CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 METHODOLOGY BEHIND S HIPMENTS MODEL METHO DOLOGY .................... 9-1

9.3 DATA INPUTS AND MODEL CALIBRATION .......................................................... 9-3

9.3.1 Historical Shipments ........................................................................................................ 9-3

9.3.2 Markets and Model Calibration ....................................................................................... 9-4

9.3.2.1 Replacements ................................................................................................. 9-5

9.3.2.2 Model Calibration —Existing Households Without Appliance ..................... 9-6

9.3.3 Base

- Case Shipments ....................................................................................................... 9-6

9.4 EFFECT OF INCREASED PURCHASE PRICE ON SH IPMENTS ............................. 9-8

9.5 AFFECTED STOCK ..................................................................................................... 9-10

9.6 EFFECT OF CANDIDATE STANDARD LEVEL ON SH IPMENTS

FORECAST ................................................................................................................... 9-11

LIST OF TABLES

Table 9.3.1 Portable Dehumidifiers: Historical Shipments, Domestic plus Imports .............. 9-4

Table 9.3.2 Product Calss Marke Share of Dehumdifiers ....................................................... 9-7

Table 9.4.1 Change in Relative Price Elasticity After a Purchase Price Change ................. 9-10

LIST OF FIGURE S

Figure 9.3.1 Portable Dehumidifier: Survival and Retirement Functions ................................ 9-5 Figure 9.3.2 Whole-home Dehumidifier: Survival and Retirement Functions ........................ 9-6 Figure 9.3.3 Dehumidifiers: Historical and Base

- Case Shipments Forecast ........................... 9-7 Figu re 9.3.4 Dehumidifiers: Disaggregated Shipments Forecast for Base Case ...................... 9-8 Figure 9.6.1 Shipments Forecasts for Base Case and Each Standards Case .......................... 9-11

9-iCHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future product shipments are a necessary input to calculations of the national energy savings (NES) and net present value (NPV), as well as to the manufacturer impact analysis. This chapter describe s the data and methods the U.S. Department of Energy (DOE) used to forecast annual product shipments and presents results for dehumidifiers considered for this standards rulemaking. Because DOE did not have shipment data for each product classes, total his torical shipments of portable and whole -home dehumidifiers were estimated and then separated by market share of each product classes

.

To project shipments for dehumidifiers, DOE used a shipments model that is calibrated with historical shipments data. Th e shipments model estimate s shipments to specific market

segments, the results for which are then aggregated to estimate total product shipments. To estimate the impacts of potential standard levels on product shipments, the shipments model accounts for the combined effects of changes in purchase price, annual operating cost on the consumer purchase decision.

The shipments model was developed as a part of the national impacts analys is (NIA) spreadsheet

. Appendix 10 A discusses how to access the NIA spreadsheet and provides basic

instructions for its use.

The rest of this chapter explains the shipments models in more detail. Section 9.2 presents the methodology behind the shipments model; section 9.3 describes the data inputs and model calibration; section 9.4 discusses impacts on shipments from changes in product purchase price; section 9.5 discusses the affected stock; and section 9.6 presents the shipments forecast for

one of the candidate standard level s (C SL) that DOE considered

.

9.2 METHODOLOGY BEHIND SHIPMENTS MODEL METH ODOLOGY

DOE developed a national stock model for estimating annual shipments for this standards rulemaking. The model considers market segmentation as a distinct input to the shipments projectio n. As represented by the following equation, the two primary market segments for

dehumidifiers are installations in exiting households without dehumidifiers and replacements.

Sh ip DEF ( j ) = Rpl DEH ( j ) + FTO DEH ( j )

W here:

Ship DEH (j) = t otal shipments of dehumidifiers in year j

, Rpl DEH (j) = units of dehumidifiers retired and replaced in year j , and

FTO DEH (j) = shipments to existing households without dehumidifiers in year j

.

DOE’s shipments model takes an accounting approach, tracking market shares of each product class, the vintage of units in the existing stock, and expected housing stock trends. The

9-1models estimate shipments due to replacements using sales in previous years and assumptions

about the lifetime of dehumidifiers . E stimated sales attributable to replacements in a given year

therefore are equal to the total stock of the appliance minus the sum of the appliances sold in previous years that remain in the stock. As described in chapter 8 of th is preliminary technical support document (TSD), DOE determined the useful service life of dehumidifiers . DOE then

estimate d how long the appliance is likely to remain in stock. The following equation represents how DOE estimated replacement shipments.



### )( )1()( 

# ∑ ∑

0 1

__ _ 

### ageprob Ship jStockjRpl
Rtr ageMax

age j

Nj j p p 

### × =

= = W here:

Stock p (j -1) = total stock of in

- service appliances in year j-1

, prob Rtr (age) = probability that an appliance of a particular age will be retired, and

N = sta rt year for when the model begins its stock accounting (start year is specific to each product based on available historical shipments data).

Stock accounting takes product shipments, a retirement function, and initial in- service product stock as inputs to estimate the age distribution of in

- service product stocks for all years. The age distribution of in-service product stocks is a key input to both the NES and NPV

calculations —the operating costs for any year depend on the age distribution of the stock. The dependence of operating cost on the equipment age distrib ution occurs under a standards case scenario that produces increasing efficiency over time, where by older, less efficient units may have higher operating costs, while younger, more- efficient units will have lower operating costs.

DOE estimated replaceme nts using product retirement functions that it developed based on product lifetimes. DOE based the retirement function on a Weibull distribution for the product lifetime. The shipments model assumes that no units are retired below a minimum product lifetim e and all units are retired before exceeding a maximum product lifetime. The models determine the probability of retirement at a certain age for all products using a Weibull equation:



# e x 

### xP β

α

θ 

    

−

−



### = )( for x > θ and

P(x) = 1 for x ≤ θ

Where:

P(x) = probability that the appliance is still in use at age x ;

x = appliance age;

α = scale parameter, which would be the decay length in an exponential distribution; β = shape parameter, which determines the way in which the failure rate changes through time; and

θ = delay parameter, which allows for a delay before any failures occur.

9-2The retirement probability is the difference in the survival function from one year to

another.

DOE calculated total in

- service stock of a product by integrating histor ical shipments data starting from a specific year. The start year depends on the historical data available for the product. As units are added to the in- service stock, some of the older ones retire and exit the stock. To estimate future shipments, DOE developed a series of equations that define the dynamics and accounting of in-service stocks. For new units, the equation is:



### )1()1,(

_ 

### jShip agejStock
==

Where:

Stock(j, age) = the population of in- service units of a particular age,
j = year for which the in-service stock is being estimated, and Ship (j) = number of units purchased in year j

.

The above equation states that the number of one- year -old units is simply equal to the number of new units purchased the previous year. The following equation describes the accounting of the existing in-service stock of units: 

# [ ] )(1),()1,1(

_ 

### ageprob agejStock agejStock
Rtr 

### × =++

In the above equation, as the year is incremented from j to j+1 , the age is also

incremented from age to age+1 . With time, a fraction of the in- service stock is removed, that

fraction being determined by a retirement probability function, prob Rtr (age) , which is described

in section 9.3. Most replacements are made when a product wears out and fails. Over time, some of the units will be retired and removed from the stock, triggering the shipment of a new unit.

9.3 DATA INPUTS AND MODEL CALIBRATION

The sections below describe the data inputs and market segments considered for

dehumidifiers.

9.3.1 Historical Shipments

For p o rt able dehumidifiers, DOE used data on historical shipments (domestic shipments plus imports) to calibrate its s hipments model for dehumidifiers . DOE’s sources for historical shipments data were (1) DOE’s previous rulemaking for dehumidifiers 1 , and (2) d ata provided by the Association of Home Appliance Manufacturers ( AHAM ) for the period 1999 – 2011 2

. Table 9.3.1 summarizes the historical data on portable dehumidifier shipments.

9-3Table 9.3.1 Portable Dehumidifiers: Historical Shipments, Domestic plus Imports

Year Shipments (thousands) Year Shipments (thousands) Y ear Shipments (thousands)

1972 461 1986 555 2000 975

1973 646 1987 704 2001 806

1974 586 1988 673 2002 799

1975 392 1989 605 2003 1,311

1976 440 1990 743 2004 1,672

1977 314 1991 745 2005 1,957

1978 442 1992 803 2006 1,456

1979 685 1993 983 2007 2,004

1980 673 1994 1,059 2008 1,558

1981 536 1995 1,003 2009 1,700

1982 440 1996 977 2010 1,552

1983 437 1997 820 2011 1,368

1984 591 1998 1,031

1985 588 1999 950

Source: 1999

– 2011: AHAM Data Submittal, 2012. 1972

– 1998 : DOE’s Previous Rulemaking for Dehumidifiers.

DOE assumed that whole -home shipments started from 2004, and the shipments accounted for about 1 percent of the portable dehumidifiers market.

9.3.2 Markets and Model Calibration

The market for dehumidifiers is comprised primarily of replacement units for products

that ha ve been retired from service. Total dehumidifiers shipments are represented by the following equation:

Sh ip DEF ( j ) = Rpl DEH ( j ) + FTO DEH ( j )

Where:

Ship DEH (j) = t otal shipments of dehumidifiers in year j

, Rpl DEH (j) = units of dehumidifiers retired and replaced in year j , and

FTO DEH (j) = shipments to existing households without dehumidifiers in year j

.

The sections below discuss these markets in further detail.

9-49.3.2.1 Replacements

DOE used an accounting method that tracks the total stock of units by vintage to determine shipments to the replacement market

. DOE estimated a stock of dehumidifiers by vintage by integrating historical shipm ents starting from 1972. Over time, some units are retired and removed from the stock, triggering the shipment of a replacement unit. Depending on the vintage, a certain percentage of each type of unit will fail and need to be replaced. To determine when a portable dehumidifier fa ils, DOE used a product survival function based on a lifetime distribution having an average value of 11.0 years. To determine when a whole-home dehumidifier fails, DOE used a product survival function based on a lifetime distribution having an average val ue of 19.01 years. For a more complete discussion of dehumidifier lifetimes, refer

to section 8.2.2.6 of chapter 8 in this preliminary TSD

. Figure 9.3.1 and Figure 9.3.2 show the

survival and retirement function that DOE used to estimate replacement shipments for each dehumidifier type.

Figure 9.3.1 Portable Dehumidifier : Survival and Retirement Functions 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

### Lifetime Dist. for Portable Dehumidifiers

% of Retiring

% of Surviving

9-5Figure 9.3.2 Whole- home Dehumidifier: Survival and Retirement Functions

9.3.2.2 Model Calibration— Existing Households Without Appliance



### To calibrate the estimated shipments with the historical data, DOE introduced into the model a market segment identified as existing households without dehumidifiers, also

referred to as FTOs. DOE es timated that 0. 34 percent of existing households without a dehumidifier would annually purchase this product over the period 20 19 ─20 48

.

9.3.3 Base

- Case Shipments

Figure 9.3.3 shows the forecasted shipments in the base case ( i.e. , the case without new energy efficiency standards) and the historical shipments DOE used to calibrate the forecast. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

### Lifetime Dist. for Whole

- home Dehumidifiers

% of Retiring

% of Surviving

9-6Figure 9.3.3 Dehumidifiers : Historical and Base

- Case Shipments Forecast

DOE’s base

- case shipments model for dehumidifiers used the aggregate shipments, i.e. the shipments for all seven product classes, as the basis for its forecasts. In other words, DOE did not develop a separate shipments model for each dehumidifier product class. As provided in

Table 9.3.2 , DOE based market shares for each of the seven product classes . DOE used the average market shares over the period 2012 – 2048 to disaggr egate forecasted shipments into each of the seven dehumidifier product classes.

Table 9.3.2 Product C l ass Marke t Share of Dehum i difiers

Year Product Class (pints/day)

≤20.00 20.01

- 30.00 30.01

- 35.00 35.01

- 45.00 > 45.00 ≤ 8.0ft 3

Case

Volume(Wholehome) >8.0ft 3

Case

Volume(Wholehome)

Pre

2004 * 33.7 % 23.4 % 21.1 % 21.8 % 0.0 % 0.0 % 0.0 %

Post

2004 ** 29.0 % 23.4 % 21.1 % 21.8 % 3. 7% 0. 7% 0.3 %

*Source: DOE’s previous rulemaking for dehumidifiers, 2009; **Source: AHAM Data Submittal, 2012 (≤20.00 pints/day dehumidifiers); DOE’s previous rulemaking for dehumidifiers (20.01

- ≥45.00 pints/day dehumidifiers); Manufacture interview, 2013 (whole

- home dehumidif iers). 0.0000 0.5000 1.0000 1.5000 2.0000 2.5000 3.0000 3.5000Shipments (million)

Year Shipment

- Historical and Projected

Historical

Projected

9-7Figure 9.3.4 Dehumidifiers : Disaggregated Shipments Forecast for Base Case

9.4 EFFE CT OF INCREASE D PURCHASE PRICE ON SH IPMENTS

Economic theory suggests that, all else being equal, an increase in the price of a good

lead s to a decrease in demand for it. Because DOE projects that appliance standards often result in an increase in the price of the product, DOE conducted a literature review and an analysis of appliance price and efficiency data to estimate the effects on product shipments from increases in product price. DOE also considered the decreases in operating costs from higher energy efficiency and changes over time in household income. Appendix 9A explains the method DOE used to quantify the effe cts of the above variables.

In the literature, DOE found only a few studies of appliance markets that are relevant to

this rulemaking analysis and identified no studies that use time- series data of produc t prices and shipments 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 incre ase the demand for appliances. Finally, the literature suggests that consumers use relatively high implicit discount rates a when comparing appliance prices and operating costs.

DOE found insufficient data on product purchase price and operating cost to perform a thorough analysis of dynamic changes in the appliance market. Rather, it used purchase price and efficiency data specific to residential refrigerators, clothes washers, and room air conditioners during 1980– 2002 to evaluate broad market trends and conduct simple regression analyses. Th e data indicate that there has been a n increase in appl iance shipments and a decrease in appliance

a A high implicit discount rate with regard to operating costs means that consumers do not put much economic value

on the operating cost savings realized from more efficient appliances. Consumers are much more concerned with the higher purchase prices. 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50Shipments (million)

Year Replacement Units New Units

9-8purchase price and operating costs during the period. Household income also increased during

this time . To simplify the analysis, DOE combined the available economic information into one variable, termed the relative price , and used this variable in an analysis of market trends, as well

as to conduct a regression analysis. The relative price is defined using the following expression.

Income PVOCPP

Income TP RP +

==

Where:

RP = r elative price,

TP = tot al price,

Income = household income,

PP = applian ce purchase price, and

PVOC = prese nt value of operating cost.

In the above equation, DOE used an implicit discount rate of 37 percent to determine the present value of operating costs.

DOE’s analysis of market trends suggests that the relative price elasticity of demand for the three appliances is relatively inelastic ( that is

, less than 1.0). DOE’s regression analysis suggests that the relative price elasticity of demand , averaged for the three appliances, is -0.34.

Thus a relative price increase of 10 percent results in a shipments decrease of 3.4 percent. Note that, because the relative price elasticity incorporates the effe cts of three factors (purchase price, operating cost, and household income), the effe ct of any single factor is mitigated by changes to
the other two

.

The relative price elasticity of -0.34 is consistent with estimates in the literature. Nevertheless, DOE stresses that the measure is based on a small data set subject to simple statistical analysis. More important ly , the measure is based on an assumption that economic variables, including purchase price, operating costs, and household income, 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 that period, but DOE did not account for them in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative price elasticity of demand pro vides a reasonable assessment of the impact that purchase price, operating cost, and household income have on product shipments.

Because DOE’s forecasts of shipments and national impacts due to standards consider 30-year period, it needed to consider how the relative price elasticity is affected once a new

standard takes effect. DOE considered the relative price elasticity provided above to be a shortterm value. It was unable to identify sources specific to household durable goods, such as appliances, to indicate how short- and long- term price elasticities differ. To estimate how the relative price elasticity changes over time, therefore, DOE relied on a study pertaining to

automobiles. 3

, 4 Th at study show ed that the automobile price elasticity of demand changes following a change in purchase price. With increasing years after the purchase price change, the price elasticity becomes more inelastic until it reaches a terminal value around the tenth year

9-9after the price change. Table 9.4.1 shows the relative change in the price elasticity of demand for automobiles over time. DOE developed a time- series of relative price elasticities for home appliances based on the relative change in the automobile price elasticity of demand. For years

not shown in Table 9.4.1 , DOE performed a linear interpola tion to obtain the relative price elasticity.

Table 9.4.1 Change in Relative Price Elasticity After a Purchase Price Change

Number of Years After Price Change
Change in e lasticity

r elative to first year 1 2 3 5 10 20

1.00 0.78 0.63 0.4 4 0.35 0.33

Relative price elasticity

- 0.34

- 0.26

- 0.21

- 0.16

- 0.12

- 0.11

Based on the following equation, DOE estimated standards case shipments by incorporating the impact of the relative price into the base- case shipments forecast. Note that in the equation below, the relative price and the relative price elasticity are functions of the year

, because both change with time.


# ( ) 

# ( ) )()(1)()( )( )(

_

_ _ _ _ j RP jejMjNIjRpljShip
RP p BASE p BASE p BASE pSTD ∆× × + + =

Where:

Ship STD_p (j) = total shipments of product p in year j

under the standards case, Rpl BASE_p (j) = units of product p retired and replaced in year j under the base case

, NI BASE_p (j) = n umber of new home installations of product p in year j under the base

case
, M BASE_p (j) = f irst

- time owners market M of product p in year j under the base case

,

e RP (j)= r elative price elasticity in year j (equals

- 0.34 for year 1), and

Δ RP(j)= change in relative price due to a standard level in year j

.

9.5 AFFECTED STOCK

The affected stock is the in -service stock of a product that is affected by a standard level. In addition to the forecast of product shipments under both the base case and the standards case, the affected stock (which represents the difference in the appliance stock between the base case and the standards case) is a key output of DOE’s shipments models. The affected stock quantifies

the effe ct that new product shipments have on the appliance stock because of a standard level. Therefore, the affected stock consists of those in

- service units that are purchased in or after the year the standard takes effect, as desc ribed by the following equation.



## ∑ _

1 _ )( )()( yr Std j

age p p p ageStock jShipjStock Aff

= + =

Where:

9- 10Aff Stock p (j) = affected stock of units of product p of all vintages that are operational in

year j

, Ship p (j) = shipments of product p in year j

, Stock p (j) = stock of units of product p of all vintages that are operational in year j

, age = age of the units (years), and Std_yr = e ffective date of the standard.

As noted for the above equation, to calculate the affected stock, DOE must define the

effective date of the standard. For the N ES and NPV results presented in chapter 10 of this preliminary TSD , DOE assumed that new energy efficiency standards will become effective in 2019. Thus, all appliances purchased starting in 2019 are affected by the standard level.

9.6 EFFECT OF CANDIDATE STANDARD LEVEL ON SHIPMENTS

FORECAST

Th is section compares the base

- case shipments for ecast with the forecast for all the

candidate standard level s (C SL s). For all C SLs, shipments are forecasted to increase

— the effects from the increase in product purchase prices offsets the effects from decreased operating costs, resulting in a net increase in shipments.

Figure 9.6.1 shows total projected shipments of dehumidifiers in the base case and under

each standards case.

Figure 9.6.1 Shipments Forecasts for Base Case and Each Standards Case 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50Shipments (million)

Base Case CSL 1 CSL 2 CSL 3 CSL 4

9- 11REFERENCES

1 DOE –Energy Efficiency and Renewable Energy. Energy Conservation Program for Consumer

Products: Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment, Residential Dishwashers, Dehumidifiers, and Cooking Products, and Commercial Clothes Washers . 2009. Washington, D.C. (Last accessed October 10, 2013.)

< http://www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_produc ts_final_rule_tsd.html >

2 Association of Home Appliance Manufacturers. AHAM Data on Portable Dehumidifiers for DOE Efficiency Standards Rulemaking, November 12, 2012. L etter to Building Technologies Program, U.S. Department of Energy.

3 Hymans, S. Consumer Durable Spending: Explanation and Prediction

. Brookings Papers on Economic Activity . 1971. 1971(1): pp. 234– 239

.

4 Dale, L., and K.S. Fujita. An Analysis of the Price Elasticity of Demand of Household Appliances

. 2008. Lawrence Berkeley National Laboratory: Berkeley, CA. Report No.

LBNL 326E. www.osti.gov/bridge/servlets/purl/929429-Vm1S9X/929429.pdf

. (Access May 2013.)

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