T

-**;~-**



# Clothes Washer ConsumerAnalysis ·

Aprjl 1999

U.S. DepartmentofEnergy Assistant Secretary, Energy Efficiency & 

### Renewable Energy Office ofCodes and Standards Washington, DC 20585 - ---- 

# J

# ?!; pTABLE OFCONTENTS

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 ConjointAnalysis 

### 2-2 ConjointSessionDesign 

### 2-2 Full Profile ConjointAnalysis 

### 2-3 Conjoint SampleMethod 

### 2-4 ResponseRates 2-5 PurchaseProbability 

### 2-6 EquipmentChoice Model 

### 2-7 Purchase Scenarios 

### 2-9 ProbabilityScenarios 2-9 Elasticity Scenarios 

### 2-11 Summary 

### 2-12 3 FOCUS GROUPRESULTS 

### 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 SurveyResults 

### 4-2 Demographics 

### 4-2 ClothesWasher DemandCharacteristics 4-5Section 5

6 TABLE OF CONTENTS (continued)

CONJOINTANALYSIS RESULTS Page 5-1 Introduction 5-1 EquipmentChoice ModelResults 5-1 CardAttribute Definitions 5-2 EquipmentChoiceModel Approach 5-2 Importance Statistics 5-3 Overall EstimationResults 5-4 SampleSubgroup ComparisonofCoefficientEstimates 5-5 Review ofRelative ImportanceResults 5-7 Purchase Scenarios 5-9 PurchaseModel EstimationResults 5-9 Purchase ProbabilityScenarios 5-11 Full Sample ProbabilityEstimates 5-12 Regional ProbabilityEstimates 5-13 ElasticityEstimates 5-14 Full Sample Results 5-14 Low Income Households 5-16 Discretionary ClothesWasher Purchasers CONCLUSIONS 5-16 

### 6-1 APPENDICES A FOCUS GROUP ATTRIBUTE RESULTS A-1 B TOP 5ATTRIBUTES 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-1LIST OF EXHIBITS

Exhibit Page 

### 2-1 Hypothetical Example- ConjointDataUsing thePurchase Card 

### 2-7 For PurchaseProbabilityEstimation 

### 2-2 HypotheticalExample

- IndividualRanked ConjointDataFor 

### 2-9 EquipmentChoiceEstimation 

### 3-1 DOE ClothesWasher Focus Group Response Rates 

### 3-2 3-2 Focus Group Results 

### 3-3 MostImportantClothes Washer Attributes 

### 3-3 Top Twelve Clothes Washer Attributes Listed byConjoint 

### 3-5 Respondents 

### 4-1 SamplingResults 

### 4-2 4-2 Income Distribution 

### 4-3 4-3 Etlmic Background ofRespondents 

### 4-3 4-4 DistributionofEducational Achievement 4-4 

### 4-5 Distribution ofExpected Price for a New ClothesWasher 

### 4-5 4-6 Distribution ofDesired Price Range for a NewWasher 

### 4-6 4-7 Choice Sensitivityto Price Change 

### 4-6 All Respondents 

### 4-8 OptionChoice Sensitivity to Price Change 

### 4-7 RespondentswithIncome Less than 

### $25,000 Per Year 

### 4-9 OptionChoice Sensitivity to Price Change 

### 4-8 Respondents Over 

### 65 Years ofAge 5-1 Attribute Definitions 

### 5-2 5-2 Regression Coefficients andRelative Importance Statistics 

### 5-4 All Respondents 

### 5-3 Regression CoefficientEstimates for DemographicSubgroups 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 CoefficientEstimates 

### 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 ofclothes 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 valuesplaced on the clothes washerattributes canbe 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 providedinthe following chapters. Focus Groups Focus groupswereused to identifythemostimportantclothes washerattributes 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 tenmostimportant attributes ifthey were to purchase a new clotheswasher 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 laundryfor 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 ' Draft4/30/99 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 attributesbyconsumers. 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. Inthis 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 ofconjoint analysis is that it makes respondents evaluate all six attributes simultaneously and requires them to make tradeoffs,just as theywould ifthey were actuallypurchasing a newclotheswasher. The ranking information obtained during the conjoint sessions was used to estimate a logit model ofclothes washer purchases.I By examininghow 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 washerwasalso determined. Along·with the conjoint analysis, respondents at these sessions were given a short survey that contained a variety ofdemographic questions as well as questions directed atwhether theywouldrepair a brokenwasher 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 couldbeconductedfor differentpopulation 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 ofpurchase. 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 dependentvariable that is discrete reflecting two ormore distinct choices while the dependent variable ofa 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.

Draft4/30/99 1-2 Executive Summarythe 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 canbe simulated and the effect onpurchases estimated. By examining how much the probability of purchase changes, we can evaluate how sensitive consumers are to changesinprice and otherwasher 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 loadingmachines withhotwaterwash capabilityor top loadingmachines with no hot water capability. These high efficiency options were designed to coincide with an approximately40 percentimprovementin efficiency. Thirdly, the data were used to calculate the price elasticity ofdemand based on the conjoint analysis results. Price elasticity is a measure how much demand for a good · changes with a change inprice while holding everything else constant. Inthis analysis, price elasticities were calculated starting at the standard efficiency case and calibrating the purchase probability to the market purchase probabilitybased 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 resultedinthe 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 importantclothes 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. Whenprice 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

Draft4/30/99 1-3 Executive Summarysignificantly. 1bisis due to the fact that consumers value energy savings more than top load doorplacement. 

### 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 doorplacement as one ofthe 10 mostimportantattributes 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 ifthey were going to buy a new clothes washer. For these people, door placement was tied for lastinterms ofimportance, comprising only 8percentoftotal 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 thatprovides no annual efficiency savings (see Exhibit 5-8 for more details). 

### Demand is more inelasticfor those that currently own washers. Both the survey and conjoint results indicate that demand is more inelastic for respondents who currently own clothes washers thannon-owners, even withlarge 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 ifitbreaks than to start doing their laundryat 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 optto gettheir machine fixed orpurchase a new clotheswasher. As a result, 0.98 canbe considered an upper bound for the likelihood of purchase under current market conditions andis used as the startingpointfor 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 amonghigh income respondents (those with incomes at $75,000 or above), no respondents were willing to start doing their laundryelsewhere. 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 currentwasher 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 wouldresultin onlya 1.6 percent decrease in demand for clothes washers.

Draft4/30/99 1-4 Executive SummaryElasticities were also calculated for those in the sample that do not currently own a clothes washer orthatpurchased 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, theprice elasticity is-1.92,whichreflects anelastic demand or demand for washers that is sensitive to price. In this case, a 10 percent increase in priceresults ina 19 percentdecreaseindemand. 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 areincluded as appendices attheend ofthis report.

Draft 4/30/99 1-5 Executive Summary2. 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 ofpurchasing 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 changeinutility. 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 itallows 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 ofquestions designed to get at sensitivity to clotheswasher 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 chapterofthis 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 onpast conjoint analysis recruiting efforts. Following this, the

2In order to verify the reliability ofthe six attributes respondents were asked to list, at the beginning ofthe 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 surveyare discussed in the Survey Results chapterofthis report.

Draft4/30/99 2-1 Methodologyconjoint analysis technique is described as well as the model specification .for estimating the purchase probabilities. Finally, probability calculations are presented andpotentialmarket 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 ofa list ofall 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 identifywhich ten attributeswould most significantlyinfluence their selection ofa new clothes washer, assuming they had made the decision to purchase a new clothes washer. These responseswere thentotaled across all tenfocus groups to arrive at a list ofthe six mostimportantclothes 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 groupparticipants were recruited randomlybyphone to ensure a mix ofdemographic types. Each respondent waspaid a $50 incentive for participatingin 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 purchaseprobabilities and utility.
CONJOINTANALYSIS 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 moderatorrelied on a list of attributes provided by the clothes washer working group to the Department ofEnergy. Theworking groupconsisted ofmanufacturers, advocates, utilities, and otherinterested parties

. 

# . Draft4/30/99 2-2 Methodologymost 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 optionsbased onthe 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 theycould 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 valuedby 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 with4 to 6 attributes on each card. Including more cards or additional attributes tends to overwhelmrespondents 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 correlationbetween 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 studywas 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 ofmaking 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.

Draft4/30/99 2-3 MethodologtJCONJOINTSAMPLING 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 ofphone 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 werepaid 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 of400.

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 ofless 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 betweenlow incomehouseholds and the rest ofthepopulation. 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 ofthe sample. As a result, the targeted sample contained 60 respondents fromboththe 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 of550 to estimate the potential demand for furore cultural event using conjoint. Given these sample sizes, the proposed sample of400 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 MethodologyResponse 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 completionrates are described: A quick review of the survey literature will uncover a wide range of response rates

....Even so, it'spossible to state some rules ofthumb about returnrates. I feel that a response rate ofatleast 50 percentis 

### 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 highresponse 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 inrecruitingbetweenphone andon-site surveys are discussedbelow. 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 investmentfor respondents, sometimes 3 or4hours or more. After an initial refusal, repeatedlycallingis veryunlikely 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 arerecruited that actually showup for the conjoint session. To maximize attendance for on-site surveys, it is general practice to· send outreminder 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 onour experience, this method maxiinizes the completion rate for on-site surveys. Efforts beyond this, such as additional reminder phone calls, tend to resultin an even lower completion rate as respondents become irritated and are discouraged from participating.

Draft4/30/99 2-5 Methodology---------

The remainder of this chapter describes how the conjoint analysis results were used to estimatepurchaseprobabilities 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 equationform, this is given by: Purchase (0,1) = a.+**1: ~/Xii** + Eii WherePurchase = 1ifcardis ranked above thePurchase Card, 0ifrankedbelow X..=Value ofattributeJ. oncardi

IJ**~-=Coefficients** to be estimated J a. = Constant to be estimated Eii**~** randomerror termassumed to belogistically distributed A hypothetical example ofone individual's conjoint data used to estimate the purchase probabilityis showninExhibit 2-1. The shadedregionhighlights 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 equipmentpurchasesProb(Purchase) is givenby: Prob(Purchase) = exp(WX) 

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

Draft4/30/99 2-6 MethodologyRank

5 6 7 8 9 10 11 12 13 14 15 16 Exhibit2-1 HypotheticalExample

- ConjointData Using the Purchase Card For Purchase Probability Estimation

Door Purchase Price Independent Variables
"aoacitv Water Temoerature Load Size Ad"**~'iJ,;~Jt4i~~~~~ ~:t:fu~:'.'\,~**  '**:~~Vi1o~AfY?/** :**·::~'F.iJm:~w ~~!sttialiH:!K!:** **,·~;t1:wJJ~t-'1C6fci~~~~1J :;it:tN'cih:YAdi~b"iE;** **·~~~ ~:::~~j~~~f:~~~~·** **~~1t400!:~~~** i

..**·~~R~'.~Sifi1~r ~~::~~~f~~~~** **(~Siailaa@~1****~W-H6t~w~~:-&l~*~t****/~~~Mi~bi~-=~:1.ifi·~-~~ :~'i::t-t~1~~** **::v.~400'7:~ ·;;f.']~:.:f8\:!1~:** **:::~~Firiri'~~:j :\~s~'Cf1iffi;~** **;:,1wH'c;e:w-r~-i?Gg1ij~ ~j\1lfNcin~:Adf~ill~t;~** **·::f4tt£~11:%~;E~1 .t~~400~~:~t1,** **;:'~TI~~~a1±~ :::~~~~rr~t~H** **~$~rid~~(~ )~li2;~~~w~~~C61H~~** **~4~~~ii~~br~;~~~;~.)~~~** 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 Adiustable 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 ofthe washerwhile Savings is the annual dollar amount thatthewasherwill save offthe combined energy and waterbill. Door Placement can take on two values, either "Top" indicating a top loadingmachine, 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 onthe cards. This provides an estimate ofhow attribute levels affect card rankings and is used to estimate total utility for each equipment option. Using a slightly different version ofthe logit model, the rankings ofthe cards are regressed against the attribute levels onthe cards. This has a different appearance than the standardconditional logit

Draft4/30/99 2-7 Methodologysince 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 tobeestimatedin this stageis givenby:
Ranki = L1

### Pi'\+Eii Where Ranki = Therankingofcardi Xij =Valuefor attributej from card i 

## p. =Coefficients to be estimated

J

. Eii =Randomerror term assumed to belogistically 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 andhighefficiency equipmentoptions. Using the logit density function, the probability ofchoosing any option n among M different equipment options given that a purchaseis 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.

Draft4/30/99 2-8 MethodologyDependent Variable 

# I Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 

# I Exhibit2-2 

### Hypothetical Example

- IndividualRanked ConjointData 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

PURCHASESCENARIOS 

# 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 ofpurchasing a washer increases or decreases with changes in washer attributes. The analysis of purchase scenarios using the purchaseprobabilityestimatesis discussedbelow. 

### Probability Scenarios The probability estimates are used to estimate the change inpurchases due to changes in clothes washer design resulting from an efficiency standard. To evaluate the effect these changes will have onpurchases, the purchaseprobabilities were calculated using the values for attributes of the original equipment before the standard. For example, thepre-standardpurchaseprobabilityis givenby: Prob 1 = exp(WX) 

### I (l + exp(WX) )**Where~=** Estimated coefficients from the purchaselogit

Draft4/30/99 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-standardvalues for price, savings, and otherequipmentattributes: Prob2 = exp(j3'X) 

### I (1 + exp(j3'X) ) Where 

### f3 = Estimated coefficients from thepurchase logit X = Attribute values such as price, savings, axis orientation for the post-standard equipmentdesign. The effect of the standard can be shown by comparing the difference in purchase probabilitiesbefore and after the**stand~rd:** Effect ofStandard = (Prob 1 - Prob2) 

### I Prob 1• 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 othermarketfactors notexplicitly addressedinthe 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 popularholdingpriceconstant: ProbAl = exp(WX) 

### I (1 + exp(l3'X) ) Where

## f3 =Estimated coefficients from the logitpurchase model X = Pre-Standard equipment attributes (vertical axis) for choice A with the poststandardequipmentprice. Thepost-standardpurchaseprobabilityfor OptionA is givenby:

Draft 4/30/99 2-10 MethodologyProb A2 =exp(f3'X) 

### I (1 + exp(f3'X) ) Where 

### J3 = Estimated coefficients from thelogitpurchasemodel X = Post-Standard equipment options (horizontal axis) with the post-standard equipmentprice. The same method is used to calculate pre-standard and post-standard purchase probabilities for OptionBwithandwithoutthehotwater attribute: Prob81 = Purchase probability with pre-standard options (hot water) and post standardprice. Prob82= Purchaseprobability withpost-standard options (no hotwater) andpoststandardprice. In this hypothetical example, the effect of the standard on these two diffe_rent equipmentoptionscanbe evaluated the same as before. Suppose that theoptions have the followingprobabilities: 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 ofOption Bthe 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 andwill haveless ofa detrimental effect onpurchases. 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 calculatedby: Price Elasticity= (6. Prob/ 6. Price)* (Pricemid/ Probmid) Where 6. Prob = Changeinpurchaseprobability due to the change inprice
6. Price= Changeinpriceresulting from t_he efficiency standard Probmid 

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

Draft4/30/99 2-11 MethodologyPricemid = Midpointbetweenpre-standard andpost-standardprice. This number reflects how sensitive the purchase probability is to changes in price. In general, an elasticity with an absolute value ofless than one is considered inelastic or insensitive to changes inprice while an elasticity greater than one is elastic or sensitive to changesinprice. By calculating the elasticities, the sensitivity ofwasher 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 standardwill 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 completionrate is a reasonable expectation given thatrepeated call backs are notanoption. The results ofthe focus groups sessions as well as the conjoint sessions are presented in the remainingchapters ofthis report.

Draft4/30/99 Methodologi;3. FOCUS GROUPRESULTS

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 ofimportant 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 mostimportantin selecting a specific clotheswasher. 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 ineach city, with a total of 100 focus group participants divided evenly across the ten sessions. Focus group participantswere recruited.randomlybyphone to ensure a mix of demographic types. Each respondent was paid a $50 incentive for participatinginthe focus group.
FOCUS GROUPSAMPLE 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 ensuringthat a mix ofdifferent income and ethnicbackgroundswere represented inthe focus groups,there wasno sampling quota setfor these groups.

Draft4/30/99 3-1 Focus Group ResultsExhibit3-1 DOE Clothe$ WasherFocus Group ResponseRates
City Calls Recruited % Attendees Completion %of Recruited Rate Calls WashingtonDC 793 27 3.4% 16 59.3% 2.0% SanFrancisco 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 ofthe clothes washer study. The recruitment rate reflects the number ofpeople 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, completionrates tend to bemore 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 ofless than $25,000 while 20 percentcame from high income homes makingmore than $75,000 a year. Different age and race groups were also well represented in the focus groups. Ofthe 90 attendees, 12 percentwere 65 years old or older while 36 percent were in the 18 to 34 years old range. Non-white attendees comprised 26 percent ofthe focus group sample, withAfricanAmericans 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 7percent completed trade orvocational school. The majority ofparticipants, 87 percent, lived in households of three or fewer members, with 38 percent living in households oftwo. Abouthalfoftheparticipants owned theirhomes, andhalfrented.

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 percentfor the Washington DC groups.
Draft4/30/99 3-2 Focus Group ResultsFinally, although the majority ofparticipants 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 washerwere able to provide their inputto the analysis.

FOCUS GROUPRESPONSES 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 importantin clothes washers. The moderatorwould 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 newclothes washer. Exhibit 3-2 shows the top 12 attributes mentioned from all ofthe focus groups as well as thepercentage ofrespondents who listed that attribute. A complete list ofall attributes as well as the attributesbrokenoutbylocationis given inAppendix A.

Draft4/30/99 

### Exhibit 3-2 

### Focus GroupResults MostImportant Clothes WasherAttributes

Percentof Frequencyin 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% DoorPlacement 38 42% Quiet Operation 36 40% Wash Time 34 38% Warranty 33 37% Multiple Wash Cycle Options 30 33% Horizontal/VerticalAxis 25 28%

3-3 Focus Group ResultsPrice wasmentionedmostoften as animportantattribute for selecting a clothes washer, with 75 of90 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 mentionedby54 ofthe 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 thathorizontal axis machines canbe either front or top loading. The focus group discussions tended to center on doorplacementrather 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 orientationin thefinal attribute listis consistentwith 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, whichwould also tend to overwhelmrespondents. 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 importantto 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, whichis theprimarypurpose ofthis analysis. Ofthe most importantattributes from the focus groups, the ones that are most likely to be affected by an efficiency standard areprice, energy and water costs, doorplacement, capacity, and water temperatures. As shown in Exhibit 3-2, these five attributes placed in the top seven attributes in terms ofimportance 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 thatmeans different things to differentpeople, nota tangible clothes washer attribute like top or front door placement. In addition, durability is unlikely to change as the result ofan efficiencyrule. Based on the focus group results, clothes washer price, energy and water savings, capacity, water temperature, doorplacement, 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 attributeswereranked among the verymostimportantattributes among consumers.

Draft 4/30/99 3-4 Focus Group ResultsAs 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 comparisonwith the list derived during the focus group discussions where respondents were allowed to express opinions to the group.

Exhibit3-3 Top Twelve Clothes WasherAttributesListedby ConjointRespondents

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% WaterTemperature Options 27.0% Machine Size 26.3% Brand 20.7% Load Size Options 19.1% Reliability 17.0% Quiet Operation 15.2% Warranty 15.2% Door Placement 13.5%

The results of this survey are shown in 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 mostfrequently 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 attributesranked 14th, 19th and 49th onthe conjoint sessionrespondentlist. 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.

Draft4/30/99 3-5 Focus Group Results4. CONJOINTSESSIONSURVEYRESULTS

INTRODUCTION Inaddition to rankingthe clothes washer attributesby sortingthe 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 demographicinformation. 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 thatindicated onthe survey that they would consider purchasing a horizontal axis machine. A complete copy of the survey and a summary of the participantresponses canbefound inAppendix 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, thelist ofimportant attributes elicited during the conjoint sessions is compared with that derived during the focus group discussions. These surveys were. conductedindependentlyfrom the conjoint analysis.

Draft 4/30/99 4-1**Surv~** ResultsCONJOINTSESSIONSURVEYRESULTS Demographics


# I Group 

# I
East Region SouthRegion Mid-WestRegion West Region Young Elderly
·Low Income 

### Exhibit4-1 SamplingResults

Target Percent of Definition Target Sample Sample Washington, DC 100 22.2% Dallas,TX 100 26.2 Madison, WI 100 26.2 SanFrancisco Bay 100 25.3 Area, CA 18

- 24 Years 60 12.8 65 and Older 60 15.8 AnnualIncome < 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 ofthe 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 showninExhibit 4-1 above. The actual samplepopulationwas a little more than 15 percent elderly people, coming within one-halfof one percent of the target percent. The actual sample was about 12 percent young people, which fell 3 percent short ofthe target. Nearly one-quarter of the sample population had incomes below $25,000 peryear, well above the target of23 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-westregions composedjust over 25 percent of the total sample each. The eastregion was a little smaller, at 22 percent, and the westregion was the.largest, at27 percent.

Draft4/30/99 4-2 Survey ResultsIncomeRange
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 ofTotal Sample

6.2% 18.3 31.9 24.5 9.6 8.7 0.4 Percent ofthose 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 ofincome 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 6percentofthe sample earningless than $10,000peryear. A little more than 18 percent ofthe sample earned $75,000 peryear or more, with about 9 percent earning more than $100,000·per year. Well overhalfofthose surveyed (56 percent) hadincomes 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 percenthadincomes over $75,000. Exhibit4-3 EthnicBackground 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 ofnon-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

Draft4/30/99 4-3 Survey Resultsmajority (37 percent) between $25,000 and $50,000. Among those over 65 in age, 21 percentwere ofnon-Caucasianethnicity. Exhibit4-4 Distn1Jution of EducationalAchievement
Education All Respondents SomeHighSchool 1.2% HighSchool Graduate 8.5 Some College 23.9 CollegeGraduate 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 mostofthe 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 thathad completed trade orvocational schoolingwas quite small, between 2percentand 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 childrenbetween 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 halfhadjust one child, and 82 percenthad two or fewer.

· Seventy-onepercentsenttheir children topublic 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

Draft4/30/99 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 aNew Clothes Washer
All Respondents Over65 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 ofthe 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 thoughtitbetween $350 and $449. Overall, among those who had recently purchased a machine, 98 percentthought theprice wouldbebetween$350 and $649.

Draft4/30/99 4-5 Suroey ResultsExhibit4-6 Distribution of DesiredPrice Rangefor aNew 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 inwhatprice range would they like to stay within ifthey were to purchase a newwasher. Specifically, the question stated "Theprice ofa new clothes washerranges from a low of $200 to a high of $1,200. Ifyou were to purchase a new clothes washer today, what price range would you hope to stay within." Exhibit 4-6 shows the distribution ofresponses 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 34percentofrespondents. 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 stayinthe $200to $349 range and the $350 to $499 range. Exhibit4-7 Choice Sensitivity to Price Change AllRespondents Option NewWasher NewWasher Cost$400 Cost$450 Purchase thenewmachine 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

Draft4/30/99 4-6 NewWasher Cost $650 16.3% 74.7 2.6

5.2 1.1

Survey ResultsPosed 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 washerwasimagined to be 10 years old and had anexpected life of 15 years. They were offeredvarious optional approaches to this problem; they could:

• fix thewasher,

• buya newwasher,

• do laundry somewhereelse (notreplace orfix),

• 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. Ata price of$650, only 16 percentopted to purchase the newmachine. Nearly 75 percent chose to repair their machine, and 3 percent would do their laundry someplaceelse. Exhibit4-8 Option Choice Sensitivity to Price Change Respondents withIncome Less than $25,000 Per Year Option NewWasher NewWasher Cost $400 Cost$450 Purchase the newmachine 54.4% 42.7% Fix old machine for $150 37.9 47.6 Do laundry somewhereelse 4.9 3.9 Shop for a usedmachine 1.9 3.9 Refused/Don'tKnow 1.0 2.0 NewWasher 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 percentstated they would repair the old machine, and only43 percent

Draft4/30/99 4-7 Survey Resultswould purchase the new machine. The number choosing to do laundry someplace else actually falls slightly, to 4percent. At a price of$650, most (70 percent) oflowerincome

·respondents choose to fix the old machine, 12 percent would purchase the new machine. Atthisprice, 9percentstate they would choose to do laundrysomeplace else. Exhibit4-9 Option Choice Sensitivity to Price Change Respondents Over 65 Years of Age Option NewWasher NewWasher Cost $400 Cost$450 Purchase the newmachine 76.1% 67.2% Fix oldmachine for $150 20.9 31.3 Do laundrysomewhere else 0.0 0.0 Shop for a used machine 1.5 0.0 Refused/Don'tKnow 1.5 1.5 NewWasher Cost$650 31.3%

65.7 1.5 0.0 1.5
Exhibit 4-9 is similar to Exhibits 4-7 and4-8, 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 newwasher 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 Results5. CONJOINTANALYSISRESULTS

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 thatpurchaseprobabilities and utility couldbe estimated for different demographic groups. The results were also used to evaluate the relative importance ofeach ofthe 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 ofsubgroups, 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 ofeach attributeto 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 subgroupswithinthe sample.
EQUIPMENTCHOICEMODEL 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 ofits meaning. Overall estimationresults 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 ConjointAnalysisResults

### Card Attribute Definitions

Variable Price Savings 

### Exhibit5-1 AttributeDefinitions
Description Dollar value ofretail price ofmachine Dollar value ofannualwaterand energybill savings K:apacity**~inary** variable: zero for standard capacitymachine, one for extra large capacity. DoorPlacement Binary variable: zero for front loader, onefor top loader. !WaterTemp. Binary variable: zero for 'cold/warm',one for 'cold/warm/hot' washing temperature options Load Size Binary variable: zeroifthere is no load size adjustmentoption, one ifthereis
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 anypotentialbias due to the card presentation. The six attributespresentedon each cardwere as follows:

• Price

- retail cost ofclothes washer ($400, $450 or $600),

• Sav:ings

- annualenergyandwaterbill savings ($0, $10, or $50),

• Capacity

- standard orextra large capacity,

• Doorplacement

- Indicates either top orfront doorplacement,

• WaterTemperature

- Indicates whetherhotwateris available, either hot, warm and cold washcycle options, orwarmand 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 mostpreferred to the least preferred. When all the conjoint data were collected, the attribute levels on the cards wereregressed against the rankings ofthe cards:

Draft4/30/99 5-2 ConjointAnalysis ResultsRank= 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 ofeach card. Price; = Value for price oncardi Savingsi =Valuefor energy and water savings oncard i DoorPlacementi = 1for top loading, 0for front loadingoncard i Capacity; = 0for standard capacity, 1for large capacity oncardi WaterTempi = 1for hotwater available, 0ifno hotwateroncard i LoadSizei = 1ifAdjustable, 0ifnonadjustable oncardi**~·** = Coefficients to be estimated

J : Ei=randomerror term assumed to be logistically distributed Importance Statistics The results ofthe 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, theimportance statisticis defined as: IMP1= /1Yi = 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

Draft4/30/99 5-3 ConjointAnalysisResultsway, the importance statistic measures each attribute's contribution to the total utility based onthe six attributesincluded 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 statisticarereviewed.



# I Exhibit5-2 Regression Coefficients andRelativeImportance AllRespondents 

# 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 witha 99 percent degree ofconfidence. 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 ofthe price coefficient since savings is measured in tens ofdollars 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 combineboth the coefficient and attribute value to ·get an overall measure ofthe influence on total utility. The relative importance statistic can be interpreted as each attribute's contribution to total utility.

Draft4/30/99 5-4 ConjointAnalysis ResultsThe relative importance statistics show that while price is the most important attribute to consumers,itjustbarelysurpassesadjustableload size in terms ofimportance 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 lastin terms ofimpact on total consumer utility, at7percent. 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. Exhibit5-3 Regression CoefficientEstimatesfor Demographic Subgroups

CoefficientEstimates Would Would Not Full Low Recent HaveTried 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 DoorPlacement 0.383 0.330 0.352 0.460 0.300 0.303 0.264 0.709 WaterTemp. 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 prominentdifferences among segments. The subgroups shownabove include:

• lowerincome ($25,000 peryear or less),

• elderly (65 and older),

• young (18 to 24), thosewho recentlypurchased a clothes washer (withinthe past two years) ,
• those who have tried a horizontal axis machine,

• those thatwould considerpurchasinga horizontal axis machine, and

Draft 4/30/99 5-5 ConjointAnalysis Results• those thatwouldnotconsiderpurchasinga horizontal axis machine. Exhibit5-4 Regression CoefficientEstimates forRegional Subgroups

Coefficients Estimates
Full Washington San Madison Dallas Francisco Sample DC BayArea 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 WaterTemp 0.614 0.768 0.616 0.523 0.638 Load Size 0.852 1.008 0.756 0.836 0.916 Conjoint sessions wereheld infour cities: Madison (Wisconsin), WashingtonDC, Dallas (Texas) and the San Francisco Bay Area (California). Roughly onehundred participants completed the ranking exercise in each city. Coefficient estimation results for the regional subgroups, as well as the full sample, are showninExhibit 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 ofthe 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 largestfor youngpeople across the subgroups does notnecessarily implyyoungpeople place the highestrelative value onsavings. 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 andpeople in Madison are most sensitive to changes inprice. Those who have recently purchased a newwasher 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.

Draft4/30/99 5-6 ConjointAnalysis ResultsWhile most ofthe subgroups examined have a coefficient estimate for capacity ofabout 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 capacitywas for youngpeople,0.38. Conversely, the group with the smallestcoefficient for capacitywas those over 65, at0.12. 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 WashingtonDC, witha coefficient of0.19. 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 of0.77 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 coefficientwas 0.65, for the lowincome group. Review of Relative Importance Results Exhibit5-5 RelativeImportance StatisticsforDemographic Subgroups 

# D Would Would Not Full Low 18-24yrs 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 onan attribute.

Draft4130/99 5-7 ConjointAnalysisResultsExhibit5-6 RelativeImportance 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% WaterTemp 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 mostimportantsingle attribute, albeitbya slimmargin. Low incomepeople 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 percentoftotal 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 lowvalue on savings, at 8 percent oftotal utility, and youngpeopleplaced a veryhighvalue onsavings, 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 correlationbetweenage and the relativeimportance ofclotheswashercapacity. 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 ondoorplacement, 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.

Draft4/30/99 5-8 ConjointAnalysis 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 ofyoungpeople, theremaining 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, adjustableload size ran a distant second to price: 30percentfor price, and 20 percentfor adjustableload size.

PURCHASE SCENARIOS As discussed in the Methodology, the conjoint results can be used to estimate the likelihood ofpurchasing a clothes washerbased on the placement ofthe 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 subgroupswithin the sample. Purchase Model Estimation Results During the conjoint analysis, once respondents completed ranking their cards, they were asked to place the Purchase Cardin the card set to indicate which ofthe 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 onthecards above andbelow the Purchase Card. Inequationform: Purchase (0,1) =

a.+ f3'Pricei + f3'Savingsi + f3'DoorPlacementi + f3'Capacityi + f3'WaterTempi + f3'LoadSizei + Ei Where Purchase = 1ifcard is ranked above the Purchase Card, 0ifrankedbelow Pricei = Value for price oncard i Savingsj =Value for energy and water savings on card i DoorPlacementi = 1 for top loading, 0for front loading on cardi Capacityi = 0for standard capacity, 1for largecapacity oncard i WaterTempi = 1for hotwater available, 0 ifno hotwater on card i

Draft4/30/99 5-9 ConjointAnalysis ResultsLoad Sizei 

## =1 ifAdjustable, 0 ifnonadjustable oncardi
Bi = Coefficients to be estimated

a= Constant to be estimated Ei= randomerror term assumed to belogistically distributed

Exhibit5-7 PurchaseModel CoefficientEstimates 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 inthe equipmentchoice model used to estimate relative importance ofattributes. 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.

Draft4/30/99 5-10 ConjointAnalysis ResultsPurchase Probability Scenarios Once this model was estimated, theprobability ofmaking a purchasewas calculatedby combining the coefficient estimates with the six washer attributes and plugging in the logitprobabilityfunction: 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 onutilityofanefficiency standard.

Exhibit5-8 Purchase Scenariosfor Demographic Subgroups

High HighEfficiency HighEfficiency Sample Group Standard Medium Efficiency HighEfficiency FrontLoad, · FrontLoad, Efficiency Efficiency No HotWater Constant Price Front Load ConstantPrice**&NoSavin~** Fullsample 0.59 0.58 0.36 0.21 0.75 0.42 LowIncome 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 RecentPurchasers 0.55 0.54 0.33 0.17 0.67 0.40 HaveTried H-Axis 0.57 0.56 0.39 0.19 0.78 0.45 Would ConsiderH-Axis 0.56 0.55 0.42 0.21 0.78 0.43 Would NotConsiderAxis 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 ofprice 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 ofExhibit 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

Draft4/30/99 5-11 ConjointAnalysisResultswilling 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 inutility, since lower utility washer configurations will have a lower likelihood ofbeingpurchased. 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 ofpurchase. This indicates that theincrease in savings to $10 is enough to offset the increase inprice 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 ofpurchase. This results from the greater change inprice 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 of39 percent. Ifthe machine is designed to run without hot water instead ofbeing 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 ofpurchase is the combined result of changes in price, savings, and either door placement or water temperature options. As discussed in the analysis ofthe importance statistics, for the overall san;:tple, p_rice plays the greatest role in influencing utility, followed by water temperature and doorplacement. This also canbe seenwhenequipment options are changedbutpriceis held constant. When price is held constant and compared with the other estimated probabilities, the importance ofprice is apparent. Exhibit 5-8 also shows the likelihood ofpurchase for high efficiency machines thathave 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 estimatedpurchaseprobability falls almost 30 percentfrom 0.59 to 0.42.

•'

,, Draft4/30/99 5-12 ConjointAnalysis ResultsRegional Probability Estimates

Exhibit5-9 PurchaseScenariosforRegionalSubgroups

Standard Medium HighEfficienc HighEfficiency HighEfficiency High Efficiency Sample Group FrontLoad, Front Load, Efficiency Efficiency FrontLoad No HotWater ConstantPrice Constant Price R. ..,_**c:~**..:-- Washington, DC 0.57 0.58 0.50 0.29 0.75 0.48 San Francisco BayArea. 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 samepurchaseprobability analysis was donebybreakingthe sample outbyregion. These results are showninExhibit 5-9. Given the overall sampleprobability estimate of 0.59 'for a standard efficiency n;iachine, both the Washington DC and Dallas estimates arebelow the sample average while Madison and the San Francisco Bay Area are above the sampleaverage. 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 Madisongroup, movingfrom standard efficiency to front loadhigh 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 thepurchaseprobabilityby55 percentfor 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 doorplacementare allowed to vary. Whenprice is held constant, Madisonhas a 32 percent increase inpurchase probability. This indicates that the decrease in probability going to highefficiency is due primarily to theprice 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.

Draft4/30/99 5-13 ConjointAnalysis ResultsELASTICITYESTIMATES 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 ofhow 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 happenwhenprice changesbuteverythingelse 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 elasticityis calculatedby: PriceElasticity =**(~** Prob

### I**~Price)** * (Pricemid/ Prob mid)**Where~** Prob=Changeinpurchaseprobability due to the change inprice**~Price=** Changeinprice resulting from the efficiency standard Probmid = Midpoint between pre-standard and post-standard purchase probability Pricemid = Midpointbetweenpre-standard andpost-standardprice. This measures howmuch demand changes with a change inprice. Using this equation, an elasticity result greater than one in absolute magnitude means that demand is price elastic, orrelatively sensitive to changes inprices. Stated another way, when demand is elastic, anincrease in price of 10 percent will cause demand to decrease by more than 10 percent. Similarly, whenelasticity is less than one demandis considered inelastic. Inthe 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 ofowning a clothes washer

Draft4/30/99 5-14 ConjointAnalysis Resultsat all or the unwillingness of current owners to give up their clothes washer are not takeninto accountin the elasticityestimate. 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 currentwasher 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 ofpurchase is estimated at90 percentor 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. Only2percent ofrespondents said that they would start doinglaundry somewhere else, and the remainder opted to purchase a new or usedmachine or getthe old.machine fixed. The survey questions provide a starting point for calibrating the purchase probability and to calculate priceelasticities. The calibrationis donebyadjusting the constantterm 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 arehomeowners that do laundry athome. 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 thathasbroken and are looking to replace itrather than start doingtheir laundryoutside thehome. The preceding case assumes that everyone who stated they would repair or purchase a used machine wouldpurchase a newmachine at$400 ifrepairing 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 lowerbound 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 ofpurchase of0.64. 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 percentincreaseinprice results in a 16 percent decrease in clothes washer demand.

Draft4/30/99 5-15 ConjointAnalysisResultsLow 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 upperbound for the purchase probability of the low income group. When faced with the choice ofpurchasing 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 startingpoint, 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 householdsin 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 orwhohave onlyrecentlypurchased a clothes washer for the first time. Since these people are currently doingwithout a clothes washer, they canbe considered discretionary purchasers since they may or not be in the market for a new clothes washer. A separatelikelihood ofpurchase 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 upperbound likelihood ofpurchase 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 inthepopulation. Given the upper bound purchase probability of 0.59, the price elasticity estimate for discretionarypurchasers is -1.92. This reflects a greater sensitivity to price and is more elastic than the estimate for currentwasher owners.

Draft4/30/99 5-16 ConjointAnalysis ResultsThe priceelasticityfor discretionarypurchaserswas also estimated usinga lowerbound 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 lowerbound, the estimatedprice elasticity of-2.80, showing an even greater sensitivityto 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 ofpurchase (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 discretionarypurchasers.

Draft 4/30/99 5-17 ConjointAnalysisResults6. CONCLUSIONS

The objective of this studywas 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 inbothfocus 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

### Ipurchase 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 discussedbelow.
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 percentincluded 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 Exhibit5-2 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 doorplacement.
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 doorplacement as one ofthe 10 mostimportant attributes theywould 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 Conclusionsiftheywere going to buy a new clothes washer. For these people, door placement was tied for lastinterms ofimportance, comprisingonly 8percentoftotal 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 thatprovidesno annual efficiency savings (see Exhibit 5-8 for moredetails). 

### Demand is more inelasticfor those that currently own washers. Both the survey and conjoint results indicate that demand is more inelastic for respondents who currently own clothes washers thannon-owners, even with large increases inthe clothes washerprice. According to survey results, those consumers that currently own clothes washers are muchmore likely to replace their washer ifitbreaks than to start doingtheir laundryat a laundromat. Of those surveyed, only 2 percent said that iftheir machine broke they would startgoing to the laundromatordo their laundry somewhere else, while the rest optto gettheir machinefixed orpurchase a new clothes washer. As a result, 0.98 canbe considered an upper bound for the likelihood of purchase under current market conditions andis used as the startingpointfor calculating thepriceelasticity. 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 startdoing their laundry somewhere else while amonghighincome respondents (those with incomes at $75,000 or above), no respondents were willing to start doing theirlaundryelsewhere. 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 ofpurchase probabilityof0.64 results in a moreelastic estimateof-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,butdemandis still consideredinelasticwithanupperbound estimate of-0.53. For the lowerbound,priceelasticityis 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 inpurchases for this sector ofthe market. For low income segments, the same 63 percentpriceincreaseresults ina 33 percentdecrease inpurchases.

Draft4/30/99 6-2 ConclusionsDiscretionary 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. Inthis case, a 10 percentchange 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 sensitivitytoprice, withan elasticity of-2.80.

Draft 4/30/99 6-3 ConclusionsAllFocus Groups
- 10 Groups
- 90Participants

FrEICJ.!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 TemperdureOpt!ans 54 60% Doer Plo::anent 38 42% QuielOperdien 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% Bleo::tlf.iofta'le Dispense 15 17% LintRemCNd 15 17% Buttonsil)lds 14 16% Carre::ts Out-of-Bdmce 14 16% Ma:hineSize 14 16% HmdWcshdjes 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"ltCa;tS0ting; 5 6% a ecr Instructiens 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 OddShcped Items 1% Contrd LctJEjs 1% Connocted towctg Sottene 1% Cydestq:is when LidOpen 1% Electricd Camp:iib Ii1y 1% Gcs/EIectric 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% ViewingWindow 1% Waning Lights 1% Draft4/30/99 A-2 AppendixADraft4/30/99 Washington DCFocus Group Combined Group

16Participants

Price Cq:ajty Fec:ture

Wder Terrp;rdureOplicns Enag/cndWderCcsis Loa::1 SizeOpliens Doa Pla::anent Wa;h Tirre QuietOi:erdioo Reic:tility Avdlc:tility OfService HmdWa;hct:Ies Brend ButtrnsA)ids Agtdioo Sp:a:l Deicx:teO{de ExiraRinse O{de AdjOothes**Mi~Oyde** Durd:llity HaizootdNeiicd Axis SpnSp:a:l**E~Of-O{deSigid** Ma:hineSize Wcrrmty Agtdioo (sid&to-sicEA.Ji:>cbwn) Ccrreds Out-of-Bdmce Deterg:ntCcstSating; HedOwnWder Lint Rerrovd Mul1ipeWa;h OydeOplicns OnWheels TirreRerrdningInd

### 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

AppendixADraft 4/30/99 San Francisco BayAreaFocus Group Combined Group 18Participants Freq.sicy Feciure in Top 10 Durc:t:iIi1y 16 Price 16 Cq:x:d1y 12 Wcrren1y 11 LocdSizeOptim 10 Wda Terrp::rrureOplims 10 Dea P1a::an:nt 9 Quiet Op:rdim 9 Enag; mdWda Ca;ts. 8 Brend 7 Extra Rinse 7 Wa>h Tirre 7 Carecls Out-of-Bdcnce 6 Lint Rarovd 4 Ma::hineSize 4 Spn Si:ea::J 4 Avdlc:t:ili1y ofSavice 3 Cda 3 GEnfleOyde 3 Scd< Optim 3 Socp Disp::nsa 3 Actl

### a othei in MidOyde 2 Agtdim (sid9tosid9A.Jp cbwn) 2 BIe:r::h,iSoftena Disp::nsa 2 HendWa>hct:Ie Oyde 2 HaizmtdNaiic:d Axis 2 HotWda Pre;sure 2 Rarovd:ie Tub 2 Wa>ha/Orya Ccrrb:) 2 Buttms/Oids 1 OnWhe9s Spn Length Spn Only Oplim Usa Friendy Cmtrds

A-4 AppendixADraft4/30/99 Madison Focus Group Combined Group 19Participants

Fedure Fr9CJ,IEl1c.y in Top 10 Cq:x:D1y Durctili1y,.Reict:ili1yA3rmd Price Ene-gy OldWdEI Ccs1s Loa:iSizeOplims Wd€I Tan::adureOplims Quiet**O~dioo** HaizmtdNerticd Axis MultipeWa;h CydeOptims DoaSize Wa;hTirre SOO< Oplioo LintRarovd Careds Out-Of-Bdmce DetEigntCa;tSo/ing; End-Of-CydeSigid Exira Rinse Cyde Adj

## aothes Mid-Cyde Agtdioo Sr:x;a:i Ble::ctvSoft91€I DiSp:n$€I Deic:deCyde Doa Pla::::anent HendWa;hd:leCyde Heo,y Du1y Cyde SpnSp;a:i At:il1yto R&UseRinseWd€I Ba;ketfa Od:lShq:s:ilterrs Crnird Lct::es Ela:lricd Ccrrp:iitiIi1y Mota Oplirrizer

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

AppendixADallasFocus Group Combined Group 17Participants

fr6CJ.1Ency Fedure inToplO Cq::x:D1y 13 Ene-w mdWcter Ccsts 13 MultipeWcr.h Oyde0pti005 13 Loc:d SizeOptial 10 Reic:bliiy 9 BI ro:tvSoftffiE! DiSl=ffiSEJ 8 Dea P1a::e 1e1t 8 Price 8 Agtdial Sp:;ed 7 HaizaltdNEJtim Axis 7 WdEr T9Tp3'dureOpti005 7 

### a e:::r 1nstrucli005 4 ExtraRinseOyde 4 Lint REITOVd 4 Brmd 3 Cda 3 EnoOf-Oyde Sig1d 3 GEnfleOyde 3 QuietOp:rdial 3 SefOEOlingFilter 3 Tub MdErid (Plcr.tic,Metd) 3 Warm1y 3 Wcr.hTirre 3 Avdlc:bli1y ofSErvice 2 Butt005JOids 2 Ma:TlineSize 2 Mota Qudi1y,Oize 2 Pr&Wcr.h 2 Sirrpe setti**n~** 2 Scx::k Optial 2 SpnSp:;ed 2 HmdWcr.hd:les 1 r• Lid Lock 1 Sdety Spn Lengh TwoSp:;ejMota

Draft4/30/99 A-6 AppendixADraft4130/99 MiamiFocus Group Combined Group

Price Durctiliiy Fectures

Loa:!SizeOpticns Warmiy Energ,t & Weier O::sts Cq:x:riiy Wder Terrp=rdureOpticns Dcx:x Pla:::errait /lgtdienSi:;eed MuftipeWcsh CideOpticns WcshTirre ManineSize QuietOp:rdien AvdIctiliiy ofService Buttcns,Oids Extra RinseCide HaizentdNertioo Axis Rust Proof Bla:x::hJSoftener Disp:nser Careds Out-of-Bdcnce Dcx:x Size Manine Desigi On Whees Brend Cda CcnnededtoWeier Softeler Cide stop; when Lid Oi::en Gcs,£1a::tric HendWcshdjes Pre-Wcsh WaningLiQ'lts 20Participants

A-7 freqJSlcy 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

AppendixAAPPEND/XS TOP 5ATTRIBUTES FROM CONJOINTANALYS/S SESSIONS

Draft 4/30/99 B-1 Appendix BTop FiveAttributesFrom ConjointAnalysis Session 429Respondents
If you were topurchase a newclothes washertoday, whatare thefive mostimportant washer features thatwouldinfluenceyourselection?

Frequency In Attribute Top5 Percentage Out of 429 Price 241 56.2% Energy aid Woter Costs/Energy Efficiency 205 47.8 Cqxx:ity 187 43.6 Multiple Wash Cyde Options 152 35.4 lwoter Temperoture Options 116 27.0 MachineSize 113 26.3 Braid 89 20.7 Lood Size Options 82 19.1 Relictlility 73 17.0 QuietOperation 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 IAgitalion Speed 22 5.1 IAvailctlility ofService 21 4.9 Buttons/Dids 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 Hi{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 DetergentCost 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 RemovcoleTub 3 0.7 Gas/Electric 3 0.7 HorizontdNerticd Axis 2 0.5 LintRemovd 2 0.5 Hot 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 Hend Washd:lles 1 0.2 Door Size 1 0.2 Ion Wheels 1 0.2 Electricci Compatibility 1 0.2
Draft4/30/99 B-2 Appendix BAPPENDIXC CONJOINTSESSIONS SURVEYWITH RESPONSEFREQUENCIES

Draft 4/30/99 C-1 Appendix CCLOTHES WASHERANALYSISFOLLOW-UP QUESTIONNAIRE
1. Howmanypeoplecurrentlylive inyourhousehold? Response AllRespondents 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. Howmanychildren ages 6 to 18 live inyourhousehold?

__ Number All LowIncome 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 (Ifgreaterthan0)**~** Do these childrenattendpublicorprivate 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 andone 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

Draft4/30/99 C-2 Appendix C7. Doyouhave a satellite dishfor yourTV reception? Response All LowIncome 65 and Older Respondents (less than $25k/yr) Yes 4.5% 4.9% 3.0% No 95.5 95.1 97.0
8. Howmanyautomobiles does yourhousehold ownorlease?
NumberofAutos All LowIncome 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 orMore 1.1 0.0 0.0 Refused 0.2 0.0 0.0
9. What is the year, make, and model ofeach automobile? (open endedresponses)
10. Wh d ere oyoud 1 d ? o your 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 ApartmentLaundryRoom 18.7 35.9 16.4 Other 1.4 3.9 1.5 Refused 0.5 0.0 1.5
11. Have youpurchased a new clothes washerwithin the last2 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 C3. Whichcategorybestdescribes yourage? Age Category All LowIncome 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 orolder 15.8 17.5
4. Whichcategorybestdescribes youreducation?
Education Category All Low Income 65 and Older Respondents (less than $25k/yr) SomeHighSchool 1.2% 2.9% 0.0% HighSchool Graduate 8.5 16.5 10.4 SomeCollege 23.9 33.0 37.3 College Graduate 63.8 45.6 50.7 Trade/VocationalSchool 2.6 1.9 1.5
5. Doyouownorrentyourhome? 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?MonthlyRent 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

Draft4/30/99 C-3 AppendixC(Ifyes)**~How** much didyoupayfor yournew clothes washer?

____ _ PriceRange 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..Whydid youpurchase the clotheswasher? 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 washerfor yourhome 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 matchingclothes washer Other (please specify) 6.7 10.0 11.1

Draft 4/30/99 C-5 AppendixC12. The majorityofresidentialclotheswashers sold in the U.S. arevertical axis machines (see Figure 1), withhorizontalaxis machines comprisingthe remainingportion (see Figure2). Before today, didyouknow thatyoucouldbuya horizontal axismachinesfor residential use? Response All LowIncome 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 (Ifyes)**~Where** didyoulearnthis?

___________________ _ 13. Have youeverwashedclothesin a horizontal axis machine? Response All LowIncome 65 and Older Respondents (lessthan $25k/yr) Yes 57.0% 58.3% 41.8% No 42.3 40.8 55.2 Refused 0.7 1.0 1.5 (Ifyes)**~Where** didyouwash them?

__________________ _ 14. Ifyouhad topurchase a clothes washertoday, would you considerpurchasing a horizontal axis machine? Response All LowIncome 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 (Ifno)**~Why** wouldyouNOTconsiderpurchasinga horizontalaxis clotheswasher?

Draft4/30/99 C-6 AppendixC15. 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 payfor a new clotheswasher. HighestPrice 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. Whatdo you thinkthe averageprice for a new clotheswasher 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 ofanew clothes washer ranges from a low of$200 to a high of$1,200. Ifyouwere to purchase a new clotheswasher today, whatpricerangewould youhope to staywithin?
Price Range All LowIncome 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 AppendixC18. Ifyouwere to purchase a new clothes washer today, whatmeans'would you use to payfor it? PaymentMethod 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 CreditCard 41.6 30.1 62.7 StoreFinancing 10.2 14.6 6.0 CreditCard Financing 9.0 7.8 3.0 BankLoan 1.4 3.9 1.5 HomeEquityLoan 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 orpurchasing a new one with the same features for $400. Whatwouldyoudo? Response All Low Income 65 and Older Respondents (less than $25k/yr) Purchasethenewmachine at$400 63.6% 54.4 76.1 Fix yourold machine at$150 32.9 37.9 20.9 Do laundrysomewhereelse 1.7 4.9 0.0 Shop for a usedmachine 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 orpurchasing a new one with the same features for $450. Whatwouldyoudo? Response All LowIncome 65 and Older Respondents (less than $25k/yr) Purchasethenewmachine at$450 53.9% 42.7 67.2 Fix youroldmachineat$150 41.8 47.6 31.3 Do laundrysomewhere else 0.9 3.9 0.0 Shop for a usedmachine 2.1 3.9 0.0 Refused 1.1 2.0 1.5

Draft4/30/99 C-8 AppendixC21. 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. Whatwould you do?

Response All LowIncome 65 and Older Respondents (less than $25k/yr) Purchase thenewmachine at$650 16.3% 11.7 31.3 Fix youroldmachine at$150 74.7 69.9 65.7 Dolaundrysomewhereelse 2.6 8.7 1.5 Shop for a usedmachine 5.2 8.7 0.0 Refused 1.1 1.0 1.5

Draft 4/30/99 C-9 Appendix CSELECTED CROSS TABULAR RESULTS RELATIONSHIP BETWEEN EXPERIENCE WITif HORIZONTAL AXIS MACHINES AND WILLINGNESS TO CONSIDERPURCHASE: PeopleWhoHaveExperiencewithHorizontal Percent Axis ClothesWashingMachines (Total=238) WouldConsiderPurchasinga H-AxisMachine 69.3% WouldNotConsiderPurchasinga H-Axis Machine 30.7 PeopleWhoHaveNOExperience withHorizontal Percent Axis ClothesWashingMachines (Total=176) Would ConsiderPurchasinga H-AxisMachine 73.3% WouldNotConsiderPurchasing.a H-Axis Machine 26.7

Draft4/30/99 C-10 AppendixCAPPENDIXD CONJOINTANALYSIS CARDSAND INSTRUCTIONS

Draft4/30/99 D-1 Appendix D

# CLOTHES WASHERS

CLOTHES WASHER Card -1

Price ofclothes washer:

..................................................................... 

### S450 Energy and waterSavings:

............................................................... 

### S50 Wash capacity:

.............................................................................. Standard Top orfront load:

.............................................................................. Top Wash/Rinse temperature:

.................................................................... Cold, Warm Load size:.............................................................................. NonAdjustable

Draft4/30/99 D-2 Appendix DCLOTHES WASHER Card -2

Price of clothes washer:

..................................................................... 

### $ 400 Energy and water Savings:

............................................................... 

### $ 50 Wash capacity:

.............................................................................. Extra Large Top orfront Load:

.............................................................................. Front Wash/Rinse temperature:

.................................................................... Cold, Warm Load size:

............................'.................................................. NonAdjustable

CLOTHES WASHER Card -3

Price of clothes washer:

..................................................................... $ 400 Energy and water Savings:

............................................................... $ l0

. Wash capacity:

.............................................................................. Extra Large

Top orfront Load:

.............................................................................. Front Wash/Rinse temperature:

.................................................................... Cold, Warm, Hot

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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NonAdjustable

Draft4/30/99 D-3 Appendix DCLOTHES WASHER Card -4

Price ofclothes washer:

..................................................................... 

### S400 Energy and waterSavings:

............................................................... 

### S0 Wash capacity:

.............................................................................. Standard Top orfront Load:

.............................................................................. Front Wash/Rinse temperature:

.................................................................... Cold, Warm, Hot

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

CLOTHES WASHER Card -5

Price ofclothes washer:

..................................................................... 

### S450 Energy and water Savings:

............................................................... $ 0 Wash capacity:

............................................................................... Extra Large Top orfront Load:

.............................................................................. Front Wash/Rinse temperature:

.................................................................... Cold, Warm Load size:

.............................................................................. Adjustable

Draft4/30/99 D-4 Appendix DCLOTHES WASHER Card -6

Price of clothes washer:

..................................................................... $ 450 Energy and waterSavings:

............................................................... $ 0 Wash capacity:

.............................................................................. Extra Large Top orfront 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 DCLOTHES WASHER Card -8

Price ofclothes washer:

..................................................................... 

### S650 Energy and waterSavings:

............................................................... 

### $ O Wash capacity:

.............................................................................. Extra Large Top orfront Load:

.............................................................................. Top Wash/Rinse temperature:

.................................................................... Cold, Warm Load size:.............................................................................. NonAdjustable

CLOTHES WASHER Card -9

Price ofclothes washer:

..................................................................... 

### S650 Energy and waterSavings:

............................................................... 

### S50 Wash capacity:

.............................................................................. Standard Top orfront Load:

.............................................................................. Front Wash/Rinse temperature:

.................................................................... Cold, Warm, Hot Load size:

............................................................................,', Adjustable

Draft4/30/99 D-6 AppendixDCLOTHES WASHER Card -10

Price of clothes washer:

..................................................................... 

### S400 Energy and waterSavings:

............................................................... 

### S50 Wash capacity:

.............................................................................. Extra Large Top orfront Load:

.............................................................................. Top Wash/Rinse temperature:

.................................................................... Cold, Warm, Hot

Load size:

.............................................................................. Adjustable

CLOTHES WASHER Card - 11

Price of clothes washer:

..................................................................... 

### S400 Energy and water Savings:

............................................................... 

### S0 Wash capacity:

.............................................................................. Standard Top orfront Load:

.............................................................................. Front Wash/Rinse temperature:

...................................................:
................ Cold, Warm Load size:.............................................................................. NonAdjustable

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

Price ofclothes washer:

...................................................................**~.** $ 400 Energy and waterSavings:

............................................................... $ 1 O Wash capacity:

.............................................................................. Extra Large Top orfront 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 orfront Load:

.............................................................................. Front Wash/Rinse temperature:

.................................................................... Cold, Warm Load size:

.............................................................................. Adjustable

Draft4/30/99 D-8 Appendix DCLOTHES WASHER Card -14

Price of clothes washer:

..................................................................... 

### S400 Energy and water Savings:

............................................................... 

### S0 Wash capacity:

.............................................................................. Standard Top orfront Load:

.............................................................................. Top Wash/Rinse temperature:

.................................................................... Cold, Warm, Hot

Load size:

.............................................................................. Adjustable

CLOTHES WASHER Card - 15

Price of clothes washer:

..................................................................... 

### S650 Energy and water Savings:

............................................................... 

### S0 Wash capacity:

.............................................................................. Extra Large Top orfront Load:

.............................................................................. Top Wash/Rinse temperature:

.................................................................... Cold, Warm, Hot

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

Draft4/30/99 D-9 Appendix DCLOTHES WASHER Card -16

Price ofclothes washer:

..................................................................... 

### $450 Energy and waterSavings:

............................................................... $ 

### l0 Wash capacity:

............................................................................... Standard Top orfront Load:

.............................................................................. Top Wash/Rinse temperature:

.................................................................... Cold, Warm, Hot

Load size:...

. .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... . . . . . . NonAdjustable



# PURCHASE

Draft4/30/99 D-10 AppendixDINSTRUCTIONS CLOTHES WASHERS Assume thatyou are purchasing a new clothes washer. You have in front ofyou a series ofcards 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 ofyou. 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 ofa new clothes washer.

TOP OR FRONT LOAD Top or front load refers to the placement o'tthe door on the machine. "Top" means the door is
on the top ofthe machine and "Front" means the door is on the front side. Please refer to Figure l to see different examples oftop 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 amountwater 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 ofwater regardless ofthe size ofthe load.
(Instructions Continued on Backof Page)

Draft 4/30/99 D-11 Appendix DOnce 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 MORELIKELY to choose

.,.,._ ·' Product/Service I would be**~** NEITHERMORE 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 andthe 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 doso.

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 tothe 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. CardNumber 

### 97 99

""**:"'-:."'.':,:~~--·:'"** :·.::::·:/:::,::··;;·,..:-.,:. 

### 98



## ... .. : . ;**·~**·.-:··

Draft4/30/99 D-12 AppendixDAPPEND/XE CONJOINTANALYSIS COEFFICIENTESTIMATES

Draft 4/30/99 E-1 Appendix ECoefficientEstimates

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% WaterTemp. 0.614 0.024 1% 18% WaterTemp. 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 I :auicment Choice Mode 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% WaterTemp. 0.518 0.049 1% 16% WaterTemp. 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 Ch" Modi :au1cment 01ce 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 1o/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 1o/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 tCh :au1pmen 01ce Md I 0 e 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%

Draft4/30/99 E-2 Appendix ERecent 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

### choice M odeI 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% WaterTemp. 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 Ch" Mdl :au1oment 01ce 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% WaterTemp. 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 hoice 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 EMadison WI E C Modi :au1cment hoice 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% WaterTemp. 0.7683 0.052 1% 16% WaterTemp. 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 Ch
. Md I :au1cment 01ce 0 e 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% WaterTemp. 0.6164 0.052 1% 20% WaterTemp. 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% WaterTemp. 0.523 0.048 1% 17% WaterTemp. 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% WaterTemp. 0.638 0.047 1% 18"/o WaterTemp. 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 EDraft 4/30/99 APPENDIXF ELAST/CnYCALCULATIONS

F-1 Appendix FFull Sample

Attribute Description Price Dollarvalue of retail price of machine Savings DOiiarvalue of annual water and energy blll savings Capacit Binaryvartable: zero for standard capacity machine. one for extra large capacity. Door Binaryvariable: zer_ofor front loader. one for top loader. WaterTemp Binaryvortable: zero for 'celd/wam'. one for 'cold/worm/hot' washing temperature options LaadSize Blnorvvartnhle: zero If !her"' Is no loadsize 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 newprice and prababilltycells 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 differonlv In their savlnas Full Sample

Note: Based on sample of that do laundryat home. the k If I d mare orreoarsan

llkellhoodof Purchase Model

CurrentMarke

,,, ..........0 Caefflclen Condition lntercep ·0.9494 I Price ·0.006E 400 Savings 0.028E 0 Capacit 0.451€ 0 Door 0.697€ l WaterTemp 1.437€ I Load Size 1.8086 I
Sum


### -·----·-·--------

...

### -

## ------- LikelihoodofPurchase

Draft 4/30/99 Uncalibrated elastldftes for the full sample Product of Coefficient CurrentMarke 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 NewSavings fi( New Probablfll) 0.86 Savlnos Elastic/hi O.J<

F-2 Appendix FCurrent Clothes Washer Owners

Attribute Description Prtce Dollarvciue of relrni price of Savings Dollarvciue of annual wafer and energy bill Capacity Binaryvariable: zero for standard capacity machine. onetor ex1ro large Door Placement Binaryvariable: zero for front loader. onetor top Water Temp Binaryvariable: zerotor 'cold/wcrm'. onetor 'cold/worm/hot' washing Load Size Blnarvvariable: zero If there Is no load size odlustment onllon onR If

Directions.
localculate CJ)rlce etosttcltyuse the "Likelihood of Purchase Model" lothe 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 dofoundry at home. the potential market for reoolrs and reolocemen1s

Likelihood of Purchase Model

Current Market Altribute Coefficient Conditions lnlorcept**-1.576~** 0.75 Piice -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 ofPurchase

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 thatwould purchase o machine al ralher than repairor dolaundryelsewhere 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
lnlhci Price inlflci Probability New Price New Proboblily Ptlce Elasl/clty

Savings Elasticity of Demond

lnittci Savings lnlttci Proboblhly 0

Appendix FDiscretionaryWasher Purchasers

Attribute Description Price Dollarvdue of retail price of Savings DOiiarvdue ofannual water andenergy bill Capaclt Binaryvariable: zero for standard capacity machine. one for extra large Door Binaryvariable: zero for front loader. one for top WaterTemp Binaryvariable: zero for 'cold/warn'. onefor 'cold/worm/hot'wasling Load Size Blnarvvariable: zero If there ls noloadsize 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 probabilitycells 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 marketcondit1onsdifferonlv in their savlnas DiscretionaryWasher Purchasers Note: Uses those in sample who non-washerowners and those purchased washer for first time In ln<t? v<>nr<

Likelihood of Purchase Model

CurrentMarki Attribute Coefflcien Conditions lntercep -0.3991 1.4 Price -0.00651: 4CX: Savings 0.03QL 0 Capacit -0.00311 0 Door**0.186~** 1 WaterTemp 2.086' 1 Load Size l.280! 1
Sum



### -------- ----------- LlkellhoodofPurchase

Draft4/30/99 Product of Coefficient t CurrentMarke 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. uncalibratedestimate of llkellhood ofpurchase ofa 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 didnotowno washer 2years ago. sanple proportion of that recently purchased o newwasher 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 FLow 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 ofPurchase

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-5Upper Bound = 0.93

Source: Survey Ql9, 7 percentof low respondents claim that would goto 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

SavngsElasltclyor 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 S150 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

SavngsElasllclyor 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 ofMarketing, 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 ofConsumerResearch, Vol. 10, December 1983,pp. 348-361. Umesh, U. N. "Transferability of Preference Models Across Segments and Geographic Areas",Journal ofMarketingr., Vol. 51,January 1987, pp. 59-70.

Draft4/30/99 R-1 References