Health and Growth: Causality through Education

Purpose--The paper theoretically and empirically investigates the impact on human capital investment decisions and income growth of lowered life expectancy as a result of HIV/AIDS and other diseases. Design/methodology/approach--The theoretical model is a three-period overlapping generations model where individuals go through three stages in their life, namely, young, adult and old. The model extends existing theoretical models by allowing the probability of premature death to differ for individuals at different life stage, and by allowing for stochastic technological advances. The empirical investigation focuses on the effect of HIV/AIDS on life expectancy and on the role of health on educational investments and growth. We address potential endogeneity by using various strategies, such as controlling for country specific time-invariant unobservables and by using the male circumcision rate as an instrumental variable for HIV/AIDS prevalence. Findings--We show theoretically that an increased probability of premature death leads to less investment in human capital, and consequently slower growth. Empirically we show that HIV/AIDS has resulted in a substantial decline in life expectancy in African countries and these falling life expectancies are indeed associated with lower educational attainment and slower economic growth world wide. Originality/value--The theoretical and empirical findings reveal a causal link flowing from health to growth, which has been largely overlooked by the existing literature. The main implication is that health investments, that decrease the incidence of diseases like HIV/AIDS resulting on increases in life expectancy, through its complementarity with human capital investments lead to long run growth..

Improved Regression Estimation of a Multivariate Relationship with Population Data on the Bivariate Relationship

Regression coefficients specify the partial effect of a regressor on the dependent variable. Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data. We show here that such population- level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data. The method of constrained MLE is used to achieve these improvements. Its statistical properties are first described. The method constrains the weighted sum of all the covariate-specific associations (partial effects) of the regressors on the dependent variable to equal the overall association of one or more regressors, where the latter is known exactly from the population data. We refer to those regressors whose bivariate or limited multivariate relationships with the dependent variable are constrained by population data as being ‘‘directly constrained.’’ Our study investigates the improvements in the estimation of directly constrained variables as well as the improvements in the estimation of other regressor variables that may be correlated with the directly constrained variables, and thus ‘‘indirectly constrained’’ by the population data. The example application is to the marital fertility of black versus white women. The difference between white and black women’s rates of marital fertility, available from population-level data, gives the overall association of race with fertility. We show that the constrained MLE technique both provides a far more powerful statistical test of the partial effect of being black and purges the test of a bias that would otherwise distort the estimated magnitude of this effect. We find only trivial reductions, however, in the standard errors of the parameters for indirectly constrained regressors.