latin America & Caribbean
|
2018
|
1,698
|
2,110
|
online
|
|
Chile
|
Latin America & Caribbean
|
2018
|
1,000
|
2,005
|
online
|
|
Mexico
|
Latin America & Caribbean
|
2018
|
1,741
|
2,160
|
online
|
|
Peru
|
Latin America & Caribbean
|
2018
|
1,400
|
2,018
|
face to face
|
|
Russia
|
Eastern Europe
|
2017
|
1,810
|
2,143
|
online
|
|
Australia
|
Western Europe & Offshoots
|
2018
|
1,811
|
2,032
|
online
|
|
Germany
|
Western Europe & Offshoots
|
2018
|
1,528
|
2,020
|
online
|
|
United Kingdom
|
Western Europe & Offshoots
|
2022
|
2,609
|
2,007
|
online
|
|
United States
|
Western Europe & Offshoots
|
2017
|
2,596
|
2,033
|
online
|
V.2 Bridge questions: India’s rank in each survey
What is shown. For each pair of questions: the share
of Indians giving the answer described, and India’s rank among the 15
countries (1 = highest share), in WVS 2023 and in PALS 2022. Where the
ranks are close, the two surveys agree about where India stands.
india_b <- b_all |> filter(country_name == "India") |>
mutate(cell_pct = sprintf("%.0f", est), cell_rank = sprintf("%d of %d", rank, N)) |>
select(var, survey, cell_pct, cell_rank) |>
pivot_wider(names_from = survey, values_from = c(cell_pct, cell_rank))
bt <- bridges |>
left_join(india_b, by = "var") |>
left_join(rho, by = "var") |>
mutate(strength = bridge_strength(rho)) |>
select(measure, `WVS 2023 question` = wvs_q, `India %` = cell_pct_WVS, `India's rank` = cell_rank_WVS,
`PALS 2022 question` = pals_q, `India % ` = cell_pct_PALS, `India's rank ` = cell_rank_PALS,
`Bridge strength` = strength)
bt |>
select(-measure) |>
kable(caption = "Bridge questions: India's share and rank among the 15 countries", align = c("l", "c", "c", "l", "c", "c", "c")) |>
styled() |>
add_header_above(c("WVS Wave 7 (India 2023)" = 3, "PALS (2022)" = 3, " " = 1)) |>
pack_rows(index = table(factor(bt$measure, unique(bt$measure)))) |>
column_spec(c(1, 4), width = "19em") |>
footnote(general = c("Rank 1 = highest share among the 15 countries in both surveys. Percentages are not comparable across the two surveys (different scales); ranks are.",
"Bridge strength: how similarly the two questions order the 15 countries (Spearman ρ, Part VI). Strong: 0.6 or more; Moderate: 0.4 to 0.6; —: below 0.4, not established as measuring the same thing."),
general_title = "")
Bridge questions: India’s share and rank among the 15 countries
|
WVS Wave 7 (India 2023)
|
PALS (2022)
|
|
|
WVS 2023 question
|
India %
|
India’s rank
|
PALS 2022 question
|
India %
|
India’s rank
|
Bridge strength
|
|
Governance (delivery)
|
|
Fighting rising prices among the top two aims (Q154–155)
|
65
|
5 of 15
|
Same question (D06_c)
|
67
|
3 of 15
|
Strong
|
|
Accountability
|
|
More say in important government decisions among the top two aims
(Q154–155)
|
41
|
13 of 15
|
Same question (D06_b)
|
37
|
12 of 15
|
—
|
|
Protecting freedom of speech among the top two aims (Q154–155)
|
18
|
13 of 15
|
Same question (D06_d)
|
30
|
12 of 15
|
Moderate
|
|
Votes in national elections always or usually (Q222)
|
94
|
6 of 15
|
Voted in the last national election (E01; those eligible)
|
88
|
7 of 15
|
Strong
|
|
Accountability: perceived capture
|
|
Rich people buy elections, very or fairly often (Q230)
|
76
|
3 of 15
|
The government is run by a few big interests looking out for themselves
(D03_d, agree 4–6)
|
76
|
7 of 15
|
—
|
|
Not very much or no confidence in the government (Q71)
|
35
|
12 of 15
|
Disagrees that government officials use their power to improve people’s
lives (D03_e, 1–3)
|
29
|
15 of 15
|
Moderate
|
|
Satisfaction with the political system
|
|
Satisfied with how the political system is functioning (Q252, 6–10)
|
66
|
3 of 15
|
Satisfied with the political system (D02_a, 4–6)
|
72
|
1 of 15
|
Strong
|
|
Authoritarian side
|
|
Maintaining order among the top two aims (Q154–155)
|
60
|
7 of 15
|
Same question (D06_a)
|
46
|
6 of 15
|
Moderate
|
|
A strong leader who does not bother with parliament and elections is
good (Q235)
|
69
|
3 of 15
|
Strong political leaders, not citizens, should decide (B01_a, 4–6)
|
35
|
6 of 15
|
—
|
|
Experts, not government, making decisions is good (Q236)
|
73
|
3 of 15
|
Established experts, not citizens, should decide (B01_c, 4–6)
|
40
|
12 of 15
|
—
|
|
Army rule is good (Q237)
|
39
|
5 of 14
|
The military, not citizens, should decide (B01_e, 4–6)
|
39
|
3 of 15
|
—
|
|
Religious authorities interpreting the laws is essential to democracy
(Q242, 7–10)
|
33
|
2 of 15
|
Religious leaders, not citizens, should decide (B01_d, 4–6)
|
35
|
3 of 15
|
—
|
|
Greater respect for authority would be good (Q45)
|
50
|
12 of 15
|
Discipline, everyone following our leaders in unity (D07_b, agree 4–6)
|
73
|
2 of 15
|
—
|
|
Security over freedom
|
|
Government should have the right to keep people under video surveillance
in public areas (Q196, should 1–2)
|
84
|
1 of 15
|
Accepts more camera monitoring of public places against terrorism
(D09_a, half the sample, 4–6)
|
82
|
2 of 15
|
—
|
|
Government should have the right to monitor e-mails and information on
the internet (Q197, should 1–2)
|
60
|
2 of 15
|
Accepts monitoring of everyone’s phone calls and internet activity
without a warrant against terrorism (D09_a, other half, 4–6)
|
72
|
1 of 15
|
Strong
|
|
Liberal side
|
|
Free elections are essential to democracy (Q243, 7–10)
|
79
|
5 of 15
|
Having a say in politics is necessary to live freely (A03_a3, picked)
|
40
|
13 of 15
|
—
|
|
Civil rights protecting people from the state are essential to democracy
(Q246, 7–10)
|
73
|
4 of 15
|
A fair legal system is necessary to live freely (A03_c2, picked; part of
the sample)
|
45
|
14 of 15
|
—
|
|
Women having the same rights as men is essential to democracy (Q249,
7–10)
|
77
|
8 of 15
|
More say for women is necessary to live freely (A03_b5, picked; part of
the sample)
|
47
|
4 of 15
|
—
|
|
Disagrees: men make better political leaders than women (Q29)
|
45
|
11 of 15
|
Women should be preferred for leadership positions until equal
representation (C06_a, agree 4–6)
|
74
|
1 of 15
|
—
|
|
Civil rights protecting people from the state are essential to democracy
(Q246, 7–10)
|
73
|
4 of 15
|
Government should obey the laws and courts (B02_a, 1–3)
|
66
|
15 of 15
|
—
|
|
Disagrees: when jobs are scarce, men have more right to a job than women
(Q33)
|
30
|
11 of 15
|
Disagrees: when jobs are scarce, men should be preferred (C05_a, 1–3)
|
33
|
15 of 15
|
Strong
|
|
State support
|
|
State ownership of business should be increased (Q107, 6–10)
|
63
|
4 of 15
|
Same question (B03_a, 4–6)
|
47
|
7 of 15
|
Moderate
|
|
Competition is harmful (Q109, 6–10)
|
45
|
1 of 15
|
Same question (B03_b, 4–6)
|
47
|
1 of 15
|
Moderate
|
|
Government should take more responsibility to provide for everyone
(Q108, 1–4)
|
39
|
12 of 15
|
Society should provide welfare benefits for everyone in need (C04_c,
agree 4–6)
|
79
|
13 of 15
|
Strong
|
|
Inequality: redistribution
|
|
Incomes should be made more equal (Q106, 1–4)
|
21
|
12 of 15
|
The gap between the rich and the poor is a major threat (D01_a3, picked)
|
64
|
6 of 15
|
—
|
|
Governments taxing the rich and subsidising the poor is essential to
democracy (Q241, 7–10)
|
69
|
5 of 15
|
Society should support disadvantaged groups, like minorities or the poor
(C04_d, agree 4–6)
|
75
|
15 of 15
|
—
|
|
Inequality: exclusion
|
|
Success is more a matter of luck and connections than hard work (Q110,
6–10)
|
42
|
3 of 15
|
Wealth and status should be based on ancestry and contacts (B03_c, 4–6)
|
32
|
1 of 15
|
—
|
|
Immigration policy: strict limits or prohibit people coming to work
(Q130)
|
48
|
8 of 15
|
The country should have the right to reject immigrants (C01_d, agree
4–6)
|
76
|
4 of 15
|
—
|
|
When jobs are scarce, employers should prefer nationals over immigrants
(Q34, agree)
|
74
|
7 of 15
|
When jobs are scarce, citizens should be preferred over migrants (C05_b,
agree 4–6)
|
69
|
5 of 15
|
Strong
|
|
Rank 1 = highest share among the 15 countries in both
surveys. Percentages are not comparable across the two surveys
(different scales); ranks are.
|
|
Bridge strength: how similarly the two questions order the
15 countries (Spearman ρ, Part VI). Strong: 0.6 or more; Moderate: 0.4
to 0.6; —: below 0.4, not established as measuring the same thing.
|
V.3 Summary by measure
What is shown. For each measure, India’s average
rank across its bridge questions, in each survey (1 = highest among the
15 countries; 8 = middle; 15 = lowest).
b_all |>
filter(country_name == "India") |>
left_join(select(bridges, var, measure), by = "var") |>
group_by(measure, survey) |>
summarise(r = mean(rank), k = n(), .groups = "drop") |>
mutate(r = sprintf("%.1f", r)) |>
pivot_wider(names_from = survey, values_from = r) |>
mutate(measure = factor(measure, unique(bridges$measure))) |>
arrange(measure) |>
select(Measure = measure, `Bridge questions` = k, `India's average rank, WVS 2023` = WVS,
`India's average rank, PALS 2022` = PALS) |>
kable(caption = "India's average rank among the 15 countries, by measure", align = c("l", "c", "c", "c")) |>
styled()
India’s average rank among the 15 countries, by measure
|
Measure
|
Bridge questions
|
India’s average rank, WVS 2023
|
India’s average rank, PALS 2022
|
|
Governance (delivery)
|
1
|
5.0
|
3.0
|
|
Accountability
|
3
|
10.7
|
10.3
|
|
Accountability: perceived capture
|
2
|
7.5
|
11.0
|
|
Satisfaction with the political system
|
1
|
3.0
|
1.0
|
|
Authoritarian side
|
6
|
5.3
|
5.3
|
|
Security over freedom
|
2
|
1.5
|
1.5
|
|
Liberal side
|
6
|
7.2
|
10.3
|
|
State support
|
3
|
5.7
|
7.0
|
|
Inequality: redistribution
|
2
|
8.5
|
10.5
|
|
Inequality: exclusion
|
3
|
6.0
|
3.3
|
V.4 India’s nearest countries
What is shown. For each measure, the three countries
whose answers are closest to India’s across all of that measure’s bridge
questions together (the smallest average gap in percentage points), in
each survey.
nearest <- b_all |>
left_join(select(bridges, var, measure), by = "var") |>
group_by(survey, measure, var) |>
mutate(india = est[country_name == "India"]) |>
ungroup() |>
filter(country_name != "India") |>
group_by(survey, measure, country_name) |>
summarise(gap = mean(abs(est - india)), .groups = "drop") |>
group_by(survey, measure) |>
slice_min(gap, n = 3, with_ties = FALSE) |>
summarise(cell = paste0(country_name, " (", sprintf("%.0f", gap), ")", collapse = ", "), .groups = "drop") |>
pivot_wider(names_from = survey, values_from = cell) |>
mutate(measure = factor(measure, unique(bridges$measure))) |>
arrange(measure)
nearest |>
select(Measure = measure, `Nearest in WVS 2023` = WVS, `Nearest in PALS 2022` = PALS) |>
kable(caption = "India's three nearest countries by measure (average gap in percentage points)") |>
styled() |>
footnote(general = "Gap: average absolute difference from India across the measure's bridge questions, within each survey.",
general_title = "")
India’s three nearest countries by measure (average gap in percentage
points)
|
Measure
|
Nearest in WVS 2023
|
Nearest in PALS 2022
|
|
Governance (delivery)
|
Turkey (0), United Kingdom (2), Brazil (6)
|
Nigeria (2), Singapore (2), Peru (4)
|
|
Accountability
|
Singapore (6), Russia (7), Peru (7)
|
Peru (4), Chile (5), Singapore (5)
|
|
Accountability: perceived capture
|
Nigeria (16), Indonesia (19), Russia (21)
|
Indonesia (2), Australia (7), Singapore (7)
|
|
Satisfaction with the political system
|
Germany (3), Turkey (5), Indonesia (6)
|
Singapore (8), Germany (17), Indonesia (17)
|
|
Authoritarian side
|
Mexico (7), Peru (9), Brazil (10)
|
Nigeria (5), United States (6), Singapore (7)
|
|
Security over freedom
|
Indonesia (8), Nigeria (11), Australia (16)
|
Indonesia (6), Nigeria (7), United Kingdom (8)
|
|
Liberal side
|
Russia (2), Indonesia (8), Turkey (8)
|
Nigeria (13), Indonesia (14), United Kingdom (19)
|
|
State support
|
Turkey (4), Indonesia (7), Mexico (9)
|
Indonesia (9), Mexico (10), Chile (10)
|
|
Inequality: redistribution
|
Russia (1), Indonesia (3), Tunisia (6)
|
Mexico (6), Germany (6), Singapore (8)
|
|
Inequality: exclusion
|
Nigeria (4), Brazil (5), Russia (9)
|
Singapore (6), Indonesia (7), United Kingdom (8)
|
|
Gap: average absolute difference from India across the
measure’s bridge questions, within each survey.
|
What this part shows.
- On most measures the two surveys place India in the same
position.
- Accountability: near the bottom in both: 13th (WVS) and
12th (PALS) of 15 on choosing more say in government or free speech as a
top aim.
- Authoritarian side: in the top third in both (average rank
5.3 in each). India is 3rd and 6th on the strong leader, 5th and 3rd on
the army, 2nd and 3rd on religious authority.
- Governance (delivery): fighting rising prices is a top aim
more often than in most countries (5th and 3rd).
- Inequality, exclusion: India ranks high in both: success
through luck and connections (3rd) or ancestry and contacts (1st), and
jobs for nationals first (7th and 5th).
- Inequality, redistribution: India sits in the middle to
lower half in both (average rank 8.5 and 10.5). Few Indians want incomes
made more equal (12th in WVS, after the 2023 fall), though many call
taxing the rich essential (5th); in PALS the rich-poor gap is often seen
as a major threat (6th), but India is last on society supporting
disadvantaged groups (15th).
- Security over freedom: India is at the very top in both
surveys. It is 1st on giving the government the right to video
surveillance (84%) and 2nd on monitoring e-mails and the internet (60%)
in the WVS; in PALS it is 2nd on more cameras in public places (82%) and
1st on monitoring everyone’s phone calls and internet activity without a
warrant (72%).
- Satisfaction with the political system is among the
highest: 3rd in the WVS and 1st in PALS.
- The identical competition question gives the same
answer: India is 1st in both on “competition is harmful” (45%
and 47%).
- Immigration: 8th in the WVS on wanting strict
limits or a ban (48%), 4th in PALS on the country’s right to reject
immigrants (76%). Voting: high turnout but mid-table
(6th and 7th), since most of the 15 countries also report high
turnout.
- Accountability, perceived capture: Indians see elections as
bought but trust the government. India is 3rd on “rich people
buy elections” (76%) and 7th in PALS on “government run by a few big
interests” (76%). Yet few Indians lack confidence in the government
(12th) or deny that officials try to improve people’s lives (15th,
last).
- Liberal side, what democracy needs vs what freedom
needs: in the WVS India ranks high on free elections (5th) and
civil rights (4th) as essentials of democracy, but in PALS few Indians
name a say in politics (13th) or a fair legal system (14th) as necessary
for their own freedom. On women the pattern reverses: India is mid-table
on women’s equal rights (8th) and rejecting “men are better leaders”
(11th) in the WVS, but 4th on more say for women and 1st on preferring
women for leadership in PALS.
- The liberal side is where the surveys differ. In
the WVS India is 4th on rating civil rights as essential to democracy;
in PALS it is last (15th) on whether government must obey the laws and
courts. Rejecting “men first for scarce jobs” puts India 11th (WVS) and
15th (PALS). The WVS asks about democracy in the abstract; PALS asks
about concrete trade-offs, where India ranks least liberal of all 15
countries.
- Two pairs diverge because the questions differ:
experts (3rd in WVS on “experts deciding is good”, 12th in PALS on
“experts, not citizens, should decide”) and authority (12th on “more
respect for authority”, 2nd on “discipline, following our
leaders”).
- India’s nearest countries are mostly Nigeria, Turkey, Peru,
Singapore, Indonesia and Russia, depending on the measure;
Western countries appear only on single measures. There is no fixed peer
group.
- How strong each pair is, and how far India is from the
others, is measured in Part VI.
- Caution: most WVS surveys here date from 2017–2020,
India’s from 2023 and PALS from 2022, and PALS interviewed most
countries online. Ranks are robust to the different scales, but not to
these differences.
Part VI. How far is India from the others?
Part V places India by rank on each bridge question.
A rank says where India stands, but not how far it is from the others.
This part measures that distance, using each question’s full
scale (for example the whole 1–10 or 1–6 scale) rather than the
share above a cut-off. Each question is turned round where needed so
that a higher value always means the same thing as in Part V (for
example, more support for a strong leader, or more acceptance of
surveillance).
- Rank: India’s position among the 15 countries on
the full-scale mean (1 = highest).
- z-score: how far India’s mean is from the average
of the other 14 countries, in units of their spread (standard
deviation). About 0 = typical; +1 or −1 = clearly above or below; beyond
±2 = an outlier among these countries.
- Effect size (d): the difference between Indian
respondents and the respondents of the other 14 countries (each country
counting equally), divided by the spread among individuals. Conventional
reading: 0.2 small, 0.5 medium, 0.8 large.
- Bridge correlation (ρ): the rank correlation
between the WVS and PALS country means across the 15 countries. Each
survey’s questions are ordered separately, so different wording and
scales do not matter: the test is whether the two questions put the
countries in the same order. Strong: 0.6 or more;
Moderate: 0.4 to 0.6; —: below 0.4,
not established (with 15 countries, values below about 0.5 cannot be
told apart from zero). A weak pair can reflect different attitudes, real
change between the survey dates, or differences in mode.
- All figures are within each survey, weighted with each survey’s own
weight. Questions asked as a pick from a list (the aims, “necessary to
live freely”, the rich-poor gap, voting) have no longer scale, so their
full scale is the yes/no answer itself.
How to use them together. Rank shows where
India stands; z shows how far it stands from the other
countries; d shows how much Indians differ as people. z is
usually larger than d, because country averages bunch together while
individuals differ a lot within every country.
| High z, high d |
India is genuinely distinctive, as a country and in its people |
| High z, modest d |
India stands out among countries, but individuals overlap a lot |
| Low z, low d |
India is typical; its rank may rest on small gaps |
VI.1 India’s distance on each bridge question
What is shown. For each pair: India’s rank, z-score
and effect size (d) in each survey, and the correlation (ρ) between the
two surveys’ country means. Positive z and d mean India is above the
other countries, in the direction described in Part V.
dt <- dist |>
mutate(cell_r = sprintf("%d of %d", rank, N), cell_z = sprintf("%+.1f", z), cell_d = sprintf("%+.2f", d)) |>
select(var, survey, cell_r, cell_z, cell_d) |>
pivot_wider(names_from = survey, values_from = c(cell_r, cell_z, cell_d)) |>
left_join(rho, by = "var") |>
right_join(select(bridges, measure, var), by = "var") |>
mutate(Question = short[var], strength = bridge_strength(rho), rho = sprintf("%.2f", rho)) |>
select(measure, Question, cell_r_WVS, cell_z_WVS, cell_d_WVS, cell_r_PALS, cell_z_PALS, cell_d_PALS, rho, strength)
dt |>
select(-measure) |>
kable(col.names = c("Question pair", "Rank", "z", "d", "Rank", "z", "d", "ρ", "Strength"),
caption = "India on each bridge question: rank, z-score and effect size (d), and bridge correlation (ρ)",
align = c("l", rep("c", 8))) |>
styled() |>
add_header_above(c(" " = 1, "WVS Wave 7 (India 2023)" = 3, "PALS (2022)" = 3, "Bridge" = 2)) |>
pack_rows(index = table(factor(dt$measure, unique(dt$measure)))) |>
column_spec(1, width = "18em") |>
footnote(general = c("z: India vs the other 14 countries, in standard deviations of their country means. d: Indian respondents vs respondents of the other 14 countries (each country weighted equally), in individual standard deviations.",
"ρ: Spearman rank correlation between WVS and PALS country means across the 15 countries."),
general_title = "")
India on each bridge question: rank, z-score and effect size (d), and
bridge correlation (ρ)
|
|
WVS Wave 7 (India 2023)
|
PALS (2022)
|
Bridge
|
|
Question pair
|
Rank
|
z
|
d
|
Rank
|
z
|
d
|
ρ
|
Strength
|
|
Governance (delivery)
|
|
Fighting rising prices (aim)
|
5 of 15
|
+0.7
|
+0.24
|
3 of 15
|
+0.7
|
+0.18
|
0.64
|
Strong
|
|
Accountability
|
|
More say in government (aim)
|
13 of 15
|
-1.1
|
-0.20
|
12 of 15
|
-0.5
|
-0.06
|
0.20
|
—
|
|
Free speech (aim)
|
13 of 15
|
-1.0
|
-0.36
|
12 of 15
|
-1.1
|
-0.22
|
0.50
|
Moderate
|
|
Voting
|
8 of 15
|
+0.4
|
+0.16
|
7 of 15
|
+0.5
|
+0.18
|
0.81
|
Strong
|
|
Accountability: perceived capture
|
|
Rich buy elections / big interests run government
|
3 of 15
|
+1.0
|
+0.60
|
9 of 15
|
-0.1
|
-0.03
|
0.32
|
—
|
|
Little confidence in government / officials don’t help
|
12 of 15
|
-1.1
|
-0.56
|
14 of 15
|
-1.7
|
-0.65
|
0.57
|
Moderate
|
|
Satisfaction with the political system
|
|
Satisfied with the political system
|
3 of 15
|
+1.0
|
+0.46
|
1 of 15
|
+2.3
|
+0.89
|
0.79
|
Strong
|
|
Authoritarian side
|
|
Maintaining order (aim)
|
7 of 15
|
+0.1
|
+0.02
|
6 of 15
|
+0.3
|
+0.05
|
0.46
|
Moderate
|
|
Strong leader
|
1 of 15
|
+1.6
|
+0.56
|
7 of 15
|
+0.3
|
+0.06
|
-0.42
|
—
|
|
Experts
|
3 of 15
|
+1.4
|
+0.37
|
12 of 15
|
-0.9
|
-0.13
|
-0.15
|
—
|
|
Army / military
|
5 of 14
|
+0.6
|
+0.25
|
4 of 15
|
+1.1
|
+0.17
|
-0.04
|
—
|
|
Religious authority
|
3 of 15
|
+0.8
|
+0.32
|
4 of 15
|
+0.7
|
+0.18
|
0.37
|
—
|
|
Respect for authority / discipline
|
12 of 15
|
-0.8
|
-0.21
|
3 of 15
|
+1.0
|
+0.29
|
-0.34
|
—
|
|
Security over freedom
|
|
Video / camera surveillance
|
1 of 15
|
+2.3
|
+0.71
|
2 of 15
|
+1.6
|
+0.19
|
0.09
|
—
|
|
Internet / phone monitoring
|
2 of 15
|
+2.1
|
+0.65
|
1 of 15
|
+2.0
|
+0.59
|
0.74
|
Strong
|
|
Liberal side
|
|
Free elections / say in politics
|
8 of 15
|
+0.1
|
+0.02
|
13 of 15
|
-0.9
|
-0.19
|
-0.11
|
—
|
|
Civil rights / fair legal system
|
9 of 15
|
+0.2
|
+0.05
|
14 of 15
|
-2.0
|
-0.49
|
0.05
|
—
|
|
Women’s equal rights / more say for women
|
7 of 15
|
+0.3
|
+0.09
|
4 of 15
|
+0.7
|
+0.18
|
0.01
|
—
|
|
Women as leaders
|
11 of 15
|
-0.7
|
-0.34
|
1 of 15
|
+3.0
|
+0.69
|
-0.31
|
—
|
|
Civil rights / government bound by courts
|
9 of 15
|
+0.2
|
+0.05
|
14 of 15
|
-1.9
|
-0.33
|
0.24
|
—
|
|
Rejects men first for jobs
|
12 of 15
|
-1.0
|
-0.52
|
15 of 15
|
-2.4
|
-0.89
|
0.63
|
Strong
|
|
State support
|
|
State ownership
|
5 of 15
|
+0.9
|
+0.27
|
7 of 15
|
-0.0
|
-0.00
|
0.57
|
Moderate
|
|
Competition is harmful
|
1 of 15
|
+2.3
|
+0.45
|
1 of 15
|
+3.6
|
+0.58
|
0.45
|
Moderate
|
|
Government responsibility / welfare
|
14 of 15
|
-1.0
|
-0.23
|
14 of 15
|
-1.3
|
-0.28
|
0.72
|
Strong
|
|
Inequality: redistribution
|
|
Equal incomes / rich-poor gap a threat
|
14 of 15
|
-1.3
|
-0.40
|
6 of 15
|
+0.1
|
+0.04
|
0.07
|
—
|
|
Tax the rich / support the disadvantaged
|
5 of 15
|
+1.0
|
+0.36
|
15 of 15
|
-2.4
|
-0.37
|
0.04
|
—
|
|
Inequality: exclusion
|
|
Luck and connections / ancestry
|
5 of 15
|
+0.4
|
+0.07
|
1 of 15
|
+1.7
|
+0.35
|
-0.12
|
—
|
|
Restrict / reject immigrants
|
7 of 15
|
-0.2
|
-0.06
|
7 of 15
|
+0.5
|
+0.21
|
0.35
|
—
|
|
Nationals first for jobs
|
7 of 15
|
+0.5
|
+0.21
|
5 of 15
|
+0.3
|
+0.10
|
0.66
|
Strong
|
|
z: India vs the other 14 countries, in standard deviations
of their country means. d: Indian respondents vs respondents of the
other 14 countries (each country weighted equally), in individual
standard deviations.
|
|
ρ: Spearman rank correlation between WVS and PALS country
means across the 15 countries.
|
Examples from the report.
| Video surveillance (WVS) |
+2.3 |
+0.71 |
Outlier as a country and a medium-to-large difference between
people: about 76% of Indians are more accepting than the average person
elsewhere |
| Satisfaction with the political system (PALS) |
+2.3 |
+0.89 |
Outlier as a country and a large difference between
people: about 81% of Indians are more satisfied than the average person
elsewhere |
| Competition is harmful (PALS) |
+3.6 |
+0.58 |
An extreme outlier among countries (they bunch tightly), but only a
medium difference between people: many Indians and non-Indians think
alike |
| Strong leader is good (WVS) |
+1.6 |
+0.56 |
Clearly above other countries, a medium difference between
people |
| Fighting rising prices as an aim (WVS) |
+0.7 |
+0.24 |
Somewhat above average, a small difference |
| Free speech as an aim (WVS) |
−1.0 |
−0.36 |
Clearly below the other countries, a small-to-medium difference |
VI.2 Summary by measure
What is shown. For each measure: the average z-score
and effect size across its bridge questions in each survey, and the
average bridge correlation.
dist |>
left_join(select(bridges, var, measure), by = "var") |>
group_by(measure, survey) |>
summarise(z = mean(z), d = mean(d), .groups = "drop") |>
pivot_wider(names_from = survey, values_from = c(z, d)) |>
left_join(rho |> left_join(select(bridges, var, measure), by = "var") |>
group_by(measure) |> summarise(rho = mean(rho), k = n(), .groups = "drop"), by = "measure") |>
mutate(measure = factor(measure, unique(bridges$measure))) |>
arrange(measure) |>
transmute(Measure = measure, `Bridge pairs` = k,
`z, WVS` = sprintf("%+.1f", z_WVS), `z, PALS` = sprintf("%+.1f", z_PALS),
`d, WVS` = sprintf("%+.2f", d_WVS), `d, PALS` = sprintf("%+.2f", d_PALS),
`Average ρ` = sprintf("%.2f", rho)) |>
kable(caption = "India's average distance from the other 14 countries, by measure", align = c("l", rep("c", 6))) |>
styled()
India’s average distance from the other 14 countries, by measure
|
Measure
|
Bridge pairs
|
z, WVS
|
z, PALS
|
d, WVS
|
d, PALS
|
Average ρ
|
|
Governance (delivery)
|
1
|
+0.7
|
+0.7
|
+0.24
|
+0.18
|
0.64
|
|
Accountability
|
3
|
-0.6
|
-0.3
|
-0.13
|
-0.03
|
0.50
|
|
Accountability: perceived capture
|
2
|
-0.0
|
-0.9
|
+0.02
|
-0.34
|
0.44
|
|
Satisfaction with the political system
|
1
|
+1.0
|
+2.3
|
+0.46
|
+0.89
|
0.79
|
|
Authoritarian side
|
6
|
+0.6
|
+0.4
|
+0.22
|
+0.10
|
-0.02
|
|
Security over freedom
|
2
|
+2.2
|
+1.8
|
+0.68
|
+0.39
|
0.41
|
|
Liberal side
|
6
|
-0.2
|
-0.6
|
-0.11
|
-0.17
|
0.09
|
|
State support
|
3
|
+0.7
|
+0.8
|
+0.16
|
+0.10
|
0.58
|
|
Inequality: redistribution
|
2
|
-0.1
|
-1.2
|
-0.02
|
-0.17
|
0.06
|
|
Inequality: exclusion
|
3
|
+0.2
|
+0.8
|
+0.07
|
+0.22
|
0.30
|
VI.3 India’s nearest countries (statistical distance)
What is shown. For each measure, the three countries
closest to India once every bridge question is put on the same footing
(each question standardised across the 15 countries), using the average
squared difference (Euclidean distance) over the measure’s
questions.
z_near <- function(cm, survey) {
cm |>
group_by(var) |> mutate(zm = (m - mean(m)) / sd(m)) |> ungroup() |>
left_join(select(bridges, var, measure), by = "var") |>
group_by(measure, var) |> mutate(india = zm[country_name == "India"][1]) |> ungroup() |>
filter(country_name != "India", !is.na(india)) |>
group_by(measure, country_name) |>
summarise(dist = sqrt(mean((zm - india)^2)), .groups = "drop") |>
group_by(measure) |> slice_min(dist, n = 3, with_ties = FALSE) |>
summarise(cell = paste0(country_name, " (", sprintf("%.1f", dist), ")", collapse = ", "), .groups = "drop") |>
mutate(survey = survey)
}
bind_rows(z_near(cm_wvs, "WVS"), z_near(cm_pals, "PALS")) |>
pivot_wider(names_from = survey, values_from = cell) |>
mutate(measure = factor(measure, unique(bridges$measure))) |>
arrange(measure) |>
select(Measure = measure, `Nearest in WVS 2023` = WVS, `Nearest in PALS 2022` = PALS) |>
kable(caption = "India's three nearest countries by measure (distance in standard deviations)") |>
styled()
India’s three nearest countries by measure (distance in standard
deviations)
|
Measure
|
Nearest in WVS 2023
|
Nearest in PALS 2022
|
|
Governance (delivery)
|
Turkey (0.0), United Kingdom (0.1), Brazil (0.3)
|
Nigeria (0.1), Singapore (0.2), Peru (0.3)
|
|
Accountability
|
Brazil (0.8), Turkey (0.8), Peru (0.8)
|
Peru (0.5), Singapore (0.6), United Kingdom (0.7)
|
|
Accountability: perceived capture
|
Nigeria (0.7), Indonesia (0.8), United States (1.0)
|
Indonesia (0.2), Australia (0.7), United Kingdom (0.9)
|
|
Satisfaction with the political system
|
Germany (0.1), Indonesia (0.1), Turkey (0.2)
|
Singapore (0.7), Indonesia (0.9), Germany (1.1)
|
|
Authoritarian side
|
Mexico (0.6), Turkey (0.8), Brazil (0.9)
|
Nigeria (0.8), United States (0.8), Singapore (1.1)
|
|
Security over freedom
|
Indonesia (0.9), Nigeria (1.0), Australia (1.5)
|
Indonesia (0.7), United Kingdom (1.0), Tunisia (1.1)
|
|
Liberal side
|
Turkey (0.5), Russia (0.5), Singapore (0.6)
|
Indonesia (1.3), Nigeria (1.4), Peru (1.6)
|
|
State support
|
Turkey (0.6), Indonesia (0.7), Mexico (0.8)
|
United Kingdom (1.3), Indonesia (1.4), Singapore (1.5)
|
|
Inequality: redistribution
|
Indonesia (0.3), Russia (0.4), Singapore (0.4)
|
United States (1.0), Singapore (1.0), Mexico (1.3)
|
|
Inequality: exclusion
|
Nigeria (0.4), Brazil (0.4), Chile (0.4)
|
Singapore (0.5), United States (0.5), Nigeria (0.6)
|
What this part shows.
- Where India is a true outlier. On a few questions
India is far from the other 14 countries in both surveys:
- Security over freedom: z about +2 in both (video or camera
surveillance +2.3 and +1.6; internet or phone monitoring +2.1 and +2.0),
with medium-to-large effect sizes (d up to 0.7).
- Competition is harmful (the identical question): z +2.3 and
+3.6.
- Satisfaction with the political system: z +1.0 and +2.3 (d
0.46 and 0.89).
- In PALS only, India is far below the others on the concrete
liberal items: a fair legal system (z −2.0), government bound by courts
(−1.9) and rejecting men first for jobs (−2.4).
- Elsewhere India is closer to the pack than its rank
suggests. Most z-scores lie between −1 and +1 and most effect
sizes are small (below 0.3): a high or low rank often rests on modest
differences between countries.
- How good are the bridges? The pairs that sort
countries the same way in both surveys (ρ 0.6 or more) are voting
(0.81), satisfaction with the political system (0.79), internet
monitoring (0.74), government responsibility / welfare (0.72), nationals
first for jobs (0.66), fighting rising prices (0.64) and rejecting men
first for jobs (0.63). On these, comparing India across the two surveys
is well founded.
- Weak bridges. The authoritarian-side pairs barely
agree (average ρ about 0: strong leader −0.42, respect for authority
−0.34, experts −0.15, army −0.04). “Is X a good way to govern?” (WVS)
and “should X decide rather than citizens?” (PALS) sort countries
differently. The same holds for the “necessary to live freely” pairs,
the redistribution pairs and luck vs ancestry (ρ close to 0). On these,
a different picture of India in the two surveys may reflect the
questions rather than India, and the Part V comparison should be read
with care.