Happiness and Clean Air

Index Introduction Happiness in economics Data Methodology Results Conclusions

University of Washington

Valuing public goods using happiness data:The case of air quality

by Arik LevinsonJournal of Public Economics

Jorge Rojas-Vallejos

2015

Jorge Rojas-Vallejos (UW) Public Goods: Air Quality 2015 1 / 28

Content

1 Introduction

2 Happiness in economics

3 Data end Methodology

4 Results

5 Conclusions

Introduction

RemarkEstimate the willingness to pay for a local public good, air quality.

Time varies Location remains fixed

Traditional methods of estimation:1 Travel-cost models2 Hedonic regressions3 Contingent valuation surveys

Here, Levinson uses Happiness or subjective well-being to esti-mate WTP.

Introduction

Traditional methods of estimation:1 Travel-cost models

2 Hedonic regressions3 Contingent valuation surveys

Introduction

Traditional methods of estimation:1 Travel-cost models2 Hedonic regressions

3 Contingent valuation surveys

Introduction

IntroductionThe goal of this study is to obtain the average marginal rate ofsubstitution between income and air quality AMRSYQ. Levinsoncalls this term for shortness WTP. Strictly speaking, he estimates:

The trade-off between income and air quality that will leave people,on average, equally happy.

0 1 20

1

2

Quality

Inco

me

0 1 20

1

2

Quality

Inco

me

0 1 20

1

2

Quality

Inco

me

Variables

Happiness

Air Quality Weather

The impact of leaving out some of these variables is OVB.

Variables

Happiness

Air Quality

Weather

Variables

Happiness

Air Quality Weather

Variables

Happiness

Air Quality Weather

Variables

Happiness

Air Quality Weather

Variables

Happiness

Air Quality Weather

Variables

Happiness

Air Quality Weather

Happiness in Economics

ParadoxRichard Easterlin (1975) stated that happiness does not increasewith income across countries or within a country over time, but itdoes increase with income across individuals within a country atany given point in time.

The evidence is mixed about this! However, there are more evidencesupporting the paradox.

ParadoxRichard Easterlin (1975) stated that happiness does not increasewith income across countries or within a country over time, but itdoes increase with income across individuals within a country atany given point in time.

The evidence is mixed about this! However, there are more evidencesupporting the paradox.

Suppose the paradox is true, then there are two possible explana-tions:

1 People become habituated to their situations and change theirreference level of well-being,

2 Happiness depends on relative income.Richest guy in poorest town may be happier than poorest guyin richest town.

Suppose the paradox is true, then there are two possible explana-tions:

1 People become habituated to their situations and change theirreference level of well-being,

2 Happiness depends on relative income.Richest guy in poorest town may be happier than poorest guyin richest town.

Why do we care about this paradox?

The key feature of this analysis identifies the relationship betweenhappiness and the place-specific, date-specific air quality, at the placeand date where the happiness question was asked.

The author compares stated happiness by statistically similar res-pondents, at the same locale, during the same season of the sameyear, who just happen to have been surveyed on days when the airquality differed (Relative exposition to pollution).

There are a few examples of applications of these happiness mea-sures:I Airport noiseI Flood disastersI TerrorismI Weather & climate

The problem... they use annual average measures of the publicgood... this may induce bias... due to heterogeneity

RemarkAggregating environmental quality across entire countries masksmuch of its heterogeneity!

The problem...

they use annual average measures of the publicgood... this may induce bias... due to heterogeneity

The problem... they use annual average measures of the publicgood...

this may induce bias... due to heterogeneity

The problem... they use annual average measures of the publicgood... this may induce bias...

due to heterogeneity

Advantages of this approach1 Travel-cost and hedonic models may underestimate the value

of air quality. Why?

Travel-cost assumes that only people affected have value forclean air.

Hedonic methods can capture this, but it may accrue value topeople who really do not care even though they are affected.

2 No annual regional averages, then no heterogeneity.

3 No habituation bias. Any valuation derived from daily fluctua-tions will omit this effect.

4 The results are identified from short-term changes in air qualityat a given location. Hence, this mitigates concerns about unob-served local characteristics correlated with both happiness andair quality.

of air quality. Why? Travel-cost assumes that only people affected have value for

clean air. Hedonic methods can capture this, but it may accrue value to

people who really do not care even though they are affected.

Disadvantages1 It treats responses to questions about happiness as a proxy for

utility and then makes interpersonal comparisons amongrespondents.

2 It takes household income to be an exogenous determinant ofhappiness, rather than potentially determined by happiness.

As we know:

Happiness Income

As we know:

Happiness Income

As we know:

Happiness Income

As we know:

Happiness Income

Data

I EPAs Air Quality System (AQS)

I National Climate Data CenterI General Social Survey (GSS) by the National Opinion Research

Center (NORC)

QuestionTaken all together, how would you say things are these days? Wouldyou say that you are very happy (3), pretty happy (2), or not too happy(1)?

Data

I EPAs Air Quality System (AQS)I National Climate Data Center

I General Social Survey (GSS) by the National Opinion ResearchCenter (NORC)

Data

I EPAs Air Quality System (AQS)I National Climate Data CenterI General Social Survey (GSS) by the National Opinion Research

Center (NORC)

Data

Figure: Example of method around population-weighted centroid.

Data

Thiessen polygons would have been better.

Data

Thiessen polygons would have been better.Jorge Rojas-Vallejos (UW) Public Goods: Air Quality 2015 15 / 28

Methodology

Focus on PM10.6,035 useful observations out of 19,491.

Regression model:

Hijt = Pjt + ln Yi +Xijt + j + t + j yeart + ijt (1)

where:i: respondent,j: location,t: date.

Methodology

Average Marginal Rate of Substitution between pollution and in-come,

dH = 0 = dP + 1Y

dY + others = 0

Fixing dX = d = d = 0, then:

YP

dH=0

= Y

(2)

Equation (2) depends upon the functional form, but this is quite ro-bust to different specifications.

Methodology

YP

dH=0

is the average MRSYQ also called WTP. (3)

RemarkThis is the trade-off between income and air quality that will leavepeople, on average, equally happy.

Methodology

Concern 1 Decision utility versus experience utility.

Concern 2 Utility is ordinal rather than cardinal, interpersonalcomparisons not reasonable. Panel data better thanpooled cross-sectional data.

Advantage 1 People tend to show projection bias (e.g. hungry,sunny). Hedonic estimates may be distorted. Questionabout GSS imprecision should not matter.

Methodology

Results

Pretty robust estimate informs us that:

RemarkFor the average income level, an individual is willing to sacrifice$ 35 to improve in one air quality for one day, on average, holdingall else constant.

Conclusions1. This new approach is a complement to (not a replacement of) e-

xisting methods,

2. It does not underestimate the value for air quality as travel-costand hedonic methods,

3. It avoids the strategic response bias. Robust to different sam-ples of the data and empirical specifications,

4. Happiness is NOT sensitive to local levels of undetectable pollu-tants (e.g. CO),

5. There is sufficient evidence to support that there is a substantialtrade-off between income and environmental quality - a compen-sating differential for pollution.

xisting methods,