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Reduced-Form Versus Structural Modeling in Environmental and Resource Economics

Author

Listed:
  • Christopher Timmins
  • Wolfram Schlenker

    () (Department of Economics, Duke University, Durham, North Carolina 27708
    School of International and Public Affairs, Department of Economics, Columbia University, New York, New York 10027)

Abstract

We contrast structural and reduced form empirical studies in environmental and resource economics. Both methodologies have their own context-specific advantages and disadvantages, and should be viewed as complements, not substitutes. Structural models typically require a theoretical model and explicit assumptions about structural errors in order to recover the parameters of behavioral functions. These estimates may be required to measure general equilibrium welfare effects or to simulate intricate feedback loops between natural and economic processes. However, many of the assumptions used to recover structural estimates are untestable. The goal of reduced form studies is, conversely, to recover key parameters of interest using exogenous within-sample variation with as few structural assumptions as possible—reducing reliance on these assumptions assists in establishing causality in the relationship of interest. Reduced-form studies do, however, require assumptions of their own, e.g., the (quasi) randomness of an experiment with no spillover effects on the control group.

Suggested Citation

  • Christopher Timmins & Wolfram Schlenker, 2009. "Reduced-Form Versus Structural Modeling in Environmental and Resource Economics," Annual Review of Resource Economics, Annual Reviews, vol. 1(1), pages 351-380, September.
  • Handle: RePEc:anr:reseco:v:1:y:2009:p:351-380
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    File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev.resource.050708.144119
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    Citations

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    Cited by:

    1. Raja Chakir & Stéphane De Cara & Bruno Vermont, 2017. "Price-Induced Changes in Greenhouse Gas Emissions from Agriculture, Forestry, and Other Land Use: A Spatial Panel Econometric Analysis," Revue économique, Presses de Sciences-Po, vol. 68(3), pages 471-490.
    2. repec:kap:enreec:v:68:y:2017:i:3:d:10.1007_s10640-016-0044-0 is not listed on IDEAS
    3. Zhang, Wendong & Irwin, Elena G., 2013. "From Farmers' Management Decisions to Watershed Water Quality: A Spatial Economic Model of Land Management Choices," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150729, Agricultural and Applied Economics Association.
    4. Sexton, Steven E. & Sexton, Alison L., 2014. "Conspicuous conservation: The Prius halo and willingness to pay for environmental bona fides," Journal of Environmental Economics and Management, Elsevier, vol. 67(3), pages 303-317.
    5. Zipp, Katherine Y. & Lewis, David J. & Provencher, Bill, 2017. "Does the conservation of land reduce development? An econometric-based landscape simulation with land market feedbacks," Journal of Environmental Economics and Management, Elsevier, vol. 81(C), pages 19-37.
    6. Mikołaj Czajkowski & Wiktor Budziński & Danny Campbell & Marek Giergiczny & Nick Hanley, 2017. "Spatial Heterogeneity of Willingness to Pay for Forest Management," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 68(3), pages 705-727, November.
    7. Sung, Jae-hoon & Miranowski, John A., 2015. "Adaptive Behavior of U.S. Farms to Climate and Risk," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205787, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    8. Michael Brady & Elena Irwin, 2011. "Accounting for Spatial Effects in Economic Models of Land Use: Recent Developments and Challenges Ahead," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(3), pages 487-509, March.

    More about this item

    Keywords

    structural modeling; Tiebout sorting; hedonics; bioeconomic systems; general equilibrium; functional form; causality; identification; randomization; quasi-experiments;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q30 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - General
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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