An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design
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Daniel Lima Miquelluti
Vitor Augusto Ozaki
David José Miquelluti
Abstract
Objective: this article studies the efficiency of a novel regression approach, the geographically weighted quantile lasso (GWQlasso), in the modeling of yield-index relationship for weather index insurance products. GWQlasso allows regression coefficients to vary spatially, while using the information from neighboring locations to derive robust estimates. The lasso component of the model facilitates the selection of relevant explanatory variables. Methodology: a weather index insurance (WII) product is developed based on one-month standardized precipitation index (SPI) derived from a daily precipitation dataset for 41 weather stations in the state of Paraná (Brazil) for the period from 1979 to 2015. Soybean yield data are also used for the 41 municipalities from 1980 to 2015. The effectiveness of the GWQlasso product is evaluated against a classic quantile regression approach and a traditional yield insurance product using the spectral risk measure (SRM) and the mean semi-deviation. Results: while GWQlasso proved as effective as quantile regression, it outperformed the yield insurance product. Conclusion: the GWQlasso is an alternative to the crop insurance market in Brazil and other locations with limited data.
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Miquelluti, D. L., Ozaki, V. A., & Miquelluti, D. J. (2021). An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design. Journal of Contemporary Administration, 26(3), e200387. https://doi.org/10.1590/1982-7849rac2022200387.en
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Carmello, V., & Sant’anna, J. L., Neto (2016). Rainfall variability and soybean yield in Paraná state, southern Brazil. International Journal of Environmental & Agriculture Research, 2(1), 86-97. Retrieved from https://ijoear.com/Paper-January-2016/IJOEAR-JAN-2016-16.pdf
Carter, M. (2009). Intelligent design of index-insurance for smallholder farmers and pastoralists [Working paper]. IFPRI Innovations in Insuring the Poor, Focus 17-06. Washington, D.C.: IFPRI. Retrieved from https://cgspace.cgiar.org/handle/10568/894
Carter, M., Janvry, A., Sadoulet, E., & Sarris, A. (2017). Index insurance for developing country agriculture: A reassessment. Annual Review of Resource Economics, 9, 421-438. https://doi.org/10.1146/annurev-resource-100516-053352
Chakravarti, J. S. (1920). Agricultural insurance a practical scheme suited to Indian conditions. Bangalore: Government Press.
Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014). NbClust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61(6). https://doi.org/10.18637/jss.v061.i06
Chen, V. Y.-J., Deng, W.-S., Yang, T.-C., & Matthews, S. A. (2012). Geographically weighted quantile regression (GWQR): An application to US mortality data. Geographical Analysis, 44(2), 134-150. https://doi.org/10.1111/j.1538-4632.2012.00841.x
Collier, B., Skees, J., & Barnett, B. (2009). Weather index insurance and climate change: Opportunities and challenges in lower income countries. The Geneva Papers on Risk and Insurance-Issues and Practice, 34(3), 401-424. https://doi.org/10.1057/gpp.2009.11
Conradt, S., Finger, R., & Bokusheva, R. (2015). Tailored to the extremes: Quantile regression for index‐based insurance contract design. Agricultural Economics, 46(4), 537-547. https://doi.org/10.1111/agec.12180
Cotter, J., & Dowd, K. (2010). Estimating financial risk measures for futures positions: A nonparametric approach. Journal of Futures Markets, 30(7), 689-703. https://doi.org/10.1002/fut.20437
Dowd, K., Cotter, J., & Sorwar, G. (2008). Spectral risk measures: Properties and limitations. Journal of Financial Services Research, 34(1), 61-75. https://doi.org/10.1007/s10693-008-0035-6
Duarte, G. V., Braga, A., Miquelluti, D. L., & Ozaki, V. A. (2018). Modeling of soybean yield using symmetric, asymmetric and bimodal distributions: Implications for crop insurance. Journal of Applied Statistics, 45(11), 1920-1937. https://doi.org/10.1080/02664763.2017.1406902
Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1-26. https://doi.org/10.1214/aos/1176344552
Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348-1360. https://doi.org/10.1198/016214501753382273
Farias, J. R. B., Assad, E. D., Almeida, I. R., Evangelista, B. A., Lazzarotto, C., Neumaier, N., & Nepomuceno, A. L. (2001). Caracterização de risco de déficit hídrico nas regiões produtoras de soja no Brasil. Revista Brasileira de Agrometeorologia, 9(3), 415-421. Retrieved from http://www.cnpt.embrapa.br/pesquisa/agromet/pdf/revista/cap4.pdf
Franchini, J. C., Balbinot, A. A., Junior, Nitsche, P. R., Debiasi, H., & Lopes, I. O. N. (2016). Variabilidade espacial e temporal da produção de soja no Paraná e definição de ambientes de produção. Embrapa Soja-Documentos (INFOTECA-E). Retrieved from https://www.embrapa.br/busca-de-publicacoes/-/publicacao/1052786/variabilidade-espacial-e-temporal-da-producao-de-soja-no-parana-e-definicao-de-ambientes-de-producao
Giné, X., Menand, L., Townsend, R., & Vickery, J. (2010). Microinsurance: A case study of the Indian rainfall index insurance market [Policy Research Working Papers]. The World Bank. https://doi.org/10.1596/1813-9450-5459
Goodwin, B. K., Vandeveer, M. L., & Deal, J. L. (2004). An empirical analysis of acreage effects of participation in the federal crop insurance program. American Journal of Agricultural Economics, 86(4), 1058-1077. https://doi.org/10.1111/j.0002-9092.2004.00653.x
Grootveld, H., & Hallerbach, W. G. (2004). Upgrading value-at-risk from diagnostic metric to decision variables: A wise thing to do? In G. P. Szego (Ed.), Risk Measures for the 21st Century (pp. 33–50). Chichester: Wiley.
Halcrow, H. G. (1949). Actuarial structures for crop insurance. American Journal of Agricultural Economics, 31(3), 418-443. Retrieved from https://www.jstor.org/stable/1232330
Hess, U., Skees, J. R., Stoppa, A., Barnett, B. J., & Nash, J. (2005). Managing agricultural production risk: Innovations in developing countries. Washington, DC: World Bank. Retrieved from https://openknowledge.worldbank.org/handle/10986/8797
Hunter, D. R., & Lange, K. (2000). Quantile regression via an MM algorithm. Journal of Computational and Graphical Statistics, 9(1), 60-77. https://doi.org/10.1080/10618600.2000.10474866
Janvry, A. D., Ritchie, E. R., & Sadoulet, E. (2016). Weather index insurance and shock coping: Evidence from Mexico’s CADENA Program [Policy Research Working Papers]. The World Bank. Retrieved from https://openknowledge.worldbank.org/handle/10986/24632
Jensen, N., & Barrett, C. (2017). Agricultural index insurance for development. Applied Economic Perspectives and Policy, 39(2), 199-219. https://doi.org/10.1093/aepp/ppw022
Keller, T., Filho, Assad, E. D., & Lima, P. R. S. R. (2005). Regiões pluviometricamente homogêneas no Brasil. Pesquisa Agropecuária Brasileira, 40(4), 311-322. https://doi.org/10.1590/S0100-204X2005000400001
Khalil, A. F., Kwon, H.-H., Lall, U., Miranda, M. J., & Skees, J. (2007). El niño–southern oscillation–based index insurance for floods: Statistical risk analyses and application to Peru. Water Resources Research, 43(10). https://doi.org/10.1029/2006wr005281
Leblois, A., Quirion, P., Alhassane, A., & Traoré, S. (2014). Weather index drought insurance: An ex ante evaluation for millet growers in Niger. Environmental and Resource Economics, 57(4), 527-551. https://doi.org/10.1007/s10640-013-9641-3
Lier, Q. D. J. V. (2014). Water availability to plants. In W. G. Teixeira, M. B. Ceddia, M. V. Ottoni, G. K. Donnagema (Eds.), Application of Soil Physics in Environmental Analyses: Measuring, modelling and data integration (pp. 435-452). Cham: Springer. https://doi.org/10.1007/978-3-319-06013-2_18
Maestro, T., Barnett, B. J., Coble, K. H., Garrido, A., & Bielza, M. (2016). Drought index insurance for the Central Valley Project in California. Applied Economic Perspectives and Policy, 38(3), 521-545. https://doi.org/10.1093/aepp/ppw013
Markowitz, H. M. (1959). Portfolio selection efficient diversification of investments. Monograph (Yale University. Cowles Foundation for Research in Economics), 16. New York: John Wiley and Sons
McKee, T. B., Doesken, N. J., & Kleist, J. (1993, January). The relationship of drought frequency and duration to time scales (pp. 179-183). Proceedings of the Conference on Applied Climatology, Anaheim, California, USA, 8. Retrieved from https://www.droughtmanagement.info/literature/AMS_Relationship_Drought_Frequency_Duration_Time_Scales_1993.pdf
Ministério da Agricultura, Pecuária e Abastecimento (2015). Relatório das indenizações pagas entre 2006 a 2015. Programa de subvenção ao prêmio do seguro rural – PSR: protegendo o produtor rural. Retrieved from https://www.gov.br/agricultura/pt-br/assuntos/riscos-seguro/seguro-rural/documentos-seguro-rural/RelatriodeSinistralidade_PSR_2006_2015.pdf
Ministério da Agricultura, Pecuária e Abastecimento (2017). Relatório geral 2017. Programa de Subvenção ao Prêmio do Seguro Rural (PSR). Retrieved from https://www.gov.br/agricultura/pt-br/assuntos/riscos-seguro/seguro-rural/documentos-seguro-rural/RelatorioGeralPSR2017.pdf
Miquelluti, D. L. (2019). Weather index insurance design: A novel approach for crop insurance in Brazil (Doctoral dissertation). Universidade de São Paulo, Piracicaba, SP, Brazil. https://doi.org/10.11606/t.11.2019.tde-02082019-100224
Miranda, M. J. (1991). Area‐yield crop insurance reconsidered. American Journal of Agricultural Economics, 73(2), 233-242. https://doi.org/10.2307/1242708
Miranda, M. J., & Farrin, K. (2012). Index insurance for developing countries. Applied Economic Perspectives and Policy, 34(3), 391-427. https://doi.org/10.1093/aepp/pps031
Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1-2), 202-216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Mishra, P. K. (1995). Is rainfall insurance a new idea? Pioneering work revisited. Economic and Political Weekly, 30(25), A84-A88. Retrieved from https://www.jstor.org/stable/4402912
Oñate, C. A., Ozaki, V. A., & Bravo-Ureta, B. (2016, July). Impact evaluation of the Brazilian crop insurance public program “Proagro Mais”. Proceedings of the Agricultural and Applied Economics Association – AAEA 2016 Annual Meeting, Boston, Massachusetts. https://doi.org/10.22004/ag.econ.236096
Ozaki, V. A., & Shirota, R. (2005). Um estudo da viabilidade de um programa de seguro agrícola baseado em um índice de produtividade regional em Castro (PR). Revista de Economia e Sociologia Rural, 43(3), 485-503. https://doi.org/10.1590/s0103-20032005000300005
Rao, K. N. (2011). Weather index insurance: Is it the right model for providing insurance to crops? ASCI Journal of Management, 41(1), 86-101. Retrieved from https://drive.google.com/drive/folders/18Z4YazKMNL0jkgkdKCPx2QuFfPpXL5yy
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