76% said that they would have purchased a front loading washing machine even if the

rebate was not offered.

' 95% said they were at least somewhat satisfied with each RECW feature

81% said the washer was worth the price

91% said they would recommend the machine to others

Intercept Surveys (42 interviews)

Of the forty-two (42) intercept interviews conducted, thirty-two (32) were customers

shopping primarily for top-loaders, and ten (10) interviews were with customers looking for

tumble-action washers.

Top-load shoppers mentioned "high cost" more than anything else as a reason for buying

a top-loading washer rather than a tumble-action washing machine.

Thirteen (13) top-load shoppers said they were shopping for a top-loader rather than a

tumble-action washer because the tumble-action machines were too expensive.

Customers also reported that they did not want to bend over to load and unload the

clothes (mentioned by 8 top-load shoppers).

Six (6) top-load shoppers reported that they were simply used to top-loaders.

Retailer Survey (205 dealers)

Retailers believed that about half of the RECW purchasers would have purchased a

standard, top loading machine if the rebate were not available.

If the rebate were not offered, 57% thought the demand would slacken, then pick up

again.

When retailers were asked to estimate the average price of top loading machines that

have similar capacity and features to the most popular tumble actions washers. The

median response was $500.

Retailers thought 30% of customers would be willing to pay $200 more for a RECW, and

50% would pay $100 more.

About half of the retailers believe that tumble action washers will dominate the market

as consumers become more familiar with them.

Manufacturer Survey (6 U.S., 3 Importers)

Six U.S. based manufacturers of clothes washers and three major importers were

interviewed.

Manufacturers' estimates for RECW market share ranged from 2% to 25% in five years.

Future Work:

Follow-up participant, consumer awareness/acceptance, appliance dealer, and manufacturers

surveys will be conducted in the Spring of 1998, and an evaluationlmarketing report will be

published in the second quarter of 1998 to document program success in terms of market

indicators and program accomplishments.

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EE-RM-PET-100 COMMENT 88
1.3.4 Northeast Energy Efficiency Partnership (NEEP)

Reports: None at this time

Sponsors: A consortium of utilities.

Contractors: Sub-contractor: RLW Analytics

Contact: Bruce Wall, Phone: (860) 620-0076

Web Site: future web site to be called: www.tumblewasher.org

Objectives:

The market for high efficiency clothes washers will be evaluated. High efficiency clothes

washers will be promoted.

Methodology:

Washers: Any washer meeting Energy Star performance levels is eligible; currently this

means H-axis washers.

Description:

Could characterize as a market pull program; some participants will have incentives, also

consumer education will be done, point of sales marketing promotions/education.

Baseline market research will include: surveys, focus groups, interviews with retailers,

intercept interviews with consumers leaving the store. ( will follow up with repeat

interviews of the same people 12 months later, who bought or didn't buy a horizontalaxis washer)

Location:

Currently there are 18-20 participating utilities and retailers covering 5.4 million

customers in all of New England, excluding Maine.

New Jersey is expected to join in 1999. New Jersey's 1998 program will include only

new construction.

Dates: Program will officially start on April 21, 1998, although some members of the

partnership have already begun the program (e.g., Con-Electric).

Rebates: Some participants will have incentives to purchase efficient washers.

Findings:

The baseline market research is completed. It is anticipated that results of research will be

made available to the public in the future.

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1.4 INDIVIDUAL UTILITY DEMAND SIDE MANAGEMENT/MARKET

TRANSFORMATION PROGRAMS

1.4.1 Pacific Gas & Electric (PG&E) -Utility Survey

Contact: Ted Pope, Phone: (415) 973-4856

Methodologies:

Washers:

Currently all eligible machines are H-axis.

The Neptune was available in the June/July time frame.

Description:

Survey questions focused on participants satisfaction with their purchased H-axis

machines.

Rebate program participants responded to an 11 question survey.

Dates: Results from April through August 1997

Location: PG&E service territory (northern California)

Sample size: 643

Rebates:

PG&E had a customer program where they gave rebates to consumers who bought energy

efficient washers. ($75-$ 175 rebates).

In addition, water utilities also provided rebates up to $75.

Findings:

Over 90% of participants in a rebate program responded excellent or very good when asked

if they were satisfied with their washer.

Future work:

Additional survey data is being collected.

1.4.2 Commonwealth Electric -Utility Survey

Contact: DickMoran, Phone: (508) 29l-0950x 3548

Methodologies:

Washers: energy efficient washers

Description: survey of participants in a rebate program

Date: a rebate program has been running since September 1997.

Sample size: 175

Rebates: yes

Questions relevant to this study include:

Would you have purchased this washer without a rebate?

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EE-RM-PET-100 COMMENT 88
Rate your washer (in comparison to old washer) in terms of ease of use, appearance, time

to wash, and quality of wash).

Have you had any problems?

Findings:

Responses have not been officially tabulated, however, responses are leaning toward easier

to use and overwhelmingly towards cleaner clothes.

1.4.3 Consumer Publications

1.4.3.1 Consumer Reports/Consumers Union

Contact: Mark Connelly, Division Head - Home Environmental Products, Phone: (914) 378-2325

Publication: July 1997 issue of Consumer Reports magazine

Methodology:

Clothes washers were evaluated and rated

Evaluates: noise, washability and features

Compared 16 top-loading (V-axis) and 2 H-axis clothes washers

H-axis washers evaluated were: Frigidaire FWT445GE and ASKO 8005 (compact)

Findings:

"To get the best results with front-loaders, you need to use special detergent that can be

expensive".

"Little is yet known about their [H-axis models] reliability".

Spot checks on wear and tear didn't verif' that front-loaders were more gentle on clothes.

The Frigidaire did not produce dryer clothes at the end of the spin cycle.

Front-loaders are generally quieter during filling and washing and a bit noisier during the

spin cycle.

• The two front-loaders used less energy.

• H-axis washers handled unbalanced loads better than others.

1.4.3.2 Consumer - New Zealand publication

Contact: Hamish Wilson, email: hamish@consumer.org

Publication: September 1997 issue of Consumer magazine.

Methodology:

• Rated soil removal, spin efficiency, energy use, water use, gentleness, ease of use, cycle time,

noise, detergent used.

Rated more H-axis washers than U.S. consumer publications.

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EE-RM-PET-100 COMMENT 88
Tested four (4) H-axis washers and six (6) V-axis washers

Findings: (results presented in bar chart form)

• Overall, H-axis machines scored better on energy and water usage.

• Overall, H-axis machines scored better on gentleness.

• Soil removal for H-axis and V-axis were similar.

1.4.3.3 Choice - (Australian Consumer Magazine)

Publication: September 1997 issue of CHOICE magazine

Methodology:

• 21 washing machines were rated; 7 H-axis, front-loaders and 14 V-axis, top-loaders

• Washers were rated/tested for: wash capacity, cycle time, noise, water used, energy

efficiency, spin efficiency, gentleness, and dirt removal.

Findings:

• Based on a combination of reasonable performance, purchase price and 10-year running

costs, the Best Buys were the GE and a Hoover front-loaders.

• "Notably higher scores than the top-loaders (V-axis) for gentleness and water and energy

efficiency."

• Top-loaders (V-axis) wash and spin just as well as front-loaders, and they tend to have faster

cycle times."

• Top-loaders (V-axis) are usually less gentle on your clothes.

1.5 TRADE ORGANIZATIONS

1.5.1 The Soap and Detergent Association (SDA)

Publication: Detergents & Laundry Additives in High-Efficiency Washers

Contacts:

Janet Donohue, Director of Communication, Phone: (212) 725-1262

Jane Meyer, Director Consumer Education, Phone: (212) 725-1262

Background:

The Soap and Detergent Association is a national trade association (139 member), founded

in 1926. The Association represents the manufacturers of household, industrial and

institutional cleaning products and those companies that supply ingredients and finished

packaging to the industry. Over 90% of the cleaning products sold in the United States are

made by SDA members.

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EE-RM-PET-100 COMMENT 88
Methodology:

Not described, however, this document relies largely on data from tumbler washer use in

Western Europe. Powder detergent is used as the basis of comparison.

Summary:

The Soap and Detergent Association made available a publication detailing information on

detergents and their use in H-axis machines. (October 1996)

Discussed are:

• excessive sudsing

• formulating detergents for high-efficiency H-axis machines

• water temperature

• lower water volume

• clothes-to-water ratio

• powder detergents and laundry additives

• load sizes

• amount of detergent required

• availability of high-efficiency .detergent

Findings:

High-efficiency tumbler washers raise special challenges when formulating effective

detergents and laundry additives

a. Suds are more readily created. Excessive suds can overflow the machine or interfere

with the washers's proper operation.

b. Water temperature can be lower because there is less water to heat the wash tub and the

clothes.

c. Lower water volume makes it more difficult to keep soils from redepositing on fabrics.

d. Lower water volume results in a higher clothes-to water ratio. This creates a greater

tendency for fabric dyes to bleed and transfer.

e. Powder detergents and laundry additives are harder to dissolve in cooler and less water.

• High-efficiency washer load sizes are expected to be comparable to current agitator

capacities and load sizes, i.e., 7 pounds.

• The amount of detergent required for effective cleaning is most closely related to the amount

of laundry and soil being washed, not to water volume.

• As high-efficiency tumbler washers become more widely available, laundry detergents

specifically made for these machines will also become available. [two brands are now

available]

• To achieve optimal cleaning performance in high-efficiency tumbler washers, detergents

formulated for these machines should be used.

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EE-RM-PET-100 COMMENT 88
1.5.2 Arthur D. Little (ADL) Report on the behalf of AHAM, 1991

Report: Financial Impact of DOE Top Loading Horizontal Axis Standards on U.S.

Washing Machine Manufacturers; Appendix A: Description of Consumer

Survey

Date: July 1991

Sponsor: AHAM

Objective: To determine the valuation by consumers of potential top loading H-axis washing

machines relative to current products.

Methodology:

Washers: side-by-side, hands-on comparison of equivalent size, top loading V-axis and Haxis washers.

Description:

• Loading/Unloading Demonstration

• Card Sort! Conjoint Exercise

• Self-Administered Questionnaire

• Environmental Sensitivity Index (measured environmental sensitivity)

Date: 1991

Location: Mall locations in Denver and Indianapolis

Sample size: 301 participants

Topics covered:

Features relating to: adding clothing, loading method, water use and detergent use were

examined.

Findings:

• Many consumers significantly desire the features of V-axis ,top-loading washing machines.

• Many consumer do prefer low energy cost units.

• Washing machine price is the major determinant of consumer utility or satisfaction.

• No substantial differences in utility patterns exist between environmentally sensitive and

average consumers. However, low and medium income sub-groups of consumers have

greater expected sensitivity to price.

• Conjoint Results (Appendix A): Consumer preference for purchasing a washing machine

are:

• price, 49.1%;

• energy costs, 20.3%;

• ability to add more clothes, 11%;

• loading method, 10.7%;

• water use, 5.6%; and

• detergent use, 3.3%.

See (Appendix B) for detailed results of the self administered survey results.

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EE-RM-PET-100 COMMENT 88
1.5.3 EPRI (Electric Power Research Institute) Journal

Publication: EPRI Journal, November/December 1997 Issue

"The New Line on Laundry" by Leslie Lamarre

Findings:

• "Consumers also like the absence of the agitator (on H-axis machines), which frees up room

for comforters, rugs, and other bulky items they might otherwise have to take to the

laundromat." This is an insight gleaned from the Four-year EPRI, THELMA project.

• At this time, 84% of consumers are very satisfied or somewhat satisfied with their own

washing machines. Their biggest complaints are unbalanced loads and clothes tangling

around the agitator.

• After in-home use, tests indicated that the capacity concern was more visual than actual.

• The biggest barrier cited to buying an H-axis machine was the higher cost of the machines.

1.5.4 Mechanical Engineering -- Journal of the American Society of Mechanical Engineers

Article title: Energy-efficient appliances

Author: Steven Ashley, Associate Editor

Date: March 1998

Summary:

The author discusses the design of the Neptune washer (Maytag).

Findings:

• The Maytag, Neptune has a maintenance-free, low-noise, high-efficiency motor with

infinitely variable-speed control.

• The Maytag team solved the bend-and stoop problem by tilting the spinner axis of the

stainless-steel tub up 15 degrees and making the opening as large as possible.

1.6 GOVERNMENT PROJECTS

1.6.1 Energy Star Program

Contacts:

Bill Noel - Partnerships manager, DOE, Phone (202) 586-6149

Lee Bodner - Program manager; D&R International, Phone: (301) 588-9387

Mary Myers - D&R International, Phone: (301) 588-9387

Jamie Treworgy - D&R International, Phone: (301) 588-9387

Marc LaFrance - DOE, Phone: (202) 586-8423

Background:

Energy Star is a joint project of the U.S. Department of Energy (DOE) and the

Environmental Protection Agency (EPA). This collaboration concentrates on educating

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EE-RM-PET-100 COMMENT 88
consumers about the benefits of highly energy efficient home appliances, consumer

electronics, office equipment and building materials.

Programs:

• Demonstrating new Technologies in "real world" settings

Example: Bern, Kansas I Maytag Washer study

• Volume Purchases

Large institutional buyers, such as housing authorities, are organized to pooi their

resources.

• Energy Star Appliance Program (formerly the Retailer Initiative)

A retailer initiative pilot program to promote high efficiency washers. The DOE will

place "Energy Star" labels on efficient washers to help consumers differentiate them

from other machines; making it easier for consumers to recognize efficient appliances.

A list of energy efficient clothes washers is listed at the energy star web site:

http ://www.energystar.gov.

• Financing Programs

The DOE is also working on financing programs with Fannie Mae, and working with

utilities and retailers to determine the best ways to communicate efficiency benefits to

consumers.

• Energy Star Manufactured Homes Program

DOE , Washington State University, and the Idaho Department of Water Resources are

working with regional manufacturers to include high efficiency residential appliance,

including clothes washers, in manufactured homes.

Energy Star Participants reported August 97:

• SMUD (Sacramento Municipal Utility District)

• Southern California Edison

• SDA&E (San Diego Gas & Electric)

• Potomac Electric Power Co.

• Wisconsin Electric Power Company

• Wisconsin Public Service

• Portland Energy Conservation Inc.

1.6.2 Commercial Laundry Field Study

Commissioned by: Cooperative Research and Development Agreement (CRADA) with Maytag

Contractor: John Tomlinson, ORNL, Phone: (423) 574-0291

Sub-contractor: Ayers Associates, CH2M Hill

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EE-RM-PET-100 COMMENT 88
Objective:

To measure and compare energy and water use before and after changing to an energy

efficient clothes washer.

Methodology:

Washers: 10 residential V-axis washers were replaced by Maytag H-axis washers

Description:

• Energy monitoring and metering study

• Main laundry room selected with 10 washers

• Washers average 115 cycles per month

Location: Tampa, Florida apartment complex with 100 units

Findings: (Contact Tomlinson for more detail)

After replacing V-axis washers with H-axis washers:

• water use was reduced by 38%,

• hot water use was reduced by 40%,

• machine energy reduced by 47%,

• 69% were satisfied or very satisfied with the cleaning performance of the V-axis washer,

• 97% of customers interviewed were either satisfied or very satisfied with the cleaning

performance of the H-axis machine.

1.6.3 MacDill Air Force Base

Commissioned by: Department of Defense

Contact: John Tomlinson, ORNL, Phone: (423) 574-0291

Subcontractor. Ayers Associates

Objectives: [from presentation by Tomlinson on July 18, 1997]

• Improved awareness of benefits.

• Information dissemination to facility managers.

• Information on load size.

• Technical evaluation

Methodology:

Washers: V-axis large Speed Queen replaced by 5 Frigidaire, 1 Creda

Description:

• Energy and water use study in two apartment laundry rooms used by Army barracks.

• Measure energy/water consumption; measure load size

Customer impressions/habits

Incentives: H-axis machines contributed to project by manufacturers

Findings: (Contact Tomlinson for more detail)(from presentation by Tomlinson on July 18, 1997)

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EE-RM-PET-100 COMMENT 88
• H-axis washer (B) showed an increase in cu.ft I kWh of 190% over V-axis washer (A).

• H-axis washer (B) showed an increase in lb / kWh of 230% over V-axis washer (A).

1.6.4 Fort Hood Demonstration Study (metering study)

Commissioned by. Department of Defense, Forces Command Department of Army

Contacts:

• Greg Sullivan, Pacific Northwest National Laboratory (PNNL), Phone: (509) 372-6212

• Graham Parker, PNNL, Phone: (509) 375-3805

Methodology:

Washers:

• Baseline washers are Roper residential washers.

• On premise laundry machines (like commercial washers without a coin box).

• Plan to monitor Staber, Maytag, & Amana washers (a total of 18 washers).

Description:

• Will monitor: hot & cold water and mechanical energy.

• Will examine operation and maintenance expense (washers expected to be treated

roughly).

Dates:

• A 6 month project starting in December 1997.

• Will monitor H-axis washers January-June.

Location: Fort Hood - Texas (near Austin), Army Base.

Sample size:

• On-premise laundry for the barracks.

• Each barrack has 6 machines.

• Each machine is estimated to do 5-7 turns a day.

Findings: Current Status (3-23-98).

• Have monitored/metered Roper baseline washers for two months.

• Are currently monitoring Staber washers and have two months worth of data.

• Report expected in early summer of 1998.

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EE-RM-PET-100 COMMENT 88
1.6.5 Fort Hood Government Procurement -Department of Defense

Commissioned by: Department of Defense, Department of Army

Contact:

Summaiy:

• Government procurement program.

• Army bought approximately 1,200 Staber H-axis washing machines during the summer/fall

of 1997.

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EE-RM-PET-100 COMMENT 88
APPENDIX J. CLOTHES WASHER CONSUMER ANALYSIS

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TABLE OF CONTENTS

CLOTHES WASHER CONSUMER ANALYSIS J-1

J.1 EXECUTIVE SUMMARY J-1

J.2 FOCUS GROUPS J-l

J.3 CONJOiNT ANALYSIS J-2

J.4 RESULTS J-2

J.5 CONCLUSIONS J-3

J.6 METHODOLOGY J-5

J.6.l Introduction J-5

J.6.2 Focus Groups J-6

J.6.3 Conjoint Analysis J-6

J.6.4 Summary J-16

J.7 FOCUS GROUP RESULTS J-16

J.7.1 Introduction J46

J.8 CONJOiNT SESSION SURVEY RESULTS J-20

J.8. 1 Introduction J-20

J.8.2 Conjoint session Survey Results J-21

J.8.2.3 Demographics J-21

J.9 CONJOINT ANALYSIS RESULTS J-28

J.9. 1 Introduction J-28

J.lO FOCUS GROUP ATTRIBUTE RESULTS J-46

J. 11 TOP 5 ATTRIBUTES FROM CONJOINT ANALYSIS SESSIONS J-52

J.12 CONJOINT SESSIONS SURVEY WITH RESPONSE FREQUENCIES J-53

J.13 CONJOINT ANALYSIS CARDS AND INSTRUCTIONS J-62

J.14 INSTRUCTIONS J-68

J. 14.1 Clothes Washers J-68

J.15 CONJOINT ANALYSIS COEFFICIENT ESTIMATES J-70

J.16 ELASTICITY CALCULATIONS J-73

LIST OF TABLES

Table J. 1 Hypothetical Example - Conjoint Data Using the Purchase Card For Purchase

Probability Estimation J- 10

Table J.2 Hypothetical Example - Individual Ranked Conjoint Data For Equipment

Choice Estimation J-13

Table J.3 DOE Clothes Washer Focus Group Response Rates J-17

Table J.4. Focus Group Results Most Important Clothes Washer Attributes J-18

Table J.5 Top Twelve Clothes Washer Attributes Listed by Conjoint Respondents . . J-20

Table J.6 Sampling Results J-21

Table J.7 Income Distribution J-22

Table J.8 Ethnic Background of Respondents J-22

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EE-RM-PET-100 COMMENT 88
Table J.9 Distribution of Educational Achievement .......................... J-23

Table J.l0 Distribution of Expected Price for a New Clothes Washer ............. J-24

Table J-l 1 Distribution of Desired Price Range for a New Washer ................ J-25

Table J-12 Choice Sensitivity to Price Change; All Respondents ................. J-26

Table J. 13 Option Choice Sensitivity to Price Change; Respondents with

Income Less than $25,000 Per Year ............................... J-27

Table J. 14 Option Choice Sensitivity to Price Change; Respondents

Over 65 Years of Age .......................................... J-27

Table J. 15 Attribute Definitions ........................................... J29

Table J. 16 Regression Coefficients and Relative Importance; All Respondents ...... J-3 1

Table J.17 Regression Coefficient Estimates for Regional Subgroups ............. J-32

Table J. 18 Regression Coefficient Estimates for Regional Subgroups ............. J-33

Table J. 19 Relative Importance Statistics for Demographic Subgroups ............ J-34

Table J.20 Relative Importance Statistics for Regional Subgroups ................ J-3 5

Table J.21 Purchase Model Coefficient Estimates; Full Sample .................. J-37

Table J.22 Purchase Scenario for Demographic Subgroups ...................... J-38

Table J.23 Purchase Scenarios for Regional Subgroups ........................ J.39

Table J.24 All Focus Groups - 10 Groups - 90 Participants ...................... J-46

Table J-25 Washington DC Focus Group, Combined Group, 16 Participants ........ J-47

Table J.26 San Francisco Bay Area Focus Group, Combined Group, 18 Participants 1.48

Table J.27 Madison Focus Group, Combined Group, 19 Participants .............. J-49

Table J.28 Dallas Focus Group, Combined Group, 17 Participants ................ J-50

Table J.29 Miami Focus Group, Combined Group, 20 Participants ............... J-5 1

Table J.30 Top Five Attributes from Conjoint Analysis Session, 429 Respondents . . J-52

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EE-RM-PET-100 COMMENT 88
CLOTHES WASHER CONSUMER ANALYSIS

J.1 EXECUTIVE SUMMARY

The objective of this study is to determine which clothes washer attributes consumers value

most and estimate how changes in these attributes resulting from an efficiency standard may affect

consumer utility and clothes washer purchases. To this end, several different analysis methods were

used. First, focus groups were held to develop a list of clothes washer attributes consumers value.

The attributes that were cited most often by the focus groups and were also likely to be affected by

an efficiency standard were included in the second analysis method - conjoint analysis. Conjoint

analysis is a stated preference technique that requires respondents to trade off different attributes

against each other. By examining how respondents make these tradeoffs, the relative values placed

on the clothes washer attributes can be determined.

The conjoint analysis results were combined with survey data and used to evaluate the

relative importance placed on clothes washer attributes and how demand for washers might change

with an efficiency standard. The analysis methods and results are summarized in the remainder of

this introduction with more detailed descriptions provided in the following chapters.

J.2 FOCUS GROUPS

Focus groups were used to identify the most important clothes washer attributes among

consumers. The primary purpose of the focus groups was to determine which clothes washer

attributes should be included in the conjoint analysis. The focus groups were conducted to develop

a list of all equipment attributes that influence the decision to purchase a clothes washer. This was

accomplished by having the moderator guide the discussion to get an unprompted list of important

clothes washer attributes. Once the unaided list of attributes was obtained, the moderator suggested

other attributes that were not volunteered initially for the group to discuss. Focus group participants

were then asked to list their top ten most important attributes if they were to purchase a new clothes

washer today.

Focus groups were conducted in five cities; Washington DC, San Francisco Bay Area CA,

Madison WI, Miami FL, and Dallas TX. These cities were selected so that input could be obtained

from five different regions of the country. Ten focus groups were conducted, two in each city, with

a total of 90 participants. Focus group participants were recruited randomly by phone to ensure a

mix of demographic types. The only condition for participation was that the respondent had to be

the person that did the laundry for their household.

Based on the focus group results, the following six clothes washer attributes were selected

for the conjoint: clothes washer price, energy and water savings, capacity, water temperature, door

placement, and load size adjustment. This group of attributes contains five washer attributes that

would be potentially affected by an efficiency standard. In addition, these attributes were ranked

among the top ten important attributes by consumers.

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EE-RM-PET-100 COMMENT 88
J.3 CONJOINT ANALYSIS

Conjoint analysis is a stated preference survey technique that involves having respondents

sort through and rank sets of cards that reflect different equipment options. In this application,

respondents ranked cards that represented clothes washers. Each card contained different values for

the six washer attributes selected from the focus groups. The different values on the cards were

chosen to represent standard, medium, and high efficiency levels. The advantage of conjoint analysis

is that it makes respondents evaluate all six attributes simultaneously and requires them to make

tradeoffs, just as they would if they were actually purchasing a new clothes washer.

The ranking information obtained during the conjoint sessions was used to estimate a logit

model of clothes washer purchases.' By examining how respondents ranked their cards, the

importance placed on each attribute was estimated. In addition, changes in clothes washer attributes

and the impact on overall utility and the decision to purchase a clothes washer was also determined.

Along with the conjoint analysis, respondents at these sessions were given a short survey that

contained a variety of demographic questions as well as questions directed at whether they would

repair a broken washer or purchase a new one. These questions provided additional information on

consumer preferences and sensitivity to clothes washer price. Survey responses were also used to

segment the sample so that model estimation could be conducted for different population subgroups.

J.4 RESULTS

The value placed on different attributes during the conjoint analysis was measured in three

different ways. First, the data were used to calculate importance statistics for each of the six washer

attributes. The importance statistics illustrate the contribution that each attribute makes towards the

total utility associated with the clothes washer as defined by the six attributes. The larger the

importance statistic, the greater the value placed on that attribute.

Second, the data were used to estimate the probability of purchase. From the conjoint

results, the probability of purchasing a clothes washer was estimated as a function of the six

attributes. Changes in these attributes then change the probability of making a purchase. By

changing attributes such as price, savings, and door placement, different efficiency levels can be

simulated and the effect on purchases estimated. By examining how much the probability of

purchase changes, we can evaluate how sensitive consumers are to changes in price and other washer

attributes.

- A logit model differs from a standard regression model in two important aspects. First, the logit model has a

dependent variable that is discrete reflecting two or more distinct choices while the dependent variable of a standard

regression model is continuous and can take on an infinite number of values. Second, the random component of a logit

model follows a logistic distribution instead of a normal distribution that applies for a standard least squares regression

model.

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EE-RM-PET-100 COMMENT 88
The purchase scenarios were run assuming a standard efficiency machine as the base case

and comparing that with a medium efficiency machine and a high efficiency machine. The standard

efficiency option assumes a price of $400, no energy and water savings, and a top loading machine.

The medium efficiency washer has a price of $450 and energy and water savings of $10 annually,

and is a top loading machine. This is consistent with an approximately 20 percent improvement in

efficiency. The high efficiency equipment options have a price of $650, annual savings of $50, and

are either front loading machines with hot water wash capability or top loading machines with no

hot water capability. These high efficiency options were designed to coincide with an approximately

40 percent improvement in efficiency.

Thirdly, the data were used to calculate the price elasticity of demand based on the conjoint

analysis results. Price elasticity is a measure how much demand for a good changes with a change

in price while holding everything else constant. In this analysis, price elasticities were calculated

starting at the standard efficiency case and calibrating the purchase probability to the market

purchase probability based on conjoint analysis survey data. From this starting point reflecting

current market conditions, the price elasticity was calculated based on the change in price going from

a standard efficiency to a high efficiency machine.

J.5 CONCLUSIONS

The analysis techniques described above resulted in the following key conclusions.

Price is the most important clothes washer attribute. All of the analysis results show price

as the most important attribute when consumers are purchasing a new clothes washer. Price was

cited most often in the focus groups when the respondents identified their "top ten" lists of important

washer attributes. In addition, the conjoint analysis results show price as the primary attribute

respondents focused on when ranking their cards. This resulted in the highest importance statistic

of all the washer attributes used in the conjoint.

In the purchase scenarios, the purchase probabilities were more sensitive to price than any

of the other washer attributes. While the shift from a standard to a high efficiency machine resulted

in a significant drop in the estimated purchase probability, this was due to the change in price rather

than to changes in the other attributes. When price is held constant at the standard efficiency level

while the other attributes are allowed to change to reflect a high efficiency machine, the likelihood

of purchase actually increases. This is due to the benefit of additional savings from the high

efficiency machine outweighing the disutility associated with a front loading machine.

Door placement is not as important as other attributes. Although the focus groups, included

door placement as one of the 10 most important attributes, both the conjoint results and survey

results show that door placement is not as important to consumers as a number of other attributes.

In the calculation for importance (i.e., the importance statistic) door placement was second from last

in importance among the six attributes used in the conjoint. In addition, 70 percent of the survey

respondents said that they would consider purchasing a front loading machine if they were going to

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buy a new clothes washer. For these people, door placement was tied for last in terms of importance,

comprising only 8 percent of total utility.

The lower value placed on door placement was also evident in the purchase scenarios where

the benefit of the higher savings outweighed the disutility of the door placement. When price is held

constant, the higher savings combined with a front loading machine resulted in a higher estimated

purchase probability relative to the standard efficiency case with no savings and a top loading

machine.

Demand is more inelasticfor those that currently own washers. Both the survey and conjoint

results indicate that demand is more inelastic for respondents who currently own clothes washers

than non-owners, even with large increases in the clothes washer price. According to survey results,

those consumers that currently own clothes washers are much more likely to replace their washer if

it breaks than to start doing their laundry at a laundromat. Of those surveyed, only 2 percent said that

if their machine broke they would start going to the laundromat or do their laundry somewhere else,

while the rest opt to get their machine fixed or purchase a new clothes washer. As a result, 0.98 can

be considered an upper bound for the likelihood of purchase under current market conditions and is

used as the starting point for calculating the price elasticity.

An additional survey result is that the willingness to start doing laundry elsewhere decreases

as income increases. For the low income respondents, 5 percent said they would start doing their

laundry somewhere else while among high income respondents (those with incomes at $75,000 or

above), no respondents were willing to start doing their laundry elsewhere.

When the purchase probabilities are calibrated to mirror this result, changes in price result

in little change in the likelihood of making a purchase. If the likelihood of purchase is calibrated up

to 98 percent to match the survey results, the price elasticity estimate for current washer owners is

-0.16, which reflects demand for clothes washers that is relatively insensitive to price. Stated

another way, a 10 percent increase in price would result in only a 1.6 percent decrease in demand for

clothes washers.

Elasticities were also calculated for those in the sample that do not currently own a clothes

washer or that purchased one for the first time in the last 2 years. This group is likely the most

sensitive to price and can be considered discretionary clothes washer purchasers. For this group, the

price elasticity is -1.92, which reflects an elastic demand or demand for washers that is sensitive to

price. In this case, a 10 percent increase in price results in a 19 percent decrease in demand.

The remainder of this report discusses all of the research methods and analysis results in

detail. The Methodology chapter outlines the research framework and the analysis techniques used

for this study. Section J. 7, Focus Group Results, describes the results of the focus groups and how

these results were used in the conjoint portion of this study. The Survey Results chapter provides

the results from the survey that respondents filled out during the conjoint sessions. The estimation

results based on the conjoint data are presented in the Conjoint Analysis Results. The final chapter,

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Conclusions, presents a summary of the major findings of the study. Complete survey and

estimation results are included as appendices at the end of this report.

J.6 METHODOLOGY

J.6.1 Introduction

The objective of this study is to determine which clothes washer attributes consumers value

most and estimate how changes in these attributes resulting from an efficiency standard may affect

clothes washer purchases and consumer utility. As discussed in this chapter, conjoint analysis data

were used to estimate the probability of purchasing standard and high efficiency clothes washers.

These purchase probabilities provide an intuitive context to evaluate clothes washer demand, since

changes in equipment attributes are expressed as a change in the likelihood of making a clothes

washer purchase as well as a change in utility.

Focus groups were used to determine the most important clothes washer attributes. These

attributes were presented to respondents during conjoint analysis sessions.2 Conjoint analysis is

advantageous because it allows the respondent to compare washer attributes simultaneously, thereby

forcing respondents to make tradeoffs across attributes as they would if they were actually

purchasing a washer. The conjoint estimation results were combined with model parameters at

different efficiency levels to estimate the probability of purchasing a clothes washer and calculate

purchase elasticities.

During the conjoint sessions, respondents were also given a short survey to complete. This

survey asked a variety of questions designed to get at sensitivity to clothes washer price as well as

additional demographic information. These questions were also used to stratify the conjoint sample

so that purchase probabilities and utility could be estimated for different demographic groups. For

example, separate estimates were conducted for those that indicated on the survey that they would

consider purchasing a horizontal axis machine. This survey is discussed in more detail in both the

Conjoint Analysis Results and the Survey Results chapter of this report.

The layout of this chapter is as follows. First, we discuss the focus group method and

sample. Next, a detailed description of the sampling scheme for the analysis is presented. This

includes a discussion of sample stratification as well as expected completion rates based on past

conjoint analysis recruiting efforts. Following this, the conjoint analysis technique is described as

2 In order to verify the reliability of the six attributes, at the beginning of the conjoint session, respondents were asked to

list the five most important attributes on a clothes washer that they would consider when purchasing a new clothes washer.

This list was elicited with no group discussion for comparison with the list derived during the focus group discussions

where respondents were allowed to express opinions to the group. The results of this survey are discussed in the Suweji

Results chapter of this report.

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well as the model specification for estimating the purchase probabilities. Finally, probability

calculations are presented and potential market scenarios are discussed.

J.6.2 Focus Groups

The primary purpose of the focus groups was to identify the most important clothes washer

attributes. Once identified, these key washer attributes were included in the conjoint analysis. The

focus groups allowed the development of a list of all equipment attributes that influence the decision

to purchase a clothes washer. This was accomplished by the moderator encouraging an open

dialogue among focus group members to obtain an unsolicited list of clothes washer attributes. Once

the unaided list of attributes was obtained, the moderator suggested other attributes that were not

volunteered initially for the group to discuss.3 When the clothes washer attributes discussion was

completed, the moderator asked the focus group participants to identify which ten attributes would

most significantly influence their selection of a new clothes washer, assuming they had made the

decision to purchase a new clothes washer. These responses were then totaled across all ten focus

groups to arrive at a list of the six most important clothes washer attributes to consumers.

Focus groups were conducted in five cities: Washington DC, San Francisco Bay Area CA,

Madison WI, Miami FL, and Dallas TX. These cities were selected so that five different regions

of the country were represented in the focus groups. Ten focus groups were conducted, two in each

city, with a total of 90 focus group participants divided approximately evenly across the ten sessions.

Focus group participants were recruited randomly by phone to ensure a mix of demographic types.

Each respondent was paid a $50 incentive for participating in the focus group.

As discussed, the top attributes as decided during the focus groups were used in the conjoint

analysis. The remainder of this chapter describes the conjoint analysis method, the sample used for

the conjoint analysis, and how the conjoint results were used to estimate the purchase probabilities

and utility.

J.6.3 Conjoint Analysis

Conjoint session design. Conjoint analysis is a stated preference survey technique that

involves having respondents sort through and rank cards that reflect different clothes washer

equipment options. Each card describes a separate clothes washer based on the six most important

washer attributes as determined from the focus groups. Respondents rank the cards from most

preferred to least preferred. As discussed below, this ranking information was used to estimate the

probability of purchasing different clothes washer options based on the equipment attributes.

The moderator relied on a list of attributes provided by the clothes washer working group to the Department of

Energy. The working group consisted of manufacturers, advocates, utilities, and other interested parties.

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The success of conjoint sessions relied on making sure that respondents were well informed

as to what was expected during the sessions as well as with the equipment being evaluated. The

conjoint session moderator explained that respondents should rank the set of cards from most

preferred to least preferred. Photographs and additional information on high efficiency clothes

washers were available at each session. This allowed respondents to become familiar with high

efficiency washers so that they could make informed decisions while doing the conjoint analysis.

Full Profile Conjoint Analysis. Conjoint analysis is a method that enables researchers to

determine which product attributes are most valued by consumers. Past experience as well as

existing literature indicates that the most successful conjoint designs limit each exercise to ranking

16 cards at a time with 4 to 6 attributes on each card. Including more cards or additional attributes

tends to overwhelm respondents and results in less reliable data. For these reasons, the clothes

washer conjoint was designed with 16 cards that presented 6 different attributes.

The full profile conjoint method utilizes an orthogonal card design, which means that there

is zero correlation between each of the attributes on a card. This is critical to the analysis, as

correlation across attributes results in a loss of precision and makes it difficult to estimate the

importance that respondents place on each attribute. For example, consider the situation where price

and rebate are two of the attributes being evaluated and on each card savings is 10 percent of the

price. Since price and savings are perfectly correlated, there is no way to determine from the data

if a respondent is ranking the cards based on price or savings. For this reason, having an

orthogonally designed study was essential.

In addition to empirically determining the value consumers placed on each clothes washer

attribute included on the cards, the conjoint method used in the consumer analysis enabled DOE to

estimate the likelihood of making a clothes washer purchase. Upon completing the rankings the

respondents were asked to determine which clothes washers they would actually purchase given their

situation today. This was done by inserting a 'Purchase Card' in the deck after each respondent

completed the ranking process. The cards ranked above the Purchase Card were recorded as the

clothes washers the respondent would consider purchasing today while those below the Purchase

Card were recorded as clothes washers they would not consider purchasing today. As discussed

below, Purchase Card placement was used to estimate the Purchase Probability.

Conjoint Sampling Method. Given the scope and budget of the clothes washer study, the

conjoint sample size was set at 400 respondents. Individuals were phoned randomly from a database

of phone numbers in zip codes located near each conjoint session location. The sample was stratified

across several key demographic groups so that purchase probabilities could be estimated with

confidence for these groups. Finally, respondents that attended the conjoint sessions were paid a $50

incentive for their time.

One of the goals of the clothes washer study was to utilize a national sample with several

different geographic regions represented. As a result, the conjoint sessions were conducted in four

different regions: Washington, DC (East), Dallas TX (South), Madison WI (Midwest), and San

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Francisco Bay Area CA (West). The sample was divided so a goal was set at 100 respondents to be

recruited from each region, for a total targeted sample of 400.

In addition, steps were taken to insure that a large enough subsample of low-income and

elderly respondents were included in the sample to allow a statistical analysis of the data collected

from these two groups. Approximately a quarter of the sample (90 respondents) were targeted from

households with annual incomes of less than $25,000. This allowed for separate estimation of

probabilities for the low income group and determined if there were statistically significant

differences in purchase behavior between low income households and the rest of the population.

While age is related to income, it is useful to separately explore differences between younger users

(18 to 24), older users (65 and older), and the rest of the sample. As a result, the targeted sample

contained 60 respondents from both the younger and older age groups.

The sample size and segmentation process is consistent with similar conjoint applications

in the literature. For example, Currim (1981)' used a sample of 608 for a conjoint study to estimate

a model of transportation mode choice. This study segmented the sample into 10 segments, with

some segments having as few as 16 and 24 observations and with 8 of the 10 segments having less

than 90 observations. Similarly, Green and Krieger (1988)2 used a conjoint sample of only 170 to

test the sensitivity of different model specifications estimated from conjoint data. Green and Helsen

(1989) 3relied on a sample of 99 in their conjoint model to estimate apartment choice. This sample

was further reduced by a segmentation scheme that split the group into subsamples of 45 and 54

observations each. In addition, Allison and Christakis(1994)4 used a sample of 413 doctors to

estimate the importance of different medical treatments and Louviere and Hensher (1 983) used a

sample of 550 to estimate the potential demand for future cultural event using conjoint. Given these

sample sizes, the proposed sample of 400 for the clothes washer study as well the segmenting

scheme is well within the accepted range of sampling methods found in the conjoint literature.

Response Rates. There is a wide range of response rates for surveys, with no one response

level considered acceptable for all situations. Response rates typically vary by length and type of

survey and by the sensitivity of the questions asked. In The Practice of Social Research by Babbie,

p. 2426, general rules for expected completion rates are described:

A quick review of the survey literature will uncover a wide range of response rates. . . . Even

so, it's possible to state some rules of thumb about return rates. I feel that a response rate of

at least 50 percent is adequate for analysis and reporting. A response of at least 60 percent

is good. And a response rate of 70 percent is very good. You should bear in mind, however,

that these are only rough guides; they have no statistical basis, and a demonstrated lack of

response bias is far more important than a high response rate.

In addition to differences due to survey content and target samples, there is also a difference

in response rates between phone surveys and on-site surveys. On-site surveys include focus groups

and conjoint analysis sessions, where respondents have to drive to a location to complete surveys.

The differences in recruiting between phone and on-site surveys are discussed below.

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For phone surveys, it is possible to repeatedly call respondents to encourage them to complete

the survey. This can result in completion rates as high as 80 or 90 percent among qualified

respondents, with some respondents having been called ten times or more. While repeated calling

can be successful in phone surveys, it is not an effective method for increasing recruitment rates for

on-site surveys. On-site sessions require a substantial time investment for respondents, sometimes

3 or 4 hours or more. After an initial refusal, repeatedly calling is very unlikely to encourage

attendance.

Because of the difference between phone surveys and on-site surveys, the traditional response

rate for phone surveys is not appropriate for evaluating conjoint recruiting. For on-site surveys, the

appropriate statistic is attendance rate, or the percentage of those that are recruited that actually show

up for the conjoint session.

To maximize attendance for on-site surveys, it is general practice to send out reminder letters

to respondents that agree to participate. The letters remind the respondent of the date and time of

the session as well as give directions to the session site. In addition to the letters, each recruit is

called the night before the session and reminded again. Based on our experience, this method

maximizes the completion rate for on-site surveys. Efforts beyond this, such as additional reminder

phone calls, tend to result in an even lower completion rate as respondents become irritated and are

discouraged from participating.

The remainder of this chapter describes how the conjoint analysis results were used to

estimate purchase probabilities and purchase scenarios.

Purchase Probability. The decision to make a clothes washer purchase is the referred to as

Purchase Probability throughout this report. This probability is estimated from the conjoint analysis

data based on the placement of the Purchase Card. Since the purchase choice variable is discrete,

the model is estimated using a standard logit model specification. In this application, the dependent

variable has a value of one for those cards placed above the Purchase Card and a value of zero for

those below. The independent variables are the values for the attributes on each card. In equation

form, this is given by:

Purchase (0,1) = o + I3'X +

Where Purchase = 1 if card is ranked above the Purchase Card, 0 if ranked below

X = Value of attribute j on card i

= Coefficients to be estimated

Constant to be estimated

= random error term assumed to be logistically distributed

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A hypothetical example of one individual's conjoint data used to estimate the purchase

probability is shown in Table J. 1. The shaded region highlights those choices that are above the

Purchase Card, indicating that these are options the respondent would purchase.

Using the estimation results from this purchase logit, the probability of making any equipment

purchases Prob(Purchase) is given by:

Prob(Purchase) = exp(b'X) / (1 + exp(13'X))

Table J.1 Hypothetical Example - Conjoint Data Using the Purchase Card

For Purchase Probability Estimation

Dependent

Variable

Independent Variables

Rank Purchase Price Savings Door

Placement

Capacity Water

Temperature

Load Size

Adjustment

1 1 400 50 Front Standard Warm, Cold Non-Adjustable

2 1 400 50 Top Standard Hot, Warm, Cold Adjustable

3 1 400 10 Front Standard Hot, Warm, Cold Non-Adjustable

4 1 400 10 Top Standard Warm, Cold Adjustable

5 0 400 0 Front Extra Large Hot, Warm, Cold Non-Adjustable

6 0 400 0 Top Extra Large Warm, Cold Adjustable

7 0 400 0 Front Extra Large Warm, Cold Adjustable

8 0 400 0 Top Extra Large Hot, Warm, Cold Adjustable

9 0 450 50 Top Extra Large Warm, Cold Non-Adjustable

10 0 450 10 Top Extra Large Hot, Warm, Cold Non-Adjustable

11 0 450 0 Front Standard Warm, Cold Adjustable

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Table J.la

Dependent Independent

Variable Variables

Rank Purchase Price Savings Door Capacity Water Load size

Placement Temperature Adjustment

1 1 400 50 Front Standard Warm, Cold Non-Adjustable

2 1 400 50 Top Standard Hot, Warm, Cold Adjustable

3 1 400 10 Front Standard Hot, Warm, Cold Non-Adjustable

4 1 400 10 Top Standard Warm, Cold Adjustable

5 0 400 0 Front Extra Large Hot, Warm, Cold Non-Adjustable

6 0 400 0 Top Extra Large Warm, Cold Adjustable

7 0 400 0 Front Extra Large Warm, Cold Non-Adjustable

8 0 400 0 Top Extra Large Hot, Warm, Cold Adjustable

9 0 450 50 Top Extra Large Warm, Cold Non-Adjustable

10 0 450 10 Top Extra Large Hot, Warm, Cold Non-Adjustable

11 0 450 0 Front Standard Warm, Cold Adjustable

12 0 450 0 Front Standard Hot, Warm, Cold Adjustable

13 0 650 50 Front Extra Large Hot, Warm, Cold Adjustable

14 0 650 10 Front Extra Large Warm, Cold Adjustable

15 0 650 0 Top Standard Warm, Cold Non-Adjustable

In this hypothetical example, a 1 for the dependent variable (i.e., Purchase) indicates that the

respondent would consider purchasing this clothes washer, and a 0 indicates the respondent is not

inclined to purchase this clothes washer. Price reflects the retail price of the washer while Savings

is the annual dollar amount that the washer will save off the combined energy and water bill. Door

Placement can take on two values, either "Top" indicating a top loading machine, or "Front"

indicating a front loading machine. Capacity also has two values, either "Extra Large" or

"Standard." Similarly, there are two alternatives for Water Temperature, either "Hot, Warm, Cold,"

where all temperatures are available for wash cycles, or "Warm, Cold" where only warm and cold

water are available for wash cycles. Finally, Load Size Adjustment is either "Adjustable" indicating

that the machine allows the user to adjust water levels to match the wash load size, or "NonAdjustable," indicating that the water level cannot be adjusted.

Equipment Choice Model. A separate estimation is done based on the card rankings and the

values of attributes on the cards. This provides an estimate of how attribute levels affect card

rankings and is used to estimate total utility for each equipment option. Using a slightly different

version of the logit model, the rankings of the cards are regressed against the attribute levels on the

cards. This has a different appearance than the standard conditional logit since the dependent

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variable is the actual ranking rather than a zero or one value.4 However, estimation of this model

is conducted the same as with the more familiar logit specification. The equation to be estimated

in this stage is given by:

Rank = J3'X +

Where Rank1 The ranking of card i

X = Value for attribute j from card i

= Coefficients to be estimated

= Random error term assumed to be logistically distributed

Table G.-2.2 shows a hypothetical example of the individual level data that would be used

to estimate the equipment choice model.

Using these estimated results, a clothes washer choice set is constructed that reflects both

standard and high efficiency equipment options. Using the logit density function, the probability

of choosing any option n among M different equipment options given that a purchase is made is:

Prob(Equip Purchase) = exp(3'X1,) / M (exp(3'X))

A more complete description of how ranked conjoint data can be analyzed using this logit specification is

contained in "Logit Models for Sets of Ranked Items", Nicholos Christakis and Paul Allison, SocioIgicaIMethodo1o,gy,

Volume 24, 1994, PP. 199-228.

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Table J.2 Hypothetical Example - Individual Ranked Conjoint Data For Equipment

Choice Estimation

Dependent Independent

Variable Variables

Rank Price Savings Door Capacity Water Temperature Load size

Placement Adjustment

1 400 50 Front Standard Warm, Cold Non-Adjustable

2 400 50 Top Standard Hot, Warm, Cold Adjustable

3 400 10 Front Standard Hot, Warm, Cold Non-Adjustable

4 400 10 Top Standard Warm, Cold Adjustable

5 400 0 Front Extra Large Hot, Warm, Cold Non-Adjustable

6 400 0 Top Extra Large Warm, Cold Adjustable

7 400 0 Front Extra Large Warm, Cold Non-Adjustable

8 400 0 Top Extra Large Hot, Warm, Cold Adjustable

9 450 50 Top Extra Large Warm, Cold Non-Adjustable

10 450 10 Top Extra Large Hot, Warm, Cold Non-Adjustable

11 450 0 Front Standard Warm, Cold Adjustable

12 450 0 Front Standard Hot, Warm, Cold Adjustable

13 650 50 Front Extra Large Hot, Warm, Cold Adjustable

14 650 10 Front Extra Large Warm, Cold Adjustable

15 650 0 Top Standard Warm, Cold Non-Adjustable

16 650 0 Top Standard Hot, Warm, Cold Non-Adjustable

Purchase Scenarios. The estimation results provide an intuitive means for evaluating the

importance of washer attributes. This is done by estimating the effect an efficiency standard has

on utility by looking at how the likelihood of purchasing a washer increases or decreases with

changes in washer attributes. The analysis of purchase scenarios using the purchase probability

estimates is discussed below.

Probability Scenarios. The probability estimates are used to estimate the change in

purchases due to changes in clothes washer design resulting from an efficiency standard. To

evaluate the effect these changes will have on purchases, the purchase probabilities were

calculated using the values for attributes of the original equipment before the standard. For

example. the pre-standard purchase probability is given by:

Prob1 = exp(3'X) / (1 + exp('X))

Where 1 = Estimated coefficients from the purchase logit

X = Attribute values such as price, savings, axis orientation for the pre-standard equipment

design.

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This pre-standard purchase probability is compared with the post-standard purchase probability.

The post-standard probability is calculated using the same formula with post-standard values for

price, savings, and other equipment attributes:

Prob2 = exp(3'X) / (1 + exp(f3'X))

Where f3 Estimated coefficients from the purchase logit

X = Attribute values such as price, savings, axis orientation for the post-standard

equipment design.

The effect of the standard can be shown by comparing the difference in purchase probabilities

before and after the standard:

Effect of Standard = (Prob1 -Prob2)/ Prob1.

For example, if the pre-standard purchase probability is 0.15 and the post-standard

purchase probability is 0.10 then the effect of the standard is (0.15 - 0.10)/0.15 or 0.33, which

indicates a 33 percent reduction in the likelihood of purchasing a clothes washer. This result

reflects the effect of the standard through those equipment attributes used in the analysis.

Additional effects may also occur through demographic or other market factors not explicitly

addressed in the conjoint analysis.

In order to determine the value placed on attributes independently of price and operating

costs, it is useful to look at changes in attributes while holding price and operating costs constant.

Holding these attributes constant allows the evaluation of different equipment configurations that

will comply with the standard independent of price considerations. As a hypothetical example,

suppose that there are two different equipment designs that comply with the new standard. The

first option meets the standard utilizing a horizontal axis. The second option meets the standard

using a vertical axis and no hot water option for the rinse and wash cycles. The same probability

calculations can be used to determine which equipment configuration is more popular holding

price constant:

ProbAl = exp(3'X) / (1 + exp(3'X'))

Where j3 = Estimated coefficients from the logit purchase model

X = Pre-Standard equipment attributes (vertical axis) for choice A with the post-standard

equipment price.

The post-standard purchase probability for Option A is given by:

Prob = exp('X / (1 + exp('X))

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Where f3 = Estimated coefficients from the logit purchase model

X = Post-Standard equipment options (horizontal axis) with the post-standard equipment

price.

The same method is used to calculate pre-standard and post-standard purchase probabilities for

Option B with and without the hot water attribute:

ProbBl = Purchase probability with pre-standard options (hot water) and post standard price.

ProbB2 = Purchase probability with post-standard options (no hot water) and post-standard

price.

In this hypothetical example, the effect of the standard on these two different equipment options

can be evaluated the same as before. Suppose that the options have the following probabilities:

ProbAl = 0.20 ProbBI = 0.15

Prob = 0.10 ProbB2 = 0.10

The effect of the efficiency standard on Option A is (0.20 -0.10) /0.20 = 0.50 or a 50 percent

reduction in the likelihood of making a purchase. In the case of Option B the change is a 33

percent reduction in the purchase probability. In this example, meeting the standard using the

Option B equipment design utilizes more popular equipment attributes and will have less of a

detrimental effect on purchases.

Elasticity Scenarios. The sensitivity of purchases to changes in equipment attributes can

also be evaluated by calculating the elasticity associated with each equipment attribute. The

purchase elasticities are calculated using the price and equipment options as well as the estimated

purchase probabilities. For the elasticity with respect to price, the elasticity is calculated by:

Price Elasticity = (A Prob/ A Price) * (Priced/ Probmid)

Where A Prob = Change in purchase probability due to the change in price

A Price = Change in price resulting from the efficiency standard

Probmid = Midpoint between pre-standard and post-standard purchase probability

Pricemid = Midpoint between pre-standard and post-standard price.

This number reflects how sensitive the purchase probability is to changes in price. In

general, an elasticity with an absolute value of less than one is considered inelastic or insensitive

to changes in price while an elasticity greater than one is elastic or sensitive to changes in price.

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By calculating the elasticities, the sensitivity of washer purchases to changes in each of

the equipment attributes used in the purchase decision can be evaluated. However, each

elasticity provides information on only one individual attribute while holding all other attributes

constant. To evaluate the effect of changing several equipment attributes simultaneously,

examining the change in overall purchase probabilities as described above is appropriate.

J.6.4 Summary

In the clothes washer consumer analysis a combination of focus groups and conjoint

analysis were used to determine which washer attributes consumers value most, and to estimate

the effect that changes in these attributes due to an efficiency standard will have on clothes

washer purchases. The attributes that are used in conjoint sessions were determined from focus

groups to ensure input from as broad a range of customers as possible. As described above, the

conjoint analysis results were used to estimate clothes washer purchase probabilities according to

the stated preference of consumers.

In this analysis the sample was designed to obtain a large enough sample for key

demographic groups so that differences across groups could be empirically explored. In addition,

the sample allocated for demographic groups are consistent with similar conjoint studies found in

the literature. Finally, experience with recruiting for on-site surveys shows that a 65 to 70

percent completion rate is a reasonable expectation given that repeated call backs are not an

option.

The results of the focus groups sessions as well as the conjoint sessions are presented in

the remaining chapters of this report.

J.7 FOCUS GROUP RESULTS

J.7.1 Introduction

The primary purpose of the focus groups is to identify the most important clothes

washer attributes among consumers for use in the conjoint analysis. The focus groups were

conducted to develop a list of all equipment attributes that influence the decision to purchase a

clothes washer. This was accomplished by having the moderator guide the discussion to get an

unsolicited list of important clothes washer attributes. Once the unaided list of attributes was

obtained, the moderator suggested other attributes that were not volunteered initially for the

group to discuss. Once the complete attribute list was determined, questions were asked to

determine which attributes are considered the most important in selecting a specific clothes

washer.

Focus groups were conducted in five cities; Washington DC, San Francisco Bay Area

CA, Madison WI, Miami FL, and Dallas TX. These cities were selected so that five different

regions were represented in the focus groups. Ten focus groups were conducted, two in each

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city, with a total of 100 focus group participants divided evenly across the ten sessions. Focus

group participants were recruited randomly by phone to ensure a mix of demographic types.

Each respondent was paid a $50 incentive for participating in the focus group.

Focus Group Sample. The criterion for recruiting for focus group participants was

that the respondent had to be the person that did the laundry for the household. While attention

was paid to ensuring that a mix of different income and ethnic backgrounds were represented in

the focus groups, there was no sampling quota set for these groups.

Table J.3 DOE Clothes Washer Focus Group Response Rates

City Calls Recruited % Attendees Completion % of

Recruited Rate Calls

Washington, DC 793 27 3.4% 16 59.3% 2.0

San Francisco Bay Area, CA 880 23 2.6% 18 78.3% 2.0

Madison, WI 795 24 3.0% 19 79.2% 2.4%

Dallas, TX 903 26 2.9% 17 65.4% 1.9%

Miami, FL 3922 29 0.7% 20 69.0% 0.5%

Total 7,293 129 1.8% 90 69.8% 1.2%

Table J.3 shows the completion rate for the focus groups conducted for the first portion

of the clothes washer study. The recruitment rate reflects the number of people called to the number

that are recruited to participate in the focus group. The completion rate is the number that actually

attend the focus group relative to the number recruited. As evidenced by Miami, regional

differences, in this case severe language barriers, can result in dramatically different recruitment

rates. However, completion rates tend to be more consistent across regions, ranging from

approximately 60 to 80 percent.5

Overall, focus group participants came from a broad range of demographic groups.

Across the entire sample, 19 percent came from household with annual incomes of less than $25,000

while 20 percent came from high income homes making more than $75,000 a year. Different age

and race groups were also well represented in the focus groups. Of the 90 attendees, 12 percent were

65 years old or older while 36 percent were in the 18 to 34 years old range. Non-white attendees

comprised 26 percent of the focus group sample, with African Americans comprising 15 percent.

The attendance rate for the Washington DC focus groups is skewed downward due to bad weather for the

second of the two sessions. The first group had II of 14 recruits show up for a completion rate of 79 percent. With

the bad weather, only 5 of 13 showed up for the second group for a completion rate of 39 percent, which lowers the

overall completion rate down to 59 percent for the Washington DC groups.

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Almost 80 percent of the respondents had completed at least some college, and 56 percent

had completed college. About 13 percent had high school degrees, and the remaining 7 percent

completed trade or vocational school. The majority of participants, 87 percent, lived in households

of three or fewer members, with 38 percent living in households of two. About half of the

participants owned their homes, and half rented.

Finally, although the majority of participants did their laundry at home, a fair number

used a laundromat or apartment laundry room. Specifically, 88 percent of the focus group sample

did their laundry at home, 12 percent used a laundromat or apartment laundry room. The latter group

ensures that those that might be in the market in the future for a clothes washer were able to provide

their input to the analysis.

Focus Group Responses. As discussed previously, each focus group was conducted so

that the first part of the session was spent eliciting from the group those attributes that they felt were

most important in clothes washers. The moderator would then suggest other attributes to get

comments from the group. Once the complete attribute list was derived, respondents were asked to

rank the top ten attributes that they would look for when purchasing a new clothes washer.

Table J.4 shows the top 12 attributes mentioned from all of the focus groups as well as

the percentage of respondents who listed that attribute. A complete list of all attributes as well as

the attributes broken out by location is given in Section J. 10.

Table J.4 Focus Group Results Most Important Clothes Washer Attributes

Feature Frequency in Top 10 Percent of Respondent (n=90)

Price 75 83

Capacity 73 81

Energy & Water Costs 65 72

Load Size Options 61 68

Durability 54 60

Water Temperature Options 54 60

Door Placement 38 42

Quiet Operation 36 40

Wash Time 34 38

Warranty 33 37

Multiple Wash Cycle Options 30 33

Horizontal/Vertical Axis 25 28

Price was mentioned most often as an important attribute for selecting a clothes washer,

with 75 of 90 respondents mentioning price. This was followed by capacity, mentioned by 73 of 90

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respondents, and by energy and water costs, which was listed by 65 of the respondents. The ability

to adjust the water to fit different wash load sizes was mentioned by 61 respondents, and durability

and water temperature options were mentioned by 54 of the respondents.

One important item to note is that door placement and axis orientation were kept as two

distinct washer attributes. The focus group moderators spent time explaining to each group the

difference between vertical and horizontal axis machines and emphasized that horizontal axis

machines can be either front or top loading. The focus group discussions tended to center on door

placement rather than axis orientation, with respondents citing issues such as bending over and the

ability to add clothes in mid-cycle as being important. The fact that door placement is cited more

often than axis orientation in the final attribute list is consistent with these discussions.

As discussed in the Methodology, the conjoint analysis is limited to a maximum of six

attributes. This has been shown to be about the upper limit of attributes that respondents can

reasonably expected to consider when ranking the cards. In addition, more than six attributes will

result in a conjoint design that utilizes 25 cards rather than the desired 16, which would also tend to

overwhelm respondents.

Ideally, the top six attributes from the focus groups would be used for the conjoint

analysis so that the conjoint utilizes those attributes that make up the largest share of overall utility.

However, it is also important to include those attributes that are likely to be affected by an efficiency

rule. If these attributes are omitted, then the conjoint analysis results will not be able to estimate

changes in utility resulting from the standard, which is the primary purpose of this analysis.

Of the most important attributes from the focus groups, the ones that are most likely to

be affected by an efficiency standard are price, energy and water costs, door placement, capacity, and

water temperatures. As shown in Table J.4, these five attributes placed in the top seven attributes

in terms of importance in the focus groups. To include these attributes, the decision was made to

drop durability as an attribute in the conjoint analysis. Although important, durability is difficult to

quantify because it is a performance characteristic that means different things to different people, not

a tangible clothes washer attribute like top or front door placement. In addition, durability is unlikely

to change as the result of an efficiency rule.

Based on the focus group results, clothes washer price, energy and water savings,

capacity, water temperature, door placement, and load size adjustment were selected as attributes to

use in the conjoint analysis. This group of attributes contains the five attributes that will be

potentially affected by an efficiency standard. In addition, these attributes were ranked among the

very most important attributes among consumers.

As stated previously, during the conjoint sessions, respondents were first asked to list the

five most important attributes on a clothes washer that they would consider when purchasing a new

clothes washer. This list was elicited with no group discussion, for comparison with the list derived

during the focus group discussions where respondents were allowed to express opinions to the group.

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Table J.5 Top Twelve Clothes Washer Attributes Listed by Conjoint Respondents

Attribute

___________________________________________

Percent of Respondents that Listed Attribute

Among the Five Most Important

Price 56.2

Energy and Water Costs/Energy Efficiency 47.8

Capacity 43.6

Multiple Wash Cycle Options 35.4

WaterTemperature Options 27.0

Machine Size 26.3

Brand 20.7

Load Size Options 19.1

Reliability 17.0

Quiet Operation 15.2

Warranty 15.2

Door Placement 13.5

The results of this survey are shown in Table J.5, above. The complete list can be found

in Section J. 11. All six of the attributes shown on the clothes washer cards are among the twelve

most frequently cited by the respondents. This list is fairly similar to the list derived during the focus

group discussions. Price, capacity, energy and water savings, were the top three on both lists. Nine

of the twelve attributes are among the top 12 attributes in both lists. The conjoint session

participants' 'top twelve' list includes machine size, brand, and reliability, while the focus group list

did not. These attributes ranked 22", th and 1 7th on the focus group list, respectively. The focus

groups had durability, wash time, and horizontal/vertical axis on the top twelve list, while these

attributes ranked 14th 19th and 49th on the conjoint session respondent list.

The results of the conjoint analysis using the attributes and the relative importance placed

on these attributes is discussed in the Conjoint Analysis Results chapter of this report.

J.8 CONJOINT SESSION SURVEY RESULTS

J.8.1 Introduction

In addition to ranking the clothes washer attributes by sorting the 16 cards, respondents

were given a short survey to complete after they finished ranking the conjoint cards. This survey

asked a variety of questions designed to get at sensitivity to clothes washer price as well as collect

additional demographic information. These questions were used to stratify the conjoint sample so

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that purchase probabilities and utility could be estimated for different demographic groups. For

example, separate estimates were conducted for those that indicated on the survey that they would

consider purchasing a horizontal axis machine. A complete copy of the survey and a summary of

the participant responses can be found in Appendix C.

This section presents a summary of the major findings of these surveys. Findings are presented by

demographic group and by clothes washer demand attributes. These discussions also incorporate

some exploration of relationships between question responses. Finally, the list of important

attributes elicited during the conjoint sessions is compared with that derived during the focus group

discussions. These surveys were conducted independently from the conjoint analysis.

J.8.2 Conjoint session Survey Results

J.8.2.3 Demographics

Table J.6 Sampling Results

Group Definition Target Percent of Actual Percent of

Sample Target Sample Actual

Sample Sample

East Region Washington, DC 100 22.2% 95 21.8%

South Region Dallas, TX 100 26.2 111 25.5

Mid-West Region Madison, WI 100 26.2 111 25.5

West Region San Francisco Bay Area, CA 100 25.3 118 27.1

Young 18-24Years 60 12.8 53 12.2

Elderly 65 And Older 60 15.8 67 15.4

Low Income Annual Income <$25,000 90 24.4 107 24.6

The respondents were recruited to represent diverse segments of the consumer market for

clothes washers. Particular attention was paid to age, income level, and region of the country. The

target and actual sample composition in these key age, income and regional categories are shown in

Table J.6 above. The actual sample population was a little more than 15 percent elderly people,

coming within one-half of one percent of the target percent. The actual sample was about 12 percent

young people, which fell 3 percent short of the target. Nearly one-quarter of the sample population

had incomes below $25,000 per year, well above the target of 23 percent. The actual sample was

also fairly evenly distributed across the four regional categories: east, south, mid-west, and west, as

was the target sample. The south and mid-west regions composed just over 25 percent of the total

sample each. The east region was a little smaller, at 22 percent, and the west region was the largest,

at 27 percent.

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Table J.7 Income Distribution

Income Range Percent of Total Sample Percent of those 65 and Older

Less than $10,000 6.2 3.0

$10,000 - $25,000 18.3 23.9

$25,000 - $50,000 31.9 44.8

$50,000 - $75,000 24.5 10.4

$75,000-$l00,000 9.6 11.9

$100,000 and over 8.7 6.0

Refused 0.4 0.0

A more detailed examination of the income distribution of respondents is shown in Table J.7.

There was a significant diversity of income levels among the participants. Table J. 7 shows the

distribution of income levels among all respondents, and those 65 and older. Fewer than 25 percent

of the sample had incomes of less than $25,000 per year, with 6 percent of the sample earning less

than $10,000 per year. A little more than 18 percent of the sample earned $75,000 per year or more,

with about 9 percent earning more than $100,000 per year. Well over half of those surveyed (56

percent) had incomes between $25,000 and $75,000 per year. The median income range was

$25,000 to $50,000. Among those over 65 years old, 27 percent had incomes below $25,000 per

year, and 18 percent had incomes over $75,000.

Table J.8 Ethnic Background of Respondents

Group Percent of Sample

Caucasian/White 75.2

African-American/Black 14.9

Latino/Hispanic 5.3

Asian

Other

3.0

16

As shown in Table J.8 above, the sample had a fair representation of different ethnic

backgrounds. Twenty five percent of those surveyed were of non-Caucasian ethnicity. A little less

than 15 percent were black, 5 percent were Hispanic, and 3 percent Asian. About 60 percent of nonCaucasian respondents had incomes over $25,000, with the majority (37 percent) between $25,000

and $50,000. Among those over 65 in age, 21 percent were of non-Caucasian ethnicity.

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Table J.9 Distribution of Educational Achievement

Education Percent of All Respondents

Some High School 1.2

High School Graduate 8.5

Some College 23.9

College Graduate 63.8

Trade Vocational School 2.6 ___________

Table J.9 shows the distribution of the respondents' educational achievement. Most

respondents were college graduates (64 percent), with most of the remaining 36 percent having some

college or finishing high school. Among those with income less than $25,000 per year, 79 percent

had at least some college. The percentage among all three groups that had completed trade or

vocational schooling was quite small, between 2 percent and 3 percent.

About 63 percent of respondents live by themselves or with one other person, while 22.1

percent lived in households of four or more people. About 75 percent of respondents had no children

between 6 and 18 year old living in their household, with the remaining 25 percent having at least

one such child in their household. Ofthose who did have children, about half had just one child, and

82 percent had two or fewer. Seventy-one percent sent their children to public school.

A large portion of the sample, 60 percent, owned their homes, while the other 40 percent

rents. The typical respondent did their laundry at home. Over 72 percent of those surveyed did

laundry at home. The second most common place was an apartment laundry room, at 19 percent.

A little more than 7 percent did laundry at a laundromat. Among respondents with incomes under

$25,000, 13 percent used a laundromat, 45 percent did their laundry at home, and 35 percent in an

apartment laundry room. Among respondent over 65 years of age, only 3 percent did their laundry

at a laundromat, 18 percent in an apartment laundry room, and 76 percent at home.

More than 17 percent of respondents had purchased a new clothes washer within the past two

years. With regard to horizontal axis clothes washers, 57 percent of those surveyed had used them

in the past, and 70 percent stated that they would consider purchasing such a machine. Among those

over 65 in age, 40 percent had used horizontal machines in the past, and 60 percent would consider

purchasing such a machine.

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Clothes Washer Demand Characteristics.

Table J.10 Distribution of Expected Price for a New Clothes Washer

All Over 65 Years Income Under

Respondents Old $25,000

Less than $350 0.7% 0.0% 1.0%

$350 to $449 29.8% 28.4% 26.2%

$450 to $549 48.9% 55.2% 52.4%

$550 to $649 14.9% 10.4% 12.6%

$650 to $750 4.0% 3.0% 4.9%

Greater than $750 0.5% 1.5% 1.0%

Refused/Don't Know 0.9% 1.5% 1.9%

In order to assess peoples' expectations of the price of a new clothes washer, we asked them

how much they think a new clothes washer would cost. Table J- 10 shows the distribution of all

responses, as well as those for the elderly and lower income segments. Among those who had not

recently purchased a new clothes washer, 47 percent thought the price would be between $450 and

$549. The second most popular response was $350 to $449, at 29 percent. Overall, among those

who had not recently purchased a washer, 90 percent thought the price would be between $350 and

$649. Among those who had recently purchased a machine, the results were quite similar. About

52 percent thought the price would be between $450 and $549, and 30 percent thought it between

$350 and $449. Overall, among those who had recently purchased a machine, 98 percent thought

the price would be between $350 and $649.

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Table J.llDistribution of Desired Price Range for a New Washer

Price Range All Over 65 Years Income Under

Respondents Old $25,000

$200-349 6.9% 4.5% 16.5%

$350 to $499 5 1.3% 46.3% 56.3%

$500 to $649 34.0% 35.8% 22.3%

$650 to $849 5.9% 10.4% 3.9%

$850 to $999 1.4% 3.0% 0.0%

$1,000 to $1,200 0.0% 0.0% 0.0%

Refused 0.5% 0.0% 1.0%

As a final approach to identifying consumers' willingness to pay for clothes washers, we

asked in what price range would they like to stay within if they were to purchase a new washer.

Specifically, the question stated "The price of a new clothes washer ranges from a low of $200 to

a high of $1,200. If you were to purchase a new clothes washer today, what price range would you

hope to stay within." Table J. 11 shows the distribution of responses to this question. The exhibit

shows that over half (51 percent) hope to pay between $350 and $499. The second most popular

category was $500 to $649, with 34 percent of respondents.

Those over 65 years old expressed similar opinions regarding what they hoped to pay for a

new clothes washer. Over 46 percent of elderly respondents hoped to stay between $350 and $499,

while nearly 36 percent hoped to stay between $500 and $649. Among those with incomes below

$25,000, a greater portion of respondents wanted to stay in the $200 to $349 range and the $350 to

$499 range.

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Table J.12 Choice Sensitivity to Price Change; All Respondents

Option New Washer

Cost $400

New Washer

Cost $450

New Washer

Cost $650

Purchase the new machine 63.6% 53.9% 16.3%

Fix oldmachine for $150 32.9% 41.8% 74.7%

Do laundry somewhere else 1.7% 0.9% 2.6%

Shop for a used machine 0.9% 1.1% 1.1%

Refused/Don't Know 0.9% 1.1% 1.1%

Posed with the problem of a hypothetical broken washer, Table J. 12 shows how the

respondents' solution choices changed with the price of a new washer. More specifically,

respondents were asked to imagine that their clothes washer had broken. This washer was imagined

to be 10 years old and had an expected life of 15 years. They were offered various optional

approaches to this problem; they could:

fix the washer,

buy a new washer;

do laundry somewhere else (not replace of fix),

or shp for a used machine.

This question was posed with three different prices for a new machine, $400, $450, and $650.

The cost to fix the machine stayed constant, at $150. At a price of $400 for a new machine, nearly

64 percent stated they would purchase a new machine , while less than 2 percent said they would do

laundry somewhere else, and 33 percent said they would fix the broken machine. When the price

rose to $450, the number willing to purchase a new machine fell to 54 percent, most deciding to

repair instead, which rose to 41 percent. At a price of $650, only 16 percent opted to purchase the

new machine. Nearly 75 percent chose to repair their machine, and 3 percent would do their laundry

someplace else.

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