摘要
This paper’s principle aim is to investigate if the level of fine particular matter (PM10) affects the impact of economic development on cancer mortality. At this scope, we consider a polynomial model with the number of cancer deaths as dependent variable for a panel of 26 Organization for Economic Cooperation and Development Countries (OECD) during the 1990-2013 time span. The covariates are PM10, income, public health-expenditure, the share of urban population, the number of daily sold cigarettes and alcohol daily consumption. For the scope of our investigation, we implement a quartile division of covariates using the level of PM10 as reference variable in order to estimate the effect of the same variable for each subgroup. Technically, we first use PM10 values to construct quartiles. Then, we use cancer-mortality rates by PM10 quartile to run separate regressions for each quartile. We also calculate the social costs arising from cancer deaths caused by PM10 emissions. According to our results, increasing pollution levels weaken the moderating effect of income and health expenditure on cancer-deaths. As far as PM10 is concerned, it seems that it increasingly affects cancer-deaths until it reaches a threshold level, then its influence on the number of deaths lowers down. Finally, we simulate that a 1% increase in PM10 emissions leads to an increase of 0.205 cancer-deaths every 100.000 inhabitants. In terms of social costs, these deaths amount to 881.500 US$.
This paper’s principle aim is to investigate if the level of fine particular matter (PM10) affects the impact of economic development on cancer mortality. At this scope, we consider a polynomial model with the number of cancer deaths as dependent variable for a panel of 26 Organization for Economic Cooperation and Development Countries (OECD) during the 1990-2013 time span. The covariates are PM10, income, public health-expenditure, the share of urban population, the number of daily sold cigarettes and alcohol daily consumption. For the scope of our investigation, we implement a quartile division of covariates using the level of PM10 as reference variable in order to estimate the effect of the same variable for each subgroup. Technically, we first use PM10 values to construct quartiles. Then, we use cancer-mortality rates by PM10 quartile to run separate regressions for each quartile. We also calculate the social costs arising from cancer deaths caused by PM10 emissions. According to our results, increasing pollution levels weaken the moderating effect of income and health expenditure on cancer-deaths. As far as PM10 is concerned, it seems that it increasingly affects cancer-deaths until it reaches a threshold level, then its influence on the number of deaths lowers down. Finally, we simulate that a 1% increase in PM10 emissions leads to an increase of 0.205 cancer-deaths every 100.000 inhabitants. In terms of social costs, these deaths amount to 881.500 US$.
