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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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    Cited by:

    1. Dave Donaldson, 2022. "Blending Theory and Data: A Space Odyssey," Journal of Economic Perspectives, American Economic Association, vol. 36(3), pages 185-210, Summer.
    2. Laura A. Bakkensen & Xiangying Shi & Brianna D. Zurita, 2018. "The Impact of Disaster Data on Estimating Damage Determinants and Climate Costs," Economics of Disasters and Climate Change, Springer, vol. 2(1), pages 49-71, April.
    3. Dong, Changgui & Wiser, Ryan & Rai, Varun, 2018. "Incentive pass-through for residential solar systems in California," Energy Economics, Elsevier, vol. 72(C), pages 154-165.
    4. 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.
    5. 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.
    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. 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.
    8. Phuong Nguyen-Hoang & Ryan Yeung & Alexander Bogin, 2014. "No Base Left Behind," Public Finance Review, , vol. 42(4), pages 439-465, July.
    9. Ciccone, Alice, 2018. "Environmental effects of a vehicle tax reform: Empirical evidence from Norway," Transport Policy, Elsevier, vol. 69(C), pages 141-157.
    10. Day, Brett & Bateman, Ian & Binner, Amy & Ferrini, Silvia & Fezzi, Carlo, 2019. "Structurally-consistent estimation of use and nonuse values for landscape-wide environmental change," Journal of Environmental Economics and Management, Elsevier, vol. 98(C).
    11. Ciccone, Alice, 2015. "Environmental Effects of a Vehicle Tax Reform: Empirical Evidence from Norway," Memorandum 03/2015, Oslo University, Department of Economics.
    12. Klaus Glenk & Robert J. Johnston & Jürgen Meyerhoff & Julian Sagebiel, 2020. "Spatial Dimensions of Stated Preference Valuation in Environmental and Resource Economics: Methods, Trends and Challenges," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(2), pages 215-242, February.
    13. Ciccone, Alice, 2014. "Is it all about CO2 emissions? The environmental effects of a tax reform for new vehicles in Norway," Memorandum 19/2014, Oslo University, Department of Economics.
    14. Wiktor Budziński & Danny Campbell & Mikołaj Czajkowski & Urška Demšar & Nick Hanley, 2018. "Using Geographically Weighted Choice Models to Account for the Spatial Heterogeneity of Preferences," Journal of Agricultural Economics, Wiley Blackwell, vol. 69(3), pages 606-626, September.
    15. 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.
    16. 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.
    17. Chen, Bowen & Villoria, Nelson B., 2018. "Food Price Variability and Import Dependence: A Country Panel Analysis," 2018 Annual Meeting, August 5-7, Washington, D.C. 274285, Agricultural and Applied Economics Association.
    18. Martin Machay, 2018. "Land Degradation in the Calorie Model: Dynamics of the Stationary State," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 66(6), pages 1543-1547.
    19. Kasper Vrolijk & Misato Sato, 2023. "Quasi-Experimental Evidence on Carbon Pricing," The World Bank Research Observer, World Bank, vol. 38(2), pages 213-248.
    20. 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.
    21. Elena G. Irwin, 2010. "New Directions For Urban Economic Models Of Land Use Change: Incorporating Spatial Dynamics And Heterogeneity," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 65-91, February.
    22. Vrolijk, Kasper & Sato, Misato, 2023. "Quasi-experimental evidence on carbon pricing," LSE Research Online Documents on Economics 118404, London School of Economics and Political Science, LSE Library.
    23. Laura A. Bakkensen & Robert O. Mendelsohn, 2016. "Risk and Adaptation: Evidence from Global Hurricane Damages and Fatalities," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(3), pages 555-587.

    More about this item

    Keywords

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

    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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