T - ;~-

Clothes Washer 

Consumer Analysis · 

Aprjl 1999 

U.S. Department of Energy 

Assistant Secretary, 

Energy Efficiency & Renewable Energy 

Office of Codes and Standards 

Washington, DC 20585 

- ----

J ?!; p 
TABLE OF CONTENTS 

Section Page 

1 EXECUTIVE SUMMARY 1-1 

Focus Groups 1-1 

Conjoint Analysis 1-2 

Results 1-2 

Conclusions 1-3 

2 METHODOLOGY 2-1 

Introduction 2-1 

Focus Groups 2-2 

Conjoint Analysis 2-2 

Conjoint Session Design 2-2 

Full Profile Conjoint Analysis 2-3 

Conjoint Sample Method 2-4 

Response Rates 2-5 

Purchase Probability 2-6 

Equipment Choice Model 2-7 

Purchase Scenarios 2-9 

Probability Scenarios 2-9 

Elasticity Scenarios 2-11 

Summary 2-12 

3 FOCUS GROUP RESULTS 3-1 

Introduction 3-1 

Focus Group Sample 3-1 

Focus Group Responses 3-3 

4 CONJOINT SESSION 'SURVEY RESULTS 4-1 

Introduction 4-1 

Conjoint Session Survey Results 4-2 

Demographics 4-2 

Clothes Washer Demand Characteristics 4-5 
Section 

5 

6 

TABLE OF CONTENTS 

(continued) 

CONJOINT ANALYSIS RESULTS 

Page 

5-1 

Introduction 5-1 

Equipment Choice Model Results 5-1 

Card Attribute Definitions 5-2 

Equipment Choice Model Approach 5-2 

Importance Statistics 5-3 

Overall Estimation Results 5-4 

Sample Subgroup Comparison of Coefficient Estimates 5-5 

Review of Relative Importance Results 5-7 

Purchase Scenarios 5-9 

Purchase Model Estimation Results 5-9 

Purchase Probability Scenarios 5-11 

Full Sample Probability Estimates 5-12 

Regional Probability Estimates 5-13 

Elasticity Estimates 5-14 

Full Sample Results 5-14 

Low Income Households 5-16 

Discretionary Clothes Washer Purchasers 

CONCLUSIONS 

5-16 

6-1 

APPENDICES 

A FOCUS GROUP ATTRIBUTE RESULTS A-1 

B TOP 5 ATTRIBUTES FROM CONJOINT ANALYSIS SESSIONS B-1 

C CONJOINT SESSIONS SURVEY WITH RESPONSE C-1 

FREQUENCIES 

D CONJOINT ANALYSIS CARDS AND INSTRUCTIONS D-1 

E CONJOINT ANALYSIS COEFFICIENT ESTIMATES E-1 

F ELASTICITY CALCULATIONS F-1 

REFERENCES R-1 
LIST OF EXHIBITS 

Exhibit Page 

2-1 Hypothetical Example- Conjoint Data Using the Purchase Card 2-7 

For Purchase Probability Estimation 

2-2 Hypothetical Example - Individual Ranked Conjoint Data For 2-9 

Equipment Choice Estimation 

3-1 DOE Clothes Washer Focus Group Response Rates 3-2 

3-2 Focus Group Results 3-3 

Most Important Clothes Washer Attributes 

3-3 Top Twelve Clothes Washer Attributes Listed by Conjoint 3-5 

Respondents 

4-1 Sampling Results 4-2 

4-2 Income Distribution 4-3 

4-3 Etlmic Background of Respondents 4-3 

4-4 Distribution of Educational Achievement 4-4 

4-5 Distribution of Expected Price for a New Clothes Washer 4-5 

4-6 Distribution of Desired Price Range for a New Washer 4-6 

4-7 Choice Sensitivity to Price Change 4-6 

All Respondents 

4-8 Option Choice Sensitivity to Price Change 4-7 

Respondents with Income Less than $25,000 Per Year 

4-9 Option Choice Sensitivity to Price Change 4-8 

Respondents Over 65 Years of Age 

5-1 Attribute Definitions 5-2 

5-2 Regression Coefficients and Relative Importance Statistics 5-4 

All Respondents 

5-3 Regression Coefficient Estimates for Demographic Subgroups 5-5 

5-4 Regression Coefficient Estimates for Regional Subgroups 5-6 

5-5 Relative Importance Statistics for Demographic Subgroups 5-7 

5-6 Relative Importance Statistics for Regional Subgroups 5-8 

5-7 Purchase Model Coefficient Estimates 5-10 

Full Sample 

5-8 Purchase Scenarios for Demographic Subgroups 5-11 

5-9 Purchase Scenarios for Regional Subgroups 5-13 
7. 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 

surrunarized in the remainder of this introduction with more detailed descriptions 

provided in the following chapters. 

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 

' 

Draft 4/30/99 1-1 Executive Summan; 
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. 

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.I 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. 

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 

1 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. 

Draft 4/30/99 1-2 Executive Summary 
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. 

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. 

Conclusions 

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

Price is an important attribute. The focus group and conjoint results indicate that price is 

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

Several other attributes, however, virtually tied with price in terms of importance. In 

the focus groups, 83 percent of the respondents included price in their top ten list of 

important clothes washer attributes, while 81 percent included capacity in that same list 

(see Exhibit 3-2 for more details). In the conjoint analysis, price had the highest relative 

importance score (26 percent), followed closely by the availability of a load size option 

(25 percent) (see Exhibit 5-2 for more details). 

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 increases 

Draft 4/30/99 1-3 Executive Summary 
significantly. 1bis is due to the fact that consumers value energy savings more than top 

load door placement. 

Door placement is not as important as other attributes. Both the focus groups and the 

conjoint analysis indicate that door placement is not as important as other clothes 

washer attributes. In the focus group sessions, less than half of the participants (i.e., 42 

percent) included door placement as one of the 10 most important attributes they would 

consider when purchasing a new clothes washer. Of the six attributes included in the 

conjoint analysis, the focus group participants viewed door placement as the least 

important (see Exhibit 3-2 for more details). In the conjoint analysis, door placement 

was second from last in importance among the six attributes included in the conjoint 

(see Exhibit 5-2 for more details). In the conjoint session survey, 70 percent of the 

survey respondents said that they would consider purchasing a front loading machine 

if they were going to 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. 

When price is held constant in the analysis, the purchase probability statistics indicate 

that consumers are more likely to purchase a front load washer that saves them $50 

annually than a top load washer that provides no annual efficiency savings (see Exhibit 

5-8 for more details). 

Demand is more inelastic for 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 

~ecreases 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. 

Draft 4/30/99 1-4 Executive Summary 
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. Chapter 3, 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, 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. 

Draft 4/30/99 1-5 Executive Summary 
2. METHODOLOGY 

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 

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

session, 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 t~ the group. The results of this 

survey are discussed in the Survey Results chapter of this report. 

Draft 4/30/99 2-1 Methodology 
conjoint analysis technique is described as well as the model specification .for 

estimating the purchase probabilities. Finally, probability calculations are presented 

and potential market scenarios are discussed. 

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 attribt!tes. Once the unaid.ed 

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. 

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 

3 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 . . 

Draft 4/30/99 2-2 Methodology 
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 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. 

Draft 4/30/99 2-3 MethodologtJ 
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 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) used a conjoint sample of only 170 to test the 

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

Helsen (1989) relied 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, Christakis and 

Allison (1994) used a sample of 413 doctors to estimate the importance of different 

medical treatments and Louviere and Hensher (1983) used a sample of 550 to estimate 

the potential demand for furore 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. 

Draft 4/30/99 2-4 Methodology 
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 Earl Babbie (Fifth edition, 1989, p. 242), 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. 

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 maxiinizes 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. 

Draft 4/30/99 2-5 Methodology 
---------

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) = a.+ 1: ~/Xii + Eii 

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

X.. = Value of attribute J. on card i IJ 

~-=Coefficients to be estimated J 

a. = Constant to be estimated 

Eii ~ random error term assumed to be logistically distributed 

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

probability is shown in Exhibit 2-1. 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(WX) I (l + exp(WX)) 

Draft 4/30/99 2-6 Methodology 
Rank 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

Exhibit 2-1 

Hypothetical Example - Conjoint Data Using the Purchase Card 

For Purchase Probability Estimation 

Door 

Purchase Price 

Independent 

Variables 

"aoacitv 

Water 

Temoerature 

Load Size 

Ad" 

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0 400 0 Front Extra Laroe Hot Warm Cold Non-Adiustable 

0 400 0 Too Extra Larae Warm Cold Adiustable 

0 400 0 Front Extra Larae Warm Cold Non-Adiustable 

0 400 0 . Ton Extra Laroe Hot Warm Cold Adiustable 

0 450 50 Too Extra Larae Warm Cold Non-Adiustable 

0 450 10 Too Extra Laroe Hot Warm Cold Non-Adiustable 

0 450 0 Front Standard Warm Cold Adiustable 

0 450 0 Front Standard Hot Warm Cold Adi us table 

0 650 50 Front Extra Laroe Hot Warm Cold Adiustable 

0 650 10 Front Extra Larae Warm Cold Adiustable 

0 650 0 Too Standard Warm Cold Non-Adiustable 

0 650 0 Ton Standard Hot Warm Cold Non-Adiustable 

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 "Non-Adjustable," indicating that the water level cannot 

be adjusted. 

Equipment Choice Model 

A separate estimation is done based on the card ran.kings 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 

Draft 4/30/99 2-7 Methodology 
since the dependent 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: 

Ranki = L1Pi'\ + Eii 

Where Ranki = The ranking of card i 

Xij =Value for attribute j from card i 

p. =Coefficients to be estimated J . 

Eii = Random error term assumed to be logistically distributed 

Exhibit 2-2 shows a hypothetical example of the individual level data that would be 

used to estimate the equipment choice z:nodel. 

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: 

4 A mor~ 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, Sociological 

Methodology, Volume 24, 1994, pp. 199-228. 

Draft 4/30/99 2-8 Methodology 
Dependent 

Variable 

I Rank 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

I 

Exhibit 2-2 

Hypothetical Example - Individual Ranked Conjoint Data 

For Equipment Choice Estimation 

Independent 

Variables 

IG:J II Pl Door I I Water Load Size 

Savin!!s lacement Ca~acity Temoerature Adiustment 

400 50 Front Standard Warm Cold Non-Adiustable 

400 50 Too Standard Hot Warm Cold Adiustable 

400 10 Front Standard Hot Warm Cold Non-Adiustable 

400 10 Too Standard Warm Cold Adiustable 

400 0 Front Extra.Larae Hot. Warm Cold Non-Adiustable 

400 0 Too Extra Larae Warm Cold Adiustable 

400 0 Front Extra Larae Warm Cold Non-Adiustable 

400 0 Too Extra Larae Hot Warm Cold Adiustable 

450 50 Too Extra Larae Warm Cold Non-Adiustable 

450 10 Too Extra Larae Hot Warm Cold Non-Adiustable 

450 0 Front Standard Warm Cold Adiustable 

450 0 Front Standard Hot Warm Cold · Adiustable 

650 50 Front Extra Larae Hot Warm Cold Adiustable 

650 10 Front Extra Larae Warm Cold Adiustable 

650 0 Too Standard Warm Cold Non-Adiustable 

650 0 Too Standard Hot Warm Cold Non-Adiustable 

PURCHASE SCENARIOS 

I 

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(WX) I (l + exp(WX) ) 

Where~= Estimated coefficients from the purchase logit 

Draft 4/30/99 2-9 Methodologi; 
X =Attribute values such as price, savings, axis orientation for the pre-standard equipment 

design. 

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(j3'X) I (1 + exp(j3'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 stand~rd: 

Effect of Standard = (Prob1 - Prob2) I 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 re$ult 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 

i:nore popular holding price constant: 

Prob Al = exp(WX) I (1 + exp(l3'X) ) 

Where f3 =Estimated coefficients from the logit purchase model 

X = Pre-Standard equipment attributes (vertical axis) for choice A with the poststandard equipment price. 

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

Draft 4/30/99 2-10 Methodology 
Prob A2 = exp(f3'X) I (1 + exp(f3'X) ) 

Where J3 = 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: 

Prob81 = Purchase probability with pre-standard options (hot water) and post 

standard price. 

Prob8 2 = Purchase probability with post-standard options (no hot water) and poststandard price. 

In this hypothetical example, the effect of the standard on these two diffe_rent 

equipment options can be evaluated the same as before. Suppose that the options have 

the following probabilities: 

ProbA1 = 0.20 

ProbA2 = 0.10 

Prob81 = 0.15 

Prob82 = 0.10 

The effect of the efficiency standard on Option A is (0.20 - 0.10) I 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 

1s calculated by: 

Price Elasticity= (6. Prob/ 6. Price)* (Pricemid/ Probmid) 

Where 6. Prob = Change in purchase probability due to the change in price 

6. Price= Change in price resulting from t_he efficiency standard 

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

probability 

Draft 4/30/99 2-11 Methodology 
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. 

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. 

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. 

Draft 4/30/99 Methodologi; 
3. FOCUS GROUP RESULTS 

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 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. 

Draft 4/30/99 3-1 Focus Group Results 
Exhibit 3-1 

DOE Clothe$ 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 880 23 2.6% 18 78.3% 2.0% 

Bay Area, CA 

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% 

Exhibit 3-1 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. 

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. 

5 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 11 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. 

Draft 4/30/99 3-2 Focus Group Results 
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. 

Exhibit 3-2 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 Appendix A. 

Draft 4/30/99 

Exhibit 3-2 

Focus Group Results 

Most Important Clothes Washer Attributes 

Percent of 

Frequency in Respondents 

Feature ToplO (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% 

3-3 Focus Group Results 
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 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 clqthes in midcycle 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 Exhibit 3-2, 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. 

Draft 4/30/99 3-4 Focus Group Results 
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. 

Exhibit 3-3 

Top Twelve Clothes Washer Attributes Listed by Conjoint Respondents 

Percent of 

Respondents that 

Listed Attribute 

Among the Five Most 

Attribute lmoortant 

Price 56.2% 

Energy and Water Costs/Energy Efficiency 47.8% 

Capacity 43.6% 

Multiple Wash Cycle Options 35.4% 

Water Temperature 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 Exhibit 3-3, above. The complete list can be 

found in Appendix B. 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 22nd , 16th , and 11" 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. 

Draft 4/30/99 3-5 Focus Group Results 
4. CONJOINT SESSION SURVEY RESULTS 

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 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. 

Draft 4/30/99 4-1 Surv~ Results 
CONJOINT SESSION SURVEY RESULTS 

Demographics 

I Group I 

East Region 

South Region 

Mid-West Region 

West Region 

Young 

Elderly 

·Low Income 

Exhibit4-1 

Sampling Results 

Target Percent of 

Definition Target Sample Sample 

Washington, DC 100 22.2% 

Dallas, TX 100 26.2 

Madison, WI 100 26.2 

San Francisco Bay 100 25.3 Area, CA 

18 - 24 Years 60 12.8 

65 and Older 60 15.8 

Annual Income < 90 $25,000 24.4 

Actual Percent of 

Actual Sample Sample 

95 21.8% 

111 25.5 

111 25.5 

118 27.1 

53 12.2 

67 15.4 

. 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 Exhibit 4-1 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. 

Draft 4/30/99 4-2 Survey Results 
Income Range 

Less than $10,000 

$10,000 - $25,000 

$25,000 - $50,000 

$50,000- $75,000 

$75,000 - $100,000 

$100,000 and over 

Refused 

Exhibit4-2 

Income Distribution 

Percent of Total 

Sample 

6.2% 

18.3 

31.9 

24.5 

9.6 

8.7 

0.4 

Percent of those 

65 and older 

3.0% 

23.9 

44.8 

10.4 

11.9 

6.0 

0.0 

A more detailed examination of the income distribution of respondents is shown in 

Exhibit 4-2. There was a significant diversity of income levels among the participants. 

Exhibit 4-2 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. 

Exhibit4-3 

Ethnic Background of Respondents 

I Group I Percent of 

Sample 

Caucasian/White 75.2% 

African-American/Black 14.9% 

Latino /Hispanic 5.3% 

Asian 3.0% 

Other 1.6% 

As shown in Exhibit 4-3 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 non-Caucasian respondents had incomes over $25,000, with the 

Draft 4/30/99 4-3 Survey Results 
majority (37 percent) between $25,000 and $50,000. Among those over 65 in age, 21 

percent were of non-Caucasian ethnicity. 

Exhibit4-4 

Distn1Jution of Educational Achievement 

Education All 

Respondents 

Some High School 1.2% 

High School Graduate 8.5 

Some College 23.9 

College Graduate 63.8 

Trade/Vocational 2.6 

School 

Exhibit 4-4 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 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. Of those 

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 hom~s, 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 

Draft 4/30/99 4-4 Survey Results 

~· 
horizontal machines in the past, and 60 percent would consider purchasing such a 

machine. 

Clothes Washer Demand Characteristics 

Exhibit4-5 

Distnoution of Expected Price for a New Clothes Washer 

All Respondents Over 65 years old Income under 

$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 0.9 1.5 1.9 

Know 

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. Exhibit 4-5 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. 

Draft 4/30/99 4-5 Suroey Results 
Exhibit4-6 

Distribution of Desired Price Range for a New Washer 

Price Range All Respondents Over 65 Years Old Income under 

$25,000 

$200-$349 6.9% 4.5% 16.5% 

$350-$499 51.3 46.3 56.3 

$500-$649 34.0 35.8 22.3 

$650-$849 5.9 10.4 3.9 

$850-$999 1.4 3.0 0.0 

$1,000-$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." Exhibit 4-6 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 

$64~, 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. 

Exhibit4-7 

Choice Sensitivity to Price Change 

All Respondents 

Option New Washer New Washer 

Cost$400 Cost $450 

Purchase the new machine 63.6% 53.9% 

Fix old machine for $150 32.9 41.8 

Do laundry somewhere 1.7 0.9 

else 

Shop for a used machine 0.9 2.1 

Refused 0.9 1.1 

Draft 4/30/99 4-6 

New Washer 

Cost $650 

16.3% 

74.7 

2.6 

5.2 

1.1 

Survey Results 
Posed with the problem of a hypothetical broken washer, Exhibit 4-7 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 or fix), 

• or shop 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 staye~ 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. 

Exhibit4-8 

Option Choice Sensitivity to Price Change 

Respondents with Income Less than $25,000 Per Year 

Option New Washer New Washer 

Cost $400 Cost$450 

Purchase the new machine 54.4% 42.7% 

Fix old machine for $150 37.9 47.6 

Do laundry somewhere else 4.9 3.9 

Shop for a used machine 1.9 3.9 

Refused/Don't Know 1.0 2.0 

New Washer 

Cost $650 

11.7% 

69.9 

8.7 

8.7 

1.0 

Exhibit 4-8 shows the same results for the low income portion of the sample. Among 

respondents with yearly incomes of $25,000 or less, the results show a much greater 

tendency to choose to go someplace else to do laundry or repair the old machine. At a 

price of $400 for a new machine nearly ·5 percent stated they would do laundry 

someplace else, and 38 percent stated they would fix the old machine. The number 

choosing to purchase the new machine for $400 is 54 percent.. At a price of $450 for a 

new machine, 48 percent stated they would repair the old machine, and only 43 percent 

Draft 4/30/99 4-7 Survey Results 
would purchase the new machine. The number choosing to do laundry someplace else 

actually falls slightly, to 4 percent. At a price of $650, most (70 percent) of lower income 

· respondents choose to fix the old machine, 12 percent would purchase the new 

machine. At this price, 9 percent state they would choose to do laundry someplace else. 

Exhibit4-9 

Option Choice Sensitivity to Price Change 

Respondents Over 65 Years of Age 

Option New Washer New Washer 

Cost $400 Cost$450 

Purchase the new machine 76.1% 67.2% 

Fix old machine for $150 20.9 31.3 

Do laundry somewhere else 0.0 0.0 

Shop for a used machine 1.5 0.0 

Refused/Don't Know 1.5 1.5 

New Washer 

Cost $650 

31.3% 

65.7 

1.5 

0.0 

1.5 

Exhibit 4-9 is similar to Exhibits 4-7 and 4-8, except it displays the results for the elderly 

portion of the sample. Among respondents over 65, the exhibit shows a greater 

relucta'nce to do laundry someplace else, or repair the old machine. At a price of $400 

and $450 for a new machine, no elderly respondents stated they would do laundry 

someplace else. At the $400 price, only 21 percent of elderly stated they would fix the 

old machine versus 33 percent for the entire sample. The number of elderly 

respondents choosing to purchase the new machine for $400 comprise 76 percent, 

significantly higher than the 64 percent result in the entire sample. At a price of $450 

for a new machine, the results are similar. At a price of $650, the number choosing to 

purchase a new machine drops to 31 percent, which is almost double the percent of the 

whole sample who indicated they would purchase a new washer at this price. At $650, 

a very small percentage would do laundry somewhere else, while most (66 percent) 

would choose to fix the old machine. 

Draft 4/30/99 4-8 Survey Results 
5. CONJOINT ANALYSIS RESULTS 

INTRODUCTION 

This section presents the results of the conjoint data analysis. Results are presented for 

the full sample, as well as· various sample subgroups. Survey questions were used to 

stratify the conjoint sample so that purchase probabilities and utility could be estimated 

for different demographic groups. The results were also used to evaluate the relative 

importance of each of the clothes washer attributes for the various groups. In addition, 

the potential impact of efficiency standards on the likelihood of purchasing a new 

clothes washer is analyzed and presented for a number of subgroups, as well as the full 

sample. 

The section begins w~th a review of the equipment choice model approach, and then 

presents the results for the full sample. There is also an explanation of the relative 

importance statistic, which is used to identify which attributes were most important in 

determining consumer utility. This is followed by an examination of the coefficients 

and the implied relative importance of each attribute to respondents.· 

A detailed review of the equipment choice model coefficient estimation results is 

presented next, concentrating on the differences in results among various sample 

subgroups. The relative importance of attributes is then compared and contrasted by 

sample subgroup. 

Finally, the purchase model estimation results are presented. In addition, analysis was 

conducted on the sensitivity of the likelihood of purchasing a washer to various 

equipment options reflecting different efficiency levels. This analysis was conducted 

over the entire sample as well as for different subgroups within the sample. 

EQUIPMENT CHOICE MODEL RESULTS 

This section presents the results of the equipment choice modeling and begins with 

teview of the clothes washer card attributes, and definitions of specific attributes used 

in the analysis. (A copy of the cards can be found in Appendix D.) This is followed by 

an explanation of the relative importance statistic, including the method used to derive 

the statistic and an explanation of its meaning. Overall estimation results are presented, 

which includes the coefficient estimates and relative importance statistics for the full 

sample. This is followed by sections that compare coefficient and importance statistics 

across various sample subgroups. 

Draft 4/30/99 5-1 Conjoint Analysis Results 
Card Attribute Definitions 

Variable 

Price 

Savings 

Exhibit 5-1 

Attribute Definitions 

Description 

Dollar value of retail price of machine 

Dollar value of annual water and energy bill savings 

K:apacity ~inary variable: zero for standard capacity machine, one for extra large capacity. 

Door Placement Binary variable: zero for front loader, one for top loader. 

!Water Temp. Binary variable: zero for 'cold/warm', one for 'cold/warm/hot' washing temperature options 

Load Size Binary variable: zero if there is no load size adjustment option, one if there is 

Exhibit 5-1 describes the attributes used in the equipment choice model. As stated 

previously, participants were asked to rank a series of cards, each with six attributes. 

The order in which the attributes wer~ presented on each card was varied across the 

groups. This was done to avoid any potential bias due to the card presentation. The six 

attributes presented on each card were as follows: 

• Price - retail cost of clothes washer ($400, $450 or $600), 

• Sav:ings - annual energy and water bill savings ($0, $10, or $50), 

• Capacity - standard or extra large capacity, 

• Door placement - Indicates either top or front door placement, 

• Water Temperature - Indicates whether hot water is available, either hot, warm and 

cold wash cycle options, or warm and cold options only, 

• Load size - adjustable water level (to match load size) or non-adjustable (one water 

level.) 

Equipment Choice Model Approach 

The equipment choice model analyzes the choice of a specific equipment option, given 

that the decision to purchase a washer has already been made. In the conjoint analysis 

session the respondents ranked 16 clothes washer cards, from the most preferred to the 

least preferred. When all the conjoint data were collected, the attribute levels on the 

cards were regressed against the rankings of the cards: 

Draft 4/30/99 5-2 Conjoint Analysis Results 
Rank= 

13'Price, + 13'Savings, + 13'DoorPlacement, + 13'Capacity, + 13'WaterTempi + 13'LoadSize, + E, 

Where Rank = Rank value from 1 to 16, based upon the respondents' relative 

assessment of each card. 

Price; = Value for price on card i 

Savingsi =Value for energy and water savings on card i 

DoorPlacementi = 1 for top loading, 0 for front loading on card i 

Capacity; = 0 for standard capacity, 1 for large capacity on card i 

WaterTempi = 1 for hot water available, 0 if no hot water on card i 

LoadSizei = 1 if Adjustable, 0 if nonadjustable on card i 

~· = Coefficients to be estimated J : 

Ei = random error term assumed to be logistically distributed 

Importance Statistics 

The results of the equipment choice model can be used to infer the relative importance 

of each attribute to the consumer's total utility. Specifically, the coefficient estimates 

from the equipment choice model can be used to calculate an "importance statistic." 

This statistic measures the importance of one design attribute, relative to that of all 

other design attributes in determining a card's total utility. · 

The total utility of each card can be calculated by inserting attribute values into the 

estimated regression equation: 

Total Utilityi = 

WPricei + WSavingsi + WDoorPlacementi + f3'Capacityi + f3'WaterTemg + f3'LoadSizei 

Using the coefficient estimates and the values for the attributes used in the conjoint 

analysis, the importance statistic is defined as: 

IMP1 = /1 Yi = The maximum utility change due to attribute i 

!iy The_maximum_utility _change_due_to_all_attributes 

The importance statistic measures the percentage of the total maximum change in 

utility across all card choices that is attributable to a single attribute. Stated another 

Draft 4/30/99 5-3 Conjoint Analysis Results 
way, the importance statistic measures each attribute's contribution to the total utility 

based on the six attributes included in the conjoint analysis. 

Overall Estimation Results 

This section describes the results of the equipment choice model estimation for the full 

sample. The estimated coefficients for each clothes washer attribute, as well as each 

relative importance statistic are reviewed. 

I 

Exhibit 5-2 

Regression Coefficients and Relative Importance 

All Respondents 

I Coefficient Standard Significance Relative 

Error Level Importance 

Price -0.00359 0.000 1% 26% 

Savings 0.010 0.001 1% 14% 

Capacity 0.248 0.024 1% 7% 

Door Placement 0.383 0.024 1% 11% 

Water Temp. 0.614 0.024 1% 18% 

Load Size 0.852 0.024 1% 25% 

100% 

The results of this estimation are shown in Exhibit 5-2. (See Appendix E for additional 

subgroup statistics.) The coefficient estimate for price is negative and significant and 

the estimate for savings is positive and significant. All of the remaining attributes are 

statistically significant, with positive coefficient estimates. A positive coefficient for 

"Capacity" indicates people prefer extra-capacity machines to standard capacity. 

Regarding door placement, respondents indicated a preference for top-loaders over 

front-loaders. Having a hot water wash option was attractive, as was the ability to 

adjust the water level to match the size of the load. All of these coefficients are 

significant at the 1 percent level of significance, which means that the estimates are 

significantly different from zero with a 99 percent degree of confidence. 

While coefficients estimates do provide some information on the influence of the 

variable on total utility, it is misleading to look only at the coefficient to gauge the 

influence of that variable. For example, the savings coefficient is ten times the 

magnitude of the price coefficient since savings is measured in tens of dollars and pr:ice 

in hundreds of dollars. Only looking at the magnitude of the coefficients would give 

the misleading impression that savings is considered much more important than price. 

To address this issue, relative importance statistics are calculated that combine both the 

coefficient and attribute value to ·get an overall measure of the influence on total utility. 

The relative importance statistic can be interpreted as each attribute's contribution to 

total utility. 

Draft 4/30/99 5-4 Conjoint Analysis Results 
The relative importance statistics show that while price is the most important attribute 

to consumers, it just barely surpasses adjustable load size in terms of importance in total 

utility based on the six washer attributes. Together, these two attributes contribute 

about half of the total utility. Having a hot water wash option was the third most 

important attribute, contributing about 18 percent of total utility. Door placement 

finished second-to-last in importance, with 11 percent of total utility, and capacity 

ranked last in terms of impact on total consumer utility, at 7 percent. 

Sample Subgroup Comparison of Coefficient Estimates 

This section provides a more detailed review of the coefficient estimation results, 

including an examination of the differences in results between various sample 

subgroups. 

Exhibit 5-3 

Regression Coefficient Estimates for Demographic Subgroups 

Coefficient Estimates 

Would Would Not 

Full Low Recent Have Tried Consider Consider 

Sample Income 65 & older 18-24 yrs old Purchasers Horizontal Purchasing Purchasing 

Axis H-Axis H-Axis 

Machine Machine 

Price -0.00359 -0.00397 -0.00246 -0.00519 -0.00239 -0.00346 -0.00367 -0.00351 

Savings 0.010 0.010 0.006 0.016 0.005 0.009 0.011 0.006 

Capacity 0.248 0.255 0.124 0.375 0.173 0.260 0.266 0.194 

Door Placement 0.383 0.330 0.352 0.460 0.300 0.303 0.264 0.709 

Water Temp. 0.614 0.518 0.524 0.547 0.520 0.624 0.653 0.533 

Load Size 0.852 0.648 0.817 0.657 0.861 0.867 0.834 0.927 

Exhibit 5-3 presents coefficient estimates for various demographic subgroups of the 

sample. The same statistical regression analysis performed on the whole sample, 

discussed above, was performed on various subgroups of the sample to assess any 

prominent differences among segments. The subgroups shown above include: 

• lower income ($25,000 per year or less), 

• elderly (65 and older), 

• young (18 to 24), 

those who recently purchased a clothes washer (within the past two years) , 

• those who have tried a horizontal axis machine, 

• those that would consider purchasing a horizontal axis machine, and 

Draft 4/30/99 5-5 Conjoint Analysis Results 
• those that would not consider purchasing a horizontal axis machine. 

Exhibit 5-4 

Regression Coefficient Estimates for Regional Subgroups 

Coefficients Estimates 

Full Washington San 

Madison Dallas Francisco Sample DC Bay Area 

Price -0.00359 -0.00477 -0.00319 -0.00336 -0.00357 

Savings 0.010 0.016 0.010 0.005 0.011 

Capacity 0.248 0.483 0.191 0.156 0.235 

Door Placement 0.383 0.558 0.178 0.523 0.339 

Water Temp 0.614 0.768 0.616 0.523 0.638 

Load Size 0.852 1.008 0.756 0.836 0.916 

Conjoint sessions were held in four cities: Madison (Wisconsin), Washington DC, Dallas 

(Texas) and the San Francisco Bay Area (California). Roughly one hundred participants 

completed the ranking exercise in each city. Coefficient estimation results for the 

regional subgroups, as well as the full sample, are shown in Exhibit 5-4. 

The signs of the estimated coefficients for each regional subgroup are consistent with 

those for the overall sample, indicating a directional consensus on the desirability of 

attributes. Moreover, the magnitudes of the coefficients for each subgroup are 

generally similar to those of the overall sample. 

As stated earlier, the coefficient estimates provide information regarding the sensitivity 

to different attributes, but do not provide information regarding the attributes relative 

importance to any one group. Thus, the simple fact that the coefficient for savings is 

largest for young people across the subgroups does not necessarily imply young people 

place the highest relative value on savings. 

The coefficient for price is fairly consistent. The range of estimated coefficients varies 

between -0.002 to -0.005, with an overall coefficient estimate of -0.004. Young people 

and people in Madison are most sensitive to changes in price. Those who have recently 

purchased a new washer and those over 65 years old are least sensitive to price. 

Young people and people from Madison are the most sensitive to savings, while those 

who have recently purchased a machine and :people from Dallas are the least sensitive. 

The difference between the highest and the lowest coefficient estimates is a factor of 

more than three, 0.005 versus 0.016. Older people and those who would not consider 

purchasing a horizontal axis machine are also relatively insensitive to savings. 

Draft 4/30/99 5-6 Conjoint Analysis Results 
While most of the subgroups examined have a coefficient estimate for capacity of about 

0.2, there are some notable exceptions. Again, the Madison group stands out from the 

rest, with the highest coefficient for capacity at 0.48. Also, age seems to be relevant to 

the sensitivity to clothes washer capacity. The second largest estimated coefficient for 

capacity was for young people, 0.38. Conversely, the group with the smallest coefficient 

for capacity was those over 65, at 0.12. 

The respondents who indicated that they would not consider purchasing a horizontal 

axis machine were quite sensitive to door placement, and had the highest estimated 

coefficient, 0.71. Conversely, restricting the sample to those who would consider 

purchasing a horizontal axis machine resulted in a lower than average coefficient for 

door placement, 0.26. The group least sensitive to door placement were those in 

Washington DC, with a coefficient of 0.19. 

Having a hot water wash temperature option was important for all of the subgroups, 

and the coefficients are fairly consistent, varying from a low of 0.51 for the low income 

group to a high of 0.77 for the Madison group. 

An adjustable load size option had high coefficient estimates across all of the 

subgroups, but the Madison group stands out again with the highest estimate, 1.0. The 

lowest estimated coefficient was 0.65, for the low income group. 

Review of Relative Importance Results 

Exhibit 5-5 

Relative Importance Statistics for Demographic Subgroups 

D Would Would Not 

Full Low 18-24 yrs Recent Have Tried Consider Consider 

65 & older Horizontal Purchasing Purchasing Sample Income old Purchasers Axis H-Axis H-Axis 

...... ,.h ;""' -- .... ~ 

Price 26% 30% 22% 31% 22% 26% 26% 25% 

Savings 14% 16% 11% 19% 9% 13% 15% 9% 

Capacity 7% 8% 5% 9% 6% 8% 8% 5% 

Door Placement 11% 10% 13% 11% 11% 9% 8% 20% 

Water Temp 18% 16% 19% 13% 19% 19% 19% 15% 

Load Size 25% 20% 30% 16% 32% 26% 24% 26% 

Exhibit 5-5 compares the relative importance statistics of each attribute across the 

demographic and regional subgroups. Again, the relative importance statistic identifies 

the contribution of each attribute to respondents' perception of total utility. It can be 

compared across the subgroups to determine which subgroup places the most emphasis 

on an attribute. 

Draft 4130/99 5-7 Conjoint Analysis Results 
Exhibit 5-6 

Relative Importance Statistics for Regional Subgroups 

I I Full Sample Washington San 

Madison Dallas Francisco DC Bay Area 

Price 26% 25% 26% 27% 25% 

Savings 14% 16% 16% 8% 15% 

Capacity 7% 10% 6% 5% 7% 

Door Placement 11% 12% 6% 17% 10% 

Water Temp 18% 16% 20% 17% 18% 

Load Size 25% 21% 25% 27% 26% 

Exhibit 5-6 presents relative importance statistics for regional subgroups. For most of 

the subgroups, price contributed a litt_le over 25 percent to total utility, and was the 

most important single attribute, albeit by a slim margin. Low income people and young 

people (who are likely also to have lower incomes) placed the highest relative value on 

price, 30 percent and 31 percent, respectively. On the other side, recent purchasers and 

people over 65 responded more strongly to adjustable load size than to price. For both 

groups, price comprised 22 percent of total utility. 

For most groups, savings contributed between 9 percent and 16 percent to total utility, 

and was not in the top three important attributes. Those from Dallas placed an 

unusually low value on savings, at 8 percent of total utility, and young people placed a 

very high value on savings, 19 percent. 

A large capacity clothes washer was not very important to most subgroups, with 

relative importance ratings varying from a low of 5 percent for elderly people· and 

people from Dallas, to a high of 10 percent for people from Madison. Young people 

also placed a higher than average value on capacity, 9 percent. There appears to be 

some correlation between age and the relative importance of clothes washer capacity. 

Door placement ranked fifth out of six attributes for the full sample. For most of the 

subgroups the relative importance was similar. Not surprisingly, those who would not 

consider purchasing a horizontal axis machine placed the highest relative importance 

on door placement. Door placement was the third most important attribute for this 

subgroup. People from Dallas also placed a relatively high importance on door 

placement, at 17 percent of total utility. Older people placed a somewhat higher than 

average importance on door placement, 13 percent. 

For most subgroups, as well as the full sample, a hot water wash option was the third 

most important attribute. Relative importance statistics vary from a low of 13 percent 

for young people to a high of 20 percent for the Washington DC group. Most 

subgroups found hot water to contribute between 16 percent and 19 percent to total 

utility. 

Draft 4/30/99 5-8 Conjoint Analysis Results 

r• 
Adjustable load size was almost invariably either the first or second most important 

attribute to each subgroup. Most subgroups found this attribute contributed 20 percent 

to 30 percent of to total utility. Adjustable load size was the single most important 

attribute to the elderly, recent purchasers, those who would not consider purchasing 

horizontal axis machines, and those from the San Francisco Bay Area. Adjustable load 

size tied price as the most important attribute for the Dallas subgroup, and those who 

have tried horizontal axis machines. With the exception of young people, the remaining 

subgroups found adjustable load the second most important attribute. Age seems to be 

correlated to the relative importance of adjustable load size. The elderly subgroup 

placed the highest relative importance on this attribute, 30 percent, while young people 

placed the lowest, 16 percent. It should be noted that among low income people, 

adjustable load size ran a distant second to price: 30 percent for price, and 20 percent for 

adjustable load size. 

PURCHASE SCENARIOS 

As discussed in the Methodology, the conjoint results can be used to estimate the 

likelihood of purchasing a clothes washer based on the placement of the purchase card. 

This section discusses these estimation results as well as some scenarios for different 

equipment options reflecting different efficiency levels. These probabilities are 

estimated for the entire group as well as different subgroups within the sample. 

Purchase Model Estimation Results 

During the conjoint analysis, once respondents completed ranking their cards, they 

were asked to place the Purchase Card in the card set to indicate which of the 16 clothes 

washers indicated on the cards they would actually considering purchasing. Based on 

this information, the probability of making a purchase can be estimated based on the 

attributes on the cards above and below the Purchase Card. In equation form: 

Purchase (0,1) = 

a.+ f3'Pricei + f3'Savingsi + f3'DoorPlacementi + f3'Capacityi + f3'WaterTempi + f3'LoadSizei + Ei 

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

Pricei = Value for price on card i 

Savingsj =Value for energy and water savings on card i 

DoorPlacementi = 1 for top loading, 0 for front loading on card i 

Capacityi = 0 for standard capacity, 1 for large capacity on card i 

WaterTempi = 1 for hot water available, 0 if no hot water on card i 

Draft 4/30/99 5-9 Conjoint Analysis Results 
Load Sizei = 1 if Adjustable, 0 if nonadjustable on card i 

Bi = Coefficients to be estimated 

a= Constant to be estimated 

Ei = random error term assumed to be logistically distributed 

Exhibit 5-7 

Purchase Model Coefficient Estimates 

Full Sample 

Coefficient Standard Significance 

Error Level 

Intercept -0.949 0.224 1% 

Price -0.0066 0.000 1% 

Savings 0.029 0.002 1% 

Capacity 0.452 0.072 1% 

Door Placement 0.698 0.075 1% 

Water Temp. 1.438 0.071 1% 

Load Size 1.809 0.071 1% 

Exhibit 5-7 shows the coefficient estimates for the purchase model using the entire 

sample. The coefficient estimates for the sample subgroups are given in Appendix E of 

this report. As these results show, the coefficient estimates are very similar to those 

found in the equipment choice model used to estimate relative importance of attributes. 

All of the coefficient estimates are statistically significant at the 1 percent level. The 

intercept term reflects the average effect of all the factors that influence the decision to 

purchase a clothes washer that are not captured in the six attributes included in the 

conjoint. Price is negative and is large in magnitude, which indicates that washer price 

was an important determinant in the decision to make a clothes washer purchase. 

Similarly, hot water and the ability to adjust the amount of water to fit the size of the 

wash load were also very important factors in determining the likelihood of purchase. 

Door placement was also influential, but not considered as important as hot water or 

adjustable load size. However, the importance of door placement did tend to vary 

across different demographic segments, as shown in the purchase scenarios discussed 

below. 

Draft 4/30/99 5-10 Conjoint Analysis Results 
Purchase Probability Scenarios 

Once this model was estimated, the probability of making a purchase was calculated by 

combining the coefficient estimates with the six washer attributes and plugging in the 

logit probability function: 

Prob(Purchase) = exp(P'X) I (1:+ WX) 

Where WX reflects the sum of the coefficient estimates used in the conjoint analysis as 

shown in Exhibit 5-7. By using different values for price, savings, and the equipment 

attributes to simulate different efficiency levels, this equation can be used to determine 

the overall effect on utility of an efficiency standard. 

Exhibit 5-8 

Purchase Scenarios for Demographic Subgroups 

High High Efficiency High Efficiency 

Sample Group Standard Medium Efficiency High Efficiency Front Load, · Front Load, 

Efficiency Efficiency No Hot Water Constant Price 

Front Load Constant Price &NoSavin~ 

Full sample 0.59 0.58 0.36 0.21 0.75 0.42 

Low Income 0.65 0.63 0.41 0.26 0.78 0.50 

Elderly 0.59 0.59 0.39 0.26 0.66 0.39 

Young 0.64 0.61 0.29 0.28 0.76 0.44 

Recent Purchasers 0.55 0.54 0.33 0.17 0.67 0.40 

Have Tried H-Axis 0.57 0.56 0.39 0.19 0.78 0.45 

Would Consider H-Axis 0.56 0.55 0.42 0.21 0.78 0.43 

Would Not Consider Axis 0.67 0.64 021 0.23 0.62 0.35 

Exhibit 5-8 shows purchase scenarios probability estimates for different sample 

subgroups. These probabilities are calculated for standard efficiency, medium 

efficiency, and several high efficiency equipment options. 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. To judge the effect of price in these scenarios, the high efficiency option is 

also calculated holding price constant at $400, while having a front loading machine 

with $50 annual savings. 

The first row of Exhibit 5-8 shows the purchase probability estimates for the full sample 

for a variety of washer efficiency levels. For the full sample, the initial likelihood of 

purchase estimate is 59 percent, meaning that 59 percent of those surveyed would be 

Draft 4/30/99 5-11 Conjoint Analysis Results 
willing to purchase the standard efficiency clothes washer. This provides a starting 

point from which to compare changes in attributes and the effect these will have on the 

likelihood of purchase. In this sense, examining the changes in purchase probability 

reflects the change in utility, since lower utility washer configurations will have a lower 

likelihood of being purchased. 

Full Sample Probability Estimates 

When the probability is calculated using numbers for the medium efficiency washer, 

there is virtually no change in the estimated likelihood of purchase. This indicates that 

the increase in savings to $10 is enough to offset the increase in price from $400 to $450. 

This suggests that a washer design that increases efficiency with a modest increase in 

price and savings that has a top loading design will have a minimal effect on overall 

purchases. 

The high efficiency . equipment options tend to have much greater effect on the 

likelihood of purchase. This results from the greater change in price as well as changing 

the design of the machine to be either a front loader or to have the machine clean 

without using hot water. As shown in Exhibit 5-8, a high efficiency front loading 

washer at a price of $650 and annual savings of $50 will decrease the likelihood of 

purchase from 0.59 to 0.36, a decrease of 39 percent. If the machine is designed to run 

without hot water instead of being a front loader, the decrease i:S even greater. In this 

case, the likelihood of purchase probability goes from 0.59 to 0.21, a decrease of 64 

percent. 

For these high efficiency options, the changing likelihood of purchase is the combined 

result of changes in price, savings, and either door placement or water temperature 

options. As discussed in the analysis of the importance statistics, for the overall san;:tple, 

p_rice plays the greatest role in influencing utility, followed by water temperature and 

door placement. This also can be seen when equipment options are changed but price is 

held constant. When price is held constant and compared with the other estimated 

probabilities, the importance of price is apparent. 

Exhibit 5-8 also shows the likelihood of purchase for high efficiency machines that have 

the standard efficiency ($400) price. In the case where savings is $50 annually and the 

machine is a front loader, then the purchase probability is estimated to increase from 

0.59 to 0.75. This shows that the increase in savings more. than offsets the decrease in 

utility due to switching from a top loader to a front loader. When price and savings are 

held constant and the washer is changed from a top loader to a front loader, the 

estimated purchase probability falls almost 30 percent from 0.59 to 0.42. 

•' 

,, 

Draft 4/30/99 5-12 Conjoint Analysis Results 
Regional Probability Estimates 

Exhibit 5-9 

Purchase Scenarios for Regional Subgroups 

Standard Medium High Efficienc High Efficiency High Efficiency High Efficiency 

Sample Group Front Load, Front Load, Efficiency Efficiency Front Load No Hot Water Constant Price Constant Price R. ..,_ c:~ .. :--

Washington, DC 0.57 0.58 0.50 0.29 0.75 0.48 

San Francisco Bay Area. CA 0.60 0.59 0.41 0.19 0.80 0.43 

Madison. WI 0.63 0.60 0.30 0.18 0.83 0.45 

Dallas. TX 0.58 0.57 0.26 0.20 0.58 0.32 

The same purchase probability analysis was done by breaking the sample out by region. 

These results are shown in Exhibit 5-9. Given the overall sample probability estimate of 

0.59 'for a standard efficiency n;iachine, both the Washington DC and Dallas estimates 

are below the sample average while Madison and the San Francisco Bay Area are above 

the sample average. 

The biggest differences for all of the sample subgroups come from the regional 

estimates for standard and high efficiency machines in Madison and Dallas. · For the 

Madison group, moving from standard efficiency to front load high efficiency machines 

decreases the purchase probability by 52 percent, compared with only a 39 percent 

decrease for the entire sample. Similarly, the same move from standard to high 

efficiency decreases the purchase probability by 55 percent for the Dallas group. 

While both groups are sensitive to the move from standard to high efficiency, they are 

reacting to different attributes. This is shown when price is held constant and savings 

and door placement are allowed to vary. When price is held constant, Madison has a 32 

percent increase in purchase probability. This indicates that the decrease in probability 

going to high efficiency is due primarily to the price increase. 

For Dallas, holding pri<;:e constant results in no increase in the purchase probability 

from the standard efficiency case. Dallas is the only subgroup for which this is true. 

For the Dallas group, door placement is relatively more important. Thus, the relatively 

large increase in savings just offsets the disutility of a front loading machine. For all 

other demographic groups the increase in savings more _than makes up for the front 

loading design. This latter result is also reflected in the importance statistics, where 

door placement had the highest importance rating in Dallas relative to the other cities, 

as well as different sample subgroups. 

Draft 4/30/99 5-13 Conjoint Analysis Results 
ELASTICITY ESTIMATES 

In addition to calculating the importance statistics and purchase probabilities, price 

elasticities were also calculated to provide a different measure of the importance of 

price. These results are discussed below. 

Price elasticity provides a measure of how sensitive consumer demand is to changes in 

price. Unlike the purchase scenarios, where price was varied along with other washer 

attributes, elasticity calculations examine the effect of changes in price while all other 

attributes remain constant. As a result, price elasticity addresses the question of what 

will happen when price changes but everything else remains the same. 

In this analysis, the current market conditions are assumed to be those of the standard 

efficiency washer. Under these conditions, machines are top loaders with normal 

capacity, adjustable load sizing, and hot water. In addition price is set at $400 and 

energy and water savings are assumed to be zero. From this starting point, price 

elasticity is calculated by: 

Price Elasticity = (~ Prob I ~Price) * (Pricemid/ Prob mid) 

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

~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 measures how much demand changes with a change in price. Using this equation, 

an elasticity result greater than one in absolute magnitude means that demand is price 

elastic, or relatively sensitive to changes in prices. Stated another way, when demand is 

elastic, an increase in price of 10 percent will cause demand to decrease by more than 10 

percent. Similarly, when elasticity is less than one demand is considered inelastic. In the 

inelastic case, a 10 percent increase in price will cause demand to decrease by less than 

10 percent. The specific calculations for the elasticity estimates are included in 

AppendixF. 

Full Sample Results 

For the entire sample result, the initial price elasticity going from a price of $400 to the 

high efficiency price of $650 results in an price elasticity estimate of -2.0, meaning that 

in general clothes washer purchases are sensitive to changes in price. However, this 

calculation is based on stated preference data and is not calibrated to actual purchase 

behavior. As a result, other factors such as the convenience of owning a clothes washer 

Draft 4/30/99 5-14 Conjoint Analysis Results 
at all or the unwillingness of current owners to give up their clothes washer are not 

taken into account in the elasticity estimate. 

To address this issue, the esti;mated purchase probabilities can be calibrated to other 

measures of the likelihood of purchase to give a more realistic starting point for the 

elasticity calculations. For example, the uncalibrated likelihood of purchase estimate 

for the entire sample is 0.59. ·For some market segments, this estimate is likely to be 

quite low. For example, consider those who are current washer owners and are used to 

doing their laundry at home. For this group, if a machine breaks, and they are faced 

with purchasing a new machine or going to the laundromat, the likelihood of purchase 

is estimated at 90 percent or greater. 

To provide additional information on this issue, survey questions were asked to 

determine how likely consumers would be to· replace a broken clothes washer. 

Respondents were asked if they would repair a broken machine for $150, purchase a 

new one for $400, or do clothes elsewhere. Only 2 percent of respondents said that they 

would start doing laundry somewhere else, and the remainder opted to purchase a new 

or used machine or get the old .machine fixed. 

The survey questions provide a starting point for calibrating the purchase probability 

and to calculate price elasticities. The calibration is done by adjusting the constant term 

in the purchase probability equation so that the resulting purchase probability for the 

standard efficiency machine equals 0.98. This is done for that segment of the sample 

that are homeowners that do laundry at home. For this demographic group, the price is 

changed from $400 to $650, which results in the purchase probability falling from 0.98 

to 0.91. Using these numbers to calculate the price elasticity yields an estimate of -0.16, 

which indicates that washer demand is relatively insensitive to price changes. Again, 

this applies to those customers that currently own a clothes washer that has broken and 

are looking to replace it rather than start doing their laundry outside the home. 

The preceding case assumes that everyone who stated they would repair or purchase a 

used machine would purchase a new machine at $400 if repairing is not an option. As a 

result, this should be considered an upper bound. A lower bound estimate can be 

derived from the survey questions as well. The lower bound assumes that all those that 

said that they would repair their machine or purchase a used machine would start 

·going to the laundromat if repairing were not an option. This lower bound case 

produces a likelihood of purchase of 0.64. 

Using the 0.64 as a starting point for the lower bound likelihood of purchase, a price 

change from $400 to $650 results in a price elasticity estimate of -1.64. This reflects a 

more elastic demand for clothes washer, where changes in price will have a greater 

impact on purchases than in the upper bound scenario. In this lower bound case, a 10 

percent increase in price results in a 16 percent decrease in clothes washer demand. 

Draft 4/30/99 5-15 Conjoint Analysis Results 
Low Income Households 

The same elasticity calculation was estimated for low income households. Using those 

respondents with annual household incomes of $25,000 or less, the purchase probability 

was estimated. Similar to the previous example, survey questions were used to 

estimate an upper bound for the purchase probability of the low income group. When 

faced with the choice of purchasing a new washer for $400 or repairing the old one for 

$150, 7 percent of low income respondents said that they would start doing their 

laundry elsewhere or shop for a used machine. Using this result, 0.93 was used as an 

upper bound for the purchase probability for use in the price elasticity estimate. From 

this starting point, increasing the price from $400 to $650 gives a price elasticity.of -0.53 

for low income households. While still considered inelastic, this estimate is greater in 

magnitude than for home owners in general and indicates low income households are 

more concerned with price than households on average that currently have clothes 

washers. 

The same survey questions were used to determine the lower bound by assuming all 

those that would repair at $150 would start going to the laundromat if repairing was 

not an option. In this case, the purchase probability for low income households is 0.54. 

This results in a price elasticity estimate of -2.10, which is elastic and greater than the 

estimate for households in general. 

Discretionary Clothes Washer Purchasers 

The preceding estimates calculated using those respondents that currently own clothes 

washers. A separate segment of the population are those that do not currently own 

clothes washers or who have only recently purchased a clothes washer for the first time. 

Since these people are currently doing without a clothes washer, they can be considered 

discretionary purchasers since they may or not be in the market for a new clothes 

washer. 

A separate likelihood of purchase model was estimated for these people based on those 

respondents that do not currently own clothes washers or that recently purchased a 

washer for the first time. For this group, the upper bound likelihood of purchase is set 

at 0.59. This is the uncalibrated purchase probability estimate for the entire sample. 

This was chosen as the upper bound because the entire conjoint analysis exercise was 

presented in a manner that made the purchase decision a discretionary purchase. That 

is, respondents were asked to go through the thought process as if they were currently 

making the decision to purchase a clothes washer, which is similar to this group of 

customers in the population. 

Given the upper bound purchase probability of 0.59, the price elasticity estimate for 

discretionary purchasers is -1.92. This reflects a greater sensitivity to price and is more 

elastic than the estimate for current washer owners. 

Draft 4/30/99 5-16 Conjoint Analysis Results 
The price elasticity for discretionary purchasers was also estimated using a lower bound 

purchase probability of 0.10. This lower bound reflects the sample proportion of those 

previously did own a clothes washer that purchased one within the last two years. 

Using this lower bound, the estimated price elasticity of-2.80, showing an even greater 

sensitivity to price. 

These results show that sensitivity to price depends largely on which market segment 

the consumer falls under. Those that currently own clothes washer or that otherwise 

might have a high initial likelihood of purchase (such as the new construction market) 

are less sensitive to changes in price. As prices increase, demand for clothes washers 

will fall only slightly for these segments of the market if all other washer attributes are 

held constant. Other segments such as low income segments or that portion of the 

population that has for whatever reason does not currently own a clothes washer are 

more sensitive to price. As price increases for these segments, demand for clothes 

washers will fall at an even greater rate than the price increase for low income and 

discretionary purchasers. 

Draft 4/30/99 5-17 Conjoint Analysis Results 
6. CONCLUSIONS 

The objective of this study was 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. The analysis 

methods described in this report use several techniques to address these issues. 

Information obtained in both focus groups and conjoint analysis allowed respondents to 

state which clothes washer attributes were most important. Surveys were used to 

determine how respondents would react to different price levels and repair I purchase 

scenarios. The conjoint analysis allowed purchase probabilities to be estimated, which 

in turn were used to estimate price elasticities and calculate importance statistics for 

each of the washer attributes. These different analysis techniques resulted in several 

key conclusions that are discussed below. 

Price is an important attribute. The focus group and conjoint results indicate that price is 

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

Several other attributes, however, virtually tied with price in terms of importance. In 

the focus groups, 83 percent of the respondents included price in their top ten list of 

important clothes washer attributes, while 81 percent included capacity in that same list 

(see Exhibit 3-2 for more details). In the conjoint analysis, price had the highest relative 

importance score (26 percent), followed closely by the availability of a load size option 

(25 percent) (see Exhibit 5-2 for more details). 

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 increases 

significantly. This is due to the fact that consumers value energy savings more than top 

load door placement. 

Door placement is not as important as other attributes. Both the focus groups and the 

conjoint analysis indicate that door placement is not as important as other clothes 

washer attributes. In the focus group sessions, less than half of the participants (i.e., 42 

percent) included door placement as one of the 10 most important attributes they would 

consider when purchasing a new clothes washer. Of the six attributes included in the 

conjoint analysis, the focus group participants viewed door placement as the least 

important (see Exhibit 3-2 for more details). In the conjoint analysis, door placement 

was second from last in importance among the six attributes included in the conjoint 

(see Exhibit 5-2 for more details). In the conjoint session survey, 70 percent of the 

survey respondents said that they would consider purchasing a front loading machine 

Draft 4/30/99 6-1 Conclusions 
if they were going to 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. 

When price is held constant in the analysis, the purchase probability statistics indicate 

that consumers are more likely to purchase a front load washer that saves them $50 

annually than a top load washer that provides no annual efficiency savings (see Exhibit 

5-8 for more details). 

Demand is more inelastic for 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 only a very small 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 is -0.16, which reflects demand for clothes washers that is 

relatively insensitive to price. A lower bound estimate using a likelihood of purchase 

probability of 0.64 results in a more elastic estimate of-1.64. 

A similar result holds for low income households, which includes those with annual 

incomes of $25,000 or less. For low income households, consumers are slightly more 

sensitive to price, but demand is still considered inelastic with an upper bound estimate 

of-0.53. For the lower bound, price elasticity is estimated to be -2.1. 

These results suggest that households that currently own clothes washers will likely 

continue to purchase new ones when needed, even with large price increases. For 

example, using the upper bound elasticity estimate for all current clothes washer 

owners, a 63 percent price increase from $400 to $650 results in only a 10 percent 

decrease in purchases for this sector of the market. For low income segments, the same 

63 percent price increase results in a 33 percent decrease in purchases. 

Draft 4/30/99 6-2 Conclusions 
Discretionary purchasers, which include those consumers that do not currently own 

clothes washers, are more sensitive to price. This is due in part to discretionary 

purchasers also being low income households. Of the discretionary purchasers, 47 

percent had annual incomes of $25,000 or less. For this group the price elasticity is -

1.92, which reflects a demand for washers that is more sensitive to price than current 

washer owners. In this case, a 10 percent change increase in price results in a 19 percent 

decrease in demand. This reflects an upper bound purchase probability estimate for 

this group; the lower bound purchase probability estimate results in even a greater 

sensitivity to price, with an elasticity of-2.80. 

Draft 4/30/99 6-3 Conclusions 
All Focus Groups - 10 Groups - 90 Participants 

Fr EICJ.!Elrlcy Percent~ 

Feaure inT2e10 out of 90 

Price 75 83% 

Cqxx:i!y 73 81% 

Enegf & Wctg Gaits 65 72% 

LocdSizeOp11ons 61 68% 

Durct:ility 54 60% 

Weter T emperdure Opt! ans 54 60% 

Doer Plo::anent 38 42% 

Quiel Operdien 36 40% 

Wa.hTime 34 38% 

warmly 33 37% 

MultipeWcsh CydeOptlens 30 33% 

HorizontdNeticd Axis 25 28% 

Agtdion Speed 22 24% 

ExtraRinseCyde 22 24% 

Avdlctility of Sevice 17 19% 

Brmd 17 19% 

Raictility 17 19% 

Bl eo::tlf.iofta'le Dispense 15 17% 

LintRemCNd 15 17% 

Buttonsil)lds 14 16% 

Carre::ts Out-of-Bdmce 14 16% 

Ma:hineSize 14 16% 

Hmd Wcshdjes 13 14% 

Sock Opt! en 12 13% 

Doer Size 11 12% 

SpnSpeed 11 12% 

EnC}()l-0fdeSig1d 8 9% 

AddOothes in MidCyde 8% 

Colcr 8% 

Daia:teCyde 6 7% 

GmtieCyde 6 7% 

Deleg3"lt Ca;t S0ting; 5 6% 

a ecr I nstructi ens 4 4% 

On Whees 4 4% 

Rust Proof 4 4% 

Agtdion (side to sldeAJp down) 3 3% 

Pre-Wcsh 3 3% 

Sef Oeming Filter 3 3% 

Socp Dispense 3 3% 

Tub Materid (PlcsticMetd) 3 3% 

Hecwy Duty Cyde 2 2% 

HotWde Pressure 2 2% 

Ma:hineDesig1 2 2% 

Motcr Qudi1yf.iize 2 2% 

RemCNd:leTub 2 2% 

Simpeseltings 2 2% 

Spn Len'1t1 2 2% 

Wcsheil)rye Combo 2 2% 

Abl1y to Re-Use RlnseWde 1% 

Beske! fcr Odd Shcped Items 1% 

Contrd LctJEjs 1% 

Connocted tow ctg Sottene 1% 

Cydestq:is when Lid Open 1% 

El ectri cd Camp:ii b Ii 1y 1% 

Gcs/EI ectri c 1% 

Hect Own Water 1% 

lid Lad< 1% 

Motor Optimize 1% 

Sdety 1% 

Spn Only Optien 1% 

Time Remdning lncla:itor 1% 

Use Friendy Controls 1% 

TWO Speed Motcr 1% 

Viewing Window 1% 

Waning Lights 1% 

Draft 4/30/99 A-2 Appendix A 
Draft 4/30/99 

Washington DC Focus Group 

Combined Group 

16 Participants 

Price 

Cq:ajty 

Fec:ture 

Wder Terrp;rdureOplicns 

Enag/ cndWder Ccsis 

Loa::1 Size Opli ens 

Doa Pla::anent 

Wa;h Tirre 

Qui et Oi:erdi oo 

Reic:tility 

Avdlc:tility Of Service 

H md W a;hct:I es 

Brend 

ButtrnsA)ids 

Ag tdi oo Sp:a:l 

Deicx:teO{de 

Exira Rinse O{d e 

Adj Oothes Mi~Oyde 

Durd:llity 

HaizootdNeiicd Axis 

SpnSp:a:l 

E~Of-O{deSigid 

Ma:hi ne Size 

Wcrrmty 

Agtdioo (sid&to-sicEA.Ji:>cbwn) 

Ccrreds Out-of-Bdmce 

Deterg:nt Ccst Sating; 

HedOwnWder 

Lint Rerrovd 

Mul1ipeWa;h OydeOplicns 

On Wheels 

Ti rre Rerrd ni ng Ind cx:ta 

ViEMlingWincbN 

A-3 

Frecμsncy 

in Top 10 

16 

15 

13 

12 

10 

9 

9 

8 

8 

7 

7 

6 

6 

5 

4 

4 

3 

3 

3 

3 

2 

2 

2 

Appendix A 
Draft 4/30/99 

San Francisco Bay Area Focus Group 

Combined Group 

18 Participants 

Freq.sicy 

Feciure in Top 10 

Durc:t:i I i1y 16 

Price 16 

Cq:x:d1y 12 

Wcrren1y 11 

Locd Size Optim 10 

Wda Terrp::rrureOplims 10 

Dea P1 a::an:nt 9 

Qui et Op:rdim 9 

Enag; mdWda Ca;ts. 8 

Brend 7 

Extra Rinse 7 

Wa>h Tirre 7 

Carecls Out-of-Bd cnce 6 

Lint Rarovd 4 

Ma::hineSize 4 

Spn Si:ea::J 4 

Avdlc:t:ili1y of Savice 3 

Cda 3 

GEnfleOyde 3 

Scd< Optim 3 

Socp Disp::nsa 3 

Actl a othei in Mid Oyd e 2 

Agtdi m (si d9 to si d9A.Jp cbwn) 2 

BI e:r::h,iSoftena Di sp::nsa 2 

H end W a>hct:I e Oyd e 2 

HaizmtdNaiic:d Axis 2 

HotWda Pre;sure 2 

Rarovd:i e Tub 2 

Wa>ha/Orya Ccrrb:) 2 

Buttms/Oids 1 

OnWhe9s 

Spn Length 

Spn Only Oplim 

Usa Friendy Cmtrds 

A-4 Appendix A 
Draft 4/30/99 

Madison Focus Group 

Combined Group 

19 Participants 

Fedure 

Fr 9CJ,IEl1c.y 

in Top 10 

Cq:x:D1y 

Durctili1y,.Reict:ili1yA3rmd 

Price 

Ene-gy OldWdEI Ccs1s 

Loa:i Size Oplims 

Wd€I T an::adure Oplims 

Quiet O~dioo 

HaizmtdNerticd Axis 

MultipeWa;h CydeOptims 

DoaSize 

Wa;hTirre 

SOO< Oplioo 

LintRarovd 

Careds Out-Of-Bdmce 

DetEignt Ca;t So/i ng; 

End-Of-CydeSigid 

Exira Rinse Cyd e 

Adj a othes Mi d-Cyd e 

Agtdioo Sr:x;a:i 

Ble::ctvSoft91€I Di Sp:n$€I 

Deic:deCyde 

Doa Pl a::::anent 

H end Wa;hd:le Cyde 

Heo,y Du1y Cyde 

SpnSp;a:i 

At:il1yto R&UseRinseWd€I 

Ba;ket fa Od:l Shq:s:i lterrs 

Crnird Lct::es 

El a:lri cd Ccrrp:ii ti Ii 1y 

Mota Opli rrizer 

A-5 

18 

18 

17 

16 

14 

13 

10 

9 

9 

8 

8 

7 

6 

4 

4 

3 

3 

2 

2 

2 

2 

2 

2 

2 

2 

l 

1 

1 

1 

1 

Appendix A 
Dallas Focus Group 

Combined Group 

17 Participants 

f r6CJ.1Ency 

Fedure inToplO 

Cq::x:D1y 13 

Ene-w mdWcter Ccsts 13 

MultipeWcr.h Oyde0pti005 13 

Loc:d Size Opti al 10 

Reic:bliiy 9 

BI ro:tvSoftffiE! Di Sl=ffiSEJ 8 

Dea P1a::e 1e1t 8 

Price 8 

Agtdial Sp:;ed 7 

HaizaltdNEJtim Axis 7 

W dEr T 9Tp3'dure Opti 005 7 

a e:::r 1nstrucli005 4 

Extra Rinse Oyd e 4 

Lint REITOVd 4 

Brmd 3 

Cda 3 

EnoOf-Oyd e Si g1d 3 

GEnfleOyde 3 

Quiet Op:rdial 3 

Sef OEOling Filter 3 

Tub MdErid (Plcr.tic,Metd) 3 

Warm1y 3 

Wcr.hTirre 3 

Avdlc:bli1y of SErvice 2 

B utt005JO ids 2 

Ma:TlineSize 2 

Mota Qudi1y,Oize 2 

Pr&Wcr.h 2 

Si rrp e setti n~ 2 

Scx::k Opti al 2 

SpnSp:;ed 2 

H md Wcr.hd:les 1 r• 

Lid Lock 1 

Sdety 

Spn Lengh 

Two Sp:;ej Mota 

Draft 4/30/99 A-6 Appendix A 
Draft 4130/99 

Miami Focus Group 

Combined Group 

Price 

Durctiliiy 

Fectures 

Loa:! Size Opti cns 

Warmiy 

Energ,t & Weier O::sts 

Cq:x:riiy 

Wder T errp=rdure Opti cns 

Dcx:x Pla:::errait 

/lgtdi en Si:;eed 

MuftipeWcsh CideOpticns 

WcshTirre 

Mani ne Size 

Qui et Op:rdi en 

Avd I cti liiy of Service 

Buttcns,Oid s 

Extra Rinse Cid e 

HaizentdNertioo Axis 

Rust Proof 

Bla:x::hJSoftener Disp:nser 

Careds Out-of-Bdcnce 

Dcx:x Size 

Mani ne Desi gi 

On Whees 

Brend 

Cda 

Ccnneded to Weier Softeler 

Cid e stop; when Lid Oi::en 

Gcs,£1 a::tri c 

H end Wcshdjes 

Pre-Wcsh 

Waning LiQ'lts 

20 Participants 

A-7 

f reqJSlcy in 

ToplO 

18 

17 

17 

17 

16 

15 

11 

10 

8 

7 

7 

6 

6 

5 

5 

4 

4 

4 

3 

3 

3 

2 

2· 

l 

l 

Appendix A 
APPEND/XS 

TOP 5 ATTRIBUTES FROM CONJOINT ANAL YS/S SESSIONS 

Draft 4/30/99 B-1 Appendix B 
Top Five Attributes From Conjoint Analysis Session 

429 Respondents 

If you were to purchase a new clothes washer today, what are the five most important 

washer features that would influence your selection? 

Frequency In 

Attribute Top5 Percentage Out of 429 

Price 241 56.2% 

Energy aid W oter Costs/Energy Efficiency 205 47.8 

Cqxx:ity 187 43.6 

Multiple Wash Cyde Options 152 35.4 

lw oter T emperoture Options 116 27.0 

Machine Size 113 26.3 

Braid 89 20.7 

Lood Size Options 82 19.1 

Relictlility 73 17.0 

Quiet Operation 65 15.2 

Worraity 65 15.2 

Door Plo::ement 58 13.5 

tolor 56 13.1 

Durcoility 52 12.1 

Simple Settings 43 10.0 

Bleo::h..Sottener Dsipenser 33 7.7 

Mo::hine Design 28 6.5 

User Friendly Controls 27 6.3 

lwosh Time 26 6.1 

Delicate Cyde 26 6.1 

IAgi tali on Speed 22 5.1 

IAvailctlility of Service 21 4.9 

Buttons/Di ds 21 4.9 

IGentteCyde 19 4.4 

Extra Rinse Cycle 18 4.2 

lspin Speed 18 4.2 

H eory Duty Cycle 18 4.2 

Rated H i{flly by Consumer Reports 17 4.0 

Agitation (side-to-side or up-down) 13 3.0 

Setf-Cleoiing Filter 13 3.0 

Corrects Out-of-Bdaice 8 1.9 

lrub Materid (Plastic/Meld) 8 1.9 

Wosher/Dryer Combo 7 1.6 

:control Leoets 7 1.6 

End of Cyde Signd 6 1.4 

isod< Option 5 1.2 

Detergent Cost Savings 5 1.2 

Pre-Wash 5 1.2 

lsoq:> Dispenser 5 1.2 

!Ability to Re-Use Rinse Water 5 1.2 

lsatety 5 1.2 

!Time Remaining Indicator 5 1.2 

Clea Instructions 4 0.9 

Motor Qudity..Size 4 0.9 

Warning Li{flts 4 0.9 

IAdd Clothes in Mid Cyde 3 0.7 

Removcole Tub 3 0.7 

Gas/Electric 3 0.7 

H orizontdNerticd Axis 2 0.5 

LintRemovd 2 0.5 

H ot Water Pressure 2 0.5 

Spin Length 2 0.5 

!connected to Water Softener 2 0.5 

Heat Own Water 2 0.5 

id Lock 2 0.5 

H end W ashd:lles 1 0.2 

Door Size 1 0.2 

Ion Wheels 1 0.2 

Electricci Compatibility 1 0.2 

Draft 4/30/99 B-2 Appendix B 
APPENDIXC 

CONJOINT SESSIONS SURVEY WITH RESPONSE FREQUENCIES 

Draft 4/30/99 C-1 Appendix C 
CLOTHES WASHER ANALYSIS FOLLOW-UP QUESTIONNAIRE 

1. How many people currently live in your household? 

Response All Respondents Low-Income 

(less then 

$25k/yr) 

1 27.2% 35.0% 

2 35.5 33.0 

3 14.7 10.7 

4 '13.9 9.7 

5 4.7 6.8 

6 2.8 2.9 

7 0.5 0.0 

8 0.0 0.0 

9 0.2 1.0 

10 0.2 0.0 

Refused 0.2 1.0 

2. How many children ages 6 to 18 live in your household? __ 

Number All Low Income 

Respondents (less than 

$25k/yr) 

0 74.5% 75.7% 

1 12.3 9.7 

2 8.7 9.7 

3 2.6 2.9 

4 0.9 1.9 

5 0.2 0.0 

Refused 0.7 0.0 

(If greater than 0) 

~ Do these children attend public or private schools? 

_Public 

_Private 

65 and Older 

43.3% 

38.8 

7.5 

7.5 

1.5 

0.0 

0.0 

0.0 

0.0 

0.0 

1.5 

65 and Older 

91.0% 

3.0 

4.5 

0.0 

0.0 

0.0 

1.5 

_Both (e.g., one attends a public school and one attends a private school) 

Response All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Public 71.4% 72.0% 80.0% 

Private 22.9 28.0 20.0 

Both 4.8 0.0 0.0 

Refused 1.0 0.0 0.0 

Draft 4/30/99 C-2 Appendix C 
7. Do you have a satellite dish for your TV reception? 

Response All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Yes 4.5% 4.9% 3.0% 

No 95.5 95.1 97.0 

8. How many automobiles does your household own or lease? 

Number of Autos All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

0 6.9% 14.6% 6.0% 

1 40.4 51.5 47.8 

2 36.4 22.3 40.3 

3 9.5- 2.9 3.0 

4 5.4 8.7 3.0 

5 or More 1.1 0.0 0.0 

Refused 0.2 0.0 0.0 

9. What is the year, make, and model of each automobile? (open ended responses) 

10. Wh ere d oyou d o your 1 d aun lry. ? 

Location All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Home 72.1% 46.6% 77.6% 

Laundromat 7.3 13.6 3.0 

Apartment Laundry Room 18.7 35.9 16.4 

Other 1.4 3.9 1.5 

Refused 0.5 0.0 1.5 

11. Have you purchased a new clothes washer within the last 2 years? 

Response All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Yes 17.3% 9.7% 26.9% 

No 82.3 89.3 70.1 

Refused 0.5 1.0 3.0 

Draft 4/30/99 C-4 Appendix C 
3. Which category best describes your age? 

Age Category All Low Income 

Respondents (less than 

$25klvr) 

18 to 24 12.8% 30.1% 

25 to34 18.7 19.4 

35 to44 19.9 15.5 

45 to 54 18.7 6.8 

55 to 64 14.2 10.7 

65 or older 15.8 17.5 

4. Which category best describes your education? 

Education Category All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Some High School 1.2% 2.9% 0.0% 

High School Graduate 8.5 16.5 10.4 

Some College 23.9 33.0 37.3 

College Graduate 63.8 45.6 50.7 

Trade/Vocational School 2.6 1.9 1.5 

5. Do you own or rent your home? 

Response All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Own 60.0% 30.1% 89.6% 

Rent 39.5 68.9 10.4 

Refused 0.5 1.0 0.0 

6. Which range coincides with the monthly rent or mortgage payment for your primary 

residence? 

Monthly Rent All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Less than $200 11.6% 14.6% 34.3% 

$200 to $499 18.7 38.8 25.4 

$500 to $999 35.9 34.0 9.0 

$1,000 to $1,499 17.5 3.9 7.5 

$1,500 to $2,000 6.4 1.9 1.5 

Greater than $2,000 4.3 1.9 3.0 

Refused 5.7 4.9 19.4 

Draft 4/30/99 C-3 Appendix C 
(If yes) 

~How much did you pay for your new clothes washer? ____ _ 

Price Range All Low Income 65 and Older 

Respondents (less than 

$25k/vr) 

$200-$299 4.1% 20.0% 0.0% 

$300-$399 11.0 0.0 11.l 

$400-$499 37.0 30.0 33.3 

$500-$599 16.4 2:0.0 16.7 

$600-$699 8.2 0.0 16.7 

$700-$799 1.4 0.0 0.0 

$800-$899 0.0 0.0 0.0 

$900-$999 2.7 0.0 0.0 

$1000-$1100 5.5 0.0 5.6 

Refused 13.7 30.0 16.7 

l..Why did you purchase the clothes washer? 

Response All Low Income 65 and Older 

Respondents (less than 

$25klvr) 

Replaced your old clothes washer 56.0% 40.0% 50.0% 

because it was not working 

properly 

Replaced your old clothes washer 13.3 0.0 27.8 

because you wanted a newer 

model 

Been washing clothes outside 6.7 0.0 5.6 

home and decided to purchase a 

clothes washer for your home 

Moved to a new residence and 16.0 30.0 0.0 

needed a clothes washer 

Bought a new clothes dryer and 1.3 0.0 0.0 

wanted a matching clothes washer 

Other (please specify) 6.7 10.0 11.1 

Draft 4/30/99 C-5 AppendixC 
12. The majority of residential clothes washers sold in the U.S. are vertical axis machines (see 

Figure 1), with horizontal axis machines comprising the remaining portion (see Figure 2). 

Before today, did you know that you could buy a horizontal axis machines for residential 

use? 

Response All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Yes 43.0% 32.0% 40.3% 

No 56.3 66.0 56.7 

Refused 0.7 1.9 3.0 

(If yes) 

~Where did you learn this? ___________________ _ 

13. Have you ever washed clothes in a horizontal axis machine? 

Response All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Yes 57.0% 58.3% 41.8% 

No 42.3 40.8 55.2 

Refused 0.7 1.0 1.5 

(If yes) 

~Where did you wash them? __________________ _ 

14. If you had to purchase a clothes washer today, would you consider purchasing a horizontal 

axis machine? 

Response All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Yes 70.0% 72.8% 56.7% 

No 28.4 25.2 40.3. 

Refused 1.7 1.9 3.0 

(If no) 

~Why would you NOT consider purchasing a horizontal axis clothes washer? 

Draft 4/30/99 C-6 Appendix C 
15. Suppose you went home today and learned that your clothes washer does not work. You 

decide to purchase a new clothes washer. Please indicate the highest price you could 

afford to pay for a new clothes washer. 

Highest Price All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

$300 3.1% 6.8% 1.5% 

$400 18.7 34.0 19.4 

$500 35.5 37.9 31.3 

$600 19.4 10.7 17.9 

$700 7.3 1.0 7.5 

$800 5.0 1.0 6.0 

$900 0.5 0.0 0.0 

$1000 9.0 4.9 11.9 

Refused 1.6 3.9 4.5 

16. What do you think the average price for a new clothes washer is today? 

Price All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Less than $350 0.7% 1.0% 0.0% 

$350-$449 29.8 26.2 28.4 

$450-$549 48.9 52.4 52.2 

$550-$649 14.9 12.6 11.9 

$650-$750 4.0 4.9 3.0 

Greater than $750 0.5 1.0 1.5 

Refused 0.9 1.9 3.0 

17. 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? 

Price Range All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

$200-$349 6.9% 16.5% 4.5% 

$350-$499 51.3 56.3 44.8 

$500-$649 34.0 22.3 38.8 

$650-$849 5.9 3.9 10.4 

$850- $999 1.4 0.0 1.5 

$1,000-$1,200 0.0 0.0 0.0 

Refused 0.5 1.0 0.0 

Draft 4/30/99 C-7 Appendix C 
18. If you were to purchase a new clothes washer today, what means 'would you use to pay for 

it? 

Payment Method All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Cash 10.2% 14.6% 7.5% 

Check 24.3 21.4 16.4 

Credit Card 41.6 30.1 62.7 

Store Financing 10.2 14.6 6.0 

Credit Card Financing 9.0 7.8 3.0 

Bank Loan 1.4 3.9 1.5 

Home Equity Loan 0.7 1.0 1.5 

Other 2.1 5.8 1.5 

Refused 0.5 1.0 0;0 

19. Imagine you have a clothes washer that is 10 years old, which the manufacturer claims 

should last for 15 years, on average. This machine is not working properly and you call a 

service technician to look into the problem. The technician gives you the option of fixing 

your 10 years old clothes washer for $150 or purchasing a new one with the same features 

for $400. What would you do? 

Response All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Purchase the new machine at $400 63.6% 54.4 76.1 

Fix your old machine at $150 32.9 37.9 20.9 

Do laundry somewhere else 1.7 4.9 0.0 

Shop for a used machine 0.9 1.9 1.5 

Refused 0.9 1.0 1.5 

20. Imagine you have a clothes washer that is 10 years old, which the manufacturer claims 

should last of 15 years, on average. This machine is not working properly and you call a 

service technician to look into the problem. The technician gives you the option of fixing 

your 10 years old clothes washer for $150 or purchasing a new one with the same features 

for $450. What would you do? 

Response All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Purchase the new machine at $450 53.9% 42.7 67.2 

Fix your old machine at $150 41.8 47.6 31.3 

Do laundry somewhere else 0.9 3.9 0.0 

Shop for a used machine 2.1 3.9 0.0 

Refused 1.1 2.0 1.5 

Draft 4/30/99 C-8 AppendixC 
21. Imagine you have a clothes washer that is 10 years old, which the manufacturer claims 

should last for 15 years, on average. This machine is not working properly and you call a 

service technician to look into the problem. The technician gives you the option of fixing 

your 10 years old clothes washer for $150 or purchasing a new one with the same features 

for $650. What would you do? 

Response All Low Income 65 and Older 

Respondents (less than 

$25k/yr) 

Purchase the new machine at $650 16.3% 11.7 31.3 

Fix your old machine at $150 74.7 69.9 65.7 

Do laundry somewhere else 2.6 8.7 1.5 

Shop for a used machine 5.2 8.7 0.0 

Refused 1.1 1.0 1.5 

Draft 4/30/99 C-9 Appendix C 
SELECTED CROSS TABULAR RESULTS 

RELATIONSHIP BETWEEN EXPERIENCE WITif HORIZONTAL AXIS MACHINES AND 

WILLINGNESS TO CONSIDER PURCHASE: 

People Who Have Experience with Horizontal Percent 

Axis Clothes Washing Machines (Total=238) 

Would Consider Purchasing a H-Axis Machine 69.3% 

Would Not Consider Purchasing a H-Axis Machine 30.7 

People Who Have NO Experience with Horizontal Percent 

Axis Clothes Washing Machines (Total=176) 

Would Consider Purchasing a H-Axis Machine 73.3% 

Would Not Consider Purchasing.a H-Axis Machine 26.7 

Draft 4/30/99 C-10 AppendixC 
APPENDIXD 

CONJOINT ANALYSIS CARDS AND INSTRUCTIONS 

Draft 4/30/99 D-1 Appendix D 
CLOTHES WASHERS 

CLOTHES WASHER Card -1 

Price of clothes washer: ..................................................................... S 450 

Energy and water Savings: ............................................................... S 50 

Wash capacity: .............................................................................. Standard 

Top or front load: .............................................................................. Top 

Wash/Rinse temperature: .................................................................... Cold, Warm 

Load size:.............................................................................. NonAdjustable 

Draft 4/30/99 D-2 Appendix D 
CLOTHES WASHER Card -2 

Price of clothes washer: ..................................................................... $ 400 

Energy and water Savings: ............................................................... $ 50 

Wash capacity: .............................................................................. Extra Large 

Top or front Load: .............................................................................. Front 

Wash/Rinse temperature: .................................................................... Cold, Warm 

Load size: ............................ '.................................................. NonAdjustable 

CLOTHES WASHER Card -3 

Price of clothes washer: ..................................................................... $ 400 

Energy and water Savings: ............................................................... $ l 0 

. Wash capacity: .............................................................................. Extra Large 

Top or front Load: .............................................................................. Front 

Wash/Rinse temperature: .................................................................... Cold, Warm, 

Hot 

Load size:.................. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NonAdjustable 

Draft 4/30/99 D-3 Appendix D 
CLOTHES WASHER Card -4 

Price of clothes washer: ..................................................................... S 400 

Energy and water Savings: ............................................................... S 0 

Wash capacity: .............................................................................. Standard 

Top or front Load: .............................................................................. Front 

Wash/Rinse temperature: .................................................................... Cold, Warm, 

Hot 

Load size:.............................................................................. NonAdjustable 

CLOTHES WASHER Card -5 

Price of clothes washer: ..................................................................... S 450 

Energy and water Savings: ............................................................... $ 0 

Wash capacity: ............................................................................... Extra Large 

Top or front Load: .............................................................................. Front 

Wash/Rinse temperature: .................................................................... Cold, Warm 

Load size: .............................................................................. Adjustable 

Draft 4/30/99 D-4 Appendix D 
CLOTHES WASHER Card -6 

Price of clothes washer: ..................................................................... $ 450 

Energy and water Savings: ............................................................... $ 0 

Wash capacity: .............................................................................. Extra Large 

Top or front Load: .............................................................................. Front 

Wash/Rinse temperature: .................................................................... Cold, Warm, 

Hot 

Load size: .............................................................................. Adjustable 

CLOTHES WASHER Card -7 

Price of clothes washer: ..................................................................... $ 400 

Energy and water Savings: ............................................................... $ 0 

Wash capacity: .............................................................................. Standard 

Top or front Load: .............................................................................. Top 

Wash/Rinse temperature: .................................................................... Cold, Warm 

Load size: .............................................................................. Adjustable 

Draft 4/30/99 0-5 Appendix D 
CLOTHES WASHER Card -8 

Price of clothes washer: ..................................................................... S 650 

Energy and water Savings: ............................................................... $ O 

Wash capacity: .............................................................................. Extra Large 

Top or front Load: .............................................................................. Top 

Wash/Rinse temperature: .................................................................... Cold, Warm 

Load size:.............................................................................. NonAdjustable 

CLOTHES WASHER Card -9 

Price of clothes washer: ..................................................................... S 650 

Energy and water Savings: ............................................................... S 50 

Wash capacity: .............................................................................. Standard 

Top or front Load: .............................................................................. Front 

Wash/Rinse temperature: .................................................................... Cold, Warm, 

Hot 

Load size: ............................................................................ ,', Adjustable 

Draft 4/30/99 D-6 Appendix D 
CLOTHES WASHER Card -10 

Price of clothes washer: ..................................................................... S 400 

Energy and water Savings: ............................................................... S 50 

Wash capacity: .............................................................................. Extra Large 

Top or front Load: .............................................................................. Top 

Wash/Rinse temperature: .................................................................... Cold, Warm, 

Hot 

Load size: .............................................................................. Adjustable 

CLOTHES WASHER Card - 11 

Price of clothes washer: ..................................................................... S 400 

Energy and water Savings: ............................................................... S 0 

Wash capacity: .............................................................................. Standard 

Top or front Load: .............................................................................. Front 

Wash/Rinse temperature: ................................................... : ................ Cold, Warm 

Load size:.............................................................................. NonAdjustable 

Draft 4/30/99 D-7 Appendix D 
CLOTHES WASHER Card -12 

Price of clothes washer: ................................................................... ~. $ 400 

Energy and water Savings: ............................................................... $ 1 O 

Wash capacity: .............................................................................. Extra Large 

Top or front Load: .............................................................................. Top 

Wash/Rinse temperature: .................................................................... Cold, Warm 

Load size: .............................................................................. Adjustable 

CLOTHES WASHER Card -13 

Price of clothes washer: ..................................................................... $ 650 

Energy and water Savings: ............................................................... $ 10 

Wash capacity: .............................................................................. Standard 

Top or front Load: .............................................................................. Front 

Wash/Rinse temperature: .................................................................... Cold, Warm 

Load size: .............................................................................. Adjustable 

Draft 4/30/99 D-8 Appendix D 
CLOTHES WASHER Card -14 

Price of clothes washer: ..................................................................... S 400 

Energy and water Savings: ............................................................... S 0 

Wash capacity: .............................................................................. Standard 

Top or front Load: .............................................................................. Top 

Wash/Rinse temperature: .................................................................... Cold, Warm, 

Hot 

Load size: .............................................................................. Adjustable 

CLOTHES WASHER Card - 15 

Price of clothes washer: ..................................................................... S 650 

Energy and water Savings: ............................................................... S 0 

Wash capacity: .............................................................................. Extra Large 

Top or front Load: .............................................................................. Top 

Wash/Rinse temperature: .................................................................... Cold, Warm, 

Hot 

Load size:.............................................................................. NonAdjustable 

Draft 4/30/99 D-9 Appendix D 
CLOTHES WASHER Card -16 

Price of clothes washer: ..................................................................... $ 450 

Energy and water Savings: ............................................................... $ l 0 

Wash capacity: ............................................................................... Standard 

Top or front Load: .............................................................................. Top 

Wash/Rinse temperature: .................................................................... Cold, Warm, 

Hot 

Load size:... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . NonAdjustable 

PURCHASE 

Draft 4/30/99 D-10 Appendix D 
INSTRUCTIONS 

CLOTHES WASHERS 

Assume that you are purchasing a new clothes washer. You have in front of you a series of cards 

that contain different combinations of clothes washer prices. expected annual energy and 

water savings. and different washer features such as capacity, wash and rinse water 

temperatures. and load size adjustments. We're going to ask you to sort these cards according 

to your preference for the features described on each. These features are defined below and 

are also shown in the laminated exhibits you have in front of you. Please read these instructions 

and rook at the exhibits carefully to make sure you understand the definitions before you begin 

sorting through the cards. 

PRICE 

The price refers to the cost of a new clothes washer. 

TOP OR FRONT LOAD 

Top or front load refers to the placement o't the door on the machine. "Top" means the door is 

on the top of the machine and "Front" means the door is on the front side. Please refer to Figure 

l to see different examples of top loading and front loading clothes washers. 

ENERGY AND WATER SAVINGS 

The savings shown on the card reflects an average expected bill savings (both water and 

energy) for an entire year using the clothes washer. 

WASH/RINSE TEMPERATURE 

This feature refers to the water temperature options available on the machine for the wash and 

rinse cycles. The two categories are "Cold, Warm, Hot'" and "Cold, Warm". 

WASH CAPACITY 

Wash capacity refers to the amount of clothes that can be washed in one wash load. The 

categories are "Standard" and "Extra Large". The "Extra Large" capacity is 20 percent larger 

than the standard tub. 

LOAD SIZE 

Load size refers to a feature that allows you to adjust the amount water during the wash cycle to 

match a small, medium, or large wash load. "Adjustable" means that the water level can be 

adjusted to match the load size. while "Non-adjustable" indicates a washer that uses the same 

amount of water regardless of the size of the load. 

(Instructions Continued on Back of Page) 

Draft 4/30/99 D-11 Appendix D 
Once you've looked through the cards, sort them into three piles in the rough order of your 

preference for them. To help you, we have provided marker cards labeled: 

Product/Service 

I would be 

MORE LIKELY 

to choose 

.,.,._ ·' 

Product/Service I would be ~ NEITHER MORE nor 

LESS LIKELY 

to choose 

~ 

Product/Service I would be ~ LESS LIKELY l 

to choose · 

After you have sorted the cards into three piles, pick up the cards in your "More Likely" pile. 

Sort them according to your preference for them so that the one you most prefer is on the top 

and the one you least prefer is on the bottom. 

Repeat this sorting procedure for the cards in your "Neither More Nor Less Likely" pile. Then, sort 

the cards in your "Less Likely" pile. If you feel inclined to move a card to a different stack during 

the sorting procedure, please feel free to do so. 

Please review your three piles to ensure that the cards are sorted from most preferred to least 

preferred. 

We would now like you to record the clothes washer you prefer most, second most, and so on 

down to the clothes washer you prefer least. 

Note that there is a number located on the upper right hand corner of each card. On your 

response sheet, write down the number of the cards under the column labeled "Card Number'' 

beginning with the top card on your "More Likely" pile. When you reach the bottom of the first 

pile, proceed to the "Neither More Nor Less Likely" pile and then to the "Less Likely" pile. When 

you are done, the number of your least preferred card should be written on the last line of the 

"Card Number" column and every line should have only one card number entered. 

Card Number 

97 

99 "" :"'-:."'.':,:~~--·:'" 

:·.::::·:/:::,::··;;·,..:-.,:. 98 .. : . ; ·~ ·.-:·· ... 

Draft 4/30/99 D-12 AppendixD 
APPEND/XE 

CONJOINT ANALYSIS COEFFICIENT ESTIMATES 

Draft 4/30/99 E-1 Appendix E 
Coefficient Estimates 

All Respondents 

Equipment Choice Model Likelihood of Purchase 

Coefficient Standard Significance Relative 

Error Level Importance Coefficient Standard Significance Relative 

Error Level lmoortance 

Intercept -0.949 0.224 1% 0% 

Price -0.0036 0.000 1 % 26% Price -0.0066 0.000 1% 22% 

Savings 0.010 0.001 1% 14% Savings 0.029 0.002 1% 19% 

Capacity 0.248 0.024 1 % 7% Capacity 0.452 0.072 1% 6% 

Door Placement 0.383 0.024 1% 11% Door Placement 0.698 0.075 1% 9% 

Water Temp. 0.614 0.024 1% 18% Water Temp. 1.438 0.071 1% 19% 

Load Size n.<>i::? n.n24 1% 25% Ln::idSiz,. 1.ROQ 0.071 1% ?4% 

100% 100% 

Low Income Less than S25k 

E :auicment Choice Mode I Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

r.neffiroi,.nt Error Level lmoortance Coeffici,.nt Error Level lmoortance 

Intercept -0.230 0.387 55% 0% 

Price -0.0040 0.000 1% 30% Price -0.0066 0.001 1% 24% 

Savings 0.010 0.001 1% 16% Savings 0.025 0.003 1% 19% 

Capacity 0.255 0.049 1% 8% capacity 0.369 0.132 1% 5% 

Door Placement 0.330 0.049 1% 10% Door Placement 0.624 0.134 1% 9% 

Water Temp. 0.518 0.049 1% 16% Water Temp. 1.262 0.130 1% 19% 

Load Size 0.648 0.049 1% 20% Load Size 1.llOO 0.129 1% 24% 

100% 100% 

Elderly, 65 and older 

E :au1cment Ch" 01ce Modi e Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

Cneffi,.;"nt Error Level lmoortance Coeffici"nt Error Level lmoortance 

Intercept -1.963 0.542 1% 0% 

Price -0.0025 0.000 1 o/o 22"/o Price -0.0044 0.001 1% 16% 

Savings 0.006 0.001 1% 11% Savings 0.022 0.004 1% 17% 

Capacity 0.124 0.061 4% 5% Capacity 0.447 0.177 1% 7% 

Door Placement 0.352 0.061 1 o/o 13°!0 Door Placement 0.794 0.183 1% 12% 

Water Temp. 0.524 0.062 1% 19% Water Temp. 1.392 0.176 1% 21% 

Load Size 0.817 0.063 1 o/o 30% Load Size 1.873 0.179 1% 28% 

100% 100% 

Young, 18-24 

E :au1pmen tCh 01ce Md 0 e I Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

Coefficient Error Level lmoortance Coefficient Error Level lmoortance 

Intercept 0.628 0.537 24% 0% 

Price -0.0052 0.000 1% 31% Price -0.0081 0.001 1% 28% 

Savings O.Q16 0.002 1% 19% Savings 0.027 0.004 1% 19% 

Capacity 0.375 0.069 1% 9% Capacity 0.634 0.186 1% 9% 

Door Placement 0.460 O.Q70 1% 11% Door Placement 0.818 0.188 1% 11% 

Water Temp. 0.547 0.071 1% 13% Water Temp. 0.867 0.181 1% 12% 

Load Size O.R57 0.069 1% 16% Load Size 1.""" 0.180 1% 21% 

100% 100% 

Draft 4/30/99 E-2 Appendix E 
Recent Purchasers 

E ui ment Choice Model Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

Coefficient Error Level Im rtance Coefficient Error Level lmoortance 

Intercept -1.596 0.573 1% 0% 

Price -0.0024 0.000 1% 22% Price -0.0056 0.001 1% 20% 

Savings 0.005 0.001 1% 9% Savings 0.022 0.004 1% 16% 

Capacity 0.173 0.059 1% 6% Capacity 0.562 0.180 1% 8% 

Door Placement 0.300 0.059 1% 11% Door Placement 0.605 0.187 1% 8% 

Water Temp. 0.520 0.059 1% 19% Water Temp. 1.468 0.176 1% 21% 

Load Size 1% Load Size 1.976 0.1R3 1% 2R% 

100% 100% 

Have Tried Horizontal Axis 

E :auioment c hoice M ode I Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

Coefficient Error Level lmoortance Coefficient Error Level lmoortance 

Intercept -0.766 0.309 1% 0% 

Price -0.0035 0.000 1% 26% Price -0.0069 0.001 1% 23% 

Savings 0.009 0.001 1% 13% Savings 0.030 0.002 1% 20% 

Capacity 0.260 0.032 1% 8% Capacity 0.455 0.099 1% 6% 

Door Placement 0.303 0.033 1% 9% Door Placement 0.484 0.102 1% 6% 

Water Temp. 0.624 0.033 1% 19% Water Temp. 1.483 0.095 1% 20% 

Load Size n.R67 0.""" 1% 2f;% Load Size, 1.841 0.097 1% 25% 

100% 100% 

Would Consider Purchasing H-Axis Machine 

E :au1oment Ch" 01ce Mdl 0 e Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

Coefficient Error Level lmoortance Coefficient Error Level lmoortance 

Intercept -0.953 0.252 1% 0% 

Price -0.0037 0.000 1% 26% Price -0.0063 0.001 1% 22% 

Savings 0.011 0.001 1% 15% Savings 0.031 0.002 1% 21% 

Capacity 0.266 0.028 1% 8% Capacity 0.482 0.083 1% 7% 

Door Placement 0.264 0.028 1% 8% Door Placement 0.499 0.085 1% 7% 

Water Temp. 0.653 0.029 1% 19% Water Temp. 1.520 0.082 1% 21% 

Load Size 0.""" 0.029 1°/o 24% Load Size 1.671 0.081 1% 23% 

100% 100% 

Would Not Consider Purchasing H-Axis Machine 

E C M :au1oment ho ice odel Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

CN>ffiroiF>nt Error Level lmoortance CnPfficiPnt Error Level lmoortance 

Intercept -1.296 0.513 i% 0% 

Price -0.0035 0.000 1% 25% Price -0.0072 0.001 1% 22% 

Savings 0.006 0.001 1% 9% Savings 0.022 0.004 1% 14% 

Capacity 0.194 0.046 1% 5% Capacity 0.392 0.149 1% 5% 

Door Placement 0.709 0.047 1% 20% Door Plact!ment 1.347 0.163 1% 16% 

Water Temp. 0.533 0.046 1% 15% Water Temp. 1.245 0.144 1% 15% 

Load Size "027 0.047 1% 26% Load Size 2.273 0.156 1%, 2R% 

100% 100% 

Draft 4/30/99 E-3 Appendix E 
Madison WI 

E C :au1cment hoice Modi e Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

Coefficient Error Level lmoortance Coefficient Error Level lmoortance 

Intercept 0.358 0.49 46% 0% 

Price -0.0048 0.000 1% 25% Price -0.0098 0.00 1% 27% 

Savings 0.0157 0.001 1% 16% Savings 0.036 0.00 1% 20% 

Capacity 0.4834 0.052 1% 10% Capacity 0.662 0.15· 1% 7o/o 

Door Placement 0.5579 0.052 1% 12"/o Door Placement 0.747 0.16 1% 8% 

Water Temp. 0.7683 0.052 1% 16% Water Temp. 1.427 0.15 1% 16% 

Load Size 1oon n.no:? 1% 21% Load Size 1.D'>A n 1c; 1% 21% 

100% 100% 

Washington DC 

E :au1cment Ch 01ce . Md 0 e I Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

Coefficient Error Level lmoortance Coefficient Error Level lmoortance 

Intercept -1.359 0.408 1% 0% 

Price -0.0032 0.000 1% 26% Price -0.0045 0.001· 1% 18% 

Savings 0.0099 0.001 1% 16% Savings 0.024 0.003 1% 19% 

Capacity 0.1912 0.052 1% 6% Capacity 0.521 0.140 1% 8% 

Door Placement o.1n9 0.052 1% S°Ai Door Placement 0.394 0.141 1% 6% 

Water Temp. 0.6164 0.052 1% 20% Water Temp. 1.284 0.137 1% 20% 

Load Size 07559 o_nc;? '1% 25% Load Size 1.7fl7 0138 1% 28% 

100% 100% 

Dallas TX 

Eauicment Choice Model Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

Coefficient Error Level lmoortance Coefficient Error Level lmoortance 

Intercept -2.039 0.510 1% Oo/o 

Price -0.0034 0.000 1% 27% Price -0.0054 0.001 1% 19% 

Savings 0.005 0.001 1% 8% Savings 0.022 0.004 1% 15% 

Capacity 0.156 0.048 1% 5% Capacity 0.325 0.151 3o/o 4% 

Door Placement 0.523 0.048 1% 17% Door Placement 1.105 0.164 1% 15% 

Water Temp. 0.523 0.048 1% 17% Water Temp. 1:472 0.152 1% 20% 

Load Size 0.836 0.049 1% 27% Load Size 1 '477 0.158 1% 27% 

100% 100°/o 

San Francisco Bav Area 

E ui ment Choice Model Likelihood of Purchase 

Standard Significance Relative Standard Significance Relative 

Error Level Im nee Coefficient Error Level lmoortance 

Intercept -0.985 0.45 3% 0% 

Price -0.0036 0.000 1% 25% Price -0.0070 0.00 1% 22% 

Savings 0.011 0.001 1% 15% Savings 0.034 0.00 1% 21% 

Capacity 0.235 0.046 1% 7% Capacity 0.312 0.15 3% 4% 

Door Placement 0.339 0.047 1% 10% Door Placement 0.684 0.15 1% 9% 

Water Temp. 0.638 0.047 1% 18"/o Water Temp. 1.747 0.15 1% 22% 

Load Size 0.91 .048 1% % Load Size 1.74Q 0.14 1% 22"/o 

100% 100% 

Draft 4/30/99 E-4 Appendix E 
Draft 4/30/99 

APPENDIXF 

ELAST/CnY CALCULATIONS 

F-1 Appendix F 
Full Sample 

Attribute Description 

Price Dollar value of retail price of machine 

Savings DOiiar value of annual water and energy blll savings 

Capacit Binary vartable: zero for standard capacity machine. one for extra large capacity. 

Door Binary variable: zer_o for front loader. one for top loader. 

Water Temp Binary vortable: zero for 'celd/wam'. one for 'cold/worm/hot' washing temperature options 

LaadSize Blnorv vartnhle: zero If !her"' Is no load size adtustment ootlon one If there Is 

Directions. 

To calculateprlce elasllcllillse the "Ukellhood of Purchase Model" to the left. Fiii in the lnlttal Price and probability, and new price and prababillty cells In the "Prlc• 

Demand" table to generate results for the specific price 

Slmllarlv when calculattnn Savlnns elasttcllv be sure the lnlttal and new market condlttons differ onlv In their savlnas 

Full Sample 

Note: Based on sample of 

that do laundry at home. the 

mare k If orreoarsan I d 

llkellhood of Purchase Model 

Current Marke ,,, .......... 0 Caefflclen Condition 

lntercep ·0.9494 I 

Price ·0.006E 400 

Savings 0.028E 0 

Capacit 0.451€ 0 

Door 0.697€ l 

Water Temp 1.437€ I 

Load Size 1.8086 I 

Sum 

-·----·-·--------... -------- Likelihood of Purchase 

Draft 4/30/99 

Uncalibrated elastldftes for the full sample 

Product of 

Coefficient 

Current Marke Price Elasticity of Demand Vnhu> 

·0.949L Initial Price 400 

-2.64 Initial Probabillty 0.59 

0 New Price 650 

0 New Probabllll) 0.21 

0.697l Pdce Elasticity ·2.00 

1.4378 

J.808l 

Savings Elasllclty of Demand 

0.354< 

59% lnittal Savings 0 

lnlttal Probability 0.59 

New Savings fi( 

New Probablfll) 0.86 

Savlnos Elastic/hi O.J< 

F-2 Appendix F 
Current Clothes Washer Owners 

Attribute Description 

Prtce Dollar vciue of relrni price of 

Savings Dollar vciue of annual wafer and energy bill 

Capacity Binary variable: zero for standard capacity machine. one tor ex1ro large 

Door Placement Binary variable: zero for front loader. one tor top 

Water Temp Binary variable: zero tor 'cold/wcrm'. one tor 'cold/worm/hot' washing 

Load Size Blnarv variable: zero If there Is no load size odlustment onllon onR If 

Directions. 

lo calculate CJ)rlce etosttcltyuse the "Likelihood of Purchase Model" lo the left. Fiii in 1he lnltlci Pnce and probability, and new prtce and probability cells In the "Pr1ce Elosttclty of 

Demond" table to generate results for the specific price change 

Slmiloriy. when cciculohnQ SovlnQs elostldtv. be sure 1he lnlttci and new morl<et conditions differ only In their sovlnm esttmoles. 

Current Clothes Washer Owners 

Note: Based on sample of homeowners 

t11ol do foundry at home. the potential 

market for reoolrs and reolocemen1s 

Likelihood of Purchase Model 

Current Market 

Altribute Coefficient Conditions 

lnlorcept -1.576~ 0.75 

Pi ice -0.00611 400 

Savings 0026£ 50 

Copoc1ly 0.6152 0 

Door Placement 06231 1 

water lernp 1.4591 1 

Load Size 1.9271 1 

Sum 

------·--·------ ----· Likelihood of Purchase 

Draft 4/30/99 

Product of 

Coefficient and 

Curren1 Market 

Vciue 

-1.162675 

-2.444 

1.44 

0 

0.6231 

1.4596 

1.9276 

2.023625 

66% 

Upper Bound = 0. 98 

Source: Survey Q19. 2 percent of sample 

that would go 1o laundromat rather 1han 

purchase new machine at $400. This 

that all others would purchase o new 

repar1ng were not an 

F-3 

Price Elasttclty of Demond 

lnlttci Price 400 

lnittci Probability 0.96 

New Price 650 

New Probability 0.91 

Ptlce Elasllclty - Savings Elasllclty of Demond 

lnlttci Savings 

inlttci Probability 

New Savings 

New Probability 

Savin s Elasllclty 

-0.16 

0 

0.96 

Lower Bound = 0.64 

Source: Survey Q 19. proportion of people 

sample that would purchase o machine al 

ralher than repair or do laundry elsewhere 

purchase o used machine. This assumes the 

ihose that chose io repair waud siarr gOlng 

laundromat If repartng were not an 

Price ElosHctty of Demond 

lnl hci Price 

inlflci Probability 

New Price 

New Proboblily 

Ptlce Elasl/clty 

Savings Elasticity of Demond 

lnittci Savings 

lnlttci Proboblhly 

0 

Appendix F 
Discretionary Washer Purchasers 

Attribute Description 

Price Dollar vdue of retail price of 

Savings DOiiar vdue of annual water and energy bill 

Capaclt Binary variable: zero for standard capacity machine. one for extra large 

Door Binary variable: zero for front loader. one for top 

Water Temp Binary variable: zero for 'cold/warn'. one for 'cold/worm/hot' wasling 

Load Size Blnarv variable: zero If there ls no load size odlustment oollon. one If 

Directions. 

To calculateprlce elastlclt\{Jse the "Likelihood of Purchase Model" to the left. Fill In the Initial Price and probability, and new price and probability cells in the "Price El 

Demand" table to generate results for the specific price change 

Slmllarlv. when calculaHna Savinas elasHcitv. be sure the inlt1al and new market condit1ons differ onlv in their savlnas 

Discretionary Washer Purchasers 

Note: Uses those in sample who 

non-washer owners and those 

purchased washer for first time In 

ln<t? v<>nr< 

Likelihood of Purchase Model 

Current Marki 

Attribute Coefflcien Conditions 

lntercep -0.3991 1.4 

Price -0.00651: 4CX: 

Savings 0.03QL 0 

Capacit -0.00311 0 

Door 0.186~ 1 

Water Temp 2.086' 1 

Load Size l.280! 1 

Sum 

------------------- Llkellhood of Purchase 

Draft 4/30/99 

Product of 

Coefficient 

t Current Marke 

Value 

-0.5587• 

-2.63:: 

0 

0 

0.186~ 

2.086: 

1.280! 

0.3628( 

59% 

F-4 

Upper Bound = 0.59 

Source: Full sanple. uncalibrated estimate of 

llkellhood of purchase of a standard 

machine. Conjoint results used since the 

reflects o pure non-dlscreflonary purchase 

clothes 

Price Elasticity of Demand 

lnlttal Price 4()( 

lnlttal Probabllif\ 0.5< 

New Price 65C 

New Probablllr 0.2 

Price E/astlclfy -1.9' 

Lower Bound = 0. IO 

Source: Of those that did not own o 

washer 2 years ago. sanple proportion of 

that recently purchased o new washer wllhlr 

2yeors. 

Price Elasticity of Demand 

Initial Price 40C 

lnlttal Proboblli'1 0.1 

New Price 65C 

New Probabllit 0.0 

Price E/astlc/fy -2.8( 

Appendix F 
Low Income Households 

Altrl:Jute 

Price 

Savings 

Capacity 

Door Placement 

Waler Temp 

Load Size 

Di'ectlons. 

Descrptkln 

Dollar value of retrnl price of 

Dollar value of annual water and energy bill 

Binary vartable: zero far standard capacity machine. one for extra large 

Binary variable: zero far front loader. one for top 

Binary variable: zero for 'cold/warm', one for 'cold/warm/hot' washing 

r 'f I I I II If 

To calculate CJ>rlce elasllclly use the "Likelihood of Purchase Model" to the left. Fill in the Initial Price and probability, and new price and probability cells In the "Price Elasticity of 

Demand" table to generate results for the specific price change 

S1milarlv. when colculalinn Sovinns elosllcllv. be sure the lnillol and new market conditions differ onlv In their sovlnos estimates. 

Low Income Households 

Nale: Includes those In sample with 

household annual incomes of $25.00J 

or lass 

Llkeltlood or Purchase Model 

Current Market 

Attribute Coefficient Cond1llons 

Intercept ·0.23026725 3 

Pf1Ce -0006588n 400 

Savings 002537415 50 

Capacity 0.36867825 0 

Door Placement 0.62377134 I 

Water Temp 1.26219647 I 

Load Size 1.59953402 1 

Sum 

··------------------------ --------------- -·------------ LfkeUhood of Purchase 

Draft 4/30/99 

Product of 

Coefficient and 

Current Market 

Value 

-0.690801756 

-26355704 

1.268707725 

0 

0.623771343 

1.262196472 

1.599534028 

1.427837412 

81% 

F-5 

Upper Bound = 0.93 

Source: Survey Ql9, 7 percent of low 

respondents claim that would go to 

rather fhon repair or purchase new 

$400. fhls assumes that respondents that chose 

repair al $150 would choose lo purchase a 

machine If repairing ware not on 

Price Elasflcly or Demand 

lnlllol Price 

lnlliol Probability 

New Price 

New Probability 

Price Elastlclty 

Savngs Elasltcly or Demand 

lnlliol Savings 

Initial Probability 

Savin 

0 

Lower Bound = 0.54 

Source: Survey Q19, proportion of people 

Income respondents that would do 

elsewhere or shop for a used machine roth 

repair at S 150 or purchase a new 

$650. ll1is assumes 11101 oil lllosa that choo' 

repair would start going to Iha 

repairing were not an 

Price Elasflcly or Demand 

Initial Price 

lnlliol Probability 

New Price 

New Probability 

Price Elasllclty 

Savngs Elasllcly or Demand 

Initial Probability 

New Savings 

New Probability 

Savin s Elasllclt 

0 

Appendix F 
REFERENCES 

Allison, Paul D. and Nicholas A. Christakis, "Logit Models for Sets of Ranked Items", 

Sociological Methodology, Vol. 24, 1994, pp. 199-228. 

Babbie, Earl. The Practice of Social Research, Fifth Edition, Wadsworth Publishing 

Company, Belmont, California, 1989. 

Currim, Irnran S., "Using Segmentation Approaches for Better Prediction and 

Understanding from Consumer Mode Choice Models", Journal of Marketing 

Research, Vol. 18, August 1981, pp. 301-309. 

Green, Paul E., and Abba M. Krieger, "Segmenting Markets with Conjoint Analysis", 

Journal of Marketing, Vol. 55, October 1991, pp. 20-30. 

Green, Paul E., and Kristiann Helsen, "Cross-Validation Assessment of Alternatives to 

Individual-Level Conjoint .Analysis: A Case Study", Journal of Marketing 

Research, Vol. 24, August 1989, pp. 346-350. 

Louviere, Jordan J., and David A. Hensher, "Using Discrete Choice Models with 

Experimental Design Data to Forecast Consumer Demand for a Unique Cultural 

Event", Journal of Consumer Research, Vol. 10, December 1983, pp. 348-361. 

Umesh, U. N. "Transferability of Preference Models Across Segments and Geographic 

Areas", Journal of Marketingr., Vol. 51, January 1987, pp. 59-70. 

Draft 4/30/99 R-1 References