IDEAS home Printed from https://ideas.repec.org/a/spr/envsyd/v40y2020i1d10.1007_s10669-019-09748-w.html
   My bibliography  Save this article

Market-based methods for monetizing uncertainty reduction

Author

Listed:
  • Roger Cooke

    (Resorses for the Future)

  • Alexander Golub

    (American University)

Abstract

New measurement systems are often expensive and need a solid economic justification. Traditional tools based on the value of information are sometimes difficult to apply. When risks are traded in a market, it may be possible to use market instruments to monetize the reductions in uncertainty. This paper illustrates such market-based methods with a satellite system designed to reduce uncertainty in predicting soil moisture in the USA. Soil moisture is a key variable in managing agricultural production and predicting crop yields. Using data on corn and soybean futures, we find that a 30% reduction in the weather-related component of uncertainty in corn and soybean futures pricing yields a yearly US consumer surplus of $1.44 billion. The total present value of information from the satellite system for the USA—calculated with a 3% discount rate—is about $22 billion, assuming the system is in operation for 20 years. The global value of the improvements in weather forecasting could be $63 billion.

Suggested Citation

  • Roger Cooke & Alexander Golub, 2020. "Market-based methods for monetizing uncertainty reduction," Environment Systems and Decisions, Springer, vol. 40(1), pages 3-13, March.
    • Handle: RePEc:spr:envsyd:v:40:y:2020:i:1:d:10.1007_s10669-019-09748-w
    DOI: 10.1007/s10669-019-09748-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10669-019-09748-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10669-019-09748-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Westcott, Paul C. & Jewison, Michael, 2013. "Weather Effects on Expected Corn and Soybean Yields," Agricultural Outlook Forum 2013 146846, United States Department of Agriculture, Agricultural Outlook Forum.
    2. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    3. paolo pianca, 2005. "Simple Formulas to Option Pricing and Hedging in the Black- Scholes Model," Finance 0511005, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zachary A. Collier & James H. Lambert & Igor Linkov, 2020. "Interdisciplinary mathematical methods for societal decision-making and resilience," Environment Systems and Decisions, Springer, vol. 40(1), pages 1-2, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2021. "Sustainable Supply Chains with Blockchain, IoT and RFID: A Simulation on Order Management," Sustainability, MDPI, vol. 13(11), pages 1-23, June.
    2. Valeria Costantini & Francesco Crespi & Giovanni Marin & Elena Paglialunga, 2016. "Eco-innovation, sustainable supply chains and environmental performance in European industries," LEM Papers Series 2016/19, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Lee, Alice J. & Ames, Daniel R., 2017. "“I can’t pay more” versus “It’s not worth more”: Divergent effects of constraint and disparagement rationales in negotiations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 141(C), pages 16-28.
    4. Hussain, Hadia & Murtaza, Murtaza & Ajmal, Areeb & Ahmed, Afreen & Khan, Muhammad Ovais Khalid, 2020. "A study on the effects of social media advertisement on consumer’s attitude and customer response," MPRA Paper 104675, University Library of Munich, Germany.
    5. A. G. Fatullayev & Nizami A. Gasilov & Şahin Emrah Amrahov, 2019. "Numerical solution of linear inhomogeneous fuzzy delay differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 18(3), pages 315-326, September.
    6. Cyril Chalendard, 2015. "Use of internal information, external information acquisition and customs underreporting," Working Papers halshs-01179445, HAL.
    7. Arun Advani & William Elming & Jonathan Shaw, 2023. "The Dynamic Effects of Tax Audits," The Review of Economics and Statistics, MIT Press, vol. 105(3), pages 545-561, May.
    8. Philippe Aghion & Ufuk Akcigit & Matthieu Lequien & Stefanie Stantcheva, 2017. "Tax Simplicity and Heterogeneous Learning," NBER Working Papers 24049, National Bureau of Economic Research, Inc.
    9. Marie Bjørneby & Annette Alstadsæter & Kjetil Telle, 2018. "Collusive tax evasion by employers and employees. Evidence from a randomized fi eld experiment in Norway," Discussion Papers 891, Statistics Norway, Research Department.
    10. Chuangen Gao & Shuyang Gu & Jiguo Yu & Hai Du & Weili Wu, 2022. "Adaptive seeding for profit maximization in social networks," Journal of Global Optimization, Springer, vol. 82(2), pages 413-432, February.
    11. Koessler, Frederic & Laclau, Marie & Renault, Jérôme & Tomala, Tristan, 2022. "Long information design," Theoretical Economics, Econometric Society, vol. 17(2), May.
    12. Jamal El-Den & Pratap Adikhari & Pratap Adikhari, 2017. "Social media in the service of social entrepreneurship: Identifying factors for better services," Journal of Advances in Humanities and Social Sciences, Dr. Yi-Hsing Hsieh, vol. 3(2), pages 105-114.
    13. Annette Alstadsæter & Wojciech Kopczuk & Kjetil Telle, 2019. "Social networks and tax avoidance: evidence from a well-defined Norwegian tax shelter," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(6), pages 1291-1328, December.
    14. Xiongnan Jin & Sejin Chun & Jooik Jung & Kyong-Ho Lee, 0. "A fast and scalable approach for IoT service selection based on a physical service model," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    15. Jun Hong Park & Sang Ho Kook & Hyeonu Im & Soomin Eum & Chulung Lee, 2018. "Fabless Semiconductor Firms’ Financial Performance Determinant Factors: Product Platform Efficiency and Technological Capability," Sustainability, MDPI, vol. 10(10), pages 1-22, September.
    16. Sebastian Kaumanns, 2019. "“Some fuzzy math”: relational information on debt value adjustments by managers and the financial press," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 755-794, December.
    17. Samuel J Gershman, 2015. "A Unifying Probabilistic View of Associative Learning," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-20, November.
    18. Arun Advani, 2022. "Who does and doesn't pay taxes?," Fiscal Studies, John Wiley & Sons, vol. 43(1), pages 5-22, March.
    19. Steve Fortin & Ahmad Hammami & Michel Magnan, 2021. "Re‐exploring Fair Value Accounting and Value Relevance: An Examination of Underlying Securities," Abacus, Accounting Foundation, University of Sydney, vol. 57(2), pages 220-250, June.
    20. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.

    More about this item

    Keywords

    Value of information; Options pricing; SMAP; Bachelier formula; Black–Scholes-Merton model;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:envsyd:v:40:y:2020:i:1:d:10.1007_s10669-019-09748-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.