placestats

Infrastructure score vs. Married by 18

Fades after adjusting for income

Across 134 countries, Infrastructure score and Married by 18 rise and fall together at r = -0.66 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.32, which could easily be chance (q = 0.064, 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: direction is not consistent across regions.

Comparing countries across the middle half of married by 18, 6.6% to 28% of women aged 20-24, infrastructure score is worse by 0.637 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.18 typical gaps.

Link before adjusting (r)-0.657p = 6.7e-18 · 134 countries
After adjusting for national income-0.315p = 0.000 · likely range [-0.46, -0.15]
p, allowing for regions0.014standard error 0.085 · 6 regions
Check for luck (q)0.064across 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.

Infrastructure score · typical gaps (standard deviations)link with income +0.90 · proxy
Married by 18 · % of women aged 20-24link with income -0.60 · moderate

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

Every country as a dot

Infrastructure score · typical gaps (standard deviations) →Married by 18 · % of women aged 20-24 →-1.011.680%76.3%Afghanistan: 0.116 typical gaps (standard deviations), 28.7% of women aged 20-24Angola: -0.152 typical gaps (standard deviations), 23.1% of women aged 20-24Albania: 1.14 typical gaps (standard deviations), 11.8% of women aged 20-24Argentina: 1.25 typical gaps (standard deviations), 15.5% of women aged 20-24Armenia: 1.09 typical gaps (standard deviations), 5.3% of women aged 20-24Azerbaijan: 1.19 typical gaps (standard deviations), 15% of women aged 20-24Burundi: -0.835 typical gaps (standard deviations), 19% of women aged 20-24Belgium: 1.64 typical gaps (standard deviations), 0.1% of women aged 20-24Benin: 0.00537 typical gaps (standard deviations), 27.5% of women aged 20-24Burkina Faso: -0.245 typical gaps (standard deviations), 38.2% of women aged 20-24Bangladesh: 0.649 typical gaps (standard deviations), 47.2% of women aged 20-24Bosnia and Herzegovina: 1.29 typical gaps (standard deviations), 3.5% of women aged 20-24Belarus: 1.48 typical gaps (standard deviations), 4.7% of women aged 20-24Belize: 0.573 typical gaps (standard deviations), 33.5% of women aged 20-24Bolivia: 0.886 typical gaps (standard deviations), 14.5% of women aged 20-24Barbados: 1.31 typical gaps (standard deviations), 29.2% of women aged 20-24Bhutan: 0.847 typical gaps (standard deviations), 9% of women aged 20-24Central African Republic: -1.01 typical gaps (standard deviations), 61% of women aged 20-24Chile: 1.25 typical gaps (standard deviations), 0% of women aged 20-24China: 1.66 typical gaps (standard deviations), 2.8% of women aged 20-24Cote d'Ivoire: 0.207 typical gaps (standard deviations), 25.8% of women aged 20-24Cameroon: 0.557 typical gaps (standard deviations), 29.8% of women aged 20-24Congo, Dem. Rep.: -0.672 typical gaps (standard deviations), 29.9% of women aged 20-24Congo, Rep.: 0.312 typical gaps (standard deviations), 27.3% of women aged 20-24Colombia: 1.01 typical gaps (standard deviations), 23.4% of women aged 20-24Comoros: 0.259 typical gaps (standard deviations), 20.7% of women aged 20-24Cabo Verde: 0.806 typical gaps (standard deviations), 8.4% of women aged 20-24Costa Rica: 1.19 typical gaps (standard deviations), 17.1% of women aged 20-24Djibouti: 0.0105 typical gaps (standard deviations), 6.5% of women aged 20-24Denmark: 1.65 typical gaps (standard deviations), 0.7% of women aged 20-24Dominican Republic: 0.956 typical gaps (standard deviations), 31.5% of women aged 20-24Algeria: 0.932 typical gaps (standard deviations), 3.8% of women aged 20-24Ecuador: 0.989 typical gaps (standard deviations), 22.2% of women aged 20-24Egypt, Arab Rep.: 0.864 typical gaps (standard deviations), 15.8% of women aged 20-24Ethiopia: -0.309 typical gaps (standard deviations), 40.3% of women aged 20-24Finland: 1.48 typical gaps (standard deviations), 0.1% of women aged 20-24Fiji: 0.665 typical gaps (standard deviations), 4% of women aged 20-24Gabon: 0.665 typical gaps (standard deviations), 13.3% of women aged 20-24United Kingdom: 1.6 typical gaps (standard deviations), 0% of women aged 20-24Georgia: 1.29 typical gaps (standard deviations), 9.4% of women aged 20-24Ghana: 0.59 typical gaps (standard deviations), 16.1% of women aged 20-24Guinea: 0.156 typical gaps (standard deviations), 46.5% of women aged 20-24Gambia, The: 0.215 typical gaps (standard deviations), 23.1% of women aged 20-24Guinea-Bissau: -0.158 typical gaps (standard deviations), 25.7% of women aged 20-24Equatorial Guinea: -0.269 typical gaps (standard deviations), 29.5% of women aged 20-24Guatemala: 0.576 typical gaps (standard deviations), 29.5% of women aged 20-24Guyana: 0.809 typical gaps (standard deviations), 32.3% of women aged 20-24Honduras: 0.417 typical gaps (standard deviations), 34% of women aged 20-24Haiti: -0.312 typical gaps (standard deviations), 14.9% of women aged 20-24Indonesia: 0.742 typical gaps (standard deviations), 16.3% of women aged 20-24India: 0.527 typical gaps (standard deviations), 22.3% of women aged 20-24Iran, Islamic Rep.: 0.97 typical gaps (standard deviations), 16.7% of women aged 20-24Iraq: 1.04 typical gaps (standard deviations), 27.9% of women aged 20-24Israel: 1.31 typical gaps (standard deviations), 0.8% of women aged 20-24Jamaica: 1.06 typical gaps (standard deviations), 26.9% of women aged 20-24Jordan: 0.776 typical gaps (standard deviations), 9.9% of women aged 20-24Kazakhstan: 1.09 typical gaps (standard deviations), 6.1% of women aged 20-24Kenya: 0.22 typical gaps (standard deviations), 12.5% of women aged 20-24Kyrgyz Republic: 0.943 typical gaps (standard deviations), 9.2% of women aged 20-24Cambodia: 0.689 typical gaps (standard deviations), 17.9% of women aged 20-24Kiribati: 0.485 typical gaps (standard deviations), 18.4% of women aged 20-24Lao PDR: 0.399 typical gaps (standard deviations), 30.5% of women aged 20-24Lebanon: 0.731 typical gaps (standard deviations), 6% of women aged 20-24Liberia: -0.767 typical gaps (standard deviations), 24.9% of women aged 20-24Libya: 0.512 typical gaps (standard deviations), 4.1% of women aged 20-24St. Lucia: 0.881 typical gaps (standard deviations), 24% of women aged 20-24Sri Lanka: 0.669 typical gaps (standard deviations), 9.8% of women aged 20-24Lesotho: -0.0187 typical gaps (standard deviations), 13.4% of women aged 20-24Lithuania: 1.29 typical gaps (standard deviations), 0.2% of women aged 20-24Morocco: 0.928 typical gaps (standard deviations), 13.7% of women aged 20-24Moldova: 1.23 typical gaps (standard deviations), 12.2% of women aged 20-24Madagascar: -0.434 typical gaps (standard deviations), 38.8% of women aged 20-24Maldives: 1.11 typical gaps (standard deviations), 2.2% of women aged 20-24Mexico: 1.13 typical gaps (standard deviations), 18.2% of women aged 20-24North Macedonia: 1.36 typical gaps (standard deviations), 7.5% of women aged 20-24Mali: -0.495 typical gaps (standard deviations), 48.5% of women aged 20-24Myanmar: 0.298 typical gaps (standard deviations), 16% of women aged 20-24Montenegro: 1.37 typical gaps (standard deviations), 5.8% of women aged 20-24Mongolia: 1.03 typical gaps (standard deviations), 8.1% of women aged 20-24Mozambique: -0.592 typical gaps (standard deviations), 48.4% of women aged 20-24Mauritania: 0.0018 typical gaps (standard deviations), 36.6% of women aged 20-24Malawi: -0.672 typical gaps (standard deviations), 38.7% of women aged 20-24Namibia: 0.203 typical gaps (standard deviations), 6.9% of women aged 20-24Niger: -0.7 typical gaps (standard deviations), 76.3% of women aged 20-24Nigeria: -0.0861 typical gaps (standard deviations), 33.4% of women aged 20-24Nicaragua: 0.574 typical gaps (standard deviations), 35.2% of women aged 20-24Norway: 1.68 typical gaps (standard deviations), 0% of women aged 20-24Nepal: 0.616 typical gaps (standard deviations), 34.9% of women aged 20-24Naoero: 0.872 typical gaps (standard deviations), 18.2% of women aged 20-24Oman: 1.03 typical gaps (standard deviations), 4% of women aged 20-24Pakistan: 0.386 typical gaps (standard deviations), 18.3% of women aged 20-24Panama: 0.927 typical gaps (standard deviations), 26.4% of women aged 20-24Peru: 0.907 typical gaps (standard deviations), 17.9% of women aged 20-24Philippines: 0.696 typical gaps (standard deviations), 9.4% of women aged 20-24Papua New Guinea: -0.611 typical gaps (standard deviations), 27.3% of women aged 20-24Paraguay: 0.928 typical gaps (standard deviations), 21.6% of women aged 20-24West Bank and Gaza: 0.774 typical gaps (standard deviations), 13.4% of women aged 20-24Qatar: 1.06 typical gaps (standard deviations), 1.7% of women aged 20-24Romania: 1.45 typical gaps (standard deviations), 6.9% of women aged 20-24Russian Federation: 1.32 typical gaps (standard deviations), 6.2% of women aged 20-24Rwanda: 0.0662 typical gaps (standard deviations), 5.5% of women aged 20-24Saudi Arabia: 1.67 typical gaps (standard deviations), 3.4% of women aged 20-24Sudan: -0.572 typical gaps (standard deviations), 34.2% of women aged 20-24Senegal: 0.482 typical gaps (standard deviations), 30.2% of women aged 20-24Singapore: 1.33 typical gaps (standard deviations), 0.1% of women aged 20-24Solomon Islands: 0.0941 typical gaps (standard deviations), 21.3% of women aged 20-24Sierra Leone: -0.336 typical gaps (standard deviations), 29.6% of women aged 20-24El Salvador: 0.822 typical gaps (standard deviations), 19.7% of women aged 20-24Serbia: 1.38 typical gaps (standard deviations), 5.5% of women aged 20-24Sao Tome and Principe: 0.187 typical gaps (standard deviations), 28% of women aged 20-24Suriname: 0.901 typical gaps (standard deviations), 36% of women aged 20-24Eswatini: 0.534 typical gaps (standard deviations), 1.9% of women aged 20-24Chad: -0.72 typical gaps (standard deviations), 60.6% of women aged 20-24Togo: -0.0274 typical gaps (standard deviations), 24.8% of women aged 20-24Thailand: 1.06 typical gaps (standard deviations), 17% of women aged 20-24Tajikistan: 0.389 typical gaps (standard deviations), 8.8% of women aged 20-24Turkmenistan: 0.294 typical gaps (standard deviations), 6.1% of women aged 20-24Timor-Leste: 0.389 typical gaps (standard deviations), 14.9% of women aged 20-24Tonga: 0.595 typical gaps (standard deviations), 10.1% of women aged 20-24Trinidad and Tobago: 1.22 typical gaps (standard deviations), 4.2% of women aged 20-24Tunisia: 0.956 typical gaps (standard deviations), 2.4% of women aged 20-24Turkiye: 1.19 typical gaps (standard deviations), 14.7% of women aged 20-24Tuvalu: 0.208 typical gaps (standard deviations), 1.8% of women aged 20-24Tanzania: -0.0741 typical gaps (standard deviations), 29.1% of women aged 20-24Uganda: -0.319 typical gaps (standard deviations), 34% of women aged 20-24Ukraine: 0.97 typical gaps (standard deviations), 9.1% of women aged 20-24Uruguay: 1.41 typical gaps (standard deviations), 24.6% of women aged 20-24Uzbekistan: 1.42 typical gaps (standard deviations), 3.4% of women aged 20-24Viet Nam: 1.17 typical gaps (standard deviations), 14.6% of women aged 20-24Vanuatu: -0.0444 typical gaps (standard deviations), 20.9% of women aged 20-24Samoa: 0.0591 typical gaps (standard deviations), 7.4% of women aged 20-24South Africa: 0.709 typical gaps (standard deviations), 3.6% of women aged 20-24Zambia: -0.161 typical gaps (standard deviations), 23.9% of women aged 20-24Zimbabwe: 0.0863 typical gaps (standard deviations), 33.7% of women aged 20-24BDIBELBGDBRBCAFDJIDNKNERNORTCDTUV

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6576.7e-18134
By rank (Spearman), not adjusted-0.6371.3e-16134
Standard (Pearson), adjusted for national income-0.3150.000134
By rank (Spearman), adjusted for national income-0.2590.003134

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 134 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: +0.342 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.333 (without DJI) to -0.269 (without GUY). 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 (+0.047).

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.

nr
East Asia & Pacific20-0.17
Europe & Central Asia25-0.13
Latin America & Caribbean24-0.30
Middle East, North Africa, Afghanistan & Pakistan17+0.25
Sub-Saharan Africa42-0.43

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 third45-0.40
Middle third44-0.16
Richest third45-0.39

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
Christians76-0.31
Muslims42-0.46

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
France24-0.36
Never a European colony19-0.24
Spain19-0.43
United Kingdom43-0.16

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 = -0.252 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 0years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Not enough history inside each country to look at this over time. That needs countries measured for several years in a row, and at least one of these measures is measured too rarely.

The data


typical gaps (standard deviations)

% of women aged 20-24
Years
Norway NOR1.680%2024, 2022
Saudi Arabia SAU1.673.4%2024
China CHN1.662.8%2024, 2020
Denmark DNK1.650.7%2024, 2021
Belgium BEL1.640.1%2024, 2020
United Kingdom GBR1.60%2024, 2021
Belarus BLR1.484.7%2024, 2019
Finland FIN1.480.1%2024, 2017
Romania ROU1.456.9%2024, 2021
Uzbekistan UZB1.423.4%2024, 2022
Uruguay URY1.4124.6%2024, 2013
Serbia SRB1.385.5%2024, 2019
Montenegro MNE1.375.8%2024, 2018
North Macedonia MKD1.367.5%2024, 2019
Singapore SGP1.330.1%2024, 2025

wdi
World Bank, World Development Indicators

Sources

Infrastructure score

Infrastructure score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 5 measures from 1 publisher. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Electricity access, Slum share of urban population, Mobile subscriptions, Fixed broadband, Internet users.

Married by 18

Women who were first married by age 18 (% of women ages 20-24)

UNICEF · CC BY 4.0 · updated quarterly

From household surveys, mostly in lower-income countries, and only in years a survey ran. No year reaches sixty countries, so the latest value used is up to a decade old for some. Rich countries rarely report it at all, which shapes any link with income.

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 6 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 Infrastructure score and Married by 18

This page shows that two measures go together. It does not show that Infrastructure score changes Married by 18, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Infrastructure score or Married by 18