Across 190 countries, Refugees originating and Regulatory quality rise and fall together at r = -0.28 (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 = -0.13, q = 0.049, allowing for the many pairs tested). Caution: correlation moves sharply once outliers are trimmed.
Comparing countries across the middle half of refugees originating, 2.16 to 131 per 100,000 people, regulatory quality is worse by 0.0127 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.00375 points.
Link before adjusting (r)-0.277p = 0.000 · 190 countries
After adjusting for national income-0.131p = 0.073 · likely range [-0.27, +0.01]
p, allowing for regions0.005standard error 0.031 · 7 regions
Check for luck (q)0.049across 17,292 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.
Refugees originating · per 100,000 peoplelink with income -0.49 · moderate
Regulatory quality · index (approx. -2.5 to 2.5)link with income +0.83 · proxy
“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
-0.277
0.000
190
By rank (Spearman), not adjusted
-0.554
1.1e-16
190
Standard (Pearson), adjusted for national income
-0.131
0.073
190
By rank (Spearman), adjusted for national income
-0.305
2.0e-5
190
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 190 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: +0.146 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.155 (without CAF) to -0.041 (without SYR). 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: SYR (+0.090).
Fragile: this link may not hold up.
correlation moves sharply once outliers are trimmed
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
East Asia & Pacific
31
-0.42
Europe & Central Asia
48
+0.01
Latin America & Caribbean
34
+0.02
Middle East, North Africa, Afghanistan & Pakistan
22
-0.15
Sub-Saharan Africa
46
-0.15
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
64
-0.46
Middle third
63
-0.00
Richest third
63
-0.26
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
109
-0.02
Muslims
49
-0.25
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.
n
r
France
25
-0.59
Never a European colony
21
-0.05
Spain
20
+0.08
United Kingdom
63
-0.27
By official ideology
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
Nationalist
25
-0.25
No strong official ideology
116
-0.11
By party system
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
A few parties banned
26
+0.03
No parties banned
120
+0.02
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 = -0.304 after dropping 29 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.
Loading…
Over time, within countries
Everything above compares countries with each other at one moment. This section compares each country only with itself over 29years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.
The link also shows up within countries over time (r = -0.196). It holds even when looking only at changes from one year to the next (r = -0.057). This is the strongest kind of evidence available here: it holds when each country is compared only with itself, and it is not just a product of shared trends.
Across countries (adjusted)-0.131for comparison
Within countries-0.211each country vs itself
…and vs the same year elsewhere-0.196q = 1.0e-5 · 193 countries
Year-on-year changes-0.057q = 0.038
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.
4,731 country-years (one country in one year), 193 countries, 1996–2024, about 25 years each on average. Share of the variation that happens within countries over time: 42% for Refugees originating, 6% for Regulatory quality. 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): -0.196. 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 data
per 100,000 people
index (approx. -2.5 to 2.5)
Years
Syrian Arab Republic SYR
24,124
-1.81
2024, 2025
Ukraine UKR
13,524
-0.282
2024, 2025
Afghanistan AFG
13,521
-1.44
2024, 2025
Central African Republic CAF
13,009
-1.31
2024, 2025
Somalia, Fed. Rep. SOM
4,703
-1.7
2024, 2025
Sudan SDN
4,151
-1.74
2024, 2025
Myanmar MMR
2,427
-1.46
2024, 2025
Burundi BDI
2,295
-0.868
2024, 2025
Rwanda RWA
1,747
0.255
2024, 2025
Azerbaijan AZE
1,482
0.0512
2024, 2025
Mali MLI
1,446
-0.808
2024, 2025
Haiti HTI
1,259
-1.51
2024, 2025
El Salvador SLV
1,132
-0.142
2024, 2025
Congo, Dem. Rep. COD
970
-1.31
2024, 2025
Bhutan BTN
939
-0.0934
2024, 2025
UNHCR Refugee Data Finder; rate per population from Our World in Data Kaufmann, D. and A. Kraay, Worldwide Governance Indicators, World Bank
Refugees originating from the country, per 100,000 people
United Nations High Commissioner for Refugees · via Our World in Data · CC BY 4.0 · updated annual
People recognised as refugees abroad, compared with the population of the country they left. It is zero for most of the world and in the thousands for a dozen countries at war. So it is put on a log scale (which squeezes very large values together) wherever it is used for adjusting, and its links lean on those few countries. The "leave one out" check shows by how much. It has a "better" direction and sits under governance. But it is an outcome of governance, not a measure of it, so it is kept out of the governance score.
Data used: most recent value in 2010-2025; values come from 2022–2025. 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 17,292 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Refugees originating and Regulatory quality
This page shows that two measures go together. It does not show that Refugees originating changes Regulatory quality, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Refugees originating or Regulatory quality