www.ccp.uea.ac.uk

Gain and Pain: Consumer’s Central Expectations and Market

‘Rationality’

(Re)Interpreting the findings

Yoonhee Tina Chang, University of BathCatherine Waddams, CCP

Consumers and Competition PolicyGrowing interest in consumer behaviour to

discipline markets‘Informational’ remedies even when adverse effect

on supply side

How does consumer activity in markets respond to their prior expectations?

Are those expecting high gains and low search/switch costs more active?

What else affects their search and switching levels?

Uniquely we use consumers’ own estimates of expected gain/costs

How much is the most you think you could save if you shopped around for. . .?

How much of your own time would it take to find information to decide whether and to whom to switch?

Once you have found all the necessary information to choose a new supplier, how much of your own time would it take to switch?

These provide central estimates

How important is the consumers’ confidence in that estimate (variance, not directly measured)?

Testing “market” rationality? Not a test of individual rationality, i.e. whether

consumer behaviour is internally consistent, since comparing across different consumers

Except comparison of activity across different markets: do consumers use different trade off between gain and

pain in different markets?

But for given expected costs and benefits, what determines whether a consumer will search and switch?

Do these factors indicate that ‘variance’ of estimates is important (not measured directly)?

What are the policy implications?

1. Motivation

2. Context and Relation to other studies

3. Model, Survey and descriptive statistics

4. Results

5. Conclusions and Policy Implications

Context and relation to literatureTwo strands:

Consumers maximise their own utility subject to limitations of time and understanding (bounded rationality)? Simon (1991) Ellison (2005); but may be ‘non rational’, unstable preferences? (DellaVigna, 2007; Rottemberg, 2008)

For market and policy may be enough that in aggregate consumers behave as if rational -

Role of search and switching costs (Klemperer 1995, Farrell and Klemperer, 2006); implications for firms’ responses, competition policy (Garrod et al., 2008)

Relation to empirical evidence

Consumer demographics used as proxies for search and switching costs; gains based on potential savings available in market e.g. Giulietti et al. (2005)

– we have direct estimates and the characteristics so can separate out effects

Switching costs greater than search costs: Sturluson (2003) Swedish electricity; Giulietti et al.(2005) UK gas

Across markets large differences in ‘interest’ are not captured by demographic variables: Pomp et al (2005)

3. Model, Survey and descriptive statistics

Searching and switching each a (different) function of:expected gain; +

anticipated time spent searching and switching; -, -

All as estimated by respondents

& relation between these (dependent on income which affects value of time and money) -

High refusal rate for income, so use as surrogates education -

and age U shaped

Other Variables affecting activity

Other demographics: gender ?

Attitudes: importance of trusting supplier - market knowledge +

Experience: switched in other markets (in survey)+ Market Dummies: advertising? certainty of

estimates?

Choosing households, identifying activity

2027 representative adults interviewed face to face in homes using quota sampling in 2005

Identified those who bought each of 10 products, Do you have a choice of supplier in your region?Are you singly or jointly responsible for choosing

supplier?

If so: In the last three years have you:Looked around for a new provider?Switched provider, apart from moving home?

Gain and Pain Revisited

How much is the most you think you could save if you shopped around for. . .?

How much of your own time would it take to find information to decide whether and to whom to switch?

Once you have found all the necessary information

to choose a new supplier, how much of your own time would it take to switch?

Last two derived from realised time compared to their expectations for those who had searched and switched

Demographics, Attitudes and Experience

Demographic information: age, gender, education

Attitudes:How important is to you to trust your supplier for

each product? ‘Savvy’ if knew no choice of provider for piped

water supply

Experience: if switched in other (of our) markets

Consumer awareness/activity across markets %

Market Aware Searched SwitchedAverage 86 23 17Electricity 88 28 24Mobile 90 29 24Fixed line 84 14 8Calls 82 17 13Broadband 68 26 17Car insurance 85 40 29Mortgage 78 22 14Current account 93 8 5

Consistent with national picture

Gains (£/month) and pain (hours) Market Expect

gainsExpected

search time

Expected switch time

Average 9.4 5.2 5.2Electricity 7.7 7.3 8.3Mobile 8.0 5.4 4.2Fixed line 6.0 6.1 5.5Calls 6.6 4.5 4.7Broadband 5.7 1.9 2.2Car insurance 12.0 3.9 2.4Mortgage 37.9 4.5 4.9Current account 3.7 9.0 10.4

With large variations

4. Modelling & Results: practicalities/ summaries

Modelled as simultaneous decision to search and switch, conditioned on awareness of choice

High correlation between exsetime and exswtime means couldn’t be used together: used exsetime for search; exsewtime for switching part of the equation

Diagnostics:

No of obs. 2388

Log-likelihood -2256.195

rho 0.8412

Wald Chi2(43) 443.29‡

McFadden's LRI 0.145

*, **, *** sig @ 10%, 5% 1%

(i) direct gain/pain trade off Dependent

variables Search Coef. Switch Coef.

Joint MFX

Exgainmax (£/ m) -0.0095* 0.0023 -0.0004 Exsetime (hours) -0.0027* -0.0002* Exswtime (hours) -0.0032** -0.0007** Age (age/ 100) -2.9167*** -2.9423*** -0.8722*** Agesq 1.8769* 2.6441** 0.7159*** Education (in years) -0.0004 -0.0192 -0.0040

Consumers who expect higher gains, lower search/switch time, are more likely to be active;

Education no direct effect, U-shaped age curve as expected

An extra hour switching deters more than searching

(ii) Demographics, attitudes and experience

Dependent variables

Search Coef. Switch Coef. Joint MFX

Gender (1=m; 0=f) -0.1494* -0.2926*** -0.0740*** Savvy -0.1398* -0.1107 -0.0352 Trust important 0.1313** -0.0308 0.0064 Switched in other markets

-0.3157 0.0694 -0.0146

Default: female, not savvy, trust not important, not switched

For this group, females are more active; others do not affect joint searching and switching on their own

(iii) Market dummies, relative to electricity

Dependent variables

Search Coef. Switch Coef. Joint MFX

Mobile phone 0.1247 0.1052 0.0338 Fixed phone -0.3979*** -0.5770*** -0.1304*** Calls -0.2863*** -0.3184*** -0.0824*** Broadband -0.2787** -0.3357*** -0.0840*** Car insurance 0.4918*** 0.2149** 0.0908*** Mortgage -0.2819** -0.2684* -0.0730** Curr Bank Acc -0.9397*** -1.0781*** -0.2033***

Holding everything else constant, consumers trade off gain and pain differently in different

markets.More confident of their central estimates in some

markets than others? Advertising effect?(Insurance has an annual contract/reminder)

(iv) Interaction terms: Confidence matters

Dependent variables Search coef. Switch Coef. JOINT MFX Exgainmax*age 0.0293** 0.0027** Exgainmax*mortgage -0.0057* -0.0012* Savvy*swother 0.3326*** 0.0257*** Swother*gender 0.3569*** 0.1963** 0.0751** Swother*education 0.0483* 0.0546*** 0.0157*** Savvy*trust 0.1729* 0.0352* Trust*gender 0.2302** 0.0468**

For older people, gain more important: experienceFor mortgages, more gain means(even)less activity: uncertainty

For ‘savvy’, men & better educated, switching in othermarkets increases activity: confidence in estimates

For those who believe it is important to trust supplier, more knowledgeable and men are more likely to switch: confidence?

5. ConclusionsThe only test of individual (ir)rationality is from the

difference between markets: more likely to be due to different confidence in central estimates

Whatever individual rationality, the market appears ‘rational’: higher expected gains and lower expected time searching and switching means more activity, when these expectations are directly measured

Other factors seem to matter much more independently of their effect on expected gains and costs. We interpret these as affecting an individual’s confidence in her own estimates of expected values:

Could we confirm through other tests?(We know full post code and occupation)

Is there an alternative hypothesis?

Policy Implications ~ To increase activity authorities should: Increase expected gains and lower expected time

to search and switch across consumersParticularly lower expected switch timeNudge through annual reminders

But more importantly, increase confidence

Exploit experience of switching in some markets Increase education about markets in general Provide more information about particular

markets