placestats

Homicide rate vs. Redistribution

Fades after adjusting for income

Across 45 countries, Homicide rate and Redistribution rise and fall together at r = -0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.25, which could easily be chance (q = 0.32, 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: leave-one-out range spans 0.22 in r.

Comparing countries across the middle half of redistribution, 23.2% to 38.1% reduction in the Gini, homicide rate is better by 5.29 per 100,000 population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 2.67 per 100,000 population.

Link before adjusting (r)-0.531p = 0.000 · 45 countries
After adjusting for national income-0.250p = 0.101 · likely range [-0.51, +0.05]
p, allowing for regions0.373standard error 0.260 · 7 regions
Check for luck (q)0.319across 12,497 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.

Homicide rate · per 100,000 populationlink with income -0.38 · moderate
Redistribution · % reduction in the Ginilink with income +0.50 · moderate

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

Every country as a dot

Homicide rate · per 100,000 population →Redistribution · % reduction in the Gini →0.22943.72.67%48.3%Australia: 0.854 per 100,000 population, 27.7% reduction in the GiniAustria: 0.876 per 100,000 population, 41.4% reduction in the GiniBelgium: 1.08 per 100,000 population, 47.7% reduction in the GiniBulgaria: 1.09 per 100,000 population, 26% reduction in the GiniBrazil: 19.3 per 100,000 population, 13.8% reduction in the GiniCanada: 1.98 per 100,000 population, 29.3% reduction in the GiniSwitzerland: 0.597 per 100,000 population, 21.2% reduction in the GiniChile: 6.35 per 100,000 population, 8.68% reduction in the GiniChina: 0.502 per 100,000 population, 6.22% reduction in the GiniCosta Rica: 17.7 per 100,000 population, 12.6% reduction in the GiniCzechia: 0.768 per 100,000 population, 43.5% reduction in the GiniGermany: 0.911 per 100,000 population, 38.7% reduction in the GiniDenmark: 0.841 per 100,000 population, 37.5% reduction in the GiniSpain: 0.691 per 100,000 population, 34.4% reduction in the GiniEstonia: 1.54 per 100,000 population, 33.5% reduction in the GiniFinland: 0.982 per 100,000 population, 47.2% reduction in the GiniFrance: 1.35 per 100,000 population, 42.2% reduction in the GiniUnited Kingdom: 1.12 per 100,000 population, 29.7% reduction in the GiniGreece: 0.84 per 100,000 population, 36.9% reduction in the GiniCroatia: 0.667 per 100,000 population, 33.1% reduction in the GiniHungary: 0.723 per 100,000 population, 29.4% reduction in the GiniIndia: 2.82 per 100,000 population, 2.67% reduction in the GiniIreland: 0.654 per 100,000 population, 41.2% reduction in the GiniIceland: 1.29 per 100,000 population, 35.7% reduction in the GiniIsrael: 1.63 per 100,000 population, 23.2% reduction in the GiniItaly: 0.568 per 100,000 population, 36.2% reduction in the GiniJapan: 0.229 per 100,000 population, 34.1% reduction in the GiniKorea, Rep.: 0.477 per 100,000 population, 18.2% reduction in the GiniLithuania: 2.63 per 100,000 population, 29.7% reduction in the GiniLuxembourg: 1.53 per 100,000 population, 35.7% reduction in the GiniLatvia: 2.5 per 100,000 population, 28.1% reduction in the GiniMexico: 24.9 per 100,000 population, 3.77% reduction in the GiniNetherlands: 0.691 per 100,000 population, 34.2% reduction in the GiniNorway: 0.725 per 100,000 population, 39.5% reduction in the GiniNew Zealand: 1.46 per 100,000 population, 28.7% reduction in the GiniPoland: 0.802 per 100,000 population, 41.9% reduction in the GiniPortugal: 0.72 per 100,000 population, 36.5% reduction in the GiniRomania: 1.12 per 100,000 population, 38.1% reduction in the GiniRussian Federation: 7.82 per 100,000 population, 27% reduction in the GiniSlovak Republic: 1.12 per 100,000 population, 48.3% reduction in the GiniSlovenia: 0.566 per 100,000 population, 42.4% reduction in the GiniSweden: 1.15 per 100,000 population, 33.1% reduction in the GiniTurkiye: 3.23 per 100,000 population, 18.6% reduction in the GiniUnited States: 5.76 per 100,000 population, 22.1% reduction in the GiniSouth Africa: 43.7 per 100,000 population, 12.8% reduction in the GiniBELBRACHLCHNFININDMEXSVKZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5310.00045
By rank (Spearman), not adjusted-0.4380.00345
Standard (Pearson), adjusted for national income-0.2500.10145
By rank (Spearman), adjusted for national income-0.3030.04645

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 45 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: +0.280 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.405 (without ZAF) to -0.183 (without MEX). 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: ZAF (-0.155).

Fragile: this link may not hold up.
  • leave-one-out range spans 0.22 in r

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
Europe & Central Asia31-0.37

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 third15-0.05
Middle third15-0.36
Richest third15-0.47

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
Christians36-0.49

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 = -0.219 after dropping 7 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 13years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Within countries over time, there is no link that is clearly more than chance (r = +0.020, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose homicide rate moved did not tend to see their redistribution move with it.

Across countries (adjusted)-0.250for comparison
Within countries+0.017each country vs itself
…and vs the same year elsewhere+0.020q = 0.899 · 38 countries
Year-on-year changes+0.121q = 0.029
Read with care.
  • Almost all of this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.

427 country-years (one country in one year), 38 countries, 2011–2023, about 11 years each on average. Share of the variation that happens within countries over time: 4% for Homicide rate, 5% for Redistribution. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.

Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.

With one measure years behind the other

0.30−0.3-60+10years Redistribution trails Homicide rate →lag -6: r = 0.190 (n = 213)lag -5: r = 0.225 (n = 245)lag -4: r = 0.234 (n = 277)lag -3: r = 0.128 (n = 332)lag -2: r = 0.083 (n = 364)lag -1: r = 0.041 (n = 399)lag +0: r = 0.020 (n = 427)lag +1: r = -0.056 (n = 430)lag +2: r = -0.043 (n = 428)lag +3: r = -0.006 (n = 424)lag +4: r = 0.005 (n = 418)lag +5: r = 0.020 (n = 413)lag +6: r = 0.049 (n = 410)lag +7: r = 0.051 (n = 401)lag +8: r = 0.072 (n = 384)lag +9: r = 0.062 (n = 376)lag +10: r = 0.072 (n = 379)

Same year (lag 0): +0.020. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.

The largest value on this curve (+0.23 at lag -4) is higher than the same-year one by 0.21. That gap is not a finding. It is the largest of 17 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.

The data


per 100,000 population
% reduction in the Gini		Years
South Africa ZAF43.712.8%2022, 2017
Mexico MEX24.93.77%2023, 2022
Brazil BRA19.313.8%2023, 2022
Costa Rica CRI17.712.6%2023, 2024
Russian Federation RUS7.8227%2024, 2017
Chile CHL6.358.68%2023, 2022
United States USA5.7622.1%2023
Turkiye TUR3.2318.6%2023, 2022
India IND2.822.67%2022, 2011
Lithuania LTU2.6329.7%2023
Latvia LVA2.528.1%2023
Canada CAN1.9829.3%2023
Israel ISR1.6323.2%2022
Estonia EST1.5433.5%2023
Luxembourg LUX1.5335.7%2022, 2023

UN Office on Drugs and Crime, Intentional Homicide Victims
OECD databases as named on each indicator: PISA, Social Expenditure Database (SOCX), Income Distribution Database, Development Assistance Committee statistics, Family Database, Government at a Glance, Corporate Tax Statistics, employment and environment statistics

Sources

Homicide rate

Intentional homicide rate (per 100,000 population)

United Nations Office on Drugs and Crime · via Our World in Data · CC BY 3.0 IGO · updated annual

Counts victims of intentional homicide (murder). Homicide is the crime least often left unreported. So it is the only crime figure similar enough across countries to use here. Rates of assault, theft and rape reflect how often people report crimes and how police record them, at least as much as how much crime there is, so they are left out on purpose. Even homicide figures shift when a country switches between police records and public-health records.

Redistribution

Reduction in the Gini coefficient from taxes and transfers

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator · comparable from 2011 only

The Gini before taxes and payments minus the Gini after them, as a share of the first. It shows how much of the inequality produced by the market the government removes. It is built from the before-tax Gini above, so their pair is built from the same numbers and flagged. No "better" direction is set: a country may redistribute little because its market incomes are already equal.

How this was computed

Data used: most recent value in 2010-2025; values come from 2011–2024. 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 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Homicide rate and Redistribution

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

More links with Homicide rate or Redistribution