placestats

Infrastructure score vs. Women's age at first marriage (census-based)

Fades after adjusting for income

Across 176 countries, Infrastructure score and Women's age at first marriage (census-based) rise and fall together at r = +0.67 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.05, which could easily be chance (q = 0.56, 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: sign flips when a single country is removed.

Comparing countries across the middle half of women's age at first marriage (census-based), 21.9 to 27.9 years, infrastructure score is better by 0.674 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0299 typical gaps.

Link before adjusting (r)+0.669p = 3.2e-24 · 176 countries
After adjusting for national income+0.049p = 0.520 · likely range [-0.10, +0.20]
p, allowing for regions0.224standard error 0.036 · 7 regions
Check for luck (q)0.556across 14,952 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
Women's age at first marriage (census-based) · yearslink with income +0.75 · high

Both of these rise and fall closely with national income, so a strong link between them before adjusting was close to certain. Read the adjusted figure, not the first one.

“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) →Women's age at first marriage (census-based) · years →-1.011.721734Aruba: 0.917 typical gaps (standard deviations), 28.5 yearsAfghanistan: 0.116 typical gaps (standard deviations), 21.5 yearsAngola: -0.152 typical gaps (standard deviations), 21.5 yearsAlbania: 1.14 typical gaps (standard deviations), 24.2 yearsArgentina: 1.25 typical gaps (standard deviations), 24.6 yearsArmenia: 1.09 typical gaps (standard deviations), 24.2 yearsAustralia: 1.5 typical gaps (standard deviations), 29.9 yearsAustria: 1.37 typical gaps (standard deviations), 27.1 yearsAzerbaijan: 1.19 typical gaps (standard deviations), 24.2 yearsBurundi: -0.835 typical gaps (standard deviations), 22.8 yearsBelgium: 1.64 typical gaps (standard deviations), 26.3 yearsBenin: 0.00537 typical gaps (standard deviations), 20.7 yearsBurkina Faso: -0.245 typical gaps (standard deviations), 20 yearsBangladesh: 0.649 typical gaps (standard deviations), 18.8 yearsBulgaria: 1.25 typical gaps (standard deviations), 32.6 yearsBahrain: 1.17 typical gaps (standard deviations), 26.9 yearsBahamas, The: 1.25 typical gaps (standard deviations), 27.9 yearsBosnia and Herzegovina: 1.29 typical gaps (standard deviations), 25.7 yearsBelarus: 1.48 typical gaps (standard deviations), 22.5 yearsBelize: 0.573 typical gaps (standard deviations), 20.5 yearsBermuda: 1.68 typical gaps (standard deviations), 31.9 yearsBolivia: 0.886 typical gaps (standard deviations), 24 yearsBrazil: 1.18 typical gaps (standard deviations), 23.9 yearsBarbados: 1.31 typical gaps (standard deviations), 17 yearsBrunei Darussalam: 1.22 typical gaps (standard deviations), 26.1 yearsBhutan: 0.847 typical gaps (standard deviations), 23.8 yearsBotswana: 0.5 typical gaps (standard deviations), 22.9 yearsCentral African Republic: -1.01 typical gaps (standard deviations), 17.3 yearsCanada: 1.56 typical gaps (standard deviations), 27.2 yearsSwitzerland: 1.7 typical gaps (standard deviations), 30.9 yearsChile: 1.25 typical gaps (standard deviations), 27.4 yearsChina: 1.66 typical gaps (standard deviations), 25.4 yearsCote d'Ivoire: 0.207 typical gaps (standard deviations), 23 yearsCameroon: 0.557 typical gaps (standard deviations), 22.2 yearsCongo, Dem. Rep.: -0.672 typical gaps (standard deviations), 20.9 yearsCongo, Rep.: 0.312 typical gaps (standard deviations), 21.9 yearsColombia: 1.01 typical gaps (standard deviations), 22.7 yearsComoros: 0.259 typical gaps (standard deviations), 22.6 yearsCosta Rica: 1.19 typical gaps (standard deviations), 26.4 yearsCayman Islands: 1.58 typical gaps (standard deviations), 28 yearsCyprus: 1.47 typical gaps (standard deviations), 26.4 yearsCzechia: 1.48 typical gaps (standard deviations), 32.4 yearsGermany: 1.65 typical gaps (standard deviations), 31.7 yearsDjibouti: 0.0105 typical gaps (standard deviations), 29.9 yearsDenmark: 1.65 typical gaps (standard deviations), 31.9 yearsDominican Republic: 0.956 typical gaps (standard deviations), 21.5 yearsAlgeria: 0.932 typical gaps (standard deviations), 28.2 yearsEcuador: 0.989 typical gaps (standard deviations), 21.8 yearsEgypt, Arab Rep.: 0.864 typical gaps (standard deviations), 22.3 yearsSpain: 1.55 typical gaps (standard deviations), 27.7 yearsEstonia: 1.45 typical gaps (standard deviations), 33.6 yearsEthiopia: -0.309 typical gaps (standard deviations), 21.3 yearsFinland: 1.48 typical gaps (standard deviations), 30.7 yearsFrance: 1.7 typical gaps (standard deviations), 32 yearsFaroe Islands: 1.3 typical gaps (standard deviations), 31 yearsMicronesia, Fed. Sts.: -0.101 typical gaps (standard deviations), 25.3 yearsGabon: 0.665 typical gaps (standard deviations), 22.9 yearsUnited Kingdom: 1.6 typical gaps (standard deviations), 27 yearsGeorgia: 1.29 typical gaps (standard deviations), 22.8 yearsGhana: 0.59 typical gaps (standard deviations), 23.7 yearsGuinea: 0.156 typical gaps (standard deviations), 19.8 yearsGambia, The: 0.215 typical gaps (standard deviations), 21.5 yearsGuinea-Bissau: -0.158 typical gaps (standard deviations), 24.1 yearsEquatorial Guinea: -0.269 typical gaps (standard deviations), 20.5 yearsGreece: 1.57 typical gaps (standard deviations), 29.2 yearsGreenland: 1.24 typical gaps (standard deviations), 32.9 yearsGuatemala: 0.576 typical gaps (standard deviations), 22.7 yearsGuyana: 0.809 typical gaps (standard deviations), 19.2 yearsHong Kong SAR, China: 1.57 typical gaps (standard deviations), 29.9 yearsHonduras: 0.417 typical gaps (standard deviations), 21.2 yearsCroatia: 1.28 typical gaps (standard deviations), 27.4 yearsHaiti: -0.312 typical gaps (standard deviations), 24.9 yearsHungary: 1.5 typical gaps (standard deviations), 32.9 yearsIndonesia: 0.742 typical gaps (standard deviations), 22.4 yearsIndia: 0.527 typical gaps (standard deviations), 21.4 yearsIreland: 1.43 typical gaps (standard deviations), 32.4 yearsIran, Islamic Rep.: 0.97 typical gaps (standard deviations), 23.1 yearsIraq: 1.04 typical gaps (standard deviations), 22.7 yearsIceland: 1.53 typical gaps (standard deviations), 32.4 yearsIsrael: 1.31 typical gaps (standard deviations), 26.2 yearsItaly: 1.36 typical gaps (standard deviations), 32.2 yearsJamaica: 1.06 typical gaps (standard deviations), 25.3 yearsJordan: 0.776 typical gaps (standard deviations), 24.6 yearsJapan: 1.45 typical gaps (standard deviations), 29.2 yearsKazakhstan: 1.09 typical gaps (standard deviations), 22.4 yearsKenya: 0.22 typical gaps (standard deviations), 21.9 yearsKyrgyz Republic: 0.943 typical gaps (standard deviations), 21.2 yearsCambodia: 0.689 typical gaps (standard deviations), 21.6 yearsKiribati: 0.485 typical gaps (standard deviations), 21.6 yearsKorea, Rep.: 1.72 typical gaps (standard deviations), 31.5 yearsKuwait: 0.878 typical gaps (standard deviations), 25.7 yearsLao PDR: 0.399 typical gaps (standard deviations), 20.7 yearsLiberia: -0.767 typical gaps (standard deviations), 22.6 yearsSt. Lucia: 0.881 typical gaps (standard deviations), 21.9 yearsSri Lanka: 0.669 typical gaps (standard deviations), 23.9 yearsLesotho: -0.0187 typical gaps (standard deviations), 22.2 yearsLithuania: 1.29 typical gaps (standard deviations), 32.7 yearsLuxembourg: 1.59 typical gaps (standard deviations), 30.7 yearsLatvia: 1.28 typical gaps (standard deviations), 30.4 yearsMacao SAR, China: 1.33 typical gaps (standard deviations), 30.3 yearsMorocco: 0.928 typical gaps (standard deviations), 24.4 yearsMoldova: 1.23 typical gaps (standard deviations), 25 yearsMaldives: 1.11 typical gaps (standard deviations), 22.5 yearsMexico: 1.13 typical gaps (standard deviations), 23.2 yearsMarshall Islands: 0.406 typical gaps (standard deviations), 24.2 yearsNorth Macedonia: 1.36 typical gaps (standard deviations), 25.5 yearsMali: -0.495 typical gaps (standard deviations), 18.8 yearsMalta: 1.63 typical gaps (standard deviations), 28 yearsMyanmar: 0.298 typical gaps (standard deviations), 22.9 yearsMontenegro: 1.37 typical gaps (standard deviations), 26.3 yearsMongolia: 1.03 typical gaps (standard deviations), 23 yearsMozambique: -0.592 typical gaps (standard deviations), 18.7 yearsMauritania: 0.0018 typical gaps (standard deviations), 21.8 yearsMauritius: 1.17 typical gaps (standard deviations), 23.9 yearsMalawi: -0.672 typical gaps (standard deviations), 20.4 yearsMalaysia: 1.09 typical gaps (standard deviations), 25.7 yearsNamibia: 0.203 typical gaps (standard deviations), 30.3 yearsNiger: -0.7 typical gaps (standard deviations), 17.2 yearsNigeria: -0.0861 typical gaps (standard deviations), 21.2 yearsNicaragua: 0.574 typical gaps (standard deviations), 21.1 yearsNetherlands: 1.63 typical gaps (standard deviations), 32.4 yearsNorway: 1.68 typical gaps (standard deviations), 32.1 yearsNepal: 0.616 typical gaps (standard deviations), 20.1 yearsOman: 1.03 typical gaps (standard deviations), 25.6 yearsPakistan: 0.386 typical gaps (standard deviations), 23.2 yearsPanama: 0.927 typical gaps (standard deviations), 21 yearsPeru: 0.907 typical gaps (standard deviations), 23.3 yearsPhilippines: 0.696 typical gaps (standard deviations), 23.4 yearsPalau: 0.865 typical gaps (standard deviations), 28.5 yearsPapua New Guinea: -0.611 typical gaps (standard deviations), 21.4 yearsPoland: 1.24 typical gaps (standard deviations), 26.6 yearsPuerto Rico (US): 1.14 typical gaps (standard deviations), 29.2 yearsPortugal: 1.6 typical gaps (standard deviations), 30.1 yearsParaguay: 0.928 typical gaps (standard deviations), 21.9 yearsWest Bank and Gaza: 0.774 typical gaps (standard deviations), 22.1 yearsRomania: 1.45 typical gaps (standard deviations), 25.9 yearsRussian Federation: 1.32 typical gaps (standard deviations), 24.4 yearsRwanda: 0.0662 typical gaps (standard deviations), 24.3 yearsSaudi Arabia: 1.67 typical gaps (standard deviations), 26.6 yearsSudan: -0.572 typical gaps (standard deviations), 21.9 yearsSenegal: 0.482 typical gaps (standard deviations), 22.2 yearsSingapore: 1.33 typical gaps (standard deviations), 27.9 yearsSolomon Islands: 0.0941 typical gaps (standard deviations), 22.4 yearsSierra Leone: -0.336 typical gaps (standard deviations), 23.1 yearsEl Salvador: 0.822 typical gaps (standard deviations), 22.6 yearsSan Marino: 1.5 typical gaps (standard deviations), 31.3 yearsSerbia: 1.38 typical gaps (standard deviations), 26.6 yearsSao Tome and Principe: 0.187 typical gaps (standard deviations), 21 yearsSuriname: 0.901 typical gaps (standard deviations), 29.3 yearsSlovak Republic: 1.4 typical gaps (standard deviations), 31 yearsSlovenia: 1.42 typical gaps (standard deviations), 34 yearsSweden: 1.57 typical gaps (standard deviations), 31 yearsEswatini: 0.534 typical gaps (standard deviations), 26.5 yearsSint Maarten (Dutch part): 1.04 typical gaps (standard deviations), 28 yearsChad: -0.72 typical gaps (standard deviations), 18.8 yearsTogo: -0.0274 typical gaps (standard deviations), 21.3 yearsThailand: 1.06 typical gaps (standard deviations), 22.5 yearsTajikistan: 0.389 typical gaps (standard deviations), 20.7 yearsTurkmenistan: 0.294 typical gaps (standard deviations), 23.5 yearsTimor-Leste: 0.389 typical gaps (standard deviations), 23 yearsTonga: 0.595 typical gaps (standard deviations), 25.3 yearsTrinidad and Tobago: 1.22 typical gaps (standard deviations), 29.2 yearsTunisia: 0.956 typical gaps (standard deviations), 28.2 yearsTurkiye: 1.19 typical gaps (standard deviations), 23.4 yearsTanzania: -0.0741 typical gaps (standard deviations), 21 yearsUganda: -0.319 typical gaps (standard deviations), 20.7 yearsUkraine: 0.97 typical gaps (standard deviations), 23 yearsUruguay: 1.41 typical gaps (standard deviations), 23 yearsUnited States: 1.53 typical gaps (standard deviations), 27.5 yearsSt. Vincent and the Grenadines: 1.22 typical gaps (standard deviations), 21.2 yearsVirgin Islands (U.S.): 0.721 typical gaps (standard deviations), 30.9 yearsViet Nam: 1.17 typical gaps (standard deviations), 22.8 yearsSamoa: 0.0591 typical gaps (standard deviations), 23.8 yearsSouth Africa: 0.709 typical gaps (standard deviations), 27.2 yearsZambia: -0.161 typical gaps (standard deviations), 21.8 yearsZimbabwe: 0.0863 typical gaps (standard deviations), 21.1 yearsBDIBLRBRBCAFDJIESTFRANAMNERSVNTCDVCT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6693.2e-24176
By rank (Spearman), not adjusted+0.7282.6e-30176
Standard (Pearson), adjusted for national income+0.0490.520176
By rank (Spearman), adjusted for national income+0.1900.012176

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 176 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: +0.620 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.014 (without GUY) to +0.074 (without BRB). 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 (-0.063).

Fragile: this link may not hold up.
  • sign flips when a single country is removed
  • 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 & Pacific26+0.09
Europe & Central Asia50+0.13
Latin America & Caribbean32-0.08
Middle East, North Africa, Afghanistan & Pakistan17-0.36
Sub-Saharan Africa42+0.30

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 third59+0.07
Middle third58+0.01
Richest third59+0.45

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
Christians105+0.07
Muslims42+0.11

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
France22+0.03
Never a European colony20+0.00
Spain20+0.38
United Kingdom50+0.06

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 = +0.046 after dropping 1 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)

years
Years
Korea, Rep. KOR1.7231.52024, 2015
Switzerland CHE1.730.92024, 2017
France FRA1.7322024, 2013
Bermuda BMU1.6831.92024, 2016
Norway NOR1.6832.12024, 2018
Saudi Arabia SAU1.6726.62024, 2017
China CHN1.6625.42024, 2016
Denmark DNK1.6531.92024, 2018
Germany DEU1.6531.72024, 2018
Belgium BEL1.6426.32024, 2011
Malta MLT1.63282024, 2011
Netherlands NLD1.6332.42024, 2018
Portugal PRT1.630.12024, 2011
United Kingdom GBR1.6272024, 2011
Luxembourg LUX1.5930.72024, 2018

wdi
World Bank Gender Statistics, from UN Population Division, World Marriage Data

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.

Women's age at first marriage (census-based)

Age at first marriage, female

World Bank · CC BY 4.0 · updated annual

The average age at which women first marry. It is worked out from census and survey counts of how many women in each age group have never married, by the UN Population Division (World Marriage Data), so it covers far more countries than registered marriages do. Most countries have a figure only every few years, and the series ends in 2019. No "better" direction is set.

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 14,952 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Infrastructure score and Women's age at first marriage (census-based)

This page shows that two measures go together. It does not show that Infrastructure score changes Women's age at first marriage (census-based), or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Infrastructure score or Women's age at first marriage (census-based)