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Air Pollution, Health, and Avoidance Behavior: Evidence from South Korea

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Abstract

Using detailed data on the beneficiaries of the Korean National Health Insurance Service (c.2006–2015), this paper estimates the health effects of air pollution in South Korea while controlling for avoidance behaviors. In particular, we investigate changes in respiratory hospitalization rates due to increases in PM\(_{10}\) and O\(_{3}\) concentrations. To address the endogeneity of air pollution, this paper applies the historical average concentration of air pollution, which includes rich information about the meteorological and geographical factors that affect regional air pollution levels, as an instrumental variable and compares the results with other count data models. We find that a 10 \(\upmu\)g/m\(^{3}\) increase in PM\(_{10}\) and a 10 ppb increase in O\(_{3}\) lead to an increase in daily respiratory hospital visits of up to 10.39% [95% confidence interval (CI) 4.04–16.80] and 10.93% (95% CI 9.23–12.63), resulting in additional health care costs of US$67 million and US$70 million, respectively. This paper also shows that the effects of PM\(_{10}\) and O\(_{3}\) are elevated in highly populated cities, children, and patients without chronic respiratory diseases.

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Notes

  1. A tertiary hospital is a hospital with highly specialized staff and technical equipment, which could include, for example, cardiology, intensive care, and specialized imaging units, clinical services highly differentiated by function, hospitals that have teaching activities, and hospitals with 300 to 1500 beds (Hensher et al. 2006).

  2. OECD Health Statistics, https://www.oecd.org/els/health-systems/health-data.htm.

  3. Every year, a patient in the sample is assigned a new ID number from the NHIS.

  4. The average exchange rate for the study period (2006–2015) is 1100 KRW per USD.

  5. This paper has actual alert information for \(\hbox {PM}_{{10}}\) in 2015.

  6. The alert is issued on an hourly basis and at the discretion of local governments; therefore, even if the air pollution level exceeds the standard, an alert may not be issued.

  7. The results in columns (2) and (4) are the same as the results in Tables 5 and 6.

  8. The daily ozone level is considered to be “Good” if the daily average concentration is less than 37, which is the sum of the mean ozone level and one standard deviation above the mean ozone level (\(\overline{O_{3}}\)+1SD). The ozone level is considered to be “Polluted” if the daily average is between \(\overline{O_{3}}\)+1SD and \(\overline{O_{3}}\)+2SD. If the daily average ozone level is greater than \(\overline{O_{3}}\)+2SD, it is considered to be “Very polluted.” See Table 3 for the summary statistics for ozone.

  9. Instead of using tercile dummies, I also tried a nonlinear functional approach in the form of a quadratic equation. This nonlinear IV approach shows lower test statistic values for under-identification and weak instruments (for \(\hbox {PM}_{{10}}\)), or extremely larger statistic values that may imply model misspecification (for \(\hbox {O}_{{3}}\)).

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Acknowledgements

I thank Walter Thurman, Roger von Haefen, Kelly Zering, Zachary Brown, Billy Pizer, and conference participants at the WCERE 2018 and AEA/ASSA 2018 Annual Meeting for their helpful comments. The content is solely the responsibility of the author.

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  1. Energy Environment Policy and Technology, Graduate School of Energy and Environment (KU-KIST Green School), Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea

    Moon Joon Kim

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Appendices

Appendices

See Tables 12 and 13.

Table 12 Lagged effects of air pollution
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Table 13 Effects of the mean air pollution and westerly wind on daily air pollution concentrations
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Kim, M.J. Air Pollution, Health, and Avoidance Behavior: Evidence from South Korea. Environ Resource Econ 79, 63–91 (2021). https://doi.org/10.1007/s10640-021-00553-1

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