placestats

Government consumption vs. Premature NCD mortality

Holds up after adjusting for income

Across 165 countries, Government consumption and Premature NCD mortality rise and fall together at r = +0.07 (on a scale from −1 to +1, where 0 means no link). There is almost no link to explain.

Comparing countries across the middle half of government consumption, 12.2% to 20.6% of GDP, premature ncd mortality is worse by 0.514% probability, ages 30-70 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 1.35% probability, ages 30-70.

Link before adjusting (r)+0.066p = 0.401 · 165 countries
After adjusting for national income+0.236p = 0.002 · likely range [+0.09, +0.38]
p, allowing for regions0.003standard error 0.051 · 7 regions
Check for luck (q)0.040across 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.

Government consumption · % of GDPlink with income +0.28 · independent
Premature NCD mortality · % probability, ages 30-70link with income -0.70 · high

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

Every country as a dot

Government consumption · % of GDP →Premature NCD mortality · % probability, ages 30-70 →2.71%60.6%6.9%44.1%Afghanistan: 23.6% of GDP, 32.7% probability, ages 30-70Angola: 15.8% of GDP, 24.7% probability, ages 30-70Albania: 12.8% of GDP, 10% probability, ages 30-70United Arab Emirates: 12.2% of GDP, 11.6% probability, ages 30-70Argentina: 14.9% of GDP, 13.4% probability, ages 30-70Armenia: 10.2% of GDP, 20.6% probability, ages 30-70Australia: 23.1% of GDP, 8.3% probability, ages 30-70Austria: 21.9% of GDP, 9.9% probability, ages 30-70Azerbaijan: 15.5% of GDP, 17.4% probability, ages 30-70Burundi: 20.6% of GDP, 24.8% probability, ages 30-70Belgium: 24% of GDP, 9.2% probability, ages 30-70Benin: 8.87% of GDP, 20.8% probability, ages 30-70Burkina Faso: 17.8% of GDP, 23.4% probability, ages 30-70Bangladesh: 5.8% of GDP, 18.1% probability, ages 30-70Bulgaria: 20.8% of GDP, 25.6% probability, ages 30-70Bahrain: 14.1% of GDP, 14.8% probability, ages 30-70Bahamas, The: 12.9% of GDP, 20.4% probability, ages 30-70Bosnia and Herzegovina: 20.3% of GDP, 17.2% probability, ages 30-70Belarus: 18.9% of GDP, 23.8% probability, ages 30-70Belize: 15.9% of GDP, 15.4% probability, ages 30-70Bolivia: 16.5% of GDP, 19.9% probability, ages 30-70Brazil: 19.1% of GDP, 14.5% probability, ages 30-70Brunei Darussalam: 23.8% of GDP, 14.9% probability, ages 30-70Bhutan: 20% of GDP, 15.9% probability, ages 30-70Botswana: 31.5% of GDP, 19% probability, ages 30-70Central African Republic: 10.3% of GDP, 30.8% probability, ages 30-70Canada: 21.9% of GDP, 9.7% probability, ages 30-70Switzerland: 12% of GDP, 7.5% probability, ages 30-70Chile: 15.1% of GDP, 9.4% probability, ages 30-70China: 16.7% of GDP, 15.9% probability, ages 30-70Cote d'Ivoire: 8.71% of GDP, 22.7% probability, ages 30-70Cameroon: 11.1% of GDP, 24.3% probability, ages 30-70Congo, Dem. Rep.: 15.9% of GDP, 25.1% probability, ages 30-70Congo, Rep.: 14.9% of GDP, 21.2% probability, ages 30-70Colombia: 15.5% of GDP, 10.3% probability, ages 30-70Comoros: 9.01% of GDP, 20.2% probability, ages 30-70Cabo Verde: 22.9% of GDP, 17.2% probability, ages 30-70Costa Rica: 14% of GDP, 9.9% probability, ages 30-70Cyprus: 18.5% of GDP, 8.5% probability, ages 30-70Czechia: 20% of GDP, 14.2% probability, ages 30-70Germany: 22.5% of GDP, 11.6% probability, ages 30-70Djibouti: 15% of GDP, 20.6% probability, ages 30-70Denmark: 23.2% of GDP, 10.7% probability, ages 30-70Dominican Republic: 11.7% of GDP, 17.4% probability, ages 30-70Algeria: 18.3% of GDP, 13.3% probability, ages 30-70Ecuador: 12.8% of GDP, 13.2% probability, ages 30-70Egypt, Arab Rep.: 5.45% of GDP, 26% probability, ages 30-70Spain: 19.2% of GDP, 9.3% probability, ages 30-70Estonia: 21.5% of GDP, 15.5% probability, ages 30-70Ethiopia: 5.84% of GDP, 16.8% probability, ages 30-70Finland: 26.1% of GDP, 9.6% probability, ages 30-70Fiji: 18.9% of GDP, 37.9% probability, ages 30-70France: 24.4% of GDP, 10.2% probability, ages 30-70Gabon: 11.2% of GDP, 18.1% probability, ages 30-70United Kingdom: 21.2% of GDP, 11% probability, ages 30-70Georgia: 13.8% of GDP, 22.1% probability, ages 30-70Ghana: 4.8% of GDP, 22.2% probability, ages 30-70Guinea: 16.7% of GDP, 23.6% probability, ages 30-70Gambia, The: 10.2% of GDP, 21.8% probability, ages 30-70Guinea-Bissau: 15.5% of GDP, 25.3% probability, ages 30-70Equatorial Guinea: 32.4% of GDP, 20.3% probability, ages 30-70Greece: 18.2% of GDP, 12% probability, ages 30-70Guatemala: 11.5% of GDP, 14.6% probability, ages 30-70Honduras: 15.2% of GDP, 17.8% probability, ages 30-70Croatia: 24.3% of GDP, 15.9% probability, ages 30-70Haiti: 5.53% of GDP, 31.6% probability, ages 30-70Hungary: 20.8% of GDP, 21.7% probability, ages 30-70Indonesia: 7.53% of GDP, 21.9% probability, ages 30-70India: 10.8% of GDP, 23.6% probability, ages 30-70Ireland: 11.2% of GDP, 9.6% probability, ages 30-70Iran, Islamic Rep.: 13.3% of GDP, 13.9% probability, ages 30-70Iraq: 22.2% of GDP, 22.7% probability, ages 30-70Iceland: 26.1% of GDP, 8.5% probability, ages 30-70Israel: 25% of GDP, 7.6% probability, ages 30-70Italy: 18.3% of GDP, 9.1% probability, ages 30-70Japan: 20.1% of GDP, 8% probability, ages 30-70Kazakhstan: 10.8% of GDP, 21.2% probability, ages 30-70Kenya: 11.2% of GDP, 18% probability, ages 30-70Kyrgyz Republic: 12.2% of GDP, 18.8% probability, ages 30-70Cambodia: 5.65% of GDP, 22.6% probability, ages 30-70Kiribati: 59% of GDP, 44.1% probability, ages 30-70Korea, Rep.: 17.6% of GDP, 6.9% probability, ages 30-70Kuwait: 25.8% of GDP, 9.3% probability, ages 30-70Lao PDR: 14% of GDP, 26.5% probability, ages 30-70Lebanon: 4.9% of GDP, 11.9% probability, ages 30-70Libya: 32.3% of GDP, 19.8% probability, ages 30-70Sri Lanka: 6.74% of GDP, 13.9% probability, ages 30-70Lesotho: 36.4% of GDP, 36.3% probability, ages 30-70Lithuania: 19.3% of GDP, 19.8% probability, ages 30-70Luxembourg: 19.8% of GDP, 7.8% probability, ages 30-70Latvia: 22.9% of GDP, 22.7% probability, ages 30-70Morocco: 18% of GDP, 22% probability, ages 30-70Moldova: 18.5% of GDP, 24.7% probability, ages 30-70Madagascar: 14.6% of GDP, 25.9% probability, ages 30-70Maldives: 17.5% of GDP, 9% probability, ages 30-70Mexico: 11.8% of GDP, 16.3% probability, ages 30-70North Macedonia: 16.4% of GDP, 19.6% probability, ages 30-70Mali: 13.1% of GDP, 23% probability, ages 30-70Malta: 17.5% of GDP, 9.8% probability, ages 30-70Montenegro: 17.4% of GDP, 18% probability, ages 30-70Mongolia: 14.6% of GDP, 26.3% probability, ages 30-70Mozambique: 19.2% of GDP, 26.6% probability, ages 30-70Mauritania: 16% of GDP, 18.7% probability, ages 30-70Mauritius: 14.2% of GDP, 21.7% probability, ages 30-70Malawi: 18.2% of GDP, 24.9% probability, ages 30-70Malaysia: 12.4% of GDP, 19.9% probability, ages 30-70Namibia: 22.3% of GDP, 23.1% probability, ages 30-70Niger: 12.2% of GDP, 20.2% probability, ages 30-70Nicaragua: 11.4% of GDP, 12.6% probability, ages 30-70Netherlands: 25.9% of GDP, 9.9% probability, ages 30-70Norway: 22.6% of GDP, 7.8% probability, ages 30-70Nepal: 8.38% of GDP, 19.4% probability, ages 30-70New Zealand: 21.1% of GDP, 10.3% probability, ages 30-70Oman: 18.7% of GDP, 14.6% probability, ages 30-70Pakistan: 9.32% of GDP, 25.5% probability, ages 30-70Panama: 12.4% of GDP, 10.9% probability, ages 30-70Peru: 13.1% of GDP, 12.3% probability, ages 30-70Philippines: 15.1% of GDP, 31.9% probability, ages 30-70Poland: 21.3% of GDP, 17% probability, ages 30-70Puerto Rico (US): 7.7% of GDP, 10.1% probability, ages 30-70Portugal: 17% of GDP, 10.5% probability, ages 30-70Paraguay: 10.9% of GDP, 16.1% probability, ages 30-70West Bank and Gaza: 17% of GDP, 18.3% probability, ages 30-70Qatar: 12.9% of GDP, 12.1% probability, ages 30-70Romania: 17.3% of GDP, 21.5% probability, ages 30-70Russian Federation: 19.1% of GDP, 22.4% probability, ages 30-70Rwanda: 15.1% of GDP, 20% probability, ages 30-70Saudi Arabia: 20.6% of GDP, 13.6% probability, ages 30-70Sudan: 17.9% of GDP, 21.2% probability, ages 30-70Senegal: 15.4% of GDP, 21.4% probability, ages 30-70Singapore: 10.3% of GDP, 10.3% probability, ages 30-70Solomon Islands: 26.2% of GDP, 40.6% probability, ages 30-70Sierra Leone: 5.95% of GDP, 23.9% probability, ages 30-70El Salvador: 16.4% of GDP, 12.6% probability, ages 30-70Somalia, Fed. Rep.: 8.11% of GDP, 27.6% probability, ages 30-70Serbia: 18.5% of GDP, 20.7% probability, ages 30-70Sao Tome and Principe: 10.3% of GDP, 19.5% probability, ages 30-70Suriname: 13.3% of GDP, 18.7% probability, ages 30-70Slovak Republic: 21.4% of GDP, 16.6% probability, ages 30-70Slovenia: 21.2% of GDP, 11.6% probability, ages 30-70Sweden: 26.4% of GDP, 7.9% probability, ages 30-70Eswatini: 21.5% of GDP, 32.3% probability, ages 30-70Seychelles: 25.1% of GDP, 15.6% probability, ages 30-70Syrian Arab Republic: 2.71% of GDP, 21.3% probability, ages 30-70Chad: 9.9% of GDP, 23.4% probability, ages 30-70Togo: 13.3% of GDP, 25.4% probability, ages 30-70Thailand: 16.7% of GDP, 14.5% probability, ages 30-70Tajikistan: 11.3% of GDP, 18.1% probability, ages 30-70Turkmenistan: 12.4% of GDP, 25.7% probability, ages 30-70Timor-Leste: 60.6% of GDP, 20.1% probability, ages 30-70Tonga: 23.5% of GDP, 26.9% probability, ages 30-70Tunisia: 19.9% of GDP, 13% probability, ages 30-70Turkiye: 13.9% of GDP, 15.4% probability, ages 30-70Tanzania: 9.78% of GDP, 18.8% probability, ages 30-70Uganda: 11.7% of GDP, 21.5% probability, ages 30-70Ukraine: 38.9% of GDP, 24.8% probability, ages 30-70Uruguay: 17.6% of GDP, 17.4% probability, ages 30-70United States: 13.6% of GDP, 13.7% probability, ages 30-70Uzbekistan: 15.9% of GDP, 24.6% probability, ages 30-70Viet Nam: 9.38% of GDP, 20.1% probability, ages 30-70Vanuatu: 21.4% of GDP, 36.8% probability, ages 30-70Samoa: 18.6% of GDP, 32.3% probability, ages 30-70South Africa: 18.9% of GDP, 22.7% probability, ages 30-70Zambia: 12.7% of GDP, 23.6% probability, ages 30-70Zimbabwe: 12.2% of GDP, 31.2% probability, ages 30-70CAFCHEEGYHTIISLKIRLBNLSOPRISWETLS

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.0660.401165
By rank (Spearman), not adjusted-0.1600.040165
Standard (Pearson), adjusted for national income+0.2360.002165
By rank (Spearman), adjusted for national income+0.1320.093165

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 165 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: -0.170 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.126 (without KIR) to +0.297 (without TLS). 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 (-0.110).

Fragile: this link may not hold up.
  • leave-one-out range spans 0.17 in r
  • 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 & Pacific22-0.12
Europe & Central Asia48+0.05
Latin America & Caribbean22-0.24
Middle East, North Africa, Afghanistan & Pakistan21-0.03
Sub-Saharan Africa44+0.33

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 third55+0.46
Middle third55+0.16
Richest third55-0.19

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
Christians99+0.31
Muslims47-0.04

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
France25-0.19
Never a European colony20+0.13
Spain19+0.28
United Kingdom47+0.39

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 = +0.060 after dropping 6 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 22years. 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 = +0.043, 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 government consumption moved did not tend to see their premature ncd mortality move with it.

Across countries (adjusted)+0.236for comparison
Within countries+0.004each country vs itself
…and vs the same year elsewhere+0.043q = 0.723 · 171 countries
Year-on-year changes-0.008q = 0.892
Read with care.
  • Almost all of the second indicator's variation is between countries, so the within-country estimate rests on a small part of the data.

3,512 country-years (one country in one year), 171 countries, 2000–2021, about 21 years each on average. Share of the variation that happens within countries over time: 17% for Government consumption, 9% for Premature NCD mortality. 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 Premature NCD mortality trails Government consumption →lag -10: r = -0.163 (n = 2,559)lag -9: r = -0.148 (n = 2,716)lag -8: r = -0.133 (n = 2,872)lag -7: r = -0.109 (n = 3,037)lag -6: r = -0.087 (n = 3,197)lag -5: r = -0.067 (n = 3,351)lag -4: r = -0.037 (n = 3,504)lag -3: r = -0.002 (n = 3,533)lag -2: r = 0.019 (n = 3,522)lag -1: r = 0.033 (n = 3,518)lag +0: r = 0.043 (n = 3,512)lag +1: r = 0.078 (n = 3,493)lag +2: r = 0.110 (n = 3,479)lag +3: r = 0.124 (n = 3,461)lag +4: r = 0.124 (n = 3,443)lag +5: r = 0.137 (n = 3,422)lag +6: r = 0.152 (n = 3,399)lag +7: r = 0.181 (n = 3,375)lag +8: r = 0.175 (n = 3,349)lag +9: r = 0.166 (n = 3,327)lag +10: r = 0.147 (n = 3,300)

Same year (lag 0): +0.043. 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 (+0.18 at lag +7) is higher than the same-year one by 0.14. 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 GDP
% probability, ages 30-70		Years
Timor-Leste TLS60.6%20.1%2024, 2021
Kiribati KIR59%44.1%2024, 2021
Ukraine UKR38.9%24.8%2025, 2021
Lesotho LSO36.4%36.3%2025, 2021
Equatorial Guinea GNQ32.4%20.3%2025, 2021
Libya LBY32.3%19.8%2025, 2021
Botswana BWA31.5%19%2025, 2021
Sweden SWE26.4%7.9%2025, 2021
Solomon Islands SLB26.2%40.6%2024, 2021
Finland FIN26.1%9.6%2025, 2021
Iceland ISL26.1%8.5%2025, 2021
Netherlands NLD25.9%9.9%2025, 2021
Kuwait KWT25.8%9.3%2024, 2021
Seychelles SYC25.1%15.6%2025, 2021
Israel ISR25%7.6%2025, 2021

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

Sources

Government consumption

General government final consumption expenditure (% of GDP)

World Bank · CC BY 4.0 · updated quarterly

What government buys directly, as a share of the economy: wages, defence, and the running of schools and hospitals. It is the widely reported half of government size. Payments such as pensions and benefits are handed out, not spent on goods and services, so they are not in here. That understates government in the countries with the largest welfare systems. Government expense covers those, but for fewer countries. Compared with income per person, this answers the question the per-person dollar figure cannot. Rich countries spend far more per person because they have far more per person. Whether their governments are a larger share of what there is needs a share of GDP, not an amount per person. No "better" direction is set: a bigger or smaller government is a political question, not a better or worse one.

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.

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 Government consumption and Premature NCD mortality

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

More links with Government consumption or Premature NCD mortality