8A-i APPENDIX 8A. ESTIMATION OF POTENT IAL EQUIPMENT PRICE TRENDS FOR
FLUORESCENT BALLASTS

TABLE OF CONTENTS

8A.1 INTRODUCTION

.................................................................................................. 8A

- 1 8A.2 DATA EVALUATION AND ANALYSIS

............................................................ 8A

- 28A-1 APPENDIX 8A. ESTIMATION OF POTENT IAL 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 analys is. In its Notice of Data Availability (NODA) published on February 22, 2011 ( 76 FR 9696) , DOE stated that it may consider improving r egulatory 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 ex amined. 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 state d in the NODA that e xamination 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 tre nds. E conomic 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 pric e 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 C urve Analyses for Energy Demand T echnologies. 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 prod ucing 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 h istorical pri ce s 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 repr esent 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 a n 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 data8A-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 el ectronic 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

s hift 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 a nnual

s hipments 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 li near 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 rulemak ing. 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 pro duct 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 .pdf8A-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) = P o X -b

,

where b (the learning rate parameter) and P o (the price or cost o f 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 pri ces: 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 shif t 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 R 2 is equal to 0.68. T he 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 4001990199319961999200220052008201120142017202020232026202920322035203820412044Millions

Existing Technologies Scenario Emerging Technologies Scenario8A-5 Table 8A. 2 Estimation Results

With cumulative shipments through 204 3 projected to reach 11.4 billion (compared with 5 b illion 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 price s would drop 19 percent compared to the 2010 real price value s . 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 E xisting 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 10,000Fitted PPI

Cumulative Shipments in millions

Fit to Historica PPI Projected Historical data