Preston curve

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international dollars, the y-axis shows life expectancy
at birth. Each dot represents a particular country.
Data points of income per head and life-expectancy of individual countries

The Preston curve is an

empirical cross-sectional relationship between life expectancy and real per capita income. It is named after Samuel H. Preston who first described it in 1975.[1][2] Preston studied the relationship for the 1900s, 1930s and the 1960s and found it held for each of the three decades. More recent work has updated this research.[3]

The relationship between life expectancy and income

The Preston curve indicates that individuals born in richer countries, on average, can expect to live longer than those born in poor countries. However, the link between income and life expectancy flattens out. This means that at low levels of per capita income, further increases in income are associated with large gains in life expectancy, but at high levels of income, increased income has little associated change in life expectancy. In other words, if the relationship is interpreted as being causal, then there are diminishing returns to income in terms of life expectancy.[4]

Improvements in health technology shift the Preston Curve upwards. In panel A, the new technology is equally applicable in all countries regardless of their level of income. In panel B, the new technology has a disproportionately larger effect in rich countries. In panel C, poorer countries benefit more.

A further significant finding of Preston's study was that the curve has shifted upwards during the 20th century. This means that life expectancy has increased in most countries, independently of changes in income. Preston credited education, better technology,

HIV/AIDS epidemic, even if their per capita incomes have increased during this time.[4]

Overall Preston found that improvements in health technology (the upwards shifts in the curve) accounted for 75% to 90% of the increase in life expectancy, while income growth (movement along the curve) was responsible for the rest.[5]

Analysis of more recent data, for example by Michael Spence and Maureen Lewis, suggests that the "fit" of the relationship has become stronger in the decades since Preston's study.[6] Though the source of income growth, rather than growth itself has been shown to be significant, with Ryan Edwards finding divergences from the Preston Curve partially explained by the size of the mining sector (a mining dominated economy).[7]

While the relationship between income and life expectancy is log linear on average, any one individual country can lie above or below curve. Those below the curve, such as South Africa or Zimbabwe, have life expectancy levels that are lower than would be predicted based on per capita income alone. Countries above the curve, such as Tajikistan, have life expectancies that are exceptionally high given their level of economic development.[5] In 2000, the USA lay just below the curve, indicating that it had a slightly lower life expectancy than other rich countries.[8]

If the relationship is estimated with

chronic diseases.[8]

Implications

The fact that the relationship between income and health is concave indicate that a transfer of income from the rich to the poor might increase the average health of a society.[3] This policy prescription will have this effect only if the relationship between income and health is causal – i.e. if higher income causes longer life expectancy (see below). If the relationship is driven by other factors, if it is spurious, or if it is in fact health that leads to higher income, then this policy outcome will no longer be true.[3]

The existence of the Preston curve has been used by

Larry Summers to argue that poor countries should focus on economic growth, and that health improvements will come about spontaneously as a result of increases in income.[9] According to these authors, in 1990 better economic performance could have prevented more than half a million child deaths worldwide.[9] However, the upward shifts of the Preston curve still imply that the main portion of gains in life expectancy has come about as a result of improved health technology rather than just increases in per capita income.[3][5] Preston did, however, acknowledge that in the poorest countries economic growth may be necessary for improvements in health, as even the most inexpensive technologies have a cost of adoption that poor countries may not be able to afford.[10]

Preston's work has also contributed to the broadening of the definition of economic development.[3] Gary Becker et al. have included longevity in a more general welfare measure and have illustrated that increases in life expectancy have made up a large portion of increases in overall global welfare since the 1960s.[11] In the same work, Becker et al. also found that while cross-country incomes have diverged, the distribution of health has converged.[11]

Criticisms and shortcomings

Lack of longitudinal evidence

The Preston curve is a relationship found in cross-country data - that is, it holds for a sample of countries taken at a particular point in time. Some research however suggests that a similar relationship does not hold in

AIDS epidemic in Sub-Saharan Africa). This suggests that over time changes in income may have no impact on health or even be negatively related.[6]

Causality

A further limitation of the

tropical zones.[13]

The problem of reverse causality between health and income means that any estimates of the impact of income on life expectancy could mistakenly reflect the influence of life expectancy (more generically, health) on income instead. As such, studies which do not account for this potential two-way causation may overestimate the importance of income for life expectancy. In economic research, this kind of problem has traditionally been dealt with through the use of

instrumental variables which allow the researcher to separate out one effect from another.[9] This strategy requires identification of an "instrument" – i.e. a variable which correlates with per capita income but not with the error term in the linear regression. However, since any variable which is likely to correlate with income is also likely to correlate strongly with health and life expectancy this is a difficult task. Some research suggests that in low and middle-income countries, the causality does indeed go from income to health, while the opposite is true for rich countries.[14]

References

  1. PMID 14758412
    .
  2. PMID 17550952
    .
  3. ^
    PMID 17550948
    .
  4. ^
    ISBN 978-0-444-53100-1
    .
  5. ^
    ISBN 978-0-387-70811-9
    .
  6. ^
    ISBN 978-0-8213-7659-1. {{cite book}}: |author1= has generic name (help)CS1 maint: multiple names: authors list (link
    )
  7. doi:10.1016/j.worlddev.2015.10.013
    .
  8. ^ a b Anne Case and Angus Deaton, "Health and wellbeing in Udaipur and South Africa", 2006, pg. 4, pg. 39
  9. ^
    JSTOR 146149
    .
  10. ^ Angus Deaton, "Health in the age of globalization", 2004
  11. ^
    S2CID 12760521. Archived from the original
    (PDF) on 2009-10-10. Retrieved 2010-01-08.
  12. S2CID 618837
    .
  13. ^ United Nations Millennium Project, 2006
  14. ^ Erkan Erdil, Hakan Yetkiner, "A Panel Data Approach for Income-Health Causality"

External links

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