Do Contraceptive use (any method) and Human capital (PWT) go together across countries?

Across 114 countries the link before adjusting for anything is r = +0.62, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.56.

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 +0.003 (84 countries, 709 country-years). Using year-to-year changes, it is —.

Does Contraceptive use (any method) cause Human capital (PWT)?

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.

Contraceptive use (any method) vs. Human capital (PWT)

Fades after adjusting for income

Across 114 countries, Contraceptive use (any method) and Human capital (PWT) 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 +0.31, which could easily be chance (q = 0.31, 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.

Comparing countries across the middle half of human capital (PWT), 2.04 to 3.14 points, contraceptive use (any method) is 20.1% higher at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 14.9%.

Link before adjusting (r)+0.617p = 2.7e-13 · 114 countries

After adjusting for national income+0.308p = 0.001 · likely range [+0.13, +0.47]

p, allowing for regions0.032standard error 0.111 · 7 regions

Check for luck (q)0.308across 18,622 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.

Contraceptive use (any method) · %link with income +0.56 · moderate

Human capital (PWT) · index (1 = no schooling)link with income +0.82 · proxy

“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	+0.617	2.7e-13	114
By rank (Spearman), not adjusted	+0.562	7.5e-11	114
Standard (Pearson), adjusted for national income	+0.308	0.001	114
By rank (Spearman), adjusted for national income	+0.198	0.035	114

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 114 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: +0.309 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.272 (without GUY) to +0.340 (without KGZ). 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: GUY (-0.036).

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	15	-0.03
Europe & Central Asia	24	+0.13
Latin America & Caribbean	22	+0.43
Sub-Saharan Africa	37	+0.50

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	38	+0.32
Middle third	38	+0.18
Richest third	38	+0.37

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	70	+0.37
Muslims	29	+0.14

By last European coloniser

	n	r
France	19	+0.14
Never a European colony	15	-0.22
Spain	17	+0.14
United Kingdom	36	+0.45

By official ideology

	n	r
Nationalist	18	+0.18
No strong official ideology	76	+0.43

By party system

	n	r
A few parties banned	19	+0.13
No parties banned	82	+0.34

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 = +0.308 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.

Within countries over time, there is no link that is clearly more than chance (r = +0.003, 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 contraceptive use (any method) moved did not tend to see their human capital (PWT) move with it.

Across countries (adjusted)+0.308for comparison

Within countries+0.493each country vs itself

…and vs the same year elsewhere+0.003q = 0.990 · 84 countries

Year-on-year changes—q = —

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.
Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.

709 country-years (one country in one year), 84 countries, 1990–2023, about 8 years each on average. Share of the variation that happens within countries over time: 8% for Contraceptive use (any method), 11% for Human capital (PWT). 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): +0.003. 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

	%	index (1 = no schooling)	Years
Finland FIN	85.5%	3.6	2015, 2023
China CHN	84.5%	2.75	2017, 2023
Korea, Rep. KOR	84.2%	3.87	2021, 2023
Colombia COL	81%	2.68	2015, 2023
Nicaragua NIC	80.4%	2.41	2012, 2023
Germany DEU	80.3%	3.71	2011, 2023
Brazil BRA	80.2%	3.36	2013, 2023
France FRA	78.4%	3.31	2010, 2023
Ecuador ECU	77.9%	2.83	2018, 2023
Iran, Islamic Rep. IRN	77.4%	2.77	2010, 2023
Peru PER	77.3%	2.79	2023
Chile CHL	76.3%	3.24	2016, 2023
United Kingdom GBR	76.1%	3.85	2012, 2023
Estonia EST	75.1%	3.58	2014, 2023
United States USA	73.9%	3.83	2019, 2023

World Bank, World Development Indicators
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table", American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt. Data: Penn World Table version 11.0, Groningen Growth and Development Centre, doi:10.34894/FABVLR.

Sources

Contraceptive use (any method)

Contraceptive prevalence, any method (% of married women ages 15-49)

World Bank · CC BY 4.0 · updated quarterly

The share of married women aged 15 to 49 who use any form of birth control, including traditional ones such as withdrawal, from national surveys. Only married women (and women living with a partner) are counted. Most countries have a survey only every few years.

Human capital (PWT)

Human capital index, from years of schooling (Penn World Table)

Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)

"Human capital" means the skills and knowledge workers bring to their jobs. This index measures it from schooling alone. It starts from how many years adults spent in school, then counts the early years for more than the later ones, following studies of how much higher pay tends to be with each extra year. A score of 1 means no schooling; the US is about 3.8. It uses the same schooling figures as mean years of schooling, so the two are close by construction. For that reason it is left out of the education score, which already counts years of schooling.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2024. 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 18,622 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Contraceptive use (any method) and Human capital (PWT)

This page shows that two measures go together. It does not show that Contraceptive use (any method) changes Human capital (PWT), or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.