Why Do Countries with Similar GDP Have Different HDI Scores?

How we compared

The dataset is the same verified 2022 cross-section used across CivlensAI’s research: 18 countries matched on ISO codes, with GDP per capita from the World Bank and the Human Development Index, Education Index, and Income Index from UNDP Human Development Report data — every observation referring to 2022.

Before looking at any outcomes, we fixed one selection rule: two countries count as income-near peers if their GDP per capita differs by no more than 10% of the pair average. Applying that rule to the sample produces exactly eleven pairs; all eleven are shown in the table above, and none were selected by hand.

One methodological note matters here. The HDI is a composite that already contains an education component and an income component, so its sub-readings are related to it by construction. In UNDP’s methodology the income component is based on gross national income (converted for price differences), which is a different measure from the World Bank’s GDP per capita used for pairing.

What the comparison shows

Similar income does not produce uniform development profiles — but neither does it guarantee divergence. Of the eleven income-near pairs, six show HDI readings within 0.02 of each other, effectively indistinguishable at this resolution. Five pairs diverge by 0.02 or more, up to a maximum gap of 0.03.

Both outcomes are real features of this data. The rest of this analysis is about what separates the second group from the first.

Finding 1 — Same income, same income index, different development

France ($42,661) and New Zealand ($42,335) are near-twins on income: their GDP per capita differs by less than 1%, and their UNDP Income Index readings are identical at 0.85. Yet their overall HDI differs by 0.03 (0.94 vs 0.91) — and the difference tracks almost entirely through education, where New Zealand records 0.92 against France’s 0.88.

This is the cleanest case in the sample: with income conditions held about as close as the data allow, the education reading moves in step with the overall index.

Finding 2 — Lower income, higher human-development index

South Korea’s GDP per capita ($33,147) sits about 7% below Italy’s ($35,657), yet South Korea’s HDI is 0.03 points higher (0.93 vs 0.90). This pair also shows the largest education gap between any income-near peers in the sample: 0.05 (0.91 vs 0.86).

An honest caveat: this case depends on the 10% threshold. Under a stricter 5% rule it would drop out of scope entirely. It survives here because the rule was fixed before cases were selected — not because the pair was chosen first.

Finding 3 — Similar GDP does not mean a similar income component

Singapore ($82,807) and the United States ($76,329) fall within 8.1% of each other on World Bank GDP per capita, and their overall HDI readings are close (0.94 vs 0.92). But their UNDP Income Index readings differ by 0.07 — the widest income-component gap between any peers in the sample.

The likely explanation is definitional rather than substantive: UNDP’s income component is built on gross national income adjusted for price levels, while the pairing uses World Bank GDP. The two measures often move together, but they are not the same quantity — and this pair shows how much that distinction can matter even between income-near countries.

Education is where similar economies separate

Across all eleven pairs, education-index gaps run up to 0.05, consistently wider than the corresponding overall-HDI gaps, which never exceed 0.03. Wherever similarly wealthy economies record meaningfully different development profiles in this data, the education reading is doing most of the separating.

That observation describes the structure of these particular indexes and this particular sample. It suggests where to look next — not what any country should do, and not that education causes development outcomes.

Why this does not establish causation

Everything above is a description of co-movement in one year of published data for eighteen countries. The HDI mathematically contains the education and income readings discussed here, so gaps in those components flow directly into the composite by construction; nothing in this analysis identifies education, or anything else, as an independent driver of development.

A small cross-section can also shift noticeably under different samples or thresholds — as Finding 2’s sensitivity to the 10% rule demonstrates. These patterns are reasons to examine more data, not conclusions about how development works.

Limitations

• Small sample: 18 economies and 11 rule-selected pairs, treated as illustrative rather than representative.
• Threshold sensitivity: the 10% pairing rule was fixed before case selection, but individual findings (notably Italy–South Korea) depend on it.
• Component dependence: the HDI contains the education and income readings, so component gaps translate into composite gaps by construction.
• Definitional mismatch: the pairing uses World Bank GDP per capita, while UNDP’s income component reflects GNI adjusted for price differences.
• Single-year observational cross-section: no trends, no controls, and no basis for causal inference.
• At this resolution, small index differences (0.01–0.02) may not be meaningful; larger stated gaps are highlighted only where the rule surfaces them.

Data and Methodology

This analysis uses indicator data already presented on CivlensAI. How that data is collected, organized, and updated is described on the Methodology page.

Sources

• UNDP Human Development Report
• World Bank Open Data

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