what people do when they say they are conserving electricity

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Energy Policy 36 (2008) 1945–1956

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What people do when they say they are conserving electricity

James Woods�

Economics Department, Portland State University, 1721 SW Broadway, Portland, OR 97220, USA

Received 8 October 2007; accepted 2 January 2008

Available online 2 April 2008

Abstract

Econometric practitioners must always make the case that existing data may be used to forecast future responses to price changes. In

residential electricity markets this means providing assurances that either territories with different prices are similar enough to be used as

a guide, or that households are still able to react to price changes with the same conservation measures they have in the past. This article

presents the results of a conservation behavior survey conducted both concurrent with and immediately after the last California

electricity crisis in 2000–2001. The survey used open-ended questions that provide some assurance that there are still conservation

behaviors that may be performed, as well as raw data that may be used to construct new closed-ended questions. The prevalence of

conservation behaviors is modeled with a forgetfulness process, necessary when using data from open-ended questions, and implemented

with a generalized method of moments (GMM) estimator.

r 2008 Elsevier Ltd. All rights reserved.

Keywords: Residential conservation behavior; Survey methodology; Electricity

1. Introduction

Economic analysis of the consequence of rising andfalling electricity prices on residential consumption hasalways fit easily into a neoclassical framework. We canobserve changes in electricity prices and households’reactions to those prices. That information can be usedto make forecasts about reactions to future price changes.

This frequentist, neoclassical way of looking at thedemand for electricity makes strong assumptions about theuniformity of response across both space and time. Itsupposes that the statistical data generation process, thetheoretical source of price and usage data, as well as thephysical data generating process, how people do things andthe equipment they use to do it, will be the same in thefuture as it has been in the past. It supposes that there areobservations of agents’ reactions to prices in the ranges weexpect to see in the future. Alternately, it supposes that wecan generalize from reactions to price changes of similarsize but in different levels. It even supposes that we can

e front matter r 2008 Elsevier Ltd. All rights reserved.

pol.2008.01.001

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generalize from the reactions of price decreases to forecastthe effect of price increases.Whether you are a Frequentist defining a viable data set,

or a Bayesian forming prior beliefs, what data can be usedfor residential electricity demand estimation and forecast-ing is a crucial question. It is essential to look into thebehavioral determinants of electricity demand, part of thephysical data generating process, to ensure that there areenough common conservation behaviors to allow house-holds to react to price increases in ways similar to whatthey have in the past.Practitioners must make the case that past data is useful

when making inferences about future behavior. Currentreal residential electricity prices are at levels not seen sincethe mid-1990s. Practitioners must show that householdshave similar equipment and practices as then if they wish touse data from the 1990s. Those mid-1990s prices are fromthe middle of a long decline in real electricity pricesextending back to the early 1980s. Practitioners must showthat data from periods of price decreases can be used toforecast responses to price increases.This paper will describe the results of two open-ended

surveys of conservation behaviors performed by Californiaresidential electricity users in 2001 and 2003—concurrent

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ARTICLE IN PRESSJ. Woods / Energy Policy 36 (2008) 1945–19561946

with, and somewhat after, the most recent electricity crisis.Because the survey is about behaviors, it can informeconometric analysis. Because the questions are open-ended, they can inform future surveys’ closed-endedquestions.

While open-ended questions allow for a rich set ofresponses, analyzing them requires methodology that cancontrol for respondents not remembering all their con-servation actions during an interview. Section 4 describes amodel of performing conservation actions and recallingthose actions during an interview. Several systems ofmoment conditions, M-Estimators, are proposed andpresented in Section 5. These will show that manyreasonable and well-known conservation behaviors arenot practiced by large sections of the population.

This paper will show that because households can stillreact by adopting existing conservation strategies, we canexpect forecasts of the effects of increases in real electricityprices to be reasonably similar to what they have been inthe past.

2. Background and literature

At the heart of residential electricity demand estimationis data about electricity use, the prices and othercircumstances that induced that use. Data can come fromcross-sections that differ in price or time-series data over aperiod where price changes have occurred.

There is always spatial variation in residential electricityprices—different territories have different prices. Much ofthe previous literature, starting with Houthakker et al.(1974), took the approach of exploiting those differences toestimate demand. Houthakker et al. (1974) began thiscross-sectional approach with limited time-series data andnoted that data on equipment and appliances is incom-plete. Equipment should slowly change over time inresponse to prices, taste, and technology. The authorsproposed an error-correction model, often referred to as aflow-adjustment model, to compensate.

Other papers used the same flow-adjustment model withdifferent units of analysis. Archibald et al. (1982) usedindividual data from the ‘‘National Survey of Lifestylesand Household Energy Use’’. Halvorsen and Larsen (2001)performed similar analysis with Norwegian data. Garcia-Cerrutti (2000) used county level data, correcting forheterogeneity, to estimate the similar flow-adjusted de-mands.

The most interesting result from the early literature thatexploited cross-sectional differences in price is about thestability of demand estimates. Yang (1978) showedinstability in demand estimates for the 1973–1975 period,corresponding to the transition year of the last sustainedreal price increases. This suggests that estimates ofresponse to future price increases, using cross-sectionalprice differences, are unlikely to be accurate whenconfronted with widespread electricity price increases.

These analyses are representative of a number of studiesthat have depended on an overwhelming consistency ofresponse across units of analysis with respect to increasesand decreases in prices. While states, counties and house-holds in different utility districts have been used as units ofanalysis in electricity demand estimation, there is evidence,as in Aigner and Leamer (1984), that it is inappropriate.That paper showed that, in well-designed experiments,reactions to price changes are different in differentlocations. This is true even when controlling for location-specific variables like weather.Because of Aigner and Leamer (1984), each practitioner

must strongly justify their use of data across serviceterritories. They must have evidence that the onlyuncontrolled difference between the service territories isprice. While this is possible using household level dataalong territory boarders—that only yields one observableprice difference.Historical prices from a single location have similar

conceptual difficulties. Behavioral economics has longconcerned itself with asymmetric responses to priceincreases and decreases. Prospect theory, introduced inTversky and Kahnneman (1991) for risk-free choice,explains this with reference price effects and transactionutility. Prospect theory effects have been studied innumerous markets including telephone service, in Bidwellet al. (1995), and grocery story purchased goods, in Hardieet al. (1993).Young et al. (1983) noted similar effects in residential

electricity markets but did not identify them as prospecttheory effects since the study predates the 1991 introduc-tion. Young gives evidence that reactions to price changesare, in both the long and short run, asymmetric. This givesadditional evidence that we should be cautious about usingpast data on the effects of real price decreases to predict theeffects of price increases.It is therefore important to go beyond simple price and

use data, to the physical data generating process, whatpeople do, to provide evidence that households still haveways to easily decrease electricity use in response to higherprices and not assume they can act. This is an importanttask not just for the Frequentist deciding how many yearsof data to use, but for the Bayesian developing prior beliefsabout future use or future reactions to price changes.

2.1. The sociological tradition

The kind of behavioral study needed for this assurance isnot of interest to economists alone. The sociologicalapproach in surveys is to ask about what people do.Kilkeary (1975), Walker and Draper (1975), Bultena(1976), Cunningham and Lopreato (1977), and Perlmanand Warren (1977) performed surveys during the last greatprice increase of the 1970s. Each of these surveys employedclosed-ended questions about conservation behaviors.Frequently the focus of sociological survey research

was on knowledge of conservation behaviors, as in

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Table 1

Completed telephone interviews by utility service territory

Survey 1 Survey 2

Sample Respondents Sample Respondents Response

rate (%)

PG&E 3500 399 355 198 55.8

SDG&E 3500 411 369 207 56.1

LADWP 3500 244 208 107 51.4

SMUD 1166 216 196 101 51.5

SCE 1200 396 354 202 57.1

J. Woods / Energy Policy 36 (2008) 1945–1956 1947

Scheffler et al. (1979), and assessments of the difficulty ofadapting some conservation behaviors, e.g., Gladhart et al.(1980). What these sociologists have done is collected dataon the determinants of demand elasticity—something thateconomists could use to differentiate heterogeneous popu-lations.

The limitation of all these surveys, even the later surveysused in Marsden (1980) and Marsden and McKinney(1980), is that they are filled with closed-ended questionsabout behavior. Open-ended questions are a rare but idealprecursor to closed-ended questions.

Interest in this kind of behavioral research waned in the1980s, but in the 1990s small, industry-sponsored surveysbegan to ask about a standardized list of behaviors, thatare now de rigueur in impact evaluation studies.1 Un-fortunately, the questions are not subject to the open-endedpre-test suggested in Lazerfeld (1944) over seven decadesago. Without this kind of pre-test there is a constant risk ofhaving the wrong closed-ended questions.

The surveys used in this paper can be used to reformulatethe common questions developed early in the 1990s aboutconservation behavior. They may also provide some directassurance that there are still some consumers that have notimplemented all the conservation behaviors.

3. Data

The data used in this analysis includes two surveys ofCalifornia consumers (Lutzenhiser, 2001). Surveys wereconducted by telephone, one immediately following themost recent California electricity crisis in 2001, andanother approximately one year later in 2003.

The first telephone survey of 1666 randomly selectedresidential electricity consumers was conducted during themonths of September and October 2001. Recall that thiswas a time when threats of brownouts were in the news andsome households were experiencing some increases inelectricity prices. Californians reduced electricity usagethat summer by almost 7% and peak monthly summerdemand by 8–14% compared to 2000. There were norolling blackouts or stage 3 alerts during summer 2001 andjust two stage 1 and two stage 2 alerts. In spite of the lackof rolling blackouts this may be thought of as a survey ofcrisis response.

The survey sample was stratified by utility territory, withinterviews of between 200 and 400 households conducted ineach of the five major California utility service territories(Table 1).2

1Many examples can be seen at the California Measurement Advisory

Committee (CALMAC) website, http://www.calmac.org.2Year 2 sample based on Year 1 respondents who agreed to be

resurveyed. Dual frame samples of 1000 Random Digit Dial

(RDD)+2500 Directory Listed (DL) for Pacific Gas and Electric (PG&E)

and Los Angeles Department of Water and Power (LADWP). For

Sacramento Municipal Utility District (SMUD) a dual frame of 566

RDD+600 DL numbers. Southern California Edison (SCE) supplied a

random sample of 5000 customers, 1200 were called.

The sampling frame was constructed from utilitycustomer accounts and random phone number samples,assuring that all households in the five utility territorieswere equally likely to be selected.The second survey was conducted from late October

2002 to early January 2003. This was after most percep-tions of an electricity crises had ended and may be thoughtof as a post-crisis survey. Of the 1482 households from thefirst survey that agreed to be reinterviewed, a total of 792second-year surveys were completed, as well as anadditional 23 partially completed surveys, for a total of815 fully and partially completed surveys—a 55% responserate.

3.1. Survey questions

Most recent residential phone surveys have used closed-end questions to elicit information about conservationbehaviors and equipment. For example, the ResidentialEnergy Consumption Survey (RECS) administered by theEnergy Information Administration, asks closed-endedquestions about equipment, e.g., heaters, coffee pots, etc.,as well as average use, but does not focus on behavioralpatterns within the household (Energy Information Ad-ministration, 2001).In the research reported here, respondents who indicated

that their energy-using practices had changed in any way asa result of the summer 2001 energy situation were asked todescribe those changes—an open-ended response. Forexample, we asked respondents whether they had ‘‘madeany changes in energy use’’ and, if so, ‘‘what those changeswere,’’ rather than eliciting responses from lists of possibleconservation actions.While closed-ended questions are quite common in

survey research, they suffer from interpretation and biaseffects. First, well-constructed questions are crafted so thatat least 90% of the respondents will know what is beingasked. This requires extensive field-testing of questions, aswell as preliminary interviews and open-ended questions.These preliminary stages are often skipped. As a result,many closed-ended questions are crafted by experts usingthe language of experts. The interpretation of thesequestions by the respondent is often cloudy since they

http://www.calmac.org


may not recognize the terminology or frame the behaviorexactly the same way. This adds a certain amount ofinterpretive noise to the data.

The bias induced by untested closed-ended questions hasbeen established under experimental conditions. The bestexemplar of the measured bias may be seen in Schumanand Presser (1979). The authors showed that closed-endedquestions, when some common responses are excluded, donot result in a simple reallocation of responses butwholesale changes in the relative frequency of options.The closed-ended questions also foreclose later open-endedresponses on the same topic. This means that if you providean incomplete list of options and then ask if, ‘‘there isanything else?’’ you are unlikely to collect all the responsesthat you would with a single open-ended question.

Second, while closed-ended questions lowers the cost ofdata collection, repeated use of closed-ended questions insurvey after survey, without intervening open-ended tests,will eventually detach expert knowledge and opinion fromwhat is occurring in the field. This survey confirmed thatthere are many more conservation behaviors, and beha-viors that are thought to conserve electricity, than anyexpert could frame. Following Schuman and Presser(1979), this suggests that many surveys conducted in thepast decade are heavily biased.

Third, closed-ended questions are often phrased so thatit is clear that all, ‘‘Yes’’, responses are good responses,indicating that the respondent is a rational and frugalconsumer. This means that if there is a doubt about theinterpretation of a question, the respondent is more likelyto answer in the affirmative or give the answer that pleasesthe interviewer.

Introducing a lie scale, often seen in psychological andpersonality inventories, can reduce this problem. The liescale is implemented by repeating questions or askingmany similar questions. A lack of consistency results inrejecting the test or removing the respondent from theanalysis. Because of the number of questions required foran accurate lie scale, they are almost never included as partof a phone survey.

Finally, long lists of questions about conservationbehaviors induce autocorrelation in responses. A commonsolution to this problem is to randomize the order of thequestions. This ensures that the population frequencies areunbiased for each of the questions but does little to correctthe correlation of questions for each survey respondent.

This is not to say that open-ended responses to questionsare easier to collect and analyze. There are separateinterpretation and bias issues, but the benefits outweighthe costs when a survey of this type has not been conductedfor some time.

Instead of having the survey respondent interpret thequestion, the analyst must interpret the answer. On the facethis looks like a transfer of the interpretation problem.What it actually represents is a transfer of the interpreta-tion task to the individuals most likely to understand boththe question and the response. The analyst, because they

see so many responses to the same question, is able to drawout commonalities from all the responses and construct aneffective categorization. The result is data with much lessinterpretative noise than a survey of closed-ended ques-tions.Open-ended questions explicitly treat the interpretation

problem while the analysis of closed-ended questionstypically ignores the problem. Section 3.2 will furtherdescribe the process used to categorize conservationbehaviors.Open-ended questions come with their own source of

bias. While closed-ended questions tend to be biased in thepositive, open-ended questions tend to be biased in thenegative. Closed-ended questions act as a prompt, remind-ing survey respondents of their own behavior. Open-endedquestions provide few such prompts, so there are manyopportunities for respondents to forget about, or notreport, conservation behaviors they would if asked directly.There are many justifications for this apparent forgetful-

ness. A respondent that has just installed ceiling insulation,a large and expensive change, may not mention turning offlights, since she did not think it was important relative tothe insulation installation. Some behaviors may happeninfrequently or were last implemented sometime before thesurvey, such as reducing the water heater temperaturesetting for the summer. There is also the possibility that therespondent may be unaware of all the energy conservationbehaviors performed by all household members. Thus,many conservation behaviors may not be mentionedduring the interview.This is the primary statistical problem encountered when

analyzing open-ended questions. Modeling the respon-dents’ ability to remember, acknowledge and have aware-ness of conservation behaviors during the interview is thetopic of Section 4.1. This model pre-supposes that ahousehold can be reinterviewed and is a variation on thecommon test/re-test methodology.

3.2. Categorization of behaviors

Once the surveys where completed the open-endedresponses were independently coded by multiple analysts,with disagreements among them negotiated, and ultimatelycategorized into nearly 100 different types of conservationbehaviors. This procedure of parallel, independent, cate-gorization followed by negotiation ensures that at least twoindividuals understand the question, the answer, and thedefinition of the category. This cannot be said for closed-ended questions. The uncertainty about how to interpretquestions and their answers is rarely addressed orstatistically treated.While this 100 category data may be used to craft closed-

ended questions in future surveys, it is much too difficult toanalyze directly. For the purposes of this paper, the resultsare presented using a collapsed coding scheme of 11categories (Table 2). It should be noted that this particulartaxonomy is not the most reasonable from every point of

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Table 2

Reported conservation behaviors

Shell improvement Hardware-related one-time improvements to the

house (windows, insulation, a new fixed appliance like

water heater, AC, furnace, etc.)

Light bulbs Hardware related purchase/use of compact

fluorescent bulbs or other energy saving/low-watt

bulbs

Appliances Hardware-related purchased/use of new non-fixture-

type appliances (refrigerator, washer/dryer, window

AC, fans, etc.)

Lights behaviors Behaviors related to turning off lights or using fewer

lights

Sm. equip

behaviors

Behaviors related to household appliances (turn off,

use less, unplug)

Lg. equip

behaviors

Behaviors related to pools, spas, irrigation motors

(turn off, use less often)

Not using A/C

behavior

Behavior related to not using the AC at all

Other heat/cool

behaviors

Behaviors related to heating or cooling other than not

using the AC at all (e.g., use AC less, use ceiling fans,

draw curtains, night venting, thermostat up/down)

H2O behaviors Behaviors related to using less water or using less hot

water (e.g., shorter showers, wash in cold/warm water,

turn water heater down)

Peak behaviors Behaviors related to using energy during off-peak

hours (postpone major appliance use until evening,

e.g., washing, cooking, cleaning)

Vague behaviors Behaviors stated in general terms (e.g., ‘‘over-all

conserver’’, ‘‘be less comfortable’’, ‘‘use little energy’’)


view or for every research question. There are an infinitenumber of categorizations and some are more useful thanothers in answering different kinds of questions. Thesebroad categories represent a compromise between tradi-tional categories of conservation behavior and categoriza-tion based on behavioral/non-behavioral, fixture/non-fixturepurchase, and a residual, i.e., ‘‘other’’, category.

Once the coding and reallocation into broad categorieswas complete, the data for each respondent consisted of alist of:

�
Conservation behaviors that they said they performed inthe first-year survey. � Conservation behaviors that they said they are continu-
ing to perform in the second-year survey.
� Conservation behaviors that they said they have
discontinued in the second-year survey.
� Conservation behaviors that they said they have
initiated as a new action in the second-year survey.

Each of these behaviors is explicitly allocated to one ofthe eleven categories shown in Table 2.

Analyzing the count of behaviors in each category doesnot yield useful information. The respondent could haveseveral shell improvements in one year, e.g., they reportedinsulation added to the floor, and walls as two separateitems, and then the next year, described it as just ‘‘addedinsulation’’, which would be translated into a single

behavior. Rather than have the analysis dependent on thedegree to which the respondents subdivide their behaviors,the analysis will focus on the existence of conservationbehaviors in each category rather than the count.This focus on the existence rather than the count induces

complications in analysis since it is possible to discontinue‘‘Night Venting’’ and continue, ‘‘Using Ceiling Fans’’, bothpart of the ‘‘Other Heat/Cool Behavior’’ category butseparate actions. If this is treated as both continued anddiscontinued, the described behavioral states are notexclusive. There are computational advantages to havingone category treated as a residual category and is acommon assumption in logistic and multinomial regressionmodels. This analysis will treat the continuation of aconservation behavior and the discontinuation of adifferent behavior, within each of the 11 categories, asexclusively continued behavior.Section 4, which follows, will focus on how actual

conservation behavior is estimated from reported conserva-tion behavior in the survey. This analysis can only beperformed when households are reinterviewed and cannotbe performed with a single survey observation.

4. Estimating conservation behavior

Responses to open-ended questions about conservationbehavior are not a perfect measure of behavior. Therespondent may forget to mention a conservation behavior.They may not mention a conservation behavior because itis so obvious, like turning off the lights. They may also notmention a conservation behavior because they have alreadymentioned several more important conservation behaviors.Surveys give an indication of behavior, not the behavioritself!In order for survey respondents to report that they

performed a conservation behavior they must both per-form the behavior and recall the behavior, a compoundevent. The notation in Eq. (1) shows that the probability ofreporting an action, i, in period one, Ps

t¼1; i, is simply theproduct of the probability of recalling action i, Pi

r, and theprobability of performing the action in the first year, Pa

t¼1; i.This relationship holds for each of the conservationactions, I, and is expected to differ across conservationactions. In other words, it is expected that some behaviors,like less frequent dishwasher use, will be less frequentlyrecalled than shell improvements, but each has a chance ofbeing forgotten. Thus, the probability of reporting anaction is given as

Pst¼1;i ¼ Pr

i Pat¼1;i. (1)

A reasonable assumption implicit in this model of recallis that people will not report actions that they did notperform. One of the reasons for using open-ended surveyquestions without prompting is that they minimize theprobability of false statement but at the cost of a largerrecall risk. Closed-ended questions have the opposite risksand costs.


This paper we will assume that there is no chance of asurvey respondent reporting a conservation action they didnot perform. Without this kind of asymmetry assumptionon recall bias, or additional experimental controls throughrepeated sampling, it is not possible to observationallyidentify behaviors using reported behaviors.

Given this asymmetry assumption, there is always achance that the respondent will forget to tell the interviewerabout a behavior, 1–Pi

r, and there will always be a weakunderstatement of the behaviors in both the first andsecond-year survey. For each conservation category, if weassume that the recall probability, Pi

r, is the same in bothsurvey years, we can estimate the true count of behaviors inboth periods, even the behaviors the respondents forgot toreport in both surveys.

Once again, we must assume that the recall probability isthe same in both periods or it is not possible toobservationally identify any of the recall probabilities orprobabilities of action no matter how many observationsor how many times we interview the survey respondents.The only way to empirically identify the different recallprobabilities is to interview each respondent twice during aperiod small enough so that there is no change in behavior,but long enough so the two interviews can be treated asbeing independent. This kind of test/re-test methodology isnot practical for a phone survey over a short-time horizonbecause of the large increase in non-participation bias.

In the interest of clarity, the subscript indicatingconservation actions will be suppressed in the discussionthat follows.

4.1. Conditional probabilities

Behaviors in the second year are potentially, but notnecessarily, dependent on what the respondents did theprevious year. For example, the probability that a house-hold will actually turn off unused lights this year may bedependent on if they turned them off last year, not on ifthey reported the behavior to an interviewer.

Eq. (2) expresses the sentiment that telling an interviewerthat a conservation action is being performed in the secondyear depends on performing the conservation action,Pa

t¼2ja;t¼1Pri þ Pa

t¼2j�a;t¼1ð1� Pri Þ, when the respondent did,

Pat¼2ja;t¼1, or did not, Pa

t¼2j�a;t¼1, perform a conservationaction in the previous year. Note that not performing theconservation action the previous year is indicated by the,�a, in the probability subscript:

Pat¼2 ¼ PrðPa

t¼2ja;t¼1Pri þ Pa

t¼2j�a;t¼1ð1� Pri ÞÞ. (2)

Conservation actions are only reported to the inter-viewer if they recalled the action during the interview,which happens with probability, Pr. The probability ofrecall, Pr, is assumed to be the same in both years, butdifferent across conservation actions. Weakening thisassumption is impossible with only one repeat surveyunless a similar assumption is made about the probabilityof action, i.e., we must assume that the probability of the

conservation actions is the same in both years to estimatedifferent recall probabilities in each year.Reported behavior in the second year is subject to the

same forgetfulness process as the first year. So, it is possiblefor a respondent to turn off lights in both years and not tellan interviewer about it in either year.The implication is that in each year and for each

behavior, the respondents can be in one of three states:(P)erformed and (R)eported an action (PR), performed butdid not report an action (P(�R)), or, did not perform anaction (�P). Again, this analysis will ignore the possibilitythat the respondents will report conservation behavior theydid not perform. It is not possible to observe the state werethe agent reports a behavior they did not perform (�P)R.The respondents can be thought of as transitioning from

one state to another. For example, conditional on therespondent performing and reporting an action theprevious period, there is a probability that they will eitherperform and report the action (PR), perform and notreport (P(�R)), or not perform, (�P) an action in yeartwo. This is usually represented as a state transition matrix:

Pt¼2Rt¼2

Pt¼2ð�Rt¼2Þ

�Pt¼2

0B@

1CA ¼

A1 A2 A3

A4 A5 A6

A7 A8 A9

0B@

1CA

Pt¼1Rt¼1

Pt¼1ð�Rt¼1Þ

�Pt¼1

0B@

1CA.

(3)

The element, A2, in the behavioral state transition matrixis interpreted as the probability of performing an actionand reporting it in year two given that you performed anaction but did not report it in year one. The probability ofbeing in PR, P(�R) and P states in the second period canbe calculated if the probability of each state in the firstperiod is known. The main difficulty is that these states arenot directly observable; we have only reported actions.Those not reporting an action may not have performed aconservation action, �P, or performed one and then notremembered to tell the interviewer, (�R)P. This represen-tation still holds when the probability of performing theaction in the second year is independent of performing theaction in the first. There is simply a restriction on thetransition probabilities.The nomenclature of these transition probabilities will be

used in Section 4.2 to describe the joint year-one/year-twobehavioral state of the households, which will then beconnected to the joint year-one/year-two observable state.

4.2. Connecting observed states to behavioral states

The surveys structure what can and cannot be observed.The first-year survey asked people what they did. Thesecond-year survey asked what conservation actions theywere continuing, what they discontinued, and what theyinitiated as a new behavior.The two surveys combined allowed for the construction

of a year-one/year-two observational state, O, for eachconservation behavior and survey respondent. Each of

ARTICLE IN PRESSJ. Woods / Energy Policy 36 (2008) 1945–1956 1951

these observational states, O, can be associated with one ormore of the unobservable behavioral states, A.

Section 4.1 described the following year-one/year-twobehavioral states:

�
A1—performed and reported a behavior in first-yearsurvey, and performed and reported a behavior insecond-year survey. � A2—performed but did not report a behavior in first-
year survey, and performed and reported a behavior insecond-year survey.
� A3—did not perform a behavior in first-year survey, and
performed and reported a behavior in second-yearsurvey.
� A4—performed and reported a behavior in first-year
survey, and performed but did not report a behavior insecond-year survey.
� A5—performed and did not report a behavior in first-
year survey, and performed but did not report abehavior in second-year survey.
performed but did not report a behavior in second-yearsurvey.
� A7—performed and reported a behavior in first-year
survey, and did not perform a behavior in second-yearsurvey.
� A8—performed but did not report a behavior in first-
year survey, and did not perform a behavior in second-year survey.
did not perform a behavior in second-year survey.

These states correspond to the transition probabilitiesdescribed in Eq. (3).

Observable (O) in the surveys are the following year-one/year-two states:

1.
Reports a behavior in year one and then in year two:(a) O1 reports continuing the behavior (A1);(b) O2 reports discontinuing the behavior (A7);(c) O3 reports initiating the action as a new behavior
(A1);(d) O4 reports nothing (A4 or A7).

2.
Does not report a behavior in year one and then in yeartwo:(a) O5 reports continuing the behavior (A2);(b) O6 reports discontinuing the behavior (A8);(c) O7 reports initiating the action as a new behavior
(A3);(d) O8 reports nothing. (A5, A6, A8, or A9).

It is important to note that there is an associationbetween the behavioral states, A, and the observable states,O. Behavioral state A2 and observational state O5 exactlyoverlap. Some behavioral states, for example A7, areassociated with more than one observational state.

This mapping can then be translated into probabilitystatements about the observational state of each individual,i, e.g., O1,i, in terms of both the behavioral states, e.g., A1,and the primal probabilities of performing and recalling aconservation behavior, i.e., Pa

t¼2j�a;t¼1;Pat¼2ja;t¼1;P

at¼1, and

Pr, assuming that all respondents are drawn from the samepopulation. A system of moment conditions, shown inEq. (4), can be identified for each conservation behavior:

EO1;i ¼ A1 ¼ ðPrPa

t¼1ÞðPrPa

t¼2ja;t¼1Þ,


t¼1Þð1� Pat¼2ja;t¼1Þ,


t¼1ÞðPrPa

t¼2ja;t¼1Þ,

EO4;i ¼ ðA4 _ A7Þ ¼ ðPrPa

t¼2ja;t¼1Þ

� ðPrPat¼1ÞðP

r � Pat¼2ja;t¼1Þ

� ðPrPat¼1Þð1� Pa

t¼2ja;t¼1Þ

� ðPrPat¼1ÞðP

rPat¼2ja;t¼1Þ,

EO5;i ¼ A2 ¼ ðð1� PrÞPat¼1ÞðP

rPat¼2ja;t¼1Þ,

EO6;i ¼ A8 ¼ ðð1� PrÞPat¼1Þð1� Pa

t¼2ja;t¼1Þ,

EO7;i ¼ A3 ¼ ð1� Pat¼1ÞðP

rPat¼2j�a;t¼1Þ,

EO8;i ¼ ðA5 _ A6 _ A8 _ A9Þ ¼ ð1� PrPat¼1Þ

� ðð1� PrÞPat¼1ÞðP

rPat¼2ja;t¼1Þ

� ðð1� PrÞPat¼1Þð1� Pa

t¼2ja;t¼1Þ

� ð1� Pat¼1ÞðP

rPat¼2j�a;t¼1Þ. (4)

Each of the equations above shows the expectedprobability of an observational state, E(O)n, the behaviorstates it represents, An, and the component primalprobabilities. For example, the probability of observingstate O1 depends on the joint event of performing aconservation action in year one and reporting the action tothe interviewer, PrPa

t¼1, and the probability of reporting theaction in year two given that conservation behaviorcontinued in the second year, PrPa

t¼2ja;t¼1. Note that theequations describing the expectation of observing theobservable state four, EO4,i, and eight, EO8,i, are shownas residual probabilities. This system can be simplifiedfurther if the probabilities of performing a conservationaction in the second year are independent of the first year’sconservation action.The appropriate method for estimating this system is

through a generalized method of moments (GMM) frame-work, which, unlike traditional regression analysis, doesnot require exogenous observable variables to describe theendogenous observable states. GMM estimators are alsofrequently referred to as M-Estimators.M-Estimators use sample moments, e.g., mean or

covariance, to define model parameters. In this case, weare using the fraction of respondents in each observablestate to define the probability of recall and other primalprobabilities.Perhaps the simplest, non-trivial, example of an

M-Estimator is calculating the l parameter for a samplebelieved to be distributed exponentially, i.e., that it has the

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Table 3

State probabilities by behavior

Cont.

(O1)

Discont.

(O2)

Init.

(O3)

Nothing

(O4)

Reports in year one and then

Shell improvement 0.026 0 0.019 0.035

Light bulbs 0.066 0 0.011 0.083

Appliances 0.012 0 0.006 0.050

Lights behaviors 0.263 0 0.034 0.226

Sm. equip behaviors 0.099 0 0.017 0.154

Lg. equip behaviors 0.019 0 0.006 0.042

Not using A/C behavior 0.015 0 0.025 0.068

Other heat/cool

behaviors

0.150 0 0.024 0.126

H2O behaviors 0.031 0 0.004 0.099

Peak behaviors 0.035 0 0.002 0.098

Vague behaviors 0.014 0 0.002 0.053

Cont.

(O5)

Discont.

(O6)

Init.

(O7)

Nothing

(O8)

No report in year one and then

Shell improvement 0.067 0.001 0.015 0.833

Light bulbs 0.050 0 0.009 0.779

Appliances 0.051 0.001 0.014 0.863

Lights behaviors 0.074 0 0.018 0.381

Sm. equip behaviors 0.061 0 0.020 0.646

Lg. equip behaviors 0.009 0.001 0.002 0.917

Not using A/C behavior 0.034 0.001 0.066 0.787

Other heat/cool

behaviors

0.191 0 0.020 0.485

H2O behaviors 0.052 0 0.004 0.806

Peak behaviors 0.060 0 0.004 0.798

Vague behaviors 0.079 0.002 0.012 0.834

J. Woods / Energy Policy 36 (2008) 1945–19561952

probability density function le�lx. The mean of thatdistribution is l�1, so the M-Estimator of, l, is simplythe inverse of the sample mean.

The moment conditions shown in Eq. (4) form an over-identified system of M-Estimators since there are moremoment conditions than parameters. Primal probabilities,e.g., Pr, are estimated by minimizing the sum of squareddifferences between the observed sample moments, in thiscase the fraction of households in each of the observationalstates, and the expectation based on the modeled primalprobabilities.

Minimizing the sum of squares of the differences inmoment conditions results in consistent estimators(Davidson and Mackinnon, 1993, p. 592). M-estimatorsare, in general, asymptotically normal (Davidson andMackinnon, 1993, p. 593). This requires, among otherthings, differentiability almost everywhere. Because theestimated parameters are probabilities, which are boundedon the [0,1] interval, this standard result does not hold. Asa consequence, all confidence intervals in this paper are theresult of simple percentile bootstrap replicates (Efron andTibshirani, 1993).

The section that follows will show the sample momentsof each of the observable states for each of the conserva-tion behaviors as well as estimates of the primalprobabilities defined by those sample moments.

5. Empirical estimates

This analysis does not attempt to assign a specific causeto behaviors. There is no attempt to attribute the cause ofthese conservation behaviors to price, media attention,utility incentives or social pressure. That kind of analysiswould be possible provided that additional information onutility price changes, media minutes and column inchesdevoted to energy topics, remodel price indexes and othervariables were appended to the data set. Because of thislimitation, some of the statements about the differencesbetween crisis year and post-crisis year actions may seemmild, but causal statements cannot be made with theavailable data.

As with all theoretically feasible estimators and estima-tion procedures, there are complications not treated in thetheory. In this case, combinations of rare observationalstates cause estimation problems.

Table 3 shows the raw probabilities of each observa-tional state for each conservation behavior. This illustratesseveral potential problems with the proposed M-Estima-tors. First, the state where households report a conserva-tion behavior the first year and then report discontinuingthe behavior the second, O2, has no observations. State six,where the households reported no conservation behaviorthe first year and reported discontinuing the behavior thesecond, is also comparatively rare.

Both of these rare states involve the transition fromperforming a conservation action to not performing theaction. This is critical to linking the behavior of households

during the 2001 survey, the crisis period, and the later post-crisis survey. In order to empirically identify a probability,there must be observations of all states. Relatively smallfractions of the population in the discontinued state wouldrequire very large sample sizes to achieve reasonableconfidence intervals. Because the primal probabilitiesinteract so strongly, these uncertainties will feedback intoother probability estimates causing uncertainty in thoseestimates.Table 4 shows the estimated primal probabilities for the

system of moment conditions described by Eq. (4). Theparentheses below each point estimate shows 95% con-fidence intervals determined by a simple percentile boot-strap of 1000 replicates, a common suggestion forconfidence interval estimation. Similar estimates may bemade for each of the service territories covered by thesurveys. Because survey responses are so similar in eachterritory, differing, except in one rare circumstance, by onlya few percentage points, the separate estimates would notprovide additional information.In Table 4, column Pa

t¼1 represents the probability ofperforming that specific conservation action during thecrisis period of the first survey. The two columns, Pa

t¼2ja;t¼1

and Pat¼2ja;t¼1 show the probability of performing the

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Table 4

Probability estimates: conditional year 2 action

Behavior Pr Pat¼1 Pa

t¼2j�a;t¼1 Pat¼2j�a;t¼1

Shell improvement 0.297 0.324 0.08 0.983

(0.248, 0.357) (0.252, 0.397) (0.038, 0.129) (0.883, 1)

Light bulbs 0.713 0.232 0.018 0.39

(0.301, 0.892) (0.203, 0.387) (0.007, 0.055) (0.304, 0.871)

Appliances 0.116 0.539 0.275 0.956

(0.065, 0.568) (0.139, 0.878) (0.025, 1) (0.312, 1)

Lights behaviors 0.978 0.626 0.05 0.33

(0.908, 1) (0.592, 0.66) (0.027, 0.079) (0.305, 0.361)

Sm. equip behaviors 0.817 0.36 0.04 0.305

(0.701, 0.926) (0.33, 0.397) (0.021, 0.064) (0.259, 0.37)

Lg. equip behaviors 0.883 0.083 0.003 0.237

(0.731, 1) (0.064, 0.1) (0.0.007) (0.163, 0.327)

Not using AC behavior 0.445 0.247 0.198 0.454

(0.285, 0.58) (0.208, 0.336) (0.117, 0.365) (0.315, 0.788)

Other heat or cool behaviors 0.345 0.892 0.549 0.977

(0.311, 0.394) (0.777, 0.988) (0.202, 1) (0.909, 1)

H2O behaviors 0.689 0.201 0.009 0.242

(0.1, 0.8) (0.175, 0.904) (0.002, 0.037) (0.175, 0.97)

Peak behaviors 0.651 0.212 0.01 0.283

(0.097, 0.79) (0.185, 1) (0.002, 0.146) (0.2, 0.976)

Vague behaviors 0.187 0.49 0.129 1

(0.093, 0.241) (0.362, 0.848) (0.05, 1) (0.967, 1)


conservation behavior in the second, post-crisis year,conditional on crisis year action.

The estimates of households purchasing compact fluor-escent light (CFLs) bulbs, the ‘‘light bulb’’ behavior, arevery well behaved. 23.2% of the survey respondentspurchased the light bulbs in the first year of the surveyand 71.3% reported this to the interviewer. PurchasingCFLs, is very sticky behavior. Only 1.8% of the householdswho did not purchase the bulbs during the first survey yearpurchased them in the second, while 39% of those who didpurchase them the first year did so in the second.

The estimates of several of the conservation behaviorsare of concern. The Appliance, Water Conservation (H20),Peak and Vague behaviors all have large confidenceintervals for some of the primal probabilities. For example,the 95% confidence interval for the probability ofperforming a peak reducing behavior in the first year isfrom 18.5% to 100%. This uncertainty echoes intovirtually all the probability estimates for that conservationbehavior.

The large confidence intervals associated with the vagueconservation behaviors can be attributed to the definitionof the category—it is a residual category and represents ahousehold doing something to conserve electricity but theaction is unclear. In addition to the risk of being forgottenby the respondent, conservation actions also carry the riskof being miscategorized. For example, the same behavior,say reducing the thermostat set point, may be described thefirst year as, ‘‘just saving electricity’’, while in the secondyear is may be described more explicitly as, ‘‘turning downthe thermostat.’’

Econometric practitioners will be pleased to note thatthere are no universally performed behaviors, with theexception of the ‘‘Other Heat or Cool Behaviors’’,essentially turning up or down the thermostat. This meansthat there are still behaviors that individual households canuse to react to price changes. In other words, we have notreached a boundary where price changes are met byextremely small changes in consumption.Policy makers should concern themselves with the

prevalence of ‘‘Other Heat or Cool Behaviors.’’ Because89.2% of the population already adjusts their thermostatto a certain extent, the effort to convince more people to doso has hit declining returns. Effort should be diverted toinducing people to make larger changes in their thermostatsettings.Table 5 shows similar results for the system of moment

conditions where conservation action in the second year isindependent of action in the first year. This table makes iteasy for the reader to compare difference in behaviorbetween the crisis survey and post-crisis survey.The primal probability estimates shown in Table 5 are

very similar to the estimates shown in Table 4. ColumnPa

t¼1 describes the probability of performing a conservationbehavior during the crisis period while Pa

t¼2 describes theprobability of performing a behavior during the post-crisisperiod.The probability of performing the ‘‘Lights Behaviors’’ in

the crisis year is virtually the same, 63% and 62.6%,whether second year performance is conditional on, orindependent from, first-year performance. The same holdstrue for the probability of recalling the action.

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Table 5

Probability estimates: independent year two action

Behavior Pr Pat¼1 Pa

t¼2

Shell improvement 0.096 0.745 0.95

(0.077, 0.125) (0.677, 0.778) (0.914, 0.992)

Light bulbs 0.435 0.24 0.228

(0.146, 0.537) (0.205, 0.662) (0.15, 0.927)

Appliances 0.359 0.144 0.11

(0.073, 0.48) (0.119, 0.819) (0.055, 0.986)

Lights behaviors 0.972 0.63 0.329

(0.903, 1) (0.596, 0.664) (0.306, 0.361)

Sm. equip

behaviors

0.658 0.37 0.27

(0.548, 0.78) (0.336, 0.407) (0.216, 0.338)

Lg. equip

behaviors

0.535 0.083 0.024

(0.405, 0.674) (0.064, 0.102) (0.012, 0.07)

Not using AC

behavior

0.339 0.242 0.272

(0.151, 0.441) (0.204, 0.482) (0.167, 0.785)

Other heat or cool

behaviors

0.341 0.902 0.98

(0.304, 0.378) (0.819, 0.985) (0.926, 1)

H2O behaviors 0.563 0.2 0.117

(0.465, 0.656) (0.174, 0.23) (0.072, 0.189)

Peak behaviors 0.537 0.21 0.147

(0.15, 0.627) (0.181, 0.708) (0.092, 0.935)

Vague behaviors 0.103 0.817 0.991

(0.082, 0.384) (0.167, 0.876) (0.153, 1)

Table 6

Fraction ‘‘no reported’’ second year behavior

Behavior No behavior (O4+O8)

Shell improvement 0.868

Light bulbs 0.862

Appliances 0.914

Lights behaviors 0.608

Sm. equip behaviors 0.801

Lg. equip behaviors 0.960

Not using A/C behavior 0.856

Other heat/cool behaviors 0.612

H2O behaviors 0.905

Peak behaviors 0.896

Vague behaviors 0.888

J. Woods / Energy Policy 36 (2008) 1945–19561954

What this table makes clear is that in the post-crisissurvey there was a large decrease in the number ofhouseholds shutting off or using fewer lights. That fractiondecreased from 63% of the population down to 32.9% ofthe population. Lights are the canary in the coal mine.They are the first measure people turn to in a crisis and thefirst they drop when the crisis abates.

In spite of the strength of this table in illustrating theprevalence of conservation actions in the crisis and post-crisis years, some conservation actions should not bethought of as being independent from year to year. Theestimates of the prevalence of ‘‘Shell Improvements’’illustrate this most clearly. Table 5 shows shell improve-ments during the crisis year by 74.5% of the populationfollowed by 95% the next year. Clearly this is not possibleand is an artifact of households reporting the same changein both years. This is one of the hazards of mixingquestions about one-time purchases with ongoing beha-viors in self-report questionnaires.

The largest differences in parameter estimates betweenthe two models occurred in the behaviors with the greatestuncertainty about primal probabilities when second-yearbehavior was conditional on the first. Once again,Appliance, Water, Peak and Vague behaviors show thelargest uncertainty in the estimates of primal probabilities.

While the uncertainty in these parameter estimates isunsettling, there are several reasonable explanations. First,the categories are very broad and cover a wide range ofmore precise behaviors. The categorization methodologytreated continuation of any of those more precise behaviorsas continuation of all behaviors. Some of the uncertaintymay be because of this aggregation effect. Smaller

aggregations could be used but it would increase therelative scarcity of some observable states.Second, Table 5 illustrates a critical commonality among

those behaviors with uncertain estimates of primalprobabilities—all show extreme estimates of second-yearbehavior. Appliance, Water and Peak are estimated to havethe first, second and third lowest estimates of second periodaction. The Vague behavior has the highest estimatedsecond-year action. This indicates that at the heart of theestimation problem is the empirical identification second-year behaviors.Table 6 shows the fraction of respondents that made no

reports of a conservation behavior the second year. Thismeans that they did not report continuing, discontinuing orinitiating the conservation behavior.The table illustrates that, except for large equipment

behaviors, the behaviors with the largest parameteruncertainty have the smallest fraction of reported behaviorin the second year. With those smaller fractions, a muchlarger sample would have been required to empiricallyidentify those primal probabilities.

5.1. Comparison to naive estimates

In spite of the difficulty in identifying some of the primalprobabilities, the benefit of decomposing reported con-servation behavior into an action and a report of an actionis quite useful. This can be seen by comparing the rawpercentage of households that reported a conservationaction and the estimated probability of performing theconservation action.Table 7 shows, as ‘‘Naive’’, the raw percentage of

households that reported a conservation behavior the firstyear. This includes not just those households that reporteda conservation behavior in the first survey, but those thatreported discontinuing or continuing a conservationbehavior in the second year when they did not report abehavior in the first. This is equal to observational statesone through six. First year estimates of conservation actionshown in Table 4 are shown in the column labeled‘‘Conditional’’ while the results from Table 5 are shownin the column labeled, ‘‘Independent’’.

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Table 7

Probability of first year behavior

Behavior Naive Conditional Independent

O1–O6

Shell improvement 0.115 0.324 0.745

Light bulbs 0.127 0.231 0.240

Appliances 0.071 0.538 0.143

Lights behaviors 0.373 0.625 0.629

Sm. equip behaviors 0.177 0.360 0.369

Lg. equip behaviors 0.036 0.082 0.082

Not using A/C behavior 0.077 0.247 0.241

Other heat/cool behaviors 0.366 0.892 0.901

H2O behaviors 0.089 0.201 0.199

Peak behaviors 0.098 0.212 0.209

Vague behaviors 0.099 0.490 0.816


With the exception of ‘‘Shell Improvement’’ and theconservation behaviors with large confidence intervals, theestimated probabilities of first-year conservation behaviorare very similar (shown in bold). What is striking is howmuch larger these estimates are relative to what is directlyobserved in the survey data. This is a direct consequence ofthe understatement problem and not related to theaggregation of small conservation behaviors into broadcategories. Aggregating conservation behaviors into largecategories increases the estimated recall rate and reducesthe difference between the naive estimates of conservationaction and the estimated probabilities.

Only 12.76% of households actually mentioned purchas-ing CFLs. This is in sharp contrast to 24.04% that wereestimated to have purchased CFLs. The difference isbecause of the low recall rate during the interviews.

Households perform conservation action far morefrequently than they report it. Therefore, when conductingsurveys with open-ended questions this understatementbias should be taken seriously and treated, when possible,with repeated surveys.

Most of the population, 89.23%, is doing somethingabout reducing heating and cooling expenditures. This areais rife with energy saving ideas but it is clear thathouseholds understand and implement many behaviors inthis area. If households are going to react to increases inthe price of electricity through these behaviors, we mustlook towards a greater intensity rather than greaterproliferation of these behaviors.

CFLs are still only used by 24.04% of households. Whilemuch effort has gone into promoting CFLs, the surveyshows that, as of 2001, many more people could bepurchasing and using these lamps in existing fixtures.

6. Summary and conclusions

What you can learn about the future based on availabledata is a concern for all applied econometricians. Withrespect to electricity demand, the common approach is tomake an informed decision about what data is applicable

to the problem of forecasting the responses to future pricechanges. What data is chosen is very dependent on theeconometrician’s beliefs about how similar households arein different service territories and in different times.Some of the literature suggests caution when using price

differences in different territories to estimate the reactionsto future price changes, but using past data also providessimilar hazards. Equipment changes and concerns aboutsymmetric responses to price increases and decreases limitthe applicability of past data. What can be done to help theFrequentist econometrician with the data problem, andhelp the Bayesian econometrician form prior beliefs, is toshow that households can react to price increases withconservation behaviors in the same way they have in thepast.The survey results presented in this paper perform that

important function as well as providing a list of commonconservation behaviors collected via open-ended questions.The responses to open-ended questions will allow others toconstruct closed-ended questions that do not bias con-servation responses and allow for a more accurateassessment of policy initiatives.The use of open-ended questions does not come without

a cost—the observed prevalence of conservation behaviorsis biased downward because open-ended questions do notprovide the same prompting as closed-ended questions.This paper presented a unique method of using repeated,open-ended survey data to correct for this bias by modelinga forgetfulness process. This model, implemented withGMM estimators produced estimates of:

�
the probability a household will recall a specificconservation behavior during the interview; � the probability of performing the conservation action
during the first year of the survey—the summer of 2001,a crisis year for California;
� the probability of performing the conservation behavior
in the second year of the survey—a post-crisis observa-tion;
� probabilities of performing the conservation behavior in
the second year of the survey conditional on performingthe action during the first, crisis, year.

The recall probabilities demonstrate downward bias inresponse for open-ended questions. The remaining prob-abilities show the prevalence of some conservationbehaviors during the last California electricity crisis andimmediately after.Three results stand out most clearly. First, turning off

lights is not sustained. Households move to this behaviormost strongly during crisis periods and abandon it whenthe crisis is over. Second, Californians turn their thermo-stats up/down to save electricity. Policy actions aimed atencouraging more households to make this change will seelittle additional response. Further improvements arepossible if households change their thermostat settings by


more than they have in the past—that is, by increasing thescale of the behavior rather than the scope.

The third, and perhaps most important result is thatwhile not every household is performing every conservationmeasure. When electricity prices rise, households can anddo act in well-known ways to reduce use and react to thoseprice increases. This information can be used to supporttheir use of data from the recent past as well as othernearby territories to forecast the effects of price changes.

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