placestats

Infrastructure score vs. Unintended pregnancy rate

Fades after adjusting for income

Across 142 countries, Infrastructure score and Unintended pregnancy rate rise and fall together at r = -0.68 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.21, which could easily be chance (q = 0.10, 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 unintended pregnancy rate, 34.3 to 86.7 per 1,000 women aged 15-49, infrastructure score is worse by 0.826 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.127 typical gaps.

Link before adjusting (r)-0.685p = 5.6e-21 · 142 countries
After adjusting for national income-0.211p = 0.012 · likely range [-0.36, -0.05]
p, allowing for regions0.051standard error 0.087 · 7 regions
Check for luck (q)0.101across 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
Unintended pregnancy rate · per 1,000 women aged 15-49link with income -0.69 · 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) →Unintended pregnancy rate · per 1,000 women aged 15-49 →-1.011.7210.9145Angola: -0.152 typical gaps (standard deviations), 120 per 1,000 women aged 15-49Albania: 1.14 typical gaps (standard deviations), 16 per 1,000 women aged 15-49Argentina: 1.25 typical gaps (standard deviations), 69 per 1,000 women aged 15-49Armenia: 1.09 typical gaps (standard deviations), 52.6 per 1,000 women aged 15-49Australia: 1.5 typical gaps (standard deviations), 37.4 per 1,000 women aged 15-49Azerbaijan: 1.19 typical gaps (standard deviations), 92.9 per 1,000 women aged 15-49Burundi: -0.835 typical gaps (standard deviations), 98.1 per 1,000 women aged 15-49Belgium: 1.64 typical gaps (standard deviations), 21 per 1,000 women aged 15-49Benin: 0.00537 typical gaps (standard deviations), 85.9 per 1,000 women aged 15-49Burkina Faso: -0.245 typical gaps (standard deviations), 75.2 per 1,000 women aged 15-49Bangladesh: 0.649 typical gaps (standard deviations), 58.9 per 1,000 women aged 15-49Bulgaria: 1.25 typical gaps (standard deviations), 28.6 per 1,000 women aged 15-49Bosnia and Herzegovina: 1.29 typical gaps (standard deviations), 20 per 1,000 women aged 15-49Belarus: 1.48 typical gaps (standard deviations), 18.7 per 1,000 women aged 15-49Belize: 0.573 typical gaps (standard deviations), 70.9 per 1,000 women aged 15-49Bolivia: 0.886 typical gaps (standard deviations), 105 per 1,000 women aged 15-49Brazil: 1.18 typical gaps (standard deviations), 67.2 per 1,000 women aged 15-49Barbados: 1.31 typical gaps (standard deviations), 76.9 per 1,000 women aged 15-49Bhutan: 0.847 typical gaps (standard deviations), 47.5 per 1,000 women aged 15-49Botswana: 0.5 typical gaps (standard deviations), 96.8 per 1,000 women aged 15-49Central African Republic: -1.01 typical gaps (standard deviations), 90.2 per 1,000 women aged 15-49Canada: 1.56 typical gaps (standard deviations), 31.5 per 1,000 women aged 15-49Switzerland: 1.7 typical gaps (standard deviations), 20.3 per 1,000 women aged 15-49Chile: 1.25 typical gaps (standard deviations), 70 per 1,000 women aged 15-49China: 1.66 typical gaps (standard deviations), 64.8 per 1,000 women aged 15-49Cote d'Ivoire: 0.207 typical gaps (standard deviations), 96.4 per 1,000 women aged 15-49Cameroon: 0.557 typical gaps (standard deviations), 83.4 per 1,000 women aged 15-49Congo, Dem. Rep.: -0.672 typical gaps (standard deviations), 117 per 1,000 women aged 15-49Congo, Rep.: 0.312 typical gaps (standard deviations), 106 per 1,000 women aged 15-49Colombia: 1.01 typical gaps (standard deviations), 62.7 per 1,000 women aged 15-49Comoros: 0.259 typical gaps (standard deviations), 95.2 per 1,000 women aged 15-49Cabo Verde: 0.806 typical gaps (standard deviations), 102 per 1,000 women aged 15-49Costa Rica: 1.19 typical gaps (standard deviations), 51.8 per 1,000 women aged 15-49Czechia: 1.48 typical gaps (standard deviations), 34.9 per 1,000 women aged 15-49Germany: 1.65 typical gaps (standard deviations), 21.3 per 1,000 women aged 15-49Djibouti: 0.0105 typical gaps (standard deviations), 50.4 per 1,000 women aged 15-49Denmark: 1.65 typical gaps (standard deviations), 29.8 per 1,000 women aged 15-49Dominican Republic: 0.956 typical gaps (standard deviations), 83.7 per 1,000 women aged 15-49Ecuador: 0.989 typical gaps (standard deviations), 79.7 per 1,000 women aged 15-49Spain: 1.55 typical gaps (standard deviations), 20.3 per 1,000 women aged 15-49Estonia: 1.45 typical gaps (standard deviations), 38.1 per 1,000 women aged 15-49Ethiopia: -0.309 typical gaps (standard deviations), 79.5 per 1,000 women aged 15-49Finland: 1.48 typical gaps (standard deviations), 32.6 per 1,000 women aged 15-49France: 1.7 typical gaps (standard deviations), 29 per 1,000 women aged 15-49Gabon: 0.665 typical gaps (standard deviations), 112 per 1,000 women aged 15-49United Kingdom: 1.6 typical gaps (standard deviations), 36.2 per 1,000 women aged 15-49Georgia: 1.29 typical gaps (standard deviations), 93.6 per 1,000 women aged 15-49Ghana: 0.59 typical gaps (standard deviations), 102 per 1,000 women aged 15-49Guinea: 0.156 typical gaps (standard deviations), 69.8 per 1,000 women aged 15-49Gambia, The: 0.215 typical gaps (standard deviations), 53.7 per 1,000 women aged 15-49Guinea-Bissau: -0.158 typical gaps (standard deviations), 99.5 per 1,000 women aged 15-49Greece: 1.57 typical gaps (standard deviations), 33 per 1,000 women aged 15-49Guatemala: 0.576 typical gaps (standard deviations), 62.8 per 1,000 women aged 15-49Guyana: 0.809 typical gaps (standard deviations), 76.5 per 1,000 women aged 15-49Hong Kong SAR, China: 1.57 typical gaps (standard deviations), 27.8 per 1,000 women aged 15-49Honduras: 0.417 typical gaps (standard deviations), 67.8 per 1,000 women aged 15-49Croatia: 1.28 typical gaps (standard deviations), 33.5 per 1,000 women aged 15-49Haiti: -0.312 typical gaps (standard deviations), 107 per 1,000 women aged 15-49Hungary: 1.5 typical gaps (standard deviations), 27.3 per 1,000 women aged 15-49Indonesia: 0.742 typical gaps (standard deviations), 39.9 per 1,000 women aged 15-49India: 0.527 typical gaps (standard deviations), 62.4 per 1,000 women aged 15-49Iceland: 1.53 typical gaps (standard deviations), 32.6 per 1,000 women aged 15-49Italy: 1.36 typical gaps (standard deviations), 28.3 per 1,000 women aged 15-49Jamaica: 1.06 typical gaps (standard deviations), 86.9 per 1,000 women aged 15-49Japan: 1.45 typical gaps (standard deviations), 21.4 per 1,000 women aged 15-49Kazakhstan: 1.09 typical gaps (standard deviations), 57.5 per 1,000 women aged 15-49Kenya: 0.22 typical gaps (standard deviations), 113 per 1,000 women aged 15-49Kyrgyz Republic: 0.943 typical gaps (standard deviations), 39.7 per 1,000 women aged 15-49Cambodia: 0.689 typical gaps (standard deviations), 69.2 per 1,000 women aged 15-49Korea, Rep.: 1.72 typical gaps (standard deviations), 34.5 per 1,000 women aged 15-49Lao PDR: 0.399 typical gaps (standard deviations), 50.7 per 1,000 women aged 15-49Liberia: -0.767 typical gaps (standard deviations), 106 per 1,000 women aged 15-49St. Lucia: 0.881 typical gaps (standard deviations), 68.7 per 1,000 women aged 15-49Sri Lanka: 0.669 typical gaps (standard deviations), 38.9 per 1,000 women aged 15-49Lesotho: -0.0187 typical gaps (standard deviations), 98.9 per 1,000 women aged 15-49Lithuania: 1.29 typical gaps (standard deviations), 25.6 per 1,000 women aged 15-49Latvia: 1.28 typical gaps (standard deviations), 33.8 per 1,000 women aged 15-49Moldova: 1.23 typical gaps (standard deviations), 26.2 per 1,000 women aged 15-49Madagascar: -0.434 typical gaps (standard deviations), 94.8 per 1,000 women aged 15-49Maldives: 1.11 typical gaps (standard deviations), 59.5 per 1,000 women aged 15-49Mexico: 1.13 typical gaps (standard deviations), 60.3 per 1,000 women aged 15-49North Macedonia: 1.36 typical gaps (standard deviations), 23.2 per 1,000 women aged 15-49Mali: -0.495 typical gaps (standard deviations), 67.6 per 1,000 women aged 15-49Myanmar: 0.298 typical gaps (standard deviations), 35.5 per 1,000 women aged 15-49Montenegro: 1.37 typical gaps (standard deviations), 10.9 per 1,000 women aged 15-49Mongolia: 1.03 typical gaps (standard deviations), 31.4 per 1,000 women aged 15-49Mozambique: -0.592 typical gaps (standard deviations), 88.2 per 1,000 women aged 15-49Mauritania: 0.0018 typical gaps (standard deviations), 67.6 per 1,000 women aged 15-49Malawi: -0.672 typical gaps (standard deviations), 115 per 1,000 women aged 15-49Namibia: 0.203 typical gaps (standard deviations), 104 per 1,000 women aged 15-49Niger: -0.7 typical gaps (standard deviations), 49.2 per 1,000 women aged 15-49Nigeria: -0.0861 typical gaps (standard deviations), 68.2 per 1,000 women aged 15-49Nicaragua: 0.574 typical gaps (standard deviations), 56.3 per 1,000 women aged 15-49Netherlands: 1.63 typical gaps (standard deviations), 17.5 per 1,000 women aged 15-49Norway: 1.68 typical gaps (standard deviations), 34.2 per 1,000 women aged 15-49Nepal: 0.616 typical gaps (standard deviations), 59.7 per 1,000 women aged 15-49New Zealand: 1.51 typical gaps (standard deviations), 40.9 per 1,000 women aged 15-49Pakistan: 0.386 typical gaps (standard deviations), 70.9 per 1,000 women aged 15-49Panama: 0.927 typical gaps (standard deviations), 73.1 per 1,000 women aged 15-49Peru: 0.907 typical gaps (standard deviations), 89.5 per 1,000 women aged 15-49Philippines: 0.696 typical gaps (standard deviations), 70.8 per 1,000 women aged 15-49Papua New Guinea: -0.611 typical gaps (standard deviations), 79.8 per 1,000 women aged 15-49Poland: 1.24 typical gaps (standard deviations), 28.7 per 1,000 women aged 15-49Puerto Rico (US): 1.14 typical gaps (standard deviations), 54.3 per 1,000 women aged 15-49Portugal: 1.6 typical gaps (standard deviations), 20.9 per 1,000 women aged 15-49Paraguay: 0.928 typical gaps (standard deviations), 67.4 per 1,000 women aged 15-49Romania: 1.45 typical gaps (standard deviations), 44.4 per 1,000 women aged 15-49Russian Federation: 1.32 typical gaps (standard deviations), 64.2 per 1,000 women aged 15-49Rwanda: 0.0662 typical gaps (standard deviations), 97.6 per 1,000 women aged 15-49Senegal: 0.482 typical gaps (standard deviations), 61.3 per 1,000 women aged 15-49Singapore: 1.33 typical gaps (standard deviations), 18.8 per 1,000 women aged 15-49Solomon Islands: 0.0941 typical gaps (standard deviations), 114 per 1,000 women aged 15-49Sierra Leone: -0.336 typical gaps (standard deviations), 88.2 per 1,000 women aged 15-49El Salvador: 0.822 typical gaps (standard deviations), 57.6 per 1,000 women aged 15-49Somalia, Fed. Rep.: -0.395 typical gaps (standard deviations), 99.6 per 1,000 women aged 15-49Serbia: 1.38 typical gaps (standard deviations), 21 per 1,000 women aged 15-49Sao Tome and Principe: 0.187 typical gaps (standard deviations), 130 per 1,000 women aged 15-49Suriname: 0.901 typical gaps (standard deviations), 66.9 per 1,000 women aged 15-49Slovak Republic: 1.4 typical gaps (standard deviations), 27.3 per 1,000 women aged 15-49Slovenia: 1.42 typical gaps (standard deviations), 33.2 per 1,000 women aged 15-49Sweden: 1.57 typical gaps (standard deviations), 35.8 per 1,000 women aged 15-49Eswatini: 0.534 typical gaps (standard deviations), 111 per 1,000 women aged 15-49Chad: -0.72 typical gaps (standard deviations), 59.6 per 1,000 women aged 15-49Togo: -0.0274 typical gaps (standard deviations), 100 per 1,000 women aged 15-49Thailand: 1.06 typical gaps (standard deviations), 38.3 per 1,000 women aged 15-49Tajikistan: 0.389 typical gaps (standard deviations), 36.9 per 1,000 women aged 15-49Turkmenistan: 0.294 typical gaps (standard deviations), 22.8 per 1,000 women aged 15-49Timor-Leste: 0.389 typical gaps (standard deviations), 49.6 per 1,000 women aged 15-49Tonga: 0.595 typical gaps (standard deviations), 49.4 per 1,000 women aged 15-49Trinidad and Tobago: 1.22 typical gaps (standard deviations), 83.9 per 1,000 women aged 15-49Tanzania: -0.0741 typical gaps (standard deviations), 105 per 1,000 women aged 15-49Uganda: -0.319 typical gaps (standard deviations), 145 per 1,000 women aged 15-49Ukraine: 0.97 typical gaps (standard deviations), 27.1 per 1,000 women aged 15-49Uruguay: 1.41 typical gaps (standard deviations), 41.5 per 1,000 women aged 15-49United States: 1.53 typical gaps (standard deviations), 34.7 per 1,000 women aged 15-49Uzbekistan: 1.42 typical gaps (standard deviations), 28.6 per 1,000 women aged 15-49Viet Nam: 1.17 typical gaps (standard deviations), 85.5 per 1,000 women aged 15-49Vanuatu: -0.0444 typical gaps (standard deviations), 86.9 per 1,000 women aged 15-49Samoa: 0.0591 typical gaps (standard deviations), 66.4 per 1,000 women aged 15-49South Africa: 0.709 typical gaps (standard deviations), 81.5 per 1,000 women aged 15-49Zambia: -0.161 typical gaps (standard deviations), 123 per 1,000 women aged 15-49Zimbabwe: 0.0863 typical gaps (standard deviations), 73.9 per 1,000 women aged 15-49AZEBOLCHECODDEUGEOMWINERNLDTCDTKMUGA

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6855.6e-21142
By rank (Spearman), not adjusted-0.7188.5e-24142
Standard (Pearson), adjusted for national income-0.2110.012142
By rank (Spearman), adjusted for national income-0.2990.000142

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 142 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: +0.474 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.244 (without TKM) to -0.167 (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 (+0.044).

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 & Pacific21+0.12
Europe & Central Asia43-0.09
Latin America & Caribbean26-0.20
Sub-Saharan Africa42-0.05

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 third48-0.13
Middle third47-0.19
Richest third47-0.62

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
Christians96-0.32
Muslims27-0.09

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
France21+0.19
Never a European colony16-0.10
Spain17-0.06
United Kingdom38-0.22

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 = -0.211 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 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)

per 1,000 women aged 15-49
Years
Korea, Rep. KOR1.7234.52024, 2019
Switzerland CHE1.720.32024, 2019
France FRA1.7292024, 2019
Norway NOR1.6834.22024, 2019
China CHN1.6664.82024, 2019
Denmark DNK1.6529.82024, 2019
Germany DEU1.6521.32024, 2019
Belgium BEL1.64212024, 2019
Netherlands NLD1.6317.52024, 2019
Portugal PRT1.620.92024, 2019
United Kingdom GBR1.636.22024, 2019
Greece GRC1.57332024, 2019
Sweden SWE1.5735.82024, 2019
Hong Kong SAR, China HKG1.5727.82024, 2019
Canada CAN1.5631.52024, 2019

wdi
Bearak, J. M., Popinchalk, A., Beavin, C. et al. (2022), 'Country-specific estimates of unintended pregnancy and abortion incidence: a global comparative analysis of levels in 2015-2019', BMJ Global Health 7(3), e007151. Guttmacher Institute. Supplementary appendix, tables A3-A5.

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.

Unintended pregnancy rate

Unintended pregnancies per 1,000 women aged 15-49 (model estimate, 2015-2019)

Guttmacher Institute (Bearak et al. 2022, BMJ Global Health) · CC BY-NC 4.0 · updated one edition (2015-2019 estimates, published 2022)

The Guttmacher Institute's estimate of pregnancies that were not wanted then or not wanted at all, per 1,000 women aged 15 to 49, averaged over 2015-2019. Whether a pregnancy was intended comes from what women say in surveys, so it is an estimate built from those surveys and a statistical model. Each figure has a range: Uganda's is 145, but it could be from 123 to 169.

How this was computed

Data used: most recent value in 2010-2025; values come from 2019–2024. 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 Unintended pregnancy rate

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

More links with Infrastructure score or Unintended pregnancy rate