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Minnesota’s State Timber Sale Program: An effort to stabilize logger stumpage bidding Photo: Potlatch Corp. Photo: MCEA Ross Brown.

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Presentation on theme: "Minnesota’s State Timber Sale Program: An effort to stabilize logger stumpage bidding Photo: Potlatch Corp. Photo: MCEA Ross Brown."— Presentation transcript:

1 Minnesota’s State Timber Sale Program: An effort to stabilize logger stumpage bidding Photo: Potlatch Corp. Photo: MCEA Ross Brown

2 Outline Background and Description of Timber Sales Problem Description Problem Statement Alternatives Evaluation Criteria Data Sources and Methods of Analysis Analysis Results Conclusions/Recommendations

3 Background/Stakeholders –MN owns ~4 million acres of forest land (25%) and sells the right to harvest trees on their land. –DNR Forester appraises the value of wood and sets up a sale. –Standing trees are sold to loggers at a public auction (oral or sealed bid). –Loggers harvest trees and sell the wood to wood products mills (OSB, lumber, paper). –Revenue from timber sales primarily goes to state K- 12 education. BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

4 DNR Timber Sale Program Goals –Maximize revenue for K-12 schools –Forest sustainability and multiple uses –Support local communities and wood products industry BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

5 Problem Description 2005: Excellent market for MN wood products loggers made very high stumpage bids, higher than the present value of the wood. 2006: Housing market declines, no market for MN wood products mills reduce production. Loggers are left with expensive stumpage contracts, but no mills are willing to buy the wood at high prices. Loggers are forced to forfeit their contracts BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

6 How did it get so bad?

7 Excessive bidding at oral auctions –Bidders may get caught up in the excitement of the oral auction. –May place bids that are higher than their true willingness to pay. BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

8 Problem Statement Loggers are submitting stumpage bids that do not reflect the true present value of the wood. Possible Causes: Loggers are speculating about future prices. Loggers place excessive bids when they get “caught up” in the auction format. BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

9 Policy Alternatives No Action Decrease Contract Lengths –More 2-3 year contracts More Sealed Bid Auctions –Fewer oral auctions Use 2 nd Price Sealed Bid Auctions –Highest bid wins the auction, but only has to pay the second highest bid price. BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

10 - Economic Efficiency Will the policy encourage loggers to make bids that reflect true willingness to pay? - Equity Will policy provide adequate opportunities for small loggers to purchase stumpage? - Social Acceptability Will the policy be acceptable to all stakeholders? Evaluation Criteria BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

11 Data & Methods Regression Analysis –Data: Records of all MN Timber Sales since 1994 (~13000 records). –How did different sale characteristics (e.g., contract length, auction format) influence logger stumpage bidding? Interview with DNR Forester and DNR Timber Sales Program Supervisor –How do different stakeholders perceive the problem? –How would stakeholders feel about the various policy alternatives? BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

12 OLS Regression –Use all 2005 pulpwood sales where the logger size was known (n=555). –Dependent Variable = % Bid Up i.e. how much higher the selling price was than the appraised price. –Possible Predictors: contract length, type of auction, number of different products/species, size of logger, etc. BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions Analysis Results

13 Independent Variables CoefficientsP-values Percent Bolts # species/products Medium Firm Small Firm Sealed Bid Over the counter sale Contract Length R 2 =0.161 n=555 BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

14 Interview Results –Contract length was identified as a factor that may contribute to higher stumpage bids. Current sales are being reduced to three years. Not much resistance to reduced contract length. –DNR is trying to increase sealed bid auctions. Many loggers don’t like them because they “leave money on the table.” –Second price sealed bids have not been seriously considered. Potentially a good solution. Concerns about collusion. BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

15 Analysis Alternative Criteria for Evaluation Economic/ EffectivenessEquity Social Acceptability No Action - ++/- Shorter Contract Length - no impact + no impact + More Sealed Bid Sales ? rational bids - high price +/- 2 nd Price Sealed Bid ? rational bids + low price +/-

16 Conclusions/Recommendation There is no evidence that contract length influences stumpage bidding. Sealed bids may help produce more thoughtful, rational bids, but many loggers do not like to leave money on the table and prices are higher. 2 nd price sealed bids may be the best option because they can help reduce excessive bids without loggers worrying about leaving money on the table. –Political and social acceptability are still uncertain. BackgroundEvaluation Criteria Problem DescriptionData & Methods Problem StatementAnalysis Results AlternativesConclusions

17 Questions? Photo: City Pages


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