Do Homicide rate and Market-income Gini go together across countries?

Across 45 countries the link before adjusting for anything is r = +0.55, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.22.

Does the link hold within countries over time, not just across countries?

Comparing each country only with itself across 2011-2023, and setting aside what happened worldwide in each year, the link is -0.056 (38 countries, 427 country-years). Using year-to-year changes, it is +0.023.

Does Homicide rate cause Market-income Gini?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Homicide rate vs. Market-income Gini

Fades after adjusting for income

Across 45 countries, Homicide rate and Market-income Gini rise and fall together at r = +0.55 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.38, which could easily be chance (q = 0.51, 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: sign flips when a single country is removed.

Comparing countries across the middle half of market-income gini, 0.434 to 0.508 points, homicide rate is worse by 5.89 per 100,000 population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 3.77 per 100,000 population.

Link before adjusting (r)+0.545p = 0.000 · 45 countries

After adjusting for national income+0.377p = 0.012 · likely range [+0.09, +0.61]

p, allowing for regions0.204standard error 0.264 · 7 regions

Check for luck (q)0.508across 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

Market-income Gini · index, 0 to 1link with income -0.36 · moderate

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

Every country as a dot

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	+0.545	0.000	45
By rank (Spearman), not adjusted	+0.220	0.147	45
Standard (Pearson), adjusted for national income	+0.377	0.012	45
By rank (Spearman), adjusted for national income	+0.092	0.551	45

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: +0.168 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.068 (without ZAF) to +0.546 (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 (-0.445).

Fragile: this link may not hold up.

sign flips when a single country is removed
leave-one-out range spans 0.61 in r
correlation moves sharply once outliers are trimmed
much weaker within regions than across them (largely a between-region difference)

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.

	n	r
Europe & Central Asia	31	-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.

	n	r
Poorest third	15	+0.42
Middle third	15	-0.10
Richest third	15	+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.

	n	r
Christians	36	+0.42

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 = +0.092 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 = -0.056, 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 market-income gini move with it.

Across countries (adjusted)+0.377for comparison

Within countries-0.051each country vs itself

…and vs the same year elsewhere-0.056q = 0.747 · 38 countries

Year-on-year changes+0.023q = 0.734

Read with care.

Almost all of the second indicator's variation is between countries, 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: 13% for Homicide rate, 5% for Market-income Gini. 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

Same year (lag 0): -0.056. 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.

This curve peaks at the edge of the window (lag -6), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.

The data

	per 100,000 population	index, 0 to 1	Years
South Africa ZAF	43.7	0.709	2022, 2017
Mexico MEX	24.9	0.416	2023, 2022
Brazil BRA	19.3	0.523	2023, 2022
Costa Rica CRI	17.7	0.526	2023, 2024
Russian Federation RUS	7.82	0.434	2024, 2017
Chile CHL	6.35	0.491	2023, 2022
United States USA	5.76	0.506	2023
Turkiye TUR	3.23	0.525	2023, 2022
India IND	2.82	0.508	2022, 2011
Lithuania LTU	2.63	0.505	2023
Latvia LVA	2.5	0.473	2023
Canada CAN	1.98	0.433	2023
Israel ISR	1.63	0.449	2022
Estonia EST	1.54	0.468	2023
Luxembourg LUX	1.53	0.461	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.

Market-income Gini

Gini coefficient of market income

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

Inequality of household income before taxes and government payments, for the whole population. It is not the Gini already on the site, which is of income after taxes and payments. The gap between the two shows what the government does. The number of countries jumps from 24 to 34 in 2011, when the OECD changed its definition of income, so the series is used from 2011 on.

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 Market-income Gini

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