This study is an empiriacl analysis of effects of government intervention on the health care delivery system in Korea. The purposes of this study are to find out the effects of government intervention on the per capita national health expenditure(per capita NHE), crude mortality rate(CMR), and institutional efficiency. Here, the institutional efficiency is defined as a formula shown below: log$frac{100-curde mortality rate }{per capita NHE}$$ imes$100. The formula indicates that the instiutional efficiency increases if the CMR and/or per capita NHE goes down. In the meantime the government intervention is measured by six independent variables: I) the degree of social developments, ii) the numberr of physicians per 100, 000 population, iii) the proportion of specialists among the total physicians, iv) the proportion of public expenditure among the NHE, v) the proportion of public beds to the total number of beds, vi) the proportion of physicians working at the public sector to the total number of physicians. In the above six independent variables iv), v) and vi) are the ones that reflect the degree of government intervention. In actual calculation, the two independent variables v) and vi) are integrated into a new variable based on one to one correspondence. The materials used are the time-series data from 1970 through 1990 in Korea. A path analysis and the time-series regression analysis were adopted to estimate and examine the causal relationship between variables involved. And decomposition of the effect of causal relationship is made to find net effect, direct and indirect effect. The major findings are as follows; 1. The effect of public expenditure, number of physicians per 100, 000 population, the proportion of specialists among the total physicians and social development shows a positive relationship with per capita NHE. Only if the government intervention would be counted, the effects of the number of physicians and the proportion of specialists succeed in containing per capita NHE. 2. In additionn to the above four variables, one additional variable, per capita NHE, was also responsible for the reduction of CMR. The factor of social development found to be the most potent predictor of the CMR reduction. However, the CMR reduction due to government intervention was negligible. 3. Meanwhile, the above four variables were found to was have negative effects on the institutional efficiency. The reverse is true when the government intervention is counted. For example, the number of physicians and the proportion of specialists have played a positive role in raising institutional efficiency via goverment intervention. This comes from the factual effect that the increment of institutional efficiency via the reduction of per capita NHE is bigger than via the reduction of CMR.