placestats

Employment in agriculture vs. Unintended pregnancy rate

Fades after adjusting for income

Across 142 countries, Employment in agriculture and Unintended pregnancy rate rise and fall together at r = +0.56 (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 = 0.74, 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, employment in agriculture is 19.9% of employment higher at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 1.2% of employment the other way.

Link before adjusting (r)+0.557p = 6.0e-13 · 142 countries
After adjusting for national income-0.048p = 0.576 · likely range [-0.21, +0.12]
p, allowing for regions0.567standard error 0.079 · 7 regions
Check for luck (q)0.743across 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.

Employment in agriculture · % of employmentlink with income -0.86 · 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

Employment in agriculture · % of employment →Unintended pregnancy rate · per 1,000 women aged 15-49 →0.0954%85.3%10.9145Angola: 51.9% of employment, 120 per 1,000 women aged 15-49Albania: 30.8% of employment, 16 per 1,000 women aged 15-49Argentina: 7% of employment, 69 per 1,000 women aged 15-49Armenia: 27% of employment, 52.6 per 1,000 women aged 15-49Australia: 2.18% of employment, 37.4 per 1,000 women aged 15-49Azerbaijan: 34.2% of employment, 92.9 per 1,000 women aged 15-49Burundi: 85.3% of employment, 98.1 per 1,000 women aged 15-49Belgium: 0.985% of employment, 21 per 1,000 women aged 15-49Benin: 40.2% of employment, 85.9 per 1,000 women aged 15-49Burkina Faso: 51.8% of employment, 75.2 per 1,000 women aged 15-49Bangladesh: 44.3% of employment, 58.9 per 1,000 women aged 15-49Bulgaria: 4.69% of employment, 28.6 per 1,000 women aged 15-49Bosnia and Herzegovina: 16.7% of employment, 20 per 1,000 women aged 15-49Belarus: 9.48% of employment, 18.7 per 1,000 women aged 15-49Belize: 16.1% of employment, 70.9 per 1,000 women aged 15-49Bolivia: 24.7% of employment, 105 per 1,000 women aged 15-49Brazil: 7.63% of employment, 67.2 per 1,000 women aged 15-49Barbados: 1.25% of employment, 76.9 per 1,000 women aged 15-49Bhutan: 42.4% of employment, 47.5 per 1,000 women aged 15-49Botswana: 18% of employment, 96.8 per 1,000 women aged 15-49Central African Republic: 66.1% of employment, 90.2 per 1,000 women aged 15-49Canada: 1.1% of employment, 31.5 per 1,000 women aged 15-49Switzerland: 2.24% of employment, 20.3 per 1,000 women aged 15-49Chile: 6.07% of employment, 70 per 1,000 women aged 15-49China: 21.7% of employment, 64.8 per 1,000 women aged 15-49Cote d'Ivoire: 45.4% of employment, 96.4 per 1,000 women aged 15-49Cameroon: 41.9% of employment, 83.4 per 1,000 women aged 15-49Congo, Dem. Rep.: 58.5% of employment, 117 per 1,000 women aged 15-49Congo, Rep.: 36.4% of employment, 106 per 1,000 women aged 15-49Colombia: 14.2% of employment, 62.7 per 1,000 women aged 15-49Comoros: 33.6% of employment, 95.2 per 1,000 women aged 15-49Cabo Verde: 9.25% of employment, 102 per 1,000 women aged 15-49Costa Rica: 11.5% of employment, 51.8 per 1,000 women aged 15-49Czechia: 2.71% of employment, 34.9 per 1,000 women aged 15-49Germany: 1.07% of employment, 21.3 per 1,000 women aged 15-49Djibouti: 1.15% of employment, 50.4 per 1,000 women aged 15-49Denmark: 1.62% of employment, 29.8 per 1,000 women aged 15-49Dominican Republic: 7.01% of employment, 83.7 per 1,000 women aged 15-49Ecuador: 32.5% of employment, 79.7 per 1,000 women aged 15-49Spain: 3.37% of employment, 20.3 per 1,000 women aged 15-49Estonia: 2.96% of employment, 38.1 per 1,000 women aged 15-49Ethiopia: 60.1% of employment, 79.5 per 1,000 women aged 15-49Finland: 3.49% of employment, 32.6 per 1,000 women aged 15-49France: 2.33% of employment, 29 per 1,000 women aged 15-49Gabon: 28.9% of employment, 112 per 1,000 women aged 15-49United Kingdom: 0.851% of employment, 36.2 per 1,000 women aged 15-49Georgia: 34% of employment, 93.6 per 1,000 women aged 15-49Ghana: 34.6% of employment, 102 per 1,000 women aged 15-49Guinea: 47.4% of employment, 69.8 per 1,000 women aged 15-49Gambia, The: 27.6% of employment, 53.7 per 1,000 women aged 15-49Guinea-Bissau: 61.1% of employment, 99.5 per 1,000 women aged 15-49Greece: 10.7% of employment, 33 per 1,000 women aged 15-49Guatemala: 29.1% of employment, 62.8 per 1,000 women aged 15-49Guyana: 12.2% of employment, 76.5 per 1,000 women aged 15-49Hong Kong SAR, China: 0.182% of employment, 27.8 per 1,000 women aged 15-49Honduras: 22.4% of employment, 67.8 per 1,000 women aged 15-49Croatia: 4.33% of employment, 33.5 per 1,000 women aged 15-49Haiti: 46.6% of employment, 107 per 1,000 women aged 15-49Hungary: 4.11% of employment, 27.3 per 1,000 women aged 15-49Indonesia: 27.3% of employment, 39.9 per 1,000 women aged 15-49India: 41.6% of employment, 62.4 per 1,000 women aged 15-49Iceland: 3.61% of employment, 32.6 per 1,000 women aged 15-49Italy: 3.35% of employment, 28.3 per 1,000 women aged 15-49Jamaica: 13.9% of employment, 86.9 per 1,000 women aged 15-49Japan: 2.81% of employment, 21.4 per 1,000 women aged 15-49Kazakhstan: 11.3% of employment, 57.5 per 1,000 women aged 15-49Kenya: 45.8% of employment, 113 per 1,000 women aged 15-49Kyrgyz Republic: 15.4% of employment, 39.7 per 1,000 women aged 15-49Cambodia: 33.4% of employment, 69.2 per 1,000 women aged 15-49Korea, Rep.: 5.1% of employment, 34.5 per 1,000 women aged 15-49Lao PDR: 68.6% of employment, 50.7 per 1,000 women aged 15-49Liberia: 41.1% of employment, 106 per 1,000 women aged 15-49St. Lucia: 7.47% of employment, 68.7 per 1,000 women aged 15-49Sri Lanka: 25.9% of employment, 38.9 per 1,000 women aged 15-49Lesotho: 30.9% of employment, 98.9 per 1,000 women aged 15-49Lithuania: 4.66% of employment, 25.6 per 1,000 women aged 15-49Latvia: 6.66% of employment, 33.8 per 1,000 women aged 15-49Moldova: 50.5% of employment, 26.2 per 1,000 women aged 15-49Madagascar: 69.2% of employment, 94.8 per 1,000 women aged 15-49Maldives: 11.3% of employment, 59.5 per 1,000 women aged 15-49Mexico: 11.4% of employment, 60.3 per 1,000 women aged 15-49North Macedonia: 8.5% of employment, 23.2 per 1,000 women aged 15-49Mali: 63% of employment, 67.6 per 1,000 women aged 15-49Myanmar: 45% of employment, 35.5 per 1,000 women aged 15-49Montenegro: 4.19% of employment, 10.9 per 1,000 women aged 15-49Mongolia: 27% of employment, 31.4 per 1,000 women aged 15-49Mozambique: 73% of employment, 88.2 per 1,000 women aged 15-49Mauritania: 32.1% of employment, 67.6 per 1,000 women aged 15-49Malawi: 65% of employment, 115 per 1,000 women aged 15-49Namibia: 22.3% of employment, 104 per 1,000 women aged 15-49Niger: 73% of employment, 49.2 per 1,000 women aged 15-49Nigeria: 33.5% of employment, 68.2 per 1,000 women aged 15-49Nicaragua: 30.5% of employment, 56.3 per 1,000 women aged 15-49Netherlands: 1.7% of employment, 17.5 per 1,000 women aged 15-49Norway: 2.5% of employment, 34.2 per 1,000 women aged 15-49Nepal: 22.9% of employment, 59.7 per 1,000 women aged 15-49New Zealand: 5.42% of employment, 40.9 per 1,000 women aged 15-49Pakistan: 36.2% of employment, 70.9 per 1,000 women aged 15-49Panama: 16% of employment, 73.1 per 1,000 women aged 15-49Peru: 23.1% of employment, 89.5 per 1,000 women aged 15-49Philippines: 20.5% of employment, 70.8 per 1,000 women aged 15-49Papua New Guinea: 36.9% of employment, 79.8 per 1,000 women aged 15-49Poland: 6.22% of employment, 28.7 per 1,000 women aged 15-49Puerto Rico (US): 1.14% of employment, 54.3 per 1,000 women aged 15-49Portugal: 2.78% of employment, 20.9 per 1,000 women aged 15-49Paraguay: 16% of employment, 67.4 per 1,000 women aged 15-49Romania: 10.9% of employment, 44.4 per 1,000 women aged 15-49Russian Federation: 5.11% of employment, 64.2 per 1,000 women aged 15-49Rwanda: 35.1% of employment, 97.6 per 1,000 women aged 15-49Senegal: 29.6% of employment, 61.3 per 1,000 women aged 15-49Singapore: 0.0954% of employment, 18.8 per 1,000 women aged 15-49Solomon Islands: 42.5% of employment, 114 per 1,000 women aged 15-49Sierra Leone: 44.5% of employment, 88.2 per 1,000 women aged 15-49El Salvador: 13.9% of employment, 57.6 per 1,000 women aged 15-49Somalia, Fed. Rep.: 30% of employment, 99.6 per 1,000 women aged 15-49Serbia: 18% of employment, 21 per 1,000 women aged 15-49Sao Tome and Principe: 12.1% of employment, 130 per 1,000 women aged 15-49Suriname: 6.98% of employment, 66.9 per 1,000 women aged 15-49Slovak Republic: 2.16% of employment, 27.3 per 1,000 women aged 15-49Slovenia: 3.66% of employment, 33.2 per 1,000 women aged 15-49Sweden: 1.77% of employment, 35.8 per 1,000 women aged 15-49Eswatini: 14.5% of employment, 111 per 1,000 women aged 15-49Chad: 71.7% of employment, 59.6 per 1,000 women aged 15-49Togo: 37.6% of employment, 100 per 1,000 women aged 15-49Thailand: 28.6% of employment, 38.3 per 1,000 women aged 15-49Tajikistan: 43.3% of employment, 36.9 per 1,000 women aged 15-49Turkmenistan: 21.9% of employment, 22.8 per 1,000 women aged 15-49Timor-Leste: 46% of employment, 49.6 per 1,000 women aged 15-49Tonga: 23.6% of employment, 49.4 per 1,000 women aged 15-49Trinidad and Tobago: 4.49% of employment, 83.9 per 1,000 women aged 15-49Tanzania: 64.6% of employment, 105 per 1,000 women aged 15-49Uganda: 64.9% of employment, 145 per 1,000 women aged 15-49Ukraine: 14.1% of employment, 27.1 per 1,000 women aged 15-49Uruguay: 8.07% of employment, 41.5 per 1,000 women aged 15-49United States: 1.52% of employment, 34.7 per 1,000 women aged 15-49Uzbekistan: 25.2% of employment, 28.6 per 1,000 women aged 15-49Viet Nam: 25% of employment, 85.5 per 1,000 women aged 15-49Vanuatu: 44.5% of employment, 86.9 per 1,000 women aged 15-49Samoa: 26.9% of employment, 66.4 per 1,000 women aged 15-49South Africa: 5.41% of employment, 81.5 per 1,000 women aged 15-49Zambia: 54.5% of employment, 123 per 1,000 women aged 15-49Zimbabwe: 54.3% of employment, 73.9 per 1,000 women aged 15-49BDICPVLAOMDAMNEMWINERNLDSGPSTPSWZUGA

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5576.0e-13142
By rank (Spearman), not adjusted+0.6031.9e-15142
Standard (Pearson), adjusted for national income-0.0480.576142
By rank (Spearman), adjusted for national income+0.0120.886142

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: +0.510 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.079 (without AGO) to -0.008 (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 (+0.040).

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 & Pacific21-0.29
Europe & Central Asia43+0.38
Latin America & Caribbean26+0.22
Sub-Saharan Africa42-0.03

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+0.02
Middle third47-0.21
Richest third47+0.31

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
Christians96-0.03
Muslims27+0.04

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
France21-0.14
Never a European colony16+0.42
Spain17+0.23
United Kingdom38+0.05

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 = -0.048 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


% of employment
per 1,000 women aged 15-49		Years
Burundi BDI85.3%98.12025, 2019
Mozambique MOZ73%88.22025, 2019
Niger NER73%49.22025, 2019
Chad TCD71.7%59.62025, 2019
Madagascar MDG69.2%94.82025, 2019
Lao PDR LAO68.6%50.72025, 2019
Central African Republic CAF66.1%90.22025, 2019
Malawi MWI65%1152025, 2019
Uganda UGA64.9%1452025, 2019
Tanzania TZA64.6%1052025, 2019
Mali MLI63%67.62025, 2019
Guinea-Bissau GNB61.1%99.52025, 2019
Ethiopia ETH60.1%79.52025, 2019
Congo, Dem. Rep. COD58.5%1172025, 2019
Zambia ZMB54.5%1232025, 2019

World Bank, World Development Indicators
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

Employment in agriculture

Employment in agriculture (% of total employment)

International Labour Organization · CC BY 4.0 · updated quarterly

Estimated with a model by the ILO. The classic measure of a country moving from farming to industry and services. It is one of the measures most closely linked to income on the site. No "better" direction is set, because a farming country is not doing badly by farming.

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 2019–2025. 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 Employment in agriculture and Unintended pregnancy rate

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

More links with Employment in agriculture or Unintended pregnancy rate