What If Climate Change Impacts The World's Top Three Rice Exporters? : An Econometric Analysis Of Panel Data
Keywords:
Productivity and Climate change relationship, Rice exporters, Econometric analysis, Panel dataAbstract
This research paper examines the impact of climate change on the top three rice exporters: India, Vietnam, and Thailand. By analyzing Effects and Risks of Climate Change for Rice Production, the study aims to understand the consequences of climate change on rice production, assess vulnerability, and provide insights for policymakers.
Using panel data analysis to eliminate the trend and variance of the data, by converting the data into the form of a Natural logarithm to reduce the impact of long-term influences on time, before using it for econometric analysis according to the OLS equation, and the Stochastic Production Function theory framework, the study establishes the climate-productivity relationship. Feasible Generalized Least Squares (FGLS) are used to address heterogeneity and autocorrelation. Numerical simulations are conducted to assess future changes in rice production under different climate scenarios, utilizing climate variable coefficients from econometric analysis.
The findings reveal the sensitivity of rice yields to Temperature mean (p<0.00) and Precipitation (p<0.00) variations. They provide insights into potential future changes in rice production. Overall, the study highlights the urgency of implementing adaptation and mitigation strategies to ensure the sustainability and resilience of the rice sector in the face of climate change challenges.
References
Antle, J. M. (1983). Testing the Stochastic Structure of Production: A Flexible Moment-Based Approach. Journal of Business & Economic Statistics, 1(3), 192-201. https://doi.org/10.1080/07350015.1983.10509339
Antle, j. M. (2010). Asymmetry, Partial Moments, and Production Risk [https://doi.org/10.1093/ajae/aaq077]. American Journal of Agricultural Economics, 92(5), 1294-1309. https://doi.org/https://doi.org/10.1093/ajae/aaq077
Antle, J. M., & Havenner, A. (1983). FORMULATING AND ESTIMATING DYNAMIC STOCHASTIC PRODUCTION MODELS (83-8, Issue. https://ageconsearch.umn.edu/record/225711/files/ageconucdavis-83-8.pdf
Battese, G. E., Rambaldi, A. N., & Wan, G. H. (1997). A Stochastic Frontier Production Function with Flexible Risk Properties. Journal of Productivity Analysis, 8(3), 269-280. https://doi.org/10.1023/A:1007755604744
Breusch, T. S., & Pagan, A. R. (1980). The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics. The review of economic studies, 47(1), 239-253. https://doi.org/10.2307/2297111
Di Falco, S., & Chavas, J.-P. (2006). Crop genetic diversity, farm productivity and the management of environmental risk in rainfed agriculture. European Review of Agricultural Economics, 33(3), 289-314. https://doi.org/10.1093/eurrag/jbl016
Di Falco, S., & Chavas, J.-P. (2009). On Crop Biodiversity, Risk Exposure, and Food Security in the Highlands of Ethiopia. American Journal of Agricultural Economics, 91(3), 599-611. https://doi.org/https://doi.org/10.1111/j.1467-8276.2009.01265.x
Dinar, A., & Mendelsohn, R. (2011). Handbook on Climate Change and Agriculture. Edward Elgar Publishing.
FAOSTAT, F. (2020). Online statistical database: Food balance. In: FAOSTAT.
Greene, W. H. (2000). Econometric analysis (4th ed.). Prentice Hall.
Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74.
Isik, M., & Devadoss, S. (2006). An analysis of the impact of climate change on crop yields and yield variability. Applied Economics, 38(7), 835-844. https://doi.org/10.1080/00036840500193682
Jatuporn, C., & Takeuchi, K. (2022). Assessing the impact of climate change on the agricultural economy in Thailand: an empirical study using panel data analysis. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-022-22743-0
Just, R. E., & Pope, R. D. (1978). Stochastic specification of production functions and economic implications. Journal of Econometrics, 7(1), 67-86. https://doi.org/https://doi.org/10.1016/0304-4076(78)90006-4
Just, R. E., & Pope, R. D. (1979). Production Function Estimation and Related Risk Considerations. American Journal of Agricultural Economics, 61(2), 276-284. https://doi.org/https://doi.org/10.2307/1239732
Kim, K., & Chavas, J.-P. (2003). Technological change and risk management: an application to the economics of corn production. Agricultural Economics, 29(2), 125-142. https://doi.org/https://doi.org/10.1016/S0169-5150(03)00081-1
Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1-24.
Pakeechai, K., Sinnarong, N., Autchariyapanitkul, K., & Supapunt, P. (2020). The impacts of climate change factors on rice production and climate-smart agriculture in the watershed areas of central Thailand. RMUTSB ACADEMIC JOURNAL (HUMANITIES AND SOCIAL SCIENCES), 5(2), 196-218.
Pakeechay, K. (2020). The Climate Smart Agriculture For Rice Production In The Central Region of Thailand (การเกษตรที่ปราดเปรื่องเรื่องสภาพภูมิอากาศส าหรับการผลิตข้าว ในภาคกลาง ประเทศไทย) Maejo University]. Maejo University, Chiang Mai.
Saha, A., Havenner, A., & Talpaz, H. (1997). Stochastic production function estimation: small sample properties of ML versus FGLS. Applied Economics, 29(4), 459-469. https://doi.org/10.1080/000368497326958
Sinnarong, N. (2013). Essays on the Impact of Climate Change in Agricultural Production [Doctoral Dissertation of Applied Economics, National Chung Hsing University, Taiwan]. Taiwan.
Sinnarong, N., Chen, C.-C., McCarl, B., & Tran, B.-L. (2019). Estimating the potential effects of climate change on rice production in Thailand. Paddy and Water Environment, 17, 1-9. https://doi.org/10.1007/s10333-019-00755-w
Sinnarong, N., Kuson, S., Nunthasen, W., Puphoung, S., & Souvannasouk, V. (2022). The potential risks of climate change and weather index insurance scheme for Thailand's economic crop production. Environmental Challenges, 8, 100575. https://doi.org/https://doi.org/10.1016/j.envc.2022.100575
Statista. (2023). https://www.statista.com/statistics/255947/top-rice-exporting-countries-worldwide-2011/Studenmund, A. (2011). using Econometrics: A practical Guide, pears-on. New York, 440-447.
Wang, C., Deser, C., Yu, J.-Y., Dinezio, P., & Clement, A. (2017). El Niño and Southern Oscillation (ENSO): A review. In (Vol. 8, pp. 85-106). https://doi.org/10.1007/978-94-017-7499-4_4
Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data MIT press. Cambridge, ma, 108(2), 245-254.
World Bank. (2019). The World Bank Group: Climate Change Knowledge Portal. https://climateknowledgeportal. worldbank. org/country/pakistan/climate-sector-healthAccessed, 10.
Yu, Y., Clark, J. S., Tian, Q., & Yan, F. (2 0 2 2 ) . Rice yield response to climate and price policy in highlatitude regions of China. Food Security, 14(5), 1143-1157. https://doi.org/10.1007/s12571-021-01253-w
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