Abstract
This paper analyses the influencing factors of farmers’ use of price hedging instruments (PHIs) based upon a discrete choice experiment with German grain farmers. A mixed logit model is used to determine whether farmers’ choices of PHIs against cash sales are influenced by their price expectation, their risk attitude and their available storage capacities. The results show that farmers with a price expectation below the actual price level have a higher preference for using PHIs against cash sales in general and that the individual degree of risk aversion can have a significant impact on farmers’ choices of a specific PHI. A generally lower preference of farmers with available storage capacities for using PHIs as assumed in many theoretical contributions in the literature, however, cannot be confirmed.
Acknowledgements
We would like to thank the anonymous referees and the editors for their helpful comments and suggestions. We gratefully acknowledge financial support from the German Research Foundation (DFG).
Appendix 1: Decision-making situation and choice-sets of the experiment
The online survey was put online during January 2012 and was brought to farmers’ notice through online newsletters of two agricultural consulting companies. In addition, students from the University of Göttingen were also asked to make farmers aware of the experiment.
All participating farmers made the six choice-sets of the DCE in one sitting, although they had the possibility to interrupt the experiment. Farmers were not offered an incentive or compensation to participate in the experiment. In addition, any feedback was provided.
The choice-sets differed only in the actual spot market price which the farmer was asked to assume while deciding between the marketing instruments. To avoid an order effect when presenting the choice-sets to the farmers, the sequence of the choice-sets was randomly chosen by the computer. In the following, the DCE is presented.
The six following choice-sets are presented to the respondent in a random order:
Appendix 2: Stata 12 code
In addition to the independent variables depicted in Table 2 of the article, the dependent variable “Choice” specifies the choice made by a farmer in a specific choice occasion. This variable is dummy-coded with 1 = alternative is chosen and 0 = alternative is not chosen. Furthermore we need two other variables: “Number” and “Occasion”. “Number” is a numeric identifier for the farmers (range from 1 to 136). In this way it is taken into account that the farmer answer six choice-sets. “Occasion” is a numeric identifier for the choice occasions (range from 1 to 6).
To integrate independent variables which do not vary over alternatives into the model it is necessary to generate interaction terms with the alternative specific constants. This procedure leads to four variables per independent variable (cf. the above-mentioned command for STATA).
For more information regarding the STATA-command “mixlogit” readers are referred to the STATA help-file.
Mixlogit
choice
sequence_forward
sequence_managed
sequence_futures
sequence_option
age_forward
age_managed
age_futures
age_option
education_forward
education_managed
education_futures
education_option
price_expectation_forward
price_expectation_managed
price_expectation_futures
price_expectation_option
risk_attitude_forward
risk_attitude_managed
risk_attitude_futures
risk_attitude_option
storage_forward
storage_managed
storage_futures
storage_option,
rand(asc_forward asc_managed asc_futures asc_option)
group(occasion) id(number) nrep(500)
References
Barry, P. J., and D. R.Fraser. 1976. “Risk Management in Primary Agricultural Production: Methods, Distribution, Rewards, and Structural Implications.” American Journal of Agricultural Economics58:286–95.10.2307/1238980Search in Google Scholar
Beal, D. J.1996. “Emerging Issues in Risk Management in Farm Firms.” Review of Marketing and Agricultural Economics64:336–47.Search in Google Scholar
Bhat, C. R.2001. “Quasi-Random Maximum Simulated Likelihood Estimation of the Mixed Multinomial Logit Model.” Transportation Research Part B: Methodological35:677–93.10.1016/S0191-2615(00)00014-XSearch in Google Scholar
Boxall, P. C., and W. L.Adamowicz. 2002. “Understanding Heterogeneous Preferences in Random Utility Models: A Latent Class Approach.” Environmental and Resource Economics23:421–46.10.1023/A:1021351721619Search in Google Scholar
Breustedt, G., J.Müller‐Scheeßel, and U.Latacz‐Lohmann. 2008. “Forecasting the Adoption of GM Oilseed Rape: Evidence from a Discrete Choice Experiment in Germany.” Journal of Agricultural Economics59:237–56.10.1111/j.1477-9552.2007.00147.xSearch in Google Scholar
De Palma, A., J. G.Myers, and Y.Papageorgiou. 1994. “Rational Choice under Imperfect Ability to Choose.” American Economic Review84:419–40.Search in Google Scholar
DeShazo, J. R., and G.Fermo. 2002. “Designing Choice Sets for Stated Preference Methods: The Effects of Complexity on Choice Consistency.” Journal of Environmental Economics and Management44:123–43.10.1006/jeem.2001.1199Search in Google Scholar
Dohmen, T., A.Falk, D.Huffman, U.Sunde, J.Schupp, and G. G.Wagner. 2011. “Individual Risk Attitudes: Measurement, Determinants, and Behavioral Consequences.” Journal of the European Economic Association3:522–50.10.1111/j.1542-4774.2011.01015.xSearch in Google Scholar
Eales, J. S., B. K.Engel, R. J.Hauser, and S. R.Thompson. 1990. “Grain Price Expectations of Illinois Farmers and Grain Merchandisers.” American Journal of Agricultural Economics72:701–8.10.2307/1243040Search in Google Scholar
Espinosa‐Goded, M., J.Barreiro‐Hurlé, and E.Ruto. 2010. “What Do Farmers Want from Agri-Environmental Scheme Design? A Choice Experiment Approach.” Journal of Agricultural Economics61:259–73.10.1111/j.1477-9552.2010.00244.xSearch in Google Scholar
European Commission. 2005. “Communication from the Commission to the Council on Risk and Crisis Management in Agriculture.” COM (2005) 74, Brussels.Search in Google Scholar
Franken, J. R. V., J. M. E.Pennings, and P.Garcia. 2012. “Crop Production Contracts and Marketing Strategies: What Drives Their Use?” Agribusiness28:324–40.10.1002/agr.21293Search in Google Scholar
Garcia, P., and R. M.Leuthold. 2004. “A Selected Review of Agricultural Commodity Futures and Options Markets.” European Review of Agricultural Economics31:235–72.10.1093/erae/31.3.235Search in Google Scholar
Goodwin, B. K., and T. C.Schroeder. 1994. “Human Capital, Producer Education Programs, and the Adoption of Forward-Pricing Methods.” American Journal of Agricultural Economics76:936–47.10.2307/1243753Search in Google Scholar
Greene, W. H., and D. A.Hensher. 2003. “A Latent Class Model for Discrete Choice Analysis: Contrasts with Mixed Logit.” Transportation Research Part B37:681–98.10.1016/S0191-2615(02)00046-2Search in Google Scholar
Hensher, D. A., and W. H.Greene. 2003. “The Mixed Logit Model: The State of Practice.” Transportation30:133–76.10.1023/A:1022558715350Search in Google Scholar
Hole, A. R.2007. “Estimating Mixed Logit Models Using Maximum Simulated Likelihood.” The StataJournal7:388–401.Search in Google Scholar
Holt, M. T., and J. A.Brandt. 1985. “Combining Price Forecasting with Hedging of Hogs: An Evaluation Using Alternative Measures of Risk.” The Journal of Futures Markets5:297–309.10.1002/fut.3990050302Search in Google Scholar
Irwin, J. R., G. H.McClelland, and W. D.Schulze. 1992. “Hypothetical and Real Consequences in Experimental Auctions for Insurance against Low Probability Risks.” Journal of Behavioral Decision Making5:107–16.10.1002/bdm.3960050203Search in Google Scholar
Katchova, A. L., and M. J.Miranda. 2004. “Characteristics Affecting Marketing Contract Decisions.” American Journal of Agricultural Economics86:88–102.10.1111/j.0092-5853.2004.00564.xSearch in Google Scholar
Kuehberger, A., M.Schulte-Mecklenbeck, and J.Perner. 2002. “Framing Decisions: Hypothetical and Real.” Organizational Behavior and Human Decision Processes89:1162–75.10.1016/S0749-5978(02)00021-3Search in Google Scholar
Louviere, J. D., D. A.Hensher, and J. D.Swait. 2010. Stated Choice Methods – Analysis and Applications. Cambridge: University Press.Search in Google Scholar
Loy, J. P., and A.Pieniadz. 2009. “Optimal Grain Marketing Revisited: A German and Polish Perspective.” Outlook on Agriculture38:47–54.10.5367/000000009787762761Search in Google Scholar
Lusk, J. L., J.Roosen, and J. A.Fox. 2003. “Demand for Beef from Cattle Administered Growth Hormones or Fed Genetically Modified Corn: A Comparison of Consumers in France, Germany, the United Kingdom, and the United States.” American Journal of Agricultural Economics85:16–29.10.1111/1467-8276.00100Search in Google Scholar
Mahul, O.2003. “Hedging Price Risk in the Presence of Crop Yield and Revenue Insurance.” European Review of Agricultural Economics30:217–39.10.1093/erae/30.2.217Search in Google Scholar
McFadden, D.1986. “The Choice Theory Approach to Market Research.” Marketing Science5:275–97.10.1287/mksc.5.4.275Search in Google Scholar
Musser, W. N., G. F.Patrick, and D. T.Eckman. 1996. “Risk and Grain Marketing Behavior of Large-Scale Farmers.” Applied Economic Perspectives and Policy18:65–77.10.2307/1349667Search in Google Scholar
Park, A.2006. “Risk and Household Grain Management in Developing Countries.” The Economic Journal116:1088–115.10.1111/j.1468-0297.2006.01124.xSearch in Google Scholar
Patrick, G. F., B. F.Blake, and S. H.Whitacker. 1980. “Farmers’ Goals and Risk Aversion: Some Preliminary Analyses.” Risk Analysis in Agriculture: Research and Educational Developments. Department of Agricultural Economics AE-4492, University of Illinois.Search in Google Scholar
Patrick, G. F., B. F.Blake, and S. H.Whitacker. 1983. “Farmers’ Goals: Uni- or Multi-Dimensional?” American Journal of Agricultural Economics65:315–20.10.2307/1240881Search in Google Scholar
Paulson, N. D., A. L.Katchova, and S. H.Lence. 2010. “An Empirical Analysis of the Determinants of Marketing Contract Structures for Corn and Soybeans.” Journal of Agricultural & Food Industrial Organization8:1–23.10.2202/1542-0485.1282Search in Google Scholar
Peck, A. E.1975. “Hedging and Income Stability: Concepts, Implications and an Example.” American Journal of Agricultural Economics57:410–19.10.2307/1238403Search in Google Scholar
Saha, A., and J.Stroud. 1994. “A Household Model of on-Farm Storage under Price Risk.” American Journal of Agricultural Economics76:522–34.10.2307/1243663Search in Google Scholar
Sartwelle, J., D.O’Brien, W. J.Tierney, and T.Eggers. 2000. “The Effect of Personal and Farm Characteristics upon Grain Marketing Practices.” Journal of Agricultural and Applied Economics32:95–111.10.1017/S1074070800027851Search in Google Scholar
Shapiro, B. I., and W. B.Brorsen. 1988. “Factors Affecting Farmers’ Hedging Decisions.” Applied Economic Perspectives and Policy10:145–53.10.1093/aepp/10.2.145Search in Google Scholar
Sumpsi, J. M., F.Amador, and C.Romero. 1997. “On Farmers’ Objectives: A Multi-Criteria Approach.” European Journal of Operational Research96:64–71.10.1016/0377-2217(95)00338-XSearch in Google Scholar
Train, K.2009. Discrete Choice Methods with Simulation. Cambridge: University Press.Search in Google Scholar
Wisman, D. B., and I. P.Levin. 1996. “Comparing Risky Decision Making Under Conditions of Real and Hypothetical Consequences.” Organizational Behavior and Human Decision Processes66:241–50.10.1006/obhd.1996.0053Search in Google Scholar
©2014 by De Gruyter
