Do Economy free of state ownership and Premature NCD mortality go together across countries?

Across 167 countries the link before adjusting for anything is r = -0.24, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.28.

Does the link hold within countries over time, not just across countries?

Comparing each country only with itself across 2000-2021, and setting aside what happened worldwide in each year, the link is -0.012 (173 countries, 3,795 country-years). Using year-to-year changes, it is +0.015.

Does Economy free of state ownership cause Premature NCD mortality?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Economy free of state ownership vs. Premature NCD mortality

Holds up after adjusting for income

Across 167 countries, Economy free of state ownership and Premature NCD mortality rise and fall together at r = -0.24 (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 = -0.18, q = 0.048, allowing for the many pairs tested). Caution: direction is not consistent across regions.

Comparing countries across the middle half of economy free of state ownership, -0.0675 to 1.32 points, premature NCD mortality is better by 2.25% probability, ages 30-70 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 1.26% probability, ages 30-70.

Link before adjusting (r)-0.242p = 0.002 · 167 countries

After adjusting for national income-0.182p = 0.019 · likely range [-0.32, -0.03]

p, allowing for regions0.005standard error 0.042 · 7 regions

Check for luck (q)0.048across 17,292 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.

Economy free of state ownership · score (higher = less state-owned)link with income +0.21 · 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

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	-0.242	0.002	167
By rank (Spearman), not adjusted	-0.283	0.000	167
Standard (Pearson), adjusted for national income	-0.182	0.019	167
By rank (Spearman), adjusted for national income	-0.200	0.010	167

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: +0.061 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.202 (without FJI) to -0.162 (without TKM). 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: FJI (-0.021).

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.

	n	r
East Asia & Pacific	20	+0.06
Europe & Central Asia	48	-0.15
Latin America & Caribbean	23	-0.26
Middle East, North Africa, Afghanistan & Pakistan	22	-0.60
Sub-Saharan Africa	46	+0.00

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.

	n	r
Poorest third	56	+0.00
Middle third	55	-0.15
Richest third	56	-0.38

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.

	n	r
Christians	99	-0.14
Muslims	48	-0.35

By last European coloniser

	n	r
France	25	-0.01
Never a European colony	21	-0.30
Spain	19	-0.04
United Kingdom	49	-0.23

By official ideology

	n	r
Nationalist	24	-0.07
No strong official ideology	116	-0.08

By party system

	n	r
A few parties banned	26	+0.16
No parties banned	121	-0.17

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 = -0.202 after dropping 3 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 = -0.012, 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 economy free of state ownership moved did not tend to see their premature NCD mortality move with it.

Across countries (adjusted)-0.182for comparison

Within countries-0.036each country vs itself

…and vs the same year elsewhere-0.012q = 0.896 · 173 countries

Year-on-year changes+0.015q = 0.588

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.

3,795 country-years (one country in one year), 173 countries, 2000–2021, about 22 years each on average. Share of the variation that happens within countries over time: 5% for Economy free of state ownership, 10% 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

Same year (lag 0): -0.012. 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.

This curve peaks at the edge of the window (lag +10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.

The data

	score (higher = less state-owned)	% probability, ages 30-70	Years
Japan JPN	2.98	8%	2025, 2021
Switzerland CHE	2.22	7.5%	2025, 2021
Peru PER	2.21	12.3%	2025, 2021
Latvia LVA	2.17	22.7%	2025, 2021
Luxembourg LUX	2.12	7.8%	2025, 2021
Guatemala GTM	2.12	14.6%	2025, 2021
Lithuania LTU	2.08	19.8%	2025, 2021
Belgium BEL	2.08	9.2%	2025, 2021
Germany DEU	1.96	11.6%	2025, 2021
West Bank and Gaza PSE	1.94	18.3%	2025, 2021
Czechia CZE	1.91	14.2%	2025, 2021
Cyprus CYP	1.85	8.5%	2025, 2021
Benin BEN	1.81	20.8%	2025, 2021
Somalia, Fed. Rep. SOM	1.81	27.6%	2025, 2021
Chile CHL	1.76	9.4%	2025, 2021

Coppedge, M. et al., V-Dem Dataset v16, Varieties of Democracy (V-Dem) Project; Lindberg, S. I. et al., Varieties of Party Identity and Organization (V-Party) Dataset. Read from the V-Dem Institute's vdemdata package.
World Bank, World Development Indicators

Sources

Economy free of state ownership

State ownership of the economy (higher = less owned by the state)

V-Dem Institute, University of Gothenburg · CC BY-SA 4.0 · updated annual (V-Dem); V-Party per election, to 2019

Does the state own or directly control the important parts of the economy? Experts answer from 0 ("virtually all valuable capital belongs to the state") to 4 ("very little valuable capital belongs to the state"), and V-Dem turns their answers into a score that runs from about -4 to +4. So a low score means a mostly state-run economy, as in North Korea or Eritrea, and a high score a mostly private one. It is about ownership and control, not taxes or welfare: a country can tax and spend a great deal while the state owns almost nothing. Today it lines up only weakly with how rich a country is. Oil states such as Saudi Arabia, Qatar and Kuwait are rich while the state owns much of the economy, and some of the poorest countries, such as Somalia, Haiti and the Central African Republic, have little state ownership. The link with income was stronger in the 1960s and has weakened every decade since. Within Europe and Central Asia, where former communist countries sold off state firms, it is still strong. No "better" direction is set: how much the state should own is a political question.

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 2021–2025. 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 17,292 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Economy free of state ownership and Premature NCD mortality

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