@article{henderson_joshi_tanger_boby_hubbard_pelkki_hughes_mcconnell_miller_nowak_et al._2017, title={Standard Procedures and Methods for Economic Impact and Contribution Analysis in the Forest Products Sector}, volume={115}, ISSN={["1938-3746"]}, DOI={10.5849/jof.16-041}, abstractNote={Economic contributions from forestry and forest products help define the importance of this industry to a state or regional economy. IMPLAN input-output modeling software has proven helpful to conduct this analysis and is commonly used in the United States. However, input-output modeling and the results of economic impact or contribution analyses can vary substantially, depending on the modeling assumptions of the analyst, creating confusion among end users as comparisons are made among studies. Southern Regional Extension Forestry and the Southern Group of State Foresters invited forest and regional economists from the Southern Region to a summit in Little Rock, Arkansas, in 2015 to discuss concerns and issues with respect to collection, calculation, and delivery of information on the economic role of forestry and the forest products industry to the southern region. This article discusses major issues identified and recommendations suggested at the Little Rock Summit. Management and Policy Implications The recommendations from the Little Rock Summit participants have strong policy implications. Economic contribution analyses of the forestry and FPI (Figure 1) are used by economic development agencies and policymakers as they strive to support sustainable economic development in their region, particularly as it relates to workforce development and industrial recruitment and enhancement. Inconsistent model assumptions that provide different results might confuse and misguide policymakers as they often consider findings from the scientific community for policy decisions. Most inconsistencies created among economic impact or contribution analysis could be easily identified by stakeholders with more thorough and consistent reporting from the analyst(s). Inconsistent results also affect the credibility of these analyses. When results are inconsistent, decisionmakers can see the data as unreliable and choose other criteria to make the decision. Most of the results of input-output analysis are presented in concise fact sheets or brochures, and, thus, it is not possible to provide a detailed explanation of the methods used. In such cases, the Little Rock Summit consensus was to produce a detailed report that could be listed as a source or reference in the more abbreviated or concise reports.}, number={2}, journal={JOURNAL OF FORESTRY}, author={Henderson, James E. and Joshi, Omkar and Tanger, Shaun and Boby, Leslie and Hubbard, William and Pelkki, Matthew and Hughes, David W. and McConnell, T. Eric and Miller, Wayne and Nowak, Jarek and et al.}, year={2017}, month={Mar}, pages={112–116} }
 @article{jeuck_cubbage_abt_bardon_mccarter_coulston_renkow_2014, title={Assessing Independent Variables Used in Econometric Modeling Forest Land Use or Land Cover Change: A Meta-Analysis}, volume={5}, ISSN={1999-4907}, url={http://dx.doi.org/10.3390/f5071532}, DOI={10.3390/f5071532}, abstractNote={We conducted a meta-analysis on 64 econometric models from 47 studies predicting forestland conversion to agriculture (F2A), forestland to development (F2D), forestland to non-forested (F2NF) and undeveloped (including forestland) to developed (U2D) land. Over 250 independent econometric variables were identified from 21 F2A models, 21 F2D models, 12 F2NF models, and 10 U2D models. These variables were organized into a hierarchy of 119 independent variable groups, 15 categories, and 4 econometric drivers suitable for conducting simple vote count statistics. Vote counts were summarized at the independent variable group level and formed into ratios estimating the predictive success of each variable group. Two ratios estimates were developed based on (1) proportion of times the independent variables had statistical significance and (2) proportion of times independent variables met the original study authors’ expectations. In F2D models, we confirmed the success of popular independent variables such as population, income, and urban proximity estimates but found timber rents and site productivity variables less successful. In F2A models, we confirmed success of popular explanatory variables such as forest and agricultural rents and costs, governmental programs, and site quality, but we found population, income, and urban proximity estimates less successful. In U2D models, successful independent variables found were urban rents and costs, zoning issues concerning forestland loss, site quality, urban proximity, population, and income. In F2NF models, we found poor success using timber rents but high success using agricultural rents, site quality, population, and income. Success ratios and discussion of new or less popular, but promising, variables was also included. This meta-analysis provided insight into the general success of econometric independent variables for future forest-use or -cover change research.}, number={7}, journal={Forests}, publisher={MDPI AG}, author={Jeuck, James and Cubbage, Frederick and Abt, Robert and Bardon, Robert and McCarter, James and Coulston, John and Renkow, Mitch}, year={2014}, month={Jul}, pages={1532–1564} }