18

- i CHAPTER 18

. IMPACT OF STANDARDS ON CONSUMER WELFARE : EVIDENCE FROM A 2007 CHANGE IN ENERGY EFFICIENCY REQUIREMENTS

FOR CLOTHES WASHERS

TABLE OF CO NTENTS

18.1 INTRODUCTION

................................ ................................ ................................ ......... 18

- 1

18.2 THE 2007 CHANGE IN E NERGY EFFICIENCY REQ UIREMENTS

....................... 18

- 1

18.3 IMPLICIT VALUE OF CH ANGES IN WASHER QUAL ITY

................................ .... 18

- 5

18.4 THE DATA AND ASSOCIA TED LIMITATIONS

................................ ..................... 18

- 6

18.5 PRICE AND QUALITY CH ANGES OVER TIME

................................ ..................... 18

- 9

18.6 REGRESSION DISCONTIN UITY

................................ ................................ ............. 18

- 15

18.7 DISCUSSION

................................ ................................ ................................ .............. 18

- 17

LIST OF TABLES

Table 18.4.1 Summary statistics of the full sample

................................ ................................ 18

- 7

Table 18.4.2 Summary statistics of the limited sample

................................ .......................... 18

- 8

Table 18.6.1 Summary of regressions

................................ ................................ ................... 18

- 17

LIST OF FIGURE S

Figure 18.2.1 The changed standard reduces the supply of low

- efficiency units

.................... 18

- 3 Figure 18.2.2 The standard change shifts out demand for high

- efficiency units.

.................... 18

- 4 Figure 18.5.1 Units sold, full sample

................................ ................................ ..................... 18

- 10 Figure 18.5.2 Units sold, limited sample.

................................ ................................ ............... 18

- 11 Figure 18.5.3 Washer prices, full sample.

................................ ................................ .............. 18

- 12 Figure 18.5.4 Washer prices, limited sample.

................................ ................................ ........ 18

- 13 Figure 18.5.5 Market share, full sample.

................................ ................................ ................ 18

- 14 Figure 18.5.6 Market share, limited sample.

................................ ................................ .......... 18

- 1518

- 1 CHAPTER 18. IMPACT OF STANDARDS ON CONSUME R WELFARE: EVIDENCE

FROM A 2007 CHANGE I N ENERGY EFFICIENCY REQUIREMENTS FOR

CLOTHES WASHERS

18.1 INTRODUCTION

In this chapter, DOE sets forth the results of a panel analysis conducted o n energy conservation standards for clothes washers that required compliance in 2007. DOE conducted the panel analysis to explore the potential i mpacts of efficiency standards for clothes washers,

gene rally, on consumer welfare. Specifically, the New York Times published a 2011 article on energy efficiency that discussed the impact of efficiency standards on clothes washer performance. Notwithstanding the fact that Consumer Reports, whose clothes wash er data was cited, subsequently rebutted the finding s asserted in the New York Times article, DOE investigated potential consumer welfare impacts in this panel analysis. (The article and rebuttal

statement are available at http://www.nytimes.com/2011/03/15/science/15letters1.html?pagewanted=all .) DOE was able

conduct th is analysis because it had specific information available for clothes washers. DOE did not consider the analysis in its adoption of the standards in the direct final rule, and DOE’s

inclusion of th is analys is is not indicative of any change or addition to DOE’s analytical methodology.

18.2 THE 2007 CHANGE IN E NERGY EFFICIENCY REQUIREME NTS

A 2007 policy change for clothes washers increased both the minimum threshold of energy efficiency and the threshold for ENERGY STAR ® certification. The minimum threshold increased from a modified energy factor (MEF) of 1.04 to 1.2 6; ENERGY STAR certification

increased the MEF from 1.42 to 1.72. The MEF measures the ratio of the capacity of a washer to

the total energy used in one cycle.

The policy change could have had a wide range of effects. To consider those effects, it may he lp to think of the washer market as compos ed of a large number of submarkets for different classes of washer that vary in quality, functions, size, and efficiency, among other factors. The policy change effectively banned one portion of the market. Note, h owever, that the change in policy affected only the manufacture of washer units, not unit sales, so the supply of banned units was not completely eliminated due to the policy change.

The policy change had two primary effects: (1) it sharply reduced the supply of low

efficiency units, reducing quantity and increasing price ( Figure 18.2.1 ); (2) higher prices and less availability of low

- efficiency units shifted dem and for higher efficiency units outward, increasing quantity and, presumably, price ( Figure 18.2.2 , panel A). This simple analysis assumes perfect competition and d ecreasing returns to scale in the production of washer units. If,18

- 2 however, washer manufactures have pricing power and/or there are increasing returns to scale in washer production (cost per unit declines with volume), prices for higher efficiency units may fall ( Figure 18.2.2 , panel B).

The net welfare effect of the policy change would need to account for changing surplus measures across the continuum of washer unit types. Changes in the ENERGY STAR label would influence flow of information, which could further shift demands. Finally, the change in policy may cause suppliers to introduce new products into the market, which would then feed back and affect pre

- existing models. The new products would be useful to monopolistically competitive firms that attempt to discriminate across buyers. A lthough strategic pricing effects are likely important, we generally abstract from these issues and assume perfect competition. Because the policy change was announced well in advance, it likely would influence production, pricing, and purchasing behavior well before the policy was enacted, and decisions made prior to the policy change likely would influence production, pricing, and purchasing behavior long after the policy change. The long transition period would be especially important for durable goods such as washers. Furthermore, it is our understanding that the policy change affected the manufacture of washers and not necessarily the sale of washers, so units manufactured before 2007 but not meeting the 2007 standard might nevertheless have been sold after 2007. Similarly, washers manufactured before 2007 not meeting the ENERGY STAR criteria may have been labeled as ENERGY STAR after 2007.18

- 3 Figure 18.2.1 The changed standard reduces the supply of low

- efficiency units

Panel A shows a hypothetical market with supply and demand for low

- efficiency washer units. The policy change causes the supply curve to shift inward, causing a loss in consumer surplus equal to the shaded area. In p anel B, demand for low

- efficiency units is flatter, and the loss in consumer surplus is greater. We cannot, however, identify the supply curve from the policy's effects on price and quantity.18

- 4 Figure 18.2.2 The standard change shifts out demand for high

- efficiency units.

Panel A shows a hypothetical mark et with supply and demand for high

- efficiency units. The policy change causes prices of low

- efficiency units to increase, which causes demand for high

- efficiency units to shift out, which increases quantity and price as well as consumer and producer surplu ses. In p anel B, supply is downward

- sloping because of increasing returns to scale, and price decreases rather than increasing. We cannot identify the size of surplus benefits because the slope of the demand curve cannot be identified from the policy's eff ects on price and quantity.18

- 5 Putting aside issues related to market transition and non

- competitive pricing, the event of the policy change still cannot fully identify welfare changes due to the policy. We can only identify changes in prices and quantities , and these effects only under strong assumptions about the effects of potentially confounding factors occurring at about the same time of the policy change. Taking estimated changes in prices and quantities as given, what we plausibly can

estimate is the loss in consumer surplus in the low

- efficiency market ( Figure 18.2.1 ). This value is identifiable because changes in quantity and price occur along the demand curve for those washers. If price increases are large, it implies the demand curve is relatively stee p and the loss in consumer surplus will be large (panel A); if the price increase is small, it implies the demand curve is relatively flat, and the loss in consumer surplus is small (panel B).

What we cannot estimate is the gain in consumer surplus due to the shift in demand for high

- efficiency washers ( Figure 18.2.2 ), because the changes in price and quantity occur along the supply curve. But the less the price increases, the greater the gain in consumer surplus, all else being held the same. And if price actually decreases due to economies of scale (panel B of Figure 18.2.2 ), one might be able to estimate a lower bound on the increase in consumers' surplus. 1

Concurrently with changes in policy are technological changes driven by non

- policy

factors and shifts in demand from non

- policy factors. These factor s include changes in energy prices, the collapse of the housing market, the recession that began at the end of 2007, and the subsequent financial crisis and prolonged downturn in the aggregate economy. Although we attempt to control for these factors as we ll as possible by using standard panel regression techniques, they nevertheless may confound the estimated effects of the 2007 policy change.

18.3 IMPLICIT VALUE OF CH ANGES IN WASHER QUAL ITY

Some concern has been voiced regarding the effects of the policy chan ge on the quality and characteristics of washing machines. Although some attributes of washers (such as energy efficiency, capacity, soil sensors, spin speed, and wear on the clothes) arguably have improved with the change in standards, other features may be less desirable, such as a longer wash cycle or quality of the cleaning. Quality, after all, depends on the tastes and preferences of individual buyers, so changes that some buyers perceive as improvements likely are perceived as detriments by other buye rs.

Changes in consumers’ surplus should embody the economic value of those changes. For example, if changes in washer design made necessary by the 2007 policy change were particularly undesirable to some consumers, then the relative scarcity of banned units that were manufactured before 2007 but still available after 2007 should see a significant increase in price, as in panel A of Figure 18.2.1 . If, however, the new units are close substitutes for the banned units, demand for the old units will be relatively elastic (flatter), as in panel B of Figure 18.2.1

.

1 This calculation is beyond the scope of the current analysis, but may be feasible.18

- 6 Alternatively, one might be able to obtain direct objective, non

- economic measures of changes in washer quality. Such measure would require testing specific washers by other means. Engineers, for example, could test whether washers manufactured before the ban tended to last longer without malfunction or breakage or could test the length of wash, the ability of washers to remove soils, wear on the clothes, and so on. Consumer surveys might also assess subjective statements about ease of use and whether increased capacity was preferred to shorter wash cycles. The data used in this study do not contain such measures. If we held objective measures for these characteristics, one might also attempt to attach economic values to them by relating objective washer quality character istics to observed prices.

18.4 THE DATA AND ASSOCIA TED LIMITATIONS

We have data on large but varying shares of total sales of washers purchased in the United States, with measures for units sold and average price broken down by washer brand and

model. Values are reported for each month. It is not clear to what extent and at what times different retailers enter and exit the sample. The data include a limited number of attributes for each model, plus an FTC measure of energy efficiency in terms of kilowatt hours per year (kWh/y) for standard usage. This measure of energy efficiency cannot be perfectly mapped into the MEF measure, probably because it does not fully account for energy used to heat the water and dry the clothes, in addition to energy used by the was her itself.

If precise MEF data were available and matched to each washer, one approach might be to consider separately four categories of washers: (1) those that met the minimum standard before and after the 2007 change; (2) those that met the minimum standard before but not after 2007; (3) those that met the ENERGY STAR criteria before and after 2007; and (4) units that met the ENERGY STAR criteria before but not after the policy change. Because we do not have MEF data, we proxy for washers that may have been closer and farther from the 2007 minimum and ENERGY STAR specifications using the FTC measure of efficiency (kWh/y). We do not have this measure for all units, but we have it for a significant majority of models in our data. Specifically, we divide units into ENE RGY STAR and non

- ENERGY STAR, and we further

divide each of these sub

- samples by the median FTC measure of efficiency (kWh/y). We consider these four categories of washer separately for the full sample of washers, which includes entry and exit of units, pl us a limited sample of washers that includes only model numbers that were sold both before and after the 2007 policy change. Summary statistics for these data, broken down by sub

- sample and category, are reported in Table 18.4.1 and Table

18.4.2

. The limited sample facilitates “apples to apples” comparisons of w asher prices before and after the 2007 policy changes, which might be obscured in analysis of the full sample, because the full sample includes a large number washers that were either discontinued or introduced around the time of the policy change18

- 7 Table 18.4.1 Summary statistics of the full sample

ENERGY STAR High

- Efficiency Low

- Efficiency

Pre

- 2007 Post

- 2007 Pre

- 2007 Post

- 2007 Non

- ENERGY STAR High

- Efficiency Low

- Efficiency

Pre

- 2007 Post

- 2007 Pre

- 2007 Post

- 2007

Units sold/month 16,933 95,301 39,941 44,649 14,291 70,580 46,999 14,763 Average price (current $) 899.64 797.48 722.38 738.53 264.13 409.36 349.16 422.5 Standard deviation of price 162.41 256.03 280.75 236.5 74.74 149.63 134.33 113.78

Front

- loading (%) 98.9 97.3 47.5 32.1 1.1 4.8 0.0 0.0 Top

- loading (%) 1.1 2.7 52.5 67.9 98.9 95.2 100.0 100.0 Average FTC (kWh/y) 193.80 176.12 342.21 328.05 446.84 402.98 715.61 735.42

Standard deviation of FTC 25.10 25.53 121.12 74.50 65.94 56.06 131.72 112.76 Average no. cycles 7.92 9.95 13.08 13.02 7.52 10.16 10.05 13.81 Standard deviation of no. cycles 2.33 3.63 4.77 3.86 3.64 4.83 4.76 5.14 Average rpm 1,066 1,145 949 1,091 678 874 714 649 Standard deviation of rpm 128 125 197 185 131 294 204 116 Side by side (%) 22.3 14.6 55.4 68.8 89.1 90.2 89.9 95.3 Portable side by side (%) 0.0 0.0 0.0 0.0 9.8 0.6 0.7 0.2 Stackable (%) 75.9 84.7 40.7 30.7 1.1 2.3 0.0 0.0 Prestacked (%) 0.0 0.2 3.6 0.4 0.0 6.8 9.5 4.5 Portable stackable (%) 0.6 0.3 0.0 0.0 0.0 0.1 0.0 0.0 Washer/dryer combo (%) 0.7 0.0 0.3 0.1 0.0 0.0 0.0 0.0 Portable washer/dryer combo (%) 0.4 0.2 0.0 0.0 0.0 0.0 0.0 0.0 Not specified (%) 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 Load sensor (%) 79.2 53.7 30.8 27.2 0.0 6.3 0.2 0.1

Soil sensor (%) 6.5 2.2 25.8 17.2 0.6 6.1 11.7 17.1 Load and soil sensor (%) 10.7 32.1 3.4 17.1 1.1 0.0 0.0 0.0 No sensor (%) 1.1 8.9 30.7 37.5 96.7 84.2 76.7 82.0 Not specified (%) 2.5 3.1 9.3 1.1 1.7 3.4 11.3 0.8 Average capacity (cu. ft.) 3.59 3.88 3.52 4.06 2.95 3.30 3.29 3.48 Standard deviation of capacity 0.27 0.37 0.29 0.36 0.25 0.32 0.39 0.32 Electronic control (%) 96.8 98.3 58.3 80.3 1.3 4.4 2.0 0.2 Mechanical control (%) 3.2 1.7 41.7 19.7 98.7 95.6 98.0 99.8

Notes: F ull sample includes all data having an FTC energy efficiency rating. High

- and low

- efficiency values are based on the median value of the FTC rating

across model numbers in sample. D ata include a varying number of retailers over time ; the sample of retaile rs may not be representative of the population. Monthly data are for 2004

– 2009, 36 months before 2007 and 35 months after because of missing data in December 2008. All averages and standard deviation s are weighted by total units sold during that period. Ch aracteristics such as No. of cycles, rpm, and Capacity are given as ranges; middle point of range is used to calculate the average: 1,009.5 rpm is used for the range 1,000

– 1,119 rpm . For ranges with one bounda ry, the boundary is used in calculation: 2.5 cu . ft. includes those less than 2.5 cu. ft., and 4.5 cu. ft. those 4.5+ cu. ft. “Not specified” data are treated as missing and ex cluded from calculation of c haracteristic average.18

- 8 Table 18.4.2 Summary statistics of the limited sample

ENERGY STAR High

- Efficiency Low

- Efficiency

Pre

- 2007 Post

- 2007 Pre

- 2007 Post

- 2007 Non

- ENERGY STAR High

- Efficiency Low

- Efficiency

Pre

- 2007 Post

- 2007 Pre

- 2007 Post

- 2007

Units sold/month 21,657 24,376 31,064 13,630 17,748 19,339 32,801 1,829 Average price (current $) 903 744 701 782 309 359 352 407 Standard deviation of price 189 245 268 174 117 111 143 176

Front

- loading (%) 97.6 90.3 40.2 23.2 0.1 0.0 0.0 0.0 Top

- loading (%) 2.4 9.7 59.8 76.8 99.9 100.0 100.0 100.0 Average FTC (kWh/y) 178.82 160.51 342.80 269.26 413.54 358.43 731.75 511.47

Standard deviation of FTC 10.64 19.93 150.50 68.42 42.84 76.44 151.21 96.83 Average no. cycles 9.94 8.98 12.88 11.34 9.91 7.76 9.48 9.90 Standard deviation of no. cycles 4.04 3.40 4.80 2.53 5.86 4.25 3.97 4.22 Average rpm 1,050 1,117 949 1,040 1,124 626 641 726 Standard deviation of rpm 148 129 197 144 243 29 59 224 Side by side (%) 12.1 37.4 62.8 78.3 92.5 97.4 87.5 78.4 Portable side by side (%) 0.0 0.0 0.0 0.0 5.4 1.1 0.2 0.5 Stackable (%) 86.8 60.7 32.8 20.2 0.0 0.0 0.0 0.0 Prestacked (%) 0.4 0.1 4.0 1.1 2.1 1.5 12.3 21.1 Portable stackable (%) 0.5 1.2 0.0 0.0 0.0 0.0 0.0 0.0 Washer/dryer combo (%) 0.0 0.0 0.4 0.3 0.0 0.0 0.0 0.0 Portable washer/dryer combo (%) 0. 2 0. 6 0.0 0.0 0.0 0.0 0.0 0.0 Load sensor (%) 60.4 61.8 35.9 67.3 0.7 0.1 0.0 0.0 Soil sensor (%) 15.5 4.3 25.2 7.9 20.5 17.2 2.6 4.9 Load and soil sensor (%) 10.8 19.4 1.8 22.5 0.0 0.0 0.0 0.0 No sensor (%) 11.6 13.6 28.7 1.5 76.3 82.0 90.2 91.7 Not specified (%) 1.7 0.8 8.4 0.8 2.5 0.7 7.2 3.4 Average capacity (cu. ft.) 3.61 3.71 3.51 3.50 3.02 3.27 3.37 3.32 Standard deviation of capacity 0.24 0.48 0.30 0.57 0.27 0.19 0.41 0.21 Electronic control (%) 89.2 92.0 60.0 91.3 0.9 0.3 0.2 0.5 Mechanical control (%) 10.8 8.0 40.0 8.7 99.1 99.7 99.8 99.5

Limited sample , subset of full sample, includes all washer models observed at least once before and after 2007 policy change. High

- and low

- efficiency

values based on median value of FTC rating across model numbers in limited sample. Data include varying number of retailers over time ; sample of retailers may not represent population. Mon thly data are for 2004

– 2009, 36 months before 2007 and 35 months after because of miss ing data in De cember

2008. A vera ges and standard deviations weighted by total units sold during period. Characteristics such as rpm and Ca pacity given as ranges; middle point

of range used to calculate average: 1,009.5 rpm is used for 1,000

– 1,119 rpm. If range has one b ounda ry, boundary is used in calculation: 2.5 cu. ft. includes

those less than 2.5 cu. ft ; 4.5 cu. ft. those 4.5+ cu. ft. “Not specified” data are treated as missing and excluded from calculation of characteristic av erage.18

- 9 18.5 PRICE AND QUALITY CHANGES OVER TIME

The data described above are plotted in Figure 18.5.1 through Figure 18.5.6 . The figure s show prices, units sold, and shares of units sold for each of th e four categories of washer, for

both the full sample and the limited sample that includes only units sold both before and after the 2007 policy change. The average price taken across all units sold in the sample (individual washers weighted by quantity sold) also is shown. The data appear to show a slight discontinuity in quantities and prices a t about the ti me of the policy change. Prices decline for high

- efficiency

units and increase slightly for low

- efficiency units. Note that all prices are in current dollars (not adjusted for infl ation). Changes in quantities, with a strong shift toward more e ffi cient ENERGY STAR units, appear to be trending before and well after the policy change. The longer

- run trends obviously are di ffi cult to di sentangle from other factors infl uencing technological advance and demand. Despite the marked shift in purchasing behavior toward more e ffi cient units, nominal average prices show little if any trend upward, even in the full sample. Because the price of

more

- e ffi ci ent units appear s to be falling faster than for less

- e ffi cient units, the concurrent trends in prices and quant ities provide some evidence of scale economies.18

- 10 Figure 18.5.1 Units sold, full sample

The full sample includes all data having an FTC energy efficiency rating. High

- and low

efficiency values are selected according to the median value of the FTC energy rating taken across model numbers in this sample. Note that the data include a varying number of retailers over time, and the sample of retailers may not b e representative of the population.18

- 11 Figure 18.5.2 Units sold, limited sample.

The limited sample is a subset of the full sample that includes all washer models observed in the data at least once before and once after the 2007 policy change. High

- and low

- efficien cy values are selected according to the median value of the FTC energy rating taken across model numbers in this limited sample. Note that the data include a varying number of retailers over time, and the sample of retailers may not be representative of th e population.18

- 12 Figure 18.5.3 Washer prices, full sample.

The full sample includes all data having an FTC energy efficiency rating. High

- and low

efficiency values are selected according to the median value of the FTC energy rating taken

across model numbers in this sample. Note that the data include a varying number of retailers over time, and the sample of retailers may not be representative of the population. The lines for each category of washer indicate the value for the median washer sold in that category, and t he band around the line marks the first and third quartiles. The thick dashed line is the average price across all models, with models weighted by number of units sold.18

- 13 Figure 18.5.4 Washer prices, limited sample.

The limited sample is a subset of the full sample tha t includes all washer models observed in the data at least once before and after the 2007 policy change. High

- and low

- efficiency values are selected according to the median value of the FTC energy rating taken across model numbers in this limited sample. Note that the data include a varying number of retailers over time, and the sample of retailers may not be representative of the population. The lines for each category of washer indicate the value for the median washer sold in that category, and the band around the line marks the first and third quartiles. The thick dashed line is the average price across all models, with models weighted by number of units sold.18

- 14 Figure 18.5.5 Market share, full sample.

The full sample includes all data having an FTC energy

- efficiency rating. High

- and low

efficiency values are selected according to the median value of the FTC energy rating taken across model numbers in this sample. Note that the data include a varying number of retailers over time, and the sample of retailers may not be representative of the population.18

- 15 Figure 18.5.6 Market share, limited sample.

The limited sample is a subset of the full sample that includes all washer models observed in the data at least once before and after the 2007 policy change. High

- and low

- efficiency v alues are selected according to the median value of the FTC energy rating taken across model numbers in this limited sample. Note that the data include a varying number of retailers over time, and the sample of retailers may not be representative of the po pulation

.

18.6 REGRESSION DISCONTIN UITY

Here we us e a regression

- discontinuity framework to attempt to discern model

- speci fi c effects of the policy on price and quantity of units sold. This approach to regression includes

fi xed effects for each washer model and smooth functions of time to account for other factors that changed at about the time of the policy change

. Fi xed effects of the model remove all model

speci fi c features of washers so that price effects cannot be attributed to change in quality or

characteristics over time. In effect , we attempt to explain how individual model price s change

over time , and whether changes were unusually sharp near the time of the 2007 policy change

.

The effect of the pol icy itself is estimated using a dummy variable to capture effects around the speci fi c time of the policy change under the maintained assumption that the change itself is purely exogenous. It is important to note that the design of the regression discontinu ity is not especially well suited to this particular policy change because, as described in the opening

section

, (a) the change was anticipated in advance and likely in fl uenced behavior long before and18

- 16 long after the event of the change, and (b) many other factors likely were infl uencing the appliance industry around the time of the policy change.

Baseline regression models estimated are speci fi ed as follows:

We also estimate models that include more robust controls for time, achieved by interacting smooth functions of time with each of the four categories ( c ) of washer described in earlier analysis. These controls allow for unobserved time

- varying effects related to energy efficiency . The models incorporating trend interactions are:

All regressions are estimated using both the full sample of washers and the limited sample for which transactions were observed both before and after the 2007 policy change. The smooth functions of time are estimated using a na tural cubic spline with four knots and three degrees of freedom.

Results from these regressions are reported in Table 18.6.1

. These results indicate that the poli cy change caused prices to fall and quantities to increase. These effects are largest for relatively efficient units (low kWh/y) and smaller for relatively inefficient units. The signs

reverse for the most inefficient units.18

- 17 Table 18.6.1 Summary of regressions

Price Effect of Policy Quantity Effect of Policy β γ Adj. R 2 β γ Adj. R 2

Estimate/(t

- statistic)

Basic

— full sample - 162.40 0.21 0.86 580.26

- 1.00 0.57 (

- 15.56) (11.16) (5.64) (

- 5.36)

Robust

— full sample - 65.55 0.04 0.87 694.18

- 1.36 0.57 (

- 5.43) (1.43) (5.64) (

- 5.24)

Basic

— limited sample - 113.61 0.20 0.88 823.12

- 1.20 0.54 (

- 11.76) (12.24) (7.79) (

- 6.73)

Robust

— limited sample - 24.82

- 0.007 0.90 600.09

- 0.64 0.54 (

- 2.07) (

- 0.315) (4.37) (

- 2.34)

Notes: All regression models include fixed effects for each model of washing machine. Basic models include a natural spline of time (on a monthly scale) with four knots. Robust models include a separate natural spline of

time for each of the fours classes of washer as classified in the data section and plotted in the above figures. The β coefficient is for the dummy variable indicating the post

- 2007 period. The γ coefficient is the effect on the interaction of the year

- 2007 dummy variable and a unit's FTC e nergy

- efficiency rating, measured in kWh of

energy use per year.

18.7 DISCUSSION

The analysis can estimate some, but not all, effects of the 2007 changes in energy standards using panel regression techniques and a regression discontinuity design that exploits changes in prices and quantity that occur a t about the time of the policy change. The analysis suggests that standard changes reduced prices and increased quantities of more energy e ffi cient units, and reduced quantities and slightly increased prices of le ss

- e ffi cient units, some of which likely were banned from manufacture in 2007 but still sold after 2007.

Because the policy change would have increased demand for more e ffi cient units, the observed fall in prices would seem possible under two scenarios: ( a) there are significant economies of scale in production, which caused average production costs to decline with higher sales; or (b) washer markets are imperfectly competitive, and the policy change caused not just an outward shift in demand for higher ef ficiency units, but a change in the elasticity of demand for higher efficiency units. In the lat t er case, it is easy to imagine that the new demand for higher efficiency units would be more elastic to price than earlier demand. Specific ally, new buyers of high

- efficiency unit s may have preferences that are less brand

- or feature

- specific than those of buyers of high

- efficiency washers before the policy change. A more elastic demand could incite monopolistically competitive producers to lower prices. Of cour se, a combination of these two explanations is also possible.

Other factors likely changing around the time of the policy change might bias the above inferences. The most logical alternative explanation for the decline in prices is the rapid

slowdown in t he housing market and the aggregate economy that occurred around this time. The

r esulting downward shift in demand could have encouraged fi rms to lower prices in general . This explanation, however, cannot reconcile the strong shift in the share of sales to ward more18

- 18 e ffi cient washers. In general, we see larger growth in sales and larger declines in prices for higher efficiency units as compared to less

- e ffi cient units. This pattern cannot easily be reconciled by a general inward shift in demand due to a slow ing economy.

Another possibility is that technological advance in production of relatively more e ffi cient machines occurred simultaneously with the policy. If this were the case, the policy may not have

caused the decline in price, but the advance neverth eless would have made the welfare costs of the policy change much smaller.

Yet another possibility is that rising energy prices in 2007 and 2008 precipitated higher demand for high

- efficiency washers. But even if energy prices were more responsible than the policy change for the shift in demand away from low

- efficiency units and toward higher efficiency units, the observed decline in washer prices would seem possible only if there were significant economies of scale and/or changes in strategic pricing by monopolistically competitive fi rms.

In general, these results are di ffi cult to reconcile with the idea that the policy change caused significant welfare losses, and are consistent with plausible scenarios in which the policy change actually improved welfa re. Because of the basic challenges of identi fi cation described earlier,

total estimates of welfare are not possible. It may be possible , however, to construct a lower bound on the overall change in consumer welfare.