placestats

Premature NCD mortality vs. Pupil-teacher ratio

Fades after adjusting for income

Across 167 countries, Premature NCD mortality and Pupil-teacher ratio rise and fall together at r = +0.51 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.03, which could easily be chance (q = 0.90, 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: sign flips when a single country is removed.

Comparing countries across the middle half of premature ncd mortality, 12.8% to 23.1% probability, ages 30-70, pupil-teacher ratio is worse by 9.2 pupils per teacher at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 0.453 pupils per teacher the other way.

Link before adjusting (r)+0.512p = 1.5e-12 · 167 countries
After adjusting for national income-0.031p = 0.688 · likely range [-0.18, +0.12]
p, allowing for regions0.822standard error 0.134 · 7 regions
Check for luck (q)0.899across 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.

Premature NCD mortality · % probability, ages 30-70link with income -0.70 · high
Pupil-teacher ratio · pupils per teacherlink with income -0.83 · proxy

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

Premature NCD mortality · % probability, ages 30-70 →Pupil-teacher ratio · pupils per teacher →6.9%44.1%8.383.4Afghanistan: 32.7% probability, ages 30-70, 48.8 pupils per teacherAngola: 24.7% probability, ages 30-70, 50 pupils per teacherAlbania: 10% probability, ages 30-70, 17.6 pupils per teacherUnited Arab Emirates: 11.6% probability, ages 30-70, 24.5 pupils per teacherArmenia: 20.6% probability, ages 30-70, 15.4 pupils per teacherAntigua and Barbuda: 12.1% probability, ages 30-70, 12.4 pupils per teacherAustria: 9.9% probability, ages 30-70, 10 pupils per teacherAzerbaijan: 17.4% probability, ages 30-70, 15.4 pupils per teacherBurundi: 24.8% probability, ages 30-70, 42.5 pupils per teacherBelgium: 9.2% probability, ages 30-70, 11.3 pupils per teacherBenin: 20.8% probability, ages 30-70, 39.2 pupils per teacherBurkina Faso: 23.4% probability, ages 30-70, 39.7 pupils per teacherBangladesh: 18.1% probability, ages 30-70, 30.1 pupils per teacherBulgaria: 25.6% probability, ages 30-70, 15.2 pupils per teacherBahrain: 14.8% probability, ages 30-70, 11.9 pupils per teacherBahamas, The: 20.4% probability, ages 30-70, 19.4 pupils per teacherBosnia and Herzegovina: 17.2% probability, ages 30-70, 16.9 pupils per teacherBelarus: 23.8% probability, ages 30-70, 19.2 pupils per teacherBelize: 15.4% probability, ages 30-70, 19.8 pupils per teacherBolivia: 19.9% probability, ages 30-70, 17.8 pupils per teacherBrazil: 14.5% probability, ages 30-70, 20.2 pupils per teacherBarbados: 14% probability, ages 30-70, 14 pupils per teacherBrunei Darussalam: 14.9% probability, ages 30-70, 9.9 pupils per teacherBhutan: 15.9% probability, ages 30-70, 34.7 pupils per teacherBotswana: 19% probability, ages 30-70, 23.7 pupils per teacherCentral African Republic: 30.8% probability, ages 30-70, 83.4 pupils per teacherSwitzerland: 7.5% probability, ages 30-70, 9.93 pupils per teacherChile: 9.4% probability, ages 30-70, 17.8 pupils per teacherChina: 15.9% probability, ages 30-70, 16.4 pupils per teacherCote d'Ivoire: 22.7% probability, ages 30-70, 41.8 pupils per teacherCameroon: 24.3% probability, ages 30-70, 44.8 pupils per teacherCongo, Dem. Rep.: 25.1% probability, ages 30-70, 33.2 pupils per teacherCongo, Rep.: 21.2% probability, ages 30-70, 44.4 pupils per teacherColombia: 10.3% probability, ages 30-70, 23.3 pupils per teacherComoros: 20.2% probability, ages 30-70, 28.1 pupils per teacherCabo Verde: 17.2% probability, ages 30-70, 21.1 pupils per teacherCosta Rica: 9.9% probability, ages 30-70, 12.2 pupils per teacherCyprus: 8.5% probability, ages 30-70, 12 pupils per teacherCzechia: 14.2% probability, ages 30-70, 18.9 pupils per teacherGermany: 11.6% probability, ages 30-70, 12.3 pupils per teacherDjibouti: 20.6% probability, ages 30-70, 29.4 pupils per teacherDenmark: 10.7% probability, ages 30-70, 10.7 pupils per teacherDominican Republic: 17.4% probability, ages 30-70, 18.9 pupils per teacherAlgeria: 13.3% probability, ages 30-70, 24.3 pupils per teacherEcuador: 13.2% probability, ages 30-70, 24.3 pupils per teacherEgypt, Arab Rep.: 26% probability, ages 30-70, 23.7 pupils per teacherSpain: 9.3% probability, ages 30-70, 13.1 pupils per teacherEstonia: 15.5% probability, ages 30-70, 11.3 pupils per teacherEthiopia: 16.8% probability, ages 30-70, 55.1 pupils per teacherFinland: 9.6% probability, ages 30-70, 13.7 pupils per teacherFiji: 37.9% probability, ages 30-70, 28 pupils per teacherFrance: 10.2% probability, ages 30-70, 18.2 pupils per teacherMicronesia, Fed. Sts.: 40.5% probability, ages 30-70, 19.7 pupils per teacherGabon: 18.1% probability, ages 30-70, 24.5 pupils per teacherUnited Kingdom: 11% probability, ages 30-70, 15.1 pupils per teacherGeorgia: 22.1% probability, ages 30-70, 8.98 pupils per teacherGhana: 22.2% probability, ages 30-70, 27 pupils per teacherGuinea: 23.6% probability, ages 30-70, 47.1 pupils per teacherGambia, The: 21.8% probability, ages 30-70, 36.1 pupils per teacherGuinea-Bissau: 25.3% probability, ages 30-70, 51.9 pupils per teacherEquatorial Guinea: 20.3% probability, ages 30-70, 23.2 pupils per teacherGreece: 12% probability, ages 30-70, 9.38 pupils per teacherGrenada: 17.1% probability, ages 30-70, 16.4 pupils per teacherGuatemala: 14.6% probability, ages 30-70, 20.3 pupils per teacherGuyana: 25.4% probability, ages 30-70, 23.2 pupils per teacherHonduras: 17.8% probability, ages 30-70, 25.6 pupils per teacherCroatia: 15.9% probability, ages 30-70, 13.5 pupils per teacherHungary: 21.7% probability, ages 30-70, 10.8 pupils per teacherIndonesia: 21.9% probability, ages 30-70, 17 pupils per teacherIndia: 23.6% probability, ages 30-70, 32.7 pupils per teacherIreland: 9.6% probability, ages 30-70, 16.1 pupils per teacherIran, Islamic Rep.: 13.9% probability, ages 30-70, 28.5 pupils per teacherIceland: 8.5% probability, ages 30-70, 10.1 pupils per teacherIsrael: 7.6% probability, ages 30-70, 12.1 pupils per teacherItaly: 9.1% probability, ages 30-70, 11.5 pupils per teacherJamaica: 20.2% probability, ages 30-70, 24.8 pupils per teacherJordan: 11.6% probability, ages 30-70, 18.5 pupils per teacherJapan: 8% probability, ages 30-70, 15.7 pupils per teacherKazakhstan: 21.2% probability, ages 30-70, 17.2 pupils per teacherKenya: 18% probability, ages 30-70, 30.7 pupils per teacherKyrgyz Republic: 18.8% probability, ages 30-70, 25 pupils per teacherCambodia: 22.6% probability, ages 30-70, 41.7 pupils per teacherKiribati: 44.1% probability, ages 30-70, 25.5 pupils per teacherKorea, Rep.: 6.9% probability, ages 30-70, 16.3 pupils per teacherKuwait: 9.3% probability, ages 30-70, 8.88 pupils per teacherLao PDR: 26.5% probability, ages 30-70, 22.3 pupils per teacherLebanon: 11.9% probability, ages 30-70, 12.5 pupils per teacherLiberia: 22% probability, ages 30-70, 22.3 pupils per teacherSt. Lucia: 15.5% probability, ages 30-70, 14.7 pupils per teacherSri Lanka: 13.9% probability, ages 30-70, 21.7 pupils per teacherLesotho: 36.3% probability, ages 30-70, 32.9 pupils per teacherLithuania: 19.8% probability, ages 30-70, 13.5 pupils per teacherLuxembourg: 7.8% probability, ages 30-70, 8.3 pupils per teacherLatvia: 22.7% probability, ages 30-70, 11.5 pupils per teacherMorocco: 22% probability, ages 30-70, 26.8 pupils per teacherMoldova: 24.7% probability, ages 30-70, 17.9 pupils per teacherMadagascar: 25.9% probability, ages 30-70, 39.8 pupils per teacherMaldives: 9% probability, ages 30-70, 10.2 pupils per teacherMexico: 16.3% probability, ages 30-70, 26.6 pupils per teacherNorth Macedonia: 19.6% probability, ages 30-70, 14.9 pupils per teacherMali: 23% probability, ages 30-70, 37.8 pupils per teacherMalta: 9.8% probability, ages 30-70, 12.9 pupils per teacherMyanmar: 24% probability, ages 30-70, 24.4 pupils per teacherMongolia: 26.3% probability, ages 30-70, 30.4 pupils per teacherMozambique: 26.6% probability, ages 30-70, 55.3 pupils per teacherMauritania: 18.7% probability, ages 30-70, 34.3 pupils per teacherMauritius: 21.7% probability, ages 30-70, 16.2 pupils per teacherMalawi: 24.9% probability, ages 30-70, 58.7 pupils per teacherMalaysia: 19.9% probability, ages 30-70, 11.7 pupils per teacherNamibia: 23.1% probability, ages 30-70, 25.1 pupils per teacherNiger: 20.2% probability, ages 30-70, 36.3 pupils per teacherNigeria: 17.7% probability, ages 30-70, 37.6 pupils per teacherNicaragua: 12.6% probability, ages 30-70, 30.2 pupils per teacherNetherlands: 9.9% probability, ages 30-70, 11.8 pupils per teacherNorway: 7.8% probability, ages 30-70, 8.59 pupils per teacherNepal: 19.4% probability, ages 30-70, 19.7 pupils per teacherNew Zealand: 10.3% probability, ages 30-70, 14.9 pupils per teacherOman: 14.6% probability, ages 30-70, 9.67 pupils per teacherPakistan: 25.5% probability, ages 30-70, 44.1 pupils per teacherPanama: 10.9% probability, ages 30-70, 22 pupils per teacherPeru: 12.3% probability, ages 30-70, 17.4 pupils per teacherPhilippines: 31.9% probability, ages 30-70, 29.1 pupils per teacherPapua New Guinea: 28.5% probability, ages 30-70, 35.5 pupils per teacherPoland: 17% probability, ages 30-70, 10.2 pupils per teacherPuerto Rico (US): 10.1% probability, ages 30-70, 13.5 pupils per teacherPortugal: 10.5% probability, ages 30-70, 12.4 pupils per teacherParaguay: 16.1% probability, ages 30-70, 24.2 pupils per teacherWest Bank and Gaza: 18.3% probability, ages 30-70, 24.5 pupils per teacherQatar: 12.1% probability, ages 30-70, 12.2 pupils per teacherRomania: 21.5% probability, ages 30-70, 19.3 pupils per teacherRussian Federation: 22.4% probability, ages 30-70, 21.3 pupils per teacherRwanda: 20% probability, ages 30-70, 59.5 pupils per teacherSaudi Arabia: 13.6% probability, ages 30-70, 13.8 pupils per teacherSenegal: 21.4% probability, ages 30-70, 36.3 pupils per teacherSingapore: 10.3% probability, ages 30-70, 14.7 pupils per teacherSolomon Islands: 40.6% probability, ages 30-70, 25.4 pupils per teacherSierra Leone: 23.9% probability, ages 30-70, 27.5 pupils per teacherEl Salvador: 12.6% probability, ages 30-70, 26.9 pupils per teacherSerbia: 20.7% probability, ages 30-70, 14.3 pupils per teacherSao Tome and Principe: 19.5% probability, ages 30-70, 31.2 pupils per teacherSuriname: 18.7% probability, ages 30-70, 13.4 pupils per teacherSlovak Republic: 16.6% probability, ages 30-70, 15.5 pupils per teacherSlovenia: 11.6% probability, ages 30-70, 13.8 pupils per teacherSweden: 7.9% probability, ages 30-70, 12.2 pupils per teacherEswatini: 32.3% probability, ages 30-70, 26.6 pupils per teacherSeychelles: 15.6% probability, ages 30-70, 14.5 pupils per teacherChad: 23.4% probability, ages 30-70, 56.9 pupils per teacherTogo: 25.4% probability, ages 30-70, 40.1 pupils per teacherThailand: 14.5% probability, ages 30-70, 16.6 pupils per teacherTajikistan: 18.1% probability, ages 30-70, 22.3 pupils per teacherTimor-Leste: 20.1% probability, ages 30-70, 26.9 pupils per teacherTonga: 26.9% probability, ages 30-70, 21.6 pupils per teacherTunisia: 13% probability, ages 30-70, 16.9 pupils per teacherTurkiye: 15.4% probability, ages 30-70, 17 pupils per teacherTanzania: 18.8% probability, ages 30-70, 50.6 pupils per teacherUganda: 21.5% probability, ages 30-70, 42.7 pupils per teacherUkraine: 24.8% probability, ages 30-70, 13 pupils per teacherUruguay: 17.4% probability, ages 30-70, 11 pupils per teacherUnited States: 13.7% probability, ages 30-70, 14.2 pupils per teacherUzbekistan: 24.6% probability, ages 30-70, 21.5 pupils per teacherSt. Vincent and the Grenadines: 23.6% probability, ages 30-70, 14.4 pupils per teacherViet Nam: 20.1% probability, ages 30-70, 20.3 pupils per teacherVanuatu: 36.8% probability, ages 30-70, 26.6 pupils per teacherSamoa: 32.3% probability, ages 30-70, 30.2 pupils per teacherSouth Africa: 22.7% probability, ages 30-70, 30.3 pupils per teacherZambia: 23.6% probability, ages 30-70, 42.1 pupils per teacherZimbabwe: 31.2% probability, ages 30-70, 36.4 pupils per teacherAFGCAFCHEETHFSMKIRLUXNORRWASLB

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5121.5e-12167
By rank (Spearman), not adjusted+0.6325.1e-20167
Standard (Pearson), adjusted for national income-0.0310.688167
By rank (Spearman), adjusted for national income+0.1300.096167

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 167 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: +0.481 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.062 (without GUY) to +0.006 (without FSM). 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: FSM (+0.038).

Fragile: this link may not hold up.
  • sign flips when a single country is removed
  • correlation moves sharply once outliers are trimmed
  • sign flips once outliers are trimmed

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 & Pacific24-0.01
Europe & Central Asia46+0.08
Latin America & Caribbean27+0.06
Middle East, North Africa, Afghanistan & Pakistan19+0.62
Sub-Saharan Africa44+0.06

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 third56-0.19
Middle third55+0.24
Richest third56+0.29

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
Christians103-0.16
Muslims43+0.39

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+0.50
Never a European colony20+0.02
Spain19-0.24
United Kingdom52-0.05

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 = +0.147 after dropping 9 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 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 = -0.195) but mostly disappears in year-to-year changes (r = -0.033). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.031for comparison
Within countries+0.177each country vs itself
…and vs the same year elsewhere-0.195q = 0.005 · 163 countries
Year-on-year changes-0.033q = 0.459
The direction flips. Comparing each country only with itself gives +0.18. Also taking out what happened to the whole world each year gives -0.19. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.
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.

2,362 country-years (one country in one year), 163 countries, 2000–2019, about 15 years each on average. Share of the variation that happens within countries over time: 8% for Premature NCD mortality, 5% for Pupil-teacher ratio. 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

0.30−0.3-100+10years Pupil-teacher ratio trails Premature NCD mortality →lag -10: r = -0.002 (n = 2,625)lag -9: r = -0.015 (n = 2,647)lag -8: r = -0.037 (n = 2,659)lag -7: r = -0.045 (n = 2,663)lag -6: r = -0.059 (n = 2,689)lag -5: r = -0.078 (n = 2,702)lag -4: r = -0.111 (n = 2,734)lag -3: r = -0.138 (n = 2,715)lag -2: r = -0.162 (n = 2,626)lag -1: r = -0.169 (n = 2,508)lag +0: r = -0.195 (n = 2,362)lag +1: r = -0.204 (n = 2,236)lag +2: r = -0.202 (n = 2,095)lag +3: r = -0.172 (n = 1,972)lag +4: r = -0.138 (n = 1,845)lag +5: r = -0.124 (n = 1,706)lag +6: r = -0.087 (n = 1,584)lag +7: r = -0.039 (n = 1,467)lag +8: r = -0.025 (n = 1,330)lag +9: r = -0.029 (n = 1,177)lag +10: r = -0.039 (n = 1,024)

Same year (lag 0): -0.195. 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


% probability, ages 30-70

pupils per teacher
Years
Kiribati KIR44.1%25.52021, 2017
Solomon Islands SLB40.6%25.42021, 2018
Micronesia, Fed. Sts. FSM40.5%19.72021, 2015
Fiji FJI37.9%282021, 2012
Vanuatu VUT36.8%26.62021, 2015
Lesotho LSO36.3%32.92021, 2017
Afghanistan AFG32.7%48.82021, 2018
Samoa WSM32.3%30.22021, 2010
Eswatini SWZ32.3%26.62021, 2017
Philippines PHL31.9%29.12021, 2017
Zimbabwe ZWE31.2%36.42021, 2013
Central African Republic CAF30.8%83.42021, 2016
Papua New Guinea PNG28.5%35.52021, 2016
Tonga TON26.9%21.62021, 2015
Mozambique MOZ26.6%55.32021, 2018

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

Sources

Premature NCD mortality

Mortality from cardiovascular disease, cancer, diabetes or chronic respiratory disease between ages 30 and 70 (%)

World Health Organization · CC BY 4.0 · updated quarterly

The series ends in 2021.

Pupil-teacher ratio

Pupil-teacher ratio, primary

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

The World Bank's copy stops at 2019.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2021. 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 Premature NCD mortality and Pupil-teacher ratio

This page shows that two measures go together. It does not show that Premature NCD mortality changes Pupil-teacher ratio, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Premature NCD mortality or Pupil-teacher ratio