1AI+Wellbeing Institute, ICLA, Japan.
2University of Tokyo, Tokyo, Japan.
*e-mail: ricketts.john@c2c.ac.jp
Global GDP has tripled since 1970, yet life satisfaction in wealthy nations has not increased1,2. This disconnect—the Easterlin paradox—reflects a robust empirical pattern: wellbeing rises with income but with sharply diminishing returns3. Early income gains secure basic capabilities; later gains increasingly fund status competition, complexity and defensive spending. Here we show that what matters for national wellbeing is not how much an economy spends, but how it allocates that spending. Across 30 countries, we decompose system overhead into Institutional Infrastructure (universal systems, safety nets), Remediation Load (preventable disease, pollution, disasters) and Extractive Dissipation (rent-seeking, arms races). The share devoted to infrastructure—the Institutional Ratio—is strongly associated with life satisfaction (r = +0.80, P < 0.001) and trust (r = +0.90, P < 0.001), while total overhead shows no association (r = −0.18, not significant). Given diminishing returns, we ask: how much wellbeing does a society achieve per income doubling? Finland achieves 19% higher efficiency than the United States despite lower GDP4,5—a gap explained by composition. These findings suggest that improving wellbeing within ecological limits requires redirecting economic activity toward institutional capacity rather than aggregate expansion.
The relationship between economic growth and human wellbeing is weaker than commonly assumed. While GDP serves as the dominant metric of national success, cross-country and longitudinal evidence consistently shows that beyond moderate income levels, additional GDP is associated with progressively smaller gains in life satisfaction1–3. This diminishing-returns pattern, first documented by Easterlin6 and recently confirmed using 50 years of data from 166 countries3, suggests that early income secures basic capabilities while later income funds other activities. The question becomes: what are those activities, and do they matter for wellbeing?
We propose that the answer lies in composition rather than volume. Economic activity can be decomposed into direct capability enhancement (education, healthcare delivery, meaningful work) and system overhead (administration, coordination, security, remediation). We hypothesize that overhead is not uniformly waste but contains functionally distinct components with different relationships to wellbeing.
To test this, we decompose economic overhead into three categories and examine whether composition, summarized by the Institutional Ratio, is associated with wellbeing outcomes.
We estimated three components of system overhead for 30 countries (see Methods): Institutional Infrastructure (I: social spending, healthcare administration), Remediation Load (R: preventable disease, climate disasters, pollution) and Extractive Dissipation (E: excess military, finance, advertising, healthcare pricing, monopoly rents), each as percentage of GDP. The Institutional Ratio is IR = 100 × I/(I+R+E), in percentage points.
The Institutional Ratio is strongly associated with life satisfaction (r = +0.80, P < 0.001; Fig. 1) and social trust (r = +0.90, P < 0.001). Total overhead shows no significant association with happiness (r = −0.18, P = 0.35) or any outcome (Table 1). This pattern—composition predicts, volume does not—is the central finding.
Fig. 1 | Institutional Ratio is associated with happiness. Each point represents one country, coloured by region. Dashed line shows linear fit. r = +0.80 (95% bootstrap CI [0.62, 0.90], 10,000 resamples), P < 0.001. Association remains significant controlling for log GDP per capita (partial r = +0.54, P = 0.002). N = 30.
| Happiness | Trust | Gini | Bottom 40% | |
|---|---|---|---|---|
| Institutional Infrastructure | +0.80*** | +0.93*** | −0.69*** | +0.74*** |
| Remediation Load | −0.81*** | −0.85*** | +0.55** | −0.62*** |
| Extractive Dissipation | −0.51** | −0.58*** | +0.69*** | −0.75*** |
| Total overhead (I+R+E) | −0.18 | −0.08 | +0.15 | −0.08 |
| Institutional Ratio | +0.80*** | +0.90*** | −0.75*** | +0.81*** |
Pearson correlations, N = 30. ***P < 0.001, **P < 0.01 (two-tailed). All *** results survive Bonferroni and FDR correction. Total overhead shows no significant association.
In multiple regression predicting happiness from IR and log GDP per capita, both are significant (Table 2). Each 10 percentage point increase in IR is associated with 0.27 higher happiness (0–10 scale), controlling for income. The model explains 74% of variance.
| Predictor | β | s.e. | t | P |
|---|---|---|---|---|
| Intercept | −1.72 | 2.06 | −0.83 | 0.41 |
| Institutional Ratio (pp) | 0.027 | 0.008 | 3.37 | 0.002 |
| log10(GDP per capita) | 1.56 | 0.49 | 3.16 | 0.004 |
N = 30. R² = 0.74. Code: lm(Happiness ~ IR + log10(GDP_pc)).
Given diminishing returns, we ask: how effectively do countries convert resources into flourishing? We define Wellbeing Efficiency as:
WE = Happiness / log10(GDP per capita)
Because log transforms multiplicative growth into an additive scale, WE is interpretable as wellbeing per income doubling (Fig. 2). Finland achieves WE of 1.64; the United States 1.37—a 19% gap. IR is associated with WE (r = +0.68, P < 0.001).
Fig. 2 | Wellbeing Efficiency varies across countries. WE = Happiness / log10(GDP per capita). Finland (1.64) achieves 19% higher efficiency than USA (1.37). Bars coloured by region. N = 30.
A sceptical reader might ask whether 'institutions correlate with trust' is tautological. Our claim is specific: overhead composition is associated with wellbeing, while total overhead is not. The United States spends one-third more on overhead than Denmark (~18% versus ~13.5% GDP), yet Denmark achieves higher wellbeing. What differs is composition: Denmark allocates 63% of overhead to infrastructure versus 25% for the US.
Extractive Dissipation shows moderate negative association with happiness (−0.51, P < 0.01) and strong positive association with inequality (+0.69, P < 0.001; Fig. 3). This is consistent with extraction operating as a transfer mechanism.
Fig. 3 | Extraction is associated with inequality. r = +0.69, P < 0.001. Countries with higher Extractive Dissipation show higher Gini coefficients. N = 30.
Table 3 presents composition for selected countries. Nordic countries allocate 59–63% of overhead to infrastructure; the United States allocates 25%.
| Country | E | I | R | Total | IR | WE | Happy | Trust |
|---|---|---|---|---|---|---|---|---|
| Denmark | 3.2 | 8.5 | 1.8 | 13.5 | 63 | 1.57 | 7.58 | 74% |
| Finland | 3.5 | 8.0 | 2.0 | 13.5 | 59 | 1.64 | 7.74 | 64% |
| Germany | 4.0 | 7.5 | 3.0 | 14.5 | 52 | 1.39 | 6.58 | 45% |
| USA | 9.0 | 4.5 | 4.5 | 18.0 | 25 | 1.37 | 6.73 | 37% |
| Brazil | 5.5 | 3.0 | 5.5 | 14.0 | 21 | 1.47 | 6.27 | 10% |
E, I, R as % GDP. IR in percentage points. Full data in Supplementary Table 1.
Results are robust across specifications (Extended Data Table 1): excluding Nordic countries (r = +0.73); Spearman rank (rs = +0.85); OECD only (r = +0.86, n = 23); log-ratio log(I/(E+R)) (r = +0.80); removing outcome-adjacent terms (r = +0.77). Residual-based efficiency yields consistent findings (r = +0.38, P = 0.04). Non-OECD (n = 7) shows directionally consistent but underpowered association (r = +0.71, P = 0.07).
These findings offer a compositional account of the Easterlin paradox. If GDP growth primarily expands extraction and remediation rather than institutional infrastructure, it would not improve wellbeing. The paradox is not that growth fails but that growth of the wrong composition fails.
The framework maps onto an intuitive heuristic: teach, heal, or steal. Institutional infrastructure builds capacity that compounds. Remediation repairs upstream failures—necessary but not generative. Extraction transfers value without creating it.
We emphasize that our cross-sectional design cannot establish causality. Reverse causation is plausible: high-trust societies may sustain institutions more easily. Omitted variables—culture, history, state capacity—likely confound. We do not claim that IR causes wellbeing; we show that composition is a stronger predictor than income or total overhead. Causal identification is future work.
Limitations include: component estimates rely on proxies with uncertainty (Extended Data Table 3 shows sensitivity); the sample is OECD-weighted (23 of 30); within-category correlations are high (VIF 1.7–6.9), though single-proxy robustness confirms results.
Fig. 1 | Institutional Ratio is associated with happiness. Each point represents one country, coloured by region. Dashed line shows linear fit. r = +0.80 (95% bootstrap CI [0.62, 0.90], 10,000 resamples), P < 0.001. Association remains significant controlling for log GDP per capita (partial r = +0.54, P = 0.002). N = 30.
Fig. 2 | Wellbeing Efficiency varies across countries. WE = Happiness / log10(GDP per capita). Finland (1.64) achieves 19% higher efficiency than USA (1.37). Bars coloured by region. N = 30.
Fig. 3 | Extraction is associated with inequality. r = +0.69, P < 0.001. Countries with higher Extractive Dissipation show higher Gini coefficients. N = 30.
We analysed 30 countries selected ex ante for data availability and regional diversity: 23 OECD members plus Brazil, Costa Rica, India, South Africa, Chile, Mexico and Turkey. The sample is not globally representative; external validity is uncertain for excluded regions.
WE = H / log10(GDPpc), where H is life satisfaction (Cantril ladder, 0–10) from World Happiness Report 20244 and GDPpc is PPP-adjusted 2023 dollars from World Bank5.
Why log? (i) Wellbeing rises with income concavely—early gains matter most—and log captures this. (ii) Log transforms multiplicative growth to additive scale, so WE = wellbeing per income doubling. (iii) Rankings are base-invariant. (iv) Results replicate using residual-based efficiency.
Worked example (Finland): H = 7.74, GDP = $53,000; log10(53000) = 4.724; WE = 7.74/4.724 = 1.64.
Three components, each as % GDP. No outcome (happiness, inequality) appears in any predictor.
Extractive Dissipation (E): E = EMilitary + EFinance + EAdvertising + EHealthcare + EMonopoly. EMilitary = max(Military − 1.5, 0), threshold = OECD median (SIPRI). EFinance = max(Finance − 5.0, 0), threshold = OECD median (BIS). EAdvertising = 0.7 × Advertising (GroupM). EHealthcare = max(Health − OECDmedian, 0) × AdminExcess (OECD). EMonopoly = markup rents7.
Institutional Infrastructure (I): I = ISocial + IHealthAdmin. ISocial = min(SocialSpending, 15), OECD SOCX excluding health. IHealthAdmin = 0.5 × AdminCosts for UHC ≥ 80, else 0.2×8.
Remediation Load (R): R = RNCD + RClimate + RPollution + RMental. RNCD = 0.4 × NCDSpending (WHO preventable). RClimate = disaster costs (Munich Re). RPollution = environmental protection (OECD). RMental = 0.2 × MentalHealth (fixed).
Institutional Ratio: IR = 100 × I/(I+R+E). Worked example (Denmark): E = 3.2, I = 8.5, R = 1.8; IR = 100 × 8.5/13.5 = 63 pp.
Extended Data Table 3 shows ±25% variation in all thresholds yields r = 0.78–0.82.
Pearson/Spearman correlations with 95% bootstrap CIs (10,000 country resamples). Partial correlations control log GDP. OLS regression. Bonferroni and Benjamini–Hochberg FDR. Log-ratio robustness: log(I/(E+R)). R 4.3.2.
Open: World Happiness Report, World Bank, OECD, SIPRI, WHO GBD. Restricted: GroupM (by request); Munich Re (derived values in Supplementary Table 1). Supplementary Table 1 provides all country-level values.
R code in Supplementary Information.
We thank colleagues at the AI+Wellbeing Institute for comments.
J.R. conceived the study, developed the framework and wrote the manuscript. C.D.B. contributed to data analysis, robustness checks and revision.
The authors declare no competing interests.
Supplementary Information is available for this paper.
Correspondence and requests for materials should be addressed to J.R.
Reprints and permissions information is available at www.nature.com/reprints.
| Specification | N | r | P |
|---|---|---|---|
| Full sample (Pearson) | 30 | +0.80 | <0.001 |
| Full sample (Spearman) | 30 | +0.85 | <0.001 |
| Excluding Nordic | 26 | +0.73 | <0.001 |
| OECD only | 23 | +0.86 | <0.001 |
| Non-OECD | 7 | +0.71 | 0.07 |
| Log-ratio | 30 | +0.80 | <0.001 |
| Residual WE | 30 | +0.38 | 0.04 |
| No outcome terms | 30 | +0.77 | <0.001 |
| Model | IR β | GovEff β | R² |
|---|---|---|---|
| Happiness ~ IR + logGDP | 0.027** | — | 0.74 |
| Happiness ~ IR + logGDP + GovEff | 0.024* | 0.18 | 0.75 |
| Happiness ~ GovEff + logGDP | — | 0.89*** | 0.71 |
| Parameter | Base | Range | r range |
|---|---|---|---|
| Military baseline | 1.5% | 1.0–2.0% | 0.78–0.82 |
| Finance baseline | 5.0% | 4.0–6.0% | 0.79–0.81 |
| Ad non-informational | 70% | 50–90% | 0.79–0.81 |
| SOCX cap | 15% | 12–18% | 0.78–0.82 |
| UHC threshold | 80 | 70–90 | 0.79–0.81 |
| NCD preventable | 40% | 30–50% | 0.79–0.81 |