Final Paper Econ 495

7/28/2019 Final Paper Econ 495

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T h e a u t h o r w o u l d l i k e t o t h a n k P r o f e s s o r J e f f r e y S m i t h f o r h i s g u i d a n c e

t h r o u g h t h i s c l a s s , w i t h o u t w h i c h t h i s p a p e r w o u l d n o t h a v e b e e n

p o s s i b l e . A l l e r r o r s a r e t h e r e s p o n s i b i l i t y o f t h e a u t h o r .

Demand-Side Management Programs for

Energy Efficiency in the United States:How effective are they?

Eugene [email protected]# 10748740


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I. Introduction

The topic of climate change has dominated the airwaves in recent years. Climate

scientists have constantly emphasized that the global economy has to reduce its average

production of carbon emissions from 48 gigatonnes a year to 35 gigatonnes a year by 2030.

Otherwise, we would exceed the 450-ppm threshold that is hypothesized to trigger a catastrophic

climate change. To meet this challenge, we are essentially faced with a supply and demand

problem: We want to increase the supply of clean energy to meet current needs, and we want to

decrease the overall demand for energy such that we can reduce carbon emissions. Yet, we are

faced with numerous market failures in the realm of energy conservation (Dumagan 1993). For

instance, in the case of consumption fossil fuel generated energy, end user consumers in the

United States do not internalize the cost of increased carbon output. Instead, it is smaller island

states in the Indian and Pacific oceans that suffer from the dangers of global warming. To resolve

this issue of market failure, we can approach the issue from both the supply and demand side.

Consider figure 1, as suggested by Gillingham et al. (2009):

Fig 1. Market transformation conceptualized as a change in demand and supply.


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The figure on the left depicts a reduction in demand for energy while keeping production

output constant. This is coupled with an increase in the consumption of capital. A classic

example of such a demand shift in energy use would be a household switching an old and

energy-inefficient refrigerator for a newer energy efficient energy star rated refrigerator. The

new refrigerator would still produce the same amount of effect, but would consume less energy

at the expense of the household needing to pay for a more expensive refrigerator. Put another

way, this figure reflects a change in the way we use energy in general. The figure on the right

reflects a supply change, specifically the increase in renewable energy production leading to the

substitution of renewable energy for fossil fuel generated energy. Here, the figure reflects a

decrease in economic output due directly to the reduction in use of fossil fuel generated energy.

This reduction in output is made up for by an increase in economic output due to consumption of

renewable energy. In other words, renewable energy has become cheap enough that it becomes a

good substitute for non-renewable energy. Note that energy supply overall do not decrease: we

are simply substituting one type of energy for another. In this literature survey, we focus on the

demand side of the equation.

On the demand side, policy makers have been trying to foster policies that promote

energy conservation by consumers. This has come in the form of subsidies for purchase of

energy efficient products or maintaining higher energy efficiency building standards (such as the

LEED certification program), taxation on energy consumption (such as a systems benefit charge

or carbon tax) or educating the public on decreasing their carbon footprint. Programs like these

could be managed privately by utilities, or publicly, by state regulatory bodies. Indeed, as

attenuation of energy demand usually comes at lower cost than changing the way we produce


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energy, management on the demand side has been the policy of choice for a long time. Over the

past 40 years, states in the US have already been actively involved in various measures to

improve energy efficiency of the end user (Gillingham 2004, 2009). These measures are

collectively called demand-side management. In this paper, I will review the existing literature

regarding their actual effectiveness and cost-effectiveness.

II. Background

Demand-side management (DSM) programs first emerged in the late 1970s as a response

to rising oil and gas prices and perceived long-term shortages in energy supply (Loughran and

Kulick 2004). The primary purpose of these programs was to decrease peak demand for

electricity. Peak demand refers to the spike in energy use during the midday when economic

activity was at its highest. Due to the increasing marginal cost of producing electricity, utilities

usually found it more cost-effective to convince consumers to shift energy use to times of lower

demand, or simply reduce their demand. Such programs encompassed a range of activities,

including transfer payments such as subsidies to pay for appliances with higher energy

efficiencies, to informational activities to educate consumers on how to better manage their

energy consumption.

The continued existence of DSM programs depended primarily on their perceived

positive effect on energy efficiency (Loughran and Kulick 2004). In effect, most of the perceived

positive effect, prior to DSM implementation, depended on ex ante engineering estimates of

saving accrued from DSM program measures over time. Engineering estimates usually assume

full ideal conditions, such as an equipment being used to the end of its useful lifetime. A benefit-

cost analysis is then the simple ratio of the energy saved in the equipments lifetime, and the cost

to purchase that equipment. This is often referred to as the expected life cycle cost of a measure,


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and is expressed in cents saved per kWh. The Lovins estimate of life cycle cost, which put

average life cycle cost at 0.6 cents/kWh with a 70% increase in energy efficiency, and the

Electric Power Research Institute (EPRI) estimate, at 2.6 cents/kWh with 30% increase in energy

efficiency, is derived this way (Fickett, Gellings, and Lovins 1990, Joskow and Marron 1991).

In reality, engineering estimates of energy savings usually do not coincide with economic

estimates. In fact, economic estimates of energy savings are usually much lower than

engineering estimates. For instance, a cellphone might have an expected useful lifespan of 5

years. In reality, consumer preferences are monotonic in product quality, and consumers are

making an intertemporal choice. Thus, a rational consumer would prefer a new phone over a

older one. Witness the current structure of the cellphone market, where most consumers change

cellphones ever two years due to the way service provider contracts are structured.

Moreover, most ex-ante engineering estimates do not take into account of free rider

effects. Free riders are defined as program participants who, in the absence of the program,

would still have implemented a certain measure promoted by that program (Train 1994,

Loughran and Kulick 2004). In this context, free riders are people who would have invested in

energy conservation measures even without the existence of DSM program incentives.

Engineering estimates that ignore free rider effects usually overestimate the amount of savings

obtained. Compare equation (1), which is the common engineering estimate of savings, to

equation (2), an economic estimate that takes into account of free ridership:

(1)

(2)

where = engineering lifetime of new appliance, = economic lifetime of new appliance,

= fraction who are free riders and = reduction in energy due to use of a more


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energy efficient appliance. Clearly, the inflation of the appliance lifetime and energy savings

attributed to using a more energy efficient appliance, leads to an overestimation in overall energy

saved.

In addition, estimates that do take into account of free rider effects rarely take into

account thefulleffects of free riders. In essence, the estimated savings are calculated by taking

savings at time t subtracted by an estimate of the savings attributed to person who would have

invested in a certain measure at time t given that the program did not exist. Equation (3)

conceptualizes this method of estimation:

(3)

where =1 if customers participate in the program at time tand 0 otherwise, and = 1 if there

is a program at time tand 0 otherwise, and T= time period considered when DSM program was

in place. Hence, such a formulation assumes that probability of free ridership at time t is

invariant to treatment effects.

In reality, probability of free ridership is not invariant to treatment effects, and in fact

suffers from dynamic diffusion (Joskow 1991, Loughran and Kulick 2004). Free ridership cannot

simply be seen as the number of people at time t who would have implemented a measure

regardless of the existence of a program. Instead, we have to consider participants at time t, who

would have implemented a measure at time t+h, regardless of the existence of a program at time

t+h, as free riders too. This is because only savings for the time period between t and t+h can

truly be attributed to DSM spending, whilst any future savings would have happened regardless

of the program. Hence, true free ridership should be formulated as such:

(4)


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such that the savings that can truly be attributed to DSM spending is:

(5)

where gives us the observed amount of energy saved, and

reflects the free rider effects of equation (4)

that we have to discount from the observed energy savings. Figure 2 gives a visual depiction of

this relation:

c0 is the cost of adoption of energy saving measures without DSM programs, c1 is the cost with

DSM programs, t is the time when a person adopts energy saving measures and T is the end of

the DSM program of interest. Anyone above the dotted line would adopt energy saving measures

since their marginal benefit is greater than their marginal cost, and the true savings accrued due

Person A

Person B

Marginal benefit

time

c0

c1

ta,0

ta,1

tb,1

tb,0

T

Fig 2. Graph showing 2 persons with different upward-slopingmarginal benefits for adopting an energy saving measure over time


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to the DSM program is the energy savings between t0 and t1. As we can see, person A adopts

energy saving measures earlier than person B given a DSM program, but would have adopted the

same measures earlier too without a DSM program. As a result, the net savings accrued due to a

DSM program is lower for person A than B. In general, then, the net savings attributable to DSM

spending is dependent on each persons marginal benefit curve. Most estimates assume that each

persons marginal benefit curve is fixed and thus invariant to treatment. However, it is more

probable that the existence of a program would shift a persons marginal benefit curve inwards.

For instance, person B, upon knowing about the existence of a program, assumes the marginal

benefit curve of person A. Therefore, the estimated savings from such a person attributable to the

program will change. This change is not taken into account in most estimates.

In this study, Ill review six existing studies that seek to quantify the cost-effectiveness of

DSM measures using economic analysis. Ill first provide a summary of each evaluation,

followed by a critical analysis of their methodology and results, where possible. Finally, Ill take

a big picture view of the existing literature, taking into account existing economic theory

regarding consumer behavior and welfare, and give a qualified conclusion on the effectiveness of

DSM programs.

III. Discussion of Literature

Most of the recent research drew their data from Form EIA-861. Since 1986, the U.S.

Department of Energy (DOE) has required all utilities to report data on electricity generation,

sales, revenues by consumer type (residential or commercial, and if commercial, what

industries), and state / territory served. In addition, since 1989, the DOE has required utilities to

report annual DSM costs and energy savings attributable to DSM programs (Loughran 2004).


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However, there has been much criticism of the Form EIA-861 DSM data (Horowitz 2007). Most

criticism centered on the fact that the estimated savings reported by utilities were either biased or

simply incorrectly measured. Most utilities were offered incentives such as subsidies or cash

transfers to increase energy savings through DSM programs, and thus had an incentive to slightly

inflate their success. Moreover, most estimates were based on ex-ante engineering estimates, and

suffer from the errors mentioned in the preceding paragraph. As a result, it is suggested that data

from Form EIA-861 not be used literally, but as an indicator of how committed utilities are to

promoting energy efficiency (Horowitz 2007). In other words, the DSM data should not be seen

as the actual amount of energy saved, but how much energy the utilities hoped to save.

The Lovins and EPRI estimates were one of the very first attempts to quantify the

average life cycle cost of electricity efficiency spending. However, as mentioned, their estimates

were based off ex-ante engineering estimates that probably suffer serious miscalculation (due to

systematic over-estimation of the amount of energy saved from adopting a measure) and

underestimation of free rider effects (leading to selection bias). In fact, their low estimates of the

life cycle cost of DSM measures rang raised a significant number of alarms. Joskow and Marron

(1991) were one of the first to respond to the Lovins and EPRI estimates.

Joskow and Marron (1991)

Joskow and Marron attempted to quantify the life cycle cost in terms of the ratio of total

resource cost and total energy savings. Total resource cost (TRC) was taken to be the sum of

measure and non-measure cost, and is given by equation (6):

(6)

where = total direct installed cost of a specific measure (shared between the utility and the

consumer), = total direct cost incurred by the utility to implement specific subprograms over


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and above direct measure costs (such as education, advertising etc), = administration cost to

maintain the program, and = total direct cost incurred by a participant if there was no program

in place, all levelized by a discount rate where . With this interpretation,

is viewed as the net present value of total measure cost, and is the net

present value of total non-measure cost. The average life cycle cost is then the total resource cost

and the lost in revenue by utilities from a reduction in energy sales due to energy saved.

Interestingly, the authors do not include the marginal social cost of government subsidies in

encouraging utilities to conduct DSM programs. Therefore, such an estimate of cost might be an

underestimate.

Joskow and Marron obtained their data from 13 utilities. Unfortunately, we cannot tell

from the paper their rationale for choosing these 13 utilities. The authors also chose to keep their

sources confidential, which made it impossible to infer from their selection the reasons for these

13 utilities. In addition, the authors do not inform the reader about the specific evaluation method

used by each utility in deriving their cost and savings estimate. However, looking at the broader

picture, this paper was meant as a pilot evaluation in DSM cost effectiveness, and not meant to

be taken at face value. Regarding this, the authors have made their point very clear.

The authors inform us that these estimates almost certainly suffer from measurement

error and selection bias that make estimated costs lower than they actually are. Firstly, utilities

estimate the lifetime of a measure using ex-ante engineering estimates. As has been described

before the ex-ante engineering estimates are often much higher than economic estimates. In

addition, most evaluations do not take into account of free rider effects. For the few evaluations

which attempted to take into account of free rider effects, the evaluations rarely took into account


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of dynamic diffusion as described in equation (5), and simply used equation (4). To justify their

claim, the authors did a sensitivity test of their estimates by separately varying the expected

economic life of a measure and the extent of free ridership according to equation (5) in their

models. The authors consistently find that the estimates of life cycle cost increase after those two

factors are taken into account.

Parfomak (1996)

Parfomak (1996) was one of the more cited papers in the early literature. Parfomak

chooses to run a regression on change in logged values of electricity sales on change in logged

conservation and a range of control variables, by taking first differences of the independent and

dependent variables. Conservation here refers to the amount of energy saved as measured ex-post

by utilities. The study, a panel data analysis, is then simply the regression:

(7)

where X is set of control variables, takes first differences in time, electricity_sales (in GWhs)

is the pool electrical sales across all utilities, conservation = amount of energy saved attributed to

DSM spending (in GWh), is a dummy variable for utility k at time t ,and is the error term.

First differences in log levels help to detrend the data. Therefore, this regression investigates how

much a change in electricity demand is a result of conservation measures. Specifically, the

coefficient gives us the percentage change in electrical demand due to a 1% change in

conservation levels. If = 1, we can conclude that utilities have correctly estimated the change

in electrical demand due to conservation (i.e. a 1% change in conservation levels leads to a 1%

change in electrical demand levels). If > 1, then utilities have underestimatedthe effects of

DSM spending. If < 1, then utilities have overestimatedthe effects of DSM spending.


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The author draws data directly from the utilities, and also through federal archives. He

uses data from 1970 to 1993, from 39 utilities in 10 states in the Northeast region and California,

primarily because of substantial historical conservation activity, but also because, in the case of

the Northeastern States, of the large number of companies operating in the region, and

geographic proximity. In this case, the results of this study should be viewed critically, as it

clearly suffers from selection bias. Studies (Joskow et al 1991, Gillingham 2009) show that there

is usually serial correlation between the level of conservation, the change in levels of

conservation, and utility experience in managing DSM programs. If we select on utilities that

have longer experiences with DSM programs, then this regression only tells us the effect of a

change in levels of conservation on a change in electricity demand conditioned on utilities with

long experiences with DSM programs. We cannot generalize this to other programs with shorter

experiences.

The author pooled data from 38 utilities, less Southern California Edison, which was an

outlier. This relied on the assumption that the control variables for the 38 utilities were common

across all states and sufficient to remove omitted variable bias, and the only difference in the

utilities was their change in level of conservation. To correct for group-wise heteroskedasticity in

the data, the author chooses to estimate the regression using weighted-least squares, as opposed

to ordinary least squares. The authors also chose to run separate regressions for each state, in an

effort to find out if the earlier assumption (regarding the removal of omitted variable bias) holds.

In fact, their state-by-state WLS shows that the assumption does not hold, but the author chooses

to ignore this by claiming that the state-by-state WLS regressions generated unreliable

coefficient estimates and large standard errors. The Durbin-Watson statistics for the regression

was 1.80, but was greater than 2 for some of the state-by-state regressions, indicating potential


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serial correlation in the error terms. This suggests the use of a heteroskedastic autocorrelated

consistent (HAC) estimator. However, the author chooses to ignore this potential error by

claiming the lack of systemic serial correlation.

The authors find an estimated of -0.994, with standard errors of 0.281. At first glance,

this estimation indicates that utilities have been rather successful in estimating the change in

demand attributable to conservation, since a change in the independent variable has a one-to-one

effect on the dependent variable. The authors then test the null hypothesis of = 0 (i.e. change

in conservation levels having no effect on change in demand), against the alternative hypothesis

of = -0.994, and find that we can reject the null at 5% significance levels. In particular, the

confidence interval at 95% is [-0.43 , -1.56]. Unfortunately, this large confidence interval

prevents us from telling with certainty that utilities are accurately estimating conservation

effects.

Interestingly, the author does not mention that most of the utilities in fact do notestimate

conservation effects very well. Out of the 38 utilities sampled for this regression, only 5 utilities

produced estimates that fall within the 95% confidence interval. 27 utilities overestimatedthe

effect of conservation by a factor of 2 to 6 (compared to the mean), while 7 dramatically

underestimatedthe effects of conservation by a factor of 5 to 9 (again compared to the mean). A

separate regression done for Southern California Edison also finds that we cannot reject the null

that changes in estimated conservation levels have no effect on change in demand. This is

primarily due to the small sample size, leading to larger standard errors.

While the author argues that first differences are sufficient to take into account of a time

trend, I argue that the author has no justification that the time trend is in fact constant. In

particular, first differences do not remove the time effects if they are non-deterministic. It might


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have been more advisable to simply use fixed time effects in his regression (i.e. adding dummy

variables for each year).

In addition, the author does not truly take into account free rider effects. As argued

earlier, DSM spending can lead to dynamic diffusion of free riders. This implies that free

ridership is nota secular time trend, such that first differences cannot take into account of free

riders. Train (1994) show that in fact first differences do not remove free rider effects in the

context of consumption change.

Loughran and Kulick (2004)

Loughran and Kulick (L&K), like Parfomak, employ panel data for their analysis. They

used data from 1989 to 1999, which they took from the DOE through form EIA-861. The

regression specification for the L&K model is:

(8)

where is change in log electrical sales in MWh; is log DSM expenditure in

dollars per kWh; is lagged effect of DSM spending with n lags and the depreciation of

current and past DSM spending; and are utility-level and state-level covariates; is

electrical output in terms of number of customers and Gross State Product; is the year fixed

effect; and is the error term. Logged specifications are used so that the coefficients can be seen

as elasticities. The first two terms give the current and lagged effect of DSM spending, and the

next 4 terms are control variables. is used as a proxy for change in quantity of

electricity demanded. In essence, we can use the estimated coefficient to calculate the arc

elasticity of DSM spending, and thus determine the life cycle cost of DSM spending at the mean.


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For this regression, the authors dropped all observations that returned data claiming zero DSM

expenditure or were located in Alaska, Hawaii, the District of Columbia and U.S. territories. This

accounted for almost 70% of all utilities observed.

Using a variety of specifications in their model, the authors find that DSM life cycle cost

ranged from $0.14 to $0.22 per kWh for a reduction in mean electricity sales of between 0.3%

and 0.4%. In comparison, utility estimates ranged from $0.02 to $0.03 per kWh for a reduction in

electricity sales of between 1.8% and 2.3%. The authors find that, testing the null hypothesis that

utility reports for reduction in electricity sales attributable to DSM spending is true (i.e. =

0.018 for the lower bound, = 0.023 for the upper bound), against the alternate hypothesis that

utility estimates are wrong (as suggested by the study), we can reject the null for three of the five

specification at a 5% significance level. Therefore, the authors conclude that utility estimates

probably are systematic overestimates of savings and underestimates of costs.

In addition to studying the life cycle cost of DSM spending, the authors also investigated

the effect of any DSM spending on change in electrical sales. Using first differences, the authors

obtain the regression specification:

(9)

where is 1 if there is a DSM program, and 0 otherwise; and is a vector of fixed state

effects. The authors find that =-0.019, and is significant at a 10% significance level. When

controlling for the level of electricity sales in 1989, =0.004, but is statistically insignificant.

Both regression specifications suffer the same issues as the one in Parfomak (1996).

Specifically, the free rider issue is not sufficiently addressed. While the time fixed effects

variable in equation (9) does take into account of a secular time trend, it does not take into


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account of endogenity of consumer choices (i.e. dynamic diffusion of free riders as a result of

program implementation). In addition, the regression for equation (10) suffers from the selection

bias mentioned by Train (1994).

Besides the issue with selection bias, the authors also choose to take the ratio of the

unweighted average of costs and savings to determine the life cycle cost of DSM spending. In

effect, the literature shows that increasing DSM program sizes (and hence spending) reduces the

ratio of costs and savings (i.e. reduces the life cycle cost of DSM spending for a certain

program). There are many reasons proposed for this, such as increasing returns to scale, larger

program sizes reflecting longer experience with DSM programs (Joskow et al 1991, Gillingham

2004, 2009). While there is no consensus on causality, it is agreed that a correlation exists. Thus,

a simple average of ratio will not suffice to obtain the life cycle cost of DSM spending. Instead, a

weighted average is more suitable. This is discussed further in Auffhammer et al. (2008).

Auffhammer, Blumstein and Fowlie (2008)

As mentioned in the preceding discussion, the point estimate of life cycle cost in

Loughran and Kulick (2004) is a simple point estimate of the ratio of unweighted costs and

savings, and is probably inaccurate. Auffhammer et al. take issue with this, and produce their

own weighted estimates.

Auffhammer et al. uses the exact same dataset and regression specification as Loughran

and Kulick, which they obtained from the authors. Unsurprisingly, the authors find that the

weighted average savings were higher than the unweighted average savings, such that the

weighted life cycle cost is lower than the unweighted life cycle cost. Auffhammer et al. gives the

follow specification for their weighting procedure:


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(10)

Where is the counterfactual unobserved situation consumption where DSM

programs do not exist; is the observed level of electricity consumption with DSM in

place; n indexes the utilities; and t indexes time.

In addition, the authors make a claim against the statistical tests that Loughran and

Kulick (2004) apply. In particular, they argue that the predicted savings are not independent

within utilities by construction, such that standard assumptions, used in constructing standard

errors for the samples, cannot apply. In fact, the test statistic employed by Loughran and Kulick

is biased towards rejecting the null. Auffhammer et al suggests using a heteroskedasticity robust

nonparametric residual bootstrap (wild bootstrap) in constructing a distribution of sample

savings, and constructing a Hansen confidence interval based on the estimates from the

bootstrap. Auffhammer et al. find that the confidence interval becomes larger, and that we can no

longer reject the null that utility estimated savings are true.

White and Reiss (2008)

White and Reiss do not specifically review DSM programs, unlike the other papers in this

literature survey. Instead, they investigate the demand response during Californias energy crisis

between 2000 and 2001.

The Californian energy crisis was a period of time between 2000 and 2001 when

wholesale electrical prices rose dramatically by more than 2000% from their prices a year ago.


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Prior to 1996, the Californian electricity market was tightly regulated, and prices rarely

fluctuated. A deregulatory wave in the mid 1990s eventually led to the partial deregulation of the

market, where wholesale prices were allowed to float while retail prices, except for the market

region under San Diego Electric and Gas (SDE&G), remained capped. This arbitrage opportunity

led to market manipulation by traders, leading to extremely high prices in the wholesale market

(Borenstein 2002). While most utilities were being forced to buy electricity at high prices in the

wholesale market, and to sell them at a lost in the retail market, SDG&E, which had completed

its contractual demands, had its retail price pegged to the wholesale price. Therefore, all price

changes were transferred directly to the consumers in SDG&Es market. At that point in time,

consumers had little reason to expect the sudden increase in prices. Therefore, this crisis could be

construed as an exogenous supply shock.

In 2001, with consumers facing extremely high prices, the state government instituted a

cap on the retail markets in San Diego. As a result, many public utilities were falling into

bankruptcy as they were essentially forced to subsidize the high retail price of electricity. This

culminated in supply shortages and rolling blackouts across California. As a result, the state

government engaged in a campaign to advise consumers to reduce electricity usage. Eventually,

the state obtained FERC approval to place a price cap on the wholesale markets, and prices

stabilized across all markets and the crisis ended. Using this crisis as a quasi-experiment, White

and Reiss studied the change in demand as a result of both an exogenous price shock and public

pressures. Unlike the rest of the studies I surveyed, this paper does not investigate the

effectiveness of DSM programs. It instead investigates the elasticity of electrical demand, and is

useful in helping to determine what combination of price changes and public education programs


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are necessary to bring about a desired change in electrical consumption. It also investigates if

high prices provoke a structural change in the consumption of electricity.

The authors obtained their data from the Household Electricity Research Billing Sample

(HERBS), a five-year panel of San Diego Gas and Electric Company residential utility bills.

The authors modeled household responses by analyzing the electricity consumption

change between one billing period and the same billing period 12 months earlier. This 12-month

differencing removes seasonal effects. The difference in consumption is regressed on a dummy

indicator for pre-crisis months and post crisis months, as well as the change in contemporaneous

weather related covariates, thus controlling for weather effects. In effect, this method reduces the

error terms of the regression to white noise. The regression specification is:

(11)

where is the consumption level of the ith household in cohort c at month m, and are

the indicator variables for whether month m in pre- or post- crisis respectively, and w is a vector

of contemporaneous weather-related activities. In essence, the regression estimates the year-on-

year change in electrical consumption pre-crisis, during the crisis, and post-crisis. This

regression is run on consumption pre-crisis ( and both in pre-crisis months), during the

crisis without a price cap ( in the post-crisis months and both in pre-crisis months),

and post-crisis ( and both in post-crisis months).

The authors found that consumer responses can be very fast when faced with a sudden price

shock. During the period of the crisis, average consumption fell by 13% in 60 days. Moreover,

the effect of a price shock has significant lasting effects. While California suffered the expected

rebound effect after a retail price cap was put back in place, consumption levels only return to


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two-thirds of its original consumption level, before falling again. This suggests that extremely

high prices can provoke a structural change in the way people consume electricity.

Unfortunately, the long-term effect of the price shock suffers from confounding treatment

with the public appeals by the state government to reduce electricity usage. While the authors

point to the significant decrease in electricity consumption as evidence that public appeals do

work, these efforts came on the back of a massive price shock. The high prices might have left an

indelible mark on consumers, permanently changing their behavior. Thus, public appeals might

not be as effective as the authors think they do.

Arimura, Li, Newell and Palmer (2011)

Arimura et al. revisit the issue of cost-effectiveness of DSM spending. Unlike the

previous papers, which are in effect simply OLS regressions after first differences, Arimura et al.

attempts to address endogenity in the models by postulating a baseline model, then estimating

the parameters of the baseline model by separately using a Generalized Method of Moments

(GMM) and an Instrumental Variables (IV) technique. In addition, the authors also account for

the lagged effect of DSM spending. The baseline specification is:

(12)

where , and is the gamma function, such

that taking first differences, we get:

(13)


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The population parameters are to be estimated by Non-linear Least Squares (NLS),

GMM and IV separately; is the electricity sales at time tin utility u, is DSM spending

per person at time tby utility u; are fixed year effects; are fixed utility effects; and is the

error term. gives the effect of DSM spending. NLS does not account for endogenity in the

model, and provides a basis for comparison with GMM and IV, which do account for

endogenity.

In the GMM treatment, the authors used the procedures suggested by Charmberlain

(1987) and Newey and McFadden (1994) to construct optimal instruments for estimation. In the

case of the IV treatment, the authors used political leanings and extent of environmentalism as

instruments. The former was quantified by the percentage of voters who voted for the

Republican candidate in the last political election, whilst the latter was proxied by the average

League of Conservative Voters (LCV) environmental scores of federal legislators who represent

voters in the utilitys service territory. It is unclear why the authors chose this specific set of

instruments for the IV treatment. Indeed, one would be led to believe that political leanings and

the extent of environmentalism would exert their own direct effects on electrical consumption,

and would thus make bad instruments.

The main takeaways from this paper are as follow:

Firstly, we find that the estimates for NLS, GMM and IV are not statistically different at

the 5% level. This indicates that endogenity in measure implementation might not be a big

concern using this baseline specification. Hence, we can choose to simply estimate the model

using NLS, as opposed to GMM and IV, which dictates the use of instruments (and are thus

more challenging to do).


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Secondly, the lagged effect of DSM spending is still statistically significant at the 5%

level after a 15 year lag. This indicates that DSM spending is effective over the long run. More

importantly, the effect of DSM spending often peaks (and becomes statistically significant) only

a few years after implementation. Hence, if we are to effectively assess the effect of DSM

spending, we need to look at both cross-sectional and time-series data, as opposed to simply

cross-sectional data over a single year.

Finally, the average cost estimate is about 5 cents per kWh, lower than the average

national retail price of electricity in 2006 of 9.1 cents per kWh. This suggests that DSM

programs are capable of producing low cost reductions in energy use. In addition, the authors

show that the null that utility estimates are true cannot be rejected at the 10% level.

IV. Synthesis and conclusion

Are DSM programs effective, and if so how cost effective are they? In order to arrive at a

quantifier of effectiveness, we have to first be able to accurately account for costs and savings.

Estimation of costs is clearly not a huge factor. After, one can easily see from a utilitys balance

sheet the amount earmarked for DSM programs. However, estimates of savings run into a

barrage of endogeneity and measurement problems, such as secular time trends, selection biases

arising from dynamic diffusion of free riders and accounting differences. How has the literature

evolved to address these issues?

Joskow (1991) was one of the first papers to address this issue. While the authors do not

suggest solutions to the inherent problems of savings estimation, the authors do use ex-ante

engineering estimates to produce a range of alternate scenarios that take into account of a

hypothesized amount selection bias. It is shown that real costs could be more than twice as large

as estimated cost. In subsequent years, various papers have increasingly attempted to address the


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issue of selection bias, first through a first differences method (Parfomak 1996), to accounting

for time trend effects (Loughran and Kulick 2004), to using a general method of moments and

instrumental variables (Arimura 2011). In addition, differences in accounting methods and

hypothesis testing were also taken into account (Auffhammer et al. 2008). Intriguingly, despite

Joskow (1991)s assertion that utility estimates are probably wrong, only Loughran and Kulick

(2004) have shown that the null of utility estimates being correct can be rejected. As mentioned

in the preceding statements to section III, the existence of DSM programs are justified by their

current cost estimates, which are deemed effective. If the literature cannot prove otherwise, this

implies that they are probably truly effective.

Moreover, Reiss and White (2008) showed that DSM programs have a significant

extended lag effect of up to 15 years. In addition, they show that public appeals for energy

conservation do in fact have a significant effect on consumers. Taken together, this suggests that

DSM programs are in fact effective and useful.

In conclusion, the literature appears to suggest that, despite our doubts and questions

regarding the effectiveness of DSM programs on energy conservation, we have no proof to reject

the claims that they are not effective. Moreover, we find that the effect of DSM spending has a

low depreciation rate, to the point where they are effective even 15 years from now. If we

assume that market failures exist in the realm of energy conservation (Dumagan 1993), then

according to traditional welfare economic theory, we should certainly implement DSM programs

as a step forward in energy conservation.


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