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Forest Fires, Smoky Kitchens, and Human Health in Indonesia

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Abstract

Burning tropical forests to establish lucrative agricultural crops ignores potentially important health externalities of the resulting air pollution. These health externalities are often poorly understood, especially if other environmental hazards, such as indoor pollution, are not taken into account. Given the potential for joint, contemporaneous harms, we estimate the impacts of outdoor and indoor air pollution on respiratory health in Indonesia. To address the endogeneity of air pollution exposure, we use panel fixed effects estimation and instrument for outdoor pollution using upwind forest fire intensity. We find that outdoor air pollution exposure reduces lung capacity and decreases overall health status. Subgroup analysis reveals that these impacts are higher among the youngest and oldest individuals in our sample. Critically, we find suggestive evidence that outdoor air pollution exposure is more harmful to the health of individuals living in households that use clean cooking fuels. Thus, policies aimed at reducing environmental health harms are not substitutable—that is, reductions in both indoor and outdoor air pollution exposures are necessary for achieving health targets.

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Notes

  1. As of 2020, Indonesia had 34 provinces, 416 kabupatens (regencies), and 7246 kecamatans (districts) (Evans 2020). These geographical units vary in size both in terms of population and area. Administrative units in Indonesia—particularly at lower levels such as kecamatans—are periodically restructured, leading to some ambiguity in their exact numbers and sizes.

  2. In the sanitation context, this interplay between private and social benefits of improved sanitation due to externalities associated with unimproved sanitation practices such as open defecation have been examined to better understand patterns of latrine adoption and disadoption (Cameron et al. 2021; Guiteras et al. 2019; Kresch et al. 2020; Pakhtigian et al. 2022).

  3. Data are available at http://www.rand.org/labor/FLS/IFLS/download.html.

  4. Over the course of survey waves, household splits (e.g., example children getting married and forming new households) were followed and retained in the sample, thus increasing the sample size over time.

  5. The shapefile is publicly available at https://gadm.org/download_country.html.

  6. Descriptive statistics for the 2000 IFLS wave, which we use for a supplementary analysis, are available in Appendix Table A1.

  7. Due to the testing process, lung capacity was measured only among individuals above the age of nine leading to a smaller sample size for this outcome.

  8. Given these low levels of asthma diagnoses in the sample, which are likely an under report due to the availability of medical services, we do not include asthma as an outcome in our empirical analysis.

  9. Appendix Figure A1 depicts the distribution for IFLS wave 3.

  10. If we conducted the analysis among households that were either always “dirty fuel” or “clean fuel” users we would be left with a highly selective sample as the LPG Conversion Program induced fuel use change (dirty to clean) among the majority of households in Indonesia during this time (Thoday et al. 2018).

  11. We also estimate our panel regression (versions of Eq. 1) by cooking fuel and age subgroups and present the results in our supplementary appendix.

  12. Appendix Table A2 reports the first stage results of the relationship between the log of upwind kabupaten fire season intensity and fire season PM\(_{2.5}\). We report the first stage F-stat for each regression reported in Table 3, noting that the first stages are identical for the respiratory symptoms and overall health outcomes and slightly different for the lung capacity outcome, due to sample construction.

  13. While somewhat unintuitive, these patterns could again reflect the temporal mismatch between respiratory symptoms—for which only the previous month is considered—and PM\(_{2.5}\) exposures, which are measured annually.

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Authors and Affiliations

  1. School of Public Policy, Pennsylvania State University, 322 Pond Lab, University Park, Pennsylvania, PA, 16802, USA

    Emily L. Pakhtigian

  2. Sanford School of Public Policy, Duke University, 201 Science Drive, Durham, NC, 27708, USA

    Subhrendu K. Pattanayak

  3. Lee Kuan Yew School of Public Policy, National University of Singapore, 469B Bukit Timah Road, Singapore, 250771, Singapore

    Jie-Sheng Tan-Soo

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Contributions

Conceptualization [E.L. Pakhtigian, S.K. Pattanayak, J.-S. Tan-Soo]; Methodology [E.L. Pakhtigian, S.K. Pattanayak, J.-S. Tan-Soo]; Formal analysis and investigation [E.L. Pakhtigian]; Writing-original draft [E.L. Pakhtigian, S.K. Pattanayak, J.-S. Tan-Soo]; Writing-review and editing [E.L. Pakhtigian, S.K. Pattanayak, J.-S. Tan-Soo].

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Pakhtigian, E.L., Pattanayak, S.K. & Tan-Soo, JS. Forest Fires, Smoky Kitchens, and Human Health in Indonesia. Environ Resource Econ (2024). https://doi.org/10.1007/s10640-024-00865-y

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