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Price behaviour under competition in UK Domestic Electricity Supply

Monica Giulietti, Jesus Otero and Michael Waterson

Presented by Michael Waterson, University of Warwick

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Basic questions

• How competitive has the domestic electricity supply market become?

• What strategies do suppliers appear to be pursuing?- firm side of picture

• Related work- GWW on consumer switching behaviour in gas, Econ J, October 2005

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Positive features of the market

• There are now a number of studies focussing on pricing, but:

• All our prices are transactions prices (no “bait and switch” etc.)

• Nearly everyone buys the product• They spend a significant proportion of their

income on it (3%+)• We can observe behaviour over a period

of time

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Plan of presentation

• Market opening- institutional features

• Naïve Theory/ Question- competitive?

• Empirical model- descriptive analysis, making distinction between search and switching costs

• Empirical results

• Interpretation in the light of theory

• Conclusions

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G en eration

M a rk e t

T ra n s m is s io n

D is trib u tio n

S u p p ly to c o n s u m e rs

E lectricityin th e U K

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Basic market structure in electricity

• Transmission and distribution regulated, generation and supply competitive.

• Since May 1999, all UK consumers have been able to choose their electricity supplier.

• Since March 2002 there has been no supply price regulation

• Incumbent in each area is a default supplier. Other suppliers (“entrants”) are incumbents from other areas, plus the incumbent in gas supply, and a small number of independent suppliers.

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Pricing in supply

• Each firm sells under an (essentially common) range of tariff structures, but they have different tariffs.

• No “special deals” to retain consumers- i.e. no price discrimination within area allowed.

• For most people (>2/3, source OFGEM 2004), price is the main reason why they would move electricity supplier.

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Switching supplier

• Switching is quite straightforward • Significant switching has occurred- Nearly 50% are no

longer with incumbent; significant “churn”

In cu m b en t

3 8 .6 %

2 2 .7 %E n tran ts

3 9 .7 % c h u rnh e re

T o ta l sw itch es in 2 0 0 5 : 4 .5 m

S o u rc e : O F G E M 2 0 0 6

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Questions

• Do prices “converge” quickly? Do they converge at all?- relative prices

• Has the market quickly become competitive?- “naïve theory” position

• What are the implications for search and switching costs?

• Assumption:- firms know what they doing, in terms of pricing strategy.

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Key magnitudes

• The difference, IM, between the incumbent’s price and the median price offered by entrants.

• The size of the gap, ML, between the lowest available price and the median price offered by entrants.

• The magnitude of the range, HL, between the lowest and highest price offered by market entrants.

• The whole difference, IL, between the incumbent’s price and the price set by the lowest price supplier.

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Expectations

• If new switchers face no search costs, we might expect ML and HL to be very small; if search costs fall over time, ML and HL shrink over time, and vice versa.

• As N shrinks, HL is likely to decline.• If there are no switching costs, and the first

search is very cheap (most people- almost 90% according to OFGEM- have been contacted by a new supplier), we would expect IM to be very small.

• The gap IL may shrink over time.

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Data

• Essentially, “prices”• Bimonthly data for 6+ years• Three bill types (DD, QB, PP)• Fourteen areas (regional), each with a

different incumbent• Two levels of consumption• Between 18 and 6 companies active, with

shakeout• Significant “headroom”

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Difference LM between median and lowest bills (Direct Debit)

0

2

4

6

8

10

12

14

16

18

20

Feb-

99

Jun-

99

Oct

-99

Feb-

00

Jun-

00

Oct

-00

Feb-

01

Jun-

01

Oct

-01

Feb-

02

Jun-

02

Oct

-02

Feb-

03

Jun-

03

Oct

-03

Feb-

04

Jun-

04

Oct

-04

Feb-

05

Jun-

05

Oct

-05

Feb-

06

Jun-

06

Oct

-06

Per

cen

tag

e

HIGH USERS (4950) LOW USERS (1650)

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Bill range (HL), excluding incumbent (DD)

10

15

20

25

30

35

40

Feb-

99

Jun-

99

Oct

-99

Feb-

00

Jun-

00

Oct

-00

Feb-

01

Jun-

01

Oct

-01

Feb-

02

Jun-

02

Oct

-02

Feb-

03

Jun-

03

Oct

-03

Feb-

04

Jun-

04

Oct

-04

Feb-

05

Jun-

05

Oct

-05

Feb-

06

Jun-

06

Oct

-06

Per

cen

tag

e

HIGH USERS (4950) LOW USERS (1650)

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IM and IL, high consumption

0

5

10

15

20

25

30

35

Feb

-99

Jun-

99

Oct

-99

Feb

-00

Jun-

00

Oct

-00

Feb

-01

Jun-

01

Oct

-01

Feb

-02

Jun-

02

Oct

-02

Feb

-03

Jun-

03

Oct

-03

Feb

-04

Jun-

04

Oct

-04

Feb

-05

Jun-

05

Oct

-05

Feb

-06

Jun-

06

Oct

-06

Pe

rce

nta

ge

IL (Incumbent - Lowest) Average RANDOM gain

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Descriptive regression model

, , 1

, , ,1

*rpc t rpc rpc rpc t

K

l rpc t l r t rpc tk

Y Tr Y

Y Nfirms

where Y is ML, HL, IM or IL.

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Preliminary questions

• Are we dealing with a stationary series?

• Answer: essentially, yes in each case.

• All series are stationary around a trend

• There is evidence of some form of structural break in Spring 2002- we split the sample at this point

• Final price controls removed, plus rise in fuel input prices

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Regressors Y = ML Y = HL

Feb. 99–Apr. 02

Jun. 02–Dec. 06

Feb. 99–Apr. 02

Jun. 02–Dec. 06

Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat

Y(-1) -0.33 -8.63 -0.52 -12.30 -0.18 -6.29 -0.25 -7.06

NFIRMS 0.10 1.69 0.66 7.06 0.35 3.19 1.19 8.05

Group 1

Tr*EA*DD -0.13 -4.76 0.08 3.61 -0.19 -3.12 0.34 8.16

Tr*EM*DD -0.09 -2.30 0.08 3.45 -0.17 -2.28 0.31 7.33

Restricted model. Trend interaction by region and product. Jun. 02–Dec. 06 (extract)

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Regressors

Y=IM Y=IL

  Feb. 99–Apr. 02 Jun. 02–Dec. 06 Feb. 99–Apr. 02 Jun. 02–Dec. 06

  Coeff t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat

Y(-1) -0.2 -3.69 -0.31 -9.94 -0.47 -9.96 -0.3 -10.34

NFIRMS 0.09 2.55 -0.27 -2.38 0.14 2.35 0.54 3.69

Group 1                

Tr*EA*DD 0.15 3.44 -0.01 -0.2 0.22 5.89 0.11 3.11

Tr*EM*DD 0.13 3.35 0 -0.06 0.23 4.85 0.11 3.18

Restricted model. Trend interaction by region and product. Jun. 02–Dec. 06 (cont)

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Results (DD case)• Results differ across the periods up to April

2002 and June 2002 to December 2006.• ML and HL decline in the first period.

However, they rise, particularly HL, in the second period.

• IM rises slightly in the first period and remains constant in the second.

• As N falls, HL and ML decline. However, IM rises as N falls in second period.

• Speed of adjustment- all variables are trend stationary. Rate of convergence speeds up in second period.

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Explaining the results

• Naïve (Bertrand- type) theory does not work

• Incumbency confers an advantage, but other factors in addition. Specific prediction on IL shrinking not borne out

• Some unexpected results, over time• Search models

– Anderson de Palma, passive search – Stahl-type mixed strategy modelling

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Passive search model?

• Anderson- de Palma model is in some ways promising: Consumers have little or no prior experience of the product and consumers search passively. Positive relationship between HL and N, also.

• But- firms are both “good” and “bad” buys over time

• (Lach) No significant correlation between Feb 99 price and t price after around 12 months.

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Randomising of prices?- reasonably so

East Midlands: Direct Debit, High Consumption (4950)

250

300

350

400

450

500

550

600

British Gas Powergen London (EDF) Npower Scottish and Southern Scottish Power

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Mixed strategy equilibrium model

• Mixed price equilibrium search models (Stahl et al) imply in these circumstances that – Price dispersion persists– Randomising of prices– Number of suppliers may or may not influence

dispersion– If proportion doing complete search rises,

average price falls.

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Mixed strategy equilibrium reconsidered

• However, average prices do not show a particular tendency to fall (n.b recent experience)

• But internet usage has increased significantly over time, and proportion searching for energy prices through this method has also increased

• So a puzzle?

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What about reswitching?

• Recall that only a minority of switches is from the incumbent to an entrant

• Hence, the decision making process for an entrant becomes more complex

In cu m b en t

3 8 .6 %

2 2 .7 %E n tran ts

3 9 .7 % c h u rnh e re

T o ta l sw itch es in 2 0 0 5 : 4 .5 m

S o u rc e : O F G E M 2 0 0 6

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Modelling an entrant after the initial period

1

1

Locals accepting current priceSwitchers from other entrants

1

1

Switchers from incumbent

1 11 1 ,

1 ,

H p N k

EkH p

v E p N k

kH p

p p r G H p F c F g c dcN N

F c F g c dcN

1

Consumers with lower if lowest price

1N

G H p F p

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Lognormal(1,4) simulations; n=15 and n=6

• Note that as time passes, industry moves from =1 to n falls from around 15 to 6.

• Implication is that average price and range change over time – two separate forces.

= 1

= 0.5

= 0

0

0.5

1

50 55 60 65 70 75 80 85 90 95 100

= 1

= 0.5

= 0

0

0.5

1

50 55 60 65 70 75 80 85 90 95 100

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Concluding remarks

• Despite search costs falling over time and people used to switching, plus very active switching:– Significant dispersion in prices across entrants and

between entrants and incumbent persists– Firms can take advantage of their captured as well as

captive customers (BG)– We do not necessarily expect further price

convergence, due to mix of forces.

• Is the market competitive??