8A-i

APPENDIX 8A. ESTIMATION OF POTENTIAL EQUIPMENT PRICE TRENDS FOR 

FLUORESCENT BALLASTS

TABLE OF CONTENTS

8A.1 INTRODUCTION .................................................................................................. 8A-1

8A.2 DATA EVALUATION AND ANALYSIS ............................................................ 8A-2
8A-1

APPENDIX 8A. ESTIMATION OF POTENTIAL EQUIPMENT PRICE TRENDS FOR 

FLUORESCENT BALLASTS

8A.1 INTRODUCTION

In developing the proposed standards, DOE assumes that the manufacturer costs and 

retail prices of products meeting various efficiency levels remain fixed, in real terms, after 2010 

(the year for which the engineering analysis estimated costs) and throughout the period of the 

analysis. In its Notice of Data Availability (NODA) published on February 22, 2011 (76 FR 

9696), DOE stated that it may consider improving regulatory analysis by addressing equipment 

price trends. Consistent with the NODA, DOE examined historical producer price indices (PPI) 

for fluorescent ballasts and found both positive and negative real price trends depending on the 

specific time period examined. Therefore, in the absence of a definitive trend, DOE assumes in 

its price forecasts that the real prices of fluorescent ballasts are constant in time and that 

fluorescent ballast prices will trend the same way as prices in the economy as a whole. 

In the following paragraphs, another potential method for forecasting long-term price 

trends is presented. DOE expects that improvements in the presented methods will be made in 

future revisions to its analysis.

DOE stated in the NODA that examination of historical price data for certain appliances 

and equipment that have been subject to energy conservation standards indicates that the 

assumption of constant real prices and costs may, in many cases, over-estimate long-term 

appliance and equipment price trends. Economic literature and historical data suggest that the 

real costs of these products may in fact trend downward over time according to “learning” or 

“experience” curves, or alternatively that the price trends for certain sectors of the US economy 

may be different than the price trends for the economy as a whole. A draft paper, “Using the 

Experience Curve Approach for Appliance Price Forecasting,” posted on the DOE web site at 

http://www1.eere.energy.gov/buildings/appliance_standards/supplemental_info_equipment_pric

e_forecasting.html, provides a summary of the data and literature currently available to DOE that 

is relevant to price forecasts for selected appliances and equipment. 

The extensive literature on the “learning” or “experience” curve phenomenon is typically 

based on observations in the manufacturing sector.a In the experience curve method, the real cost 

of production is related to the cumulative production or “experience” with a manufactured 

product. To explain the empirical relationship, DOE would use the theory of technology learning

to substantiate a decline in the cost of producing a given product as firms accumulate experience 

with the technology. A common functional relationship used to model the evolution of 

production costs in this case is: 

Y = aX-b

 

a In addition to the draft paper mentioned above, see Weiss, M., Junginger, H.M., Patel, M.K., Blok, K., (2010a). A 

Review of Experience Curve Analyses for Energy Demand Technologies. Technological Forecasting & Social 

Change. 77:411-428. 
8A-2

where a is an initial price (or cost), b is a positive constant known as the learning rate parameter, 

X is cumulative production, and Y is the price as a function of cumulative production. Thus, as 

experience (production) accumulates, the cost of producing the next unit decreases. The 

percentage reduction in cost that occurs with each doubling of cumulative production is known 

as the learning rate (LR), given by:

LR = 1 – 2-b 

In typical learning curve formulations, the learning rate parameter is derived using two 

historical data series: cumulative production and price (or cost). 

DOE examined historical prices through the use of the Bureau of Labor Statistics’ (BLS)

Producer Price Index (PPI) and GDP deflator, available from the Bureau of Economic Analysis 

(BEA). The PPI data for fluorescent lamp ballasts (FLB) is available for 1982 -2009 and is used 

to represent aggregate prices. Figure 8A.1 below shows the PPI data series used. 

Figure 8A.1 Historical PPI data for Fluorescent Lamp Ballasts

Inflation-adjusted price indices were calculated by dividing the fitted PPI series by the

GDP deflator for the same years. The GDP deflator was used as opposed to the consumer price 

index (CPI) because the majority of ballasts are shipped to commercial and industrial customers. 

8A.2 DATA EVALUATION AND ANALYSIS

Figure 1 shows an apparent price trend in ballasts which is trending downward from 1982 

to 2004, but shows an increase in the real producer price index from 2004 to 2008. DOE 

implemented an efficiency standard for ballasts in 2005 that resulted in a substantial change in 

the market structure for fluorescent ballasts at that time. Table 8A.1 summarizes the historical 

change in the ballast market structure and DOE’s estimate of how the market shares of different 

ballast types may evolve in the near-term future. 

0.5

0.6

0.7

0.8

0.9

1

1.1

1980 1985 1990 1995 2000 2005 2010 2015

Original PPI data
8A-3

Table 8A. 1 Estimated Market Shares for Ballasts Operating T12, T8 and T5 Fluorescent 

Lamps

Percentage of Shipments (Market Share)

Year T12 T8 T5 Comments

1995 68 32 0 T5 not present in U.S. market until approx. 

2002.

2000 45 55 0

2005 7 80 13 Corresponds with compliance date for the 2000 

Ballast Rule.

2010 6 79 15 T8 market share eroded by T5.

2015 4 78 18

2020 4 74 22

DOE believes that the increase in PPI after 2005 is due to the requirements of the 2000 

Ballast Rule going into effect (compliance date: April 1, 2005), resulting in an increase in 

average ballast prices. The 2000 Ballast Rule TSD (chapter 3) shows that electronic ballasts 

were, on average, about 54 percent more expensive than the baseline magnetic ballasts they were 

intended to replace (and were about twice as expensive for several ballast types). When the rule 

went into effect in 2005, all but 5 percent of ballast shipments were not covered by the rule, 

mostly in residential and sign ballast applications. DOE believes that price increase observed 

after 2005 is attributable to the phase-in of the 2000 Ballast Rule requirements and the required 

shift from magnetic to more expensive electronic ballast designs. 

As electronic ballasts displaced magnetic designs as the predominant products, electronic 

ballast prices decreased, even though they were still higher than prices for magnetic ballasts. 

Based on these price decreases and as explained in the discussion below, DOE expects that, in 

the future, the PPI is likely to resume a downward trend. 

In order to perform an experience curve fit, DOE assembled a time-series of annual 

shipments for 1990-2005 for fluorescent lamp ballasts (for calculating cumulative production)

from the Census Bureau.b

 Shipments prior to 1990 were extrapolated backward based on a 

linear trend to the estimated start of commercial production in 1940. DOE adjusted historical 

shipments data to include non-NEMA manufacturers. 

Projected shipments after 2009 were obtained from the base case projections made for the 

NIA (see chapter 10 of this TSD). These figures were adjusted to include ballasts not covered by 

the current rulemaking. For fluorescent lamp ballasts, DOE developed two base case shipments 

scenarios addressing the uncertainty about market penetration of light emitting diode (LED) 

technologies into the FLB market. Therefore, DOE developed two separate sets of future product 

prices. The Existing Technologies scenario corresponds to no LED penetration, while the 

Emerging Technologies scenario depicts aggressive LED penetration. Historical and projected 

annual shipments for these two cases are depicted on Figure 8A.2 below. 

 

b U.S. Census Bureau, Current Industrial Reports, Fluorescent Lamp Ballasts: 2005. July 2006. Washington, D.C. 

<http://www.census.gov/industry/1/mq335c055.pdf
8A-4

Figure 8A.2 Historical and Projected Annual Shipments for Fluorescent Lamp Ballasts 

To estimate potential product price trends, DOE performed a least-squares power-law fit 

on the FLB price index versus cumulative shipments. The form of the fitting equation is: 

P(X) = PoX-b, 

where b (the learning rate parameter) and Po (the price or cost of the first unit of production) are 

obtained by fitting the model to the data. DOE notes that the cumulative shipments on the right 

hand side of the equation can depend on price, so there is an issue with simultaneity where the 

independent variable is not truly independent. DOE’s use of a simple least squares fit is 

equivalent to an assumption of no significant first price elasticity effects in the cumulative 

shipments variable. 

To model the 2005 shift in market structure, DOE also included a dummy variable in the 

model to account for the effect of 2000 Ballast Rule on ballast prices: the variable is equal to 

0 before 2005 (the year 2000 Ballast Rule went into effect) and equal to 1.0 for 2005 and after. 

The resulting model assumes that experience curve price trend applies to the entire ballast market 

with the effect of a market shift being a simple shift to a proportionally more expensive ballast 

type. 

The estimated parameters of the model fit and their confidence intervals are presented in 

Table 8A.2 below. The model’s R2 is equal to 0.68. The estimated learning rate (defined as the 

fractional reduction in price expected from each doubling of cumulative production) is 19 

percent. 

0

50

100

150

200

250

300

350

400

1990199319961999200220052008201120142017202020232026202920322035203820412044

Millions

Existing Technologies Scenario Emerging Technologies Scenario
8A-5

Table 8A.2 Estimation Results 

With cumulative shipments through 2043 projected to reach 11.4 billion (compared with 

5 billion in 2010), the modeled trend predicts a drop of 24% in real price compared to prices in 

the economy as a whole in the Existing Technologies scenario. In the Emerging Technologies 

scenario, the model forecasts that ballast prices would drop 19 percent compared to the 2010 real 

price values. Figure 8A.3 below shows the model fit for the historical years and projected values 

for the period after 2009 for Existing Technologies scenario. 

Figure 8A.3 Model Fit for Existing Technologies Scenario 

Lower 95% Upper 95%

Intercept 2.76 1.977 3.553

Log shipments -0.30 -0.382 -0.213

2005 Dummy 0.07 0.032 0.099

Parameter Estimate 

Confidence Interval

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0 2,000 4,000 6,000 8,000 10,000

Fitted PPI

Cumulative Shipments in millions

Fit to Historica PPI Projected Historical data