The impact of price frames on
consumer decision making
May 2010
OFT1226
© Crown copyright 2010
This publication (excluding the OFT logo) may be reproduced free of charge in
any format or medium provided that it is reproduced accurately and not used in
a misleading context. The material must be acknowledged as crown copyright
and the title of the publication specified.
FOREWORD BY AMELIA FLETCHER
This report was commissioned by the Office of Fair Trading (OFT) from London
Economics in association with Steffen Huck and Brian Wallace (University
College London). It examines the impact on consumer decision-making of
different ways in which prices can be framed.
Consumers deal with relatively complex price frames on a daily basis, both on
the high street and the Internet. Typical examples are'3 for 2' 'price was x now
y', 'sale ends tomorrow', and 'price is x while stocks last'. When these offers
are genuine they can clearly benefit consumers. But what about when they do
not necessarily represent a genuine offer? Can consumers see through the
different ways of framing prices, or are they in some way tricked?
This report uses a controlled economic experiment to isolate and address
precisely this issue. By using a comparative metric the authors are able to rank,
in terms of their potential magnitude, five different pricing frames. Further, the
authors investigate whether and where in the decision making process any
behavioural biases occur and what impact the frames have on a retailer's sales.
Finally, repetition enables the authors to analyse the impact of learning.
The views of this paper are those of authors and do not necessarily reflect the
views of the OFT nor the legal position under existing competition or consumer
law which the OFT applies in exercise of its enforcement functions. Rather the
aim of the report is to shed some evidence on this interesting issue, and
promote economic debate in this area.
This report is part of the OFT's Economic Discussion Paper series. If you would
like to comment on the paper, please write to me, Amelia Fletcher, at the
address below. The OFT welcomes suggestions for future research topics on all
aspects of UK competition and consumer policy.
Dr Amelia Fletcher,
Chief Economist
Office of Fair Trading,
Fleetbank House,
2-6 Salisbury Square
London EC4Y 8JX
[email protected]
CONTENTS
Chapter/Annexe
1 Executive summary
Page
5
2 Introduction
15
3 Review of emprical and theory literature on price framing
21
4 The experiment
36
5 Experimental results
54
6 External validity and implications for non-Laboratory markets
88
7 Policy recommendations
92
A Screen shots from the experiment
94
B Web trawl of pricing practices
109
C Theory: Optimal search strategy
126
D Regression details
132
E Personality test
164
A feedback questionnaire
165
B Experiment instructions
166
C References
170
1
EXECUTIVE SUMMARY
1.1
This report presents a controlled economic experiment that analyses
whether or not the way prices are presented or 'framed' to consumers
has effects on consumer decision making and consumer welfare.1
1.2
The experiment was completed by London Economics with Steffen Huck
and Brian Wallace.2
1.3
The experiment was designed to create a comparative metric alongside
which each of the frames can be compared with a baseline treatment
where sellers show clear per-unit prices. This comparative metric enables
a ranking of price frames in terms of the potential magnitude of impact
on consumer decisions.
1.4
The experiment isolates the effect of price frames and as such, is not
constructed to explicitly examine their potential benefits. There may,
however, be benefits if these price frames reflect genuinely better offers
or efficiencies.
The price frames
1.5
The price frames tested in this controlled experiment are the following:
A baseline treatment in which consumers see straight per-unit
prices.
1
For information on what experiments in economics are see, OFT 2009 and Ofcom 2010. A
brief explanation is provided in chapter 2 of this report.
2
Steffen Huck is a Specialist Consultant with London Economics, and Head of the Economics
Department at University College London. Brian Wallace is a Senior Research Fellow at
University College London.
OFT1226 | 5
Drip pricing where the consumers see only part of the full price up
front and price increments are dripped through the buying process.
Sales in which a sale price is given and a pre-sale price is also given
as a reference to the consumer, 'was £2 is now £1' (actual prices
are identical to the baseline treatment).
Complex pricing where the unit price requires some computations,
'3 for the price of 2'.
Baiting in which sellers may promote a special price but there is only
a limited number of goods actually available at that price.
Time limited offers where the special price is only available for a
pre-defined short period of time.
The experiment design
1.6
The experiments were conducted at the University College London (UCL)
Experimental Laboratory, and used 166 participants (also called subjects)
drawn from across the UCL student population. Each subject participated
in the unit-pricing baseline and two of the five price frames.
1.7
Subjects participated in 30 rounds of the experiment, with each subject
experiencing each of his three frames 10 times. This repetition allows
subjects to learn about the experiment and adapt their behaviour. This
allows us to determine whether any potential effects of the price frames
can be overcome through experience. We discuss the effect of learning
below.
1.8
The full design is presented in detail in Chapter 4.
Results
1.9
The evidence from the controlled experiment shows that, in contrast to
the predictions of standard economic theory, price frames do matter for
consumer decision making and welfare. Consumers make more mistakes
and achieve lower consumer welfare under the price frames we
investigate as compared to straight unit pricing (the baseline).
OFT1226 | 6
1.10
We use a number of measures to compare across the price frames. Each
measure is explained in detail in Chapter 5. Here we provide an
overview.
Ranking of the price frames
1.11
Using the consumer welfare metrics (Chapter 5), we can rank the price
frames (1 - 5) in terms of which cause the worst problems, defined as
welfare losses, for consumers. Welfare losses are a measure of actual
monetary earnings in each of the 5 price frames and the baseline
treatment as compared to potential monetary earnings if subjects
behaved optimally. Optimal behaviour is what a fully rational subject that
knew the experimental environment would do.
1.12
The ranking of the price frames, starting with the worst – that which
causes the greatest welfare loss - is as follows:
(1) drip pricing
(2) time limited offers
(3) baiting
(4) sales, and
(5) complex pricing.
1.13
If we consider why there are behavioural problems giving rise to
consumer welfare losses we can explore two types of errors the subjects
may be making. First, subjects may search 'too much' or 'too little'
between shops before making a decision to buy. Searching 'too
much' or 'too little' is a measure of actual search behaviour compared to
optimal search behaviour. Optimal search behaviour is what a fully
rational subject that knows the experimental environment would do in
each of the frames and the baseline. The optimal search strategy can be
derived analytically and is shown in the annexe. Second, consumers may
buy 'too many' or 'too few' units of a good given their utility (or pay-off)
function. Again 'too many' or 'too few' units is a measure of actual
OFT1226 | 7
purchasing behaviour in each of the price frames and the baseline as
compared to optimal purchasing behaviour.
1.14
We observe that drip pricing and time-limited offers which generated the
biggest welfare losses are also the price frames in which subjects make
the most errors. The most prevalent error under these two price frames
is that subjects buy at the first shop at prices that are too high, that is,
at prices where they should continue their search.
1.15
Purchasing errors are less frequent but do occur. Buying too many or too
few units occurs equally often and the overall sales volume is virtually
constant across the different frames.
1.16
We also analyse the impact of search costs on the quality of consumer
decision making. There are two main effects. First, search errors
decrease as search costs increase. The intuition is that buying at the
first shop tends to be optimal for higher search costs. Hence, consumers
who tend to buy at the first shop 'come-what-may' behave optimally if
search costs are indeed high, but not if search costs are low. Second,
however, search costs have the opposite impact on purchasing errors. It
seems if consumers go through more costly (longer) search this raises
the chance that they buy more units of the good than they optimally
should. Intuitively this is because consumers fall prey to the sunk cost
fallacy, that is,, they appear to justify earlier incurred (sunk) costs
through high levels of consumption. Nonetheless, overall it is the first
effect on search errors which dominates implying that price frames have
a stronger (worse) effect on in markets with low search costs.3
Learning
1.17
If we consider subjects' behaviour over time, we can assess if any
learning happens and if errors decline as subjects become more
experienced.
3
With very large search costs the effect of the sunk cost fallacy could, of course, start to
dominate such that the overall effect of search costs on consumer detriment from price frames
could well be U-shaped.
OFT1226 | 8
1.18
We observe that errors generally do decline as experience increases
although at a slowing rate such that learning cannot completely
eradicate the problem. Moreover, in the time-limited frame there is no
learning at all.
1.19
Likewise, we observe that in all frames consumer welfare losses decline
(that is, subject pay-offs increase) over time except in the time-limited
frame.
Behavioural forces
1.20
What are the behavioural forces driving errors and welfare losses? The
data from the experiment allows us to explore this in detail. Further, the
responses to the voluntary qualitative questionnaire, conducted at the
end of the experiment, also shed some light.
1.21
In summary the behavioural forces at work can be identified as follows:
1) Drip pricing: If a consumer sees a low base price and they make the
decision to buy the good, they shift their reference point because
they imagine already possessing the good. Later, when they realise
that there are additional costs and charges, it is thus more difficult
for them to give up the good which they already have 'in their
basket'. Therefore, they purchase the good despite the increase in
price. This is in line what is known in the literature as loss aversion or
the endowment effect.
Subjects reported in the questionnaire feeling disappointed in this
frame because they felt they were receiving a good deal when they
saw the base price. Subjects reported that they still bought the good
after they found out the additional charges, but felt cheated and
annoyed because their pay-off was reduced.
2) Time-limited offers: Consumers believe erroneously that if they leave
the store then the prices will go up (note: the prices may go up or
down in this experiment), as such they have a tendency to buy at the
first store they go to; or, if they do not buy at the first store, they
will buy at the second store if the price at the second store is at all
OFT1226 | 9
profitable. This erroneous pre-conception is self-enforcing. As
subjects do not return to the first shop they cannot learn that their
beliefs are false. The main behavioural issue in this frame is, hence,
of a cognitive nature.
The responses support this finding, subjects reported that they felt
'compelled to buy' or it was 'something not to be missed'. Some
reported they were enticed to buy without further searching. Many
reported being confused and those who did venture back to the first
shop reported finding it strange that the price upon return to a shop
may be lower than when they first visited.
3) Baiting: Consumers choose a shop using the advertising they see
(this is the only time subjects see any price information before going
to a shop). They choose the store based on the deal they are being
offered in the advertisement. This choice raises their willingness to
pay because they expect to get a deal and again envisage owning the
good. Not buying the good at a higher price would be perceived as a
loss. Therefore, it is once again loss aversion or the endowment
effect working that is driving consumer behaviour.
Subjects reported that this frame enticed then to go quickly to the
shop with the best deal so as not to miss out. They reported anger
and frustration if they missed out on the offer in the experiment,
however, they reported they may buy anyway.
4) Complex pricing: Consumers tend to buy at the first store they visit,
and they tend to buy the offer even if it may not be the best thing for
them to do (for example, it may be more profitable to simply buy one
unit instead of the '3 for 2' deal). These errors can be attributed to
cognitive failure. They have a significant yet comparatively small
consequences for consumer welfare.4
4
The effect on consumer welfare loss due to this price frame is small but significant (at five per
cent) as compared to the welfare loss incurred in the baseline (Table 5.1). The effect on search
and purchasing errors are not significant (Tables 5.3 and 5.4).
OFT1226 | 10
Subjects reported that they did realise that in some instances it may
be better not to buy the deal but instead buy just one unit of the
good. However, 32 per cent of those who chose to answer this
question reported that they always chose to buy the offer.
5) Sales frame: While this frame had no significant effect on consumer
welfare there was a small and significant increase in deviations from
the optimal search rule. The meaningless information 'was £X is now
£Y' seems to be used by consumers although it clearly should not be.
Use of information that should be discarded is a cognitive error that
drives behaviours in this frame.
41 per cent of subjects reported that the special offer did influence
their decision making. These subjects reported that they were
'getting a good deal', 'receiving a discount', 'saving money' and
therefore were more tempted to buy the product.
Sellers
1.22
5
Shops in this experiment were computerised and as such they did not
respond endogenously (within the experiment environment) to the
behaviour of the consumers.5 We observe that the overall sales volume
of both shops taken together is virtually constant across the different
frames (Table 1.1), and that in time-limited offers, one of the most
detrimental frames for consumers, firms actually experience reduced
sales as compared to the baseline. The effect on shops of the different
price frames is in the distribution of sales. We find that the first shop
visited gains in drip pricing, time-limited offers, baiting and complex
pricing (Table 1.1). Therefore, firms may engage in activities to
It is possible to have both consumers and firms active in experiments.
OFT1226 | 11
encourage consumers to visit their shops first when elaborate pricing
frames are used.6
1.23
Sales for complex pricing (Table 1.1) are substantially higher but
whether this would translate into higher profits for sellers depends on
unit costs which are unmodelled in this experiment.7
Summary of main findings
The main findings for each price frame, as discussed above, are summarised in
the table overleaf.
6
In complex pricing subjects tend to undersearch and buy too often at the first shop they visit.
It is not that in this frame subjects buy too many units of a good, but that they buy at all when
it would be better for them to continue to search. These errors are small (Table 1.1) but
nonetheless our observations suggest that there is an attraction created by complex pricing
which entices consumers to buy the first offer they see.
7
The sellers do not have production costs. The prices sellers offer to consumers is selected
randomly from a pre-determined distribution of prices. Of course it would be possible to
introduce endogenous firms into the experiment in order to test firms' response to consumers'
decisions. Not including firms was a design choice made at the beginning of this experiment.
OFT1226 | 12
Table 1.1: Summary of main findings
Price
frame
Significantly
more
purchasing
errors
Significantly
more search
errors
Significant
consumer
welfare
losses
Baseline
Number of
units sold
by shops
Sales
by
shops
1.58
125
First
shop
visited
benefits?
Behavioural
biases
Learning
helps
Drip
pricing
Yes,
substantially
Yes
Large
1.59
124
Yes
Endowment
effect/loss
aversion/maybe
sunk cost
fallacy
Yes
Time-ltd
offers
Yes
Yes
Medium
1.59
121
Yes
Cognitive
errors/maybe
sunk cost
fallacy
No
Baiting
Yes
Yes
Medium
1.61
124
Yes,
strongly
Endowment
effect/loss
aversion/sunk
cost fallacy
Yes
Complex
pricing
No
No
Small
2.73
221
Yes
Cognitive
errors/sunk cost
fallacy
Yes
Sales
frame
Yes
Yes
No
1.59
125
No
Cognitive
errors/sunk cost
fallacy
Yes
Note: The measure 'significantly more purchasing errors' shows if subjects in the experiment either bought
'too many' units or 'too few' units than was optimal in the relevant price frame as compared to purchasing
errors that subjects made (buying too few or too many units than was optimal) in the baseline treatment
with straight per unit pricing. 'Significantly more search errors' shows if subjects in the experiment either
searched 'too little' or 'too much' than was optimal in the relevant price frame as compared to search
errors that subjects made (searching too little or too much than was optimal) in the baseline treatment.
'Significant consumer welfare losses' shows if actual earnings compared to optimal earnings in each price
frame are less than actual earnings compared to optimal earnings in the baseline. 'Number of units sold by
shops' reports the number of units sold by the two shops in this experiment. 'Sales by shops' reports the
turnover for both shops in the experiment. 'First shop visited benefits' reports if the distribution of sales in
this experiment is to the advantage of the first shop visited under each price frame. 'Behavioural biases'
shows which behavioural bias is causing actual behaviour to differ from optimal behaviour in each price
frame. 'Learning helps' indicates if errors in purchasing and/or search errors decline over time as subjects
gain more experience in the price frames.
OFT1226 | 13
Policy
1.24
1.25
From a policy point of view, this experiment has several broad
implications. These are the following:
•
Price frames do change consumer behaviour which is in contrast to
what orthodox economic theory on consumer behaviour would
predict.
•
Furthermore, change in behaviour is for the worse. Price frames do
cause consumer detriment. The worst culprits are drip pricing and
time-limited offers.8
•
Overall, the negative effects on consumer welfare are greater when
search costs in the market are low. However, with higher search
costs consumers become prone to the sunk cost fallacy and this
effect could become much stronger if search costs are substantially
higher.
•
Due to learning through experience the adverse effects of all frames
except time-limited offers are presumably greater in markets for less
frequently purchased items while time-limited offers can also do
harm in markets for goods that are purchased with high frequency.
Therefore, policymakers may be particularly worried about drip pricing in
markets for goods that are not purchased (particularly) frequently and
time-limited offers in markets for goods that are purchased with high
frequency.
8
Particularly when the offer is not a genuine offer. As is discussed in detail in Chapter 5,
consumers believe the shop's claim in this price frame, but if the claim is not true, and in fact
prices can decrease below the offer (after the offer has expired), consumers never learn this
because they do not go away and then return to shop again (in order to learn).
OFT1226 | 14
2
INTRODUCTION
2.1
While prices are assumed to take on a crucial role for economic
behaviour, their presentation, or framing, has historically received little
focus in economic research because how prices are presented has been
deemed irrelevant. Yet, sellers use many different ways of presenting or
framing prices in the marketplace and they often change these frames.9
As changing the presentation of prices is costly, we find ourselves in the
paradoxical situation where standard economics suggests on the one
hand, that price framing is irrelevant for consumer behaviour but must,
on the other hand, have some impact on demand because otherwise
firms would not spend money on manipulating them. This study is
designed to throw some light on this paradox.
2.2
The question we ask is simple and entirely empirical. We simply set out
to analyse whether different price frames do or do not have an effect on
consumer choice. Of course, one would expect that the literature should
have long since answered this question but as our literature review
reveals this is not the case (section 3). Existing evidence on price frames
is sparse, scattered and often based on questionable data. In fact, we
believe that this is the first study that provides comprehensive evidence
on the impact of price on consumer behaviour in a systematic, fully
incentivized environment.
2.3
Specifically, we use an experimental approach studying the impact of
price frames on consumer choices in a laboratory environment. The box
below provides a brief summary of what experiments in economics are.
9
The annex presents a web trawl of price frames actually used by different shops in seven
different consumer good markets: airfares, package holidays, accommodation and hotels, theatre
tickets, electronic goods, computers and equipment and furniture. Industries chosen are
illustrative only; the experiment is concerned with the impact of pricing frames in general and
not just for these industries listed. The web trawl was used as background information to inform
the design of the treatments for this experiment.
OFT1226 | 15
Box 1: Experiments in economics
Economic experiments allow policy-makers to:
Test if there are problems in markets. For example, do consumers have
problems making the best decision under different price frames as tested in this
experiment for the OFT.
Pre-test policy interventions and remedies to either compare alternative
interventions or to fine tune a given intervention. See for example the Ofcom
experiment, 2010, used to test the performance of alternative price revelation
mechanisms on consumer behaviour in telecommunications markets.
Ex-post checking of existing policy interventions in order to identify if the
interventions are not working as well as they could, and they could be changed
to improve the outcomes in our economies. See for example the HewlettPackard experiment, 200110, used to test the impact of minimum advertised
prices on HP retailer behaviour.
Experiments are an empirical method, and act as a complement to field data
collected via survey methods, and simulations that use computing power to
model the behaviour of consumers and firms.
The hallmarks of experiments in economics are control, treatment and
replication. Control means individual decisions made in the experiment are
induced by the incentives created in the experiment and by no other factors.
Treatment is the ability to change specific incentives or features of a policy and
to identify how individual decisions change as a result, thus, establishing true
causality, that is, why behaviour is changing. Replication is the ability for the
experiment to be conducted multiple times by the same researcher, by different
researchers and across different populations, in order to verify the results.
Experiments use real people to make real economic decisions in controlled
10
Charness.G. and Chen.K. (2001).
OFT1226 | 16
environments. Experiments induce behaviour by creating real monetary
incentives (and disincentives) that are directly linked to the decisions that
participants (also called subjects) make in the experiments. This is called
induced decision making and incentivisation which alternative survey methods
do not employ.
Experiments set-up simplified environments that mirror complex real world
environments. Experiments are designed such that they focus on the main
behavioural drivers of interest and reduce other confounding factors on
behaviour. This simplification allows the experiments to establish causality.
Causality is the identification of what is really driving behaviour and why.
The two main criticisms of experiments are:
(1) representativeness of the participants, and
(2) extrapolation beyond the laboratory.
In regard to the former, subject pool selection is an area of continued debate
and research in experimentation both in psychology and economics. Overall the
research suggests that the fundamental underlying behaviour, if it is robust,
given the incentives tested in the experiment, will hold across all types of
subject pool. However, there may be some difference in the magnitude of
outcomes if the subjects have had prior experience, or if the cognitive ability of
subjects is different. We discuss subject pool choice for this experiment
completed for the OFT in chapter 6.
Extrapolation is often addressed by concept of parallelism where the
experimenter tackles the criticism through conducting new experiments that
investigate the precise nature of the criticism. For example, if the critique is that
certain experiments do not have external validity because in real markets there
are many more participants, the researcher can carry out new experiments
doubling the number of market participants to find out whether this is a real
issue or not. Again we discuss extrapolation of this experiment for the OFT in
chapter 6.
OFT1226 | 17
2.4
This environment retains all the crucial features of real consumer choice
problems: Goods are on offer at multiple shops, consumers might want
to buy single or multiple units and they can search among the different
shops. At the same time, the environment is simple and highly controlled
which allows us to make clean-cut inferences on how price frames
affect consumer behaviour and, ultimately, consumers' welfare.
2.5
Notice that our design isolates the pure effects of framing, that is, the
presentation of prices, holding the actual price levels constant. In many
real-life markets there are sometimes genuine offers that may or may not
be framed in one of the ways that we study here. In these cases the
effect of such offers on consumers is the following: there is the direct
(positive) effect of lower prices and in addition there may be adverse
effects from the price framing. In this study we isolate the latter. (Only
in the case of '3 for 2' do we examine actually lower prices.)
2.6
Similarly, we isolate the pure effects of each price frame. In real-life
markets, some of these frames might appear in combination with one
another). Moreover, consumers might have to compare different frames
which would be another complication we abstract from in this study.
2.7
Our main result is that how prices are framed (or presented) has a
profound impact on consumer choice. We compare simple per-unit
pricing ('This good costs £1 per unit') with five other price frames: sales
('was £2 is now £1'), complex pricing ('3 for 2'), time-limited offers ('£1
only today'), baiting ('£1 while stocks last' with the possibility that
stocks are very small such that consumers are very likely to face a
substantially higher price once they check out the offer) and drip pricing
(where the effective full price of a good is only revealed in 'drips', for
example, with shipping and handling charges that have to be paid on top
of the basic price). We find that all of these practices have some adverse
effect on consumer behaviour and that most of them significantly impact
on consumer welfare.
2.8
Our research also identifies two price frames that are particularly
detrimental for consumers: time-limited offers and, even more so, drip
pricing. As we will discuss in some detail these are extremely strong
OFT1226 | 18
results as they have been detected in comparatively weak treatments in
a comparatively smart subject pool. If anything, our experiment will,
therefore, underestimate the true harm done by these practices.
2.9
We also discuss how our findings relate to known biases in economic
behaviour and how they are likely to impact on market outcomes and
competition between firms.
2.10
In our experiment human subjects (UCL students registered on a
database for economics experiments) participate in a sequence of oneperson decision tasks that mirror the repeated purchasing of different
goods. Before we go into the detail of the experimental design it is
perhaps useful to offer a rough overview of how the experiment works.
Subjects work on their own on a computer screen and are rewarded for
their performance. Specifically, they receive a monetary payoff
(reflecting consumption utility; the satisfaction one derives from the
consumption of a good) for each unit of each good they buy but, of
course, they also have to pay the price that the shops charge. In the
entire experiment there are always two shops. A priori, these shops are
entirely identical. Subjects who play the role of consumers have to
choose which shop to visit first and we will call this shop the first shop
and the other the second shop. Going to a shop involves some
(monetary) search costs reflecting the time costs of search on the
internet or the actual travel costs in the case of visiting physical stores.
2.11
Our main experimental manipulation is how prices are framed when the
consumer subject arrives at a shop. In our baseline subjects see simple
per-unit prices. The five more elaborate price frames that we analyse are
then modelled on price frames used in the field. The precise wording is
explained in detail further below.
2.12
With this experiment we can examine the effect of price frames on
consumers in two ways. First, we can simply examine whether price
frames have an effect on consumer welfare (reflected in the experiment
by subjects’ total pay). Since some pricing strategies convey a real
advantage or disadvantage to consumers (for example, under '3 for 2'
effective prices are really lower) we will always compare the achieved
OFT1226 | 19
welfare to the welfare that would be obtained under optimal behaviour.
Optimal behaviour can be precisely computed for all six different
environments and we will explain it in some detail in this report.
2.13
The second way of looking at our data is by comparing actual choices
with the optimal strategy. This allows us to identify errors in consumer
decision making and how they are triggered by the different price
frames. A more detailed analysis of errors also allows us to indentify
where in the process errors occur which is important for understanding
the root causes of the consumer detriment.
2.14
It turns out that the two main root causes for errors in consumer
decision making are to be found in a phenomenon called the endowment
effect (where consumers’ willingness to pay shifts once they believe
themselves already in possession of a good) and in cognitive errors
where available information is simply not processed properly and
consumers’ beliefs about what shops are actually doing are severely
biased.
2.15
In what follows we shall first review the surprisingly small literature on
price frames. We then move on to our experiment, spending some time
on the precise setup and design before analysing the data. Finally, we
discuss the implications of our findings for markets outside the
laboratory and offer some policy advice.
OFT1226 | 20
3
REVIEW OF EMPRICAL AND THEORY LITERATURE ON PRICE
FRAMING
3.1
From this review, the first thing to notice is that the literature on price
framing and the impact on consumer behaviour is surprisingly patchy. In
particular, the empirical evidence available is thin. Even within the
marketing literature the effects of different price frames have not been
systematically explored. Moreover, some of the existing studies suffer
from methodological problems. Methodological problems include, for
example, using purely hypothetical choice problems or relying simply on
perception or recall data. It is well-known that in many contexts the link
between hypothetical choice and actual choice is rather tenuous and
questionnaire and perception data often unreliable (see, for example
Hertwig and Ortmann (2001)).11
3.2
We also discuss some of the literature on price transparency which is
indirectly related. If price framing makes price comparisons more difficult
this could amount to reduced price transparency in markets. Again there
is not a large amount of evidence on the role of price transparency but
there are some useful sources that we briefly discuss.
3.3
The relevant theory papers, investigate how firms would optimally
choose prices and price frames if consumers struggle with comparing
framed prices. However, it is not perfectly clear how safe the
assumption that consumers struggle in comparing prices is.
11
For a more detailed discussion of the methodological strengths and weaknesses of different
qualitative and quantitative methods for testing consumer behaviour see also 'Road Testing
Consumer Remedies', OFT, 2009
(www.oft.gov.uk/advice_and_resources/resource_base/economic-research/)
OFT1226 | 21
3.4
We present this literature below.12
Empirical evidence on price framing
3.5
There is some limited evidence on some forms of price framing. This
evidence comes from experimental research and from consumer surveys.
Does framing matter?
3.6
On a fundamental level the literature suggests that, in supermarkets,
consumers often grab products without checking the price at all
(Dickson and Sawyer 1990). Consumers’ knowledge or recall of prices
immediately after shopping appears poor. Of course, if this is indeed true
it would mitigate any effect of different price frames. If consumers grab
products without checking prices, price frames become irrelevant.13
Partitioned pricing (drip pricing)
3.7
One price frame which has received some attention is partitioned pricing
(which covers drip pricing).14 Morwitz, Greenleaf and Johnson (1998)
show how partitioned pricing lowers consumers’ recalled price for the
good and increases demand. For example, in a fully incentivised auction
experiment in which participants bid for a jar for pennies, one group of
participants/buyers was given a bid form which told them that they must
12
Notice that we do not discuss the wider literature on behavioural economics and biases in
decision making. While this literature is mostly based on evidence from laboratory experiments
there are also some studies that have shown biases in real-life consumer decision making
(DellaVigna and Malmendier (2006) demonstrate, for example, the role of biases in time
preferences for taking out gym memberships.) For a general overview of field evidence on
behavioural biases, see DellaVigna (2009).
13
Notice, however, that the interpretation of such recall data crucially rests on what you are
willing to assume about consumers’ memory. It is, of course, entirely possible that consumers
pay close attention to the prices while picking goods in the aisles and then simply forget the
prices when they check out.
14
This also covers to opt-in/opt-out pricing.
OFT1226 | 22
pay 15 per cent in addition to their bid if they win the auction. A second
group of participants was given a bid form that told them that the price
they will pay if they win will be their bid price. Buyers’ premiums are
often charged by auctioneers, and such a set-up is a form of partitioned
or drip pricing because the buyer does not see the total price of the good
but instead must calculate it from its parts. Morwitz et., al, found that
the partitioned pricing increased demand for the good as compared to
the situation where the buyers’ premium was not separated. In a second
experiment, subjects were shown the price of telephones and then later
asked to recall the total price of the different phones. One group of
participants was shown the total price of the good including any
surcharges such as shipping and handling, while a second group was
shown partitioned prices where the surcharges were reported separately
to the base price for the phones. The surcharges were either reported as
the base price plus the surcharge in dollars (that is, $69.85 plus $12.95
for shipping and handling) or base price plus the surcharge in percentage
terms (that is, $69.95 plus 18.5 per cent for shipping and handling).
Participants in the partitioned price group consistently recalled lower
total prices as compared to those in the total price group. Further, those
given base price plus surcharge in dollars recalled higher total prices as
compared to those given base price with the surcharge in percentage
terms.
3.8
One reason for these observations put forward by Morwitz, et al, is the
use of heuristics by consumers. Namely, consumers may regard the base
price and the surcharge as separate pieces of information. Therefore,
instead of calculating the mathematical summation of all the price parts
(which may require some cognitive effort) consumers are anchoring to
the first piece of information seen which is generally the base price and
then attribute less importance to later pieces of information (the
surcharges).
3.9
An interesting field experiment was conducted by Hossain and Morgan
(2006). This was a natural field experiment implemented using 'eBay'.
Buyers in an eBay auction made bids for CDs and Xbox games. In the
auctions there was a reserve price (that is, the minimum price that will
be accepted by the seller such that bid prices below the reserve will not
OFT1226 | 23
be accepted) and separate (fixed) shipping and handling costs. In this
field experiment it was observed that when the reserve price (which is
the opening bid essentially) is low as compared to the retail price of the
good, and the shipping and handling costs are high, then the auction
always results in a higher sale price than a situation in which the reserve
price is high (relative to retail) and the shipping and handling is low. The
authors present a number of theories that may explain this behaviour.
Bidders may be loss averse, which is similar to the anchoring argument
posed by Morwitz above, namely that consumers treat the base price
separately from the shipping and handling fees and focus on the base
price in order to ‘win’ the good they want and have invested effort in
bidding for. Therefore, the consumer imagines they have the good, they
have 'won' it, and when they subsequently see the additional costs it is
hard for them to give the good up (lose it). Further, consumers may
simply ignore or miss the additional costs.
3.10
In a follow-up paper Brown, Hossain and Morgan (mimeo 2007) sold
different iPod models on Yahoo Taiwan and eBay Ireland. They find that
shrouding low shipping charges is a money-losing strategy for the seller
but that raising shrouded shipping charges does increase revenue. In this
instance shipping charges were either shown on the search page for
each auction or they were shrouded because buyers had to read each
individual auction page to learn the shipping costs. Brown, Hossain and
Morgan attribute their findings to a model where some consumers are
fully rational while others are either naive and overlook the shipping
charge or they are suspicious in the sense of always assuming that
shipping charges are very high.
3.11
These studies imply that drip pricing may generate bias in behaviour
such that consumers end up paying more, and buying more, when priceparts are dripped as opposed to a total price. In addition the relative size
of the drips may be important. If the additional surcharges are a large
proportion of the total cost then buyers may take greater account of the
surcharges when considering the base price, and as such the drip pricing
may have less effect on behaviour.
OFT1226 | 24
Reference pricing
3.12
The marketing literature suggests that consumers use reference prices
when they evaluate goods that are on offer. These reference prices may
be internal (and be based on advertising, associations the product
generates or on remembered prices) or they may be external (prices
formerly charged for the product that are still on display). Unfortunately,
it is not always clear what such reference prices would mean from an
economic point of view and whether they can be reliably inferred from
the data.
3.13
Evidence of reference pricing suggests that consumers are sceptical
when they see an offer/sale price and a reference price, but not sceptical
enough for the reference price not to be potentially misleading. One such
study by Blair and Landon (1981) ran a field experiment in a shopping
centre. The experiment was not incentivised, but instead used
presentation cards to elicit respondents’ valuation of savings on
electrical goods when exposed to an offer price with a reference price
(shown as, for example, list price $69.95, sale price $44.95) as
compared to an offer price with no reference (shown as sale price
$44.95). Participants that were shown only the offer price perceived
that the saving due to the offer was lower as compared to participants
that were shown the offer price and the reference price. However, those
that saw the reference price did not fully believe the reference. For
example, the offer price for a television was shown as $459. The
reference price was shown as $544. In this study the authors found that
respondents who saw only the offer price reported that they believed the
average saving from the full retail price was $58, while those that were
also shown the reference price reported a believed saving of $65, but
not the full referenced difference of $85. This observation held across
well known brands and lesser known brands. Therefore, in this study the
authors observe that reference price claims are discounted by about 25
per cent, but do have an impact on consumers' perceptions of the
savings they are receiving.
3.14
In a survey of research on reference pricing Biswas, Wilson, and Licata
(1993) also report mixed evidence. While the majority of studies do find
OFT1226 | 25
effects of reference pricing on consumer perceptions there are several
that do not.
3.15
A fairly recent study by Chandrashekaran and Grewal (2006) shows how
subjects’ internal reference prices (their perceptions of 'fair prices',
market prices, and 'acceptable prices') respond to the observation of
sales prices. It is shown that subjects’ internal reference prices are
revised in the direction of sales prices. However, these results are not
derived from choice data, nor is it entirely clear how these adjustments
would affect actual choice. In other words, it is entirely unclear whether
this finding has any implication for consumer behaviour and welfare.
3.16
A study completed by the University of Nottingham Business School for
the Office of Fair Trading in 2005, used non-incentivised web-based
experiments to test consumers’ beliefs about different (external)
reference prices for four product types – televisions, holidays,
chocolates and books. The study implemented three different
experiments. Experiment 1 compared between time limited (external)
reference prices and non time limited. Experiment 2 explored how the
reference price was presented - as an absolute discount, a percentage
discount or a discount in relation to the recommended retail price.
Experiment 3 compared sales in stores to sales via the internet. Five
outcome variables were analysed – search intentions, purchase
intentions, transaction value (the consumer’s perceived value of the
deal), acquisition value (the satisfaction or utility derived from the good
as compared to the price paid for the good) and quality (the consumer’s
perceived quality of the good). The experiments observed that for
situations where there is a high reference price and the price of the
product is 'high' (that is, in the case of televisions and holidays but not
books or chocolates) then transaction value, purchase intentions,
acquisition value and quality perceptions are significantly greater than for
the same product when the reference price is smaller. The authors
suggest that this indicates that high reference prices can lead to
consumer detriment by artificially increasing transaction value,
acquisition value and quality and this is particularly strong for goods
which are a larger portion of a consumer’s budget (high ticket items).
OFT1226 | 26
3.17
Alford and Biswas (2002) report that consumers’ psychological makeup
drives the extent to which they are influenced by offers. Specifically the
authors look at two psychological features which they call price
consciousness and sale proneness. Price consciousness is the degree to
which the consumer focuses exclusively on paying a lower price, such
that a highly price conscious consumer will have lower perceptions of
the offer value when faced with a reference price. In other words they
will discount the reference claim to a greater extent. Sale proneness on
the other hand, is the degree to which the consumer will respond to an
offer because of the form in which the sale price is presented.
Consumers that have high sale proneness will have higher perceptions of
the offer value. In other words, they will attach less discount to the
reference price. In this study the authors did find that participants who
were ranked as price conscious placed less weight on the reference than
participants who were ranked as low price conscious while participants
who were ranked as sale prone put more weight on the reference price.
Interpreting these findings requires some care. First, as most studies in
this field it is based on a purely hypothetical choice situation, not real
(incentivized) choices. Second, the data analysis ignores the problem
that 'psychological makeup' is endogenous. Whether a consumer is price
conscious or not may be driven by the same factors that also drive how
they respond to reference pricing (for example, their degree of
impatience). In other words, there is no proof of causality..
3.18
A number of studies have investigated if the form of the offer matters.
DelVecchio, Krishnan, and Smith (2007) analyse the effect of using
cents-off promotions versus percentage-off promotions on participants’
perceptions of the non-sale price. They ran a number of experimental
studies using university students and the goods shampoo and canned
drinks (however these experiments were not incentivised). The authors
observed that a cents-off frame lead participants to estimate a lower
non-sale price as compared to a percentage-off frame, even though both
frames resulted in the same savings as compared to the non-sale prices.
Therefore, in this study a percentage-off frame leads consumers to
believe the savings are greater as compared the use of cents-off
promotion.
OFT1226 | 27
3.19
The Nottingham study (discussed above) also investigated the form of
offers. The observations were mixed. Namely, when the reference price
was high and the ticket price of the good was high (that is, TVs and
holidays but not chocolates and books), then using the recommended
retail price as a reference did have an effect on consumers’ perceived
value of the deal (transaction value) and their intention to purchase.
However, when considering acquisition value (defined above) using the
recommended retail price as a reference had the greatest impact when
discounts were low to medium for televisions and high for holidays.
3.20
Of course, one aspect of promotions and sales is that they simply
change the relative prices of goods within one store and a key strategic
decision that store owners have to take is which goods, and how many
alternatives, they want to offer consumers. A review of these issues can
be found in Simonson (1999). One relevant finding Simonson discusses
is that there is more switching from low-quality to high-quality options if
the high-quality option is advertised than the other way round. Of
course, this might be a simple consequence of the relevant cross-price
elasticities. There is, not necessarily, anything particularly mysterious
about this.
3.21
The limited literature on reference pricing implies that consumers can be
influenced by a reference price, and thereby perceive that the saving on
the good may be greater than if they did not have a reference price. It
also suggests, however, that consumers are sceptical of reference prices
and thereby discount their beliefs about the saving represented by the
reference.
Time-limited offers
3.22
There is very little published research in this area. Devlin, Ennew,
McKechnie, and Smith (2007) analysed time-limited promotions in the
context of durables (TVs) and came up with a very clear conclusion:
Time-limited price offers do not impact upon consumers perceptions of a
good’s value, nor do they have an impact on consumers' behaviour in
terms of search and buying choice. As such this study suggests that
OFT1226 | 28
marketing managers, policy makers and regulators should not be
concerned about this form of pricing.
3.23
Cialdini (2009) draws upon the psychology literature to explain why
sellers use time limits when advertising products. Cialdini argues that
time limits, which state that the price offered will not be available at a
later time, are used by sellers to generate a want for the product even if
previously there was none. Cialdini basis his explanation for this
behaviour upon the proposed human belief that things which are difficult
to get are typically better than those that are easy to get and as such
the availability of an item is a 'short-cut' method for determining quality
of a good (Lynn, 1989). Further, Cialdini asserts that humans do not like
to lose their freedoms, which psychologists term 'reactance theory', and
as such whenever humans feel their choices are limited the need to
maintain choice, and the goods and services associated with the
choices, increases (Brehm and Brehm, 1981). Therefore scarcity invoked
through time limits aims to interfere with humans’ prior access to a good
such that the human will react against this loss of freedom by wanting
the good more. Cialdini provides anecdotal examples of this effect –
electrical goods, gym memberships and automobile sales - in his book
Influence: Science and Practice.
Complex pricing and baiting sales
3.24
We were not able to locate meaningful evidence on the effects of
complex '3 for 2' pricing. There is, however, an interesting study on the
effects of 'multiple-unit pricing' where offers are explained through the
price you get when buying more than one unit ('4 units for £2' instead
of '50p per unit'). Wansink, Kent, and Hoch (1998) report results from a
field experiment to compare such multiple-unit presentations of offers
with straight single-unit offer frames. Utilizing a rather impressive data
set from 86 stores that are randomly assigned to one of the two offer
frames they find that, for a wide range of products, sales under the
multiple-unit frame were, on average, 32 per cent higher than under the
single-unit frame. The multiple-unit frames typically used the format 'buy
two for x'. The observed effect is not only highly significant but also
very large. Rational consumers would, of course, not exhibit this effect
OFT1226 | 29
provided there is no confusion about whether the offer is also effective if
you buy a single unit (that is, that the per unit price is not higher if a
single unit is bought). In all cases, the offer would have been effective
for single units. But the authors cannot rule out that shoppers actually
perceived prices to be non-linear (in which case this would provide
evidence for complex pricing). If shoppers thought they would have to
pay a higher per-unit price for a single unit, then increasing demand
when faced with the 'buy two for x' pricing might be perfectly rational.
(Notice that, technically speaking, consumers’ choice sets would be nonconvex.) Unfortunately, the study does not have exit interviews that
could have clarified this issue. Without exit interviews it is impossible to
understand what is really driving this result; misperception of the nature
of pricing or some sort of confusion triggered by the multiple-unit
pricing.
3.25
Similarly, we could not locate much empirical work on the effects of
traditional forms of baiting. However, Ellison and Ellison (2005) analyse
a market characterized by the presence of price search engines. Price
search engines are designed to make consumer search and price
comparison easier, and as such, reduce friction in the market such that
the price of identical goods should be identical. However, retailers may
seek to put friction back into the market by making price search more
difficult and thereby less of a threat to profitability. One such method is
to prevent price comparison sites including the listed price. Ellison and
Ellison, using field data from an actual price comparison site in the US
observed a more subtle and highly successful version of 'bait and
switch' where sellers advertise a low-quality product at a very low price
as a bait and then try to convince consumers to switch to better quality
more expensive products once they are in their online store (despite the
cheap product being available).
Conclusions from the empirical studies
3.26
On balance we find the evidence on price frames rather thin and shaky
and believe that this OFT research will make a large contribution to the
literature.
OFT1226 | 30
Empirical evidence on price transparency
3.27
There has not been much research on the effects of price transparency
for consumer behaviour and market outcomes. There are however a few
notable exceptions, most of which focus on the strategic behaviour of
firms and the resulting impact on consumers as opposed to specifically
testing consumer behaviour. Davis and Holt (1996) test the Diamond
paradox that predicts that for homogeneous goods (that is identical
goods) the smallest consumer search costs can raise equilibrium prices
from the competitive to monopoly levels. This extreme prediction is not
borne out in the laboratory data although market prices are increasing in
search costs. More support for the Diamond prediction is reported in an
experiment by Cason and Friedman (2003). For policy applications this
implies that relatively small search costs are less of a worry than theory
predicts. Rather, the amount of attention the regulator might want to
pay to search costs should be increasing in these search costs.
3.28
Lynch and Ariely (2000) study the role of price transparency in a field
experiment with online wine retailers. They show how lower costs for
price searches increases price sensitivity of consumers. However, they
also show that this does not necessarily induce ruinous price
competition, where firms prefer to exit the market, if it is offset by a
simultaneous increase in quality transparency. Therefore, in a policy
context, if sellers can differentiate themselves based on quality
features/differences, then even if the cost of searching for price
information is very low, competitive pressures exerted by consumers do
not lead to excessive competition where quality cannot be profitably
maintained.
3.29
In general, there is some evidence that online markets tend to increased
price transparency and, thus, price competition which, of course, is
precisely the reason why online retailers react to this with more attempts
to obfuscate prices (see our discussion of Ellison and Ellison above). The
best evidence available so far stems from comparing internet and
traditional markets for cars and life insurance. Morton, Zettelmeyer, and
Silva-Risso (2001) estimate that online cars are two per cent cheaper
than cars bought offline, controlling for the type of car. In a similar vein,
OFT1226 | 31
Brown and Goolsbee (2002) demonstrate that life insurance premia fell
between eight per cent and 15 per cent through the 1990s in response
to the rise of the internet in the consumers’ homes.
3.30
Notice, however, that there is also clear evidence that the internet does
not eliminate price dispersion. While Brown and Goolsbee show that
price dispersion in life insurance markets declined as internet use became
more common (after an initial increase), Baye, Morgan, and Scholten
(2004) show that there is substantial price dispersion in online markets
for consumer electronics. They utilize over four million price observations
from the price comparison site Shopper.com and find that price
dispersion in internet electronic markets depends crucially on the number
of firms competing which is in line with standard economic models
predicting price dispersion.
3.31
Baye et al. (2006) study the introduction of the Euro as a natural field
experiment. In online markets the common currency makes price
comparisons easier and, thus, increases price transparency. The authors
show that, quite counter-intuitively, this can increase prices due to
strategic responses by retailers. Essentially, the intuition for this follows
three steps. First, more transparency implies more competitive pressure
for consumers who shop around by comparing prices. Second, more
competitive pressure in this market means reduced profits for firms.
Third, reduced profits for targeting consumers who shop around
increases the incentives to simply rely on 'loyal' consumers and charge
higher prices to them.
3.32
Remarkably, this is borne out in the data (taken from Kelkoo on gaming
consoles, computer games, music CDs, PDAs, printers and scanners).
After the introduction of the Euro, the authors document a price increase
of 3 per cent that can be attributed to the increased transparency (that
is, after controlling for cost, demand and market structure effects).
Theoretical literature
3.33
With the rise of behavioural economics several researchers in industrial
organisation have become interested in modelling markets in which at
least some consumers are not perfectly rational. This is still a small but
OFT1226 | 32
rapidly growing literature and there are a few studies that explicitly focus
on price framing.
3.34
Armstrong and Zhou (2010) study how time-limited offers might arise
endogenously. If firms can track consumers they have an incentive to
offer buy-now discounts which, in equilibrium, helps to raise market
prices.
3.35
Gabaix and Laibson (2006) analyse markets where sellers can shroud the
prices of expensive add-ons. Such examples include the sale of printers
and the add-ons for ink cartridges. Or, consumer credit products and the
additional costs for late minimum payments. Consumers that are fully
rational (called Bayesian consumers) will always understand in these
markets that shrouded prices are likely to be high that is, the devil is
always in the detail. Consequently, shrouding would disappear in a
purely Bayesian world because competition would drive firms that
tricked consumers through the use of shrouding out of the market. But
when consumers are not fully rational (non-Bayesian consumers) they do
not always understand that shrouded prices are likely to be high. In this
case not even perfect competition can eliminate shrouding. This arises
because in situations where consumers can be tricked (that is, nonBayesian), then the benefit from ‘training’ them to recognise that high
add-on costs may be shrouded accrues to none of the firms. By
informing consumers that high cost elements may be shrouded, firms
will loose customers and profits to other firms that do shroud because
consumers are not fully rational. On the other side of the coin,
sophisticated Bayesian consumers might actually prefer to buy from the
shrouding firms. This is because they know that the add-ons are high
price but benefit from buying their desired quantity of the cheap nonshrouded component while fully integrating the high add-ons into their
decision making. Of course for those consumers that are not
sophisticated, they may continue to be tricked by not recognising that
high cost elements may be shrouded and thereby not fully integrated the
high add-ons into their decision making.
OFT1226 | 33
3.36
This theoretical work implies that drip pricing with shrouding can indeed
lead to reduced outcomes for consumers because they don’t realise that
the (often) compulsory add-ons are high price.
3.37
Chioveanu and Zhou (2009) study oligopoly markets (markets in which
there are only a small number of firms) where identical sellers of an
identical (homogenous) product choose both, prices and price frames.
The authors assume that price frames have two effects on consumer
behaviour. Consumers may fail to compare prices correctly because of
the complexity of a price frame and/or because of the difficulty
comparing different frames. Remarkably, these behavioural biases can
completely overturn standard intuition about the functioning of markets.
Indeed it is shown that an increase in the number of firms can increase
industry profits and harm consumers (while standard theory would
assume that new entrants reduce industry profits, thereby reducing
prices and benefiting consumers). This arises because the framing acts
as a form of price differentiation meaning consumers cannot compare
prices for identical goods.
3.38
This work implies that how a price is framed may be important for
example, Morwitz et al, (1998) found that how a discount is framed
might influence consumers’ assessment of the partitioned price (above).
However, the complexity of the framing may be of more importance
when considering its effect on consumer behaviour. For example, multipart tariffs for mobile phone contracts or mortgage contracts with
different features can require complex computations in order to directly
compare across different offers. In this (latter) situation firms may be
able to take advantage of this raising price and increasing profits at a
cost to consumers.
3.39
In related work, Piccione and Spiegler (2009) analyse duopoly markets
where consumers make price comparisons only with a certain probability
that is assumed to depend on the price frames chosen by firms. Broadly
in line with the results by Chioveanu and Zhou, they argue that product
differentiation can also be viewed as a means to reduce consumers’
ability of comparing prices effectively.
OFT1226 | 34
3.40
From a policy point of view, this opens up an entirely new question.:
Where do firms engage in product differentiation that is not driven by
responses to different tastes of consumers but essentially just designed
to make comparison more difficult? In this context, Bakos (2001) makes
the interesting observation that in online markets (where, generally, price
comparison is easier) there has been proliferation of customization and
product differentiation in minute details.
Conclusions of the literature review
3.41
We have undertaken a review of both the marketing and economic
literature in regard to price framing. We were surprised to find that the
published literature is very limited. Further, while there is some evidence
to suggest that how prices are framed impact upon consumer behaviour,
this evidence is not very strong.
3.42
The implications of this for the OFT is that the Market Study into
Advertising and Pricing, and this study using controlled economic
experiments to test and observe behaviour under different price frames,
is, we believe, one of the first to systematically study the impact of
price framing on consumer behaviour in markets.
OFT1226 | 35
4
THE EXPERIMENT
The experimental environment
4.1
The task is to analyse all of the five pricing practices (plus a baseline
with straight unit pricing) in a way that allows the OFT to compare
consumer decision making between the different practices and the
baseline. This created several constraints when designing the
experimental environment.15 Specifically, we need an environment with
multiple shops in which multiple units of at least one good can be
purchased. Moreover, we need scope for an advertising stage such that
the experimental environment needs to mirror not only the shops but
also the consumer’s home where he might be reached by some
advertising before actually going to a shop.
4.2
We opted to include all these facets in the simplest manner. The
philosophy behind this is that we want to minimize the noise in decision
making that originates from the sheer complexity of the basic
environment. The less noise that originates from this baseline
complexity, the sharper will be the results of the price-frame
comparison.
4.3
For the implementation of the five different price frames we have opted
for 'typical' incarnations as inspired by the web trawl (Annex B). For
example, we have opted for two price drips which we found to be
common.16 Of course, our comparisons between different price frames
are a function of our design choices. For example, complex pricing could
be much more complex than '3 for 2' (we could imagine highly nonlinear tariffs) to an extent where consumers completely fail to
understand the marginal prices of extra units. Our design choice has
15
An experimental environment is the experiment setting. It is the simplified setting which
mirrors the important real world features that have a bearing on the policy questions to be tested
in the experiment.
16
In the web trawl we did find cases where there were up to 4 compulsory price drips.
OFT1226 | 36
been to opt for simple generic versions of all price frames in order to
maximize possibilities for comparability.
4.4
As we have opted for a simple basic environment and simple incarnation
of the price frames informed by the web trawl. The implications of a
simple environment for external validity will be highly asymmetric: If we
find that consumer subjects do well in the experiment, it would be hard
to extrapolate this finding. However, if we find that they are doing
badly, we might feel more assured that in more complicated real-life
environments the problems are likely to be at least as severe as
identified in the experiment.17
4.5
Our basic environment is designed in the following way. There are two
shops, both of which sell the good that the consumer wishes to buy. At
the start of each round, the consumer is at home. The consumer can go
back and forth between his home and the two shops as often as he likes
and buy units of the good at the shops (up to four units in total). Unless
he receives advertising, he does not know the price of the good at either
shop until he visits it.
4.6
All goods are of the same quality. Again this allows us to focus on the
pure effect of the frames. In many real-life markets prices (current or
former) may serve as a quality signal which renders the decision
problems much more complicated and confounds the issue of price
framing.18
17
Of course, markets might have some self-healing power. For example, firms could establish a
reputation for not using certain confusing practices. We discuss these issues in a section below
devoted to the external validity of our study.
18
Some of the literature on reference pricing (called sales in this experiment), for example,
Simonson (1999), suggests that the reference price can encourage consumers to switch from
low-quality to high-quality options (Chapter 3).
OFT1226 | 37
4.7
In the baseline, each shop draws a random price from a price interval.19
For the purposes of our presentation, let us choose the price interval as
[60, 120] (in the experiment the range was sometimes different, but
could always be normalized to this interval). Both shops draw their
prices from a uniform distribution over this interval. That is, all prices
between 60 and 120 are equally likely.
4.8
Exogenous prices have the crucial advantage of easing comparability
between treatments. There are, of course, consequences for the
interpretation of our experiment. Clearly, in an environment where firms
can adjust prices optimally the effects of any suboptimal consumer
behaviour would presumably be more pronounced. In this sense,
exogenous prices help us to compare treatments but imply that we will
underestimate the consumer detriment (if any) because firms are static
and do not respond to consumer behaviour.
4.9
The consumer has a utility (payoff) function with decreasing marginal
utility.20 For the first unit he buys he receives a payoff of 120, for the
second unit 80, for the third unit 20, and for the fourth unit 10. So the
total payoff for buying one, two, three or four units is 120, 200, 220 or
230 respectively. Notice that with straight unit pricing, as in the
baseline, the consumer will never buy more than two units as the
marginal utility of the third unit is smaller than the lowest possible price
of 60. Notice also that we never allow the purchase of more than four
units.
4.10
Each time the consumer travels to a shop he has to pay some
search/travel costs, c. The search costs vary in the experiment, being
19
The price interval is selected before the experiment begins. The subjects playing the role of
consumers in the experiment are told what the price interval is at the beginning of the
experiment.
20
Marginal utility is the additional satisfaction (payoff) the consumer derives if he buys and
consumes another unit of the good in question. Marginal utility decreases as the consumer gets,
and consumes, more units of the good.
OFT1226 | 38
randomly chosen each round. Specifically, we examine three different
levels of c, low (2), medium (6), and high (12).
4.11
The decision process a consumer must therefore undertake is
summarised in the box below and a simple schematic diagram of the
process is reproduced in the subsequent figure.
Box 2: Consumer decision making process
I can go to shop 1 or shop 2 to buy up to four units of the good available in this
time period. Whenever I go to a shop, I incur a travel cost which will be
deducted from the points I receive when I purchase a unit of the good. The two
shops have different prices. I can only find out what price each shop is selling a
unit of the good at by travelling to the shop. At the home screen (see Figure A.1
in the annex for a shot of the home screen), I can see that the travel cost is two
per journey in this time period.
I will go to a shop. Suppose I choose to go to Shop 1. I see a price of 105 (as in
Figure A.2). I know that I receive 120 points from buying 1 unit, for two units I
receive 200 points, and for three units I receive 220. So the additional points
(my marginal utility) are 80 and 20 for buying the second and third unit.
I have already incurred a travel cost of two points. Therefore if I buy one unit at
105, my earnings will be 120 – 105 – 2 = 13 points. If I buy two units at this
shop, then my earnings will be 200 – 105 * 2 – 2 = -12 points. If I buy three
units at this shop, then my earnings are 220 – 3 * 105 – 2 = -97 points.
If instead I choose to go to shop 2 to find out the price of the good there, I will
incur another travel cost (for travelling from home to Shop 2). This means my
travel costs, for visiting the two shops, will be 2 * 2= 4. To make it worth my
while, the price at Shop 2 will need to be lower than at Shop 1. If the price at
shop 2 is higher than shop 1, then I may want to come back to shop 1 again. In
this instance I will incur a third travel cost meaning my total travel cost will be 2
* 3 = 6.
Looking at the price range, the lowest possible price is 60 and the highest is
120. I have observed 105 for a unit at Shop 1. If I choose not to visit Shop 2,
OFT1226 | 39
then it will be best to buy 1 unit at Shop 1 (because I get 13 points, better than
-12 or -97).
Should I go to Shop 2? Well, the price at Shop 1 is quite high and so it is likely
that the price at Shop 2 will be lower. If it is at least two points lower (that is,
lower than 103), then I will more than cover the extra travel cost. This is quite
likely. Even if it is higher than Shop 1, I can always come back to Shop 1 and
have only lost the cost of two extra trips (a cost of four points).
So, I will go to Shop 2 and see the price there. Once there, I will do the same
calculations as I have just done here to decide the optimal number of units to
buy (or whether it is better to go back to Shop 1).
Figures A.3, A.4, A.5, and A.6 show the buying and results process. For
simplicity, suppose rather than visit Shop 2, I chose to buy 2 units at Shop 1
and then end the round. I must confirm my purchase – see Figure A.3. If I
confirm my purchase, I successfully buy 2 units (at 105 each) – see Figure A.4.
If I then go home and click 'I’m done' on the home screen to finish the period, I
am prompted to confirm my decision – Figure A.5. Upon confirmation of my
exit, the results are shown – see Figure A.6.
OFT1226 | 40
Figure 4.1: Consumer decision process in the baseline treatment
The implementation of the pricing practices
4.12
The experiment studies five different pricing practices and compares
these practices to a baseline of, straight, per-unit prices. As described
previously, in this baseline treatment, consumers see the per-unit price a
shop charges once they visit this shop. This price stays constant within
one round. That is, if the consumer returns to the shop he will still get
the same price. This holds for both shops. Notice that we design the
experiment in a way such that both shops always employ the same type
of price framing.
4.13
The setup for the sales frame is almost identical. The selling prices are
determined in precisely the same way as for the baseline and the only
difference to the baseline is that consumers are additionally informed
about a former price. Specifically, they are told that the former price is a
price that is chosen randomly between the actual selling price and the
OFT1226 | 41
maximum possible price. They are also told about the consequent
'discount' that this represents in percentage terms. As such, it should be
easy for the subjects to see that the sales information is actually entirely
meaningless. Even though subjects are not told how the former price is
generated it is apparent that the good has not been sold previously at
this price. Of course, it might have been to some other subjects in some
earlier round but for the subject faced with the decision at hand this is
irrelevant information and should easily be identified as such. The screen
shot for the sales frame is presented in figure A.14.
4.14
Figure 4.2 presents a schematic of the consumer decision process in the
sales frame.
Figure 4.2: Consumer decision making process in the sales frame
4.15
Drip pricing is also virtually identical to the baseline. Actual selling prices
are determined in precisely the same manner. This time, however,
consumers learn about the selling price only in drips. Once they visit a
shop, they see a base price (with no mention of additional charges).
OFT1226 | 42
Once they decide to buy one or multiple units, they see a first drip and
need to click ok to proceed. If they do so, they are informed about a
second drip and need to ok this as well. Finally, they see the total price
(and its decomposition into base price plus shipping plus handling) and
they need to click one more time to confirm their purchase. So the only
difference to the baseline is that subjects need to click two more times
to learn the actual selling price. As mentioned above, actual selling
prices are determined in precisely the same way as in the baseline. The
actual selling price is then decomposed by the computer into the base,
the first drip and the second drip. The first drip is randomly chosen to be
between five per cent and 15 per cent of the selling price, and the
second drip is randomly chosen to be between 10 per cent and 20 per
cent of the selling price. The base price is the remainder. The screen
shots for drip pricing are presented in figures A.8 to A.10.
4.16
Figure 4.3 shows a schematic of the consumer decision making process
in the drip pricing frame.
OFT1226 | 43
Figure 4.3: Consumer decision making process in the drip pricing
frame
4.17
Under complex pricing ('3 for 2') the unit prices are again determined in
the same way but consumers are not charged for a third unit if they
wish to buy one. They are informed of this offer once they enter the
shop and the offer remains in place for an entire round. Figure A.7
presents a screen shot for complex pricing.
4.18
Figure 4.4 shows a schematic of the consumer decision making process
in the complex pricing frame.
OFT1226 | 44
Figure 4.4: Consumer decision making process in the complex
pricing frame
4.19
In time-limited offers, consumers are told when they enter the first shop
that the price they are confronted with now is only on offer at this
instance. If consumers come back at a later stage, the price will have
changed. Specifically, the shop simply draws a new price from the same
distribution, that is, sometimes consumers will actually get a price upon
return that is below the time-limited offer price. The other shop engages
in the same tactics (that is if the consumer had visited the other shop
first, he would also have been confronted with a time-limited offer).
However, once the time limit passes both shops offer a price that then
remains fixed. This means that the consumer never experiences a timelimited offer at the second shop (simply because the offer there was also
just valid for one time period). Figure A.15 shows a screen shot for the
time limited offer frame.
OFT1226 | 45
4.20
Figure 4.5 shows a schematic of the consumer decision making process
in the time-limited offer frame
Figure 4.5: Consumer decision making process in the time-limited
price frame
4.21
Finally, under baiting both stores advertise prices. That is, in contrast to
all other treatments, consumers have some price information at their
home screen. These prices are under a generic 'while stocks last'
qualification that is printed next to the price information that subjects
can see at the home screen. Specifically, if the selling price is between
60 and 72, the advertised price is equal to the selling price. If the selling
price is greater than 72, then there is a 50 per cent chance that the
advertised price will be the selling price and a 50 per cent chance that
the advertised price will be some price randomly drawn from the interval
[60, 72] (a 'bait'). When a consumer visits their first shop, they will see
OFT1226 | 46
whether the price is real or a bait. The selling price at the other shop is
now randomly drawn. If the consumer returns to the home screen, the
advertising has gone. Figures A.11 to A.13 show the screen shots for
the baiting frame.
4.22
Figure 4.6 presents a schematic of the consumer decision making
process in the baiting frame.
Figure 4.6: Consumer decision making process in the baiting price
frame
The consumer’s optimal search strategy in the baseline
4.23
The first decision is to choose one of the two shops to visit first. As
there is no history and no information about the two shops, this is
inevitably a random (and, hence, meaningless) decision. (Of course, this
changes in the presence of some advertising, as in our treatments on
OFT1226 | 47
baiting.) So, without loss of generality, we can call the shop the
consumer chooses first 'shop 1'.
4.24
Once the consumer is at shop 1, he can see the unit price that the shop
charges in this period. He can then either buy as many units as he
desires (up to a maximum of four) or he can also decide not to buy and
return to the home screen empty-handed. He can then travel to shop 2
(or indeed go back to shop 1 but that would, of course, not be
reasonable as no new information would be revealed), again paying the
travel/search costs, c. At shop 2 the same rules apply. That is, the
consumer learns the unit price charged at shop 2 and can buy as many
units as he desires. Again, he can also return empty-handed and, if he
desires, return to shop 1. He can, of course, also return to shop even if
he has bought some units already, either at shop 2 or even at shop 1,
but, as is easy to see, that would also not be rational.
4.25
We can analytically derive the optimal consumer search strategy. The
optimal search strategy is what a fully rational consumer should do. Here
we summarize the solution. The theory is presented in detail in Annex C.
4.26
Notice first that the marginal utilities and prices used in this experiment
mean that it is only ever optimal to buy one or two units in total. The
marginal utility of the third unit is always less than the price.
4.27
At shop 1, there are two cut-off prices, p’ and p’’. p’ is the cut-off for
buying one unit or two units and p’’ is the cut-off for buying at shop 1
or searching more by visiting shop 2.
4.28
If the price at shop 1, p1, is above p’’, then the consumer will not buy at
shop 1, but rather go to shop 2 and see what the price is there. The
consumer may return to shop 1 later, depending on the price at shop 2
and the prevailing search costs.
4.29
If p1 is below p’’, the consumer will do all their shopping at shop 1. If
p1 is below p’, the consumer will buy two units at shop 1 and end the
round. If p1 is above p’ he will buy one unit at shop 1 and end the
round.
OFT1226 | 48
4.30
As described above, in case of a price p1 above p’’, the consumer will
travel to shop 2. There are four different possibilities that can then arise:
(i) He can buy two units at shop 2 and end the round. (ii) He can buy
one unit at shop 2 and end the round. (iii) He can decide not buy at shop
2 and return to shop 1 to buy two units there. (iv) He can decide not to
buy at shop 2, return to shop 1 and buy one unit there.
4.31
Which of these four options is optimal depends on the search costs and
the two prices p1 and p2. The higher the search costs, the higher the
cut-off p’’. Table 4.1 shows the cut-offs for the search costs we have
implemented.
Table 4.1: p’’ cut-offs for the various treatments
Search cost
Baseline*
Complex
Time-limited
offer
c=2
74.5
74.5
79.2
c=6
86.3
83.9
85.8
c = 12
97.8
91.9
94.8
Note: The optimal strategies, and therefore the p'' cut-offs for the sales, drip pricing and baiting
frames are the same as the baseline (single unit straight pricing). This is explained further in the
following sub-section of this chapter.
The consumer’s optimal search strategy in the price frames
4.32
Two of the five price frames leave the optimal search strategy from the
baseline completely untouched because the true unit price does not
change and is clearly discernible to the unbiased consumer. In the sales
frame, the consumer directly sees the true unit price and just receives
some completely irrelevant additional information (the former price)21 and
under drip pricing the true price gets clearly revealed, albeit only in drips.
21
In practice, former prices might often serve as a signal of quality. By abstracting from such
quality issues we can measure the pure effect of framing a price as a sales price.
OFT1226 | 49
So in both setups, a fully rational consumer will simply adopt the same
search strategy as that we have derived as optimal above.
4.33
Optimal search is, however, slightly different under the other three
frames. Under complex pricing, however, the derivation of the optimal
search strategy is essentially identical. Under '3 for 2' we can revisit our
old analysis and simply change the marginal utility of the second unit
(80). Specifically, we replace it by the sum of the marginal utility of the
second and third unit (80 + 20 = 100). The logic is simple. As the third
unit gives you some positive utility you will always take it if you do buy
two units. In other words, you will never refuse the offer and check out
with just two units. However, you might still sometimes prefer just to
buy one unit. This changes the cut-offs. Table 4.1 shows the relevant
cut-offs for complex pricing for each search cost level.
4.34
For the remaining two frames, the structure of the analysis also changes
slightly. First, let us consider time-limited offers. If a time-limited offer is
not taken and the consumer returns home, both shops draw new prices.
Consequently, if a consumer does not take the time-limited offer and
returns to his home screen, he faces a situation that is identical to the
original straight pricing baseline because, from now on, the shops do
simply stick to one price and refrain from any further such offers. This
implies that essentially, when the consumer sees the time-limited offer at
shop 1 (the shop he visited first), he has the choice between buying now
and ending the round or playing the original game. This means the
consumer can simply compute the utility he would receive from buying
optimally one or two units now (for the time-limited offer price) and
compare this with the ex ante expected utility he receives from playing
the baseline game, assuming, of course, he plays this optimally. This
generates the different cut-offs shown in Table 4.1.
4.35
Finally, let us consider baiting. Under baiting the consumer needs to take
into account that low prices in the range from [60, 72] might turn out to
be baits while with higher prices he can rest assured that they will turn
out to be true. This solution necessitates knowing the precise rule the
firms employ which, in the experiment, is initially not the case but,
through repetition, may be able to be learnt approximately. As the
OFT1226 | 50
baiting offer is only available initially, the rational consumer essentially
has to take just one additional decision that goes beyond the optimal
baseline search strategy. At the home screen, he has to decide which
shop to visit first! Notice that this is indeed the only frame where the
initial choice of shop is meaningful. Once he is at the first shop and the
true price gets revealed, the consumer is back to the original problem of
the baseline. It is, of course, this elegance of the rational benchmark
solution that has inspired the specific design here, that is the decision to
have the baits only initially and revert to a new price draw after that (for
both firms).
4.36
The optimal choice of shops is not simply determined by the lowest
advertised price. As low prices may be baits, but high prices tend to be
honest, the consumer needs to work out average expected payoffs
based on the offered prices. For prices that look like potential baits, but
that are not extremely low, the consumer might actually be better off to
seek out a shop that advertises a higher but honest price.
Experimental procedures
4.37
A total of 166 subjects participated in this experiment. Each subject was
confronted with the baseline and two of the five frames. Subjects played
for thirty rounds, ten for each type of price frame. The sequence in
which they faced the different frames was random. This repetition
enables subjects to learn about the environment and adapt their
behaviour. This enables us to determine whether any potential effects of
the price frames can be overcome through experience.
4.38
In order to enhance attention (and make sure that each round was
viewed as a truly new round) we scaled payoffs in four different ways.
Specifically, subjects faced four different goods, GREEN, ORANGE, BLUE
and RED. Utilities and prices for each good were obtained from the
model above through different ways of up scaling (the model above
details the utilities and prices for RED). This ensures that the basic
problem is always identical, regardless of the specific goods subjects
could buy. The search cost was randomly chosen each period from low,
OFT1226 | 51
medium and high. The actual payoff, price and search costs are given in
Table 4.2.
Table 4.2: Parameters for different goods
Product
C
0
(search
units
costs)
1 unit
2
units
3
units
4
units
Price
Range
GREEN
1,3,6
0
60
100
110
115
30 to 60
ORANGE
1,3,6
0
80
140
170
195
50 to 80
0
110
180
190
190
50 to
110
0
120
200
220
230
60 to
120
BLUE
RED
2,6,12
2,6,12
4.39
There are 10 combinations of two out of five price frames and we
implemented all of them. That is, we studied ten different groups of
subjects where each group of subjects is characterized by a combination
of two price frames subjects are faced with in addition to the baseline
that every subject experiences. This allows us both within and between
subject comparisons, maximizing the statistical power we get for our
data analysis.
4.1
In addition to the 30 rounds of the experiments, each subject undertook:
22
1.
pre-experiment test to ensure that they understood the
experimental instructions
2.
12-question IQ test (incentivised)
3.
15-question personality test22
We found no correlation between personality and behaviour in the experiment.
OFT1226 | 52
4.
feedback questionnaire about the experiment.
The personality test and the feedback questionnaire are reproduced in
the annexe.
4.2
Experimental sessions lasted on average 145 minutes and average
earnings were approximately £20 which included a £5 show-up fee.
OFT1226 | 53
5
5.1
EXPERIMENTAL RESULTS
In our analysis, we proceed as follows. We first study whether price
frames have any effect on consumer welfare. To make treatments
comparable we define losses in consumer welfare relative to what
consumers could have achieved under optimal behaviour.23 In a second
step, we employ econometrics to analyse errors in behaviour,
distinguishing errors in search and errors in purchasing. Finally, we will
zoom in more closely on search patterns and purchasing behaviour, in
order to understand what the root causes of poor performance are and
how they relate to known behavioural phenomena.
Consumers
Consumer welfare
5.2
We now turn to the first fundamental question this research addresses:
do price frames matter for consumer welfare? Despite our extremely
simple environment and a rather smart subject pool, we find that price
frames do matter.
5.3
Since the amount of consumer welfare obtainable under optimal
behaviour differs between the different frames, we need to normalize
achieved payoffs in an appropriate manner. In order to do this, we take,
for each of the 4895 observations we have, the difference between the
actual achieved payoff and the payoff that would have resulted from
following the optimal decision rule. We call this variable the consumer’s
loss. If a consumer could have achieved a payoff of 87 under optimal
behaviour but only achieved a payoff of 69 then his loss is 87 – 69 =
18. Often we look at the average loss a consumer has made in a
particular environment which is simply calculated as the arithmetic mean
of all the losses in all rounds.
23
The optimal strategy is the one a subject that knows the experimental environment and is fully
rational would employ.
OFT1226 | 54
5.4
Additionally, we compute a further welfare indicator, the extra loss
relative to the baseline loss. This is computed as follows. For each
subject we have three average loss variables, one for the baseline, and
two for the two price frames encountered.24 The extra loss a subject
incurred under a price frame is then simply defined as the difference
between the average loss in this price frame and the average loss in the
baseline. We can then also compute the extra loss made on average by
all subjects under a particular price frame. This difference-in-difference
approach controls for both different earning potentials under different
frames and subject-specific differences in performance levels.
5.5
Table 5.1 shows both loss and extra loss for all price frames and the
baseline. Ranks are assigned according to extra loss. The magnitude of
losses as shown in Table 5.1 refers to the normalized model that we
sketched above. One unit of loss translates to £0.02 in each round of
the experiment. So, for example, the average loss under time-limited
offers of 7.64 translates into a monetary loss of £1.58 over 10 rounds
in which time-limited offers are experienced. Given average earnings of
£15 on top of the show-up fees over thirty rounds, this shows that the
loss is substantial: more than a third of what subjects make on average
in ten rounds.
24
Each subject did three treatments including the baseline within a two-hour session.
OFT1226 | 55
Table 5.1: Welfare losses under the different price frames
Rank
Frame
Loss
Extra Loss
1
Baseline
5.66
0
2
Complex
7.47
0.81**
3
Sales
7.10
2.01
4
Baiting
5.83
2.04**
5
Time-ltd offers
7.64
2.13**
6
Drips
10.69
3.23***
Note: Stars indicate significant differences to baseline, ** 5 per cent, *** 1 per cent
5.6
For each price frame, we test whether the average performance is
significantly different from the baseline performance. With the exception
of the sales frame, differences are indeed significant. The significance
levels are 1.8 per cent for complex pricing, 1.6 per cent for time-limited
offers, and 2.0 per cent for baiting. For drip pricing the difference in
performance even reaches a significance of 0.1 per cent.25
5.7
Two main results emerge from Table 5.1. First, price framing is indeed
detrimental for consumer welfare and this seems to be the case for all
frames, with the potential exception of the sales frame. Second, drip
pricing emerges quite clearly as the worst culprit with the biggest
average loss and the biggest average extra loss.
Errors in consumer behaviour
5.8
There are generally two types of errors subjects can make in our
experiment: Errors in their search activity and errors in purchasing
25
This means that subjects' extra losses in the price frames were significantly larger (or smaller)
than the extra losses incurred by subjects in the baseline.
OFT1226 | 56
behaviour. Errors in search activity occur where a consumer makes more
or fewer visits to the shops than is optimal. Specifically, there are two
types of search errors. A consumer makes a search error if he buys at
the present shop but should optimally have continued his search. Or,
vice versa, if he continues his search but should have optimally bought
at the present shop. Errors in purchasing behaviour occur where subjects
do not buy the optimal amount of the good. For example, they buy one
unit when it would be optimal to buy two units given prices and marginal
utilities of consumption. Notice that search and purchasing errors can
also occur together. For example, when a consumer buys one unit at the
first shop while according to the optimal strategy he should have bought
two units at the second.
5.9
There are some arguments for why in the context of this study it might
be more important to focus on errors rather than overall performance as
the performance measures above are sensitive to the precise parameters
chosen in the experiment. For example, the losses would have been
much bigger if the value of the goods had been higher.
5.10
In order to get a first grip on errors in decision-making, we examine each
of the 4895 rounds played by our 166 subjects. Whenever the observed
behaviour in a given round departs from the optimal decision rule, we
classify the round as a round with an error. We then regress errors on
prices, search costs, the scaling factor and the different price frames
(where each frame is captured by a binary dummy with the baseline
serving as the reference). We use probit regressions and cluster the
standard errors on the subject level in order to account for dependencies
resulting from repeated measurement. Table 5.2 shows the estimated
marginal effects. (All detailed regression results are contained in the
technical appendix where we also include estimations from linear
probability models that throughout confirm the robustness of our
results.)
•
The way to read Table 5.2 is the following: the estimated coefficients
show if subjects in the experiment either searched 'too much' or 'too
little' or bought 'too many' units or 'too few' units (overall errors)
than was optimal in the relevant price frame as compared to the
OFT1226 | 57
overall errors that subjects made in the baseline treatment with
straight per unit pricing.
Table 5.2: Probit estimation of errors in decision making.
Variable
Coefficient
Standard error (p-value)
p1 (price at 1st shop
visited)
0.393
0.070 ***
p2 (price at 2nd shop
visited)
0.218
0.050 ***
c (search cost)
-0.516
0.214 **
Highvalue (the scaling;
Green, Blue, Orange or
Red goods)
0.012
0.014
d2 (Complex pricing)
0.019
0.026
d3 (Drip pricing)
0.143
0.026 ***
d4 (Baiting)
0.091
0.026 ***
d5 (Sales)
0.069
0.025 ***
d6 (Time limited offers)
0.185
0.25 ***
Note: The estimation model is probit regression 1.a. in the annexe.
5.11
The coefficients shown in this table are to be read as the percentage
increase in errors if the explanatory variable increases by one unit. With
the exception of complex pricing, we observe significantly more
erroneous behaviour (relative to the optimal behaviour benchmark) in all
price frames, including the sales frame which increases the error rate by
almost seven per cent. This higher error rate induced by the simple 'was
X is now Y' statement appears striking, the reference price is quite
obviously meaningless in the experimental environment.
OFT1226 | 58
5.12
The highest marginal effect on error rates are observed under timelimited offers and drip pricing with 19 per cent and 14 per cent more
errors respectively than in the baseline. Baiting generates around nine
per cent more errors.
5.13
With respect to the other variables, we make the following three
observations. (i) The 'high value good' variable is not significant. Hence,
there are no effects of the scaling and our results are robust to the
incentives. In other words, higher stakes do not reduce errors. (ii) Lower
prices are associated with lower errors, presumably because with low
prices it is easier to detect when purchase of two goods is optimal.
Moreover, as we will see in more detail below, consumers exhibit in
several treatments a tendency 'to buy anyway' (instead of optimally
continuing their search). And this behaviour is obviously only erroneous
when prices are high.26 (iii) With higher search costs consumers make
fewer errors.
5.14
Tables 5.3 and 5.4 contain two further regressions that analyse the
quality of choices in more detail. Specifically, we distinguish between
two kinds of errors, errors in search behaviour and errors in purchasing
behaviour. Errors in search occur whenever a consumer continues his
search while the optimal strategy prescribes immediate purchase or vice
versa. Errors in purchasing behaviour occur when subjects buy a
suboptimal number of units. While determining the optimal number of
units is a rather simple task (it just requires a very basic understanding
of the marginal utility/pay-off table) the decision about search is more
demanding.
5.15
Table 5.3 can be read in the following way: The estimated coefficients
show If subjects in the experiment either searched 'too little' or 'too
much' than was optimal in the relevant price frame as compared to
26
Notice the difference between price and value. High value refers to a higher multiplier for
both, consumer payoff and price. Changing this multiplier leaves the decision-relevant (relative)
price completely unaffected.
OFT1226 | 59
search errors that subjects made (searching too little or too much than
was optimal) in the baseline treatment.
Table 5.3: Probit estimation of errors in search behaviour
variable
coefficient
standard error
p1 (price at 1st shop
visited)
0.206
0.067 ***
p2 (price at 2nd shop
visited)
0.221
0.042 ***
C (search costs)
-0.463
0.189 **
Highvalue (the scaling;
Green, Blue, Orange or
Red goods)
0.007
0.013
d2 (Complex pricing)
0.018
0.024
d3 (Drip pricing)
0.095
0.027 ***
d4 (Baiting)
0.071
0.024 ***
d5 (Sales)
0.047
0.022 **
d6 (Time limited offers)
0.185
0.026 ***
Note: The estimation model is probit regression 1.b. in the annexe.
5.16
The estimation results for errors in search are almost identical to the
estimations for errors in general. This is not surprising given that errors
in search are the main source of suboptimal behaviour.
5.17
Table 5.4 can be read in the following way: The estimated coefficients
show if subjects in the experiment either bought 'too many' units or 'too
few' units than was optimal in the relevant price frame as compared to
purchasing errors that subjects made (buying too few or too many units
than was optimal) in the baseline treatment with straight per unit pricing.
OFT1226 | 60
Table 5.4: Probit estimation of errors in purchasing behaviour
variable
coefficient
standard error
p1 (price at 1st shop
visted)
0.620
0.061 ***
p2 (price at 2nd shop
visited)
-0.156
0.051 ***
C (search costs)
0.479
0.202 **
Highvalue ((the scaling;
Green, Blue, Orange or
Red goods)
0.026
0.014
d2 (Complex pricing)
0.013
0.025
d3 (Drip pricing)
0.142
0.028 ***
d4 (Baiting)
0.095
0.026 ***
d5 (Sales)
0.060
0.024 **
d6 (Time limited offers)
0.211
0.025 ***
Note: The estimation model is probit regression 1.c. in the annexe.
5.18
While it is less common that subjects buy the incorrect number of units
(we will document this in more detail further below) it does happen and
the estimation results in Table 5.4 reveal some clear patterns. Once
again, price frames increase error rates (and once again time-limited
offers and drip pricing are the worst culprits). But some of the other
variables have an effect on purchasing behaviour that is rather different
from their effect on search behaviour. Specifically, subjects make more
errors in purchasing the right number of units if the price at the second
store is comparatively lower; errors increase in search costs; and for
high-value goods there are more errors than for low-value goods. All
three effects point to a psychological mechanism whereby consumers
get overly keen to buy more of a good after being frustrated by high
OFT1226 | 61
prices at the first shop and having spent much on extra search to travel
to the second. This justification of earlier incurred costs is an incarnation
of the sunk cost fallacy and appears to be the main source of purchasing
errors beyond pure cognitive failure to trade off marginal utility and unit
price.
5.19
Notice that the regressions suggest a slightly different ranking of the five
price frames than our welfare results. There are a number of reasons
behind this. First of all, we do not control for the size of errors in the
regressions (precisely to offer an alternative view on consumers’
performance that is less dependent on our parameter choices). Second,
we do not account for multiple errors in the regressions. For example,
subjects who search excessively (and through these multiple errors lose
substantial amounts of money) are classified for the purpose of the
regressions in the same way as a subject who makes a single mistake.
5.20
Notice that we have also estimated linear-probability models as a
robustness check. (See the technical appendix for details, linear
regression model 1.a. – 1.c.). The stability of the results is remarkable:
the marginal effects reported above are virtually identical to the
parameter estimates obtained through the linear estimations.
The role of search costs
5.21
As we have seen above search costs have an ambiguous effect on the
quality of consumer’s choice behaviour. Higher search costs reduce, on
average, search errors but increase purchasing errors. In order to better
understand the effect of search costs we need to investigate their
differential impact on behaviour across the different treatments.
5.22
We investigate whether search costs have treatment effects by rerunning our regressions including interactions between search costs and
treatment dummies. (See the technical appendix for all detailed
estimation results, estimation models probit 6.a. – 6.c.). In the general
error regressions we find that search costs as such lose their
significance but do show up negatively if interacted with drip pricing (1.11), baiting (-1.32), and time-limited offers (-1.51). In other words, we
OFT1226 | 62
find that higher search costs reduce errors only under these three price
frames and not in any of the others.
5.23
Decomposing errors, we find the following. For search errors, the picture
is virtually identical to what we have seen in the general error
estimations. Search costs have no effects in the baseline nor under
complex pricing or the sales frame. On the other hand, they do reduce
search errors under drip pricing, baiting, and time-limited offers.
5.24
For purchasing errors a different pattern emerges. The search cost
variable is significantly positive that is, purchasing errors increase with
search costs. However, for drip pricing and time-limited offers this effect
is wiped out (which can be seen by comparing the significant
coefficients of the relevant interaction terms with the estimated
coefficient of the general search cost term).
5.25
In all, we have to conclude that price frames have a stronger effect on
consumer behaviour in markets with lower search costs. In particular,
drip pricing, baiting and time-limited offers cause more consumer
detriment if search costs are comparatively low. We will revisit the
causes for these effects further below when we discuss the behavioural
biases that are driving these deviations from optimal consumer choice.
The lessons so far
5.26
We have seen very clean evidence now that identifies drip pricing as the
price frame that is most detrimental to consumers, both in terms of
errors and consequences. Time-limited offers come second (with
intermediate welfare losses but even more errors than drip pricing),
which is surprising given that the literature so far has either ignored or
exonerated time-limited offers. Consumers have the least problems with
complex prices and sales frames where the former has no effect on
errors and the latter no effects on welfare. They are, however, both not
completely unproblematic. In the middle is baiting with systematically
more errors and a significant welfare loss.
OFT1226 | 63
5.27
In order to better understand the behavioural forces that are the root
cause of inferior consumer decision making stems we will now study
search and purchasing behaviour in much finer detail.
Zooming in
5.28
Before we actually turn to a detailed analysis of consumer behaviour
under the different price frames, we need carefully to examine the
baseline.
5.29
Table 5.5 shows all observations we have for our subjects at the first
shop in the baseline. The rows indicate what would have been optimal,
the columns what has actually been chosen. So, the first row ('0') in the
table indicates all situations where the optimal strategy prescribes
further search (the purchase of zero units at the first shop). The second
row contains all cases where consumers should have bought one unit
and the third all cases where consumers should have bought two units.
The columns indicate the actual number of units purchased.
5.30
While the table shows that subjects do generally very well in this
situation (78.6 per cent of all choices are optimal) it also reveals two
interesting asymmetries. First of all, it shows that errors are typically
errors in search while purchasing the wrong number of units is much
rarer. In fact, 90.5 per cent of all errors are search errors. The second
asymmetry occurs within the class of search errors. While subjects do
not buy in 86.8 per cent of all cases when it is optimal to continue to
search, they buy optimally only in 68.3 per cent of all cases where it is
optimal to buy. In other words, there is a clear tendency to oversearch.
This is particularly dramatic when subjects should optimally buy just one
unit. In this case only 50.8 per cent of choices are correct.
OFT1226 | 64
Table 5.5: Optimal vs actual choices at the first shop visited in the
baseline (straight per unit pricing)
Actual choice
Optimal
choice
5.31
0
1
2
3
4
Total
0
804
93
25
1
3
926
1
83
99
11
1
1
195
2
119
14
410
3
4
550
Total
1006
206
446
5
8
1671
Next we turn to what happens at the second shop (where we only track
those subjects who went there optimally).27 The rate of optimal
behaviour here is 86.7 per cent even higher than at the first shop. This
partly reflects that it is an easier decision (as now all uncertainty has
been resolved and all prices are known) but is also due to a selection
effect. After all, the table only contains those subjects who have already
made one correct decision. The asymmetries also largely disappear.
There is no clear pattern of oversearch once consumers have reached
the second store.
27
Notice that the number of cases in all tables that show behaviour at the second shop does not
always coincide with number of cases in the (0,0) box of the first-shop tables. This is due to the
fact that in some instances subjects did not buy at all.
OFT1226 | 65
Table 5.6: Optimal vs actual choices at the second shop in the
baseline (straight per unit pricing)
Actual choice
Optimal
choice
0
1
2
3
4
Total
0
144
17
2
0
0
163
1
24
313
31
3
3
374
2
2
18
234
3
3
260
Total
170
348
267
6
6
797
5.32
It is interesting to examine the stores’ sales volume, relative to what
they would sell under optimal consumer behaviour. The first shop sold
0.69 units per customer which compares to 0.78 units that are predicted
under optimal consumer behaviour.28 In other words, in the baseline the
first shop reaches only 88.4 per cent of its sales potential. In contrast,
the second store achieves 103.4 per cent of its sales potential.
5.33
Let us now examine the same two tables for drip pricing. Table 5.7
shows behaviour at the first, and Table 5.8 at the second shop.
Comparing Table 5.7 with the baseline reveals how dramatic the effect
of drip pricing really is. The rate of optimal decisions falls to 70.9 per
cent. But even more strikingly, the phenomenon of relative oversearch
that was so prevalent in the baseline not only disappears but is actually
replaced by a stark pattern of undersearch. In other words, while many
consumers who should have bought at the first store in the baseline
decided to check out the second store, this is reversed under drip
pricing. Consumers who should continue to search are lured into buying.
28
Notice that these numbers can be directly computed from the tables showing optimal vs.
actual choice.
OFT1226 | 66
In 26.9 per cent of all cases where consumers should not have bought
from the first shop they do so now. This compares to just 11.0 per cent
in the baseline.
5.34
As a consequence drip pricing means that many more units are sold at
store 1. The first store sells more units as predicted, and not fewer as in
the baseline. It reaches 111.8 per cent of its sales potential, a relative
increase of 26.5 per cent over the baseline.
5.35
These findings appear all the more remarkable as the difference between
the two environments is really rather small. In essence, it just requires
two extra clicks to see the true full price under drip pricing. Everything
else is completely identical. Moreover, the environment is very simple
and the subject population is highly selected and presumably much more
capable of sophisticated behaviour than the average consumer.
Table 5.7: Optimal vs actual choices at the first shop in the drip
pricing frame
Actual choice
Optimal
choice
5.36
0
1
2
3
4
Total
0
274
68
28
0
5
375
1
25
39
7
0
0
71
2
39
7
137
0
6
189
Total
338
114
172
0
11
635
Turning to Table 5.8 we also find that consumers’ performance does not
much improve at the second shop with just 72.6 per cent of optimal
choices (compared to 86.7 per cent in the baseline). However, the
second store does not benefit to the same extent as the first store, even
conditional on being visited. In fact, it only reaches 97.2 per cent of its
sales potential.
OFT1226 | 67
Table 5.8: Optimal vs actual choices at the second shop in the drip
pricing frame
Actual choice
Optimal
choice
0
1
2
3
4
Total
0
40
6
2
0
0
48
1
25
73
21
1
0
120
2
5
11
83
2
1
102
Total
70
90
106
3
1
270
5.37
Tables 5.9 and 5.10 show the same data for consumer behaviour at the
first and second shop for time-limited offers. Table 5.9 for the first shop
looks remarkably similar to Table 5.7 for the first shop under drip pricing.
Again the phenomenon of oversearch is reversed and consumers tend to
buy when optimal behaviour suggests they should not. However, the
achieved sales volume is slightly smaller than under drip pricing (albeit
still much better than under the baseline). With time-limited offers for the
first store reaches 101.2 per cent of its sales potential.
5.38
While consumer behaviour at the first store under time-limited offers is
qualitatively similar to that under drip pricing, there is a marked
difference between Tables 5.10 and 5.8 which show consumer
behaviour at the second shop. While the second shop could not benefit
from drip pricing (if anything consumers reversed to oversearch when
facing drips at the second shop), it does benefit substantially from the
time-limited offers consumers faced at the first shop. It (the second
shop) reaches an enormous 140.4 per cent of its sales potential. From
that it becomes apparent that consumers fear to face substantially
higher prices upon return to the first store. In that sense, time-limited
offers do the trick, they do confuse consumers substantially. But,
OFT1226 | 68
somewhat ironically, the competition benefits more from this than the
shop first visited by the consumer.
Table 5.9: Optimal vs actual choices at the first shop in the time
limited offers frame
Actual choice
Optimal
choice
0
1
2
3
4
Total
0
297
69
9
0
1
376
1
22
32
7
0
0
61
2
28
15
178
1
0
222
Total
347
116
194
1
1
659
Table 5.10: Optimal vs actual choices at the second shop in the time
limited offers frame
Actual choice
Optimal
choice
5.39
0
1
2
3
4
Total
0
117
70
3
0
0
190
1
5
24
5
1
0
35
2
1
7
60
0
0
68
Total
123
101
68
1
0
293
Tables 5.11 and 5.12 show the same data for baiting, Tables 5.13 and
5.14 for complex pricing, and Tables 5.15 and 5.16 for the sales frame.
OFT1226 | 69
5.40
Just as in drip pricing and time-limited offers, a baiting strategy
eradicates oversearch and generates a pattern of undersearch with many
consumers (even) buying two units at the first shop where a continued
search would have been optimal. There is no indication of consumers’
'punishing' sellers that lured them into their shops with low offers that
then turn out to be unavailable.
5.41
In contrast to time-limited offers, there are no particular effects of
baiting on consumer behaviour at the second shop. Generally, consumers
do very well at the second shop with the vast majority of observations in
Table 5.12 on the main diagonal (meaning actual choice was optimal).
5.42
Baiting is the only treatment where the initial choice of shops is nonrandom. It is worthy to note that subjects in the experiment invariably
chose the shop with the lower advertised price even in those cases
when the optimal strategy suggests to choose the more expensive
store.29
5.43
Table 5.13 and 5.14 for complex ('3 for 2') prices looks, of course,
considerable different from the others as it is never optimal to buy two
units (when the third is effectively free). Once again, there is
undersearch instead of oversearch with more than 20 per cent of
consumers buying the 3-for-2 offer at the first shop when, in fact, they
should continue their search.
5.44
A particularly interesting case arises at the second store where it can be
optimal to buy only a single item of the good on offer. This is
psychologically demanding as it requires that consumers decide on
purpose to buy the product at a higher per-unit price than the price that
is on offer. Due to the parameter constellations this case does not arise
very often: There are only 41 observations. In 23 cases consumers do
29
Of course, it is not easy to learn this peculiarity in the experiment. Remember that subjects
are not told the precise baiting strategy that shops employ and even if they did know it, it would
require some non-trivial computations to determine the optimal rule for choosing among the two
shops.
OFT1226 | 70
optimally buy one unit and there is no discernible pattern in deviations.
Not buying is almost as frequent as buying the offer.
5.45
Finally, let us investigate the sales frame, in many ways the weakest
treatment as it should be easy for subjects to understand that the former
price that is mentioned is entirely meaningless. While the welfare losses
that subjects incurred under the sales frame were statistically not
significant we did find significantly higher error rates in our econometric
estimations. As Tables 5.15 and 5.16 show these errors mainly arise at
the first shop where only around 60 per cent of all choices are optimal.
Interestingly, even the sales frame destroys the strong pattern of
oversearch that we detected in the baseline. However, this is not
reversed into an undersearch pattern. Rather over- and undersearch are
roughly equally frequent.
Table 5.11: Optimal vs actual choices at the first shop visited under
baiting
Actual choice
Optimal
choice
0
1
2
3
4
Total
0
187
84
36
2
0
309
1
21
40
17
0
0
78
2
18
17
234
1
3
273
Total
226
141
287
3
3
660
OFT1226 | 71
Table 5.12: Optimal vs actual choices at the second shop visited
under baiting
Actual choice
Optimal
choice
0
1
2
3
4
Total
0
28
4
0
0
0
32
1
7
62
2
0
0
71
2
1
9
64
1
2
77
Total
36
75
66
1
2
180
Table 5.13: Optimal vs actual choices at the first shop under
complex pricing ('3 for 2')
Actual choice
Optimal
choice
0
1
2
3
4
Total
0
347
23
1
102
0
473
3
34
1
6
214
2
257
Total
381
24
7
316
2
730
OFT1226 | 72
Table 5.14: Optimal vs actual choices at the second shop under
complex pricing ('3 for 2')
Actual choice
Optimal
choice
0
1
2
3
4
Total
0
52
4
0
6
0
62
1
8
23
0
10
0
41
3
8
13
5
214
2
242
Total
68
40
5
230
2
345
Table 5.15: Optimal vs actual choices at the first shop visited under
a sales frame
Actual choice
Optimal
choice
0
1
2
3
4
Total
0
300
56
10
0
0
366
1
30
43
2
0
1
76
2
53
12
152
0
1
218
Total
383
111
164
0
2
660
OFT1226 | 73
Table 5.16: Optimal vs actual choices at the second shop visited
under the sales frame
Actual choice
Optimal
choice
0
1
2
3
4
Total
0
42
9
0
0
1
52
1
16
106
13
1
1
137
2
1
11
95
2
0
109
Total
59
126
108
3
2
298
Table 5.17: Achieved sales potential
Frame
per cent sales potential
shop 1
per cent sales potential
shop 2
Baseline
88.4
103.4
Complex
122.9
97.5
Sales
87.3
98.3
Baiting
117.9
96.9
Time-ltd offers
101.2
140.4
Drip pricing
111.8
97.2
5.46
Table 5.17 compares all treatments by showing the achieved sales
potentials for the two shops. Notice that this table is not equivalent to
actual total sales as it does not include sales through return visits or
sales to consumers who strayed from the optimal strategy at a previous
OFT1226 | 74
stage. Therefore, the table is more indicative of consumer behaviour
than of total profitability for firms which will analyse separately below.
5.47
The table reveals one more stark effect, the large sales volume at shop 1
under complex pricing. This is simply due to the fact that 21.6 per cent
of those customers that should continue to search actually buy three
units at the first store. Of course, these numbers cannot directly be
compared to those under the other price frames as effective prices are
much lower when '3 for 2' offers are purchased.
5.48
The overall picture from this more detailed analysis is that price frames
are effective because they mainly change behaviour at the first shop. To
some extent they reduce inefficient oversearch. However, in particular
under drip pricing, and as we can see from Table 5.11 also under
baiting, the picture actually reverses and consumers buy too often at the
first shop. Relative to the baseline this implies even worse outcomes for
consumers.
5.49
Given the general tendency of experimental subjects to explore
environments they are faced with rather more fully than they may in the
field, our results presumably underestimate the consumer detriment
caused by these practices simply because an experiment like this will
always bias subjects towards some oversearch. And clearly, with less
search, the baseline would perform better and the different price frames
would perform worse.
Learning and IQ
5.50
Given that the decision environment in this experiment is not completely
trivial and given that subjects take the same kind of decisions
repeatedly, there is, just as in real life, ample scope for learning. In this
sub-section we discuss whether there is any evidence for learning and
whether learning is different across the different price frames.
5.51
In a first step we run the regressions on errors in subjects’ behaviour
(errors are documented above in Tables 5.2, 5.3, and 5.4) again, this
time controlling for a linear time trend (see the technical annexe for
precise details, estimation model probit 2.a. – 2.c.). Crucially, the
OFT1226 | 75
estimates on the coefficients reported above turn out to be robust to the
inclusion of such a time trend.
5.52
The linear time trend variable turns out to be highly significant and
important in terms of its impact. The estimate is that per period the error
rate falls by 0.58 percentage points. This may initially look like a small
number but remember that there are thirty periods altogether. A linear
approximation would, thus, suggest that subjects’ error rate is more than
15 percentage points lower at the end of the experiment than at the
beginning. Allowing for non-linear effects (through a quadratic term)
suggests that learning is levelling out towards the end of the experiment.
Comparing the size of the learning effects with the overall error rates
that we have examined above we can conclude that learning
substantially reduces but does not eliminate erroneous behaviour.
5.53
Decomposing the errors into search and purchasing errors, similar
pictures emerge although the speed of learning is slower when it comes
to search errors. These are estimated to decline by 0.22 percentage
points per period while purchasing errors decline by 0.53 percentage
points.
5.54
In a further set of estimations we also add results from an aptitude (IQ)
test that we conducted at the end of the experiment. The aptitude score
is between zero and 12 and the marginal effect of one extra point is
estimated to be 2.06 percentage points for overall errors, 0.88
percentage points for search errors, and 2.02 percentage points for
purchasing errors in the expected direction. All three coefficients are
highly significant.
5.55
In a further stage we add interaction terms between price frames and
the linear time trend in order to examine whether learning speeds differ
between different time trends. For the overall error rate we find no
differences in learning speed between the baseline and the different price
frames with the exception of time-limited offers. For time-limited offers
learning is significantly slower (and very significantly so). In fact, the
estimate is so big that it completely wipes out the general learning trend.
On balance, we find that there is no learning at all under time-limited
OFT1226 | 76
offers. Consumers appear to believe that the first shop will always
charge a higher price upon return and therefore typically do not return.
Hence, they can never learn that their belief is wrong. To what extent
this holds in real-life markets will crucially depend on the precise
informational structure. For example, for shops located along a high
street, consumers might notice if time-limited offers advertised in a
window display turn out to be repeated over time or are replaced by
even lower prices. In other markets where such information does not
come for free it would be similarly hard to learn as in our experiment.
5.56
The same findings hold for the decomposed search and purchasing
errors: There is no differential speed of learning between the baseline
and the other price frames with the exception of time-limited offers
where there is simply no learning at all. Neither search, nor purchasing
errors are reduced over time in the presence of time-limited offers. In
fact, search errors are even slightly increasing over time for time-limited
offers.
5.57
Clearly, this examination of learning renders our finding that consumers
have trouble with time-limited offers substantially more worrying. Not
only is it difficult for subjects to optimally adjust their search strategy in
the presence of time-limited offers they also are not able to improve their
performance over time.
5.58
As we have evidence on declining error rates that are attributed to
learning we can also examine whether consumer welfare increases over
time. For that purpose we conduct simple non-parametric tests to
compare subject welfare in the first half of the experiment with subject
welfare in the second half of the experiment. (All test statistics are to be
found in the technical appendix.)
5.59
Welfare is found to be significantly increasing in the second half of the
experiment under all price frames (including the baseline) with one
exception: time-limited offers. This is shown in the following table we
report welfare loss in the first and second half of the experiment
sessions by price frame.
OFT1226 | 77
Table 5.18: Welfare loss across first and second half of the
experiment sessions
Frame
first half
second half
Average
Baseline
8.65
2.56
5.66
Complex
9.87
5.07
7.47
Sales
10.64
3.56
7.10
Baiting
8.03
3.63
5.83
Time-ltd offers
8.72
6.46
7.64
Drip pricing
14.98
6.04
10.69
Behavioural forces at work
5.60
We conclude our empirical analysis of consumer behaviour by reflecting
on what the data tell us about behavioural forces at work. We briefly
discuss each of the five pricing practices under investigation.
5.61
Drip pricing: As we have seen drip pricing turns consumers who tend to
search too much into consumers who tend search too little. This effect
is particularly striking as in our experiment the drips are revealed through
just two mouse clicks and consumers can see the total price very clearly
before they make their final purchasing decision. The objective costs of
going through the drips are very close to zero (just a few seconds that
pass for the two clicks). Accordingly, it is completely implausible to
attribute the change in behaviour to increased costs of search and sunk
costs. In principle, consumers might rationally decide to accept higher
prices after being led through complicated drips if they were to expect
equally costly practices elsewhere. Accepting a higher price at the first
outlet would after all avoid the costs of clicking through a labyrinth at a
competing outlet. Notice that this is an entirely rational response and has
nothing to do with the so-called sunk cost fallacy where consumers
OFT1226 | 78
ignore that they cannot recover the costs of (failed) activities and,
consequently, may 'throw good money after bad'. Here the extra search
costs are so tiny that any explanation along the sunk costs line is simply
not justified. Rather the data suggest that consumers who see a low
base price and do not yet know that the effective price will go up
through 'shipping and handling' charges experience an increase in their
willingness to pay for the good which is in line with loss aversion and
the so-called endowment effect.30 Consumers who decide to buy the
product at the low price experience a shift in their reference point as
they already imagine departing with the good. Changing the initial
decision, that is, giving up the good that is already in the virtual basket
would be perceived as a loss. This loss can be avoided by purchasing the
product despite an increased price.
5.62
Time-limited offers: Time-limited offers eliminate oversearch at the first
store but have an even more dramatic effect on the sales at the second
store. Consumers who have rejected the time-limited offer at the first
store simply tend not to return to it even if the price at the second store
is comparatively high. The underlying problem in consumer behaviour is
obvious: As the consumers believe the store (that is, as they erroneously
believe that prices will go up) they have a) a tendency to buy more now
at the first store, and b) if they don’t buy at the first store, they buy at
the second shop if this appears at all profitable. This false preconception
can then never be revised, simply because consumers do not learn that
time-limited offers are not real offers with true discounts but that prices
can go up and down once they expire. Consumers simply do not
understand the real mechanism because of naive beliefs, confusion and
too little exploration. In all, this effect appears to be due to purely
cognitive problems.
5.63
Baiting: As under drip pricing consumers buy too much at the first shop
under baiting while their behaviour at the second shop is largely optimal.
As baiting generates expectations about good deals, it appears once
30
This is similar to the findings of previous researchers such as Morwitz, Greenleaf and Johnson
(1998), and Hossain and Morgan (2006) discussed in chapter 3.
OFT1226 | 79
again that it is loss aversion and the endowment effect that is at work.
Consumers pick a store from which they expect a good deal and this act
in itself raises their willingness to pay for the good in comparison to the
baseline. In contrast, to standard experiments on the endowment effect
it is, once again, anticipated or imagined ownership that causes the
positive shift in consumers’ valuation of the good.
5.64
Complex pricing: This frame is different from the other price frames in
that it actually lowers the prices. Controlling for that, we find that
consumers make slightly higher welfare losses under complex pricing
than under the baseline. Error rates are not significantly higher than in
the baseline but the type of error is very different. Instead of oversearch
consumers undersearch and buy too often at the first shop. Notice that
they do not simply buy too many units of the good (which would follow
from an endowment-effect like shift in valuations) but that they do buy
the offer. This suggests that the offer has an attraction beyond the mere
reduced price or that consumers are cognitively limited.
5.65
Sales frame: Finally, we find that somewhat surprisingly even the
meaningless by-line 'was X' where X is a higher price than the current
price eradicated oversearch. However, the sale frame is not able to
trigger loss aversion or the endowment effect to the point where
consumers start to undersearch. Rather it generates evenly distributed
errors suggesting mainly cognitive problems with processing information
that optimally should be discarded.
5.66
In light of the behavioural forces that we have identified it is worthwhile
to revisit the issue of how search costs impact on consumer choice. As
we have seen earlier, search errors are falling in search costs under drip
pricing, baiting and time-limited offers – precisely those treatments
where we now conclude that search errors are driven by loss aversion.
So why and how do search costs matter in these treatment? The nexus
is simple. Loss aversion increases consumers willingness to pay at the
first shop (once they imagine themselves as owner of the good). Now
we simply need to observe that the larger the search costs, the more
likely it is that the decision to buy at a comparatively high price is
actually optimal! In other words, in environments with large search costs
OFT1226 | 80
it matters less if consumers want to buy straight away because they
would experience not buying as a loss.
5.67
We have also argued that purchasing errors are either due to cognitive
failure or to the sunk cost fallacy and we have seen earlier that they
increase with higher search costs under all treatments with the
exception of time-limited offers and drip pricing (but including the
baseline). The basic intuition for the impact of search costs on the sunk
costs fallacy is obvious. The higher the search costs consumers have
spent, the bigger their urge to justify them through purchasing too many
units. It is less clear why this interacts with the different treatments.
One possibility is that, insofar purchasing errors occur after prolonged
search, that those who are prone to loss aversion are less likely to make
them (simply because they tend to buy straight away at the first shop).
Furthermore, if these biases are correlated (as recent research by Burks
et al. (2008) suggests) then this implies that under those treatments that
trigger loss aversion there will be fewer biased consumers who do long
searches. In other words, the heterogeneous treatment effect on
purchasing errors is quite plausibly simply due to a selection effect. This
would then suggest that the sunk cost fallacy occurs indeed independent
of price framing.
Feelings
5.68
At the end of each session, and before the subjects left the laboratory,
they were asked to complete a qualitative questionnaire about their
experiences with the price frames in the experiment.31
5.69
We now turn to the individual price frames and subjects' reported
experiences.
5.70
In the drip pricing frame subjects reported annoyance and irritation
because the additional charges were not shown with the base price.
31
The questionnaire was optional, and was not incentivised. Further, as previously mentioned
this is controlled laboratory experiment and as such we used university students. Therefore, this
survey is not designed to be representative of the general population.
OFT1226 | 81
Subjects often reported feeling disappointment because they felt they
were receiving a good deal when they saw the base price, and only after
they had decided to buy the good were they then told that they would
incur additional charges. Subjects reported that they still bought the
good even after they were informed of the additional costs, but felt
cheated/annoyed because their profit on the units were reduced.
5.71
In the sales frame, 59 per cent of responses we received reported that
the 'special offer - discounted price' had no influence on their decision
making; subjects continued to make their decision based on the post sale
price. However, 41 per cent of subjects reported that the special offer
did influence their decision making.32 These subjects felt they were
'getting a good deal', 'saving money', 'receiving a discount' and
therefore were more tempted to buy the product.
5.72
In the complex pricing frame, subjects reported that they recognised that
the offer may not always be the best choice for them. Subjects report
that they would buy only one unit if the return to them was better than
buying two and getting the third unit for free. However, 32 per cent of
respondents to this question reported that always chose the offer.
5.73
Baiting, subjects' reported that the frame enticed them to go to the shop
with the lower offer first and to do it quickly (click quickly) so as not to
miss out. Subjects reported anger, frustration and disappointment if the
offer price had changed from the advertised price when they got to the
store. However, they may buy anyway.
5.74
Time limited offers, subjects reported that this frame 'compelled [them]
to buy', or it was 'something not to be missed'. Subjects reported they
found it strange that the price upon return to the shop may be lower
than when they first visited. And, some subjects reported that the
special offer enticed them to buy without searching further.
32
This is in line with our results which find that the essentially meaningless sales frame in this
experiment does encourage subjects to reduce oversearch.
OFT1226 | 82
5.75
If we consider the subjects' experience in the experiment session as
whole we find the following qualitative observations.
5.76
In regard to annoying aspects:
5.77
5.78
•
he additional costs in the drip pricing frame were by far the most
reported annoyance to subjects.
•
subjects reported annoyance if the baiting price was not available
once they travelled to the shop.
•
subjects were annoyed if the price at the second shop visited was
higher than the first visited shop, and
•
annoyance arose because subjects wanted to know the prices before
choosing to travel.
When asked about enjoyable aspects subjects reported the following:
•
making a profit was, not surprisingly, the most enjoyable aspect
reported by subjects
•
subjects enjoyed the choice aspect of the experiment, namely the
decision to continue to search or not
•
similarities with real shopping experiences – 'felt like high street
shopping with the travel costs', 'shipping and handling is like online
shopping', 'makes me realise the calculations I do subconsciously
when shopping'.
Finally we asked subjects to report on how they thought the experiment
mirrored actual shopping experiences. Subjects reported the following:
•
many subjects reported that the price frames mirrored well those they
have experienced when shopping
•
subjects reported that they encountered the same trade-off in real life
as to whether to keep shopping around for a better deal or to simply
accept a price that is reasonable (to them) in the first shop they visit
OFT1226 | 83
•
there were different feelings about the travel costs. Some subjects
reported that the travel costs reflected the real costs incurred to
search or not, but others felt that the travel costs were too high
because it is only 'their time' and 'going for a walk' which they must
invest to go to another shop, while others reported that they always
check prices online which is quick and easy before choosing to buy
anything
•
the experiment did not capture the emotional aspect of shopping, for
example, 'if I love it I will buy it no matter the price or whether I need
it'
•
quality differences between the same type of product was not
included which is sometimes important when comparing between
shops in the real world.
Sellers and total welfare
5.79
In our analysis we have so far very much focussed on the effect of price
frames on consumer behaviour and welfare although we had some
results that pointed towards the effect on firms and, generally, the two
are of course closely linked.
5.80
While shops were completely computerized it is still interesting to ask
how their performance is affected by price frames. Price frames that
perform well for the shops are not necessarily those that are detrimental
for consumers. Some price frames could hurt both buyers and sellers. Of
course, one would expect that in a market environment mainly those
price frames are used that actually improve sellers’ performance.
5.81
Table 5.18 shows average number of units sold by the two shops under
the different price frames as well as average turnover. The table also
contains units sold and turnover for the entire industry. These industry
performance indicators are perhaps the most important ones as, a
specific shop, is in our experiment equally likely to become the first or
the second shop.
OFT1226 | 84
Table 5.18: Average number of units sold by the two shops
Frame
Units
shop 1
Units
shop 2
All units
Sales
shop 1
Sales
shop 2
All sales
(Industry
indicator)
(Industry
indicator)
Baseline
.95
.63
1.58
73
52
125
Complex
1.67
1.06
2.73
133
88
221
Sales
.95
.64
1.59
73
52
125
Baiting
1.23
.38
1.61
94
30
124
Time-ltd
1.02
.57
1.59
79
42
121
Drip
pricing
.99
.60
1.59
77
47
124
5.82
The table shows impressively that, contrary to what we might have
expected, the shops gain nothing from employing the different price
frames. In fact, under time-limited offers, which is one of the two most
detrimental practices for consumers, shops experience even reduced
sales.
5.83
Sales for complex pricing are, of course, substantially higher but whether
this would translate into higher profits depends entirely on unit costs
which remain unmodelled. If unit costs are equal to the average market
price (as suggested by a Bertrand model) then complex pricing would not
be profitable. If there is a higher average mark-up (say if costs were
equal to the lowest price ever charged) then complex pricing would be
profitable for firms.
5.84
While the other price frames have essentially no effect on industry
performance there are dramatic shifts in the distribution of sales,
generally to the advantage of the shop that is first visited (which is in
line with what we have said above about the reversal from oversearch to
OFT1226 | 85
undersearch). While in our experiment, the sequence in which shops are
visited is essentially random (with the exception of baiting), firms can in
many markets influence the order in which consumers search, for
example through advertising or sponsored links in search engines. Our
results suggest that the incentives to engage in such activities are
substantially increased through elaborate price framing. We would thus
predict that, across different markets, the occurrence of price framing is
positively correlated with the amounts firms spend on enticing
consumers to search their offers first. This is a prediction that could be
easily tested using data from auctions for sponsored search.
5.85
Of course, such activities to change consumers’ search order would
reduce total welfare even further. In our setup, price framing reduces
total welfare already in any case as it harms consumers and does not
benefit firms. However, contests for being searched first would render
the picture even bleaker.
Summary
5.86
In Table 5.19 we attempt a summary of our main findings, indicating for
each price frame how it impinges on welfare and errors, how it affects
stores; which kind of behavioural bias (if any) can be identified to be
driving the data; and whether learning helps consumers to overcome or
reduce the effects of the bias.
OFT1226 | 86
Table 5.19: Summary of main findings
Significant
welfare
losses
Significantly
more errors
first shop
benefits
Behavioural
biases
Learning
helps
Drip
pricing
Large
Yes,
substantially
Yes
Endowment
Yes
effect/loss
aversion/maybe
sunk cost
fallacy
Time-ltd
offers
Medium
Yes
Yes
Cognitive
errors/maybe
sunk cost
fallacy
No
Baiting
Medium
Yes
Yes,
strongly
Endowment
effect/loss
aversion/sunk
cost fallacy
Yes
Complex
pricing
Small
No
Yes
Cognitive
errors/sunk
cost fallacy
Yes
Sales
frame
No
Yes
No
Cognitive
errors/sunk
cost fallacy
Yes
OFT1226 | 87
6
EXTERNAL VALIDITY AND IMPLICATIONS FOR NONLABORATORY MARKETS
6.1
External validity for studies of this nature is typically highly asymmetric.
Both simplification and stylization of the decision problems and the
highly selected subject pool imply that we are more likely to observe
good or even optimal performance in the laboratory than in the field
under real-life conditions. This implies that whenever we find close to
perfect performance external validity is severely limited. If student
subjects do well in a simple task it is hard to conclude from that the
general population would do well in a more complicated task. However,
the other way round things look much brighter. If a highly selected
student sample does badly in a simple decision environment that also
offers scope for repetition and learning, it would be very surprising if the
general population did much better in more complicated situations.
6.2
Given this asymmetry, the design choices we took were risky. We
designed a very simple search environment that was, given our
requirement (multiple units, two stages) pretty much minimal. It would
have been difficult to come up with an even simpler environment.
Similarly, the implementation of the price frames was typically simple
and subjects could experience them repeatedly in almost identical
manner. The risk of these design choices was that we might have found
close to optimal performance in all treatments in which case we would
have learned very little from this study.
6.3
The alternative strategy to design a more complicated environment
would have entailed different risks. In more complicated environments
decision errors and noise will invariably go up and accordingly it will be
more difficult to detect differences between treatments for given sample
sizes.
6.4
However, as we have documented we have observed substantial
difference between treatments with surprisingly poor performance in
some. The results on drip pricing stand out. Being 'just two clicks away'
from the baseline its effects on search patterns and performance are
dramatic. Outside the laboratory where drip mechanics are more
OFT1226 | 88
elaborate and it is more time consuming to reach a stage where full
prices are clearly visible it is likely that the effects that stem from loss
aversion or the endowment effect will be even stronger. On top of that,
more elaborate drips will also increase the true costs of searching for the
price which will enhance the effects from loss aversion. Similarly, we
have not tried to optimize the drip sizes and it would be very bewildering
if we had, by accident, stumbled across the most effective drips. Once
again, this suggests that, if anything, we might still underestimate the
true consumer detriment resulting from drip pricing.
6.5
Given our basic design choices and selection of subjects the observation
holds, of course, more generally: there is a built-in tendency to
underestimate consumer detriment for all price frames. This also implies
that the sales frames which receives almost a clean bill of health in this
study could potentially be more harmful than we detect. As soon as we
are in environment where former prices contain some hard information
there is much more scope for consumers to process this information in a
less than adequate way. On the other hand, references to former (higher)
prices could also serve as a useful signal of quality.
6.6
In this context it is important to notice that, with the exception of
complex (3 for 2) pricing, we isolate the pure effect of price frames and
not of offers. Of course, real offers with lower prices might benefit
consumers even if their presentation confuses them or triggers some
behavioural biases. The net effect of lower prices and the adverse
consequences we measure here might still be positive. What this study
shows, however, is that also real offers could benefit consumers more if
presented in a straight way as in our baseline treatment.
6.7
In our experiment, both price frames and prices themselves are
exogenously fixed while in real markets they are, of course, chosen.
Given our consumer data it appears clear that certain price frames will
allow firms to charge higher prices (in particular those that trigger loss
aversion and the endowment effect as these effects are akin to
increased willingness to pay or an outward shift of the demand curve).
Consumers would then suffer doubly, from their direct negative
consequences we measure in this experiment and from the higher prices.
OFT1226 | 89
6.8
One aspect of endogenous choice of price frames that we have not
studied at all is that sellers in the same market might choose different
price frames which makes price comparisons much harder. As Chioveanu
and Zhou (2009) show these effects can even overturn standard
intuition on how the number of firms in a market relates to consumer
welfare. With added confusion from a greater variety of price frames,
consumers might actually suffer from the entry of additional sellers.
6.9
On the other hand firms may elect to not use price frames that annoy
customers. Firms may seek to establish a reputation for not using
annoying practices, such a drip pricing.
6.10
While we can neither validate nor reject these theoretical predictions we
can say a little about how our findings on relevant behavioural biases
would impact on markets. Clearly, the strongest force that causes
consumer detriment in our experiment is the endowment effect or loss
aversion. Consumers’ imagination of owning a good shifts their
willingness to pay. We observe strong evidence on this in both drip
pricing and baiting. In the field there will be many other practices of hot
selling that play on these effects. If the consumer tries out a product in a
shop it will give him some objective information about how the product
handles but it also makes envisaging ownership easier and what we
have seen here is that envisaging ownership is all that is needed to
increase willingness to pay.
6.11
There are, of course, many institutional and physical details that will
matter for the effect of these practices in non-laboratory markets. For
example, it might be easier to encourage the imagination of ownership
for some goods than for others. By thinking about the product
characteristics that make imagination of ownership easier, we could then
derive comparative static predictions about in which markets we would
expect to observe certain price frames more frequently.
6.12
Similarly, there might be particular characteristics of sellers that tinge
the decision problems in real-life markets. For example, for closing-down
sales (where, say, the consumer can see that a building is about to be
OFT1226 | 90
torn down) the time-limitedness of offers might be more credible than for
other 'mid season' sales.
6.13
For one important aspect of real-life markets we do have some indication
in our data, the role of search costs. Our analysis suggests that the
detrimental effects of loss aversion increase with lower search costs.
This is intuitive: Buying too early too often, tends to coincide with
optimal behaviour when search costs are high. On the other hand, we
have found that the sunk cost fallacy (that drives some of the
purchasing errors) increases with search costs. Again this seems
plausible for real-world applications. The more time and money
consumers have spent on search, the more desperate they might be to
justify these high search costs through making (too many) purchases. In
our experiment, this effect is much smaller than the effect of search
errors which can be traced to loss aversion. However, this may well be a
consequence of our parameter choice. For example, in markets with very
high search costs (for example, because of location in just one city in a
larger geographical area, say, some sort of fair) it is plausible that
consumers might be very frustrated to leave empty-handed and would
thus be tempted to buy more than they would have bought had the
market taken place at their doorstep.
6.14
Summarising, let us stress however again that we have good reason to
believe in the general external validity of our results – that these
practices do cause consumer detriment and that what we identify in the
lab is probably rather the tip of the ice berg as there are many aspects of
real-life markets that will accentuate the problems we document here.33
33
Although inevitably there are also likely to be some factors in real life which will mitigate
concerns about practices (even when frames are false offers) such as the desire for firms to
build reputation, and consumers to learn about honest firms, as discussed in the next section.
OFT1226 | 91
7
POLICY RECOMMENDATIONS
7.1
From a policy point of view, this experimental study has two broad
implications. We identify price frames that clearly cause consumer
detriment. As they do so in a comparatively simple environment and
with a comparatively sophisticated subject pool, it appears clear that
these practices are also harmful outside the laboratory.
7.2
Our findings suggest that firms will be tempted to confuse consumers
through drips, baits or time-limited offers. Clearly, the occurrence of any
of these three practices which do not represent genuine offers might
provide reason to worry.
7.3
While drip pricing has previously been known to be problematic, timelimited offers have never before been identified as a source of consumer
confusion and detriment. As we have shown, consumer errors under
time-limited offers are particularly severe in that they are not reduced
through learning (at least in this setting). Thus, even in markets for
goods that are purchased frequently, one would expect that time-limited
offers are used and indeed problematic.
7.4
Consequently, one may be particularly worried about drip pricing in
markets for not particularly frequently purchased goods or time-limited
offers in markets for goods that are purchased with high frequency.34
7.5
We also have clear indication that in this experimental setting these
effects are particularly pronounced in environments with low search
costs for consumers. However, this result would require further analysis
before clear policy conclusions could be drawn from it. Not least
because, with higher search costs consumers become prone to the sunk
cost fallacy and this effect could become much stronger if search costs
are substantially higher.
34
Clearly, as a seller one would rather employ frames that are robust to learning for frequently
purchased items.
OFT1226 | 92
7.6
If the main effect of price frames is that they shift demand from one firm
to another, enforcement (which helps to promote a level playing field)
may be welcomed not only by consumers but also by firms. But this
needs more research into environments with endogenous prices. This
could be done in the same experimental framework.
7.7
If consumers are annoyed by some price frames, there is some scope for
the self-healing powers of the market. Firms may gain a reputation for
not using such practices. This is more likely to work for practices that
are indeed perceived as an annoyance such as drip pricing. In contrast,
time-limited offers might, in fact, be perceived as something positive by
consumers who will not even be aware of their struggle with
understanding the true nature of such offers. Accordingly, one would
have less hope that the market can overcome the occurrence of such
positively perceived practices. These issues could be investigated in a
similar experimental study where firms are no longer simple static
computers.
OFT1226 | 93
A
SCREEN SHOTS FROM THE EXPERIMENT
Figure A.1: Home Screen in baseline
OFT1226 | 94
Figure A.2: Shop 1 screen in baseline
OFT1226 | 95
Figure A.3: Purchase confirmation dialogue in baseline
OFT1226 | 96
Figure A. 4: Purchase notification
OFT1226 | 97
Figure A.5: Confirm exit from period
OFT1226 | 98
Figure A.6: Results screen
OFT1226 | 99
Figure A.7: Shop 1 screen in Complex pricing
OFT1226 | 100
Figure A.8: Drip 1 screen
OFT1226 | 101
Figure A.9: Drip 2 screen
OFT1226 | 102
Figure A.10: Confirmation in drip pricing
OFT1226 | 103
Figure A.11: Home screen in baiting
OFT1226 | 104
Figure A.12: Shop 2 screen in baiting (price different from
advertised)
OFT1226 | 105
Figure A.13: Home screen in baiting after returning home
OFT1226 | 106
Figure A.14: Shop 1 screen in sales price frame
OFT1226 | 107
Figure A.15: Shop 1 screen in Time limited offer
OFT1226 | 108
B
WEB TRAWL OF PRICING PRACTICES
B.1
Here we provide details of the web trawl. The purpose of the trawl was
to collect information on the practices used by different firms in the UK.
The web trawl was completed between the 28 September and the 9
October 2009.
B.2
This trawl was completed as background information to guide the design
of the experiment treatments. Industries chosen for the web trawl are
illustrative only; the experiment is concerned with the impact of pricing
frames in general and not just for these industries listed.
B.3
B.4
The web trawl includes the following industries:
•
airfares
•
package holidays
•
accommodation and hotels
•
theatre tickets
•
electronic goods (for example, audiovisual and CD players)
•
computers and computer equipment, and
•
furniture.
A summary of the pricing frames used by different firms is presented in
the tables below.
OFT1226 | 109
Table B.1: Drip pricing
Industry and
Firm
Number of
compulsory drips
Airfares
Carrier 1
ƒ
ƒ
ƒ
Headline price does
not include
taxes/fees
Online check-in
(£5.00 pp)
Handling charge for
payment (£5.00 pp,
per flight), not
shown until last
page.
3 compulsory drips
Number of optional
drips
ƒ
ƒ
ƒ
ƒ
ƒ
Carrier 2
ƒ
ƒ
ƒ
Headline price
includes government
taxes and airport
fees.
Handling charge for
booking £2.95
Credit card fee 2.5
per cent of total cost
or £4.00 whichever
is lowest.
2 compulsory drips
Carrier 3
ƒ
ƒ
ƒ
ƒ
Price includes taxes
fees and charges
(which can be seen
separately at bottom
of screen)
Price includes
handling fee (but not
applicable from UK).
Price includes 1 hold
bag up to 23kg.
Credit card payment
method £4.50
Priority boarding (£3
pp)
Bags in hold (£10
first bag, £20
second and third
each , max 3)
Add bulk sports
item, sports
equipment, musical
instrument, ski
(£30)
Add baby equipment
(£10)
Request SMS
confirmation (£1.00)
Up to 5 optional drips
ƒ
Priority boarding
(£8.00 pp)
ƒ
Bags in hold (£8.00
per bag up to max 8
with total weight
20kg)
ƒ
Extra kilos in hold
(£21 per 3 kilo lots
up to 30kg max)
ƒ
Add sports
equipment (£18.50
per unit of
equipment)
ƒ
Carbon offset
programme
depending on
distance
Up to 5 optional drips
ƒ
Carbon offset
depending on
distance.
1 optional drip
Shrouded drips
Other
details/feat
ures
Handling charge
not seen until last
page.
List of all fees
and charges
available
using link on
booking page.
No (clear)
mention of
additional
cost of
food/drink on
board.
Found no
shrouding
1 bag per
person is the
default.
Travel
insurance
included is
the default.
No mention
(clear) of
additional
cost of
food/drink on
board.
Found no
shrouding
Uses selling
feature that
all charges
are included
in price,
including food
on board.
List of any
fees and
charges
available on
booking page
OFT1226 | 110
Industry and
Firm
Carrier 3
Carrier 4
Number of
compulsory drips
1 compulsory drip (credit
card)
ƒ
Headline price
includes taxes fees
and charges which
can be seen
separately.
ƒ
Drip for credit charge
fee.
1 compulsory drip
ƒ
Headline price does
not include taxes
and charges.
ƒ
Check-in fees: online
no bags £0, online
1+bags £30, airport
0 bags £3.00,
airport 1+ bags
£6.00
ƒ
Handling fees for
debit card (3.5 per
cent), credit card
fees (3.5 per cent +
2.25 per cent),
PayPal (£3.49 + 1.5
per cent)
Number of optional
drips
Shrouded drips
No optional drips but
travel insurance is
available if wanted.
Found no
shrouding
Other
details/feat
ures
1 optional drip
ƒ
ƒ
ƒ
ƒ
ƒ
Bags in hold (£9.99
per bag max of 3
max weight 22kg)
Seat selection:
standard £6.99,
extra leg room
£15.99.
Add sports
equipment £20 up
to 20kg
Travel insurance
£6.50 pp.
Meals onboard
(£8.00)
Found no
shrouding
List of fees
and charges
available on
booking page.
No shrouding
found
List of fees
and charge
on booking
page
5 optional drips
3 compulsory drips
(unless check in online
with 0 bags)
Carrier 5
ƒ
Headline price does
not include taxes
and fees.
ƒ
ƒ
Payment by credit
card £4.00 pp
No charge for debit
card
1 compulsory drip
1 optional drip
Package
holidays
Company 1
ƒ
Credit card
charge
1 compulsory drip
Company 2
ƒ
ƒ
Fuel supplement
(20£)
TOD charges
Offset
emissions
(4.41£)
ƒ
Book a car (7684.86£)
ƒ
Airport parking
(30.95£)
ƒ
Travel insurance
(21.68-43.36£)
ƒ
Credit card fee
ƒ
5 optional drips
ƒ
Upgrading
possibility of
accommodation
No shrouding
found
No shrouding
found
First price
includes
optional
OFT1226 | 111
Industry and
Firm
Number of
compulsory drips
Number of optional
drips
(30£)
ƒ
2 compulsory drips
ƒ
ƒ
Shrouded drips
(84-238£)
World Care
Fund donation
(2£ per person)
Insurance
(19.98-37.98£)
Credit card
charge if don’t
pay by debit
card
donation
Coach
transfer is
free and
optional
Online saving
possibility
(386£)
4 optional drips
Company 3
ƒ
No compulsory
drips
ƒ
ƒ
ƒ
Company 4
ƒ
System updates
(price differs
from the one
shown at first )
1 compulsory drip
Company 5
ƒ
ƒ
ƒ
Adult
supplement
(105£)
Fuel supplement
(10£)
Late booking fee
(15£)
3 compulsory drips
Company 6
ƒ
ƒ
Airport taxes
Fuel
supplements
Junior suite
(98£)
No flight meals
included
Insurance and
excess waiver
Credit card
charge if don’t
pay by debit
card
3 optional drips
ƒ
Accommodation
options – half
board (98£)
ƒ
Resort transfer
(20£ per
person)
ƒ
Insurance
(19.99£ per
person)
3 optional drips
ƒ
Price of the
holiday depends
on the flight
time one selects
(varies up to
30£)
ƒ
In flight meal
(15£)
ƒ
Extra luggage
(5£)
ƒ
Resort transfer
(20£)
ƒ
Seats together
Mid-hl (12£)
5 optional drips
ƒ
Transfer (227361£)
ƒ
Car hire (140-
Other
details/feat
ures
Family holiday
choice shows
price only per
person at first
No flight
meals
Holiday+fligh
t party price
includes
taxes, online
discount,
donation to
World Care
Fund and
return airport
transfer
Credit card charge
not shown
Price changes
due to the
system
updates
(increases
and
decreases)
Credit card charge
not shown
Allowed 15kg
luggage
Online
booking
discount 10£
Credit card charge
not shown
A notice that
it is a fair
price that
OFT1226 | 112
Industry and
Firm
Number of
compulsory drips
ƒ
ƒ
1 piece of hold
luggage
Full financial
security of
ABTA and ATOL
membership (no
prices shown for
the services)
Number of optional
drips
ƒ
Shrouded drips
285£)
Car parking (5888£)
includes all
UK airport
taxes, fuel
supplements,
1 piece of
hold luggage
per
passenger,
and full
financial
security of
both ABTA
and ATOL
Membership
3 optional drips
4 compulsory drips
Theatre tickets
Provider 1
ƒ
Booking fee
(4.95£)
No optional drips
No shrouding
found
No optional drips
No shrouding
found
No optional drips
No shrouding
found
It is actually
said in the
first place
what are the
extra costs
No optional drips
No shrouding
found
No optional drips
No shrouding
found
The
commission
and delivery
fees is listed
alongside the
ticket price at
the outset
All the extra
costs are
listed
alongside at
the outset
No optional drips
No shrouding
1 compulsory drip
Provider 2
ƒ
ƒ
ƒ
Provider 3
Provider 4
Provider 5
Provider 6
Service charge
(5.30£)
Order processing
fee (jncluded)
Delivery charge
for customers
who live outside
of Republic of
Ireland, Northern
Ireland and
Great Britain
(3€)
3 compulsory drips
ƒ
Booking fee
(3.50£)
ƒ
Transaction fee
(2.25£)
2 compulsory drips
ƒ
Commission fee
(30.70£)
ƒ
Delivery charge
depending the
country
2 compulsory drips
ƒ
Delivery charge
(7.50-40£)
ƒ
Booking fee
(13.35£)
ƒ
IVA Tax (2.14£)
3 compulsory drips
ƒ
Delivery (1£)
Other
details/feat
ures
On the first
page it says
'from 10£',
actual price
49.50£
Notes that all
prices
exclude fees
and charges
The delivery
OFT1226 | 113
Industry and
Firm
Number of
compulsory drips
Number of optional
drips
Shrouded drips
Other
details/feat
ures
found
cost is listed
alongside at
the outlet
The booking
cost is listed
alongside at
the outlet.
1 compulsory drip
Provider 7
ƒ
ƒ
Booking fee
(1.95£)
Delivery cost
(1.79-1.95£)
No optional drips
No shrouding
found
1 compulsory drip
Furniture
Company 1
ƒ
Delivery fee
(35£)
Delivery cost
is not shown
not until last
page
No optional drips
No shrouding
found
1 compulsory
drip
Company 2
ƒ
Company 3
1 compulsory drip
ƒ
Delivery fee
(5.80£)
Company 4
Company 5
Delivery (5.808.70£)
1 compulsory drip
ƒ
Delivery costs
(5-20£ or free if
purchase over
400£)
1 compulsory drip
ƒ
Delivery fee
(25£)
ƒ
2 year insurance
(40-99.99£)
1 optional drip
No optional drips
No shrouding
found
Delivery cost
is being
mentioned on
the left
corner of the
page, not
easily
noticeable
Chance to
collect
purchased
items at store
No shrouding
found
No optional drips
No shrouding
found
No optional drips
No shrouding
found
1 compulsory drip
Company 6
ƒ
Pre-paid returns
(4.95£)
No shrouding
found
Free delivery
statement on
first page of
purchase
•
Accessories like
speakers,
number pads
etc. (4.9999.95£)
No shrouding
found
Free delivery
shown at the
check out
No shrouding
Subtotal
No compulsory drips
Computers
Company 1
Company 2
No compulsory drips
Up to 8 optional
drips
•
'Other customer
OFT1226 | 114
Industry and
Firm
Number of
compulsory drips
Number of optional
drips
No compulsory drips
Company 3
•
Norton antivirus
was added to
the basket even
when to remove
the tick (29£)
1 compulsory tick
Company 4
No compulsory drips
Company 5
•
Company 6
1 compulsory drip
•
Delivery charge
(5.65£)
Delivery charge
(3.40£)
also bought'
variation of
products offered
(16-27£)
Up to 7 optional
drips
•
Offers to
supplement the
product bought
(14-69£)
•
'essential'
accessories
(4.99/6.86£)
Up to 7 optional
drips (possibility to
view even more)
No optional drips
No optional drips
Shrouded drips
Other
details/feat
ures
found
including VAT
Free delivery
No shrouding
found
No shrouding
found
No shrouding
found
No delivery
charge
VAT included
No optional drips
No shrouding
found
Price with
and without
Vat is shown
No optional drips
No shrouding
found
Free delivery
with Super
Saver
Shipping
1 compulsory drip
Audivisual &
DVD
Company 1
Company 2
•
Delivery charge
(international fee
6.48$)
1 compulsory drip
•
Delivery charge
(6.99-15.99£)
Company 3
1 compulsory drip
No compulsory drips
Company 4
No compulsory drips
•
Accessories
N number of
optional drips
•
Accessories,
products and
services
•
Standard (free)
or premium
delivery
N number of drips
•
Extra warranty
(40£)
•
Cable offer
(29£)
•
Delivery choice
(next day,
Saturday) up to
No shrouding
found
No shrouding
found
Standard
delivery free
No shrouding
found
5 days
standard
delivery free
OFT1226 | 115
Industry and
Firm
Number of
compulsory drips
Number of optional
drips
Shrouded drips
Other
details/feat
ures
No shrouding
found
Price
including and
excluding
VAT shown
on the first
page
Free delivery
after 3 days
9.95£
•
Company 5
Delivery charge
(10£)
3 optional drips
No optional drips
1 compulsory drip
Company 6
No compulsory drips
No optional drips
No shrouding
found
Table B.2: Sales
Industry and
Firm
Existence of
reference pricing
Example of
reference pricing
Shrouded
references
Other
details/feat
ures
Airfares
Carrier 1
None
none
None
Carrier 2
none
none
Carrier 3
none
none
Carrier 3
none
none
Carrier 4
yes
Carrier 5
none
15 per cent off across
Spain; Winter Sun Offer
10 per cent
none
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
Package
holidays
Company 1
Yes
Company 2
Yes
Company 3
No shrouding
found
Hotel sale extended up
to 50 per cent off
Save 10 per cent
guaranteed on all
holidays departing in
November – ends soon;
massive winter
clearance 10 per cent
guaranteed savings in
November and
December
No shrouding
found
No shrouding
found
None
None
Company 4
Yes
Company 5
Yes
Summer 2010:
guaranteed 10 per cent
off
Prices from only ...£
No shrouding
found
No shrouding
found
To find
special offers
and deals,
one has to
click on the
section
before
No shrouding
OFT1226 | 116
Industry and
Firm
Existence of
reference pricing
Example of
reference pricing
Shrouded
references
Company 6
Yes
(Mexico, Madeira etc)
Bargains (Cyprus,
Turkey etc)
found
No shrouding
found
Theatre tickets
Provider 1
Yes
Up to 50 per cent off
theatre;
No shrouding
found
Provider 2
none
none
Provider 3
none
none
Provider 4
none
none
Provider 5
none
none
Provider 6
Yes
Provider 7
yes
Half price and discount
theatre tickets
'Concert tickets on sale'
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
Furniture
Company 1
none
none
Company 2
yes
Company 3
yes
Save at least £400
today (shows the
'original' price and the
amount one saves)
Save £40 on a
dishwasher ('original'
price and final price
shown)
Company 4
yes
Company 5
yes
Company 6
none
Company 7
yes
Autumn sale now on,
prices reduced now up
to 50 per cent;
clearance sale no up to
70 per cent
yes
1TB hard drive only
£23.99; Acer 5536
Laptop save £150;
massive stock clearance
£140, £30 etc
25 per cent off selected
Bosch power tools; up
to ½ price selected
furniture;
Save 1/3 off on all
modular and
freestanding furniture;
save 15 per cent all
sofas; save 40 per cent
piano wall hung fires
none
Other
details/feat
ures
No shrouding
found
Not sure if the
'original' price is
accurate
Not sure if the
'original' price is
accurate
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
Computers
Company 1
No shrouding
found
OFT1226 | 117
Industry and
Firm
Existence of
reference pricing
Example of
reference pricing
Shrouded
references
Company 2
none
none
Company 3
yes
Company 4
yes
Company 5
none
Various products '£x,
sale save over £x, was
£x', save up to £200 on
large kitchen appliances
SAVE £45 was £244.95
inc vat £199.00 inc vat
none
No shrouding
found
Not sure if the
'original' price is
accurate
Company 6
none
none
Audivisual &
DVD
Company 1
yes
DVDs up to 80 per cent
off;
Company 2
yes
Company 3
none
Denon DM37DAB
save $50 £199;
none
Company 4
none
none
Company 5
none
none
Company 6
none
none
Other
details/feat
ures
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
Info not on
the front
page, one has
to click on
the selection
first
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
OFT1226 | 118
Table B.3: BAITING SALES
Industry and
Firm
Existence of baiting
sales
Example of baiting
sales
Shrouded
baiting
Airfares
Carrier 1
yes
No shrouding
found
Carrier 2
Yes
Homepage: Beds from
£8; shuttle direct from
£7
Munich from 29.99£; ski
flights to Geneva from
23.99£ etc
Carrier 3:
yes
Carrier 3
Yes
Carrier 4:
Yes
Carrier 5:
Yes
Package
holidays
Company 1
Yes
Company 2
Yes
Company 3:
yes
Company 4
Yes
Company 5
none
Winter escapes from
only £99 pp; summer
2010 holidays from
£140
Last minute holidays:
from £149 pp; summer
2010 cruises from only
£419 pp
none
Company 6
none
none
Other
details/feat
ures
No shrouding
found
Edinburgh escape from
185£ per person; Hong
Kong city breaks from
579£ per person etc
Hotels all over the world
(LA, Jamaica etc) from
43£ (60£, 100£ etc);
Caribbean holidays from
515£
Winter sun getaways
from £29.99 one way
including taxes
Cairo and Tel Aviv from
$558
No shrouding
found
Hotels (London, Paris,
Barcelona etc) from £35,
Costa del Sol from £214
etc
All inclusive holidays
from only £267
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
To find
special offers
and deals,
one has to
click on the
section
before
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
Theatre tickets
OFT1226 | 119
Industry and
Firm
Existence of baiting
sales
Example of baiting
sales
Shrouded
baiting
Provider 1
Yes
Priscilla queen of the
desert from £20
No shrouding
found
Provider 2
none
none
Provider 3
none
none
Provider 4
none
none
Provider 5
none
none
Provider 6
yes
Provider 7
none
Blood Brothers discount
tickets from £20
none
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
Furniture
Company 1
none
none
Company 2
Company 3
none
yes
none
1000s DVDs & CDs
from £2.95; gift
experiences from only
£5
Company 4
yes
Company 5
yes
Company 6
none
Up to ½ price on
selected furniture
Save 1/3 off on all
modular and
freestanding furniture
none
Company 7
yes
Autumn sale now on,
prices reduced now up
to 50 per cent;
clearance sale no up to
70 per cent
Company 1
yes
Company 2
yes
No shrouding
found
No shrouding
found
Company 3
yes
Great range of laptops
from £249
Inspiron 546 feat. 6GB
RAM and large hard
drive from £299;
inspiron 15 with 500GB
hard drive from £349
Save up to half price on
sat navs; save up to 1/3
on Panasonic phones
Company 4
none
none
Company 5
none
none
Company 6
yes
Samsung Netbook range
from £249.99, laptops
No shrouding
found
No shrouding
found
No shrouding
found
Other
details/feat
ures
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
Computers
No shrouding
found
To see the
price one has
to click to go
further
OFT1226 | 120
Industry and
Firm
Existence of baiting
sales
Example of baiting
sales
Shrouded
baiting
Other
details/feat
ures
Audivisual &
DVD
yes
from under £300
5MP3 albums at £5
songs from 29p; autumn
offers: CDs from £2.98
No shrouding
found
Info not on
the front
page, one has
to click on
the selection
first
Company 1
none
none
Company 2
yes
Company 3
yes
Your dream installation
fully installed for only
£1999.95 incredible!
Free delivery on all
orders LG KC550 Pay as
You Go Mobile Phone
£49.99 free delivery
Company 4
none
none
Company 5
none
none
Company 6
none
none
No shrouding
found
No shrouding
found
No shrouding
found
Not sure how
much does
one save,
since there is
no 'before'
price
No shrouding
found
No shrouding
found
No shrouding
found
OFT1226 | 121
Table B.4: TIME LIMITED PRICE OFFERS
Industry and
Firm
Existence of time
limited offers
Example of time
limited offers
Shrouded
offers
Airfares
Carrier 1
Yes
Carrier 2
Yes
Book until midnight
8.10.09 from $1
Book a hotel by 9
October 9 (the same
offer as '5 for the price
of 1')
No shrouding
found
No shrouding
found
Carrier 3
None
none
Carrier 4
Yes
Carrier 5
None
Asia and Africa: book by
9th October; last minute
deals all over the world
None
No shrouding
found
No shrouding
found
Carrier 6
Yes
Earn £30 by joining
Company Rewards
before 31 December
Package
holidays
Company 1
Yes
Company 2
Yes
20 meal and show deals
for £20 for a limited
time period
Save 10 per cent
guaranteed on all
holidays departing in
November – ends soon;
massive winter
clearance 10 per cent
guaranteed savings in
November and
December
Company 3
none
none
Company 4
Yes
Company 5
none
Hurry – escape before
the deals do!
none
Company 6
none
none
Theatre tickets
Provider 1
Yes
Provider 2
none
Chicago theatre tickets
– 48 hr sale up to 50
per cent off
none
Provider 3
none
none
Provider 4
none
none
Provider 5
none
none
Other
details/feat
ures
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
To find
special offers
and deals,
one has to
click on the
section
before
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
OFT1226 | 122
Industry and
Firm
Existence of time
limited offers
Example of time
limited offers
Provider 6
none
none
Provider 7
none
none
Furniture
Company 1
none
none
Company 2
Company 3
none
none
none
none
Company 4
yes
Company 5
none
Guaranteed kitchen
installations before
Christmas when you
order before 11 October
none
Company 6
none
none
Company 7
none
none
Company 1
none
none
Company 2
yes
Company 3
none
Save up to £327 offers
end 14.10.09; 14 days
of deals, offer ends
14.10.09
none
Company 4
none
none
Company 5
none
none
Company 6
none
none
Audivisual &
DVD
Company 1
none
none
Company 2
none
none
Company 3
none
none
Company 4
none
none
Company 5
none
none
Company 6
none
none
Shrouded
offers
Other
details/feat
ures
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
Computers
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
OFT1226 | 123
Table B.5: COMPLEX PRICING
Industry and
Firm
Existence of
complex pricing
Example of complex
pricings
Shrouded
complex
pricing
Other
details/feat
ures
Airfares
Carrier 1
none
none
None
Carrier 2
Yes
Hotel offer: 5 for the
price of 1
No shrouding
found
No shrouding
found
Carrier 3
none
none
Carrier 4
none
none
Carrier 5
Yes
Carrier 6
none
1000’s 3 for 2 hotel
rooms
none
Package
holidays
Company 1
none
none
Company 2
none
none
Company 3
none
none
Company 4
none
none
Company 5
none
none
Company 6
none
none
Theatre tickets
Provider 1
none
none
No shrouding
found
Provider 2
none
none
Provider 3
none
none
Provider 4
none
none
Provider 5
none
none
Provider 6
none
none
Provider 7
none
none
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
Furniture
Company 1
none
none
Company 2
Company 3
none
none
none
none
Company 4
yes
½ price kitchen units
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No shrouding
found
No t sure how
OFT1226 | 124
Industry and
Firm
Existence of
complex pricing
Example of complex
pricings
Shrouded
complex
pricing
when you buy 3 or more
much is the units
price
No shrouding
found
No shrouding
found
No shrouding
found
Company 5
yes
Company 6
none
Buy one, get one free
(different products)
none
Company 7
none
none
Company 1
none
none
Company 2
none
none
Company 3
none
none
Company 4
none
none
Company 6
none
none
Audivisual &
DVD
Company 1
yes
Buy one get one free on
Disney DVD & Blue Ray
Company 2
yes
Company 3
none
Buy all 3 together
for only £379.95
none
Company 4
none
none
Company 5
none
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OFT1226 | 125
C
THEORY: OPTIMAL SEARCH STRATEGY
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D
REGRESSION DETAILS
Variables used in regressions:
Dependent variables:
label var err_v 'Indicator variable for error in the number of visits made'. These
are search errors.
label var err_n 'Indicator variable for error in the number units bought'. These are
purchasing errors.
label var err 'Indicator variable for any type of error '. These are overall errors.
Independent variables:
subject: Subject number
price_strategy: Pricing strategy (1=baseline, 2=complex, 3=drip, 4=bait,
5=reference, 6=time limited)
p1: Price at first shop visited
p2: Price at second shop visited
c: Search cost
highvaluegood: Dummy for whether the good was high value
aptitude: Aptitude (0 = worst, 12 = best)
d2: Dummy for complex pricing
d3: Dummy for drip pricing
d4: Dummy for baiting
d5: Dummy for reference pricing (sales)
d6: Dummy for time limited offers
pd2: Interaction between period and d2
pd3: Interaction between period and d3
pd4: Interaction between period and d4
pd5: Interaction between period and d5
pd6: Interaction between period and d6
cd2: Interaction between c and d2
cd3: Interaction between c and d3
OFT1226 | 132
cd4: Interaction between c and d4
cd5: Interaction between c and d5
cd6: Interaction between c and d6
Regressions included:
For each specification, there are three probit regressions using the three different
dependent variables. The specifications are as follows:
1. 'base' specification using independent variables: p1 p2 highvaluegood c d2
d3 d4 d5 d6
2. 'base' plus period using independent variables: p1 p2 highvaluegood c d2 d3
d4 d5 d6 period
3. 'base' plus aptitude using independent variables: p1 p2 highvaluegood c d2
d3 d4 d5 d6 aptitude
4. 'base' plus aptitude and period using independent variables: p1 p2
highvaluegood c d2 d3 d4 d5 d6 aptitude period
5. 'base' plus aptitude, period and interaction between period and treatment
dummy using independent variables: p1 p2 highvaluegood c d2 d3 d4 d5 d6
period aptitude pd2 pd3 pd4 pd5 pd6
6. 'base' plus aptitude, period and interaction between cost and treatment
dummy using independent variables: p1 p2 highvaluegood c d2 d3 d4 d5 d6
period aptitude cd2 cd3 cd4 cd5 pd6
Following this, and included as a robustness check, we perform exactly the
same sequence of 18 regressions using linear probability model (that is, using
the reg command rather than probit).
OFT1226 | 133
Probit regressions
1. 'Base' regressions
a. Reported in Table 5.2: Probit estimation of overall errors in decision
making
dprobit err p1 p2 highvaluegood c d2 d3 d4 d5 d6, cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(9) = 132.78
Prob > chi2 = 0.0000
Log pseudolikelihood = -3227.2947
Pseudo R2 = 0.0270
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | dF/dx
Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .39767 .0688223 5.84 0.000 .746815 .262781 .532559
p2 | .2200164 .0498589 4.41 0.000 .745319 .122295 .317738
highva~d*| .0111229 .0139256 0.80 0.424 .503689 -.016171 .038417
c | -.5291529 .2172676 -2.44 0.014 .055726 -.95499 -.103316
d2*| .0188239 .0252819 0.75 0.455 .145563 -.030728 .068376
d3*| .1544238 .0253296 6.17 0.000 .12662 .104779 .204069
d4*| .0788943 .0262817 3.03 0.002 .131605 .027383 .130405
d5*| .066988 .0254915 2.67 0.007 .131605 .017026 .116951
d6*| .1731629 .0257119 6.76 0.000 .131406 .122768 .223557
---------+-------------------------------------------------------------------obs. P | .374676
pred. P | .3713474 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 134
b. Reported in Table 5.3: Probit estimation of errors in search behaviour
dprobit err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6, cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(9) = 95.85
Prob > chi2 = 0.0000
Log pseudolikelihood = -2909.8074
Pseudo R2 = 0.0220
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .2234849 .0672924 3.35 0.001 .746815 .091594 .355376
p2 | .2162868 .0414883 5.19 0.000 .745319 .134971 .297602
highva~d*| .0045715 .0128177 0.36 0.721 .503689 -.020551 .029694
c | -.5120419 .1901925 -2.70 0.007 .055726 -.884812 -.139271
d2*| .0244149 .024193 1.02 0.306 .145563 -.023003 .071832
d3*| .0979155 .0259545 3.92 0.000 .12662 .047046 .148785
d4*| .0648617 .02431 2.74 0.006 .131605 .017215 .112508
d5*| .0463191 .0223851 2.13 0.033 .131605 .002445 .090193
d6*| .178912 .0259026 7.24 0.000 .131406 .128144 .22968
---------+-------------------------------------------------------------------obs. P | .2803589
pred. P | .2762831 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 135
c. Reported in Table 5.4: Probit estimation of errors in purchasing
behaviour
dprobit err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6, cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(9) = 264.01
Prob > chi2 = 0.0000
Log pseudolikelihood = -2858.4259
Pseudo R2 = 0.0525
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .622151 .0597762 11.16 0.000 .746815 .504992 .73931
p2 | -.1465648 .0504143 -2.90 0.004 .745319 -.245375 -.047755
highva~d*| .0261268 .0135208 1.94 0.053 .503689 -.000373 .052627
c | .4850114 .205159 2.34 0.019 .055726 .082907 .887116
d2*| .0153017 .0241285 0.64 0.523 .145563 -.031989 .062593
d3*| .1551735 .0274357 5.99 0.000 .12662 .1014 .208947
d4*| .080523 .0264786 3.14 0.002 .131605 .028626 .13242
d5*| .0571337 .0255033 2.32 0.020 .131605 .007148 .107119
d6*| .1975555 .0259052 7.90 0.000 .131406 .146782 .248329
---------+-------------------------------------------------------------------obs. P | .289332
pred. P | .2779313 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 136
2. Base regressions including period (time trend) to account for learning
a. Reported in paragraph 5.52 (overall errors)
.
dprobit err p1 p2 highvaluegood c d2 d3 d4 d5 d6 period , cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(10) = 177.45
Prob > chi2 = 0.0000
Log pseudolikelihood = -3201.4808
Pseudo R2 = 0.0348
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .4028919 .0692408 5.88 0.000 .746815 .267182 .538601
p2 | .213841 .0497624 4.29 0.000 .745319 .116308 .311373
highva~d*| .0093243 .0140595 0.66 0.507 .503689 -.018232 .03688
c | -.5504067 .2193025 -2.52 0.012 .055726 -.980232 -.120582
d2*| .0201932 .0255385 0.79 0.427 .145563 -.029861 .070248
d3*| .1549183 .0256867 6.11 0.000 .12662 .104573 .205263
d4*| .0809213 .0265381 3.08 0.002 .131605 .028908 .132935
d5*| .0681888 .0257969 2.69 0.007 .131605 .017628 .11875
d6*| .172756 .0258861 6.71 0.000 .131406 .12202 .223492
period | -.0057894 .0008282 -6.98 0.000 15.2881 -.007413 -.004166
---------+-------------------------------------------------------------------obs. P | .374676
pred. P | .3701252 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
b. Reported in paragraph 5.53 (search errors)
dprobit err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6 period , cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(10) = 105.55
Prob > chi2 = 0.0000
Log pseudolikelihood = -2905.4031
Pseudo R2 = 0.0235
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .2258963 .0670752 3.40 0.001 .746815 .094431 .357361
p2 | .2135218 .0414073 5.14 0.000 .745319 .132365 .294679
highva~d*| .0039001 .0128066 0.30 0.761 .503689 -.0212 .029001
c | -.5200878 .1900081 -2.75 0.006 .055726 -.892497 -.147679
d2*| .0250345 .0242547 1.05 0.295 .145563 -.022504 .072573
d3*| .0976357 .0260292 3.90 0.000 .12662 .046619 .148652
d4*| .0655612 .0243515 2.77 0.006 .131605 .017833 .113289
d5*| .0465966 .022437 2.14 0.033 .131605 .002621 .090572
d6*| .178545 .0259155 7.23 0.000 .131406 .127751 .229339
period | -.0022006 .0007915 -2.76 0.006 15.2881 -.003752 -.000649
---------+-------------------------------------------------------------------obs. P | .2803589
pred. P | .275887 (at x-bar)
OFT1226 | 137
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
c. Reported in paragraph 5.53 (purchasing errors)
dprobit err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6 period , cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(10) = 309.22
Prob > chi2 = 0.0000
Log pseudolikelihood = -2832.7844
Pseudo R2 = 0.0610
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .6286775 .0604273 11.19 0.000 .746815 .510242 .747113
p2 | -.1552339 .0502741 -3.09 0.002 .745319 -.253769 -.056698
highva~d*| .0245179 .0135889 1.81 0.070 .503689 -.002116 .051152
c | .471413 .2066389 2.26 0.024 .055726 .066408 .876418
d2*| .0173205 .0244133 0.72 0.474 .145563 -.030529 .06517
d3*| .1553828 .0278635 5.91 0.000 .12662 .100771 .209994
d4*| .0825454 .0268375 3.18 0.001 .131605 .029945 .135146
d5*| .0582946 .0257837 2.34 0.019 .131605 .007759 .10883
d6*| .19809 .0260925 7.87 0.000 .131406 .14695 .24923
period | -.0053769 .0008142 -6.67 0.000 15.2881 -.006973 -.003781
---------+-------------------------------------------------------------------obs. P | .289332
pred. P | .2757784 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 138
3. Base regressions including aptitude (IQ test) no time trend
a. Aptitude score and overall errors (no time trend): This is not reported
in the main document but provided for additional information
. dprobit err p1 p2 highvaluegood c d2 d3 d4 d5 d6 aptitude , cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(10) = 136.47
Prob > chi2 = 0.0000
Log pseudolikelihood = -3204.5041
Pseudo R2 = 0.0339
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .3925986 .0693896 5.73 0.000 .746815 .256597 .5286
p2 | .2152091 .049812 4.32 0.000 .745319 .117579 .312839
highva~d*| .0103742 .0138171 0.75 0.453 .503689 -.016707 .037455
c | -.5364496 .2173673 -2.48 0.013 .055726 -.962482 -.110417
d2*| .0232376 .0255684 0.91 0.361 .145563 -.026875 .073351
d3*| .1478671 .0244522 6.11 0.000 .12662 .099942 .195792
d4*| .0838768 .0259858 3.26 0.001 .131605 .032946 .134808
d5*| .0629328 .0250123 2.56 0.010 .131605 .01391 .111956
d6*| .1773022 .0253245 7.04 0.000 .131406 .127667 .226937
aptitude | -.020245 .0060059 -3.40 0.001 9.61416 -.032016 -.008474
---------+-------------------------------------------------------------------obs. P | .374676
pred. P | .3705894 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
b. Aptitude score and search errors (no time trend): This is not reported
in the main document but provided for additional information
dprobit err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6 aptitude , cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(10) = 100.17
Prob > chi2 = 0.0000
Log pseudolikelihood = -2904.7271
Pseudo R2 = 0.0237
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .220039 .0672009 3.30 0.001 .746815 .088328 .35175
p2 | .2141076 .0416203 5.13 0.000 .745319 .132533 .295682
highva~d*| .0042712 .01278 0.33 0.738 .503689 -.020777 .029319
c | -.5128878 .1901901 -2.70 0.007 .055726 -.885654 -.140122
d2*| .0260137 .0241937 1.09 0.275 .145563 -.021405 .073432
d3*| .0940223 .0253924 3.84 0.000 .12662 .044254 .14379
d4*| .0669203 .024382 2.83 0.005 .131605 .019132 .114708
d5*| .0443031 .0222778 2.04 0.041 .131605 .000639 .087967
d6*| .1806035 .0258304 7.34 0.000 .131406 .129977 .23123
aptitude | -.0087638 .0043133 -2.04 0.041 9.61416 -.017218 -.00031
---------+-------------------------------------------------------------------obs. P | .2803589
OFT1226 | 139
pred. P | .2759511 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
c. Aptitude score and purchasing errors (no time trend): This is not
reported in the main document but provided for additional information
. dprobit err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6 aptitude , cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(10) = 280.93
Prob > chi2 = 0.0000
Log pseudolikelihood = -2831.8944
Pseudo R2 = 0.0613
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .6192829 .0606703 11.05 0.000 .746815 .500371 .738195
p2 | -.1545655 .0504147 -3.07 0.002 .745319 -.253377 -.055754
highva~d*| .0256831 .0135018 1.91 0.056 .503689 -.00078 .052146
c | .4813031 .2043267 2.33 0.020 .055726 .08083 .881776
d2*| .0196412 .0243649 0.81 0.416 .145563 -.028113 .067395
d3*| .1475948 .0268628 5.79 0.000 .12662 .094945 .200245
d4*| .0861477 .0258976 3.45 0.001 .131605 .035389 .136906
d5*| .0536591 .0249353 2.23 0.026 .131605 .004787 .102531
d6*| .202571 .0254268 8.29 0.000 .131406 .152735 .252407
aptitude | -.0199857 .0059569 -3.40 0.001 9.61416 -.031661 -.00831
---------+-------------------------------------------------------------------obs. P | .289332
pred. P | .2762123 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 140
4. Base regressions including aptitude (IQ test) with time trend
a. Reported in paragraph 5.54 Aptitude score and overall errors (with
time trend)
dprobit err p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude , cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(11) = 179.25
Prob > chi2 = 0.0000
Log pseudolikelihood = -3178.0582
Pseudo R2 = 0.0419
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .3979432 .0699006 5.76 0.000 .746815 .260941 .534946
p2 | .2088008 .0496715 4.19 0.000 .745319 .111446 .306155
highva~d*| .0086067 .0139609 0.62 0.537 .503689 -.018756 .03597
c | -.5592682 .2193316 -2.56 0.010 .055726 -.98915 -.129386
d2*| .0247506 .0258238 0.96 0.335 .145563 -.025863 .075364
d3*| .1483134 .0248002 6.05 0.000 .12662 .099706 .196921
d4*| .0861345 .0262493 3.32 0.001 .131605 .034687 .137582
d5*| .064239 .0253229 2.58 0.010 .131605 .014607 .113871
d6*| .1769457 .0254744 6.98 0.000 .131406 .127017 .226875
period | -.0058771 .0008369 -7.01 0.000 15.2881 -.007517 -.004237
aptitude | -.0205834 .0060692 -3.42 0.001 9.61416 -.032479 -.008688
---------+-------------------------------------------------------------------obs. P | .374676
pred. P | .3693388 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 141
b. Reported in paragraph 5.54 Aptitude score and search errors (with
time trend)
dprobit err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude , cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(11) = 111.85
Prob > chi2 = 0.0000
Log pseudolikelihood = -2900.2538
Pseudo R2 = 0.0252
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .2224458 .0669998 3.35 0.001 .746815 .091129 .353763
p2 | .2113218 .0415244 5.07 0.000 .745319 .129935 .292708
highva~d*| .0036092 .0127683 0.28 0.777 .503689 -.021416 .028635
c | -.5211159 .1899626 -2.75 0.006 .055726 -.893436 -.148796
d2*| .0266448 .024253 1.11 0.265 .145563 -.02089 .07418
d3*| .0937191 .0254602 3.82 0.000 .12662 .043818 .14362
d4*| .0676542 .0244233 2.85 0.004 .131605 .019785 .115523
d5*| .044614 .0223347 2.05 0.040 .131605 .000839 .088389
d6*| .1802525 .0258386 7.32 0.000 .131406 .12961 .230895
period | -.0022186 .0007919 -2.78 0.005 15.2881 -.003771 -.000667
aptitude | -.0088248 .0043276 -2.05 0.040 9.61416 -.017307 -.000343
---------+-------------------------------------------------------------------obs. P | .2803589
pred. P | .2755517 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 142
c. Reported in paragraph 5.54 Aptitude score and purchasing errors
(with time trend)
dprobit err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude , cluster(subject )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(11) = 318.94
Prob > chi2 = 0.0000
Log pseudolikelihood = -2805.6533
Pseudo R2 = 0.0700
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .6257942 .0614191 11.06 0.000 .746815 .505415 .746173
p2 | -.1636083 .0502934 -3.27 0.001 .745319 -.262182 -.065035
highva~d*| .0240438 .0135759 1.78 0.076 .503689 -.002565 .050652
c | .4663176 .2057147 2.24 0.025 .055726 .063124 .869511
d2*| .0218483 .0246595 0.90 0.371 .145563 -.026484 .07018
d3*| .1477884 .027274 5.72 0.000 .12662 .094332 .201244
d4*| .0884172 .0262874 3.49 0.000 .131605 .036895 .13994
d5*| .0549567 .0252275 2.26 0.024 .131605 .005512 .104402
d6*| .2031956 .0255897 8.28 0.000 .131406 .153041 .25335
period | -.0054489 .0008251 -6.65 0.000 15.2881 -.007066 -.003832
aptitude | -.0202333 .00601 -3.42 0.001 9.61416 -.032013 -.008454
---------+-------------------------------------------------------------------obs. P | .289332
pred. P | .274009 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 143
5. Base regressions including aptitude (IQ test) and interaction between price
frames and time trend
a. Reported in paragraph 5.55 Interaction terms between price frames
and time trend (overall errors)
. dprobit err p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude pd2 pd3 pd4 pd5 pd6 , cluster(subject > )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(16) = 188.84
Prob > chi2 = 0.0000
Log pseudolikelihood = -3171.6004
Pseudo R2 = 0.0438
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .3988921 .0700601 5.77 0.000 .746815 .261577 .536207
p2 | .208588 .0495926 4.20 0.000 .745319 .111388 .305788
highva~d*| .0076987 .0140764 0.55 0.584 .503689 -.019891 .035288
c | -.5503808 .2183509 -2.53 0.011 .055726 -.978341 -.122421
d2*| .0374593 .0472915 0.80 0.424 .145563 -.05523 .130149
d3*| .195949 .0456059 4.33 0.000 .12662 .106563 .285335
d4*| .1134488 .0473532 2.43 0.015 .131605 .020638 .206259
d5*| .0946806 .0449673 2.14 0.032 .131605 .006546 .182815
d6*| .0693022 .0447374 1.57 0.116 .131406 -.018381 .156986
period | -.0057981 .0014002 -4.16 0.000 15.2881 -.008542 -.003054
aptitude | -.0206289 .0060978 -3.41 0.001 9.61416 -.03258 -.008677
pd2 | -.0008274 .0027187 -0.30 0.761 2.25464 -.006156 .004501
pd3 | -.0030261 .0025407 -1.19 0.233 1.91366 -.008006 .001954
pd4 | -.0017216 .0025692 -0.67 0.503 2.04686 -.006757 .003314
pd5 | -.0019452 .002564 -0.76 0.449 2.03968 -.006971 .00308
pd6 | .0069187 .0024703 2.81 0.005 1.96191 .002077 .01176
---------+-------------------------------------------------------------------obs. P | .374676
pred. P | .3691438 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 144
b. Reported in paragraph 5.56 Interaction terms between price frames
and time trend (search errors)
. dprobit err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude pd2 pd3 pd4 pd5 pd6 , cluster(subject
>t)
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(16) = 120.26
Prob > chi2 = 0.0000
Log pseudolikelihood = -2896.6522
Pseudo R2 = 0.0265
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .2223229 .0670359 3.35 0.001 .746815 .090935 .353711
p2 | .2112912 .04139 5.09 0.000 .745319 .130168 .292414
highva~d*| .0032695 .0128579 0.25 0.799 .503689 -.021931 .028471
c | -.5155089 .1899918 -2.72 0.007 .055726 -.887886 -.143132
d2*| .0031518 .0437461 0.07 0.942 .145563 -.082589 .088893
d3*| .1213581 .0474757 2.69 0.007 .12662 .028307 .214409
d4*| .0758175 .0470318 1.67 0.095 .131605 -.016363 .167998
d5*| .0608236 .047497 1.32 0.186 .131605 -.032269 .153916
d6*| .0976527 .0431666 2.36 0.018 .131406 .013048 .182258
period | -.0027117 .0014193 -1.90 0.057 15.2881 -.005493 .00007
aptitude | -.0088159 .0043357 -2.04 0.041 9.61416 -.017314 -.000318
pd2 | .0015213 .002584 0.59 0.556 2.25464 -.003543 .006586
pd3 | -.0016748 .002524 -0.66 0.507 1.91366 -.006622 .003272
pd4 | -.000486 .0024458 -0.20 0.842 2.04686 -.00528 .004308
pd5 | -.0010077 .0026198 -0.38 0.701 2.03968 -.006142 .004127
pd6 | .0048932 .0022014 2.22 0.027 1.96191 .000579 .009208
---------+-------------------------------------------------------------------obs. P | .2803589
pred. P | .2753392 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 145
c. Reported in paragraph 5.56 Interaction terms between price frames
and time trend (purchasing errors)
dprobit err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude pd2 pd3 pd4 pd5 pd6 , cluster(subjec
>t)
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(16) = 345.89
Prob > chi2 = 0.0000
Log pseudolikelihood = -2799.2758
Pseudo R2 = 0.0721
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .6283741 .0615721 11.10 0.000 .746815 .507695 .749053
p2 | -.1646668 .0500515 -3.30 0.001 .745319 -.262766 -.066568
highva~d*| .0234938 .0136437 1.73 0.084 .503689 -.003247 .050235
c | .4765438 .2048793 2.30 0.022 .055726 .074988 .8781
d2*| .0112102 .0452586 0.25 0.803 .145563 -.077495 .099915
d3*| .1843825 .0484549 4.03 0.000 .12662 .089413 .279352
d4*| .1108439 .0445429 2.60 0.009 .131605 .023541 .198146
d5*| .0523911 .0460994 1.17 0.243 .131605 -.037962 .142744
d6*| .0879082 .0463086 1.97 0.049 .131406 -.002855 .178671
period | -.0060872 .0013844 -4.45 0.000 15.2881 -.008801 -.003374
aptitude | -.0202804 .0060356 -3.41 0.001 9.61416 -.03211 -.008451
pd2 | .0007137 .0027253 0.26 0.793 2.25464 -.004628 .006055
pd3 | -.0021542 .0023679 -0.91 0.363 1.91366 -.006795 .002487
pd4 | -.0013412 .0025483 -0.53 0.599 2.04686 -.006336 .003653
pd5 | .0001732 .0024392 0.07 0.943 2.03968 -.004607 .004954
pd6 | .0068452 .0023735 2.90 0.004 1.96191 .002193 .011497
---------+-------------------------------------------------------------------obs. P | .289332
pred. P | .2734712 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 146
6. Base regressions including interaction between search costs and treatment
a. Reported in paragraph 5.22 Interaction terms between search costs and
treatment (overall errors)
. dprobit err p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude cd2 cd3 cd4 cd5 cd6 , cluster(subject
>)
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(16) = 190.86
Prob > chi2 = 0.0000
Log pseudolikelihood = -3174.0818
Pseudo R2 = 0.0431
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .3962835 .0699802 5.74 0.000 .746815 .259125 .533442
p2 | .2097658 .0497116 4.21 0.000 .745319 .112333 .307199
highva~d*| .0079886 .0139478 0.57 0.567 .503689 -.019348 .035326
c | .1238768 .3822887 0.32 0.746 .055726 -.625395 .873149
d2*| .0528838 .0453782 1.18 0.239 .145563 -.036056 .141823
d3*| .2126438 .0428422 4.96 0.000 .12662 .128675 .296613
d4*| .1618088 .0435639 3.75 0.000 .131605 .076425 .247193
d5*| .0998216 .0451835 2.25 0.024 .131605 .011264 .18838
d6*| .2643349 .0506616 5.12 0.000 .131406 .16504 .36363
period | -.0058292 .0008321 -6.99 0.000 15.2881 -.00746 -.004198
aptitude | -.0209384 .0060683 -3.48 0.001 9.61416 -.032832 -.009045
cd2 | -.4891798 .6655608 -0.74 0.462 .008049 -1.79366 .815295
cd3 | -1.114248 .5958725 -1.87 0.062 .007022 -2.28214 .053641
cd4 | -1.322431 .6199777 -2.13 0.033 .007185 -2.53756 -.107297
cd5 | -.6181273 .6063966 -1.02 0.308 .007444 -1.80664 .570388
cd6 | -1.509331 .7061585 -2.14 0.032 .007424 -2.89338 -.125286
---------+-------------------------------------------------------------------obs. P | .374676
pred. P | .3693055 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 147
b. Reported in paragraph 5.23 Interaction terms between search costs and
treatment (search errors)
dprobit err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude cd2 cd3 cd4 cd5 cd6 ,
cluster(subject > t )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(16) = 129.53
Prob > chi2 = 0.0000
Log pseudolikelihood = -2888.82
Pseudo R2 = 0.0291
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .2206325 .0670083 3.33 0.001 .746815 .089299 .351966
p2 | .2126269 .0413668 5.13 0.000 .745319 .13155 .293704
highva~d*| .0027607 .012686 0.22 0.828 .503689 -.022103 .027625
c | .3459962 .3405603 1.01 0.310 .055726 -.32149 1.01348
d2*| .0432098 .0416566 1.06 0.288 .145563 -.038436 .124855
d3*| .1835859 .0450089 4.28 0.000 .12662 .09537 .271802
d4*| .198842 .0428474 4.88 0.000 .131605 .114863 .282821
d5*| .0668572 .0406978 1.70 0.088 .131605 -.012909 .146624
d6*| .3145075 .050635 6.39 0.000 .131406 .215265 .41375
period | -.0021322 .0007885 -2.69 0.007 15.2881 -.003678 -.000587
aptitude | -.0093272 .0043372 -2.16 0.031 9.61416 -.017828 -.000827
cd2 | -.2692348 .6201286 -0.43 0.664 .008049 -1.48466 .946195
cd3 | -1.442658 .5538867 -2.59 0.010 .007022 -2.52826 -.35706
cd4 | -2.168836 .6172764 -3.51 0.000 .007185 -3.37868 -.958996
cd5 | -.376066 .5389976 -0.70 0.485 .007444 -1.43248 .68035
cd6 | -2.079233 .6060126 -3.44 0.001 .007424 -3.267 -.89147
---------+-------------------------------------------------------------------obs. P | .2803589
pred. P | .2750024 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 148
c. Reported in paragraph 5.24 Interaction terms between search costs and
treatment (purchasing errors)
dprobit err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude cd2 cd3 cd4 cd5 cd6 ,
cluster(subject > t )
Probit regression, reporting marginal effects Number of obs = 5015
Wald chi2(16) = 332.42
Prob > chi2 = 0.0000
Log pseudolikelihood = -2802.5502
Pseudo R2 = 0.0710
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | dF/dx Std. Err. z P>|z| x-bar [ 95 per cent C.I. ]
---------+-------------------------------------------------------------------p1 | .6250536 .0614056 11.06 0.000 .746815 .504701 .745406
p2 | -.1625912 .050294 -3.24 0.001 .745319 -.261166 -.064017
highva~d*| .0236248 .0135655 1.75 0.081 .503689 -.002963 .050213
c | 1.048708 .3492857 2.97 0.003 .055726 .364121 1.7333
d2*| .0599929 .0433454 1.42 0.155 .145563 -.024962 .144948
d3*| .2158993 .0470321 4.85 0.000 .12662 .123718 .308081
d4*| .1118733 .044836 2.61 0.009 .131605 .023996 .19975
d5*| .1059318 .0472655 2.35 0.019 .131605 .013293 .198571
d6*| .2872089 .0499563 5.96 0.000 .131406 .189296 .385121
period | -.0054247 .0008206 -6.66 0.000 15.2881 -.007033 -.003816
aptitude | -.0204506 .0060048 -3.46 0.001 9.61416 -.03222 -.008681
cd2 | -.6355408 .5631849 -1.13 0.259 .008049 -1.73936 .468281
cd3 | -1.05095 .5401403 -1.95 0.052 .007022 -2.10961 .007706
cd4 | -.3616208 .5810001 -0.62 0.534 .007185 -1.50036 .777118
cd5 | -.8203628 .5571246 -1.47 0.141 .007444 -1.91231 .271581
cd6 | -1.271652 .6221084 -2.04 0.041 .007424 -2.49096 -.052342
---------+-------------------------------------------------------------------obs. P | .289332
pred. P | .2736706 (at x-bar)
-----------------------------------------------------------------------------(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
OFT1226 | 149
Non-parametric test consumer welfare (paragraph 5.58)
by price_strategy : signrank diff_u =diff_u2
-----------------------------------------------------------------------------> price_strategy = 1
Wilcoxon signed-rank test
sign | obs sum ranks expected
-------------+--------------------------------positive | 56 4182 7245
negative | 105 10308 7245
zero | 9 45 45
-------------+--------------------------------all | 170 14535 14535
unadjusted variance 413036.25
adjustment for ties -2.13
adjustment for zeros -71.25
---------adjusted variance 412962.88
Ho: diff_u = diff_u2
z = -4.766
Prob > |z| = 0.0000
-----------------------------------------------------------------------------> price_strategy = 2
Wilcoxon signed-rank test
sign | obs sum ranks expected
-------------+--------------------------------positive | 26 799.5 1350
negative | 46 1900.5 1350
zero | 1 1 1
-------------+--------------------------------all | 73 2701 2701
unadjusted variance 33087.25
adjustment for ties -0.25
adjustment for zeros -0.25
---------adjusted variance 33086.75
OFT1226 | 150
Linear regressions performed to check robustness
1. 'Base' regressions
a. Linear estimation of overall errors in decision making
reg err p1 p2 highvaluegood c d2 d3 d4 d5 d6, cluster(subject )
Linear regression
Number of obs = 5015
F( 9, 169) = 15.83
Prob > F = 0.0000
R-squared = 0.0354
Root MSE = .47586
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .3843403 .0651998 5.89 0.000 .2556293 .5130513
p2 | .2177066 .0486591 4.47 0.000 .1216487 .3137646
highvalueg~d | .0110253 .0135952 0.81 0.419 -.015813 .0378635
c | -.5025095 .2105306 -2.39 0.018 -.918118 -.086901
d2 | .0194859 .0237183 0.82 0.412 -.0273363 .0663082
d3 | .149702 .0245467 6.10 0.000 .1012443 .1981597
d4 | .0767684 .024637 3.12 0.002 .0281326 .1254042
d5 | .0638656 .0242424 2.63 0.009 .0160086 .1117226
d6 | .1684747 .0246646 6.83 0.000 .1197843 .217165
_cons | -.1146047 .0653518 -1.75 0.081 -.2436158 .0144064
------------------------------------------------------------------------------
b. Linear estimation of errors in search behaviour
reg err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6, cluster(subject )
Linear regression
Number of obs = 5015
F( 9, 169) = 10.98
Prob > F = 0.0000
R-squared = 0.0268
Root MSE = .44356
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .2190497 .0634417 3.45 0.001 .0938095 .3442899
p2 | .2189598 .0410745 5.33 0.000 .1378747 .3000449
highvalueg~d | .0049569 .012704 0.39 0.697 -.0201221 .0300359
c | -.4940624 .1845707 -2.68 0.008 -.8584236 -.1297013
d2 | .0237349 .0220786 1.08 0.284 -.0198503 .0673202
d3 | .0923835 .0239344 3.86 0.000 .0451346 .1396325
d4 | .0631902 .0221352 2.85 0.005 .0194931 .1068873
d5 | .0431921 .0204709 2.11 0.036 .0027806 .0836036
d6 | .1738838 .0243593 7.14 0.000 .1257961 .2219715
_cons | -.0733924 .0573267 -1.28 0.202 -.1865611 .0397763
OFT1226 | 151
c. Linear estimation of errors in purchasing behaviour
reg err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6, cluster(subject )
Linear regression
Number of obs = 5015
F( 9, 169) = 29.92
Prob > F = 0.0000
R-squared = 0.0611
Root MSE = .43983
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .5882927 .055456 10.61 0.000 .4788169 .6977685
p2 | -.128347 .0488204 -2.63 0.009 -.2247233 -.0319706
highvalueg~d | .0252613 .0131397 1.92 0.056 -.0006779 .0512004
c | .4790518 .1984353 2.41 0.017 .0873206 .870783
d2 | .0149644 .0216724 0.69 0.491 -.027819 .0577479
d3 | .1437444 .0257071 5.59 0.000 .0929959 .1944928
d4 | .0738574 .0232609 3.18 0.002 .027938 .1197768
d5 | .0509222 .0228032 2.23 0.027 .0059064 .095938
d6 | .1847231 .0235767 7.83 0.000 .1381803 .2312659
_cons | -.1548482 .0584492 -2.65 0.009 -.2702329 -.0394635
2. Base regressions including period (time trend) to account for learning
a. Linear estimation (overall errors)
reg err p1 p2 highvaluegood c d2 d3 d4 d5 d6 period , cluster(subject )
Linear regression
Number of obs = 5015
F( 10, 169) = 19.71
Prob > F = 0.0000
R-squared = 0.0452
Root MSE = .47349
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .3849301 .0652101 5.90 0.000 .2561987 .5136615
p2 | .2109145 .0483101 4.37 0.000 .1155455 .3062835
highvalueg~d | .0091038 .0136333 0.67 0.505 -.0178097 .0360173
c | -.5144887 .2108786 -2.44 0.016 -.9307843 -.0981932
d2 | .02087 .0237399 0.88 0.381 -.025995 .067735
d3 | .148945 .0246001 6.05 0.000 .100382 .197508
d4 | .0785549 .0246309 3.19 0.002 .029931 .1271788
d5 | .0652308 .0242331 2.69 0.008 .0173922 .1130694
d6 | .1668361 .0247087 6.75 0.000 .1180587 .2156135
period | -.0055898 .0008006 -6.98 0.000 -.0071704 -.0040093
_cons | -.0231943 .066322 -0.35 0.727 -.1541206 .1077319
-------------------------------------------------------------------------------
OFT1226 | 152
b. Linear estimation (search errors)
reg err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6 period , cluster(subject )
Linear regression
Number of obs = 5015
F( 10, 169) = 11.04
Prob > F = 0.0000
R-squared = 0.0284
Root MSE = .44324
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .2192702 .0633432 3.46 0.001 .0942244 .3443161
p2 | .2164203 .0410221 5.28 0.000 .1354386 .2974021
highvalueg~d | .0042385 .0126904 0.33 0.739 -.0208136 .0292906
c | -.4985413 .1844228 -2.70 0.008 -.8626104 -.1344723
d2 | .0242524 .0220776 1.10 0.274 -.019331 .0678358
d3 | .0921005 .0239483 3.85 0.000 .0448242 .1393769
d4 | .0638581 .0221411 2.88 0.004 .0201494 .1075669
d5 | .0437025 .0204785 2.13 0.034 .0032758 .0841292
d6 | .1732711 .0243627 7.11 0.000 .1251767 .2213656
period | -.00209 .000786 -2.66 0.009 -.0036416 -.0005384
_cons | -.0392153 .0602411 -0.65 0.516 -.1581372 .0797066
c. Linear estimation (purchasing errors)
reg err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6 period , cluster(subject )
Linear regression
Number of obs = 5015
F( 10, 169) = 31.88
Prob > F = 0.0000
R-squared = 0.0702
Root MSE = .43772
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .588828 .0557822 10.56 0.000 .4787083 .6989477
p2 | -.1345112 .0484762 -2.77 0.006 -.2302081 -.0388143
highvalueg~d | .0235174 .013098 1.80 0.074 -.0023393 .0493741
c | .4681801 .1986009 2.36 0.020 .0761219 .8602382
d2 | .0162205 .021713 0.75 0.456 -.0266431 .0590842
d3 | .1430574 .0257707 5.55 0.000 .0921834 .1939314
d4 | .0754787 .0232998 3.24 0.001 .0294826 .1214748
d5 | .0521612 .0228149 2.29 0.023 .0071224 .0972
d6 | .183236 .0236425 7.75 0.000 .1365633 .2299087
period | -.0050731 .0007818 -6.49 0.000 -.0066163 -.0035298
_cons | -.0718888 .0590577 -1.22 0.225 -.1884746 .0446971
OFT1226 | 153
3. Base regressions including aptitude (IQ test) no time trend
a. Linear Estimation: Aptitude score and overall errors (no time trend). This
is not reported in the main document but provided for additional
information
reg err p1 p2 highvaluegood c d2 d3 d4 d5 d6 aptitude , cluster(subject )
Linear regression
Number of obs = 5015
F( 10, 169) = 14.94
Prob > F = 0.0000
R-squared = 0.0444
Root MSE = .4737
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .3767392 .0651553 5.78 0.000 .2481162 .5053623
p2 | .2113991 .0482665 4.38 0.000 .1161162 .3066819
highvalueg~d | .0103371 .0134045 0.77 0.442 -.0161246 .0367989
c | -.5034809 .2090071 -2.41 0.017 -.9160819 -.0908799
d2 | .0230307 .0237248 0.97 0.333 -.0238044 .0698659
d3 | .1422743 .0233686 6.09 0.000 .0961423 .1884063
d4 | .0804488 .0242342 3.32 0.001 .0326082 .1282895
d5 | .05954 .0236212 2.52 0.013 .0129095 .1061706
d6 | .1712666 .0241892 7.08 0.000 .1235146 .2190186
aptitude | -.0201029 .0059812 -3.36 0.001 -.0319104 -.0082954
_cons | .0895888 .0870977 1.03 0.305 -.0823507 .2615283
OFT1226 | 154
b. Linear Estimation: Aptitude score and search errors (no time trend). This
is not reported in the main document but provided for additional
information
reg err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6 aptitude , cluster(subject )
Linear regression
Number of obs = 5015
F( 10, 169) = 10.28
Prob > F = 0.0000
R-squared = 0.0288
Root MSE = .44314
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .2156845 .0632746 3.41 0.001 .090774 .3405949
p2 | .2161673 .0412416 5.24 0.000 .1347523 .2975823
highvalueg~d | .0046523 .0126463 0.37 0.713 -.0203129 .0296174
c | -.4944925 .1844263 -2.68 0.008 -.8585685 -.1304165
d2 | .0253043 .0219837 1.15 0.251 -.0180936 .0687023
d3 | .0890951 .0234131 3.81 0.000 .0428752 .1353149
d4 | .0648196 .0221579 2.93 0.004 .0210776 .1085616
d5 | .041277 .0203521 2.03 0.044 .0010998 .0814542
d6 | .1751199 .0242684 7.22 0.000 .1272116 .2230281
aptitude | -.0089002 .0044762 -1.99 0.048 -.0177367 -.0000637
_cons | .0170106 .0735236 0.23 0.817 -.1281324 .1621535
------------------------------------------------------------------------------
c. Linear Estimation: Aptitude score and purchasing errors (no time trend).
This is not reported in the main document but provided for additional
information
reg err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6 aptitude , cluster(subject )
Linear regression
Number of obs = 5015
F( 10, 169) = 28.69
Prob > F = 0.0000
R-squared = 0.0713
Root MSE = .43746
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .5806488 .0555212 10.46 0.000 .4710444 .6902532
p2 | -.1346901 .0486209 -2.77 0.006 -.2306727 -.0387076
highvalueg~d | .0245693 .0130293 1.89 0.061 -.0011518 .0502904
c | .478075 .1969125 2.43 0.016 .08935 .8667999
d2 | .0185292 .0216601 0.86 0.394 -.0242301 .0612885
d3 | .1362748 .0247686 5.50 0.000 .0873792 .1851704
d4 | .0775586 .0226555 3.42 0.001 .0328344 .1222828
d5 | .0465722 .0222881 2.09 0.038 .0025733 .0905712
d6 | .1875307 .023013 8.15 0.000 .1421009 .2329606
aptitude | -.0202163 .0061826 -3.27 0.001 -.0324214 -.0080112
_cons | .0504971 .0847426 0.60 0.552 -.1167933 .2177876
OFT1226 | 155
4. Base regressions including aptitude (IQ test) with time trend
a. Linear Estimation: Aptitude score and overall errors (with time trend)
reg err p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude , cluster(subject )
Linear regression
Number of obs = 5015
F( 11, 169) = 18.46
Prob > F = 0.0000
R-squared = 0.0543
Root MSE = .47128
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .3772766 .0651867 5.79 0.000 .2485916 .5059617
p2 | .2045141 .0479022 4.27 0.000 .1099504 .2990777
highvalueg~d | .0083975 .0134437 0.62 0.533 -.0181416 .0349367
c | -.5155485 .2092547 -2.46 0.015 -.9286384 -.1024587
d2 | .0244505 .0237431 1.03 0.305 -.0224207 .0713217
d3 | .1414571 .0234118 6.04 0.000 .0952399 .1876744
d4 | .0822747 .0242268 3.40 0.001 .0344487 .1301008
d5 | .0608824 .0236056 2.58 0.011 .0142825 .1074823
d6 | .1696376 .0242211 7.00 0.000 .1218228 .2174524
period | -.0056277 .0008027 -7.01 0.000 -.0072123 -.0040431
aptitude | -.020252 .0059918 -3.38 0.001 -.0320804 -.0084237
_cons | .1831338 .0874546 2.09 0.038 .0104895 .355778
------------------------------------------------------------------------------
b. Linear Estimation: Aptitude score and search errors (with time trend)
reg err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude , cluster(subject )
Linear regression
Number of obs = 5015
F( 11, 169) = 10.58
Prob > F = 0.0000
R-squared = 0.0304
Root MSE = .44282
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .2158856 .0631799 3.42 0.001 .0911621 .3406092
p2 | .2135899 .0411839 5.19 0.000 .1322887 .2948911
highvalueg~d | .0039262 .0126325 0.31 0.756 -.0210117 .0288641
c | -.49901 .1842637 -2.71 0.007 -.862765 -.135255
d2 | .0258358 .0219822 1.18 0.242 -.0175594 .069231
d3 | .0887892 .0234236 3.79 0.000 .0425486 .1350298
d4 | .0655032 .0221631 2.96 0.004 .0217509 .1092554
d5 | .0417795 .0203588 2.05 0.042 .0015893 .0819698
d6 | .17451 .0242712 7.19 0.000 .1265962 .2224239
period | -.0021067 .0007857 -2.68 0.008 -.0036577 -.0005558
aptitude | -.008956 .0044778 -2.00 0.047 -.0177956 -.0001164
_cons | .0520288 .0746798 0.70 0.487 -.0953967 .1994543
------------------------------------------------------------------------------
OFT1226 | 156
c. Linear estimation: Aptitude score and purchasing errors (with time trend)
reg err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude , cluster(subject )
Linear regression
Number of obs = 5015
F( 11, 169) = 29.81
Prob > F = 0.0000
R-squared = 0.0807
Root MSE = .4353
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .5811369 .055869 10.40 0.000 .4708458 .6914279
p2 | -.1409431 .0482658 -2.92 0.004 -.2362246 -.0456617
highvalueg~d | .0228077 .0129886 1.76 0.081 -.0028332 .0484486
c | .467115 .1969811 2.37 0.019 .0782545 .8559755
d2 | .0198186 .021695 0.91 0.362 -.0230095 .0626468
d3 | .1355326 .0248259 5.46 0.000 .0865237 .1845415
d4 | .0792169 .022696 3.49 0.001 .0344128 .124021
d5 | .0477914 .0222951 2.14 0.033 .0037787 .0918041
d6 | .1860513 .0230631 8.07 0.000 .1405224 .2315801
period | -.0051111 .0007851 -6.51 0.000 -.006661 -.0035613
aptitude | -.0203517 .0061947 -3.29 0.001 -.0325806 -.0081229
_cons | .1354551 .0857042 1.58 0.116 -.0337335 .3046438
------------------------------------------------------------------------------
OFT1226 | 157
5. Base regressions including aptitude (IQ test) and interaction between price
frames and time trend
a. Liner estimation: Interaction terms between price frames and time trend
(overall errors)
reg err p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude pd2 pd3 pd4 pd5 pd6 , cluster(subject )
Linear regression
Number of obs = 5015
F( 16, 169) = 13.46
Prob > F = 0.0000
R-squared = 0.0568
Root MSE = .47089
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .3771078 .0652229 5.78 0.000 .2483512 .5058644
p2 | .2040824 .0477327 4.28 0.000 .1098532 .2983116
highvalueg~d | .0076402 .0135429 0.56 0.573 -.0190948 .0343751
c | -.5054014 .2078975 -2.43 0.016 -.915812 -.0949909
d2 | .037421 .0449334 0.83 0.406 -.051282 .126124
d3 | .1953679 .0438268 4.46 0.000 .1088495 .2818863
d4 | .1115643 .0451669 2.47 0.015 .0224003 .2007283
d5 | .0951622 .043486 2.19 0.030 .0093166 .1810079
d6 | .072565 .043916 1.65 0.100 -.0141297 .1592596
period | -.0053366 .0012772 -4.18 0.000 -.0078579 -.0028153
aptitude | -.0202384 .0059997 -3.37 0.001 -.0320824 -.0083944
pd2 | -.0008417 .0024809 -0.34 0.735 -.0057393 .004056
pd3 | -.0035654 .0024701 -1.44 0.151 -.0084416 .0013108
pd4 | -.0018882 .0024134 -0.78 0.435 -.0066524 .0028761
pd5 | -.0022197 .0024425 -0.91 0.365 -.0070415 .0026021
pd6 | .0065071 .0024687 2.64 0.009 .0016336 .0113806
_cons | .1788373 .089109 2.01 0.046 .0029272 .3547475
------------------------------------------------------------------------------
OFT1226 | 158
b. Linear estimation: Interaction terms between price frames and time
trend (search errors)
. reg err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude pd2 pd3 pd4 pd5 pd6 ,
cluster(subject )
Linear regression
Number of obs = 5015
F( 16, 169) = 7.83
Prob > F = 0.0000
R-squared = 0.0319
Root MSE = .4427
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .2152265 .0632046 3.41 0.001 .0904544 .3399986
p2 | .2137568 .0410381 5.21 0.000 .1327435 .29477
highvalueg~d | .0036083 .0127172 0.28 0.777 -.0214968 .0287133
c | -.493282 .1840931 -2.68 0.008 -.8567003 -.1298638
d2 | .0042466 .0416249 0.10 0.919 -.0779252 .0864184
d3 | .1223118 .0447887 2.73 0.007 .0338945 .2107291
d4 | .0760956 .04367 1.74 0.083 -.0101134 .1623047
d5 | .0591564 .0450419 1.31 0.191 -.0297608 .1480737
d6 | .1004524 .0421358 2.38 0.018 .0172722 .1836326
period | -.0024335 .0012911 -1.88 0.061 -.0049823 .0001153
aptitude | -.0089475 .0044824 -2.00 0.048 -.0177963 -.0000988
pd2 | .0014008 .0023995 0.58 0.560 -.0033361 .0061377
pd3 | -.00222 .0025422 -0.87 0.384 -.0072386 .0027987
pd4 | -.0006743 .0023445 -0.29 0.774 -.0053025 .0039539
pd5 | -.0011161 .0025334 -0.44 0.660 -.0061172 .003885
pd6 | .0049538 .0023613 2.10 0.037 .0002925 .0096152
_cons | .057128 .0766873 0.74 0.457 -.0942604 .2085165
OFT1226 | 159
c. Linear estimation: Interaction terms between price frames and time
trend (purchasing errors)
reg err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude pd2 pd3 pd4 pd5 pd6 ,
cluster(subject )
Linear regression
Number of obs = 5015
F( 16, 169) = 22.24
Prob > F = 0.0000
R-squared = 0.0829
Root MSE = .43498
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .5816763 .0559091 10.40 0.000 .471306 .6920465
p2 | -.1404681 .0479951 -2.93 0.004 -.2352153 -.0457209
highvalueg~d | .0221594 .0130499 1.70 0.091 -.0036024 .0479212
c | .477269 .1960103 2.43 0.016 .090325 .8642129
d2 | .0178651 .0419834 0.43 0.671 -.0650144 .1007446
d3 | .1875255 .0457952 4.09 0.000 .0971212 .2779297
d4 | .1082362 .0403947 2.68 0.008 .028493 .1879795
d5 | .0548557 .0444816 1.23 0.219 -.0329555 .1426668
d6 | .0968941 .044949 2.16 0.033 .0081602 .1856279
period | -.0051491 .0011782 -4.37 0.000 -.0074751 -.0028231
aptitude | -.0203441 .0062037 -3.28 0.001 -.0325908 -.0080974
pd2 | .0001282 .002341 0.05 0.956 -.0044931 .0047496
pd3 | -.0034397 .0023807 -1.44 0.150 -.0081394 .00126
pd4 | -.0018611 .0022774 -0.82 0.415 -.0063569 .0026347
pd5 | -.0004564 .0023133 -0.20 0.844 -.0050231 .0041102
pd6 | .005971 .0024994 2.39 0.018 .0010368 .0109051
_cons | .1349531 .0880065 1.53 0.127 -.0387806 .3086868
OFT1226 | 160
6. Base regressions including interaction between search costs and treatment
a. Linear estimation: Interaction terms between search costs and treatment
(overall errors). Referred to in paragraph 5.2.
reg err p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude cd2 cd3 cd4 cd5 cd6 , cluster(subject
)
Linear regression
Number of obs = 5015
F( 16, 169) = 13.49
Prob > F = 0.0000
R-squared = 0.0560
Root MSE = .4711
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .3761938 .0651845 5.77 0.000 .2475131 .5048745
p2 | .2051198 .0480376 4.27 0.000 .1102887 .2999508
highvalueg~d | .0079281 .013412 0.59 0.555 -.0185486 .0344049
c | .1429074 .3526965 0.41 0.686 -.5533509 .8391657
d2 | .0482192 .0418448 1.15 0.251 -.0343865 .130825
d3 | .2049976 .0411469 4.98 0.000 .1237695 .2862257
d4 | .1535119 .0402235 3.82 0.000 .0741067 .232917
d5 | .0930471 .0422811 2.20 0.029 .0095801 .1765142
d6 | .2563992 .0500176 5.13 0.000 .1576594 .355139
period | -.0055708 .0007983 -6.98 0.000 -.0071467 -.0039949
aptitude | -.0206072 .0059922 -3.44 0.001 -.0324365 -.0087779
cd2 | -.422031 .6216148 -0.68 0.498 -1.649161 .8050991
cd3 | -1.143424 .5824744 -1.96 0.051 -2.293287 .0064391
cd4 | -1.289201 .5862457 -2.20 0.029 -2.446509 -.1318926
cd5 | -.5789729 .5792171 -1.00 0.319 -1.722406 .5644598
cd6 | -1.54217 .7042568 -2.19 0.030 -2.932444 -.1518962
_cons | .1495132 .0884576 1.69 0.093 -.025111 .3241374
------------------------------------------------------------------------------
OFT1226 | 161
b. Linear estimation: Interaction terms between search costs and treatment
(search errors)
reg err_v p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude cd2 cd3 cd4 cd5 cd6 ,
cluster(subject )
Linear regression
Number of obs = 5015
F( 16, 169) = 8.46
Prob > F = 0.0000
R-squared = 0.0354
Root MSE = .4419
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_v | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .2149247 .0630822 3.41 0.001 .0903942 .3394553
p2 | .2148667 .0410987 5.23 0.000 .1337337 .2959997
highvalueg~d | .0035045 .0125274 0.28 0.780 -.0212258 .0282347
c | .3256471 .3103176 1.05 0.295 -.2869511 .9382453
d2 | .0368799 .037047 1.00 0.321 -.0362545 .1100144
d3 | .1698329 .0402472 4.22 0.000 .0903809 .2492849
d4 | .1810451 .0372284 4.86 0.000 .1075526 .2545377
d5 | .0577631 .0359549 1.61 0.110 -.0132155 .1287417
d6 | .3015525 .0486231 6.20 0.000 .2055656 .3975394
period | -.0020197 .0007816 -2.58 0.011 -.0035626 -.0004769
aptitude | -.0094729 .0044791 -2.11 0.036 -.018315 -.0006307
cd2 | -.189733 .582171 -0.33 0.745 -1.338997 .9595309
cd3 | -1.458678 .5479735 -2.66 0.009 -2.540433 -.3769234
cd4 | -2.096243 .573799 -3.65 0.000 -3.228979 -.9635056
cd5 | -.2955704 .5094517 -0.58 0.563 -1.301279 .7101384
cd6 | -2.256467 .6486409 -3.48 0.001 -3.536949 -.9759846
_cons | .0096037 .0766216 0.13 0.900 -.141655 .1608623
------------------------------------------------------------------------------ z and P>|z| correspond to the
test of the underlying coefficient being 0
OFT1226 | 162
c. Linear estimation: Interaction terms between search costs and treatment
(purchasing errors)
reg err_n p1 p2 highvaluegood c d2 d3 d4 d5 d6 period aptitude cd2 cd3 cd4 cd5 cd6 ,
cluster(subject )
Linear regression
Number of obs = 5015
F( 16, 169) = 21.21
Prob > F = 0.0000
R-squared = 0.0817
Root MSE = .43528
(Std. Err. adjusted for 170 clusters in subject)
-----------------------------------------------------------------------------| Robust
err_n | Coef. Std. Err. t P>|t| [95 per cent Conf. Interval]
-------------+---------------------------------------------------------------p1 | .5803583 .0558194 10.40 0.000 .4701653 .6905513
p2 | -.1404074 .048253 -2.91 0.004 -.2356637 -.045151
highvalueg~d | .0224876 .0129686 1.73 0.085 -.0031136 .0480889
c | .9580039 .3050051 3.14 0.002 .3558932 1.560115
d2 | .0508532 .0353083 1.44 0.152 -.018849 .1205553
d3 | .1862716 .0413694 4.50 0.000 .1046043 .2679389
d4 | .0960869 .036041 2.67 0.008 .0249383 .1672354
d5 | .0883287 .03928 2.25 0.026 .010786 .1658714
d6 | .2523042 .0448162 5.63 0.000 .1638325 .3407759
period | -.0050886 .0007814 -6.51 0.000 -.0066311 -.0035461
aptitude | -.0205489 .0061923 -3.32 0.001 -.0327731 -.0083247
cd2 | -.5555199 .5116064 -1.09 0.279 -1.565482 .4544425
cd3 | -.9127406 .5368703 -1.70 0.091 -1.972577 .1470954
cd4 | -.2974583 .5430896 -0.55 0.585 -1.369572 .7746551
cd5 | -.7239608 .5178312 -1.40 0.164 -1.746212 .29829
cd6 | -1.177779 .6535352 -1.80 0.073 -2.467923 .1123647
_cons | .1099432 .0858383 1.28 0.202 -.0595103 .2793967
------------------------------------------------------------------------------
OFT1226 | 163
E
PERSONALITY TEST
The personality survey consisted of the following statements. Subjects
had to choose how strongly they agreed or disagreed with them.
I am somebody who:
1. Is often worried.
2. Is communicative and talkative.
3. Works systematically and thoroughly.
4. Has original ideas.
5. Is sometimes a little tough on others.
6. Tends to be lazy.
7. Becomes nervous easily.
8. Is sociable and can let their hair down.
9. Values artistic experiences.
10. Can forgive others.
11. Is reserved.
12. Has a vivid imagination.
13. Treats others respectfully and politely.
14. Is relaxed and can cope with stress easily.
15. Solves tasks efficiently and effectively.
Developed by Schupp and Gerlitz , DIW German Institute for Economic
Research, 2005.
OFT1226 | 164
A
FEEDBACK QUESTIONNAIRE
Here are the questions that we asked the subject in the questionnaire. Note that
not all subjects received all parts of question 3.
1.
Did you feel the instructions were clear?
2.
What was your strategy during the experiment (in terms of shops to visit,
where to purchase and number of units to buy)?
3.
Do you have any comments on the shopping experience where you
encountered a:
a. price advertised on the home screen that sometimes was not available
at the shop?
b. price that only existed for the first visit to the shop?
c. 3 for 2 offer
d. price that was advertised at the shop as reduced from a higher price
e. situation where you needed to add shipping and handling after choosing
how many units to buy
4.
Were there any aspects of the shopping experience that you enjoyed?
Please explain what and why.
5.
Were there any aspects of the shopping experience that you found
annoying? Please explain what and why.
6.
How did the experiment, pricing practices you encountered and your
behaviour mirror/reflect your experience of shopping?
7.
How old are you in years?
8.
Which gender are you?
9.
What subject, if any, are you studying?
10. Any other comments.
OFT1226 | 165
B
EXPERIMENT INSTRUCTIONS
EXPERIMENTAL INSTRUCTIONS
Welcome to our experiment! In the course of this experiment you can earn a
substantial amount of money. The precise amount will depend on your choices
and some luck. We kindly ask you to remain silent throughout the entire
experiment. Do not attempt to communicate with your neighbours and do not
try to look at their screens. If you have any questions, please, raise your hand
and we will come and answer it in private.
This experimental session will consist of several on-screen stages:
1.
2.
3.
4.
5.
Quiz (to ensure you understand the instructions)
Experiment (described in the instructions below)
Multiple choice quiz
Questionnaire about yourself
Feedback questionnaire about the experiment
Your payment for this session will consist of the amounts you earn in Stages 2,
3 together with the £5 show up fee. We will pay you in cash. You will need to
sign a receipt, which we will supply.
In this experiment, we simulate the idea of shopping for different products. The
experiment will have 30 periods and in each period, you can buy zero or more
units of a product that is available to buy at two different shops (the same
product is available at both shops). By buying units of the product, you will get
some points.
At the end of the experiment, the total sum of all the points you earned over the
30 periods will be converted into pounds, at a rate of £1 for 50 points.
OFT1226 | 166
Let us describe the experiment. You can buy four different products. They are
called GREEN, ORANGE, BLUE, and RED. For each of these products, you can
see in the attached table (on page 3) how many points you will get depending
on the number of units you buy in a period. For example, if you buy a total of
two units of GREEN in a period you get 100 points. Or for a total of three units
of RED you get 220 points.
The prices for these products will also vary from period to period and shop to
shop. The table shows you, for each good, the price range in points you can
usually expect. This is the price is per unit. The price will be randomly chosen
each period from the price range and the price for each shop will be determined
separately.
At the beginning of each period, you will see your home screen. This will inform
you of which product is available to buy in this period (only one product will be
sold in any one period and it is chosen at random) so that you know which row
of the table is relevant. The product available will also be identified by the colour
of the screen surround. On your home screen, there are two buttons,
representing the two shops where you can buy the product. They will be
labelled 'Go To Shop 1' and 'Go To Shop 2'. There is also a button labelled 'I’m
done' which ends the period and brings up the results.
If you want to go to a shop to buy something (or simply to check the price), you
can do so by clicking on this shop’s button. Every time you visit a shop, you will
incur some costs (think of time it takes to get there, petrol, etc). The cost
(denoted in points) is displayed on the home screen (the cost is the cost to
make one return trip to a shop).
Once you are at the shop, you will see the price of the good that is available to
buy in the period and, if you want to buy, you can enter the number of units
you wish to buy (the maximum total number of units you can buy in any one
period is 4). A box will appear where you need to confirm your purchase. Once
your purchase is confirmed (or if you choose to cancel), you will be returned to
the Shop screen (not to your home screen). If you make a purchase, a box will
briefly appear informing you of the number of units you have successfully
bought.
OFT1226 | 167
If you don’t want to buy anything (because you find the prices too high or you
first want to check out the other shop) or once you have finished shopping, you
can click the Go home button to return to the home screen.
You can go back and forth between your home screen and the shops as often
as you like but notice that you are charged for every trip you make. Once you
are done with your shopping for a period, you can click the I’m done button at
the bottom right corner of your home screen and proceed to the results screen,
where you are informed of your decisions and earnings in the period.
Your earnings in each period depend on the number of units of the product you
bought as shown in the attached table. From these points, we take away your
travel costs and the amount of points you spent on buying the product.
For example, suppose in a period the GREEN product is available to buy and that
travel costs are 3 and you take the following actions:
1. Go to shop 2 and observe a price of 48 per unit for GREEN.
2. Go back to the home screen.
3. Go to shop 1 and observe a price of 31 per unit for GREEN.
4. Buy 3 units of GREEN at shop 1. [You are still at shop 1 after the purchase]
5. Go to home screen.
6. Click 'I’m done'
Then your earnings for the period would be:
Points for buying 3 units of GREEN: 110
Minus travel costs (two visits to the shops): 2 * 3 = 6
Minus cost of buying 3 units in shop 1: 3 * 31 = 93
Your earnings for the period would be: 110 – 6 – 93 = 11 points
OFT1226 | 168
The prices shown above are purely for example and the strategy of the shopper
in this case may or may not be good. The real prices for GREEN that you will
encounter will be randomly drawn each period between 30 and 60.
Before the experiment, there will be a short quiz on the screen to check that you
understand these instructions. You will need to get all the answers right before
you can proceed to the experiment, but you can have a second, third, etc. go if
you get an answer wrong.
After the experiment, there will be another on-screen quiz consisting of 12
multiple choice questions. Instructions will appear on the screen. If you get more
than 10 correct, we will pay you an extra £2 or if you get more than eight
correct we will pay an extra £1. You have a total of eight minutes to complete
the quiz – it will time out after this point.
Following this quiz, there will be two short questionnaires where we ask you to
give us your thoughts on the experiment, your strategy and about yourself.
At the end of the experiment, you will be asked to fill in a receipt. We will call
you up to the front, one by one, to pay you in cash. After this, you are free to
go.
Product
0 units 1 unit
2 units 3 units 4 units
Price
Range
GREEN
0
60
100
110
115
30 to 60
ORANGE
0
80
140
170
195
50 to 80
BLUE
0
110
180
190
190
50 to 110
RED
0
120
200
220
230
60 to 120
OFT1226 | 169
C
REFERENCES
Armstrong.M and Zou.J (2010) Conditioning prices on search behaviour,
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