Abstract
Asian developing countries are frequently devastated by tropical cyclones. While the literature is replete with their impacts on economic growth, the impacts on critical sustainable development indicators, namely income inequality, health, and human capital accumulation, have seldom been explored. In this study, we measure the direct, indirect, and spillover impacts of tropical cyclones on the sustainable development of eight developing countries in Asia. This study uses the dynamic generalized method of moments model to estimate the impact of occurrences and casualties in these countries over 28 years. Our results indicate that recurrent tropical cyclones increase income inequality, reaching the threshold at 0.4 cyclone. The mortality rate tends to rise, which decreases after 2.5 cyclonic occurrences. Similarly, cyclones seem to initially reduce the expected years of schooling, which starts increasing after one cyclonic occurrence. These weakening impacts provide evidence of negative feedback loops. We also find evidence of domino effects and gender effects. The resilience factors are controlled for, as it helps the developing countries recover from the vicious cycles. The feedback loops can be broken by taking timely interventions, mitigation and adaptation.
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Notes
Henceforth, the notation \({\mathrm{TC}}_{n}\) is used to represent tropical cyclonic occurrences and TC_deaths is used for casualties from tropical cyclones, unless otherwise defined.
Specifications using higher-order cubic terms of tropical cyclonic occurrences were also tested, which were insignificant. This implies that the cubic specification does not yield any statistically meaningful explanatory power over the squared specification.
The ‘xtdpdgmm’ command of STATA 16 is used to estimate the dynamic panel data regression models (Kripfganz 2019).
We have taken the antilog of the estimated threshold and subtracted 1 from the estimated value. Accordingly, the formula becomes \(\left(-\frac{{\delta }_{1}+{\delta }_{2}}{{2\delta }_{3}}\right)=\mathrm{ln}(1+\widehat{{\mathrm{TC}}_{n}})\). Therefore, \(\widehat{{\mathrm{TC}}_{n}}=\mathrm{exp}\left(-\frac{{\delta }_{1}+{\delta }_{2}}{{2\delta }_{3}}\right)-1\). Note that \({\delta }_{1}\mathrm{ and }{\delta }_{2}\) are the coefficients of \({{\mathrm{TC}}_{n}}_{i,t} and {\mathrm{TC}}_{{n}_{i, t-1}}\), respectively. We have estimated the threshold values \(\widehat{{\mathrm{TC}}_{n}}\) considering both \({\delta }_{1}\mathrm{and }{\delta }_{2}\) or anyone based on their statistical sifgnificance.
Accordingly, the estimation of the threshold value of \({\mathrm{TC}}_{n}\) for income inequality \(\left(-\frac{0.7}{2*(-1.117)}\right)=\mathrm{ln}\left(1+\widehat{{\mathrm{TC}}_{n}}\right) \mathrm{gives} (1+\widehat{{\mathrm{TC}}_{n}})=1.4.\) Hence, the threshold of \({\mathrm{TC}}_{n}\) (\(\widehat{{\mathrm{TC}}_{n}}\)) for income inequality is 0.4 (\(=1.4-1\)).
The estimation of the threshold value of \({\mathrm{TC}}_{n}\) for mortality rate \(\left(-\frac{3.983}{2*(-1.609)}\right)=\mathrm{ln}\left(1+\widehat{{\mathrm{TC}}_{n}}\right) \mathrm{gives} (1+\widehat{{\mathrm{TC}}_{n}})=3.448\sim 3.5.\) Hence, the threshold of \({\mathrm{TC}}_{n}\) (\(\widehat{{\mathrm{TC}}_{n}}\)) for mortality rate is 2.5 (\(=3.5-1\)).
The estimation of the threshold value of \({\mathrm{TC}}_{n}\) for expected years of schooling \(\left(-\frac{(-4.002)}{2*(3.335)}\right)=\mathrm{ln}\left(1+\widehat{{\mathrm{TC}}_{n}}\right)\mathrm{gives} (1+\widehat{{\mathrm{TC}}_{n}})=1.83\sim 2.\) Hence, the threshold of \({\mathrm{TC}}_{n}\) (\(\widehat{{\mathrm{TC}}_{n}}\)) for expected years of schooling is 1 (\(=2-1\)).
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Sen, S., Nayak, N.C. & Mohanty, W.K. Impact of tropical cyclones on sustainable development through loops and cycles: evidence from select developing countries of Asia. Empir Econ 65, 2467–2498 (2023). https://doi.org/10.1007/s00181-023-02431-9
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DOI: https://doi.org/10.1007/s00181-023-02431-9
Keywords
- Developing countries
- Tropical cyclones
- Sustainable development indicators
- Feedback loops
- Resilience
- Asia