placestats

Environment score vs. Equality score

Holds up after adjusting for income

Across 162 countries, Environment score and Equality score rise and fall together at r = -0.34 (on a scale from −1 to +1, where 0 means no link). It holds up almost unchanged when only countries with similar income (GDP per person) are compared (r = -0.25, q = 0.032, allowing for the many pairs tested).

Comparing countries across the middle half of environment score, -0.378 to 0.29 typical gaps, equality score is worse by 0.326 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.189 typical gaps.

Link before adjusting (r)-0.340p = 9.7e-6 · 162 countries
After adjusting for national income-0.252p = 0.001 · likely range [-0.39, -0.10]
p, allowing for regions0.002standard error 0.050 · 7 regions
Check for luck (q)0.032across 14,952 pairs

How to read these.r runs from −1 (one goes up as the other goes down) to +1 (they rise together), and 0 means no link. Adjusting for national income means comparing countries with the same GDP per person, so differences in wealth can’t explain the link. Income is used in percentage terms, since an extra $1,000 means more to a poor country than to a rich one. p is how likely a link this strong would be if there were really none. Below 0.05 means it is unlikely to be chance. Very small values are written in short form: 1.1e-32 means a decimal point followed by 31 zeros and then 11, so very close to zero. The likely range is where the true figure probably lies, and a range that crosses zero means the link could easily be chance. Countries in the same region tend to resemble each other, so the third figure allows for that, which gives a fairer p. The standard error is the typical size of the error in r. Finally, testing this many pairs means some will look strong by luck alone. A q below 0.05 means this link is unlikely to be one of those.

Environment score · typical gaps (standard deviations)link with income -0.20 · independent
Equality score · typical gaps (standard deviations)link with income +0.64 · high

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

Environment score · typical gaps (standard deviations) →Equality score · typical gaps (standard deviations) →-1.361.41-2.511.06Angola: 0.161 typical gaps (standard deviations), -1.52 typical gaps (standard deviations)Albania: 0.126 typical gaps (standard deviations), 0.74 typical gaps (standard deviations)United Arab Emirates: -0.764 typical gaps (standard deviations), 0.92 typical gaps (standard deviations)Argentina: -0.619 typical gaps (standard deviations), -0.0243 typical gaps (standard deviations)Armenia: 0.171 typical gaps (standard deviations), 0.842 typical gaps (standard deviations)Australia: -0.479 typical gaps (standard deviations), 0.472 typical gaps (standard deviations)Austria: 0.246 typical gaps (standard deviations), 0.632 typical gaps (standard deviations)Burundi: -0.0681 typical gaps (standard deviations), -1.63 typical gaps (standard deviations)Belgium: -0.458 typical gaps (standard deviations), 0.895 typical gaps (standard deviations)Benin: 0.414 typical gaps (standard deviations), -0.24 typical gaps (standard deviations)Burkina Faso: -0.0452 typical gaps (standard deviations), -0.796 typical gaps (standard deviations)Bangladesh: -0.492 typical gaps (standard deviations), 0.51 typical gaps (standard deviations)Bulgaria: 0.511 typical gaps (standard deviations), 0.142 typical gaps (standard deviations)Bosnia and Herzegovina: -0.345 typical gaps (standard deviations), 0.688 typical gaps (standard deviations)Belarus: -0.309 typical gaps (standard deviations), 1.03 typical gaps (standard deviations)Belize: 0.901 typical gaps (standard deviations), 0.119 typical gaps (standard deviations)Bolivia: 0.485 typical gaps (standard deviations), 0.0335 typical gaps (standard deviations)Brazil: 0.586 typical gaps (standard deviations), -0.529 typical gaps (standard deviations)Barbados: -0.776 typical gaps (standard deviations), 0.434 typical gaps (standard deviations)Bhutan: 1.31 typical gaps (standard deviations), 0.799 typical gaps (standard deviations)Botswana: 0.111 typical gaps (standard deviations), -1.27 typical gaps (standard deviations)Central African Republic: 0.95 typical gaps (standard deviations), -1.88 typical gaps (standard deviations)Canada: -0.387 typical gaps (standard deviations), 0.62 typical gaps (standard deviations)Switzerland: -0.314 typical gaps (standard deviations), 0.51 typical gaps (standard deviations)Chile: -0.186 typical gaps (standard deviations), -0.0433 typical gaps (standard deviations)China: -0.489 typical gaps (standard deviations), 0.369 typical gaps (standard deviations)Cote d'Ivoire: -0.00841 typical gaps (standard deviations), -0.127 typical gaps (standard deviations)Cameroon: 0.149 typical gaps (standard deviations), -0.675 typical gaps (standard deviations)Congo, Dem. Rep.: 0.825 typical gaps (standard deviations), -2.33 typical gaps (standard deviations)Congo, Rep.: 0.909 typical gaps (standard deviations), -1.38 typical gaps (standard deviations)Colombia: 0.211 typical gaps (standard deviations), -0.906 typical gaps (standard deviations)Comoros: 0.371 typical gaps (standard deviations), 0.565 typical gaps (standard deviations)Cabo Verde: -0.266 typical gaps (standard deviations), -0.375 typical gaps (standard deviations)Costa Rica: 0.544 typical gaps (standard deviations), -0.197 typical gaps (standard deviations)Cyprus: 0.125 typical gaps (standard deviations), 0.61 typical gaps (standard deviations)Czechia: -0.0912 typical gaps (standard deviations), 0.96 typical gaps (standard deviations)Germany: 0.291 typical gaps (standard deviations), 0.483 typical gaps (standard deviations)Djibouti: -0.758 typical gaps (standard deviations), -0.607 typical gaps (standard deviations)Denmark: -0.462 typical gaps (standard deviations), 0.709 typical gaps (standard deviations)Dominican Republic: 0.273 typical gaps (standard deviations), 0.176 typical gaps (standard deviations)Algeria: -0.942 typical gaps (standard deviations), 0.851 typical gaps (standard deviations)Ecuador: 0.265 typical gaps (standard deviations), -0.287 typical gaps (standard deviations)Egypt, Arab Rep.: -0.655 typical gaps (standard deviations), 0.763 typical gaps (standard deviations)Spain: 0.143 typical gaps (standard deviations), 0.498 typical gaps (standard deviations)Estonia: 0.148 typical gaps (standard deviations), 0.665 typical gaps (standard deviations)Ethiopia: 0.224 typical gaps (standard deviations), -0.344 typical gaps (standard deviations)Finland: 0.21 typical gaps (standard deviations), 0.857 typical gaps (standard deviations)Fiji: -0.00523 typical gaps (standard deviations), 0.552 typical gaps (standard deviations)France: 0.104 typical gaps (standard deviations), 0.607 typical gaps (standard deviations)Micronesia, Fed. Sts.: 1.41 typical gaps (standard deviations), -0.539 typical gaps (standard deviations)Gabon: 0.838 typical gaps (standard deviations), 0.156 typical gaps (standard deviations)United Kingdom: -0.167 typical gaps (standard deviations), 0.565 typical gaps (standard deviations)Georgia: 0.13 typical gaps (standard deviations), 0.381 typical gaps (standard deviations)Ghana: 0.0042 typical gaps (standard deviations), -1.07 typical gaps (standard deviations)Guinea: 0.564 typical gaps (standard deviations), 0.435 typical gaps (standard deviations)Gambia, The: -0.0847 typical gaps (standard deviations), -0.359 typical gaps (standard deviations)Guinea-Bissau: 1.13 typical gaps (standard deviations), -0.51 typical gaps (standard deviations)Equatorial Guinea: 0.635 typical gaps (standard deviations), -0.00131 typical gaps (standard deviations)Greece: 0.286 typical gaps (standard deviations), 0.503 typical gaps (standard deviations)Grenada: -0.00211 typical gaps (standard deviations), -0.0995 typical gaps (standard deviations)Guatemala: 0.0972 typical gaps (standard deviations), -0.409 typical gaps (standard deviations)Honduras: 0.44 typical gaps (standard deviations), -0.592 typical gaps (standard deviations)Croatia: 0.352 typical gaps (standard deviations), 0.7 typical gaps (standard deviations)Haiti: -0.248 typical gaps (standard deviations), -0.965 typical gaps (standard deviations)Hungary: -0.203 typical gaps (standard deviations), 0.656 typical gaps (standard deviations)Indonesia: -0.0353 typical gaps (standard deviations), 0.365 typical gaps (standard deviations)India: -0.45 typical gaps (standard deviations), 1.05 typical gaps (standard deviations)Ireland: -0.609 typical gaps (standard deviations), 0.768 typical gaps (standard deviations)Iran, Islamic Rep.: -0.896 typical gaps (standard deviations), 0.31 typical gaps (standard deviations)Iraq: -1.06 typical gaps (standard deviations), 0.712 typical gaps (standard deviations)Iceland: -0.648 typical gaps (standard deviations), 0.895 typical gaps (standard deviations)Israel: -0.327 typical gaps (standard deviations), 0.227 typical gaps (standard deviations)Italy: -0.13 typical gaps (standard deviations), 0.443 typical gaps (standard deviations)Jamaica: 0.249 typical gaps (standard deviations), 0.109 typical gaps (standard deviations)Jordan: -0.817 typical gaps (standard deviations), 0.498 typical gaps (standard deviations)Japan: 0.488 typical gaps (standard deviations), 0.55 typical gaps (standard deviations)Kazakhstan: -0.982 typical gaps (standard deviations), 0.759 typical gaps (standard deviations)Kenya: -0.239 typical gaps (standard deviations), -0.947 typical gaps (standard deviations)Kyrgyz Republic: -0.649 typical gaps (standard deviations), 0.8 typical gaps (standard deviations)Kiribati: -0.15 typical gaps (standard deviations), 0.994 typical gaps (standard deviations)Korea, Rep.: 0.0613 typical gaps (standard deviations), 0.544 typical gaps (standard deviations)Lao PDR: 0.194 typical gaps (standard deviations), 0.261 typical gaps (standard deviations)Lebanon: -0.563 typical gaps (standard deviations), 0.248 typical gaps (standard deviations)Liberia: 0.44 typical gaps (standard deviations), -0.456 typical gaps (standard deviations)St. Lucia: 0.251 typical gaps (standard deviations), -0.0757 typical gaps (standard deviations)Sri Lanka: 0.377 typical gaps (standard deviations), 0.202 typical gaps (standard deviations)Lesotho: -0.0865 typical gaps (standard deviations), -1.22 typical gaps (standard deviations)Lithuania: -0.162 typical gaps (standard deviations), 0.341 typical gaps (standard deviations)Luxembourg: 0.228 typical gaps (standard deviations), 0.504 typical gaps (standard deviations)Latvia: 0.159 typical gaps (standard deviations), 0.473 typical gaps (standard deviations)Morocco: -0.723 typical gaps (standard deviations), 0.0727 typical gaps (standard deviations)Moldova: -0.6 typical gaps (standard deviations), 0.897 typical gaps (standard deviations)Madagascar: 0.0256 typical gaps (standard deviations), -1.46 typical gaps (standard deviations)Maldives: -0.811 typical gaps (standard deviations), 0.754 typical gaps (standard deviations)Mexico: -0.208 typical gaps (standard deviations), -0.0512 typical gaps (standard deviations)Marshall Islands: 1.27 typical gaps (standard deviations), 0.344 typical gaps (standard deviations)North Macedonia: 0.199 typical gaps (standard deviations), 0.412 typical gaps (standard deviations)Mali: -0.342 typical gaps (standard deviations), -0.544 typical gaps (standard deviations)Malta: -0.267 typical gaps (standard deviations), 0.6 typical gaps (standard deviations)Myanmar: -0.0285 typical gaps (standard deviations), 0.408 typical gaps (standard deviations)Montenegro: 0.725 typical gaps (standard deviations), 0.402 typical gaps (standard deviations)Mongolia: -0.55 typical gaps (standard deviations), 0.623 typical gaps (standard deviations)Mozambique: 0.668 typical gaps (standard deviations), -2.51 typical gaps (standard deviations)Mauritania: -0.813 typical gaps (standard deviations), 0.336 typical gaps (standard deviations)Mauritius: -0.67 typical gaps (standard deviations), 0.31 typical gaps (standard deviations)Malawi: 0.29 typical gaps (standard deviations), -1.72 typical gaps (standard deviations)Malaysia: -0.0832 typical gaps (standard deviations), 0.0991 typical gaps (standard deviations)Namibia: 0.287 typical gaps (standard deviations), -1.55 typical gaps (standard deviations)Niger: -0.0158 typical gaps (standard deviations), -1.01 typical gaps (standard deviations)Nigeria: -0.142 typical gaps (standard deviations), -0.587 typical gaps (standard deviations)Nicaragua: 0.266 typical gaps (standard deviations), -0.387 typical gaps (standard deviations)Netherlands: -0.417 typical gaps (standard deviations), 0.958 typical gaps (standard deviations)Norway: -0.266 typical gaps (standard deviations), 0.909 typical gaps (standard deviations)Nepal: 0.446 typical gaps (standard deviations), 0.652 typical gaps (standard deviations)Naoero: -1.36 typical gaps (standard deviations), 0.326 typical gaps (standard deviations)Pakistan: -0.261 typical gaps (standard deviations), -0.0801 typical gaps (standard deviations)Panama: 0.629 typical gaps (standard deviations), -0.497 typical gaps (standard deviations)Peru: 0.321 typical gaps (standard deviations), 0.00216 typical gaps (standard deviations)Philippines: -0.169 typical gaps (standard deviations), 0.0429 typical gaps (standard deviations)Poland: 0.261 typical gaps (standard deviations), 0.794 typical gaps (standard deviations)Portugal: 0.0342 typical gaps (standard deviations), 0.479 typical gaps (standard deviations)Paraguay: 0.000094 typical gaps (standard deviations), -0.156 typical gaps (standard deviations)West Bank and Gaza: -0.918 typical gaps (standard deviations), 0.292 typical gaps (standard deviations)Qatar: -1.07 typical gaps (standard deviations), 0.421 typical gaps (standard deviations)Romania: -0.023 typical gaps (standard deviations), 0.707 typical gaps (standard deviations)Russian Federation: -0.306 typical gaps (standard deviations), 0.539 typical gaps (standard deviations)Rwanda: 0.0329 typical gaps (standard deviations), -0.821 typical gaps (standard deviations)Sudan: -0.493 typical gaps (standard deviations), 0.212 typical gaps (standard deviations)Senegal: 0.476 typical gaps (standard deviations), -0.104 typical gaps (standard deviations)Solomon Islands: 0.565 typical gaps (standard deviations), -0.732 typical gaps (standard deviations)Sierra Leone: 0.222 typical gaps (standard deviations), -0.683 typical gaps (standard deviations)El Salvador: -0.26 typical gaps (standard deviations), 0.0323 typical gaps (standard deviations)Serbia: -0.0414 typical gaps (standard deviations), 0.524 typical gaps (standard deviations)Sao Tome and Principe: 0.815 typical gaps (standard deviations), -0.236 typical gaps (standard deviations)Suriname: 0.523 typical gaps (standard deviations), 0.129 typical gaps (standard deviations)Slovak Republic: 0.381 typical gaps (standard deviations), 1.06 typical gaps (standard deviations)Slovenia: 0.723 typical gaps (standard deviations), 1.02 typical gaps (standard deviations)Sweden: 0.257 typical gaps (standard deviations), 0.733 typical gaps (standard deviations)Eswatini: -0.382 typical gaps (standard deviations), -1.85 typical gaps (standard deviations)Seychelles: 1.37 typical gaps (standard deviations), 0.575 typical gaps (standard deviations)Syrian Arab Republic: -0.83 typical gaps (standard deviations), 0.456 typical gaps (standard deviations)Chad: 0.0555 typical gaps (standard deviations), -0.729 typical gaps (standard deviations)Togo: 0.37 typical gaps (standard deviations), -0.634 typical gaps (standard deviations)Thailand: -0.0809 typical gaps (standard deviations), 0.524 typical gaps (standard deviations)Tajikistan: -0.207 typical gaps (standard deviations), 0.206 typical gaps (standard deviations)Timor-Leste: 0.626 typical gaps (standard deviations), -0.346 typical gaps (standard deviations)Tonga: -0.421 typical gaps (standard deviations), 0.87 typical gaps (standard deviations)Tunisia: -0.741 typical gaps (standard deviations), 0.483 typical gaps (standard deviations)Turkiye: -0.549 typical gaps (standard deviations), -0.0783 typical gaps (standard deviations)Tuvalu: 0.231 typical gaps (standard deviations), -0.0409 typical gaps (standard deviations)Tanzania: 1.04 typical gaps (standard deviations), -1.21 typical gaps (standard deviations)Uganda: 0.0236 typical gaps (standard deviations), -1.16 typical gaps (standard deviations)Ukraine: -0.368 typical gaps (standard deviations), 0.966 typical gaps (standard deviations)Uruguay: -0.769 typical gaps (standard deviations), 0.134 typical gaps (standard deviations)United States: -0.491 typical gaps (standard deviations), 0.00761 typical gaps (standard deviations)Uzbekistan: -0.714 typical gaps (standard deviations), 0.499 typical gaps (standard deviations)Viet Nam: -0.262 typical gaps (standard deviations), 0.322 typical gaps (standard deviations)Vanuatu: 0.276 typical gaps (standard deviations), 0.079 typical gaps (standard deviations)Samoa: 0.0432 typical gaps (standard deviations), 0.098 typical gaps (standard deviations)South Africa: -0.667 typical gaps (standard deviations), -1.12 typical gaps (standard deviations)Zambia: 1.13 typical gaps (standard deviations), -2.37 typical gaps (standard deviations)Zimbabwe: 0.403 typical gaps (standard deviations), -1.72 typical gaps (standard deviations)BDIBTNCODDZAIRQMHLMOZNRUSWZSYCZAFZMB

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.3409.7e-6162
By rank (Spearman), not adjusted-0.3291.9e-5162
Standard (Pearson), adjusted for national income-0.2520.001162
By rank (Spearman), adjusted for national income-0.2550.001162

The rank version uses only each country’s place in line, from lowest to highest, so a few extreme countries count for less. The adjusted and unadjusted versions use the same 162 countries: those that report both measures and GDP per person. So the gap between them comes from the adjustment, not from a change in which countries are counted. Drop in the size of r after adjusting: +0.088 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.278 (without BTN) to -0.225 (without ZMB). The bar above shows that range on a scale from −1 to +1, with the solid line at the full result. The single country that moves r most: ZMB (+0.027).

Within each region

Countries are not separate, unrelated cases, and neighbors share a lot. A link that holds inside each region is far more believable than one that shows up only when comparing regions.

nr
East Asia & Pacific23-0.42
Europe & Central Asia46-0.16
Latin America & Caribbean24-0.21
Middle East, North Africa, Afghanistan & Pakistan16-0.32
Sub-Saharan Africa45-0.12

By income third

If the link shows up only in the richest third of countries, it is a feature of rich countries, not a general pattern.

nr
Poorest third54-0.36
Middle third54-0.30
Richest third54+0.07

By largest religious group

Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.

nr
Christians103-0.11
Muslims39-0.26

By last european coloniser

Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.

nr
France24-0.31
Never a European colony16-0.13
Spain20-0.45
United Kingdom48-0.27

Without outliers (countries far outside the middle half of countries on either measure, by more than one and a half times the width of that middle half): r = -0.254 after dropping 8 countries.

Two series, one country

Both measures over the years for one country you pick. The two charts are stacked so they share only the years along the bottom.

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Over time, within countries

Everything above compares countries with each other at one moment. This section compares each country only with itself over 0years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Not enough history inside each country to look at this over time. That needs countries measured for several years in a row, and at least one of these measures is measured too rarely.

The data


typical gaps (standard deviations)

typical gaps (standard deviations)
Years
Micronesia, Fed. Sts. FSM1.41-0.5392024, 2013
Seychelles SYC1.370.5752024, 2018
Bhutan BTN1.310.7992024, 2022
Marshall Islands MHL1.270.3442024, 2019
Guinea-Bissau GNB1.13-0.512024, 2021
Zambia ZMB1.13-2.372024, 2022
Tanzania TZA1.04-1.212024, 2018
Central African Republic CAF0.95-1.882024, 2021
Congo, Rep. COG0.909-1.382024, 2011
Belize BLZ0.9010.1192024, 2018
Gabon GAB0.8380.1562024, 2017
Congo, Dem. Rep. COD0.825-2.332024, 2020
Sao Tome and Principe STP0.815-0.2362024, 2017
Montenegro MNE0.7250.4022024, 2021
Slovenia SVN0.7231.022024, 2023

wdi
wdi

Sources

Environment score

Environment score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 3 measures from 1 publisher. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Forest area, CO2 per capita, Protected land.

Equality score

Equality score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 2 measures from 1 publisher. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Extreme poverty rate, Gini index.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2025. To adjust for income, each measure is first predicted from GDP per person (in percentage terms, on a log scale) with a straight-line fit. Only what is left over is then compared. This is called a partial correlation. The line is drawn the standard way (ordinary least squares), which picks the line that comes closest to all the countries at once. The error margins allow for countries in the same region resembling each other, using 7 World Bank regions. The q-value corrects for testing all 14,952 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Environment score and Equality score

This page shows that two measures go together. It does not show that Environment score changes Equality score, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Environment score or Equality score