Do Male labor participation and Old-age spending go together across countries?

Across 42 countries the link before adjusting for anything is r = -0.77, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.78.

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

Comparing each country only with itself across 1990-2023, and setting aside what happened worldwide in each year, the link is -0.359 (42 countries, 1,214 country-years). Using year-to-year changes, it is -0.070.

Does Male labor participation cause Old-age spending?

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.

Male labor participation vs. Old-age spending

Holds up after adjusting for income

Across 42 countries, Male labor participation and Old-age spending rise and fall together at r = -0.77 (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 = -0.76, q = 0.0011, allowing for the many pairs tested).

Comparing countries across the middle half of old-age spending, 5.23% to 9.69% of GDP, male labor participation is 6.23% of men 15+ lower at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 6.07% of men 15+.

Link before adjusting (r)-0.766p = 3.3e-9 · 42 countries

After adjusting for national income-0.757p = 1.0e-8 · likely range [-0.86, -0.59]

p, allowing for regions2.4e-6standard error 0.019 · 5 regions

Check for luck (q)0.001across 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.

Male labor participation · % of men 15+link with income -0.06 · independent

Old-age spending · % of GDPlink with income +0.17 · independent

Neither of these is closely tied to national income, so adjusting for income is a real test here, not a formality.

“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	-0.766	3.3e-9	42
By rank (Spearman), not adjusted	-0.785	7.7e-10	42
Standard (Pearson), adjusted for national income	-0.757	1.0e-8	42
By rank (Spearman), adjusted for national income	-0.785	1.2e-9	42

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 42 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: +0.009 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.799 (without HRV) to -0.735 (without ITA). 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: HRV (-0.042).

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
Europe & Central Asia	30	-0.68

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	14	-0.76
Middle third	14	-0.75
Richest third	14	-0.69

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	35	-0.76

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 = -0.757 after dropping 0 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 34years. 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 = -0.359) but mostly disappears in year-to-year changes (r = -0.070). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.757for comparison

Within countries-0.543each country vs itself

…and vs the same year elsewhere-0.359q = 0.000 · 42 countries

Year-on-year changes-0.070q = 0.460

Read with care.

The association is much weaker in year-on-year changes than in levels, which points to shared country-specific trends.

1,214 country-years (one country in one year), 42 countries, 1990–2023, about 29 years each on average. Share of the variation that happens within countries over time: 19% for Male labor participation, 12% for Old-age spending. 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): -0.359. 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

	% of men 15+	% of GDP	Years
Peru PER	80.9%	1.87%	2025, 2023
Iceland ISL	79.6%	3.29%	2025, 2021
Mexico MEX	77.1%	5.11%	2025, 2023
Colombia COL	75.7%	5.7%	2025, 2023
New Zealand NZL	74.7%	5.06%	2025, 2022
Korea, Rep. KOR	72%	3.62%	2025, 2022
Chile CHL	71.9%	3.28%	2025, 2023
Netherlands NLD	71.9%	7.19%	2025, 2021
Turkiye TUR	71.9%	4.88%	2025, 2021
Japan JPN	71.5%	8.29%	2025, 2022
Switzerland CHE	71.4%	6.32%	2025, 2021
Australia AUS	70.7%	4.46%	2025, 2022
Ireland IRL	70.1%	2.75%	2025, 2021
Estonia EST	69.8%	6.82%	2025, 2021
Costa Rica CRI	69.8%	4.58%	2025, 2022

World Bank, World Development Indicators
OECD databases as named on each indicator: PISA, Social Expenditure Database (SOCX), Income Distribution Database, Development Assistance Committee statistics, Family Database, Government at a Glance, Corporate Tax Statistics, employment and environment statistics

Sources

Male labor participation

Labor force participation rate, male (% of male population 15+)

International Labour Organization · CC BY 4.0 · updated quarterly

Old-age spending

Public spending on old-age benefits

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator

Public pensions and other benefits for older people. It mostly reflects how old a country is and how its pensions are set up. That is why its strongest link is with the share of people aged 65 and over.

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 5 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 Male labor participation and Old-age spending

This page shows that two measures go together. It does not show that Male labor participation changes Old-age spending, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.