Aversion to Extreme Temperature, Climate Change, and Quality of Life

Aversion to Extreme Temperature, Climate Change, and Quality of Life

David Albouy, University of Michigan and NBERWalter Graf, University of MichiganRyan Kellogg, University of Michigan and NBERHendrik Wolff, University of Washington

April 21, 2023

Preliminary – Comments Wanted!

4

2009: atmospheric CO2 = 383ppm

5

Present and Future Temperature Data

6

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

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? Depends on

1) How much people value (i.e. are willing to pay) those changes per unit (reduction in cold or heat), which may vary by person.

2) Changes in the climate, which varies by location and scenario.

7

8San Francisco

Average Daily Temperature Distribution

RED:2090-2100Projected

A2 scenario from CCSM 3.0 in IPCC (2007)

BLUE:1960-90 Normals

9

Boston

San Francisco

Houston

Average Daily Temperature Distribution

RED:2090-2100Projected

A2 scenario from CCSM 3.0 in IPCC (2007)

BLUE:1960-90 Normals

County Temperature Data

10

365

1

365

1

0 65

0 65

HDD = Annual Heating Degree Days = max ,

CDD = Annual Cooling Degree Days = max ,

dd

dd

T

T

County Temperature Data

11

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

16

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

17

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



18

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



19

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



116% Increase

33% Decrease

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)

20

21

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)

22

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)

23

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.

24








25








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)

26





o Preference heterogeneity across households, sorting! Recover distribution of marginal willingness to pay for climate Method follows IO lit., Bajari and Benkard (2005)

27





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)

28

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

29

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 variables30

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!

31

A Hedonic Model of Welfare Changes

o Value of a location depends on amenities Zk

e.g. heating degree days, distance to water body etc.

o Price of amenity k = βk = (∂U/∂Zk) / (∂U/∂income)

o Change in household amenity value = Σk(βk x ΔZk) Gains and losses do not show up in GDP

*There may be effects on firm productivity that would be in GDP

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34

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.

35




Quality of Life , Cost of Livingj j

jj j j

j

QOL COL

Incomeu QOL Consumption QOL

COL

jjj IncomedCOLdQOLd lnlnln Log-linearize around the national average

36




Quality of Life , Cost of Livingj j

jj j j

j

QOL COL

Incomeu QOL Consumption QOL

COL

jjj IncomedCOLdQOLd lnlnln Log-linearize around the national average

jK

jK

jj ZZQOLd ...ln 11Second-stage

regression

37

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

39

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, TN

Tucson, 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 Dif

fere

ntia

lHousing Costs versus Wage Levels across Metro Areas, 2000

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Mean Std. Dev

Avg annual heating degree days (1000s), 1961-1990 data 4.960 2.133Avg annual cooling degree days (1000s), 1961-1990 data 1.257 0.768Projected 2100 heating degree days (1000s), IPCC A2 3.337 1.657Projected 2100 cooling degree days (1000s), IPCC A2 2.711 1.021Precipitation (meters) 1.448 0.532Dummy for bordering ocean 0.082 0.275Dummy for bordering a Great Lake 0.027 0.161Average land slope (degrees) 1.104 1.451Population density (log of people per sq. mile) 10.234 1.402Percent high school graduates 0.773 0.087Percent college graduates (bachelors) 0.165 0.078Population 89312 291113Quality of life differential (in logs) -0.017 0.050Productivity differential (in logs) -0.063 0.107

Apart from climate and projected climate, all variables are based on the year 2000 census

Data include 3105 counties

TABLE 1: DESCRIPTIVE STATISTICS FOR COUNTY-LEVEL DATASET

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

42

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 Y

Other 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


43



(0.004) (0.003) (0.003) (0.003)


(0.010) (0.008) (0.006) (0.007)


Other Controls Y Y







44



(0.004) (0.003) (0.003) (0.003)


(0.010) (0.008) (0.006) (0.007)


Other Controls Y Y







45



(0.004) (0.003) (0.003) (0.003)


(0.010) (0.008) (0.006) (0.007)


Other Controls Y Y






Effect on Overall Welfare Relatively Stable across Specifications with Controls

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Dependent Variable: QOL + ProductivityNo Controls Controls 1 Controls 2 Controls 3

(1) (2) (3) (4)

Heating-Degree Days (1000s) -0.046*** -0.016* -0.015*** -0.017***

(0.010) (0.009) (0.006) (0.007)

Cooling-Degree Days (1000s) -0.112*** -0.055*** -0.038*** -0.038***

(0.024) (0.021) (0.012) (0.012)


Other Controls Y Y



TABLE 2c: TOTAL WELFARE AND TEMPERATURE

Robust standard errors clustered by MSA/CMSA shown in parentheses. *** p<.01, ** p<.05, * p<.10Natural Controls: Precipitation, ocean and Great Lake Coast dummies, average land slope. Other Controls: Percent with HS and BA, population density.

The Estimated Temperature Loss Function is Asymmetric

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Slope = βhdd Slope = -βcdd = -1.9βhdd

Avg. Daily Temp

23ºF

42 HDD

January in Ann Arbor

7ºF

58 HDD

January in Fargo

80ºF

15 CDD

July in Atlanta

111ºF

36 CDD

July in Death Valley

50ºF

15 HDD

January in Austin

65ºF

0

Second Law of Thermodynamics: Cheaper to heat than to cool.

Second Law of Wardrobes: Clothing is bounded below by zero.

Step 2: Predict Welfare changes in 2100

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Welfare Change, Population Growth and Discounting

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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

j

Welfare 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

Welfare Change, Population Growth and Discounting

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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

j

Welfare 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

Mobility Response

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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.

_j

j j

j

Welfare alt Pop Pop QOL

Loss from Hotter Summer Exceeds Gain from Warmer Winters

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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 $325.7

Change 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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Sum -0.023 -$280.1(0.007) ($86.8)




Sum -0.026 -$327.7





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Sum -0.023 -$280.1(0.007) ($86.8)




Sum -0.026 -$327.7





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Sum -0.023 -$280.1(0.007) ($86.8)




Sum -0.026 -$327.7





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Sum -0.023 -$280.1(0.007) ($86.8)




Sum -0.026 -$327.7





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Sum -0.023 -$280.1(0.007) ($86.8)




Sum -0.026 -$327.7




Mobility responses reduce mitigate welfare impacts by 10%

We improve upon the simple empirical model in two substantial ways

1. Allow climate to enter the 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

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RICHER ESTIMATION

We improve upon the simple empirical model in two substantial ways

1. Allow climate to enter the 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

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RICHER ESTIMATION

We use “binned” temperature data to flexibly model MWTP for exposure to heat / cold

o 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 The HDD/CDD specification can be seen as a restrictive functional form for f(t):

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βHDD∙(65 – t) if t < 65

βCDD∙(t – 65) if t ≥ 65f(t) =

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

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f(t) = β0 + β1S1(t) + β2S2(t) + β3S3(t) + β4S4(t)


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:

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f(t) = β0 + β1S1(t) + β2S2(t) + β3S3(t) + β4S4(t)


( )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

Flexible Temperature Specification: Value of Daily Average Temperature

Willingness to pay for daily temperatureo Generally consistent with simpler functional form: greater WTP to

avoid heat than to avoid coldo Assume that WTP curves are horizontal outside the domain of

observed present temperatures (conservative!)

67Controls, with state FE

Present, 2050, and 2100 average U.S. climate

More flexible homogenous taste model predicts welfare losses of 2% to 3.8%

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Percent of income

Billions of $2008

Percent of income

Billions of $2008

-0.020 -$250.0 -0.007 -$88.7(0.010) ($125.6) (0.006) ($69.7)

-0.038 -$468.1 -0.023 -$280.1(0.016) ($203.5) (0.007) ($86.8)

HDD AND CDD specificationSpline specification

Controls, no fixed effects

Controls, with fixed effects

Estimated welfare losses:

o WTP to avoid extreme heat exceeds the WTP to avoid extreme coldo Welfare losses are concentrated in the Southo Estimated impact is sensitive to inclusion of state FE

Heterogeneity

o South presumably has distaste for cold and prefers higher temp. 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

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The impact on overall welfare of modeling heterogeneity is ambiguous, ex ante

Method to (Locally) Identify Households’ MWTP

o Bajari and Benkard (2005) show how to identify each household’s preferences using the local hedonic gradient

70

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

CDD

MWTP

MWTP

QOL

SF HOU

o Bajari and Benkard (2005) show how to identify each household’s preferences using the local hedonic gradient

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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

CDD

MWTP

MWTP

P

SF HOU

Method to (Locally) Identify Households’ MWTP

Local linear regression

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*j j j jk k

k

QOL Z

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

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74San Francisco

Ann Arbor Boston

Estimated MWTP curves at selected cities

Houston

WTP, with 95% c.i. Present, 2050, and 2100 average U.S. climate

Estimated Marginal Distaste for Cold

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Estimated Marginal Distaste for Heat

Geographic Distribution of Tastes for Mild Weather/Aversion to Extreme Weather

o MWTP to avoid cold weather Highest in Southwest and coasts North, Mountains more resilient (at least around

freezing)

o MWTP to avoid hot weather Highest along Pacific and in Northeast Middle latitudes less resilient.

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Aggregate welfare change is fairly stable over specifications with controls: 2-3% of income

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"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$

Regressions using wages or housing costs alone are unstable relative to QOL regressions

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Natural controls

Natural controls + state FE All controls

All controls + state FE

Wage regressionsPercent of income -0.032 0.033 -0.017 0.052

Billions of $2008 -$401.4 $414.2 -$212.6 $648.3

House price regressions

Percent of income -0.106 -0.049 -0.073 -0.013

Billions of $2008 -$1,320.5 -$609.0 -$900.5 -$154.9

Results underscore importance of using the “right” QOL measure in estimating preferences

Damage function is convex over time and with temperature over both A2 and A1F1 scenarios.

81

"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

Results broadly indicate that climate change is likely to diminish quality of life

o Point estimates of aggregate impact are -2% to -3% of GDP in preferred specificationo Confidence intervals rule out positive aggregate impacto Welfare losses most severe in California and the Southwest

o Methods improve on prior literatureo Quality of life measureo Flexible MWTP specification for each temperature bino Allowance for heterogeneity and sorting

o Migration appears unlikely to substantially mitigate the estimated welfare losses

82

Thank You

83

86

Distribution of MWTP for 1000 HDD (With all controls and state FE)

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Distribution of MWTP for 1000 CDD (With all controls and state FE)

• Aggregate estimates of welfare change under heterogeneity of the same magnitude as with homogenous preferences

• Estimates less precise but more stable across specifications

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

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Preview of preliminary work with richer specification on climate

o We have acquired binned climate data at 10 degree intervalso That is, # of days between 10-20oF, 20-30oF, etc…o Future work: data binned at 1 degree intervals

o Flexible functional form: value of a marginal day in each bin is a 4th order polynomial of the midpoint temperature of the bin

o “Bliss point” not necessarily 65oF

o We use local linear regressions to assess the shape of this polynomial at each location

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Other Possible Improvements of the Specification

o Weather variables

1) Rainfall – no robustly significant estimate…

2) Humidity, sunshine, within-day changes and interactions with temperature – currently no climate projections available

o PUMA level regressions to take advantage of within-county microclimates (Santa Monica vs. East LA)

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Future Work: Mobility Responses

Estimates are a valid first-order approximation with mobility. Envelope Theorem Holding preferences and technology constant over time.

Mobility creates second order effects (envelope theorem) Migration will require new housing (or crowding) in the North. May account for local housing supply elasticities (Saiz 2008) Minimum effect given by changes in welfare using the future

distribution of the population after the mobility reaction.

*Given current demographic trends, a larger population will be in the South when climate change starts to bite. Will Detroit see a return in population? (try the UP!) Housing depreciates slowly: should we build more in Las Vegas?

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Aversion to Extreme Temperature, Climate Change, and Quality of Life

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