placestats

Unintended pregnancy rate vs. Vulnerable employment

Fades after adjusting for income

Across 142 countries, Unintended pregnancy rate and Vulnerable employment rise and fall together at r = +0.63 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.11, which could easily be chance (q = 0.70, 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, vulnerable employment is worse by 27.6% of employment at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 3.46% of employment.

Link before adjusting (r)+0.626p = 8.2e-17 · 142 countries
After adjusting for national income+0.115p = 0.175 · likely range [-0.05, +0.28]
p, allowing for regions0.530standard error 0.172 · 7 regions
Check for luck (q)0.698across 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.

Unintended pregnancy rate · per 1,000 women aged 15-49link with income -0.69 · high
Vulnerable employment · % of employmentlink with income -0.87 · proxy

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

Unintended pregnancy rate · per 1,000 women aged 15-49 →Vulnerable employment · % of employment →10.91453.81%90.2%Angola: 120 per 1,000 women aged 15-49, 66.3% of employmentAlbania: 16 per 1,000 women aged 15-49, 46% of employmentArgentina: 69 per 1,000 women aged 15-49, 22.3% of employmentArmenia: 52.6 per 1,000 women aged 15-49, 32.8% of employmentAustralia: 37.4 per 1,000 women aged 15-49, 9.12% of employmentAzerbaijan: 92.9 per 1,000 women aged 15-49, 53.8% of employmentBurundi: 98.1 per 1,000 women aged 15-49, 83.7% of employmentBelgium: 21 per 1,000 women aged 15-49, 11% of employmentBenin: 85.9 per 1,000 women aged 15-49, 78.5% of employmentBurkina Faso: 75.2 per 1,000 women aged 15-49, 80.6% of employmentBangladesh: 58.9 per 1,000 women aged 15-49, 57.3% of employmentBulgaria: 28.6 per 1,000 women aged 15-49, 6.92% of employmentBosnia and Herzegovina: 20 per 1,000 women aged 15-49, 18.1% of employmentBelarus: 18.7 per 1,000 women aged 15-49, 6.33% of employmentBelize: 70.9 per 1,000 women aged 15-49, 29.8% of employmentBolivia: 105 per 1,000 women aged 15-49, 62.7% of employmentBrazil: 67.2 per 1,000 women aged 15-49, 26.1% of employmentBarbados: 76.9 per 1,000 women aged 15-49, 18.8% of employmentBhutan: 47.5 per 1,000 women aged 15-49, 69% of employmentBotswana: 96.8 per 1,000 women aged 15-49, 25.6% of employmentCentral African Republic: 90.2 per 1,000 women aged 15-49, 79.4% of employmentCanada: 31.5 per 1,000 women aged 15-49, 9.56% of employmentSwitzerland: 20.3 per 1,000 women aged 15-49, 9.6% of employmentChile: 70 per 1,000 women aged 15-49, 20.9% of employmentChina: 64.8 per 1,000 women aged 15-49, 33.4% of employmentCote d'Ivoire: 96.4 per 1,000 women aged 15-49, 69.4% of employmentCameroon: 83.4 per 1,000 women aged 15-49, 67.6% of employmentCongo, Dem. Rep.: 117 per 1,000 women aged 15-49, 81.5% of employmentCongo, Rep.: 106 per 1,000 women aged 15-49, 75.9% of employmentColombia: 62.7 per 1,000 women aged 15-49, 43% of employmentComoros: 95.2 per 1,000 women aged 15-49, 56.8% of employmentCabo Verde: 102 per 1,000 women aged 15-49, 34.3% of employmentCosta Rica: 51.8 per 1,000 women aged 15-49, 23.4% of employmentCzechia: 34.9 per 1,000 women aged 15-49, 13.5% of employmentGermany: 21.3 per 1,000 women aged 15-49, 4.34% of employmentDjibouti: 50.4 per 1,000 women aged 15-49, 30.3% of employmentDenmark: 29.8 per 1,000 women aged 15-49, 5.4% of employmentDominican Republic: 83.7 per 1,000 women aged 15-49, 38% of employmentEcuador: 79.7 per 1,000 women aged 15-49, 50.1% of employmentSpain: 20.3 per 1,000 women aged 15-49, 10.1% of employmentEstonia: 38.1 per 1,000 women aged 15-49, 7.09% of employmentEthiopia: 79.5 per 1,000 women aged 15-49, 84.1% of employmentFinland: 32.6 per 1,000 women aged 15-49, 9.46% of employmentFrance: 29 per 1,000 women aged 15-49, 8.5% of employmentGabon: 112 per 1,000 women aged 15-49, 29.8% of employmentUnited Kingdom: 36.2 per 1,000 women aged 15-49, 11.5% of employmentGeorgia: 93.6 per 1,000 women aged 15-49, 42.4% of employmentGhana: 102 per 1,000 women aged 15-49, 60% of employmentGuinea: 69.8 per 1,000 women aged 15-49, 79.2% of employmentGambia, The: 53.7 per 1,000 women aged 15-49, 63.5% of employmentGuinea-Bissau: 99.5 per 1,000 women aged 15-49, 74.9% of employmentGreece: 33 per 1,000 women aged 15-49, 23.1% of employmentGuatemala: 62.8 per 1,000 women aged 15-49, 36.2% of employmentGuyana: 76.5 per 1,000 women aged 15-49, 25.3% of employmentHong Kong SAR, China: 27.8 per 1,000 women aged 15-49, 5.76% of employmentHonduras: 67.8 per 1,000 women aged 15-49, 33.9% of employmentCroatia: 33.5 per 1,000 women aged 15-49, 6.35% of employmentHaiti: 107 per 1,000 women aged 15-49, 76% of employmentHungary: 27.3 per 1,000 women aged 15-49, 7.83% of employmentIndonesia: 39.9 per 1,000 women aged 15-49, 50% of employmentIndia: 62.4 per 1,000 women aged 15-49, 71.6% of employmentIceland: 32.6 per 1,000 women aged 15-49, 8.4% of employmentItaly: 28.3 per 1,000 women aged 15-49, 14.3% of employmentJamaica: 86.9 per 1,000 women aged 15-49, 31.6% of employmentJapan: 21.4 per 1,000 women aged 15-49, 7.49% of employmentKazakhstan: 57.5 per 1,000 women aged 15-49, 24.8% of employmentKenya: 113 per 1,000 women aged 15-49, 63.8% of employmentKyrgyz Republic: 39.7 per 1,000 women aged 15-49, 29% of employmentCambodia: 69.2 per 1,000 women aged 15-49, 52.3% of employmentKorea, Rep.: 34.5 per 1,000 women aged 15-49, 17.7% of employmentLao PDR: 50.7 per 1,000 women aged 15-49, 76.4% of employmentLiberia: 106 per 1,000 women aged 15-49, 76.9% of employmentSt. Lucia: 68.7 per 1,000 women aged 15-49, 22% of employmentSri Lanka: 38.9 per 1,000 women aged 15-49, 39.2% of employmentLesotho: 98.9 per 1,000 women aged 15-49, 32.6% of employmentLithuania: 25.6 per 1,000 women aged 15-49, 10.4% of employmentLatvia: 33.8 per 1,000 women aged 15-49, 8.62% of employmentMoldova: 26.2 per 1,000 women aged 15-49, 58.6% of employmentMadagascar: 94.8 per 1,000 women aged 15-49, 84.4% of employmentMaldives: 59.5 per 1,000 women aged 15-49, 16.9% of employmentMexico: 60.3 per 1,000 women aged 15-49, 25.3% of employmentNorth Macedonia: 23.2 per 1,000 women aged 15-49, 12.3% of employmentMali: 67.6 per 1,000 women aged 15-49, 75.8% of employmentMyanmar: 35.5 per 1,000 women aged 15-49, 62.9% of employmentMontenegro: 10.9 per 1,000 women aged 15-49, 14.3% of employmentMongolia: 31.4 per 1,000 women aged 15-49, 38.4% of employmentMozambique: 88.2 per 1,000 women aged 15-49, 83.7% of employmentMauritania: 67.6 per 1,000 women aged 15-49, 53.3% of employmentMalawi: 115 per 1,000 women aged 15-49, 59.9% of employmentNamibia: 104 per 1,000 women aged 15-49, 29.9% of employmentNiger: 49.2 per 1,000 women aged 15-49, 83.7% of employmentNigeria: 68.2 per 1,000 women aged 15-49, 66.5% of employmentNicaragua: 56.3 per 1,000 women aged 15-49, 38.9% of employmentNetherlands: 17.5 per 1,000 women aged 15-49, 13.4% of employmentNorway: 34.2 per 1,000 women aged 15-49, 4.17% of employmentNepal: 59.7 per 1,000 women aged 15-49, 77% of employmentNew Zealand: 40.9 per 1,000 women aged 15-49, 12.6% of employmentPakistan: 70.9 per 1,000 women aged 15-49, 55.5% of employmentPanama: 73.1 per 1,000 women aged 15-49, 37.9% of employmentPeru: 89.5 per 1,000 women aged 15-49, 47.5% of employmentPhilippines: 70.8 per 1,000 women aged 15-49, 33.6% of employmentPapua New Guinea: 79.8 per 1,000 women aged 15-49, 71.2% of employmentPoland: 28.7 per 1,000 women aged 15-49, 15% of employmentPuerto Rico (US): 54.3 per 1,000 women aged 15-49, 10.3% of employmentPortugal: 20.9 per 1,000 women aged 15-49, 9.66% of employmentParaguay: 67.4 per 1,000 women aged 15-49, 36.1% of employmentRomania: 44.4 per 1,000 women aged 15-49, 13% of employmentRussian Federation: 64.2 per 1,000 women aged 15-49, 4.93% of employmentRwanda: 97.6 per 1,000 women aged 15-49, 39.4% of employmentSenegal: 61.3 per 1,000 women aged 15-49, 63% of employmentSingapore: 18.8 per 1,000 women aged 15-49, 9.3% of employmentSolomon Islands: 114 per 1,000 women aged 15-49, 68.7% of employmentSierra Leone: 88.2 per 1,000 women aged 15-49, 86% of employmentEl Salvador: 57.6 per 1,000 women aged 15-49, 30.8% of employmentSomalia, Fed. Rep.: 99.6 per 1,000 women aged 15-49, 46% of employmentSerbia: 21 per 1,000 women aged 15-49, 23.9% of employmentSao Tome and Principe: 130 per 1,000 women aged 15-49, 35% of employmentSuriname: 66.9 per 1,000 women aged 15-49, 18.4% of employmentSlovak Republic: 27.3 per 1,000 women aged 15-49, 11.8% of employmentSlovenia: 33.2 per 1,000 women aged 15-49, 10.6% of employmentSweden: 35.8 per 1,000 women aged 15-49, 6.87% of employmentEswatini: 111 per 1,000 women aged 15-49, 28.4% of employmentChad: 59.6 per 1,000 women aged 15-49, 90.2% of employmentTogo: 100 per 1,000 women aged 15-49, 75% of employmentThailand: 38.3 per 1,000 women aged 15-49, 48% of employmentTajikistan: 36.9 per 1,000 women aged 15-49, 28% of employmentTurkmenistan: 22.8 per 1,000 women aged 15-49, 28.3% of employmentTimor-Leste: 49.6 per 1,000 women aged 15-49, 58.6% of employmentTonga: 49.4 per 1,000 women aged 15-49, 33.7% of employmentTrinidad and Tobago: 83.9 per 1,000 women aged 15-49, 21.2% of employmentTanzania: 105 per 1,000 women aged 15-49, 83.7% of employmentUganda: 145 per 1,000 women aged 15-49, 74.4% of employmentUkraine: 27.1 per 1,000 women aged 15-49, 19% of employmentUruguay: 41.5 per 1,000 women aged 15-49, 24.4% of employmentUnited States: 34.7 per 1,000 women aged 15-49, 3.81% of employmentUzbekistan: 28.6 per 1,000 women aged 15-49, 33.3% of employmentViet Nam: 85.5 per 1,000 women aged 15-49, 51.4% of employmentVanuatu: 86.9 per 1,000 women aged 15-49, 58% of employmentSamoa: 66.4 per 1,000 women aged 15-49, 41.6% of employmentSouth Africa: 81.5 per 1,000 women aged 15-49, 11.7% of employmentZambia: 123 per 1,000 women aged 15-49, 70.2% of employmentZimbabwe: 73.9 per 1,000 women aged 15-49, 68.2% of employmentBLRCODDEUGABMDAMNENERSTPSWZTCDUGAZMB

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6268.2e-17142
By rank (Spearman), not adjusted+0.6551.0e-18142
Standard (Pearson), adjusted for national income+0.1150.175142
By rank (Spearman), adjusted for national income+0.1470.083142

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: +0.511 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.086 (without TJK) to +0.147 (without STP). 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: STP (+0.032).

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 & Pacific21-0.03
Europe & Central Asia43+0.40
Latin America & Caribbean26+0.59
Sub-Saharan Africa42-0.23

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+0.10
Middle third47-0.03
Richest third47+0.51

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+0.22
Muslims27+0.30

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
France21-0.00
Never a European colony16+0.63
Spain17+0.75
United Kingdom38-0.10

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 = +0.115 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


per 1,000 women aged 15-49
% of employment		Years
Uganda UGA14574.4%2019, 2025
Sao Tome and Principe STP13035%2019, 2025
Zambia ZMB12370.2%2019, 2025
Angola AGO12066.3%2019, 2025
Congo, Dem. Rep. COD11781.5%2019, 2025
Malawi MWI11559.9%2019, 2025
Solomon Islands SLB11468.7%2019, 2025
Kenya KEN11363.8%2019, 2025
Gabon GAB11229.8%2019, 2025
Eswatini SWZ11128.4%2019, 2025
Haiti HTI10776%2019, 2025
Liberia LBR10676.9%2019, 2025
Congo, Rep. COG10675.9%2019, 2025
Tanzania TZA10583.7%2019, 2025
Bolivia BOL10562.7%2019, 2025

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.
World Bank, World Development Indicators

Sources

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.

Vulnerable employment

Vulnerable employment, total (% of total employment)

International Labour Organization · CC BY 4.0 · updated quarterly

Self-employed workers and unpaid family workers, who have no employer and usually no protection. The ILO estimates it with a model for every country and year, which is why it is complete where surveys are not.

How this was computed

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

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

More links with Unintended pregnancy rate or Vulnerable employment