placestats

Abortion rate (estimate) vs. Informal employment

Holds up after adjusting for income

Across 114 countries, Abortion rate (estimate) and Informal employment rise and fall together at r = +0.59 (on a scale from −1 to +1, where 0 means no link). It holds up almost unchanged when only countries with similar income (GDP per person) are compared (r = +0.46, q = 0.046, allowing for the many pairs tested).

Comparing countries across the middle half of abortion rate (estimate), 16 to 36.2 per 1,000 women aged 15-49, informal employment is worse by 29.9% of employment at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 12.4% of employment.

Link before adjusting (r)+0.586p = 7.3e-12 · 114 countries
After adjusting for national income+0.461p = 2.8e-7 · likely range [+0.30, +0.59]
p, allowing for regions0.005standard error 0.095 · 6 regions
Check for luck (q)0.046across 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.

Abortion rate (estimate) · per 1,000 women aged 15-49link with income -0.50 · moderate
Informal employment · % of employmentlink with income -0.88 · proxy

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

Every country as a dot

Abortion rate (estimate) · per 1,000 women aged 15-49 →Informal employment · % of employment →5.1880.30.7%98.5%Angola: 33.4 per 1,000 women aged 15-49, 95.3% of employmentArgentina: 33.2 per 1,000 women aged 15-49, 51.6% of employmentArmenia: 42.8 per 1,000 women aged 15-49, 76.1% of employmentBurundi: 25.6 per 1,000 women aged 15-49, 98% of employmentBelgium: 7.78 per 1,000 women aged 15-49, 1.37% of employmentBenin: 31.9 per 1,000 women aged 15-49, 96.3% of employmentBurkina Faso: 30.5 per 1,000 women aged 15-49, 93.8% of employmentBangladesh: 35.3 per 1,000 women aged 15-49, 84% of employmentBulgaria: 10.2 per 1,000 women aged 15-49, 1.2% of employmentBosnia and Herzegovina: 11 per 1,000 women aged 15-49, 16.6% of employmentBolivia: 37.8 per 1,000 women aged 15-49, 83.9% of employmentBrazil: 32.3 per 1,000 women aged 15-49, 35.6% of employmentBarbados: 44.6 per 1,000 women aged 15-49, 61.8% of employmentBotswana: 30.7 per 1,000 women aged 15-49, 75.6% of employmentSwitzerland: 5.18 per 1,000 women aged 15-49, 1.48% of employmentChile: 35.9 per 1,000 women aged 15-49, 26.4% of employmentCote d'Ivoire: 35.3 per 1,000 women aged 15-49, 92.1% of employmentCameroon: 33.9 per 1,000 women aged 15-49, 87.1% of employmentCongo, Dem. Rep.: 32.7 per 1,000 women aged 15-49, 96.3% of employmentColombia: 27.1 per 1,000 women aged 15-49, 56% of employmentComoros: 30.6 per 1,000 women aged 15-49, 88.8% of employmentCabo Verde: 49.1 per 1,000 women aged 15-49, 60.6% of employmentCosta Rica: 21.1 per 1,000 women aged 15-49, 36.1% of employmentCzechia: 6.86 per 1,000 women aged 15-49, 2.26% of employmentGermany: 5.77 per 1,000 women aged 15-49, 2.49% of employmentDjibouti: 20.6 per 1,000 women aged 15-49, 47.6% of employmentDenmark: 11.6 per 1,000 women aged 15-49, 2.82% of employmentDominican Republic: 36.6 per 1,000 women aged 15-49, 54.7% of employmentEcuador: 27.9 per 1,000 women aged 15-49, 68.5% of employmentSpain: 8.73 per 1,000 women aged 15-49, 2.2% of employmentEstonia: 15.9 per 1,000 women aged 15-49, 2.4% of employmentEthiopia: 24.4 per 1,000 women aged 15-49, 77.7% of employmentFinland: 8.09 per 1,000 women aged 15-49, 2.19% of employmentFrance: 14.4 per 1,000 women aged 15-49, 3.15% of employmentUnited Kingdom: 13.1 per 1,000 women aged 15-49, 6.51% of employmentGeorgia: 80.3 per 1,000 women aged 15-49, 51.5% of employmentGhana: 36.6 per 1,000 women aged 15-49, 78% of employmentGambia, The: 16.4 per 1,000 women aged 15-49, 83.9% of employmentGuinea-Bissau: 58.5 per 1,000 women aged 15-49, 94.8% of employmentGreece: 15 per 1,000 women aged 15-49, 3.58% of employmentGuatemala: 18.9 per 1,000 women aged 15-49, 75.9% of employmentGuyana: 36.4 per 1,000 women aged 15-49, 51% of employmentHonduras: 20.7 per 1,000 women aged 15-49, 82.6% of employmentCroatia: 16.5 per 1,000 women aged 15-49, 1.17% of employmentHaiti: 38.1 per 1,000 women aged 15-49, 91.6% of employmentHungary: 13.6 per 1,000 women aged 15-49, 1.31% of employmentIndonesia: 25 per 1,000 women aged 15-49, 81% of employmentIndia: 48 per 1,000 women aged 15-49, 88.1% of employmentIceland: 12.7 per 1,000 women aged 15-49, 1.97% of employmentItaly: 18.9 per 1,000 women aged 15-49, 3.42% of employmentJamaica: 38.1 per 1,000 women aged 15-49, 54.6% of employmentKenya: 42.7 per 1,000 women aged 15-49, 86.5% of employmentKyrgyz Republic: 31.2 per 1,000 women aged 15-49, 57.5% of employmentCambodia: 45 per 1,000 women aged 15-49, 89.4% of employmentKorea, Rep.: 21 per 1,000 women aged 15-49, 29.1% of employmentLao PDR: 34 per 1,000 women aged 15-49, 90.5% of employmentLiberia: 42.3 per 1,000 women aged 15-49, 89.3% of employmentSt. Lucia: 37.2 per 1,000 women aged 15-49, 28.3% of employmentSri Lanka: 27.9 per 1,000 women aged 15-49, 67.6% of employmentLesotho: 23.5 per 1,000 women aged 15-49, 89.1% of employmentLithuania: 7.15 per 1,000 women aged 15-49, 3.2% of employmentLatvia: 10.3 per 1,000 women aged 15-49, 2.17% of employmentMoldova: 18.2 per 1,000 women aged 15-49, 52.2% of employmentMadagascar: 59.9 per 1,000 women aged 15-49, 96.1% of employmentMaldives: 42 per 1,000 women aged 15-49, 47.3% of employmentMexico: 30.6 per 1,000 women aged 15-49, 56.4% of employmentNorth Macedonia: 14.3 per 1,000 women aged 15-49, 11.1% of employmentMali: 22.5 per 1,000 women aged 15-49, 94.4% of employmentMyanmar: 26 per 1,000 women aged 15-49, 80.7% of employmentMongolia: 21 per 1,000 women aged 15-49, 27.1% of employmentMozambique: 40.4 per 1,000 women aged 15-49, 95.5% of employmentMauritania: 17 per 1,000 women aged 15-49, 89.3% of employmentMalawi: 31.4 per 1,000 women aged 15-49, 92.7% of employmentNamibia: 29.2 per 1,000 women aged 15-49, 55.6% of employmentNiger: 15.2 per 1,000 women aged 15-49, 98.5% of employmentNigeria: 32.7 per 1,000 women aged 15-49, 93.1% of employmentNicaragua: 15.3 per 1,000 women aged 15-49, 81.8% of employmentNetherlands: 7.15 per 1,000 women aged 15-49, 2.63% of employmentNorway: 10.8 per 1,000 women aged 15-49, 1.43% of employmentNepal: 41.3 per 1,000 women aged 15-49, 81.5% of employmentPakistan: 43.1 per 1,000 women aged 15-49, 88.7% of employmentPanama: 29.6 per 1,000 women aged 15-49, 58.7% of employmentPeru: 42.4 per 1,000 women aged 15-49, 70.5% of employmentPoland: 10.5 per 1,000 women aged 15-49, 8.62% of employmentPortugal: 6.77 per 1,000 women aged 15-49, 4.73% of employmentParaguay: 34 per 1,000 women aged 15-49, 64.5% of employmentRomania: 31 per 1,000 women aged 15-49, 2.21% of employmentRussian Federation: 45.7 per 1,000 women aged 15-49, 19.3% of employmentRwanda: 27.8 per 1,000 women aged 15-49, 84.3% of employmentSenegal: 15.3 per 1,000 women aged 15-49, 89.2% of employmentSierra Leone: 45.4 per 1,000 women aged 15-49, 93.1% of employmentEl Salvador: 24 per 1,000 women aged 15-49, 64% of employmentSomalia, Fed. Rep.: 28.7 per 1,000 women aged 15-49, 81.9% of employmentSerbia: 12.7 per 1,000 women aged 15-49, 17.8% of employmentSuriname: 31 per 1,000 women aged 15-49, 50.9% of employmentSlovak Republic: 7.15 per 1,000 women aged 15-49, 4.37% of employmentSlovenia: 8.13 per 1,000 women aged 15-49, 0.7% of employmentSweden: 17.3 per 1,000 women aged 15-49, 2.83% of employmentEswatini: 29.3 per 1,000 women aged 15-49, 53.3% of employmentChad: 17.4 per 1,000 women aged 15-49, 96.9% of employmentTogo: 32.1 per 1,000 women aged 15-49, 92.3% of employmentThailand: 25 per 1,000 women aged 15-49, 63.2% of employmentTajikistan: 25.8 per 1,000 women aged 15-49, 63.6% of employmentTimor-Leste: 34 per 1,000 women aged 15-49, 80.6% of employmentTonga: 25 per 1,000 women aged 15-49, 59.2% of employmentTanzania: 37.5 per 1,000 women aged 15-49, 90.8% of employmentUganda: 43 per 1,000 women aged 15-49, 94.5% of employmentUruguay: 11.4 per 1,000 women aged 15-49, 28.5% of employmentViet Nam: 64 per 1,000 women aged 15-49, 67% of employmentVanuatu: 39.2 per 1,000 women aged 15-49, 72.4% of employmentSamoa: 42.8 per 1,000 women aged 15-49, 51.5% of employmentSouth Africa: 29.7 per 1,000 women aged 15-49, 34.8% of employmentZambia: 34.6 per 1,000 women aged 15-49, 82.7% of employmentZimbabwe: 18.2 per 1,000 women aged 15-49, 85% of employmentCHECZEDEUGEOGNBMDGNERRUSSENTCDVNM

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5867.3e-12114
By rank (Spearman), not adjusted+0.5655.9e-11114
Standard (Pearson), adjusted for national income+0.4612.8e-7114
By rank (Spearman), adjusted for national income+0.3250.000114

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: +0.125 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.442 (without GUY) to +0.481 (without MDG). 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: MDG (+0.020).

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 & Pacific12+0.22
Europe & Central Asia35+0.60
Latin America & Caribbean23+0.10
Sub-Saharan Africa37+0.01

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 third38+0.16
Middle third38+0.26
Richest third38+0.65

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
Christians80+0.47
Muslims21+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
France17+0.04
Spain16+0.43
United Kingdom28+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 = +0.471 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


per 1,000 women aged 15-49
% of employment		Years
Georgia GEO80.351.5%2019, 2024
Viet Nam VNM6467%2019, 2024
Madagascar MDG59.996.1%2019, 2022
Guinea-Bissau GNB58.594.8%2019, 2022
Cabo Verde CPV49.160.6%2019, 2015
India IND4888.1%2019, 2024
Russian Federation RUS45.719.3%2019, 2024
Sierra Leone SLE45.493.1%2019, 2018
Cambodia KHM4589.4%2019
Barbados BRB44.661.8%2019, 2016
Pakistan PAK43.188.7%2019, 2025
Uganda UGA4394.5%2019, 2021
Samoa WSM42.851.5%2019, 2022
Armenia ARM42.876.1%2019, 2017
Kenya KEN42.786.5%2019

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.
United Nations Global SDG Indicators Database; series compiled by the International Labour Organization (8.3.1) and the UN Office on Drugs and Crime (16.3.2)

Sources

Abortion rate (estimate)

Abortions 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 all abortions, legal or not, per 1,000 women aged 15 to 49 (women of childbearing age), averaged over 2015-2019. Where a country has good counts, the estimate stays close to them. Where it does not, including most countries where abortion is banned, it is worked out by a statistical model from surveys, births, birth control use and similar countries. Only 75 of the 166 countries with any data had counts reliable enough to use directly, so for many countries this is an informed estimate with a wide range: Georgia's is 80, but it could be anywhere from 46 to 133. The official count of legal abortions is shown separately. No "better" direction is set.

Informal employment

Share of employment that is informal

International Labour Organization · via Our World in Data · CC BY 3.0 IGO · updated annual

Workers with no contract, no social protection or no registered employer, as the ILO (the UN's labour agency) makes them comparable across national labour force surveys. The World Bank's copy of this series was retired, so it comes from the UN's database for the Sustainable Development Goals.

How this was computed

Data used: most recent value in 2010-2025; values come from 2012–2025. 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 14,952 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Abortion rate (estimate) and Informal employment

This page shows that two measures go together. It does not show that Abortion rate (estimate) changes Informal employment, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Abortion rate (estimate) or Informal employment