placestats

Road traffic deaths vs. Secondary enrollment

Fades after adjusting for income

Across 175 countries, Road traffic deaths and Secondary enrollment rise and fall together at r = -0.62 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.23, which could easily be chance (q = 0.14, allowing for the many pairs tested, and allowing for countries in the same region being alike). So the first figure mostly shows richer countries differing from poorer ones, not a link between these two things. Caution: direction is not consistent across regions.

Comparing countries across the middle half of secondary enrollment, 70.2% to 102% gross, road traffic deaths is better by 7.75 per 100,000 population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 3.36 per 100,000 population.

Link before adjusting (r)-0.621p = 5.2e-20 · 175 countries
After adjusting for national income-0.231p = 0.002 · likely range [-0.37, -0.09]
p, allowing for regions0.003standard error 0.049 · 7 regions
Check for luck (q)0.139across 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.

Road traffic deaths · per 100,000 populationlink with income -0.71 · high
Secondary enrollment · % grosslink with income +0.71 · high

Both of these rise and fall closely with national income, so a strong link between them before adjusting was close to certain. Read the adjusted figure, not the first one.

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

Every country as a dot

Road traffic deaths · per 100,000 population →Secondary enrollment · % gross →064.63.35%143%Afghanistan: 15.9 per 100,000 population, 59.6% grossAngola: 26.1 per 100,000 population, 51.5% grossAlbania: 11.7 per 100,000 population, 108% grossAndorra: 7.6 per 100,000 population, 102% grossUnited Arab Emirates: 8.9 per 100,000 population, 102% grossArgentina: 14.1 per 100,000 population, 106% grossArmenia: 20 per 100,000 population, 90.9% grossAntigua and Barbuda: 0 per 100,000 population, 109% grossAustralia: 4.9 per 100,000 population, 132% grossAustria: 4.9 per 100,000 population, 101% grossAzerbaijan: 6.7 per 100,000 population, 91.1% grossBurundi: 35.5 per 100,000 population, 23.1% grossBelgium: 5.8 per 100,000 population, 143% grossBenin: 26.8 per 100,000 population, 43.6% grossBurkina Faso: 31 per 100,000 population, 30% grossBangladesh: 15.3 per 100,000 population, 64.3% grossBulgaria: 9.2 per 100,000 population, 94.8% grossBahrain: 5.2 per 100,000 population, 98.8% grossBahamas, The: 7.7 per 100,000 population, 79.5% grossBosnia and Herzegovina: 13.5 per 100,000 population, 87.9% grossBelarus: 7.6 per 100,000 population, 94.8% grossBelize: 22.6 per 100,000 population, 86.5% grossBolivia: 21.1 per 100,000 population, 90.5% grossBrazil: 16 per 100,000 population, 104% grossBarbados: 8.2 per 100,000 population, 104% grossBrunei Darussalam: 7.5 per 100,000 population, 91.8% grossBhutan: 16.2 per 100,000 population, 86.2% grossBotswana: 26.4 per 100,000 population, 70.6% grossCentral African Republic: 37.7 per 100,000 population, 15.8% grossCanada: 5.3 per 100,000 population, 104% grossSwitzerland: 2.2 per 100,000 population, 101% grossChile: 14.9 per 100,000 population, 106% grossChina: 17.4 per 100,000 population, 92.3% grossCote d'Ivoire: 24.1 per 100,000 population, 65.3% grossCameroon: 30.2 per 100,000 population, 44.8% grossCongo, Dem. Rep.: 34.9 per 100,000 population, 55.5% grossCongo, Rep.: 29.7 per 100,000 population, 50.5% grossColombia: 15.4 per 100,000 population, 102% grossComoros: 26.6 per 100,000 population, 54.8% grossCabo Verde: 26.8 per 100,000 population, 96.4% grossCosta Rica: 14.8 per 100,000 population, 122% grossCyprus: 5.8 per 100,000 population, 107% grossCzechia: 5.9 per 100,000 population, 97.6% grossGermany: 3.8 per 100,000 population, 100% grossDominica: 10.9 per 100,000 population, 103% grossDenmark: 3.7 per 100,000 population, 126% grossDominican Republic: 64.6 per 100,000 population, 76.4% grossAlgeria: 20.9 per 100,000 population, 105% grossEcuador: 20.1 per 100,000 population, 92.8% grossEgypt, Arab Rep.: 10.1 per 100,000 population, 84.8% grossSpain: 3.9 per 100,000 population, 119% grossEstonia: 4.5 per 100,000 population, 109% grossEthiopia: 28.2 per 100,000 population, 33.9% grossFinland: 3.9 per 100,000 population, 142% grossFiji: 13.5 per 100,000 population, 98.1% grossFrance: 5.1 per 100,000 population, 104% grossGabon: 23.9 per 100,000 population, 70.3% grossUnited Kingdom: 3.2 per 100,000 population, 112% grossGeorgia: 12.4 per 100,000 population, 101% grossGhana: 25.7 per 100,000 population, 75.7% grossGuinea: 29.7 per 100,000 population, 38.6% grossGambia, The: 29.6 per 100,000 population, 107% grossGreece: 8.3 per 100,000 population, 105% grossGrenada: 8 per 100,000 population, 101% grossGuatemala: 22.9 per 100,000 population, 49.6% grossGuyana: 22.3 per 100,000 population, 91.4% grossHonduras: 16.1 per 100,000 population, 51.8% grossCroatia: 7.9 per 100,000 population, 105% grossHungary: 7.7 per 100,000 population, 107% grossIndonesia: 11.3 per 100,000 population, 97.2% grossIndia: 15.6 per 100,000 population, 79.6% grossIreland: 3.1 per 100,000 population, 131% grossIran, Islamic Rep.: 21.5 per 100,000 population, 85.1% grossIceland: 2 per 100,000 population, 109% grossIsrael: 3.9 per 100,000 population, 98.5% grossItaly: 5.3 per 100,000 population, 101% grossJamaica: 15.1 per 100,000 population, 84.5% grossJordan: 17 per 100,000 population, 91.8% grossJapan: 3.6 per 100,000 population, 102% grossKazakhstan: 12.7 per 100,000 population, 97.6% grossKenya: 28.3 per 100,000 population, 84.3% grossKyrgyz Republic: 12.7 per 100,000 population, 91.7% grossCambodia: 19.6 per 100,000 population, 58.8% grossKiribati: 1.9 per 100,000 population, 90.3% grossKorea, Rep.: 8.6 per 100,000 population, 96.3% grossKuwait: 15.4 per 100,000 population, 95.4% grossLao PDR: 17.9 per 100,000 population, 50.5% grossLebanon: 16.4 per 100,000 population, 63.8% grossLiberia: 38.9 per 100,000 population, 37.7% grossSt. Lucia: 29.8 per 100,000 population, 87.9% grossSri Lanka: 19.7 per 100,000 population, 88% grossLesotho: 31.9 per 100,000 population, 59.4% grossLithuania: 8.1 per 100,000 population, 105% grossLuxembourg: 4.1 per 100,000 population, 109% grossLatvia: 8.1 per 100,000 population, 101% grossMorocco: 17 per 100,000 population, 90% grossMoldova: 7.3 per 100,000 population, 99.3% grossMadagascar: 29.2 per 100,000 population, 34.1% grossMaldives: 1.6 per 100,000 population, 79.4% grossMexico: 12.8 per 100,000 population, 103% grossMarshall Islands: 5.7 per 100,000 population, 85.8% grossNorth Macedonia: 5.1 per 100,000 population, 90.1% grossMali: 22.7 per 100,000 population, 38.9% grossMalta: 4.1 per 100,000 population, 103% grossMyanmar: 20.4 per 100,000 population, 77% grossMontenegro: 7.6 per 100,000 population, 93.9% grossMongolia: 21 per 100,000 population, 96.8% grossMozambique: 30 per 100,000 population, 37.2% grossMauritania: 25.6 per 100,000 population, 36% grossMauritius: 12.2 per 100,000 population, 90.7% grossMalawi: 33.4 per 100,000 population, 36.6% grossMalaysia: 22.5 per 100,000 population, 80.3% grossNamibia: 34.8 per 100,000 population, 82% grossNiger: 25.5 per 100,000 population, 19.9% grossNigeria: 20.7 per 100,000 population, 46.9% grossNicaragua: 16.9 per 100,000 population, 70.2% grossNetherlands: 4 per 100,000 population, 138% grossNorway: 2.1 per 100,000 population, 116% grossNepal: 16.3 per 100,000 population, 90.4% grossNew Zealand: 9.6 per 100,000 population, 114% grossOman: 10.6 per 100,000 population, 95.1% grossPakistan: 13 per 100,000 population, 48.3% grossPanama: 13.9 per 100,000 population, 76.6% grossPeru: 13.6 per 100,000 population, 108% grossPhilippines: 12 per 100,000 population, 85.3% grossPalau: 4.78 per 100,000 population, 95.7% grossPapua New Guinea: 12.6 per 100,000 population, 52.1% grossPoland: 9.4 per 100,000 population, 108% grossPortugal: 8.2 per 100,000 population, 120% grossParaguay: 22 per 100,000 population, 79.1% grossWest Bank and Gaza: 5.3 per 100,000 population, 89.1% grossQatar: 7.3 per 100,000 population, 99.6% grossRomania: 10.3 per 100,000 population, 81.6% grossRussian Federation: 12 per 100,000 population, 92.5% grossRwanda: 29.4 per 100,000 population, 48.8% grossSaudi Arabia: 35.9 per 100,000 population, 111% grossSudan: 26.8 per 100,000 population, 43.5% grossSenegal: 23.5 per 100,000 population, 44.1% grossSingapore: 2.1 per 100,000 population, 103% grossSolomon Islands: 16.5 per 100,000 population, 49% grossSierra Leone: 33 per 100,000 population, 86.9% grossEl Salvador: 20.9 per 100,000 population, 62.1% grossSan Marino: 0 per 100,000 population, 60.4% grossSomalia, Fed. Rep.: 27.4 per 100,000 population, 3.35% grossSerbia: 7.5 per 100,000 population, 89.8% grossSao Tome and Principe: 27.9 per 100,000 population, 90.4% grossSuriname: 15.3 per 100,000 population, 76.1% grossSlovak Republic: 6.1 per 100,000 population, 93.2% grossSlovenia: 5.1 per 100,000 population, 108% grossSweden: 3.1 per 100,000 population, 132% grossEswatini: 33.5 per 100,000 population, 82% grossSeychelles: 11.3 per 100,000 population, 70.8% grossSyrian Arab Republic: 14.9 per 100,000 population, 38.7% grossChad: 32.4 per 100,000 population, 23.5% grossTogo: 28.7 per 100,000 population, 63.4% grossThailand: 32.2 per 100,000 population, 92.1% grossTajikistan: 15.7 per 100,000 population, 86.9% grossTurkmenistan: 13.5 per 100,000 population, 94.6% grossTimor-Leste: 11.9 per 100,000 population, 84.7% grossTonga: 33 per 100,000 population, 92.4% grossTrinidad and Tobago: 9.3 per 100,000 population, 95.7% grossTunisia: 16.5 per 100,000 population, 90.3% grossTurkiye: 6.7 per 100,000 population, 118% grossTanzania: 31.1 per 100,000 population, 28.2% grossUkraine: 10.2 per 100,000 population, 86.2% grossUruguay: 14.8 per 100,000 population, 119% grossUnited States: 12.7 per 100,000 population, 97.5% grossUzbekistan: 11.7 per 100,000 population, 98.1% grossSt. Vincent and the Grenadines: 7.4 per 100,000 population, 125% grossViet Nam: 30.6 per 100,000 population, 93.4% grossVanuatu: 14.9 per 100,000 population, 135% grossSamoa: 13 per 100,000 population, 87.4% grossSouth Africa: 22.2 per 100,000 population, 104% grossZambia: 20.5 per 100,000 population, 56.8% grossZimbabwe: 41.2 per 100,000 population, 52.4% grossBDIBELCAFDOMFINGMBNLDSAUSMRSOMSYR

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6215.2e-20175
By rank (Spearman), not adjusted-0.6557.6e-23175
Standard (Pearson), adjusted for national income-0.2310.002175
By rank (Spearman), adjusted for national income-0.2440.001175

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 175 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: +0.389 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.254 (without SMR) to -0.206 (without KIR). 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: KIR (+0.026).

Fragile: this link may not hold up.
  • 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 & Pacific26-0.15
Europe & Central Asia50-0.05
Latin America & Caribbean29-0.39
Middle East, North Africa, Afghanistan & Pakistan19+0.16
Sub-Saharan Africa43+0.19

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 third59-0.30
Middle third58-0.29
Richest third58-0.18

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
Christians104-0.33
Muslims45-0.16

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
France23+0.12
Never a European colony21-0.38
Spain18-0.41
United Kingdom57-0.15

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 = -0.238 after dropping 4 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 20years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

The link shows up within countries over time (r = +0.148) but mostly disappears in year-to-year changes (r = +0.017). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.231for comparison
Within countries-0.103each country vs itself
…and vs the same year elsewhere+0.148q = 0.005 · 163 countries
Year-on-year changes+0.017q = 0.680
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.

2,590 country-years (one country in one year), 163 countries, 2000–2019, about 16 years each on average. Share of the variation that happens within countries over time: 8% for Road traffic deaths, 13% for Secondary enrollment. 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 Secondary enrollment trails Road traffic deaths →lag -10: r = 0.065 (n = 2,024)lag -9: r = 0.088 (n = 2,073)lag -8: r = 0.088 (n = 2,130)lag -7: r = 0.104 (n = 2,180)lag -6: r = 0.121 (n = 2,217)lag -5: r = 0.123 (n = 2,274)lag -4: r = 0.122 (n = 2,346)lag -3: r = 0.133 (n = 2,411)lag -2: r = 0.148 (n = 2,499)lag -1: r = 0.143 (n = 2,566)lag +0: r = 0.148 (n = 2,590)lag +1: r = 0.161 (n = 2,615)lag +2: r = 0.168 (n = 2,616)lag +3: r = 0.156 (n = 2,616)lag +4: r = 0.142 (n = 2,625)lag +5: r = 0.149 (n = 2,584)lag +6: r = 0.122 (n = 2,454)lag +7: r = 0.104 (n = 2,314)lag +8: r = 0.071 (n = 2,179)lag +9: r = 0.067 (n = 2,042)lag +10: r = 0.078 (n = 1,905)

Same year (lag 0): +0.148. 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 data


per 100,000 population
% gross		Years
Dominican Republic DOM64.676.4%2019, 2024
Zimbabwe ZWE41.252.4%2019, 2013
Liberia LBR38.937.7%2019, 2020
Central African Republic CAF37.715.8%2019, 2017
Saudi Arabia SAU35.9111%2019, 2024
Burundi BDI35.523.1%2019, 2025
Congo, Dem. Rep. COD34.955.5%2019, 2023
Namibia NAM34.882%2019, 2024
Eswatini SWZ33.582%2019, 2016
Malawi MWI33.436.6%2019, 2023
Tonga TON3392.4%2019, 2024
Sierra Leone SLE3386.9%2019, 2023
Chad TCD32.423.5%2019, 2024
Thailand THA32.292.1%2019, 2025
Lesotho LSO31.959.4%2019, 2017

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

Sources

Road traffic deaths

Mortality from road traffic injury (per 100,000 population)

World Health Organization · CC BY 4.0 · updated quarterly

The World Bank's copy stops at 2019.

Secondary enrollment

School enrollment, secondary (% gross)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

How this was computed

Data used: most recent value in 2010-2025; values come from 2012–2025. 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 Road traffic deaths and Secondary enrollment

This page shows that two measures go together. It does not show that Road traffic deaths changes Secondary enrollment, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Road traffic deaths or Secondary enrollment