Climate Change, Temperatures and Quality of Life: Projections for 2100 Hendrik Wolff

1. Climate Change, Temperatures and Quality of Life: Projections for 2100 Hendrik Wolff Department of Economics, University of Washington with D. Albouy, W. Graf and R. Kellogg

5. 2010: atmospheric • CO2 = 390ppm

6. Present and Future Temperature Data

7. Average Daily Temperature Distribution RED: 2090-2100 Projected A2 scenario from CCSM 3.0 in IPCC (2007) BLUE: 1960-90 Normals San Francisco

8. Average Daily Temperature Distribution Boston Houston RED: 2090-2100 Projected A2 scenario from CCSM 3.0 in IPCC (2007) BLUE: 1960-90 Normals San Francisco

9. Future Temperature Data Future temperatures in 2100: IPCC Assessment Report • A2 scenario: +3.5°C/6.3°F • “moderate” compared to MIT model (2009): +5.2°C/ 9.4°F

10. Will Higher Temperatures from Climate Change be Good or Bad in the Daily Lives of Americans? • Reduces the severity of cold winters: GAIN • Increases the severity of hot summers: LOSS.

11. Will Higher Temperatures from Climate Change be Good or Bad in the Daily Lives of Americans? • Reduces the severity of cold winters: GAIN • Increases the severity of hot summers: LOSS. • Will the LOSS outweigh the GAIN? This depends on • How much people value changes in cold or heat, which may vary by person. • Changes in the climate, which varies by location and scenario.

12. County Temperature Data

13. County Temperature Data • Drawback: • 1 day of 115 F & 4 days of 65 F  50 CDD • 5 days of 75 F  50 CDD

21. 33% Decrease 116% Increase

22. How Important Are These Temperature Changes? • Price of consumption of climate amenities? • We talk about weather all the time… • Outdoor recreation, skiing, BBQ…. • In 2005 the U.S. spent ~$180bn on heating and cooling • 1.5% of GDP  willingness to pay for comfort • Welfare changes may be at least as important as value of climate change to agriculture (ag = 1.2% of GDP)

23. Existing climate change literature has generally not focused on amenity values From a recent review of the literature on estimating damages from climate change: “The effects of climate change that have been quantified and monetized include the impacts on agriculture and forestry, water resources, coastal zones, energy consumption, air quality, and human health….Many of the omissions seem likely to be relatively small in the context of those items that have been quantified.” • (Tol, 2009, J Econ Perspectives)

24. Existing climate change literature has generally not focused on amenity values From a recent review of the literature on estimating damages from climate change: “The effects of climate change that have been quantified and monetized include the impacts on agriculture and forestry, water resources, coastal zones, energy consumption, air quality, and human health….Many of the omissions seem likely to be relatively small in the context of those items that have been quantified.” • (Tol, 2009, J Econ Perspectives)

25. Existing literature on climate amenity values • Wage-only hedonic regressions (low wage  high amenity) • Hoch and Drake (1974): 2.25 ºC cooling reduces real income by 2% • Moore (1998): 4.5 ºC warming benefits workers by $30-100 billion • Hedonics including local prices and wages • Nordhaus (1996): doubling of CO2 -0.17% of GDP (noisy) Adjusts w for cost of living (29 regions “issue should be flagged”) • Cragg and Kahn (1999) : over 1940-1990, mild weather has been capitalized more into prices, less into wages • Discrete choice of migrants’ location decisions (state level) • Cragg and Kahn (1997) finds high WTP for mild climate (~$1000 to $20000 for a 5.2oC reduction in July temperature) • Timmins (2007) forecasts migration in Brazil.

26. Existing literature on climate amenity values • Wage-only hedonic regressions (low wage  high amenity) • Hoch and Drake (1974): 2.25 ºC cooling reduces real income by 2% • Moore (1998): 4.5 ºC warming benefits workers by $30-100 billion • Hedonics including local prices and wages • Nordhaus (1996): doubling of CO2 -0.17% of GDP (noisy) Adjusts w for cost of living (29 regions “issue should be flagged”) • Cragg and Kahn (1999) : over 1940-1990, mild weather has been capitalized more into prices, less into wages • Discrete choice of migrants’ location decisions (state level) • Cragg and Kahn (1997) finds high WTP for mild climate (~$1000 to $20000 for a 5.2oC reduction in July temperature) • Timmins (2007) forecasts migration in Brazil.

27. Existing literature on climate amenity values • Wage-only hedonic regressions (low wage  high amenity) • Hoch and Drake (1974): 2.25 ºC cooling reduces real income by 2% • Moore (1998): 4.5 ºC warming benefits workers by $30-100 billion • Hedonics including local prices and wages • Nordhaus (1996): doubling of CO2 -0.17% of GDP (noisy) Adjusts w for cost of living (29 regions “issue should be flagged”) • Cragg and Kahn (1999) : over 1940-1990, mild weather has been capitalized more into prices, less into wages • Discrete choice of migrants’ location decisions (state level) • Cragg and Kahn (1997) finds high WTP for mild climate (~$1000 to $20000 for a 5.2oC reduction in July temperature) • Timmins (2007) forecasts migration in Brazil.

28. This paper contributes to the literature by… • Richer hedonic model based on housing costs and wages • Cost of living approximates housing & non-housing costs • Wage differences taken after federal taxes • Based on Albouy (NBER, 2008, JPE, 2009) • Uses climate change projections that vary by county • Allows for distributional analysis of welfare impact • Parallels literature on agricultural impacts (Deschênes and Greenstone 2007, Schlenker et al. 2006, Fisher et al. 2009) • Preference heterogeneity across households, sorting! • Recover distribution of marginal willingness to pay for climate • Method follows IO lit., Bajari and Benkard (2005)

29. This paper contributes to the literature by… • Richer hedonic model based on housing costs and wages • Cost of living approximates housing & non-housing costs • Wage differences taken after federal taxes • Based on Albouy (NBER, 2008, JPE, 2009) • Uses climate change projections that vary by county • Allows for distributional analysis of welfare impact • Parallels literature on agricultural impacts (Deschênes and Greenstone 2007, Schlenker et al. 2006, Fisher et al. 2009) • Preference heterogeneity across households, sorting! • Recover distribution of marginal willingness to pay for climate • Method follows IO lit., Bajari and Benkard (2005)

30. This paper contributes to the literature by… • Richer hedonic model based on housing costs and wages • Cost of living approximates housing & non-housing costs • Wage differences taken after federal taxes • Based on Albouy (NBER, 2008, JPE, 2009) • Uses climate change projections that vary by county • Allows for distributional analysis of welfare impact • Parallels literature on agricultural impacts (Deschênes and Greenstone 2007, Schlenker et al. 2006, Fisher et al. 2009) • Preference heterogeneity across households, sorting • Recover distribution of marginal willingness to pay for climate without relying on functional form assumption for utility • Method follows IO lit., Bajari and Benkard (2005)

31. Our approach broadly proceeds via two stagesStage 1 Hedonics: estimate preferences for climate Stage 2: using estimated preferences: predict welfare loss/gain for 2100

32. Stage 1 - Hedonics • Core idea: use cross-sectional variation in climate, wages, and prices to identify preferences • Benefits of cross-section vs. time series approach • No substantial longitudinal variation in climate • Cross-section allows for climate adaptation • Cost: concerns regarding omitted variables • No instrument available for climate • Will examine robustness of results to different specifications and control variables

33. Stage 2 welfare loss/gain predictions • Use spatially heterogeneous climate change predictions from the IPCC (A2 scenario) for 2100 • Account for migration responses, mitigating welfare impacts. • We do NOT account for: - discounting and population growth issues. - We hold preferences and technology constant until 2100!

34. What we are and are not measuring • The amenity value of changes in daily average temperatures • Direct consumption of outdoor temperatures • Indoor temperatures to degree imperfectly mitigated. • Discomfort and health effects • Loss or gain of outdoor recreational opportunities • Non-housing expenditures (e.g. automobile) • NOT Measuring • Out of sample indoor energy costs • Rising sea levels and land loss • Extreme weather events or water shortages. • Productivity effects, e.g. agricultural or urban

35. Estimates of Amenity Values and Quality of Life Standard equilibrium assumption Households are homogenous and fully mobile, and thus receive the same utility u in any inhabited city j.

36. Estimates of Amenity Values and Quality of Life Standard equilibrium assumption Households are homogenous and fully mobile, and thus receive the same utility u in any inhabited city j.

37. Estimates of Amenity Values and Quality of Life Standard equilibrium assumption Households are homogenous and fully mobile, and thus receive the same utility u in any inhabited city j. Log-linearize around the national average

38. Estimates of Amenity Values and Quality of Life Standard equilibrium assumption Households are homogenous and fully mobile, and thus receive the same utility u in any inhabited city j. Log-linearize around the national average Second-stage regression

39. Estimates of Amenity Values and Quality of Life Standard equilibrium assumption Households are homogenous and fully mobile, and thus receive the same utility u in any inhabited city j. Log-linearize around the national average Second-stage regression Price of the city

40. Estimates of Amenity Values and Quality of Life Standard equilibrium assumption Households are homogenous and fully mobile, and thus receive the same utility u in any inhabited city j. Log-linearize around the national average Second-stage regression Price of the city Z = Vector of K Amenities

42. Wage and Housing-Cost Differentials Data (2000) Calculated in wage and price regressions from 5% Census IPUMS using county dummies (derived from PUMAs). Wage differential • Sample: full-time workers (male & female) 25 to 55 • Controls: education, experience, industry, occupation, race, immigrant, language ability, etc. interacted with gender Housing-cost (rent or imputed-rent) differential • Sample: moved within last 10 years • Controls: Type and age of building, size, rooms, acreage, kitchen, etc. interacted with tenure.

44. Homogenous-Taste Results Suggest that CDDs Have Larger QOL Impact than HDDs

45. Homogenous-Taste Results Suggest that CDDs Have Larger QOL Impact than HDDs

46. Homogenous-Taste Results Suggest that CDDs Have Larger QOL Impact than HDDs

47. Homogenous-Taste Results Suggest that CDDs Have Larger QOL Impact than HDDs

48. Step 2: Predict Welfare changes in 2100

49. Welfare Change, Population Growth and Discounting

50. Welfare Change, Population Growth and Discounting