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FACTORS AFFECTING THE WILLING TO JOIN IN COFFEE CROP INSURANCE IN DAK LAK PROVINCE, VIETNAM: A NOVEL APPLICATION OF BAYESIAN MODEL AVERAGING APPROACH
Corresponding Author(s) : Le Dinh Thang
Humanities & Social Sciences Reviews,
Vol. 8 No. 5 (2020): September
Abstract
Purpose of the study: this paper aims to determine factors affecting the willingness to join crop insurance. Besides, this paper is the purpose of developing a coffee tree insurance program.
Methodology: The authors used a systematic random sampling technique. The authors used the Bayesian Model Average (BMA) that calculated the probability of all independent variables affecting the dependent variable with significance level 0.05. Besides, the data based on 480 coffee farmers in Dak Lak province, Vietnam.
Main Findings: Authors calculated the probability of all independent variables affecting the dependent variable with significance level 0.05. Independent variables, including loans, drought risks, educational level, experiences, and productivity.
Applications of this study: This result is a vital science document for insurance companies and managers to apply and suggest recommendations for developing coffee tree insurance in the future.
Novelty/Originality of this study: Vietnam is an agricultural country, 60-70% of the population lives in rural areas, and agricultural insurance should have a considerable market. Farmers’ agrarian insurance cultivated the coffee trees that are currently underdeveloped and challenging.
Keywords
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- Abraham Falola, Opeyemi Eyitayo Ayin, and Babatola Olasunkanmi Agboola (2018). Willingness to take agricultural insurance by cocoa farmers in Nigeria. International Journal of Food and Agricultural Economics, 1(1), 97-107. https://www.foodandagriculturejournal.com/97.pdf
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- Aidoo R, Mensah Osei J, Wie P, Awunyo-Vitor D. (2014). Prospects of crop insurance as a risk management tool among arable crop farmers in Ghana. Asian Economic Financial Review, 4(3), 341-354.
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- Carter, M. R., and C. B. Barrett (2006). The Economics of Poverty Traps and Persistent Poverty: An Asset-Based Approach. Journal of Development Studies, 42(1), 178-199. https://doi.org/10.1080/00220380500405261 DOI: https://doi.org/10.1080/00220380500405261
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References
Abraham Falola, Opeyemi Eyitayo Ayin, and Babatola Olasunkanmi Agboola (2018). Willingness to take agricultural insurance by cocoa farmers in Nigeria. International Journal of Food and Agricultural Economics, 1(1), 97-107. https://www.foodandagriculturejournal.com/97.pdf
Adam Was and Paweł Kobus (2018). Factors determining the crop insurance level in Pola,nd taking into account the level of farm subsidizing. The Common Agricultural Policy of the European Union, 3(1), 125-146. https://doi.org/10.30858/pw/9788376587431.11 DOI: https://doi.org/10.30858/pw/9788376587431.11
Aidoo R, Mensah Osei J, Wie P, Awunyo-Vitor D. (2014). Prospects of crop insurance as a risk management tool among arable crop farmers in Ghana. Asian Economic Financial Review, 4(3), 341-354.
A. Lawrence Gould (2018). BMAâ€Mod: A Bayesian model averaging strategy for determining doseâ€response relationships in the presence of model uncertainty. Biometrical Journal, 61(5), 1-13. https://doi.org/10.1002/bimj.201700211 DOI: https://doi.org/10.1002/bimj.201700211
Axel Theorell, Katharina Nöh (2018). Model Uncertainty Analysis for Metabolic Network Inference: A Case Study in Bayesian Model Averaging. IFAC-PapersOnLine, 51(19), 124-135, ISSN 2405-8963. https://doi.org/10.1016/j.ifacol.2018.09.010 DOI: https://doi.org/10.1016/j.ifacol.2018.09.010
BalmaIssaka, Yakubu, Buadu Latif Wumbei, Joy Buckner, and Richard Yeboah Nartey (2016). willingness to participate in the market for crop drought index insurance among farmers in Ghana. African Journal of Agricultural Research, 11(4), 1257-1265. https://doi.org/10.5897/AJAR2015.10326 DOI: https://doi.org/10.5897/AJAR2015.10326
Barnett, B. J., C. B. Barrett, and J. R. Skees (2006). Poverty Traps and Index-Based Risk Transfer Products. Department of Agricultural and Applied Economics: University of Georgia. https://doi.org/10.2139/ssrn.999399 DOI: https://doi.org/10.2139/ssrn.999399
Barrett, C. B., and B. M. Swallow (2006). Fractal Poverty Traps. World Development, 34(1), 1-15. https://doi.org/10.1016/j.worlddev.2005.06.008 DOI: https://doi.org/10.1016/j.worlddev.2005.06.008
Barrett, C. B., and J. G. McPeak (2005). Poverty Traps and Safety Nets.†Poverty, Inequality, and Development: Essays in Honor of Erik Thorbecke. A. de Janvry, and R. Kanbur, eds. Norwell, MA: Kluwer Academic Publishers. https://doi.org/10.1007/0-387-29748-0_8 DOI: https://doi.org/10.1007/0-387-29748-0_8
Bruce J. Sherrick, Peter J. Barry, Paul N. Ellinger, Gary D. Schnitkey (2004). Factors Influencing Farmers’ Crop Insurance Decisions. American Journal of Agricultural Economics, 86(1), 103-114 https://doi.org/10.1111/j.0092-5853.2004.00565.x
Carter, M. R., and C. B. Barrett (2006). The Economics of Poverty Traps and Persistent Poverty: An Asset-Based Approach. Journal of Development Studies, 42(1), 178-199. https://doi.org/10.1080/00220380500405261 DOI: https://doi.org/10.1080/00220380500405261
Carter, M. R., P.D. Little, T. Mogues, and W. Negatu (2007). Poverty Traps and Natural Disasters in Ethiopia and Honduras. World Development, 35(1), 835-856. https://doi.org/10.1016/j.worlddev.2006.09.010 DOI: https://doi.org/10.1016/j.worlddev.2006.09.010
Chao Feng, Lu-Xuan Sun, Yin-Shuang Xia (2020). Clarifying the “gains†and “losses†of transport climate mitigation in China from technology and efficiency perspectives. Journal of Cleaner Production, 263(1), 14-23. https://doi.org/10.1016/j.jclepro.2020.121545 DOI: https://doi.org/10.1016/j.jclepro.2020.121545
Danso-Abbeam G, Setsoafia ED, Gershon I, Ansah K. (2014). Modeling farmer’s investment in agrochemicals: the experience of smallholder cocoa farmers in Ghana. Res Appl Econ, 6(4), 1-15. https://doi.org/10.5296/rae.v6i4.5977 DOI: https://doi.org/10.5296/rae.v6i4.5977
Elvis Dartey Okoffo, Elisha Kwaku Denkyirah, Derick Taylor Adu & Benedicta Yayra Fosu-Mensah (2016). Double-e‑hurdle model estimation of cocoa farmers’ willingness to pay for crop insurance in Ghana. Okoffo et al. SpringerPlus, 4(2), 1-19. https://springerplus.springeropen.com/track/pdf/10.1186/s40064-016-2561-2
Filippa Pyk and Assem Abu Hatab (2018). Fairtrade and Sustainability: Motivations for Fairtrade Certification among Smallholder Coffee Growers in Tanzania. Sustainability, 10(5), 1-18. https://doi.org/10.3390/su10051551 DOI: https://doi.org/10.3390/su10051551
Fonta, W.M., Sanfo, S., Kedir, A.M. et al. (2018). Estimating farmers’ willingness to pay for weather index-based crop insurance uptake in West Africa: Insight from a pilot initiative in Southwestern Burkina Faso. Agric Econ, 6(1), 1-10. https://doi.org/10.1186/s40100-018-0104-6 DOI: https://doi.org/10.1186/s40100-018-0104-6
Girma Gezimu Gebre, Hiroshi Isoda, Dil Bahadur Rahut Yuichiro Amekawa, Hisako Nomura (2019). Gender differences in agricultural productivity: evidence from maize farm households in southern Ethiopia. GeoJournal, 3(1), 21-34. https://link.springer.com/article/10.1007%2Fs10708-019-10098-y
Guoqiang Tang, Yingzhao Ma, DiLong, LingzhiZhong, Yang Hong (2016). Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. Journal of Hydrology, 533(1), 152-167. https://doi.org/10.1016/j.jhydrol.2015.12.008 DOI: https://doi.org/10.1016/j.jhydrol.2015.12.008
Hasen, M., & Mekonnen, H. (2017). The impact of agricultural cooperatives membership on the wellbeing of smallholder farmers: Empirical evidence from Eastern Ethiopia. Agricultural and Food Economics, 5(6), 1-20. https://doi.org/10.1186/s40100-017-0075-z DOI: https://doi.org/10.1186/s40100-017-0075-z
Jennifer A. Hoeting, David Madigan, Adrian E. Raftery, and Chris T. Volinsky (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382-401. https://doi.org/10.1214/ss/1009212519 DOI: https://doi.org/10.1214/ss/1009212519
John Mano Raj (2014). Marketing of rain fall insurance in coffee: a concept failure or promotion failure? A Journal of Radix International Educational and Research Consortium, 3(3),1-12.
Khanal Arjun Prasad, Khanal Suman, Dutta Jay Prakash, Dhakal Shiva Chand,ra and Kattel Rishi Ram (2019). An assessment of factors determining the productivity of coffee the western hills of Nepal. International Journal of Agricultural Sciences and Veterinary Medicine, 7(2),11-17. https://www.researchgate.net/publication/333479102
Khalil Ur Rahman; Songhao Shang; Muhammad Shahid; Yeqiang Wen; Zeeshan Khan (2020). Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Mul tisatellite Precipitation Products over Pakistan. J. Hydrometeor, 21(1), 17-37. https://doi.org/10.1175/JHM-D-19-0087.1 DOI: https://doi.org/10.1175/JHM-D-19-0087.1
Koloma, Y. (2015). Crop Microinsurance for Maize Farmers in Burkina Faso: Access and Agriculture Performance in the Dandé Village. Strategic Change, 24(1), 115-129. https://doi.org/10.1002/jsc.2001 DOI: https://doi.org/10.1002/jsc.2001
Kenneth W. Sibiko, Prakashan C. Veettil, and Matin Qaim (2018). Small farmers’ preferences for weather index insurance: insights from Kenya. Sibiko et al. Agric & Food Secityur, 1(1), 1-14. https://doi.org/10.1186/s40066-018-0200-6 DOI: https://doi.org/10.1186/s40066-018-0200-6
Krzysztof Drachal (2018). Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices. Sustainability, 10(8), 1-27. https://doi.org/10.3390/su10082801 DOI: https://doi.org/10.3390/su10082801
Lele Lu, Hanchen Wang, Sophan Chhin, Aiguo Duan, Jianguo Zhang, Xiongqing Zhang (2019). A Bayesian Model AveraginA approach for moelling tree mortality in relation tthe o site, competiti,on and climatic factors for Chinese fir plantations. Forest Ecology and Management, 40(1), 169-177. https://doi.org/10.1016/j.foreco.2019.03.003 DOI: https://doi.org/10.1016/j.foreco.2019.03.003
Madigan, D., Raftery, A.., (1994). Model selection and accounting for model uncertainty in graphical models using Occam’s window. J. Am. Stat. Assoc, 89(428), 1535-1546. https://doi.org/10.1080/01621459.1994.10476894 DOI: https://doi.org/10.1080/01621459.1994.10476894
Man, Georg (2015). Competition and the growth of nations: International evidence from Bayesian model averaging. Economic Modelling, 51(1), 491-501. https://doi.org/10.1016/j.econmod.2015.08.012 DOI: https://doi.org/10.1016/j.econmod.2015.08.012
Mark F.J. Steel (2019). Model Averaging and its Use in Economics. Department of Statistics, University of Warwick, 3(1), 1-106. https://arxiv.org/pdf/1709.08221.pdf
Notaro, Vincenza & Liuzzo, Lorena & Freni, Gabriele (2016). A BMA Analysis to Assess the Urbanization and Climate Change Impact on Urban Watershed Runoff. Procedia Engineering. 154(1), 868-876. https://doi.org/10.1016/j.proeng.2016.07.461 DOI: https://doi.org/10.1016/j.proeng.2016.07.461
Okoffo, E.D., Denkyirah, E.K., Adu, D.T. et al. (2016). Double-hurdle model estimation of cocoa farmers’ willingness to pay for crop insurance in Ghana. SpringerPlus, 5(1), 873-879. https://doi.org/10.1186/s40064-016-2561-2 DOI: https://doi.org/10.1186/s40064-016-2561-2
Rafia Afroz, Rulia Akhtar, Puteri Farhana (2017). Willingness to pay for crop insurance to adopt flood risk by Malaysian farmers: an empirical investigation of Kedah. International Journal of Economics and Financial Issues, 7(1), 1-9.
Raftery, A. (1995). Bayesian model selection in social research. In: Marsden, R.V.(Ed.), Sociological Methodology. Blackwell: Cambridge, Mass. https://doi.org/10.2307/271063 DOI: https://doi.org/10.2307/271063
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