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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.

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slide1

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

slide5

2010: atmospheric

  • CO2 = 390ppm
slide7

Average Daily Temperature Distribution

RED:

2090-2100

Projected

A2 scenario from CCSM 3.0 in IPCC (2007)

BLUE:

1960-90

Normals

San Francisco

slide8

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

future temperature data
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
will higher temperatures from climate change be good or bad in the daily lives of americans
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 higher temperatures from climate change be good or bad in the daily lives of americans1
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.
county temperature data1
County Temperature Data
  • Drawback:
    • 1 day of 115 F & 4 days of 65 F  50 CDD
    • 5 days of 75 F  50 CDD
slide21

33%

Decrease

116%

Increase

how important are these temperature changes
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)
existing climate change literature has generally not focused on amenity values
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)
existing climate change literature has generally not focused on amenity values1
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)
existing literature on climate amenity values
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.
existing literature on climate amenity values1
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.
existing literature on climate amenity values2
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.
this paper contributes to the literature by
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)
this paper contributes to the literature by1
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)
this paper contributes to the literature by2
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)
slide31

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

stage 1 hedonics
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
stage 2 welfare loss gain predictions
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!

what we are and are not measuring
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
estimates of amenity values and quality of life
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.

estimates of amenity values and quality of life1
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.

estimates of amenity values and quality of life2
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

estimates of amenity values and quality of life3
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

estimates of amenity values and quality of life4
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

estimates of amenity values and quality of life5
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

wage and housing cost differentials data 2000
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.
loss from hotter summer exceeds gain from warmer winters4
Loss from Hotter Summer Exceeds Gain from Warmer Winters

Mobility responses reduce mitigate welfare impacts by 10%

we improve upon the simple empirical model in two ways
We improve upon the simple empirical model in two ways

RICHER ESTIMATION

  • Allow climate to enter utility function in a non-linear way
    • Model WTP as a flexible function of the number of days spent at any given temperature
    • Maximum WTP no longer restricted to be at 65oF
we improve upon the simple empirical model in two ways1
We improve upon the simple empirical model in two ways

RICHER ESTIMATION

  • Allow climate to enter utility function in a non-linear way
    • Model WTP as a flexible function of the number of days spent at any given temperature
    • Maximum WTP no longer restricted to be at 65oF
  • Allow climate preferences to be heterogeneous, with households sorting to their optimal location
we use binned temperature data to flexibly model mwtp for exposure to heat cold
We use “binned” temperature data to flexibly model MWTP for exposure to heat / cold
  • Present-day climate data: average number of days spent in each one-degree temperature bin (e.g. 65 – 66oF)
    • Courtesy of Deschênes and Greenstone
we use binned temperature data to flexibly model mwtp for exposure to heat cold1
We use “binned” temperature data to flexibly model MWTP for exposure to heat / cold
  • Present-day climate data: average number of days spent in each one-degree temperature bin (e.g. 65 – 66oF)
    • Courtesy of Deschênes and Greenstone
  • Define f(t) as the MWTP for an additional day in temperature bin t
    • Our aim is to estimate the function f(t)
we use binned temperature data to flexibly model mwtp for exposure to heat cold2
We use “binned” temperature data to flexibly model MWTP for exposure to heat / cold
  • Present-day climate data: average number of days spent in each one-degree temperature bin (e.g. 65 – 66oF)
    • Courtesy of Deschênes and Greenstone
  • Define f(t) as the MWTP for an additional day in temperature bin t
    • Our aim is to estimate the function f(t)
  • Example: HDD/CDD can be seen as a restrictive functional form for f(t):

βHDD∙(65 – t) if t < 65

βCDD∙(t – 65) if t ≥ 65

f(t) =

slide63

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

  • Rather than rely on the HDD / CDD specification, we model f(t) as a flexible spline
    • Where S1 through S4 are the basis functions of a cubic spline. Maximum MWTP is no longer restricted to 65oF

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

slide65

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

  • Rather than rely on the HDD / CDD specification, we model f(t) as a flexible spline
    • Where S1 through S4 are the basis functions of a cubic spline. Maximum MWTP is no longer restricted to 65oF
  • Estimation:
    • where Nit denotes the number of days at temperature t
  • Rearranging:

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

flexible temperature specification
Flexible Temperature Specification:

Willingness to pay for daily temperature

  • Generally consistent with simpler HDD/CDD functional form: greater WTP to avoid heat than to avoid cold
  • Welfare loss 2-3.8%
  • Assume that WTP curves are horizontal outside the domain of observed present temperatures (conservative!)

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

Controls, with state FE

heterogeneity
Heterogeneity
  • South presumably has distaste for cold and prefers heat

 Their welfare loss will be lower with heterogeneity

  • North presumably doesn’t mind cold, but may be more vulnerable to heat

 Their welfare loss could be higher with heterogeneity

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

method to locally identify households mwtp
Method to (Locally) Identify Households’ MWTP
  • Bajari and Benkard (2005) show how to identify each household’s preferences using the local hedonic gradient

QOL

  • Step 1: Estimate the hedonic price function flexibly.
  • Obtain a local price for climate at each location
  • Step 2: Household’s local MWTP is given by the FOC

MWTP

MWTP

SF

HOU

CDD

slide70

Method to (Locally) Identify Households’ MWTP

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

P

  • Note: we cannot identify the shape of the WTP curve away from the household’s current location
  • We conservatively assume a linear WTP

MWTP

MWTP

SF

HOU

CDD

local linear regression2
Local linear regression
  • We use weighted LS to estimate βj*at each j*
    • That is, we run a separate weighted OLS regression at each j*
    • Weights are normal kernels on the difference between Zj* and Zj
  • This approach allows βk’sto vary smoothly across characteristic space
slide74

Estimated MWTP San Francisco in % of income

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

% of income * 10^(-1)

slide75

Estimated MWTP curves at selected cities

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

WTP, with 95% c.i.

Ann Arbor

Boston

Houston

San Francisco

slide78
With Heterogeneity: Aggregate welfare change is more stable over specifications with controls: 2-3% of income
damage function is convex over time and with temperature over both a2 and a1f1 scenarios1
Damage function is convex over time and with temperature over both A2 and A1F1 scenarios.

Welfare impacts for 2050 A2 forecast: <0.7% of income

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

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

conclusions
Conclusions
  • Preliminary results show
    • Evidence of substantial heterogeneity in households’ valuations of hot and cold weather
    • Projections of QOL impacts are therefore heterogeneous as well
    • Point estimates of overall effect range from 2% to 3.0% loss in income.
    • First study to consider heterogeneity in preferences for amenities on county level