Dynamic Structural Econometric Modeling of the Ethanol Industry

  • C.-Y. Cynthia Lin LawellEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 195)


This chapter reviews some of the papers my co-authors and I have written developing and estimating dynamic structural econometric models of dynamic games in the ethanol industry. These structural econometric models model the dynamic and strategic decisions made by ethanol firms and enable us to analyze the effects of government policy. Analyses that ignore the dynamic implications of government policies, including their effects on incumbent ethanol firms’ investment, production, and exit decisions and on potential entrants’ entry behavior, may generate incomplete estimates of the impact of the policies and misleading predictions of the future evolution of the fuel ethanol industry. In Thome and Lin Lawell [50], we estimate a model of the investment timing game in corn ethanol plants in the United States. In Yi and Lin Lawell [53, 54], we estimate a model of the investment timing game in ethanol plants worldwide that allows for the choice among different feedstocks. In Yi et al. [55], we estimate a structural econometric model of ethanol firms’ investment, production, entry, and exit decisions in order to analyze the effects of government subsidies and the Renewable Fuel Standard on the U.S. fuel ethanol industry. The results of our research will help determine which policies and factors can promote fuel-ethanol industry development.


Ethanol industry Dynamic structural econometric model Dynamic game 


  1. 1.
    Abbott, P., Hurt, C., Tyner, W.E.: Whats Driving Food Prices?. Farm Foundation Issue Report (2008)Google Scholar
  2. 2.
    Abbott, P., Hurt, C., Tyner, W.E.: What’s Driving Food Prices?. March 2009 Update in Farm Foundation Issue Report 2009 (2009)Google Scholar
  3. 3.
    Abbott, P., Hurt, C., Tyner, W.E.: What’s Driving Food Prices in 2011?. Farm Foundation Issue Report 2011 (2011)Google Scholar
  4. 4.
    Aguirregabiria, V., Mira, P.: Dynamic discrete choice structural models: a survey. J. Econom. 156(1), 38–67 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Bajari, P., Benkard, C.L., Levin, J.: Estimating dynamic models of imperfect competition. Econometrica 75(5), 1331–1370 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Bajari, P., Chernozhukov, V., Hong, H., Nekipelov, D.: Identification and efficient semiparametric estimation of a dynamic discrete game. NBER Working paper 21125 (2015)Google Scholar
  7. 7.
    Carroll, C.L., Carter, C.A., Goodhue, R.E., Lin Lawell, C.-Y.C.: Supply chain externalities and agricultural disease. Working paper (2017a)Google Scholar
  8. 8.
    Carroll, C.L., Carter, C.A., Goodhue, R.E., Lin Lawell, C.-Y.C.: The economics of decision-making for crop disease control. Working paper (2017b)Google Scholar
  9. 9.
    Carter, C.A., Rausser, G.C., Smith, A.: Commodity booms and busts. Annu. Rev. Resour. Econ. 3, 87–118 (2011)CrossRefGoogle Scholar
  10. 10.
    Lin Lawell, C.-Y.C.: Wind turbine shutdowns and upgrades in Denmark: Timing decisions and the impact of government policy. Working paper, Cornell University (2017)Google Scholar
  11. 11.
    de Gorter, H., Drabik, D., Just, D.R.: How biofuels policies affect the level of grains and oilseed prices: theory, models and evidence. Glob. Food Secur. 2, 82–88 (2013)CrossRefGoogle Scholar
  12. 12.
    de Gorter, H., Drabik, D., Just, D.R.: The Economics of Biofuel Policies: Impacts on Price Volatility in Grain and Oilseed Markets. Palgrave-McMillan, New York (2015)CrossRefGoogle Scholar
  13. 13.
    de Gorter, H., Drabik, D., Just, D.R., Kliauga, E.M.: The impact of OECD biofuels policies on developing countries. Agric. Econ. 44, 477–486 (2013)CrossRefGoogle Scholar
  14. 14.
    Dixit, A.K., Pindyck, R.S.: Investment Under Uncertainty. Princeton University Press, Princeton (1994)Google Scholar
  15. 15.
    DOE [Deparment of Energy] (2008). Energy Time Lines: Ethanol. Revised June, 2008. Accessed Jan 2009Google Scholar
  16. 16.
    Eidman, V.R.: Ethanol economics of dry mill plants. Corn-Based Ethanol in Illinois and the US: A Report from the Department of Agricultural and Consumer Economics, University of Illinois, 22–36. (2007)Google Scholar
  17. 17.
    Ellinger, P.N.: Assessing the financial performance and returns of ethanol production: a case study analysis. Corn-Based Ethanol in Illinois and the US: A Report from the Department of Agricultural and Consumer Economics, University of Illinois, 38–62. (2007)Google Scholar
  18. 18.
    Ellison, G., Glaeser, E.L.: The geographic concentration of industry: does natural advantage explain agglomeration? Am. Econ. Rev. 89(2), 311–316 (1999)CrossRefGoogle Scholar
  19. 19.
    Fowlie, M., Reguant, M., Ryan, S.P.: Market-based emissions regulation and industry dynamics. J. Polit. Econ. 124(1), 249–302 (2016)CrossRefGoogle Scholar
  20. 20.
    Goetz, S.: State- and county-level determinants of food manufacturing establishment growth: 1987–93. Am. J. Agric. Econ. 79, 838–850 (1997)CrossRefGoogle Scholar
  21. 21.
    Gonzalez, A.O., Karali, B., Wetzstein, M.E.: A public policy aid for bioenergy investment: case study of failed plants. Energy Policy 51, 465–473 (2012)CrossRefGoogle Scholar
  22. 22.
    Herath Mudiyanselage, N., Lin, C.-Y.C., Yi, F.: An analysis of ethanol investment decisions in Thailand. Theor. Econ. Lett. 3(5A1), 14–20 (2013)CrossRefGoogle Scholar
  23. 23.
    Hochman, G., Sexton, S.E.: The economics of biofuel policy and biotechnology. J. Agric. Food Ind. Organ. 6(2), (2008). Article 8Google Scholar
  24. 24.
    Lade, G.E., Lin, C.-Y.C.: A report on the economics of Californias low carbon fuel standard and cost containment mechanisms. Prepared for the California Air Resources Board. Institute of Transportation Studies, University of California at Davis, Research Report UCD-ITS-RR-13–23 (2013)Google Scholar
  25. 25.
    Lade, G.E., Lin, C.-Y.C.: Controlling compliance costs for Californias LCFS with a price ceiling. University of California at Davis Institute of Transportation Studies, Policy brief (2014)Google Scholar
  26. 26.
    Lade, G.E., Lin Lawell, C.-Y.C. Lin, C.-Y.C.: The design of renewable fuel policies and cost containment mechanisms. Working paper (2017)Google Scholar
  27. 27.
    Lade, G.E., Lin Lawell, C.-Y.C.: The design and economics of low carbon fuel standards. Res. Transp. Econ. 52, 91–99 (2015)CrossRefGoogle Scholar
  28. 28.
    Lade, G.E., Lin, C.-Y.C., Smith, A.: Ex post costs and renewable identification number (RIN) prices under the Renewable Fuel Standard. Resources for the Future Discussion Paper 15-22. (2015)Google Scholar
  29. 29.
    Lade, G.E., Lin Lawell, C.-Y.C., Smith, A.: Policy shocks and market-based regulations: Evidence from the Renewable Fuel Standard. Working paper (2017)Google Scholar
  30. 30.
    Lambert, D.M., Wilcox, M., English, A., Stewart, L.: Ethanol plant location determinants and county comparative advantage. J. Agric. Appl. Econ. 40, 117–135 (2008)CrossRefGoogle Scholar
  31. 31.
    Lin, C.-Y.C.: Estimating strategic interactions in petroleum exploration. Energy Econ. 31(4), 586–594 (2009)CrossRefGoogle Scholar
  32. 32.
    Lin, C.-Y.C.: On designing and analyzing policies for renewable fuels. California State Controller John Chiang statement of general fund cash receipts and disbursements 6(12), 4–5 (2012)Google Scholar
  33. 33.
    Lin, C.-Y.C.: Containing the costs of California’s low carbon fuel standard. California State Controller John chiang statement of general fund cash receipts and disbursements 7(12), (2013a)Google Scholar
  34. 34.
    Lin, C.-Y.C.: On designing and analyzing policies for renewable fuels. Energy Dimensions. (2013b)
  35. 35.
    Lin, C.-Y.C.: Strategic decision-making with information and extraction externalities: a structural model of the multi-stage investment timing game in offshore petroleum production. Rev. Econ. Stat. 95(5), 1601–1621 (2013c)CrossRefGoogle Scholar
  36. 36.
    Lin, C.-Y.C., Zhang, W., Rouhani, O., Prince, L.: The implications of an E10 ethanol-blend policy for California. Agric. Resour. Econ. Update 13(2), 1–4 (2009)Google Scholar
  37. 37.
    Ma, X., Lin Lawell, C.-Y.C., Rozelle, S.: Estimating peer effects: a structural econometric model using a field experiment of a health promotion program in rural China. Working paper, Cornell University (2017)Google Scholar
  38. 38.
    Mitchell, D.: A note on rising food prices. Policy research Working Paper # 4862, The World Bank Development Prospects Group, July 2008 (2008)Google Scholar
  39. 39.
    Pakes, A., Ostrovsky, M., Berry, S.: Simple estimators for the parameters of discrete dynamic games (with entry and exit examples). RAND J. Econ. 38(2), 373 (2007)CrossRefGoogle Scholar
  40. 40.
    Poudel, B.N., Paudel, K.P., Timilsina, G., Zilberman, D.: Providing numbers for a food versus fuel debate: an analysis of a future biofuel production scenario. Appl. Econ. Perspect. Policy 34(4), 637–668 (2012)CrossRefGoogle Scholar
  41. 41.
    Rajagopal, D., Sexton, S., Roland-Holst, D., Zilberman, D.: Challenge of biofuel: filling the tank without emptying the stomach? Environ. Res. Lett. 2(4), 1–9 (2007)CrossRefGoogle Scholar
  42. 42.
    Reiss, P.C., Wolak, F.A.: In: Heckman, J.J., Leamer, E.E. (eds.) Structural Econometric Modeling: Rationales and Examples from Industrial Organization. Handbook of Econometrics, vol. 6A, pp. 4277–4415. Stanford, California (2007)Google Scholar
  43. 43.
    Runge, C.F., Senauer, B.: How Biofuels Could Starve the Poor. Foreign Affairs, 41–53. (2007)Google Scholar
  44. 44.
    Ryan, S.P.: The costs of environmental regulation in a concentrated industry. Econometrica 80(3), 1019–1061 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  45. 45.
    Sarmiento, C., Wilson, W.W.: Spatial competition and ethanol plant location decisions. July 2008, 2008 Annual Meeting. American Agricultural Economics Association, Orlando, Florida 6175, (2008)Google Scholar
  46. 46.
    Sarmiento, C., Wilson W.W., Dahl, B. Spatial competition and ethanol plant location decisions. Agribusiness 28(3), 260–273 (2012)Google Scholar
  47. 47.
    Schmit, T.M., Luo, J., Conrad, J.M.: Estimating the influence of US ethanol policy on plant investment decisions: a real options analysis with two stochastic variables. Energy Econ. 33(6), 1194–1205 (2011)CrossRefGoogle Scholar
  48. 48.
    Schmit, T.M., Luo, J., Tauer, L.W.: Ethanol plant investment using net present value and real options analyses. Biomass Bioenergy 33(10), 1442–1451 (2009)CrossRefGoogle Scholar
  49. 49.
    Si, S., Chalfant, J.A., Lin Lawell, C.-Y.C., Yi, F.: The effects of China’s biofuel policies on agricultural and ethanol markets. Working paper, Cornell University (2017)Google Scholar
  50. 50.
    Thome, K.E., Lin Lawell, C.-Y.C.: Investment in corn-ethanol plants in the Midwestern United States. Working paper, Cornell University (2017)Google Scholar
  51. 51.
    Urbanchuk, J.M.: Economic Impacts on the Farm Community of Cooperative Ownership of Ethanol Production. National Corn Growers Association Report (2006)Google Scholar
  52. 52.
    Wright, B.: Global biofuels: key to the puzzle of grain market behavior. J. Econ. Persp. 28(1), 73–98 (2014)CrossRefGoogle Scholar
  53. 53.
    Yi, F., Lin Lawell, C.-Y.C.: Ethanol plant investment in Canada: a structural model. Working paper, Cornell University (2017a)Google Scholar
  54. 54.
    Yi, F., Lin Lawell, C.-Y.C.: What factors affect the decision to invest in a fuel ethanol plant?: a structural model of the ethanol investment timing game. Working paper, Cornell University (2017b)Google Scholar
  55. 55.
    Yi, F., Lin Lawell, C.-Y.C., Thome, K.E.: The effects of subsidies and mandates: a dynamic model of the ethanol industry. Working paper, Cornell University (2017)Google Scholar
  56. 56.
    Zilberman, D., Hochman, G., Rajagopal, D., Sexton, S., Timilsina, G.: The impact of biofuels on commodity food prices: assessment of findings. Am. J. Agric. Econ. 95, 275–281 (2012)Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Cornell UniversityIthacaUSA

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