Munich Personal RePEc Archive
Estimating the impact of time-of-use
pricing on Irish electricity demand
Di Cosmo, Valeria and Lyons, Sean and Nolan, Anne
Economic and Social Research Institute, Dublin, Trinity College,
Dublin
July 2012
Online at
https://mpra.ub.uni-muenchen.de/39971/
1
Est imat ing t he Impact of Time-of-Use Pricing on Irish
Elect ricit y Demand
Valeria Di Cosmo,
ab*Sean Lyons,
abAnne Nolan
aba
Econom ic & Social Research Inst itut e, Dublin
b
Trinit y College Dublin
* Corresponding aut hor: Whit aker Square, Sir John Rogerson’s Quay, Dublin 2. Em ail: valeria.dicosm o@esri.ie. Tel: +353 1 863 2033.
We are grat eful for funding from t he ESRI Energy Policy Research Cent re. The sponsors had no role in t he design of t he st udy; t he collect ion, analysis, and interpret at ion of dat a; t he w rit ing of t he report; or t he decision t o submit t he paper for publicat ion. We w ould like t o t hank Conor Devit t for out st anding research assist ance, Richard Tol and Laura M alaguzzi Valeri for helpful advice, part icipant s at t he ESRI and IEA 2012 conferences and 2012 ESRI Environm ental Econom ics Sem inar for comm ent s and suggest ions. The usual disclaimer applies.
Abst ract
Elect ricit y dem and traditionally exhibit s a subst antial peak during a sm all num ber of hours each day. Policym akers are aw are of t he pot ential efficiency savings t hat m ay be generat ed from a shift in energy consum ption aw ay from peak t im es. Sm art m eters, in conjunct ion w it h t im e-of-use (TOU) pricing, can facilit ate an im provem ent in energy efficiency by providing consum ers wit h enhanced inform at ion about electricit y consum ption and cost s, and t hereby encourage a shift aw ay from consum ption during peak hours. In 2009-10, t he Irish Commission for Energy Regulation (CER) co-ordinat ed a random ised cont rolled t rial in t he Irish residential elect ricity m arket . Sm art m et ers, w hich replaced t he exist ing mechanical m eter readers, w ere int roduced in approxim at ely 5,000 households. Part icipants w ere divided int o cont rol and treatm ent groups, w it h t reat ment groups exposed t o a variet y of TOU t ariffs and inform ation st im uli (in-hom e display (IHD) unit s, m ont hly billing, et c.). Dat a w as collect ed over approxim ately 18 m ont hs, w it h t he first half year being used as a cont rol period. This paper analyses t he response of Irish households t o t he introduction of TOU t ariffs and inform ation st im uli. We examine how households responded t o t he different TOU t ariffs, at different tim es of t he day (peak, day and night) and in conjunct ion w it h different inform at ion st imuli. Finally, w e examine t he variation in our result s across households of differing socio-econom ic st at us (as proxied by educat ion levels). We find t hat TOU t ariffs and inform at ion st imuli have a significant effect in reducing electricit y consum ption in Ireland, part icularly during peak hours. However, w hile households reduce peak dem and significant ly aft er t he int roduct ion of TOU t ariffs and associated inform at ion, there is lit tle increm ent al response t o increasing differentials bet w een peak and off-peak prices.
JEL codes: Q41, D12
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1
Intr oduction
Elect ricit y dem and t radit ionally exhibit s a subst ant ial peak during a sm all num ber of hours each day. Policym akers are aw are of t he pot ent ial efficiency savings t hat m ay be generat ed from a shift in energy consum pt ion aw ay from peak t im es. Sm art m et ers, in conjunct ion w it h t im e-of-use (TOU) pricing, can facilit at e an im provem ent in energy efficiency by providing consum ers w it h enhanced inform at ion about elect ricit y consum pt ion and cost s, and t hereby encourage a shift aw ay from
consum pt ion during peak hours.
In t he EU, a num ber of recent pieces of legislation have prom ot ed t he use of sm art m et ering, including t he Elect ricit y Direct ive 2009/ 72/ EC, w hich requires M em ber St at es t o ensure t he im plem ent at ion of int elligent m et ering syst em s and t o carry out a cost -benefit analysis of t he syst em by Sept em ber 2012 (Com mission for Energy Regulat ion, 2011b). In Ireland in M ay 2009 t he first Nat ional Energy Efficiency Act ion Plan (NEEAP) was adopt ed in line wit h EU requirement s, and
included a com m it m ent t o encourage m ore energy efficient behaviour by households t hrough t he int roduct ion of sm art m et ers (Com m ission for Energy Regulat ion, 2011a).
In 2007, t he Irish Comm ission for Energy Regulat ion (CER) announced t heir int ent ion t o int roduce a t rial sm art m et ering experim ent in t he Irish resident ial and sm all-t o-m edium ent erprise (SM E) elect ricit y m arket s.1 Sm art m et ers, w hich replaced t he existing m echanical m et er readers, w ere int roduced in approxim at ely 5,000 households and 650 SM Es. Part icipant s w ere divided int o cont rol
and t reat m ent groups, w it h t reat m ent groups exposed t o a variet y of t im e-of-use (TOU) t ariffs and inform at ion st im uli (in-hom e display (IHD) unit s, m ont hly billing, et c.). Dat a w as collect ed over t he period 14 July 2009 t o 31 Decem ber 2010, and as t he experim ent began on 1 January 2010, six m ont hs of pre-t rial dat a are available for bot h t he cont rol and t reat m ent groups.
Num erous ot her count ries have experim ent ed w it h t he use of sm art m et ers (e.g US, Canada and Denm ark)2, and t here is a grow ing int ernat ional lit erat ure analysing t he im pact of TOU t ariffs on resident ial and com mercial elect ricit y consum pt ion. The availabilit y of high-qualit y dat a on a large and represent at ive sam ple allow s us t o est im at e t he im pact of TOU pricing on elect ricit y consum pt ion in Ireland for t he first t ime.3 Ireland is an int erest ing case st udy as m uch of t he int ernat ional lit erat ure focuses on t he US w here the use of air condit ioning for resident ial use is com m on. As in Ireland t here is no dem and of air condit ioning during t he sum mer, t he t rial result s
show t he im pact of different TOU and st im uli on resident ial elect ricit y dem and net of t he air condit ioning effect s, w hich account s for a large part of t he household responses in t he US (Faruqui and Sergici, 2009). In addit ion, t he dat a also allow us t o invest igat e t he im pact of a variet y of
1
There w ere t hree dist inct st rands t o t he w ork; t echnology trials, cust omer behaviour t rials and a cost -benefit analysis for t he nat ional roll-out of sm art m eters (Comm ission for Energy Regulation, 2011a).
2
See w ww .ont ario-hydro.com / index.php?page=current_rat es and
w w w .ct energyinfo.com / dpuc_t im e_of_day_rat es.ht m [last accessed 01 Sept em ber 2011] for exam ple. Darby (2006) m aint ain that TOU pricing is m ost com m on in part s of t he w orld wit h sum mer and w inter peaks allied w it h supply const raint s: California, Ont ario, the nort h east ern st at es of t he US and part s of Aust ralia. For evidence on Denm ark see Gleerup et al. (2010).
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3
inform at ion st im uli on elect ricit y consum pt ion. Finally, limit ed socio-econom ic inform at ion on t he part icipat ing households is also available.4
The first aim of t his paper is t herefore t o disent angle t he effect s of t he different TOU t ariffs (peak, day and night ) on resident ial elect ricit y5 consum pt ion during different t im es of t he day. Our result s show t hat different inform at ion st im uli lead t o differences in household responses during different t im es of t he day. In part icular, t he presence of an IHD t hat indicat es t he quant it y and cost of elect ricit y consum ed on a real-t im e basis leads households t o cont ract t heir consum pt ion during t he
peak hours, and t he m agnit ude of t he cont ract ion increases as t he rat io of peak t o off-peak prices increases. How ever, t he ext ent of t he additional reduct ion in peak dem and due t o a st eepening t ariff schedule is very sm all in absolut e t erm s. The ot her st im uli (i.e., bi-m ont hly and m ont hly paper billing) also give rise t o reduct ions in peak dem and w hen TOU t ariffs are em ployed, but for t hem t here is lit t le evidence of furt her reduct ions as t he rat io of peak t o off-peak prices rises furt her.
Second, w e invest igat e t he det erm inant s of elect ricit y consum pt ion during different t im es of t he day. We find t hat cont rolling for day of t he w eek, public holidays, climat ic condit ions and household appliance ow nership, t he presence of different TOU t ariffs affect s household elect ricit y consum pt ion during t he peak hours, but does not lead t o a significant change in elect ricit y usage during t he day and night periods.
Finally, w e exam ine t he variat ion in our result s across different socio-econom ic groups, as proxied by t he highest level of educat ion com plet ed by t he chief incom e earner of t he household. We find t hat households w it h higher educat ion levels respond t o TOU t ariffs during t he peak period (consist ent w it h t he overall result s not ed above), but t hat households w it h low educat ion levels are less responsive t o TOU t ariffs.
Sect ion 2 discusses previous research in t he area. Sect ion 3 describes our dat a, w hile Sect ion 4 out lines t he met hodology em ployed in t his paper. Sect ion 5 present s and discusses em pirical result s, w hile Sect ion 6 sum m arises and concludes.
2
Liter atur e r eview
Est im at es of t he price elast icit y of elect ricit y dem and in t he resident ial sect or can be very different depending on t he t ype of dat a used (t ime-series, cross-sect ion, panel), cont ext (nat ional, regional or local econom y), size of t he variat ion in price and t im e periods covered (see also Alberini et al., 2011). Here w e focus on st udies t hat , sim ilar t o t he approach used in t his paper, use m icro-dat a on households and t hat exam ine t he im pact of price and inform at ion st im uli on elect ricit y dem and.
4
As described in Sect ion 3, t he quality of t he dat a relating to household income w as poor, and as a result , t he educat ion level of t he chief incom e earner is used t o indicate household socio-econom ic st at us.
5
4
The ext ent t o w hich price elasticit ies differ across populat ion groups is a com m on focus of research in t his area. Baker et al. (1989) use dat a from t he Brit ish Fam ily Expendit ure Survey over t he period 1972-1983 t o analyse household expendit ure on electricit y, gas and ot her fuels. Prices are nat ional averages. They find a significant ow n-price elasticit y of -0.758 for elect ricit y dem and, wit h considerable variat ion in t he est im at ed ow n-price elast icit y across different household t ypes (e.g., by presence of children, t ype of heat ing, incom e, et c.). Alberini et al. (2011) est im at e price elast icit ies of energy (elect ricit y and gas) dem and using dat a on over 74,000 households in t he 50 largest m et ropolit an areas in t he US over t he period 1997-2007. They report price elast icit ies of
dem and for elect ricit y use t hat range from -0.67 t o -0.86, w it h t he elast icit ies slight ly higher in poorer households.
As TOU pricing is becoming m ore com m on, so t oo are st udies evaluat ing households’ responses t o TOU pricing. Bart usch et al. (2011) examine t he im pact of t he int roduct ion of a dem and-based TOU t ariff on a pilot basis t o a group of 500 households in Sw eden. Using dat a before and aft er t he
int roduct ion of t he TOU t ariff, t hey find t hat t ot al elect ricit y consum pt ion declined by 11.1 per cent and 14.2 per cent in t he first t w o years aft er t he change t o TOU pricing (w it h t he size of t he reduct ions higher in t he wint er m ont hs). They also find a shift in elect ricit y dem and from t he peak t o off-peak period of 0.8 and 1.2 percent age point s in t he first t w o years (w it h t he shift great er during t he sum m er m ont hs). Filippini (2011) analyse elect ricit y dat a at t he cit y level for 22 Sw iss cit ies over t he period 2000 t o 2006. They find t hat t he ow n-price elast icit ies vary bet w een -0.80 and -0.89
during t he peak period and bet w een -0.90 and -0.95 during t he off-peak period (posit ive cross-price elast icit ies im ply t hat peak and off-peak elect ricit y are subst it ut es). An earlier st udy, also using Swiss dat a, found sim ilar result s (Filippini, 1995). M at sukaw a (2001) exam ine t he im pact of TOU pricing on resident ial elect ricit y dem and in Japan. The result s show t hat (1) household response t o t he high price of t he peak period is relat ively m odest , and (2) t he relat ive m agnit udes of t he price and
select ion effect s (i.e., part icipat ion in t he t rial) depend on t he ow nership of w at er heat ers.
Ham et al. (1997) discuss t he im port ance of account ing for select ion w hen using experim ent al dat a (t he bias induced by volunt ary participat ion in such init iat ives is also discussed by Aubin et al., 1995). They m easure t he responsiveness of sm all com m ercial cust om ers t o TOU pricing using dat a from a TOU experim ent conduct ed by Ont ario Hydro. Part icipant s w ere randomly assigned t o cont rol and
t reat m ent groups, but approxim at ely half of t he t reatm ent group refused t o part icipat e. Allowing for select ion has a significant im pact on t he param et er est im at es. Nonet heless, t hey find a significant reduct ion of 15 per cent in elect ricit y consum pt ion w hen t he peak period is relat ively short in lengt h (approxim at ely 5 hours) and t he peak/ off-peak price different ial is approxim at ely six t o one. For t he ot her t w o t reat m ent s, w here t he lengt h of t he peak period is longer and t he price different ial is sm aller, no significant reduct ion is observed. Ow nprice elast icit ies of dem and are est im at ed t o be
-0.134 in t he w int er and -0.114 in t he sum m er.
5
expensive t o serve power during t hese crit ical periods and even a m odest reduct ion in dem and can be very cost -effect ive (Faruqui and Sergici, 2009). A com prehensive review of 15 experim ent s
(largely based in t he US)6 w it h dynam ic pricing of elect ricit y w as undert aken by Faruqui and Sergici (2009). They find conclusive evidence t hat households (residential cust om ers) respond t o higher prices by low ering use. The m agnit ude of t he price response depends on several fact ors, such as t he m agnit ude of t he price increase, t he presence of cent ral air condit ioning and t he availabilit y of enabling t echnologies such as t w o-w ay program m able com m unicat ing t herm ost at s. Across t he experiment s st udied, TOU pricing induces a drop in peak dem and t hat ranges bet w een t hree t o six
percent and crit ical-peak pricing t ariffs induce a drop in peak dem and t hat ranges bet w een 13 t o 20 percent . When accom panied w it h enabling t echnologies, t he lat t er set of t ariffs lead t o a drop in peak dem and in t he 27 t o 44 percent range. Wolak (2011) exam ines w het her households in Washingt on DC face a ‘fixed cost of t aking act ion’ w hen responding t o dynam ic hourly prices; he finds t hat t he m agnit ude of t he average hourly percent age dem and reduct ion from hourly pricing is roughly equal t o t he est im at ed percent age dem and reduct ion over a longer durat ion of high prices.
Charles River Associat es (2005) examine t he im pact of t he California St at ewide Pricing Pilot (SPP) on resident ial and indust rial elect ricit y dem and (a TOU and t w o dynam ic pricing t ariffs w ere t est ed). The experim ent involved over 2,500 cust om ers and ran from July 2003 t o Decem ber 2004. The SPP also t est ed an inform at ion t reat m ent t hat urged cust om ers t o reduce dem and on crit ical days in t he absence of t im e-varying price signals. For resident ial cust om ers, t he est im at ed average reduct ion in
peak-period energy use on crit ical days w as 13.1 percent . Im pact s varied across clim at e zones, from a low of –7.6 percent in t he relat ively mild clim at e of zone 1 t o a high of –15.8 percent in t he hot clim at e of zone 4. The average im pact on norm al w eekdays w as -4.7 percent , w it h a range across clim at e zones from –2.2 percent t o –6.5 percent . They also found t hat households w it h cent ral air condit ioning w ere m ore price responsive and produced great er absolut e and percent age reduct ions
in peak-period energy use t han did households w it hout air condit ioning. TOU im pact s w ere less significant , due in part t o t he sm all sam ple size, w hile t he inform at ion-only t reat m ent s w ere sim ilarly insignificant .
As in our experim ent , TOU pricing is oft en com bined w it h various inform at ion st im uli. Darby (2006) reviews t he lit erat ure on t he im pact of feedback (bot h direct in t he form of m et ers or display
m onit ors, and indirect in t he form of frequent , accurat e billing) on household energy use. She finds t hat overall t he lit erat ure dem onst rat es t hat clear feedback is a necessary elem ent in learning how t o cont rol fuel use m ore effect ively over a long period of t im e and inst ant aneous direct feedback in com binat ion w it h frequent , accurat e billing (a form of indirect feedback) is needed as a basis for sust ained dem and reduct ion. There is som e indicat ion t hat high energy users m ay respond m ore t han low users t o direct feedback. In t erms of indirect feedback, hist oric feedback (com paring w it h
previous recorded periods of consum pt ion) appears t o be m ore effect ive t han com parat ive or norm at ive inform at ion (com paring w it h ot her households, or w it h a t arget figure).
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6
Gans et al. (2011) est im at e t he effect of real-t ime usage inform at ion on resident ial elect ricit y consum pt ion in Nort hern Ireland. They exploit t he int roduct ion in an exogenous change in t he t ype of inform at ion provided t o one set of cust om ers, i.e., t hose on prepaym ent account s, in April 2002. From t hat dat e, prepaym ent cust om ers received im m ediat e feedback about t heir elect ricit y consum pt ion via keypad m et ers.7 They use dat a from 18 w aves of Cont inuous Household Survey of Nort hern Ireland (from 1990 t o 2009), w hich is m erged w it h price and plan inform at ion from t he elect ricit y ut ilit y, and w eat her dat a (t he final sam ple size is over 45,000 observat ions). They find t hat
households using t he keypad use 15-20 per cent less elect ricit y t han ot her households, even cont rolling for housing t ype, heat ing, household charact erist ics and select ion int o t he plan. Their est im at ed ow n-price and incom e elast icit ies are -0.72 and 0.04 respect ively. Also in Ireland, Dulleck
et al. (2004) use m ont hly t im e-series dat a of household elect ricit y use over t he period 1976 t o 1993 t o exam ine t he im pact of dem and m anagement policies t hat provided inform at ion and offered m inor incent ives t o cust om ers (e.g., inform at ion leaflet s w it h households’ elect ricit y bills). They find
t hat t he int roduct ion of inform at ion program s reduces long-t erm elect ricit y usage by 7 per cent .
In a st at ist ical analysis of the Irish dat a t o w hich w e apply econom et rics lat er in t his paper, Com mission for Energy Regulat ion (2011a) finds t hat applicat ion of TOU t ariffs w it h a select ion of inform at ional stim uli reduce overall household elect ricit y use by an average of 2.5 per cent and peak dem and by 8.8 per cent . They also find t hat households w it h an IHD or w it h high pre-t rial dem and
reduced dem and m ore t han ot hers, but t hat increases in t he rat io of peak t o off-peak prices beyond t he init ial st ep t est ed do not lead t o furt her st at ist ically significant reduct ions in dem and. They conclude t hat dem and is highly price inelast ic.
3
Data
The resident ial com ponent of t he t rial involved over 5,000 households (cust om ers of Elect ric
Ireland8) w ho w ere asked t o part icipat e in t he t rial.9 In order t o assess w het her TOU pricing and inform at ion st im uli led t o a change in household elect ricit y consum pt ion, half-hourly dat a w ere collect ed for each household over t he period 14 July 2009 t o 31 Decem ber 2010.10 Households w ere random ly assigned t o eit her t he cont rol or t reat m ent groups for t he com m encem ent of t he experiment on 1 January 2010. The cont rol group w as billed on t heir norm al t ariff and saw no
changes t o t heir bill. They received none of t he inform at ion st im uli and w ere request ed t o cont inue using t heir elect ricit y as norm al (Comm ission for Energy Regulat ion, 2011a). Benchm ark pre-t rial dat a is available for all households (bot h cont rol and t reat m ent ) for t he period 14 July 2009-31 Decem ber 2009.
7
The keypad m et ers combine a rechargeable card control wit h an int eract ive display t hat allow s consumers t o easily monit or t heir elect ric usage and cost . In Novem ber 2010, t hey account ed for just over one-t hird of residential electricit y cust om ers.
8
At t he t im e of recruit ment (m id-2008), Electric Ireland cust om ers represent ed 100 per cent of resident ial electricit y cust om ers in Ireland (Com mission for Energy Regulat ion, 2011a).
9
We focus on resident ial electricit y participant s in t his paper, as t he publicly-released m icro-dat a relate only t o residential part icipant s in t he t rial.
10
7
Treat m ent households w ere random ly assigned t o different TOU t ariff groups and t o different
inform at ion st im uli groups. The allocat ion of t reat m ent households bet w een t ariffs and inform at ion st im ulus groups w as decided by t he regulat or at t he end of 2009. In order t o allocat e t he t reat ed groups bet w een different t ariffs and inform at ion st imuli a principal com ponent analysis w as applied t o ident ify t he m ain household charact erist ics and t o opt im ally com bine int erest in energy reduct ion and usage profile. Given t hese com binat ions, t he part icipant s w ere random ly allocat ed t o different t reat m ent groups.11
Four TOU t ariffs w ere t est ed. TOU prices referred t o peak (17:00-18:59 M onday-Friday, excluding public holidays), day (08:00-16:59; 19:00-22:59 M onday-Friday, plus 17:00-18:59 public holidays, Sat urday and Sunday) and night (23:00-07:59) periods (based on syst em dem and peaks). A w eekend t ariff w as also t est ed (w hereby t he night rat e applied all day Sat urday and Sunday, wit h separat e peak, day and night t ariffs for w eekdays). In com parison w it h t he init ial flat-rat e t ariff, t he elect ricit y price associat ed w it h peak hour consum pt ion rose up t o a m axim um of 166 per cent of it s init ial
value, w hile t he price of elect ricit y during t he day and night w as decreased by a m axim um of 13 per cent and 37 per cent respect ively. The TOU t ariffs w ere designed t o be neut ral in com parison w it h t he st andard flat-rat e t ariff t o ensure t hat t he average part icipant w ho did not change t heir elect ricit y consum pt ion w ould not be financially penalised.
The regulat ion aut horit y st at es t hat “ Throughout t he Trial all part icipant s t est ing t ime-of-use t ariffs w ere guarant eed t hat t hey w ould not pay more for t heir elect ricity t han if t hey had been on t he normal Elect ric Ireland t ariff (14.1c per unit ex VAT). Accordingly, all part icipant s received a balancing credit at t he end of t he benchmark period and in January 2011. The small number of individuals w ho incurred cost s above t his average w ere recompensed on a case by case basis” .12
The base TOU t ariff (Tariff A) reflect s t he underlying cost of energy t ransm ission, dist ribut ion, generat ion and supply (Comm ission for Energy Regulat ion, 2011a). Table 1 set s out t he various price
levels applying in t he cont rol and t reat m ent periods.13
[insert Table 1 here]
In addit ion, t reat m ent groups were also subject ed t o one of four inform at ion st im uli.14 In Ireland, elect ricit y cust om ers t ypically receive bi-m ont hly paper bills. Households in t he t reat m ent group
w ere random ly assigned t o one of four groups; bi-m ont hly billing, m ont hly billing, bi-m ont hly billing plus IHD st im ulus, bi-m ont hly billing plus overall load reduct ion (OLR) st im ulus. OLR refers t o households w ho received €20 (plus t heir energy savings) if t hey reached a m ont hly t arget (based on
11
For a com plet e descript ion of t he allocat ion betw een st imulus and t ariff groups see Comm ission for Energy Regulat ion (2011a).
12
These paym ent s ranged from €30 under Tariff A t o €90 under t ariff D (Comm ission for Energy Regulation, 2011 a pp.8).
13
There is som e debat e in t he literat ure over w het her households respond t o average or m arginal prices. It has been argued t hat households respond t o average price, w hich is easily calculat ed and observable (see Alberini et al., 2011 for exam ple).
14
8
hist oric t rend m inus 10 per cent ). As t he precise prices faced by households in t he OLR group could not be det erm ined, w e excluded t hese households (n=940) from our analysis.15 Am ong t he t reat m ent group t herefore, 13 dist inct groups defined by com binat ions of t he various TOU t ariffs and inform at ion st im uli are ident ified. Table 2 out lines t he num bers of cont rol and t reat m ent observat ions available for analysis.16
[insert Table 2 here]
The qualit y of t he dat a on elect ricit y consum pt ion is very high. Only a sm all percent age of households w ere excluded due t o incom plet e records (i.e., due t o signal problem s im pact ing on t he ret urn of half-hourly sm art m et er readings). Det ailed inform at ion on each of t he part icipat ing households w as also collect ed, bot h before and aft er t he t rial period. Inform at ion on household com posit ion, appliance ow nership and use, as w ell as at t it udes t ow ards energy conservat ion and t he environm ent w as collect ed. As det ailed below , w e also exam ine t he response of different household
t ypes t o t he various TOU t ariffs and inform at ion stim uli. This requires det ailed inform ation on household com posit ion and socio-econom ic st at us. We use an indicat or of t he highest level of educat ion com plet ed by t he chief incom e earner of t he household in order t o ident ify different household t ypes as t here are som e problem s w it h ot her pot ent ial indicat ors.17
As t he cont rol period st art ed on t he 14t h of July 2009, w e also exclude t he first seven m ont hs of t he t reat m ent period t o correct ly est im at e household responses t o t he int roduct ion of t ariffs and inform at ion st im uli. The final sam ple size is 967,756 observat ions, across 2,831 households (768 in t he cont rol group, and 2,063 in t he t reat m ent group).
The m ain focus of t his paper is t he est im at ion of t he react ion of households t o different TOU t ariffs
and different inform at ion st im uli. How ever, elect ricity dem and is also affect ed by ot her fact ors. As discussed in Sect ion 2, previous research has highlight ed t he im port ance of t he w eat her and t he num ber of appliances in each household in det ermining elect ricit y consum pt ion.In addit ion t o price and inform at ion st im uli, w e t herefore include in our analysis t he num ber of elect ric appliances ow ned by t he household18, and proxies for t he t em perat ure and climat e variables in t he form of heat ing degree days (HDD) and sunshine hours for each individual day over t he period 14 July 2009 –
15
A sam ple of the inform ation provided w ith t he bills can be found on pp.85 of Commission for Energy Regulat ion (2011a).
16
Half-hourly dat a w ere aggregated t o daily t ot als.
17
For exam ple, t he indicat or of household income is poorly recorded (m any m issing observat ions, and an analysis of t he summ ary st atist ics indicat es t hat t he wording of the quest ion caused confusion am ong households in relat ion t o w het her responses should be annual, m ont hly or w eekly incom e, or pre- or post -t ax. In addition, inform at ion on the num ber and ages of individuals in t he household did not allow us t o distinguish am ong households wit h children of different ages.
18
9
31 Decem ber 2010.19 We also include a cat egorical variable t hat indicat e t he day of t he w eek, and a binary variable t hat indicat es public holidays.
M oreover, w e creat e a dumm y variable w hich is equal t o 1 for households t hat have elect ric heat ing, and w e int eract t his variable w it h t he HDD indicat or, t o cont rol for het erogeneit y in t he response t o t em perat ure am ong households t hat have different heat ing m et hods. In t his w ay, w e also cont rol for pot ent ial effect s on elect ricit y consum pt ion during t he m ont hs of Novem ber and Decem ber 2010, w hen it w as unusually cold in Ireland (see Figure 1).
4
Methodology
The m ain advant age of t he experiment conduct ed on sm art m et ering and TOU pricing in Ireland is t hat our dat a are unaffect ed by t he select ion bias t hat usually charact erises t his t ype of analysis.20 While init ial part icipat ion in t he experim ent w as not random , households w ere subsequent ly random ly assigned t o eit her t he cont rol or t reat m ent groups. This m eans t he sam ple w as collect ed
w it h t he object ive t hat t he t reat m ent and t he cont rol groups should not have any significant differences apart from t he t reat m ent . In order t o t est t he effect iveness of t his approach, w e est im at ed a probit m odel in w hich t he dependent variable was t he probabilit y of being part of t he t reat ed group and t he independent variables were household charact erist ics (age of t he individual w ho responded t o t he household quest ionnaire, appliances used by t he household, level of educat ion of t he chief incom e earner of t he household). None of t hese variables proved t o be
significant at t he st andard significance levels, as highlight ed in Table 3. M oreover, a com parison of t he m eans of different variables t hat sum m arise t he household charact erist ics did not show any significant difference. 21
[insert Table 3 here]
As w e also have inform at ion from t he benchm ark period for cont rol and t reat m ent groups, t he nat ural choice of est im at or for t he react ion t o different t ariffs and st im uli is t he difference-in-difference est im at or. This t echnique allow s us t o correct ly est im at e t he difference-in-difference in t he m eans bet w een t he cont rol and t he t reat m ent groups in the t reat m ent period, cont rolling for com m on t rends across t he t w o groups during t he cont rol period.
Let us denot e
it as t he m ean of t he out com e of t he group i at t im e t, in w hich i is equal t o 0 (cont rolgroup) or 1 (t reat m ent group) and t is equal t o 0 (cont rol period) or t o 1 (t reat m ent period). As t he only difference bet w een t he households w ho populat e our sam ple is t he t reat m ent , w e est im at e t he difference-in-differences (
−
) , using t he random effect s est im at or for panel dat a.
19
Inform ation on HDD and sunshine hours is available for Dublin Airport only. In any case, m ore det ailed inform ation on t he regional locat ion of households is not available.
20
See Card and Kruger , 1984), am ong ot hers.
21
10
We est im at e t hree different versions of our m odel: a benchm ark case; a m odel in w hich t he sam ple is divided by t he highest educat ion level of t he chief incom e earner of t he household; and m odels in
w hich w e dist inguish bet w een addit ional household t ypes (based on t he age of t he survey respondent , and t he occupancy st at us of t he household (rent , ow ned out right and ow ned w it h m ort gage)).
Impact of different TOU tariffs on electricity demand
In order t o t est t he im pact of a change in t he t ariff st ruct ure, given t he different inform at ion st im uli,
w e est im at e t he follow ing equat ion:
, = + + + +
+ + + +
ℎ
++
α
+ + ++
(1)
in w hich , is t he daily consum pt ion of elect ricit y in t he t hree different t im e of t he day (peak, day,
night ), is t he dum m y variable indicat ing t hat t he household w as exposed t o t ariff A during t he t reat m ent period, is t he dumm y variable indicat ing t hat t he household w as exposed t o t ariff B during t he t reat m ent period, et c. is t he dum m y variable indicat ing t hat t he household w as exposed t o t he w eekend t ariff during t he t reat m ent period (t his t ariff w as
applied only t o consum ers facing t he bi-m ont hly billing inform ation st im ulus). is t he dum m y variable for t he t reat m ent period, is t he dum m y variable for t he t reat ed group, is t he
dum m y variable for public holidays, Wkdays are dumm ies w hich are equal t o 1 on t he various days of t he w eek, HDD is a variable t hat reflect s t he heat ing degree days, sunshine is a variable t hat reflect s sunshine hours (not included in t he night specificat ion), Appliances is a count variable of t he num ber of appliances ow ned by t he household and ElecHeat is a dum m y variable indicat ing t hat t he household has an elect ric heat ing syst em . The variable HDDElecHeat is a variable t hat int eract s t he HDD w it h t he ElecHeat dum m y; t his variable should cont rol for high elect ricit y consum pt ion during t he w int er of 2010, in w hich t he t em perat ures in Ireland w ere except ionally low , as well as t he different ial response t o TOU t ariffs am ong households w it h different heat ing t ypes. The coefficient s
−
represent our difference-in-difference est im at es (i.e., t he effect of t he f our TOU t ariffs on household elect ricit y consum pt ion). We est im at e nine different specificat ions of t he m odel, w hich represent different com binat ions of t im e of day (peak, day, night ) and inform at ion st im ulus (bim ont hly billing, m ont hly billing, IHD).The t reat m ent period dum m y t hat w e include in our analysis simply indicat es t he differences in t he dependent variable bet w een t he cont rol and t he t reat m ent period, t hat is:
11
consum pt ion. On t he cont rary, w e expect t hat t he t reat m ent group dum m y w ill be alw ays
insignificant as t he t reat ment and t he cont rol groups are not st at ist ically different t o each ot her (as dem onst rat ed above).
Differential response to TOU pricing and information stimuli by household education level
To correct ly disent angle t he differences in elect ricit y consum pt ion bet w een households w it h
different socio-econom ic charact erist ics w e re-est im at e m odel (1) for different subsam ples of t he init ial sam ple. The response rat e t o t he incom e quest ion in t he pre-t rial survey w as poor, and t he inform at ion on household com posit ion (e.g., num ber and ages of children) is not det ailed enough t o const ruct a household com posit ion variable. Inst ead, w e use inform at ion on t he highest educat ion level of t he chief incom e earner of t he household. We disaggregat e households on t he basis of w het her t he chief incom e earner had a t hird level qualificat ion or not (38.3 per cent of households
are t hus classified as ‘high educat ion households’, w hile 61.7 percent are classified as ‘low educat ion households’).
The educat ion level of t he household (proxied by t hat of t he chief incom e earner) m ay have non-t rivial effecnon-t s on elecnon-t ricinon-t y consum pnon-t ion during differennon-t non-t im es of non-t he day: on one hand, high educat ion households m ay be m ore concerned about t he efficient use of t heir appliances, and we
t herefore m ight observe a higher cont ract ion in consum pt ion during t he peak hours am ong t hese households t han am ong low educat ion households. On t he ot her hand, educat ion can (at least part ially) pick up som e of t he incom e effect s, and so we m ight expect t hat low educat ion households m ight be m ore concerned about price t han t he high educat ion households.
Differential response to TOU pricing and information stimuli by alternative household
characteristics
While t he educat ion level of t he chief incom e earner is our m ain indicat or of household socio-econom ic st at us, w e also ran t he m odels using alt ernat ive household sub-sam ples. First , w e dist inguish bet w een households of different ages, as proxied by t he age of t he survey respondent . We consider 4 different age groups: young people, aged 18-34; adult s aged 35-54; adult s in t he last st age of t heir career (55-64) and ret ired people (i.e., t hose aged 65+). Second, w e also consider
12
5
Empir ical Results
22Impact of different TOU tariffs on electricity demand
Tables 4-6 present t he result s of t he difference-in-difference analysis of t he int roduct ion of TOU t ariffs for t he peak, day and night periods respect ively (w it h t he sam ples furt her disaggregat ed by inform at ion st im uli). The t reat m ent period dumm y is st rongly negat ive and significant in bot h t he peak and day specificat ions. The sam e result em erges from t he analysis performed by Faruqui and
Sergici (2009); how ever, our result s are not direct ly com parable as t heir st udy account s only for differences in t he pricing st ruct ures before and aft er the t reat m ent period, w hereas our analysis also assesses t he im pact of differences in inform at ion st imuli. As expect ed, t he consum pt ion of elect ricit y decreases m ore during peak t han day hours. M oreover, in our analysis elect ricit y consum pt ion decreases even during t he night hours, but t his decline is not st at ist ically significant . As expect ed, t he t reat m ent group dum m y is alw ays insignificant , w it h t he except ion of t he night specificat ion
w here it is som et im es w eakly significant . 23
Variables relat ing t o t he day of t he w eek are largely significant , and have signs t hat are consist ent w it h expect at ions (i.e., relative t o Wednesdays, peak consum pt ion is low er, and day consum pt ion is higher on w eekends). Peak period elect ricit y consum pt ion is also significant ly low er on public holidays (and day consum pt ion correspondingly higher).
The influence of t he w eat her is highly significant . The effect s of HDD and sunshine hours are posit ive and negat ive respect ively.24 When HDD is int eract ed w it h t he indicat or for elect ric heat ing, t he effect of HDD is m ore st rongly posit ive, indicat ing t he part icular burden t hat low t em perat ures place on t hose t hat rely on elect ric heat ing. Finally, t he num ber of appliances inst alled in each house is posit ive and significant in all t he different specificat ions of t he m odel.
From Table 4 it is clear t hat consum pt ion during t he peak hours is negat ively affect ed by t he init ial int roduct ion of TOU t ariffs. How ever, across t he different inform at ion st im uli, t here are differences in bot h t he m agnit ude of t he effect s, and how consum pt ion responds t o increasing t ariffs. For exam ple, in t he peak period m odel, elect ricit y consum pt ion is alw ays low er under t ariff D (wit h t he
22
For t he sm art m et ering experim ent analysed in t his paper, a st at ist ical analysis of t he im pact of t he TOU t ariffs and inform ation st im uli on t ot al and peak dem and was also carried out on behalf of t he Com mission for Energy Regulat ion by The Research Perspect ive and Insight St at ist ical Consult ing (Comm ission for Energy Regulat ion, 2011a). They found t hat overall, t he int roduct ion of t he TOU t ariffs and t he inform ation st imuli result ed in st at ist ically significant reduct ions in t ot al elect ricity consum ption of 2.5 per cent and peak electricit y consum ption of 8.8 per cent . These result s w ere used subsequent ly in t he cost -benefit analysis of t he sm art m et ering t rial. They also found t hat t he stim ulus com bining bi-m ont hly bill, energy usage st at em ent and elect ricit y m onit or w as m ore effective than ot her inform at ion st im uli in reducing peak usage wit h a peak shift of 11.3 per cent , and t hat households w it h higher elect ricit y consumpt ion w ere m ore responsive t o TOU pricing and t he inform at ion st im uli.
23
For t he night specification t his dumm y might include a composit ional difference betw een t he t reatm ent and cont rol groups t hat exist s in t he night t im e (and w hich w as not apparent in t he overall result s present ed in Table 3).
24
13
highest rat io of peak t o night prices) t han under t ariff A (wit h t he lowest ). In t he households w here IHDs are inst alled, t here is a linear relat ionship bet w een t he size of t he t ariff applied and t he
cont ract ion in elect ricit y consum pt ion. How ever, w hen t he st im ulus is charact erised by t he provision of less frequent inform at ion (bi-m ont hly or m ont hly paper billing), t he m agnit ude of t he reduct ion is different across t he different t ariffs. For inst ance, w hen t he households receive a bi-m ont hly bill, t he cont ract ion in elect ricit y consum pt ion during t he peak is higher under t ariff B t han t ariff A, w hich is plausible. How ever, households t hat face t ariff C do not respond t o t he increase in t he peak period elect ricit y t ariff, alt hough t hose on t ariff D do respond significant ly. A similar nonlinearit y in the
consum pt ion cont ract ion in t he peak period under t he four different t ariffs is associat ed w it h t he m ont hly billing st im ulus, alt hough t he pat t ern is closer t o t hat observed for t he IHD st im ulus.
Alt hough t he IHD st im ulus is associat ed w it h t he m ost consist ent -looking price response, it is st ill w eak in absolut e t erm s. The rat io of peak t o night prices rises from about 1.7 in Tariff A t o 4.2 in Tariff D as per Table 1. This is a subst ant ial relat ive price change. Nevert heless, t he associat ed
reduct ion in peak usage is only 1 per cent for each st ep change in t ariff and a t ot al of 4.5 per cent from Tariff A t o D. M ore t han a doubling of t he peak/ night rat io leads t o a reduct ion of less t han 5 per cent in peak dem and. These result s show som e consist ency w it h previous research. Reiss and Whit e (2005) found a non-linear react ion bet w een t he changes in elect ricit y dem and and t he applied elect ricit y prices in California. Pollit t and Shaorshadze (2011) and It o (2010) discuss t he possibilit y t hat t he lack of cont inuous inform at ion might affect consum er react ions. Allcot t and M ullainat han
(2010) and Allcot t (2011) highlight how consum ers believes can be syst em at ically biased w hen t hey are evaluat ing energy cost s.
In t he Irish experiment , m ont hly and bi-m ont hly billing m ight not provide sufficient inform at ion t o households, w ho t hen cannot regulat e t heir behaviour consist ent ly w it h t he t ariff applied. In
cont rast , t he provision of real-t im e inform at ion on bot h t he quant it y and cost of elect ricit y consum ed via t he IHD seem s t o result in m ore consist ent behaviour am ong t he t reat m ent group households (at least in t he peak period). Overall, household responses m ay be dom inat ed by applicat ion of som e sim ple heurist ic: t hey know peak prices are now higher t han ot her t im es of day and t hey change behaviour t o reflect t his, but furt her increases in t he different ial are eit her not fully perceived or evoke only a w eak response for som e ot her reason.
Elect ricit y consum pt ion during t he day and night is less responsive t o TOU t ariffs. As Table 1 highlight s, t he changes from t he cont rol period for t he day t ariff were quit e low (ranging from -2.2 per cent under t ariff A t o -12.6 per cent under t ariff D), so it is perhaps underst andable t hat households did not change t heir consum pt ion significant ly.
14
p.m , m aking it difficult t o shift t he usage of m any appliances (cooker, show er and w ashing m achine) t o t hese hours.
Differential response to TOU pricing and information stimuli by household education level
To ascert ain w het her t he response t o TOU pricing is different across households w it h different educat ion levels, w e run t he m odels on t he subsam ples of low educat ion and high educat ion households. Tables 7 and 8 present t he result s of t he difference-in-difference analysis for t he low - and high educat ion households w it h IHDs respect ively.25
[insert Tables 7 and 8 here]
Focussing on t he peak period first , t he result s indicate t hat low educat ion households respond only t o higher peak prices w hen receiving a m ont hly bill, or in possession of an IHD. The react ion of high educat ion households t o t he peak pricing st ruct ure is sim ilar, alt hough t he effect s are slight ly sm aller
in m agnit ude, and t here is som e response t o higher peak prices am ong high educat ion households w ho receive a bi-m ont hly bill. This suggest s once again t hat regular feedback in t he form of an IHD is m ore effect ive in reducing peak-period elect ricit y consum pt ion t han ot her st im uli, and t he result s also provide som e evidence t o suggest t hat t his effect is st ronger for low educat ion households. As w it h t he baseline result s, day consum pt ion is largely unaffect ed by TOU pricing. High educat ion households are sim ilarly unaffect ed by TOU pricing for night consum pt ion, w hile TOU pricing has a
significant effect on night consum pt ion for low educat ion households w ho have an IHD. The effect s show t hat decreasing night prices are associat ed w it h increasing consum pt ion, w hich suggest t hat low educat ion households w it h an IHD are responding t o TOU t ariffs by shift ing consum pt ion t o t he night hours. There is no such effect for low educat ion households w it h t he bi-m ont hly or m ont hly billing opt ions how ever.
Differential response to TOU pricing and information stimuli by alternative household
characteristics
Split t ing t he sam ple using alt ernat ive indicat ors of household socio-econom ic st at us confirm s t he general result s. How ever, som e int erest ing conclusions m ight be draw n for t he age groups and t he house occupancy t ype. First , adult s (aged 35-54) are t he m ost responsive t o changes in t he peak
prices, w hen IHD is inst alled. Second, households w ho are rent ing t heir apart ment seem t o be less responsive t o change in peak pricing t han households w ho live in t heir ow n houses. The last result can be underst ood by considering t hat som et im e t he rent is inclusive of t he ut ilit y bills; t his affect s t he incent ives in changing t he elect ricit y consum pt ion in presence of different t ariffs and st im uli.26
25
Result s for households on t he bi-m ont hly and m ont hly billing opt ions are available on request from t he aut hors.
26
15
7
Discussion, Summar y and Conclusions
The analysis in t his paper present s est im at es of t he response of a sam ple of Irish households t o TOU t ariffs and inform at ion st im uli in t he residential elect ricit y m arket . The qualit y of t he dat a, along w it h t he careful experim ent al design, allow s us t o exam ine t hese issues for t he first t im e in Ireland. While t he im pact of TOU t ariffs and inform at ion st imuli has been examined in ot her count ries, t he applicat ion t o Ireland present s evidence for a count ry w it h a very different clim at e t o t hat analysed
in m ost recent analyses (i.e., a t em perat e clim at e w it h no household air condit ioning).
Our result s show t hat TOU t ariffs and inform at ion st im uli are effect ive in influencing elect ricit y consum pt ion. In t erm s of inform at ion st im uli, t he provision of an IHD is part icularly significant . It m ust be not ed t hat our result s are not direct ly com parable w it h t hose of t he st at ist ical analysis of t he dat a (Com m ission for Energy Regulat ion, 2011a). The st at ist ical analysis involved a before-aft er
analysis of elect ricit y consum pt ion under t he different TOU t ariffs and inform at ion st im uli. In addit ion, t he researchers did not im pose any param et ric assum pt ions on t he relat ionship bet w een elect ricit y consum pt ion and prices/ inform at ion st imuli and t hey im put ed m issing values for t he cases in w hich elect ricit y consum pt ion readings w ere m issing. Our analysis furt her cont rols for possible sources of het erogeneit y across households (e.g., appliance ow nership), and t his allow s us t o separat e out t he pure effect of t he variation in t he tariffs and t he presence of t he st im uli from t he
environm ent al and household specific charact erist ics.
Our result s are part icularly int erest ing as t hey highlight how t he presence of different TOU t ariffs, in com binat ion w it h different inform ation st im uli, affect s household elect ricit y consum pt ion during different t im es of t he day. The result s of different TOU t ariffs indicat e t hat TOU pricing is only st at ist ically significant in influencing household elect ricit y consum pt ion during t he peak period. This
is not surprising given t he sharp increases in peak period prices t hat w ere observed bet w een t he cont rol and t reat m ent periods, w hile t he changes for t he day and night periods w ere m uch sm aller (see Table 1). How ever, w e do observe a non-linear response t o TOU t ariffs for t he peak period for households t hat received a bim ont hly or m ont hly paper bill, in cont rast t o t he result s for households w it h an IHD w here t he response is linear. The m agnit ude of t he result s for m ont hly paper billing are
closer t o t he result s for t he IHD st im ulus, w hile t he result s for t he bim ont hly paper billing opt ion are sm aller in m agnit ude. This is consist ent w it h t he research not ed above t hat st resses t he im port ance of regular and easily underst ood feedback in influencing consum er energy use.
While t here is a general t endency for peak usage t o f all w hen TOU t ariffs are in place regardless of inform at ion t reat m ent , addit ional increases in t he rat io of peak t o night prices only result s in limit ed
16
In order t o underst and how different groups react t o t he sam e changes in t he TOU t ariffs w e split our sam ple in t w o, considering low and high educat ion households separat ely. Our result s show
t hat , for t he peak period, regular feedback in t he form of an IHD is part icularly effect ive in reducing peak-period elect ricit y consum pt ion, and t he result s also provide som e evidence t o suggest t hat t his effect is st ronger for low educat ion households. The fact t hat high educat ion households respond in a linear w ay t o increasing peak prices is consist ent w ith t he research of It o (2010) w ho suggest s t hat individuals w it h higher educat ion levels are bet t er able t o underst and prices and inform at ion st im uli. How ever, t he larger m agnit ude of t he effect s for low educat ion households and t he finding t hat
t hese households shift elect ricit y consum pt ion t ow ards t he night period is suggest ive of great er price sensit ivit y on t he part of low educat ion households, perhaps due t o t he correlation bet w een educat ion level and incom e. The fact t hat t he lat t er effect is significant only for households w it h IHDs reinforces t he im port ance of easily-understood, inst ant aneous feedback in influencing elect ricit y consum pt ion.
In t he cont ext of European clim at e policy t arget s and t he im port ance of m at ching elect ricit y supply and dem and, t hese result s have im port ant policy im plicat ions. They indicat e t hat TOU pricing can be effect ive in influencing peak period household elect ricit y consum pt ion, and suggest t han t he price response is m ore consist ent w hen accom panied by real-t im e feedback in t he form of an IHD. How ever, t he w eakness of responses t o furt her relat ive price increases m ay suggest t hat t he scope for dem and response is quickly exhaust ed or t hat consum ers use sim ple heurist ics w hen considering
17
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18
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M at sukaw a, I. (2001) Household Response t o Opt ional Peak-Load Pricing of Elect ricit y. Journal of Regulat ory Economics, 20(3), 249-267.
Pollit t , M . & Shaorshadze, I. (2011) The Role of Behavioural Econom ics in Energy and Clim at e Policy.
Available at : ht t p:/ / w w w .eprg.group.cam.ac.uk/ w
p-cont ent / uploads/ 2011/ 12/ EPRG1130_M ain.pdf [last accessed 29 M ay 2012].
Reiss, P. & Whit e, M . (2005) Household Elect ricit y Dem and Revisit ed. Review of Economic St udies,
72(3), 853-883.
19
[image:20.595.73.519.117.326.2]Appendix
Table 1: Cont rol and Treat m ent Period Tariffs (€ cent s per kWh, including VAT)
Tariff Cont rol Period
Treat m ent Period % change
Peak, Day and Night
Peak Day Night Peak Day Night
Cont rol 16.24 16.00 16.00 16.00 -1.5 -1.5 -1.5
Tariff A 16.24 22.70 15.89 13.62 39.8 -2.2 -16.1
Tariff B 16.24 29.51 15.32 12.46 81.7 -5.6 -23.1
Tariff C 16.24 36.32 14.76 11.35 123.7 -9.1 -30.1
Tariff D 16.24 43.13 14.19 10.22 165.6 -12.6 -37.1
Tariff W/ E 16.24 33.03 14.45 11.35 103.4 -11.0 -30.1
Not e: The control and treatm ent period prices for t he control group are slight ly different as t he control period electricit y t ariff w as reduced (for all cust om ers of Electric Ireland) in Oct ober 2009. The treatm ent period price for t he control group t herefore reflect s t he new low er t ariff t hat w as charged for all part icipant s from Oct ober 2009 – Decem ber 2009, and for cont rol group part icipant s from January 2010.
Table 2: Num ber of Households involved in t he Trial
Tariff Det ail Bim ont hly M ont hly IHD Tot
Cont rol Group 768 n/ a n/ a 768
A 226 241 232 699
B 90 98 93 281
C 250 245 233 728
D 93 96 90 279
W/ E 76 n/ a n/ a 76
Tot 1,503 680 648 2,831
Source: Commission for Energy Regulation, 2011a.
[image:20.595.72.374.438.562.2]20
Table 3 Probit result s
treatg Coef. Std. Err.
age group1 0.609 0.630 age group2 0.644 0.626 age group3 0.576 0.624 age group4 0.719 0.625 age group5 0.528 0.625 appliances -0.082 0.059 high educat ion 0.012 0.086 occupancy 0.055 0.072 incom e group 0.025 0.029 elect ric heat ing 0.021 0.150
_cons 0.036 0.651
[image:21.595.72.251.121.319.2]21
Table 4: Est im at ion Result s – Peak
Bimonthly M onthly IHD
Tariff A -0.0484* * -0.0509* * -0.0571* * *
(0.0195) (0.0209) (0.0205)
Tariff B -0.102* * -0.0848* * * -0.0673* *
(0.0408) (0.0244) (0.0294)
Tariff C -0.00777 -0.0818* * * -0.0846* * *
(0.0204) (0.0239) (0.0201)
Tariff D -0.0625* * -0.160* * * -0.102* * *
(0.028) (0.0325) (0.0377)
Tariff W -0.176* * * - -
(0.0474) - -
Tp -0.0435* * * -0.0426* * * -0.0430* * *
(0.0098) (0.0098) (0.0098)
Tg 0.0421 0.0543 0.0552
(0.0369) (0.0369) (0.0381)
DBankHoliday -0.0276* * -0.0320* * * -0.0189*
(0.0115) (0.0112) (0.0112)
Sunday -0.138* * * -0.134* * * -0.132* * *
(0.0092) (0.0092) (0.0094)
M onday 0.00526 0.00735 0.00436
(0.0054) (0.0054) (0.0052)
Tuesday 0.00988* * 0.00750 0.00886*
(0.0047) (0.0049) (0.0046)
Thursday -0.0319* * * -0.0402* * * -0.0322* * *
(0.0052) (0.0056) (0.0052)
Friday -0.0760* * * -0.0832* * * -0.0769* * *
(0.0063) (0.0062) (0.0065)
Saturday -0.0821* * * -0.0876* * * -0.0806* * *
(0.0088) (0.0086) (0.0091)
Sunshine -0.0219* * * -0.0217* * * -0.0214* * *
(0.0005) (0.0005) (0.0005)
HDD 0.0392* * * 0.0385* * * 0.0383* * *
(0.0007) (0.0008) (0.0007)
Elect ricHeat ing -0.236* * * -0.170* * -0.214* * *
(0.0772) (0.07) (0.075)
HDDElecHeat 0.0121* * * 0.0118* * * 0.0151* * *
(0.0039) (0.0037) (0.0037)
Appliances 0.208* * * 0.204* * * 0.191* * *
(0.0282) (0.0297) (0.028)
Const ant -0.563* * * -0.554* * * -0.522* * *
(0.0758) (0.0798) (0.075)
22
Table 5: Est im at ion Result s – Day
Bimonthly M onthly IHD
Tariff A -0.0130 -0.0260 0.00542
(0.0165) (0.0173) (0.0186)
Tariff B -0.0411 -0.0188 -0.00117
(0.0358) (0.023) (0.024)
Tariff C 0.0300* -0.0155 -0.00723
(0.0156) (0.0207) (0.0163)
Tariff D -0.00821 -0.0470* -0.00705
(0.0226) (0.0255) (0.0253)
Tariff W -0.0765* - -
(0.0418) - -
Tp -0.0335* * * -0.0325* * * -0.0330* * *
(0.0088) (0.0088) (0.0088)
Tg 0.0238 0.0361 0.0438
(0.0319) (0.0324) (0.0326)
DBankHoliday 0.0888* * * 0.0769* * * 0.0814* * *
(0.0082) (0.0083) (0.0084)
Sunday 0.0773* * * 0.0799* * * 0.0739* * *
(0.0061) (0.006) (0.0065)
M onday -0.00883* * * -0.00386 -0.00741* *
(0.0033) (0.0032) (0.0031)
Tuesday -0.000581 0.000725 0.00192
(0.0027) (0.0028) (0.0027)
Thursday -0.0187* * * -0.0208* * * -0.0172* * *
(0.0028) (0.0031) (0.0029)
Friday -0.0226* * * -0.0272* * * -0.0227* * *
(0.0035) (0.0036) (0.0039)
Saturday 0.0540* * * 0.0522* * * 0.0479* * *
(0.0056) (0.0053) (0.006)
Sunshine -0.00986* * * -0.00968* * * -0.00960* * *
(0.0003) (0.0004) (0.0004)
HDD 0.0224* * * 0.0215* * * 0.0218* * *
(0.0006) (0.0006) (0.0006)
Elect ricHeat ing -0.190* * * -0.114* -0.127*
(0.0718) (0.0651) (0.0649)
HDDElecHeat 0.0118* * * 0.0135* * * 0.0128* * *
(0.0039) (0.0034) (0.0035)
Appliances 0.186* * * 0.174* * * 0.154* * *
(0.0256) (0.0267) (0.0251)
Const ant -0.635* * * -0.606* * * -0.556* * *
(0.0688) (0.0717) (0.0675)
Observations 513,165 494,377 483,578 St andard errors in parenthesis
23
Table 6: Est im at ion Result s - Night
Bimonthly M onthly IHD
Tariff A 0.0186 -0.00189 0.0316*
(0.0163) (0.0175) (0.0177)
Tariff B 0.0102 0.0361 0.0303
(0.0316) (0.0266) (0.0295)
Tariff C 0.0660* * * 0.0211 0.0382* *
(0.0175) (0.0208) (0.0182)
Tariff D 0.0450 0.0112 0.0388
(0.0296) (0.0298) (0.0237)
Tariff W -0.0419 - -
(0.0316)
Tp -0.0106 -0.00866 -0.00975
(0.0089) (0.0089) (0.0089)
Tg 0.0549* 0.0682* * 0.0668* *
(0.0318) (0.0334) (0.0337)
DBankHoliday 0.00776 -0.00244 0.00619
(0.0066) (0.0064) (0.0066)
Sunday -0.0418* * * -0.0405* * * -0.0407* * *
(0.0048) (0.0045) (0.0047)
M onday -0.0132* * * -0.00983* * * -0.00953* * *
(0.0026) (0.0026) (0.0025)
Tuesday -0.00193 -0.00107 -0.00134
(0.0021) (0.0018) (0.0018)
Thursday 0.00418* * 0.00357* 0.00463* *
(0.002) (0.002) (0.0019)
Friday 0.0244* * * 0.0190* * * 0.0224* * *
(0.0026) (0.0025) (0.0027)
Saturday -0.0176* * * -0.0170* * * -0.0178* * *
(0.0044) (0.0043) (0.0044)
HDD 0.0110* * * 0.00956* * * 0.0103* * *
(0.0007) (0.0007) (0.0007)
Elect ricHeat ing -0.233* * * -0.150* * -0.194* * *
(0.0692) (0.0652) (0.0651)
HDDElecHeat 0.0132* * * 0.0131* * * 0.0137* * *
(0.0039) (0.0035) (0.004)
Appliances 0.0992* * * 0.0915* * * 0.0861* * *
(0.0252) (0.0278) (0.0258)
Const ant -1.158* * * -1.137* * * -1.126* * *
(0.0682) (0.0746) (0.0692)
Observations 512,853 494,200 483,355 St andard errors in parenthesis
24
Table 7: Est im at ion result – Educat ion (high), IHD
Peak Day Night
Tariff A -0.0846* * * -0.0172 0.00414 (0.0296) (0.0227) (0.026) Tariff B -0.114* * -0.0492 -0.0159 (0.0561) (0.0496) (0.0583) Tariff C -0.106* * * -0.0233 0.00241 (0.0292) (0.0246) (0.0274) Tariff D -0.0673 0.0192 0.0240 (0.0630) (0.0483) (0.0447) Tp -0.0238 -0.0164 -0.00329 (0.0178) (0.0153) (0.016)
Tg 0.0719 0.0482 0.0138
(0.0659) (0.0557) (0.0568) DBankHoliday -0.0638* * * 0.0573* * * -0.0303* * (0.0213) (0.0159) (0.0121) Sunday -0.0814* * * 0.0929* * * -0.0646* * * (0.0165) (0.0117) (0.0081) M onday 0.0307* * * -0.000661 -0.00923* * (0.00892) (0.00555) (0.0044) Tuesday 0.0188* * -0.00120 0.000606 (0.00801) (0.005) (0.0031) Thursday -0.0321* * * -0.0194* * * 0.00162 (0.00870) (0.0054) (0.0035) Friday -0.0835* * * -0.0282* * * 0.0124* * * (0.0106) (0.0063) (0.0042) Saturday -0.0820* * * 0.0457* * * -0.0414* * * (0.0157) (0.0114) (0.0076) Sunshine -0.0230* * * -0.0106* * * - (0.000817) (0.0006) -
HDD 0.0394* * * 0.0224* * * 0.0122* * * (0.00118) (0.0009) (0.0011) HDDElecHeat 0.0135* 0.0104 0.0137 (0.00707) (0.0084) (0.0088) Appliances 0.218* * * 0.177* * * 0.0771* (0.0507) (0.0447) (0.0439) Const ant -0.611* * * -0.596* * * -0.988* * *
(0.142) (0.126) (0.123)
Observations 173,393 173,506 173,333
St andard errors in parenthesis * * * p<0.01, * * p<0.05, * p<0.1
