Geography, Institutions and Human Development: A Cross-Country Investigation Using Bayesian Model Averaging

This paper examines the role of long standing institutions – identified through geography, disease ecology, colonial legacy, and some direct measures of political and economic governance – on human development and its non income components across countries. The study employs a novel econometric technique called the Bayesian Model Averaging that allows us to select the relevant predictors by experimenting with a host of competing sets of variables. It constructs estimates as weighted average of OLS estimates for every possible combination of included variables. This is particularly useful in situations when there is model uncertainty and theory provides only a weak guidance on the selection of appropriate predictors. Of the 25 variables that we tried, three stand out in terms of their degree of importance and their robustness across various specifications. These include malaria ecology, KKZ index of good governance and fertility rate. Our finding on the dominant and robust role of malaria ecology in explaining differences in human development across countries, even in the presence of variables that directly and indirectly measure the quality of institutions, is extremely fascinating. It shows that malaria ecology has a direct negative impact on human development and this effect appears to be over and above its effect via institutions. Some of the other measures of climate and geography as well as those of colonial legacy are important as long as we do not control for some direct measures of the performance of political and economic institutions such as the KKZ index of good governance and democracy score. Once we control for these and other conditioning variables such as public spending on health and education; fertility rates; and measures of health infrastructure, the importance of geography and colonial legacy disappears.