Like every Friday, from Raj Nallari and Breda Griffith's lecture notes on Economic Policies for Poverty Reduction.

Evidence from cross-country growth regressions suggests that improvements in health make a large contribution to economic growth, and the initial health of a population has been identified as one of the most robust factors contributing to economic growth as economies adjust over time to their steady-state output level when growth then begins to slow.  Bloom, Canning and Sevilla (2004) found that one extra year of life expectancy raised steady-state GDP per capita by about 4 percent (Bloom, Canning and Jamison, 2004). Each 10 percent improvement in life expectancy at birth is associated with a rise in economic growth of between 0.3 and 0.4 percentage points per year, all other growth factors held constant. This means that the difference in annual growth between a rich country, where life expectancy at birth is around 77 years on average, and a typical poor country, 49 years on average, is about 1.6 percentage points a year that, over time, adds to a substantial effect.  Thus, health status explains part of the difference in growth rates among rich and poor countries, even after controlling for other macroeconomic variables.  Furthermore, Bloom and Sachs (1998) showed that more than half of the growth disparity between Africa and East Asia was statistically explained by disease burden, demography and geography.

A virtuous cycle characterizes the health-growth relationship. Improvements in health increase economic growth that in turn facilitate further health improvements.  This pattern of cumulative causation increases for a time before diminishing returns to health set in as demographics take over and the population ages.   This non-linear relationship was also identified by Preston in 1975.

Preston (1975), in examining the cross-country relationship between average income and increases in life expectancy, found that increases in average income among poor countries are strongly associated with increases in life expectancy. As income per head rises, the relationship flattens out. Thus for rich countries, the relationship is weak or absent.  If this nonlinear relationship also holds within countries, then we would expect that the more equal a country is in terms of income per head, the higher average life-expectancy and transfers from rich to poor should increase overall average life expectancy. Deaton (2001) finds that the effect of income on reducing the probability of death at the bottom of the income distribution is much greater than its effect at the top of the distribution. Thus, a redistribution of income, even without an increase in average income should bring about an improvement in average health.  Similarly, among poor countries, a redistribution of income to poorer countries within the group should improve infant and child mortality near the bottom of the distribution.  However, as noted by Deaton (2001), average  income matters more than income inequality  for population health in poor countries.

However, focusing solely on GDP per capita as a measure of a country’s economic performance misses the fact that health indicators vary widely for the same income level.  A more accurate measure is the concept of “full income” “that captures the value of changes in life expectancy by including them in a measure of economic welfare” (Bloom, Canning and Jamison, 2005, p.12).  A proxy for full income is the ‘value of a statistical life’, i.e. the willingness to pay to avoid risks and is defined as the observed amount required accepting a risk divided by the level of the risk.  Bloom, Canning and Jamison give the example of a worker who demands and gets US$500 extra a year to accept a more risky but similar job where the increase in the mortality rate is 1 in 10,000. Thus, the VSL is (US$500/1/10,000) = US$5,000,000.  Based on research by Viscusi and Aldy (2003), a country’s range of values for VSL lies between 100 to 200 times its GDP per capita. VSLs for richer countries are more likely to lie nearer to 200 given that the willingness to pay to avoid risks increases with income.

Bloom, Canning and Jamison (2005) estimate full income for Africa in an attempt to measure the impact of AIDS on full income.  Even though life expectancy in sub-Saharan Africa has declined to 46 years and almost 21 percent of deaths were directly attributable to AIDS (numbers are for 2001), little impact was found on GDP per capita.  This does not preclude GDP per capita decreasing in the long-run as education and savings rates may fall because of high mortality rates. Two studies undertaken respectively for the WHO and the IMF both concluded “that the AIDS epidemic in the 1990s had far more adverse economic consequences than its effects on per capita GDP would suggest” (Bloom, Canning and Jameison, 2004, p. 13).  Using the change in GDP per capita and the value of changes in mortality rates  i.e. by calculating the impact of AIDS on mortality rates  as a measure of full income, the authors suggest that income declined by 1.7 percent a year from 1990 to 2000, ‘far higher than existing estimates of the effect of AIDS on GDP’ (Bloom, Canning and Jamison, 2004, p. 13).  Furthermore, improvements in adult health prior to 1990 suggested larger economic benefits relative to changes in GDP per capita. The exhibit below shows that when comparing full income with GDP per head, Kenya’s economic performance before 1990 was significantly underestimated and overestimated since then.

Comparison of Full-Income and GDP per head for Kenya, 1960-2000

                Source: Bloom, Canning and Jamison, 2004