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Incentive Payment Programs for Environmental Protection: A Framework for Eliciting and Estimating Landowners' Willingness to Participate

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  • Siikamäki, Juha

    ()
    (Resources for the Future)

  • Layton, David

Abstract

This paper considers the role of incentive payment programs in eliciting, estimating, and predicting landowners’ conservation enrollments. Using both program participation and the amount of land enrolled, we develop two econometric approaches for predicting enrollments. The first is a multivariate censored regression model that handles zero enrollments and heterogeneity in the opportunity cost of enrollments by combining an inverse hyperbolic sine transformation of enrollments with alternative-specific correlation and random parameters. The second is a beta-binomial model, which recognizes that in practice elicited enrollments are essentially integer valued. We apply these approaches to Finland, where the protection of private nonindustrial forests is an important environmental policy problem. We compare both econometric approaches via cross-validation and find that the beta-binomial model predicts as well as the multivariate censored model yet has fewer parameters. The beta-binomial model also facilitates policy predictions and simulations, which we use to illustrate the framework.

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Bibliographic Info

Paper provided by Resources For the Future in its series Discussion Papers with number dp-05-57.

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Date of creation: 07 Dec 2005
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Handle: RePEc:rff:dpaper:dp-05-57

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Related research

Keywords: protection; endangered; voluntary; incentive; tobit; beta-binomial; stated preferences;

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References

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  14. Cooper, Joseph C. & Keim, Russ, 1996. "Incentive payments to encourage farmer adoption of water quality protection practices," MPRA Paper 24779, University Library of Munich, Germany.
  15. Lori Lynch & Sabrina J. Lovell, 2003. "Combining Spatial and Survey Data to Explain Participation in Agricultural Land reservation Programs," Land Economics, University of Wisconsin Press, vol. 79(2), pages 259-276.
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