This brief note presents a preview of a study we are conducting on the reasons why countries have opted for different levels of health spending. We follow a model analyzed in Becker, Phillipson and Soares, and Hall and Jones. In this model the countries have a utility function that depends on the consumption and health status.
The state of health is a function of spending on health care. It is assumed that is determined by a Cobb-Douglas production function, implying that not all countries produce the same health care with the same health spending. We use the inverse of the infant mortality rate as a measure of the health status. The functional form of the equation involves diminishing returns if the parameter is lower than one, and increasing if it is larger than one.
Once calculated the production functions and optimized the utility functions we can calculate the income elasticity of spending on health, this is, how much the country decided to spend on health given their income level, the objective of the research.
In this first deliverable we show preliminary estimates of the production functions and we make some thoughts on the major findings. For this, we required annual data of the countries from 1995 to 2006, period for which there is information of health status and total expenditure. The coefficients of the production function (as well as the standard deviation) are shown in the chart, where the countries are sorted from lowest to highest state of health.
In general the result shows what has already been well documented: that LAC countries have a low productivity of health spending, as well as the United States. However, there is one aspect to be noted: Peru and Chile are the only countries that have a coefficient greater than one, that is, they made efficient use of health resources, at least in this period. In the case of Peru it is explained because there was a significant reduction in the infant mortality rate between 1996 and 2006 from 43 to 21 deaths per one thousand live births (more than 51%). This was achieved with improvements in health service coverage in rural areas, which helped reduce the gap that long existed between urban and rural indicators.
Chile’s case is similar, as this also shows a reduction in child mortality linked to improvements in health interventions and socio-economics conditions. In 2000, infant mortality dropped to 8.9 per 1,000 live births.
The results show that you can reduce infant morality through public health programs, but also with policies on other fronts to generate synergies with health policies, for example, education, sewage and firm floor.
Finally, we can not abstract us from the fact that simple analysis like this one has limitations: i) the infant morality rate is not an indicator of the full health status. For example, it is found that when using other indicators, access to expensive treatments, for example, the United States is better assessed; ii) the fact that we were unable to get data prior to 1995, implies that we are only capturing recent facts.
The state of health is a function of spending on health care. It is assumed that is determined by a Cobb-Douglas production function, implying that not all countries produce the same health care with the same health spending. We use the inverse of the infant mortality rate as a measure of the health status. The functional form of the equation involves diminishing returns if the parameter is lower than one, and increasing if it is larger than one.
Once calculated the production functions and optimized the utility functions we can calculate the income elasticity of spending on health, this is, how much the country decided to spend on health given their income level, the objective of the research.
In this first deliverable we show preliminary estimates of the production functions and we make some thoughts on the major findings. For this, we required annual data of the countries from 1995 to 2006, period for which there is information of health status and total expenditure. The coefficients of the production function (as well as the standard deviation) are shown in the chart, where the countries are sorted from lowest to highest state of health.
In general the result shows what has already been well documented: that LAC countries have a low productivity of health spending, as well as the United States. However, there is one aspect to be noted: Peru and Chile are the only countries that have a coefficient greater than one, that is, they made efficient use of health resources, at least in this period. In the case of Peru it is explained because there was a significant reduction in the infant mortality rate between 1996 and 2006 from 43 to 21 deaths per one thousand live births (more than 51%). This was achieved with improvements in health service coverage in rural areas, which helped reduce the gap that long existed between urban and rural indicators.
Chile’s case is similar, as this also shows a reduction in child mortality linked to improvements in health interventions and socio-economics conditions. In 2000, infant mortality dropped to 8.9 per 1,000 live births.
The results show that you can reduce infant morality through public health programs, but also with policies on other fronts to generate synergies with health policies, for example, education, sewage and firm floor.
Finally, we can not abstract us from the fact that simple analysis like this one has limitations: i) the infant morality rate is not an indicator of the full health status. For example, it is found that when using other indicators, access to expensive treatments, for example, the United States is better assessed; ii) the fact that we were unable to get data prior to 1995, implies that we are only capturing recent facts.
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