placestats

HIV prevalence vs. Youth not in work or study

Holds up after adjusting for income

Across 137 countries, HIV prevalence and Youth not in work or study rise and fall together at r = +0.31 (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 = +0.24, q = 0.035, 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, youth not in work or study is worse by 0.875% of 15 to 24 year olds at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.558% of 15 to 24 year olds.

Link before adjusting (r)+0.313p = 0.000 · 137 countries
After adjusting for national income+0.236p = 0.006 · likely range [+0.07, +0.39]
p, allowing for regions0.003standard error 0.048 · 7 regions
Check for luck (q)0.035across 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.

HIV prevalence · % of adults aged 15-49link with income -0.40 · moderate
Youth not in work or study · % of 15 to 24 year oldslink with income -0.63 · high

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

Every country as a dot

HIV prevalence · % of adults aged 15-49 →Youth not in work or study · % of 15 to 24 year olds →0.1%23.4%3.98%51%Afghanistan: 0.1% of adults aged 15-49, 43.8% of 15 to 24 year oldsAngola: 1.6% of adults aged 15-49, 24.9% of 15 to 24 year oldsAlbania: 0.1% of adults aged 15-49, 21% of 15 to 24 year oldsUnited Arab Emirates: 0.1% of adults aged 15-49, 8.92% of 15 to 24 year oldsArgentina: 0.4% of adults aged 15-49, 15.2% of 15 to 24 year oldsArmenia: 0.4% of adults aged 15-49, 20.2% of 15 to 24 year oldsAntigua and Barbuda: 1.1% of adults aged 15-49, 20.8% of 15 to 24 year oldsAustralia: 0.1% of adults aged 15-49, 8.53% of 15 to 24 year oldsAzerbaijan: 0.1% of adults aged 15-49, 9.64% of 15 to 24 year oldsBurundi: 0.8% of adults aged 15-49, 11.2% of 15 to 24 year oldsBelgium: 0.2% of adults aged 15-49, 8.15% of 15 to 24 year oldsBenin: 0.7% of adults aged 15-49, 14.2% of 15 to 24 year oldsBurkina Faso: 0.5% of adults aged 15-49, 24.5% of 15 to 24 year oldsBangladesh: 0.1% of adults aged 15-49, 15.4% of 15 to 24 year oldsBulgaria: 0.1% of adults aged 15-49, 10.9% of 15 to 24 year oldsBelize: 1.2% of adults aged 15-49, 24.9% of 15 to 24 year oldsBolivia: 0.4% of adults aged 15-49, 8.02% of 15 to 24 year oldsBrazil: 0.7% of adults aged 15-49, 18.7% of 15 to 24 year oldsBarbados: 1% of adults aged 15-49, 29% of 15 to 24 year oldsBhutan: 0.2% of adults aged 15-49, 15.3% of 15 to 24 year oldsBotswana: 15.7% of adults aged 15-49, 38% of 15 to 24 year oldsCanada: 0.1% of adults aged 15-49, 13.3% of 15 to 24 year oldsSwitzerland: 0.2% of adults aged 15-49, 6.99% of 15 to 24 year oldsChile: 0.7% of adults aged 15-49, 13.4% of 15 to 24 year oldsCote d'Ivoire: 1.7% of adults aged 15-49, 18.8% of 15 to 24 year oldsCameroon: 2.5% of adults aged 15-49, 23.2% of 15 to 24 year oldsCongo, Dem. Rep.: 0.7% of adults aged 15-49, 31.4% of 15 to 24 year oldsColombia: 0.6% of adults aged 15-49, 22% of 15 to 24 year oldsComoros: 0.1% of adults aged 15-49, 21.2% of 15 to 24 year oldsCabo Verde: 0.9% of adults aged 15-49, 28% of 15 to 24 year oldsCosta Rica: 0.6% of adults aged 15-49, 19.4% of 15 to 24 year oldsGermany: 0.2% of adults aged 15-49, 7.58% of 15 to 24 year oldsDjibouti: 0.9% of adults aged 15-49, 44% of 15 to 24 year oldsDenmark: 0.1% of adults aged 15-49, 6.9% of 15 to 24 year oldsDominican Republic: 1% of adults aged 15-49, 24.7% of 15 to 24 year oldsAlgeria: 0.1% of adults aged 15-49, 20.9% of 15 to 24 year oldsEcuador: 0.4% of adults aged 15-49, 17.7% of 15 to 24 year oldsEgypt, Arab Rep.: 0.1% of adults aged 15-49, 23.8% of 15 to 24 year oldsSpain: 0.3% of adults aged 15-49, 10.5% of 15 to 24 year oldsEstonia: 0.7% of adults aged 15-49, 10.2% of 15 to 24 year oldsEthiopia: 0.7% of adults aged 15-49, 17.5% of 15 to 24 year oldsFiji: 1% of adults aged 15-49, 20.8% of 15 to 24 year oldsFrance: 0.3% of adults aged 15-49, 12.6% of 15 to 24 year oldsGabon: 2.8% of adults aged 15-49, 20.2% of 15 to 24 year oldsUnited Kingdom: 0.2% of adults aged 15-49, 14.4% of 15 to 24 year oldsGeorgia: 0.4% of adults aged 15-49, 14.7% of 15 to 24 year oldsGhana: 1.5% of adults aged 15-49, 23.9% of 15 to 24 year oldsGuinea: 1.3% of adults aged 15-49, 34.3% of 15 to 24 year oldsGambia, The: 1.3% of adults aged 15-49, 36.5% of 15 to 24 year oldsGuinea-Bissau: 2.2% of adults aged 15-49, 13.2% of 15 to 24 year oldsGreece: 0.3% of adults aged 15-49, 10.4% of 15 to 24 year oldsGuatemala: 0.2% of adults aged 15-49, 24% of 15 to 24 year oldsGuyana: 1.6% of adults aged 15-49, 44.8% of 15 to 24 year oldsHonduras: 0.2% of adults aged 15-49, 29% of 15 to 24 year oldsCroatia: 0.1% of adults aged 15-49, 8.08% of 15 to 24 year oldsHaiti: 1.6% of adults aged 15-49, 21.1% of 15 to 24 year oldsIndonesia: 0.4% of adults aged 15-49, 21.4% of 15 to 24 year oldsIndia: 0.2% of adults aged 15-49, 25.6% of 15 to 24 year oldsIreland: 0.2% of adults aged 15-49, 5.67% of 15 to 24 year oldsIraq: 0.1% of adults aged 15-49, 36.9% of 15 to 24 year oldsIceland: 0.1% of adults aged 15-49, 3.98% of 15 to 24 year oldsItaly: 0.3% of adults aged 15-49, 12% of 15 to 24 year oldsJamaica: 1.1% of adults aged 15-49, 19.7% of 15 to 24 year oldsJordan: 0.1% of adults aged 15-49, 29.4% of 15 to 24 year oldsKazakhstan: 0.3% of adults aged 15-49, 9.49% of 15 to 24 year oldsKenya: 3% of adults aged 15-49, 18.5% of 15 to 24 year oldsKyrgyz Republic: 0.3% of adults aged 15-49, 19.3% of 15 to 24 year oldsCambodia: 0.5% of adults aged 15-49, 6.32% of 15 to 24 year oldsLao PDR: 0.4% of adults aged 15-49, 22.5% of 15 to 24 year oldsLebanon: 0.1% of adults aged 15-49, 23.5% of 15 to 24 year oldsLiberia: 0.9% of adults aged 15-49, 39.9% of 15 to 24 year oldsSt. Lucia: 0.4% of adults aged 15-49, 25.5% of 15 to 24 year oldsSri Lanka: 0.1% of adults aged 15-49, 17.2% of 15 to 24 year oldsLesotho: 17.1% of adults aged 15-49, 38% of 15 to 24 year oldsLithuania: 0.2% of adults aged 15-49, 14.9% of 15 to 24 year oldsLuxembourg: 0.3% of adults aged 15-49, 10.2% of 15 to 24 year oldsLatvia: 0.5% of adults aged 15-49, 9.89% of 15 to 24 year oldsMorocco: 0.1% of adults aged 15-49, 35.3% of 15 to 24 year oldsMoldova: 0.9% of adults aged 15-49, 9.05% of 15 to 24 year oldsMadagascar: 0.5% of adults aged 15-49, 24.3% of 15 to 24 year oldsMaldives: 0.1% of adults aged 15-49, 26.3% of 15 to 24 year oldsMexico: 0.5% of adults aged 15-49, 16.2% of 15 to 24 year oldsNorth Macedonia: 0.1% of adults aged 15-49, 19.5% of 15 to 24 year oldsMali: 0.8% of adults aged 15-49, 30.1% of 15 to 24 year oldsMalta: 0.3% of adults aged 15-49, 7.84% of 15 to 24 year oldsMyanmar: 0.9% of adults aged 15-49, 15% of 15 to 24 year oldsMontenegro: 0.1% of adults aged 15-49, 14.2% of 15 to 24 year oldsMongolia: 0.1% of adults aged 15-49, 9.9% of 15 to 24 year oldsMozambique: 11.5% of adults aged 15-49, 19.2% of 15 to 24 year oldsMauritania: 0.1% of adults aged 15-49, 44.1% of 15 to 24 year oldsMalawi: 6.2% of adults aged 15-49, 39.4% of 15 to 24 year oldsMalaysia: 0.3% of adults aged 15-49, 10.2% of 15 to 24 year oldsNamibia: 9% of adults aged 15-49, 31.9% of 15 to 24 year oldsNiger: 0.2% of adults aged 15-49, 15.7% of 15 to 24 year oldsNigeria: 1.2% of adults aged 15-49, 11.9% of 15 to 24 year oldsNetherlands: 0.1% of adults aged 15-49, 4.14% of 15 to 24 year oldsNorway: 0.1% of adults aged 15-49, 5.82% of 15 to 24 year oldsNepal: 0.1% of adults aged 15-49, 34.8% of 15 to 24 year oldsNew Zealand: 0.1% of adults aged 15-49, 12.8% of 15 to 24 year oldsOman: 0.1% of adults aged 15-49, 25.5% of 15 to 24 year oldsPakistan: 0.2% of adults aged 15-49, 28.5% of 15 to 24 year oldsPanama: 1.1% of adults aged 15-49, 15.1% of 15 to 24 year oldsPeru: 0.4% of adults aged 15-49, 22.8% of 15 to 24 year oldsPhilippines: 0.3% of adults aged 15-49, 12.4% of 15 to 24 year oldsPapua New Guinea: 1.5% of adults aged 15-49, 27.7% of 15 to 24 year oldsPortugal: 0.5% of adults aged 15-49, 7.02% of 15 to 24 year oldsParaguay: 0.5% of adults aged 15-49, 18.6% of 15 to 24 year oldsRomania: 0.2% of adults aged 15-49, 17% of 15 to 24 year oldsRwanda: 2% of adults aged 15-49, 15.3% of 15 to 24 year oldsSaudi Arabia: 0.1% of adults aged 15-49, 16.4% of 15 to 24 year oldsSudan: 0.2% of adults aged 15-49, 46.5% of 15 to 24 year oldsSenegal: 0.3% of adults aged 15-49, 35.2% of 15 to 24 year oldsSingapore: 0.1% of adults aged 15-49, 6.46% of 15 to 24 year oldsSolomon Islands: 0.1% of adults aged 15-49, 7.04% of 15 to 24 year oldsSierra Leone: 1.4% of adults aged 15-49, 25.9% of 15 to 24 year oldsEl Salvador: 0.4% of adults aged 15-49, 20.5% of 15 to 24 year oldsSomalia, Fed. Rep.: 0.1% of adults aged 15-49, 51% of 15 to 24 year oldsSerbia: 0.1% of adults aged 15-49, 12.7% of 15 to 24 year oldsSuriname: 1.6% of adults aged 15-49, 18.2% of 15 to 24 year oldsSlovak Republic: 0.1% of adults aged 15-49, 8.75% of 15 to 24 year oldsSlovenia: 0.1% of adults aged 15-49, 7.13% of 15 to 24 year oldsSweden: 0.1% of adults aged 15-49, 5.58% of 15 to 24 year oldsEswatini: 23.4% of adults aged 15-49, 36.5% of 15 to 24 year oldsChad: 0.9% of adults aged 15-49, 42.3% of 15 to 24 year oldsTogo: 1.6% of adults aged 15-49, 13% of 15 to 24 year oldsThailand: 1% of adults aged 15-49, 12.4% of 15 to 24 year oldsTajikistan: 0.2% of adults aged 15-49, 41.3% of 15 to 24 year oldsTimor-Leste: 0.2% of adults aged 15-49, 29.1% of 15 to 24 year oldsTunisia: 0.1% of adults aged 15-49, 22.6% of 15 to 24 year oldsTuvalu: 0.4% of adults aged 15-49, 34.3% of 15 to 24 year oldsTanzania: 3.5% of adults aged 15-49, 12.8% of 15 to 24 year oldsUganda: 4.9% of adults aged 15-49, 13.8% of 15 to 24 year oldsViet Nam: 0.4% of adults aged 15-49, 10.3% of 15 to 24 year oldsVanuatu: 0.1% of adults aged 15-49, 29.3% of 15 to 24 year oldsSouth Africa: 17.2% of adults aged 15-49, 34.6% of 15 to 24 year oldsZambia: 9.4% of adults aged 15-49, 26.7% of 15 to 24 year oldsZimbabwe: 9.8% of adults aged 15-49, 36% of 15 to 24 year oldsISLLSOMOZMRTNLDSDNSOMSWESWZZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.3130.000137
By rank (Spearman), not adjusted+0.2780.001137
Standard (Pearson), adjusted for national income+0.2360.006137
By rank (Spearman), adjusted for national income+0.0540.530137

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 137 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: +0.077 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.185 (without BWA) to +0.272 (without MOZ). 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: BWA (-0.052).

Fragile: this link may not hold up.
  • correlation moves sharply once outliers are trimmed
  • sign flips 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 & Pacific18+0.16
Europe & Central Asia35-0.24
Latin America & Caribbean23+0.30
Middle East, North Africa, Afghanistan & Pakistan14+0.42
Sub-Saharan Africa40+0.25

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 third46+0.01
Middle third45+0.54
Richest third46+0.63

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+0.48
Muslims37-0.36

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
France22-0.07
Never a European colony14-0.39
Spain16-0.01
United Kingdom43+0.35

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 = -0.029 after dropping 15 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.

Loading…

Over time, within countries

Everything above compares countries with each other at one moment. This section compares each country only with itself over 35years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Within countries over time, there is no link that is clearly more than chance (r = -0.084, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose hiv prevalence moved did not tend to see their youth not in work or study move with it.

Across countries (adjusted)+0.236for comparison
Within countries-0.069each country vs itself
…and vs the same year elsewhere-0.084q = 0.430 · 97 countries
Year-on-year changes-0.022q = 0.638
Read with care.
  • Almost all of this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.

1,739 country-years (one country in one year), 97 countries, 1990–2024, about 18 years each on average. Share of the variation that happens within countries over time: 2% for HIV prevalence, 19% for Youth not in work or study. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.

Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.

With one measure years behind the other

0.20−0.2-100+10years Youth not in work or study trails HIV prevalence →lag -10: r = -0.096 (n = 933)lag -9: r = -0.102 (n = 1,006)lag -8: r = -0.114 (n = 1,090)lag -7: r = -0.138 (n = 1,167)lag -6: r = -0.148 (n = 1,229)lag -5: r = -0.164 (n = 1,318)lag -4: r = -0.050 (n = 1,406)lag -3: r = -0.004 (n = 1,490)lag -2: r = -0.048 (n = 1,587)lag -1: r = -0.051 (n = 1,669)lag +0: r = -0.084 (n = 1,739)lag +1: r = -0.073 (n = 1,755)lag +2: r = -0.066 (n = 1,741)lag +3: r = -0.060 (n = 1,725)lag +4: r = -0.057 (n = 1,710)lag +5: r = -0.086 (n = 1,693)lag +6: r = -0.089 (n = 1,671)lag +7: r = -0.092 (n = 1,650)lag +8: r = -0.093 (n = 1,627)lag +9: r = -0.095 (n = 1,606)lag +10: r = -0.094 (n = 1,578)

Same year (lag 0): -0.084. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.

The largest value on this curve (-0.16 at lag -5) is higher than the same-year one by 0.08. That gap is not a finding. It is the largest of 21 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.

The data


% of adults aged 15-49
% of 15 to 24 year olds		Years
Eswatini SWZ23.4%36.5%2024, 2023
South Africa ZAF17.2%34.6%2024
Lesotho LSO17.1%38%2024
Botswana BWA15.7%38%2024
Mozambique MOZ11.5%19.2%2024, 2022
Zimbabwe ZWE9.8%36%2024
Zambia ZMB9.4%26.7%2024
Namibia NAM9%31.9%2024, 2018
Malawi MWI6.2%39.4%2024
Uganda UGA4.9%13.8%2024, 2021
Tanzania TZA3.5%12.8%2024
Kenya KEN3%18.5%2024, 2021
Gabon GAB2.8%20.2%2024
Cameroon CMR2.5%23.2%2024, 2021
Guinea-Bissau GNB2.2%13.2%2024, 2022

World Bank, World Development Indicators
World Bank, World Development Indicators

Sources

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.

Youth not in work or study

Share of youth not in education, employment or training, total (% of youth population)

International Labour Organization · CC BY 4.0 · updated quarterly

From labour force surveys, so yearly for about a hundred countries and occasional elsewhere.

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 HIV prevalence and Youth not in work or study

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

More links with HIV prevalence or Youth not in work or study