The Determinants of Health Status: Evidence from Asian Developing Countries

The paper estimates a health production function for Asian developing countries based on the Grossman (1972) theoretical model that treats social, economic, and environmental factors as inputs of the production system. In estimating this function, socioeconomic and environmental factors such as income per capita, literacy rate, food availability, health expenditure, health services, urbanization rate, population, and carbon dioxide emission are specified as determinants of health status. The parameters of the function are estimated by one-way and two-way fixed and random effects model of panel data analyses. The results of the one-way fixed effect model suggest that an increase in GDP per capita, food availability and literacy rate, and decrease in carbon dioxide emissions are strongly associated with an improvement in life expectancy at birth. Overall, the results imply that a health policy may focus on the provision of health services and environmental aspects may do little to improve the current health status of the region.


Introduction
Health is now universally regarded as an important index of human development. Ill health is both the cause and effect of poverty, illiteracy and ignorance. Policies of human development not only raise the income of the people but also improve other components of their standard of living, such as life expectancy, health, literacy, knowledge and control over their destiny. While it is true that health is not everything, it is also true that without health, everything else is nothing. An analysis of health status is an important aspect of human resource development. Improvements in health do not only improve the productivity of the labor force, but they also help to improve the impact of other forms of human capital formation, e.g.
education. In most developing countries health status is difficult to determine as the question arises as to what measures should be used as indicators of health status. However, the improvement in health status can be observed most obviously from increases in life expectancy which is a better measure for cross country comparison than age and disease-specific mortality or morbidity, which are more difficult to compare at the international level. The remaining sections of the paper are organized as follows. The next sections outline literature review and empirical framework derived from the Grossman (1972) theoretical model and then describes data and the econometric methods to be followed in the estimation process. The last two sections will present and interpret the results and draw some conclusions.

Literature Review
In most empirical studies, per capita income, adult literacy, nutritional status and the availability of health services are included as important determinants of health status and per capita income and adult literacy are significant determinants of life expectancy in less developed countries but nutritional status is not statistically significant [1][2][3][4][5][6][7]. The study by Grosse and Perry [8]  Socioeconomic factors have also long been considered as important determinants of health outcomes, which are now widely known as "social determinants of health" (SDH) [9] . Reviewing both experimental and observational studies, it is concluded that there is abundant evidence at both the microeconomic and macroeconomic levels showing that a variety of health indicators are positively associated with different dimensions of economic prosperity and the causal pathways linking health and economic outcomes run in both directions and health capital has a positive impact on aggregate economic output [10]. According to the analysis, about one-fourth of economic growth was attributable to health capital accumulation, and health condition equivalent to one additional year of life expectancy is correlated with higher economic growth of up to 4% per year.

The Framework
Grossman (1972)1 developed a theoretical health production function, which can be specified as: where H is a measure of individual health output and X is a vector of individual inputs to the health production function F. The elements of the vector include: nutrient intake, income, consumption of public goods, education, time devoted to health related procedures, initial individual endowments like genetic makeup, and community endowments such as the environment.
This theoretical model was designed for analysis of health production at micro level. The interest here is, however, to analyze the production system at macro level. To switch from micro to macro analysis, without losing the theoretical ground, the elements of the vector X were represented by per capita variables and regrouped into sub-sectoral vectors of economic, social, and environmental factors as: Where Y is a vector of per capita economic variables, S is a vector of per capita social variables, and V is a vector of per capita environmental factors. In its scalar form, Equation 2 can be rewritten as ( )  (4) and rearranging it yields: where i = 1,2,3; j = 1,2,3; and k = 1,2 and Ω is an estimate of the initial health stock of the region.

Variables
For the purpose of this study life expectancy at birth is used as health status indicators. GDP is per capita gross domestic product.
The growth of GDP is expected to improve health status in a country.
As incomes grow households can spend more money on looking after their health. Health expenditure per capita is expected to have a positive influence on health status. This variable has important implications for policy purposes as well. Improvements in food availability are expected to improve the nutritional status of a nation, and consequently the health status. However, the effect may be very strong at the initial changes in food availability and then may be slower after a certain level of nutrition has been achieved. Education (literacy) is expected to have a positive effect on health.
However, it is difficult to say that its coefficient will reveal the net effect of changes in human capital as it may be correlated with the distribution of wealth. The availability of physicians indicates the availability of health services. If physician increases, it implies that the price of health services is rising, and the health status will deteriorate and vice versa. Population variable is introduced the in the function to correct the food availability index. Keeping all else constant, the larger population size, the lesser the food availability; hence, we expect a negative coefficient for the population variable.

Estimation Method
For estimation of the parameters under consideration, a panel data analytic approach is employed. An econometric model is specified for Equation 5 in its general form. In order to provide an empirical exposition of the model, the specification is given as follows: (6) where h*(g, t) is natural logarithm of life expectancy in country g at year t, and X*(g, t) is vector of explanatory variables (y 1 ,y 2 ,y 3 , s 1 , s 2 , s 3 , v 1 , v 2 ) for g = 1,2,..., m (number of countries), t = 1,2,..., T (number of years), Ф is vertical vector of parameters (α1, α2, α3, β1, β2, β3, γ1, γ2); Ψ (g, t) is a classical stochastic disturbance term  Table 2 suggests that a One-Way error component regression model is superior to Two-Way. Next, for the choice between random effects (GLS estimator) and fixed effect estimator, a Hausman test is performed. This implies that the preferable estimates of the parameters in Equation 6 can be given by one-way fixed effect model.   Table 2    So, there is also some indication of harmful effects from carbon dioxide emission.

Discussion
The study has estimated a health production function for

Conclusion
The main conclusion of this study is that GDP per capita, food