On the Risk Efficiency of a Weather Index Insurance Product for the Brazilian Semiarid Region

Mateus P. Lavorato aFederal University of Viçosa, Viçosa, Minas Gerais, Brazil

Search for other papers by Mateus P. Lavorato in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-4425-3718
and
Marcelo J. Braga aFederal University of Viçosa, Viçosa, Minas Gerais, Brazil

Search for other papers by Marcelo J. Braga in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Weather index insurance (WII) has long been advertised as a viable alternative to crop yield insurance. WII products were first developed to assist climate-vulnerable farmers from developing countries where establishing a well-structured crop insurance market is expressively difficult due to the poor transport infrastructure and the prevalence of sparsely distributed small-scale farms. In Brazil, the semiarid region stands out as the one that concentrates the ideal conditions for the implementation of a WII product since it houses thousands of climate-vulnerable farmers. With this in mind, we designed and priced a WII product for farmers from the semiarid region of Brazil and posteriorly investigated its risk efficiency. To do so, we first investigated crop yield responses to aridity, enabling the selection of locations for which the WII product was posteriorly assessed. Second, we grouped selected locations into specific contracts according to geographical proximity and evaluated each of these contracts to attest the risk efficiency of the proposed WII product using the method of stochastic efficiency with respect to a function (SERF), which identifies utility efficient alternatives for a range of risk attitudes. Our results show that the WII product may be effective in protecting farmers from adverse variations in production revenue, possibly being attractive for utility-maximizer farmers that are sufficiently risk averse.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mateus P. Lavorato, mateus.lavorato@ufv.br

Abstract

Weather index insurance (WII) has long been advertised as a viable alternative to crop yield insurance. WII products were first developed to assist climate-vulnerable farmers from developing countries where establishing a well-structured crop insurance market is expressively difficult due to the poor transport infrastructure and the prevalence of sparsely distributed small-scale farms. In Brazil, the semiarid region stands out as the one that concentrates the ideal conditions for the implementation of a WII product since it houses thousands of climate-vulnerable farmers. With this in mind, we designed and priced a WII product for farmers from the semiarid region of Brazil and posteriorly investigated its risk efficiency. To do so, we first investigated crop yield responses to aridity, enabling the selection of locations for which the WII product was posteriorly assessed. Second, we grouped selected locations into specific contracts according to geographical proximity and evaluated each of these contracts to attest the risk efficiency of the proposed WII product using the method of stochastic efficiency with respect to a function (SERF), which identifies utility efficient alternatives for a range of risk attitudes. Our results show that the WII product may be effective in protecting farmers from adverse variations in production revenue, possibly being attractive for utility-maximizer farmers that are sufficiently risk averse.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Mateus P. Lavorato, mateus.lavorato@ufv.br
Save
  • Abdi, M. J., N. Raffar, Z. Zulkafli, K. Nurulhuda, B. M. Rehan, F. M. Muharam, N. Ain Khosim, and F. Tangang, 2022: Index-based insurance and hydroclimatic risk management in agriculture: A systematic review of index selection and yield-index modelling methods. Int. J. Disaster Risk Reduct., 67, 102653, https://doi.org/10.1016/j.ijdrr.2021.102653.

    • Search Google Scholar
    • Export Citation
  • Adeyinka, A. A., C. Krishnamurti, T. N. Maraseni, and S. Chantarat, 2016: The viability of weather-index insurance in managing drought risk in rural Australia. Int. J. Rural Manage., 12, 125142, https://doi.org/10.1177/0973005216660897.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. R., and J. L. Dillon, 1992: Risk Analysis in Dryland Farming Systems. FAO, 109 pp.

  • Araghi, A., C. J. Martinez, J. Adamowski, and J. E. Olesen, 2018: Spatiotemporal variations of aridity in Iran using high‐resolution gridded data. Int. J. Climatol., 38, 27012717, https://doi.org/10.1002/joc.5454.

    • Search Google Scholar
    • Export Citation
  • Bouveyron, C., G. Celeux, T. B. Murphy, and A. E. Raftery, 2019: Model-Based Clustering and Classification for Data Science: With Applications in R. Cambridge University Press, 446 pp.

  • Cai, R., D. Yu, and M. Oppenheimer, 2014: Estimating the spatially varying responses of corn yields to weather variations using geographically weighted panel regression. J. Agric. Resour. Econ., 39, 230252.

    • Search Google Scholar
    • Export Citation
  • Choudhury, A., J. Jones, A. Okine, and R. Choudhury, 2016: Drought-triggered index insurance using cluster analysis of rainfall affected by climate change. J. Insur. Issues, 39, 169186.

    • Search Google Scholar
    • Export Citation
  • Companhia Nacional de Abastecimento, 2019: Calendário de plantio e colheita de grãos no Brasil (Grain planting and harvesting calendar in Brazil). National Food Supply Company, 75 pp., https://www.conab.gov.br/outras-publicacoes/item/download/28424_34d371f808b23d9bd37b9101c8ed5094.

  • Conradt, S., R. Finger, and M. Spörri, 2015: Flexible weather index-based insurance design. Climate Risk Manage., 10, 106117, https://doi.org/10.1016/j.crm.2015.06.003.

    • Search Google Scholar
    • Export Citation
  • da Silva, V. D. P. R., 2004: On climate variability in Northeast of Brazil. J. Arid Environ., 58, 575596, https://doi.org/10.1016/j.jaridenv.2003.12.002.

    • Search Google Scholar
    • Export Citation
  • De Martonne, E., 1926: Une nouvelle function climatologique: L’indice d’aridité (A new climatological function: The aridity index). La Meteor., 2, 449459.

    • Search Google Scholar
    • Export Citation
  • Dillon, J. L., and P. L. Scandizzo, 1978: Risk attitudes of subsistence farmers in Northeast Brazil: A sampling approach. Amer. J. Agric. Econ., 60, 425435, https://doi.org/10.2307/1239939.

    • Search Google Scholar
    • Export Citation
  • Ender, M., and R. Zhang, 2015: Efficiency of weather derivatives for Chinese agriculture industry. China Agric. Econ. Rev., 7, 102121, https://doi.org/10.1108/CAER-06-2013-0089.

    • Search Google Scholar
    • Export Citation
  • Fathelrahman, E. M., J. C. Ascough II, D. L. Hoag, R. W. Malone, P. Heilman, L. J. Wilesand, and R. S. Kanwar, 2011: Economic and stochastic efficiency comparison of experimental tillage systems in corn and soybean under risk. Exp. Agric., 47, 111136, https://doi.org/10.1017/S0014479710000979.

    • Search Google Scholar
    • Export Citation
  • Fotheringham, A. S., C. Brunsdon, and M. Charlton, 2002: Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley and Sons, 288 pp.

  • Guerra, A. F., G. C. Rodrigues, O. C. Rocha, and W. Evangelista, 2003: Necessidade hídrica no cultivo de feijão, trigo, milho e arroz sob irrigação no bioma Cerrado (Water requirements in the cultivation of beans, wheat, corn and rice under irrigation in the Cerrado biome). Embrapa, 15 pp.

  • Hardaker, J. B., J. W. Richardson, G. Lien, and K. D. Schumann, 2004: Stochastic efficiency analysis with risk aversion bounds: A simplified approach. Aust. J. Agric. Resour. Econ., 48, 253270, https://doi.org/10.1111/j.1467-8489.2004.00239.x.

    • Search Google Scholar
    • Export Citation
  • Hardaker, J. B., G. Lien, J. R. Anderson, and R. B. Huirne, 2015: Coping with Risk in Agriculture: Applied Decision Analysis. CABI, 288 pp.

  • Heimfarth, L. E., and O. Musshoff, 2011: Weather index‐based insurances for farmers in the North China Plain. Agric. Finance Rev., 71, 218239, https://doi.org/10.1108/00021461111152582.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2019: Summary for policymakers. Climate Change and Land, P. R. Shukla et al., Eds., Cambridge University Press, 36 pp., https://www.ipcc.ch/site/assets/uploads/sites/4/2020/02/SPM_Updated-Jan20.pdf.

  • Kath, J., S. Mushtaq, R. Henry, A. Adeyinka, and R. Stone, 2018: Index insurance benefits agricultural producers exposed to excessive rainfall risk. Wea. Climate Extremes, 22, 19, https://doi.org/10.1016/j.wace.2018.10.003.

    • Search Google Scholar
    • Export Citation
  • Kath, J., S. Mushtaq, R. Henry, A. A. Adeyinka, R. Stone, T. Marcussen, and L. Kouadio, 2019: Spatial variability in regional scale drought index insurance viability across Australia’s wheat growing regions. Climate Risk Manage., 24, 1329, https://doi.org/10.1016/j.crm.2019.04.002.

    • Search Google Scholar
    • Export Citation
  • Kusuma, A., B. Jackson, and I. Noy, 2018: A viable and cost-effective weather index insurance for rice in Indonesia. Geneva Risk Insur. Rev., 43, 186218, https://doi.org/10.1057/s10713-018-0033-z.

    • Search Google Scholar
    • Export Citation
  • Li, C., and S. Managi, 2022: GWPR.light: Geographically weighted panel regression, version 0.2.1. R package, https://CRAN.R-project.org/package=GWPR.light.

  • Lien, G., S. Størdal, J. B. Hardaker, and L. J. Asheim, 2007: Risk aversion and optimal forest replanting: A stochastic efficiency study. Eur. J. Oper. Res., 181, 15841592, https://doi.org/10.1016/j.ejor.2005.11.055.

    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., 2008: Vulnerabilidade, impactos e adaptação à mudança do clima no semi-árido do Brasil (Vulnerability, impacts and adaptation to climate change in semi-arid Brazil). Parcerias Estratégicas, 13, 149176.

    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., and M. Bernasconi, 2015: Regional differences in aridity/drought conditions over Northeast Brazil: Present state and future projections. Climatic Change, 129, 103115, https://doi.org/10.1007/s10584-014-1310-1.

    • Search Google Scholar
    • Export Citation
  • Markowitz, H., 1952: Portfolio selection. J. Finance, 7, 7791, https://doi.org/10.1111/j.1540-6261.1952.tb01525.x.

  • Melo, R. F., and T. V. Voltolini, 2019: Agricultura Familiar Dependente de Chuva no Semiárido (Rain-Dependent Family Farming in the Semiarid Region). Embrapa, 467 pp.

  • Ministério da Agricultura, Pecuária e Abastecimento, 2020: Atlas do seguro rural (Rural insurance atlas). MAPA, accessed 14 August 2020, https://mapa-indicadores.agricultura.gov.br/publico/extensions/SISSER/SISSER.html.

  • Miquelluti, D. L., V. A. Ozaki, and D. J. Miquelluti, 2022: An application of geographically weighted quantile lasso to weather index insurance design. Rev. Adm. Contemp., 26, 118, https://doi.org/10.1590/1982-7849rac2022200387.en.

    • Search Google Scholar
    • Export Citation
  • Odening, M., and O. Musshoff, and W. Xu, 2007: Analysis of rainfall derivatives using daily precipitation models: Opportunities and pitfalls. Agric. Finance Rev., 67, 135156, https://doi.org/10.1108/00214660780001202.

    • Search Google Scholar
    • Export Citation
  • Oliver, J. E., 2005: Encyclopedia of World Climatology. Springer, 854 pp.

  • Park, C. E., and Coauthors, 2018: Keeping global warming within 1.5°C constrains emergence of aridification. Nat. Climate Change, 8, 7074, https://doi.org/10.1038/s41558-017-0034-4.

    • Search Google Scholar
    • Export Citation
  • Parodi, P., 2014: Pricing in General Insurance. CRC Press, 586 pp.

  • Pellicone, G., T. Caloiero, and I. Guagliardi, 2019: The De Martonne aridity index in Calabria (southern Italy). J. Maps, 15, 788796, https://doi.org/10.1080/17445647.2019.1673840.

    • Search Google Scholar
    • Export Citation
  • Pour, S. H., A. K. Abd Wahab, and S. Shahid, 2020: Spatiotemporal changes in aridity and the shift of drylands in Iran. Atmos. Res., 233, 104704, https://doi.org/10.1016/j.atmosres.2019.104704.

    • Search Google Scholar
    • Export Citation
  • Saldanha, R., R. Akbarinia, P. Valduriez, M. Pedroso, V. Ribeiro, C. Cardoso, E. Pena, and F. Porto, 2023: Brclimr: Fetch zonal statistics of weather indicators for Brazilian municipalities, version 0.1.2. R package, https://CRAN.R-project.org/package=brclimr.

  • Raucci, G. L., R. Lanna, F. Silveira, and D. H. D. Capitani, 2019: Development of weather derivatives: Evidence from the Brazilian soybean market. Ital. Rev. Agric. Econ., 74, 1728, https://doi.org/10.13128/rea-10850.

    • Search Google Scholar
    • Export Citation
  • R Core Team, 2021: R: A language and environment for statistical computing. R Foundation for Statistical Computing, https://www.R-project.org/.

  • Schlenker, W., and M. J. Roberts, 2009: Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl. Acad. Sci. USA, 106, 15 59415 598, https://doi.org/10.1073/pnas.0906865106.

    • Search Google Scholar
    • Export Citation
  • Scrucca, L., M. Fop, T. B. Murphy, and A. E. Raftery, 2016: mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. R J., 8, 289317, https://doi.org/10.32614/RJ-2016-021.

    • Search Google Scholar
    • Export Citation
  • Shen, Z., and M. Odening, 2013: Coping with systemic risk in index‐based crop insurance. Agric. Econ., 44 (1), 113, https://doi.org/10.1111/j.1574-0862.2012.00625.x.

    • Search Google Scholar
    • Export Citation
  • Silva, A. F., and A. Regitano-Neto, 2019: As principais culturas anuais e bianuais na agricultura familiar (The main annual and biannual crops in family farming). Agricultura Familiar Dependente de Chuva no Semiárido (Rain-Dependent Family Farming in the Semiarid Region), Melo, R. F. and T. V. Voltolini, Eds., Embrapa, 45–83.

  • Soto-Caro, A., 2019: ceRtainty: Certainty equivalent, version 1.0.0. R package, https://CRAN.R-project.org/package=ceRtainty.

  • Stoppa, A., and U. Hess, 2003: Design and use of weather derivatives in agricultural policies: The case of rainfall index insurance in Morocco. Int. Conf. Agricultural Policy Reform and the WTO: Where Are We Heading, Capri, Italy, The World Bank, 17 pp., https://www.farm-d.org/app/uploads/2019/05/Design-and-Use-of-Weather-Derivatives-Morocco.pdf.

  • Turvey, C. G., J. Du, Y. He, and A. Ortiz-Bobea, 2021: A vulnerability index for priority targeting of agricultural crops under a changing climate. Climatic Change, 166, 34, https://doi.org/10.1007/s10584-021-03135-8.

    • Search Google Scholar
    • Export Citation
  • Wang, H., N. Adusumilli, D. Gentry, and L. Fultz, 2020: Economic and stochastic efficiency analysis of alternative cover crop systems in Louisiana. Exp. Agric., 56, 651661, https://doi.org/10.1017/S0014479720000216.

    • Search Google Scholar
    • Export Citation
  • World Bank, 2011: Weather index insurance for agriculture: Guidance for development practitioners. Agriculture and Rural Development Discussion Paper 50, 116 pp., https://documents1.worldbank.org/curated/en/590721468155130451/pdf/662740NWP0Box30or0Ag020110final0web.pdf.

  • Xavier, A. C., C. W. King, and B. R. Scanlon, 2016: Daily gridded meteorological variables in Brazil (1980–2013). Int. J. Climatol., 36, 26442659, https://doi.org/10.1002/joc.4518.

    • Search Google Scholar
    • Export Citation
  • Xiao, Y., and J. Yao, 2019: Double trigger agricultural insurance products with weather index and yield index. China Agric. Econ. Rev., 11, 299316, https://doi.org/10.1108/CAER-01-2018-0021.

    • Search Google Scholar
    • Export Citation
  • Yu, D., 2010: Exploring spatiotemporally varying regressed relationships: The geographically weighted panel regression analysis. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 38, 134139.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 425 425 19
Full Text Views 45 45 3
PDF Downloads 47 47 2