placestats

Equality score vs. HIV prevalence

Holds up after adjusting for income

Across 130 countries, Equality score and HIV prevalence rise and fall together at r = -0.58 (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 = -0.60, q = 0.0078, allowing for the many pairs tested). Caution: correlation moves sharply once outliers are trimmed.

Comparing countries across the middle half of hiv prevalence, 0.1% to 1% of adults aged 15-49, equality score is worse by 0.12 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0932 typical gaps.

Link before adjusting (r)-0.584p = 3.0e-13 · 130 countries
After adjusting for national income-0.598p = 7.6e-14 · likely range [-0.70, -0.47]
p, allowing for regions0.018standard error 0.184 · 7 regions
Check for luck (q)0.008across 12,497 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.

Equality score · typical gaps (standard deviations)link with income +0.64 · high
HIV prevalence · % of adults aged 15-49link with income -0.40 · moderate

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

Every country as a dot

Equality score · typical gaps (standard deviations) →HIV prevalence · % of adults aged 15-49 →-2.511.060.1%23.4%Angola: -1.52 typical gaps (standard deviations), 1.6% of adults aged 15-49Albania: 0.741 typical gaps (standard deviations), 0.1% of adults aged 15-49United Arab Emirates: 0.921 typical gaps (standard deviations), 0.1% of adults aged 15-49Argentina: -0.0236 typical gaps (standard deviations), 0.4% of adults aged 15-49Armenia: 0.843 typical gaps (standard deviations), 0.4% of adults aged 15-49Australia: 0.473 typical gaps (standard deviations), 0.1% of adults aged 15-49Burundi: -1.63 typical gaps (standard deviations), 0.8% of adults aged 15-49Belgium: 0.895 typical gaps (standard deviations), 0.2% of adults aged 15-49Benin: -0.24 typical gaps (standard deviations), 0.7% of adults aged 15-49Burkina Faso: -0.796 typical gaps (standard deviations), 0.5% of adults aged 15-49Bangladesh: 0.51 typical gaps (standard deviations), 0.1% of adults aged 15-49Bulgaria: 0.143 typical gaps (standard deviations), 0.1% of adults aged 15-49Belize: 0.12 typical gaps (standard deviations), 1.2% of adults aged 15-49Bolivia: 0.0341 typical gaps (standard deviations), 0.4% of adults aged 15-49Brazil: -0.529 typical gaps (standard deviations), 0.7% of adults aged 15-49Barbados: 0.435 typical gaps (standard deviations), 1% of adults aged 15-49Bhutan: 0.8 typical gaps (standard deviations), 0.2% of adults aged 15-49Botswana: -1.27 typical gaps (standard deviations), 15.7% of adults aged 15-49Central African Republic: -1.88 typical gaps (standard deviations), 2.4% of adults aged 15-49Canada: 0.62 typical gaps (standard deviations), 0.1% of adults aged 15-49Switzerland: 0.491 typical gaps (standard deviations), 0.2% of adults aged 15-49Chile: -0.0426 typical gaps (standard deviations), 0.7% of adults aged 15-49Cote d'Ivoire: -0.126 typical gaps (standard deviations), 1.7% of adults aged 15-49Cameroon: -0.674 typical gaps (standard deviations), 2.5% of adults aged 15-49Congo, Dem. Rep.: -2.33 typical gaps (standard deviations), 0.7% of adults aged 15-49Congo, Rep.: -1.38 typical gaps (standard deviations), 3.2% of adults aged 15-49Colombia: -0.906 typical gaps (standard deviations), 0.6% of adults aged 15-49Comoros: 0.565 typical gaps (standard deviations), 0.1% of adults aged 15-49Cabo Verde: -0.374 typical gaps (standard deviations), 0.9% of adults aged 15-49Costa Rica: -0.196 typical gaps (standard deviations), 0.6% of adults aged 15-49Germany: 0.484 typical gaps (standard deviations), 0.2% of adults aged 15-49Djibouti: -0.607 typical gaps (standard deviations), 0.9% of adults aged 15-49Denmark: 0.709 typical gaps (standard deviations), 0.1% of adults aged 15-49Dominican Republic: 0.177 typical gaps (standard deviations), 1% of adults aged 15-49Algeria: 0.852 typical gaps (standard deviations), 0.1% of adults aged 15-49Ecuador: -0.286 typical gaps (standard deviations), 0.4% of adults aged 15-49Egypt, Arab Rep.: 0.764 typical gaps (standard deviations), 0.1% of adults aged 15-49Spain: 0.498 typical gaps (standard deviations), 0.3% of adults aged 15-49Estonia: 0.666 typical gaps (standard deviations), 0.7% of adults aged 15-49Ethiopia: -0.344 typical gaps (standard deviations), 0.7% of adults aged 15-49Fiji: 0.553 typical gaps (standard deviations), 1% of adults aged 15-49France: 0.608 typical gaps (standard deviations), 0.3% of adults aged 15-49Gabon: 0.157 typical gaps (standard deviations), 2.8% of adults aged 15-49United Kingdom: 0.566 typical gaps (standard deviations), 0.2% of adults aged 15-49Georgia: 0.382 typical gaps (standard deviations), 0.4% of adults aged 15-49Ghana: -1.07 typical gaps (standard deviations), 1.5% of adults aged 15-49Guinea: 0.435 typical gaps (standard deviations), 1.3% of adults aged 15-49Gambia, The: -0.358 typical gaps (standard deviations), 1.3% of adults aged 15-49Guinea-Bissau: -0.509 typical gaps (standard deviations), 2.2% of adults aged 15-49Greece: 0.503 typical gaps (standard deviations), 0.3% of adults aged 15-49Guatemala: -0.409 typical gaps (standard deviations), 0.2% of adults aged 15-49Honduras: -0.592 typical gaps (standard deviations), 0.2% of adults aged 15-49Croatia: 0.701 typical gaps (standard deviations), 0.1% of adults aged 15-49Haiti: -0.964 typical gaps (standard deviations), 1.6% of adults aged 15-49Indonesia: 0.358 typical gaps (standard deviations), 0.4% of adults aged 15-49India: 0.836 typical gaps (standard deviations), 0.2% of adults aged 15-49Ireland: 0.769 typical gaps (standard deviations), 0.2% of adults aged 15-49Iraq: 0.713 typical gaps (standard deviations), 0.1% of adults aged 15-49Iceland: 0.895 typical gaps (standard deviations), 0.1% of adults aged 15-49Italy: 0.444 typical gaps (standard deviations), 0.3% of adults aged 15-49Jamaica: 0.11 typical gaps (standard deviations), 1.1% of adults aged 15-49Jordan: 0.499 typical gaps (standard deviations), 0.1% of adults aged 15-49Kazakhstan: 0.76 typical gaps (standard deviations), 0.3% of adults aged 15-49Kenya: -0.947 typical gaps (standard deviations), 3% of adults aged 15-49Kyrgyz Republic: 0.801 typical gaps (standard deviations), 0.3% of adults aged 15-49Lao PDR: 0.261 typical gaps (standard deviations), 0.4% of adults aged 15-49Lebanon: 0.249 typical gaps (standard deviations), 0.1% of adults aged 15-49Liberia: -0.456 typical gaps (standard deviations), 0.9% of adults aged 15-49St. Lucia: -0.0751 typical gaps (standard deviations), 0.4% of adults aged 15-49Sri Lanka: 0.202 typical gaps (standard deviations), 0.1% of adults aged 15-49Lesotho: -1.22 typical gaps (standard deviations), 17.1% of adults aged 15-49Lithuania: 0.341 typical gaps (standard deviations), 0.2% of adults aged 15-49Luxembourg: 0.505 typical gaps (standard deviations), 0.3% of adults aged 15-49Latvia: 0.474 typical gaps (standard deviations), 0.5% of adults aged 15-49Morocco: 0.0733 typical gaps (standard deviations), 0.1% of adults aged 15-49Moldova: 0.895 typical gaps (standard deviations), 0.9% of adults aged 15-49Madagascar: -1.46 typical gaps (standard deviations), 0.5% of adults aged 15-49Maldives: 0.754 typical gaps (standard deviations), 0.1% of adults aged 15-49Mexico: -0.0506 typical gaps (standard deviations), 0.5% of adults aged 15-49North Macedonia: 0.413 typical gaps (standard deviations), 0.1% of adults aged 15-49Mali: -0.544 typical gaps (standard deviations), 0.8% of adults aged 15-49Malta: 0.6 typical gaps (standard deviations), 0.3% of adults aged 15-49Myanmar: 0.408 typical gaps (standard deviations), 0.9% of adults aged 15-49Montenegro: 0.403 typical gaps (standard deviations), 0.1% of adults aged 15-49Mongolia: 0.623 typical gaps (standard deviations), 0.1% of adults aged 15-49Mozambique: -2.51 typical gaps (standard deviations), 11.5% of adults aged 15-49Mauritania: 0.336 typical gaps (standard deviations), 0.1% of adults aged 15-49Malawi: -1.72 typical gaps (standard deviations), 6.2% of adults aged 15-49Malaysia: 0.0997 typical gaps (standard deviations), 0.3% of adults aged 15-49Namibia: -1.55 typical gaps (standard deviations), 9% of adults aged 15-49Niger: -1.01 typical gaps (standard deviations), 0.2% of adults aged 15-49Nigeria: -0.587 typical gaps (standard deviations), 1.2% of adults aged 15-49Netherlands: 0.958 typical gaps (standard deviations), 0.1% of adults aged 15-49Norway: 0.91 typical gaps (standard deviations), 0.1% of adults aged 15-49Nepal: 0.652 typical gaps (standard deviations), 0.1% of adults aged 15-49Pakistan: -0.0796 typical gaps (standard deviations), 0.2% of adults aged 15-49Panama: -0.497 typical gaps (standard deviations), 1.1% of adults aged 15-49Peru: 0.00275 typical gaps (standard deviations), 0.4% of adults aged 15-49Philippines: 0.0435 typical gaps (standard deviations), 0.3% of adults aged 15-49Portugal: 0.48 typical gaps (standard deviations), 0.5% of adults aged 15-49Paraguay: -0.155 typical gaps (standard deviations), 0.5% of adults aged 15-49Qatar: 0.421 typical gaps (standard deviations), 0.1% of adults aged 15-49Romania: 0.708 typical gaps (standard deviations), 0.2% of adults aged 15-49Rwanda: -0.82 typical gaps (standard deviations), 2% of adults aged 15-49Sudan: 0.213 typical gaps (standard deviations), 0.2% of adults aged 15-49Senegal: -0.103 typical gaps (standard deviations), 0.3% of adults aged 15-49Solomon Islands: -0.732 typical gaps (standard deviations), 0.1% of adults aged 15-49Sierra Leone: -0.683 typical gaps (standard deviations), 1.4% of adults aged 15-49El Salvador: 0.0329 typical gaps (standard deviations), 0.4% of adults aged 15-49Serbia: 0.525 typical gaps (standard deviations), 0.1% of adults aged 15-49Suriname: 0.129 typical gaps (standard deviations), 1.6% of adults aged 15-49Slovak Republic: 1.06 typical gaps (standard deviations), 0.1% of adults aged 15-49Slovenia: 1.02 typical gaps (standard deviations), 0.1% of adults aged 15-49Sweden: 0.734 typical gaps (standard deviations), 0.1% of adults aged 15-49Eswatini: -1.85 typical gaps (standard deviations), 23.4% of adults aged 15-49Syrian Arab Republic: 0.457 typical gaps (standard deviations), 0.1% of adults aged 15-49Chad: -0.729 typical gaps (standard deviations), 0.9% of adults aged 15-49Togo: -0.634 typical gaps (standard deviations), 1.6% of adults aged 15-49Thailand: 0.525 typical gaps (standard deviations), 1% of adults aged 15-49Tajikistan: 0.207 typical gaps (standard deviations), 0.2% of adults aged 15-49Timor-Leste: -0.345 typical gaps (standard deviations), 0.2% of adults aged 15-49Tunisia: 0.484 typical gaps (standard deviations), 0.1% of adults aged 15-49Tuvalu: -0.0403 typical gaps (standard deviations), 0.4% of adults aged 15-49Tanzania: -1.21 typical gaps (standard deviations), 3.5% of adults aged 15-49Uganda: -1.56 typical gaps (standard deviations), 4.9% of adults aged 15-49Viet Nam: 0.323 typical gaps (standard deviations), 0.4% of adults aged 15-49Vanuatu: 0.0795 typical gaps (standard deviations), 0.1% of adults aged 15-49South Africa: -1.12 typical gaps (standard deviations), 17.2% of adults aged 15-49Zambia: -2.37 typical gaps (standard deviations), 9.4% of adults aged 15-49Zimbabwe: -1.72 typical gaps (standard deviations), 9.8% of adults aged 15-49BDICODLSOMDGMOZNLDSVKSVNSWZZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5843.0e-13130
By rank (Spearman), not adjusted-0.7021.4e-20130
Standard (Pearson), adjusted for national income-0.5987.6e-14130
By rank (Spearman), adjusted for national income-0.6101.7e-14130

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 130 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: -0.013 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.622 (without COD) to -0.569 (without SWZ). 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: SWZ (+0.028).

Fragile: this link may not hold up.
  • correlation moves sharply once outliers are trimmed
  • direction is not consistent across regions

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 & Pacific14+0.55
Europe & Central Asia34+0.03
Latin America & Caribbean21+0.12
Middle East, North Africa, Afghanistan & Pakistan13-0.75
Sub-Saharan Africa41-0.62

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 third44-0.60
Middle third43-0.68
Richest third43-0.61

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
Christians85-0.60
Muslims34-0.46

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
France24-0.67
Spain16-0.13
United Kingdom38-0.74

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 = -0.422 after dropping 17 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)
% of adults aged 15-49		Years
Slovak Republic SVK1.060.1%2023, 2024
Slovenia SVN1.020.1%2023, 2024
Netherlands NLD0.9580.1%2021, 2023
United Arab Emirates ARE0.9210.1%2018, 2024
Norway NOR0.910.1%2023, 2024
Moldova MDA0.8950.9%2023, 2024
Iceland ISL0.8950.1%2019, 2024
Belgium BEL0.8950.2%2023, 2024
Algeria DZA0.8520.1%2011, 2024
Armenia ARM0.8430.4%2024
India IND0.8360.2%2022, 2024
Kyrgyz Republic KGZ0.8010.3%2024
Bhutan BTN0.80.2%2022, 2024
Ireland IRL0.7690.2%2023, 2024
Egypt, Arab Rep. EGY0.7640.1%2021, 2024

wdi
World Bank, World Development Indicators

Sources

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.

HIV prevalence

Prevalence of HIV, total (% of population ages 15-49)

UNAIDS · CC BY 4.0 · updated quarterly

Estimates from a statistical model. Very lopsided: a few southern African countries sit about ten times higher than everyone else. So a link with this is largely a link with them, and the "leave one out" check on each pair page shows by how much.

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 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Equality score and HIV prevalence

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

More links with Equality score or HIV prevalence