Preston curve

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. 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, vaccinations, improved provision of public health services, oral rehydration therapy and better nutrition with these exogenous improvements in health. 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. 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. If the relationship is estimated with nonparametric regression then it produces a version of the curve which has a "hinge" – i.e. a kink in the relationship where the slope of the regression equation falls off significantly. This point occurs around the per capita income level of $2,045 (data for the year 2000) which is about the per capita income level of India. This level of income is generally associated with a crossing of a "epidemiological transition", where countries change from having most of their mortality occur due to infant mortality to that due to old age mortality, and from prevalence of infectious diseases to that of chronic diseases. ==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. According to these authors, in 1990 better economic performance could have prevented more than half a million child deaths worldwide. Preston's work has also contributed to the broadening of the definition of economic development. In the same work, Becker et al. also found that while cross-country incomes have diverged, the distribution of health has converged. ==Criticisms and shortcomings==

Much of Angus Deaton's The Great Escape: Health, Wealth, and the Origins of Inequality is concerned with thinking about the meaning and implications of the curve. 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 time series and longitudinal data within individual countries. In particular, per capita incomes between countries have generally diverged over time, while life expectancies, and other health indicators such as the infant mortality rates, have converged (this trend was interrupted in the 1990s with the outbreak of the 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. Likewise there is evidence that more healthy individuals save more and thus contribute to the faster accumulation of physical capital of an economy. 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. ==References==