Do Migrant share of population and Regulatory quality go together across countries?

Across 194 countries the link before adjusting for anything is r = +0.56, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.64.

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

Comparing each country only with itself across 2000-2024, and setting aside what happened worldwide in each year, the link is -0.143 (197 countries, 1,167 country-years). Using year-to-year changes, it is —.

Does Migrant share of population cause Regulatory quality?

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.

Migrant share of population vs. Regulatory quality

Fades after adjusting for income

Across 194 countries, Migrant share of population and Regulatory quality 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 +0.19, which could easily be chance (q = 0.19, 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 migrant share of population, 1.52% to 14.2% of population, regulatory quality is better by 0.415 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.105 points.

Link before adjusting (r)+0.560p = 2.1e-17 · 194 countries

After adjusting for national income+0.192p = 0.008 · likely range [+0.05, +0.32]

p, allowing for regions0.055standard error 0.081 · 7 regions

Check for luck (q)0.192across 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.

Migrant share of population · % of populationlink with income +0.73 · high

Regulatory quality · index (approx. -2.5 to 2.5)link with income +0.83 · proxy

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

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	+0.560	2.1e-17	194
By rank (Spearman), not adjusted	+0.639	1.2e-23	194
Standard (Pearson), adjusted for national income	+0.192	0.008	194
By rank (Spearman), adjusted for national income	+0.083	0.253	194

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 194 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: +0.368 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.176 (without GUY) to +0.211 (without KWT). 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: KWT (+0.019).

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.

	n	r
East Asia & Pacific	31	+0.27
Europe & Central Asia	51	+0.11
Latin America & Caribbean	35	+0.43
Middle East, North Africa, Afghanistan & Pakistan	22	+0.37
Sub-Saharan Africa	46	-0.30

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	65	+0.02
Middle third	64	+0.09
Richest third	65	+0.02

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	111	+0.26
Muslims	49	+0.37

By last european coloniser

	n	r
France	25	-0.19
Never a European colony	21	+0.13
Spain	20	-0.28
United Kingdom	63	+0.19

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 = +0.220 after dropping 16 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 25years. 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 shows up within countries over time (r = -0.143) but mostly disappears in year-to-year changes (r = —). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)+0.192for comparison

Within countries-0.118each country vs itself

…and vs the same year elsewhere-0.143q = 0.011 · 197 countries

Year-on-year changes—q = —

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.

1,167 country-years (one country in one year), 197 countries, 2000–2024, about 6 years each on average. Share of the variation that happens within countries over time: 3% for Migrant share of population, 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): -0.143. 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

	% of population	index (approx. -2.5 to 2.5)	Years
Qatar QAT	76.7%	0.966	2024
United Arab Emirates ARE	74%	1.13	2024
Aruba ABW	68%	0.981	2024
Kuwait KWT	67.3%	0.404	2024
Virgin Islands (U.S.) VIR	66.9%	1.1	2024
Macao SAR, China MAC	59.3%	1.37	2024
Andorra AND	59.1%	1.1	2024
Bahrain BHR	52.3%	0.95	2024
Luxembourg LUX	51.2%	1.84	2024
Singapore SGP	48.7%	2.13	2024
Jordan JOR	45.7%	0.276	2024
Oman OMN	43.2%	0.688	2024
Cayman Islands CYM	42.9%	1.07	2024
Hong Kong SAR, China HKG	41.3%	1.74	2024
Saudi Arabia SAU	40.3%	0.71	2024

World Bank, World Development Indicators
Kaufmann, D. and A. Kraay, Worldwide Governance Indicators, World Bank

Sources

Migrant share of population

International migrant stock (% of population)

UN Population Division, International Migrant Stock · CC BY 4.0 · updated quarterly

People living outside the country they were born in, as a share of the population. It counts people at one moment, while the net migration already on the site counts moves over a year. Published every five years until 2020, and for 2024.

Regulatory quality

Regulatory Quality (estimate)

World Bank · CC BY 4.0 · updated annual

How this was computed

Data used: most recent value in 2010-2025; values come from 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 Migrant share of population and Regulatory quality

This page shows that two measures go together. It does not show that Migrant share of population changes Regulatory quality, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.