What Can We Learn from Benefit Transfer Errors? Evidence from 20 Years of Research on Convergent Validity

نویسندگان

  • Sapna Kaul
  • Kevin J. Boyle
  • Nicolai V. Kuminoff
  • Christopher F. Parmeter
  • Jaren C. Pope
چکیده

We develop a nonparametric approach to meta-analysis and use it to identify modeling decisions that affect benefit transfer errors. The meta-data describe the results from 31 empirical studies testing the convergent validity of benefit transfers. They evaluated numerous methodological procedures, collectively reporting 1071 transfer errors. Our meta-regressions identify several important findings, including: (1) the median absolute error is 39%; (2) function transfers outperform value transfers; (3) transfers describing environmental quantity generate lower transfer errors than transfers describing quality changes; (4) geographic site similarity is important for value transfers; (5) contingent valuation generates lower transfer errors than other valuation methods; and (6) combining data from multiple studies tends to reduce transfer errors.

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تاریخ انتشار 2013