placestats

Physicians vs. Redistribution

Holds up after adjusting for income

Across 45 countries, Physicians and Redistribution 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 +0.33 (q = 0.024, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak positive link remains. Caution: much weaker within regions than across them (largely a between-region difference).

Comparing countries across the middle half of redistribution, 23.2% to 38.1% reduction in the Gini, physicians is better by 0.772 per 1,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.508 per 1,000 people.

Link before adjusting (r)+0.560p = 6.3e-5 · 45 countries
After adjusting for national income+0.332p = 0.028 · likely range [+0.04, +0.57]
p, allowing for regions0.000standard error 0.044 · 7 regions
Check for luck (q)0.024across 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.

Physicians · per 1,000 peoplelink with income +0.85 · proxy
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

Physicians · per 1,000 people →Redistribution · % reduction in the Gini →0.7236.582.67%48.3%Australia: 4.09 per 1,000 people, 27.7% reduction in the GiniAustria: 5.51 per 1,000 people, 41.4% reduction in the GiniBelgium: 3.57 per 1,000 people, 47.7% reduction in the GiniBulgaria: 4.33 per 1,000 people, 26% reduction in the GiniBrazil: 2.36 per 1,000 people, 13.8% reduction in the GiniCanada: 2.82 per 1,000 people, 29.3% reduction in the GiniSwitzerland: 4.48 per 1,000 people, 21.2% reduction in the GiniChile: 3.33 per 1,000 people, 8.68% reduction in the GiniChina: 3.11 per 1,000 people, 6.22% reduction in the GiniCosta Rica: 2.69 per 1,000 people, 12.6% reduction in the GiniCzechia: 4.35 per 1,000 people, 43.5% reduction in the GiniGermany: 4.53 per 1,000 people, 38.7% reduction in the GiniDenmark: 4.5 per 1,000 people, 37.5% reduction in the GiniSpain: 4.29 per 1,000 people, 34.4% reduction in the GiniEstonia: 3.47 per 1,000 people, 33.5% reduction in the GiniFinland: 3.61 per 1,000 people, 47.2% reduction in the GiniFrance: 3.28 per 1,000 people, 42.2% reduction in the GiniUnited Kingdom: 3.3 per 1,000 people, 29.7% reduction in the GiniGreece: 6.58 per 1,000 people, 36.9% reduction in the GiniCroatia: 3.91 per 1,000 people, 33.1% reduction in the GiniHungary: 3.46 per 1,000 people, 29.4% reduction in the GiniIndia: 0.723 per 1,000 people, 2.67% reduction in the GiniIreland: 3.88 per 1,000 people, 41.2% reduction in the GiniIceland: 4.37 per 1,000 people, 35.7% reduction in the GiniIsrael: 3.8 per 1,000 people, 23.2% reduction in the GiniItaly: 4.19 per 1,000 people, 36.2% reduction in the GiniJapan: 2.65 per 1,000 people, 34.1% reduction in the GiniKorea, Rep.: 2.61 per 1,000 people, 18.2% reduction in the GiniLithuania: 4.47 per 1,000 people, 29.7% reduction in the GiniLuxembourg: 2.98 per 1,000 people, 35.7% reduction in the GiniLatvia: 3.4 per 1,000 people, 28.1% reduction in the GiniMexico: 2.59 per 1,000 people, 3.77% reduction in the GiniNetherlands: 3.88 per 1,000 people, 34.2% reduction in the GiniNorway: 4.97 per 1,000 people, 39.5% reduction in the GiniNew Zealand: 3.61 per 1,000 people, 28.7% reduction in the GiniPoland: 4.03 per 1,000 people, 41.9% reduction in the GiniPortugal: 5.85 per 1,000 people, 36.5% reduction in the GiniRomania: 3.63 per 1,000 people, 38.1% reduction in the GiniRussian Federation: 5.11 per 1,000 people, 27% reduction in the GiniSlovak Republic: 3.7 per 1,000 people, 48.3% reduction in the GiniSlovenia: 3.37 per 1,000 people, 42.4% reduction in the GiniSweden: 4.41 per 1,000 people, 33.1% reduction in the GiniTurkiye: 2.24 per 1,000 people, 18.6% reduction in the GiniUnited States: 3.68 per 1,000 people, 22.1% reduction in the GiniSouth Africa: 0.794 per 1,000 people, 12.8% reduction in the GiniAUTBELCHNFINGRCINDMEXPRTRUSSVKZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5606.3e-545
By rank (Spearman), not adjusted+0.4670.00145
Standard (Pearson), adjusted for national income+0.3320.02845
By rank (Spearman), adjusted for national income+0.3230.03345

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: +0.228 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.289 (without GRC) to +0.359 (without BEL). 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: GRC (-0.043).

Fragile: this link may not hold up.
  • much weaker within regions than across them (largely a between-region difference)
  • much weaker within income groups than across them (mostly a gap between richer and poorer countries)

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+0.10

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+0.18
Middle third15+0.19
Richest third15+0.10

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+0.28

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 = +0.317 after dropping 3 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 = +0.013, 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 physicians moved did not tend to see their redistribution move with it.

Across countries (adjusted)+0.332for comparison
Within countries+0.071each country vs itself
…and vs the same year elsewhere+0.013q = 0.941 · 37 countries
Year-on-year changes-0.023q = 0.830
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.

397 country-years (one country in one year), 37 countries, 2011–2023, about 11 years each on average. Share of the variation that happens within countries over time: 4% for Physicians, 8% 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

0.20−0.2-50+10years Redistribution trails Physicians →lag -5: r = 0.150 (n = 218)lag -4: r = 0.180 (n = 253)lag -3: r = 0.085 (n = 288)lag -2: r = 0.033 (n = 328)lag -1: r = 0.013 (n = 359)lag +0: r = 0.013 (n = 397)lag +1: r = -0.026 (n = 416)lag +2: r = -0.028 (n = 418)lag +3: r = -0.037 (n = 419)lag +4: r = -0.035 (n = 419)lag +5: r = -0.052 (n = 420)lag +6: r = -0.040 (n = 419)lag +7: r = -0.001 (n = 418)lag +8: r = 0.016 (n = 416)lag +9: r = 0.007 (n = 415)lag +10: r = -0.020 (n = 413)

Same year (lag 0): +0.013. 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 (+0.18 at lag -4) is higher than the same-year one by 0.17. That gap is not a finding. It is the largest of 16 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 1,000 people
% reduction in the Gini		Years
Greece GRC6.5836.9%2022, 2023
Portugal PRT5.8536.5%2022, 2023
Austria AUT5.5141.4%2023
Russian Federation RUS5.1127%2022, 2017
Norway NOR4.9739.5%2023, 2022
Germany DEU4.5338.7%2022
Denmark DNK4.537.5%2021, 2022
Switzerland CHE4.4821.2%2022
Lithuania LTU4.4729.7%2022, 2023
Sweden SWE4.4133.1%2021, 2023
Iceland ISL4.3735.7%2023, 2019
Czechia CZE4.3543.5%2022, 2023
Bulgaria BGR4.3326%2022, 2023
Spain ESP4.2934.4%2022, 2023
Italy ITA4.1936.2%2022, 2023

World Bank, World Development Indicators
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

Physicians

Physicians (per 1,000 people)

World Health Organization · CC BY 4.0 · updated quarterly

Reported irregularly, so the latest value in the time window used is from a different year for different countries. Hospital beds, already on the site, have no "better" direction, since more beds is not plainly better. More doctors is closer to plainly better, so a direction is set on that basis.

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 Physicians and Redistribution

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

More links with Physicians or Redistribution