placestats

Infrastructure score vs. Labor share of income

Holds up after adjusting for income

Across 137 countries, Infrastructure score and Labor share of income rise and fall together at r = +0.17 (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 = +0.38, q = 0.014, allowing for the many pairs tested).

Comparing countries across the middle half of labor share of income, 42.6% to 58.6% of GDP, infrastructure score is better by 0.147 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.13 typical gaps.

Link before adjusting (r)+0.172p = 0.044 · 137 countries
After adjusting for national income+0.376p = 6.6e-6 · likely range [+0.22, +0.51]
p, allowing for regions0.003standard error 0.080 · 7 regions
Check for luck (q)0.014across 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.

Infrastructure score · typical gaps (standard deviations)link with income +0.90 · proxy
Labor share of income · % of GDPlink with income +0.08 · independent

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

Every country as a dot

Infrastructure score · typical gaps (standard deviations) →Labor share of income · % of GDP →-1.011.7216.4%75.1%Aruba: 0.917 typical gaps (standard deviations), 64.5% of GDPAngola: -0.152 typical gaps (standard deviations), 22.3% of GDPAlbania: 1.14 typical gaps (standard deviations), 70.5% of GDPArgentina: 1.25 typical gaps (standard deviations), 52.8% of GDPArmenia: 1.09 typical gaps (standard deviations), 50.9% of GDPAustralia: 1.5 typical gaps (standard deviations), 54.6% of GDPAustria: 1.37 typical gaps (standard deviations), 60.8% of GDPAzerbaijan: 1.19 typical gaps (standard deviations), 39.2% of GDPBurundi: -0.835 typical gaps (standard deviations), 60.6% of GDPBelgium: 1.64 typical gaps (standard deviations), 58.6% of GDPBenin: 0.00537 typical gaps (standard deviations), 61.7% of GDPBurkina Faso: -0.245 typical gaps (standard deviations), 42.6% of GDPBulgaria: 1.25 typical gaps (standard deviations), 51.7% of GDPBahrain: 1.17 typical gaps (standard deviations), 29.4% of GDPBahamas, The: 1.25 typical gaps (standard deviations), 39.7% of GDPBosnia and Herzegovina: 1.29 typical gaps (standard deviations), 67.1% of GDPBelarus: 1.48 typical gaps (standard deviations), 57% of GDPBermuda: 1.68 typical gaps (standard deviations), 58.6% of GDPBolivia: 0.886 typical gaps (standard deviations), 49.8% of GDPBrazil: 1.18 typical gaps (standard deviations), 51.1% of GDPBarbados: 1.31 typical gaps (standard deviations), 75.1% of GDPBotswana: 0.5 typical gaps (standard deviations), 27.8% of GDPCentral African Republic: -1.01 typical gaps (standard deviations), 16.4% of GDPCanada: 1.56 typical gaps (standard deviations), 65.9% of GDPSwitzerland: 1.7 typical gaps (standard deviations), 65.3% of GDPChile: 1.25 typical gaps (standard deviations), 44% of GDPChina: 1.66 typical gaps (standard deviations), 56.1% of GDPCote d'Ivoire: 0.207 typical gaps (standard deviations), 33.2% of GDPCameroon: 0.557 typical gaps (standard deviations), 64.4% of GDPColombia: 1.01 typical gaps (standard deviations), 47.8% of GDPCabo Verde: 0.806 typical gaps (standard deviations), 62.6% of GDPCosta Rica: 1.19 typical gaps (standard deviations), 53.1% of GDPCuracao: 1.18 typical gaps (standard deviations), 67.9% of GDPCayman Islands: 1.58 typical gaps (standard deviations), 49.1% of GDPCyprus: 1.47 typical gaps (standard deviations), 53.7% of GDPCzechia: 1.48 typical gaps (standard deviations), 51.2% of GDPGermany: 1.65 typical gaps (standard deviations), 63.7% of GDPDjibouti: 0.0105 typical gaps (standard deviations), 63.4% of GDPDenmark: 1.65 typical gaps (standard deviations), 60.1% of GDPDominican Republic: 0.956 typical gaps (standard deviations), 42.7% of GDPEcuador: 0.989 typical gaps (standard deviations), 53% of GDPEgypt, Arab Rep.: 0.864 typical gaps (standard deviations), 36.2% of GDPSpain: 1.55 typical gaps (standard deviations), 59.4% of GDPEstonia: 1.45 typical gaps (standard deviations), 60.7% of GDPFinland: 1.48 typical gaps (standard deviations), 58.3% of GDPFiji: 0.665 typical gaps (standard deviations), 48.9% of GDPFrance: 1.7 typical gaps (standard deviations), 63.1% of GDPGabon: 0.665 typical gaps (standard deviations), 27.5% of GDPUnited Kingdom: 1.6 typical gaps (standard deviations), 59.5% of GDPGeorgia: 1.29 typical gaps (standard deviations), 46.7% of GDPGuinea: 0.156 typical gaps (standard deviations), 42.2% of GDPGreece: 1.57 typical gaps (standard deviations), 54% of GDPGuatemala: 0.576 typical gaps (standard deviations), 46.9% of GDPHong Kong SAR, China: 1.57 typical gaps (standard deviations), 52.7% of GDPHonduras: 0.417 typical gaps (standard deviations), 54.7% of GDPCroatia: 1.28 typical gaps (standard deviations), 61.3% of GDPHungary: 1.5 typical gaps (standard deviations), 53.7% of GDPIndonesia: 0.742 typical gaps (standard deviations), 50.9% of GDPIndia: 0.527 typical gaps (standard deviations), 53.3% of GDPIreland: 1.43 typical gaps (standard deviations), 30.9% of GDPIran, Islamic Rep.: 0.97 typical gaps (standard deviations), 39.3% of GDPIraq: 1.04 typical gaps (standard deviations), 29.6% of GDPIceland: 1.53 typical gaps (standard deviations), 61.7% of GDPIsrael: 1.31 typical gaps (standard deviations), 49.9% of GDPItaly: 1.36 typical gaps (standard deviations), 51.2% of GDPJamaica: 1.06 typical gaps (standard deviations), 60.9% of GDPJordan: 0.776 typical gaps (standard deviations), 49.3% of GDPJapan: 1.45 typical gaps (standard deviations), 58.7% of GDPKazakhstan: 1.09 typical gaps (standard deviations), 37.3% of GDPKenya: 0.22 typical gaps (standard deviations), 54% of GDPKyrgyz Republic: 0.943 typical gaps (standard deviations), 40% of GDPKorea, Rep.: 1.72 typical gaps (standard deviations), 54.6% of GDPKuwait: 0.878 typical gaps (standard deviations), 24.9% of GDPLao PDR: 0.399 typical gaps (standard deviations), 39.8% of GDPLebanon: 0.731 typical gaps (standard deviations), 44.5% of GDPSri Lanka: 0.669 typical gaps (standard deviations), 38.1% of GDPLesotho: -0.0187 typical gaps (standard deviations), 66.1% of GDPLithuania: 1.29 typical gaps (standard deviations), 57.1% of GDPLuxembourg: 1.59 typical gaps (standard deviations), 49.8% of GDPLatvia: 1.28 typical gaps (standard deviations), 62.2% of GDPMacao SAR, China: 1.33 typical gaps (standard deviations), 32.2% of GDPMorocco: 0.928 typical gaps (standard deviations), 46.5% of GDPMoldova: 1.23 typical gaps (standard deviations), 51.3% of GDPMexico: 1.13 typical gaps (standard deviations), 38.1% of GDPNorth Macedonia: 1.36 typical gaps (standard deviations), 52.5% of GDPMalta: 1.63 typical gaps (standard deviations), 45.9% of GDPMongolia: 1.03 typical gaps (standard deviations), 40.6% of GDPMozambique: -0.592 typical gaps (standard deviations), 54% of GDPMauritania: 0.0018 typical gaps (standard deviations), 40.4% of GDPMauritius: 1.17 typical gaps (standard deviations), 45.6% of GDPMalaysia: 1.09 typical gaps (standard deviations), 39.7% of GDPNamibia: 0.203 typical gaps (standard deviations), 52.9% of GDPNiger: -0.7 typical gaps (standard deviations), 39.1% of GDPNigeria: -0.0861 typical gaps (standard deviations), 31.2% of GDPNicaragua: 0.574 typical gaps (standard deviations), 52% of GDPNetherlands: 1.63 typical gaps (standard deviations), 55.9% of GDPNorway: 1.68 typical gaps (standard deviations), 38.4% of GDPNew Zealand: 1.51 typical gaps (standard deviations), 55.4% of GDPOman: 1.03 typical gaps (standard deviations), 30.3% of GDPPanama: 0.927 typical gaps (standard deviations), 32.8% of GDPPeru: 0.907 typical gaps (standard deviations), 40.1% of GDPPhilippines: 0.696 typical gaps (standard deviations), 44.9% of GDPPoland: 1.24 typical gaps (standard deviations), 54.8% of GDPPortugal: 1.6 typical gaps (standard deviations), 61.7% of GDPParaguay: 0.928 typical gaps (standard deviations), 44.5% of GDPQatar: 1.06 typical gaps (standard deviations), 17.7% of GDPRomania: 1.45 typical gaps (standard deviations), 50% of GDPRussian Federation: 1.32 typical gaps (standard deviations), 52.9% of GDPRwanda: 0.0662 typical gaps (standard deviations), 74.1% of GDPSaudi Arabia: 1.67 typical gaps (standard deviations), 28% of GDPSudan: -0.572 typical gaps (standard deviations), 63.5% of GDPSenegal: 0.482 typical gaps (standard deviations), 52.3% of GDPSingapore: 1.33 typical gaps (standard deviations), 43.9% of GDPSierra Leone: -0.336 typical gaps (standard deviations), 59.6% of GDPEl Salvador: 0.822 typical gaps (standard deviations), 52.1% of GDPSerbia: 1.38 typical gaps (standard deviations), 63.7% of GDPSao Tome and Principe: 0.187 typical gaps (standard deviations), 73.8% of GDPSuriname: 0.901 typical gaps (standard deviations), 45.8% of GDPSlovak Republic: 1.4 typical gaps (standard deviations), 53.2% of GDPSlovenia: 1.42 typical gaps (standard deviations), 63.3% of GDPSweden: 1.57 typical gaps (standard deviations), 61% of GDPEswatini: 0.534 typical gaps (standard deviations), 61.2% of GDPChad: -0.72 typical gaps (standard deviations), 48.5% of GDPTogo: -0.0274 typical gaps (standard deviations), 50.1% of GDPThailand: 1.06 typical gaps (standard deviations), 43.8% of GDPTajikistan: 0.389 typical gaps (standard deviations), 43.6% of GDPTrinidad and Tobago: 1.22 typical gaps (standard deviations), 33.8% of GDPTunisia: 0.956 typical gaps (standard deviations), 56.1% of GDPTurkiye: 1.19 typical gaps (standard deviations), 42.6% of GDPTanzania: -0.0741 typical gaps (standard deviations), 58.3% of GDPUkraine: 0.97 typical gaps (standard deviations), 55.8% of GDPUruguay: 1.41 typical gaps (standard deviations), 51.9% of GDPUnited States: 1.53 typical gaps (standard deviations), 56.8% of GDPUzbekistan: 1.42 typical gaps (standard deviations), 37.9% of GDPSouth Africa: 0.709 typical gaps (standard deviations), 54.3% of GDPZambia: -0.161 typical gaps (standard deviations), 30% of GDPZimbabwe: 0.0863 typical gaps (standard deviations), 53.3% of GDPAGOBDIBRBCAFCANCHEIRLNERQATRWASAUSDN

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.1720.044137
By rank (Spearman), not adjusted+0.2370.005137
Standard (Pearson), adjusted for national income+0.3766.6e-6137
By rank (Spearman), adjusted for national income+0.3621.5e-5137

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 137 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: -0.203 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.334 (without QAT) to +0.414 (without UZB). 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: QAT (-0.042).

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 & Pacific15+0.70
Europe & Central Asia46+0.30
Latin America & Caribbean25+0.25
Middle East, North Africa, Afghanistan & Pakistan15+0.04
Sub-Saharan Africa31+0.61

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 third46+0.23
Middle third45+0.65
Richest third46+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
Christians85+0.44
Muslims33+0.26

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
France16+0.42
Never a European colony16+0.32
Spain18+0.45
United Kingdom34+0.48

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
Nationalist18+0.52
No strong official ideology92+0.39

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 banned18-0.05
No parties banned100+0.45

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 = +0.336 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 0years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Not enough history inside each country to look at this over time. That needs countries measured for several years in a row, and at least one of these measures is measured too rarely.

The data


typical gaps (standard deviations)
% of GDP		Years
Korea, Rep. KOR1.7254.6%2024, 2023
Switzerland CHE1.765.3%2024, 2023
France FRA1.763.1%2024, 2023
Bermuda BMU1.6858.6%2024, 2023
Norway NOR1.6838.4%2024, 2023
Saudi Arabia SAU1.6728%2024, 2023
China CHN1.6656.1%2024, 2023
Denmark DNK1.6560.1%2024, 2023
Germany DEU1.6563.7%2024, 2023
Belgium BEL1.6458.6%2024, 2023
Malta MLT1.6345.9%2024, 2023
Netherlands NLD1.6355.9%2024, 2023
Portugal PRT1.661.7%2024, 2023
United Kingdom GBR1.659.5%2024, 2023
Luxembourg LUX1.5949.8%2024, 2023

wdi
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

Infrastructure score

Infrastructure score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 5 measures from 1 publisher. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Electricity access, Slum share of urban population, Mobile subscriptions, Fixed broadband, Internet users.

Labor share of income

Labor share of GDP (Penn World Table)

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

The share of a country's income (GDP) that is paid to people for their work: wages, salaries and benefits, plus an estimate of what the self-employed, such as farmers and shopkeepers, earn for their work. The rest goes to the owners of land, buildings and machines as profit, rent and interest. Where there is little data on the self-employed, the Penn World Table fills in a value by a fixed rule, so figures for poorer countries are rougher, and a few countries show the same value every year. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2022–2024. 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 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 Infrastructure score and Labor share of income

This page shows that two measures go together. It does not show that Infrastructure score changes Labor share of income, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Infrastructure score or Labor share of income