An analysis of the effect of the inequality of income to the inequality of health: Using Panel Analysis of the OECD Health data from 1980 to 2013
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This study aims to analyze panel data using OECD Health data of 34 years to examine how significant the inequality of income is to the inequality of health. The data was from OECD’s pooled Health data of 32 countries from 1980 to 2013. The process of determining analysis model was as follows; First, through the descriptive statistics, we examined averages and standard deviation of variables. Second, Lagrange multiplier test has done. Third, through the F-test, we compared Least squares method and Fixed effect model. Lastly, by Hausman test, we determined proper model and examined effective factor using the model. As a result, rather than Pooled OLS Model, Fixed Effect Model was shown as effective in order to consider the characteristics of individual in the panel. The results are as follows: First, as relative poverty rate(β=-19.264, p<.01) grows, people’s life expectancy decreases. Second, as the rate of smoking(β=-.125, p<.05) and the rate of unemployment (β=-.081, p<.01) grows, people’s life expectancy decreases. Third, as health expenditure(β=.414, p<.01) shares more amount of GDP and as the number of hospital beds(β=-.190, p<.05) grows, people’s life expectancy increases.
Abstract
This study aims to analyze panel data using OECD Health data of 34 years to examine how significant the inequality of income is to the inequality of health. The data was from OECD’s pooled Health data of 32 countries from 1980 to 2013. The process of determining analysis model was as follows; First, through the descriptive statistics, we examined averages and standard deviation of variables. Second, Lagrange multiplier test has done. Third, through the F-test, we compared Least squares method and Fixed effect model. Lastly, by Hausman test, we determined proper model and examined effective factor using the model. As a result, rather than Pooled OLS Model, Fixed Effect Model was shown as effective in order to consider the characteristics of individual in the panel. The results are as follows: First, as relative poverty rate(β=-19.264, p<.01) grows, people’s life expectancy decreases. Second, as the rate of smoking(β=-.125, p<.05) and the rate of unemployment (β=-.081, p<.01) grows, people’s life expectancy decreases. Third, as health expenditure(β=.414, p<.01) shares more amount of GDP and as the number of hospital beds(β=-.190, p<.05) grows, people’s life expectancy increases.
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