Definition
The Preston Curve is a cross-country scatter plot showing life expectancy (LE) on the vertical axis against per capita income on the horizontal axis, first constructed by Samuel Preston (1975). At any given point in time, richer countries have longer life expectancy. But the entire curve has shifted upward dramatically over the 20th century — the same income level supports much longer life in 1990 than in 1930. Preston's own estimate was that only 10–25% of the mid-20th century cross-country LE improvement could be attributed to income growth; the remainder reflected autonomous improvements from public health knowledge and technology. The upward shift, not the slope, is the central empirical regularity and the basis for the claim that knowledge and technology — not income — are the ultimate determinants of mortality.
Key Ideas
- Cross-sectional income-LE association: At any given year, richer countries have longer life expectancy. The curve is concave — each additional dollar of income yields diminishing LE gains as countries grow richer. This slope is the foundation of the "wealthier is healthier" claim (Pritchett and Summers 1996).
- Upward shift over time: The entire curve has shifted upward substantially from 1930 to 1960 to 2000. A country at income level X in 2000 had far longer LE than a country at the same X in 1930. This shift is not explained by income growth within the period; it reflects technology and knowledge diffusion.
- Null in time-series regressions: Cross-country panel regressions tracking changes in income vs. changes in LE over 10–40 year periods find near-zero correlation. Income growth explains only ~10–15% of LE improvements; the remainder is accounted for by technology transfer, public health investment, and institutional factors independent of income.
- The cross-sectional slope is not causal: The observed income-LE association conflates (a) income → health (nutrition, healthcare access), (b) health → income (reverse causality: sick people earn less), and (c) common confounders (stable institutions that produce both growth and good public health). True causal income effects are smaller than the cross-sectional correlation implies.
- Technology diffusion mechanism: Public health knowledge — germ theory, oral rehydration therapy, vaccination protocols, drug regimens — can be transmitted to low-income countries rapidly and cheaply, explaining why poor countries today have LE far above what their position on the pre-1950 curve would predict.
How It Works
Preston (1975) pooled cross-country income-LE observations from multiple time points, fit a concave curve for each period, and measured the upward shift over time. The fraction of LE improvement attributable to income is the curve slope × average income change; the remainder is the "shift" term, attributable to exogenous technology and public health advances.
Cutler, Deaton, and Lleras-Muney (2006) synthesize post-Preston evidence: income elasticity of LE ≈ 0.10–0.15 in cross-country long-run growth regressions, and even this modest estimate is an overstatement of the causal income effect because of health-to-income reverse causality and institutional confounders.
The three phases of mortality decline documented by Cutler et al. — (1) improved nutrition, (2) macro public health, (3) post-1930s medicine — each produced upward shifts in the Preston Curve without necessarily requiring income growth first. Water purification alone (Cutler and Miller 2005) accounts for ~50% of US urban mortality reduction in the first third of the 20th century at low additional cost.
Why It Matters
- Policy for developing countries: If income were the driver, growth-first strategies (trade liberalization, macroeconomic stabilization) would be the priority. If technology and institutional capacity are the drivers, direct public health investment can succeed even at low income levels — as demonstrated by India and China's periods of rapid health improvement at moderate income levels and by the failures of growth without deliberate health policy.
- Interpretation of cross-country health data: The Preston Curve warns against using cross-sectional income-LE associations to infer causal income effects. The same LE level can be achieved through very different combinations of income, public health infrastructure, and institutional quality.
- Within-country gradient distinction: The Preston Curve is a cross-country relationship; the income-mortality gradient within rich countries (Waldron 2007, Chetty et al. 2016) is a within-country relationship. Both show income-LE associations, but they operate through different mechanisms and have different policy implications. See Income-Mortality Gradient.
- Demographic transition context: The upward shift of the Preston Curve maps directly onto the worldwide convergence of mortality rates documented in the Demographic Transition — the same technology diffusion story, seen at the aggregate level.
Open Questions
- Does the curve continue to shift upward at the same rate, or is the shift slowing as cheap, easily diffusible health technologies (vaccines, water treatment, oral rehydration) are exhausted? Future shifts may require more expensive, institution-intensive innovations.
- Is there an income threshold below which additional income does not translate into LE gains regardless of technology access?
- How much of the remaining cross-sectional slope reflects genuine causal income effects vs. reverse causality (health → income) and institutional confounders? The causal share is likely below 15% but has not been definitively estimated.
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