climate change, temperatures and quality of life: projections for 2100 hendrik wolff

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

7San Francisco

Average Daily Temperature Distribution

RED:2090-2100Projected

A2 scenario from CCSM 3.0 in IPCC (2007)

BLUE:1960-90 Normals

8

Boston

San Francisco

Houston

Average Daily Temperature Distribution

RED:2090-2100Projected

A2 scenario from CCSM 3.0 in IPCC (2007)

BLUE:1960-90 Normals

9

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.

10

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.

11

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

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

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

Gaussian kernel, bandwidth = .2. 10000+ HDDs (mainly Alaska) put in last bin

Population-Weighted Change in Heating and Cooling Degree Days: 2000-2100

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



20

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



21

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)

22

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

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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27








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)

28





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

29





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)

30

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

31

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

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

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

34

35

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.

36




Quality of Life , Cost of Livingj j

jj j j

j

QOL COL

Incomeu QOL Consumption QOLCOL

37





jj j j

j

QOL COL


jjj IncomedCOLdQOLd lnlnln Log-linearize around the national average

38





jj j j

j

QOL COL



jK

jK

jj ZZQOLd ...ln 11Second-stage

regression

39





jj j j

j

QOL COL



jK

jK


regression

Price of the city

40





jj j j

j

QOL COL



jK

jK


regression

Price of the city Z = Vector of K Amenities

41

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

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.

ln ij i j ijw w w ww X

ln ij i j ijp p p pp X

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

44

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


45











46











47










Step 2: Predict Welfare changes in 2100

49

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

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

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

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


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


54




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



Sum -0.026 -$327.7(0.012) ($148.8)





55




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



Sum -0.026 -$327.7(0.012) ($148.8)





56




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



Sum -0.026 -$327.7(0.012) ($148.8)





57




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



Sum -0.026 -$327.7(0.012) ($148.8)




Mobility responses reduce mitigate welfare impacts by 10%

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

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

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



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



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) =

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

Cubic spline basis functions

67

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

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

Heterogeneity in Preferences

70

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

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

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

Local linear regression

74

*j j j jk k

k

QOL Z


75

*j j j jk k

k

QOL Z


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

77

Estimated MWTP San Francisco in % of incomePresent, 2050, and 2100 average U.S. climate% of income * 10^(-1)

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

Estimated Marginal Distaste for Cold

80

Estimated Marginal Distaste for Heat

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$

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

82

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

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

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

Thank You

87

climate change, temperatures and quality of life: projections for 2100 hendrik wolff

Documents

effects of climate change

value of climate change

temperature changes

climate amenities

higher temperatures

county temperature data12

comfort welfare changes

severity of cold winters