consumer choice in economics - cedmfinding: chinese consumers more open to bevs u.s. consumers want...

1© 2017 Electric Power Research Institute, Inc. All rights reserved.

Consumer choice in Economics

▪ Key assumptions:

– Completeness: When facing a choice between two goods, a consumer can rank them so

that either a ≻ b, b ≻a, or a ∼ b

– Transitivity: Consumers’ rankings are logically consistent: if a≻b and b≻c, then a≻c

– More is Better (local non satiation): All else the same, more of a commodity is better than

less.

Firm’s Decisions in Economics

▪ Firms are profit maximizers/cost minimizers

▪ Strategic considerations in this realm as well (e.g., Nash Equilibrium)


Consumer choice in Psychology

▪Choice set dependence

– Adding an inferior option can

draw attention to a better,

"dominant" one

– Adding an extreme option can

draw attention to a

"compromise"

▪ Hummer, mid-sized truck,

small truck

▪Reference dependence

– The status quo is a salient frame of

reference

– Options that are currently owned

are unlikely to be traded

– Options that are strictly better than

what is currently owned are

preferred to those that require

tradeoffs


Consumer choice in Sociology

▪Social meaning

– How alternatives enter the

choice set

– Meaning attributed to those

alternatives

– Choosing an LED lightbulb over an

incandescent might reflect a desire

to join with others in doing the right

thing (e.g., for the environment), or

a desire to oppose incandescent

bulb manufacturers.

▪Social status

– Choose what those of high

status choose

– Those with power can determine

the options that are available


A taxonomyData & methods used to understand and predict choices

Data Class

Data Type

Methods

Data Sources

SP: Stated Preferences (hypothetical alternatives)

RP: Revealed Preferences (historical data)

Judgments“How much would

you pay?”

Choices“Which option would

you choose?”

Salesrecord of prior

purchases

Contingent

Valuation

Multiattribute

Uility

Elicitation

Discrete

Choice

Experiments

Econometric

Choice

Models

Predictive

Analytics


Overview of Methods


1. Contingent valuation

▪What is it?

– Questionnaire that asks repeated

questions about willingness to pay

▪How is it used?

– Approximates how much someone

values different alternatives

– Represent population willingness to

pay to determine things like market

share at a price point“How much would you be willing to

pay for a 10W LED?”

▪ Advantages– Easy to understand and answer

– Inexpensive to implement, can be done over the phone

– Does not require modeling expertise

▪ Disadvantages

– Susceptible to subtle forms of bias (e.g., anchoring)

– Other trade-offs not explicit (color quality)

– Task is unlike real world


2. Multi-attribute utility theory

▪What is it?

– Help decision-maker define what is

important and how to make

tradeoffs among attributes

▪How is it used?

– Figure out multi-attribute utility

function for decision-maker

– Use this to evaluate set of

alternatives and choose the one

that maximizes that utility function

▪ Advantages– Iterative process that helps decision-

makers understand what they want

– Provides a good representation of the utility function for an individual

▪ Disadvantages– It’s not easy to scale up to many

decision makers / individuals

– Very time consuming

– Elicitor can induce bias


Example: Alternative energy in Wisconsin

Elicitor

“Consider fatalities versus the permanent

unusable land of an energy facility. Would you

rather lose 600 people or lose 2000 acres?”

Decision-maker

“Lose the 2000 acres.”

Elicitor

“Ok, would you rather lose 100 additional

people, or the 2000 acres?”

Decision-maker

“I'd still rather lose the acres.”

Elicitor

“How many people, on a first guess, would you be willing to give up to be indifferent to these 2000 acres?”

Decision-maker

“That's pretty tough. That's permanent commitment for land. But relative to fatalities, it just doesn't seem that important to me.”

Elicitor

“How about 110 people?”

Decision-maker

“That's probably in the neighborhood...Maybe 105, how does that sound?”

(Keeney, 1980)


3. Discrete choice survey experiments

▪What is it?

– Respondents make a series of

choices among alternatives that

vary on their attributes

▪ How is it used?

– Infer how much respondents are

willing to pay for different attributes

of alternatives

– Make forecast for new

products/services

▪ Advantages

– Can model tradeoffs and heterogeneity

– Established theory in psychology and economics

▪ Disadvantages– Susceptible to bias (choice set, reference)

– Moderate resource requirement

– Choices in study context may not reflect market context


Examples from prior work from CMU using dicrete choice

(Helveston et al., 2015)

Conjoint Discrete Choice Surveys for US and Chinese Consumers


Finding: Chinese consumers more open to BEVs

▪U.S. consumers want BEVs to be $10,000 to $20,000 cheaper than otherwise-equivalent gasoline vehicles to adopt at similar levels.

▪ In contrast, Chinese consumers are willing to adopt BEVs at similar levels if they have sufficient range.

▪So, China could potentially adopt BEVs at mainstream levels first

▪China’s market is already the largest: – Could affect global market

Hybrid electric

vehicle

Plug-in hybrid

electric vehicles

Battery

electric

vehicles

Average Willingness to Pay

Estimated using choice-based conjoint surveys fielded in the U.S. and in China

14© 2017 Electric Power Research Institute, Inc. All rights reserved. 14

Lighting choices and the importance of labels

Source: FTC, http://www.ftc.gov/opa/2010/06/lightbulbs.shtm

Examples from prior work from CMU using dicrete choice

Min, Azevedo, Bruine de Bruin, 2013.

Now

mandated on

packaging

15© 2017 Electric Power Research Institute, Inc. All rights reserved. 15

One group was shown energy cost estimates. The other was not.

Min, Azevedo, Bruine de Bruin, 2013.


How do consumers compare lamp prices to energy

savings, and how does that impact lighting choices?

▪When weighing purchase cost vs. efficiency, our respondents

acted as though they were using a discount rate of:

– 550% when operating cost estimates were not shown

– 110% when operating cost estimates were shown

▪So, showing energy cost estimates to consumers results in

higher value placed on future energy savings

– But they still overvalue purchase cost relative to energy cost


4. Revealed Preferences: Econometric choice models

▪ Disadvantages

– Model construction can be complex

– Some attributes may be

unmeasured, leading to bias

▪Advantages

– Real purchases in the market

are used; high fidelity to choice

context

▪ How is it used?

– Estimate customer willingness to

pay for product attributes

– Forecast market share of existing

and new alternatives

▪What is it?

– Construct a model from large

datasets of consumer purchases

– Uses info from consumers and

product characteristics


5. Revealed Preferences: Predictive analytics

▪What is it?

– Uses lots of info from consumers

and product characteristics ("big

data")

– Models may be human

interpretable or black box

▪ How is it used?

– Estimate customer willingness to

pay for product attributes

– Forecast market share of existing

and new alternatives

▪ Advantages

– Real purchases in the market are used; high fidelity to choice context

– Can automatically deal with many product/consumer characteristics

▪ Disadvantages

– Models may be difficult to interpret

– Some attributes may be

unmeasured, leading to bias


Comparison of Methods


Comparison of methodsContingent

Valuation

Multi-Attribute

Utility Elicitation

Discrete Choice

Experiments

Econometric

Choice Models

Predictive

Analytics

Data Type SP: Judgments SP: Choices SP: Choices RP: Sales RP: Sales

Key Advantage Requires little

modeling; easy to

implement and

understand

Deliberative;

Interactive

Controlled

experiment; useful

for products that

don’t yet exist

Uses real purchase

behavior in market

context

Uses real purchase

behavior in market

context

Key Disadvantage Hypothetical

judgments unlike

tasks consumers

face; bias from

survey design &

context

Extremely time

consuming; requires

extensive training

and resources;

cannot examine

heterogeneity

Hypothetical

choices may not

match choices in

marketplace; bias

from survey design

& context

Lack of controlled

experiment causes

bias if not controlled

for; measurement

error & limited info

about options

consumers faced

Lack of controlled

experiment causes

bias if not controlled

for; results based

on correlation and

not causation

Appropriate When Respondent and

interviewer time are

limited;

services/goods

relatively familiar

Interested in what a

single decision-

maker cares about

and how she

weights different

attributes

Choice process in

the survey is similar

to market context;

respondents are

willing to give more

time

Able to use natural

experiments or stat

techniques to

control for sources

of bias

Sales prediction

desired but

causality

unimportant


Suitability of methods

Contingent

Valuation

Multi-Attribute

Utility Elicitation

Discrete Choice

Experiments

Econometric

Choice Models

Predictive

Analytics

Consumer

preference

heterogeneity

Not suitable Not suitable Yes Yes Yes

Many alternatives

with varying

attributes

Not suitable Not suitable Yes Yes Yes

Tradeoffs among

attributes

Provides no

information

Provided directly Estimated from

choice data

Estimated from

choice data

Estimated from

choice data

Scalability Can accommodate

large samples

Limited to small

samples

Can accommodate

large samples

Large samples

typically needed

Large samples

typically needed

Challenges Task is typically

unlike decisions the

respondent makes

in the real world

Cumbersome to

implement. Most

appropriate for a

single decision-

maker.

Designing the

choice task to be

well-specified, to

minimize heuristic

choice strategies,

and avoid

misspecification

Unobserved

information and

measurement error.

Model specification

to manage sources

of bias.

Unobserved

information and

measurement error.

Models may be

"black box" and hard

to interpret. No

causality.


Literature Review of Choice Studies

for Energy Service-Related Products

Alex


Literature review

Residential

customers

Commercial/business

customers

PV EVs Rates PV EVs Rates

Stated

preferenceContingent valuation 2 1 1 0 0 1

Multi-attribute utility

theory

0 0 0 0 0 0

Discrete choice

experiments

5 17 6 0 0 1

Rating 3 0 3 0 0 0

Revealed

preferenceEconometric estimation

on market choice data

2 1 11 0 0 2

Predictive analytics 1 0 14 0 0 1

Combined Pooled stated / revealed

preference models

0 1 7 0 0 1

Other Parameters set by

modeler judgment

informed by literature

2 5 0 0 0 0

Alex


Solar PV (14 studies)

▪ Explanatory literature

– Mostly stated preference

– Some studies include comparisons from

several microgeneration options (PV, PV

+ storage, wind).

– Studies from many parts of the world

including the U.S., Australia, Japan, and

Kenya.

▪ Predictive literature

▪ Focused on key factors to diffusion, policy

suggestions, and potential future markets.

▪ Mostly econometric analyses, with some

machine learning studies

▪ Key findings

– Motivations for adopting PV include:

▪ Earning money from installation

▪ Energy independence

▪ Health/Global warming benefits

– Key barriers/considerations include:

▪ Initial cost & pay-back (3-5 years)

▪ Aesthetics

▪ Panel warranty & time on market

▪ Lack of info/familiarity

– Installation in the region increases the

probability of PV adoption ~0.8%


Electric vehicles (27 studies + 4 models)

▪ Explanatory literature

– Peer-reviewed academic studies

– Estimate willingness to pay for vehicle attributes

– Segmentation of customer groups

– Discrete choice surveys with existing alternatives

▪ Key findings

– Battery electric vehicles are less

desirable due to range, cost, and time to refuel

▪ Plug-in hybrids viewed more

favorably

▪ Some subgroups prefer battery

vehicles

– Consumers are sensitive to restrictions on their charging behavior

– PEVs will continue to be a small

market, but policy can increase sales

▪ Predictive literature– Government studies used for

program/policy planning

– Key parameters are based on secondary analyses of data and expert judgment


Electricity plans (10 studies)

▪Literature

– Peer-reviewed academic studies

– Mostly residential populations (2 w/ some commercial)

– Methods

▪ 6 discrete choice surveys

▪ 3 rating/ranking

▪ 1 contingent valuation

– Several randomized experiments testing framing/information

▪ Key findings

– Status quo (fixed rate) pricing is almost always preferred to dynamic pricing (TOU, CPP, DLC)

– Randomized experiments show mixed/small impact of plan framing (e.g., money gained vs. avoided losses)

– Providing program information can increase willingness to participate (e.g., more info on plan, environmental and system reasons)

– There is substantial heterogeneity in preferences (e.g., risk aversion, income group)


Electricity plans in the commercial sector

▪ Previous work– Much less work done to date

– Commonalities with residential customers (e.g., care about "green" reputation)

▪ Challenges/Considerations

– Agency: difficulty determining who the decision-maker is:

▪ Using revealed preferences might be advantageous, as the DM is implicitly defined

– For businesses and organizations, the choice process might be complex and rule-based

▪ High dimensional models (predictive analytics) on sales data can learn these complex rules

▪ Might need additional firm characteristics (e.g., revenue, debt)

– Possible willingness to adopt new schemes if matching with firm’s mission (e.g, experiential goods)

– Information/education on the benefits or generally on how the program works can help increase respondents' willingness to enroll

Goett, Hudson, and Train (2000)

1. Ranked fixed bill plans over seasonal pricing, which

was in turned ranked higher than time-of-day pricing.

2. Respondents required a 35% discount in the electricity

rate in order to be willing to switch from flat rate to

hourly rates.

3. Respondents had a preference toward plans with less

fluctuating pricing

4. Huber and Train (2001) obtained similar results.

5. Firm usage characteristics affect likelihood of

adoption, also prior experience


Summing Up


Concluding thoughts

– Collect relevant sales data in a standardized format (useful for RP

studies)

– Use small experiments to test different offerings based on the results of

choice models (helps calibrate models, test causality)

– Carefully select control groups, avoid possible contamination in RCTs

– Include behavioral (anchoring, status quo biases, framing, moral

suasion) and economic (risk aversion, response to marginal vs. average

prices) considerations when providing information

consumer choice in economics - cedmfinding: chinese consumers more open to bevs u.s. consumers want...

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