18-i

CHAPTER 18. IMPACT OF STANDARDS ON CONSUMER WELFARE:

EVIDENCE FROM A 2007 CHANGE IN ENERGY EFFICIENCY REQUIREMENTS 

FOR CLOTHES WASHERS

TABLE OF CONTENTS

18.1 INTRODUCTION ......................................................................................................... 18-1

18.2 THE 2007 CHANGE IN ENERGY EFFICIENCY REQUIREMENTS....................... 18-1

18.3 IMPLICIT VALUE OF CHANGES IN WASHER QUALITY.................................... 18-5

18.4 THE DATA AND ASSOCIATED LIMITATIONS ..................................................... 18-6

18.5 PRICE AND QUALITY CHANGES OVER TIME ..................................................... 18-9

18.6 REGRESSION DISCONTINUITY............................................................................. 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 FIGURES

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........................................................................... 18-15
18-1

CHAPTER 18. IMPACT OF STANDARDS ON CONSUMER WELFARE: EVIDENCE 

FROM A 2007 CHANGE IN ENERGY EFFICIENCY REQUIREMENTS FOR 

CLOTHES WASHERS

18.1 INTRODUCTION

In this chapter, DOE sets forth the results of a panel analysis conducted on energy 

conservation standards for clothes washers that required compliance in 2007. DOE conducted 

the panel analysis to explore the potential impacts of efficiency standards for clothes washers, 

generally, 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 washer data was 

cited, subsequently rebutted the findings 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 this 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 this analysis is not indicative of any change or addition to DOE’s analytical 

methodology. 

18.2 THE 2007 CHANGE IN ENERGY EFFICIENCY REQUIREMENTS

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 1.26; 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 help to think of the washer market as composed 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, however, 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 lowefficiency units, reducing quantity and increasing price (Figure 18.2.1); (2) higher prices and less 

availability of low-efficiency units shifted demand for higher efficiency units outward, 

increasing quantity and, presumably, price (Figure 18.2.2, panel A). This simple analysis 

assumes perfect competition and decreasing 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. Although 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 panel 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 market 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 surpluses. In panel 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 effects 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 steep 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 factors 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 well 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 CHANGES IN WASHER QUALITY

Some concern has been voiced regarding the effects of the policy change 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 buyers. 

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 characteristics to observed prices.

18.4 THE DATA AND ASSOCIATED 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 washer 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 ENERGY 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, plus 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 18.4.1and Table 

18.4.2. The limited sample facilitates “apples to apples” comparisons of washer 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 change
18-7

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: Full 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. Data include a varying number of retailers over time; the sample of retailers 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 deviations 

are weighted by total units sold during that period. Characteristics 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 boundary, 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 excluded from calculation of characteristic 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. Monthly data are for 2004–2009, 36 months before 2007 and 35 months after because of missing data in December 

2008. Averages and standard deviations weighted by total units sold during period. Characteristics such as rpm and Capacity 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 boundary, 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 average.
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 figures 

show prices, units sold, and shares of units sold for each of the 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 at about the time 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 inflation). Changes in quantities, with a strong shift toward more efficient ENERGY 

STAR units, appear to be trending before and well after the policy change. The longer-run trends 

obviously are difficult to disentangle from other factors influencing technological advance and 

demand. Despite the marked shift in purchasing behavior toward more efficient units, nominal 

average prices show little if any trend upward, even in the full sample. Because the price of 

more-efficient units appears to be falling faster than for less-efficient units, the concurrent trends 

in prices and quantities 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 lowefficiency 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.
18-11

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- 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.
18-12

Figure 18.5.3 Washer prices, full sample.

The full sample includes all data having an FTC energy efficiency rating. High- and lowefficiency 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 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.
18-13

Figure 18.5.4 Washer prices, 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 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 lowefficiency 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 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.

18.6 REGRESSION DISCONTINUITY

Here we use a regression-discontinuity framework to attempt to discern model-specific 

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

fixed 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. Fixed effects of the model remove all modelspecific 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 prices change 

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

The effect of the policy itself is estimated using a dummy variable to capture effects around

the specific 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 discontinuity 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 influenced behavior long before and 
18-16

long after the event of the change, and (b) many other factors likely were influencing the 

appliance industry around the time of the policy change.

Baseline regression models estimated are specified 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 natural 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 

policy 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. R2 β γ Adj. R2

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 energy-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 at about the time of the policy change. The analysis suggests that 

standard changes reduced prices and increased quantities of more energy efficient units, and 

reduced quantities and slightly increased prices of less-efficient 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 efficient 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 efficiency units, but a change in the elasticity of demand 

for higher efficiency units. In the latter case, it is easy to imagine that the new demand for higher 

efficiency units would be more elastic to price than earlier demand. Specifically, new buyers of 

high-efficiency units 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 course, 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 the housing market and the aggregate economy that occurred around this time. The 

resulting downward shift in demand could have encouraged firms to lower prices in general. This 

explanation, however, cannot reconcile the strong shift in the share of sales toward more 
18-18

efficient washers. In general, we see larger growth in sales and larger declines in prices for 

higher efficiency units as compared to less-efficient units. This pattern cannot easily be 

reconciled by a general inward shift in demand due to a slowing economy.

Another possibility is that technological advance in production of relatively more efficient

machines occurred simultaneously with the policy. If this were the case, the policy may not have 

caused the decline in price, but the advance nevertheless 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 firms.

In general, these results are difficult 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 welfare. Because of the basic challenges of identification 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.