Potential applications of economic tools to weather and society interactions

1. Potential applications of economic tools to weather and society interactions Steve Stewart Presented at WAS*IS Boulder, CO July 2006 Steve Stewart Hydrology and Water Resources, University of Arizona

2. Topics • Non-market valuation • Riparian restoration • Weather forecasts • Impact assessment • Economic experiment • Behavior under extreme Steve Stewart Hydrology and Water Resources, University of Arizona

3. Value of improved weather/climate information • Many ways to value (Katz & Murphy 97) • http://www.isse.ucar.edu/HP_rick/esig.html • Many values to assess • Better information can lead to social gains or losses (what is the metric for value?) Steve Stewart Hydrology and Water Resources, University of Arizona

4. Uncertain Supply – Estimating snow Arizona/New Mexico: 39% Utah: 60% Colorado: 63% Sierra Nevada: 67% Snow Contributions to Annual Precipitation From Serreze et al., Water Resources Research 35(7), July 1999 Current approaches for estimating SWE and forecasting runoff are highly inaccurate. Steve Stewart Hydrology and Water Resources, University of Arizona

5. Uncertain Supply – Measuring precipitation Sparse rain gauge distribution Mountain blockage of radar Source: Maddox, et. al. Weather and Forecasting, 2002. Steve Stewart Hydrology and Water Resources, University of Arizona

6. Douglas “Lake”, East Tennessee Steve Stewart Hydrology and Water Resources, University of Arizona

7. Dillon Reservoir, Summit County, CO Steve Stewart Hydrology and Water Resources, University of Arizona

8. Blue Ridge Parkway, VA Steve Stewart Hydrology and Water Resources, University of Arizona

9. Elk, Yellowstone National Park Steve Stewart Hydrology and Water Resources, University of Arizona

10. Buffalo, Yellowstone National Park Steve Stewart Hydrology and Water Resources, University of Arizona

11. Steve Stewart Hydrology and Water Resources, University of Arizona Bald Eagle. Near Muddy Gap, WY

12. Steve Stewart Hydrology and Water Resources, University of Arizona 11 endangered mussels, Clinch River Valley, TN-VA

13. System Changes Valuing River Restoration – integrating biology, hydrology and economics Hydrology Biology • Environmental Economics: Valuing Changes • Hope to Improve Efficiency/effectiveness of Restoration: • About $1 Billion Spent per year (National River Restoration Science Synthesis) Steve Stewart Hydrology and Water Resources, University of Arizona

14. Rio Salado de Oeste in Phoenix • San Pedro National Riparian Conservation Area • Rio Grande Bosque in central New Mexico Different Riparian Resources Steve Stewart Hydrology and Water Resources, University of Arizona

15. Use and Non-Use Values • Natural resource supplies • Recreation opportunities • Property value • Biodiversity conservation • Flood control • Scenic value & inspiration • Air and water quality enhancement • Tourism (regional) • Long-term community impact Sources of Riparian Value expanded from Postel & Richter, 2002 Steve Stewart Hydrology and Water Resources, University of Arizona

16. Instream Flow • Changing Preferences • Scarcity of Riparian Corridors • Restoration vs. Extraction • Legal Barriers • Valuation Difficulties Steve Stewart Hydrology and Water Resources, University of Arizona

17. Non-Market Goods: Public Goods Consumer Surplus Price Demand Quantity • Non-exclusive • Non-rival • Where is the market? • River Restoration • National Defense • Weather/climate information Steve Stewart Hydrology and Water Resources, University of Arizona

18. Non-Market Valuation Methods • Stated Preference • Hypothetical Market • Contingent Valuation Method • Choice Experiment • Revealed Preference • Observed Behavior, Related Market • Hedonics • Travel Cost Method • Avoidance behavior Steve Stewart Hydrology and Water Resources, University of Arizona

19. Valuation of restoration of the Albuquerque Rio Grande BosqueStewart and Weber + =$??? Steve Stewart Hydrology and Water Resources, University of Arizona

20. Albuquerque Bosque • US Army Corps – Study Partner • Policy Context - Actual Ongoing Restoration • Public Input Needed – Survey Steve Stewart Hydrology and Water Resources, University of Arizona

21. Survey Development • How do people use and perceive the bosque? • What is bosque restoration worth? • What are recreation amenities worth? Steve Stewart Hydrology and Water Resources, University of Arizona

22. Trails Describing Bosque Restoration • Define Attributes & Levels • Focus Group Meetings • What hits the public radar • Research Partner Meetings • What work is planned Steve Stewart Hydrology and Water Resources, University of Arizona

23. Metrics of Bosque Restoration 1. Bird and Wildlife Habitat (Habitat Suitability Index) (5, 7, 8) - HSI Scale 0-10 2. Vegetation Density (Full, Moderate, No Thinning) - Appearance - Water Use - Fire Risk • Native vs. Non-Native Trees(Native Dom, Equal, Non-Native Dom) • - Cottonwood, New Mexico Olive - Tamarisk, Russian Olive • 4.Natural River Processes (Some, none) - Naturalized Flooding • - Removing Bank Stabilization • 5. Cost: Increase in sales tax ($125 - $0) Steve Stewart Hydrology and Water Resources, University of Arizona

24. Bosque Usage Patterns • Travel Cost Model • Willingness to Pay • Restoration State • Avoid Loss • Choice Experiment • Ecosystem services Survey Instrument Steve Stewart Hydrology and Water Resources, University of Arizona

25. Steve Stewart Hydrology and Water Resources, University of Arizona

26. Whither Homo Economicus? The role of economic experiments and behavioral economics in weather/climate research Steve Stewart Hydrology and Water Resources, University of Arizona

27. Whither Homo Economicus? • Rationality • Expected utility • Individuals may pursue goals other than maximization of expected utility provided that those goals are self-consistent • Problem in the lab is measurement of these goals • Rationality is usually a useful approximation Steve Stewart Hydrology and Water Resources, University of Arizona

28. Why Experimental Economics? • EE examines the fundamentals of economic behavior • With EE, we are committing to models that are behavioral in character • Complements traditional methods • EE is or will eventually become a traditional method Steve Stewart Hydrology and Water Resources, University of Arizona

29. Economic Experiments Tests of predictions of existing theory • Test inclusion of variables on which the theory is silent • Speak to policy issues/support a position • Demonstration (forensic economics) • How? Remove context/Induce value Steve Stewart Hydrology and Water Resources, University of Arizona

30. Recipe for Behavioral Game Theory (Camerer) • Begin with a game or situation in which standard game theory makes a bold prediction • If behavior is different than predicted, seek explanation • Extend formal game theory to incorporate the results Steve Stewart Hydrology and Water Resources, University of Arizona

31. Individual Value Formulation • What information do individuals and groups use to make decisions? • How do risk, uncertainty, and ambiguity affect those decisions? • How individuals make decisions vs. how humans interact Steve Stewart Hydrology and Water Resources, University of Arizona

32. Lessons from Cognitive psychology • Evaluability (Hsee, et al) • Framing of low probability events • individuals don’t relate well to probabilities stated as “the probability of a major storm event is .005” • Loss aversion (Kahneman & Tversky 1979) • Gains and losses evaluated wrt reference point • Value losses more than equivalent gains • Loss avoidance (Cachon & Camerer 1996) • Rule out certain behaviors on the part of others (competitors, colleagues, EM agencies, etc) Steve Stewart Hydrology and Water Resources, University of Arizona

33. Experiment: Information as Insurance • Ganderton, et al 2000 JRU • Examine value of improved geologic mapping information to prevent earthquake losses • Natural disasters as low probability/high loss events • In many areas natural disasters are inevitable, yet individuals don’t take measures to avoid, prepare, or insure from loss. Steve Stewart Hydrology and Water Resources, University of Arizona

34. Nature E YES P $0 N Buy insurance? E Small E loss Large E loss NO P Small P loss E = episodic event P = periodic event N = no event Large P loss N $0 Steve Stewart Hydrology and Water Resources, University of Arizona

35. 32*2*5 = 90 possible combinations Steve Stewart Hydrology and Water Resources, University of Arizona

36. Maximize Expected Utility? • EU theory: buy information (insurance) if: E(utility w/information) >= E(utility w/o info) • Buy Policy if: Cost <= E(loss) + risk premium • May not hold for low probability events Steve Stewart Hydrology and Water Resources, University of Arizona

37. Expected Utility and Low probability/high loss events • Evidence that individuals do not maximize expected utility when probability of loss is low or magnitude of loss is very large • Camerer and Kunreuther (1989); Thaler (80,83) • Conjunction fallacy • Optimism • Threshold effects Steve Stewart Hydrology and Water Resources, University of Arizona

38. Analysis • Logit Prob(purchase policy) • Buy = const*-cost* -wealth* +exposure* -experience* +smallloss +medloss –smallprob* -medprob* + lowprob* -riskindex* • Value of a policy E(WTP) = $22.4 Steve Stewart Hydrology and Water Resources, University of Arizona

39. WTP/E(loss) Steve Stewart Hydrology and Water Resources, University of Arizona

40. Analysis (2) • Value of policy to insure against loss is greater for risk averse individuals than for others. There is a significant risk premium. • Suggests that the value of information to reduce uncertainty is higher for risk averse subjects as well. • If theory breaks down with low prob/high loss events, how can we determine whether reductions in uncertainty (improved forecasts) have value? Steve Stewart Hydrology and Water Resources, University of Arizona

41. Thanks! Steve Stewart Hydrology and Water Resources, University of Arizona

42. Cropping choices in conjoint Steve Stewart Hydrology and Water Resources, University of Arizona