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Explaining the density of Esperanto speakers with language and politics
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--- | |
title: "Esperanto correlations" | |
output: html_notebook | |
--- | |
From the blog post Frequencies of Esperanto Speakers in all countries I have the main variable. | |
```{r} | |
prefix='../../Esperanto/L/language failes/' | |
lf=list.files(prefix) | |
esperanto_file='../../Linguistic differences/asjp-contribution-ESPERANTO.csv' | |
lf=c(lf,esperanto_file) | |
res=matrix(0,nrow=100, ncol=length(lf)) | |
cnames=rep("", length(lf))#names of the languages | |
for(i in 1:length(lf)){ | |
l=lf[i] | |
#still haven't learnt regular expresions: | |
name=strsplit( strsplit(l, "-")[[1]][3], ".", fixed=T)[[1]][1] | |
cnames[i]=name | |
#load file and extrat relevant information. | |
ad=read.csv(paste0(prefix,l)) | |
res[as.numeric(ad$Parameter_ID),i]=as.character(ad$Value) | |
} | |
res=data.frame(res) | |
colnames(res) <- cnames | |
``` | |
```{r} | |
library(stringdist) | |
#this will be th LDN distance matrix in the nd | |
resulting_matrix=matrix(0, nrow=ncol(res), ncol=ncol(res)) | |
#this is the denominator in the average. | |
counter_matrix=resulting_matrix | |
#function that calculates the longest word for each entry in the outer product v1%o%v2 | |
len_f=function(v1,v2){ | |
res=matrix(0, nrow=length(v1),ncol=length(v2)) | |
for(i in 1:length(v1)){ | |
for(j in 1:length(v2)){ | |
res[i,j]=max(nchar(v1[i]), nchar(v2[j])) | |
} | |
} | |
return(res) | |
} | |
#calculates the sum and denominator in the average cummulatively for all entries. | |
for(i in 1:nrow(res)){ | |
#x is the translation of a certain term to all languages | |
x=as.character(unlist(res[i,])) | |
#calculates levenshtein differences and rem the missing values | |
to_add=stringdistmatrix(x,x, method="lv")*(x!="0")%o%(x!="0") | |
#the len_f(x,x) term is the N in LDN. | |
counter_matrix=counter_matrix+len_f(x,x)*(to_add>0) | |
resulting_matrix=resulting_matrix+to_add | |
cat(i) | |
} | |
#the average is calculated. | |
resulting_matrix[counter_matrix>0]=resulting_matrix[counter_matrix>0]/counter_matrix[counter_matrix>0] | |
rownames(resulting_matrix) <- cnames | |
colnames(resulting_matrix) <- cnames | |
``` | |
```{r} | |
linguistic_differences=resulting_matrix[nrow(resulting_matrix),] | |
write.csv(cbind(rownames(resulting_matrix), linguistic_differences), file='esperanto_difs.csv', quote=F, row.names = F) | |
``` | |
```{r} | |
language_proportions=read.csv('languages.txt', fill=T, header=F, | |
col.names=c('number','country','L1', | |
'P1','L2','P2','L3','P3', | |
'L4','P4','L5','P5','L6','P6'), | |
stringsAsFactors = F | |
) | |
row_to_number=function(x){ | |
res=0 | |
total_prob=0 | |
for(i in 1:6){ | |
if(!is.na(x[i*2])){ | |
#print(i) | |
#print(res) | |
#print(total_prob) | |
p=as.numeric(x[i*2]) | |
#print(p) | |
#print(as.character(x[(i-1)*2+1])) | |
#print(linguistic_differences[as.character(x[(i-1)*2+1])]) | |
total_prob=total_prob+p | |
res=res+as.numeric(linguistic_differences[as.character(x[(i-1)*2+1])])*p | |
} | |
} | |
res=res/total_prob | |
return(res) | |
} | |
country_to_esperanto=apply(language_proportions[,3:12],1,row_to_number) | |
names(country_to_esperanto) <- language_proportions[,2] | |
``` | |
```{r} | |
esperanto_numbers=read.csv('uploadable.csv', header=F) | |
country_codes=as.character(esperanto_numbers$V1) | |
rplace=function(x, old_val, new_val){ | |
index=which(x==old_val) | |
x[index]=new_val | |
return(x) | |
} | |
esperanto_rates=esperanto_numbers$V2 | |
df=data.frame(esperanto=esperanto_rates^2) | |
rownames(df) <- country_codes | |
``` | |
From http://data.worldbank.org I get GDP data and population size and internet usage | |
```{r} | |
pop=read.csv('pop.csv', skip = 4) | |
rownames(pop) <- pop$Country.Code | |
df$pop=pop[rownames(df), 'X2015'] | |
gdp=read.csv('gdp.csv', skip=4) | |
rownames(gdp) <- gdp$Country.Code | |
df$GDP=gdp[rownames(df), 'X2015'] | |
df$GDPcapita=df$GDP/df$pop | |
www=read.csv('www.csv',skip=4) | |
rownames(www) <- www$Country.Code | |
df$www=www[rownames(df), 'X2015'] | |
``` | |
```{r} | |
ujo=read.table('esperantujodirectory2.txt', header=T) | |
df$continent=ujo[rownames(df), 'continent'] | |
``` | |
#unused variable | |
```{r} | |
prof=read.csv('english_profeciency.txt', sep='\t', header=F) | |
prof=prof[substr(as.character(prof$V1),1,1)!='-',] | |
rownames(prof) <- prof$V1 | |
df$prof=prof[rownames(df),'V4'] | |
``` | |
```{r} | |
div=read.csv('Linguistic Diversity.txt', sep='\t', header=T, comment.char = '#') | |
div=div[substr(as.character(div$ï..CODE),1,1)!='-',] | |
rownames(div) <- div$ï..CODE | |
df$div=as.numeric(as.character(div[rownames(df),'Index'])) | |
``` | |
Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*. | |
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file). | |
```{r} | |
df$ling_dist=country_to_esperanto[rownames(df)] | |
``` | |
```{r} | |
df_tmp=read.csv('goodcountryrankings.txt', header=T) | |
df_cop=df_tmp[rownames(df), colnames(df_tmp)[-1]] | |
df=cbind(df,df_cop) | |
``` | |
```{r} | |
df$europe=(df$continent=='E') | |
``` | |
```{r} | |
#this has profound effects (meaning that it introduces a bias.) | |
#library('DMwR') | |
#df2=knnImputation(df[, !names(df) %in% "esperanto"]) | |
#df2$esperanto=df$esperanto | |
#df=df2 | |
``` | |
```{r} | |
l=lm(log(esperanto) ~ div+ling_dist+www+GDPcapita+continent+science+culture+peace+order+climate+equality+health, data=df[1:(nrow(df)-1),]) | |
summary(l) | |
plot(l) | |
``` | |
```{r} | |
library('car') | |
vif(l) | |
``` | |
```{r} | |
plot(df$ling_dist, log(df$esperanto)) | |
``` | |
```{r} | |
h=summary(l) | |
tvals=h$coefficients[-c(5:7),3] | |
signed_tvals=tvals[-1]*c(c(1,-1,1,1), rep(-1,7)) | |
midpoints=barplot(signed_tvals, xaxt="n", ylab='T statistic',ylim=c(-2.3,2.3), main='Joint analysis') | |
labs <- c('Lang. div.','Eo. simil.', 'Internet','GDP per c', 'Science','Culture','Peace','Order','Climate','Equality','Health') | |
text(cex=1, x=midpoints, y=-1.1*sign(signed_tvals), labs, xpd=TRUE, srt=90) | |
abline(h=qt(0.975,125), col='red') | |
abline(h=-qt(0.975,125), col='red') | |
``` | |
```{r} | |
tr=c("div","ling_dist","www" ,"GDPcapita" , "science", "culture","peace" , "order" , "climate", "equality" , "health") | |
sgn=c(1,-1,1,1,rep(-1,7)) | |
res=rep(0,11) | |
for(n in 1:length(tr)){ | |
lmarg=lm(as.formula(paste("log(esperanto) ~ ", tr[n])), data=df[2:(nrow(df)-1),]) | |
res[n]=summary(lmarg)$coefficients[2,3]*sgn[n] | |
} | |
midpoints=barplot(res, xaxt="n", ylab='T statistic',ylim=c(-15,15), main='Marginal analyses') | |
labs <- c('Lang. div.','Eo. simil.', 'Internet','GDP per c', 'Science','Culture','Peace','Order','Climate','Equality','Health') | |
text(cex=1, x=midpoints, y=-6.1*sign(res), labs, xpd=TRUE, srt=90) | |
abline(h=qt(0.975,125), col='red') | |
abline(h=-qt(0.975,125), col='red') | |
``` | |
```{r} | |
l=lm(log(esperanto) ~ div+ling_dist+www+GDPcapita+continent+science+culture+peace+order+climate+equality+health, data=df[1:(nrow(df)-1),], na.action=na.exclude) | |
nams=names(l$fitted.values) | |
expected=l$fitted.values | |
observed=na.omit(log(df[nams, 'esperanto'][1:(nrow(df)-1)])) | |
residuals=observed-expected | |
limits=quantile(residuals, p=c(0.08,0.94)) | |
bad=which(residuals<limits[1]) | |
good=which(residuals>limits[2]) | |
plot(expected, observed, pch=21, main='Model prediction', | |
xlab='log model density ', ylab='log observed density') | |
points(expected[bad], observed[bad], bg='red', pch=21) | |
points(expected[good], observed[good], bg='green',pch=21) | |
abline(0,1, col='red') | |
``` | |
```{r} | |
bads=cbind(exp(l$fitted.values[nams[bad]])*df[nams[bad],'pop'], df[nams[bad],'esperanto']*df[nams[bad],'pop'])/1000000 | |
goods=cbind(exp(l$fitted.values[nams[good]])*df[nams[good],'pop'], df[nams[good],'esperanto']*df[nams[good],'pop'])/1000000 | |
library(xtable) | |
print(xtable(bads, digits=1), type='html') | |
print(xtable(goods, digits=1), type='html') | |
``` | |
```{r} | |
print(l$coefficients) | |
print(df['GBR',]) | |
cofs=l$coefficients[c(1,2,3,4,5,6,9,10,11,12,13,14,15)] | |
adr=c(1,df['GBR',c('div','ling_dist','www','GDPcapita','continent','science','culture','peace','order','climate','equality','health')]) | |
dfex=na.exclude(df[,c('div','ling_dist','www','GDPcapita','continent','science','culture','peace','order','climate','equality','health')]) | |
fit_value=function(x){ | |
continent=x[5] | |
y=x | |
y[5]=0 | |
contrib=0 | |
if(continent=='E'){ | |
contrib=l$coefficients[6] | |
} | |
if(continent=='F'){ | |
contrib=l$coefficients[7] | |
} | |
if(continent=='M'){ | |
contrib=l$coefficients[8] | |
} | |
f=as.numeric(y)*as.numeric(cofs[-1]) | |
f[5]=contrib | |
return(f) | |
} | |
a=apply(apply(dfex, 1,fit_value),1,mean) | |
b=exp(fit_value(dfex['GBR',])-a) | |
base=exp(sum(a)+cofs[1]) | |
test=prod(b)*base*df['GBR','pop']/1000000 | |
print(b) | |
print(base*df['GBR','pop']/1000000) | |
``` | |
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linguistic_differences | ||
---|---|---|
AFAR | 0.953367875647668 | |
AFRIKAANS | 0.781420765027322 | |
ALBANIAN | 0.88780487804878 | |
AMHARIC | 0.930693069306931 | |
AMOY_MINNAN_CHINESE | 0.924107142857143 | |
AZERBAIJANI_NORTH | 0.922279792746114 | |
B41_SHIRA | 0.883977900552486 | |
BAMBARA | 0.881578947368421 | |
BANGANDU | 0.890173410404624 | |
BARI_SUDAN | 0.894736842105263 | |
BAULE | 0.922535211267606 | |
BELARUSIAN | 0.862244897959184 | |
BELIZE_KRIOL_ENGLISH | 0.694117647058824 | |
BEMBE | 0.880681818181818 | |
BENGALI | 0.827225130890052 | |
BHOJPURI | 0.878787878787879 | |
BISLAMA | 0.820987654320988 | |
BORONG | 0.855721393034826 | |
BOSNIAN | 0.753846153846154 | |
BULGARIAN | 0.803030303030303 | |
BULU | 0.911242603550296 | |
CANTONESE | 0.906779661016949 | |
CAPE_VERDEAN_CREOLE | 0.590163934426229 | |
CATALAN | 0.729166666666667 | |
CENTRAL_AYMARA | 0.917355371900826 | |
CLASSICAL_NAHUATL | 0.887179487179487 | |
CROATIAN | 0.771573604060914 | |
CZECH | 0.807106598984772 | |
DANISH | 0.836065573770492 | |
DATOOGA_DIALECT_2 | 0.938202247191011 | |
DINKA_SOUTHEASTERN | 0.918367346938776 | |
DUTCH | 0.777173913043478 | |
DZONGKHA | 0.905405405405405 | |
EASTERN_FARSI | 0.832460732984293 | |
EASTERN_FRISIAN | 0.740740740740741 | |
ECHIE | 0.909090909090909 | |
ENGLISH | 0.733333333333333 | |
ESTONIAN | 0.9 | |
EWE_ADANGBE | 0.865671641791045 | |
FANG | 0.931506849315068 | |
FIJIAN | 0.894736842105263 | |
FINNISH | 0.941489361702128 | |
FONGBE | 0.9 | |
FRENCH | 0.703703703703704 | |
FULA | 0.916666666666667 | |
FULFULDE | 0.935643564356436 | |
GALICIAN | 0.6524064171123 | |
GEORGIAN | 0.940677966101695 | |
GIKUYU | 0.912790697674419 | |
GREEK | 0.792079207920792 | |
GUARANI | 0.918103448275862 | |
HAITIAN_CREOLE | 0.761904761904762 | |
HAUSA | 0.845410628019324 | |
HEBREW | 0.864583333333333 | |
HERERO | 0.891304347826087 | |
HINDI | 0.807692307692308 | |
HMONG_DAW | 0.92948717948718 | |
HUNGARIAN | 0.91304347826087 | |
ICELANDIC | 0.848341232227488 | |
INDONESIAN | 0.848484848484849 | |
IRISH_GAELIC | 0.898058252427185 | |
ITALIAN | 0.572916666666667 | |
J321_MARAMA | 0.917197452229299 | |
KABIYE | 0.918518518518519 | |
KALANGA | 0.863636363636364 | |
KARIMOJONG | 0.870192307692308 | |
KAZAKH | 0.93048128342246 | |
KHMER | 0.926724137931034 | |
KHMU | 0.905263157894737 | |
KICHE | 0.946808510638298 | |
KINYARWANDA | 0.878947368421053 | |
KIRUNDI | 0.880281690140845 | |
KITUBA | 0.908496732026144 | |
KOONGO | 0.90625 | |
KOREAN | 0.904761904761905 | |
KURDISH_CENTRAL | 0.823204419889503 | |
KWANGALI | 0.843137254901961 | |
KWANYAMA | 0.875776397515528 | |
KYRGYZ | 0.861702127659574 | |
L31a_LUBA_KASAYI | 0.884393063583815 | |
LAO | 0.898477157360406 | |
LATVIAN | 0.884422110552764 | |
LINGALA | 0.883977900552486 | |
LITHUANIAN | 0.848739495798319 | |
LOMWE | 0.879781420765027 | |
LOZI_2 | 0.863905325443787 | |
LUGANDA | 0.8743961352657 | |
LUO | 0.904255319148936 | |
LUXEMBOURGISH | 0.782608695652174 | |
M64_TONGA_1 | 0.890243902439024 | |
MACEDONIAN | 0.8125 | |
MACHAME | 0.895061728395062 | |
MAITHILI | 0.885496183206107 | |
MAKASAE | 0.915151515151515 | |
MAKONDE | 0.919431279620853 | |
MAKWA_ALUA | 0.87431693989071 | |
MALAGASY_PLATEAU_SIHANAKA_AMBATONDRAZAKA | 0.824742268041237 | |
MALANG | 0.898395721925134 | |
MALAY | 0.8743961352657 | |
MALDIVIAN | 0.876811594202899 | |
MALINKE | 0.875 | |
MALTESE | 0.882926829268293 | |
MAMBAI | 0.902173913043478 | |
MANDARIN | 0.915178571428571 | |
MANDINKA | 0.886010362694301 | |
MAORI | 0.877659574468085 | |
MAPUDUNGUN_3 | 0.914141414141414 | |
MARATHI | 0.869109947643979 | |
MAURITIAN | 0.764397905759162 | |
MBEDE | 0.936507936507937 | |
MBOSHI | 0.867052023121387 | |
MENDE | 0.913907284768212 | |
MISKITO | 0.913333333333333 | |
MONGOLIAN | 0.919354838709677 | |
MOORE | 0.888888888888889 | |
NAMA | 0.877094972067039 | |
NEPALI | 0.86046511627907 | |
NGUNGWEL | 0.925 | |
NORTHERN_PASHTO | 0.878306878306878 | |
NORWEGIAN_BOKMAAL | 0.82122905027933 | |
NUER | 0.938461538461538 | |
NYANJA | 0.870466321243523 | |
OLD_TURKIC | 0.942857142857143 | |
PERSIAN | 0.832460732984293 | |
PIJIN | 0.790575916230366 | |
POLISH | 0.869346733668342 | |
PORTUGUESE | 0.722513089005236 | |
PROTO_KAREN | 0.92 | |
PROTO_MAYAN | 0.934131736526946 | |
PROTO_PHILIPPINE | 0.952054794520548 | |
PROTO_QUECHUA | 0.897142857142857 | |
PUNJABI_MAJHI | 0.82 | |
ROMA | 0.889570552147239 | |
ROMANIAN | 0.645833333333333 | |
RUSSIAN | 0.87719298245614 | |
SANGO | 0.859756097560976 | |
SENA | 0.889473684210526 | |
SENUFO_CEBAARA | 0.85427135678392 | |
SERBOCROATIAN | 0.796875 | |
SHAN | 0.900763358778626 | |
SHONA | 0.888297872340426 | |
SINHALA | 0.872340425531915 | |
SIWA_BERBER | 0.915492957746479 | |
SLOVAK | 0.792553191489362 | |
SLOVENIAN | 0.774358974358974 | |
SOMALI | 0.896373056994819 | |
SOTHO_SOUTHERN | 0.903448275862069 | |
SPANISH | 0.658031088082902 | |
SRANAN_TONGO | 0.770114942528736 | |
STANDARD_ARABIC | 0.940217391304348 | |
STANDARD_GERMAN | 0.744791666666667 | |
SUNDANESE | 0.907216494845361 | |
SUSU | 0.931578947368421 | |
SUZHOU_WU | 0.954314720812183 | |
SWAHILI | 0.880829015544041 | |
SWAZI | 0.887323943661972 | |
SWEDISH | 0.841530054644809 | |
TAJIK | 0.83419689119171 | |
TAMASHEQ | 0.935643564356436 | |
TAMIL | 0.859223300970874 | |
TELUGU | 0.890995260663507 | |
TETUN_DILI | 0.894736842105263 | |
THAI | 0.9 | |
THEMNE | 0.902597402597403 | |
TIGRE | 0.914691943127962 | |
TIGRIGNA | 0.907834101382488 | |
TIGRINYA | 0.912037037037037 | |
TIKAR_AKUEN | 0.944444444444444 | |
TOK_PISIN | 0.773869346733668 | |
TOKYO_JAPANESE | 0.913875598086124 | |
TSHANGLA | 0.935643564356436 | |
TSONGA | 0.887700534759358 | |
TSWANA | 0.890547263681592 | |
TUMBUKA_ZAMBIE | 0.906976744186046 | |
TURKISH | 0.916666666666667 | |
TURKMEN | 0.916666666666667 | |
TWI_ASANTE | 0.917582417582418 | |
TWI_FANTE | 0.937984496124031 | |
UKRAINIAN | 0.757731958762887 | |
UMBUNDU | 0.875 | |
URDU | 0.845744680851064 | |
UZBEK | 0.865591397849462 | |
VALPEI | 0.861702127659574 | |
VIETNAMESE | 0.903954802259887 | |
W_OROMO | 0.948979591836735 | |
WE_SOUTHERN | 0.911764705882353 | |
WELSH | 0.835106382978723 | |
WEST_GREENLANDIC | 0.911504424778761 | |
WESTERN_ARMENIAN | 0.91005291005291 | |
WOLOF | 0.868613138686131 | |
XHOSA | 0.919075144508671 | |
YAKOUBA | 0.953488372093023 | |
YANGON_BURMESE | 0.892857142857143 | |
YAO_1 | 0.896174863387978 | |
YORUBA | 0.898936170212766 | |
ZANDE | 0.89261744966443 | |
ZARMA | 0.840425531914894 | |
ZIMBABWE_NDEBELE | 0.890995260663507 | |
ZULU | 0.891402714932127 | |
ESPERANTO | 0 |
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continent name ujo pop uea lernu www pas edu nat | |
AFG A afghanistan 0 27.7 1 48 - 0 0 0 | |
ALB E albanien 0 2.8 14 64 0.6 1 5 33 | |
DZA F algeriet 1 39.2 6 503 0.15 1 8 0 | |
AND E andorra - 0.078 0 167 - 0 8 0 | |
AGO F angola 1 21.5 2 42 0.19 0 0 - | |
ARG M argentino 27 41.5 29 1965 0.60 16 61 140 | |
ARM E armenien 0 3.0 12 92 - 0 1 27 | |
AUS A australio 23 23.1 40 2568 0.83 16 52 130 | |
AUT E austria 4 8.6 44 780 0.81 8 23 79 | |
AZE E azerbadj 1 9.8 1 112 - 0 2 0 | |
BHS M bahamas 1 0.378 1 15 - 0 0 0 | |
BGD A bangladesh 0 156.6 2 49 0.39 0 1 0 | |
BLR E belarus 5 9.5 12 425 0.54 5 6 0 | |
BEL E belgien 16 11.3 143 1695 0.82 37 106 360 | |
BLZ M belize 0 0.380 1 14 - 0 0 0 | |
BEN F benin 0 10.6 14 69 - 6 6 500 | |
BTN A bhutan 0 0.8 0 8 - 0 0 0 | |
BOL M bolivia 1 11.0 2 330 - 0 3 0 | |
BIH E bosnien 1 3.8 13 175 0.72 3 7 300 | |
BWA F botswana 0 2.2 0 8 - 0 0 0 | |
BRA M brasilien 299 200.4 382 16845 0.60 137 577 385 | |
BRN A brunei 0 0.417 0 13 - 0 0 0 | |
BGR E bulgarien 7 7.2 45 622 0.53 11 10 140 | |
BFA F burkina 1 19 0 15 - 0 0 0 | |
BDI F burundi 0 10.1 15 41 - 3 9 95 | |
KHM A cambodja 0 15.6 2 26 - 1 1 - | |
CMR F cameroun 0 22.7 4 35 - 0 1 - | |
CAN M canada 27 35.2 67 3982 0.86 25 63 115 | |
CAF F centralafr 0 5.0 0 26 - 2 0 0 | |
CHL M chile 7 17.6 10 1541 0.67 4 11 - | |
COL M colombia 13 47.12 24 3307 0.52 13 42 - | |
COG F congoBraz 0 4.7 3 13 - 0 0 0 | |
CUB M cuba 2 11.3 35 152 0.26 10 13 350 | |
CYP E cypern 0 0.8 2 64 - 0 2 - | |
DNK E danmark 9 5.7 86 995 0.95 16 22 140 | |
DJI F djibouti 0 0.9 0 8 - 0 0 0 | |
DOM M domrep 2 10.4 3 551 0.52 1 0 0 | |
EGY F egypt 1 82.1 1 364 0.5 1 0 0 | |
ECU M equador 2 15.7 4 642 0.4 1 6 0 | |
GNQ F equatorialguinea 0 1.2 0 5 - 0 0 0 | |
ERI F eritrea 0 5.3 0 7 - 0 0 0 | |
EST E estland 2 1.3 17 151 0.8 2 2 100 | |
TLS A etimor 1 1.2 4 11 - 0 0 0 | |
ETH F etiopien 0 101.9 1 43 - 0 0 0 | |
FJI A fiji - 0.9 0 8 - 0 0 0 | |
PHL A filip 12 98.4 6 584 0.44 1 3 0 | |
FIN E finland 6 5.5 97 1105 0.92 12 35 300 | |
FRA E france 86 66.4 734 7061 0.89 234 637 650 | |
GAB F gabon 0 1.8 0 19 - 0 0 0 | |
GMB F gambia 0 1.9 0 7 - 0 0 0 | |
GEO E georgien 0 3.7 3 71 - 2 2 0 | |
GHA F ghana 0 28.3 5 60 - 3 2 - | |
GRC E greece 1 10.8 11 383 0.6 2 5 65 | |
GRL E greenland 0 0.056 0 7 - 0 0 0 | |
GTM M guatem 3 15.5 2 438 0.20 1 3 - | |
GIN F guinea 0 12.9 0 10 - 0 0 0 | |
GNB F guineabissau 0 1.5 0 7 - 0 0 0 | |
GUY M guyana 0 0.746 0 3 - 0 0 0 | |
HTI M haiti 1 11.1 0 48 - 0 0 0 | |
NLD E holland 25 17 205 2182 0.94 32 106 235 | |
HND M honduras 2 8.1 2 199 0.18 0 1 0 | |
HKG A hongkong - 7.4 2 1048 - 1 3 - | |
ISL E iceland 1 0.323 14 92 0.97 1 2 50 | |
IND A indien 3 1252 14 1371 0.35 4 7 150 | |
IDN A indon 8 249.9 9 1136 0.35 0 15 0 | |
IRQ A irak 2 36.8 0 53 - 0 0 0 | |
IRN A iran 57 77.5 60 1781 0.31 17 39 40 | |
IRL E irland 5 4.6 10 627 0.78 2 2 63 | |
ISR A israel 6 8.1 64 1812 0.71 3 15 160 | |
ITA E italien 29 59.8 171 2997 0.58 27 151 910 | |
CIV A ivory 0 22.7 1 53 - 0 0 100 | |
JAM M jamaica 0 2.7 0 27 - 0 0 0 | |
JPN A japan 10 127.3 378 1399 0.9 11 62 1298 | |
JOR A jordan 1 9.8 0 68 - 0 0 0 | |
KAZ A kasak 2 17.0 8 286 0.54 0 10 0 | |
CPV F kapverde - 0.531 0 23 - 0 0 0 | |
KEN F kenya 0 44.3 3 65 0.39 1 0 - | |
CHN A kina 17 1357 135 8273 0.52 38 91 1144 | |
KGZ A kirigistan 1 6.1 1 37 - 0 2 0 | |
COD F KongoDR 2 67.5 16 118 0.022 3 18 456 | |
KOR A korea 4 50.2 87 1520 0.85 11 44 250 | |
CRI M kostariko 4 4.9 8 636 0.46 3 7 34 | |
HRV E kroatien 4 4.2 32 409 0.67 3 16 316 | |
KWT A kuwait 0 4.2 0 46 - 0 0 0 | |
LAO A laos 0 6.5 0 17 - 0 0 0 | |
LSO F lesotho 0 1.9 0 1 - 0 0 0 | |
LVA E letland 1 2.0 18 160 0.75 1 5 102 | |
LBN A libanon 1 6.0 1 88 - 0 0 0 | |
LBR F liberia 0 4.1 0 8 - 0 0 0 | |
LBY F libya 0 6.4 0 21 - 0 0 0 | |
LIE E liechtenstein - 0.037 0 8 - 0 0 0 | |
LTU E litauen 8 3.0 43 5127 0.68 13 32 960 | |
LUX E luxemborg 2 0.57 17 86 0.94 4 5 104 | |
MDG F madagaskar 1 22.9 6 66 0.61 3 4 70 | |
MKD E makedonien 1 2.1 4 48 - 2 2 45 | |
MWI F malawi 0 16.8 2 7 - 0 3 0 | |
MYS A malaysia 2 31.9 0 662 - 1 5 0 | |
MLD A maldives - 0.344 0 4 - 0 0 0 | |
MLI F mali 0 18.3 1 21 - 0 1 - | |
MTA E malta - 0.425 3 28 0.69 0 1 3 | |
MAR F maroko 1 33.0 5 1096 0.56 0 4 0 | |
MRT F mauretania 0 3.7 0 16 - 0 0 0 | |
MAU F mauritius - 1.3 0 23 - 0 0 0 | |
MEX M mexico 40 122.3 26 5162 0.51 19 67 145 | |
MDA E moldova 0 3.6 1 102 - 1 3 0 | |
MNG A mongoliet 2 2.8 19 58 0.16 3 2 50 | |
MNE E montenegro 0 0.621 1 11 - 0 0 - | |
MOZ F mozambique 0 26.4 0 14 - 0 0 0 | |
MMR A myanmar 1 54.6 1 25 - 0 1 0 | |
NAM F namibia 0 2.3 0 21 - 0 0 0 | |
NPL A nepal 7 31.0 26 115 - 2 4 147 | |
NCL A newkaledonia 0 0.268 2 46 - 1 6 - | |
NZL A newzealand 8 4.5 21 577 0.83 5 6 46 | |
NIC M nicaragua 1 6.1 19 295 0.16 1 8 - | |
NER F niger 0 20.7 3 25 - 1 1 - | |
NGA F nigeria 1 173.6 10 77 0.46 2 2 142 | |
NOR E norge 7 5.2 36 826 0.95 14 14 144 | |
OMN A oman 0 4.6 0 7 - 0 0 0 | |
PAK A pakistano 5 182.1 40 195 0.18 29 2 450 | |
PAN M panamo 0 3.9 0 214 0.43 0 1 0 | |
PNG A papua 0 8.2 0 0 - 0 0 0 | |
PRY M paraguay 0 6.8 0 147 0.37 0 1 0 | |
PER M peru 6 30.4 9 1815 0.39 2 13 - | |
POL E polen 48 38.5 162 4556 0.76 62 197 620 | |
PRT E portugal 5 10.3 11 1025 - 2 17 95 | |
PRI M puertorico 3 3.5 1 288 - 0 2 0 | |
QAT A qatar 0 2.6 0 37 - 0 1 0 | |
ROU E romania 5 19.8 35 422 0.5 3 17 51 | |
RUS E rusland 43 144.0 107 7343 0.71 76 193 148 | |
RWA F rwanda 0 11.6 4 21 - 0 0 0 | |
SLB A salomon 0 0.642 0 3 - 0 0 0 | |
SLV M salvadoro 1 6.3 1 218 0.23 3 2 0 | |
SAU A saudi 0 28.8 0 163 0.61 0 0 0 | |
CHE E schweiz 9 8.1 85 935 0.87 29 44 170 | |
SEN F senegal 0 14.8 4 129 - 6 12 - | |
SRB E serbien 5 7.1 28 234 0.72 5 15 - | |
SLE F sierraleone 0 7.1 0 10 - 0 0 0 | |
SVK E slovakia 2 5.4 30 1418 0.78 5 28 80 | |
SVN E slovenien 0 2.1 14 233 - 1 18 106 | |
SOM F somalia 0 11.1 0 3 - 0 0 0 | |
ESP E spanien 60 46.4 131 5876 0.84 47 207 285 | |
LKA A srilanka 0 21.2 3 31 - 0 0 - | |
SSD F ssudan 0 12.1 0 1 - 0 0 0 | |
ZAF F sudafriko 10 53.0 5 288 0.49 6 2 54 | |
SDN F sudan 0 41.2 0 35 - 0 1 0 | |
SUR M surinam 0 0.541 0 7 - 0 0 0 | |
SWE E sverige 19 9.9 103 1609 0.95 30 46 275 | |
SWZ F swaziland 0 1.3 1 6 - 1 1 0 | |
SYR A syria 1 18.6 0 82 - 0 1 0 | |
TJK A tadjstikistan 1 8.6 3 14 - 1 3 25 | |
TWN A taiwan 5 23.5 10 1771 0.8 3 10 - | |
TZA F tanzania 1 55.2 9 23 - 1 1 - | |
TCD F tchad 0 14.5 3 2 - 0 0 0 | |
THA A thailand 3 67.0 5 277 0.29 3 3 0 | |
CZE E tjekkiet 11 10.5 86 1724 0.74 23 64 105 | |
TGO F togo 0 7.1 28 81 - 6 14 300 | |
TTO M trinidad 0 1.4 0 28 - 0 0 0 | |
TUN F tunesien 0 11.2 5 241 - 0 0 0 | |
TKM A turkmenistan 0 4.8 0 5 - 0 0 0 | |
TUR A tyrkiet 13 74.9 14 1193 0.46 0 14 0 | |
DEU E tyskland 76 81.5 480 6684 0.88 109 226 1086 | |
ARE A uae 2 9.9 1 173 - 2 2 0 | |
UGA F uganda 0 36.9 6 18 - 0 1 - | |
GBR E uk 55 65.1 119 5484 0.82 28 90 385 | |
UKR E ukraine 13 42.9 47 2042 0.62 27 38 160 | |
HUN E ungarn 39 9.8 80 7950 0.73 35 234 120 | |
URY M uruguay 2 3.4 13 278 0.58 2 4 30 | |
USA M usa 210 318.9 245 30152 0.88 76 354 700 | |
UZB A usbekistan 0 31.6 2 72 - 1 1 0 | |
VUT A vanuatu 0 0.277 0 2 - 1 0 0 | |
VEN M venezuela 16 30.4 7 1156 0.55 4 16 32 | |
VNM A vietnam 5 89.7 63 1287 0.44 0 30 200 | |
YEM A yemen 0 27.5 0 28 - 0 0 0 | |
ZMB F zambia 0 15.9 0 15 - 1 2 0 | |
ZWE F zimbabwe 0 14.2 1 30 - 1 1 0 |
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"Data Source","World Development Indicators", | |
"Last Updated Date","2017-02-01", | |
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This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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country,science,culture,peace,order,climate,equality,health | |
ALB,Albania,121,51,148,118,87,49,126 | |
DZA,Algeria,105,135,121,117,98,134,143 | |
AGO,Angola,161,149,128,144,36,96,144 | |
ARG,Argentina,67,57,30,39,86,116,100 | |
ARM,Armenia,48,124,95,111,140,44,69 | |
AUS,Australia,17,43,33,9,54,66,7 | |
AUT,Austria,2,4,71,1,23,18,23 | |
AZE,Azerbaijan,120,142,113,91,49,119,44 | |
BHS,Bahamas,125,46,68,139,37,40,153 | |
BGD,Bangladesh,119,138,59,61,132,123,94 | |
BLR,Belarus,37,94,97,75,69,148,52 | |
BEL,Belgium,12,1,105,18,30,6,9 | |
BLZ,Belize,108,83,54,107,139,154,151 | |
BEN,Benin,85,99,14,105,162,32,111 | |
BOL,Bolivia,151,128,32,54,131,135,148 | |
BIH,Bosnia and Herzegovina,25,66,151,43,109,70,128 | |
BWA,Botswana,147,62,117,106,104,141,62 | |
BRA,Brazil,98,63,37,42,33,158,32 | |
BRN,Brunei Darussalam,140,86,11,127,79,127,123 | |
BGR,Bulgaria,22,29,43,45,42,82,81 | |
BFA,Burkina Faso,89,116,6,109,154,53,115 | |
BDI,Burundi,61,158,38,153,152,133,133 | |
KHM,Cambodia,154,125,144,93,123,62,103 | |
CMR,Cameroon,87,137,28,79,136,43,84 | |
CAN,Canada,21,22,36,8,29,24,3 | |
CAF,Central African Republic,69,159,162,141,134,140,162 | |
TCD,Chad,143,134,86,146,115,146,150 | |
CHL,Chile,64,101,15,24,40,19,66 | |
CHN,China,60,102,16,97,121,118,27 | |
COL,Colombia,65,98,57,122,58,37,124 | |
COG,Congo,158,97,149,120,52,98,122 | |
CRI,Costa Rica,79,47,40,23,85,47,101 | |
HRV,Croatia,33,35,106,90,16,95,51 | |
CYP,Cyprus,5,34,73,13,6,28,55 | |
CZE,Czech Republic,3,8,107,34,48,152,38 | |
CIV,Côte d'Ivoire,115,136,88,150,82,113,95 | |
COD,Democratic Republic of the Congo,130,163,116,152,127,76,97 | |
DNK,Denmark,4,2,49,3,19,3,6 | |
DOM,Dominican Republic,131,118,126,37,72,77,56 | |
ECU,Ecuador,138,91,13,51,114,109,79 | |
EGY,Egypt,52,100,2,98,118,69,45 | |
SLV,El Salvador,136,65,101,149,128,87,73 | |
GNQ,Equatorial Guinea,162,152,159,161,65,161,163 | |
EST,Estonia,32,6,56,67,89,129,42 | |
FJI,Fiji,59,76,85,162,100,73,31 | |
FIN,Finland,7,16,65,12,26,4,4 | |
FRA,France,13,20,47,15,8,20,18 | |
GAB,Gabon,150,148,157,73,53,83,138 | |
GEO,Georgia,81,90,62,112,84,11,35 | |
DEU,Germany,11,7,44,2,17,30,13 | |
GHA,Ghana,76,52,12,71,116,79,156 | |
GRC,Greece,35,42,133,32,11,65,53 | |
GTM,Guatemala,106,73,10,102,156,117,57 | |
GIN,Guinea,91,120,35,104,159,94,161 | |
GNB,Guinea-Bissau,68,126,147,81,145,57,145 | |
GUY,Guyana,137,55,75,154,120,85,41 | |
HTI,Haiti,135,89,123,159,113,120,159 | |
HND,Honduras,129,107,98,96,158,84,137 | |
HUN,Hungary,9,17,60,50,39,63,39 | |
ISL,Iceland,19,37,112,28,1,108,46 | |
IND,India,62,80,27,100,106,124,37 | |
IDN,Indonesia,160,111,19,62,138,35,70 | |
IRN,Iran,118,157,109,64,94,157,74 | |
IRQ,Iraq,163,160,150,156,73,149,98 | |
IRL,Ireland,18,13,51,17,13,23,17 | |
ISR,Israel,39,74,122,68,10,74,54 | |
ITA,Italy,40,24,74,16,5,42,20 | |
JAM,Jamaica,72,81,48,76,108,54,88 | |
JPN,Japan,50,40,22,38,18,60,16 | |
JOR,Jordan,49,141,21,110,130,41,26 | |
KAZ,Kazakhstan,148,133,26,143,129,114,40 | |
KEN,Kenya,56,129,70,57,144,110,68 | |
KWT,Kuwait,132,71,118,136,68,71,59 | |
KGZ,Kyrgyzstan,94,115,76,101,101,156,76 | |
LAO,Laos,73,144,140,157,71,142,106 | |
LVA,Latvia,29,21,90,58,43,125,63 | |
LBN,Lebanon,66,61,153,84,93,22,89 | |
LSO,Lesotho,122,109,80,78,119,144,93 | |
LBR,Liberia,70,156,158,35,161,16,118 | |
LBY,Libya,156,161,161,130,147,153,146 | |
LTU,Lithuania,24,33,141,51,32,100,71 | |
LUX,Luxembourg,43,12,100,5,35,14,15 | |
MDG,Madagascar,103,95,17,148,141,102,160 | |
MWI,Malawi,111,153,91,76,107,59,125 | |
MYS,Malaysia,53,44,46,70,153,9,61 | |
MLI,Mali,139,96,124,82,133,106,142 | |
MRT,Mauritania,157,143,163,129,160,105,109 | |
MEX,Mexico,83,85,84,69,90,36,92 | |
MDA,Moldova,42,45,4,66,67,88,72 | |
MNG,Mongolia,133,154,23,137,142,75,64 | |
MNE,Montenegro,58,54,134,63,81,27,78 | |
MAR,Morocco,82,132,41,125,103,33,87 | |
MOZ,Mozambique,116,93,120,95,124,115,130 | |
NAM,Namibia,77,59,103,114,102,112,48 | |
NLD,Netherlands,15,5,42,4,15,10,10 | |
NZL,New Zealand,6,28,31,19,31,26,14 | |
NIC,Nicaragua,126,114,127,108,148,81,82 | |
NER,Niger,110,121,7,128,151,92,158 | |
NGA,Nigeria,145,151,25,115,88,111,127 | |
NOR,Norway,41,36,58,6,2,17,8 | |
OMN,Oman,144,127,50,88,78,68,77 | |
PAK,Pakistan,102,150,110,87,126,90,49 | |
PAN,Panama,92,68,83,25,125,45,75 | |
PNG,Papua New Guinea,152,112,78,85,96,132,108 | |
PRY,Paraguay,159,105,39,31,117,163,43 | |
PER,Peru,112,103,96,86,77,64,114 | |
PHL,Philippines,146,108,102,55,61,29,85 | |
POL,Poland,31,19,53,30,46,89,50 | |
PRT,Portugal,46,18,131,40,4,12,25 | |
QAT,Qatar,153,110,69,65,51,58,33 | |
KOR,Republic of Korea,30,32,92,29,83,72,28 | |
MCD,Macedonia,27,70,125,59,62,93,119 | |
ROU,Romania,51,27,29,47,28,138,80 | |
RUS,Russian Federation,47,82,104,132,63,143,29 | |
RWA,Rwanda,114,162,55,151,47,122,65 | |
SAU,Saudi Arabia,100,147,82,134,112,39,11 | |
SEN,Senegal,101,119,115,116,157,25,112 | |
SRB,Serbia,28,38,114,48,105,34,104 | |
SLE,Sierra Leone,123,145,93,56,137,97,99 | |
SVK,Slovakia,45,15,77,46,24,99,102 | |
SVN,Slovenia,16,14,152,20,12,104,21 | |
ZAF,South Africa,26,79,1,33,143,160,36 | |
ESP,Spain,34,23,94,22,21,48,19 | |
LKASri Lanka,113,131,24,89,91,50,110 | |
SUR,Suriname,142,113,143,135,110,151,155 | |
SWZ,Swaziland,95,72,155,99,59,139,58 | |
SWE,Sweden,8,3,52,11,7,1,1 | |
CHE,Switzerland,10,31,61,10,3,2,5 | |
SYR,Syria,128,146,160,94,135,131,132 | |
TZA,Tanzania,141,123,5,83,74,121,140 | |
THA,Thailand,80,77,34,21,155,80,30 | |
TGO,Togo,109,106,9,140,163,8,135 | |
TTO,Trinidad and Tobago,84,87,81,92,99,21,152 | |
TUN,Tunisia,78,84,8,41,55,52,120 | |
TUR,Turkey,55,58,72,126,60,103,22 | |
UGA,Uganda,75,130,79,60,70,136,134 | |
UKR,Ukraine,14,75,137,49,95,126,96 | |
ARE,United Arab Emirates,96,56,154,80,64,51,24 | |
GBR,United Kingdom,1,11,64,14,22,5,2 | |
USA,United States of America,20,53,66,26,34,46,12 | |
URY,Uruguay,127,78,3,44,9,128,91 | |
VUT,Vanuatu,90,66,136,138,20,147,131 | |
VEN,Venezuela,149,139,111,74,97,162,136 | |
VNM,Viet Nam,86,88,87,145,146,67,47 | |
YEM,Yemen,97,155,135,124,149,86,83 | |
ZMB,Zambia,117,140,67,53,45,150,107 | |
ZWE,Zimbabwe,88,117,108,142,150,78,90 |
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1,AFG,EASTERN_FARSI,0.5,NORTHERN_PASHTO,0.35,UZBEK,0.11 | |
2,AGO,PORTUGUESE,0.712,UMBUNDU,0.23,KOONGO,0.082 | |
3,ALB,ALBANIAN,0.988,GREEK,0.005 | |
4,AND,CATALAN,1.0 | |
5,ARE,STANDARD_ARABIC,1.0 | |
6,ARG,SPANISH,1.0 | |
7,ARM,WESTERN_ARMENIAN,0.979,KURDISH_CENTRAL,0.01 | |
8,AUS,ENGLISH,0.768,MANDARIN,0.016 | |
9,AUT,STANDARD_GERMAN,0.886,TURKISH,0.023 | |
10,AZE,AZERBAIJANI_NORTH,0.925,RUSSIAN,0.015 | |
11,BDI,KIRUNDI,1.0 | |
12,BEL,DUTCH,0.6,FRENCH,0.4 | |
13,BEN,FRENCH,0.35,FONGBE,0.24 | |
14,BFA,MOORE,0.4,FRENCH,0.15,FULFULDE,0.0836 | |
15,BGD,BENGALI,0.988 | |
16,BGR,BULGARIAN,0.768,TURKISH,0.08,ROMA,0.044 | |
17,BHS,ENGLISH,1.0 | |
18,BIH,BOSNIAN,0.529,SERBOCROATIAN,0.308,CROATIAN,0.146 | |
19,BLR,RUSSIAN,0.702,BELARUSIAN,0.234 | |
20,BLZ,ENGLISH,0.278,SPANISH,0.2502,BELIZE_KRIOL_ENGLISH,0.197,PROTO_MAYAN,0.046 | |
21,BOL,SPANISH,0.607,PROTO_QUECHUA,0.212,CENTRAL_AYMARA,0.146 | |
22,BRA,PORTUGUESE,1.0 | |
23,BRN,MALAY,0.657,MANDARIN,0.103 | |
24,BTN,TSHANGLA,0.28,DZONGKHA,0.24,NEPALI,0.22 | |
25,BWA,TSWANA,0.782,KALANGA,0.079 | |
26,CAF,SANGO,0.92 | |
27,CAN,ENGLISH,0.588,FRENCH,0.216 | |
28,CHE,STANDARD_GERMAN,0.637,FRENCH,0.204,ITALIAN,0.065,SERBOCROATIAN,0.015 | |
29,CHL,SPANISH,0.77,MAPUDUNGUN_3,0.05 | |
30,CHN,MANDARIN,0.662,AMOY_MINNAN_CHINESE,0.062,SUZHOU_WU,0.061 | |
31,CIV,BAULE,0.25,SENUFO_CEBAARA,0.25,YAKOUBA,0.25,WE_SOUTHERN,0.25 | |
32,CMR,FRENCH,0.18,BULU,0.2,TIKAR_AKUEN,0.2,BANGANDU,0.2 | |
33,COD,FRENCH,0.31,KITUBA,0.15,LINGALA,0.15,SWAHILI,0.15,L31a_LUBA_KASAYI,0.15 | |
34,COG,KITUBA,0.32,NGUNGWEL,0.124,TUMBUKA_ZAMBIE,0.112 | |
35,COL,SPANISH,1.0 | |
36,CPV,CAPE_VERDEAN_CREOLE,1.0 | |
37,CRI,SPANISH,1.0 | |
38,CUB,SPANISH,1.0 | |
39,CYP,GREEK,0.809 | |
40,CZE,CZECH,0.949,SLOVAK,0.02 | |
41,DEU,STANDARD_GERMAN,0.95,TURKISH,0.018 | |
42,DJI,SOMALI,0.619,AFAR,0.36,STANDARD_ARABIC,0.07 | |
43,DNK,DANISH,1.0 | |
44,DOM,SPANISH,1.0 | |
45,DZA,STANDARD_ARABIC,0.72,SIWA_BERBER,0.274 | |
46,ECU,SPANISH,0.85 | |
47,EGY,STANDARD_ARABIC,1.0 | |
48,ERI,TIGRINYA,0.5,TIGRE,0.25 | |
49,ESP,SPANISH,0.74,CATALAN,0.17,GALICIAN,0.07 | |
50,EST,ESTONIAN,0.673,RUSSIAN,0.297 | |
51,ETH,AMHARIC,0.327,W_OROMO,0.316,TIGRIGNA,0.061,SOMALI,0.06 | |
52,FIN,FINNISH,0.927,SWEDISH,0.055 | |
53,FJI,FIJIAN,0.54 | |
54,FRA,FRENCH,1.0 | |
55,GAB,FANG,0.3,MBEDE,0.3,B41_SHIRA,0.3,FRENCH,0.05 | |
56,GBR,ENGLISH,0.98,WELSH,0.012 | |
57,GEO,GEORGIAN,0.71,RUSSIAN,0.09,WESTERN_ARMENIAN,0.07 | |
58,GHA,TWI_ASANTE,0.148,EWE_ADANGBE,0.127,TWI_FANTE,0.099,BORONG,0.046 | |
59,GIN,FULA,0.4,MALINKE,0.3,SUSU,0.2 | |
60,GMB,MANDINKA,0.38,FULA,0.212,WOLOF,0.18 | |
61,GNB,CAPE_VERDEAN_CREOLE,0.44,PORTUGUESE,0.11 | |
62,GNQ,SPANISH,0.676 | |
63,GRC,GREEK,0.99 | |
64,GRL,WEST_GREENLANDIC,0.87,DANISH,0.12 | |
65,GTM,SPANISH,0.6,KICHE,0.064 | |
66,GUY,ENGLISH,1.0 | |
67,HKG,CANTONESE,0.908,ENGLISH,0.028 | |
68,HND,SPANISH,1.0 | |
69,HRV,CROATIAN,0.961 | |
70,HTI,HAITIAN_CREOLE,0.9 | |
71,HUN,HUNGARIAN,0.936 | |
72,IDN,MALANG,0.337,INDONESIAN,0.172,SUNDANESE,0.136 | |
73,IND,HINDI,0.41,BENGALI,0.081,TELUGU,0.072,MARATHI,0.07,TAMIL,0.059,URDU,0.05 | |
74,IRL,ENGLISH,0.95,IRISH_GAELIC,0.0234 | |
75,IRN,PERSIAN,0.58,OLD_TURKIC,0.26,KURDISH_CENTRAL,0.09 | |
76,IRQ,STANDARD_ARABIC,0.6,KURDISH_CENTRAL,0.2,TURKMEN,0.05 | |
77,ISL,ICELANDIC,1.0 | |
78,ISR,HEBREW,0.49,STANDARD_ARABIC,0.18,RUSSIAN,0.15 | |
79,ITA,ITALIAN,1.0 | |
80,JAM,ENGLISH,1.0 | |
81,JOR,STANDARD_ARABIC,1.0 | |
82,JPN,TOKYO_JAPANESE,1.0 | |
83,KAZ,KAZAKH,0.644 | |
84,KEN,GIKUYU,0.18,J321_MARAMA,0.117,LUO,0.11 | |
85,KGZ,KYRGYZ,0.647,UZBEK,0.136,RUSSIAN,0.125 | |
86,KHM,KHMER,0.95 | |
87,KOR,KOREAN,1.0 | |
88,KWT,STANDARD_ARABIC,1.0 | |
89,LAO,LAO,0.55,KHMU,0.11,HMONG_DAW,0.05 | |
90,LBN,STANDARD_ARABIC,0.9 | |
91,LBR,MANDINKA,0.2, | |
92,LBY,STANDARD_ARABIC,0.8,SIWA_BERBER,0.16 | |
93,LIE,STANDARD_GERMAN,1.0 | |
94,LKA,SINHALA,0.74,TAMIL,0.18 | |
95,LSO,SOTHO_SOUTHERN,0.9,ZULU,0.5 | |
96,LTU,LITHUANIAN,0.82,RUSSIAN,0.08,POLISH,0.05 | |
97,LUX,LUXEMBOURGISH,0.52,FRENCH,0.16 | |
98,LVA,LATVIAN,0.582,RUSSIAN,0.375 | |
99,MAR,STANDARD_ARABIC,0.4,SIWA_BERBER,0.4 | |
100,MAU,MAURITIAN,0.857,BHOJPURI,0.053,FRENCH,0.041 | |
101,MDA,ROMANIAN,0.758,RUSSIAN,0.16 | |
102,MDG,MALAGASY_PLATEAU_SIHANAKA_AMBATONDRAZAKA,0.697,FRENCH,0.2 | |
103,MEX,SPANISH,0.927,CLASSICAL_NAHUATL,0.014 | |
104,MKD,MACEDONIAN,0.665,ALBANIAN,0.251,TURKISH,0.035 | |
105,MLD,MALDIVIAN,1.0 | |
106,MLI,BAMBARA,0.8 | |
107,MMR,YANGON_BURMESE,0.66,SHAN,0.06,PROTO_KAREN,0.05 | |
108,MNE,SERBOCROATIAN,0.779,BOSNIAN,0.053,ALBANIAN,0.053 | |
109,MNG,MONGOLIAN,0.95 | |
110,MOZ,MAKWA_ALUA,0.253,PORTUGUESE,0.107,TSONGA,0.103,SENA,0.075,LOMWE,0.07 | |
111,MRT,STANDARD_ARABIC,0.8,SIWA_BERBER,0.1 | |
112,MTA,MALTESE,0.902,ENGLISH,0.06 | |
113,MWI,NYANJA,0.8,YAO_1,0.101 | |
114,MYS,MALAY,0.67, | |
115,NAM,KWANYAMA,0.48,NAMA,0.11,AFRIKAANS,0.11,KWANGALI,0.1,HERERO,0.1 | |
116,NCL,FRENCH,1.0 | |
117,NER,HAUSA,0.497,ZARMA,0.255,TAMASHEQ,0.084 | |
118,NGA,HAUSA,0.29,YORUBA,0.21,ECHIE,0.18 | |
119,NIC,SPANISH,0.71,MISKITO,0.025 | |
120,NLD,DUTCH,0.9,EASTERN_FRISIAN | |
121,NOR,NORWEGIAN_BOKMAAL,0.98 | |
122,NPL,NEPALI,0.478,MAITHILI,0.121,BHOJPURI,0.074 | |
123,NZL,ENGLISH,0.912,MAORI,0.039 | |
124,OMN,STANDARD_ARABIC,1.0 | |
125,PAK,PUNJABI_MAJHI,0.58,NORTHERN_PASHTO,0.08,URDU,0.0 | |
126,PAN,SPANISH,0.93 | |
127,PER,SPANISH,0.841,PROTO_QUECHUA,0.13 | |
128,PHL,PROTO_PHILIPPINE,1.0 | |
129,PNG,TOK_PISIN,1.0 | |
130,POL,POLISH,0.978 | |
131,PRI,SPANISH,0.945,ENGLISH,0.053 | |
132,PRT,PORTUGUESE,1.0 | |
133,PRY,GUARANI,0.51,SPANISH,0.49 | |
134,QAT,STANDARD_ARABIC,1.0 | |
135,ROU,ROMANIAN,0.91,HUNGARIAN,0.067 | |
136,RUS,RUSSIAN,1.0 | |
137,RWA,KINYARWANDA,1.0 | |
138,SAU,STANDARD_ARABIC,1.0 | |
139,SDN,STANDARD_ARABIC,1.0 | |
140,SEN,WOLOF,0.8 | |
141,SLB,PIJIN,1.0 | |
142,SLE,THEMNE, 0.375,MENDE,0.295 | |
143,SLV,SPANISH,1.0 | |
144,SOM,SOMALI,1.0 | |
145,SRB,SERBOCROATIAN,0.883,HUNGARIAN,0.038 | |
146,SSD,DINKA_SOUTHEASTERN,0.481,NUER,0.254,BARI_SUDAN,0.144,ZANDE,0.12 | |
147,SUR,DUTCH,0.6,SRANAN_TONGO,0.2 | |
148,SVK,SLOVAK,0.839,HUNGARIAN,0.107 | |
149,SVN,SLOVENIAN,0.911,SERBOCROATIAN,0.045 | |
150,SWE,SWEDISH,1.0 | |
151,SWZ,SWAZI,0.95 | |
152,SYR,STANDARD_ARABIC,1.0 | |
153,TCD,STANDARD_ARABIC,1.0 | |
154,TGO,EWE_ADANGBE,0.4,FRENCH,0.3,KABIYE,0.16, | |
155,THA,THAI,0.3,LAO,0.24 | |
156,TJK,TAJIK,1.0 | |
157,TKM,TURKMEN,0.72,RUSSIAN,0.12,UZBEK,0.09 | |
158,TLS,TETUN_DILI,0.36,MAMBAI,0.129,MAKASAE,0.097 | |
159,TTO,ENGLISH,1.0 | |
160,TUN,STANDARD_ARABIC,1.0 | |
161,TUR,TURKMEN,0.85,UZBEK,0.05,RUSSIAN,0.04 | |
162,TWN,AMOY_MINNAN_CHINESE,1.0 | |
163,TZA,MAKONDE,0.33,DATOOGA_DIALECT_2,0.33,MACHAME,0.33 | |
164,UGA,LUGANDA,0.5,KARIMOJONG,0.5 | |
165,UKR,UKRAINIAN,0.778,RUSSIAN,0.173 | |
166,URY,SPANISH,1.0 | |
167,USA,ENGLISH,0.8,SPANISH,0.124 | |
168,UZB,UZBEK,0.8,RUSSIAN,0.055,TAJIK,0.5 | |
169,VEN,SPANISH,0.9 | |
170,VNM,VIETNAMESE,0.857 | |
171,VUT,VALPEI,0.667,BISLAMA,0.337 | |
172,YEM,STANDARD_ARABIC,1.0 | |
173,ZAF,ZULU,0.227,XHOSA,0.16,AFRIKAANS,0.135,ENGLISH,0.096 | |
174,ZMB,BEMBE,0.334,NYANJA,0.147,M64_TONGA_1,0.114,LOZI_2,0.055 | |
175,ZWE,SHONA,0.7,ZIMBABWE_NDEBELE,0.2 |
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CODE Country Count Percent Established Immigrant Total Mean Median Index Coverage | |
AFG Afghanistan 42 0.59 41 1 22,964,800 560,117 8,000 0.790 98% | |
ALB Albania 12 0.17 8 4 2,801,786 280,179 4,220 0.503 83% | |
DZA Algeria 21 0.30 18 3 33,135,600 1,743,979 40,000 0.360 90% | |
- American Samoa 7 0.10 2 5 55,910 9,318 25,800 0.210 86% | |
AND Andorra 5 0.07 4 1 74,270 14,854 19,650 0.671 100% | |
AGO Angola 40 0.56 40 0 23,511,670 602,863 43,900 0.748 98% | |
- Anguilla 2 0.03 2 0 12,450 6,225 6,225 0.141 100% | |
- Antigua and Barbuda 5 0.07 2 3 135,000 33,750 66,500 0.515 80% | |
ARG Argentina 38 0.54 24 14 44,146,270 1,337,766 8,410 0.165 87% | |
ARM Armenia 13 0.18 7 6 3,041,450 276,495 11,135 0.053 85% | |
- Aruba 6 0.08 4 2 81,090 20,272 9,000 0.429 67% | |
AUS Australia 259 3.65 215 44 21,607,217 87,479 30 0.274 95% | |
AUT Austria 29 0.41 13 16 8,961,530 426,740 24,900 0.234 72% | |
AZE Azerbaijan 37 0.52 18 19 9,246,510 264,186 20,000 0.202 95% | |
BHS Bahamas 5 0.07 3 2 576,280 144,070 284,500 0.509 80% | |
- Bahrain 14 0.20 6 8 724,300 55,715 80,000 0.658 93% | |
BGD Bangladesh 46 0.65 41 5 193,817,420 4,404,941 15,500 0.318 96% | |
- Barbados 3 0.04 3 0 518,290 172,763 256,000 0.500 100% | |
BLR Belarus 11 0.15 4 7 9,170,400 833,673 1,230,500 0.411 100% | |
BEL Belgium 36 0.51 11 25 12,807,200 457,400 200,000 0.700 78% | |
BLZ Belize 12 0.17 8 4 541,160 45,097 14,100 0.721 100% | |
BEN Benin 56 0.79 55 1 6,635,390 122,878 60,500 0.933 96% | |
- Bermuda 3 0.04 2 1 65,610 32,805 63,000 0.076 67% | |
BTN Bhutan 31 0.44 23 8 639,800 25,592 8,000 0.827 81% | |
BOL Bolivia 45 0.63 43 2 7,335,305 170,588 400 0.565 96% | |
BIH Bosnia and Herzegovina 11 0.15 5 6 2,699,500 269,950 383,000 0.694 91% | |
BWA Botswana 38 0.54 31 7 2,213,660 63,247 5,950 0.397 92% | |
BRA Brazil 227 3.20 216 11 204,400,751 959,628 240 0.099 94% | |
- British Indian Ocean Territory 1 0.01 1 0 3,500 3,500 3,500 0.000 100% | |
- British Virgin Islands 2 0.03 2 0 39,700 19,850 19,850 0.500 100% | |
BRN Brunei 17 0.24 15 2 459,685 28,730 8,150 0.570 94% | |
BGR Bulgaria 25 0.35 11 14 8,016,320 364,378 10,500 0.226 88% | |
BFA Burkina Faso 71 1.00 71 0 12,472,930 183,425 20,500 0.721 96% | |
BDI Burundi 4 0.06 3 1 10,036,100 2,509,025 9,600 0.007 100% | |
KHM Cambodia 28 0.39 27 1 15,826,130 608,697 6,220 0.101 93% | |
CMR Cameroon 280 3.94 279 1 10,166,051 37,375 9,500 0.974 97% | |
CAN Canada 164 2.31 94 70 32,967,314 207,342 360 0.603 97% | |
- Cape Verde Islands 4 0.06 2 2 492,000 492,000 492,000 ? 25% | |
- Caribbean Netherlands 6 0.08 5 1 18,540 3,708 3,390 0.707 83% | |
- Cayman Islands 4 0.06 1 3 74,840 18,710 50,000 0.475 100% | |
CAF Central African Republic 83 1.17 72 11 3,488,143 45,301 17,350 0.959 93% | |
TCD Chad 132 1.86 131 1 8,638,559 71,988 13,150 0.933 91% | |
CHL Chile 17 0.24 11 6 17,728,013 1,772,801 10,500 0.036 59% | |
CHN China 301 4.24 299 2 1,298,861,948 4,448,157 27,700 0.521 97% | |
- China–Hong Kong 10 0.14 7 3 7,323,700 813,744 89,800 0.205 90% | |
- China–Macao 8 0.11 6 2 862,790 107,849 19,620 0.253 100% | |
TWN China–Taiwan 28 0.39 22 6 22,093,700 920,571 6,620 0.489 86% | |
COL Colombia 86 1.21 82 4 47,059,630 580,983 1,300 0.019 94% | |
- Comoros 6 0.08 6 0 764,500 152,900 40,000 0.551 83% | |
COG Congo 65 0.92 62 3 3,691,260 59,536 14,400 0.821 95% | |
- Cook Islands 5 0.07 5 0 15,000 3,000 680 0.232 100% | |
CRI Costa Rica 12 0.17 11 1 4,523,430 452,343 2,840 0.036 83% | |
HRV Croatia 25 0.35 15 10 4,433,610 192,766 10,200 0.102 92% | |
CUB Cuba 5 0.07 3 2 11,203,500 3,734,500 5,600,000 0.001 60% | |
- Curacao 6 0.08 5 1 136,910 27,382 8,550 0.285 83% | |
CYP Cyprus 15 0.21 4 11 1,531,180 117,783 154,880 0.444 87% | |
CZE Czechia 26 0.37 10 16 10,794,890 415,188 10,450 0.072 100% | |
CIV Côte d’Ivoire 99 1.39 84 15 12,308,290 146,527 25,200 0.900 85% | |
COD Democratic Republic of the Congo 212 2.99 210 2 39,893,030 199,465 26,000 0.948 94% | |
DNK Denmark 23 0.32 4 19 5,637,000 296,684 16,450 0.089 83% | |
DJI Djibouti 10 0.14 5 5 731,900 104,557 162,000 0.504 70% | |
- Dominica 5 0.07 3 2 52,800 17,600 10,000 0.313 60% | |
DOM Dominican Republic 9 0.13 4 5 9,433,500 1,572,250 159,000 0.040 67% | |
TLS East Timor 20 0.28 20 0 1,040,006 52,000 16,700 0.819 100% | |
ECU Ecuador 25 0.35 24 1 16,279,895 651,196 11,695 0.182 100% | |
EGY Egypt 23 0.32 15 8 94,879,800 4,993,674 752,000 0.512 83% | |
SLV El Salvador 5 0.07 5 0 6,278,000 2,092,667 7,500 0.003 60% | |
GNQ Equatorial Guinea 17 0.24 14 3 699,039 58,253 7,500 0.284 71% | |
ERI Eritrea 20 0.28 15 5 6,465,200 461,800 164,500 0.672 70% | |
EST Estonia 20 0.28 7 13 1,530,870 85,048 44,250 0.473 90% | |
ETH Ethiopia 90 1.27 88 2 68,260,250 784,601 48,000 0.862 97% | |
- Faroe Islands 2 0.03 2 0 48,000 48,000 48,000 ? 50% | |
FJI Fiji 21 0.30 10 11 842,340 64,795 7,700 0.632 62% | |
FIN Finland 37 0.52 12 25 5,611,580 151,664 7,585 0.172 100% | |
FRA France 41 0.58 21 20 69,428,400 1,827,063 118,000 0.252 93% | |
- French Guiana 16 0.23 13 3 79,650 7,241 925 0.549 69% | |
- French Polynesia 10 0.14 9 1 257,960 25,796 3,000 0.463 100% | |
GAB Gabon 43 0.61 43 0 1,074,960 25,594 4,000 0.846 98% | |
GMB Gambia 24 0.34 11 13 1,314,820 69,201 43,650 0.776 79% | |
GEO Georgia 27 0.38 19 8 4,882,620 187,793 17,300 0.550 96% | |
DEU Germany 92 1.30 24 68 86,065,300 1,212,187 80,000 0.336 77% | |
CHA Ghana 87 1.23 81 6 27,661,206 359,236 36,000 0.858 89% | |
- Gibraltar 3 0.04 2 1 45,200 15,067 21,200 0.511 100% | |
GRE Greece 28 0.39 16 12 11,435,480 519,795 9,700 0.123 79% | |
GRL Greenland 3 0.04 3 0 56,200 28,100 28,100 0.196 67% | |
- Grenada 4 0.06 4 0 92,250 30,750 2,300 0.064 75% | |
- Guadeloupe 4 0.06 3 1 449,300 149,767 218,650 0.083 75% | |
- Guam 9 0.13 2 7 191,000 27,286 52,150 0.736 78% | |
GTM Guatemala 26 0.37 26 0 14,519,872 558,457 32,500 0.518 100% | |
- Guernsey 4 0.06 3 1 62,720 31,360 31,360 0.007 50% | |
GIN Guinea 38 0.54 36 2 8,151,300 254,728 23,000 0.748 84% | |
GNB Guinea-Bissau 27 0.38 23 4 1,653,490 75,159 8,520 0.859 81% | |
GUY Guyana 19 0.27 17 2 1,319,093 101,469 1,215 0.514 68% | |
HTI Haiti 4 0.06 4 0 6,960,600 2,320,200 600 0.000 75% | |
HND Honduras 12 0.17 10 2 8,142,850 740,259 990 0.039 92% | |
HUN Hungary 21 0.30 17 4 10,007,110 588,654 3,710 0.033 81% | |
ISL Iceland 3 0.04 2 1 301,000 100,333 150,150 0.007 100% | |
IND India 454 6.40 448 6 1,078,672,781 2,508,541 34,400 0.914 95% | |
IDN Indonesia 709 9.99 707 2 221,455,810 314,568 3,500 0.816 99% | |
IRN Iran 81 1.14 78 3 85,993,020 1,433,217 81,000 0.638 74% | |
IRQ Iraq 27 0.38 24 3 33,742,620 1,467,070 110,000 0.761 85% | |
IRL Ireland 13 0.18 5 8 4,548,940 413,540 21,000 0.118 85% | |
- Isle of Man 2 0.03 2 0 85,000 42,500 42,500 0.000 100% | |
ISR Israel 52 0.73 35 17 8,768,485 190,619 42,500 0.719 88% | |
ITA Italy 44 0.62 34 10 79,507,930 1,939,218 100,000 0.460 93% | |
JAM Jamaica 8 0.11 4 4 2,692,540 448,757 7,500 0.017 75% | |
JPN Japan 18 0.25 15 3 129,302,863 9,235,919 5,050 0.035 78% | |
- Jersey 4 0.06 3 1 64,100 21,367 30,600 0.136 75% | |
JOR Jordan 14 0.20 10 4 5,227,000 522,700 65,300 0.498 71% | |
KAZ Kazakhstan 47 0.66 14 33 15,308,820 356,019 90,000 0.514 91% | |
KEN Kenya 72 1.01 67 5 37,110,868 545,748 178,000 0.927 94% | |
- Kiribati 3 0.04 2 1 103,990 34,663 51,745 0.019 100% | |
KWT Kuwait 7 0.10 3 4 1,749,400 349,880 507,200 0.605 71% | |
KGZ Kyrgyzstan 33 0.46 6 27 5,825,990 215,777 290,650 0.459 82% | |
LAO Laos 87 1.23 81 6 5,773,774 70,412 7,200 0.697 94% | |
LVA Latvia 16 0.23 7 9 2,363,930 168,852 8,000 0.526 88% | |
LBN Lebanon 10 0.14 7 3 4,680,570 585,071 218,000 0.198 80% | |
LSO Lesotho 5 0.07 5 0 2,214,000 553,500 47,000 0.091 80% | |
LBR Liberia 34 0.48 31 3 4,290,730 134,085 85,600 0.917 94% | |
LBY Libya 30 0.42 10 20 6,186,770 229,140 17,000 0.538 90% | |
- Liechtenstein 5 0.07 4 1 35,910 11,970 17,750 0.092 60% | |
LTU Lithuania 15 0.21 10 5 3,702,375 336,580 8,000 0.404 73% | |
LUX Luxembourg 16 0.23 4 12 460,780 28,799 66,100 0.622 100% | |
MKD Macedonia 13 0.18 11 2 2,080,670 208,067 24,900 0.495 77% | |
MDG Madagascar 20 0.28 14 6 18,266,000 1,014,778 1,130,000 0.789 90% | |
MWI Malawi 23 0.32 16 7 13,032,000 766,588 174,500 0.692 74% | |
MYS Malaysia 145 2.04 134 11 22,083,975 210,324 3,400 0.735 72% | |
MLD Maldives 2 0.03 2 0 331,000 331,000 331,000 ? 50% | |
MLI Mali 72 1.01 68 4 12,882,390 189,447 25,000 0.873 94% | |
MTA Malta 7 0.10 3 4 457,170 65,310 16,200 0.255 100% | |
- Marshall Islands 2 0.03 2 0 65,700 32,850 32,850 0.457 100% | |
- Martinique 4 0.06 3 1 427,330 142,443 213,500 0.043 75% | |
MRT Mauritania 8 0.11 7 1 3,589,900 512,843 97,800 0.228 88% | |
MAU Mauritius 14 0.20 7 7 1,234,660 94,974 28,550 0.216 93% | |
- Mayotte 5 0.07 3 2 185,000 46,250 41,700 0.438 80% | |
MEX Mexico 290 4.09 285 5 119,501,413 416,381 4,725 0.106 99% | |
- Micronesia 19 0.27 18 1 115,550 6,419 2,065 0.751 95% | |
MDA Moldova 14 0.20 10 4 3,363,260 480,466 162,000 0.389 50% | |
- Monaco 4 0.06 4 0 31,700 7,925 5,350 0.652 100% | |
MNG Mongolia 14 0.20 12 2 2,875,100 359,388 31,000 0.179 57% | |
MNE Montenegro 9 0.13 5 4 588,140 84,020 32,700 0.244 78% | |
- Montserrat 2 0.03 2 0 3,920 1,960 1,960 0.050 100% | |
MAR Morocco 14 0.20 14 0 27,713,920 2,519,447 80,000 0.461 79% | |
MOZ Mozambique 46 0.65 43 3 20,291,700 450,927 197,500 0.926 98% | |
MMR Myanmar 118 1.66 118 0 46,676,605 398,945 15,000 0.522 99% | |
NAM Namibia 32 0.45 27 5 2,748,410 88,658 12,000 0.779 97% | |
- Nauru 9 0.13 3 6 13,310 2,218 5,005 0.487 67% | |
NPL Nepal 124 1.75 120 4 26,642,691 220,188 7,780 0.755 98% | |
NLD Netherlands 45 0.63 15 30 20,448,000 639,000 220,000 0.405 71% | |
NCL New Caledonia 40 0.56 38 2 143,010 3,972 950 0.750 90% | |
NZL New Zealand 28 0.39 4 24 4,543,920 174,766 148,000 0.291 93% | |
NIC Nicaragua 11 0.15 11 0 5,454,490 495,863 740 0.052 100% | |
NER Niger 22 0.31 22 0 14,000,900 666,710 30,000 0.571 95% | |
NGA Nigeria 527 7.42 520 7 103,910,005 222,030 13,800 0.890 89% | |
- Niue 3 0.04 2 1 1,490 745 745 0.192 67% | |
- Norfolk Island 2 0.03 2 0 2,110 1,055 1,055 0.325 100% | |
- North Korea 1 0.01 1 0 23,300,000 23,300,000 23,300,000 0.000 100% | |
- Northern Mariana Islands 7 0.10 4 3 42,180 6,026 4,620 0.719 100% | |
NOR Norway 21 0.30 11 10 5,261,260 350,751 1,500 0.067 71% | |
OMN Oman 23 0.32 17 6 3,646,500 182,325 30,000 0.698 87% | |
PAK Pakistan 79 1.11 73 6 198,899,690 2,762,496 56,500 0.752 91% | |
- Palau 6 0.08 4 2 19,992 3,332 1,810 0.470 100% | |
- Palestine 10 0.14 6 4 3,686,800 409,644 1,400 0.303 90% | |
PAN Panama 19 0.27 15 4 3,432,843 264,065 12,400 0.287 68% | |
PNG Papua New Guinea 840 11.83 840 0 4,171,079 4,989 1,270 0.988 100% | |
PRY Paraguay 27 0.38 23 4 6,622,608 254,716 2,060 0.215 96% | |
PER Peru 94 1.32 93 1 29,982,662 325,898 4,210 0.339 98% | |
PHL Philippines 191 2.69 183 8 72,034,715 393,632 20,000 0.842 96% | |
- Pitcairn 2 0.03 2 0 36 36 36 ? 50% | |
POL Poland 27 0.38 21 6 38,365,810 1,534,632 12,000 0.050 93% | |
PRT Portugal 18 0.25 9 9 10,346,400 574,800 15,000 0.066 100% | |
PRI Puerto Rico 12 0.17 4 8 3,601,200 360,120 100,000 0.060 83% | |
QAT Qatar 14 0.20 3 11 1,583,200 131,933 320,500 0.825 86% | |
ROU Romania 29 0.41 23 6 21,670,430 1,031,925 19,400 0.153 72% | |
RUS Russian Federation 155 2.18 108 47 140,654,350 937,696 5,100 0.283 97% | |
RWA Rwanda 5 0.07 3 2 11,118,740 2,779,685 5,301,515 0.089 80% | |
- Réunion 8 0.11 3 5 651,200 93,029 310,500 0.452 88% | |
- Saint Barthélemy 3 0.04 3 0 7,850 2,617 1,000 0.244 100% | |
- Saint Kitts and Nevis 3 0.04 3 0 39,240 13,080 200 0.012 100% | |
- Saint Lucia 4 0.06 3 1 159,600 79,800 79,800 0.020 50% | |
- Saint Martin 5 0.07 3 2 28,500 5,700 5,000 0.689 100% | |
- Saint Pierre and Miquelon 3 0.04 2 1 6,790 2,263 3,295 0.110 100% | |
- Saint Vincent and the Grenadines 4 0.06 3 1 138,700 34,675 400 0.010 100% | |
- Samoa 3 0.04 2 1 187,000 62,333 93,400 0.174 100% | |
- San Marino 2 0.03 2 0 25,000 12,500 12,500 0.000 100% | |
SAU Saudi Arabia 24 0.34 11 13 18,723,220 851,055 250,000 0.427 92% | |
SEN Senegal 47 0.66 38 9 13,974,680 303,797 20,000 0.778 98% | |
SRB Serbia 23 0.32 16 7 7,208,080 360,404 31,000 0.224 87% | |
- Seychelles 3 0.04 3 0 85,420 28,473 4,590 0.116 100% | |
SLE Sierra Leone 26 0.37 24 2 6,977,150 290,715 138,000 0.841 92% | |
- Singapore 31 0.44 24 7 5,677,330 210,271 14,100 0.761 87% | |
- Sint Maarten 9 0.13 4 5 39,800 4,422 6,000 0.816 100% | |
SVK Slovakia 15 0.21 10 5 5,506,240 458,853 15,000 0.247 80% | |
SVN Slovenia 15 0.21 9 6 2,094,660 190,424 5,505 0.167 73% | |
SLB Solomon Islands 73 1.03 73 0 380,859 5,290 2,735 0.968 99% | |
SOM Somalia 14 0.20 13 1 9,938,659 828,222 20,000 0.350 86% | |
ZAF South Africa 42 0.59 30 12 51,004,892 1,416,803 133,500 0.871 86% | |
KOR South Korea 5 0.07 3 2 48,649,600 12,162,400 24,202,500 0.010 80% | |
SSD South Sudan 69 0.97 69 0 4,444,650 79,369 26,000 0.929 81% | |
ESP Spain 24 0.34 15 9 45,419,910 2,523,328 42,500 0.276 75% | |
LKA Sri Lanka 12 0.17 7 5 19,983,720 1,998,372 46,000 0.446 83% | |
SDN Sudan 77 1.08 76 1 32,195,500 473,463 13,000 0.307 88% | |
SUR Suriname 27 0.38 21 6 547,035 21,881 1,650 0.709 93% | |
SWZ Swaziland 7 0.10 5 2 1,105,900 157,986 19,000 0.209 100% | |
SWE Sweden 36 0.51 14 22 10,056,090 295,767 3,200 0.226 94% | |
CHE Switzerland 25 0.35 12 13 8,772,330 438,616 30,500 0.683 80% | |
SYR Syria 24 0.34 18 6 23,785,610 1,081,164 76,000 0.363 92% | |
- São Tomé e Príncipe 7 0.10 4 3 120,090 17,156 7,540 0.630 100% | |
TJK Tajikistan 33 0.46 13 20 7,580,210 303,208 27,500 0.276 76% | |
TZA Tanzania 126 1.77 125 1 48,071,890 400,599 86,000 0.871 95% | |
THA Thailand 83 1.17 70 13 52,892,781 734,622 13,800 0.752 87% | |
TGO Togo 46 0.65 44 2 4,889,380 113,707 34,300 0.905 93% | |
- Tokelau 5 0.07 2 3 2,421 484 870 0.679 100% | |
- Tonga 4 0.06 3 1 105,550 26,388 1,020 0.029 100% | |
TTO Trinidad and Tobago 9 0.13 7 2 2,632,500 329,062 20,000 0.599 89% | |
TUN Tunisia 13 0.18 6 7 11,532,500 961,042 21,200 0.122 92% | |
TUR Turkey 52 0.73 39 13 82,840,960 2,124,127 66,000 0.345 75% | |
TKM Turkmenistan 31 0.44 6 25 5,300,460 176,682 256,850 0.457 97% | |
- Turks and Caicos Islands 3 0.04 2 1 11,620 5,810 5,810 0.146 67% | |
- Tuvalu 3 0.04 3 0 10,100 5,050 5,050 0.020 67% | |
- U.S. Virgin Islands 6 0.08 3 3 167,410 33,482 52,300 0.550 83% | |
UGA Uganda 45 0.63 43 2 33,690,850 765,701 303,000 0.929 98% | |
UKR Ukraine 40 0.56 22 18 49,826,886 1,346,673 75,600 0.489 92% | |
ARE United Arab Emirates 34 0.48 7 27 5,651,610 235,484 407,000 0.707 71% | |
GBR United Kingdom 84 1.18 11 73 61,536,550 809,691 57,400 0.154 90% | |
USA United States 347 4.89 220 127 316,386,398 944,437 25 0.333 97% | |
URY Uruguay 14 0.20 4 10 3,469,050 385,450 27,000 0.111 64% | |
UZB Uzbekistan 38 0.54 10 28 30,973,900 910,997 378,000 0.466 89% | |
VUT Vanuatu 117 1.65 111 6 192,536 1,674 680 0.973 98% | |
- Vatican State 2 0.03 2 0 330 165 165 0.000 100% | |
VEN Venezuela 51 0.72 44 7 29,699,865 645,649 705 0.040 90% | |
VNM Viet Nam 111 1.56 109 2 76,935,380 761,736 13,500 0.267 91% | |
- Wallis and Futuna 3 0.04 3 0 12,960 4,320 3,900 0.548 100% | |
YEM Yemen 14 0.20 11 3 15,762,900 1,432,991 59,950 0.581 79% | |
ZMB Zambia 56 0.79 46 10 11,433,470 238,197 32,850 0.830 86% | |
ZWE Zimbabwe 23 0.32 21 2 11,688,440 649,358 145,000 0.597 78% |
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"Data Source","World Development Indicators", | |
"Last Updated Date","2017-02-01", | |
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