the$behavioral$response$to$voluntary$provision$ of ... · thispaper •...

The Behavioral Response to Voluntary Provision of an Environmental Public Good:

Evidence from Residen=al Electricity Demand

Grant Jacobsen University of Oregon

Ma.hew Kotchen

Yale University and NBER

Michael Vandenbergh Vanderbilt University

1

“Green” Goods •  Growing market for green goods, especially carbon offsets and low-‐carbon products (e.g. hybrids).

•  Financial mo=va=on is sufficient for the purchase of some green goods –  e.g., energy efficient vehicles or appliances, residenEal weatherizaEon

•  Environmental concern mo=vates the purchase of other green goods –  e.g., carbon offsets, green electricity –  Pay to improve environmental quality

•  Policies encourage purchase of both types of these goods.

2

Are green purchases mo=vated by guilt? If so, does it maKer?

3

A New Kind of Rebound Effect?

•  “Guilt-‐based” rebound effect –  TradiEonal rebound effect occurs when energy efficiency improvements lower the price of an energy service

–  Does a “guilt-‐based” rebound effect occur when green goods reduce the guilt associated with polluEng behaviors? •  “Behavioral response” •  “Non-‐pecuniary” rebound effect

•  A psychology perspecEve –  Moral licensing (Monin & Miller 2001) –  Green consumer behavior (Mazar & Zhong 2010)

4

Some Exis=ng Evidence

•  Kotchen & Moore (2008) study the behavioral response to parEcipaEon in a per-‐kWh green-‐electricity program –  Program parEcipants paid a 25% premium on their bill to increase the producEon of renewable energy

–  Changes the price of electricity –  ContribuEon amount linked to consumpEon

•  Results: –  High consump=on households less likely to join –  Par=cipants decreased consump=on aVer joining

•  LimitaEon: Could not disentangle the effect of reducing emissions from the effect of the change in the marginal price

5

This Paper

•  Evaluates a green electricity program based on fixed monthly contribu=ons –  ContribuEons are not dependent on how much electricity is consumed at the residence

–  ParEcipaEon does not change the marginal price of electricity

•  Evaluates the possibility of a “buy-‐in mentality” –  ExisEng research suggests that individuals that make small donaEons do so for different reasons than those that make larger donaEons •  Rose-‐Ackerman (1982): Minimum donors “believe they have ‘bought in’ to the enEre range of services provided by the charity”

• DellaVigna et al. (2009): Minimum donors were doing so to avoid the displease of saying “no” to fundraisers 6

Summary of Main Empirical Results

•  High-‐consump=on households more likely to par=cipate

•  Behavioral response differs with level of par=cipa=on –  Voluntary purchase of green electricity increases electricity demand for buy-‐in households by 2.5%

–  Behavioral response not large enough to outweigh environmental benefits of green-‐electricity purchase

•  Evidence of offset mo=ve, guilt-‐based (non-‐pecuniary) rebound effect and buy-‐in mentality

7

Brief Theory

•  A representaEve household solves

–  is strictly increasing and strictly concave

–  is strictly increasing and convex

–  is an indicator variable for environmental concern

}:)()({max,

mypxyhyfx yyx=+−+ γ

)(⋅f

)(⋅h

}1,0{=γ

8

Solu=on: Conven=onal Electricity

€

y

€

MB,MC

€

Ù y

€

y €

py

€

ʹ′ f (⋅)

€

ʹ′ h (⋅) + py

9

Availability of Green Electricity

•  Green electricity at price •  Concern with net emissions •  Household’s problem rewri.en

•  First-‐order condiEons for an interior soluEon

€

y − g

€

pg

€

g

€

maxx,y,g{x + f (y) −γh(y − g) : x + pyy + pgg =m}

€

y : ʹ′ f (y) = py −γ ʹ′ h (y − g)

€

g : γ ʹ′ h (y − g) = pg

10

Solu=on: Conven=onal & Green Electricity

€

y

€

MB,MC

€

Ù y

€

ı y

€

y

€

ı y − ı g €

py€

py + pg

€

ʹ′ f (⋅)

€

ʹ′ h (⋅) + py

11 PredicEon: ParEcipants increase electricity consumpEon, but net effect is beneficial for environment

Heterogeneity of Direct Benefit of Electricity

€

y

€

MB,MC

€

Ù y

€

ı y

€

y

€

ı y − ı g €

py€

py + pg

€

α ʹ′ f (⋅)

€

ʹ′ h (⋅) + py

Greater alpha:!•  Greater conv. elec. !•  More likely to participate!•  Greater increase in y!•  Greater g!•  Greater net reduction!

New parameter alpha!

12

PredicEon: If green electricity program is moEvated by guilt, then individuals that consume more electricity will be most likely to parEcipate and will make larger donaEons.

The “Buy-‐In” Mentality

Susan Rose-‐Ackerman (1982) If a “donor’s gi< to a par>cular charity is at least equal to some minimum z, the donor believes that he or she has ‘bought in’ to the en>re range of services provided by the charity”

We capture the idea with

€

γ (g;z) =1 if g < z0 if g ≥ z.⎧ ⎨ ⎩

13

The “Buy-‐In” Mentality

• Household solves

• Two possible soluEons for – No parEcipaEon – Buy-‐in parEcipaEon where solves

€

maxx, y, g

{x + f (y)−γ (g;z)h(y − g) : x + pyy + pgg = m , g = 0or g ≥ z}

€

{y , g }

€

{ Ù y , 0}

€

{y , z}

€

y

€

ʹ′ f (y) = py

14

Solu=on: To Buy In or Not?

€

y

€

MB,MC

€

Ù y

€

y €

py

€

ʹ′ h (⋅) + py Comparison of surpluses • Not par>cipate • Buy in minus

Two degrees of freedom with "

€

pgz

€

α ʹ′ f (⋅)

€

pgz

15

Buy-‐in Effect

€

y

€

MB,MC

€

Ù y

€

y €

py

€

ʹ′ h (⋅) + py

Larger increase in electricity consump2on with buy-‐in effect

With buy-‐in effect ambiguous net difference

€

α ʹ′ f (⋅)

16 ?"

Empirical Se\ng • Memphis Light, Gas and Water (MLGW) serves 430,00 customers in Shelby County, Tennessee

• Green Power Switch (GPS) program since April 2005 – Chose number of blocks to purchase – Each block is associate with 150 kWh of increased producEon of renewable energy

– Each block costs $4/month

17

GPS Online Sign-‐up

18

GPS Program Par=cipa=on

19

GPS Enrollment Levels (N = 910)

20

Data Sets

• Monthly residenEal electricity billing data – May 2003 through Dec 2008 (GPS started April 2005)

–  All 885 parEcipaEng households –  Sample of 20,205 households

• Match with selected US Census data by zip-‐code –  Income, educaEon, race, family size, populaEon density

•  Electoral data for 2000 US PresidenEal elecEon –  ProporEon for Bush, Gore, Nader 21

Summary Sta=s=cs (N = 20,205)

22

Empirical Analysis

•  The par=cipa=on decision – Are households that consume more electricity more likely to parEcipate in the GPS program and, if so, do they purchase more blocks?

•  The behavioral response to par=cipa=on – Does electricity demand increase upon parEcipaEon in the GPS program?

– Does electricity demand increase more for parEcipants that simply bought in to the GPS program? • If there is a behavioral response, what is the net effect on emissions? 23

Part 1

• The par=cipa=on decision – Are households that consume more electricity more likely to parEcipate in the GPS program and, if so, do they purchase more blocks?

24

Es=ma=on Strategy

• Regress mean daily electricity consumpEon and zip-‐code level demographic controls on parEcipaEon in the GPS program

• Probit and Truncated regression

25

Consump=on and par=cipa=on

Note: Results are opposite of what has been found for programs based on per kWh pricing 26

Part 2

• The behavioral response to par=cipa=on – Does electricity demand increase upon parEcipaEon in the GPS program?

– Does electricity demand increase more for parEcipants that simply bought in to the GPS program?

27

Es=ma=on Strategy

•  Fixed effect regression –  DV: ln(kWh/day) –  Treatment variable: AcEve enrollment –  Individual fixed effects –  Month-‐year dummies –  Clustered standard errors

•  Iden=fica=on assump=on: ParEcipants and non-‐parEcipants would have had similar consumpEon trends absent the GPS program. –  Examine potenEal biases by using different subsamples –  2 different subsamples: ParEcipants Only, IniEal Joiners and Controls

28

Behavioral Response to Par=cipa=on

€

ln(kWh /dayit ) = βEnrolledit +δMit + vi +ε it

29

Behavioral Response to Par=cipa=on

€

ln(kWh /dayit ) = β1Enrollment[1]it + β2Enrollment[>1]it +δMit + vi +ε it

30

Net Consump=on (Emissions) Households purchasing blocks > 1

€

y

€

MB,MC

€

Ù y =

€

ı y

€

ı y − ı g €

py€

py + pg

€

ʹ′ f (⋅)

€

ʹ′ h (⋅) + py

Averaged"

510 kwh/month"

Net is -510 kwh/month of conventional electricity!!

Change annual emissions!• 8,129 lbs CO2!• 35 lbs SO2!• 15 lbs NOx!

31

Net Consump=on (Emissions) for “Buy-‐In” Households

€

y

€

MB,MC

€

Ù y

€

y €

py

€

ʹ′ h (⋅) + py

€

ʹ′ f (⋅)30 kwh/month"

Averaged"

150 kwh/month"

Net is -120 kwh/month of conventional electricity!!

Change annual emissions!• 1,913 lbs CO2!• 8 lbs SO2!• 13 lbs NOx!

32

Summary

•  Evidence of offset moEve and buy-‐in mentality –  High-‐consumpEon households more likely to parEcipate

–  Behavioral response differs with level of parEcipaEon

•  Voluntary purchase of green electricity increases electricity demand 2.5% or buy-‐in households

•  Behavioral response not large enough to outweigh environmental benefits of green-‐electricity purchase

33

Broader Implica=ons

•  Standards and cerEficaEon for green-‐electricity programs –  CauEon about the buy-‐in effect –  Green-‐E sets minimum of 100 kwh/month buy-‐in

•  Some of the first evidence on the moral licensing (indulgence) hypothesis on environmental behavior –  Evidence of a non-‐pecuniary (“guilt-‐based”) rebound effect

–  Magnitude relaEvely small in our case •  Future research on other behaviors

–  Green behavior and support for environmental policy?

34

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