placestats

Government effectiveness vs. Human capital (PWT)

Fades after adjusting for income

Across 142 countries, Government effectiveness and Human capital (PWT) rise and fall together at r = +0.75 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.16, which could easily be chance (q = 0.37, 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.

Comparing countries across the middle half of human capital (PWT), 2.2 to 3.31 points, government effectiveness is better by 1.16 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.24 points.

Link before adjusting (r)+0.751p = 5.0e-27 · 142 countries
After adjusting for national income+0.162p = 0.055 · likely range [-0.00, +0.32]
p, allowing for regions0.134standard error 0.094 · 7 regions
Check for luck (q)0.367across 18,622 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.

Government effectiveness · index (approx. -2.5 to 2.5)link with income +0.85 · proxy
Human capital (PWT) · index (1 = no schooling)link with income +0.82 · 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

Government effectiveness · index (approx. -2.5 to 2.5) →Human capital (PWT) · index (1 = no schooling) →-2.042.091.263.99Angola: -0.709 index (approx. -2.5 to 2.5), 1.54 index (1 = no schooling)Albania: 0.471 index (approx. -2.5 to 2.5), 3.02 index (1 = no schooling)United Arab Emirates: 1.13 index (approx. -2.5 to 2.5), 3.3 index (1 = no schooling)Argentina: 0.276 index (approx. -2.5 to 2.5), 3.16 index (1 = no schooling)Armenia: -0.237 index (approx. -2.5 to 2.5), 3.16 index (1 = no schooling)Australia: 1.75 index (approx. -2.5 to 2.5), 3.61 index (1 = no schooling)Austria: 1.54 index (approx. -2.5 to 2.5), 3.45 index (1 = no schooling)Burundi: -1.25 index (approx. -2.5 to 2.5), 1.48 index (1 = no schooling)Belgium: 1.32 index (approx. -2.5 to 2.5), 3.17 index (1 = no schooling)Benin: -0.167 index (approx. -2.5 to 2.5), 2.04 index (1 = no schooling)Burkina Faso: -0.791 index (approx. -2.5 to 2.5), 1.38 index (1 = no schooling)Bangladesh: -0.445 index (approx. -2.5 to 2.5), 2.15 index (1 = no schooling)Bulgaria: 0.126 index (approx. -2.5 to 2.5), 3.26 index (1 = no schooling)Bahrain: 0.421 index (approx. -2.5 to 2.5), 2.82 index (1 = no schooling)Belize: -0.185 index (approx. -2.5 to 2.5), 2.92 index (1 = no schooling)Bolivia: -0.617 index (approx. -2.5 to 2.5), 3.09 index (1 = no schooling)Brazil: -0.0356 index (approx. -2.5 to 2.5), 3.36 index (1 = no schooling)Barbados: 0.611 index (approx. -2.5 to 2.5), 2.87 index (1 = no schooling)Brunei Darussalam: 0.893 index (approx. -2.5 to 2.5), 2.86 index (1 = no schooling)Botswana: 0.434 index (approx. -2.5 to 2.5), 3.05 index (1 = no schooling)Central African Republic: -1.61 index (approx. -2.5 to 2.5), 1.6 index (1 = no schooling)Canada: 1.76 index (approx. -2.5 to 2.5), 3.77 index (1 = no schooling)Switzerland: 1.97 index (approx. -2.5 to 2.5), 3.75 index (1 = no schooling)Chile: 1.04 index (approx. -2.5 to 2.5), 3.24 index (1 = no schooling)China: 0.624 index (approx. -2.5 to 2.5), 2.75 index (1 = no schooling)Cote d'Ivoire: -0.254 index (approx. -2.5 to 2.5), 1.74 index (1 = no schooling)Cameroon: -0.795 index (approx. -2.5 to 2.5), 1.95 index (1 = no schooling)Congo, Dem. Rep.: -1.67 index (approx. -2.5 to 2.5), 1.83 index (1 = no schooling)Congo, Rep.: -1.17 index (approx. -2.5 to 2.5), 2.18 index (1 = no schooling)Colombia: -0.0935 index (approx. -2.5 to 2.5), 2.68 index (1 = no schooling)Costa Rica: 0.411 index (approx. -2.5 to 2.5), 2.7 index (1 = no schooling)Cyprus: 0.833 index (approx. -2.5 to 2.5), 2.99 index (1 = no schooling)Czechia: 1.24 index (approx. -2.5 to 2.5), 3.78 index (1 = no schooling)Germany: 1.49 index (approx. -2.5 to 2.5), 3.71 index (1 = no schooling)Denmark: 1.96 index (approx. -2.5 to 2.5), 3.67 index (1 = no schooling)Dominican Republic: 0.043 index (approx. -2.5 to 2.5), 2.93 index (1 = no schooling)Algeria: -0.193 index (approx. -2.5 to 2.5), 2.51 index (1 = no schooling)Ecuador: -0.0311 index (approx. -2.5 to 2.5), 2.83 index (1 = no schooling)Egypt, Arab Rep.: -0.106 index (approx. -2.5 to 2.5), 2.86 index (1 = no schooling)Spain: 1.13 index (approx. -2.5 to 2.5), 3.07 index (1 = no schooling)Estonia: 1.5 index (approx. -2.5 to 2.5), 3.58 index (1 = no schooling)Ethiopia: -0.616 index (approx. -2.5 to 2.5), 1.54 index (1 = no schooling)Finland: 1.94 index (approx. -2.5 to 2.5), 3.6 index (1 = no schooling)Fiji: 0.529 index (approx. -2.5 to 2.5), 2.74 index (1 = no schooling)France: 1.21 index (approx. -2.5 to 2.5), 3.31 index (1 = no schooling)Gabon: -0.551 index (approx. -2.5 to 2.5), 3.04 index (1 = no schooling)United Kingdom: 1.23 index (approx. -2.5 to 2.5), 3.85 index (1 = no schooling)Ghana: 0.134 index (approx. -2.5 to 2.5), 2.66 index (1 = no schooling)Gambia, The: -0.432 index (approx. -2.5 to 2.5), 1.49 index (1 = no schooling)Greece: 0.322 index (approx. -2.5 to 2.5), 3.24 index (1 = no schooling)Guatemala: -0.776 index (approx. -2.5 to 2.5), 2.04 index (1 = no schooling)Guyana: 0.147 index (approx. -2.5 to 2.5), 2.76 index (1 = no schooling)Hong Kong SAR, China: 1.54 index (approx. -2.5 to 2.5), 3.36 index (1 = no schooling)Honduras: -0.585 index (approx. -2.5 to 2.5), 2.58 index (1 = no schooling)Croatia: 0.77 index (approx. -2.5 to 2.5), 3.81 index (1 = no schooling)Haiti: -2.04 index (approx. -2.5 to 2.5), 1.8 index (1 = no schooling)Hungary: 0.47 index (approx. -2.5 to 2.5), 3.55 index (1 = no schooling)Indonesia: 0.255 index (approx. -2.5 to 2.5), 2.34 index (1 = no schooling)India: 0.502 index (approx. -2.5 to 2.5), 2.28 index (1 = no schooling)Ireland: 1.63 index (approx. -2.5 to 2.5), 3.27 index (1 = no schooling)Iran, Islamic Rep.: -0.857 index (approx. -2.5 to 2.5), 2.77 index (1 = no schooling)Iraq: -1.02 index (approx. -2.5 to 2.5), 2.39 index (1 = no schooling)Iceland: 1.55 index (approx. -2.5 to 2.5), 3.28 index (1 = no schooling)Israel: 1.19 index (approx. -2.5 to 2.5), 3.99 index (1 = no schooling)Italy: 0.925 index (approx. -2.5 to 2.5), 3.27 index (1 = no schooling)Jamaica: 0.189 index (approx. -2.5 to 2.5), 2.65 index (1 = no schooling)Jordan: 0.111 index (approx. -2.5 to 2.5), 2.88 index (1 = no schooling)Japan: 2.04 index (approx. -2.5 to 2.5), 3.66 index (1 = no schooling)Kazakhstan: 0.103 index (approx. -2.5 to 2.5), 3.39 index (1 = no schooling)Kenya: -0.0894 index (approx. -2.5 to 2.5), 2.61 index (1 = no schooling)Kyrgyz Republic: -0.85 index (approx. -2.5 to 2.5), 3.57 index (1 = no schooling)Cambodia: -0.401 index (approx. -2.5 to 2.5), 2.06 index (1 = no schooling)Korea, Rep.: 1.5 index (approx. -2.5 to 2.5), 3.87 index (1 = no schooling)Kuwait: 0.256 index (approx. -2.5 to 2.5), 2.3 index (1 = no schooling)Lao PDR: -0.593 index (approx. -2.5 to 2.5), 2.01 index (1 = no schooling)Liberia: -1.17 index (approx. -2.5 to 2.5), 2.03 index (1 = no schooling)Sri Lanka: -0.129 index (approx. -2.5 to 2.5), 2.93 index (1 = no schooling)Lesotho: -0.64 index (approx. -2.5 to 2.5), 2.02 index (1 = no schooling)Lithuania: 1.24 index (approx. -2.5 to 2.5), 3.41 index (1 = no schooling)Luxembourg: 1.98 index (approx. -2.5 to 2.5), 3.47 index (1 = no schooling)Latvia: 0.829 index (approx. -2.5 to 2.5), 3.21 index (1 = no schooling)Macao SAR, China: 1.16 index (approx. -2.5 to 2.5), 3.05 index (1 = no schooling)Morocco: 0.177 index (approx. -2.5 to 2.5), 2.03 index (1 = no schooling)Moldova: 0.214 index (approx. -2.5 to 2.5), 3.18 index (1 = no schooling)Madagascar: -0.873 index (approx. -2.5 to 2.5), 1.72 index (1 = no schooling)Maldives: -0.241 index (approx. -2.5 to 2.5), 2.8 index (1 = no schooling)Mexico: -0.181 index (approx. -2.5 to 2.5), 2.85 index (1 = no schooling)Mali: -1.37 index (approx. -2.5 to 2.5), 1.48 index (1 = no schooling)Malta: 0.763 index (approx. -2.5 to 2.5), 3.48 index (1 = no schooling)Myanmar: -1.62 index (approx. -2.5 to 2.5), 1.94 index (1 = no schooling)Mongolia: -0.206 index (approx. -2.5 to 2.5), 2.76 index (1 = no schooling)Mozambique: -0.808 index (approx. -2.5 to 2.5), 1.29 index (1 = no schooling)Mauritania: -0.757 index (approx. -2.5 to 2.5), 1.85 index (1 = no schooling)Mauritius: 0.86 index (approx. -2.5 to 2.5), 2.7 index (1 = no schooling)Malawi: -0.603 index (approx. -2.5 to 2.5), 2.14 index (1 = no schooling)Malaysia: 0.957 index (approx. -2.5 to 2.5), 3.21 index (1 = no schooling)Namibia: 0.101 index (approx. -2.5 to 2.5), 2.31 index (1 = no schooling)Niger: -0.864 index (approx. -2.5 to 2.5), 1.26 index (1 = no schooling)Nigeria: -0.913 index (approx. -2.5 to 2.5), 2.13 index (1 = no schooling)Nicaragua: -0.894 index (approx. -2.5 to 2.5), 2.41 index (1 = no schooling)Netherlands: 1.74 index (approx. -2.5 to 2.5), 3.46 index (1 = no schooling)Norway: 1.84 index (approx. -2.5 to 2.5), 3.75 index (1 = no schooling)Nepal: -0.77 index (approx. -2.5 to 2.5), 1.93 index (1 = no schooling)New Zealand: 1.87 index (approx. -2.5 to 2.5), 3.38 index (1 = no schooling)Pakistan: -0.606 index (approx. -2.5 to 2.5), 1.76 index (1 = no schooling)Panama: 0.217 index (approx. -2.5 to 2.5), 2.97 index (1 = no schooling)Peru: -0.144 index (approx. -2.5 to 2.5), 2.79 index (1 = no schooling)Philippines: 0.195 index (approx. -2.5 to 2.5), 2.96 index (1 = no schooling)Poland: 0.796 index (approx. -2.5 to 2.5), 3.49 index (1 = no schooling)Portugal: 1.02 index (approx. -2.5 to 2.5), 2.61 index (1 = no schooling)Paraguay: -0.228 index (approx. -2.5 to 2.5), 2.73 index (1 = no schooling)Qatar: 0.996 index (approx. -2.5 to 2.5), 3.14 index (1 = no schooling)Romania: 0.371 index (approx. -2.5 to 2.5), 3.35 index (1 = no schooling)Russian Federation: -0.501 index (approx. -2.5 to 2.5), 3.34 index (1 = no schooling)Rwanda: 0.361 index (approx. -2.5 to 2.5), 1.88 index (1 = no schooling)Saudi Arabia: 0.74 index (approx. -2.5 to 2.5), 2.99 index (1 = no schooling)Sudan: -1.95 index (approx. -2.5 to 2.5), 1.65 index (1 = no schooling)Senegal: 0.251 index (approx. -2.5 to 2.5), 1.72 index (1 = no schooling)Singapore: 2.09 index (approx. -2.5 to 2.5), 3.4 index (1 = no schooling)Sierra Leone: -0.931 index (approx. -2.5 to 2.5), 1.73 index (1 = no schooling)El Salvador: -0.235 index (approx. -2.5 to 2.5), 2.38 index (1 = no schooling)Serbia: 0.0328 index (approx. -2.5 to 2.5), 3.53 index (1 = no schooling)Slovak Republic: 0.582 index (approx. -2.5 to 2.5), 3.89 index (1 = no schooling)Slovenia: 1.1 index (approx. -2.5 to 2.5), 3.66 index (1 = no schooling)Sweden: 1.82 index (approx. -2.5 to 2.5), 3.5 index (1 = no schooling)Eswatini: -0.84 index (approx. -2.5 to 2.5), 1.91 index (1 = no schooling)Syrian Arab Republic: -1.59 index (approx. -2.5 to 2.5), 2.7 index (1 = no schooling)Togo: -0.837 index (approx. -2.5 to 2.5), 2.13 index (1 = no schooling)Thailand: 0.262 index (approx. -2.5 to 2.5), 2.86 index (1 = no schooling)Tajikistan: -0.686 index (approx. -2.5 to 2.5), 3.11 index (1 = no schooling)Trinidad and Tobago: 0.234 index (approx. -2.5 to 2.5), 3.13 index (1 = no schooling)Tunisia: -0.285 index (approx. -2.5 to 2.5), 2.84 index (1 = no schooling)Turkiye: 0.0289 index (approx. -2.5 to 2.5), 2.62 index (1 = no schooling)Tanzania: -0.255 index (approx. -2.5 to 2.5), 1.77 index (1 = no schooling)Uganda: -0.311 index (approx. -2.5 to 2.5), 2.69 index (1 = no schooling)Ukraine: -0.596 index (approx. -2.5 to 2.5), 3.32 index (1 = no schooling)Uruguay: 0.863 index (approx. -2.5 to 2.5), 3.01 index (1 = no schooling)United States: 1.19 index (approx. -2.5 to 2.5), 3.83 index (1 = no schooling)Viet Nam: 0.176 index (approx. -2.5 to 2.5), 2.74 index (1 = no schooling)South Africa: -0.023 index (approx. -2.5 to 2.5), 2.9 index (1 = no schooling)Zambia: -0.39 index (approx. -2.5 to 2.5), 2.77 index (1 = no schooling)Zimbabwe: -0.886 index (approx. -2.5 to 2.5), 2.82 index (1 = no schooling)CAFCHEGMBHTIJPNKGZNORRWASDNSENSYRUKR

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7515.0e-27142
By rank (Spearman), not adjusted+0.7597.6e-28142
Standard (Pearson), adjusted for national income+0.1620.055142
By rank (Spearman), adjusted for national income+0.1700.044142

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 142 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: +0.589 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.135 (without GUY) to +0.191 (without KGZ). 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: KGZ (+0.029).

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
East Asia & Pacific19+0.72
Europe & Central Asia40+0.23
Latin America & Caribbean23+0.49
Middle East, North Africa, Afghanistan & Pakistan16+0.18
Sub-Saharan Africa37+0.14

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 third48+0.00
Middle third47+0.03
Richest third47+0.52

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
Christians87+0.18
Muslims34-0.22

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.

nr
France20-0.38
Never a European colony15+0.12
Spain18+0.19
United Kingdom45+0.50

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.

nr
Nationalist20+0.08
No strong official ideology97+0.19

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.

nr
A few parties banned20-0.34
No parties banned103+0.18

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 = +0.162 after dropping 0 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 28years. 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 = -0.030, 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 government effectiveness moved did not tend to see their human capital (PWT) move with it.

Across countries (adjusted)+0.162for comparison
Within countries+0.339each country vs itself
…and vs the same year elsewhere-0.030q = 0.771 · 145 countries
Year-on-year changes+0.001q = 0.980
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.
  • Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.

3,625 country-years (one country in one year), 145 countries, 1996–2023, about 25 years each on average. Share of the variation that happens within countries over time: 6% for Government effectiveness, 6% for Human capital (PWT). 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

0.10−0.1-100+10years Human capital (PWT) trails Government effectiveness →lag -10: r = 0.084 (n = 3,625)lag -9: r = 0.067 (n = 3,625)lag -8: r = 0.060 (n = 3,770)lag -7: r = 0.044 (n = 3,770)lag -6: r = 0.038 (n = 3,915)lag -5: r = 0.024 (n = 3,915)lag -4: r = 0.011 (n = 3,915)lag -3: r = 0.000 (n = 3,915)lag -2: r = -0.012 (n = 3,915)lag -1: r = -0.022 (n = 3,770)lag +0: r = -0.030 (n = 3,625)lag +1: r = -0.035 (n = 3,480)lag +2: r = -0.038 (n = 3,335)lag +3: r = -0.043 (n = 3,190)lag +4: r = -0.044 (n = 3,045)lag +5: r = -0.043 (n = 2,900)lag +6: r = -0.031 (n = 2,755)lag +7: r = -0.023 (n = 2,610)lag +8: r = -0.020 (n = 2,465)lag +9: r = -0.015 (n = 2,320)lag +10: r = -0.002 (n = 2,175)

Same year (lag 0): -0.030. 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 -10), 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


index (approx. -2.5 to 2.5)

index (1 = no schooling)
Years
Singapore SGP2.093.42025, 2023
Japan JPN2.043.662025, 2023
Luxembourg LUX1.983.472025, 2023
Switzerland CHE1.973.752025, 2023
Denmark DNK1.963.672025, 2023
Finland FIN1.943.62025, 2023
New Zealand NZL1.873.382025, 2023
Norway NOR1.843.752025, 2023
Sweden SWE1.823.52025, 2023
Canada CAN1.763.772025, 2023
Australia AUS1.753.612025, 2023
Netherlands NLD1.743.462025, 2023
Ireland IRL1.633.272025, 2023
Iceland ISL1.553.282025, 2023
Austria AUT1.543.452025, 2023

Kaufmann, D. and A. Kraay, Worldwide Governance Indicators, World Bank
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table", American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt. Data: Penn World Table version 11.0, Groningen Growth and Development Centre, doi:10.34894/FABVLR.

Sources

Government effectiveness

Government Effectiveness (estimate)

World Bank · CC BY 4.0 · updated annual

Human capital (PWT)

Human capital index, from years of schooling (Penn World Table)

Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)

"Human capital" means the skills and knowledge workers bring to their jobs. This index measures it from schooling alone. It starts from how many years adults spent in school, then counts the early years for more than the later ones, following studies of how much higher pay tends to be with each extra year. A score of 1 means no schooling; the US is about 3.8. It uses the same schooling figures as mean years of schooling, so the two are close by construction. For that reason it is left out of the education score, which already counts years of schooling.

How this was computed

Data used: most recent value in 2010-2025; values come from 2023–2025. 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 18,622 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Government effectiveness and Human capital (PWT)

This page shows that two measures go together. It does not show that Government effectiveness changes Human capital (PWT), or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Government effectiveness or Human capital (PWT)