added lab 4
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node_modules
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.venv
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.vscode
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Assignment III
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tmp/
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##########################################
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### Principal Component Analysis (PCA) ###
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##########################################
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## load libraries
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library(ggplot2)
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library(ggfortify)
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library(GGally)
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library(e1071)
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library(class)
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library(psych)
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library(readr)
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## set working directory so that files can be referenced without the full path
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setwd("/home/ion606/Desktop/Data Analytics/Lab 4")
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## read dataset
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wine <- read_csv("wine.data", col_names = FALSE)
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## set column names
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names(wine) <- c("Type","Alcohol","Malic acid","Ash","Alcalinity of ash","Magnesium","Total phenols","Flavanoids","Nonflavanoid Phenols","Proanthocyanins","Color Intensity","Hue","Od280/od315 of diluted wines","Proline")
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## inspect data frame
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head(wine)
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## change the data type of the "Type" column from character to factor
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####
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# Factors look like regular strings (characters) but with factors R knows
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# that the column is a categorical variable with finite possible values
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# e.g. "Type" in the Wine dataset can only be 1, 2, or 3
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####
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wine$Type <- as.factor(wine$Type)
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## visualize variables
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pairs.panels(wine[,-1],gap = 0,bg = c("red", "yellow", "blue")[wine$Type],pch=21)
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ggpairs(wine, ggplot2::aes(colour = Type))
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###
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has_pkg <- function(pkg) requireNamespace(pkg, quietly = TRUE)
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has_ggplot2 <- has_pkg("ggplot2")
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has_GGally <- has_pkg("GGally")
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has_e1071 <- has_pkg("e1071")
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has_class <- has_pkg("class")
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has_psych <- has_pkg("psych")
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has_readr <- has_pkg("readr")
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# WHY IS THIS HERE YOU MIGHT ASK???? WELL LET ME TELL YOU I SPENT TWO HOURS ON STUPID PACKAGE IMPORTS
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# OOOOOOHHH PSYCH IS IN A DIFFERENT REPO??? OH IT ISN'T??? I have a fever of 103 I DO NOT CARE
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if (has_ggplot2) { library(ggplot2) } else { warning("ggplot2 not available; plots will be skipped") }
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if (has_GGally) { library(GGally) } else { message("GGally not available; skipping ggpairs plot") }
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if (has_e1071) { library(e1071) }
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if (has_class) { library(class) } else { stop("class package not available for kNN") }
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if (!has_psych) { message("psych not available; skipping pairs.panels plot") }
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if (has_readr) { library(readr) }
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library(grid) # unit() for arrows in plots
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suppressWarnings(RNGkind(sample.kind = "Rounding"))
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# set a reproducible seed
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set.seed(4600)
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# 178 rows
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# col 1 is class label (1,2,3)
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# other 13 columns continuous predictors
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possible_paths <- c(
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"wine.data",
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"./wine.data",
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"../wine.data",
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"DAN/wine.data",
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"./DAN/wine.data"
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)
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data_path <- NA
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for (p in possible_paths) { if (file.exists(p)) { data_path <- p; break } }
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if (is.na(data_path)) stop("could not find wine.data; place this script in the DAN folder or given/ and re-run")
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if (has_readr) {
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wine <- readr::read_csv(
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file = data_path,
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col_names = FALSE,
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show_col_types = FALSE,
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progress = FALSE
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)
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} else {
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wine <- read.csv(file = data_path, header = FALSE)
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}
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colnames(wine) <- c(
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"Type",
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"Alcohol",
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"Malic_acid",
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"Ash",
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"Alcalinity_of_ash",
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"Magnesium",
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"Total_phenols",
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"Flavanoids",
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"Nonflavanoid_phenols",
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"Proanthocyanins",
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"Color_intensity",
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"Hue",
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"OD280_OD315",
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"Proline"
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)
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wine$Type <- as.factor(wine$Type)
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# put here from when I accidentally read in the wrong file repeatedly
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# left because it makes it more, "robust"
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stopifnot(nrow(wine) == 178, ncol(wine) == 14)
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print(summary(wine$Type))
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# exploratory plots (because I went down a rabbit hole and by god I'm using it)
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if (has_psych) {
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# pairs panel (psych) – colors by class
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psych::pairs.panels(
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wine[,-1],
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gap = 0,
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bg = c("red","gold","royalblue")[wine$Type],
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pch = 21,
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main = "wine (uci) – scatterplot matrix by class"
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)
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}
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if (has_GGally && has_ggplot2) {
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# ggpairs for nice matrix <3
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GGally::ggpairs(wine, ggplot2::aes(colour = Type), columns = 2:ncol(wine))
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}
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# split into train/test BEFORE!!!!!!!!!!!!!!!!!!!!!! any preprocessing to avoid leakage
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set.seed(4600)
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n <- nrow(wine)
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train_idx <- sample.int(n, size = floor(0.7 * n))
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wine_train <- wine[train_idx, , drop = FALSE]
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wine_test <- wine[-train_idx, , drop = FALSE]
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X_train <- wine_train[, -1]
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y_train <- wine_train$Type
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X_test <- wine_test[, -1]
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y_test <- wine_test$Type
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# yes
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if (any(sapply(X_train, function(x) var(x, na.rm = TRUE) == 0))) {
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warning("one or more predictors have zero variance in the training set; scale() would fail")
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}
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if (anyNA(X_train) | anyNA(X_test)) {
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stop("found NA values in predictors; handle missingness before PCA")
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}
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# project both train and test using the train-fitted pca
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pca_tr <- prcomp(X_train, center = TRUE, scale. = TRUE)
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pve_tr <- (pca_tr$sdev^2) / sum(pca_tr$sdev^2)
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pve_df <- data.frame(
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PC = paste0("PC", seq_along(pve_tr)),
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PVE = pve_tr,
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CumPVE = cumsum(pve_tr)
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)
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print("variance explained (training pca):")
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print(pve_df)
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# scree plot from training pca
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p_scree <- ggplot(pve_df, aes(x = seq_along(PVE), y = PVE)) +
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geom_line() + geom_point() +
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scale_x_continuous(breaks = 1:length(pve_df$PC), labels = pve_df$PC) +
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labs(title = "scree plot – variance explained by principal components (training pca)",
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x = "principal component", y = "proportion of variance explained") +
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theme_minimal()
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# cumulative variance plot from training pca
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p_cumvar <- ggplot(pve_df, aes(x = seq_along(CumPVE), y = CumPVE)) +
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geom_line() + geom_point() +
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scale_x_continuous(breaks = 1:length(pve_df$PC), labels = pve_df$PC) +
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labs(title = "cumulative variance explained (training pca)",
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x = "principal component", y = "cumulative proportion of variance") +
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theme_minimal()
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# ========================================================================================================
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# choose number of pcs: default to the smallest k with >= thresh cum variance
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# you can change thresh to 0.90 or 0.99 if you prefer
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pc_variance_threshold <- 0.95
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k_pcs <- which(cumsum(pve_tr) >= pc_variance_threshold)[1]
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if (is.na(k_pcs)) k_pcs <- ncol(X_train) # crashes if fails so...
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cat("chosen number of pcs (threshold =", pc_variance_threshold, "):", k_pcs, "\n")
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# project train/test into the pca space
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Z_train_full <- as.data.frame(predict(pca_tr, newdata = X_train))
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Z_test_full <- as.data.frame(predict(pca_tr, newdata = X_test))
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# for downstream modeling
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Z_train <- Z_train_full[, seq_len(k_pcs), drop = FALSE]
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Z_test <- Z_test_full[, seq_len(k_pcs), drop = FALSE]
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scores_all <- as.data.frame(predict(pca_tr, newdata = wine[,-1]))
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scores_all$Type <- wine$Type
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# loadings from training pca
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loadings <- as.data.frame(pca_tr$rotation)
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loadings$Variable <- rownames(loadings)
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top_pc1 <- loadings[order(abs(loadings$PC1), decreasing = TRUE), c("Variable","PC1")][1:5, ]
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top_pc2 <- loadings[order(abs(loadings$PC2), decreasing = TRUE), c("Variable","PC2")][1:5, ]
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print("top contributors to pc1 (training pca):"); print(top_pc1)
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print("top contributors to pc2 (training pca):"); print(top_pc2)
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# function to make convex hull data for each group
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scores <- scores_all
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hull_df <- do.call(rbind, lapply(split(scores, scores$Type), function(df) {
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pts <- df[chull(df$PC1, df$PC2), c("PC1","PC2")]
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pts$Type <- unique(df$Type)
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pts
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}))
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p_pc12 <- ggplot(scores, aes(PC1, PC2, color = Type)) +
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geom_point(size = 2, alpha = 0.85) +
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geom_polygon(data = hull_df, aes(fill = Type, group = Type), color = NA, alpha = 0.15) +
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guides(fill = "none") +
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theme_minimal() +
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labs(title = "pc1 vs pc2 by class (projected with training pca)")
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# arrow arrow arrow arrow arrow arrow arrow arrow arrow
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loading_scalefactor <- 3 * max(abs(scores$PC1), abs(scores$PC2)) # heuristic
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load_plot_df <- loadings
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load_plot_df$PC1s <- load_plot_df$PC1 * loading_scalefactor
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load_plot_df$PC2s <- load_plot_df$PC2 * loading_scalefactor
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p_biplot <- ggplot(scores, aes(PC1, PC2, color = Type)) +
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geom_point(size = 2, alpha = 0.85) +
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geom_segment(
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data = load_plot_df,
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mapping = aes(x = 0, y = 0, xend = PC1s, yend = PC2s),
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inherit.aes = FALSE,
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arrow = arrow(length = unit(0.02, "npc")),
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color = "black",
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alpha = 0.8
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) +
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geom_text(
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data = load_plot_df,
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mapping = aes(x = PC1s, y = PC2s, label = Variable),
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inherit.aes = FALSE,
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hjust = 0,
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vjust = 0
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) +
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theme_minimal() +
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labs(title = "pc1 vs pc2 with variable loadings (training pca projection)")
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# 1) kNN on original variables with standardization
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# 2) kNN on first 2 principal components only
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# helper to create metrics from a confusion matrix (rows=true, cols=pred)
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compute_metrics <- function(cm) {
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lv <- rownames(cm)
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if (is.null(lv)) lv <- as.character(1:nrow(cm))
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TP <- diag(cm)
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FP <- colSums(cm) - TP
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FN <- rowSums(cm) - TP
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precision <- TP / (TP + FP)
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recall <- TP / (TP + FN)
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f1 <- 2 * precision * recall / (precision + recall)
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acc <- sum(TP) / sum(cm)
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macro_precision <- mean(precision, na.rm = TRUE)
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macro_recall <- mean(recall, na.rm = TRUE)
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macro_f1 <- mean(f1, na.rm = TRUE)
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per_class <- data.frame(
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class = lv,
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precision = precision,
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recall = recall,
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f1 = f1,
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row.names = NULL
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)
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summary <- data.frame(
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accuracy = acc,
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macro_precision = macro_precision,
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macro_recall = macro_recall,
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macro_f1 = macro_f1
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)
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list(per_class = per_class, summary = summary)
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}
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set.seed(4600)
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ks <- seq(1, 15, by = 2)
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Kfolds <- 5
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# kNN on original vars
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X_train_scaled <- scale(X_train, center = TRUE, scale = TRUE)
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scale_center <- attr(X_train_scaled, "scaled:center")
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scale_scale <- attr(X_train_scaled, "scaled:scale")
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X_test_scaled <- scale(X_test, center = scale_center, scale = scale_scale)
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n_train_orig <- nrow(X_train_scaled)
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folds_orig <- sample(rep(1:Kfolds, length.out = n_train_orig))
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cv_acc_orig <- sapply(ks, function(k) {
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mean(sapply(1:Kfolds, function(f) {
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tr <- which(folds_orig != f)
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va <- which(folds_orig == f)
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pred_cv <- knn(train = X_train_scaled[tr, , drop = FALSE],
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test = X_train_scaled[va, , drop = FALSE],
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cl = y_train[tr], k = k)
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mean(pred_cv == y_train[va])
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}))
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})
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best_k_orig <- ks[which.max(cv_acc_orig)]
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cat("[Original vars] best k:", best_k_orig, "cv acc:", max(cv_acc_orig), "\n")
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pred_orig <- knn(train = X_train_scaled, test = X_test_scaled, cl = y_train, k = best_k_orig)
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acc_orig <- mean(pred_orig == y_test)
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cm_orig <- table(truth = y_test, pred = pred_orig)
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cat("[Original vars] held-out accuracy:", round(acc_orig, 4), "\n")
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print(cm_orig)
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metrics_orig <- compute_metrics(cm_orig)
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print(metrics_orig$summary)
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print(metrics_orig$per_class)
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# kNN on first 2 PCs only
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Z2_train <- Z_train_full[, 1:2, drop = FALSE]
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Z2_test <- Z_test_full[, 1:2, drop = FALSE]
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n_train_2pc <- nrow(Z2_train)
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folds_2pc <- sample(rep(1:Kfolds, length.out = n_train_2pc))
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cv_acc_2pc <- sapply(ks, function(k) {
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mean(sapply(1:Kfolds, function(f) {
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tr <- which(folds_2pc != f)
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va <- which(folds_2pc == f)
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pred_cv <- knn(train = Z2_train[tr, , drop = FALSE],
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test = Z2_train[va, , drop = FALSE],
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cl = y_train[tr], k = k)
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mean(pred_cv == y_train[va])
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}))
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})
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best_k_2pc <- ks[which.max(cv_acc_2pc)]
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cat("[First 2 PCs] best k:", best_k_2pc, "cv acc:", max(cv_acc_2pc), "\n")
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pred_2pc <- knn(train = Z2_train, test = Z2_test, cl = y_train, k = best_k_2pc)
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acc_2pc <- mean(pred_2pc == y_test)
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cm_2pc <- table(truth = y_test, pred = pred_2pc)
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cat("[First 2 PCs] held-out accuracy:", round(acc_2pc, 4), "\n")
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print(cm_2pc)
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metrics_2pc <- compute_metrics(cm_2pc)
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print(metrics_2pc$summary)
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print(metrics_2pc$per_class)
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# ===========================================================================================
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outputs_dir <- "outputs"
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if (!dir.exists(outputs_dir)) dir.create(outputs_dir, recursive = TRUE, showWarnings = FALSE)
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# plots
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if (exists("p_pc12") && inherits(p_pc12, "ggplot")) ggsave(filename = file.path(outputs_dir, "pc12_scatter.png"), plot = p_pc12, width = 8, height = 6, dpi = 300)
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if (exists("p_biplot") && inherits(p_biplot, "ggplot")) ggsave(filename = file.path(outputs_dir, "pc12_biplot.png"), plot = p_biplot, width = 8, height = 6, dpi = 300)
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if (exists("p_scree") && inherits(p_scree, "ggplot")) ggsave(filename = file.path(outputs_dir, "pca_scree.png"), plot = p_scree, width = 8, height = 6, dpi = 300)
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if (exists("p_cumvar") && inherits(p_cumvar, "ggplot")) ggsave(filename = file.path(outputs_dir, "pca_cumvar.png"), plot = p_cumvar, width = 8, height = 6, dpi = 300)
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|
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# top contributors/vars to PC1 and PC2
|
||||
write.csv(top_pc1, file = file.path(outputs_dir, "top_contributors_pc1.csv"), row.names = FALSE)
|
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write.csv(top_pc2, file = file.path(outputs_dir, "top_contributors_pc2.csv"), row.names = FALSE)
|
||||
|
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# confusion matrices as wide CSV and pretty text
|
||||
write.csv(as.matrix(cm_orig), file = file.path(outputs_dir, "confusion_original_wide.csv"))
|
||||
writeLines(capture.output(cm_orig), con = file.path(outputs_dir, "confusion_original.txt"))
|
||||
|
||||
write.csv(as.matrix(cm_2pc), file = file.path(outputs_dir, "confusion_2pc_wide.csv"))
|
||||
writeLines(capture.output(cm_2pc), con = file.path(outputs_dir, "confusion_2pc.txt"))
|
||||
|
||||
# metrics
|
||||
write.csv(metrics_orig$per_class, file = file.path(outputs_dir, "metrics_original_per_class.csv"), row.names = FALSE)
|
||||
write.csv(metrics_orig$summary, file = file.path(outputs_dir, "metrics_original_summary.csv"), row.names = FALSE)
|
||||
write.csv(metrics_2pc$per_class, file = file.path(outputs_dir, "metrics_2pc_per_class.csv"), row.names = FALSE)
|
||||
write.csv(metrics_2pc$summary, file = file.path(outputs_dir, "metrics_2pc_summary.csv"), row.names = FALSE)
|
||||
|
||||
# summary
|
||||
metrics_compare <- data.frame(
|
||||
model = c("original_variables", "first_2_pcs"),
|
||||
accuracy = c(metrics_orig$summary$accuracy, metrics_2pc$summary$accuracy),
|
||||
macro_precision = c(metrics_orig$summary$macro_precision, metrics_2pc$summary$macro_precision),
|
||||
macro_recall = c(metrics_orig$summary$macro_recall, metrics_2pc$summary$macro_recall),
|
||||
macro_f1 = c(metrics_orig$summary$macro_f1, metrics_2pc$summary$macro_f1)
|
||||
)
|
||||
write.csv(metrics_compare, file = file.path(outputs_dir, "metrics_comparison.csv"), row.names = FALSE)
|
||||
|
||||
# The below was made with help from ChatGPT because the psych package is confusing
|
||||
if (!interactive() && has_ggplot2) {
|
||||
pdf("Rplots_pca_fixed.pdf", width = 8, height = 6)
|
||||
if (has_psych) {
|
||||
psych::pairs.panels(
|
||||
wine[,-1],
|
||||
gap = 0,
|
||||
bg = c("red","gold","royalblue")[wine$Type],
|
||||
pch = 21,
|
||||
main = "wine (uci) – scatterplot matrix by class"
|
||||
)
|
||||
}
|
||||
|
||||
if (exists("p_scree") && inherits(p_scree, "ggplot")) print(p_scree)
|
||||
if (exists("p_pc12") && inherits(p_pc12, "ggplot")) print(p_pc12)
|
||||
dev.off()
|
||||
}
|
||||
@@ -0,0 +1,5 @@
|
||||
pred
|
||||
truth 1 2 3
|
||||
1 15 2 0
|
||||
2 1 19 1
|
||||
3 0 1 15
|
||||
@@ -0,0 +1,4 @@
|
||||
"","1","2","3"
|
||||
"1",15,2,0
|
||||
"2",1,19,1
|
||||
"3",0,1,15
|
||||
|
@@ -0,0 +1,5 @@
|
||||
pred
|
||||
truth 1 2 3
|
||||
1 17 0 0
|
||||
2 1 18 2
|
||||
3 0 0 16
|
||||
@@ -0,0 +1,4 @@
|
||||
"","1","2","3"
|
||||
"1",17,0,0
|
||||
"2",1,18,2
|
||||
"3",0,0,16
|
||||
|
@@ -0,0 +1,4 @@
|
||||
"class","precision","recall","f1"
|
||||
"1",0.9375,0.882352941176471,0.909090909090909
|
||||
"2",0.863636363636364,0.904761904761905,0.883720930232558
|
||||
"3",0.9375,0.9375,0.9375
|
||||
|
@@ -0,0 +1,2 @@
|
||||
"accuracy","macro_precision","macro_recall","macro_f1"
|
||||
0.907407407407407,0.912878787878788,0.908204948646125,0.910103946441156
|
||||
|
@@ -0,0 +1,3 @@
|
||||
"model","accuracy","macro_precision","macro_recall","macro_f1"
|
||||
"original_variables",0.944444444444444,0.944444444444444,0.952380952380952,0.94522732169791
|
||||
"first_2_pcs",0.907407407407407,0.912878787878788,0.908204948646125,0.910103946441156
|
||||
|
@@ -0,0 +1,4 @@
|
||||
"class","precision","recall","f1"
|
||||
"1",0.944444444444444,1,0.971428571428571
|
||||
"2",1,0.857142857142857,0.923076923076923
|
||||
"3",0.888888888888889,1,0.941176470588235
|
||||
|
@@ -0,0 +1,2 @@
|
||||
"accuracy","macro_precision","macro_recall","macro_f1"
|
||||
0.944444444444444,0.944444444444444,0.952380952380952,0.94522732169791
|
||||
|
Binary file not shown.
|
After Width: | Height: | Size: 344 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 227 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 101 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 105 KiB |
@@ -0,0 +1,6 @@
|
||||
"Variable","PC1"
|
||||
"Flavanoids",0.430570697054093
|
||||
"Total_phenols",0.388556731445086
|
||||
"OD280_OD315",0.379238757892512
|
||||
"Proanthocyanins",0.318149910146199
|
||||
"Nonflavanoid_phenols",-0.292569052362651
|
||||
|
@@ -0,0 +1,6 @@
|
||||
"Variable","PC2"
|
||||
"Color_intensity",-0.504116493512561
|
||||
"Alcohol",-0.480328824227057
|
||||
"Ash",-0.369020648548877
|
||||
"Proline",-0.3555672525193
|
||||
"Hue",0.300324646690879
|
||||
|
+178
@@ -0,0 +1,178 @@
|
||||
1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
|
||||
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
|
||||
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
|
||||
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
|
||||
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
|
||||
1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
|
||||
1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
|
||||
1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
|
||||
1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
|
||||
1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
|
||||
1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
|
||||
1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
|
||||
1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
|
||||
1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
|
||||
1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
|
||||
1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
|
||||
1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
|
||||
1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
|
||||
1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
|
||||
1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
|
||||
1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
|
||||
1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
|
||||
1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
|
||||
1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
|
||||
1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
|
||||
1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
|
||||
1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
|
||||
1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
|
||||
1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
|
||||
1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
|
||||
1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
|
||||
1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
|
||||
1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
|
||||
1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
|
||||
1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
|
||||
1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
|
||||
1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
|
||||
1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
|
||||
1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
|
||||
1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
|
||||
1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
|
||||
1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
|
||||
1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
|
||||
1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
|
||||
1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
|
||||
1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
|
||||
1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
|
||||
1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
|
||||
1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
|
||||
1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
|
||||
1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
|
||||
1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
|
||||
1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
|
||||
1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
|
||||
1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
|
||||
1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
|
||||
1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
|
||||
1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
|
||||
1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
|
||||
2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
|
||||
2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
|
||||
2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
|
||||
2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
|
||||
2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
|
||||
2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
|
||||
2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
|
||||
2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
|
||||
2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
|
||||
2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
|
||||
2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
|
||||
2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
|
||||
2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
|
||||
2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
|
||||
2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
|
||||
2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
|
||||
2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
|
||||
2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
|
||||
2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
|
||||
2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
|
||||
2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
|
||||
2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
|
||||
2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
|
||||
2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
|
||||
2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
|
||||
2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
|
||||
2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
|
||||
2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
|
||||
2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
|
||||
2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
|
||||
2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
|
||||
2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
|
||||
2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
|
||||
2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
|
||||
2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
|
||||
2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
|
||||
2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
|
||||
2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
|
||||
2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
|
||||
2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
|
||||
2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
|
||||
2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
|
||||
2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
|
||||
2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
|
||||
2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
|
||||
2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
|
||||
2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
|
||||
2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
|
||||
2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
|
||||
2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
|
||||
2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
|
||||
2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
|
||||
2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
|
||||
2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
|
||||
2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
|
||||
2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
|
||||
2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
|
||||
2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
|
||||
2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
|
||||
2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
|
||||
2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
|
||||
2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
|
||||
2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
|
||||
2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
|
||||
2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
|
||||
2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
|
||||
2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
|
||||
2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
|
||||
2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
|
||||
2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
|
||||
2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
|
||||
3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
|
||||
3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
|
||||
3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
|
||||
3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
|
||||
3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
|
||||
3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
|
||||
3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
|
||||
3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
|
||||
3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
|
||||
3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
|
||||
3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
|
||||
3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
|
||||
3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
|
||||
3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
|
||||
3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
|
||||
3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
|
||||
3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
|
||||
3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
|
||||
3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
|
||||
3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
|
||||
3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
|
||||
3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
|
||||
3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
|
||||
3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
|
||||
3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
|
||||
3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
|
||||
3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
|
||||
3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
|
||||
3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
|
||||
3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
|
||||
3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
|
||||
3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
|
||||
3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
|
||||
3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
|
||||
3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
|
||||
3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
|
||||
3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
|
||||
3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
|
||||
3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
|
||||
3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
|
||||
3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
|
||||
3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
|
||||
3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
|
||||
3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
|
||||
3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
|
||||
3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
|
||||
3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
|
||||
3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
|
||||
@@ -0,0 +1,100 @@
|
||||
1. Title of Database: Wine recognition data
|
||||
Updated Sept 21, 1998 by C.Blake : Added attribute information
|
||||
|
||||
2. Sources:
|
||||
(a) Forina, M. et al, PARVUS - An Extendible Package for Data
|
||||
Exploration, Classification and Correlation. Institute of Pharmaceutical
|
||||
and Food Analysis and Technologies, Via Brigata Salerno,
|
||||
16147 Genoa, Italy.
|
||||
|
||||
(b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au
|
||||
(c) July 1991
|
||||
3. Past Usage:
|
||||
|
||||
(1)
|
||||
S. Aeberhard, D. Coomans and O. de Vel,
|
||||
Comparison of Classifiers in High Dimensional Settings,
|
||||
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
|
||||
Mathematics and Statistics, James Cook University of North Queensland.
|
||||
(Also submitted to Technometrics).
|
||||
|
||||
The data was used with many others for comparing various
|
||||
classifiers. The classes are separable, though only RDA
|
||||
has achieved 100% correct classification.
|
||||
(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
|
||||
(All results using the leave-one-out technique)
|
||||
|
||||
In a classification context, this is a well posed problem
|
||||
with "well behaved" class structures. A good data set
|
||||
for first testing of a new classifier, but not very
|
||||
challenging.
|
||||
|
||||
(2)
|
||||
S. Aeberhard, D. Coomans and O. de Vel,
|
||||
"THE CLASSIFICATION PERFORMANCE OF RDA"
|
||||
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
|
||||
Mathematics and Statistics, James Cook University of North Queensland.
|
||||
(Also submitted to Journal of Chemometrics).
|
||||
|
||||
Here, the data was used to illustrate the superior performance of
|
||||
the use of a new appreciation function with RDA.
|
||||
|
||||
4. Relevant Information:
|
||||
|
||||
-- These data are the results of a chemical analysis of
|
||||
wines grown in the same region in Italy but derived from three
|
||||
different cultivars.
|
||||
The analysis determined the quantities of 13 constituents
|
||||
found in each of the three types of wines.
|
||||
|
||||
-- I think that the initial data set had around 30 variables, but
|
||||
for some reason I only have the 13 dimensional version.
|
||||
I had a list of what the 30 or so variables were, but a.)
|
||||
I lost it, and b.), I would not know which 13 variables
|
||||
are included in the set.
|
||||
|
||||
-- The attributes are (dontated by Riccardo Leardi,
|
||||
riclea@anchem.unige.it )
|
||||
1) Alcohol
|
||||
2) Malic acid
|
||||
3) Ash
|
||||
4) Alcalinity of ash
|
||||
5) Magnesium
|
||||
6) Total phenols
|
||||
7) Flavanoids
|
||||
8) Nonflavanoid phenols
|
||||
9) Proanthocyanins
|
||||
10)Color intensity
|
||||
11)Hue
|
||||
12)OD280/OD315 of diluted wines
|
||||
13)Proline
|
||||
|
||||
5. Number of Instances
|
||||
|
||||
class 1 59
|
||||
class 2 71
|
||||
class 3 48
|
||||
|
||||
6. Number of Attributes
|
||||
|
||||
13
|
||||
|
||||
7. For Each Attribute:
|
||||
|
||||
All attributes are continuous
|
||||
|
||||
No statistics available, but suggest to standardise
|
||||
variables for certain uses (e.g. for us with classifiers
|
||||
which are NOT scale invariant)
|
||||
|
||||
NOTE: 1st attribute is class identifier (1-3)
|
||||
|
||||
8. Missing Attribute Values:
|
||||
|
||||
None
|
||||
|
||||
9. Class Distribution: number of instances per class
|
||||
|
||||
class 1 59
|
||||
class 2 71
|
||||
class 3 48
|
||||
Binary file not shown.
@@ -1,74 +0,0 @@
|
||||
Type,Abbreviation,Variable,IssueCategory,PolicyObjective,Weight,Description
|
||||
EPI,EPI,Environmental Performance Index,,,,The 2024 EPI combines 58 indicators across 11 issue categories
|
||||
PolicyObjective,ECO,Ecosystem Vitality,,,45.00%,The Ecosystem Vitality policy objective measures how well countries are preserving
|
||||
IssueCategory,BDH,Biodiversity & Habitat,,Ecosystem Vitality,25.00%,The Biodiversity and Habitat issue category assesses countries’ actions toward retaining natural ecosystems and protecting the full range of biodiversity within their borders. It consists of 12 indicators: protection of marine key biodiversity areas
|
||||
Indicator,MKP,Marine KBA Protection,Biodiversity & Habitat,Ecosystem Vitality,3.00%,Percentage of area designated as Key Biodiversity Areas (KBA) within a country's exclusive economic zone(s) that is covered by marine protected areas. Marine Protected Area data comes from the March 2024 release of the World Database on Protected Areas (https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA). Data on KBAs
|
||||
Indicator,MHP,Marine Habitat Protection,Biodiversity & Habitat,Ecosystem Vitality,3.00%,Percentage of important marine and coastal habitats -- coral reefs
|
||||
Indicator,MPE,Marine Protection Stringency,Biodiversity & Habitat,Ecosystem Vitality,0.50%,This pilot indicator estimates the stringency of marine protected areas (MPAs) by comparing total fishing effort on a given year inside versus outside MPAs within a country's exclusive economic zone(s). A score of 100 indicates that fishing efforts inside a country's MPAs is 1% or less than the fishing effort outside MPAs
|
||||
Indicator,PAR,Protected Areas Representativeness Index,Biodiversity & Habitat,Ecosystem Vitality,3.00%,The Protected Areas Representativeness Index (PARI) indicator measures how well terrestrial protected areas represent the ecological diversity of a country. This metric is calculated by CSIRO (https://geobon.org/ebvs/indicators/protected-area-representatives-connectedness-indices/) using high-resolution remote sensing data and biological records of species' locations. A score of 100 indicates that a country's terrestrial protected areas nearly perfectly represent the country's ecosystem diversity
|
||||
Indicator,SPI,Species Protection Index,Biodiversity & Habitat,Ecosystem Vitality,4.00%,The Species Protection Index (SPI) measures how well a country's terrestrial protected areas overlap with the ranges of its vertebrate
|
||||
Indicator,TBN,Terrestrial Biome Protection (national weights),Biodiversity & Habitat,Ecosystem Vitality,2.50%,We derive the terrestrial biome protection indicator by first calculating the proportion of each biome in a country that lies within a protected area. We then give greater weight to biomes that are relatively rare within a country – and less weight to prevalent biomes – before aggregating the proportions. A score of 100 indicates that a country protects at least 30% of each of its biome types
|
||||
Indicator,TKP,Terrestrial KBA Protection,Biodiversity & Habitat,Ecosystem Vitality,2.50%,Percentage of area designated as Key Biodiversity Areas (KBA) within a country's territory that is covered by protected areas. Protected Area data comes from the March 2024 release of the World Database on Protected Areas (https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA). Data on KBAs
|
||||
Indicator,PAE,Protected Area Effectiveness,Biodiversity & Habitat,Ecosystem Vitality,0.50%,This pilot indicator measures the percentage of a country's terrestrial protected areas in which the area of croplands and buildings is increasing by more than 0.5% per year.
|
||||
Indicator,PHL,Protected Human Land,Biodiversity & Habitat,Ecosystem Vitality,0.50%,This pilot indicator measures the percentage of the total terrestrial area protected in a country that is covered by croplands and buildings.
|
||||
Indicator,RLI,Red List Index,Biodiversity & Habitat,Ecosystem Vitality,3.00%,The Red List Index (RLI) tracks the overall extinction risk for species in a country
|
||||
Indicator,SHI,Species Habitat Index,Biodiversity & Habitat,Ecosystem Vitality,2.00%,The Species Habitat Index (SHI) measures the proportion of suitable habitats for a country's species that remain intact
|
||||
Indicator,BER,Bioclimatic Ecosystem Resilience,Biodiversity & Habitat,Ecosystem Vitality,0.50%,The Bioclimatic Ecosystem Resilience Index (BERI) measures the capacity of natural ecosystems to retain species diversity in the face of climate change
|
||||
IssueCategory,ECS,Forests,,Ecosystem Vitality,5.00%,The Forests issue category (previously called Ecosystem Services) measures trends in area and integrity of countries' forests. It includes five indicators: loss of humid tropical primary forests
|
||||
Indicator,PFL,Primary Forest Loss,Forests,Ecosystem Vitality,1.50%,Humid tropical primary forests are the most biodiverse terrestrial ecosystems on the planet and provide irreplaceable ecosystem services. This indicator measures annual losses of tree cover in these critical ecosystems relative to their extent in 2001.
|
||||
Indicator,IFL,Intact Forest Landscape Loss,Forests,Ecosystem Vitality,1.50%,Intact forest landscapes are large and pristine mosaics of forests and naturally treeless ecosystems and play a disproportionate role storing carbon
|
||||
Indicator,FCL,Tree cover loss weighted by permanency,Forests,Ecosystem Vitality,1.25%,Not all tree cover loss is the same. Depending on what drives tree cover loss
|
||||
Indicator,TCG,Net change in tree cover,Forests,Ecosystem Vitality,0.50%,Forest landscapes are highly dynamic
|
||||
Indicator,FLI,Forest Lanscape Integrity,Forests,Ecosystem Vitality,0.25%,Going beyond measuring changes in tree cover
|
||||
IssueCategory,FSH,Fisheries,,Ecosystem Vitality,2.00%,The Fisheries issue category measures the health and sustainability of the world’s fisheries. It is made up of five indicators: fish stock status
|
||||
Indicator,FSS,Fish Stock Status,Fisheries,Ecosystem Vitality,0.30%,Fish stock status is the percentage of a country’s total catch that comes from overexploited or collapsed stocks
|
||||
Indicator,FCD,Fish Catch Discarded,Fisheries,Ecosystem Vitality,0.40%,The proportion of a country’s total catch in the global ocean that is discarded
|
||||
Indicator,BTZ,Bottom Trawling in EEZ,Fisheries,Ecosystem Vitality,0.50%,The proportion of the total catch in a country’s exclusive economic zone(s) caught by any country using bottom trawling and dredging. This indicator measures whether countries allow bottom trawling in the marine regions under their jurisdiction. A score of 100 indicates that no fish is caught with bottom trawling inside a country's exlusive economic zone(s)
|
||||
Indicator,BTO,Bottom Trawling in Global Ocean,Fisheries,Ecosystem Vitality,0.70%,The proportion of a country’s total catch across the global ocean caught by bottom trawling and dredging. This indicator measures how much countries use bottom trawling
|
||||
Indicator,RMS,Regional Marine Trophic Index,Fisheries,Ecosystem Vitality,0.10%,A measurement of how fast the trophic level of fish stocks changed over the last decade. The decline of the trophic level of fish catches is a phenomenon commonly known as “fishing down the food web”. A score of 100 indicates that the mean trophic level increased 0.015 per year over the last decade
|
||||
IssueCategory,APO,Air Pollution,,Ecosystem Vitality,6.00%,The Air Pollution issue category (previously called Acid Rain) measures countries' contribution and exposure to air pollution. It consists of two indicators measuring trends in the emissions of acid rain precursors (sulfur dioxide and nitrogen oxides)
|
||||
Indicator,OEB,Ozone exposure KBAs,Air Pollution,Ecosystem Vitality,0.50%,As a proxy of ozone pollution effects on biodiversity
|
||||
Indicator,OEC,Ozone exposure croplands,Air Pollution,Ecosystem Vitality,0.50%,As a proxy of ozone pollution effects on crop productivity
|
||||
Indicator,NXA,Adjusted emissions growth rate for nitrous oxides,Air Pollution,Ecosystem Vitality,2.50%,We measure the average annual rate of nitrogen oxides emissions over the years 2013 to 2022 and adjust for economic trends to isolate change due to policy effort rather than economic fluctuation. A score of 100 indicates a country is cutting emissions by ≥3.94% per year
|
||||
Indicator,SDA,Adjusted emissions growth rate for sulfur dioxide,Air Pollution,Ecosystem Vitality,2.50%,We measure the average annual rate of sulfur dioxide emissions over the years 2013 to 2022 and adjust for economic trends to isolate change due to policy effort rather than economic fluctuation. A score of 100 indicates a country is cutting emissions by ≥3.94% per year
|
||||
IssueCategory,AGR,Agriculture,,Ecosystem Vitality,3.00%,The Agriculture issue category measures efforts to produce food and other agricultural products while minimizing the threats of agriculture to the environment. It is based on four indicators: the Sustainable Nitrogen Management Index (SNMI)
|
||||
Indicator,SNM,Sustainable Nitrogen Management Index,Agriculture,Ecosystem Vitality,1.20%,The Sustainable Nitrogen Management Index (SNMI) seeks to balance efficient application of nitrogen fertilizer with maximum crop yields as a measure of the environmental performance of agricultural production. The 2024 EPI uses the SNMI as a proxy for agricultural drivers of environmental damage. A score of 100 indicates that a country is optimizing both crop yields and fertilizer application
|
||||
Indicator,PSU,Phosphorus Surplus,Agriculture,Ecosystem Vitality,0.10%,The difference between phosphorus inputs (as fertilizer) and outputs (as harvested crops)
|
||||
Indicator,PRS,Pesticide Pollution Risk,Agriculture,Ecosystem Vitality,0.50%,The risk agricultural pesticide pollution to biodiversity. A score of 100 indicates minimal risk
|
||||
Indicator,RCY,Relative Crop Yield,Agriculture,Ecosystem Vitality,1.20%,The average yield of 17 major crops
|
||||
IssueCategory,WRS,Water Resources,,Ecosystem Vitality,5.00%,The Water Resources issue category measures the extent to which humans are mitigating our threats to aquatic ecosystems through the generation and mismanagement of wastewater. It consists of four indicators: wastewater generation
|
||||
Indicator,WWG,Wastewater generated,Water Resources,Ecosystem Vitality,0.50%,Total amount of municipal wastewater generated per person each year. A score of 100 indicates the country has one of the lowest (≤5th percentile) rates of wastewater generation in the world
|
||||
Indicator,WWC,Wastewater collected,Water Resources,Ecosystem Vitality,2.00%,Percentage of wastewater collected for treatment. Sometimes measured as the percentage of the population connected to urban or independent wastewater treatment facilities.
|
||||
Indicator,WWT,Wastewater treated,Water Resources,Ecosystem Vitality,2.00%,Percentage of wastewater that undergoes at least primary treatment.
|
||||
Indicator,WWR,Wastewater reused,Water Resources,Ecosystem Vitality,0.50%,Percentage of wastewater reused after treatment
|
||||
PolicyObjective,HLT,Environmental Health,,,25.00%,The Environmental Health policy objective measures how well countries are protecting their populations from environmental health risks. It comprises 25% of the total EPI score and is made up of four issue categories: Air Quality
|
||||
IssueCategory,AIR,Air Quality,,Environmental Health,17.00%,The Air Quality issue category measures the impacts of air pollution on human health in each country. It consists of seven indicators: anthropogenic PM2.5 exposure
|
||||
Indicator,HPE,Anthropogenic PM2.5 exposure ,Air Quality,Environmental Health,6.50%,We measure the exposure to fine particulate matter (PM2.5) from satellite-derived ground-level measurements weighted by population density. We exclude the population-weighted fraction of exposure to PM2.5 from windblown dust
|
||||
Indicator,HFD,Household solid fuels,Air Quality,Environmental Health,6.50%,We measure household solid fuels using the number of age-standardized disability-adjusted life-years lost per 100
|
||||
Indicator,OZD,Ozone exposure,Air Quality,Environmental Health,1.50%,We measure ozone exposure using the number of age-standardized disability-adjusted life-years lost per 100
|
||||
Indicator,NOD,NO2 exposure,Air Quality,Environmental Health,1.00%,We measure nitrogen dioxide exposure using the number of age-standardized disability-adjusted life-years lost per 100
|
||||
Indicator,SOE,SO2 exposure,Air Quality,Environmental Health,0.50%,We measure exposure to ground-level sulfur dioxide using a country’s ambient ground-level concentration. The pollutant concentration is population-weighted to better capture the exposure levels in geographic areas with a higher human population density. A score of 100 indicates a country has among the lowest exposure in the world (≤5th-percentile)
|
||||
Indicator,COE,CO exposure,Air Quality,Environmental Health,0.50%,We measure exposure to ground-level carbon monoxide using a country’s ambient ground-level concentration. The pollutant concentration is population-weighted to better capture the exposure levels in geographic areas with a higher human population density. A score of 100 indicates a country has among the lowest exposure in the world (≤1st-percentile)
|
||||
Indicator,VOE,VOC exposure,Air Quality,Environmental Health,0.50%,We measure exposure to ground-level volatile organic compounds using a country’s ambient ground-level concentration. The pollutant concentration is population-weighted to better capture the exposure levels in geographic areas with a higher human population density. We estimate volatile organic compounds as the summed concentration of ethane
|
||||
IssueCategory,H2O,Sanitation & Drinking Water,,Environmental Health,5.00%,The Sanitation & Drinking Water issue category measures how well countries protect human health from environmental risks on two indicators: unsafe drinking water and unsafe sanitation.
|
||||
Indicator,USD,Unsafe sanitation ,Sanitation & Drinking Water,Environmental Health,2.00%,We measure unsafe sanitation using the number of age-standardized disability-adjusted life-years lost per 100
|
||||
Indicator,UWD,Unsafe drinking water,Sanitation & Drinking Water,Environmental Health,3.00%,We measure unsafe drinking water using the number of age-standardized disability-adjusted life-years lost per 100
|
||||
IssueCategory,HMT,Heavy Metals,,Environmental Health,2.00%,The Heavy Metals issue category measures the direct impacts of heavy metal pollution exposure on human health in each country. It is based on one indicator
|
||||
Indicator,LED,Lead exposure,Heavy Metals,Environmental Health,2.00%,We measure lead exposure using the number of age-standardized disability-adjusted life-years lost per 100
|
||||
IssueCategory,WMG,Solid Waste,,Environmental Health,1.00%,The Waste Management issue category recognizes the threats of solid waste to human and environmental health. It is based on three indicators: municipal solid waste generation per capita
|
||||
Indicator,WPC,Waste Generated Per Capita,Waste Management,Environmental Health,0.40%,We measure the total municipal solid waste produced per person each year.
|
||||
Indicator,SMW,Controlled Solid Waste,Waste Management,Environmental Health,0.20%,Controlled solid waste refers to the percentage of household and commercial waste generated in a country that is collected and treated in a manner that controls environmental risks. This metric counts waste as “controlled” if it is treated through recycling
|
||||
Indicator,WRR,Waste Recovery Rate,Waste Management,Environmental Health,0.40%,We assess the quality of municipal solid waste management by rewarding waste treatment methods that mitigate environmental impacts and recover materials and energy (composting
|
||||
PolicyObjective,PCC,Climate Change ,,,30.00%,TheClimate Change policy objective comprises 30% of the total EPI score and is made up of a single issue category: Climate Change Mitigation.
|
||||
IssueCategory,CCH,Climate Change Mitigation,,Climate Change ,30.00%,The Climate Change Mitigation issue category measures progress to combat global climate change
|
||||
Indicator,CDA,Adjusted emissions growth rate for carbon dioxide,Climate Change Mitigation,Climate Change ,7.50%,We calculate the CO2 growth rate indicator as the average annual rate of CO2 emissions over the years 2013–2022. We then adjust for economic trends to isolate change due to policy effort rather than economic fluctuation. A score of 100 indicates a country is cutting emissions by ≥13% per year
|
||||
Indicator,CDF,Adjusted emissions growth rate for carbon dioxide (country-specific targets),Climate Change Mitigation,Climate Change ,0.50%,This pilot indicator measures the average annual growth rate of CO2 emissions over the years 2013–2022
|
||||
Indicator,CHA,Adjusted emissions growth rate for methane,Climate Change Mitigation,Climate Change ,3.00%,We calculate the CH4 growth rate indicator as the average annual rate of CH4 emissions over the years 2013-2022. We then adjust for economic trends to isolate change due to policy effort rather than economic fluctuation. A score of 100 indicates a country is cutting emissions by ≥5% per year
|
||||
Indicator,FGA,Adjusted emissions growth rate for F-gases,Climate Change Mitigation,Climate Change ,2.00%,We calculate the F-gases growth rate indicator as the average annual rate of F-gases emissions over the years 2013-2022. A score of 100 indicates a country is cutting emissions by ≥12% per year
|
||||
Indicator,NDA,Adjusted emissions growth rate for nitrous oxide,Climate Change Mitigation,Climate Change ,1.00%,We calculate the nitrous oxide growth rate indicator as the average annual rate of nitrous oxide emissions over the years 2013-2022. We then adjust for economic trends to isolate change due to policy effort rather than economic fluctuation. A score of 100 indicates a country is cutting emissions by ≥5% per year
|
||||
Indicator,BCA,Adjusted emissions growth rate for black carbon,Climate Change Mitigation,Climate Change ,1.50%,We calculate the black carbon growth rate indicator as the average annual rate of black carbon emissions over the years 2013-2022. We then adjust for economic trends to isolate change due to policy effort rather than economic fluctuation. A score of 100 indicates a country is cutting emissions by ≥5% per year
|
||||
Indicator,LUF,Net carbon fluxes due to land cover change,Climate Change Mitigation,Climate Change ,1.00%,The sum of carbon fluxes (both emissions and sinks) from land use
|
||||
Indicator,GTI,GHG growth rate adjusted by emissions intensity,Climate Change Mitigation,Climate Change ,6.00%,Average annual growth rate in greenhouse gas emissions over the last decade adjusted to account for declines in GDP and for how close countries are to a target of zero emissions intensity of GDP.
|
||||
Indicator,GTP,GHG growth rate adjusted by per capita emissions,Climate Change Mitigation,Climate Change ,6.00%,Average annual growth rate in greenhouse gas emissions over the last decade adjusted to account for declines in GDP and for how close countries are to a target of zero emissions per capita.
|
||||
Indicator,GHN,Projected emissions in 2050,Climate Change Mitigation,Climate Change ,1.00%,The projected emissions levels in 2050 indicator captures whether countries are on track to reach zero emissions of four greenhouse gases by 2050. These greenhouse gases include carbon dioxide
|
||||
Indicator,CBP,Projected cumulative emissions to 2050 relative to carbon budget,Climate Change Mitigation,Climate Change ,0.50%,This pilot indicator of projected cumulative emissions to 2050 indicator captures whether countries exceed their allocated share of the remaining carbon budget in the decarbonization journey until 2050. The indicator includes emissions of carbon dioxide
|
||||
|
@@ -1,181 +0,0 @@
|
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code,iso,country,region,population,gdp,EPI.old,EPI.new,ECO.old,ECO.new,BDH.old,BDH.new,MKP.old,MKP.new,MHP.old,MHP.new,MPE.old,MPE.new,PAR.old,PAR.new,SPI.old,SPI.new,TBN.old,TBN.new,TKP.old,TKP.new,PAE.old,PAE.new,PHL.old,PHL.new,RLI.old,RLI.new,SHI.old,SHI.new,BER.old,BER.new,ECS.old,ECS.new,PFL.old,PFL.new,IFL.old,IFL.new,FCL.old,FCL.new,TCG.old,TCG.new,FLI.old,FLI.new,FSH.old,FSH.new,FSS.old,FSS.new,FCD.old,FCD.new,BTZ.old,BTZ.new,BTO.old,BTO.new,RMS.old,RMS.new,APO.old,APO.new,OEB.old,OEB.new,OEC.old,OEC.new,NXA.old,NXA.new,SDA.old,SDA.new,AGR.old,AGR.new,SNM.old,SNM.new,PSU.old,PSU.new,PRS.old,PRS.new,RCY.old,RCY.new,WRS.old,WRS.new,WWG.old,WWG.new,WWC.old,WWC.new,WWT.old,WWT.new,WWR.old,WWR.new,HLT.old,HLT.new,AIR.old,AIR.new,HPE.old,HPE.new,HFD.old,HFD.new,OZD.old,OZD.new,NOD.old,NOD.new,SOE.old,SOE.new,COE.old,COE.new,VOE.old,VOE.new,H2O.old,H2O.new,USD.old,USD.new,UWD.old,UWD.new,HMT.old,HMT.new,LED.old,LED.new,WMG.old,WMG.new,WPC.old,WPC.new,SMW.old,SMW.new,WRR.old,WRR.new,PCC.old,PCC.new,CCH.old,CCH.new,CDA.old,CDA.new,CDF.old,CDF.new,CHA.old,CHA.new,FGA.old,FGA.new,NDA.old,NDA.new,BCA.old,BCA.new,LUF.old,LUF.new,GTI.old,GTI.new,GTP.old,GTP.new,GHN.old,GHN.new,CBP.old,CBP.new
|
||||
4,AFG,Afghanistan,Southern Asia,41454761,2116,18,30.7,21.1,31.2,25.6,32.1,NA,NA,NA,NA,NA,NA,0.3,0.3,0.6,9.1,0.3,11.7,24.6,24.6,79.5,79.5,80.9,80.9,75.8,75.6,67.8,66.4,50.1,50.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,5.2,42,19.4,18,39.9,37.6,0,34.2,0,55.5,45,41.1,35.3,31.2,100,97.2,60.6,52,40.2,41.9,9,9,89.7,89.7,0,0,0,0,0,0,17.7,18.2,16.8,15.8,22.8,18.8,1.1,3.6,7.5,10.3,33.4,34.9,63.5,58.7,51,50.2,36.5,37.1,26.2,32.3,20.7,31,22.3,33.1,0,0,0,0,25.2,25.2,60.6,60.6,0,0,2.4,2.4,13.7,40.2,13.7,40.2,0,38.4,0,100,4.2,48,36.8,4.3,9.3,50.8,1.2,39.3,42.2,44.4,0.7,35,8.1,46.7,17.9,22.6,97.7,97.7
|
||||
8,ALB,Albania,Eastern Europe,2811655,22730,45.9,52.1,50.3,51.8,50.9,50.6,21.2,25.5,24.4,24.4,100,96.7,46.4,46.4,54.4,56.8,54.2,60.7,58.4,58.4,65.2,65.2,63.4,63.4,70.5,70,78.4,53.5,44.3,45.1,58.4,62.4,NA,NA,NA,NA,65,71.7,36.5,36.5,67.5,67.5,13.7,15.8,NA,NA,24.2,24,9,3.5,5.6,7.8,100,100,80.3,74.5,25.8,25.9,23.3,21.8,100,100,100,69.2,48.4,50.4,20.3,22,35.7,36.2,67,65.7,68.6,73.6,21.8,39.2,64.8,56.6,19,26.5,10.9,49,33.4,33.4,40,43.8,32,36.5,32.8,36.6,19.3,27.6,43.6,60.2,37.5,34.5,39,44.6,59.6,64.4,48.9,47.2,68.1,71.3,67.1,73.2,65.4,70,50.5,51.5,50.7,51.5,16.4,16.4,35.6,35.6,0,0,5.3,5.3,44.1,59.4,44.1,59.4,37.8,54.7,47.5,77.2,82.4,87.6,36.8,3.8,72.3,100,65.5,100,49.7,50,43.3,56.9,44,56.3,32.7,39.5,87.6,87.6
|
||||
12,DZA,Algeria,Greater Middle East,46164219,18340,38.6,41.9,39.7,42.2,33.1,33,0,0,0,0,100,100,6.1,6.1,73.2,73.8,4.2,4.3,6,6,20.3,20.3,58.8,58.8,74.3,73.8,78.1,74,54.7,54.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,53.1,51.6,85.6,75.7,40.8,37.7,44.8,49.2,45.3,50.1,20.8,58.2,49.7,63.7,25.8,18.7,12,7.8,7.1,60.7,100,86.8,42.9,46.7,35,38.6,100,100,76.7,74.6,39.8,38.7,53.1,55.9,62.7,62.7,65,72,41.7,41.7,41.7,41.7,48,48.1,47.3,46.1,46.8,37.1,52.8,64.5,30.5,29.6,33.6,31.3,36.5,24,54,42.7,33.2,27.9,61.6,64.7,61,68.8,57.4,62,26.6,29.2,24.4,29.2,34.3,37.6,44.6,47.8,61.6,71.6,10.3,10.5,29.3,36.2,29.3,36.2,34.8,40.7,21.3,30.4,48,40.5,34.3,12.6,9.4,46.1,3.8,59.8,49.3,49.7,29.8,32.1,32.1,34.7,7.7,6.9,51.5,51.5
|
||||
24,AGO,Angola,Sub-Saharan Africa,36749906,9910,31.6,39.7,35.9,43.2,42.3,41.5,0,0,0,0,NA,NA,35.6,35.6,37.1,37.1,34.1,34.1,74.7,74.7,82.1,82.1,89.8,89.8,77.8,77.7,85.1,71.6,57.7,55.6,50.8,49.2,68.5,50.5,41.1,49.3,58.1,49.1,27.9,27.9,83.5,83.5,38.8,37.6,21,29,48.1,45.4,27.2,26.9,38.5,43.4,59.3,45.3,13.5,73.2,69.5,69.1,87.5,84.4,0,69.6,0,75.4,39.3,41,33,35.2,81.6,97.5,97,97.8,24,18.4,13.8,13.8,86.5,86.5,7.6,7.6,4.3,4.3,4.3,4.3,19.4,21.6,18.9,19.9,14.4,15.4,8.2,13.8,24.4,32.7,43.7,43.6,63.7,60.3,37.5,43.2,4.3,9.8,16.2,22.9,10,23.4,10,22.6,28.7,30.4,26.9,30.4,26.1,26.1,64.1,64.1,0,0,1.1,1.1,35.2,49.4,35.2,49.4,26.4,54.7,78.8,100,55.5,54.7,36.8,3.7,49.2,49,5.8,52.5,47.9,48,38.3,48.8,42.5,52.9,15.3,20.4,88.5,88.5
|
||||
28,ATG,Antigua and Barbuda,Latin America & Caribbean,93316,31474,54.4,55.5,52.4,53.2,52.5,52.8,74.7,74.7,33.1,33.1,60.3,88.5,NA,NA,19.6,19.9,51.5,51.5,72.9,72.9,28.3,28.3,90,90,67.3,64,NA,NA,93.7,91.4,35.4,28.7,0,0,NA,NA,70.6,59,30.2,30.2,46.1,46.1,97.6,97.3,81.5,100,NA,NA,100,100,100,100,51.3,56.4,64.6,72,40,36.2,44.4,39.6,39.5,84.8,76.3,72.9,30.2,31.4,5.2,3.8,40.2,39.8,29.4,41.2,54.1,54.1,48.2,48.2,56.5,56.7,50.8,50.8,44.4,44.4,44.4,44.4,65.7,69.6,72.8,77.8,93.9,93.5,57.3,62.7,100,100,42.2,35.1,63,68.8,82.6,85.8,87.3,91,55.6,56.9,53.3,56,56,57.5,45.8,48.5,46.1,48.5,35.6,35.6,39,39,100,100,0,0,47.1,46.4,47.1,46.4,42.1,46.3,22.8,28.8,67.9,57.4,NA,NA,52.1,57.5,57.2,59.9,50.3,50.2,39.1,42.6,36.8,39,51.5,51.6,54,54
|
||||
32,ARG,Argentina,Latin America & Caribbean,45538401,30380,45.9,46.8,41.7,47.1,32,35,13.4,17.1,9.5,33,68.2,89.1,14.9,14.9,30.9,32.9,26,27.9,40.5,40.5,37.6,37.6,71.6,71.6,54.4,54.2,69.6,48.2,39.9,39.3,35.6,48.9,47.2,62.7,44.9,51.3,21.4,44.2,0,0,72.2,72.2,52,38.5,77.5,70.5,53.4,50.8,12.8,26.8,15.8,24.6,49.9,48.3,57,80.5,80.1,77,94.4,92.5,36.7,69,62,90.4,84.1,81.4,82.6,87.7,100,100,37.3,29.5,80.8,95.3,48.5,48.5,32.8,32.8,55,55,55,55,11.8,11.8,52.1,52.9,48.1,47.6,47.9,44.6,47.8,54.5,49,48.6,11.6,18.2,65.6,66.9,66.4,64.5,14.2,17.4,64.4,68.3,61.6,70.2,60.2,67.1,68,72.8,65.8,72.8,26.6,26.6,32.5,32.5,56.1,56.1,6,6,47.1,41.4,47.1,41.4,41.8,50.8,30.7,44.8,87.6,46.6,42.6,6.8,58.4,27.3,41.7,54.3,45.1,48.5,44.9,43.4,39.9,39.1,7.4,6.6,53,53
|
||||
51,ARM,Armenia,Former Soviet States,2943393,24970,42.5,44.7,46.8,47.8,48.4,47.4,NA,NA,NA,NA,NA,NA,14.4,14.4,38.7,38.7,76.4,77.2,39.5,39.5,49.8,49.8,71.5,71.5,49.9,47.9,92.4,80.4,41.5,43.4,80.3,83.4,NA,NA,NA,NA,88.8,99.3,58.2,58.2,54.2,54.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,38.7,54.2,40.9,37.3,45.3,40.6,41.2,18.6,50.8,95.8,56.6,35.5,29.7,13.1,100,100,66.3,49.3,59.9,46.7,31.9,35.8,0,0,38,42.9,38,42.9,14.6,14.6,38.5,41.2,29.1,30.9,22.9,19.3,26.5,38.5,27.2,35.2,29.6,28.3,35.6,35.2,58.4,59.7,47.1,43.2,69.1,74.2,59.8,68.7,67.1,77.8,49.9,54.8,45.7,54.8,23.5,24.8,54.3,57.4,0,0,4.5,4.5,39.3,42.8,39.3,42.8,37.3,39,38.9,41.9,60.3,79.3,36.7,3.8,6.7,39.2,36,74.4,43.3,46.9,35.1,38.9,36,38.1,31.8,31.3,69.2,69.2
|
||||
36,AUS,Australia,Global West,26451124,71310,59,63,60.7,63.3,50.6,55.4,36.5,57.2,30.1,42.5,45,57.2,50.9,50.9,53.5,65.8,44.2,67.1,58.1,58.1,89.1,89.1,92.2,92.2,48,39.5,82.7,50.3,36.1,35.8,60.2,42.3,97.5,97.4,21.1,0,64.7,20.1,44.9,44.9,72.2,72.2,48.5,48.1,29.4,28.6,45.4,47.7,46.3,51.1,49,53.3,77.6,57.2,87.4,95.9,79,77.3,86.2,82.1,91.2,98.2,79.7,100,53.8,65.3,52.9,49.7,53.4,51.8,57.5,59,58.9,84.7,88.9,89.1,26.2,26.2,100,100,92.4,92.9,92.9,92.9,79.6,82,78.5,81,90,94.9,80.6,85.1,85.5,71,16.5,26.4,33.3,42.2,77.5,86.5,16.8,18.3,89.8,90.9,89,91.6,89.3,90.5,80.4,86.3,75.4,86.3,45.4,45.7,25.2,27.6,90.2,91.3,43.1,41.1,39.1,46.6,39.1,46.6,47.8,50.8,20.1,24,44.6,82.4,39.8,31.9,39.7,77.7,41.7,49.6,47.7,49.5,38,47.5,20.4,30.2,2.7,5.9,43.5,43.5
|
||||
40,AUT,Austria,Global West,9130429,74981,68.9,69,78.4,78.2,74.6,74.4,NA,NA,NA,NA,NA,NA,86.4,86.4,78.9,83.6,85.7,86.9,70.6,70.6,76.7,76.7,57.9,57.9,62.6,59.1,74,59.9,48.1,48,58.9,47.5,NA,NA,NA,NA,66.7,53.4,38.7,38.7,35.6,35.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,93.1,92.9,56.3,54.2,65.5,61,100,100,100,100,68.9,72.5,64.6,64,41,40.9,75.3,71.6,71.5,84.1,88.1,89.5,0,10.8,100,100,95.2,96,100,100,65.4,70,56.4,61.5,30.8,46.1,83.7,88.9,41.6,39.3,20.1,25.5,54.2,61.1,51.7,57.4,50.8,50.2,89,92.6,79.9,87.7,88.5,95.8,82.5,88.3,78.4,88.3,67.2,63.8,23.8,12.2,97.9,99.5,95.2,97.6,57.3,54.1,57.3,54.1,62.1,52.5,50.8,36.9,72.7,77.3,47.1,40.4,55.2,60.5,100,100,51.5,51.4,57.4,53,47.6,44.5,23.2,20.6,61.5,61.5
|
||||
31,AZE,Azerbaijan,Former Soviet States,10318207,25480,40.4,40.4,44.7,44.4,36.3,36.9,0.8,0.8,20,20,50,50,7.2,7.2,42.1,43.1,31.3,31.4,41.3,41.3,55.2,55.2,31,31,80.8,81.2,86,80.1,24.1,25.8,79.5,82.3,NA,NA,NA,NA,92.1,98.3,50.7,50.7,65.4,65.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,77.1,67,45,39.3,56.4,49.8,70.5,60.3,100,82.7,62.7,63,45.8,48.1,100,100,80.7,64.2,55.3,74.4,23.2,28.9,43,44,18.9,25.9,18.9,25.9,38.1,38.1,38.1,40.2,36.1,38.2,36.3,37.9,24,36.8,41.2,37.1,37.6,36.4,35.5,39.3,60.9,63.1,40.3,41.6,45,48.2,39.8,45.2,44.6,50.2,40.9,46.8,37,46.8,31.2,21.6,47.7,34.5,22.7,13.8,18.9,12.6,36.1,34.7,36.1,34.7,52.8,44.5,50.5,37.3,0,21.2,23,25.8,24,11.3,28.3,50.7,48.4,47.2,36.9,33.5,36.6,33,18.9,16.9,48.4,48.4
|
||||
44,BHS,Bahamas,Latin America & Caribbean,399440,37517,54.6,56,54.7,53.9,47,46.8,25.1,25.1,17.4,17.4,53,77.8,80.1,80.1,21.6,21.6,79.3,82.6,46.2,46.2,100,100,100,100,8.1,3.1,99.4,98.2,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,91.5,91.6,64.7,59,100,100,100,100,100,100,46.9,56,70,66.1,20.2,16.5,13.6,8.4,63.9,79.8,80,73.9,50.8,50.2,37.8,39.9,29.7,30.8,23.6,23.3,73.3,73.3,61.9,61.9,28.6,28.6,68.9,68.9,63,63,63,63,67.7,69.4,74.7,76.4,99,97.1,63,65.4,55.9,65.9,22.9,23.6,62.3,70.9,78,83,79.5,86.5,62.7,63.8,65.7,68.7,59.3,60.5,50.4,53.9,47.9,53.9,8.7,8.7,18.8,18.8,0,0,2.9,2.9,42.7,47.6,42.7,47.6,45.9,49,32.6,37.3,33.9,40.9,NA,NA,38.3,69.4,70.5,58.5,50,50.1,43.6,47.9,38.4,42.7,41.9,42.9,61.3,61.3
|
||||
48,BHR,Bahrain,Greater Middle East,1569666,66975,37.1,35.9,45.9,38.6,26.8,26,67.3,67.3,44.3,59.4,100,30.8,NA,NA,3.7,3.7,8.7,8.7,0,0,NA,NA,NA,NA,21.7,13.3,NA,NA,46.2,45.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,66.4,68.9,NA,NA,27,30.6,100,100,65.5,67.6,18.7,75.8,73.3,32.5,0,0,0,0,76,60.7,66.4,17.2,38,42.5,8.2,21.7,45.3,35.5,61.1,58.5,57.2,57.2,83.2,80.9,20.4,20,88,81,98.6,100,64.9,64.9,39.6,40.9,32.8,32.7,9.5,3.8,63.6,71.7,13.9,15.2,5.6,7.9,11,9.2,45.5,37.5,27,22.8,65.3,66.9,65.6,69.9,62.7,64.9,42.8,48,36.3,48,20,34.6,0,3.2,100,98.5,0,34,23.4,27.9,23.4,27.9,27.7,39.5,0,5.8,12.9,42.9,48.3,10.2,28.5,35.7,100,100,NA,NA,10.2,24.7,0,1.5,16,17,3.8,3.8
|
||||
50,BGD,Bangladesh,Southern Asia,171466990,10370,25.5,27.8,27.3,31.4,20.7,29.1,21,32.6,4.8,61.1,50,91.2,0,0,19.8,19.8,13.1,14,23.9,23.9,1.4,1.4,51,51,25.4,13.2,92.7,80.1,0.3,0.4,61.9,51.4,62.9,59.9,NA,NA,65.4,31.2,75,75,53.9,53.9,62.4,63.2,98.2,88.6,60.3,58.4,69.8,58.6,71.2,60.4,43.2,49,11.9,13.3,33.1,27.3,41.5,35.4,18.8,19.5,0,0,71.1,72.2,52.9,54.4,50.6,29.7,74.2,56.6,85.3,100,14.1,14,48,48.9,2.9,2.9,19.9,19.4,2,2,13.4,15,5.6,6.3,0,0,3.2,7,1.1,2.4,40.8,40,21.6,17.8,5.1,3.2,16.8,15.8,28.8,31.9,25,30.9,27,32.6,22.3,27,17.8,27,51.5,52.9,96.5,99.5,58.6,59.1,3,3.1,32.8,33,32.8,33,24.7,27.4,63.7,69.1,31.3,42.1,36.7,3.7,23.1,32.7,36.4,46.9,48.7,46.1,30.3,34.2,33.3,35.5,8.3,6.8,87.1,87.1
|
||||
52,BRB,Barbados,Latin America & Caribbean,282336,22035,50.5,53.1,34.1,35,11.8,12.5,0,0,0,0,50,100,NA,NA,1,1.2,1.6,1.6,1.7,1.7,NA,NA,NA,NA,58.9,54.7,NA,NA,19,19.5,42.5,45.4,NA,NA,NA,NA,36.6,47.6,60.1,60.1,5,5,77.9,80.4,11.8,6,86.4,77.4,100,100,100,100,NA,NA,67.8,70,48.5,43.6,54.7,48.8,41,74.8,73.1,74.6,48.7,47.9,61.6,41.4,23.8,20.3,0,13.5,71,71,49.5,49.5,33.8,33.8,55.1,55.1,48.2,48.2,48.2,48.2,76.5,77.4,85.2,85.8,100,100,75.6,76.5,100,100,20.8,25,87.1,92.5,83,85.4,94.1,96.3,59.1,59.8,58.7,61.1,58.1,59,62.7,65.7,62.2,65.7,42.9,46,32.6,8.9,100,100,24.7,56.2,50.5,56.7,50.5,56.7,47.3,59.2,38.1,56.4,61,49.5,NA,NA,42.4,53.8,55.1,54.8,54.6,66.4,40.5,56.1,39.8,55.4,44,55.2,82.3,82.3
|
||||
112,BLR,Belarus,Former Soviet States,9115680,33600,49.3,58.1,60.4,68.1,53.9,70.3,NA,NA,NA,NA,NA,NA,36.2,36.2,13.4,90.6,44.8,45.5,93.7,93.7,23.9,23.9,72.3,72.3,88.9,92.4,87.7,77.8,0,0,63.5,52.5,NA,NA,NA,NA,61.6,44.5,80.6,80.6,36.2,36.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,79.8,81.9,56.1,58.1,65,68.2,56,100,100,71.4,49,45.2,30.2,29.7,33.2,44.2,70.7,73.1,56.7,49.2,66.4,63.2,23.6,16.3,78.5,78.5,65.8,59.6,62.8,62.8,49.6,55.8,43.7,51.3,26.1,44.4,46.4,58.1,48.7,66.3,21.8,15.9,47.7,50.3,61.6,65.4,63.6,65.2,74.3,74.5,71.4,71.9,76.5,76.2,48.9,53,44.6,53,29.1,44.3,25.5,32,70.8,95.1,11.9,31.1,32.1,44.5,32.1,44.5,47,50.8,29.9,35.5,32.8,50.5,2,30.1,0,49.9,10.3,69.5,49.7,49.4,26.8,41.4,24,38.4,12.8,17.1,50.5,50.5
|
||||
56,BEL,Belgium,Global West,11712893,75199,62,66.7,61.6,69.1,53.2,66.4,45.5,45.5,80,80,48.5,47.1,70.4,70.4,38.7,96,24,51.6,46.1,46.1,3,3,70.8,70.8,93.8,92.7,45,48,16.5,16.7,48.8,43.6,NA,NA,NA,NA,47.8,47.5,48.5,48.5,13.9,13.9,7.8,8,NA,NA,15.2,15.7,0,0,0,3.2,44.5,51.4,95.4,94.3,68.1,61.6,80.8,70.2,100,100,100,100,68.9,68.5,44.7,43.6,27.6,29.3,62.1,61.6,99.6,99.6,81.6,83.6,29.3,29.3,95.2,98,81.9,84,78.2,78.2,66.5,70.8,59.5,64.8,30.4,48.7,87.1,92.7,44.6,44.8,10.7,24.9,43.1,54.7,49.9,60.2,63.4,67,86.4,88.2,82.9,87.4,86.3,88.7,74.4,81.3,68.9,81.3,70,65.1,31.9,14.3,96.6,99.3,94.9,98.9,59,59.7,59,59.7,70,56.8,59.9,41,63.3,100,28.6,60.5,39.6,61.8,100,100,48.8,47.2,59,55.8,48.1,46.2,23,19.8,64.7,64.7
|
||||
84,BLZ,Belize,Latin America & Caribbean,411106,14958,46.5,47.4,55.8,57.3,57.6,58.4,25.9,25.9,36.1,36.7,50,97.4,100,100,90.3,90.3,94.6,94.6,39.8,39.8,89.5,89.5,99.4,99.4,31.7,29.9,26,0,87.6,83.2,42,42.3,46.7,48.2,63.1,56.3,28.9,28.6,8.1,8.1,60.5,60.5,86.3,86.7,NA,NA,40.4,54.4,75.1,100,99.7,100,61.4,55.6,66,71,63.3,62.4,71,69.6,31.2,71.2,74.9,72.9,55.8,60.9,66.8,57.4,41,23.4,39.2,42,68.1,75.5,37.9,37.9,50.9,50.9,43.2,43.2,31,31,31,31,41.5,43.3,39.8,41.3,43.6,47.7,23.5,28,49.6,46.7,45.4,50.3,81.2,84.4,68.8,75,21,18.9,48.2,50.4,46.8,51.4,46.3,49.7,50.5,54.3,48.9,54.3,19.6,19.6,44.9,44.9,5.2,5.2,1.4,1.4,36.5,35.8,36.5,35.8,42.6,32.2,55.9,37.7,2.7,40.4,44.1,28.6,0,42.7,35.4,48.1,47.6,46.3,27.1,32.1,29.4,34.3,44.7,43.8,63.8,63.8
|
||||
204,BEN,Benin,Sub-Saharan Africa,14111034,4501,37.7,37.4,51,54.6,63.8,63.7,NA,NA,0,0,NA,NA,62.6,62.6,75.5,75.5,86.1,86.1,91.7,91.7,36.3,36.3,79.3,79.3,70.7,70.7,83.9,80.4,12,10.7,54.4,53.2,92.9,85.5,NA,NA,19,28.9,14.4,14.4,58.4,58.4,87.8,88.3,NA,NA,100,100,72.7,77.2,97.4,97.9,42.4,30.3,27.4,50.4,66.5,63.5,71.7,68.9,0,44.6,17.6,49.8,50.1,54,41.3,52.6,100,100,79,71.7,42.3,44.2,10,10,97.9,97.9,0.4,0.4,0,0,0,0,24.1,25.8,23.6,25,46.8,36.1,4.5,6.1,35.8,24.5,51.8,55.6,62.4,59.4,42.2,38.4,25.8,20.1,16.4,20.1,12.1,18.9,13.6,20.9,36,38.1,34.5,38.1,48.1,41.9,100,84.3,40.6,40.6,0,0.1,30.5,22.9,30.5,22.9,23.2,22,58.5,56.2,17.3,25.4,36.8,3.8,37.7,12.1,18.5,35.9,0,0,23.4,20.4,30,26.2,26.7,23.3,82.1,82.1
|
||||
64,BTN,Bhutan,Southern Asia,786385,16754,36.4,43.3,56.4,59.5,68,67.2,NA,NA,NA,NA,NA,NA,40.9,40.9,68.2,68.2,80.2,80.2,77.8,77.8,100,100,99,99,42.3,42.3,98,87.7,100,100,87.8,86.7,93.9,94.1,70.3,87.6,87,90.1,52.5,52.5,88.4,88.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,25.4,50.9,32,15.6,46.4,25.4,54.8,51.5,15.4,62.4,50.5,44.7,39.8,33.8,100,67.5,85.7,74.9,34.2,41.1,23.9,23.9,64.9,64.9,25.5,25.5,14.4,14.4,14.4,14.4,18.5,22.3,13,16.8,1.1,5.2,12.4,22.2,3.3,1.7,65.7,71.1,42.9,39.4,18.3,15.9,10.5,12.2,30.3,35,20.9,27.6,28.8,40,28.3,30.5,26.6,30.5,33.7,35.8,62.3,65.5,32.4,35.5,5.7,6.2,19.6,35.3,19.6,35.3,3.8,38.2,0,58.2,35.7,17.7,NA,NA,37.1,37.5,20.4,51.3,48.3,49.6,23,31.6,25.1,32.6,39.6,39.3,64.8,64.8
|
||||
68,BOL,Bolivia,Latin America & Caribbean,12244159,11323,41.6,44.9,58.4,55.7,67.6,63.6,NA,NA,NA,NA,NA,NA,73.3,73.3,69.1,69.4,81.5,85,66.5,66.5,46.1,46.1,95.5,95.5,53.2,51.9,69,24.3,51.8,49.9,53.2,33.7,59.9,47.1,44.3,23.6,44.4,24.4,21.9,21.9,84.7,84.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,61,73.9,100,100,100,100,27.6,55.9,57.4,81.5,62.3,59.6,49.5,61.3,82,98.6,52.8,42.8,63.9,61.7,24.2,24.2,66.7,66.7,26.4,26.4,14,14,14,14,29.5,29.4,24.8,23,23,14.9,14.8,21.9,59.1,39.2,20.9,20.4,95.3,91,63.2,53.1,0,0,42.1,46.5,36.1,44.7,39.5,47.7,41,43.3,39.3,43.3,24.8,24.5,49.7,49.8,12.6,11.8,5.9,5.5,25.8,41.4,25.8,41.4,29.6,45,33.7,60.8,24.9,45.6,10,24.3,32.7,26.1,22.7,72.6,48.7,49.2,22.2,35,29.5,40.7,17.2,18.8,62.1,62.1
|
||||
70,BIH,Bosnia and Herzegovina,Eastern Europe,3185073,22610,42.4,45.6,48.7,51.3,43.8,45.7,NA,NA,31,31,NA,NA,55.1,55.1,19.2,22,11.2,13.2,67.5,67.5,76.1,76.1,75,75,64.6,63.5,82.7,75.4,36.7,36.7,76.3,76.2,NA,NA,NA,NA,89.9,88.4,53.7,53.7,59.9,59.9,92.5,89.9,NA,NA,82.2,81,100,100,100,100,80,4.5,77.3,75.2,28.6,29.6,57.2,54.7,55,100,50.7,63.7,31.8,52.3,21,29.1,46.8,51.2,63,52.1,22.9,75.8,20.4,23,42.8,42.8,29.6,36,2.9,2.9,31.3,31.3,32.5,36,19.3,22.9,8.6,10.5,19,24.4,39.9,40.1,39.9,38.8,25.7,31.9,56.7,60.8,36.8,35.7,75.5,78.7,72.1,77.9,74.7,79.3,45.1,48.8,43.5,48.8,17.7,17.7,37.2,37.2,14.1,14.1,0,0,41.8,45.9,41.8,45.9,32.1,50.5,8.8,36,39.2,40.8,100,43.3,6,100,17.3,34.4,50.2,49.7,22.6,43.5,25.7,42.1,21.9,26.9,57.7,57.7
|
||||
72,BWA,Botswana,Sub-Saharan Africa,2480244,20800,50.9,49,68.2,74,85.9,85.8,NA,NA,NA,NA,NA,NA,84.1,84.1,93.7,93.7,87.4,87.4,78.8,78.8,46.2,46.2,54.5,54.5,92.3,92.2,95.3,94.2,59,59.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,52.5,83.5,65.6,61.9,71.3,65.6,43.6,74.9,100,100,33.7,35.3,1,6,56.1,56.8,97.6,93.8,38.5,38.5,42,42,53.3,53.3,46.7,46.7,36,36,36,36,27.2,31.7,29.2,34.5,28.8,38.9,17.4,25.5,30.1,31.3,57.5,61.2,45.3,45.1,66.2,60.8,13.5,12.3,18.1,20.1,14.8,18.4,16.9,21.3,31.4,37.3,25.6,37.3,31.4,31.4,53.8,53.8,49,49,0.3,0.3,44.1,25.1,44.1,25.1,38.6,45.1,35.1,45.7,0,0,36.6,3.7,0,0,31.6,80.7,0,0,6.4,19.3,6.6,18.7,22.4,22.9,27.5,27.5
|
||||
76,BRA,Brazil,Latin America & Caribbean,211140729,22930,46.2,53,58.2,63.8,58.1,62.2,47.4,66.4,26.7,39.9,46.9,73.4,100,100,66.7,69.6,75.6,76.7,59.7,59.7,68.5,68.5,94,94,63.8,62.2,34.2,0,23.2,22.6,55.4,44,67.4,56.3,61.7,40.2,47.8,37,20.3,20.3,75.1,75.1,48.1,47.9,58,58.6,18.3,16.5,59,54.2,61,56.8,46.7,48.4,60.3,90.6,100,100,100,99.5,43.7,82.2,76.3,95.3,80.1,81,81.3,78.8,34.4,30.2,42.8,53.5,88.5,99,49.6,55.3,15.1,15.4,63,72.2,46.9,52,40.8,40.8,40.2,42.2,36.1,36.2,44.5,39.3,27.4,35.8,50.4,35.9,9.8,15,42.8,43.8,62.8,59.8,16.5,14.2,52.6,59.4,44.9,57.6,47.9,60.6,51.5,57.4,46.6,57.4,26.2,26.2,35.3,35.3,57.8,57.8,1.4,1.4,32.9,45.5,32.9,45.5,33.1,53.4,32.4,66.8,45.3,41.8,12,26.6,35.1,30.2,34.1,69.7,47.1,47.9,34.5,45,33.5,44.4,0,0,64.3,64.3
|
||||
96,BRN,Brunei Darussalam,Asia-Pacific,458949,95046,51.8,48.5,51.5,48.9,49.1,47.4,0,0,0.6,1.1,50,57.9,20.8,20.8,89.6,89.6,100,100,44.5,44.5,100,100,99.9,99.9,52.8,51.7,74.9,33.3,100,100,54,64.2,68.7,80.3,47,51.1,52.3,65.1,47.1,47.1,75.8,75.8,44.3,41.4,NA,NA,37,35.6,24.8,34.2,31.7,48.7,28.8,50.1,64.6,41.9,83.2,84.4,79.7,83.1,68.2,67.1,0,0,20,24.1,0,0.7,12.7,10.7,16.1,22.4,20.1,49.4,67.2,67.2,27.3,27.3,76.1,76.1,68.1,68.1,68.1,68.1,62.9,70.9,58.2,68.7,71.4,76.8,70.9,72.3,70,67.7,40.6,42,56,57.8,45,52.5,0,0,85.8,87.9,84.5,88,85.6,87.9,59.5,65,57,65,35,35,34.9,34.9,100,100,2.5,2.5,43,29.2,43,29.2,31.1,30.4,0,0,46.4,73.3,36.8,3.9,31.1,38.8,20.1,72.5,47.5,47.9,28.6,27.9,4.9,5,27.4,26.8,1.6,1.6
|
||||
100,BGR,Bulgaria,Eastern Europe,6795803,41510,57.6,56.3,70.5,70.8,69.4,69.1,86.7,86.7,0,0,32.3,33,48.7,48.7,91.3,91.5,99.9,99.9,99.2,99.2,74.4,74.4,0,0,80.5,80.1,86,77.5,18.2,18.9,71.9,72.1,NA,NA,NA,NA,82,80.3,57,57,61,61,19.1,20.9,NA,NA,57.2,55.5,12.1,4.3,16.5,8.8,50,50,92.7,92.3,47.3,49,58,59,100,100,100,100,71.5,74.2,56.1,60.8,71.2,57.1,85.7,70.1,62.2,90.7,66,67.4,39.4,39.4,86.8,90.4,54.6,54.6,54.6,54.6,41.3,42.5,30.3,32,18.6,21,28.1,36.4,40.3,49.3,38.5,37.8,11.4,16,54.5,59.6,41,41.4,80.6,79.3,90.8,87.8,78.7,73.7,33.8,37.3,31.1,37.3,47.7,47.3,33.6,33.4,99.9,90.1,35.6,39.8,51.7,45.7,51.7,45.7,56.3,54.1,41.7,38.4,76,60.2,0,7.9,53.9,37.6,80.5,48.5,51.6,51,50.4,48,47.7,41.9,24.3,21.4,56.3,56.3
|
||||
854,BFA,Burkina Faso,Sub-Saharan Africa,23025776,2850,41.5,41.5,58,56.9,72.3,73.3,NA,NA,NA,NA,NA,NA,50.7,50.7,83.3,89.8,54.1,54.4,88.8,88.8,17.2,17.2,85,85,95.5,95.4,80.1,74.9,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,47.8,39.8,73.2,72.3,90.1,90.8,5.2,35.7,55.6,27.2,66.5,64,45.8,46.3,100,100,93.9,90.2,73.6,67.7,12.4,12.4,100,100,3.4,3.4,2,2,2,2,31.1,33.1,38,39.8,79.1,72.8,4.5,6.2,48.5,34.2,50.7,42.2,77.8,75.4,52.6,51.5,19.7,13.6,12.3,15.7,9,14.7,10.1,16.3,21,22.3,20.7,22.3,27.4,27.4,66.4,66.4,1.8,1.8,1.2,1.2,25,24.9,25,24.9,6.8,15.1,61.2,78.6,23.3,25.7,36.7,3.7,31.6,31.6,44.1,64.2,0,0,18.5,19.8,34.9,33.7,21.7,19.5,81.5,81.5
|
||||
108,BDI,Burundi,Sub-Saharan Africa,13689450,986,34.3,33,52.2,48.1,51.2,51.7,NA,NA,NA,NA,NA,NA,28.3,28.3,34.6,37,20.4,26.4,89.4,89.4,38.5,38.5,95.7,95.7,67.6,67.6,90.4,80.7,3.3,3.7,68.3,63.7,91.4,85.7,NA,NA,65.2,50.7,39.9,39.9,44.4,44.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,82.7,59.6,72.6,70.5,84.5,82.7,57.4,53.2,69.4,59.3,46.2,45.4,40.9,45,100,72.1,84.8,77.1,12.3,30.4,11.5,11.5,97.2,97.2,4.5,4.5,0,0,0,0,12.5,12.9,9.5,9.1,0,0,3.6,5.2,40.1,21.5,55,53.9,32.9,32.9,20.1,20.4,17.5,17.7,14.1,16.8,10.8,15.9,11.9,17.4,28.2,29.6,26.9,29.6,24,24,59,59,0,0,0.9,0.9,25.1,26.6,25.1,26.6,12.9,4.9,100,100,3.3,29.4,36.7,3.8,0,20.4,42,61.9,44.3,45.4,13.3,19.7,34.2,43,32.3,31.6,99.7,99.7
|
||||
132,CPV,Cabo Verde,Sub-Saharan Africa,522331,11397,39.6,37.9,26.4,23.1,19.1,19.8,0.1,0.1,0,0,100,100,NA,NA,0.4,3.5,9.4,9.6,1.3,1.3,0,0,91.3,91.3,69.3,69.7,NA,NA,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,80.6,70.2,93.5,61.5,48.2,38.6,NA,NA,100,100,63.6,14.8,38.5,14.3,15.5,14.8,NA,NA,4.1,12.6,60.5,15.9,26.7,28,11.4,8.1,46.9,50.5,33.1,51.9,36.1,36.1,25.6,28.7,90.4,90.4,20.9,28.6,16.5,16.5,16.5,16.5,53.8,54.7,62.8,63.1,100,100,16.6,21.4,62.8,47.8,82.7,79.9,85,88.9,77.5,81.3,94.1,95.5,33.2,36.5,28.3,34.2,31.6,38.1,46.1,45.2,48.9,45.2,20.2,20.2,49.4,49.4,0,0,1.2,1.2,45,43.4,45,43.4,41.4,43.2,77,80.5,34.6,41.9,0,21.3,39.8,48.9,48.9,41.2,42.9,52.7,38.9,42.3,40,43,49,49.1,89.7,89.7
|
||||
116,KHM,Cambodia,Asia-Pacific,17423880,8680,31,31,36.3,44,38,57.3,27.8,63.1,13.8,17.6,50,50,66.7,66.7,58.4,91.1,46.5,100,72.6,72.6,4.4,4.4,86.1,86.1,39.7,32.3,0,0,57.5,55.2,25.6,37.6,15.7,32.2,57.8,77.6,0,5.9,0,0,63.3,63.3,33.7,31.8,33.6,23,88.8,86.7,15.5,12,15.4,15.4,78.3,52,46.7,14.5,64.1,65.1,73.2,72.9,31,4.4,56.1,2.9,62,64.6,48.8,51.4,71.5,100,65.7,48.6,66.7,81.5,11.7,11.7,77.8,77.8,9.9,9.9,0,0,0,0,23.9,24.1,19.5,18.2,17,20,3.9,6.5,34.3,31.8,41.7,41.1,45.1,44.8,39.7,46.1,9,4.5,35,40.2,28.6,38.9,31.2,41,27.6,28.9,26.5,28.9,36.3,36.3,85.7,85.7,0,0,5.1,5.1,28.8,16.7,28.8,16.7,9.4,0,9.6,0,30.6,38.8,36.8,3.8,28.5,31.2,30.9,33.8,47.3,45.8,23.4,12.2,34.1,21.4,21,16.3,60.7,60.7
|
||||
120,CMR,Cameroon,Sub-Saharan Africa,28372687,5566,36,38.1,45.2,48.1,46,45,NA,NA,53.8,75.6,100,100,30.9,30.9,30,31,30.3,33.1,67.7,67.7,62.3,62.3,89.1,89.1,49.8,49,75.1,0,46.8,44.9,67.5,58.4,78.2,58.9,79.4,69.9,72.1,46.6,41.4,41.4,80,80,72.2,81.6,NA,NA,85.2,90.1,56.8,76.5,75,88,32.8,28.8,44.2,71.7,86.7,84.3,88.5,85.6,33.5,60.9,25.9,77.1,48.4,50.4,39.2,44.7,100,100,56.3,59.1,47,48.4,10.2,10.2,100,100,0.6,0.6,0,0,0,0,17.3,19.5,15.7,17,17.3,14.6,4.5,7.5,37.7,25,63.5,63,49,47.4,41.5,38.7,2.5,4.7,17.5,23,12.1,21.8,13.4,23.8,25.1,28.3,22.7,28.3,26.8,26.8,65.5,65.5,2.6,2.6,0.2,0.2,38.6,39.4,38.6,39.4,31.6,43.6,100,100,51.8,40.5,58.7,7.7,62.3,40.7,54.7,28.6,48.9,49.1,40.1,36.7,46.9,41.9,23.3,20.6,88.2,88.2
|
||||
124,CAN,Canada,Global West,39299105,64570,57.7,61.1,57.8,60.6,48.2,52.1,11.2,21.7,15.7,26.9,50.7,60.8,40.3,40.3,66.8,82.1,29.2,36.2,40.7,40.7,89.8,89.8,99,99,89.8,89.3,79.5,47.1,57.8,56.9,42.5,47.6,NA,NA,24.2,29.2,67.3,65.9,36.2,36.2,89.9,89.9,31.7,33.1,4.7,0,64.6,57.3,31.1,29,35.9,34,53.2,49.4,89.5,90.8,22.2,41.1,43,48.6,100,100,100,100,72.5,72.3,54.1,61.7,77,60,41.5,33.6,96.1,100,80.4,80.4,6.9,6.9,98.2,98.3,84,84,68,68,73.5,77.3,68.4,72.3,62.5,67,90.7,96.7,50.1,51.9,2.3,7.8,37.1,41.7,55.9,61,57.3,57.3,90.6,94.7,80.1,88,91.3,99.1,93.3,97.3,91,97.3,36,36,17.6,17.6,96,96,24.4,24.4,44.3,48.2,44.3,48.2,51.7,52.5,26,27.1,60.1,65.7,43.7,41.4,38,44.3,100,100,49.7,49.8,45.2,48.3,29.3,33.1,3.2,4.1,45.2,45.2
|
||||
140,CAF,Central African Republic,Sub-Saharan Africa,5152421,1296,43.3,38.3,58.7,56.1,68.7,71,NA,NA,NA,NA,NA,NA,37,37,59.4,76.7,59.5,59.5,94.5,94.5,87.5,87.5,99.4,99.4,79.5,79.5,93.9,77.5,48.4,47.2,77.7,68.6,79,73.4,81.2,59.6,79.5,78.7,43.4,43.4,92.7,92.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,63.3,47.7,95.1,88.3,100,100,38.5,40.6,70.8,36.2,38.3,38.3,15.8,16.2,68.7,67.9,94.6,93.7,34.8,34.8,10,10,100,100,0,0,0,0,0,0,12.1,14.4,13.2,15.6,13.4,19.9,0,1.2,13.3,7.5,56.4,61.1,79.5,77.3,32.2,32.7,0,0,6.3,8.9,4.4,8.4,4.7,9.2,13.7,15.3,12.7,15.3,20.7,20.7,50.9,50.9,0,0,0.9,0.9,45.6,31,45.6,31,71.5,19.4,100,100,50.7,49.1,36.8,3.8,60.3,55.2,56.5,83.4,49.8,49.9,2.4,0,49.5,47.1,29.8,28.2,75.1,75.1
|
||||
148,TCD,Chad,Sub-Saharan Africa,19319064,2832,33.2,35.2,50.9,49.4,61.8,60.1,NA,NA,NA,NA,NA,NA,29.8,29.8,68.8,68.8,61.5,61.5,72,72,4.4,4.4,87,87,72.3,71.6,88.6,70.2,25.8,24.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,56.6,52.4,44.6,35.5,99,93.4,27.6,32.7,62.6,67.3,41.1,42.7,30.7,30.2,100,100,60.4,73,37.8,37.8,10,10,99.9,99.9,0,0,0,0,0,0,23.5,26.4,30.3,33.6,56.6,57.1,2.6,4,34.6,24.7,67.6,66.6,84.9,86.1,49.8,54.4,0,0,1.8,4.3,0,4,0,4.5,16.1,17.5,14.8,17.5,31.2,31.2,77.1,77.1,0,0,1,1,14.2,21,14.2,21,15.8,45.8,100,100,5.6,0,36.7,3.8,8.6,0,44.8,56.7,0,0,0,0,24,18.9,15.6,11.8,39.7,39.7
|
||||
152,CHL,Chile,Latin America & Caribbean,19658835,34790,47,50,54.8,58.4,39.7,42.5,13.4,28.4,17.4,37.8,100,87.7,34.6,34.6,30.4,33.6,48.2,54.9,63.9,63.9,64.8,64.8,95.6,95.6,30,20.6,78.1,46.8,100,100,50.6,70.3,NA,NA,46.1,83.6,51.9,62.1,49.6,49.6,73.5,73.5,82.7,81.9,31,22.7,85.5,88.8,100,100,96.5,94.3,57.7,55.2,75.7,80.9,74.2,71.1,93.3,86.6,32.2,63.4,100,99.1,69.1,66.3,39.7,45.4,30.7,29.5,60.6,43.2,91,100,88.3,88.4,0,0,99.9,99.9,99.9,100,84,84,44.8,44.7,31.2,29.2,21,8.3,37.9,48,55.6,62.3,11.8,12.4,0,0,25.2,22.4,25.8,27.5,75.8,80.1,68.8,77.6,74.9,81.8,89,94,85.5,94,31.9,31,34,31.3,90.7,91.3,0.4,0.5,37,41.5,37,41.5,36.5,46.7,21.8,37.5,55.5,49.5,0,0,45.3,56,14,66.6,49.3,49.5,34.3,42.3,30.2,38.1,12.3,13.5,54.8,54.8
|
||||
156,CHN,China,Asia-Pacific,1422584933,28010,29.9,35.5,34.5,35.9,11.4,9.5,24,24,2.4,2.5,47.9,54.8,0,0,2.5,3.1,1.5,2.6,3.8,3.8,4.4,4.4,0,0,20,10.4,54.1,24.9,39.8,40.3,69.7,73.1,61.1,85.3,76.9,73.2,64.1,66,54.8,54.8,71.4,71.4,40.5,39.6,59,57.5,76.6,68.3,29.6,26.3,28.7,24.1,46.3,46,73.6,87.3,0,0,45.2,47.2,40.9,100,100,100,64.8,69,50.1,58.8,25,30.1,71.2,60.5,77.7,85.9,48.4,48.4,42.6,42.6,49,49,49,49,49,49,26,29.5,11,14.3,0,1.1,13.8,25.1,0.9,16.5,24.3,31.9,0,0,2.5,4.5,28,29.2,69.8,74.5,61.1,71.6,66.5,76.5,35.4,39.4,31.7,39.4,44.7,43.3,61.8,58.1,89.5,91.4,5.1,4.5,26,39.8,26,39.8,21,43.1,0,20.9,22.9,38.7,10.8,36.3,42.6,65.3,54.6,100,50.1,49.4,3.4,33.5,7.8,31.1,0,0,39.3,39.3
|
||||
170,COL,Colombia,Latin America & Caribbean,52321152,22190,44.9,49.4,52.9,56.4,56.1,55.5,97.1,97.2,40.9,51,67.8,75.6,67.6,67.6,45.1,54.1,48.7,53.5,81.2,81.2,64,64,97.1,97.1,17.6,14.3,56.4,0,54.9,53.5,73,57,79.5,67.1,84.5,52.7,62.4,52.1,39,39,82.6,82.6,51.6,46.2,8.1,0,63.8,42.9,52.6,51.3,66.1,62.9,51.9,55.5,42.5,84.9,100,100,100,100,38.6,64.3,39.6,99.4,57.1,59.3,52.5,47.4,25.8,28.8,40.1,48.2,64.9,78.4,28,28.1,60.3,61.8,31.8,31.8,20.2,20.2,11.3,11.3,42.8,45.2,37.6,39.6,38.5,37.6,28.9,40,66.7,58.8,20.8,23.1,57,57.9,51.7,49.2,11.9,9.4,56.5,59.7,55.8,62.1,54.3,58.1,61,65.3,55.7,65.3,27,27,30.3,30.3,56.8,56.8,8.8,8.8,34.2,42.2,34.2,42.2,30.7,49.2,32.7,65.1,42.1,34.9,36.4,4.6,55,21.5,0.8,97.2,48.9,49.2,35,41.5,35.1,40.6,9,9.3,65.8,65.8
|
||||
174,COM,Comoros,Sub-Saharan Africa,850387,3861,44,37.9,48.8,44.4,53.9,49.9,70.1,70.1,23.3,23.3,86.6,100,NA,NA,43.3,43.3,100,100,41.1,41.1,100,100,99.6,99.6,23.4,10.2,86.5,30.4,100,100,46.1,65.1,NA,NA,NA,NA,57,72.1,41.3,41.3,77.9,77.9,74.9,73.4,100,64.1,100,5.9,100,100,100,100,52.6,51.8,55.7,33.4,65,53.8,NA,NA,48.2,37.8,61.7,25,49.7,50.6,42.3,45.1,100,100,77.8,77.8,40.7,40.7,9.5,9.5,83.9,83.9,2.8,2.8,0,0,0,0,44,41.2,51.8,46.8,71.8,70.9,5.3,6.7,88.2,54.9,90.4,79.6,85.2,89.6,81.8,82.1,82.4,85.9,23.7,25.8,20,24.1,22.4,26.9,36.9,39,35,39,28,28,69.1,69.1,0,0,1,1,36.5,25.2,36.5,25.2,42.3,3.4,100,29,18.2,35.1,NA,NA,33.5,35.3,47.7,61.7,49.9,50,32.6,22.1,36.7,26.9,48.6,44.6,86.1,86.1
|
||||
188,CRI,Costa Rica,Latin America & Caribbean,5105525,31090,55.3,55.5,63.2,62.5,65.7,63.9,86,86.1,34.1,37.5,63.3,100,99.9,99.9,58.6,58.8,84.4,84.8,54.7,54.7,60.9,60.9,97.2,97.2,51.7,49.1,59.9,14,79.1,77.1,77,72.3,85.6,83.6,88.6,83.8,57.5,64,38.1,38.1,46.4,46.4,41.9,35.7,19,18,9.4,3.8,57.9,47.4,51.4,50.2,50,56,72.8,79.8,66.8,64.9,75.5,78.7,51.1,62.7,40.6,100,52.8,57,49.7,42.7,32.2,33,35,49.1,76.7,76.7,40.3,38.7,20.5,17.7,84.2,81.4,7.6,7.1,15.2,15.2,51.5,53.7,47.6,49.6,55,56.3,37.5,45.3,65.9,59.8,18.8,23.8,50.1,51.4,67.5,68.1,20.6,20.4,65.9,68,67,73.3,62.2,64.4,59.7,64.4,55.6,64.4,31.2,31.2,42.9,42.9,63.6,63.6,3.2,3.2,46.1,46.3,46.1,46.3,46.4,50.5,68.3,75.6,30.9,39.3,36.7,4.2,24.6,46.3,45.8,82.5,49.7,49.7,42.3,47.1,41.4,45.9,28.9,29.7,78.4,78.4
|
||||
384,CIV,Cote d'Ivoire,Sub-Saharan Africa,31165654,8060,35,42.5,38.4,48.9,49.7,56.3,NA,NA,2.4,2.4,100,100,37.1,37.1,48.1,84.4,76.2,76.3,94,94,68.3,68.3,96.2,96.2,70.8,70.7,0.6,0,27.6,25.6,20.7,26.2,28.6,39.5,25.6,27.2,32.2,11.4,15.3,15.3,36.5,36.5,49.3,46.4,51.2,28.4,68.8,61.8,17.7,15.9,73.9,69.2,49.4,31,27.2,75.6,76.5,76.3,87.5,87.8,24.4,49.3,63.6,99.4,44.9,42.2,34.4,32.4,100,100,51.9,45.5,49.8,45.8,11.5,11.5,90.2,90.2,6.1,6.1,0,0,0,0,30.8,33.5,34.8,37,73.8,66.9,4.1,7,53.3,31.1,45.9,38.2,69.1,67.3,49.8,47.7,17.4,13.1,19.9,24.4,15.4,23.1,16.9,25.2,28.3,30.9,26.4,30.9,23.7,23.7,57,57,1.5,1.5,1.5,1.5,33.4,40.9,33.4,40.9,43.8,42.7,100,100,27.2,39.8,36.8,3.7,38.3,38.9,11.8,60.2,45.5,46,37.1,39.8,40.2,41,23.4,22.3,92.5,92.5
|
||||
191,HRV,Croatia,Eastern Europe,3896023,51220,58.1,62.6,70.2,72.8,69.5,69.8,66.7,66.7,32.8,33.4,55.3,55.3,55.8,55.8,81.3,84.6,40.4,100,95,95,63.2,63.2,52.5,52.5,65.1,63.6,85.4,78.4,34.4,34.5,68.5,61.7,NA,NA,NA,NA,80.9,69.4,48.7,48.7,49,49,59.3,62.1,60,61.5,46.3,68.5,45.3,61.9,47.8,63,37.1,33.8,73.7,91.1,39.7,37.3,59.4,56.4,100,100,100,100,64,67.9,53.5,61.5,37.7,56.2,78.8,78.2,53.2,71.1,77.5,77,37.3,39.8,98.3,96.5,81,80.9,20.6,20.6,48.5,51.7,36,40.6,23.2,29.3,45.7,52.8,37.7,36.2,34.3,34.9,35.3,41.9,50.7,55.2,38.2,37.2,87.7,85.4,96.3,91.1,89.5,81.6,62.3,68.1,56.5,68.1,39.1,39.1,31.6,31.6,96.8,96.8,17.8,17.8,47.5,56,47.5,56,54.6,52.9,48.5,45.8,51,90.5,30.5,46,58.3,40.2,89.5,94.1,52.1,51.1,53.1,52.7,49.4,47.5,29.6,28.9,68.1,68.1
|
||||
192,CUB,Cuba,Latin America & Caribbean,11019931,,49.8,52.3,52.1,49.8,47.3,43.4,80.8,80.8,33.9,33.9,50,26.4,40.8,40.8,39.2,39.2,41.7,41.7,71.9,71.9,98.1,98.1,97.1,97.1,0,0,71.6,21.6,47.7,46.5,80.9,66,85.6,73.8,96.9,65.5,64.6,66.6,49.2,49.2,53.6,53.6,83.6,81.4,33.1,31.4,98.9,99.1,86.4,88.9,87.9,89.8,69.1,63.7,62.7,78.8,29.5,24.6,38,32.5,84.5,84.5,68.5,93.1,50.5,47,41.8,45.7,63.4,71.2,43.8,54.5,55.2,43.2,23.3,20,6.6,3.7,16.8,18.7,33.7,24.3,24.6,24.6,50.1,52,50.6,52.5,62.9,62.8,37.7,44.9,46.8,55.1,20.9,22.1,54,56.9,72.4,76.3,43.6,43.1,57,58.8,55.9,59.8,56.1,58.1,42.9,45,42.1,45,22.1,23.5,21.1,25.8,48.6,49.9,9.9,7.9,46.1,56.4,46.1,56.4,42.6,62.3,54.4,88.7,42.3,62,36.8,3.8,53.2,60.4,100,55.9,51,51.5,38.8,58.9,40,59.6,20.1,33.6,90.1,90.1
|
||||
196,CYP,Cyprus,Eastern Europe,1344976,62290,54.3,54,55.7,57.2,48.7,51.6,7.8,7.8,16,16,44.2,36.4,37.6,37.6,49.4,69.3,57.2,100,59.9,59.9,25.2,25.2,37.1,37.1,73.3,73.4,73.1,60.2,74.9,76.2,75.8,60.6,NA,NA,NA,NA,83.7,60,55.3,55.3,74.5,74.5,41.5,43.6,8.9,10.9,31.8,26.4,40.7,74.9,98.6,51.3,94.2,0,79.6,83.3,0,0,0,0,100,100,100,100,39.1,35.7,13.7,9.2,28.9,27.7,55.3,48.6,57.6,57.6,70.4,70.8,61,61,82.7,83.5,62.5,62.5,62.5,62.5,61.9,62,57,55.9,42.9,37.1,82.7,87.2,27.9,30.4,20.1,21.8,29.9,40.5,62,67.7,36.8,42.4,85.1,86.7,84,87.5,84,86.2,60.4,67.1,56,67.1,31.7,31.7,19.9,19.9,85.4,85.4,16.6,16.6,45.9,42.6,45.9,42.6,54.6,48.6,43.9,35.1,0,30.3,15.7,33.8,59.6,35.9,100,52,57.8,54.5,49.2,45.2,44.9,39.6,36,33.4,55.6,55.6
|
||||
203,CZE,Czech Republic,Eastern Europe,10809716,59210,65.4,65.6,79,78,78.5,78.7,NA,NA,NA,NA,NA,NA,63.2,63.2,94.9,96.8,63.8,69.9,98.4,98.4,65.7,65.7,31.2,31.2,91.1,89.6,75.7,68.1,8.4,8.6,49.4,22.5,NA,NA,NA,NA,57.8,17.1,38.6,38.6,17.1,17.1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,93.4,93.8,62.8,59.3,70.3,66.8,100,100,100,100,74.7,74,60.3,58.5,96.1,73.5,74.5,70.6,67.6,90.9,77.1,80.2,27.3,27.3,80.8,84.7,80.8,84.8,96.8,96.8,55.3,58.8,44.7,50.4,21.1,37.7,62.5,69.7,43.7,38.4,22,27.1,33.6,40.4,48.5,54.1,54.1,53.2,83.3,80,98.3,89.6,83.7,73.6,75.7,80.9,71.2,80.9,55.5,51.2,38.9,23.4,100,100,49.9,54.5,53,52.2,53,52.2,61.8,57.8,44.9,39.4,57.2,63.4,0,36.3,52.3,69.1,69.3,65.9,54.9,54.6,53.8,53.3,44.7,43.3,20.1,18.6,59.5,59.5
|
||||
180,COD,Dem. Rep. Congo,Sub-Saharan Africa,105789731,1842,33,39,47.9,53.1,53,51.8,NA,NA,59.6,59.6,NA,NA,33.9,33.9,51.1,62.7,41.6,45.8,66.8,66.8,74.9,74.9,97.6,97.6,62.1,62.1,71.9,0,60,54.8,57,47.1,69.5,55.4,64.7,45.8,55.7,39,32.2,32.2,75.6,75.6,73.2,73,NA,NA,100,100,19.9,18,100,100,41.5,51.3,50.6,100,100,100,100,100,39.5,100,38.3,100,39.4,39,37.7,37.5,100,100,64.8,57.8,26.9,27.6,10,10,100,100,0,0,0,0,0,0,12.3,13.8,8.1,8.2,0,0,3.5,5.1,26.5,16.3,47.6,53.9,27.3,28,24.8,26.1,0,0,18.3,25.7,10.5,24.1,11.6,26.7,27.5,27.7,27.4,27.7,23.9,23.9,58.2,58.2,1,1,1,1,29.5,40.1,29.5,40.1,29.3,51.8,100,100,20.3,29.3,36.8,3.8,57.7,42.1,42.4,40.9,47.9,47.1,23.3,30,41.8,45.5,17.3,16.4,98.3,98.3
|
||||
208,DNK,Denmark,Global West,5948136,85791,68.3,67.9,64.2,63.5,53.4,53.3,28.9,29,17.3,17.3,21.2,21.4,41.5,41.5,74.9,75.4,48,48,52.4,52.4,24.9,24.9,67.4,67.4,93.6,93.8,90.6,87.1,5.3,6.3,56,51,NA,NA,NA,NA,49.9,45.9,87,87,4.3,4.3,49.6,44.7,9.5,27.3,68.8,72.7,34.7,39.2,24.5,41.2,57.9,36.6,90.9,90.3,39.2,38.9,42.7,44.1,100,100,100,100,80.6,77.8,65.9,61.3,44.6,41,75,71.2,96.8,100,85.1,85.8,20.4,21.4,99.6,99.8,90.8,92,69.3,69.3,73.9,76.9,67.7,70.9,54.3,59,90.6,95.6,37.5,40.7,26.6,32,59.1,66.8,62.9,68.2,78,80.3,89.7,91,87.7,91.1,89.1,90.9,92.3,98.8,86.4,98.8,64.4,65.5,12.3,13.9,100,100,98.8,99.9,69.9,67.1,69.9,67.1,63.3,77.9,59.6,81.7,60.3,56.5,54.3,80.4,57.5,49.6,72.2,100,47.6,48.2,63.1,64.3,53.1,56.9,37.6,40.5,83.7,83.7
|
||||
262,DJI,Djibouti,Sub-Saharan Africa,1152944,8601,32,32.2,25.2,24.4,19.7,18.1,0,0,0,0,NA,NA,0,0,6.8,6.8,0.3,4.4,2.1,2.1,100,100,100,100,46.7,39,90.1,81.3,43.8,42.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,95.9,96.4,NA,NA,100,100,45.2,100,100,100,13.5,38.8,18.7,16.8,34.4,10.4,NA,NA,17.1,17.1,17.7,17.7,58.4,65,49.9,59.3,23.9,42.4,68.9,86.3,63.6,63.6,14.8,14.8,74.1,74.1,14.5,14.5,3.2,3.2,3.2,3.2,27.1,28.9,28.2,29.2,29.1,33.2,14.3,16.8,49.2,34.3,33.3,30,60.5,60.5,67.1,61.6,56.3,56.9,21.1,25.8,16,24.4,17.7,26.8,31.6,34.5,29.2,34.5,29.1,29.1,71.6,71.6,0,0,1.1,1.1,47.9,47.9,47.9,47.9,51.4,51.4,100,100,37.1,37.1,NA,NA,46.2,46.2,48.6,50.3,NA,NA,43,43,46.5,46.5,44.4,44.4,88.9,88.9
|
||||
212,DMA,Dominica,Latin America & Caribbean,66510,18391,49.4,49.2,42.9,40.6,27.8,27.8,0,0,3.2,3.2,50,78.3,NA,NA,24.7,24.7,70.4,70.4,43,43,52.2,52.2,95.7,95.7,4.8,0,NA,NA,100,100,79.4,56.4,89,62,NA,NA,83.5,64.3,43.2,43.2,10,10,39.6,49.6,NA,NA,18.2,33,100,100,0,24.9,45.9,37.6,75.6,68.1,44,39.8,NA,NA,49,68.6,91.5,73.3,40.9,45.7,35.5,37.5,57.6,55.6,19.8,38,56.4,56.4,44.5,44.5,50.2,50.2,49.9,49.9,39.1,39.1,39.1,39.1,55.8,57.6,59.6,62,84.1,80.8,28.5,32.2,86.3,100,50.2,43,80,85,82.3,85.5,78,83.2,51.2,51.3,51.5,53,50.8,50.2,42.9,44.8,41.8,44.8,39.6,38.4,43.9,43.5,100,95.2,5,4.8,52.8,53.6,52.8,53.6,45.3,50.7,55.1,64.4,40.7,45.9,100,100,46.7,47.3,66.4,52.1,50.1,50.6,42.1,48.1,42.6,48.8,58.8,60.9,79.1,79.1
|
||||
214,DOM,Dominican Republic,Latin America & Caribbean,11331265,28950,48.7,47.6,58.4,56.8,59.4,57.6,82.6,82.6,43.5,43.5,65.8,50,56.8,56.8,51.6,51.8,67,69.9,95.3,95.3,34.6,34.6,91,91,15.5,12,81.3,56.7,70.5,67.9,54.6,61.1,68.3,68.8,64,82.1,42.2,39.9,37.3,37.3,41.8,41.8,95.2,95.7,80.8,86.6,100,100,100,100,100,100,54.6,54.6,68,51.7,50.3,46.2,56.3,53.1,72.5,43.5,79.5,60.8,74.5,78.7,65.1,85.4,46.3,68.8,49.9,55.4,82.5,82.5,21.1,25.2,24.4,27.2,20.9,20.9,23.4,32.8,9.7,9.7,40.8,43.2,43.9,46.3,56.1,53.9,27.8,37.1,58.4,66.9,17.1,19.5,54,58.2,63.9,64.7,31.6,29.9,39.4,42.4,36,42.8,37.1,42.1,28.6,30.5,27.4,30.5,18.3,18.3,35,35,7.1,7.1,7.1,7.1,40.3,37.3,40.3,37.3,41.4,45.2,48.5,54.9,20.2,34.3,36.8,3.8,34,24,57.2,24.9,50.7,50.7,35.9,40.4,35.8,38.9,21,20.7,67,67
|
||||
218,ECU,Ecuador,Latin America & Caribbean,17980083,16890,44.1,51.2,51.2,56.7,50.7,50.3,84.2,84.2,64.4,66.3,93.4,47.8,50.1,50.1,33.8,39.2,53.7,63.9,52.4,52.4,73.3,73.3,96,96,0,0,68.4,30.1,80.3,77.6,74.8,71.3,81.6,76.7,87.3,78.5,61.6,66.5,42.5,42.5,76.4,76.4,65.4,69.9,0,0,64.8,74,72.6,83.4,80.8,91.6,48.6,43.2,41.5,85.7,100,100,92.5,87.9,31.8,68.1,33.9,100,51.1,50.1,37.5,37.9,46.5,40.3,39,34.2,50.2,69.7,37,38.7,37.5,35.5,48.9,48.9,25,29.8,36.8,36.8,42.4,44.3,37.2,37.3,33,29.3,31,39.1,79.8,65.3,15.4,23.9,68.3,69.1,55.2,50.8,16.1,14.3,56.1,63.5,47.6,62.7,50.3,64,55.3,62.3,50.1,62.3,36.4,32.4,47.5,44.4,58.9,49.5,14,11.8,34.6,48.5,34.6,48.5,36.5,52.3,37.6,64.3,35.6,62.1,36.8,3.8,32.8,45.2,29,59.1,48.7,49,32.6,48.8,33.7,50.3,16.1,20.9,77.4,77.4
|
||||
818,EGY,Egypt,Greater Middle East,114535772,21610,40.5,43.8,46.9,51.7,48.4,47.4,30.2,30.2,27.5,27.5,100,100,22.5,22.5,41.7,41.7,42.7,42.7,44.6,44.6,82.8,82.8,98.7,98.7,75.4,68.7,93.9,90.1,64.1,64,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,30.1,32.4,58.6,41.9,36.6,34.5,29.1,31.2,26.8,23.3,36.9,64.9,35.8,73.3,0,0,14.8,0,35,76,45.5,100,51.1,48.9,57.5,54.1,100,73,49.2,44.4,39.9,43.5,57.1,57.1,18.8,18.8,91.8,91.8,32.1,32.1,56.7,56.7,29.5,33.3,28,31.7,6.4,10.3,44.8,58,22.4,26.3,17.3,20.8,6.2,3.5,30.1,27.2,39.3,38.3,47.3,53,39.7,50.3,45.2,54.8,0,1.9,0,1.9,25.2,25.2,53,53,6.8,6.8,6.5,6.5,39.7,40.4,39.7,40.4,36.2,43.8,36.4,49.3,44.3,46.1,36.7,10.6,34.7,60.2,0.8,51.3,19.5,32,33.2,40.7,34.7,40.9,5.2,5.5,71.7,71.7
|
||||
222,SLV,El Salvador,Latin America & Caribbean,6309624,13173,46,41.5,45.8,44.7,36.9,36.3,13.4,13.4,38.9,38.9,50,84.2,35,35,18.1,18.8,22.8,24.1,45.5,45.5,77.9,77.9,85.2,85.2,41,37,84.5,66.7,58.5,57.9,68.1,67,82.8,79.4,NA,NA,59.3,67.6,41.8,41.8,40.2,40.2,24.8,21.6,0,0,28.8,22.6,21.3,30.9,30,18.8,53.7,55.3,91.5,85.1,54.9,54.4,57.7,55.6,94.8,82.3,94.1,100,56.5,62.3,49.8,55.8,49.9,53.3,56.1,50.1,76.3,74.7,21.2,21.2,50,50,37.4,37.4,2.5,2.5,2.5,2.5,28.5,29.8,22,22.9,8.5,10.3,19.7,27.3,53.2,45.9,21.9,32.9,33.9,35.6,47.8,48.3,3.8,2,46.7,50,41.6,48.7,45,50.9,38.2,39.6,37.8,39.6,29.2,27.4,45,40.5,53.1,53.1,1.4,1.4,61.1,46.4,61.1,46.4,51,40.8,85.6,66.7,94.1,79.8,36.9,3.7,53.3,78.2,69,45.9,49.4,51.1,53.6,44.2,54.7,44.9,35.5,30.5,85.3,85.3
|
||||
226,GNQ,Equatorial Guinea,Sub-Saharan Africa,1847549,21751,44.7,41.6,53,43.7,49,45.6,3.5,3.5,25.3,25.3,0,10.5,33.3,33.3,70.1,70.1,59.2,59.2,100,100,85.7,85.7,97.3,97.3,40,40,72.2,8.8,84.1,82.7,61.2,66.6,76.9,75.6,74.4,68.9,64.3,59.8,42.5,42.5,80,80,45,44.7,39.7,7.2,85.4,95.7,32,17.1,52.2,43.9,63.3,97.4,90.1,22.3,74.1,69.3,NA,NA,81.1,18.5,100,16.8,41.5,43.7,32.3,34.5,100,100,54.8,64.9,39.4,39.4,34.8,34.8,52.8,52.8,34.8,34.8,31.1,31.1,31.1,31.1,32.9,33.3,33.3,32.4,19.2,14.9,34.4,37.1,56.8,32.1,92.7,95,65.8,66.4,57.7,53.5,18.7,18.3,30.7,35.3,25.7,36.3,26,34.7,38,39.5,37.1,39.5,26.4,26.4,64.7,64.7,0,0,1.4,1.4,42.1,45.5,42.1,45.5,42,54.9,38.8,59.9,41.5,55.9,36.8,3.7,29.5,54.9,100,0,49,49.2,35.5,48,26.7,47.7,26.9,35.6,69.9,69.9
|
||||
232,ERI,Eritrea,Sub-Saharan Africa,3470390,1832,28.9,28.6,27.3,25.1,18.5,16.8,0,0,0,0,NA,NA,3.1,3.1,0,0,0,0,0,0,0,0,65.5,65.5,68.7,61.4,95.9,86.3,27.2,26.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,63.1,66,0,28.4,100,100,9.4,15.7,100,100,49.3,57.1,64.6,56.3,35.4,12,57.7,30.6,58.1,52.2,96.3,74.5,33.9,31.9,19.3,20.5,75.9,100,95.9,90.4,15.1,13.2,7.6,7.6,76.2,76.2,0,0,0,0,0,0,23.2,24.6,26.2,26.9,37.9,40.6,4.2,5.9,29.4,26.3,46.5,46.7,67.6,63.7,60,52.2,20.2,22.9,13,15.9,9.7,14.9,10.9,16.6,24.7,27.7,21.9,27.7,21.3,21.3,52.2,52.2,0,0,1,1,36.6,38.1,36.6,38.1,55.6,44.4,100,100,35.3,38,NA,NA,40.1,40.5,30.5,29.2,0,0,22.9,24,45,43.9,35,34.3,84.8,84.8
|
||||
233,EST,Estonia,Eastern Europe,1367196,49700,60.6,75.3,75.8,76.6,77.1,78.8,94.9,94.9,82.6,82.6,66.2,69.3,48.4,48.4,79.6,95.9,60.7,66.2,95.6,95.6,88.2,88.2,92,92,95.2,95.2,69.3,43.3,11.5,12,39.4,28.9,NA,NA,NA,NA,44.9,25.9,35.8,35.8,30.3,30.3,69.9,70.4,80.1,73.2,55.6,56.6,90.8,90.2,74.1,69,68.4,27.7,87.6,91.5,42.3,46,44.5,52.5,83.5,100,100,100,71.1,71,38.5,51.6,51.3,54.7,70.7,68.7,69.8,92.8,74.5,72.2,25.2,25.7,88,83,83,82,36,36,60.8,63.9,57,60.9,58.8,69.3,45,55.4,68.4,77.8,25.4,22.8,34.7,37.3,57.8,62.6,64.4,70.6,71.8,71.9,69.5,70.4,73.1,72.9,63.9,68.8,59.3,68.8,62.6,65.1,35.8,34.4,87.4,98.7,77.1,79,37.3,82.8,37.3,82.8,45.9,100,25.4,100,27.3,100,27.9,49.1,40.3,45.3,30.4,100,50.3,49.6,33.5,78.9,23.7,69.6,25.1,100,99.1,99.1
|
||||
748,SWZ,Eswatini,Sub-Saharan Africa,1230506,12963,40.8,38.5,39.9,38.7,30.5,30.7,NA,NA,NA,NA,NA,NA,35.2,35.2,15.2,17,13.3,14,26.6,26.6,79.5,79.5,98.1,98.1,39.6,39.6,34.5,26,64.7,65,41.1,44.6,NA,NA,NA,NA,32.1,49.1,34.9,34.9,41.3,41.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,83.2,75.3,70.4,69.5,82.3,82,100,62.8,89.4,87.7,52.3,51.7,33.3,26.4,100,100,71.5,71.8,64.6,64.6,14.5,14.5,65.7,65.7,11.9,11.9,6.4,6.4,6.4,6.4,21,21.9,23.2,22.5,26.7,24.5,6.6,12.1,35.9,23.6,55.3,62.9,35.8,36.5,43.4,40.9,19.9,16,14.2,18.1,10,17.3,10.9,18.7,18.2,25.6,13.2,25.6,22.8,22.8,55.8,55.8,0,0,1.2,1.2,60.1,52.9,60.1,52.9,40.8,42.6,72.1,75.3,100,81.1,NA,NA,41,50.2,41.9,70.9,49.8,50.1,49.9,48.3,52.6,49.6,43.6,42.9,85.2,85.2
|
||||
231,ETH,Ethiopia,Sub-Saharan Africa,128691692,4045,32,35.8,42.3,45.9,44.1,46,NA,NA,NA,NA,NA,NA,31.2,31.2,45.2,51.4,41.2,56.5,29,29,30.4,30.4,8.8,8.8,50.4,50,95,78.1,23.2,23.1,58,63.8,69.3,67.5,56.9,63.3,65.2,71.3,31.9,31.9,71.7,71.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,44.8,53.9,79.4,70.1,84.5,74.7,30.4,41.4,34.4,59.1,52.8,58.8,41,48.2,90.2,87.2,65.6,62.8,40.6,65.3,10.8,10.8,100,100,2,2,0,0,0,0,23.5,25.1,24.6,25.4,30.6,33,4.8,7.4,45.4,30.6,61.9,63.3,70.3,69.4,36,36,14.1,14,14.9,18.5,11.1,17.2,12.5,19.4,29.7,33.1,26,33.1,36.1,36.1,89.2,89.2,0,0,1,1,22.8,28.9,22.8,28.9,23.2,22.6,100,100,9.6,23.1,NA,NA,6.9,18.8,35.3,43.2,43.4,41.8,11.3,23.6,28.6,34,9.4,8.3,84.5,84.5
|
||||
242,FJI,Fiji,Asia-Pacific,924145,16003,48.1,45.8,39.6,36.9,17.9,16.2,4.3,4.3,16.8,16.9,80.6,99.5,0,0,7.5,7.5,8.1,8.1,5.5,5.5,59,59,99.3,99.3,5.7,0.1,90.9,64.9,40.2,38.4,79.2,75.3,92.8,87.6,NA,NA,80.3,72.8,40.8,40.8,83.7,83.7,76.4,75.8,53.9,33.9,41.7,40.6,100,100,100,100,48.9,51.6,81.8,70.7,78.7,75,92.8,87.8,100,66.2,100,71,62.6,63,71.9,68.2,58.6,44.2,27.3,30.3,73,73,42.4,42.4,47.9,47.9,49.1,49.1,36,36,36,36,54.8,55.7,61.2,61.7,100,100,10.2,15.8,77.2,66.2,91.8,93.8,63,64.1,95.5,94.2,46.2,47.8,34.3,36.1,33,37.5,32.7,35.1,56.7,59.6,54.5,59.6,45.2,44.7,58.5,57.5,100,100,4.4,4.2,55.6,51.1,55.6,51.1,60.5,51,100,87.6,47,61.1,36.8,3.8,68.6,75,56.8,59.9,49.7,49.8,52.6,50.9,53.4,51,45.5,45.3,86.2,86.2
|
||||
246,FIN,Finland,Global West,5601185,67077,70.5,73.7,70.1,68.4,61.8,58.7,64.7,64.9,44.4,44.7,69.4,64.2,27.4,27.4,64.1,67.9,33.9,42.4,84,84,100,100,100,100,97,97,72.5,13.5,40,38.9,61.1,60.8,NA,NA,81,93.2,45.9,31.6,41.3,41.3,50.9,50.9,89.6,90.4,88.6,80.8,94.2,95.8,94.4,91.4,100,100,59.4,25.6,92.1,92.8,36.4,51.6,49.6,62.3,100,100,100,100,68.5,66.6,43.8,37.7,51.6,50.4,60.6,59,94.5,100,84.1,84.5,32.4,32.4,99.9,100,84,85,72.6,72.6,82.5,85.6,78.5,82.2,73.4,81.9,90.6,95.3,62.5,79.6,21.9,20,64,69.3,62.7,66.9,71.3,76.7,93.4,95.2,89.2,93.5,93.3,96.3,95.2,99.3,91.9,99.3,69.5,68.4,27.1,21.5,100,100,96.7,99.4,61.3,71.8,61.3,71.8,62.7,73.7,52.5,68.5,80.4,100,41.2,64.5,57.3,54.4,63.5,100,49.4,49.3,58.2,66.6,45.6,56.9,28.1,100,87.5,87.5
|
||||
250,FRA,France,Global West,66438822,67669,65.6,67.1,68.3,68.4,60.2,61.6,47.4,48,57.4,58,43.3,44.1,71.5,71.5,67.6,71.6,64.4,83,68.4,68.4,45.3,45.3,0,0,59.6,46.2,80.7,72.8,27.1,27.6,64,58.6,NA,NA,NA,NA,66.3,65.3,49.3,49.3,43.5,43.5,45.7,43.2,23,20,31.5,39.9,42.6,51.5,37.8,48.9,53.6,44.7,93.3,92.8,61.4,60.8,55.9,53.1,100,100,100,100,77.1,72.8,65,63.7,65.2,57.4,56.6,59.8,95.1,88.6,84.2,84.2,33.8,33.8,100,100,82,82,80.3,80.3,67.9,71.5,61.3,65.2,36.3,49,86.8,91.5,55,57.5,13.8,19,50.4,62,53.6,60.6,56.8,57.4,84.4,85.9,82.8,86.7,83.2,85.3,89,94.8,84.4,94.8,56.6,59.6,24.4,23.9,92.6,100,70.8,75.2,59.6,61.3,59.6,61.3,63.4,60.9,63,59.1,67.9,69.1,36.2,73.3,71.4,70.1,100,100,51.2,51.1,57.4,59.1,50.5,53.6,10.3,12.8,77.7,77.7
|
||||
266,GAB,Gabon,Sub-Saharan Africa,2484789,24129,46,53.1,57.2,64.8,63,63.9,0,2.2,40.9,50,28.9,45,50.5,50.5,87.1,87.1,72.9,73.2,90.5,90.5,47.2,47.2,84.2,84.2,84.9,84.9,94.4,77.8,68.5,65.8,75.6,80.5,88.8,89.1,76.7,81.2,82.1,80.2,47.9,47.9,90.7,90.7,46.2,47.5,54.1,37.2,88.2,88.1,51.4,33.9,35.8,35,41.5,70.5,48.8,98.9,84.1,86.4,100,100,20.1,100,11,100,28.5,28.2,29.2,24.9,80.3,55.6,18.6,32.3,27.5,27.6,42.5,42.5,62.1,62.1,45.8,45.8,35.9,35.9,35.9,35.9,31.5,32.1,31.4,30.6,15.6,15.1,29,35.1,67.9,42.2,71.4,67.4,70.1,69.4,51.8,48.6,11.1,8.8,29.6,34.1,23.9,31.9,27.1,35.6,38.1,41.4,35.6,41.4,29,29,71.1,71.1,0,0,1.4,1.4,40.8,52.8,40.8,52.8,51.4,57.5,61.4,71.8,100,81.4,36.7,3.8,17.9,55.1,14,81.8,49.7,49.8,51.5,47.3,52,47.8,42.5,32.3,67.6,67.6
|
||||
270,GMB,Gambia,Sub-Saharan Africa,2697845,3491,36.5,37.1,40.1,35.5,40.6,38.3,0.7,0.7,56.4,56.4,NA,NA,40,40,30.9,30.9,17.9,25.3,15.5,15.5,52.2,52.2,99,99,87.4,87.3,93,41.8,0,0,41,49.9,NA,NA,NA,NA,43.7,55.4,37.3,37.3,48,48,90.8,91.1,NA,NA,98.6,100,75.7,73.9,95.8,100,1.7,78.9,48.9,26.4,50.9,51.2,66.2,65.7,20.2,18.9,67,21.1,40.3,33.7,30.5,15.4,100,96.6,67.4,55,51.1,37.8,9.5,9.5,92.6,92.6,0.7,0.7,0,0,0,0,37.5,40,44.3,46.9,96.7,93.2,4.4,5.2,41.5,28.9,50.2,58.3,32.4,29.8,54.8,51.9,28.8,29.7,21.2,24.9,17.7,23.6,19.4,25.8,22.7,23.4,23,23.4,32.6,32.6,80.4,80.4,0,0,1,1,29.6,37.2,29.6,37.2,34,26.6,100,99.3,0,43.7,NA,NA,44.4,44.5,41.5,52,0,0,21.6,33.8,30.2,41.5,39.5,40.1,94.5,94.5
|
||||
268,GEO,Georgia,Former Soviet States,3807492,29530,41,46.9,45.8,50.2,42.6,44.3,46.4,46.4,14.3,14.3,33.5,67.3,28.5,28.5,24.5,28.7,26.7,36.5,31.9,31.9,89,89,98,98,79.1,76.6,92.4,85.7,53.1,53.8,87.9,85.1,NA,NA,88.4,84,95.2,97.4,61.6,61.6,77.9,77.9,70.9,61.2,NA,NA,48.4,38.1,79.5,67.6,81,76,39.6,17.9,48.3,73.9,49.6,47.3,60,56.9,17.4,68,75.8,88.5,24.8,26,16.7,19.2,77,51.7,55.3,42.1,14.8,24,34.3,38.2,51.3,51.3,32.1,37,32.1,37,34.5,34.5,39.7,42,33,34.5,41.6,39.9,13.1,25.5,48.3,41.8,33.2,26.5,44.3,44.8,62.7,65.3,40,35.2,66.7,70.6,59,66.6,64.9,73.3,32.1,37,27.1,37,35,36.6,43.1,42.8,79.4,88,4.6,4.6,34.8,46,34.8,46,24.8,38.1,11,32.7,64.3,81.7,56.7,19.7,46.8,77.8,17.5,88.2,52,52.1,31.2,40,31.9,38.7,27.4,27.7,63.4,63.4
|
||||
276,DEU,Germany,Global West,84548231,72661,70.5,74.6,80.9,80.5,81.9,82.5,85,85.3,95,95.1,38.2,43.2,76.3,76.3,94.5,98.3,100,100,76.7,76.7,15.9,15.9,0,0,92.8,91.6,74.3,71.9,17.9,18.5,64,38.5,NA,NA,NA,NA,68.7,37.1,49.9,49.9,22.8,22.8,37.5,36.4,8.2,0,32.9,48.6,6.9,26.6,35.7,49.6,60.6,52.8,89.2,92.6,56.9,51.7,65.1,58.9,92.1,100,100,100,78.7,78.8,65.5,67.1,50,51.3,66.8,65.4,95.2,98.4,90.7,90.9,25.7,25.7,99.3,99.3,96.8,97.3,97,97,70.9,75.4,61.9,66.9,36,50,92.8,96.7,45.4,43.5,20.9,26.9,51,62.1,53.8,61.3,59.4,60.4,94.9,97.9,90.9,97.7,92.3,98.1,90.1,94.6,86.5,94.6,65.9,67.4,20.3,19.7,100,100,94.4,98.9,54.4,64.9,54.4,64.9,55.3,68.3,38.3,56.9,86.8,87,54.1,55.7,65.6,85.7,86.6,97.8,52.3,52.1,53.5,62.6,42.1,53.4,3.3,14.9,78.4,78.4
|
||||
288,GHA,Ghana,Sub-Saharan Africa,33787914,8260,34.8,36.6,45.5,46.9,48.1,45,0,0,0.1,0.1,100,56.7,40.8,40.8,79.2,79.2,49,49,90.4,90.4,69.4,69.4,92,92,46.4,45.5,78.1,42.1,17.5,16.9,41.6,25.1,65.4,35.9,NA,NA,39.9,8.5,24.5,24.5,45.2,45.2,57.2,58.6,37.1,45,21,22.2,68.7,72.7,73.9,78.4,49.2,35.8,50.6,78.3,69.3,68.5,83.6,84.6,0,57.4,19.3,100,61,70.7,46.1,51.9,100,100,68.8,64.9,61.4,89.4,14.5,14.5,65.5,65.5,12.1,12.1,6.3,6.3,6.3,6.3,29.7,31.9,30.6,32.4,58.4,49.5,5.7,9.4,51.3,35.9,56.8,55.6,62.6,60,43.8,39.6,22.1,17.4,23.6,27.4,19.7,26.6,21.1,27.9,36.1,39.5,33.2,39.5,31.5,31.3,60,59.9,28.1,27.5,4.8,4.7,22.7,24.7,22.7,24.7,21.3,26.5,55.6,65.8,0,9.1,37.6,4.1,26.6,30.4,28.3,26.9,46.2,49.2,21.2,23.6,23.4,25.9,19.3,16.6,82,82
|
||||
300,GRC,Greece,Eastern Europe,10242908,43800,58.7,67.4,65.6,67.9,62.9,62.7,72.2,72.2,39.7,39.7,43.4,47.2,36.5,36.5,56.4,56.5,100,100,85.2,85.2,75.6,75.6,18.5,18.5,57.7,56.6,90.6,85.1,44.9,44.8,68.2,58.2,NA,NA,NA,NA,73.9,60.7,48.1,48.1,66.1,66.1,42.8,47.8,45.8,30.2,36.2,46.2,50.8,49.7,37.4,51.1,59.3,73.6,68.9,88,23.5,24,37.4,38.4,79.7,98.7,100,100,63.8,61.4,55.9,52.2,100,100,55.5,50.1,62.7,72,84.3,85.4,18.1,18.1,93.4,94.7,93.4,94.7,78.1,78.1,61,61.9,53.3,53.8,44.9,41.2,71.3,73.7,30.8,44.8,19.4,26.6,32.5,37.4,58.1,63.3,46.6,45.9,91,92,82.9,85.3,95,96.4,62.9,67.3,58.4,67.3,38,39.4,27.4,26,100,100,17.7,22.4,46.1,71.3,46.1,71.3,51.4,69,38.6,64.8,50.5,96.8,14.3,58,58.6,69.1,65.3,100,51.3,52.7,47.7,66.7,41.3,60.8,17.5,100,89.9,89.9
|
||||
308,GRD,Grenada,Latin America & Caribbean,117081,20306,45.6,46,39.2,40.4,20.6,21.4,0,0,1.8,3.4,100,100,NA,NA,11.2,14.2,27.3,27.3,60.6,60.6,28.3,28.3,74.6,74.6,0,0,NA,NA,84.8,81.5,62.6,63.1,NA,NA,NA,NA,63.1,76.2,40,40,44,44,83.5,84,NA,NA,53.4,50.8,100,100,100,100,48.6,24.6,73,76.5,47.8,42.5,NA,NA,36.5,87.3,82.8,72.6,48.3,51,43.8,50.6,59.7,55.8,4.1,21.6,63.3,63.3,46.8,46.8,23.4,23.4,55.6,55.6,44.5,44.5,44.5,44.5,64.3,66.1,72.5,74.4,100,100,39.4,47.1,74.5,82.7,56.3,51.7,83.8,88.4,83,84.7,90.4,92.3,53.8,55.2,51.7,55.7,52.7,54.9,33.1,36.9,30.3,36.9,38.8,38.8,48.3,48.3,96.8,96.8,0.2,0.2,38.5,36.7,38.5,36.7,36.1,39,32.2,36.9,46.9,43.6,0,20.3,12.5,50.2,55.6,50.7,52,51.5,23.7,30.3,24.1,29.9,51.1,50.5,56.1,56.1
|
||||
320,GTM,Guatemala,Latin America & Caribbean,18124838,15390,35.3,32.6,42.8,38.6,38.2,36.8,53.8,53.8,17.8,19.2,89.6,20.6,57.6,57.6,31.9,33,65.5,66.6,35.6,35.6,46,46,88,88,11.3,7.7,0,0,82.5,78.7,46.6,33.3,39.2,41.1,61,32.2,31.7,29.5,19.8,19.8,38.5,38.5,40,39,70.9,32.3,42,35.2,21.3,44.1,31.2,38.2,66.4,54.8,63.5,49.8,79.9,80.9,63.9,60.9,65.5,37.8,70.4,53.4,59.2,56.8,62,64.7,44.9,44.3,50.2,49.5,47.4,53.1,28.7,28.7,25.3,25.3,35.5,35.5,23.9,23.9,23.9,23.9,22.2,24,19.2,20.1,7,10.9,11.9,15.8,58,59.5,34.3,38.6,37.7,37.9,41,43.1,3,0.7,27.4,31.4,25.5,31.8,25.3,31.1,33.9,38.2,32.9,38.2,24.3,24.3,58.6,58.6,1.7,1.7,1.3,1.3,34.7,30.6,34.7,30.6,49.3,33.3,85.9,56.1,18.5,26.7,36.9,3.8,22.1,32.2,21.7,17.2,46.7,48.2,38.3,32,40.1,33.3,22.5,19,73.2,73.2
|
||||
324,GIN,Guinea,Sub-Saharan Africa,14405465,4321,39.1,36.2,51,47.4,64,61.4,81.4,81.4,7.7,7.7,100,100,67.1,67.1,58.5,65,95.6,95.6,69.6,69.6,33.4,33.4,91.1,91.1,65.9,65.6,87.1,25.4,66.8,65.7,39.7,30.6,69.1,56.4,NA,NA,44.2,5,8.3,8.3,48.8,48.8,43,38.2,53.9,55.4,39.7,28.6,12.4,25.6,52.8,43.7,52.5,48.7,44.9,33.8,62.5,66.9,80.3,85.6,34.7,28.7,20.5,22,42.4,45.9,38,41,100,100,95.6,81.4,21.1,31.6,9.6,9.6,95.7,95.7,0,0,0,0,0,0,29.7,32.4,34.5,36.8,71.6,67.4,2.5,4.6,40.6,27.6,61.8,62.4,62.2,62.7,35.2,38.9,5.5,4.5,15.1,20.1,10.5,19,11.6,20.8,20.9,23.2,19.3,23.2,39.3,39.3,95.5,95.5,1.8,1.8,1.8,1.8,28.7,22.2,28.7,22.2,35.9,7.1,100,49.3,0,26.6,36.8,3.8,14.3,22.2,40.3,56,47.8,48.3,6.2,16.8,25.2,31.4,21.8,20.6,73,73
|
||||
624,GNB,Guinea-Bissau,Sub-Saharan Africa,2153339,3110,42.1,41.6,48.7,44.8,54.9,54.2,47.7,47.7,21.4,21.4,100,100,63.3,63.3,60.5,79.4,71.9,87.9,23.4,23.4,46.2,46.2,96.7,96.7,70.5,70.4,60.5,0,59.3,56.6,39.2,33.7,65.5,50.9,NA,NA,33.9,17.3,11.8,11.8,56.3,56.3,64.8,67,46.7,49.2,100,100,43.3,14.4,100,100,35.9,19.5,57.5,35,50.4,45.3,65,64.3,36.2,35.9,79.7,26.1,44,42.2,28.6,28.2,81.2,67.5,90,76.3,40,40,10,10,100,100,0,0,0,0,0,0,33,35.4,41.2,42.9,87.8,83.5,1.3,3.2,36.8,24.4,74.1,71.3,54,51.6,51.5,51.4,14.7,14,14.8,19.8,9.7,19,10.5,20.4,13.4,16,11.7,16,25,25,61.5,61.5,0,0,1,1,39.4,41.8,39.4,41.8,41.8,39.2,100,100,24.4,37.1,NA,NA,36.3,43.6,53.1,67.9,48.2,48.8,21.3,29.3,38.8,43.6,39.3,39.6,88.8,88.8
|
||||
328,GUY,Guyana,Latin America & Caribbean,826353,91380,46.4,48.6,53.3,56.2,57.2,56.1,NA,NA,0,0,NA,NA,96.9,96.9,38.3,38.3,27.8,27.8,100,100,71.3,71.3,98.5,98.5,62.5,59.4,89.2,79.8,44.1,41,81.4,79.8,92.2,89,78.3,74.6,88.7,84.4,48.2,48.2,95.8,95.8,26,31.1,55.4,50.9,29.1,29.6,19.4,22.5,22.9,25.9,55.4,57.8,35.2,61.2,100,100,84,87.1,31.4,47,40,62.4,77.6,74,57.2,55.3,66.6,100,69.4,51.4,77.9,100,27.3,27.3,52.7,52.7,29.8,29.8,20.3,20.3,20.3,20.3,53.7,55.8,63.9,65.4,100,100,18.6,27.5,100,100,39.3,37.1,97.3,99.3,78.5,77.2,23,16.1,38.4,42.2,35.5,43.5,35.4,41.4,16.2,20.3,13.8,20.3,31.1,31.1,49.6,49.6,55.1,55.1,0.5,0.5,29.4,30.6,29.4,30.6,43.4,19.6,32,0,22.3,59.9,NA,NA,36.5,44.9,51.1,39.6,49.7,49.6,31.8,30.3,34.1,24,36.6,32.8,35.5,35.5
|
||||
332,HTI,Haiti,Latin America & Caribbean,11637398,3039,28.5,36.2,29.2,36.8,26.7,30.6,42.1,52.6,22.3,26.8,50,50,45.4,45.4,11.5,21.2,6.9,25,30.9,30.9,0,0,62.9,62.9,13.7,10.5,63.7,22.2,70.1,67.7,56.6,51.4,64.8,53.3,NA,NA,55.9,52.6,48.5,48.5,40.1,40.1,87.6,86.8,64.1,47.1,NA,NA,100,100,100,100,53.9,47.2,13,55.7,27,22.9,38.4,31.6,41,57.9,0,64.8,50.9,50.6,30.9,27.5,41.2,52.9,93.3,98.3,53.7,53.7,10.7,10.7,89.9,89.9,4.2,4.2,0,0,0,0,20,22.1,23.2,25,37.1,38.4,2.3,3.4,34.4,35.9,15.5,14.4,73,75.9,57.2,59.9,33,33.8,14.6,18.1,10.5,17.4,11.3,18.6,6.4,8.4,5,8.4,21.6,21.6,52.3,52.3,1.2,1.2,1.1,1.1,34.8,47.7,34.8,47.7,30.2,51.2,100,100,29.2,42.2,NA,NA,38,47,26,42.4,43.9,48,31.8,42,37.7,48,27.8,30.2,94.3,94.3
|
||||
340,HND,Honduras,Latin America & Caribbean,10644851,7605,37.4,40.2,47,49,51.8,51,76.9,76.9,24,25.6,51.5,11.1,64.7,64.7,60.5,61.9,59.3,60.2,85.3,85.3,39.9,39.9,95.3,95.3,20.4,16.7,11.1,0,80.4,77.5,36.2,20.2,42.4,27.1,28.8,0,41.7,25.5,35,35,44.7,44.7,55,45.3,43.6,29.2,53.2,37.4,63.6,53.1,65.2,55.1,61.6,18.3,53,87.8,74.1,74.4,86.5,89.8,45.2,77.9,55.2,100,39.3,38.6,36.7,34.9,48,40.5,41.2,41.3,45.6,40.9,28.3,28.3,47.1,47.1,34.7,34.7,19.5,19.5,19.5,19.5,21.8,22.8,18.3,18.7,11.2,14.5,9.7,11.5,40.4,33.1,34.4,41.1,52.1,54.5,56.5,61.9,0,0,34.3,37.8,29.3,36.1,32.5,38.9,17.5,18.7,17.5,18.7,25.7,25.7,50.5,50.5,25.3,25.3,1.2,1.2,35.6,41.2,35.6,41.2,38.1,44.1,66.5,77.7,27.9,38.4,36.8,3.8,25.8,34.1,17.1,84,49.4,48.6,30.5,37,36.2,41.4,24.8,24.8,79.8,79.8
|
||||
348,HUN,Hungary,Eastern Europe,9686463,49150,61.6,60.1,73,73.8,66.9,67,NA,NA,NA,NA,NA,NA,38.1,38.1,83.3,83.3,75,75,83.6,83.6,8,8,38.5,38.5,71.7,71.6,74.9,77.2,6,6.3,53.7,50.1,NA,NA,NA,NA,55.5,47.4,70.6,70.6,22.5,22.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,94.1,93.3,60.8,56.8,67.7,62.9,100,100,100,100,75,69.2,64.5,67,94.6,89.7,73.7,75.6,59.8,67.1,75.8,87.1,49.3,49.3,76.6,83.5,76.6,97.9,96,96,43.7,48.1,31.9,38.7,22.5,35.5,36.5,43.6,27.6,30.8,25.9,27,24.4,31.8,52.9,58.3,48.5,49.8,76.3,74,83.1,76.9,80.5,72.1,57.3,61.9,54.4,61.9,54,51.7,35.5,32.7,100,98.3,49.4,47.3,59,49.2,59,49.2,63.8,47.5,62.3,37.4,65.8,73.3,14.6,33.7,80.5,37.2,30,100,53.6,53.6,64.2,46.4,60.1,41,100,19.4,58.4,58.4
|
||||
352,ISL,Iceland,Global West,387558,80000,62,64.3,56.8,60.9,54,54.8,46,46.1,29.2,29.2,34.8,40.9,63,63,46.7,49.7,44.6,47.6,32.2,32.2,100,100,99.7,99.7,62.6,62.5,100,100,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,57.6,47.5,17.9,39,67.6,78.5,41.9,30.9,48.6,45.1,54.2,48.1,63.5,89.8,33.5,46.6,40.5,54.6,100,95.2,10.1,100,33.6,36.5,0,0,29,28.9,29.8,46.8,69.4,69.4,76.7,76.7,20.4,20.4,86.9,86.9,79.7,79.7,79.7,79.7,87.4,89.3,88.3,89.7,100,100,92.6,96.6,51.7,58.8,32.7,29.6,74.6,87.8,70.1,75.2,94.3,95.8,93.5,95.2,89.3,93.7,93.5,96.2,88.2,95.7,82.2,95.7,40.1,39.3,19.3,21.2,94.5,84.9,33.6,34.7,48.5,48.2,48.5,48.2,43.5,49.9,19.3,28.4,63,50.9,18.3,45.8,54.2,49.2,100,100,NA,NA,40.1,48.2,26.9,35.8,36.7,38.8,46.2,46.2
|
||||
356,IND,India,Southern Asia,1438069596,11940,23.5,27.6,28.1,30.5,13,11.4,1.6,1.9,0.2,0.2,100,100,0,0,0.9,0.9,0.7,0.7,0.8,0.8,88,88,94.5,94.5,3,0,85.7,61.1,15.6,15.7,75.4,73.8,76.2,71.9,90.2,90.4,74.2,63.6,56.6,56.6,70.9,70.9,37,37,84.1,81.3,54.1,58.9,22.1,16.8,25.1,19.5,45.2,40.8,31.9,55.3,32.5,25.1,29.9,22,22.8,66.4,30.6,57,59.7,65.1,45,51.6,38.1,39.4,62.9,45.8,76.7,88.8,29.9,29.9,71.8,71.8,32.7,32.7,19.2,19.2,19.2,19.2,11.4,13.3,5.9,6.8,0,0,5.8,10,0,0,33.8,37.9,11.1,8,3.7,0,20.4,17.8,20.2,25.6,14.8,25.3,16,25.8,26.3,28.2,25.7,28.2,31.3,31.8,64.3,65.6,9.3,9.3,9.3,9.3,26.4,35,26.4,35,24.1,37.1,20.3,42.6,39.6,39.2,0,18.3,21.3,31.2,15.6,56.8,48.5,49.2,19.6,31.1,26.1,34.9,0,0,70.6,70.6
|
||||
360,IDN,Indonesia,Asia-Pacific,281190067,17520,28.1,33.8,31.7,39.3,25.5,31.5,24.9,28.6,11.3,13,84.6,96.1,14.9,14.9,3.3,43.2,38.2,38.2,43.7,43.7,77.8,77.8,97.4,97.4,31.2,19.6,34.5,0,93.7,91.8,41.2,52.7,47.5,65,63.1,60.9,16.7,31.8,36.9,36.9,66.1,66.1,40.2,39.9,64.1,60.3,60.2,58.3,27.2,26.3,33.1,31.7,50.2,31.1,21.5,46.2,90.6,92.6,67.1,62.3,0,38.9,27,41,74,72.2,50.5,52.5,74.6,49.3,65.2,59.3,97.3,99.3,36.9,36.9,39,39,45.2,45.2,29.8,29.8,29.8,29.8,20.6,25.7,16.9,22.8,24.2,23.2,9.6,17.5,37.4,28.6,40.8,47.1,18.2,17.6,26.2,40.1,8.7,8.8,29.3,33.4,26.7,36.6,24.5,31.3,26.9,30,24.5,30,26.7,26.7,48,48,31.1,31.1,3.1,3.1,28.8,32.1,28.8,32.1,30.2,34.7,23.2,30.7,9,25.9,37.4,8.5,25,21.3,48,100,45,44.2,22.9,28.9,25.2,29.6,0,0,55.3,55.3
|
||||
364,IRN,Iran,Greater Middle East,90608707,20370,41.6,41.6,45.5,45.9,43.8,42.9,66.4,66.4,22.2,22.2,100,100,10.1,10.1,24,25.4,23.2,23.2,68,68,65.5,65.5,80.2,80.2,61.9,54.2,81.6,69.6,45.7,44.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,62.4,63.3,55.5,76.5,61.5,62.6,50.2,58.4,70.2,67.8,42.6,20.2,63.9,70.7,18.7,2.4,25.2,5.2,44.1,80.1,78.3,88.1,41.2,37.8,26.9,31.6,49.4,83.7,54.4,50.2,28.7,35.1,28.1,28.7,51.8,51.8,30,31.5,21.8,21.8,21.8,21.8,40,41.6,36.6,36.9,19.8,15.8,56.8,66.6,32.6,29.3,21.8,21.8,11.7,6.7,33.3,22.9,24.4,22.6,60,65.6,52.2,61.5,59.6,68.3,22.8,26.8,20.8,26.8,31.7,31.7,52.4,52.4,19.8,19.8,17,17,37.1,35.1,37.1,35.1,37,44,12.7,22.6,40.6,35.4,35,14,100,35.9,37.8,68.7,52.9,53.4,25.4,30.5,26.5,30.4,0,0,37.5,37.5
|
||||
368,IRQ,Iraq,Greater Middle East,45074049,15260,24,30.4,27.4,33.1,21.4,20.2,0,0,0.4,0.4,NA,NA,8.8,8.8,12.8,12.8,1.9,4.9,17,17,68.1,68.1,78.2,78.2,38.4,31.6,94.7,87.1,41.5,38.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,74.2,72.7,NA,NA,72.3,68.2,81.9,75.8,82.7,76.8,100,47.1,8.7,57.3,10.2,0,26.6,9.6,0,51.1,34.9,84.6,52,49.6,28.5,31.5,53.6,52,90.5,84.2,41.4,53.2,48.9,44.1,64.9,55,27.1,28.9,71.8,60.5,28.2,28.2,30.9,32.6,26.7,27.7,10.4,4.2,31.7,51,36.6,35.8,26,24.1,14.8,10.7,36.8,33.4,22.6,23.2,52.1,57.7,46.5,58.2,48.7,57.3,24.1,24.3,22,24.3,10.9,8.6,22.5,16.7,0,0,4.8,4.8,13,24.6,13,24.6,35.3,38.7,21.4,26.8,0,12.1,36.7,5.5,0,24.7,36.4,48.1,0,0,14.6,19,20.1,25.5,5.4,4.1,36.7,36.7
|
||||
372,IRL,Ireland,Global West,5196630,133550,63.4,65.7,61,67.5,50.7,62.9,30.9,33.1,42.6,46.9,70.9,60.7,46.8,46.8,25.1,93.8,43.9,47,70.3,70.3,66.9,66.9,95.5,95.5,60.2,60.5,96.8,97.7,70,72.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,62.2,40.6,41.2,14.6,40.3,43.6,59.5,28.9,60.6,56.8,42.1,52,86.6,87.3,15.5,22.4,19.3,25.3,100,100,100,100,75.7,72.9,50.6,46.6,29.6,23.5,82.8,80.9,89.9,100,72.7,73.8,10.4,10.4,93.5,94.8,62.1,63.6,94.3,94.3,77.2,79.8,74.7,76.8,62.3,67.1,87.8,93.5,57.6,85.4,20.2,25.6,55.2,61.3,67.2,72.5,80.3,83.4,87.1,88.4,78.2,82,90.9,92.7,84.1,93.2,78.6,93.2,56.4,60.7,22.9,19.8,95.8,98.7,70.1,82.6,55.6,51.1,55.6,51.1,61.1,50.5,47.1,32,64.3,51.5,48.5,80.9,60.9,47.9,100,100,55.8,52.5,59.6,51,46.4,36.9,29.2,20.5,48.3,48.3
|
||||
376,ISR,Israel,Greater Middle East,9256314,54446,47.6,48.1,45.1,43.6,31.4,30,0,0,50,50,57.7,100,18.7,18.7,32.8,34,68.3,69.5,23.3,23.3,46.2,46.2,92.1,92.1,10.4,10.7,54.4,0,48.1,47.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,23.7,27.5,NA,NA,19.3,25.4,11.8,22.1,15.7,25.6,51.2,75.5,61.7,65.2,31.2,18.3,51.4,32.4,52.2,46.4,99.3,100,63,45.3,35.9,24.5,40.4,51.6,37.2,45.2,92.5,65.6,90.3,90.5,32.9,32.9,97.7,97.9,96.8,97,92.3,92.3,60.3,62,52.3,53.7,36.7,33.3,83,88.5,35.9,41.6,17.7,24,0,0,27.9,26.9,38.5,41.4,78.7,80.2,74.5,77.9,79.7,81.7,94.3,100,89.9,100,36.6,37.1,21.2,19.2,100,100,20.4,23.5,40.7,43.3,40.7,43.3,42.2,60.6,24.3,51.5,21,2.1,0,14.6,62.3,42.7,100,95.2,58.1,11.4,36.1,48.9,27.9,41,15.3,18.6,55.8,55.8
|
||||
380,ITA,Italy,Global West,59499453,62600,55.8,60.5,58.4,63.4,53.5,58.9,66.3,66.8,37.3,41.8,44.1,45.3,55.4,55.4,26.7,57.6,57.4,72.1,71.9,71.9,70,70,44.7,44.7,63.4,57.8,77.5,59.4,34.3,34.4,65,55,NA,NA,NA,NA,77.8,61.3,48.5,48.5,36.5,36.5,35.5,34,20.8,11.3,45.3,48.8,28.2,35.5,24.4,30.1,67.3,63.1,72,89.5,38.3,32,51.3,41.9,86.5,100,100,100,62.1,56.4,45.5,41.7,46.4,43,65.7,61.2,72.8,70.3,70.9,73.9,28.5,28.5,86.9,86.9,62.5,70,82.6,82.6,61.1,63.9,49.1,52.3,29,31.7,75.6,79.4,43.7,38.6,24.8,34.3,41.5,50.8,45.3,54.7,45.3,44.8,97.9,98.2,94.7,95.6,100,100,74.9,79.6,71.1,79.6,52.5,57.5,27.5,28.1,90,90.9,58.7,70.2,47.5,53.2,47.5,53.2,54.1,55.9,42.7,45.4,55.4,51.9,23.9,40.5,76.8,56.7,64.1,100,53.4,52.9,54.5,54.6,47.7,48,9.3,9.2,67.6,67.6
|
||||
388,JAM,Jamaica,Latin America & Caribbean,2839786,12283,47.7,48.5,49.9,49.7,39.7,38.9,76,76,19.2,20.3,50,50,44.5,44.5,31.2,32.4,62.1,63.4,58.1,58.1,52.2,52.2,52.2,52.2,0,0,45.3,0,80.6,77.8,65.2,68.7,74.8,83.2,NA,NA,58.5,64.3,45.6,45.6,50.1,50.1,84,83.2,82.3,20.6,91.4,88.5,100,100,100,100,46.1,47.5,74.9,77.4,21.9,19.1,25.2,22.6,89.1,77.5,88.7,100,60.3,55.1,67.5,54.4,46.8,42.5,53.6,55.6,53.3,56.6,40.5,40.5,49.1,49.1,46.8,46.8,33.7,33.7,33.7,33.7,38.1,39.5,34,35.8,35.2,35.9,25.9,29,49.3,50.9,27.3,33.8,38.1,41.2,63.6,67.3,41.7,43.4,53,53.8,51.9,53.4,53.3,54.1,42.2,42.7,43,42.7,25.5,25.5,37,37,50.5,50.5,1.4,1.4,52.4,54.3,52.4,54.3,52.1,58.8,63.9,75.3,61.2,54.4,30.6,24,71.6,51.8,56.6,41.1,47.1,50.2,50.6,56.8,52.7,58.1,36.3,41.5,90.2,90.2
|
||||
392,JPN,Japan,Asia-Pacific,124370947,54910,57.6,61.7,56.9,59.9,46.8,47.5,42.6,42.9,55.9,58.7,33.7,37,25,25,45.3,58.9,88.5,95.1,26.9,26.9,68.8,68.8,78.5,78.5,27.3,17.7,70.8,43.3,72.8,72.5,79.6,80.1,NA,NA,92.8,98,81.4,76.4,47.3,47.3,57.4,57.4,41,48.5,32.6,49.8,40.5,44.2,39.8,40.7,50.8,54.5,47.7,58.4,72.5,86.4,17.8,17.9,19.8,18.6,100,100,59.3,100,61.9,63.3,48.9,50.3,24.6,25.1,29.4,38.5,89.9,89.8,76.6,78.4,24.6,24.6,89.9,91.7,77.8,80.6,70.7,70.7,65.4,67.4,57.4,59.9,40.5,42.2,83.2,86.7,59.2,62.9,12.7,17.7,29.2,33.9,50.2,58.9,40.2,43.9,77.5,78.7,77.9,80.5,75.8,77.5,100,100,98.8,100,72.5,73.6,38.8,39.6,99.3,100,92.7,94.4,52.2,59.7,52.2,59.7,49.9,63.1,29.8,48.4,70.8,72.3,62.5,40.6,71.1,63.3,100,100,51,50.5,47.9,61.1,38.4,52.8,0,10,76.9,76.9
|
||||
400,JOR,Jordan,Greater Middle East,11439213,11380,37.7,47.5,36.7,50.1,30.5,32.9,NA,NA,0,0,NA,NA,31.6,31.6,5.2,13.5,3,12.2,14.9,14.9,57,57,87.1,87.1,87.6,87.4,82.5,62.3,64.9,64.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,96.2,95.7,NA,NA,100,100,100,100,100,100,49.2,26.1,13.4,85.5,11.3,4.6,27.4,21.1,21.8,100,52.1,100,34.1,38.1,31.5,31.1,51,88.4,52.6,49.9,29.1,35.9,72.5,73.3,53.4,54.3,63,63,89.3,91.2,62.1,62.1,44.6,46.6,41.1,42.9,22.6,20.4,65.2,74.2,33.6,41.1,12,18.6,0,0,37.2,32.3,37.1,36,62.3,64.3,61.1,65.6,60.1,63.5,38.8,42.8,33.8,42.8,27.3,27.3,47.4,47.4,29.4,29.4,6.2,6.2,33.2,44.6,33.2,44.6,42.1,58,50.7,78.1,0,15.7,36.7,3.8,20.2,42.4,100,100,0,0,33,45.3,34.1,47.1,21.9,24.3,78.7,78.7
|
||||
398,KAZ,Kazakhstan,Former Soviet States,20330104,43610,43.3,47.5,51.5,49.2,50.2,50,87.6,87.6,41.7,41.7,50,50,12.6,12.6,57.4,58.3,32.3,32.6,34,34,31.9,31.9,16.1,16.1,63.7,62.3,93.3,88.9,22.4,22,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,76.9,61.7,35.7,42.1,44.8,54.2,48.9,55.5,100,73.4,43.2,44.7,28.7,37.4,100,95.1,87.6,66.4,18.2,38.7,32.2,32.6,0,0,57,56,11.3,13.2,48.9,48.9,46.6,50.8,41.1,44.5,43.5,48.2,31.1,45,26.4,22.4,43.3,41.6,32.4,33,62,64.5,53.4,53.1,66.7,73.1,56,68.4,62.5,76.3,51.6,58.6,45.1,58.6,28.5,30.8,48,47,31.1,34.2,7.8,13,28.6,42.3,28.6,42.3,38.3,51,8,25.1,14.9,44.1,0,41.9,52.3,20.4,0.3,96.4,43.2,45,21.1,38.9,14.6,31.1,4.6,8.1,40.1,40.1
|
||||
404,KEN,Kenya,Sub-Saharan Africa,55339003,7520,37.3,36.9,48,49.1,44.7,43.9,58.6,58.6,20.3,20.3,43.4,59.9,38.4,38.4,51.3,54.4,40.1,40.2,54.6,54.6,31.2,31.2,5.5,5.5,34,24.9,90.1,80.7,14.9,15.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,73.9,77.1,64.1,78.7,65.5,70.9,100,90.6,74.2,78.2,30.5,21.4,60.5,72.6,78.6,70.2,84.8,74.9,13.8,45.1,40.9,100,49.3,44.2,42.7,44,54.1,44.9,85.2,83.8,38.8,27.8,38.2,38.7,48.5,48.5,7.5,8.5,76,76,0,0,27.5,27,27.1,25,31.3,32.4,6.6,8.8,58.6,31.1,60.6,64.5,49,48,28.4,26.5,18.4,17.4,16.8,21.2,12.2,20.1,13.4,22,42.2,45.7,39.6,45.7,59,51.8,100,87.7,74.9,73.4,10,5.2,29,26.5,29,26.5,25.1,24,75.4,73.3,5.4,17.2,0,24.2,0,13.2,19.4,30.1,40.9,46.7,19.5,24.6,25.1,29.3,13.6,11.8,76.9,76.9
|
||||
296,KIR,Kiribati,Asia-Pacific,132530,3612,45.2,44.1,46.8,45,33.2,31.4,23.6,23.6,8,8,99.8,100,NA,NA,NA,NA,57.7,58.3,29.7,29.7,NA,NA,NA,NA,27.9,18.9,NA,NA,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,92,91.4,79.3,94.1,82.7,82.2,100,100,100,100,62.1,16.4,86,81.1,89.1,94.3,NA,NA,100,89.9,87.5,69.7,58.3,58.6,23.1,23.8,100,100,77.8,77.8,81.9,81.9,19.5,19.5,81.9,81.9,20.4,20.4,6.3,6.3,6.3,6.3,45.9,46.2,54.9,54.5,100,100,2.5,5.1,27.9,14.6,82.5,72.9,100,100,95.4,97.6,100,100,19.6,21.6,17.1,21,18.4,22,48.6,50.3,47.3,50.3,18.8,18.8,42.3,42.3,0,0,4.7,4.7,42.6,40.9,42.6,40.9,40.5,37.9,100,95.7,28.9,30.2,NA,NA,32.6,37.8,41.7,66.7,NA,NA,34,31.7,43.6,40.3,64.8,62.8,96.6,96.6
|
||||
414,KWT,Kuwait,Greater Middle East,4838782,49736,46.8,44.9,56.7,54.8,50.7,50.6,0,0,6.5,7.4,50,100,44.2,44.2,71.2,71.5,54.8,55.3,86.8,86.8,87,87,99.8,99.8,61.8,55.3,81,67.5,48.3,49,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,19.6,23.3,NA,NA,0,0,21.1,25.8,24.5,29.1,47,63.6,63.4,51.8,0,0,0,0,42.2,44.9,72.9,79.3,62.9,59,44.5,37.1,16.2,7.7,84.3,75,78.4,78.4,87.4,87.4,30.9,30.9,100,100,88.7,88.7,88.7,88.7,50.1,50,41.6,41.5,12.4,7,76,82.5,54.7,62.4,5.2,9,0,0,34.7,30.2,13.7,11.1,75.3,76.5,77.5,81.5,72.1,73.1,54.6,60.3,48.8,60.3,59,42.6,28.9,17.2,79,59.6,79,59.6,28.5,24.9,28.5,24.9,36.6,38.6,2.1,4.7,47.8,42.6,36.2,5.8,20.5,29.9,0,75.5,NA,NA,27.6,22.1,5.2,1,11.7,9.7,0,0
|
||||
417,KGZ,Kyrgyzstan,Former Soviet States,7073516,7773,29.5,42.2,35.5,43.3,37.5,36.5,NA,NA,NA,NA,NA,NA,36.3,36.3,13.7,13.7,11.7,11.7,13.7,13.7,64.1,64.1,74.8,74.8,64.2,63.6,91.2,83.9,39.8,40.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,30.3,77.3,3,2.6,31.3,27.6,0,100,33.1,79.4,54.7,51.9,43.4,39.5,72.9,59.9,80.7,51.8,42,63.6,22.7,22.7,75.9,75.9,26.9,26.9,8.7,8.7,8.7,8.7,32.2,36.7,26.2,30.6,37.4,45.7,7.3,14.2,12.4,21.5,32.3,25.5,37.5,38.8,61.9,61.9,45.5,45.8,53.3,59.4,43.4,54.8,49.5,62.4,38.4,43.1,34.2,43.1,16.2,14.8,37.2,33.9,0,0,3.2,3.2,17.5,45.4,17.5,45.4,25.1,56.2,33.1,89.6,19.4,22,NA,NA,14.9,35.2,0,52.4,46.5,47.6,14.9,39,23.9,45.9,24.5,28,79.1,79.1
|
||||
418,LAO,Laos,Asia-Pacific,7664993,9727,24.2,26.1,35,40,39.1,50.8,NA,NA,NA,NA,NA,NA,32.9,32.9,44.8,66.2,37.3,63,63.2,63.2,74.4,74.4,97.3,97.3,44.2,43.4,0,0,93,90.5,31.8,24.5,52.3,40.4,35.9,25.7,24.6,2,14.2,14.2,55.8,55.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,16.2,16.2,54.2,56.1,53.3,55.5,30.8,16.6,15.4,0,75.4,78.5,59.8,78.1,65.9,45.5,72.2,72.2,84.4,84.4,20.9,20.9,51.2,51.2,24.7,24.7,11.8,11.8,11.8,11.8,16.5,19.2,11.8,13.7,5.3,9.9,2.7,6.8,22.9,22.2,49.8,49,47.3,49.4,18.9,34,0,0,26.2,32.3,19.6,30.9,21.7,33.3,19.1,21.9,16.7,21.9,42.6,42.6,94.9,94.9,12.1,12.1,5.5,5.5,13.4,9.6,13.4,9.6,0,0,0,0,21.8,28.8,NA,NA,14.3,24.4,22.6,52.3,47.9,48,19.9,0,24.6,0,24.9,16.5,27.6,27.6
|
||||
428,LVA,Latvia,Eastern Europe,1882396,45450,59,59.9,70.3,68.6,69.3,68.3,86.8,86.8,42.9,42.9,39.1,37.2,42.6,42.6,83,84.2,60.3,60.4,95.2,95.2,71,71,55.7,55.7,96.3,96.3,63.2,44.2,10.2,11.3,39.8,31.8,NA,NA,NA,NA,35.9,26.5,50.7,50.7,20.6,20.6,72.6,69,43.2,34.4,37.7,54.9,88.4,87.4,96.1,81.2,41.6,51,86.3,82.4,43,46.8,46.6,52.1,89.8,100,100,77.9,67,64.4,45.5,60.3,55.7,52.3,78.1,73.4,63.5,65.8,68.1,69.8,22.2,22.2,75.1,77.3,74.8,76.9,59,59,49.3,52.8,40.9,45.1,29.4,37.8,33.7,45.1,69.7,70.5,31.9,30.6,53.5,56.6,53.2,59.3,59.6,67.5,74.9,75.1,73.3,73.8,76.2,75.9,63.4,68.1,58.4,68.1,36.1,42.8,28.2,24.7,77.5,84.4,23.2,40.1,49.8,52.4,49.8,52.4,52,52.4,51,51.7,46.2,73.1,6,48.1,34.3,43.5,66.3,60.7,49.6,49.1,48.2,51.3,44.4,46.5,32.3,33.7,67.2,67.2
|
||||
422,LBN,Lebanon,Greater Middle East,5733493,11784,32.2,40.1,34.1,38.1,25.3,24.1,5,5,0,0,100,50,14.9,14.9,4.2,4.3,5.8,6.2,4.3,4.3,42.6,42.6,79.2,79.2,87.1,87.6,88.9,63.2,48.9,49,64.5,48.5,NA,NA,NA,NA,68.5,45.7,60.8,60.8,37.5,37.5,96.9,96.2,NA,NA,92.6,94,100,100,100,100,55.4,60.2,22.8,62.5,29.4,23.2,33.9,27,0,68,59.3,72,53,50.6,39.2,44.6,29.6,52.7,41.3,37.5,62.6,61.8,47.3,47.3,38.6,38.6,55,55,42.8,42.8,42.8,42.8,44.1,46.3,37.9,40,29.6,27.4,52.8,62.1,26.6,35.5,5.2,0,10,10.1,31.7,33.5,41.4,45.7,60.7,63.2,66.4,73.2,53.4,56.6,58.3,62.4,54.1,62.4,36.6,36.6,40,40,57,57,23,23,19.3,38,19.3,38,32.8,47.8,16.2,39.3,16.5,22.3,36.6,4.5,11.5,38.2,0,67,48.5,45.5,26.6,37.3,24.8,37.7,21.4,23.1,56.2,56.2
|
||||
426,LSO,Lesotho,Sub-Saharan Africa,2311472,3260,36.6,36.6,45.7,46.3,51.5,60.4,NA,NA,NA,NA,NA,NA,25.6,25.6,0.5,60.1,69.9,69.9,66.6,66.6,64.1,64.1,77.2,77.2,81,80.9,93.6,53.2,79.6,79.7,69.7,59.6,NA,NA,NA,NA,81.1,64.8,53.3,53.3,45.9,45.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,55.4,35,59.9,54.7,64.2,61.4,0,29.6,0,31.3,35.4,34.3,11.2,11,59,73.9,84.9,84,33.5,33.5,9.6,9.6,84.8,84.8,2.9,2.9,0,0,0,0,13.7,12.8,14,11.8,11.5,6.1,2.4,4.3,24.5,15,67.7,68.4,16.6,15,53.5,47.7,26,20.2,7.6,9.4,5.4,8.9,6.1,9.8,13,16.1,11.8,16.1,40.4,40.4,100,100,0,0,1,1,41.9,41.7,41.9,41.7,33.7,43.7,49.4,67.5,47.6,55.8,35.2,0,56.1,60.9,41.9,60.2,0,0,24.2,32.5,38.9,49.3,35.7,38.2,85.4,85.4
|
||||
430,LBR,Liberia,Sub-Saharan Africa,5493031,1902,32.9,34.1,32.2,30,26.5,26.5,NA,NA,4.5,4.5,100,100,24.6,24.6,14.7,15,12.7,12.7,10.3,10.3,55.9,55.9,96.9,96.9,72.4,72.4,18.7,0,70.7,69.5,50.7,42.4,69.3,49.2,77.9,68.4,33.9,6,32.1,32.1,47.8,47.8,65.9,68.8,65.3,49,99.3,96.2,13.8,21.1,99.6,99,41.2,45.7,42.3,34.1,69.3,69.8,90.2,91.6,23.5,26.8,61.7,22.7,38.4,34,24.3,22.2,91.2,70.6,72.7,49.8,36.2,36.2,9.9,9.9,99.1,99.1,0,0,0,0,0,0,32.7,36.3,39.5,43.2,78.4,76.9,4.3,5.6,60.2,33.7,77,73.6,79.2,79.8,54.7,51.7,19.7,16.2,14.2,18.7,9.9,18,10.6,19.1,24.2,25.5,22.6,25.5,28.2,28.2,69.6,69.6,0,0,1,1,34.2,38.4,34.2,38.4,29.5,39.9,100,100,6,21,NA,NA,0,35.2,38.1,55.7,48.3,48.4,18.5,28.4,29.1,40.3,35.6,36,96.1,96.1
|
||||
440,LTU,Lithuania,Eastern Europe,2854099,56000,59.6,63.9,70.3,74.3,68.6,74.8,61.1,61.1,100,100,55.5,68.8,41.7,41.7,53.4,94.5,56,56.5,86.9,86.9,50.2,50.2,41.8,41.8,96.7,96.8,89.5,77.5,0,0,51.1,45.9,NA,NA,NA,NA,45.4,38.6,79,79,16.4,16.4,81.1,80.1,NA,NA,40.5,74.4,77,81.2,92,89.8,64.3,28.9,81.3,83.2,45.7,50.8,50.2,57.1,80.4,78.2,98,100,66.7,67,42.6,61,64,78.5,74.2,76.1,65.7,68.3,70.8,73.8,35.2,35.2,76.7,79.8,72.4,77,75.9,75.9,53.4,58.8,46.3,53.2,31.3,45.2,46.4,58.4,58.3,73.8,32.2,29.5,49.1,50.9,56.2,60.4,67.5,69.7,72.1,72.5,69.2,70.4,74.1,73.9,65.4,71.5,60.2,71.5,56.8,61.3,30.5,28.1,94,93.5,64.6,78.3,48.4,52.4,48.4,52.4,50.6,48.9,42.7,40,70.1,100,0,45.8,38.1,55.5,45,41.9,48.9,48.6,47.7,50.9,41.9,43.9,27.5,28.8,61,61
|
||||
442,LUX,Luxembourg,Global West,665098,154915,71.7,75,83.1,83.6,82.7,84.9,NA,NA,NA,NA,NA,NA,89.6,89.6,93.1,100,100,100,89.2,89.2,0,0,0,0,94.3,93.2,60.8,66.4,31.3,32,52.6,46.1,NA,NA,NA,NA,57.5,51,51.4,51.4,11,11,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,95.1,94.3,66.5,61,77.1,70.2,100,100,100,100,66,62.8,51.4,44.8,36.2,35.1,84.6,80.3,72.7,75.9,92,92.4,40.8,40.8,99.4,99.4,98.5,99.4,87.8,87.8,69.7,74.6,61.2,67.1,34.4,52.9,89.5,94.5,43.2,45,13.9,23.1,59.5,68.2,51.5,58.6,57.7,58.5,91.4,93.2,88.9,93.2,90.8,93.2,90.1,97,85.3,97,63.3,63.8,12.9,13.6,100,99.8,95.4,95.9,55.8,62.4,55.8,62.4,59.6,67.4,38.7,49.3,50.7,68.7,29.7,53.2,66.5,78.6,100,100,49.8,49.6,55.9,62.5,34.9,48.4,36.5,47.5,74.1,74.1
|
||||
450,MDG,Madagascar,Sub-Saharan Africa,31195932,1990,29.2,29.9,28.4,27.7,27.4,27,15.9,19.8,10.5,14.8,81.9,55.2,34.5,34.5,38,38.9,23.5,24.3,24.5,24.5,68.3,68.3,91.1,91.1,25.2,14.9,0,0,75.1,75.2,26.7,23.5,41.9,33.8,36.9,20.6,26.5,11.3,20.5,20.5,46.3,46.3,53.8,49.3,94.9,33.1,67,54.9,65.4,51.1,63.5,52.3,44.3,45,29,31.2,81.2,71.2,97.9,85,30.7,24.9,0,18.7,52.5,48.2,47,42.6,100,100,90,80.6,42.7,36,10,10,100,100,0,0,0,0,0,0,26.2,26.5,31.6,30.8,47.7,49.6,2.2,3.5,63.9,31.4,51.3,47.1,82.1,85.7,57,64.2,16.6,18.2,9.7,12.9,6.5,12.1,7.5,13.5,21.8,23.9,20.1,23.9,25.7,25.7,63.4,63.4,0.6,0.6,0.6,0.6,33,36.1,33,36.1,25.2,34.1,100,100,36.9,38.8,36.8,3.8,38.1,75,33.5,8.2,47.6,48.9,24.6,29.9,41.7,44.7,22.2,22.2,92.4,92.4
|
||||
454,MWI,Malawi,Sub-Saharan Africa,21104482,1714,41.3,34.9,57.8,53.8,68.3,68.3,NA,NA,NA,NA,NA,NA,63.9,63.9,79.2,81.4,67.3,77.6,79.9,79.9,24.5,24.5,90.9,90.9,37.5,37.6,96.2,86.5,16.6,17.1,45.2,28.2,70.7,30.7,NA,NA,45.6,26.4,10.6,10.6,57.3,57.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,68.4,47.5,88.2,84.8,100,98.2,45.9,35.3,85.1,42.2,47.1,46.6,43.9,43.6,100,56.3,73.2,65.8,41.2,40.8,21.7,29.4,100,100,16,16,13.2,32.5,0,0,21.1,20.8,21.9,20,24,21.4,3.4,5.3,45.1,36.9,58.1,48.8,64.3,64.6,43,45.5,14.5,15.8,13.4,17.1,9.7,16,11,17.8,27.2,29.9,25.1,29.9,33.9,33.9,83.7,83.7,0,0,1,1,32.8,17.7,32.8,17.7,38.1,15.3,100,100,6.6,2.7,36.8,3.8,0,0,46.8,66.3,37,42.9,20,5.9,33.5,17.7,28.1,22.9,90.3,90.3
|
||||
458,MYS,Malaysia,Asia-Pacific,35126298,43100,34.1,41.2,33.8,39.7,28.3,28.8,6.2,6.2,6.6,12.4,39.8,86.1,14.6,14.6,56.8,57.7,31.3,42.8,40,40,80,80,96.7,96.7,11,0.1,0,0,100,100,25.8,41,36,49.9,33.8,55.9,0.7,14.5,31.1,31.1,50.1,50.1,52.4,52,51.3,51.5,87.9,87.8,39.5,39.4,41.8,41.3,54.5,48.7,31.8,60.7,88.1,88.9,60.9,53.8,28.3,68.6,12.3,48.6,65.2,63.6,56.7,63.9,46,45,14.6,19.7,86.7,83.2,45.1,48,35.3,33.2,75.4,83.1,22.9,22.9,22.9,22.9,39.2,45.7,33.8,43.2,24.2,33.7,52.8,60.2,47.6,35.1,24.6,29.5,35,37.5,31.8,46,0,0,53.5,54,54.9,57.7,52,51.6,48.6,51.3,46.2,51.3,40.1,35.4,31.9,32.7,100,47.2,18.3,32.2,30.2,39.9,30.2,39.9,35.7,41.3,9.6,17.5,52.4,44,19.8,43.5,29.1,64.3,42.4,90.2,46.1,44.8,29.3,36.2,24.6,29.2,5.3,5.1,36,36
|
||||
462,MDV,Maldives,Southern Asia,525994,34322,35.3,38.1,40.5,39.4,15.5,12,0,0,0.9,1.3,81,26.8,NA,NA,NA,NA,1.3,5.9,0,0,NA,NA,NA,NA,59.6,47.3,NA,NA,NA,NA,71.3,72.9,75.3,70.1,NA,NA,77.6,84.5,52,52,NA,NA,90.3,90.5,75.7,52.2,100,99.5,100,100,100,100,54.3,54.9,68.2,65.9,NA,NA,NA,NA,60.5,58,68.5,73.8,48.7,55.8,47,46.4,72.7,73.4,77.8,77.8,54.5,54.5,40.6,40.6,38.4,38.4,45.4,45.4,37.1,37.1,37.1,37.1,45.9,48,46.5,47.8,41.8,44.2,28,40.4,34.7,21.6,93.3,96.9,94.2,97.2,72.2,74.5,91.8,93.5,47.8,51.9,46.8,61.3,41.2,45.7,52.2,57.3,47.6,57.3,13.4,13.4,29.4,29.4,2.7,2.7,2.7,2.7,19.4,27.9,19.4,27.9,24.2,30.4,8.5,18.5,1,15.9,NA,NA,0,28.3,0.9,38.5,49.9,50,16.2,25.2,15.1,23.5,42.2,40,54.6,54.6
|
||||
466,MLI,Mali,Sub-Saharan Africa,23769127,2843,37.1,33.9,45.9,43.2,51.7,50.8,NA,NA,NA,NA,NA,NA,15.9,15.9,70.5,71,18.6,19,16.6,16.6,22,22,92.2,92.2,93.3,92.9,94.7,85.4,27.7,26.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,47.6,36.7,58.4,56.4,89.5,91.2,3,26.3,62.3,32.2,67.9,66.4,41.7,47,100,100,99.1,99.2,73.2,69.4,8.9,8.9,89.4,89.4,0,0,0,0,0,0,35.2,37.5,42.8,44.7,92.3,88.3,3.9,5.5,39.5,19.8,54.1,48.2,84.5,82.6,54.7,54.6,9.4,8.4,14.1,18.3,10.1,17.3,11.1,19,25.9,27.3,25.2,27.3,30,30,74,74,0,0,1,1,25.4,16.5,25.4,16.5,20.9,4.6,83.6,50,19,11.9,36.8,3.8,25.6,7.8,44.1,65.4,0,45.5,15.1,6.4,33.7,25.3,20.5,16.3,70.3,70.3
|
||||
470,MLT,Malta,Global West,532956,75822,62.1,66.6,55.1,65.5,47,67.7,41.7,99.3,56.6,57.9,45.5,36.8,NA,NA,10.3,34.6,94.1,95.2,99.8,99.8,0,0,0,0,75.1,75.1,NA,NA,27.1,26.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,57.3,56.9,11.2,33.6,16.9,33.2,42.2,74.4,0,73,40.2,21.8,83.3,83.3,0,0,0,0,82.3,100,100,100,57.2,43.5,48.5,16.4,23.8,21.6,45.5,39.5,74,74,51.8,51.9,17.8,18.5,100,100,0,0,100,100,68.6,72,66.2,69.9,68.4,69.9,74.5,83.4,44.3,42.6,23.7,27.4,36.3,48.2,67.6,70.5,80.2,80.9,89.7,91.7,83.9,88.9,90.5,93.6,57,63.7,51.2,63.7,27.1,27.1,14.7,14.7,93.1,93.1,6.5,6.5,66.1,63.6,66.1,63.6,56.4,67.2,61.3,78.7,59,34.7,0,44.7,89.2,79.7,33.4,100,100,100,51,63.6,45.4,59.8,43.6,57.7,89.7,89.7
|
||||
584,MHL,Marshall Islands,Asia-Pacific,38827,6688,41.9,42.6,38.2,34.9,14.9,13.4,0.2,0.2,5.1,5.1,100,100,NA,NA,3.8,3.8,2.9,2.9,0,0,NA,NA,NA,NA,49.3,39.6,NA,NA,92.5,93.1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,91.7,86.9,51.6,56.2,71.4,70.6,100,100,100,100,NA,NA,95.1,83,80.4,88,NA,NA,79.6,92.5,100,72.5,77.8,77.8,NA,NA,NA,NA,77.8,77.8,NA,NA,39.8,39.8,54.9,54.9,47.2,47.2,30.8,30.8,30.8,30.8,54.8,55.4,63.1,63.4,100,100,5.8,8,100,100,88.1,78.4,100,100,91.7,93.9,100,100,35.3,36.5,34,36.2,34.9,36.7,41.5,44.2,39.3,44.2,36.4,36.4,57.9,57.9,22.1,22.1,22.1,22.1,35,41,35,41,41.1,46.7,32.8,41.7,0,17.5,NA,NA,9.5,28.3,44,69.9,NA,NA,24.7,33.9,35.7,41.8,60.2,61.2,67.9,67.9
|
||||
478,MRT,Mauritania,Sub-Saharan Africa,5022441,8233,37.7,34.2,37.9,33.7,37.1,36.2,45.4,45.4,20.7,20.7,100,100,0.9,0.9,5.1,5.2,2,2,52.3,52.3,NA,NA,NA,NA,91,90.4,90.1,80.4,58.8,58.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,78.7,77.1,66.3,60.5,14,54.4,74.9,81.7,31.1,95.9,18,63.2,47.8,23,33.2,34.5,57.5,56.1,24.1,14.4,58.9,22.7,41.3,42.1,16.1,21.5,65,62.4,97.2,95.7,38.7,38.7,11.9,11.9,90.8,90.8,7.1,7.1,0,0,0,0,45.7,46.3,54,53.5,100,100,9.3,11.8,61.9,42.4,36.1,35.2,70.9,69.4,69,68.8,28.1,30.7,22.8,27,17.8,25.7,19.5,27.9,40.1,40.8,39,40.8,30.1,30.1,71.6,71.6,4.5,4.5,1.4,1.4,30.5,24.8,30.5,24.8,25.5,17.9,46.9,32.5,36.4,34.7,36.7,3.8,35.1,36.2,37.3,42.5,0,0,27.5,22.9,37,31.5,28.6,25.5,61.5,61.5
|
||||
480,MUS,Mauritius,Sub-Saharan Africa,1273588,32063,44.5,47.3,33.2,34.3,16.4,14.6,0.8,0.8,17.1,17.1,73.9,1.7,NA,NA,11,11,15.7,15.7,6.7,6.7,100,100,99.7,99.7,0,0,NA,NA,79.9,75.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,78.3,75.7,46.4,16.1,87.9,70.9,91.8,87.2,82.3,94.6,34.7,83.7,58.7,69.6,62.8,47,NA,NA,59.2,77.7,49.6,66,64.2,68.6,86.7,92.4,42.4,45.6,0,17.6,67.9,67.9,35.6,36.9,51.6,51.6,25,28.1,40.9,40.9,40.9,40.9,68.1,69.8,74,75.8,88.1,88.5,46.3,56.4,100,100,58.6,60.9,71,79.9,83.9,92.1,95.8,99.8,59.3,61.4,54.6,61.2,59.7,61.6,55.5,57.6,54.1,57.6,38.5,34.8,35.3,34.3,100,100,10.9,2.7,39.7,45.8,39.7,45.8,39.8,50.1,32.8,49.2,44.8,47.1,0,24.4,45.5,51.6,61.4,73.5,48.4,48.1,29.4,43.8,27.5,40.9,33.2,35.2,62.4,62.4
|
||||
484,MEX,Mexico,Latin America & Caribbean,129739759,25560,42.4,44.7,46.5,47.7,33.2,32.5,51.6,55.5,34.3,48.6,29.8,53.7,18,18,27.4,28.3,44.5,46.5,30.7,30.7,34.7,34.7,77.9,77.9,2.9,0,66.7,8.2,82.7,82,60.2,48.8,60.5,52,66.2,45.1,53.3,47.5,43.8,43.8,68.2,68.2,35,38.4,58,52.8,34.2,31.7,44,34.9,47.7,39,51.2,35.6,74.6,89.1,58.2,54.7,60.9,55.1,71,100,67.9,91.9,51.7,56.8,42.3,48.5,64.3,50.1,55,50.1,60.8,68.5,67.3,71.5,35,35,91.7,91.7,57,67.5,43.4,43.4,35,36.9,28.2,29.7,25.5,27.5,30.3,34.4,28.6,36.9,15.9,22.2,0,0,41.7,40.7,11.2,10.6,54.9,58.6,51.5,59.6,51.9,58,46.7,49.1,44.7,49.1,26.3,26.3,31.3,31.3,59.6,59.6,4.7,4.7,42.5,46.4,42.5,46.4,46.3,51.2,38.5,46.1,42,46.3,0,24.9,22.2,38.2,45.2,100,49.5,47.9,39.6,45.4,37.2,42.8,0.2,1.5,61,61
|
||||
583,FSM,Micronesia,Asia-Pacific,112630,4689,40.4,40.6,29.5,27.6,5.3,5,0,0,0,0,100,88.5,NA,NA,0,0,0,0,0,0,NA,NA,NA,NA,0,0,NA,NA,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,84.4,85.3,65.3,66,63.9,64.8,100,100,100,100,29.3,49.2,88.2,76.6,79.3,86.6,NA,NA,100,81.6,100,69.7,46.1,47,43.5,24,38.7,100,0,1.7,84.4,84.4,24.8,24.8,57.1,57.1,27.6,27.6,16.1,16.1,16.1,16.1,55.7,56.5,64.1,64.8,100,100,6.1,8.5,91,100,100,100,100,100,90.5,94,99,100,38.7,39.3,38.5,39.3,38.9,39.3,43.7,45.8,42,45.8,21.8,21.8,49.5,49.5,0,0,4.9,4.9,41.7,44.4,41.7,44.4,51,45.2,82.2,71.6,33.2,37.8,NA,NA,31,37.9,76.7,58.1,59.4,62,39.6,34.3,47.7,43.5,61,58.9,84.2,84.2
|
||||
498,MDA,Moldova,Former Soviet States,3067070,19910,42.8,45.6,44.1,48.4,41.1,53.3,NA,NA,NA,NA,NA,NA,16.2,16.2,16.1,68.4,35.3,35.3,52.9,52.9,NA,NA,NA,NA,90.2,91,57.6,58.8,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,68.7,54.2,43.1,44,46.6,48.7,51.3,60.3,80.9,51.3,47.6,49.9,46.6,55.9,100,66.1,66.4,66,17.4,35.8,23,23.3,2.4,0,36.1,36.1,16.6,18.1,16.6,16.6,37.2,40.6,30.1,34.5,27.5,35.3,14.9,23.4,35.9,57.9,39.8,37.3,30.9,32.1,60.6,65.2,64.5,64.7,61.1,62.2,57.3,59,62.8,64.3,46.9,51.2,42.3,51.2,17.2,16.2,24.8,21.7,31.9,31.8,2.3,2.9,45.6,45.4,45.6,45.4,50,43.2,63.6,51.9,71.4,71,0,23.3,43.4,29.6,33.7,61.5,51.3,55,48,42.2,49.4,42.4,35.5,32.8,72.7,72.7
|
||||
496,MNG,Mongolia,Asia-Pacific,3431932,20510,31.5,37,52.6,54.4,63.1,63.4,NA,NA,NA,NA,NA,NA,26.5,26.5,80.4,82.5,55.3,59.2,73.4,73.4,26.8,26.8,63.3,63.3,85.7,84.9,81.6,58.5,29.5,29.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,41.9,48.2,11.2,21.1,29,41.7,38.7,54.4,23.2,48.6,23.7,29,1.3,0.2,35.6,28.2,96.9,88.1,58.3,33.2,44.1,44.1,42.1,42.1,53.5,53.5,36.9,36.9,36.9,36.9,24.8,28.3,15.2,17.8,0,0,9.2,20.5,29,41,39,33.4,27.8,23.6,64,67.5,58.5,56.7,56.4,61.8,48.3,58.3,53.4,64.1,34.5,41.8,28.1,41.8,11.6,12.3,22.6,24.3,0,0,6.4,6.4,4.6,17.6,4.6,17.6,0,38.2,0,10.1,0,8.1,0,5,28.3,23.4,0,39.6,50,50.6,0,0,0,8.8,11.2,14,1.8,1.8
|
||||
499,MNE,Montenegro,Eastern Europe,633552,33620,48.3,47.6,48.2,50.2,42.2,42.1,14.5,14.5,0,0,100,75.1,65,65,43.9,47.1,27.2,42.5,50.4,50.4,84.1,84.1,86.9,86.9,49.4,48.2,72.2,53.2,40.6,40.5,65.7,67.9,NA,NA,NA,NA,79.4,79.2,40.9,40.9,65,65,30.4,47.9,NA,NA,57.2,46.4,34.4,47.4,37.3,49.7,41.2,44.6,78,87.3,24.4,24.7,23.2,22.7,0,100,0,100,44.6,43,16,17.3,11.1,12.7,75,64.1,62.4,62.4,44.1,44.1,43.8,43.8,61,61,30.6,30.6,30.6,30.6,44.9,48.5,31.1,35.3,23.3,32.4,23.2,28.4,61.7,57.7,48,43.2,38.9,43.6,60.4,64.3,43.5,42.3,94.3,97.5,93.2,100,87,95.8,55,56.1,55.5,56.1,12.7,12.7,25.6,25.6,4.1,4.1,4.1,4.1,51.3,43.1,51.3,43.1,43.1,44.6,35.2,37.5,50.9,39.4,100,20,69.8,51.5,8.6,83.7,51.9,52.7,43.7,41.8,42.1,38.8,40.7,39.3,58.3,58.3
|
||||
504,MAR,Morocco,Greater Middle East,37712505,11100,37.1,39.7,34.8,40.7,22.6,30.4,4.8,5.2,6.6,6.6,100,100,12.6,12.6,4.1,57.9,7,7.1,7.7,7.7,43.5,43.5,66.1,66.1,65.7,62.2,56.7,49.7,56.2,56.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,48.7,49.6,51.6,51,46.4,48.4,28.6,34.5,59.5,63.6,45.4,28.7,59.4,69.1,22.7,27.4,26.6,33.4,38.8,63.8,77.2,89.8,52.6,35.6,39.1,40.4,54.9,71.3,66,62.3,18.5,16.6,50,57.6,61.2,61.2,60,79,40.1,40.1,38,38,42.1,43.8,44.4,44.7,70.9,62.1,23.6,34.3,34.8,27.1,38.3,40.8,27.7,21.9,55.7,48.6,34.4,34.2,43.8,50.6,39,52.6,37.1,49.2,24.9,27.1,23.6,27.1,28.4,28.4,55.2,55.2,18.8,18.8,6.3,6.3,36.5,34.9,36.5,34.9,37,39.7,45.3,50.1,28.7,31.6,36.8,3.8,41.8,39.3,30,39.9,50.2,50.6,31.9,33.9,35,36.6,13.9,12.9,70.8,70.8
|
||||
508,MOZ,Mozambique,Sub-Saharan Africa,33635160,1730,33.6,38.6,48.4,47.8,57.3,57.3,34.2,34.2,16.6,16.6,63,54.7,68.4,68.4,56.6,69.6,86.7,89,81.8,81.8,81.7,81.7,96.8,96.8,38,29.2,80.1,67,63.8,61.5,44.1,41.5,57.2,58.3,NA,NA,40.9,32.5,0,0,69.2,69.2,59.1,66.9,59.3,96.5,31.2,50.3,45,68.3,47.1,69.8,68.3,17.1,49.1,41.3,83.5,79,97.1,91,37.8,4.1,85.3,61,36,40.1,34.2,32.1,100,100,95.2,94.2,23,20.6,9.6,9.6,96.1,96.1,0,0,0,0,0,0,24.6,25,27.9,26.9,35.9,35.2,2.5,4.3,56.9,41.7,56.7,58,79.1,81.2,59.4,62.5,17.3,17.1,16.4,20.5,12.2,19.3,13.5,21.3,13.5,16.7,11.6,16.7,31.5,31.5,78.1,78.1,0.5,0.5,0.5,0.5,18.4,35.7,18.4,35.7,19,36.8,90.7,100,7.1,34.3,70.4,22.5,43.4,46.9,100,28.9,48.2,48.7,10.8,22.9,32.8,42.6,20,20.4,90.4,90.4
|
||||
104,MMR,Myanmar,Asia-Pacific,54133798,5206,28.4,26.9,31.5,26.6,26.5,23.4,4.8,5.1,1.6,2.1,93,98.1,7.1,7.1,32.8,34.3,17.6,19.6,27.7,27.7,54.9,54.9,95.3,95.3,39.8,31.5,71.1,17,69.3,68.4,54.5,51.5,68.2,65.3,65.4,56.7,43.3,32,32.7,32.7,71.7,71.7,38.6,36,72.7,50.5,67.2,55.3,22.1,22.8,24.5,25.3,39.1,56.4,28.8,12.5,70.7,69.9,76.6,80,51,0,28.9,0,68.9,59.6,71.6,54.7,100,62.6,69.2,58,67.1,64.8,12,12,72.4,72.4,12,12,0,0,0,0,15.3,17,8.4,9.1,0,0,2.7,7.1,13.2,11.9,49.7,47.3,51.8,48.2,30.9,37.7,0.2,0,30.6,35.5,25.6,34.2,27.6,36.3,25.7,29.2,22.1,29.2,34.7,34.7,78.1,78.1,7.2,7.2,5,5,34.5,35.6,34.5,35.6,47.2,3.6,100,19.2,36.4,75.1,26.1,27.8,19.8,92.3,49.8,34.7,46.6,47.3,32.4,38.1,41.9,45.7,14.1,14.4,83.3,83.3
|
||||
516,NAM,Namibia,Sub-Saharan Africa,2963095,11730,43.8,43.8,58.6,62,70.4,69.8,7.5,7.5,7.7,7.7,96,73.4,100,100,84.4,84.6,100,100,99,99,46.2,46.2,80.1,80.1,91.2,91.1,80.1,77.2,70.9,72.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,36.7,34.2,11.1,18,59.1,58.6,28.2,20.5,34.3,35.8,52.2,42.1,59.4,84,68.6,65.8,69.2,67.5,48.3,74.9,46.5,100,22.6,25.5,12.9,10.1,72.3,69.9,99.1,97,1,7.5,29.3,29.3,59.6,59.6,32.4,32.4,20.7,20.7,20.7,20.7,25,26.6,26.4,27.4,32.1,33,10,16.2,20.8,20.7,46.5,45.6,68.2,67.8,60.2,59.9,9.2,11.1,16.8,19.8,13.2,19.2,14.2,20.2,31.1,34.3,28.9,34.3,30.5,30.5,72.1,72.1,2.7,2.7,2.7,2.7,36.8,30.3,36.8,30.3,34.4,41.1,46.5,58.5,11.7,22.2,36.9,3.7,0,23.7,4.5,50.1,0,0,20.8,26.6,23.9,31.1,26,27,48.7,48.7
|
||||
524,NPL,Nepal,Southern Asia,29964614,5348,32.2,32.9,47.4,47.4,55.6,55.6,NA,NA,NA,NA,NA,NA,13.8,13.8,48.4,48.8,55.9,56.5,80.3,80.3,53.6,53.6,76.9,76.9,50.6,50.3,97.7,95.9,85.3,84.7,81.1,80.6,93.9,93.4,55.7,67.8,88.3,91.7,57.3,57.3,72.3,72.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,15.7,15.8,21.7,9.5,31,17.1,22.3,32.6,28.3,0,65.3,65.6,76.3,70.7,100,71.7,85.6,67,46.2,59.3,11.4,11.4,81.5,81.5,8.1,8.1,0,0,0,0,13.1,14.4,6,6.2,0,0,4.4,7.1,0,0,55.7,42.7,27.3,23.8,0,0,8.8,7.6,28.2,33.8,22.2,33,23.8,34.4,20.9,21.4,21,21.4,42.7,42.7,98.4,98.4,14.6,14.6,1,1,24.4,25.8,24.4,25.8,5.6,5.2,32.3,31.4,35.1,42.6,NA,NA,18.3,27,37.3,28.6,48.7,49.1,29,27.6,35.8,32.9,20,17.5,82.1,82.1
|
||||
528,NLD,Netherlands,Global West,18092524,83823,63,67.2,64.7,67.8,54.4,61,40.2,77.7,52.9,53.2,32.7,32.6,52.8,52.8,40.2,40.5,53.5,90.1,76.8,76.8,28.3,28.3,61.4,61.4,73.8,69.6,55.6,52.9,38.6,39.5,71.2,62,NA,NA,NA,NA,69.5,64.1,84.8,84.8,5.7,5.7,25,22.5,8.3,0,26.2,29.8,7.4,11.8,34,31.9,66.3,47.9,94.3,92.6,57.7,50.5,70.1,60.9,100,100,100,100,68.4,68,46.4,45.2,16.9,17.8,57.6,55.9,99.5,100,91.4,91.3,19.4,18.1,100,100,99.4,99.5,97,97,70.3,74.2,62.7,67.4,37.4,51.1,92,96.5,49.7,44.1,12.9,20.4,52.8,64.3,56.2,65.3,66.8,70,87.1,88.2,84.8,87.7,87.1,88.5,93.9,99,89.2,99,69.5,69.6,26.2,26.4,100,100,97.5,97.7,54.4,60.7,54.4,60.7,54.8,68.3,39.8,59.1,66.1,100,83.5,17.9,100,61.9,100,100,36.6,40.5,55.5,58.3,43.2,47.9,15.6,18.6,68.7,68.7
|
||||
554,NZL,New Zealand,Global West,5172836,52983,56.6,57.7,51.3,51.3,37.7,39.6,15,27.2,12.6,24.5,83.6,61.2,28.5,28.5,49.4,50.2,86.2,86.8,16.8,16.8,97.1,97.1,99.5,99.5,0,0,80.1,59.4,79.1,81.7,60.3,67.4,NA,NA,81.1,87,53.6,52,45.1,45.1,70.9,70.9,34.3,31.4,54.6,34.4,40.2,38.8,19.4,25.6,33.4,26.7,72.4,54.7,78.7,69.1,72.3,60.2,92.5,85.2,77.5,65.6,100,71.2,75.6,72.9,63.7,52,53.1,50.9,58.8,62.3,100,100,72.7,72.7,20.2,20.2,77.1,77.1,84.1,84.1,61.8,61.8,80.6,81.5,83,83.1,97.8,96.2,80.8,85,74.5,65.2,31.2,40.6,42.8,45.1,90.6,94.6,43.8,53.5,82.4,84.8,80.4,85.6,80.5,84.2,76.5,80.8,73.2,80.8,40,39.5,16.4,15.1,100,100,33.6,33.6,44.6,47.6,44.6,47.6,53.5,53,39.9,39.2,51.1,64.6,29.1,31.8,58,54.1,57.8,68.4,52.6,51.7,44.5,48.5,30.9,35.2,18.8,19.6,47.3,47.3
|
||||
558,NIC,Nicaragua,Latin America & Caribbean,6823613,8950,46.2,47.4,58.6,58.5,65.9,66.1,97.1,97.1,40.6,40.6,50,100,85.3,85.3,72.9,72.9,69.3,69.5,92.9,92.9,66.1,66.1,96.3,96.3,48.2,40.9,0,0,64,61.6,33.8,16.7,43.2,22.8,31.8,5.7,24.7,25.2,0,0,36.1,36.1,51.7,48.9,73.4,67,35.1,43.3,28.3,41.2,43,50.1,60.5,47.6,72.3,82.4,74.3,74.5,71.6,73.7,58,68.1,93.4,100,44.8,51.6,30.3,36.8,56.8,48.3,50.9,58.8,52.1,63.6,41.3,41.3,31.5,31.5,53.7,53.7,33.3,33.3,33.3,33.3,37,39,35.7,36.7,47.1,47.7,13.6,17.6,62.2,58.3,42.1,48.2,37.1,41,69.8,73.5,13.9,12.1,42.4,47,36.4,44.7,40,48.5,41.7,47.6,38.1,47.6,21.6,21.6,49.2,49.2,7.4,7.4,1.2,1.2,34.8,37.6,34.8,37.6,47,45.9,95,92.9,24.2,30.6,36.8,3.7,23.6,30,36.7,41.1,45.4,47.5,31.3,33.3,38.3,39.5,26.6,25.5,72.4,72.4
|
||||
562,NER,Niger,Sub-Saharan Africa,26159867,1978,32.2,39.2,46.1,57,61,69.7,NA,NA,NA,NA,NA,NA,53.9,53.9,54.9,78.1,29.2,59.7,85.2,85.2,36.3,36.3,89.6,89.6,79.4,76.9,85.7,72.2,30.4,28.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,24.2,57.9,52.3,41.7,70,59.7,8.1,48.2,10.5,70.4,57.5,55.9,31.6,33.7,100,100,91,77.7,62.4,65.4,10,10,100,100,0,0,0,0,0,0,28.9,30.4,35.5,36.5,76.1,65.4,4.8,5.7,38.7,26.1,50.4,45.9,70.4,69.7,50.5,51.6,21.4,24.1,8.3,12.2,4.2,11.4,4.7,12.8,23.2,23.3,23.8,23.3,31.9,31.9,78.4,78.4,0.9,0.9,0.9,0.9,13.6,19.4,13.6,19.4,0,33.2,79.4,100,15.3,5.2,36.8,3.8,13.1,3.7,19.8,14.1,0,0,3.6,2.8,30.1,26.9,20.9,17.5,78.8,78.8
|
||||
566,NGA,Nigeria,Sub-Saharan Africa,227882945,6710,32.9,37.5,38.7,43.3,39.5,47.1,NA,NA,0.4,0.4,NA,NA,28.2,28.2,23.2,68.8,44.5,44.5,88.7,88.7,32.9,32.9,41.4,41.4,53.2,52.7,78.4,62,0,0,53.8,32.8,63.1,46.6,64,25.7,52.3,24.4,19.7,19.7,61.8,61.8,58.2,63.4,83.9,47.6,88.5,95.6,38.1,56.9,40.8,57.9,43.4,52.8,32.7,54.4,59.1,53.8,61.1,55.6,39,53.6,72.4,55.1,49.1,47.9,45.5,50,100,100,56.1,47.3,42.7,41.8,13.4,13.4,74.8,74.8,10.6,10.6,3.4,3.4,3.4,3.4,18.2,20,17,18,30.6,18.3,5.8,8.8,36.5,30,41.8,40,55.4,51.2,26.2,20.4,19.2,16.7,9.2,14.4,5.2,14,5.2,14.6,44.7,47,43,47,29.7,29.7,63.7,63.7,19,19,1.1,1.1,36.9,43.9,36.9,43.9,38.6,44.9,91.9,100,59.1,59.6,36,5.8,26.4,30,29.3,41.1,44.8,47.6,41.5,43.7,46.5,47.8,6.2,6.3,88.1,88.1
|
||||
807,MKD,North Macedonia,Eastern Europe,1831802,28720,49,50,54.1,55.1,47.3,52.8,NA,NA,NA,NA,NA,NA,42.3,42.3,34.9,53.2,28.6,44.4,26.3,26.3,76.1,76.1,94.5,94.5,77.3,75.2,80.4,67,30,30.6,66,64.6,NA,NA,NA,NA,65.9,71.1,43.3,43.3,74.3,74.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,87.5,74.7,41.9,43.7,47.2,49.7,77,100,77.4,60.6,42.1,46.7,36.8,37.7,44.9,50.6,53.5,56.7,38.2,51.3,41.9,41.9,62.5,62.5,46.9,46.9,33.7,33.7,33.7,33.7,33.6,39.1,24,29.9,12.4,21.5,19,28.7,34,56.1,35.7,36.8,15.1,21.1,54.7,61.2,40.1,38.6,65.4,70.4,63.1,71,63.3,70,37.4,43,34.3,43,31.6,31.6,42.2,42.2,73,73,0.2,0.2,54.2,51.3,54.2,51.3,53.3,47.5,51.9,42.7,48.4,78.8,22.9,24.4,68.3,79.2,50.5,100,51.2,51.1,48.1,45,47.9,43.5,34.4,32.5,65.5,65.5
|
||||
578,NOR,Norway,Global West,5519167,106540,66.7,70,67.2,72.6,63.9,71.6,93.6,93.9,9.9,11.3,62,62,98,98,19.4,67.4,55.6,58.5,73.4,73.4,99.5,99.5,99.9,99.9,84.2,83.8,83,74,100,100,71,61.4,NA,NA,78.6,72.9,69.3,53.3,43.2,43.2,69.7,69.7,50,54.2,31.6,40.2,73.3,91.9,65.9,50.5,64.4,39.6,48.3,65.7,79.2,90.9,35.1,48.4,48.3,49.2,65.4,98.6,100,100,52.7,52.3,19,18.9,44.7,43.7,32.2,26.8,73.9,97,81.9,83.3,10.1,10.1,99.2,100,84.1,86.7,76.1,76.1,84.5,86.3,81.1,82.9,78.7,85.1,97.1,100,47.5,58.1,22.6,28.9,41.8,54.5,62.9,67.3,59,59.4,96.6,97.6,94.7,97.4,97,97.8,93.9,100,86.8,100,61.3,58.3,14.9,13.2,95.1,90.4,90.9,87.3,51.1,52.6,51.1,52.6,49.3,58.8,30.9,44.4,72.7,51,64.4,52.1,86.2,47.8,94.5,100,49.8,49.7,51.5,53.2,37.6,40.3,20.8,21.7,54.6,54.6
|
||||
512,OMN,Oman,Greater Middle East,5049269,41652,39.3,51.9,48.6,65.6,38.7,56.7,37.8,73.7,26.2,65.8,97,100,42.1,42.1,12.7,19.1,9.3,72.9,53.5,53.5,71.3,71.3,98.6,98.6,68.2,61.6,95.6,61.1,75.8,74.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,93.9,88.8,100,68.8,79.8,83.6,69.1,100,70.4,100,26.5,35.3,32.2,73.6,26.1,13.7,0,0,1.1,73.9,100,100,67.1,70.7,31.2,46.4,49.8,49.3,63.1,62.8,100,100,88.6,88.4,60.7,58.6,99,99,99,99,33.1,33.1,49.7,50.1,47.7,47.5,38.4,34.4,59.3,68.3,34.6,37.2,27,29.5,29.6,24.7,60.3,51.7,36.3,33.6,67.4,69.5,73.8,78.4,61.5,63.5,30,36.7,25.8,36.7,33.8,22.7,36,19.8,32.3,24.7,32.3,24.7,16.3,32.6,16.3,32.6,15.5,47.9,0,20.1,46.7,27.6,31.9,9.4,3.1,25.2,52.3,100,100,0,11.4,32.4,0,18.9,11.7,14.2,21.9,21.9
|
||||
586,PAK,Pakistan,Southern Asia,247504495,6920,29.5,25.5,33.1,29.4,27.6,25.7,0,0,0.7,0.7,50,50,6.6,6.6,28.1,28.3,35.5,35.5,62.8,62.8,34.1,34.1,18.7,18.7,54,41.6,43.6,32.7,38.5,39,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,56.7,62.2,62,67,88.4,91.1,42.9,52.1,44.7,53.4,58.1,45.3,51.6,32.2,20.3,13.2,26.1,21.1,53.5,47.2,51.6,23.1,46.4,46.8,34.1,33.9,46,35.3,34.8,28.9,66.4,68.2,21.1,21.1,55.9,55.9,29,29,7.9,7.9,7.9,7.9,11.2,13,5.7,6.4,0,0,5,8,1.5,0,38.2,43.9,11.8,8.7,2.6,0,17.7,17.2,22.3,28.2,18.1,28.2,18.4,28.2,20.5,22.4,19.2,22.4,29.3,29.3,64.2,64.2,10.8,10.8,3.6,3.6,39.2,30,39.2,30,43.6,35.7,86.2,71.1,23.7,25.1,35,9.9,24,9.2,33.1,29.9,44.9,37.5,32.7,28.6,38.5,33,4,1,76.2,76.2
|
||||
591,PAN,Panama,Latin America & Caribbean,4458759,41292,47.6,52.9,56.4,59.1,59.1,57,76.8,76.8,23.4,23.5,52.4,88.1,68.9,68.9,70.2,71.8,93.1,93.4,84,84,72.2,72.2,97.9,97.9,11.8,8.4,51.2,0,64.9,63.5,67.4,60,77.7,72.3,71.8,66,53.8,48.3,32.5,32.5,63.3,63.3,67.1,71.6,15.9,57,42.8,50,52.7,77.1,67,82.2,81.4,100,52.1,80.9,65.5,65.6,68.2,67.5,33.1,67.5,51.7,100,40.5,50,26.4,31,49.8,41.2,49.7,53.9,53.8,68,42.5,42.9,28.6,28.5,38.3,38.3,53.4,54.4,29.6,29.6,51.2,54.9,53.3,57.5,70.2,70.6,33.5,46.2,70,60.3,33.7,43.6,61.6,65.5,66.5,69.3,31.3,32.5,46,49.1,42.1,48.4,45.2,49.5,58.4,61.6,56.3,61.6,25.7,27.5,38.4,42.3,46,47.5,2.8,2.8,31.3,41.9,31.3,41.9,25.9,45,15.5,47.2,30.3,48.1,36.7,3.7,30,40.9,2,61,48.2,48.8,27.2,43.4,24.3,40.4,24.9,27,65.6,65.6
|
||||
598,PNG,Papua New Guinea,Asia-Pacific,10389635,3542,40.1,36.5,38,35.5,23.1,19.8,0,0,3.5,3.5,100,100,0,0,7.2,8.4,10.3,11.8,3.9,3.9,79.5,79.5,98.6,98.6,51.6,41.2,91.2,50,100,100,67.8,65.3,83,73.3,71.3,59.1,75.6,64.7,49.6,49.6,88.6,88.6,85.7,88.6,84.7,66.6,67.9,75.6,99.1,98.9,97.2,98.2,5.1,87.2,74.3,68.2,100,100,100,100,100,54,90.3,69.6,58.2,61.8,48.2,57.3,100,100,67.9,75.2,63.7,57.5,9,9,86.1,86.1,0.9,0.9,0,0,0,0,37,37,40.8,40,60.4,58.1,1.6,2.9,86.2,95.4,83.1,78.4,45.5,44.7,80.6,77.6,0,0,19.3,21.2,16.8,20.2,18.3,21.9,49.6,51.7,48,51.7,36.8,36.8,71.6,71.6,38.4,38.4,1.2,1.2,46.2,37.7,46.2,37.7,48.6,36.4,99.4,76,100,29,NA,NA,63.3,47.9,34.7,44.9,49.1,49.3,53,33,57.1,37,37.7,27.9,88.7,88.7
|
||||
600,PRY,Paraguay,Latin America & Caribbean,6844146,17360,38.2,39,44.4,43.6,47.9,47.8,NA,NA,NA,NA,NA,NA,43.3,43.3,36.9,36.9,46.8,47.3,61.7,61.7,38.2,38.2,82.6,82.6,84.3,84.2,0,0,46.3,43.1,12.9,20.6,23.7,33.9,0.2,9.2,1.4,18,0,0,63.9,63.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,69,62.3,100,100,100,100,36.7,23.2,91.4,86.3,80.6,71.6,80.9,86.7,52.9,100,85.9,79.7,46.1,50.7,11.5,11.5,46.2,46.2,11.2,11.2,4.8,4.8,4.8,4.8,37.4,39.3,32.3,33.6,46.4,39.6,16.8,24.2,58.8,43.2,20.8,20.8,83.7,83.8,60.2,57.2,0,0,53.2,56.7,47.7,55.1,51.2,57.7,48.9,52.6,47.6,52.6,23.4,23.4,43.5,43.5,26.8,26.8,1.5,1.5,29.4,31.9,29.4,31.9,34.5,25.7,51.6,35.5,13.9,46.1,36.8,4,14.2,38.6,51.8,31.4,46.7,47.3,23.7,34.8,25.5,35.4,19.3,20.2,50.6,50.6
|
||||
604,PER,Peru,Latin America & Caribbean,33845617,18390,42.4,46.6,53.2,56.4,50.2,48.9,92.1,92.1,13.5,16.6,23.2,53.4,48.4,48.4,42.3,46.2,62.5,71.8,65.6,65.6,83.4,83.4,98.4,98.4,15.3,15.2,72.2,10.5,77,75.6,65.2,60.1,73.4,69.1,66,53.9,62.6,57,45.1,45.1,88.6,88.6,75.8,85,52.4,80.8,46.4,62.3,95.2,94.9,95.3,94.8,81.1,69.5,53.7,74.7,100,100,100,99.1,32.9,65.8,77.9,73.7,42.6,45.3,34.8,34.2,52.9,46.8,36,37.4,51.2,59.5,53.6,64,51.9,48.9,53.5,66.9,53.5,66.9,56.3,56.3,35.5,35.6,28,26.6,16,10.5,22.3,33.2,89.1,66.2,6.2,6.2,61.4,56.4,66.6,57.2,11.3,10.9,50.9,55.1,45.2,52.9,49.7,56.6,66.2,68.3,66.3,68.3,25.2,25.9,51,48.6,22.4,28.5,0.7,1.8,31.5,40.7,31.5,40.7,27.5,40.2,29.4,51.7,29.1,48.3,36.7,3.8,33.2,36.6,29.3,81.5,49.2,49.5,29.3,40.2,29.8,40.3,13.4,14,72,72
|
||||
608,PHL,Philippines,Asia-Pacific,114891199,12910,31.7,32,33.9,33.7,22.8,25.6,16.4,19.4,7.2,12.5,67,53.1,28.5,28.5,10.6,26.3,39.4,51.4,37.1,37.1,56,56,78.7,78.7,3,0,58.8,0,77.2,76.3,62.8,50.6,74.7,67.4,66.3,36.3,50.1,49.2,42.8,42.8,59.1,59.1,74.5,76.4,86.6,72.7,85.4,86.2,74.6,76.7,76,77.2,56.2,41.8,43.1,39.7,60.2,61.5,71.6,72.2,53.8,32.8,73.2,35.7,72.1,72.3,79.4,73.7,100,86.1,49.5,49.8,71.4,79.2,10.6,10.6,80.9,80.9,2.8,2.8,2.8,2.8,2.8,2.8,26.9,28.5,21.7,22.8,20.4,17.4,9.4,13.1,54.7,79,22.8,24.2,11.7,11.3,56,61.4,18.9,19,38.5,42.7,35.8,45.5,34.1,40.8,40.3,41.6,39.7,41.6,30.8,30,56.5,55.9,13.7,12.8,13.7,12.8,32.4,32.2,32.4,32.2,37.1,31.4,57.3,46.8,32.9,39,35.7,8.3,50,30.5,27.6,45.2,46.1,48.5,35.4,31.3,37.8,32.8,9,5.9,75.2,75.2
|
||||
616,POL,Poland,Eastern Europe,38762844,54500,62.7,64.4,78.8,79.3,81.3,81.3,82.1,82.2,79.5,79.5,69.9,58.5,66.6,66.6,98.4,99,100,100,90.9,90.9,0,0,0,0,89.5,92.3,87.1,83.6,0,0,56.2,48.4,NA,NA,NA,NA,56,45.3,69.2,69.2,22.4,22.4,58.4,57.8,85.6,50.2,68,78.5,73.8,84.2,32.7,33.6,64.4,34.8,92.8,93.5,55.8,55.8,67.1,65.7,86.9,100,100,100,59.7,68.3,52.5,60.8,39.4,43.1,71.1,72,59.8,76.3,77.2,79.2,37.9,36.3,94.2,96.9,72.6,75.2,67.4,67.4,45.6,49.9,31.6,38.5,13.8,28.8,36.5,46.7,46.4,49.6,16.2,10.8,25.8,34.9,52.8,60,61.6,63.2,84.5,80.7,91.5,83.9,89.5,78.6,60.6,65.3,56.6,65.3,57.9,58.8,41.8,36.9,100,100,53,60,52.3,53.5,52.3,53.5,50.2,49.3,30.1,28.8,93.6,85.3,14.8,100,48,49.6,47.4,64.8,53.8,53.4,45.8,48.7,39.6,39.9,7.5,7.7,53.2,53.2
|
||||
620,PRT,Portugal,Global West,10430738,51260,58.2,62.2,61.4,63.4,59.7,60.4,70,70.1,28.6,30.5,25,26.4,46.3,46.3,61.9,65.2,75.3,76.6,80,80,38.1,38.1,47.9,47.9,68.1,66.7,71.6,64,63.1,63.6,18.7,16.5,NA,NA,NA,NA,17.7,11.4,33.1,33.1,8.6,8.6,36.5,31.1,24.3,5.5,25,21.7,62.8,42.5,49.1,36.7,54.8,49,75.9,88.7,22.8,30.3,24.6,34.4,100,100,100,100,46.1,49.7,13.4,15.1,35.8,35.6,72.4,72.1,53.8,76.1,87.3,87.3,40.5,40.5,97.1,97.1,91.8,91.8,76.8,76.8,64.7,68.2,57.4,61.1,55.7,55,69.3,77,42.2,52.8,13.3,22.8,37.1,44.2,57.9,65.3,47,48,92.3,94.4,87.7,92.5,92.9,95.6,65.2,71.6,59.7,71.6,50.7,50.8,28.9,26.5,95,100,50.4,50.4,48,55.3,48,55.3,61.5,62.3,62.2,63.5,62.2,41.6,0,31,57.9,59.3,93,100,51.8,51.7,61,56.6,56.3,51.9,31.7,24.7,75.2,75.2
|
||||
634,QAT,Qatar,Greater Middle East,2979082,118760,41.5,47.2,54.1,57.4,50.5,50.2,42.9,42.9,36.2,36.2,92.3,84.9,55.3,55.3,13,25.2,44.9,44.9,94.5,94.5,64.1,64.1,98.6,98.6,50.4,39.4,86.3,66.1,54.7,54.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,88.3,88.6,NA,NA,61.7,62,100,100,100,100,0,57.4,41.7,65.3,0,0,0,0,0,56.7,90.6,100,35.5,35.7,6.1,6.7,43.9,30.8,43,39.9,63.3,63.3,86.7,86.7,20.3,20.3,100,100,89.3,89.3,89.3,89.3,49.1,50.7,41.3,42.4,0,0,88.7,96.8,26.4,31.7,5.3,6.6,21.7,19.7,46.1,37,22.1,18.8,73.7,75.1,76,80.6,70.4,71.5,57.4,64.8,46.6,64.8,42,42,30.8,31.3,100,100,24.3,23.6,15.1,28,15.1,28,21,40.7,0,5.8,0,47.2,31.2,7.2,0,14.9,100,91.2,NA,NA,0,30.5,0,0,6.3,10.2,4.1,4.1
|
||||
178,COG,Republic of Congo,Sub-Saharan Africa,6182885,6404,40.2,41.2,55.4,63.8,71.5,71.4,NA,NA,22.7,22.7,100,100,69.8,69.8,80.6,81.4,77,85,75,75,61.8,61.8,87.7,87.7,89.3,89.2,93.3,72.8,62.8,58.8,71.9,67.9,85.3,79.5,74.5,64.4,75.4,64,43.1,43.1,88.9,88.9,51.3,61,NA,NA,71.3,59.7,NA,NA,75.2,63.4,54.2,49,16,76.6,100,100,88.8,97.2,0,68.8,0,75.7,47.5,48.1,36.7,37.6,100,100,56.7,66.1,46,46.8,21.3,21.3,88.4,88.4,21.3,21.3,7.9,7.9,7.9,7.9,16.2,17.5,12.3,12.2,1.3,2.5,6.6,10.3,36.4,22.9,36.8,48.1,49.9,50.5,33.7,32.7,0,0,18.6,24.4,14.6,22.9,16.4,25.4,32.7,35.4,30.6,35.4,37.8,37.8,79.1,79.1,10.3,10.3,10.3,10.3,38.8,29.3,38.8,29.3,34,35,53,55,40.1,25.3,36.7,3.8,27.2,55.4,0,48.9,49.5,49.6,25.3,12.5,37.4,33.2,25.2,23,57.6,57.6
|
||||
642,ROU,Romania,Eastern Europe,19118479,49940,60.2,57.2,67.7,68.4,71.3,71.9,98.8,98.8,90,90,31.2,53.7,43.8,43.8,77.9,78.8,63.5,68.9,81.1,81.1,56.4,56.4,71.1,71.1,63.4,63,69.6,62.3,8.4,8.5,60.2,57.1,NA,NA,52.2,44.1,71.6,72.1,57.3,57.3,59.6,59.6,25.6,24.5,NA,NA,32.3,88.1,40,0,100,1.9,60.2,50,93.3,86.8,53.3,51.3,62.5,60,100,86,100,100,58.2,67.8,43.8,68.5,55.2,51.3,82.4,84.4,32,61.6,44.7,52.5,24.1,25.1,47.8,58.3,46.4,55.2,45.7,45.7,43.1,46.4,34.7,39.4,26.7,29.6,31.9,42.8,44.2,66.6,23.6,24.4,33,38.5,54.1,60,49.1,50.1,69.1,68.5,67.5,65.9,73,70.2,48.6,53.4,44.1,53.4,43.1,42.3,46.6,42.3,87.1,92.7,17.7,17.1,63.1,49.3,63.1,49.3,62.9,54,65.4,51.4,62.6,63,73.7,23.6,54.3,36.1,44.9,58.3,51.1,51.5,59.7,51.1,56.5,46.4,26.2,17.1,67.4,67.4
|
||||
643,RUS,Russia,Former Soviet States,145440500,48960,46.5,46.5,48.2,48.2,41.8,41,10.6,11.4,4,4.4,90.6,85.6,27.2,27.2,45.7,47.2,31.9,34,31,31,79,79,91.8,91.8,82.9,82.4,88,67.3,68,67.1,46.4,43.9,NA,NA,27,18.2,71.7,63.1,49.9,49.9,90.2,90.2,59.9,63,31.5,51.3,28.7,24.5,77,74.4,85.7,84.6,64.6,43.7,67.3,65.2,32.4,46.9,57.1,65.2,70.4,65.1,63.1,69,50.9,62.9,33.6,43.9,73.9,82.5,68.1,53,42.1,84.4,53,53,33.9,33.9,55.1,55.1,55.1,55.1,55.1,55.1,50.7,54.7,46.3,50.5,41.2,48.4,44.4,55,42.2,59.5,22.6,23.7,29.5,31.5,58.2,59.1,57.2,56.3,71.1,73.8,63,68.3,72,77.5,55.3,61.8,48.4,61.8,15.5,15.5,32.8,32.8,4,4,4,4,40.5,36.9,40.5,36.9,47,48.8,21.7,24.2,37.1,32.9,66.6,30.6,38.3,36.2,39.5,40,50,49.9,37.2,38.1,29.7,29.6,0,0,35.7,35.7
|
||||
646,RWA,Rwanda,Sub-Saharan Africa,13954471,3747,33.7,33.4,45.7,44.5,51.8,49.9,NA,NA,NA,NA,NA,NA,34.4,34.4,44.5,44.7,29.4,29.8,57,57,100,100,99.3,99.3,63.1,63.1,97.1,67.3,0,0,67,58.3,94.4,84.4,NA,NA,56.1,39.6,35.7,35.7,39.8,39.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,47.3,51.2,69.8,66.7,80.1,77.2,34,46.2,47.4,47.9,42.1,41.6,50.4,48.8,100,61.7,68.9,65.9,39.7,22.6,8.6,8.6,79.6,79.6,1.6,1.6,0,0,0,0,13.4,14.2,8.8,8.5,0,0,5.3,7,42.4,21.5,46.6,53.6,0,0,11.8,13.1,11.7,14.5,20.5,24.6,15.8,23.2,17.3,25.5,34,36.2,31.7,36.2,15,15,36.5,36.5,0,0,1,1,32.1,32.3,32.1,32.3,32.2,18,100,100,21.8,26.3,31.5,29.2,7,31,32.9,32.2,43.6,47.4,28.8,32.1,40.8,41.1,31.9,30.2,98.3,98.3
|
||||
662,LCA,Saint Lucia,Latin America & Caribbean,179285,27052,48.8,51,45.4,45.1,30.4,30.3,12,12,13.2,13.2,50,70.2,NA,NA,21.1,21.1,52,52,44.5,44.5,0,0,82.2,82.2,31.8,27.4,NA,NA,92.7,91.2,78.5,80.7,87,92.4,NA,NA,76.3,85.5,43.2,43.2,62,62,92.7,94,NA,NA,100,80.5,100,100,100,100,56.1,76.4,71,68.6,46.5,41.5,51.9,45.9,47.9,75,95,72.1,41,39.3,41.5,27.8,44.7,39.3,22.7,21.7,58.2,58.2,38.4,38.4,43.5,43.5,41.9,41.9,34.6,34.6,34.6,34.6,63.5,64.9,72.1,73.7,100,100,35.4,42.3,88.1,100,54.3,53.1,71.3,77.4,82.5,85.4,82.1,87.5,52.9,53.8,51.7,54.8,52.1,53.2,43.1,44.3,41.4,44.3,11.7,12.5,29.1,31,0.1,0.1,0.1,0.1,41.3,47.5,41.3,47.5,39.1,50.4,38,56.8,55.5,51.4,43.3,24.3,34.6,49.1,43.7,49.9,51.3,51.2,36.7,46.2,36.3,45.6,50.2,52.5,73.8,73.8
|
||||
670,VCT,Saint Vincent and the Grenadines,Latin America & Caribbean,101323,19425,53.4,54.1,49.8,50.6,31.7,35.6,9.6,9.6,10.7,10.7,65.9,100,NA,NA,9,26.4,58.1,58.1,61.6,61.6,100,100,99.6,99.6,19.3,16.2,NA,NA,96.4,95.2,81.1,75.5,90.3,81.9,NA,NA,77.3,83.9,38.3,38.3,69.9,69.9,90.6,86.1,NA,NA,42.8,43.4,100,100,100,100,45.3,90.2,76.7,72.8,46.6,41.6,NA,NA,56.7,78.5,84.7,73.3,75.8,76.9,76.4,79.7,61.8,56.5,77.8,77.8,75.5,75.5,41.5,41.5,47.2,47.2,46.7,46.7,36.1,36.1,36.1,36.1,63,64.5,72.6,74.1,100,100,34.7,40.9,100,100,66.3,67.6,69.4,74.6,82.7,85.2,88.1,91.3,50.1,51.7,48.2,51.9,49.3,51.6,26.4,28.3,25.8,28.3,37.1,37.1,42.7,42.7,100,100,0.1,0.1,49.9,49.9,49.9,49.9,49.1,49,58.9,58.7,41.6,47.4,NA,NA,36.5,56.5,60,53.3,50.2,50.2,46.8,47.9,47,47.5,58.4,58.3,79.5,79.5
|
||||
882,WSM,Samoa,Asia-Pacific,216663,6998,40.9,46.8,35.9,37.2,26.2,25.9,7,7,5.2,5.2,89.3,100,NA,NA,8.2,8.2,25.8,25.9,14.5,14.5,28.3,28.3,89.8,89.8,26.3,22.1,100,100,44.8,46,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,85.3,75.9,33.3,29.2,82.4,42.5,100,100,100,100,54.4,60.1,65.4,75.2,87.4,82.4,NA,NA,94.3,79.3,64.4,69.7,51.2,57.1,23.5,40.9,80.5,54.2,77.8,77.8,65,65,17.6,17.6,50.1,50.1,0,0,25.2,25.2,25.2,25.2,57.2,57.7,57.5,57.9,100,100,6.4,7.8,34.1,39.2,88.3,76.8,92.5,97.1,100,100,100,100,56.6,57.3,61.9,64.4,52.2,52.5,57,58.2,56.5,58.2,56,55.7,86.1,83.4,98.8,98.9,4.6,6.5,33.9,51.3,33.9,51.3,43.4,39.4,74.2,66.7,34.9,92.6,12.9,28.5,19.9,79.2,41.9,62.2,NA,NA,35.3,43.5,39.9,48.6,52.9,55.1,85.7,85.7
|
||||
678,STP,Sao Tome and Principe,Sub-Saharan Africa,NA,6205,34.2,35.9,36.2,31.6,33.6,33.6,10.1,10.1,0,0,100,100,NA,NA,0,0,100,100,59.3,59.3,4.4,4.4,82.1,82.1,33.4,33.4,NA,NA,100,100,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,83.5,72.6,15.9,0,99.7,100,86,65.2,90.7,100,50.8,26.9,41.6,20.4,54.4,51.4,NA,NA,4.4,17.7,68.3,17,39.9,31.7,41.4,20.1,98,57.4,28.7,44.8,35.6,35.6,19.8,19.8,81.7,81.7,20.6,20.6,6.7,6.7,6.7,6.7,35.4,39.5,37,40.9,52.7,56.3,6.5,10.7,34.6,20.6,100,100,100,100,74.4,70.2,84.9,88.1,31.4,37.5,24.2,36.1,26.2,38.5,36,38.1,34.8,38.1,27.8,27.8,68.4,68.4,0,0,1,1,30.4,38.6,30.4,38.6,31.8,44,79.2,100,30.3,27.6,23.6,0,12.5,26,45.1,46.4,48.7,49.9,29.2,35.9,32.8,38.6,57.6,57,92.7,92.7
|
||||
682,SAU,Saudi Arabia,Greater Middle East,32264292,65880,33.2,42.6,39.2,50.3,37.7,45.4,50.4,50.4,5.8,5.8,87.9,100,34.1,34.1,13.3,54.9,7.2,29.7,15.6,15.6,72.9,72.9,95.4,95.4,72.6,65.2,98,89.2,47.7,47.3,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,53.4,56,56.4,54.6,52.2,53.6,33,61.1,49.9,55.2,49.7,50.4,17.8,60.5,0,0,2.4,0,4.6,65.4,31.1,79.9,54.2,53.8,10.9,8.9,40.6,41.3,61.2,53.1,100,100,57.4,57.7,30.8,26.8,60.1,61,60.1,61,62.1,62.1,38,40,33.1,34.7,5.4,2.7,59.6,70.2,23.2,23,24.4,31.1,5.3,4.6,58.6,54.4,41.7,40.8,61.7,64,62.4,69.2,57.2,60.6,21.4,26.3,17.2,26.3,37.4,37.4,28.4,28.4,100,100,15,15,20,33.2,20,33.2,33.9,46.5,0.6,17,22.1,35.6,35.9,6.7,23,32.3,0,95.9,100,0,24,36.3,7.9,18.8,0,0,21,21
|
||||
686,SEN,Senegal,Sub-Saharan Africa,18077573,5056,38.6,43.3,46.6,50.9,51.5,56.5,8.6,8.9,14.3,18.4,45.4,44.9,54.3,54.3,48.5,78.2,83.6,86.8,63.8,63.8,16.8,16.8,61.5,61.5,80,78.2,93.9,89.2,7.8,7.1,67.4,63.5,97.4,97,NA,NA,48.8,28.6,46.2,46.2,71.2,71.2,56,58.4,25.2,11.3,56.8,57.5,65.7,55.8,69.3,79.8,71.1,66.2,35.8,39.7,60.9,60.1,59.6,60.7,24.3,44.9,55.5,26.2,53.5,71.1,29.5,43.9,100,86.6,87.6,86.5,65.8,90.6,12.8,12.8,67.9,67.9,4.4,4.4,8.6,8.6,8.6,8.6,40.5,42.9,47.7,49.5,100,100,4.8,6.8,48.9,33.6,50.6,46.5,15.1,14.5,57.4,55.5,29.4,30.2,21.6,27.2,15.6,25.5,17.7,28.3,33.7,35.5,32.6,35.5,24.5,24.5,59.9,59.9,0.6,0.6,1,1,24.9,32,24.9,32,26.9,33.9,63.1,76.8,27.1,33.6,36.7,3.9,34.3,24.3,0,51,0,19.6,24.1,27.8,33.6,34.7,23.3,21.6,82.1,82.1
|
||||
688,SRB,Serbia,Eastern Europe,6773201,30910,54.8,49.3,54.7,56.1,50.5,53.5,NA,NA,NA,NA,NA,NA,34.7,34.7,46.6,55.9,21.5,29.7,48.5,48.5,53.2,53.2,85.4,85.4,75.2,73.8,90.4,84.5,13.4,13.7,72.3,69.3,NA,NA,NA,NA,85.1,78,55.9,55.9,52.8,52.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,90,86.3,48.2,46.8,61.3,56.1,89,100,71.9,86.6,66.7,71.4,59.7,78.1,50.6,47.7,58.3,46.3,77.1,77.1,13.9,15.4,34.9,34.9,12.1,15.9,10.1,10.1,14.8,14.8,39.1,43.4,26.6,31.6,16.9,24.2,23.2,30.6,35.5,55.4,32.8,36.4,14.5,24.3,54,60.1,40.7,39.9,79.6,82.6,78.3,83.9,77.7,81.8,50.6,54.1,47,54.1,26.4,26.4,39.4,39.4,52.3,52.3,0.5,0.5,68.1,43.6,68.1,43.6,71.8,47.6,66.1,30.8,50.6,44.9,100,25.7,45.2,95.8,53.5,40.7,53.1,52.7,56.9,42.9,56.4,39.4,41.8,20.5,53.5,53.5
|
||||
690,SYC,Seychelles,Sub-Saharan Africa,127951,41078,52.3,48.2,46.3,48.3,33.1,51.8,47.9,94.7,4.7,50.2,100,50.5,NA,NA,12.1,18.7,73.4,73.4,89.2,89.2,28.3,28.3,95.8,95.8,6.6,0,NA,NA,66.1,64.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,75.2,75.8,10.8,16.1,58.7,56.6,100,98,100,100,47.9,51.5,74.6,17.5,71.8,62.1,NA,NA,68.8,5.7,100,20.3,54.9,58,31.4,52.3,39.3,38,77.8,77.8,57.1,57.1,51.5,51.5,34.6,34.6,58,58,49.7,49.7,49.7,49.7,71.9,71.4,82.5,80.2,91.8,92.6,58.4,66.6,91.1,59,95,97.7,94,97.3,87.3,89.1,99.8,100,51.4,53.9,51.8,58,48.2,51.1,53.9,59.6,52.4,59.6,29.6,32,24.2,30.1,95,95,2.3,2.3,43.5,27.3,43.5,27.3,44.4,30.9,25.9,6.3,36.3,33.3,NA,NA,100,29.4,82.4,11,50,50,42.4,25.6,38.9,19.8,53.2,47.2,30.8,30.8
|
||||
694,SLE,Sierra Leone,Sub-Saharan Africa,8460512,3505,34.4,39.7,41.6,38.9,47,46,5.5,5.5,39.1,39.1,75,100,24.6,24.6,59.5,67.8,42.8,42.8,63.3,63.3,85.1,85.1,96.9,96.9,79.3,79.1,76.9,0,58.4,56.7,24.1,17.4,61,34.5,NA,NA,28.2,0,4.9,4.9,27.4,27.4,77.1,75.3,64.1,79.4,52.7,96.4,66.3,38.4,52.6,98.7,17.6,0,41.2,33,56.3,59.6,52.8,48.4,23.5,30.6,64.2,27,45.9,40.1,34.2,29.1,100,100,71.8,52.8,40.7,40.7,10,10,100,100,0,0,0,0,0,0,29.5,33.1,34.4,38.2,73.5,70.3,2.3,4.5,45.2,34.3,51.8,51.8,63.6,67.8,35.1,40.4,11.9,10,13.5,17.7,9,16.9,9.8,18.3,25.9,28.2,23.9,28.2,32.8,32.8,81.1,81.1,0,0,1,1,26.9,46.8,26.9,46.8,20.1,50.3,100,100,22.8,36.4,NA,NA,12.6,49.1,49.5,64,44.9,48.1,24.6,36.2,33.5,46.8,31.8,33.4,96.8,96.8
|
||||
702,SGP,Singapore,Asia-Pacific,5789090,153737,47.5,53.8,59.2,55.9,38.3,36.6,NA,NA,0,0,NA,NA,NA,NA,63.2,63.2,16.5,16.5,14.9,14.9,100,100,91.7,91.7,57.7,48.1,NA,NA,64,65.3,61,28.8,92,37.3,NA,NA,41.9,18.7,40.7,40.7,4.1,4.1,97.9,97.1,NA,NA,NA,NA,100,100,100,100,73.5,62.6,78.6,81.8,30,27.1,28.9,26.9,64.8,85.5,100,100,60.2,61.3,33.6,34.1,0,0,38.6,46,100,100,92.4,92.4,23.7,23.7,100,100,100,100,100,100,56.8,65.8,42.3,53.6,11.3,32.7,77.9,84.7,70.6,59.9,5.3,10,0,6.5,35.7,61.2,25,30.5,97.2,99.9,94.6,100,94.5,99.8,70.3,78.6,63.3,78.6,74.6,75.5,39.3,42,100,100,97.2,96.7,25.5,41.2,25.5,41.2,43.7,51.9,19.5,31,16.1,29.1,37.7,10.6,100,26.2,26.4,100,45.6,42.8,41.2,45.6,28.2,32,17.6,18.1,39.2,39.2
|
||||
703,SVK,Slovakia,Eastern Europe,5518055,47440,66.5,65,77.5,77.8,81.9,81.8,NA,NA,NA,NA,NA,NA,62.4,62.4,94,95.8,90.1,90.1,91.4,91.4,56.5,56.5,70.8,70.8,90.1,89.4,79.2,73.7,18.4,18.2,54.2,53.5,NA,NA,NA,NA,58.7,60.1,42.1,42.1,43.2,43.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,94.5,94.1,64.3,60.4,73.6,68.3,100,100,100,100,65.6,67.4,55.3,62.9,76.6,100,82.1,79.6,47.7,64.2,57.1,59.4,26.1,26.1,57.4,57.4,64.5,70.2,57.3,57.3,55,60.5,43.3,50.6,20.2,35.8,59.3,68.7,43.7,51,31.8,32.7,29.5,36.7,51.3,57.9,47.6,48,92.9,93,100,100,89.3,88.4,63,67.1,59,67.1,48.4,53.4,38.1,27.6,98.8,97.6,33.4,57,59.3,48.9,59.3,48.9,66,51.7,58.2,37.3,70.5,48.3,13.8,44.5,46.6,44.3,48.8,82.9,50.8,51,56.3,49.5,50.8,43,29,23.6,59.1,59.1
|
||||
705,SVN,Slovenia,Eastern Europe,2118396,58150,62.6,62.5,68.3,67.7,65.3,64.8,12,12,25.9,27.8,100,100,69.9,69.9,93.9,94,100,100,87.3,87.3,58.7,58.7,54.3,54.3,72.8,71.2,75.7,52.6,42.9,42.2,67.2,58.9,NA,NA,NA,NA,85.8,67.6,47.6,47.6,37.9,37.9,46.7,36.4,NA,NA,32.5,51.9,54.2,29,97.5,32.1,56.3,41.5,93,92.7,53.6,51.7,62.2,60.6,94.6,100,100,100,58.6,56.7,39.6,43.8,32,34.6,74,70.9,67.4,65.5,67.3,72.7,37.9,38,91.7,94,56.5,67.6,42.5,42.5,55.2,59,40.4,45.8,28,35.8,52.3,58.6,44.3,42.4,28.6,33,37.1,45.7,46.6,51.9,39.8,39.2,92.5,91.5,98.1,95.7,93.1,88.7,86.6,92.5,82.3,92.5,58.2,53.6,30,26.7,83.8,77,73.5,68.9,59.9,57.5,59.9,57.5,53.3,57.9,40.4,47.1,75.3,69.6,91.8,59.1,54,50.2,81.1,100,48.8,48.9,53.3,55.7,45.9,48.4,32.1,33.6,68.5,68.5
|
||||
90,SLB,Solomon Islands,Asia-Pacific,800005,2627,40.3,41.8,34,30.2,18.8,13.2,9.5,9.5,7.3,7.3,100,99.6,0,0,0.5,0.8,0.9,1.7,5,5,100,100,99.7,99.7,30,20.4,75.7,0,100,100,51.2,42,71.5,52,50.1,23.3,61.8,44.9,45.8,45.8,72.3,72.3,80.1,84.8,83,84.7,44.1,48.8,100,100,100,100,55,47.5,84.8,83,92.7,98.1,98.7,100,100,90,100,69.7,40.1,44.6,16.5,19.4,100,100,33.6,42.1,66.2,66.2,9.7,9.7,83,83,3.5,3.5,0,0,0,0,49.9,50.6,59.9,60.1,100,100,1.2,1.6,99.2,100,97.2,98.3,94.5,96.6,97.3,95.6,31.8,33.4,31.1,33.6,27.8,31.7,30.7,34.8,27.2,28.6,26.7,28.6,19.4,19.4,43.8,43.8,0,0,4.7,4.7,42,52.8,42,52.8,37.2,59.7,100,100,16.9,25.4,NA,NA,35.9,41.5,46.8,73.2,48.9,48.8,29.2,46.1,34.9,54.1,50,54.4,100,100
|
||||
710,ZAF,South Africa,Sub-Saharan Africa,63212384,16010,38.7,42.9,44.8,49.8,38.8,40.1,35,35.2,45.4,54.6,76.8,70.3,16.3,16.3,28.9,34.8,23,29,37.8,37.8,87.3,87.3,81.8,81.8,31.3,24.4,87.7,77.2,59.3,59.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,41.1,47.8,0,22.9,54,60.1,52.6,48.1,54.6,50.2,53,54.7,61.1,85.3,65.8,60.7,67.3,62.7,56.7,97.5,60.9,82.6,49,57.2,40.5,45.8,62.7,55,72.9,73.5,57.6,62.1,54,52.4,34,34,58,56,58,56,42.3,42.3,22.5,24.2,19.4,20.4,10.4,10.3,19,25,39.4,33.6,46.6,50.4,4.8,2.8,22.2,13.3,22.5,18.4,21.5,25.3,18.2,24.9,18.8,25.5,44.6,48.6,40.4,48.6,34.5,34.5,39.9,39.9,59.5,59.5,16.5,16.5,42.8,48,42.8,48,47.6,56,30,42.3,43.4,60.2,37.9,10.6,55,42.4,34.8,88.7,47.9,49.8,34.1,43.7,35.4,45,3.4,7.2,62.3,62.3
|
||||
410,KOR,South Korea,Asia-Pacific,51748739,65580,46.2,51,47.8,49.9,28.8,32.8,41.4,41.4,14.4,15.1,20.5,27,19.1,19.1,27.2,54.4,47.6,55.8,29.1,29.1,54,54,65.4,65.4,9,0,48.8,35.4,42,40.5,63.4,57.5,NA,NA,NA,NA,71.1,62,44.9,44.9,60.3,60.3,30.5,34.9,45,31.2,34.4,40,18.2,21.2,29.8,39.5,48.8,61.5,89,87.3,31.9,22.3,36.5,25.2,100,100,97.2,100,62.9,61.8,47.6,43.6,25.9,21.1,41.4,48.3,80.4,89,85.1,86.3,14.9,14.9,92.9,94.4,92.9,94.4,93,93,55.6,57.9,42.3,44.5,9.9,12.6,81.8,89.7,45.7,39.2,6.4,13.9,0,0,10,14.3,21.7,22.2,89.2,91.1,88.9,92.7,87.7,90.1,78.6,85.4,71.6,85.4,67.5,64.7,35.2,31.4,99.9,96.7,83.6,82,36,47,36,47,37.2,52.6,8.3,29.2,48.5,42.2,23.5,41.9,58.5,38.7,100,100,51.6,51.5,30,49.3,19.3,37.6,0,4.1,49.7,49.7
|
||||
724,ESP,Spain,Global West,47911579,56660,62.1,64.2,67.5,68.5,67.2,67.3,91.3,91.4,46.3,47.4,29,33.3,59.5,59.5,70.8,75.1,79.8,93.5,64.3,64.3,49,49,63.5,63.5,57.5,55.3,83.6,67.3,37.8,38.4,50.8,44.5,NA,NA,NA,NA,53.4,42.2,51.3,51.3,42.3,42.3,33,33.7,22.5,21.9,40.4,41.3,39.5,40.3,33.8,27.4,53.3,49.7,82.2,89.3,29.8,33.9,32.3,38.1,100,100,100,100,51.8,54.1,31.3,34.9,42.8,38.5,68.4,67.8,54.3,69,80.7,80.7,25.6,25.6,89.4,89.4,88.3,88.3,71,71,63.3,64.8,55,56.2,55.1,48.8,69.7,74.2,36.3,36.1,18.1,25.5,35.8,45.4,60.7,64.5,45.7,44.3,90.1,91.6,84.7,88.6,91.8,93.6,73,77.8,69.5,77.8,50.2,50.8,29.6,29,100,100,45.9,48.1,52.8,57.2,52.8,57.2,63.7,55.1,59.4,46.3,54.1,45.7,43.3,100,69,47.2,92.6,100,51.4,51.5,61.8,56.6,55.7,50.7,22.8,12.5,72.2,72.2
|
||||
144,LKA,Sri Lanka,Southern Asia,22971617,14255,36.9,38.7,40.9,39.3,37.2,33.7,2.5,2.5,1.5,1.5,75.1,44.8,59.8,59.8,36.1,36.2,82.8,82.8,48.3,48.3,32.2,32.2,81,81,0,0,75.4,30.7,78.1,78.5,66.1,70,81.4,88.7,NA,NA,62,65.4,31,31,58.4,58.4,63.1,63.4,66,68.1,49.8,62.6,51.5,58.7,65.8,67.2,53.5,50.3,47.7,49.1,42,36.2,42.2,30.9,41.7,54.2,63.4,50.2,64.2,62.5,55.1,65.6,61.8,100,55,61.3,68.5,56.9,10.9,10.9,87.3,87.3,4,4,1.1,1.1,1.1,1.1,30.1,30.9,20.2,20.1,8.8,12.3,11.6,20.3,48.9,26.9,44.9,48.1,22.1,20.8,41.8,38.6,25.5,23.6,50.7,53.7,47.9,55.6,47.8,52.5,60.9,65.2,57.7,65.2,32.6,32.6,68.2,68.2,8.9,8.9,8.9,8.9,36.6,44.1,36.6,44.1,38.1,44.2,79.2,91,32.2,46.1,36.7,3.8,43.7,51.1,53.3,61.9,46,48.6,38.3,44.8,38.4,44.8,22.5,23.4,90.4,90.4
|
||||
729,SDN,Sudan,Greater Middle East,50042791,2513,34.7,38.6,35.5,42.3,28.7,39.1,1.9,95.5,26.5,28,50,65.6,11,11,22.5,22.5,6.6,6.6,16.9,16.9,0,0,58.1,58.1,78.4,71.7,78.7,70.2,15.6,14.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,96.6,95.8,53.9,83.9,100,100,100,100,100,100,31.7,64,62.9,62.4,56.6,49.3,63.5,52.5,37.6,41.2,19.1,88.3,40.2,48.5,17,26,100,100,83.2,74.7,35.7,55.8,9.4,9.4,75.7,75.7,4.6,4.6,0,0,0,0,32.4,34.6,34.4,35.3,53.6,55.5,7.2,11.7,37.1,28.3,48.5,48.4,69.9,63.1,59.1,58.7,17.4,22.3,32.5,39.2,24.5,36.4,27.7,41,9.7,12,7.6,12,44.5,44.5,84.8,84.8,44.8,44.8,4,4,35.4,36.2,35.4,36.2,30.4,40.3,85.3,100,40.7,41.9,38.4,3.1,42.5,43.7,34.8,33.2,0,0,32.6,32.6,41.4,43.4,14.3,13.9,79.2,79.2
|
||||
740,SUR,Suriname,Latin America & Caribbean,628886,21404,47.9,56.6,55.6,63.9,63.8,64.3,57.9,57.9,43.4,43.4,50,100,100,100,57,58.3,34.1,34.1,32.6,32.6,76.1,76.1,98.7,98.7,97.7,97.7,88.9,74.9,47.6,46.2,78.7,72.9,89.2,82.5,80.7,63.4,84.9,78.4,47.9,47.9,93.9,93.9,44.7,38.9,38.6,19.5,38.9,44.9,38.8,38.3,41.1,44.7,56.4,36.3,14.5,80.3,97,100,85.4,89.9,20.8,72.1,7.7,82.7,58.1,62.8,41.7,47.5,38,100,20.5,28.3,79.9,89.4,44.3,44.3,46.6,46.6,49.5,49.5,39.6,39.6,39.6,39.6,57.2,58.8,67.2,68.3,100,100,30.3,37,100,97.8,33,36.4,85.6,83.1,77.9,76.2,25.5,15.9,40.9,43.4,41.2,46.5,37.7,41.4,34.4,39.7,33.2,39.7,13.7,13.1,31.2,30.3,2.4,2.4,1.9,1.2,28.5,43.6,28.5,43.6,19.2,45.2,0,29.2,22.9,50.7,100,100,28.2,29,44.8,10.5,49.9,49.8,14.5,37.2,11.4,37,33.8,37.2,50.3,50.3
|
||||
752,SWE,Sweden,Global West,10551494,74147,70.3,70.5,67.3,67.3,59.5,59.9,45.1,54.8,45.8,47.5,42.8,48.9,40.2,40.2,68.3,84.9,39.7,42.8,69.3,69.3,97,97,99.4,99.4,97.5,97.5,70.5,3.5,55.8,54.9,56.7,56.2,NA,NA,73.8,83.6,44.3,33.3,32.5,32.5,53.5,53.5,56.3,52.4,82.3,26.6,73.9,48.9,74.5,76.7,41.3,50.5,66.6,35,90.2,90.6,42.2,51.9,29.2,35.4,100,100,100,100,74.4,73.2,44.4,46.6,58.6,54.6,78.3,76.5,100,100,86.3,86.3,35.5,35.5,100,99,87,88,79.8,79.8,83,85.5,78.1,81.2,73,77.1,94,99.9,55.6,66.5,22.4,17.7,68.9,76.6,64.2,69.3,74.5,77.4,96.1,97,93.8,96,96.9,97.7,96.7,100,92.5,100,72,72.7,30.6,32.7,100,99.7,99.4,99.2,64.3,62.9,64.3,62.9,64.2,64.8,69.4,70.4,72.5,96.8,56.2,59.9,53.2,35.7,60,93.3,49.5,49.5,59.1,58.1,52.3,53.1,26.1,25.7,77.1,77.1
|
||||
756,CHE,Switzerland,Global West,8870561,98146,66,68,68.7,69.4,56.9,60,NA,NA,NA,NA,NA,NA,92.9,92.9,32.8,42.9,24.7,32.8,21.7,21.7,87.5,87.5,93.5,93.5,89.7,88.3,77.2,68.7,62.3,62.3,69.8,61.1,NA,NA,NA,NA,83.5,72.3,46.2,46.2,35.2,35.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,93.7,92.5,58.8,52.1,69.9,57.5,100,100,100,100,62.4,59.6,45.1,43.1,27.2,27.5,58.6,50.9,84.9,82.5,85.5,85.5,10.1,10.1,93.9,93.9,93.9,93.9,93.9,93.9,72.3,75.6,63.8,67.5,37.2,48.2,96.7,100,43.7,45.7,20.6,25.9,62.1,71.4,50.1,59.8,48.9,49.6,97.1,98,95.6,98.2,96.5,97.9,86.3,92.2,81.1,92.2,65.8,66.8,16.1,16.9,99,100,99,100,56.5,59.4,56.5,59.4,55.8,64.1,53.4,66.3,57.5,45.1,39.1,58,57.1,63.1,100,100,50.7,50.8,53.5,60.8,47.2,53.7,23.2,27.6,78.8,78.8
|
||||
158,TWN,Taiwan,Asia-Pacific,23317145,79031,50.4,50.3,53.2,51.8,41.5,39.4,2.2,2.2,10.5,10.5,100,88.3,24,24,37.4,37.4,65.3,65.4,79.1,79.1,95.1,95.1,98.3,98.3,NA,NA,68.4,27.8,100,100,84.8,85.1,77.3,97.3,NA,NA,80.6,88.5,50.8,50.8,64.1,64.1,47.5,46.4,22,41.3,45.3,45.9,50.9,27.3,72.7,69.1,41,0,89.5,86.5,30.2,18.8,44.8,18.8,100,100,100,100,58.2,60.3,38.9,44.2,14.2,16.2,30,34.8,90.8,90.8,39.2,39.2,31.5,31.5,69.9,69.9,16.2,16.2,16.2,16.2,47,50,36.2,40.1,19,22.2,55.5,61.2,35.2,50.8,13,18.2,10.3,10.4,34.9,44.2,35.2,36.1,69.7,70.9,68.9,72.1,69.5,70.1,68,71.8,65.9,71.8,75.4,69.7,40.8,28.4,99.2,98.5,98.1,96.7,49,48.4,49,48.4,48.7,49.2,23.8,24.4,40.3,70.3,100,49.4,48.2,34.8,63.2,100,NA,NA,53.4,47.9,42,34.8,12.3,8.7,44.4,44.4
|
||||
762,TJK,Tajikistan,Former Soviet States,10389799,5533,36,31.9,45,46.2,54,53.6,NA,NA,NA,NA,NA,NA,29.5,29.5,34.2,34.7,53,53,23.1,23.1,71.3,71.3,88.8,88.8,96.4,96.4,94.4,87.7,57.3,58.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,29.5,41,4.3,0,52.2,47.5,14.5,22.7,20.8,66.3,64.8,57,71.6,57.9,74,51.9,82.6,57.2,48.2,56.5,18.6,18.6,64.3,64.3,23.9,23.9,5.2,5.2,5.2,5.2,21.1,21.7,18.1,17.4,21.4,15.1,6.1,10.9,15.8,22.9,37.2,30.9,34,34.3,53.6,54,33.2,34.4,28,31.2,22.5,28.1,26.7,33.3,28.8,33.8,23.5,33.8,22.8,22.8,53.8,53.8,0,0,3.1,3.1,34.5,18.5,34.5,18.5,47.9,0,87.3,0,0,25.4,100,89.6,0,4,18.4,35.1,26.2,31.9,45.9,9.5,53.1,16.2,34.2,22.2,70.4,70.4
|
||||
834,TZA,Tanzania,Sub-Saharan Africa,66617606,4134,37.7,43.1,52.3,59.6,61.5,65.3,67.5,67.5,37.8,37.8,100,100,100,100,45.5,72.1,96.4,98.1,82.1,82.1,56.1,56.1,79.4,79.4,5.9,0,87.7,73.4,24.7,24.8,46,54.4,73,62.6,49,73.6,42.4,40,0,0,71.3,71.3,82.2,71.7,88.6,75.2,70.8,60.7,86.1,77.7,87.2,78.9,43.6,24.1,38.3,77.7,86.9,81.8,93.4,87.6,28.5,52.7,72.4,100,48.6,48.5,39.7,44,100,100,99.7,99.6,22.6,27.4,19.4,16.1,100,100,0.9,0.9,22.7,14.4,0,0,24.5,24.9,24.9,23.9,23.8,26.1,5.1,6.9,62.9,42.2,54.7,53.7,69.8,71.6,57.9,57.9,20.4,19.6,16.7,20.6,12.6,19.4,13.9,21.4,41.6,44.1,39.4,44.1,24.1,24.1,59.2,59.2,0,0,1,1,25.6,32.5,25.6,32.5,19.1,24.2,91.8,100,19.4,31.5,NA,NA,25.7,35.6,26.4,32.2,43.5,44.7,19.6,28.5,33.2,38.1,14.2,13.6,86.3,86.3
|
||||
764,THA,Thailand,Asia-Pacific,71702435,26420,41,45.4,49,50.8,46.9,46.2,57.8,68.3,17.2,28.6,53.8,33,27,27,69.9,70.2,60.5,60.6,69.6,69.6,63.9,63.9,94.2,94.2,30.5,22,41.5,0,30.2,30.2,66.6,70.7,76,89.9,69.7,85.2,41.4,39.8,51.9,51.9,60.1,60.1,47.2,44.2,58.9,38.8,79,79.2,32.7,31.5,34.7,33.3,49,60.9,61.3,75.8,66.3,64.2,58.4,56.6,52.6,57.7,88.9,100,59.8,58.8,46.8,48.9,64.9,60.7,58.1,47.5,76.6,73.2,21.5,21.5,31.3,31.3,23.4,23.4,18.1,18.1,18.1,18.1,33.3,34.9,24.1,25.5,5.7,11.9,27.9,37,38.7,35.7,33.6,32.7,18.7,16.2,28.1,31.4,10.5,10.4,49.1,51.2,54.1,61.2,42.3,44.6,72.1,75.4,68.7,75.4,32.6,33.6,35.2,38,41.8,40.1,25.3,26,35,46,35,46,39.5,50,29.5,46.1,27.1,70.7,36,6.4,9.6,57.8,31.8,55.9,46.2,48.1,31.7,45,30.9,43.2,3,6,62.3,62.3
|
||||
626,TLS,Timor-Leste,Asia-Pacific,1384286,4697,40.2,49.7,43.9,47.6,42.9,45.3,59.7,59.9,6.5,17.7,50,100,14.8,14.8,27.3,32.2,42.8,53.5,56.2,56.2,80.2,80.2,93.1,93.1,58.1,47.6,81.6,69.3,72.1,70.7,63.3,62.6,NA,NA,NA,NA,65.6,66.1,49.5,49.5,71,71,95.5,95.5,100,100,NA,NA,NA,NA,100,100,45.3,50,50.3,67,78.5,74,94.2,89.4,36,57.9,100,70.2,50.5,48.8,26.1,18.9,54.3,45.9,95,97.1,58.9,58.9,18.9,18.9,71.5,71.5,18.5,18.5,8.7,8.7,8.7,8.7,36,36.1,38.9,38.3,65.1,62.2,8.2,8.1,50.8,38.1,44.3,44.9,86.2,90.7,76.3,75,15.4,16.9,30.3,33.1,25.8,31.5,28.7,34.1,23.6,23.8,24.1,23.8,39.7,39.7,94.3,94.3,0,0,5,5,37.9,65.2,37.9,65.2,30.4,46.2,83,100,0,100,NA,NA,31.7,39.8,44.8,45.5,46.2,47.1,0,65.7,3.5,71.8,33,100,100,100
|
||||
768,TGO,Togo,Sub-Saharan Africa,9304337,3290,36.4,35.2,45,45.9,55.3,54.7,NA,NA,0,0,NA,NA,45.7,45.7,62.4,62.6,82.6,83.1,85.4,85.4,30,30,26.3,26.3,58.9,57.7,90.1,81,9.3,8.6,50.6,42.4,83.6,61,NA,NA,49.9,25.2,20.7,20.7,59.6,59.6,56.3,58.9,NA,NA,86.3,87.7,22.4,24.3,65.6,66,45,67.4,32.9,45.3,75.3,75.5,78,76.8,0,0,38.1,78.3,41,40.3,28.7,31.9,100,99.5,82,76.1,26.4,28.8,10,10,100,100,0,0,0,0,0,0,22.8,25.3,24.9,26.8,50.8,41.6,3.4,5.5,34.6,21.1,56.9,58.8,61.5,59.1,42.4,39.8,25.2,20.4,13.2,17.8,9,17.1,9.8,18.2,28.1,30.4,26.1,30.4,26.4,26.4,64.1,64.1,1.2,1.2,1.2,1.2,35.6,28.5,35.6,28.5,35.8,28.4,100,100,19,23,36.8,3.7,14.3,30.4,8.3,21.7,43.3,47.2,22.1,23.4,33.2,32.1,30.5,28.4,88.1,88.1
|
||||
776,TON,Tonga,Asia-Pacific,104597,7811,47.5,40.2,37.3,34,15.1,15.3,0.2,0.2,3.7,3.7,100,100,NA,NA,2.9,7.9,33.1,33.1,0.5,0.5,100,100,98.1,98.1,15,9.9,NA,NA,0,0.4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,95.2,95.7,94.9,98.2,100,100,100,100,100,100,28.3,18.8,96.5,70.4,71.4,63,NA,NA,100,74.7,100,67.5,42,49.9,23.8,53.7,100,53.9,0,0,66.6,66.6,34.8,34.8,51.9,51.9,39.6,39.6,27.6,27.6,27.6,27.6,57.6,58.2,59.8,60.3,100,100,10.7,13.4,40.1,46.1,80.5,68.8,98.2,100,100,100,100,100,49.4,50.2,49.3,50.9,49,49.7,71.4,74,69.1,74,32,32,60.5,60.5,28.4,28.4,5.3,5.3,53.4,32.5,53.4,32.5,54,23.7,76.2,23.3,46.3,44.4,NA,NA,54,48.3,60.1,60.8,NA,NA,45.4,24.5,50.4,30.4,60.7,53.8,65.6,65.6
|
||||
780,TTO,Trinidad and Tobago,Latin America & Caribbean,1502932,34987,50.9,52.1,47.6,48.9,48.1,49.5,0,0,3.4,3.4,79.8,100,100,100,15.4,32.9,97.3,97.3,70.5,70.5,84.9,84.9,93.4,93.4,60.6,57.3,54.1,20.8,51.5,49.1,70.9,72.6,82.7,86.5,NA,NA,56.3,72.5,34.1,34.1,66.3,66.3,57.3,58.1,40.9,42,28.3,26.5,70.6,65.6,76.6,78.8,48.7,49.8,74.8,72.5,48.9,44.3,48,42,43.6,66.3,100,90.4,13.5,22.5,4,5.4,23.4,20.7,22.7,29.1,27.9,36.9,13.2,13.2,9.8,9.8,25.2,25.2,4.2,4.2,4.2,4.2,75,77,85,87.2,100,100,75,82.4,89.6,94.6,32.2,46.4,75,82.5,78.6,81.2,48.5,53.1,58.5,60.2,58.7,63,55.6,58.4,62.2,65.2,59.4,65.2,11.8,11.8,27.6,27.6,2.4,2.4,0.7,0.7,36,36.1,36,36.1,38.3,52.2,4.8,22.8,25.9,55.4,36.7,3.7,49.1,40.1,85.3,54.7,51.2,50.8,8.9,27.6,0,21.3,16.3,23.2,36.8,36.8
|
||||
788,TUN,Tunisia,Greater Middle East,12200431,14720,45.2,45.7,47.5,48.1,38.5,38.3,15.7,15.7,25.7,25.7,100,100,10.7,10.7,15.4,15.4,21,21.1,59.6,59.6,61.8,61.8,95.8,95.8,83.6,83.5,78.1,71.9,29.1,28.6,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,49.5,50.3,42.5,45.5,44.5,44.8,42.5,51.1,56.7,54.8,54.9,51.6,69.3,70,17.4,16.3,28,24.6,53.7,59.7,100,100,31.5,40.2,22.6,24.3,65.4,63.7,55,59.7,47.5,45.9,75,75,58.8,58.8,80.7,80.7,73.7,73.7,73.7,73.7,46.9,46.5,45.9,44.3,43.5,33,50.6,61.6,34.6,32.8,31.5,35.4,23.8,19.2,52.2,46.3,47.6,43.3,60.5,62.4,59.5,64.3,58.6,61.1,29.8,33.2,27,33.2,30.8,30.8,50.2,50.2,41.8,41.8,5.9,5.9,40.3,41.4,40.3,41.4,38.2,43.6,34.2,43,40.2,65.3,36.8,4,42.7,38.2,47.2,45.3,41.2,48.5,34.4,39.2,35.9,41,20,20.1,67.4,67.4
|
||||
792,TUR,Turkiye,Eastern Europe,87270501,41910,36,37.6,40.8,35.6,21.1,20.1,2.1,2.1,21,21,70.9,61.5,5.4,5.4,0.8,0.8,0.6,0.6,1,1,46.2,46.2,18.7,18.7,68.7,67.7,81,64,29.4,30.4,65.4,54.7,NA,NA,NA,NA,70.7,53.4,53.3,53.3,63.8,63.8,40.1,49.6,48.6,24.4,54.9,56.2,37.2,51.3,41.5,54.2,53.1,57.4,84.2,50,32.7,32.9,42.5,44.7,86.4,69.7,67.6,34.7,61.2,59.2,50.2,49.8,63.7,52.1,57.9,42.9,63.9,76,65.9,69.1,38.7,38.7,69.9,74,69.9,74,60.7,60.7,38.5,41.8,31.8,34.8,16.9,16,41.8,57.2,19.9,21.3,14.9,13.1,32.5,38.8,57.5,62.4,41,40.8,59,63.7,55.6,66.9,53.6,61.6,49.2,52.2,47.2,52.2,28,29.7,32.7,33.1,56.4,57.8,9.2,12.3,26.5,37,26.5,37,35.2,40.9,16.5,25,0,28,0,30.2,5.2,16.9,26,100,51.5,51.9,27,36.6,23.2,30.8,1,0.7,44.2,44.2
|
||||
795,TKM,Turkmenistan,Former Soviet States,7364438,26881,35.5,40.7,36.9,40.8,39.7,39.5,72.8,72.8,12.5,12.5,50,50,2.3,2.3,17.9,18.2,10.7,10.7,24.1,24.1,64.1,64.1,65.6,65.6,92.2,91.9,93,89.2,42,42.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,16.6,43.5,30.7,32.2,37.5,38.8,33.4,15.7,25.4,74.6,49.9,48.1,31.8,27,69.7,43.5,86.6,72.3,59.6,59.6,39.2,39.2,43.2,43.2,44.7,44.7,34.1,34.1,34.1,34.1,51.6,53.8,53.3,54.1,48.5,45.5,51.4,67.8,48.1,43.1,36,34.1,57,56.3,62.8,64.2,48.3,48,51.3,58.5,40.2,54.2,45.7,61.4,39.8,41.9,38.7,41.9,48.7,48.7,79.8,79.8,75.3,75.3,4.3,4.3,20.1,29.6,20.1,29.6,34.5,49.5,5.1,25.7,42.9,37.7,36.8,7.6,19.9,28.3,0,11.5,0,0,4.8,23.4,14.9,26.4,12.4,14.7,32.3,32.3
|
||||
800,UGA,Uganda,Sub-Saharan Africa,48656601,3642,31.3,35.4,43.8,44.5,54.6,51.9,NA,NA,NA,NA,NA,NA,39.1,39.1,69.5,72,59,59,77.5,77.5,17.6,17.6,62.2,62.2,24.9,15.7,89.8,63.7,0,0,31.3,34.1,40.7,46.2,31.2,35.5,44.8,29.7,0,0,43.5,43.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,44.7,54.5,83.4,76.4,83.2,77.2,9.3,30.7,46.6,69.4,47.8,48.3,36.4,40.9,100,100,97.7,92.7,31.6,32.9,12.8,12.8,96,96,8.1,8.1,0,0,0,0,17.7,18.3,14.4,14,4.5,7.7,5.5,7.5,48.9,29.2,60.5,64.7,22.8,25.4,27.1,28.9,3.5,5.9,20.5,23.6,16.3,22.1,18.1,24.6,35.9,38.3,33.7,38.3,24.3,24.3,57.3,57.3,2.6,2.6,2.2,2.2,22.8,35.7,22.8,35.7,13.4,29.5,100,100,0,33,NA,NA,0,37.1,13,24.5,46.6,48.1,12.2,33.2,21.9,40.4,17.1,18.7,92.9,92.9
|
||||
804,UKR,Ukraine,Former Soviet States,37732836,20760,47,54.6,49.4,58.5,44.8,53.8,81.5,81.5,34.2,34.2,48.4,44.1,22.4,22.4,8,64.9,43.8,43.8,54.5,54.5,42.5,42.5,0,0,79.3,79.8,73.1,71.1,0,0,58.4,54.8,NA,NA,NA,NA,58.5,53.1,70.4,70.4,32.2,32.2,33.6,33.4,79.4,18.8,37.4,27.7,29.7,40,42.1,34.8,60.7,57.6,67.5,92.9,54,54.7,56.2,59.7,70.7,100,49.6,100,69.8,76.4,56.1,71.1,100,100,63,64.7,56.6,84.5,41.4,41.7,19.5,22,55.9,55.9,34.2,34.2,34.2,34.2,43.6,48.1,34.1,40,25.1,37.9,28.8,35.6,40.8,58.8,29.3,31,30.4,35.9,59.9,64.2,63.3,64.4,74.9,76,69.9,72.2,76.3,78.5,56.2,60.7,52.9,60.7,22.6,22.2,39.7,37.5,30.2,33.3,1.8,1.3,46.3,53.9,46.3,53.9,52.5,62.6,50,65.9,53.8,73.4,0,0,28.1,41.6,55.1,62.6,50,49.8,41.3,51.7,43.1,55.3,8.1,23.9,84.9,84.9
|
||||
784,ARE,United Arab Emirates,Greater Middle East,10642081,82000,43.3,52,54.3,63.5,49.2,59.3,90.2,90.2,20.6,31.1,100,100,42.5,42.5,23.1,37.3,2.6,55.1,95.3,95.3,88,88,99.3,99.3,55.8,48.4,88,78.7,41,41.2,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,79.4,80,45,37.1,89.8,89.4,100,100,77.5,82.8,44.7,50.6,46.2,65.1,0,0,0,0,12.5,88.9,100,67.3,34.5,37.5,11.7,13.8,33.7,29.2,31.5,36.7,62.1,62.1,91.2,91.2,36.7,36.7,93.9,93.9,100,100,100,100,49.3,50.6,46,46.2,16.9,10.3,93.2,99.3,11.9,24.2,2.3,7.1,16.3,12.4,42.8,34.8,14.9,12.6,69.5,71.9,76,80.6,63.2,66.1,36.6,49.9,33.5,49.9,29.5,20,23,23.3,53,41.6,24.2,5.9,21.5,35.6,21.5,35.6,17.2,45.9,0,14,22.2,42.7,36.1,8,6.9,35.9,60.1,89.9,100,100,7.2,36.5,0,11.3,3.4,6.5,14.4,14.4
|
||||
826,GBR,United Kingdom,Global West,68682962,64384,71.4,72.7,72.8,73.3,70.4,71.9,78.1,80,44.5,54.2,66.2,70.4,60,60,56.9,57,87.6,87.9,83.5,83.5,72,72,24.5,24.5,91.3,91.4,89.2,88,47.1,49.3,46,46.4,NA,NA,NA,NA,42.3,45.6,63.6,63.6,16.1,16.1,43.2,38.1,20.9,14.9,49.2,58.4,29.2,31.7,37.1,40.3,57.5,44.1,92.1,92,38.9,43,62.2,60.5,100,100,100,100,78.6,76.9,57.4,56,50.1,48.6,83.6,77,81.6,100,79.8,79.8,25.7,25.7,85.8,85.8,85.8,85.8,85.8,85.8,74.5,77.4,67.2,69.9,43.1,52.1,93.9,98.8,59.7,66.5,4.1,12.7,38.8,51.6,57.7,66.8,68.8,71.7,95.8,98.2,94.5,100,91.9,97,89.9,95.7,85.2,95.7,61.9,65.4,28.4,29.4,97.6,97.7,77.5,85.2,66.6,67.8,66.6,67.8,65,74.3,63.1,77.3,100,58.6,46.8,56.8,68.6,52.5,100,86.5,46.9,47,62.4,67.1,54.1,61.6,18.2,100,91.6,91.6
|
||||
840,USA,United States of America,Global West,343477335,89685,57.3,57.3,54.1,54.1,41.6,41,58.7,58.7,46.1,46.1,69.6,76.9,14.6,14.6,27,39.9,37.2,37.8,36.9,36.9,71.5,71.5,89.4,89.4,47.7,45.5,68.1,20.8,46.8,46.2,53.6,51.9,92.2,87.6,24,18.1,50.3,49.9,43.9,43.9,66.5,66.5,42.5,46.5,38.4,38,37,45.1,36,46.4,41,50.6,51.5,49.1,89,89.1,28.6,31.7,35.5,36.9,100,100,100,100,78.5,83,68,76.6,62.8,80.7,56.9,57.8,77.6,100,65.7,65.7,5.6,5.6,78.2,78.2,67.7,67.7,67.7,67.7,69.7,72,63,65.8,56.3,61.5,88.3,91.6,27.1,33.8,0,6.6,29.1,35.7,53.5,60.3,38.5,35.5,95.1,96.4,84.5,90.9,98.6,100,75.4,78.6,73.6,78.6,45.3,41.7,15.6,13.3,100,93.9,47.7,44,51.6,50.1,51.6,50.1,60.8,57.7,37.4,33.3,60.7,50.5,38,46.7,53.8,55.2,100,100,50.5,50.2,52.6,52.5,35.3,35.5,0,0,50.1,50.1
|
||||
858,URY,Uruguay,Latin America & Caribbean,3388081,36010,43.2,43.9,39.7,39.1,29.8,29.4,15.1,15.1,22.1,22.1,73.4,54.2,6.3,6.3,17.4,18.3,9.7,11.6,16.7,16.7,42.6,42.6,89.3,89.3,70.4,71.3,80.1,69.9,23.5,25.9,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,44.1,34.4,61.2,51.6,55.1,53.7,16.8,26,20.6,21.1,47.5,40.1,68.2,75.8,82.4,78.9,96.7,92.6,16.4,55.5,41,92.2,72,58.2,59.1,58.6,36.1,32.5,73.4,73.8,82.3,53.4,34.1,34.1,59,59,37.7,37.7,26.3,26.3,26.3,26.3,54.8,56.5,51.1,52.3,48.4,48.6,46.7,57.7,57.8,52.3,18.7,27,68,68.6,79.3,78.4,33.9,38.8,69.3,72.5,69.1,75.6,64.9,70.4,62.1,64.7,60,64.7,30.9,30.9,36.3,36.3,66.2,66.2,7.8,7.8,38.9,40.6,38.9,40.6,34.8,46.1,34.7,53.8,44.7,38.9,94.8,50.4,39.9,46.5,0,53.3,46.3,48.2,36,37.6,31.2,31.4,21.8,21.4,38.4,38.4
|
||||
860,UZB,Uzbekistan,Former Soviet States,35652307,11596,44.3,42.9,48.2,49.3,37.7,44.4,NA,NA,NA,NA,NA,NA,10.8,10.8,19.8,36.6,13.4,32.3,26.8,26.8,75.3,75.3,75.3,75.3,91.1,90,76.9,66.1,36,36.1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,61.9,49.2,40.3,41.5,60.8,57.1,73.6,42.8,68,55.6,64.9,57.4,59.6,49.6,42.4,35.6,70.6,45.7,72,72,60.8,62.2,54.2,53.6,39,39,93.5,97.2,23.5,23.5,35.4,37.7,26.2,27.5,25.8,23.9,13.7,23.2,46.6,45.8,35.9,29.6,28.3,30.1,56,56,38.2,40.1,66.4,71.5,55.6,65.4,64.6,75.5,39.9,44.3,36,44.3,28.7,28.7,68.3,68.3,0,0,3.5,3.5,45.8,37.5,45.8,37.5,59.2,46.8,63.5,43.6,32.6,35.6,0,21.7,4.1,11.8,53.4,47.2,54,55,32.3,32.1,45.2,38.6,13,10,57.8,57.8
|
||||
548,VUT,Vanuatu,Asia-Pacific,320409,2878,35.4,44.6,30.5,36.5,17.7,18.1,0.6,0.6,1.8,1.8,100,98.1,1.7,1.7,0.6,3.6,13.7,13.7,2.9,2.9,76.1,76.1,99.1,99.1,0.3,0,98.5,98.8,100,100,77.4,81.9,83.8,81.2,91.7,94.6,86.9,79.6,48.4,48.4,88.7,88.7,78.9,84,73.2,85.6,31.1,36.3,100,100,100,99.2,NA,NA,36.1,73.5,76,80.5,78.4,88.9,42.1,73,1,69.6,40.6,42.7,11.8,13.7,100,100,30.4,42.3,67.2,67.2,17.6,17.6,77.2,77.2,15.7,15.7,7.1,7.1,7.1,7.1,48.3,49.2,57,57.5,100,100,1.4,2.5,64.5,58.5,100,100,76.8,83.7,96.6,97.7,58.3,64.9,30.6,32.4,28.2,31.2,30.2,33.2,32.8,34.5,31.5,34.5,21.9,21.9,48.1,48.1,4.4,4.4,4.4,4.4,31.9,53.7,31.9,53.7,9.3,25.4,31.3,62.8,25.2,100,NA,NA,23.8,100,22.3,61.5,49.3,50.2,16.3,47.4,31.1,57.9,48.4,59,95.1,95.1
|
||||
862,VEN,Venezuela,Latin America & Caribbean,28300854,8404,51.6,53.1,60.2,61,63.2,61.3,10.3,10.3,14.9,14.9,58.1,76.4,100,100,88.5,88.6,96.7,96.7,75.5,75.5,69.9,69.9,91.6,91.6,43.1,37.5,82.2,56.7,54.9,52.2,79.2,71.3,89.1,82.8,84.9,69.6,70.8,69.7,37.6,37.6,87.8,87.8,84.9,82.5,36.8,27.8,75,85.9,100,99,99.7,98.5,79.3,39.3,53.7,76.1,100,100,91,90.5,44.9,72.2,27.8,72.3,47.3,46.1,25.9,18.1,40,42.5,41.2,47.5,83.1,73.9,32.1,32.1,29.1,29.1,33,33,32,32,32,32,48.3,50,51.4,54.2,58.2,62.1,49,51.6,63.3,54.2,24.3,33.3,58.4,61.9,62.6,62.9,11.5,9.4,47.7,48.5,48.5,49.3,47.7,47.9,39.9,38.2,40.5,38.2,14.6,10.3,35.3,24.6,0,0,1.2,1.2,41.4,43.5,41.4,43.5,37.8,50,35,55.1,50.4,51.7,40.7,15.5,44.8,53.6,43.3,57.2,49.3,49.4,32.6,34.4,29.2,45.5,6.4,14.2,67.2,67.2
|
||||
704,VNM,Viet Nam,Asia-Pacific,100352192,17350,28.2,24.5,32.2,27.7,27.4,25.4,27.5,27.5,7,7,76.7,40,25.4,25.4,41.2,41.6,19.7,24.9,44.3,44.3,30,30,82.7,82.7,14.5,3.2,19,0,60.2,59.4,47.4,48.5,45.2,56.8,75.5,71.4,22.4,15.6,34.3,34.3,53.9,53.9,31.4,29.4,100,98.2,36.8,31.8,15.4,9,18,13.3,35.3,29,33.2,7.5,46,43.9,52.8,46.6,36.5,0,44.6,0,73.7,73,58.9,55.2,29.9,31.9,58.1,59.4,98.7,100,14.9,14.9,58.7,58.7,10,10,10,10,10,10,24.7,26.6,13.7,15.5,1.8,8,9.7,15.4,27.3,34.3,28.8,22.9,32.8,32.3,21,24.8,17.2,15.8,51.6,53.7,46.7,52.2,51.2,54.7,40.8,43.3,38.9,43.3,46.1,46.1,74.2,74.2,27.4,27.4,27.4,27.4,25.2,17.9,25.2,17.9,23.9,10.8,6.3,0,36.4,49.7,36.7,0,33.2,35.4,35.4,51.6,45.5,41.7,24.2,10.3,28.2,12,4.8,0,36.5,36.5
|
||||
894,ZMB,Zambia,Sub-Saharan Africa,20723965,4190,42.2,46.1,63.5,65.3,84.7,83.7,NA,NA,NA,NA,NA,NA,99.6,99.6,90.7,91.2,99.2,99.2,79.4,79.4,54,54,92.1,92.1,59.6,59.6,94.2,80.1,54.6,52.6,50.4,40,57.9,45.1,NA,NA,47.4,37.5,13.5,13.5,75.1,75.1,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,54.7,75.4,72.1,69.3,86.8,82.6,28.2,50.5,40.6,100,46,44.1,32.8,29.9,100,58.2,74.5,85.9,38.1,39.6,15.3,15.3,94.9,94.9,13.8,13.8,0.5,0.5,0.5,0.5,17.3,18.9,16.4,16.7,15.3,12.7,3.7,7,31.2,30.9,62.1,55.7,42,44,48.2,51.2,11.2,13.1,15.7,21.4,10,20.1,11.3,22.3,24.6,28.2,22.4,28.2,25.2,25.2,61.9,61.9,0,0,1,1,30.3,39.4,30.3,39.4,15.6,16.8,57.5,60,37.7,48.9,76.9,100,41.9,55.4,18.9,31.3,47.7,47.8,30,34.2,41.2,43.4,21.9,21.6,84,84
|
||||
716,ZWE,Zimbabwe,Sub-Saharan Africa,16340822,5071,42.4,51.7,57.1,66.3,68.7,70.5,NA,NA,NA,NA,NA,NA,64.7,64.7,67.6,72.7,92.3,94,95.4,95.4,41.8,41.8,79.4,79.4,32.7,32.7,79.2,85.1,31.8,33.6,41.4,45.7,54.1,51.6,NA,NA,36.1,45.1,20.4,20.4,63.5,63.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,41,88.7,76,69.5,90.9,84,37.7,82.1,50.1,100,27.3,35.5,27.5,31.7,65.9,57,83.5,80.8,4.1,18.7,62.5,56.7,76.5,75.1,75.9,69,61.2,53.9,0,0,21.5,22.9,21.9,22.8,27.4,26.8,3,4.9,38.7,33.5,68,68.9,53.7,55.4,49.6,53.1,16.8,17.5,17.2,19.5,14.3,19.2,14.9,19.7,23.9,27.1,22,27.1,31.8,31.8,74.9,74.9,3.3,3.3,3,3,37.1,53.5,37.1,53.5,45.3,69.1,100,100,34.7,54.2,36.8,3.8,35.1,59.8,34,34.1,35.7,43.5,26,46.2,42.3,59,23.3,32.2,96.9,96.9
|
||||
|
@@ -1,41 +0,0 @@
|
||||
from pptx import Presentation
|
||||
|
||||
# load the uploaded file
|
||||
input_path = "./tech_project_proposal_ideas_dark_theme.pptx"
|
||||
output_path = "./new_pptx.pptx"
|
||||
|
||||
def to_title_case(text: str) -> str:
|
||||
if not text:
|
||||
return text
|
||||
# title case for slide titles (major words capitalized)
|
||||
return text.title()
|
||||
|
||||
def to_sentence_case(text: str) -> str:
|
||||
if not text:
|
||||
return text
|
||||
text = text.strip()
|
||||
if not text:
|
||||
return text
|
||||
# sentence case for bullet points
|
||||
return text[0].upper() + text[1:]
|
||||
|
||||
# open ppt
|
||||
prs = Presentation(input_path)
|
||||
|
||||
for slide in prs.slides:
|
||||
for shape in slide.shapes:
|
||||
if not shape.has_text_frame:
|
||||
continue
|
||||
tf = shape.text_frame
|
||||
# heuristics: assume first shape (or largest font) in slide is the title
|
||||
if slide.shapes.index(shape) == 0 or "title" in shape.name.lower():
|
||||
for para in tf.paragraphs:
|
||||
para.text = to_title_case(para.text)
|
||||
else:
|
||||
for para in tf.paragraphs:
|
||||
para.text = to_sentence_case(para.text)
|
||||
|
||||
# save output
|
||||
prs.save(output_path)
|
||||
|
||||
output_path
|
||||
Binary file not shown.
Binary file not shown.
Reference in New Issue
Block a user