In China the annualized growth rate of GDP is negatively correlated with the annualized rate of reduction of the infant mortality rate (correlation coefficient −0.45, _t_ statistic), while in India the correlation is positive (correlation coefficient 0.77, _t_ statistic). As Deaton (2006) notes, in China the largest gains in health _preceded_ the takeoff in economic growth.

The data from India are perhaps more ambiguous: during that country’s period of relatively slow economic growth from 1965 to 1985, the correlation between changes in income and health was tight, but in more recent years, as economic growth has taken off, the rate of improvement in the infant mortality rate has fallen off.

# **_Interpreting Correlations between Health and Income: Data and Estimation Issues_**

Research on the links between health and growth are fraught with measurement problems from the selection of variables and the validity of those measures to the econometric problems common where there is reverse causality. Creative solutions to these challenges have met with mixed results, but from a policy perspective the bottom line is a tenuous link between health and growth at the macroeconomic level. This section reviews the measurement issues, the analytic constraints, and alternative options for capturing the correlations between health and income.

# _Limitations of Aggregate Measures of Health and Income_

Although relationships between aggregate measures of health and income can be informative, they have some limitations because both indicators are summary statistics of complex, multidimensional assessments of human activity and well-being.

Measuring “health” is tricky, and no measure aptly captures morbidity and mortality (Schultz 2005). In particular, the use of life expectancy or infant and child mortality rates as measures of health status is not without ambiguity, for both conceptual and practical reasons. First, these indicators attempt to measure aspects of health that might be related to productivity, including the extent to which individuals experience, or are at risk of, bad health, encompassing both morbidity and premature death. For example, in using life expectancy in cross-country analysis, we place too much weight on infant mortality, while that measure itself is an _imputed_ variable in most contexts. Mortality is also a one-time event and remains rare even in high mortality settings. Despite the heavy reliance on mortality statistics to measure health, for all these reasons mortality is a suboptimal measure of “health.” Second, at a practical level, accurate measures of life expectancy require good vital registration data, particularly on deaths. In many developing countries, these data simply do not exist, and estimates of life expectancy are based on child mortality rates, using standard life tables to impute infant mortality levels (adjusting for guesses about mortality risks in the population where necessary). While the cross-country pattern of life expectancy _levels_ is likely to be reasonably accurate, data on _changes_ in life expectancy may well embody large errors, due to the variety of (unmeasured) causes of such changes.

Third, interventions that affect morbidity but not mortality may well have important effects on productivity that will not be attributed to changes in health status if the latter are measured by 11