climate change, temperatures and quality of life: projections for 2100 hendrik wolff
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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 . 2010: atmospheric CO 2 = 390ppm. Present and Future Temperature Data. Average Daily Temperature Distribution. - PowerPoint PPT PresentationTRANSCRIPT
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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
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2010: atmospheric CO2 = 390ppm
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Present and Future Temperature Data
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7San Francisco
Average Daily Temperature Distribution
RED:2090-2100Projected
A2 scenario from CCSM 3.0 in IPCC (2007)
BLUE:1960-90 Normals
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Boston
San Francisco
Houston
Average Daily Temperature Distribution
RED:2090-2100Projected
A2 scenario from CCSM 3.0 in IPCC (2007)
BLUE:1960-90 Normals
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Future Temperature Data
Future temperatures in 2100: IPCC Assessment Report
o A2 scenario: +3.5°C/6.3°F
o “moderate” compared to MIT model (2009): +5.2°C/ 9.4°F
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Will Higher Temperatures from Climate Change be Good or Bad in the Daily Lives of Americans?
o Reduces the severity of cold winters: GAINo Increases the severity of hot summers: LOSS.
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Will Higher Temperatures from Climate Change be Good or Bad in the Daily Lives of Americans?
o Reduces the severity of cold winters: GAINo Increases the severity of hot summers: LOSS.
o Will the LOSS outweigh the GAIN? This depends on 1) How much people value changes in cold or heat,
which may vary by person.2) Changes in the climate, which varies by location
and scenario.
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County Temperature Data
12
365
1
365
1
0 65
0 65
HDD = Annual Heating Degree Days = max ,
CDD = Annual Cooling Degree Days = max ,
dd
dd
T
T
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County Temperature Data
13
365
1
365
1
0 65
0 65
HDD = Annual Heating Degree Days = max ,
CDD = Annual Cooling Degree Days = max ,
dd
dd
T
T
Drawback:
• 1 day of 115 F & 4 days of 65 F 50 CDD
• 5 days of 75 F 50 CDD
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0.0
5.1
.15
.2D
ensi
ty
0 2 4 6 8 10Present HDD (1000s) in 2000
0.0
5.1
.15
.2.2
5D
ensi
ty
0 2 4 6 8 10Future HDD (1000s) in 2100
0.1
.2.3
.4.5
Den
sity
0 2 4 6 8 10Present CDD (1000s) in 2000
0.1
.2.3
Den
sity
0 2 4 6 8 10Future CDD (1000s) in 2100
Gaussian kernel, bandwidth = .2. 10000+ HDDs (mainly Alaska) put in last bin
Population-Weighted Change in Heating and Cooling Degree Days: 2000-2100
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0.0
5.1
.15
.2D
ensi
ty
0 2 4 6 8 10Present HDD (1000s) in 2000
0.0
5.1
.15
.2.2
5D
ensi
ty
0 2 4 6 8 10Future HDD (1000s) in 2100
0.1
.2.3
.4.5
Den
sity
0 2 4 6 8 10Present CDD (1000s) in 2000
0.1
.2.3
Den
sity
0 2 4 6 8 10Future CDD (1000s) in 2100
Gaussian kernel, bandwidth = .2. 10000+ HDDs (mainly Alaska) put in last bin
Population-Weighted Change in Heating and Cooling Degree Days: 2000-2100
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0.0
5.1
.15
.2D
ensi
ty
0 2 4 6 8 10Present HDD (1000s) in 2000
0.0
5.1
.15
.2.2
5D
ensi
ty
0 2 4 6 8 10Future HDD (1000s) in 2100
0.1
.2.3
.4.5
Den
sity
0 2 4 6 8 10Present CDD (1000s) in 2000
0.1
.2.3
Den
sity
0 2 4 6 8 10Future CDD (1000s) in 2100
Gaussian kernel, bandwidth = .2. 10000+ HDDs (mainly Alaska) put in last bin
Population-Weighted Change in Heating and Cooling Degree Days: 2000-2100
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0.0
5.1
.15
.2D
ensi
ty
0 2 4 6 8 10Present HDD (1000s) in 2000
0.0
5.1
.15
.2.2
5D
ensi
ty
0 2 4 6 8 10Future HDD (1000s) in 2100
0.1
.2.3
.4.5
Den
sity
0 2 4 6 8 10Present CDD (1000s) in 2000
0.1
.2.3
Den
sity
0 2 4 6 8 10Future CDD (1000s) in 2100
Gaussian kernel, bandwidth = .2. 10000+ HDDs (mainly Alaska) put in last bin
Population-Weighted Change in Heating and Cooling Degree Days: 2000-2100
116% Increase
33% Decrease
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How Important Are These Temperature Changes?
o Price of consumption of climate amenities? We talk about weather all the time… Outdoor recreation, skiing, BBQ….
o In 2005 the U.S. spent ~$180bn on heating and cooling 1.5% of GDP willingness to pay for comfort
o Welfare changes may be at least as important as value of climate change to agriculture (ag = 1.2% of GDP)
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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)
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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)
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Existing literature on climate amenity values
o Wage-only hedonic regressions (low wage high amenity)o Hoch and Drake (1974): 2.25 ºC cooling reduces real income by 2%o Moore (1998): 4.5 ºC warming benefits workers by $30-100 billion
o Hedonics including local prices and wageso Nordhaus (1996): doubling of CO2
-0.17% of GDP (noisy)Adjusts w for cost of living (29 regions “issue should be flagged”)
o Cragg and Kahn (1999) : over 1940-1990, mild weather has been capitalized more into prices, less into wages
o Discrete choice of migrants’ location decisions (state level)o Cragg and Kahn (1997) finds high WTP for mild climate (~$1000 to
$20000 for a 5.2oC reduction in July temperature)o Timmins (2007) forecasts migration in Brazil.
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Existing literature on climate amenity values
o Wage-only hedonic regressions (low wage high amenity)o Hoch and Drake (1974): 2.25 ºC cooling reduces real income by 2%o Moore (1998): 4.5 ºC warming benefits workers by $30-100 billion
o Hedonics including local prices and wageso Nordhaus (1996): doubling of CO2
-0.17% of GDP (noisy)Adjusts w for cost of living (29 regions “issue should be flagged”)
o Cragg and Kahn (1999) : over 1940-1990, mild weather has been capitalized more into prices, less into wages
o Discrete choice of migrants’ location decisions (state level)o Cragg and Kahn (1997) finds high WTP for mild climate (~$1000 to
$20000 for a 5.2oC reduction in July temperature)o Timmins (2007) forecasts migration in Brazil.
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Existing literature on climate amenity values
o Wage-only hedonic regressions (low wage high amenity)o Hoch and Drake (1974): 2.25 ºC cooling reduces real income by 2%o Moore (1998): 4.5 ºC warming benefits workers by $30-100 billion
o Hedonics including local prices and wageso Nordhaus (1996): doubling of CO2
-0.17% of GDP (noisy)Adjusts w for cost of living (29 regions “issue should be flagged”)
o Cragg and Kahn (1999) : over 1940-1990, mild weather has been capitalized more into prices, less into wages
o Discrete choice of migrants’ location decisions (state level)o Cragg and Kahn (1997) finds high WTP for mild climate (~$1000 to
$20000 for a 5.2oC reduction in July temperature)o Timmins (2007) forecasts migration in Brazil.
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This paper contributes to the literature by…
o 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)
o 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)
o Preference heterogeneity across households, sorting! Recover distribution of marginal willingness to pay for climate Method follows IO lit., Bajari and Benkard (2005)
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This paper contributes to the literature by…
o 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)
o 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)
o Preference heterogeneity across households, sorting! Recover distribution of marginal willingness to pay for climate Method follows IO lit., Bajari and Benkard (2005)
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This paper contributes to the literature by…
o 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)
o 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)
o 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)
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Our approach broadly proceeds via two stages
Stage 1 Hedonics: estimate preferences for climate
Stage 2: using estimated preferences: predict welfare loss/gain for 2100
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Stage 1 - Hedonics
o Core idea: use cross-sectional variation in climate, wages, and prices to identify preferences
o Benefits of cross-section vs. time series approacho No substantial longitudinal variation in climateo Cross-section allows for climate adaptation
o Cost: concerns regarding omitted variableso No instrument available for climateo Will examine robustness of results to different
specifications and control variables32
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Stage 2 welfare loss/gain predictions
o Use spatially heterogeneous climate change predictions from the IPCC (A2 scenario) for 2100
o 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! 33
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What we are and are not measuring
o 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)
o 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
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Estimates of Amenity Values and Quality of Life
Standard equilibrium assumptionHouseholds are homogenous and fully mobile, and thus
receive the same utility u in any inhabited city j.
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Estimates of Amenity Values and Quality of Life
Standard equilibrium assumptionHouseholds are homogenous and fully mobile, and thus
receive the same utility u in any inhabited city j.
Quality of Life , Cost of Livingj j
jj j j
j
QOL COL
Incomeu QOL Consumption QOLCOL
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Estimates of Amenity Values and Quality of Life
Standard equilibrium assumptionHouseholds are homogenous and fully mobile, and thus
receive the same utility u in any inhabited city j.
Quality of Life , Cost of Livingj j
jj j j
j
QOL COL
Incomeu QOL Consumption QOLCOL
jjj IncomedCOLdQOLd lnlnln Log-linearize around the national average
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Estimates of Amenity Values and Quality of Life
Standard equilibrium assumptionHouseholds are homogenous and fully mobile, and thus
receive the same utility u in any inhabited city j.
Quality of Life , Cost of Livingj j
jj j j
j
QOL COL
Incomeu QOL Consumption QOLCOL
jjj IncomedCOLdQOLd lnlnln Log-linearize around the national average
jK
jK
jj ZZQOLd ...ln 11Second-stage
regression
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Estimates of Amenity Values and Quality of Life
Standard equilibrium assumptionHouseholds are homogenous and fully mobile, and thus
receive the same utility u in any inhabited city j.
Quality of Life , Cost of Livingj j
jj j j
j
QOL COL
Incomeu QOL Consumption QOLCOL
jjj IncomedCOLdQOLd lnlnln Log-linearize around the national average
jK
jK
jj ZZQOLd ...ln 11Second-stage
regression
Price of the city
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Estimates of Amenity Values and Quality of Life
Standard equilibrium assumptionHouseholds are homogenous and fully mobile, and thus
receive the same utility u in any inhabited city j.
Quality of Life , Cost of Livingj j
jj j j
j
QOL COL
Incomeu QOL Consumption QOLCOL
jjj IncomedCOLdQOLd lnlnln Log-linearize around the national average
jK
jK
jj ZZQOLd ...ln 11Second-stage
regression
Price of the city Z = Vector of K Amenities
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Dallas, TXPhiladelphia, PADetroit, MI
Washington, DCChicago, IL
Boston, MALos Angeles, CA New York, NY
San Francisco, CA
San Antonio, TXPittsburgh, PA
St. Louis, MO Houston, TXNorfolk, VA Cincinnati, OHTampa, FL Columbus, OH
Minneapolis, MN
Miami, FLPortland, OR
Denver, CO
Seattle, WA
San Diego, CA
McAllen, TX
El Paso, TX
Syracuse, NYOklahoma City, OK
New Orleans, LANashville, TNTucson, AZAlbuquerque, NM
Sarasota, FLHartford, CT
Honolulu, HI
Gadsden, ALJoplin, MO
Decatur, ILBeaumont, TX
Kokomo, INKilleen, TXSioux Falls, SD
Bloomington, ILMyrtle Beach, SCFort Walton Beach, FL
Grand Junction, CO
Wilmington, NCFlagstaff, AZMedford, OR
Santa Fe, NMNaples, FL
Salinas, CA
Santa Barbara, CA
ND MSOK ALSD KY
MT
HI
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
-0.2 -0.1 0.0 0.1 0.2Log Wage Differential
METRO POP >5.0 Million Avg Mobility Cond: slope = 1.54
1.5-5.0 Million 0.5-1.5 Million Avg Zero-Profit Cond: slope = -7.37
<0.5 Million Non-Metro Areas Avg Iso-Value Curve: slope = -.02
Log
Hou
sing
-Cos
t Diff
eren
tial
Housing Costs versus Wage Levels across Metro Areas, 2000
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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.
ln ij i j ijw w w ww X
ln ij i j ijp p p pp X
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Homogenous-Taste Results Suggest that CDDs Have Larger QOL Impact than HDDs
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No Controls Controls 1 Controls 2 Controls 3(1) (2) (3) (4)
Heating-Degree Days (1000s) -0.025*** -0.008** -0.008*** -0.019***(0.004) (0.003) (0.003) (0.003)
Cooling-Degree Days (1000s) -0.053*** -0.019** -0.014** -0.037***(0.010) (0.008) (0.006) (0.007)
Natural Controls Y Y YOther Controls Y Y
State Fixed Effects Y
R-squared 0.29 0.50 0.68 0.78Number of Counties 3105 3105 3105 3105
TABLE 2a: QUALITY OF LIFE AND TEMPERATURE
Robust standard errors clustered by MSA/CMSA shown in parentheses. *** p<.01, ** p<.05 Natural Controls: Precipitation, ocean and Great Lake Coast dummies, average land slope. Other Controls: Percent with HS and BA, population density.
Dependent Variable: Quality of Life
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Homogenous-Taste Results Suggest that CDDs Have Larger QOL Impact than HDDs
45
No Controls Controls 1 Controls 2 Controls 3(1) (2) (3) (4)
Heating-Degree Days (1000s) -0.025*** -0.008** -0.008*** -0.019***(0.004) (0.003) (0.003) (0.003)
Cooling-Degree Days (1000s) -0.053*** -0.019** -0.014** -0.037***(0.010) (0.008) (0.006) (0.007)
Natural Controls Y Y YOther Controls Y Y
State Fixed Effects Y
R-squared 0.29 0.50 0.68 0.78Number of Counties 3105 3105 3105 3105
TABLE 2a: QUALITY OF LIFE AND TEMPERATURE
Robust standard errors clustered by MSA/CMSA shown in parentheses. *** p<.01, ** p<.05 Natural Controls: Precipitation, ocean and Great Lake Coast dummies, average land slope. Other Controls: Percent with HS and BA, population density.
Dependent Variable: Quality of Life
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Homogenous-Taste Results Suggest that CDDs Have Larger QOL Impact than HDDs
46
No Controls Controls 1 Controls 2 Controls 3(1) (2) (3) (4)
Heating-Degree Days (1000s) -0.025*** -0.008** -0.008*** -0.019***(0.004) (0.003) (0.003) (0.003)
Cooling-Degree Days (1000s) -0.053*** -0.019** -0.014** -0.037***(0.010) (0.008) (0.006) (0.007)
Natural Controls Y Y YOther Controls Y Y
State Fixed Effects Y
R-squared 0.29 0.50 0.68 0.78Number of Counties 3105 3105 3105 3105
TABLE 2a: QUALITY OF LIFE AND TEMPERATURE
Robust standard errors clustered by MSA/CMSA shown in parentheses. *** p<.01, ** p<.05 Natural Controls: Precipitation, ocean and Great Lake Coast dummies, average land slope. Other Controls: Percent with HS and BA, population density.
Dependent Variable: Quality of Life
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Homogenous-Taste Results Suggest that CDDs Have Larger QOL Impact than HDDs
47
No Controls Controls 1 Controls 2 Controls 3(1) (2) (3) (4)
Heating-Degree Days (1000s) -0.025*** -0.008** -0.008*** -0.019***(0.004) (0.003) (0.003) (0.003)
Cooling-Degree Days (1000s) -0.053*** -0.019** -0.014** -0.037***(0.010) (0.008) (0.006) (0.007)
Natural Controls Y Y YOther Controls Y Y
State Fixed Effects Y
R-squared 0.29 0.50 0.68 0.78Number of Counties 3105 3105 3105 3105
TABLE 2a: QUALITY OF LIFE AND TEMPERATURE
Robust standard errors clustered by MSA/CMSA shown in parentheses. *** p<.01, ** p<.05 Natural Controls: Precipitation, ocean and Great Lake Coast dummies, average land slope. Other Controls: Percent with HS and BA, population density.
Dependent Variable: Quality of Life
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Step 2: Predict Welfare changes in 2100
49
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Welfare Change, Population Growth and Discounting
50
With no mobility or population growth, per year:
Δ Δ , Δ amenity-induced change
US Population is expected to exceed 600M by 2100. Pop growth rate .
Future may need to be disc
j jj
jWelfare Pop QOL QOL
n
0
ounted by because of consumption growth, pure time preference, exogenous probability of civilization ending.
? Set to zero.
ρ
discount ρ n
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Welfare Change, Population Growth and Discounting
51
With no mobility or population growth, per year:
Δ Δ , Δ amenity-induced change
US Population is expected to exceed 600M by 2100. Pop growth rate .
Future may need to be disc
j jj
jWelfare Pop QOL QOL
n
0
ounted by because of consumption growth, pure time preference, exogenous probability of civilization ending.
? Set to zero.
ρ
discount ρ n
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Mobility Response
52
Population will likely move in response to climate change.
Closed border assumption: a uniform decrease in QOL across
nation will not cause individuals to move.
elasticity of p
j AVGjPop n ε QOL QOL
ε
5
opulation w.r.t. to QOL: depends on housing supply, production/employment opportunities, willingness to live densely. Impossible to estimate, will be calibrated to be large: . .
Alternate we
e g ε
lfare measure to account for mobility response, lower bound.
_jj j
jWelfare alt Pop Pop QOL
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Loss from Hotter Summer Exceeds Gain from Warmer Winters
53
Price per Percent in BillionsMean 1000 of Income of 2008$
Panel A: Quality-of-Life Changes Only
Change in Heating Degree Days -1623 -0.019 0.029 $359.0Change in Cooling Degree Days 1454 -0.037 -0.052 -$639.1
Sum -0.023 -$280.1(0.007) ($86.8)
Losers as Percent of Population 87.5%Panel B: Total Welfare (QOL + Productivity) Change
Change in Heating Degree Days -1623 -0.017 0.026 $325.7Change in Cooling Degree Days 1454 -0.038 -0.053 -$653.5
Sum -0.026 -$327.7(0.012) ($148.8)
Losers as Percent of Population 91.7%
TABLE 3: TEMPERATURE AND WELFARE CHANGES, HOMOGENOUS PREFERENCES
Estimates from specification 4 using all controls and state fixed effects
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Loss from Hotter Summer Exceeds Gain from Warmer Winters
54
Price per Percent in BillionsMean 1000 of Income of 2008$
Panel A: Quality-of-Life Changes Only
Change in Heating Degree Days -1623 -0.019 0.029 $359.0Change in Cooling Degree Days 1454 -0.037 -0.052 -$639.1
Sum -0.023 -$280.1(0.007) ($86.8)
Losers as Percent of Population 87.5%Panel B: Total Welfare (QOL + Productivity) Change
Change in Heating Degree Days -1623 -0.017 0.026 $325.7Change in Cooling Degree Days 1454 -0.038 -0.053 -$653.5
Sum -0.026 -$327.7(0.012) ($148.8)
Losers as Percent of Population 91.7%
TABLE 3: TEMPERATURE AND WELFARE CHANGES, HOMOGENOUS PREFERENCES
Estimates from specification 4 using all controls and state fixed effects
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Loss from Hotter Summer Exceeds Gain from Warmer Winters
55
Price per Percent in BillionsMean 1000 of Income of 2008$
Panel A: Quality-of-Life Changes Only
Change in Heating Degree Days -1623 -0.019 0.029 $359.0Change in Cooling Degree Days 1454 -0.037 -0.052 -$639.1
Sum -0.023 -$280.1(0.007) ($86.8)
Losers as Percent of Population 87.5%Panel B: Total Welfare (QOL + Productivity) Change
Change in Heating Degree Days -1623 -0.017 0.026 $325.7Change in Cooling Degree Days 1454 -0.038 -0.053 -$653.5
Sum -0.026 -$327.7(0.012) ($148.8)
Losers as Percent of Population 91.7%
TABLE 3: TEMPERATURE AND WELFARE CHANGES, HOMOGENOUS PREFERENCES
Estimates from specification 4 using all controls and state fixed effects
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Loss from Hotter Summer Exceeds Gain from Warmer Winters
56
Price per Percent in BillionsMean 1000 of Income of 2008$
Panel A: Quality-of-Life Changes Only
Change in Heating Degree Days -1623 -0.019 0.029 $359.0Change in Cooling Degree Days 1454 -0.037 -0.052 -$639.1
Sum -0.023 -$280.1(0.007) ($86.8)
Losers as Percent of Population 87.5%Panel B: Total Welfare (QOL + Productivity) Change
Change in Heating Degree Days -1623 -0.017 0.026 $325.7Change in Cooling Degree Days 1454 -0.038 -0.053 -$653.5
Sum -0.026 -$327.7(0.012) ($148.8)
Losers as Percent of Population 91.7%
TABLE 3: TEMPERATURE AND WELFARE CHANGES, HOMOGENOUS PREFERENCES
Estimates from specification 4 using all controls and state fixed effects
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Loss from Hotter Summer Exceeds Gain from Warmer Winters
57
Price per Percent in BillionsMean 1000 of Income of 2008$
Panel A: Quality-of-Life Changes Only
Change in Heating Degree Days -1623 -0.019 0.029 $359.0Change in Cooling Degree Days 1454 -0.037 -0.052 -$639.1
Sum -0.023 -$280.1(0.007) ($86.8)
Losers as Percent of Population 87.5%Panel B: Total Welfare (QOL + Productivity) Change
Change in Heating Degree Days -1623 -0.017 0.026 $325.7Change in Cooling Degree Days 1454 -0.038 -0.053 -$653.5
Sum -0.026 -$327.7(0.012) ($148.8)
Losers as Percent of Population 91.7%
TABLE 3: TEMPERATURE AND WELFARE CHANGES, HOMOGENOUS PREFERENCES
Estimates from specification 4 using all controls and state fixed effects
Mobility responses reduce mitigate welfare impacts by 10%
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60
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We improve upon the simple empirical model in two ways
1. Allow climate to enter utility function in a non-linear wayo Model WTP as a flexible function of the number of
days spent at any given temperatureo Maximum WTP no longer restricted to be at 65oF
61
RICHER ESTIMATION
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We improve upon the simple empirical model in two ways
1. Allow climate to enter utility function in a non-linear wayo Model WTP as a flexible function of the number of
days spent at any given temperatureo Maximum WTP no longer restricted to be at 65oF
2. Allow climate preferences to be heterogeneous, with households sorting to their optimal location
62
RICHER ESTIMATION
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We use “binned” temperature data to flexibly model MWTP for exposure to heat / coldo Present-day climate data: average number of days spent
in each one-degree temperature bin (e.g. 65 – 66oF)o Courtesy of Deschênes and Greenstone
63
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We use “binned” temperature data to flexibly model MWTP for exposure to heat / coldo Present-day climate data: average number of days spent
in each one-degree temperature bin (e.g. 65 – 66oF)o Courtesy of Deschênes and Greenstone
o Define f(t) as the MWTP for an additional day in temperature bin to Our aim is to estimate the function f(t)
64
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We use “binned” temperature data to flexibly model MWTP for exposure to heat / coldo Present-day climate data: average number of days spent
in each one-degree temperature bin (e.g. 65 – 66oF)o Courtesy of Deschênes and Greenstone
o Define f(t) as the MWTP for an additional day in temperature bin to Our aim is to estimate the function f(t)
o Example: HDD/CDD can be seen as a restrictive functional form for f(t):
65
βHDD∙(65 – t) if t < 65
βCDD∙(t – 65) if t ≥ 65f(t) =
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o Rather than rely on the HDD / CDD specification, we model f(t) as a flexible spline
o Where S1 through S4 are the basis functions of a cubic spline. Maximum MWTP is no longer restricted to 65oF
66
f(t) = β0 + β1S1(t) + β2S2(t) + β3S3(t) + β4S4(t)
We use “binned” temperature data to flexibly model MWTP for exposure to heat / cold
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Cubic spline basis functions
67
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o Rather than rely on the HDD / CDD specification, we model f(t) as a flexible spline
o Where S1 through S4 are the basis functions of a cubic spline. Maximum MWTP is no longer restricted to 65oF
o Estimation:o where Nit denotes the number of days at temperature t
o Rearranging:
68
f(t) = β0 + β1S1(t) + β2S2(t) + β3S3(t) + β4S4(t)
We use “binned” temperature data to flexibly model MWTP for exposure to heat / cold
( )i it i it
QOL N f t Controlsα ε
β β
4
01
( ) ( )it k it kt k t
N f t N S t
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Flexible Temperature Specification:
Willingness to pay for daily temperatureo Generally consistent with simpler HDD/CDD functional form:
greater WTP to avoid heat than to avoid coldo Welfare loss 2-3.8%o Assume that WTP curves are horizontal outside the domain of
observed present temperatures (conservative!)
69Controls, with state FE
Present, 2050, and 2100 average U.S. climate
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Heterogeneity in Preferences
70
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Heterogeneity
o South presumably has distaste for cold and prefers heat Their welfare loss will be lower with heterogeneity
o North presumably doesn’t mind cold, but may be more vulnerable to heat Their welfare loss could be higher with heterogeneity
71
The impact on overall welfare of modeling heterogeneity is ambiguous, ex ante
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Method to (Locally) Identify Households’ MWTP
o Bajari and Benkard (2005) show how to identify each household’s preferences using the local hedonic gradient
72
o Step 1: Estimate the hedonic price function flexibly.
o Obtain a local price for climate at each location
o Step 2: Household’s local MWTP is given by the FOC
CDDMWTP
MWTP
QOL
SF HOU
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o Bajari and Benkard (2005) show how to identify each household’s preferences using the local hedonic gradient
73
o Note: we cannot identify the shape of the WTP curve away from the household’s current location
o We conservatively assume a linear WTP
CDDMWTP
MWTP
P
SF HOU
Method to (Locally) Identify Households’ MWTP
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Local linear regression
74
*j j j jk k
k
QOL Z
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Local linear regression
75
*j j j jk k
k
QOL Z
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Local linear regression
o We use weighted LS to estimate βj* at each j*o That is, we run a separate weighted OLS regression at each j*o Weights are normal kernels on the difference between Zj* and Zj
o This approach allows βk’s to vary smoothly across
characteristic space
76
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77
Estimated MWTP San Francisco in % of incomePresent, 2050, and 2100 average U.S. climate% of income * 10^(-1)
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78San Francisco
Ann Arbor Boston
Estimated MWTP curves at selected cities
Houston
WTP, with 95% c.i. Present, 2050, and 2100 average U.S. climate
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Estimated Marginal Distaste for Cold
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80
Estimated Marginal Distaste for Heat
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With Heterogeneity: Aggregate welfare change is more stable over specifications with controls: 2-3% of income
81
"Natural" controls
"Natural" controls and
state FE All controlsAll controls
and state FE
-0.024 -0.030 -0.022 -0.026(0.012) (0.018) (0.009) (0.011)
-301.1 -366.9 -269.7 -323.7(143.1) (228.8) (113.5) (135.7)
Mean QOL change as fraction of income
Aggregate QOL change in billions of 2008$
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Damage function is convex over time and with temperature over both A2 and A1F1 scenarios.
82
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Damage function is convex over time and with temperature over both A2 and A1F1 scenarios.
83
"Natural" controls
"Natural" controls and
state FE All controlsAll controls
and state FE
-0.007 -0.005 -0.005 -0.004(0.003) (0.006) (0.003) (0.004)
-80.5 -64.8 -65.1 -53.9(41.1) (72.3) (33.5) (45.0)
Mean QOL change as fraction of income
Aggregate QOL change in billions of 2008$
Welfare impacts for 2050 A2 forecast: <0.7% of income
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Regressions using wages or housing costs alone are unstable relative to QOL regressions
84
Natural controls
Natural controls + state FE All controls
All controls + state FE
Wage regressionsPercent of income -0.032 0.033 -0.017 0.052Billions of $2008 -$401.4 $414.2 -$212.6 $648.3
House price regressionsPercent of income -0.106 -0.049 -0.073 -0.013Billions of $2008 -$1,320.5 -$609.0 -$900.5 -$154.9
Results underscore importance of using the “right” QOL measure in estimating preferences
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Conclusions
o Preliminary results show
o Evidence of substantial heterogeneity in households’ valuations of hot and cold weather
o Projections of QOL impacts are therefore heterogeneous as well
o Point estimates of overall effect range from 2% to 3.0% loss in income.
o First study to consider heterogeneity in preferences for amenities on county level
85
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Thank You
87