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The Effect of Label Information on U.S. Farmers' Herbicide Choices

Published online by Cambridge University Press:  15 September 2016

Tomas Hasing ,
Carlos E. Carpio ,
David B. Willis ,
Olha Sydorovych  and
Michele Marra
Tomas Hasing
Affiliation:
Department of Applied Economics and Statistics at Clemson University in South Carolina
Carlos E. Carpio
Affiliation:
John E. Walker Department of Economics at Clemson University in South Carolina
David B. Willis
Affiliation:
John E. Walker Department of Economics at Clemson University in South Carolina
Olha Sydorovych
Affiliation:
Department of Agricultural and Resource Economics at North Carolina State University
Michele Marra
Affiliation:
Department of Agricultural and Resource Economics at North Carolina State University
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Abstract

This paper analyzes the effect of labeling information on U.S. farmers' herbicide choices. Herbicide choices reported by U.S. soybean farmers are used to estimate farmer preferences for different herbicide attributes using a mixed logit model. Our results indicate that statements displayed on pesticide labels regarding risks to human health and the environment are important components in herbicide selection. We find that farmers are willing to pay an average of $27 per acre to avoid using an herbicide labeled with the word “Warning” and $38 per acre to avoid using an herbicide labeled with the word “Danger.”

Keywords

mixed logit modelWTP to avoid human and environmental risk
Type
Research Article
Information
Agricultural and Resource Economics Review , Volume 41 , Issue 2 , August 2012 , pp. 200 - 214
Copyright
Copyright © 2012 Northeastern Agricultural and Resource Economics Association 

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References

Augustijn-Beckers, P.W., Hornsby, A.G., and Wauchope, R.D. 1994. “The SCS/ARS/CES Pesticide Properties Database for Environmental Decision-Making. II: Additional Compounds.” Reviews of Environmental Contamination and Toxicology 137: 1–82.Google Scholar
Beach, E., and Carlson, G. 1993. “A Hedonic Analysis of Herbicides: Do User Safety and Water Quality Matter?” American Journal of Agricultural Economics 75(3): 612–623.Google Scholar
Carpio, C., Sydorovych, O., and Marra, M. 2007. “Relative Importance of Environmental Attributes Using Logistic Regression.” Paper presented at the 2007 Southern Agricultural Economics Association Annual Meeting, Mobile, AL.Google Scholar
Davidson, R. and MacKinnon, J.G. 2004. Econometric Theory and Methods. New York, NY: Oxford University Press, Inc.Google Scholar
Delaplane, K. 2000. “Pesticide Usage in the United States: History, Benefits, Risks, and Trends.” Bulletin 1121, University of Georgia Cooperative Extension Service, Athens, GA.Google Scholar
Fernandez-Cornejo, J., and Jans, S. 1995. “Quality-adjusted Price and Quantity Indices for Pesticides.” American Journal of Agricultural Economics 77(3): 645–659.Google Scholar
Florax, R., Travisi, C., and Nijkamp, P. 2005. “A Meta-Analysis of the Willingness to Pay for Reduction in Pesticide Exposure.” European Review of Agricultural Economics 32(4): 441–467.Google Scholar
Golan, E., Kuchler, F., and Mitchell, L. 2000. “Economics of Food Labeling.” Agricultural Economic Report 793, Economic Research Service, U.S. Department of Agriculture, Washington, D.C.Google Scholar
Gossett, B.J. 2006. Weed Scientist, Department of Entomology, Soils, and Plant Sciences, Clemson University, Clemson, SC. Personal communication. (October).Google Scholar
Greene, W.H. 2003. Econometric Analysis. Upper Saddle River, NJ: Pearson Education, Inc.Google Scholar
Hasing, T. 2009. “The Effect of Label Information on Farmers' Pesticide Choice.” Unpublished M.S. thesis. Department of Applied Economics and Statistics, Clemson University.Google Scholar
Hensher, D., and Greene, W. 2003. “The Mixed Logit Model: The State of Practice.” Transportation 30(2): 133–176.Google Scholar
Hensher, D., Shore, N., and Train, K. 2005. “Households' Willingness to Pay for Water Service Attributes.” Environmental and Resource Economics 32(4): 509–531.Google Scholar
Higley, L., and Wintersteen, W. 1992. “A Novel Approach to Environmental Risk Assessment of Pesticides as a Basis for Incorporating Environmental Costs into Economic Injury Levels.” American Entomologist 38(1): 34–39.Google Scholar
Hubbell, B., and Carlson, G. 1998. “Effects of Insecticide Attributes on Within-Season Insecticide Product and Rate Choices: The Case of U.S. Apple Growers.” American Journal of Agricultural Economics 80(2): 382–396.Google Scholar
Judge, G.G., Griffiths, W.E., Hill, R.C., and Lee, T.C. 1980. The Theory and Practice of Econometrics. New York, NY: John Wiley and Sons.Google Scholar
Kiely, T., Donaldson, D., and Grube, A. 2004. “Pesticide Industry Sales and Usage. 2000 and 2001 Market Estimates.” U.S. Environmental Protection Agency, Washington, D.C. Google Scholar
Lohr, L., Park, T., and Wetzstein, M. 1998. “Voluntary Economic and Environmental Risk Tradeoffs in Crop Protection Decisions.” Agricultural and Resource Economics Review 27(1): 108–116.Google Scholar
Meyer, J., Horak, M., Rosenbaum, E., and Schneider, R. 2006. “Petition for the Determination of Nonregulated Status for Roundup Ready2Yield™ Soybean MON 89788.” Available at http://www.aphis.usda.gov/brs/aphisdocs/06_17801p.pdf (accessed January 19, 2010).Google Scholar
Monaco, T, Ashton, F., and Weller, S. 2002. Weed Science: Principles and Practices. New York, NY: John Wiley and Sons.Google Scholar
Nicol, A., Hurrell, A., Wahyuni, D., McDowall, W., and Chu, W. 2008. “Accuracy, Comprehensibility, and Use of Material Safety Data Sheets: A Review.” American Journal of Industrial Medicine 51 (11): 861–876.Google Scholar
Owens, N., Swinton, S., and van Ravenswaay, E. 1998. “Farmers' Willingness to Pay for Herbicide Safety Characteristics.” Paper presented at the 1998 American Agricultural Economics Association Annual Meeting, Salt Lake City, UT.Google Scholar
Revelt, D. 1999. “Three Discrete Choice Random Coefficient Papers and One Policy Crime Study.” Unpublished Ph.D. dissertation, Department of Economics, University of California, Berkley, CA.Google Scholar
Revelt, D., and Train, K. 1998. “Mixed Logit with Repeated Choices: Households’ Choices of Appliance Efficiency Level.” Review of Economics and Statistics 80(4): 647–657.Google Scholar
Rigby, D., and Burton, M. 2006. “Modeling Disinterest and Dislike: A Bounded Bayesian Mixed Logit Model of the UK Market for GM Food.” Environmental and Resource Economics 33(4): 485–509.Google Scholar
Sattler, B. 2002. “Finding Information about Chemicals in Our Environment.” In Sattler, B. and Lipscomb, J., eds., Environmental Health and Nursing Practice. New York, NY: Springer Publishing Company.Google Scholar
Singh, I., Squire, L., and Strauss, J. 1986. Agricultural Household Models: Extensions, Applications, and Policy. Baltimore, MD: The Johns Hopkins University Press.Google Scholar
Sydorovych, O., and Marra, M. 2007. “A Genetically Engineered Crop's Impact on Pesticide Use: A Revealed-Preference Index Approach.” Journal of Agricultural and Resource Economics 32(3): 476–491.Google Scholar
Sydorovych, O., and Marra, M. 2008. “Valuing the Changes in Herbicide Risks Resulting from Adoption of Roundup Ready Soybeans by U.S. Farmers: A Revealed-Preference Approach.” Journal of Agricultural and Applied Economics 40(3): 777–787.CrossRefGoogle Scholar
Teisl, M., and Roe, B. 1998. “The Economics of Labeling: An Overview of Issues for Health and Environmental Disclosure.” Agricultural and Resource Economics Review 27(2): 140–150.Google Scholar
Train, K. 1998. “Recreation Demand Models with Taste Differences over People.” Land Economics 74(2): 230–239.Google Scholar
Train, K. 2003. Discrete Choice Methods with Simulation. New York, NY: Cambridge University Press.Google Scholar
Vogue, P., Kerle, E., and Jenkins, J. 1994. “Oregon State University Extension Pesticide Properties Database.” Corvallis, OR: Oregon State University Extension Service. Available online at http://npic.orst.edu/ppdmove.htm (accessed April 14, 2010).Google Scholar
Vuong, Q.H. 1989. “Likelihood Ratio Tests for Model Selection and Non-Nested Hypothesis.” Econometrica 57(2): 307–333.Google Scholar
Wauchope, R.D., Buttler, T.M., Hornsby, A.G., Augustijn-Beckers, P.W., and Burt, J.P. 1992. “The SCS/ARS/CES Pesticide Properties Database for Environmental Decision-making.” Reviews of Environmental Contamination and Toxicology 123: 1–155.Google Scholar
Whitford, F., Barber, D., Scott, D., Edwards, C., and Caravetta, J. 2004. “Pesticides and the Label.” PPP-24, Purdue University Cooperative Extension Service, West Lafayette, IN.Google Scholar
Zandstra, B., Particka, M., and Masabni, J. 2004. “Guide to Tolerance of Crops and Susceptibility of Weeds to Herbicides.” Bulletin E-2833, Michigan State University Extension, East Lansing, MI.Google Scholar