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

$25,000 Per Year

Option New Washer

Cost $400

New Washer

Cost $450

New Washer

Cost $650

Purchase the new machine 54.5% 42.7% 11.7%

Fix oldmachine for $150 37.9% 47.6% 69.9%

Do laundry somewhere else 4.9% 3.9% 8.7%

Shop for a used machine 1.9% 4.9% 8.7%

Refused/Don't Know 1.0% 2.0% 1.0%

Table J. 13 shows the same results for the low income portion of the sample. Among

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

choose to go someplace else to do laundry or repair the old machine. At a price of $400 for a new

machine nearly 5 percent stated they would do laundry someplace else, and 38 percent stated they

would fix the old machine. The number choosing to purchase the new machine for $400 is 54

percent. At a price of $450 for a new machine, 48 percent stated they would repair the old machine,

and only 43 percent would purchase the new machine. The number choosing to do laundry

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

income respondents choose to fix the old machine, 12 percent would purchase the new machine. At

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

Table J.14 Option Choice Sensitivity to Price Change; Respondents Over 65 Years of Age

Option New Washer

Cost $400 J

New Washer

Cost $4S0 ]

] New Washer Cost

__________________________________

Purchase the new machine 76.1% 67.2%

___________________

3 1.3%

Fix oldmachine for $150 20.9 31.3 65.7

Do laundry somewhere else 0.0 0.0 1.5

Shop for a used machine 1.5 0.0 0.0

RefusedlDon't Know 1.5 1.5 1.5

Table J.l4 is similar to Table J.12 and J.13, except it displays the results for the elderly

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

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EE-RM-PET-100 COMMENT 88
number of elderly respondents choosing to purchase the new machine for $400 comprise 76 percent,

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

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

machine drops to 31 percent, which is almost double the percent of the whole sample who indicated

they would purchase a new washer at this price. At $650, a very small percentage would do laundry

somewhere else, while most (66 percent) would choose to fix the old machine.

J.9 CONJOINT ANALYSIS RESULTS

J.9.1 Introduction

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

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

sample so that purchase probabilities and utility could be estimated for different demographic

groups. The results were also used to evaluate the relative importance of each of the clothes washer

attributes for the various groups. In addition, the potential impact of efficiency standards on the

likelihood of purchasing a new clothes washer is analyzed and presented for a number of subgroups,

as well as the full sample.

The section begins with a review of the equipment choice model approach, and then presents

the results for the full sample. There is also an explanation of the relative importance statistic, which

is used to identify which attributes were most important in determining consumer utility. This is

followed by an examination of the coefficients and the implied relative importance of each attribute

to respondents.

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

next, concentrating on the differences in results among various sample subgroups. The relative

importance of attributes is then compared and contrasted by sample subgroup.

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

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

reflecting different efficiency levels. This analysis was conducted over the entire sample as well as

for different subgroups within the sample.

Equipment Choice ModeiResults. This section presents the results of the equipment choice

modeling and begins with review of the clothes washer card attributes, and definitions of specific

attributes used in the analysis. (A copy of the cards can be found in Section J.l4.) This is followed

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

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

the coefficient estimates and relative importance statistics for the full sample. This is followed by

sections that compare coefficient and importance statistics across various sample subgroups.

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EE-RM-PET-100 COMMENT 88
Table J.15 Attribute Definitions

Variable Description

Price Dollar value of retail price of machine

Savings Dollar value of annual water and energy bill savings

Capacity Binary variable: zero for standard capacity machine, one for extra large capacity.

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

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

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

Table J. 15 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 were presented on each card was varied across the groups. This was done to avoid any

potential bias due to the card presentation. The six attributes presented on each card were as follows:

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

Savings - annual energy and water bill savings ($0, $10, or $50),

Capacity - standard or extra large capacity,

Door placement - Indicates either top or front door placement,

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

wash cycle options, or warm and cold options only,

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

Equipment Choice ModelApproach. The equipment choice model analyzes the choice of

a specific equipment option, given that the decision to purchase a washer has already been made. In

the conjoint analysis session the respondents ranked 16 clothes washer cards, from the most

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

cards were regressed against the rankings of the cards:

Rank =

I3'Price + I3'Savings + 'DoorPlacement1 + 'Capacity + 'WaterTemp + 'LoadSize1 +

Where Rank Rank value from I to 16, based upon the respondents' relative assessment of each

card.

Price1 Value for price on card i

Savings1 = Value for energy and water savings on card i

DoorPlacement = I for top loading, 0 for front loading on card i

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EE-RM-PET-100 COMMENT 88
Capacity1 = 0 for standard capacity, I for large capacity on card i

WaterTemp = I for hot water available, 0 if no hot water on card i

LoadSize1 = I if Adjustable, 0 if nonadjustable on card i

= Coefficients to be estimated

random error term assumed to be logistically distributed

Importance Statistics. The results of the equipment choice model can be used to infer the

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

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

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

in determining a card's total utility.

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

regression equation:

Total Utility =

'Price + 3'Savings1 + J3'DoorPlacement1 + 'Capacity + 'WaterTemp1 + 'LoadSize

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

the importance statistic is defined as:

IMP1 = AYi = The maximum utility change due to attribute i

Ly 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 way, the importance

statistic measures each attribute's contribution to the total utility based on the six attributes included

in the conjoint analysis.

Overall Estimation Results. This section describes the results of the equipment choice

model estimation for the full sample. The estimated coefficients for each clothes washer attribute,

as well as each relative importance statistic are reviewed.

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EE-RM-PET-100 COMMENT 88
Table J.16 Regression Coefficients and Relative Importance; All Respondents

Coefficient Standard

Error

Significance

Level

Relative

Importance

Price -0.33359 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 Temperature 0.614 0.024 1% 18%

Load Size 0.852 0.024 1% 25%

_____________________________

100%

The results of this estimation are shown in Table J. 16. (See Section J. 14 for additional

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

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

with positive coefficient estimates. A positive coefficient for "Capacity" indicates people prefer

extra-capacity machines to standard capacity. Regarding door placement, respondents indicated a

preference for top-loaders over front-loaders. Having a hot water wash option was attractive, as was

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

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

different from zero with a 99 percent degree of confidence.

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

on total utility, it is misleading to look only at the coefficient to gauge the influence of that variable.

For example, the savings coefficient is ten times the magnitude of the price coefficient since savings

is measured in tens of dollars and price in hundreds of dollars. Only looking at the magnitude of the

coefficients would give the misleading impression that savings is considered much more important

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

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

importance statistic can be interpreted as each attribute's contribution to total utility.

The relative importance statistics show that while price is the most important attribute to

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

on the six washer attributes. Together, these two attributes contribute about half of the total utility.

Having a hot water wash option was the third most important attribute, contributing about 18

percent of total utility. Door placement finished second-to-last in importance, with 11 percent of total

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

Sample Subgroup Comparison of Coefficient Estimates. This section provides a more

detailed review of the coefficient estimation results, including an examination of the differences in

results between various sample subgroups.

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EE-RM-PET-100 COMMENT 88
Table J.17 Regression Coefficient Estimates for Regional Subgroups

Coefficient Estimates

Full Low 65 & 18-24 Recent Have Tried Would Would Not

Sample Income Older Yrs. Old Purchasers Horizontal Consider Consider

Axis Purchasing Purchasing

H-Axis H-Axis

Machine Machine

Price -000359 -0.00397 -0.00246 -0.00519 -0.00239 -0.00346 -0.00367 -0.00351

Savings 0.010 0.010 0.0067 0.016 0.005 0.009 0.011 0.006

Capacity 0.248 0.255 0.124 0.375 0.173 0.260 0.266 0.194

Door Placement 0.383 0.330 0.352 0.460 0.300 0.303 0.264 0.709

WaterTemperature 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

Table J. 17 presents coefficient estimates for various demographic subgroups of the sample.

The same statistical regression analysis performed on the whole sample, discussed above, was

performed on various subgroups of the sample to assess any prominent differences among segments.

The subgroups shown above include:

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

elderly (65 and older),

young (18 to 24),

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

those who have tried a horizontal axis machine,

those that would consider purchasing a horizontal axis machine, and

those that would not consider purchasing a horizontal axis machine.

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EE-RM-PET-100 COMMENT 88
Table J.18 Regression Coefficient Estimates for Regional Subgroups

Coefficients Estimates

Full Madison Washington Dallas San Francisco

_________________________

Sample

_________

DC _________Bay Area

Price -0.00359 -0.00477 -0.003 19 -0.00336 -0.00357

Savings 0.010 0.016 0.010 0.005 0.011

Capacity 0.248 0.483 0.191 0.156 0.235

Door Placement 0.383 0.558 0.178 0.523 0.339

Water Temperature 0.614 0.768 0.616 0.523 0.638

Load Size 0.852 1.008 0.756 0.836 0.916

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

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

the ranking exercise in each city. Coefficient estimation results for the regional subgroups, as well

as the full sample, are shown in Table J. 18.

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

for the overall sample, indicating a directional consensus on the desirability of attributes. Moreover,

the magnitudes of the coefficients for each subgroup are generally similar to those of the overall

sample.

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

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

one group. Thus, the simple fact that the coefficient for savings is largest for young people across

the subgroups does not necessarily imply young people place the highest relative value on savings.

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

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

in Madison are most sensitive to changes in price. Those who have recently purchased a new washer

and those over 65 years old are least sensitive to price.

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

have recently purchased a machine and people from Dallas are the least sensitive. The difference

between the highest and the lowest coefficient estimates is a factor of more than three, 0.005 versus

0.016. Older people and those who would not consider purchasing a horizontal axis machine are

also relatively insensitive to savings.

While most of the subgroups examined have a coefficient estimate for capacity of about 0.2,

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

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EE-RM-PET-100 COMMENT 88
highest coefficient for capacity at 0.48. Also, age seems to be relevant to the sensitivity to clothes

washer capacity. The second largest estimated coefficient for capacity was for young people, 0.38.

Conversely, the group with the smallest coefficient for capacity was those over 65, at 0.12.

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

machine were quite sensitive to door placement, and had the highest estimated coefficient, 0.71.

Conversely, restricting the sample to those who would consider purchasing a horizontal axis machine

resulted in a lower than average coefficient for door placement, 0.26. The group least sensitive to

door placement were those in Washington DC, with a coefficient of 0.19.

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

coefficients are fairly consistent, varying from a low of 0.51 for the low income group to a high of

0.77 for the Madison group.

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

the Madison group stands out again with the highest estimate, 1.0. The lowest estimated coefficient

was 0.65, for the low income group.

Review of Relative Importance Results

Table J.19 Relative Importance Statistics for Demographic Subgroups

Coefficient Estimates

Full Low 65 & 18-24 Recent Have Tried Would Would Not

Sample Income Older Yrs. Old Purchasers Horizontal Consider Consider

Axis Purchasing Purchasing

H-Axis H-Axis

Machine Machine

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%

DoorPlacement 11% 10% 13% 11% 11% 9% 8% 20%

Water Temperature 18% 16% 19% 13% 19% 19% 19% 15%

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

Table J. 19 compares the relative importance statistics of each attribute across the

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

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

the subgroups to determine which subgroup places the most emphasis on an attribute.

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EE-RM-PET-100 COMMENT 88
Table J.20 Relative Importance Statistics for Regional Subgroups

Coefficients Estimates

Full Madison Washington Dallas San Francisco

_________________________

Sample

_________

DC _________

Bay Area

Price 26% 25% 26% 27% 25%

Savings 14% 16% 16% 8% 15%

Capacity 7% 10% 6% 5% 7%

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

Water Temperature 18% 16% 20% 17% 18%

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

Table J.20 presents relative importance statistics for regional subgroups. For most of the

subgroups, price contributed a little over 25 percent to total utility, and was the most important single

attribute, albeit by a slim margin. Low income people and young people (who are likely also to have

lower incomes) placed the highest relative value on price, 30 percent and 31 percent, respectively.

On the other side, recent purchasers and people over 65 responded more strongly to adjustable load

size than to price. For both groups, price comprised 22 percent of total utility.

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

was not in the top three important attributes. Those from Dallas placed an unusually low value on

savings, at 8 percent of total utility, and young people placed a very high value on savings, 19

percent.

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

importance ratings varying from a low of 5 percent for elderly people and people from Dallas, to a

high of 10 percent for people from Madison. Young people also placed a higher than average value

on capacity, 9 percent. There appears to be some correlation between age and the relative

importance of clothes washer capacity.

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

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

purchasing a horizontal axis machine placed the highest relative importance on door placement.

Door placement was the third most important attribute for this subgroup. People from Dallas also

placed a relatively high importance on door placement, at 17 percent of total utility. Older people

placed a somewhat higher than average importance on door placement, 13 percent.

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

important attribute. Relative importance statistics vary from a low of 13 percent for young people

to a high of 20 percent for the Washington DC group. Most subgroups found hot water to contribute

between 16 percent and 19 percent to total utility.

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EE-RM-PET-100 COMMENT 88
Adjustable load size was almost invariably either the first or second most important attribute

to each subgroup. Most subgroups found this attribute contributed 20 percent to 30 percent of to

total utility. Adjustable load size was the single most important attribute to the elderly, recent

purchasers, those who would not consider purchasing horizontal axis machines, and those from the

San Francisco Bay Area. Adjustable load size tied price as the most important attribute for the

Dallas subgroup, and those who have tried horizontal axis machines. With the exception of young

people, the remaining subgroups found adjustable load the second most important attribute. Age

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

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

lowest, 16 percent. It should be noted that among low income people, adjustable load size ran a

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

Purchase Scenarios. As discussed in the Methodology, the conjoint results can be used to

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

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

options reflecting different efficiency levels. These probabilities are estimated for the entire group

as well as different subgroups within the sample.

Purchase Model Estimation Results. During the conjoint analysis, once respondents

completed ranking their cards, they were asked to place the Purchase Card in the card set to indicate

which of the 16 clothes washers indicated on the cards they would actually considering purchasing.

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

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

Purchase (0,1) =

a + I3'Price + 3'Savings + 3'DoorP1acement + 3'Capacity + 'WaterTemp + 3'LoadSize +

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

Price = Value for price on card i

Savings = Value for energy and water savings on card i

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

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

WaterTemp1 = 1 for hot water available, 0 if no hot water on card i

Load Size1 = 1 if Adjustable, 0 if nonadjustable on card i

B3 = Coefficients to be estimated

a = Constant to be estimated

= random error term assumed to be logistically distributed

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EE-RM-PET-100 COMMENT 88
Table J.21 Purchase Model Coefficient Estimates; Full Sample

Coefficient Standard Error Significance 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%

Table J.2 1 shows the coefficient estimates for the purchase model using the entire sample.

The coefficient estimates for the sample subgroups are given in Appendix E of this report. As these

results show, the coefficient estimates are very similar to those found in the equipment choice model

used to estimate relative importance of attributes. All of the coefficient estimates are statistically

significant at the 1 percent level. The intercept term reflects the average effect of all the factors that

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

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

an important determinant in the decision to make a clothes washer purchase. Similarly, hot water

and the ability to adjust the amount of water to fit the size of the wash load were also very important

factors in determining the likelihood of purchase. Door placement was also influential, but not

considered as important as hot water or adjustable load size. However, the importance of door

placement did tend to vary across different demographic segments, as shown in the purchase

scenarios discussed below.

Purchase Probability Scenarios. Once this model was estimated, the probability of making

a purchase was calculated by combining the coefficient estimates with the six washer attributes and

plugging in the logit probability function:

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

Where 1 'X reflects the sum of the coefficient estimates used in the conjoint analysis as shown in

Exhibit 5-7. By using different values for price, savings, and the equipment attributes to simulate

different efficiency levels, this equation can be used to determine the overall effect on utility of an

efficiency standard.

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EE-RM-PET-100 COMMENT 88
Table J.22 Purchase Scenario for Demographic Subgroups

Sample Group

Standard

Efficiency

Medium

Efficiency

High

Efficiency

Front Load

High

Efficiency No

Hot Water

High Efficiency,

Front Load,

Constant Price

High

Efficiency,

Front Load,

Constant Price

and No Savings

Full Sample 0.59 0.58 0.36 0.21 0.75 0.42

Low Income 0.65 0.63 0.41 0.26 0.78 0.50

Elderly 0.59 0.59 0.39 0.26 0.66 0.39

Young 0.64 0.61 0.29 0.28 0.76 0.44

Recent Purchasers 0.55 01.54 0.33 0.17 0.67 0.40

Have Tried H-Axis 0.57 0.56 0.39 0.19 0.78 0.45

WouldConsiderH-Axis 0.56 0.55 0.42 0.21 0.78 0.43

WouldNotConsiderAxis 0.67 0.64 0.21 0.23 0.62 0.35

Table J.22 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 inefficiency. The high efficiency equipment options have

a price of $650, annual savings of $50, and are either front loading machines with hot water wash

capability or top loading machines with no hot water capability. To judge the effect of price in these

scenarios, the high efficiency option is also calculated holding price constant at $400, while having

a front loading machine with $50 annual savings.

The first row of Table J.22 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 willing to purchase the standard

efficiency clothes washer. This provides a starting point from which to compare changes in

attributes and the effect these will have on the likelihood of purchase. In this sense, examining the

changes in purchase probability reflects the change in utility, since lower utility washer

configurations will have a lower likelihood of being purchased.

Full Sample Probability Estimates. When the probability is calculated using numbers for

the medium efficiency washer, there is virtually no change in the estimated likelihood of purchase.

This indicates that the increase in savings to $10 is enough to offset the increase in price from $400

to $450. This suggests that a washer design that increases efficiency with a modest increase in price

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

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EE-RM-PET-100 COMMENT 88
The high efficiency equipment options tend to have much greater effect on the likelihood of

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

to be either a front loader or to have the machine clean without using hot water. As shown in Table

J.22, a high efficiency front loading washer at a price of $650 and annual savings of $50 will

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

designed to run without hot water instead of being a front loader, the decrease is even greater. In this

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

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

of changes in price, savings, and either door placement or water temperature options. As discussed

in the analysis of the importance statistics, for the overall sample, price plays the greatest role in

influencing utility, followed by water temperature and door placement. This also can be seen when

equipment options are changed but price is held constant. When price is held constant and compared

with the other estimated probabilities, the importance of price is apparent.

Table J.22 also shows the likelihood of purchase for high efficiency machines that have the

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

front loader, then the purchase probability is estimated to increase from 0.59 to 0.75. This shows

that the increase in savings more than offsets the decrease in utility due to switching from a top

loader to a front loader. When price and savings are held constant and the washer is changed from

a top loader to a front loader, the estimated purchase probability falls almost 30 percent from 0.59

to 0.42.

Regional Probability Estimates

Table J.23 Purchase Scenarios for Regional Subgroups

High High High High

Standard Medium Efficiency Efficiency Efficiency, Efficiency,

Sample Group Efficiency Efficiency Front No Hot Front Load, Front Load,

Load Water Constant Constant

Price Price and No

________________ __________ __________ __________

Savings

Washington, DC 0.57 0,58 0.50

__________

0.29

_____________

0.75 0.48

SanFranciscoBay 0.60 0.59 0.41 0.19 0.80 0.43

Area, CA

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

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EE-RM-PET-100 COMMENT 88
The same purchase probability analysis was done by breaking the sample out by region.

These results are shown in Table J.23. Given the overall sample probability estimate of 0.59 for a

standard efficiency machine, both the Washington DC and Dallas estimates are below the sample

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

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

standard and high efficiency machines in Madison and Dallas. For the Madison group, moving from

standard efficiency to front load high efficiency machines decreases the purchase probability by 52

percent, compared with only a 39 percent decrease for the entire sample. Similarly, the same move

from standard to high efficiency decreases the purchase probability by 55 percent for the Dallas

group.

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

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

placement are allowed to vary. When price is held constant, Madison has a 32 percent increase in

purchase probability. This indicates that the decrease in probability going to high efficiency is due

primarily to the price increase.

For Dallas, holding price constant results in no increase in the purchase probability from the

standard efficiency case. Dallas is the oniy 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.

Elasticity Estimates. In addition to calculating the importance statistics and purchase

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

of price. These results are discussed below.

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

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

calculations examine the effect of changes in price while all other attributes remain constant. As a

result, price elasticity addresses the question of what will happen when price changes but everything

else remains the same.

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

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

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

are assumed to be zero. From this starting point, price elasticity is calculated by:

Price Elasticity = (z Prob/ L. Price) * (Pncemjd/ Prob)

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

J-40

EE-RM-PET-100 COMMENT 88
A Price = Change in price resulting from the efficiency standard

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

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

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

elasticity result greater than one in absolute magnitude means that demand is price elastic, or

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

price of 10 percent will cause demand to decrease by more than 10 percent. Similarly, when

elasticity is less than one demand is considered inelastic. In the inelastic case, a 10 percent increase

in price will cause demand to decrease by less than 10 percent. The specific calculations for the

elasticity estimates are included in Section J. 15.

Full Sample Results. For the entire sample result, the initial price elasticity going from a

price of $400 to the high efficiency price of $650 results in an price elasticity estimate of -2.0,

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

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

a result, other factors such as the convenience of owning a clothes washer at all or the unwillingness

of current owners to give up their clothes washer are not taken into account in the elasticity estimate.

To address this issue, the estimated purchase probabilities can be calibrated to other

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

calculations. For example, the uncalibrated likelihood of purchase estimate for the entire sample is

0.59. For some market segments, this estimate is likely to be quite low. For example, consider those

who are current washer owners and are used to doing their laundry at home. For this group, if a

machine breaks, and they are faced with purchasing a new machine or going to the laundromat, the

likelihood of purchase is estimated at 90 percent or greater.

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

how likely consumers would be to replace a broken clothes washer. Respondents were asked if they

would repair a broken machine for $150, purchase a new one for $400, or do clothes elsewhere.

Only 2 percent of respondents said that they would start doing laundry somewhere else, and the

remainder opted to purchase a new or used machine or get the old machine fixed.

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

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

probability equation so that the resulting purchase probability for the standard efficiency machine

equals 0.98. This is done for that segment of the sample that are homeowners that do laundry at

home. For this demographic group, the price is changed from $400 to $650, which results in the

purchase probability falling from 0.98 to 0.91. Using these numbers to calculate the price elasticity

yields an estimate of -0.16, which indicates that washer demand is relatively insensitive to price

J-41

EE-RM-PET-100 COMMENT 88
changes. Again, this applies to those customers that currently own a clothes washer that has broken

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

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

machine would purchase a new machine at $400 if repairing is not an option. As a result, this should

be considered an upper bound. A lower bound estimate can be derived from the survey questions

as well. The lower bound assumes that all those that said that they would repair their machine or

purchase a used machine would start going to the laundromat if repairing were not an option. This

lower bound case produces a likelihood of purchase of 0.64.

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

from $400 to $650 results in a price elasticity estimate of-i .64. This reflects a more elastic demand

for clothes washer, where changes in price will have a greater impact on purchases than in the upper

bound scenario. In this lower bound case, a 10 percent increase in price results in a 16 percent

decrease in clothes washer demand.

Low Income Households. The same elasticity calculation was estimated for low income

households. Using those respondents with annual household incomes of $25,000 or less, the

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

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

the choice of purchasing a new washer for $400 or repairing the old one for $150, 7 percent of low

income respondents said that they would start doing their laundry elsewhere or shop for a used

machine. Using this result, 0.93 was used as an upper bound for the purchase probability for use in

the price elasticity estimate. From this starting point, increasing the price from $400 to $650 gives

a price elasticity of-0.53 for low income households. While still considered inelastic, this estimate

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

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

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

that would repair at $150 would start going to the laundromat if repairing was not an option. In this

case, the purchase probability for low income households is 0.54. This results in a price elasticity

estimate of -2.10, which is elastic and greater than the estimate for households in general.

Discretionary Clothes Washer Purchasers. The preceding estimates calculated using those

respondents that currently own clothes washers. A separate segment of the population are those that

do not currently own clothes washers or who have only recently purchased a clothes washer for the

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

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

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

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

first time. For this group, the upper bound likelihood of purchase is set at 0.59. This is the

uncalibrated purchase probability estimate for the entire sample. This was chosen as the upper

bound because the entire conjoint analysis exercise was presented in a manner that made the

J-42

EE-RM-PET-100 COMMENT 88
purchase decision a discretionary purchase. That is, respondents were asked to go through the

thought process as if they were currently making the decision to purchase a clothes washer, which

is similar to this group of customers in the population.

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

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

the estimate for current washer owners.

The price elasticity for discretionary purchasers was also estimated using a lower bound

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

did own a clothes washer that purchased one within the last two years. Using this lower bound, the

estimated price elasticity of -2.80, showing an even greater sensitivity to price.

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

consumer falls under. Those that currently own clothes washer or that otherwise might have a high

initial likelihood of purchase (such as the new construction market) are less sensitive to changes in

price. As prices increase, demand for clothes washers will fall only slightly for these segments of

the market if all other washer attributes are held constant. Other segments such as low income

segments or that portion of the population that has for whatever reason does not currently own a

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

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

purchasers.

Conclusions. The objective of this study was to determine which clothes washer attributes

consumers value most and estimate how changes in these attributes resulting from an efficiency

standard may affect consumer utility and clothes washer purchases. The analysis methods described

in this report use several techniques to address these issues. Information obtained in both focus

groups and conjoint analysis allowed respondents to state which clothes washer attributes were most

important. Surveys were used to determine how respondents would react to different price levels

and repair/purchase scenarios. The conjoint analysis allowed purchase probabilities to be estimated,

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

the washer attributes. These different analysis techniques resulted in several key conclusions that

are discussed below.

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

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

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

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

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

of all the washer attributes used in the conjoint.

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

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

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

J-43

EE-RM-PET-100 COMMENT 88
price rather than to changes in the other attributes. When price is held constant at the standard

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

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

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

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

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

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

During the conjoint analysis, 65 percent of the respondents indicated with their placement of the

purchase card that they would be willing to purchase a front loading clothes washer. In the

calculation of importance statistics, door placement was second from last in importance among the

six attributes used in the conjoint. In addition, 70 percent of the survey respondents said that they

would consider purchasing a front loading machine if they were going to buy a new clothes washer.

For these people, door placement was tied for last in terms of importance, comprising only 8 percent

of total utility.

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

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

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

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

machine.

Demand is more inelastic for those that currently own washers. Both the survey and

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

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

results, those consumers that currently own clothes washers are much more likely to replace their

washer if it breaks than to start doing their laundry at a laundromat. Of those surveyed, only 2

percent said that if their machine broke they would start going to the laundromat or do their laundry

somewhere else, while the rest opt to get their machine fixed or purchase a new clothes washer. As

a result, 0.98 can be considered an upper bound for the likelihood of purchase under current market

conditions and is used as the starting point for calculating the price elasticity.

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

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

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

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

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

in only a very small change in the likelihood of making a purchase. If the likelihood of purchase is

calibrated up to 98 percent to match the survey results, the price elasticity estimate is -0.16, which

reflects demand for clothes washers that is relatively insensitive to price. A lower bound estimate

using a likelihood of purchase probability of 0.64 results in a more elastic estimate of -1 .64.

J-44

EE-RM-PET-100 COMMENT 88
A similar result holds for low income households, which includes those with annual incomes

of $25,000 or less. For low income households, consumers are slightly more sensitive to price, but

demand is still considered inelastic with an upper bound estimate of -0.53. For the lower bound,

price elasticity is estimated to be -2.1.

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

to purchase new ones when needed, even with large price increases. For example, using the upper

bound elasticity estimate for all current clothes washer owners, a 63 percent price increase from $400

to $650 results in only a 10 percent decrease in purchases for this sector of the market. For low

income segments, the same 63 percent price increase results in a 33 percent decrease in purchases.

Discretionary purchasers, which include those consumers that do not currently own clothes

washers, are more sensitive to price. This is due in part to discretionary purchasers also being low

income households. Of the discretionary purchasers, 47 percent had annual incomes of $25,000 or

less. For this group the price elasticity is -1.92, which reflects a demand for washers that is more

sensitive to price than current washer owners. In this case, a 10 percent change increase in price

results in a 19 percent decrease in demand. This reflects an upper bound purchase probability

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

sensitivity to price, with an elasticity of -2.80.

J-45

EE-RM-PET-100 COMMENT 88
J.10 FOCUS GROUP ATTRIBUTE RESULTS

Table J.24 All Focus Groups - 10 Gto us - 90 Partieipa!its

Feature

________________________

Freq. in

Top 10

% out

o190

Feature Freq. in

Top 10

% out

of 90

Price 75 83% On Wheels 4 4%

Capacity 73 I % Rust Proof 4 45

Energy & Water Costs 65 72% Agitation (side to side/up down) 3 3%

Load Size Options 61 6% Pre-Wash 3 3%

Durability 54 60% Self Cleaning Filter 3 3%

Water Temperature Options 54 60% Soap Dispenser 3 3%

Door Placement 38 42% Tub Material (Plastic/Metal 3 3%

Quiet Operation 36 40% Heavy Duty cycle 2 2%

Wash Time 34 3% Hot Water Pressure 2 2%

Warranty 33 37'V Machine Design 2 2%

Multiple Wash Cycle Options 30 33% Motor Quality/Size 2 2%

Horizontal/Vertical Axis 25 2i% Removable Tub 2 2%

Agitation Speed 22 24/ Simple Settings 2 2%

Extra Rinse Cycle 22 24% Spin Length 2 2%

Availability of Service 17 I Washer/Dryer Combo 2 2%

Brand 17 19% Ability to Re-Use Rinse Water 1 1%

Reliability 17 I YU Basket for Odd Shaped Items 1 1%

BleachlSoftener Dispenser 15 I •i/a Control Labels 1 1%

Lint Removal 15 I 7'4 Connected to Water Softener 1 1%

Buttons/Dials 14 16% Cycle Stops when Lid Open 1 1%

Corrects Out-of-Balance 14 I 6% Electrical Compatibility 1 1%

Machine Size 14 16% Gas/Electric 1 1%

Hand Washables 13 [4% Heat Own Water 1 1%

Soak Option 12 I 3% Lid Lock 1 1%

Door Size 12 13% Motor Optimizer 1 1%

Spin Speed 11 1.2% Safety 1 1%

End-of-Cycle Signal 8 9% Spin Only Option 1 1%

Add Clothes in Mid Cycle 7 8% Time Remaining Indicator 1 1%

Color 7 User Friendly Controls 1 1%

Delicate Cycle 6 79/n Two Speed Motor 1 1%

Gentle Cycle 6 7% Viewing Window 1 1%

Detergent Cost Savings 5 6% Warning Lights 1 1%

Clear Instructions 4 4%

J-46

EE-RM-PET-100 COMMENT 88
Table J-25 Washington DC Focus GrouD Combined GrouD. 16 Particinants

Feature Frequency

in Top 10

Feature Frequency

in Top 10

Price 16 Add Clothes Mid-Cycle 3

Capacity 15 Durability 3

Water Temperature Options 13 Horizontal/Vertical Axis 3

Energy and Water Costs 12 Spin Speed 3

Load Size Options 10 End-of-Cycle Signal 2

Door Placement 9 Machine Size 2

Wash Time 9 Warranty 2

Quiet Operation 8 Agitation (side-to-side/up-down) 1

Reliability 8 Corrects Out-of-Balance 1

Availability of Service 7 Detergent Cost Savings 1

Hand Washables 7 Heat Own Water 1

Brand 6 Lint Removal 1

Buttons/Dials 6 Multiple Wash Cycle Options 1

Agitation Speed S On Wheels 1

Delicate Cycle 4 Time Remaining Indicator 1

Extra Rinse Cycle 4 - Viewing Window 1

J-47

EE-RM-PET-100 COMMENT 88
Table J.26 San Francisco Bay Area Focus Group, Combined Group, 18 Participants

Feature Frequency

in Top 10

Feature Frequency

in Top 10

Durability 16 Color 3

Price 16 ' Gentle Cycle 3

Capacity 12 Soak Option 3

Warranty 11 Soap Dispenser 3

Load Size Option 10 Add Clothes in Mid Cycle 2

Water Temperature Options 10 Agitation (side-to-side/up-down) 2

Door Placement 9 BleachlSoftner Dispenser 2

Quiet Operation 9 Hand Washable Cycle 2

Energy and Water Costs 8 Horizontal/Vertical Axis 2

Brand 7 Hot Water Pressure 2

Extra Rinse 7 Removable Tub 2

Wash Time 7 Washer/Dryer Combo 2

Corrects Out-of-Balance 6 Buttons/Dials 1

Lint Removal 4 On Wheels 1

Machine Size 4 Spin Length 1

Spin Speed 4 Spin Only Option 1

Availability of Service 3 User Friendly Controls 1

J-48

EE-RM-PET-100 COMMENT 88
Table J.27 Madison Focus Group, Combined Group, 19 Participants

Feature Frequency

inToplO

Feature Frequency

inToplO _____________________

Capacity 18

_____________________

End-of-Cycle Signal 3

Durability/Reliability Brand 18 Extra Rinse Cycle 3

Price 17 Add Clothes Mid-Cycle 2

Energy and Water Costs 16 Agitation Speed 2

Load Size Options 14 BleachlSoftener Dispenser 2

Water Temperature Options 13 Delicate Cycle 2

Quiet Operation 10 Door Placement 2

Horizontal/Vertical Axis 9 Hand Washable Cycle 2

Multiple Wash Cycle Options 9 Heavy Duty Cycle 2

Door Size 8 Spin Speed 2

Wash Time 8 Ability to Re-Use Rinse Water 1

Soak Option 7 Basket for Odd Shaped Items 1

Lint Removal 6 Control Labels 1

Corrects Out-of-Balance 4 Electrical Compatibility 1

Detergent Cost Savings 4 - Motor Optimizer 1

J-49

EE-RM-PET-100 COMMENT 88
Table J.28 Dallas Focus Grviin. Combined Giup. 17 1arti-park%

Feature Frequency

inToplO

Feature

_____________________

Frequency

inToplO _____________________

Capacity 13 Quiet Operation 3

Energy and Water Costs 13 Self Cleaning Filter 3

Multiple Wash Cycle Options 13 Tub Material (Plastic/Metal) 3

Load Size Option 10 i Warranty 3

Reliability 9 Wash Time 3

Bleach/Softener Dispenser 8 Availability of Services 2

Door Placement 8 Buff ons/Dials 2

Price 8 Machine Size 2

Agitation Speed 7 Motor Quality/Size 2

Horizontal/Vertical Axis 7 Pre-Wash 2

Water Temperature Options 7 Simple Settings 2

Clear Instructions 4 Soak Option 2

Extra Rinse Cycle 4 Spin Speed 2

Lint Removal 4 Hand Washables 1

Brand 3 Lid Lock 1

Color 3 Safety 1

End-of-Cycle Signal 3 Spin Length 1

Gentle Cycle 3 - Two-Speed Motor 1

J-50

EE-RM-PET-100 COMMENT 88
Table J.29 Miami Focus Group. Combined 1Trum. 20 PaI'te'LDRu.tc,

Feature

___________________

Frequency

inToplO

[ Feature Frequency

inToplO

Price 18

__________________

Horizontal/Vertical Axis

-

4

Durability 17 Rust Proof 4

Load Size Options 17 Bleach/Softener Dispenser 3

Warranty 17 Corrects Out-of-Balance 3

Energy & Water Costs 16 Door Size 3

Capacity 15 Machine Design 2

Water Temperature Options 11 On Wheels 2

Door Placement 10 Brand 1

Agitation Speed 8 Color 1

Multiple Wash Cycle Options 7 Connected to Water Softener 1

Wash Time 7 Cycle Stops When Lid Open 1

Machine Size 6 Gas/Electric 1

Quiet Operation 6 Hand Washables 1

Availability of Service 5 Pre-Wash 1

Buttons/Dials 5 Warning Lights 1

Extra Rinse Cycle 4 - ________________________ ____________

J-5 1

EE-RM-PET-100 COMMENT 88
J.l1 TOP 5 ATTRIBUTES FROM CONJOINT ANALYSIS SESSIONS

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

features that would influence your selection?

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

Attribute

Frequency in

Top 5 Percentage Out of 429

l-ice 241 56.2%

Wergy and Water Cods/Biergy bficiency 205 47.8

Capacity 187 43.6

MultipleWashCycleOptions 152 35.4

Water Temperature Options 116 27.0

Machine bze 113 26.3

Brand 89 20.7

Load bce Options 82 19.1

Reliability 73 17.0

Quiet Operation 65 15.2

Warranty 65 15.2

Door Racement 58 13.5

Color 56 13.1

Durability 52 12.1

SmpIettings 43 10.0

BIeechfftener Dpeeser 33 7.7

Machine Dedgn 28 6.5

User Friendly Controls 27 6.3

Wad, Time 26 6.1

Delicate Cycle 26 6.1

Agitation eed 22 5.1

Availability ofrvice 21 4.9

Buttons'Diels 21 4.9

GentleCycle 19 4.4

btra Rinse Cycle 18 4.2

in jeed 18 4.2

Heavy Duty Cycle 18 4.2

Reted Highly by Conaimer Reports 17 4.0

Agitation (dde-to-ode or up-down) 13 3.0

lf-Cleaning Filter 13 3.0

CorrectsOut-of-Balance 8 1.9

Tub Material )Raotic/Metal) 8 1.9

Waer/DryerCombo 7 1.6

Control Labels 7 1.6

bid of Cycle bgnal 6 1.4

Sak Option 5 1.2

Detergent Cod vings 5 1.2

Re-Wad, 5 1.2

Sap DiWonoer 5 1.2

AbilitytoRe-UseRinoeWater 5 1.2

fety 5 1.2

lime Remaining Indicator 5 1.2

Qear Indructions 4 0.9

Motor Quality/Sze 4 0.9

Warning Lights 4 0.9

Add Clothooin Mid Cycle 3 0.7

Removable Tub 3 0.7

Gas'Aectric 3 0.7

HorizontelNertical Axis 2 0.5

Lint Removal 2 0.5

Hot Water Preseire 2 0.5

in Length 2 0.5

Connected to Water bdtener 2 0.5

Heat Own Water 2 0.5

Lid Lock 2 0.5

Hand Waedables 1 0.2

Door Sze 1 0.2

On Wheels 1 0.2

Bectrical Compatibility 1 0.2

J-52

EE-RM-PET-100 COMMENT 88
J.12 CONJOINT SESSIONS SURVEY WITH RESPONSE FREQUENCIES

Clothes Washer Analysis Follow-up Questionnaire

1. How many people currently live in your household?

Response

- _______________

All Respondents

________________

Low-Income

(less then

$25k/yr)

65 and Older

1 _______________ 27.2% 35.0%

________________

43.3%

2 35.5 33.0 38.8

3 14.7 10.7 7.5

4 13.9 9.7 7.5

5 4.7 6.8 1.5

6 2.8 2.9 0.0

7 0.5 0.0 0.0

8 0.0 0.0 0.0

9 0.2 1.0 0.0

10 0.2 0.0 0.0

Refused 0.2 1.0 1.5

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

Number

________________

All Respondents

________________

Low Income (less

than $25k/yr)

65 and Older

0 74.5%

_

75.7%

________________

91.0%

1 12.3 9.7 3.0

2 8.7 9.7 4.5

3 2.6 2.9 0.0

4 0.9 1.9 0.0

5 0.2 0.0 0.0

Refused 0.7 0.0 1.5

(If greater than 0)

i)r. Do these children attend public or private schools?

Public

Private

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

Response

_______________

All Respondents

_______________

Low Income (less

than $25k/yr)

65 and Older

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

J-53

EE-RM-PET-100 COMMENT 88
3. Which category best describes your age?

Age Category

________________

All

Respondents

________________

Low Income

(less than

$25k/yr)

18to24 12.8% 30.1%

25 to 34 18.7 19.4

35 to 44 19.9 15.5

45 to 54 18.7 6.8

55to64 14.2 10.7

65orolder 15.8 17.5

4. Which category best describes your education?

Education Category

________________________

All

Respondents

Low Income (less

than $25kIyr)

65 and Older

Some High School 1.2% 2.9%

____________

0.0%

High School Graduate 8.5 16.5 10.4

Some College 23.9 33.0 37.3

College Graduate 63.8 45.6 50.7

Trade/Vocational School 2.6 1.9 1.5

5. Do you own or rent your home?

Response

_______________

All

Respondents

________________

Low Income

(less than

$2SkIyr)

65 and Older

_______________

Own 60.0% 30.1% 89.6%

Rent 39.5 68.9 10.4

Refused 0.5 1.0 0.0

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

Monthly Rent All

Respondents

Low Income (less

than $25k/yr)

65 and

___________________ Older

Less than $200 11.6% 14.6% 34.3%

$200to$499 18.7 38.8 25.4

$500 to $999 35.9 34.0 9.0

$1,000to$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

J-54

EE-RM-PET-100 COMMENT 88
7. Do you have a satellite dish for your TV reception?

Response All Low Income 65 and Older

Respondents (less than

______________ ______________ $25k/yr) ______________

Yes 4.5% 4.9% 3.0%

No 95.5 95.1 97.0

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

Number of Autos

_____________________

All Respondents

________________

Low Income (less

than_$25k/yr)

65 and Older

________________

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

5orMore 1.1 0.0 0.0

Refused 0.2 0.0 0.0

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

10. Where do you do your laundry?

Location

____________________________

All

Respondents

_______________

Low Income

(less than

$2SkIyr)

65 and Older

_______________

Home 72.1% 46.6% 77.6%

Laundromat 7.3 13.6 3.0

Apartment Laundry Room 18.7 35.9 16.4

Other 1.4 3.9 1.5

Refused 0.5 0.0 1.5

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

Response

_______________

All

Respondents

_______________

Low Income

(less than

S25k/yr)

65 and Older

_______________

Yes 17.3% 9.7% 26.9%

No 82.3 89.3 70.1

Refused 0.5 1.0 3.0

J-55

EE-RM-PET-100 COMMENT 88
(If yes)

-*How much did you pay for your new clothes washer?

Price Range All Respondents

________________

Low Income (less

than $25kIyr)

65 and Older

_____________

$200-$299 4.1%

_

20.0%

________________

0.0%

$300-$399 11.0 0.0 11.1

$400-$499 37.0 30.0 33.3

$500-$599 16.4 20.0 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

-*Why did you purchase the clothes washer?

Response All Low Income (less 65 and

___________________________________________ Respondents than $25k/yr) Older

Replaced your old clothes washer because it was 56.0% 40.0% 50.0%

not working properly ___________ _______________

Replaced your old clothes washer because you 13.3 0.0

________

27.8

wanted a newer model

___________

Been washing clothes outside home and decided to 6.7

_______________

0.0

________

5.6

purchase a clothes washer for your home

Moved to a new residence and needed a clothes

_____________

16.0

_________________

30.0

_________

0.0

washer

Bought a new clothes dryer and wanted a matching

______________

1.3

__________________

0.0

__________

0.0

clothes washer

_____________ __________________

Other (please specifr) 6.7 10.0 11.1

12. The majority of residential clothes washers sold in the U.S. are vertical axis machines (see Figure 1), with

horizontal axis machines comprising the remaining portion (see Figure 2). Before today, did you know

that you could buy a horizontal axis machines for residential use?

Response All

Respondents

Low Income

(less than

$25k/yr)

65 and Older

_______________

Yes

_______________

43.0% 32.0%

_______________

40.3%

No 56.3 66.0 56.7

Refused 0.7 1.9 3.0

(If yes)

J-56

EE-RM-PET-100 COMMENT 88
-*Where did you learn this?

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

Response

_______________

All

Respondents

_______________

Low Income

(less than

$25kIyr)

65 and Older

_______________

Yes 57.0% 58.3% 41.8%

No 42.3 40.8 55.2

Refused 0.7 1.0 1.5

(If yes)

-*Where did you wash them?

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

machine?

Response

_______________

All

Respondents

_______________

Low Income

(less than

$25k/yr)

65 and Older

_______________

Yes 70.0% 72.8% 56.7%

No 28.4 25.2 40.3

Refused 1.7 1.9 3.0

(If no)

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

J-57

EE-RM-PET-100 COMMENT 88
15. Suppose you went home today and learned that your clothes washer does not work. You decide to

purchase a new clothes washer. Please indicate the highest price you could afford to pay for a new

clothes washer.

Highest Price

_______________

All

Respondents

_______________

Low Income

(less than

$25kIyr)

65 and Older

$300 3.1% 6.8%

_______________

1.5%

$400 18.7 34.0 19.4

$500 35.5 37.9 31.3

$600 19.4 10.7 17.9

$700 7.3 1.0 7.5

$800 5.0 1.0 6.0

$900 0.5 0.0 0.0

$1000 9.0 4.9 11.9

Refused 1.6 3.9 4.5

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

Price

____________________

All

Respondents

________________

Low Income

(less than

$2SkIyr)

65 and Older

________________

Less than $350 0.7% 1.0% 0.0%

$350-$449 29.8 26.2 28.4

$450-$549 48.9 52.4 52.2

$550-$649 14.9 12.6 11.9

$650-$750 4.0 4.9 3.0

Greater than $750 0.5 1.0 1.5

Refused 0.9 1.9 3.0

17. The price of a new clothes washer ranges from a low of $200 to a high of $1,200. If you were to

purchase a new clothes washer today, what price range would you hope to stay within?

Price Range

_______________

All

Respondents

_______________

Low Income

(less than

$25k/yr)

65 and Older

$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

J-58

EE-RM-PET-100 COMMENT 88
18. If you were to purchase a new clothes washer today, what means would you use to pay for it?

Payment Method

____________________

All

Respondents

Low Income (less

than $2SkIyr)

65 and Older

Cash 10.2% 14.6%

____________________

7.5%

Check 24.3 21.4 16.4

Credit Card 41.6 30.1 62.7

Store Financing 10.2 14.6 6.0

Credit Card Financing 9.0 7.8 3.0

BankLoan 1.4 3.9 1.5

HomeEguityLoan 0.7 1.0 1.5

Other 2.1 5.8 1.5

Refused 0.5 1.0 0.0

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

15 years, on average. This machine is not working properly and you call a service technician to look into

the problem. The technician gives you the option of fixing your 10 years old clothes washer for $150

or purchasing a new one with the same features for $400. What would you do?

Response

______________________________

All

Respondents

Low Income (less

than $25kIyr)

65 and

Older

Purchase the new machine at $400 63.6% 54.4 76.1

Fixyouroldmachineat$150 32.9 37.9 20.9

Do laundry somewhere else 1.7 4.9 0.0

Shop for a used machine 0.9 1.9 1.5

Refused 0.9 1.0 1.5

20. Imagine you have a clothes washer that is 10 years old, which the manufacturer claims should last of 15

years, on average. This machine is not working properly and you call a service technician to look into

the problem. The technician gives you the option of fixing your 10 years old clothes washer for $150

or purchasing a new one with the same features for $450. What would you do?

Response

______________________________

All

Respondents

Low Income (less

than $25kIyr)

65 and Older

Purchase the new machine at $450 53.9% 42.7

____________

67.2

Fix your old machine at $150 41.8 47.6 31.3

Do laundry somewhere else 0.9 3.9 0.0

Shop for a used machine 2.1 3.9 0.0

Refused 1.1 2.0 1.5

21. Imagine you have a clothes washer that is 10 years old, which the manufacturer claims should last for

15 years, on average. This machine is not working properly and you call a service technician to look into

the problem. The technician gives you the option of fixing your 10 years old clothes washer for $150

or purchasing a new one with the same features for $650. What would you do?

J-59

EE-RM-PET-100 COMMENT 88
Response All

Respondents

Low Income (less

than $25k/yr)

65 and Older

______________________________ _____________

Purchase the new machine at $650 16.3% 11.7 31.3

Fixyouroldmachineat$150 74.7 69.9 65.7

Do laundry somewhere else 2.6 8.7 1.5

Shop for a used machine 5.2 8.7 0.0

Refused 1.1 1.0 1.5

J-60

EE-RM-PET-100 COMMENT 88
Selected Cross Tabular Results

Relationship Between Experience With HorizontalAxis Machines and Willingness to Consider

Purchase:

People Who Have Experience with Horizontal Axis

Clothes Washing Machines

Percent

J'ota1238)

Would Consider Purchasing a H-Axis Machine 69.3%

Would Not Consider Purchasing a H-Axis Machine 30.7

People Who Have NO Experience with Horizontal

Axis Clothes Washing Machines

Percent

Tota1 176)

Would Consider Purchasing a H-Axis Machine 73.3%

Would Not Consider Purchasing a H-Axis Machine 26.7

J-6 1

EE-RM-PET-100 COMMENT 88
J.13 CONJOINT ANALYSIS CARDS AND INSTRUCTIONS

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CLOTHES WASHER CARD -1

Price of Clothes Washer $450

Energy and Water Savings $50

Wash Capacity Standard

Top or Front Load Top

Wash/Rinse Temperature Cold, Warm

Load Size Non-Adjustable

CLOTHES WASHER CARD -2

Price of Clothes Washer $400

Energy and Water Savings $50

Wash Capacity Extra

Top or Front Load Front

Wash/Rinse Temperature Cold, Warm

Load size Non-Adjustable

CLOTHES WASHER CARD -3

Price of Clothes Washer $400

Energy and Water Savings $10

Wash Capacity Extra

Top or Front Load Front

Wash/Rinse Temperature Cold, Warm, Hot

Load Size Non-Adjustable

J-62

EE-RM-PET-100 COMMENT 88
CLOTHES WASHER . CARD -4

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Price of Clothes Washer $400

Energy and Water Savings $0

Wash Capacity Standard

Top or Front Load Front

Wash/Rinse Temperature Cold, Warm, Hot

Load Size Non-Adjustable

CLOTHES WASHER CARD -5

Price of Clothes Washer $450

Energy and Water Savings $0

Wash Capacity Extra

Top or Front Load Front

Wash/Rinse Temperature Cold, Warm

Load Size Adjustable

CLOTHES WASHER CARD -6

Price of Clothes Washer $450

Energy and Water Savings $0

Wash Capacity Extra

Top or Front Load Front

Wash/Rinse Temperature Cold, Warm, Hot

Load Size Adjustable

J-63

EE-RM-PET-100 COMMENT 88
CLOTHES WASHER . CARD -7

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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

CLOTHES WASHER CARD -8

Price of Clothes Washer $650

Energy and Water Savings $0

Wash Capacity Extra

Top or Front Load Top

Wash/Rinse Temperature Cold, Warm

Load Size Non-Adjustable

CLOTHES WASHER CARD -9

Price of Clothes Washer $650

Energy and Water Savings $50

Wash Capacity Standard

Top or Front Load Front

Wash/Rinse Temperature Cold, Warm, Hot

Load Size Adjustable

J-64

EE-RM-PET-100 COMMENT 88
CLOTHES WASHER . CARD -10

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Price of Clothes Washer $400

Energy and Water Savings $50

Wash Capacity Extra

Top or Front Load Top

Wash/Rinse Temperature Cold, Warm, Hot

Load Size Adjustable

CLOTHES WASHER CARD -11

Price of Clothes Washer $400

Energy and Water Savings $0

Wash Capacity Standard

Top or Front Load Front

Wash/Rinse Temperature Cold, Warm

Load Size Adjustable

CLOTHES WASHER CARD -12

Price of Clothes Washer $400

Energy and Water Savings $10

Wash Capacity Extra

Top or Front Load Top

Wash/Rinse Temperature Cold, Warm

Load Size Adjustable

J-65

EE-RM-PET-100 COMMENT 88
CLOTHES WASHER . CARD -13

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Price of Clothes Washer $650

Energy and Water Savings $10

Wash Capacity Standard

Top or Front Load Front

Wash/Rinse Temperature Cold, Warm

Load Size Adjustable

CLOTHES WASHER CARD -14

Price of Clothes Washer $400

Energy and Water Savings $0

Wash Capacity Standard

Top or Front Load Top

Wash/Rinse Temperature Cold, Warm, Hot

Load Size Adjustable

CLOTHES WASHER CARD -15

Price of Clothes Washer $650

Energy and Water Savings $0

Wash Capacity Extra

Top or Front Load Top

Wash/Rinse Temperature Cold, Warm, Hot

Load Size Adjustable

J-66

EE-RM-PET-100 COMMENT 88