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The determinants of health status in Sub-Saharan Africa (SSA).

Article Excerpt
I. Introduction

The health capital of nations serves both as an important means and a basic end in efforts aimed at improving human welfare. Consequently, development specialists and policy formulators have focused their attention on a viable and efficient mechanism for improving the health status of society, (Schultz, 2004). Over the years, such efforts have produced impressive results in many regions of the world.

Although some African development specialists and policy makers have also undertaken important measures for improving the quality of life for their citizens, the health status of SSA is still considerably low and exists below that of most parts of the world. Low life expectancy at birth, high infant and maternal mortality rates, malaria and tuberculosis afflictions, and the HIV/AIDS pandemic are some of the unique images of the health status of the African content. The World Bank (2002) data set shows that a new infant born in SSA has only 42 expected life-years to live. If the same infant were born in high-income countries of the world during the same period, however, it would have expected 70 years to live. The high-income groups aside, the infant would have 46 expected years had it been from other low-income countries. Not only is the level of expected life disappointing, but also its dynamics is equally alarming. In low and middle-income countries, the average life expectancy at birth has improved from about 13 to 15 life-years from the 1960s to the 1990s, respectively; in SSA, however, it has only changed by about 7 life-years during the same period. This change is also far below the world average of about 11 years.

A similar phenomenon can also be observed from other indicators. In the 1960s, the average number of infants dying before reaching one year of age per 1,000 live births was estimated to be 154, while it was only about 27 in high-income countries. From the 1960s to 1990s, the high income and middle income countries have reduced this figure by about 77 and 65 percent, respectively, while SSA only reduced infant mortality by only 38 percent which is also below the world's average of almost 50 percent. The intended progress might have been hampered by different socioeconomic, political, and environmental factors. This study, nonetheless, perceives that the health status of SSA can be substantially improved despite the prevailing distressing health records.

This paper estimates the determinants of health status for the region based on the Grossman (1972) theoretical model. The model treats economic, social, and environmental factors as inputs of the production system. The major advantages of identifying the determinants are that estimates of the over-all effect of medical care utilization on the health status of the population can be obtained (Thornton, 2002). Policy makers and practitioners can use the above information in their search for cost effective mechanisms for providing health services and the reallocation of health resources in such a manner that the gains from health spending could be optimized.

The remaining sections of the paper are organized as follows. The next section outlines the framework, hypotheses, and data. Section three describes the empirical method derived from the Grossman (1972) theoretical model. The econometric results and interpretations are given in section four. The last section summarizes and draws conclusions based on the results.

II. The Framework, Hypotheses, and Data

To determine responsiveness of the health status of SSA to the economic, social, and environmental factors, we specify a double log-linear Cobb-Douglas production function based on the Grossman (1972) model as:

Lnh = ln[OMEGA] + [SIGMA][[alpha].sub.i] (ln[y.sub.i]) + [SIGMA][[beta].sub.j] (ln[s.sub.j]) + [SIGMA][[gamma].sub.k](ln[v.sub.k]), (1)

Where Lnh is the natural log of individual's health status measured by life expectancy at birth, [y.sub.i] is a vector of per capita economic variables, [s.sub.j] is a vector of per capita social variables, [v.sub.k] is a vector of per capita environmental factors, and [y.sub.i], [s.sub.j], and [v.sub.k] are...

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