Predicting Resource Policy Outcomes via Meta-Regression: Data Space, Model Space, and the Quest for 'Optimal Scope'

Abstract Resource-managing agencies are increasingly relying on secondary data to predict economic benefits for planned policy interventions. This `transfer of benefits' is often based on a quantitative synthesis of aggregate results for similar past interventions via Meta-Regression Models. However, this approach is generally plagued by the paucity of available studies and related small sample problems. A broadening of scope of the Meta-Regression Model by adding data from ``related, yet different" contexts or activities may circumvent these issues, but may not necessarily enhance the efficiency of transfer functions if the different contexts do not share policy-relevant parameters. We illustrate how different combinations of contexts can be interpreted as `data spaces' which can then be explored for the most promising transfer function using Bayesian Model Search techniques. Our results indicate that model-averaged benefit predictions for scope-augmented data spaces can be more robust and efficient than those flowing from the baseline context and data.

[1]  Charles Griffiths,et al.  Clean water, ecological benefits, and benefits transfer: A work in progress at the U.S. EPA , 2006 .

[2]  Amber Jessup,et al.  Valuing avoided morbidity using meta-regression analysis: what can health status measures and QALYs tell us about WTP? , 2006, Health economics.

[3]  John B. Loomis,et al.  Panel Stratification in Meta-Analysis of Economic Studies: An Investigation of Its Effects in the Recreation Valuation Literature , 2000, Journal of Agricultural and Applied Economics.

[4]  Ian J. Bateman,et al.  Contrasting Conventional with Multi-Level Modeling Approaches to Meta-Analysis: Expectation Consistency in U.K. Woodland Recreation Values , 2003, Land Economics.

[5]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[6]  V. Smith,et al.  Treating open space as an urban amenity , 2002 .

[7]  Robert J. Johnston,et al.  Characterizing the effects of valuation methodology in function-based benefits transfer , 2006 .

[8]  Laura O. Taylor,et al.  Externality effects of small-scale hazardous waste sites: evidence from urban commercial property markets , 2004 .

[9]  Bonnie G. Colby,et al.  Evaluating the Performance of Benefit Transfer: An Empirical Inquiry , 1997 .

[10]  M. Termansen,et al.  Testing Benefits Transfer of Forest Recreation Values over a Twenty-Year Time Horizon , 2007, Land Economics.

[11]  J. Bergstrom,et al.  Status of Benefits Transfer in the United States and Canada: Reply , 1999 .

[12]  Roy Brouwer,et al.  The Validity of Environmental Benefits Transfer: Further Empirical Testing , 1999 .

[13]  F. Johnson,et al.  Valuing morbidity: an integration of the willingness-to-pay and health-status index literatures. , 1997, Journal of health economics.

[14]  Subhrendu K. Pattanayak,et al.  Is Meta-Analysis a Noah's Ark for Non-Market Valuation? , 2002 .

[15]  E. George,et al.  Journal of the American Statistical Association is currently published by American Statistical Association. , 2007 .

[16]  John C. Bergstrom,et al.  Using meta-analysis for benefits transfer: Theory and practice , 2006 .

[17]  K. Kovacs,et al.  Identifying Individual Discount Rates and Valuing Public Open Space with Stated-Preference Models , 2008, Land Economics.

[18]  J. Geweke,et al.  Bayesian Treatment of the Independent Student- t Linear Model , 1993 .

[19]  Gary Koop,et al.  Bayesian Econometric Methods , 2007 .

[20]  John Geweke,et al.  Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments , 1991 .

[21]  J. Geweke,et al.  Variable selection and model comparison in regression , 1994 .

[22]  Siddhartha Chib,et al.  MARKOV CHAIN MONTE CARLO METHODS: COMPUTATION AND INFERENCE , 2001 .

[23]  A. Shapiro.  Monte Carlo Sampling Methods , 2003 .

[24]  Edward E. Leamer,et al.  Let's Take the Con Out of Econometrics , 1983 .

[25]  John M. Olin.  On MCMC sampling in hierarchical longitudinal models , 1999 .

[26]  Klaus Moeltner,et al.  Meta-analysis and benefit transfer for resource valuation-addressing classical challenges with Bayesian modeling , 2007 .

[27]  Kathleen Segerson.  The Benefits of Groundwater Protection: Discussion , 1994 .

[28]  Bradley P. Carlin,et al.  On MCMC sampling in hierarchical longitudinal models , 1999, Stat. Comput..

[29]  John B. Loomis,et al.  Reducing barriers in future benefit transfers: Needed improvements in primary study design and reporting , 2006 .

[30]  V. Chernozhukov,et al.  Bayesian Econometrics , 2007 .

[31]  S. Chib,et al.  Understanding the Metropolis-Hastings Algorithm , 1995 .

[32]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[33]  W. Wong,et al.  The calculation of posterior distributions by data augmentation , 1987 .

[34]  E. George,et al.  APPROACHES FOR BAYESIAN VARIABLE SELECTION , 1997 .

[35]  Laura O. Taylor,et al.  What Determines the Value of Life: A Meta‐Analysis , 2002 .

[36]  D. Lindley,et al.  Bayes Estimates for the Linear Model , 1972 .

[37]  Robert J. Johnston,et al.  Systematic Variation in Willingness to Pay for Aquatic Resource Improvements and Implications for Benefit Transfer: A Meta-Analysis , 2005 .