SCC76: Economics and Management of Risk in Agriculture and Natural Resources
(Multistate Research Coordinating Committee and Information Exchange Group)
SCC76: Economics and Management of Risk in Agriculture and Natural Resources
Duration: 10/01/2014 to 09/30/2019
Statement of Issues and Justification
Risk and uncertainty are pervasive in agriculture and other natural resource industries. Decisions are constrained by limited and uncertain knowledge. The unpredictability of future weather, vagaries of government policies affecting resource values and commodity prices, and the growing importance of environmental considerations in policy-making add to the uncertainty surrounding decision analysis. Although these uncertainties are widespread, their nature and impact vary across firms, regions, and government agencies.
Much progress has been made in understanding decision-making under uncertainty, but the knowledge base is incomplete. There is a continuing need to examine both short- and long-term effects of risk in agriculture and other natural resource based industries. Recent developments in both commodity and financial markets highlight the importance of understanding the ever changing nature of risk and risk management in production, both on the input and output side, as well as prices. Of a particular importance and interest is the study of the integration between the food and energy markets and the consequences of such integration with respect to risk and risk management. Better understanding of how risk management practices affect the economic and natural environment, influence the adoption of new technology, and interact with public policies will improve firm-level decision-making and aid policy-makers in addressing important policy issues related to risk. One of the strengths of the predecessor regional projects has been the national scope of the institutions represented by the participants.
Although some risk problems and research opportunities may be primarily of local interest, the earlier projects have demonstrated that the analytical procedures developed and presented by project participants can often be successfully applied to understanding and addressing a variety of local risk problems. Hence, the information exchanged among project participants generates local benefits for the institutions represented. In addition, the information exchange format creates opportunities for researchers to interact on issues of mutual interest, sometimes fostering extramural grant-writing efforts. The renewal of SCC-76 would continue the long tradition of bringing together researchers from around the country to strengthen and coordinate research and policy efforts related to risk analysis. Research conducted and presented within the predecessor regional projects focused on characterizing producer attitudes toward risk, deriving distributional aspects of commodity prices and yields, and developing improved decision models for considering risk from the perspective of the individual decision-maker. Due largely to the efforts of participants in those earlier projects, consideration of risk is now widespread in applied agricultural economics research and agricultural farm policy. Many of the methods developed under previous projects and presented at annual meetings are now widely utilized by agricultural and natural resource economists who have themselves never been formally affiliated with these projects.
Over the years, participants have thought it important to broaden the scope of the project beyond just farm-level decision-making to include broader agricultural and natural resource risk issues. This has proven quite beneficial in that it has attracted researchers with broader interests, while still maintaining a core group with interests in farm-level decision-making. Synergies have resulted as participants share cutting-edge approaches to risk analysis as practiced in specific sub-disciplinary areas. Bridging these sub-disciplinary boundaries allows participants to consider alternative approaches that may benefit their specific risk research programs. The continued participation of individuals from government entities suggests that research presented at the annual meetings has important public policy implications.
Related, Current, and Previous Work:
While risk and risk management strategies are nothing new to agriculture, federal insurance policies are still attempting to find better ways to insulate farmers from extreme risks. The formation of the Federal Crop Insurance Corporation (FCIC) by Congress in 1938 was the first major step towards insuring farmers against risk using federal funds. The goal of this program was to protect farmer’s income from crop failure or price collapse. The Great Depression and major droughts throughout the 1930s led to the formation of federal crop insurance offerings to help farmers manage risk. This program was originally plagued with low participation rates and large losses, leading to the reduction of such programs (Goodwin and Smith, 1995). Since then, many refinements to federal farm support programs have been developed and evaluated by members of the SCC76 group.
Crop Yield Distribution
This rebirth of federal crop insurance programs over the past 30 years has inspired an extensive amount of literature aimed at characterizing agricultural yields. This motivation can be found in much of the crop insurance literature that attempts to characterize conditional mean yield densities to evaluate the risks involved with crop management and accurately price crop insurance premiums. Much research has focused on the use of the normal distribution to characterize crop yields (Just and Weninger, 1999; Atwood, Shaik and Watts , 2003). Other studies focus on alternative distributions to estimate conditional yields (Ramirez et al., 2003; Sherrick et al., 2004, Nelson, 1990; Nelson and Preckel, 1989; Gallagher 1987; and Ker and Coble, 2003). Many of these studies are further examined in Goodwin and Ker (2002). The impact of spatial dependence has also been evaluated and shown to importantly determine regional actuarial performance (Bekkerman et al., 2008; Woodard et al., 2012). With the growth in revenue-based insurance policies, econometric studies evaluating more effective ways to identify the multivariate nature of revenue has led to gains in rating efficiency (Anderson et al., 2009; Holt and Chavas, 2002).
Participation, Moral Hazard, and Adverse Selection Related to Farm Policy
As crop insurance has become the dominant federal safety net for farmers in the United States farm policy, studies evaluating the impact of new policies on the demand for participating in crop insurance programs are an important element of farm policy analysis. Past studies have characterized the demand for crop insurance through empirical studies (Coble et al., 1996). Along the same lines, as crop insurance becomes more common in typical farm risk management strategies, it becomes important to identify any indirect impacts from crop insurance participation (Adhikari et al., 2013; Goodwin et al., 2004; Hennessy et al., 1997; and Ifft et al., 2013; and Turvey et al., 2002). In order to frame these empirical studies, theoretical developments have also been extended by studies such as Coble and Knight (2002) and Meyer (2002). As it is important to evaluate past phenomena, recent studies have also focused on the impact of changed in crop insurance policies as well as proposing new ideas in the development of crop insurance delivery systems (Collins and Bulut, 2013; Glauber, 2004; Paulson et al., 2013; Ramirez and Colson, 2013; Sproul et al, 2013; and Zackarias and Collins, 2013)
Livestock Marketing and Insurance
Livestock production is one that has followed a path separate from crop production. Most notably, insurance is substantially different between the two modes of production. Recent studies have evaluated the impact of new and proposed legislation on farm operations (Woodard and Baker, 2013). Studies have also focused on the development of new products specifically designed for domestic livestock producers (Hart et al., 2001) and livestock producers in developing countries (Chantarat et al., 2013; Deng et al, 2007). Other studies have characterized the multivariate relationship between cattle production and weather or ex ante variables (Belasco, 2008; Belasco et al, 2009). Theoretical developments evaluating the beef marketing chain have also provided insights into characterizing variability in beef prices (Fausti and Feuz, 1995).
The SCC will provide a scientific/professional forum to facilitate the exchange of theoretical and methodological approaches to risk analysis, and to nurture the development of original concepts and preliminary research efforts related to agriculture and natural resources.
Develop new methods of micro-level modeling of how risk affects a number of important natural resource and environmental risk issues, including forest, wildlife, and range management, ground- and surface-water pollution, the environmental sustainability of agricultural production systems, and conflicts in resource demands between agricultural and competing users.
Evaluate firm-level analysis of production, financial, marketing, and environmental risks including analysis of how these risks impact (and are impacted by) technology adoption and access to information
Disseminate firm-level analysis of various risk management strategies such as forward pricing, insurance purchasing, and diversification and how government policies affect optimal risk management strategies.
Develop and apply economic theory and the behavioral foundations of decision-making under uncertainty, including extensions of expected utility theory, challenges to expected utility theory, behavioral impacts of asymmetrically distributed information, and understanding decision-maker risk attitudes within a portfolio context.
Evaluate the impact of public policy on the risk environment of individuals, firms, and sectors within the economy, including exogenous trade shocks, food safety regulations, and commodity income support programs, changes in financial and agricultural insurance institutions, bioenergy, and resource pricing policies
Procedures and Activities
Annual meetings will continue to be held to allow for the exchange of information on risk and uncertainty. Advertisement of the meetings will be designed to reach a large number of potential participants, such as announcements placed in the bimonthly newsletter of the Agricultural and Applied Economics Association. Program schedules will be distributed prior to the meeting to attract additional participation. Predecessor projects have organized paper sessions and organized symposia at various agricultural economics professional meetings. Under the project that is now expiring, the group has published its research presentations online through the AgEcon Search website at http://ageconsearch.umn.edu/ and also several presentations from the 2008 annual meeting resulted in papers published in the April 2009 issue of JARE. Additionally, presentations from the 2012 annual meeting resulted in a special edition published in Choices Magazine.
Expected Outcomes and Impacts
- Around 30 individuals typically attend the annual meeting, though in some years attendance has been higher. Most participants are university researchers and graduate students though a number of extension economists, government employees, and private-sector employees also regularly attend. Several USDA employees have attended the most recent annual meetings, mostly from the Risk Management Agency and from the Economic Research Service. In addition, the Director of the Southern Region Risk Management Education Center (SRRMEC) often provides a discussion on the mission and objectives of the SRRMEC to the group. Economists that work for private-sector entities such as agricultural commodity organizations, consulting firms, or agricultural insurance firms also have attended regularly. Normally between 16 and 18 formal research presentations are made during the 1.5 day annual meeting. However, this number in recent meetings has been as high as 24 presentations.
- Presentations will describe cutting-edge research on risk issues related to agriculture and natural resources. The annual meeting schedule also allows ample time for informal exchanges among researchers, government employees, and private-sector participants during breaks, at mealtimes, or in the evenings. These breaks are intended to enhance existing research as well as incite new research ideas.
- Given the high level of expertise typically found in this group, research often provides important insights to inform and evaluate farm policy and trends in agriculture and its impact on farm-level decisions and the future landscape of agriculture.
- The group commonly publishes its research presentations online through the AgEcon Search website at http://ageconsearch.umn.edu/. It is anticipated that when opportunities arise the group will continue to present research findings through appropriate outlets. o In 2008, SCC-76 made arrangements with the Journal of Agricultural and Resource Economics (JARE) to publish a number of papers that were presented at the annual meeting. These papers, in the general areas of (i) Advances in Risk Modeling Theory and Methodology, (ii) Insurance and Government Policy, and (iii) Market Instruments: Weather Derivatives, Commodity Futures and Options, were published in the April 2009 issue of JARE. o Discussions among members of the SCC-76 group directly led to the formation of the new Applied Risk Analysis Section of the Agricultural and Applied Economics Association. This section has already grown to 89 members. o In 2012, SCC-76 made arrangements with Choices Magazine to publish a number of papers that were presented at the annual meeting. These six papers were published as a special issue entitled “Current Issues in Risk Management and U.S. Agricultural Policy.” This particular outlet allows the SCC-76 group’s research to reach beyond its typical audience as the magazine is targeted to non-economists and mostly targets policy-makers, extension agents, commodity organizations, and farmers.
Projected ParticipationView Appendix E: Participation
Although this project does not have a specific extension and outreach component, a key role of this group is to foster the development of the field, especially the support and furthering of new research ideas on risk and risk management. Information developed in this project will be disseminated to stakeholders and scientific audiences through refereed journal articles, conference abstracts, extension bulletins when appropriate, AgEcon Search website, and through our annual meetings and reports. The project will also continue to maintain its web page which will be updated by the Chair of the committee.
Administrative issues will be addressed during the business meeting held in conjunction with the annual meeting. During the business meeting elections will be conducted to fill the position of program chair. The program chair coordinates the program for the next annual meeting. The out-going program chair becomes the project chair and is responsible for conducting the business meeting, submitting an annual report on project activities, and maintaining communication with the administrative advisor and the Southern Association of Agricultural Experiment Station Directors.
Adhikari, S., T.O. Knight, and E.J. Belasco (2013). “Yield Guarantee Determination and the Producer Welfare Benefits of Crop Insurance.” Journal of Agricultural and Resource Economics. Vol. 38, No. 1: 78-92.
Anderson, J.D., A. Harri, and K.H. Coble (2009). “Techniques for Multivariate Simulation from Mixed Marginal Distributions with Applications to Whole-Farm Revenue Simulation.” Journal of Agricultural and Resource Economics. Vol. 34, No. 1: 53-67.
Atwood, J., S. Shaik, and M. Watts (2003) “Are crop yields normally distributed? A Reexamination.” American Journal of Agricultural Economics 85(4), 888–901
Bekkerman, A., B.K. Goodwin, and N.E. Piggott (2008). “Spatio-temporal Risk and Severity Analysis of Soybean Rust in the United States.” Journal of Agricultural and Resource Economics. Vol. 33, No. 3: 311-331.
Belasco, E.J. (2008). “The Role of Price Risk Management in Mitigating Fed Cattle Profit Exposure.” Journal of Agricultural and Resource Economics. Vol 33, No. 3: 332-348.
Belasco, E.J., S.K. Ghosh, and B.K. Goodwin (2009). “A Multivariate Evaluation of Ex ante Risks Associated with Fed Cattle Production.” American Journal of Agricultural Economics. Vol 91, No. 2: 431-443.
Chantarat, S., A.G. Mude, C.B. Barrett, M.R. Carter. 2013. “Designing Index-Based Livestock Insurance for Managing Assett Risk in Norther Kenya.” Journal of Risk and Insurance. 80 (1): 205-237.
Coble, K.H. and T.O. Knight. (2002) ‘Crop Insurance As a Tool for Price and Yield Risk Management.’ In A Comprehensive Assessment of the Role of Risk in U.S. Agriculture, ed. R. E. Just and R. D. Pope (Boston: Kluwer Academic Press) pp. 445-468.
Coble, K. H., T. O. Knight, R. D. Pope, and J. R. Williams (1996) ‘Modeling farm level crop insurance demand with panel data.’ American Journal of Agricultural Economics 78, 439–447
Collins, K. and H. Bulut. “How Will the Farm Bill’s Supplemental Revenue Programs Affect Crop Insurance?.” Choices. (Sept. 2013). Retreived from http://www.choicesmagazine.org.
Deng, X., B.J. Barnett, D.V. Vedenov, and J.W. West. 2007. “Hedging Dairy Production Losses Using Weather-Based Index Insurance.” Agricultural Economics. 36(2): 271-280.
Fausti, S. W., and D. M. Feuz (1995) ‘Production uncertainty and factor price disparity in the slaughter cattle market: Theory and evidence.’ American Journal of Agricultural Economics 77, 533–540
Gallagher, P. (1987) ‘U.S. soybean yields: Estimation and forecasting with nonsymmetric disturbance.’ American Journal of Agricultural Economics 69, 798–803.
Glauber, J. W. (2004) ‘Crop insurance reconsidered.’ American Journal of Agricultural Economics 86, 1179–1195.
Goodwin, B. K., and A. P. Ker (2002) ‘Modeling price and yield risk.’ In A Comprehensive Assessment of the Role of Risk in U.S. Agriculture, ed. R. E. Just and R. D. Pope (Boston: Kluwer Academic Press) pp. 289–323.
Goodwin, B. K., and V. H. Smith (1995) The Economics of Crop Insurance and Disaster Aid (The AEI Press).
Goodwin, B. K., M. L. Vandeveer, and J. L. Deal (2004) ‘An empirical analysis of acreage effects of participation in the federal crop insurance program.’ American Journal of Agricultural Economics 86, 1058–1077.
Hart, C. E., B. A. Babcock, and D. J. Hayes (2001) ‘Livestock revenue insurance.’ Journal of Futures Markets 21(6), 553–580.
Hennessy, D. A., B. A. Babcock, and D. J. Hayes (1997) ‘Budgetary and producer welfare effects of revenue insurance.’ American Journal of Agricultural Economics 79, 1024–1034.
Holt, M.T. and J.P. Chavas. (2002) ‘The Econometrics of Risk.’ In A Comprehensive Assessment of the Role of Risk in U.S. Agriculture, ed. R. E. Just and R. D. Pope (Boston: Kluwer Academic Press) pp. 213-242.
Ifft, J., T. Keuthe, and M. Morehart. “Farm Debt Use by Farms with Crop Insurance.” Choices. (Sept. 2013). Retreived from http://www.choicesmagazine.org.
Just, R. E., and Q. Weninger (1999) ‘Are crop yields normally distributed?’ American Journal of Agricultural Economics 81, 287–304
Ker, A. P., and K. Coble (2003) ‘Modeling conditional yield densities.’ American Journal of Agricultural Economics 85(2), 291–304
Meyer, J. (2002) ‘Expected Utility as a Paradigm for Decision Making in Agriculture.’ In A Comprehensive Assessment of the Role of Risk in U.S. Agriculture, ed. R. E. Just and R. D. Pope (Boston: Kluwer Academic Press) pp. 3-20.
Nelson, C. H. (1990) ‘The influence of distribution assumptions on the calculation of crop insurance premia.’ North Central Journal of Agricultural Economics 12, 71–78
Nelson, C. H., and P. V. Preckel (1989) ‘The conditional beta distribution as a stochastic production function.’ American Journal of Agricultural Economics 71, 370–378
Paulson, N.D., J.D. Woodard, and B.A. Babcock. 2013. “Modeling ‘Shallow Loss’ Crop Revenue Programs: Issues and Implications for the 2012 Farm Bill.” Agricultural Finance Review 73(2), 329-344.
Ramirez, O.A. and G. Colson. “Can We Do Better Than Crop Insurance? The Case for Farmer Owned Crop Insurance Savings Accounts.” Choices. (Sept. 2013). Retreived from http://www.choicesmagazine.org.
Ramirez, O. A., S. Misra, and J. Field (2003) ‘Crop-yield distributions revisited.’ American Journal of Agricultural Economics 85(1), 108–120
Sherrick, B. J., F. C. Zanini, G. D. Schnitkey, and S. H. Irwin (2004) ‘Crop insurance valuation under alternative yield distributions.’ American Journal of Agricultural Economics 86(2), 406–419
Sproul, T.W., D. Zilberman, and J.C. Cooper. “Deductibles Versus Coinsurance in Shallow-Loss Crop Insurance.” Choices. (Sept. 2013). Retreived from http://www.choicesmagazine.org.
Turvey, C. G., M. Hoy, and Z. Islam (2002) ‘The role of ex ante regulations in addressing problems of moral hazard in agricultural insurance.’ American Journal of Agricultural Economics 62(2), 103–116
Woodard, J.D. and D. Baker. “2013 Farm Bill Dairy Title Proposals Redistribute Program Benefits toward States with Larger Farms.” Choices. (Sept. 2013). Retreived from http://www.choicesmagazine.org.
Woodard, J.D., G.D. Schnitkey, B.J. Sherrick, N. Lozano-Gracia, and L. Anselin. 2012. “A Spatial Econometric Analysis of Loss Experience in the U.S. Crop Insurance Program.” Journal of Risk and Insurance, 79(1), 261-286.
Zackarias, T.P. and K.J. Collins. “Ten Considerations Regarding the Role of Crop Insurance in the Agricultural Safety Net.” Choices. (Sept. 2013). Retreived from http://www.choicesmagazine.org.