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Technical Barriers to Interstate Trade: Noxious Weed Regulations

Published online by Cambridge University Press:  26 January 2015

Munisamy Gopinath ,
He Min  and
Steven Buccola
Munisamy Gopinath
Affiliation:
Department of Agricultural and Resource Economics, Oregon State University, Corvallis, OR
He Min
Affiliation:
PayPal, Austin, TX, and a former graduate assistant, Oregon State University, Corvallis, OR
Steven Buccola
Affiliation:
Department of Agricultural and Resource Economics, Oregon State University, Corvallis, OR
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Abstract

We focus on regulations controlling the spread of noxious weeds, especially the trade effects of regulatory differences across U.S. states. We specify a gravity model for each state's seed, nursery product, and commodity trade with each other state. Within the gravity model, we examine the role of cross-state regulatory congruence arising from ecological and agronomic characteristics and interest-group lobbying. A spatial-autoregressive Tobit model is estimated with a modified expectation-maximization algorithm. Results show that weed regulatory congruence positively affects interstate trade. By fostering cross-state regulatory differences, consumer and commodity-producer lobbying reduce the value of interstate trade by about two percent per annum.

Keywords

interstate tradeinvasive speciesrent-seekingF1H7Q5
Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 42 , Issue 4 , November 2010 , pp. 617 - 630
Copyright
Copyright © Southern Agricultural Economics Association 2010

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References

Anderson, J.E. “A Theoretical Foundation for the Gravity Equation.” The American Economic Review 69(1979): 106–16.Google Scholar
Anselin, L. Spatial Econometrics: Methods and Models. Boston, MA: Kluwer Academic Publishers, 1988.Google Scholar
Anselin, L. “Space and Applied Econometrics: Introduction.” Regional Science and Urban Economics 22,3(1992):307–16.Google Scholar
Anselin, L. “Spatial Regression.” Spatial Analysis Laboratory Working Paper: IL: University of Illinois. S. Fotheringham and P. Rogerson, eds. Urbana, Champaign, 2006.Google Scholar
Anselin, L., Florax, R.J.G.M., and Rey, S.J. Advances in Spatial Econometrics: Methodology, Tools and Application. Boston, MA: Kluwer Academic Publishers, 2004.Google Scholar
Antweiler, W., Copeland, B.R., and Taylor, M.S. “Is Free Trade Good for the Environment?” The American Economic Review 91(2001): 877–908.Google Scholar
Bailey, R.G. ’Ecoregions of the United States.” Miscellaneous Publication No. 1391, U.S. Forest Service. Washington, DC: USDA, 1995.Google Scholar
Bergstrand, J.H. “The Gravity Equation in International Trade: Some Microeconomic Foundations and Empirical Evidence.” The Review of Economics and Statistics 67(1985): 474–81.Google Scholar
Blonigen, B.A., Davies, R.B., Waddell, G.R., and Naughton, H.T. “FDI in Space: Spatial Autoregressive Relationships in Foreign Direct Investment.” European Economic Review 51(2007):1303–25.CrossRefGoogle Scholar
Burt, J.W., Muir, A.A., Piovia-Scott, J., Veblen, K.E., Chang, A.L., Grossman, J.D., and Weiskel, H.W. “Preventing Horticultural Introductions of Invasive Plants: Potential Efficacy of Voluntary Initiatives.” Biological Invasions 9(2007):909–23.Google Scholar
Case, A. “Neighborhood Influence and Technological Change.” Regional Science and Urban Economics 22,3(1992):491–508.Google Scholar
Chib, S. “Bayes Inference in the Tobit Censored Regression Model.” Journal of Econometrics 51,1-2(1992):79–99.CrossRefGoogle Scholar
Cliff, A.D., and Ord, J.K. Spatial Processes: Models and Applications. London: Pion Ltd., 1981.Google Scholar
Copeland, B.R., and Taylor, M.S. Trade and Environment: Theory and Evidence. Princeton, NJ: Princeton University Press, 2003.CrossRefGoogle Scholar
Costello, C., and McAusland, C. “Protectionism, Trade, and Measures of Damage From Exotic Species Introductions.” American Journal oj Agricultural Economics 85,4(2003):964–75.CrossRefGoogle Scholar
Feenstra, R.C. Advanced International Trade: Theory and Evidence. Princeton, NJ: Princeton University Press, 2004.Google Scholar
Fleming, M. 2004. Techniques for estimating spatially dependent discrete choice models. In Anselin, L., Florax, R.J.G.M., and Rey, S.J., eds. Advances in Spatial Econometrics: Methodology, Tools and Application. Boston, MA: Kluwer Academic Publishers, pp. 145–68.CrossRefGoogle Scholar
Frankel, J.A. Regional Trading Blocks in the World Economic System. Washington, DC: Institute for International Economics, 1997.Google Scholar
Goldberg, P.K., and Maggi, G. “Protection for Sale: An Empirical Investigation.” The American Economic Review 89,5(1999): 1135–55.CrossRefGoogle Scholar
Helpman, E., and Krugman, P.R. Market Structure and Foreign Trade: Increasing Returns, Imperfect Competition and the International Economy. Cambridge, MA: MIT Press, 1985.Google Scholar
Kelejian, H.H., and Pracha, I.R. “A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model.” International Economic Review 40,2(1999):509–33.Google Scholar
LeSage, J.P. The Theory and Practice of Spatial Econometrics. Toledo, OH: Department of Economics. University of Toledo, 1999.Google Scholar
LeSage, J.P., and Pace, R.K. “Models for Spatially Dependent Missing Data.” The Journal of Real Estate Finance and Economics 29,2(2004): 233–54.Google Scholar
Maddala, G.S. Limited-Dependent and Qualitative Variables in Econometrics. New York: Cambridge University Press, 1983.Google Scholar
Margolis, M., Shogren, J.F., and Fischer, C. “How Trade Politics Affect Invasive Species Control?” Ecological Economics 52,3(2005):305–13.Google Scholar
Marsh, T.L., and Mittelhammer, R.C. 2004. “Generalized Maximum Entropy Estimation of Spatial Autoregressive Models.” Pace, R.K. and LePage, J.P., eds. Advances in Econometrics, Spatial and Spatiotemporal Econometrics. Vol. 18. Elsevier Press.Google Scholar
McMillen, D.P. “Probit with Spatial Autocorrelation.” Journal of Regional Science 32,3(1992): 335–48.Google Scholar
Min, H., Gopinath, M., Buccola, S., and McEvoy, P. “Rent-Seeking in Invasive Species Regulations: The Case of Noxious Weeds.” Land Economics 84,2(2008):306–26.Google Scholar
National Resources Inventory. 1998. Natural Resource and Conservation Service, U.S. Department of Agriculture.Google Scholar
Pimentel, D., Zuniga, R., and Morrison, D. “Update on the Environmental and Economic Costs Associated with Alien-Invasive Species in the United States.” Ecological Economics 52,3(2004): 273–88.Google Scholar
Pinkse, J., and Slade, M.E. “Contracting in Space: An Application of Spatial Statistics to Discrete-Choice Models.” Journal of Econometrics 85(1998): 125–54.Google Scholar
Porojan, A. “Trade Flows and Spatial Effects: The Gravity Model Revisited.” Open Economies Review 12,3(2001):265–80.Google Scholar
Tasker, A.V. “Noxious Weeds: Permits and Interstate Movement.” Federal Register Agriculture Archive: Noxious Weeds 66,115(2001): 32213–17.Google Scholar
Tinbergen, J. Shaping the World Economy. New York: The Twentieth Century Fund, 1962.Google Scholar
Weinhold, D. “The Importance of Trade and Geography in the Pattern of Spatial Dependence of Growth Rates.” Review of Development Economics 6,3(2002):369–82.Google Scholar