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Lawrence Berkeley National Laboratory

Title:

An Analysis of the Price Elasticity of Demand for Household Appliances

Author:

Dale, Larry

Publication Date:

06-10-2008

Publication Info:

Lawrence Berkeley National Laboratory

Permalink:

http://escholarship.org/uc/item/5qr2f2nz

Keywords:

price elasticity of demand

Abstract:

This report summarizes our study of the price elasticity of demand for home appliances, including

refrigerators, clothes washers, and dishwashers. In the context of increasingly stringent appliance

standards, we are interested in what kind of impact the increased manufacturing costs caused by

higher efficiency requirements will have on appliance sales. We begin with a review of existing

economics literature describing the impact of economic variables on the sale of durable goods. We

then describe the market for home appliances and changes in this market over the past 20 years,

performing regression analysis on the shipments of home appliances and relevant economic

variables including changes to operating cost and household income. Based on our analysis, we

conclude that the demand for home appliances is price inelastic.
An Analysis of the Price Elasticity of Demand for 

Household Appliances 

Larry Dale and K. Sydny Fujita 

February 2008 

Energy Analysis Department 

Environmental Energy Technologies Division 

Lawrence Berkeley National Laboratory 

University of California 

Berkeley, CA 94720 
DISCLAIMER 

This document was prepared as an account of work sponsored by the United States 

Government. While this document is believed to contain correct information, neither the 

United States Government nor any agency thereof, nor The Regents of the University of 

California, nor any of their employees, makes any warranty, express or implied, or 

assumes any legal responsibility for the accuracy, completeness, or usefulness of any 

information, apparatus, product, or process disclosed, or represents that its use would not

infringe privately owned rights. Reference herein to any specific commercial product, 

process, or service by its trade name, trademark, manufacturer, or otherwise, does not 

necessarily constitute or imply its endorsement, recommendation, or favoring by the 

United States Government or any agency thereof, or The Regents of the University of 

California. The views and opinions of authors expressed herein do not necessarily state or 

reflect those of the United States Government or any agency thereof or The Regents of 

the University of California. 

This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy of the 

U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
i

TABLE OF CONTENTS 

1.0 INTRODUCTION.......................................................................1 

 1.1 Literature Review...............................................................1 

 1.1.1 Price.......................................................................1 

 1.1.2 Income....................................................................2 

 1.1.3 Appliance Efficiency and Discount Rates........................2 

2.0 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

 CLOTHES WASHERS, AND DISHWASHERS.................................3 

 2.1 Physical Household and Appliance Variables.............................3 

 2.2 Economic Variables............................................................3 

3.0 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

 SHIPMENTS.............................................................................5 

 3.1 Broad Trends....................................................................5 

 3.2 Model Specification............................................................7 

 3.3 Model Discussion...............................................................7 

4.0 ANALYSIS RESULTS.................................................................8 

 4.1 Individual Appliance Model..................................................8 

 4.2 Combined Appliance Model..................................................9 

5.0 CONCLUSION...........................................................................9 

APPENDIX A. ADDITIONAL REGRESSION SPECIFICATIONS AND 

 RESULTS....................................................11 

A.1 Lower Consumer Discount Rate....................................................11 

A.2 Disaggregated Variables..............................................................11 

APPENDIX B. DATA USED IN THIS ANALYSIS..............................13 

REFERENCES..................................................................................15 

 
ii

LIST OF CHARTS 

Chart 2.1 Trends in Appliance Shipments, Housing Starts and Replacements....4 

Chart 3.1 Residual Shipments and Appliance Price....................................6 

LIST OF TABLES 

Table 1.1 Estimates of the Impact of Price, Income and Efficiency on 

 Automobile and Appliance Sales...............................................2 

Table 1.2 Discount Rates.....................................................................3 

Table 2.1 Physical Household and Appliance Variables...............................4 

Table 2.2 Economic Variables...............................................................4 

Table 3.1 Simple Estimate of Total Price Elasticity of Demand......................6 

Table 3.2 Tabular Estimation of Relative Price Elasticity of Demand..............6

Table 4.1 Individual Appliance Model Results..........................................9 

Table 4.2 Combined Appliance Model Results..........................................9 

Table A.1 Combined and Individual Results, 20% Discount Rate..................11 

Table A.2 Disaggregated Regression Results, 37% Discount Rate..................12 
iii

1.0 INTRODUCTION 

This article summarizes our study of the price elasticity of demand1 for home appliances, 

including refrigerators, clothes washers and dishwashers. In the context of increasingly 

stringent appliance standards, we are interested in what kind of impact the increased 

manufacturing costs caused by higher efficiency requirements will have on appliance 

sales. We chose to study this particular set of appliances because data for the elasticity 

calculation was more readily available for refrigerators, clothes washers, and dishwashers 

than for other appliances. 

We begin with a review of the existing economics literature describing the impact of 

economic variables on the sale of durable goods. We then describe the market for home

appliances and changes in it over the past 20 years. We conclude with summary and 

interpretation of the results of our regression analysis and present estimates of the price 

elasticity of demand for the three appliances. 

1.1 Literature Review 

There are relatively few studies measuring the impact of price, income and efficiency on 

the sale of household appliances. In this section we provide a short review of this 

literature which suggests the likely importance of these variables. 

1.1.1 Price 

The goal of many of the studies covered in this review is to measure the impact of price 

on sales in a dynamic market. One study of the automobile market prior to 1970 finds the 

price elasticity of demand to decline over time. The author explains this as the result of 

buyers delaying purchases after a price increase but eventually making the purchase out 

of necessity (Table 1.1).2 A contrasting study of household white goods also prior to 

1970, finds the elasticity of demand to increase over time as more price-conscious buyers 

enter the market.3 A recent analysis of refrigerator market survey data finds that 

consumer purchase probability decreases with survey asking price.4

 Estimates of the 

price elasticity of demand for different brands of the same product tend to vary. A

review of 41 studies of the impact of price on market share found the average brand price 

elasticity5 to be -1.75.6 The average estimate of price elasticity of demand reported in 

 

1

Price elasticity is the percent change in shipments given a percent change in price. 

2

 S. Hymens "Consumer Durable Spending: Explanation and Prediction” Brookings Papers on Economic Activity. 1971 

3

P. Golder and G. Tellis, "Beyond Diffusion: An Affordability Model of the Growth of New Consumer Durables" Journal of 

Forecasting. 1998. 

4

D. Revelt and K. Train, "Mixed Logit with Repeated Choices: Household's Choice of Appliance Efficiency Level." Review of 

Economics and Statistics. 1997 

5

Brand price elasticity is the percent change in shipments given a percent change in price for a particular brand of appliance. Sales of 

a particular brand of appliance will be more sensitive to price changes than sales of appliances in general. 

6

G. Tellis. "The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric Models of Sales". Journal of Marketing 

Research. 1988. 
2

these studies is -0.33 in the appliance market and -0.47 in the combined automobile and 

appliance markets. 

1.1.2 Income 

Higher income households are more likely to own household appliances.7 The impact of 

income on appliance shipments is explored in two econometric studies of the automobile

and appliance markets. 8 9 The average income elasticity of demand is 0.50 in the 

appliance study cited in the literature review; the average income elasticity is much larger 

in the automobile study. 

Table 1.1 Estimates of the Impact of Price, Income and Efficiency on 

 Automobile and Appliance Sales 

Product Price 

Elasticity

Income 

Elasticity

Brand Price 

Elasticity

Implicit Discount 

Rate Model Data years

Time 

Period

1 Automobiles1

-1.07 3.08 - - Linear Regression, stock adjustment Short run

2 Automobiles1

-0.36 1.02 - - Linear Regression, stock adjustment Long run

3 Cloths Dryers2

-0.14 0.26 - - Cobb-Douglas, diffusion 1947-1961 Mixed

4 Room Air Conditioner2

-.376

0.45 - - Cobb-Douglas, diffusion 1946-1962 Mixed

5 Dishwasher2

-0.42 0.79 - - Cobb-Douglas, diffusion 1947-1968 Mixed

6 Refrigerators3

-0.37 - - 39% Logit probability, using survey data. 1997 Short run

7 Various4

- - -1.767- Multiplicative regregression Mixed

8 Room Air Conditioner5

- - -1.72 - Non-linear diffusion 1949-1961 Short run

9 Cloths Dryers5

- - -1.32 - Non-linear diffusion 1963-1970 Short run

Sources:

1 S. Hymens. "Consumer Durable Spending: Explanation and Prediction" Brookings Papers on Economic Activity. 1971

2 P. Golder and G. Tellis, "Beyond Diffusion: An Affordability Model of the Growth of New Consumer Durables" Journal of Forecasting. 1998.

3 D. Revelt; K.Train,"Mixed Logit with Repeated Choices: Households Choice of Appliance Efficiency Level" Review of Economics and Statistics. 1997

4 G. Tellis. "The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric Models of Sales". Journal of Marketing Research. 1988.

5 D.Jain; R.Rao. "Effect of Price on the Demand for Durables: Modeling, Estimation and Findings" Journal of Business and Economic Statistics. 1990.

Notes:

6 Logit probability results are not directly comparable to other elasticity estimates in this table.

7 Average brand price elasticity across 41 studies.

1.1.3 Appliance Efficiency and Discount Rates 

Many studies estimate the impact of appliance efficiency on consumer appliance choice. 

Typically, this impact is summarized by the implicit discount rate, i.e., the rate consumers 

use to compare future appliance operating cost savings against an appliance purchase 

price premium. One early and much cited study concludes that consumers use a 20% 

implicit discount rate when purchasing room air conditioners (Table 1.2).10 A survey of 

several studies of different appliances suggests that the average consumer implicit 

discount rate is about 37%.11 In Section 3, we use a discount rate of 37% to estimate 

 

7

Energy Information Agency. “The Effect of Income on Appliances in U.S. Households.” U.S. Department of Energy: 

http://www.eia.doe.gov/emeu/recs/appliances/appliances.html. 2002. Accessed February 1, 2007 

8

S. Hymens "Consumer Durable Spending: Explanation and Prediction." Brookings Papers on Economic Activity. 1971

9

P. Golder and G. Tellis, "Beyond Diffusion: An Affordability Model of the Growth of New Consumer Durables" Journal of 

Forecasting. 1998.

10 J. Hausman. "Individual discount rates and the purchase and utilization of energy using durables." The Bell Journal of Economics. 

1979. 

11 K. Train. "Discount rates in consumer' energy related decisions: a review of the literature." Energy. 1985
3

discounted operating costs, based on appliance efficiency. The implications of a 20% 

discount rate are considered in Appendix A. 

Table 1.2 Discount Rates 

Product Implicit 

Discount Rate Model

Room Air Conditioners1

20% Qualitative choice, survey data

Household Appliances237%3

Assorted 

1 J. Hausman. "Individual discount rates and the purchase and utilization of energy using durables." 

The Bell Journal of Economics. 1979.

2 K. Train. "Discount rates in consumer' energy related decisions: a review of the literature." Energy. 1985 

3 Averaged across several household appliance studies referenced in this work. 

2.0 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, 

 CLOTHES WASHERS, AND DISHWASHERS 

In this section we evaluate variables that appear to account for refrigerator, clothes 

washer and dishwasher shipments, including physical variables and economic variables. 

2.1 Physical Household and Appliance Variables 

Several variables influence the sale of refrigerators, clothes washers and dishwashers. 

The most important for explaining appliance sales trends are the annual number of new

households formed (housing starts) and the number of appliances reaching the end of 

their operating life (replacements). Housing starts influence sales because new homes are 

often provided with – or soon receive – new appliances, including dishwashers and 

refrigerators. Replacements are correlated with sales because new appliances are 

typically purchased when old ones wear out. In principle, if households maintain a fixed 

number of appliances, shipments should equal housing starts plus appliance 

replacements. 

2.2 Economic variables 

Appliance price, appliance operating cost and household income are important economic 

variables affecting shipments. Low prices and operating costs encourage household 

appliance purchases and a rise in income increases householder ability to purchase 

appliances. In principle, changes in economic variables should explain changes in the

number of appliances per household. 

During the 1980 – 2002 study period, annual shipments grew 69% for clothes washers, 

81% for refrigerators and 105% for dishwashers (Table 2.1). This rising shipments trend 

is explained in part by housing starts, which increased 6% and by appliance 

replacements, which rose between 49% and 90% over the period (Table 2.1).12 For 

 

12 Appliance replacements are determined from the expected operating life of refrigerators (19 years), cloths washers (14 years) and 

dishwashers (12 years) and from past shipments. Replacements are further described in Appendix II. 
4

mature markets such as these, replacements exceed appliance sales associated with new 

housing construction. 

Table 2.1 Physical Household and Appliance Variables 

Appliance Change Change Change

1980 2002 (%) 1980 2002 (%) 1980 2002 (%)

Refrigerators 5.124 9.264 81% 1.723 1.822 6% 3.93 5.84 49%

Clothes Washers 4.426 7.492 69% 1.723 1.822 6% 3.66 5.50 50%

Dishwashers 2.738 5.605 105% 1.723 1.822 6% 1.99 3.79 90%

Shipments: Number of units sold, in millions. AHAM Factbook and Appliance Magazine.

Housing starts: Annual number of new homes constructed. U. S. Census. 

Replacements: Average of annual lagged shipments, with lag equal to expected appliance operating life, 

plus or minus 5 years.

Shipments Housing Starts Replacements

Nevertheless, it is apparent that appliance shipments increased somewhat more rapidly 

than housing starts and replacements. This is shown by comparing the beginning and end 

points of lines representing “starts plus replacements” (uppermost solid line) and 

“shipments” (diamond linked line) (Chart 2.1). In 1980 the “shipment” line begins below 

the “starts plus replacements” line. In 2002, the “shipments” line ends above the “starts 

plus replacements” line. This more rapid increase in shipments, compared to housing 

starts plus replacements, suggests that the appliance per household ratio increased over 

the study period. 

Chart 2.1 Trends in Appliance Shipment, Housing Starts and Replacements 

Economic variables, including price, cost and income, may explain this increase in appliances per 

household. Over the period, appliance prices increased 40% to 50%, operating costs fell between 

33% and 72% and median household income rose 16% (Table 2.2). 

Table 2.2 Economic Variables 

Appliance Change Change Change

1980 2002 (%) 1980 2002 (%) 1980 2002 (%)

Refrigerators 1208 726 -40% 333 94 -72% 37,447 43,381 16%

Clothes Washers 779 392 -50% 262 175 -33% 37447 43381 16%

Dishwashers 713 368 -48% 183 95 -48% 37447 43381 16%

Price. Shipment weighted retail sales price, in 1999 dollars for selected years. AHAM Fact Book, TSDs.

Operating cost. Annual electricity price times electricity consumption (UEC), for selected years. 1999 dollars. AHAM fact book.

Income. Mean household income. U.S. Census.

Price Operating Cost Household Income

Refrigerator Shipments

0

1

2

3

4

5

6

7

8

9

10

1980 1984 1988 1992 1996 2000

Millions .

New Housing

Retirements

Shipments

Clothes Washer Shipments

0

1

2

3

4

5

6

7

8

1980 1984 1988 1992 1996 2000

Millions .

Dishwasher Shipments

0

1

2

3

4

5

6

1980 1984 1988 1992 1996 2000

Millions .
5

3.0 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE 

 SHIPMENTS 

Little data is available for estimating the impact of economic variables on the demand for 

appliances. Industry operating cost data is incomplete – appliance energy use data is 

available for only 12 years of the 1980-2002 study period. Industry price data is also 

incomplete – available for only 8 years of the study period for each of the appliances. 

The lack of data suggests that regression analysis can at best evaluate broad data trends, 

utilizing relatively few explanatory variables. In this section we begin by describing 

broad trends apparent in the economic and physical household data sets. We then specify 

a simple regression model to measure these trends, making assumptions to minimize the 

number of explanatory variables. Finally, we present results of the regression analysis 

and our estimate of the price elasticity of demand for appliances. In an appendix, we also 

present the results of regression analysis performed with more complex models, and used 

to test assumptions made to specify the simple model. These results support the simple 

model specification, and estimates of the price elasticity of appliance demand measured 

with that model. 

3.1 Broad Trends 

In this section we review trends in the physical household and economic data sets and 

posit a simple approach for estimating the price elasticity of appliance demand. As 

noted above, the physical household variables (starts and appliance replacements) explain 

most of the variability in appliance shipments over the period.13 We assume the rest of 

the variability in shipments (residual shipments) is explained by economic variables, and 

present a tabular method for measuring price elasticities described below. 

To illustrate this tabular approach, we define two new variables – residual shipments and 

total price. Residual shipments are defined as the difference between shipments and 

physical household demand (starts plus replacements). Total price is defined as appliance 

price plus the present value of lifetime appliance operating cost.14 Over the study period, 

residual shipments increase 30% for refrigerators, 19% for clothes washers and 23% for 

dishwashers in proportion to total shipments. At the same time, total prices decline 47%, 

45% and 48% for refrigerators, clothes washers and dishwashers, respectively. Assuming 

that total price explains the entire change in per household appliance purchases, we 

calculate a rough estimate of the total price elasticity of demand equal to -.48 for 

refrigerators, -.32 for clothes washers and -.37 for dishwashers (Table 3.1). 

 

13 A log linear regression of the form: Shipments = a + b (Housing starts) + c (Retirements), 

indicates that these two variables explain 89% of the variation in refrigerator shipments, 97% of the 

variation in cloths washer shipments and 97% of the variation in dishwasher shipments. 

14 Present value operating cost is calculated assuming a 19 year operating life for refrigerators, 14 year 

operating life for washing machines and a 12 year operating life for dishwashers. A 37% discount rate is 

used to sum annual operating costs into a total present value operating cost. 
6

Table 3.1 Simple Estimate of Total Price Elasticity of Demand 

Appliance

Residual 

Shipments, 

millions Change Change Elasticity

1980 2002 2002 (%) 1980 2002 (%)

Refrigerators -0.5 1.6 2.1 30% 1541 820 -61% -0.48

Clothes Washers -1.0 0.2 1.1 19% 1042 567 -59% -0.32

Dishwashers -1.0 -0.01 1.0 23% 896 464 -64% -0.37

Residual Shipments, 

millions Total Price

The negative correlation between total price and residual shipments suggested by these 

negative price elasticities is illustrated in a graph of residual shipments and total price 

(Chart 3.1). 

Chart 3.1 Residual Shipments and Appliance Price 

Household income rose during the study period, making it easier for households to 

purchase appliances. Assuming that a rise in income has a similar impact on shipments 

as a decline in price, we incorporate the impact of income by defining a third variable, 

termed relative total price, calculated as total price divided by household income.15 The 

percent decline in relative price for the three appliances divided by the percent change in 

residual shipments suggests a rough estimate of relative price elasticity equal to -.40 for 

refrigerators, -.26 for clothes washers and -.30 for dishwashers (Table 3.2). 

Table 3.2 Tabular Estimation of Relative Price Elasticity of Demand 

Appliance Change Change Elasticity

1980 2002 (%) 1980 2002 (%)

Refrigerators -0.532 1.597 30% 0.041 0.019 -74% -0.40

Clothes Washers -0.953 0.174 19% 0.028 0.013 -72% -0.26

Dishwashers -0.974 -0.005 23% 0.024 0.011 -76% -0.30

Residual Shipments Relative Total Price

 

15 Recall that the income elasticity of demand cited in the literature review is .50 and the price elasticity of 

demand cited in the review averages -.35. This suggests that combining the effects of income and price 

will yield an elasticity less negative than price elasticity alone. 

Refrigerators

400

800

1200

1600

2000

-2.0 -1.0 0.0 1.0 2.0

Total Price

Residual Shipments .

Interpolation

Observation

Clothes Washers

400

600

800

1000

1200

-1.5 -1.0 -0.5 0.0 0.5

Total Price

Residual Shipments .

Dishwashers

200

400

600

800

1000

-2.0 -1.5 -1.0 -0.5 0.0 0.5

Total Price

Residual Shipments .
7

3.2 Model Specification 

The limited price data suggests using a simple regression model to estimate the impact of 

economic variables on shipments, using few explanatory variables. The equation chosen 

for this analysis includes one physical household variable (starts plus replacements) and 

one relative price variable (the sum of price plus operating cost, divided by income) 

(Equation 3.1). These variables in this model, termed the individual appliance model, are

defined in foot notes below and in Appendix B. 16 17 

Equation 3.1 Shipments = a + b (Relative Price) + c (Starts+Replacements) 

The natural logs are taken of all variables so that the estimated coefficients for each 

variable in the model may be interpreted as the percent change in shipments associated 

with the percent change in the variable. Thus, the coefficient b in this model is 

interpreted as the relative price elasticity of demand for the three appliances. 

A combined regression equation is used to estimate an average price elasticity of demand 

across the three appliances, using pooled data in a single regression (Equation 3.2). A 

combined regression specification is justified, given limited data availability and 

similarity in price and shipment behavior across appliances (Chart 3.1). Thus, the model 

represented by the combined regression equation is considered the basic model in our 

analysis of appliance shipments. 

Equation 3.2 Shipments = a + b (Relative Price) + c (Starts+Replacements) + d (CW) + 

 e (DW)18

3.3 Model Discussion 

The most important assumption used to specify this model is that changes in economic 

variables over the study period – income, price and operating cost – are responsible for 

all observed growth in residual appliance shipments. In other words, we assume other

possible explanations, such as changing consumer preferences and increases in the 

quality of appliances, had no impact. This assumption seems unlikely but without 

additional data, the impact of this assumption on the price elasticity of demand cannot be 

measured. We effectively assume that changes in consumer preferences and appliance 

characteristics, while affecting which specific models are purchased, have relatively little 

impact on the total number of appliances purchased in a year. 

Three additional assumptions used to specify this model deserve comment. The relative 

price variable is specified in the model, assuming that (1) the correct implicit discount 

 

16 Shipments is the quantity of appliances sold, housing starts is the number of new homes, replacements is 

the number at the end of their operating life and ability to pay is defined in footnote (15) below (natural 

logs taken of all variables). 

17

Income

(First Price) (Present Value Operating Cost)

Income

Total Price Relative Price +

= =

. 

18 CW and DW are dummy variables for clothes washers and dishwashers. 
8

rate is used to combine appliance price and operating cost and that (2) rising income has 

the same impact on shipments as falling total price. The starts + replacements variable is 

specified, assuming (3) that starts and replacements have similar impacts on shipments. 

To investigate the first assumption about discount rates, we calculated “present value 

operating cost” using a 20% implicit discount rate and performed a second regression 

analysis of equation 1. The results of this analysis, presented in Appendix A, indicate 

that the elasticity of relative price is relatively insensitive to changes in the discount rate. 

To investigate the second and third assumptions, we specified a regression model 

separating income from total price and replacements from starts, thus adding two 

additional explanatory variables to the basic model (Equation 3.3). 

Equation 3.3 Shipments = a + b (Total Price) + c (Income) + d (Starts) + 

 e (Replacements) + f (CW) + g (DW).

The results of the regression analysis of this model are also presented in Appendix A. 

These results suggest that the elasticity of total price (coefficient b) is relatively 

insensitive to changes in the treatment of income and starts + replacements in the model. 

4.0 ANALYSIS RESULTS 

4.1 Individual Appliance Model 

The individual appliance regression equations are specified as described in equation 3.1. 

In regression analysis of this model, the elasticity of relative price (b) is estimated to be -

0.4 for refrigerators, -0.31 for clothes washers and -0.32 for dishwashers (Table 6), 

averaging -0.35. These elasticities are similar to those reported in the literature survey 

for appliances (Table 1.1). They are remarkably similar to the price elasticity calculated 

using a tabular approach presented above (Table 3.2). 

The estimated coefficient associated with the starts + replacements variable is close to 

one. A coefficient equal to one for this variable would imply that shipments increase in 

direct proportion to an increase in starts + replacements, holding economic variables 

constant. The high R-squared values (above 95) and t-statistics (above 5) in the results 

provide a measure of confidence in this analysis, despite the very small data set. 

Table 4.1 Individual Appliance Model Results 

Coefficient tStat Coefficient tStat Coefficient tStat

Intercept -1.51 -7.26 -1.47 -8.23 -2.08 -16.78

Relative Full Price -0.40 -6.60 -0.31 -5.69 -0.32 -7.03

Starts+Replacements 1.05 5.90 1.08 6.41 1.35 11.46

R

2

0.954 0.954 0.975

Observations 23 23 23.00

Refrigerator Clothes Washer Dishwasher Variable
9

4.2 Combined Appliance Model 

The combined appliance regression equation is specified as described in equation 3.2. 

Our regression analysis indicates that the model fits the existing shipments data well 

(high R-squared) and that the variables included in the model are statistically significant 

(Table 4.2). The elasticity of relative price estimated with this model is -0.34, close to 

the average value estimated in the individual appliance models (-0.35). It is also similar 

to elasticity estimates reported in the literature survey and calculated using the tabular 

approach above. 

Table 4.2 Combined Appliance Model Result 

Variable Coefficient tStat

Intercept -1.60 -15.54

Relative Full Price -0.34 -10.74

Starts+Replacements 1.21 13.95

CW -0.20 -9.04

DW -0.32 -6.58

R

2

0.983

Observations 69

5.0 CONCLUSION 

At the beginning of this report, we describe the results of a literature search, tabular 

analysis and regression analysis of the impact of price and other variables on appliance 

shipments. In the literature, we find only a few studies of appliance markets that are 

relevant to this analysis, and no studies using time series price and shipments data after 

1980. The information that can be summarized from the literature, suggests that the 

demand for appliances is price inelastic. Other information in the literature suggests that 

appliances are a normal good, such that rising incomes increase the demand for 

appliances. Finally, the literature suggests that consumers use relatively high implicit 

discount rates, when comparing appliance prices and appliance operating costs. 

There is not enough price and operating cost data available to perform complex analysis 

of dynamic changes in the appliance market. In this analysis, we use data available for 

refrigerators, clothes washers and dishwashers to evaluate broad market trends and to 

perform simple regression analysis. 

These data indicate that there has been a rise in appliance shipments and a decline in 

appliance price and operating cost over the period. Household income has also risen 

during this time. To simplify the analysis, we combined the available economic 

information into one variable, termed relative price, and used this variable in a tabular 

analysis of market trends, and a regression analysis. 
10

Our tabular analysis of trends in the number of appliances per household suggests that the

price elasticity of demand for the three appliances is inelastic. Our regression analysis of 

these same variables suggests that the price elasticity of demand is -.35, averaged over 

the three appliances. The price elasticity is similar to estimates in the literature. 

Nevertheless, we stress that the measure is based on a small data set, using very simple 

statistical analysis. More important, the measure is based on an assumption that

economic variables, including price, income and operating costs, explain most of the 

trend in appliances per household in the United States since 1980. 

Changes in appliance quality and consumer preferences may have occurred during this 

period, but they are not accounted for in this analysis. The capacity of most appliances 

has increased since 1980, and it is likely that there have been increases in quality and 

durability as well. If these factors have impacted the sales of appliances, our estimate of 

the price elasticity of demand is an over-estimate, since some of the increases in sales 

over the 1980-2002 time period would have been driven by changing preferences rather 

than decreasing prices. 
11

APPENDIX A. ADDITIONAL REGRESSION SPECIFICATIONS AND RESULTS

As mentioned above, the implicit price variable in the basic regression model is specified using a 

37% implicit discount rate, to aggregate appliance price and operating cost. In addition, the 

implicit price variable is defined assuming that rising income has the same impact on shipments 

as falling total price. Similarly, the Starts+Replacements variable is defined assuming that 

housing starts have a similar impact on shipments as appliance replacements. 

A.1 Lower Consumer Discount Rate 

To investigate the first assumption about discount rates, we calculated “present value operating 

cost” using a 20% implicit discount rate and performed a second regression analysis of equations 

3.1 and 3.2. The estimated coefficient associated with the relative price variable in these 

regressions is almost identical to the coefficients estimated for same variable reported above 

using a 37% implicit discount rate. The elasticity of relative price calculated using a 20% 

discount rate is -.33 in the combined regression and averages -.35 for the three appliances (Table 

A.1). The elasticity of price calculated using a 37% discount rate is -.34 in the combined 

regression and averages -.35 for the three appliances. We conclude from this analysis that the 

elasticity of relative price is relatively insensitive to changes in the discount rate. 

Table A.1 Combined and Individual Results, 20% discount rate 

Variable Coefficient tStat

Intercept -1.53 -14.61

Relative Full Price -0.33 -10.69

Starts+Replacements 1.20 13.65

CW -0.18 -8.69

DW -0.32 -6.57

R

2

0.982

Observations 69

Coefficient tStat Coefficient tStat Coefficient tStat

Intercept -1.36 -6.26 -1.41 -7.49 -2.04 -17.23

Relative Full Price -0.38 -6.50 -0.32 -5.29 -0.33 -7.30

Starts+Replacements 1.04 5.73 1.06 5.83 1.34 11.64

R

2

0.953 0.950 0.977

Observations 23 23 23.00

Variable Refrigerator Clothes Washer Dishwasher

A.2 Disaggregated Variables 

To investigate the second and third assumptions, we constructed a regression model separating 

income from total price and replacements from starts, thus adding two additional explanatory 

variables to the basic model (Equation A.1). 

Equation A.1 Shipments = a + b (Total Price) + c (Income) + d (Starts) + e (Replacements) + 

 f (CW) + g (DW).

The estimated coefficient associated with the total price variable in these regressions is almost 

identical to the coefficients estimated for the relative price variable reported above. The elasticity 
12

of total price in Equation A.1 is -.36 in the combined appliance regression and averages -.35 for 

the three appliances (Table A.2). The elasticity of relative price in Equation 3.2 is -.34 in the 

combined regression and averages -.35 across the individual appliances (Tables 4.1 and 4.2). We 

conclude that the price elasticity calculated in this analysis is relatively insensitive to the 

specification of household income and starts + replacements variables in the model. 

Table A.2 Disaggregated Regression Results, 37% Discount Rate 

Variable Coefficient tStat

Intercept -2.92 -1.26

Income 0.58 2.92

Full Price -0.36 -7.06

Houseing Starts 0.44 10.02

Replacements 0.62 8.12

CW -0.24 -9.25

DW -0.46 -7.68

R2 0.985

Observations 69

Coefficient tStat Coefficient tStat Coefficient tStat

Intercept -6.19 -2.24 -6.64 -1.63 1.00 0.23

Income 0.89 3.80 0.87 2.31 0.20 0.52

Full Price -0.35 -5.48 -0.27 -2.51 -0.43 -5.18

Houseing Starts 0.41 7.38 0.25 3.29 0.62 8.24

Replacements 0.56 6.06 0.56 2.09 0.65 5.86

R2 0.984 0.958 0.979

Observations 23 23 23

Variable Refrigerator Clothes Washer Dishwasher
13

APPENDIX B. DATA USED IN THIS ANALYSIS

1. Appliance Shipments: 

Shipments are defined as the annual number of units shipped in millions. These data 

were collected from the Association of Home Appliance Manufacturers (AHAM) and 

Appliance Magazine as annual values for each year, 1980-2002. 

2. Appliance Price: 

Price is defined as the shipments weighted retail sales price of the unit in 1999 dollars. 

Price values for 1980, 1985, 1986, 1991, 1993, 1994, 1998, and 2002 were collected from 

The AHAM. Fact Book and Department of Energy Technical Support Documents. Price 

values for other years were interpolated from these eight years of data.

3. Housing Starts: 

Housing starts data were collected from U.S. Census construction statistics (C25 reports) 

as annual values for each year, 1980-2002. 

4. Replacements: 

Retirement-driven replacements are estimated with the assumption that some fraction of 

sales arise from consumers replacing equipment at the end of its useful life. Since each 

appliance has a different expected lifespan (19 years for refrigerators19, 14 years for 

clothes washers20, 12 years for dishwashers21), replacements are calculated differently for 

each appliance type. Replacements are estimated as the average of shipments 14-24 

years previous for refrigerators, 9-19 years previous for clothes washers, and 7 to 17 

years previous for dishwashers. Historical shipments data were collected from AHAM 

and Appliance Magazine. 

5. Annual Electricity Consumption: 

Electricity Use (UEC) is defined as the energy consumption of the unit in kilowatt-hours. 

Electricity consumption is dependent on appliance capacity and efficiency. These data 

were provided by AHAM for 1980, 1990-1997 and 1999-2002. Data were interpolated in 

the years for which data were not available. 

6. Operating Cost: 

Operating Cost is the present value of the electricity consumption of an appliance over its 

expected lifespan. The life spans of refrigerators, clothes washers and dishwashers are 

assumed to be 19, 14, and 12 years respectively. Discount rates of 20%22 and 37%23

were used, producing similar estimates of price elasticity. A study by Hausman 

 

19 Duemling, Reubin. 1999 “Product Life of Refrigerators.” University of California, Berkeley, Energy and 

Resources Group masters thesis. 

20 U.S. Department of Energy. 2000. “Technical Support Document (TSD): Energy Efficiency Standards 

for Consumer Products: Clothes Washers.” 

21U.S. Department of Energy. “Technical Support Document: Home Appliances.” 

22 Hausman, Jerry A. 1979 “Individual discount rates and the purchase and utilization of energy-using 

durables.” 

23 Train, Kenneth E. and Terry Atherton. 1995 The Energy Journal, “Rebates, loans, and customers’ 

choice of appliance efficiency level: combining stated- and revealed-preference data.” 
14

recommended a discount rate of “about 20%” in its introduction, and presented results 

ranging from 24.1% to 29% based on his calculations for room air conditioners. A study 

by Train suggests a range of implicit discount rates averaging 35% for appliances. 

7. Income: 

Median annual household income in 2003 dollars. This data was collected for each year, 

1980-2002, from Table H-6 of the U.S. Census, 

http://www.census.gov/hhes/income/histinc/h06ar.html (accessed February 1, 2007). 
15

REFERENCES 

Association of Home Appliance Manufacturers. Fact Book. 2003. 

Duemling, R. Product Life of Refrigerators. University of California, Berkeley, Energy 

and Resources Group Master’s Thesis. 1999. 

Energy Information Agency. The Effect of Income on Appliances in U.S. Households. 

U.S. Department of Energy. 2002. 

Golder, P. and G. Tellis. Beyond Diffusion: An Affordability Model of Growth of New 

Consumer Durables. Journal of Forecasting. 1998. 

Hausman, J. Individual Discount Rates and the Purchase and Utilization of Energy Using 

Durables. The Bell Journal of Economics. 1979. 

Hymens, S. Consumer Durable Spending: Explanation and Prediction. Brookings Papers 

on Economic Activity. 1971. 

Jain, D. and R. Rao. Effect of Price on the Demand for Durables: Modeling, Estimation 

and Findings. Journal of Business and Economic Statistics. 1990. 

Revelt, D. and K. Train. Mixed Logit with Repeated Choices: Household’s Choice of 

Appliance Efficiency Level. Review of Economics and Statistics. 1997 

Tellis, G. The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric 

Models of Sales. Journal of Marketing Research. 1998. 

Train, K. Discount Rates in Consumer Energy Related Decisions: a Review of the 

Literature. Energy. 1985. 

Train, K. and T. Atherton. Rebates, Loans, and Customers’ Choice of Appliance 

Efficiency Level: Combining Stated- and Revealed-Preference Data. The Energy Journal. 

1995.