Yeah, thank you very much for the very warm welcome and for the invitation to this very
illustrious lecture series.
It's a great privilege to be here and to be talking to you.
In a sense, what we're going to go through in the next hour or so is three lectures.
I'm sorry.
So in a sense, you get three for the price of one.
We start with a very basic overview on experimental methods and on gathering empirical data.
As Patrick mentioned, it's going to be very, very basic.
So please, if this is all common to you, if this is too basic, just let me know.
We'll skip through quickly.
I have enough material for an entire week probably of lectures.
So no problem.
I'm also happy to take questions while we go.
As Patrick was saying, I'm collaborating with economists in the Max Planck Institute
in Bonnendaire.
It's very common not to wait till the end of the lecture to ask questions, but you're
bombarded with questions right while you go.
And I'm happy to entertain any questions as we go.
Maybe also what I want to mention is that as it's a very basic introduction to the idea
of legal or to the idea of experiments and more specifically legal experiments, this
is by no means complete or in that sense.
So I will not be able to talk to you about the most recent econometric methods.
And I'm sparing questions like pre and post tests that one could do with experiments.
But yeah, we can discuss that if you're interested in that later.
So the first part of the presentation is going to deal with empirical research in general.
And then we'll discuss properties of experiments and design features of experiments, both with
a few specific points that are special to legal experiments or to experiments that deal
with legal questions, if you wish.
And then I'm briefly going to touch on the topic of the validity of results, which is
a question you have generally with empirical data.
And I'm going to tackle it more specifically with a view to experiments.
And then following that, I'll give you two brief examples where most of what we discussed
in the first part, you'll see it's two very different types of experiment.
One is on sticky rebates.
It's on specific rebate schemes.
And one is on a public law question, which is gambling, sport bets.
So let's start with empirical research, with the basics of empirical research.
Why is it that we collect empirical data?
What are we interested in?
There's two large fields that you can work on with empirical data.
First of all, you can collect empirical data to describe social realities.
It's just a descriptive method.
You want to know how the real world is.
The second field, and that's especially with view to experiments, the more interesting
one is we want to learn something about causal connections of social factors.
So however, if we collect empirical data, all we can see is correlations.
We cannot see causalities from the data right away.
Social tests help us establish correlations, see whether there's correlation between different
social factors, but they cannot help us tell anything about causality.
Causality can only be assumed, in a sense, as a logical consequence according to our
understanding of the world.
So if we, from how we see the world, can assume that the correlation can only work in one
direction, then we can say that we have causality there.
Two important terms that are crucial to all empirical research is that of dependent and
independent variable.
The dependent variable is the social phenomenon that you want to explain, whereas the independent
variable is the factor that could possibly explain the dependent variable.
So this is the two, and well, obviously you can have a multitude of dependent and independent
variables.
Oftentimes you'll have one dependent and several independent variables to explain the dependent
one.
Now to give a bit of flesh to the bones, I want to just give you a few examples to see
what I'm talking about.
For example, we could collect data on the democratic standards established in a society,
and we could see whether there are correlations with economic strength.
Now with statistical tests, we can see whether there is connections, in a sense, of correlations
between those two variables.
But if we want to conclude something for causalities, then we can't say whether a democracy
lead it to a higher economic strength, or whether economic strength is an important
prerequisite for a strong democracy, or whether both factors actually interact.
The same holds, for instance, if you look at the educational level of parents and
children, then it's the opposite.
If you see a correlation between the educational level of parents and children, then it's quite
probable that it's not the education level of children that affected the education level
of their parents, but rather the other way around.
So that's where you could probably establish correlations.
One of the problems that we have generally when dealing with empirical data is that in
establishing causalities or correlations, we have to be very careful not to overlook
variables that affect both our observed variables.
For instance, you could check the body height of siblings of brothers and sisters, and if
you see that there's a correlation, this correlation is not due to any causality.
You wouldn't say that the older child's body height had influence on the younger child's
body height.
Rather, probably the influential factor would be the body height of the parents.
So there is an unobserved variable that influences both of them.
So you have to make sure that you get the whole picture.
Another very famous example is the question of the number of firemen and the amount of
damage in a fire.
So you can establish a correlation that the more firemen are at a fire site, the higher
is the damage caused by the cause there.
So this could lead you to the conclusion that the more firemen there is, the more damage
there.
So you should send less firemen so that the damage caused by the fire would be fewer.
But of course, this is not true.
If the fire is greater, then obviously there's more firemen, and therefore also the damage
is higher.
So again, you have an unobserved variable there that you have to take into consideration.
Now how do we find a proper research question?
First of all, we have to identify and define concepts that we actually want to investigate.
We have to see what are the characteristics, and we have to define the concepts.
And this is one of the crucial points where actually we'll see differences between empirical
research across the different disciplines, namely with the definition of the concepts
that we want to study and therefore the wording of the research question.
For instance, just to pick up the example of democracy we were having earlier on, the
characteristics of democracy would probably be different when viewed from a political
scientist's view or from a lawyer's view.
So it's very important to have a clear-cut definition of what is really your research
interest.
Then you have to operationalize the concept.
So now assume we have a good definition of democracy, then how do we actually want to
measure to what extent democracy is implemented in a certain country?
So you'll have to look for indicators or for proxies that can actually measure the concept.
And then to go on with your empirical research, you'll usually have to develop hypotheses
which are either based on theory or which are explorative in nature.
Now what is the right empirical method for our research question?
Basically there's two interesting research paths that we could go through.
One is to collect field data and one is to conduct an experiment.
Field data is the data that we observe in the real world.
So for instance you have statistical yearbooks of the national statistical bureaus or from
OECD, the World Bank, and so on and so forth.
Or you can yourself conduct questionnaire studies.
These are very helpful to conduct broader research questions, but oftentimes it's difficult
to have complete control over the variables that you want to observe.
Or to control for random effects and for noise, you'll have to apply a strong apparatus
of econometric methods to make sure you really see the actual effects you're after.
In contrast you can conduct experiments where the basic idea is that you only vary one factor
per treatment, while all other factors are kept constant.
And you can control for these factors usually because you're conducting the experiment in
a laboratory in a very clean environment where you've control over all external factors.
And you can in certain tasks and settings make sure that you only vary one factor across
the different treatments.
And for the rest of the time you will, for the rest of the effects, for the random effects,
you can neglect them because you can randomly assign your subjects to the different treatments
so that noise shouldn't have an effect on your observations.
Now the downside of this is that the research questions that you can actually deal with
are rather tiny.
And that's basically because you can only vary one single factor, so your research question
has to be focused on this one single factor, at least in theory.
This is just to give you a basic idea of how you conduct different empirical research paths.
And then the control over the data and the different variables here is gained not by
econometric methods, or at least not at first sight, but rather by the experimental design.
The design of the experiment is such that your data should be clean.
Now of course we can have combinations of these different approaches, and this is what
most of the time we see, we have oftentimes single factors which are observed in the field.
So we try to get the cleanliness of experiments into our field research, and also in experiments
what we see a lot in recent time is that we have a whole battery of sophisticated econometric
methods that we apply to the experimental data.
Now what are the properties of experiments?
First of all, what are the different types of experiments?
We have conventional lab experiments, that's what we're basically going to talk about in
the following few couple of minutes.
And what we're also going to talk about in the second example is the framed field experiment,
where we have in a sense a conventional experiment, but with field context that is added to it.
So it's not really out in the field, it's not that people don't realize they're in an experiment,
so that they know they're in an experiment, but they have a complete field context.
Just one little hint to quasi-experiments, there's a very nice study by Sloan et al.
on the effect of the right to carry arms and crime, and what he did is that he collected
data from Seattle and Vancouver, Seattle being in the States and having a right to carry,
whereas Vancouver in Canada has a strict regulation on owning arms, and the rest of the factors
that Sloan and his colleagues deem relevant, both cities are the same.
So in a sense, of course it's rather a field thing, but the idea is that this could be
something closer to an experiment, that's why they call it a quasi-experiment.
Now what does an experiment actually look like?
I brought you a few pictures from the Bond Econ lab, most of the time you work on computers,
there's software that is designed specifically for the experiment that you want to conduct,
there's cabins, everyone sits in one of these cabins with one of the computers, in the beginning
what we see here, you introduce the subjects to the experiment, you read to them the instructions,
you discuss questions, often at times you have a little test to see whether they really
understood the task, then they go to the cabins and they can still ask questions or sometimes
they have problems with the software and you can help them, and well yeah, you have a good
control center, a star trek like command central, where as an experimenter you sit
and feel very powerful, and actually are keen to see why people enter their data, whether
really what your hypothesis was turns to be true or not.
What are the basic properties of an experiment?
What are the basic terms, what are the basic ideas that we deal with?
First of all, I already introduced the word treatment, a treatment is a particular condition
of an experiment, usually you have at least two treatments, first the main effect treatment
where you want to see the effect that you hypothesize about, or that is in your research
question and then you also have a control treatment in which the effect should not occur,
so you have two groups, the subjects are sent to the groups randomly, so there should be
no effect from the people through the assignment.
Then you have a session, if you remember the pictures, of course I mean in such a lab you
have maybe 12 or 24 seats, but you need a few more observations, so you'll go through
a number of sessions and repeat the experiment with different groups, so a session is a specific
date and time, and this is interesting because with some tasks, especially it's been shown
with mathematical tasks that involve a bit of math, you see that groups that are early
in the morning are worse off than those later in the day, so it's also effects that you
can have per session.
A subject, I used the word a couple of times, a participant in an experiment, a matching
group is a group set together in a session of people interacting with each other, so often
we want to see how people, in these experiments we want to see how people interact in social
dilemma situations, and in these situations you form matching groups where for instance
four subjects play a game together or so.
And then one of the key words in experimental studies is observation, it's the observed
action of a subject in the first place, but more distinctly we have to be very careful
about the statistically independent observation.
The idea is that in interaction setting, behavior of one person is potentially affected by the
behavior of others, or of other people that she interacts with, so therefore the observation
is not independent, so as I described if you have a matching group of four, you would see
the action of four people, but as these four people might react on each other, it would
not be four observations, or four statistically independent observations, but only one statistically
independent observation.
This is a very important concept when it comes to analyzing your data after all, because
you have to apply different statistical tests and be careful about being clear in here.
And this is one of the criticisms that come up every time that we discuss experiments
in our institute, if someone starts off and then someone finds the question whether he's
really sure that what he's designing is so many or rather only half the number of observations
that he thought, because there's some sort of interaction going on.
What about incentives?
Experiments normally use monetary incentives as opposed, for example, to questionnaires,
and especially experimental economists consider this a crucial feature of experiments.
This is questioned by psychologists.
Psychologists think this is not necessary to pay subjects for each and every question,
so you don't need to incentivize every action, you can just pay them for showing up.
But economists strongly consider it essential that you have to pay the subjects to reveal
their true behavior.
If you think a moment about it, this is quite plausible.
If you are asked in a questionnaire, are you a fair person, and I don't know, do you help
others who are in need, or do you say what you mean, all these things, probably every
one of us would say, yeah, of course, I'm an Oscar, I'm honest, I'm whatever, right?
I would help everyone, anyone who's in need, really, I would help them out.
But then when it comes to a situation where you actually have to act that way, and where
you see, oh, helping someone out costs me something, then your reaction might be different,
and in fact, was shown to be different.
One issue is low stakes versus high stakes.
What you can usually earn during such an experiment as a subject is normally an average
one hour, or pay, say, if the experiment takes an hour, then you usually earn what
your opportunity costs are, right?
So oftentimes what you see is the question, do these experimental results also hold when
they're really high stakes?
If your decision was not about 1 euro 50, but rather about 1,500 euros, would the decision
still be the same?
There have been a whole bunch of studies to show, or to see whether there's a difference
between low and high stakes, and what you could basically say is that the effects are,
in general, very robust, so they're always shown the same direction.
Sometimes they're a bit stronger in high-stake environments, but usually you don't have a
problem there.
Yeah, instead of implementing incentives via money, you could also use real effort tasks,
so people actually, I don't know, solve crossword puzzles, or I don't know, have to build something
with LEGO or so.
What are different design features or design types of experiments?
Those are what we differentiate between within-subject and across-subject design.
The within-subject design, in a within-subject design, subject participate in more than one
treatment, and this has a bunch of advantages.
For instance, it allows individual comparisons, so you can see how the same person reacts
in situation A and in situation B. It allows to control for individual fixed effects, and
there's a number of more powerful statistical tests that you can apply if you have a within-subject
design.
However, there's also a bunch of problems.
For instance, your subject might anticipate or might learn from the first situation, and
so the reaction in the second setting might not be all clean, as if you had only asked
them once.
You also can have order effect problems, so it might matter in which order you presented
the different tasks to them.
You can control for all that, so there's both advantages and disadvantages, and in the across-subject
design, each subject only participates in one treatment and only in one treatment.
In the examples I've brought you, we'll see that, especially in the gambling paradigm,
we have both.
We have a within-subject test, and as a robustness check, we have an across-subject design, and
as a robustness check, we also have a within-subject design.
Different research questions may require that you pick either of them, and important is
that different designs require different statistical tests.
Then we differentiate between one-shot and repeated games or observations.
A one-shot game, obviously, you have one decision, it's taken once, and then the thing is over,
whereas in repeated observations, you have to take the same decision several times, and
as an experimenter, you can observe learning, you can observe certain dynamics, convergence,
and so on and so forth.
There's a whole bunch of problems with repeated observations.
If they're finitely repeated, then you have a problem when your subjects are economists,
they start doing backward induction, especially if you have a unique Nash equilibrium, so
actually what they're going to play in the first round.
The solution, you start with what you should do in the last round, and then you try to
anticipate what you should do in the round before, and so on and so forth.
If you have very good economists in your experiment, this might lead to a complete breakdown of
your experiment, as I've seen once in a public good setting, where the economists from the
first round on played the way that we had expected them to play in the last round.
Then we have another differentiation between partner and stranger design.
If you have matching groups, so if you have people interact with each other, then you
can decide whether you have always the same people interact with each other, or whether
the people who interact with each other change every time.
Both again have different advantages and disadvantages.
With partner design, you lose the number of independent, you have fewer independent observations.
In the stranger design, you need more subjects to actually show your effect.
In the partner design, you might have reputation effects if you always play with the same people.
You might want that, and you might not want that.
It depends on your research question.
Then there is a way to elicit entire strategies.
Sometimes you don't only want to know how people act.
Sometimes you have situations that only occur with a certain probability, but you want to
see how people would react also in the situation that doesn't occur.
What you can do is you can apply the strategy method and ask people to give all their choices
upfront what they would do in which situation, how they would react and how they would act
in each situation.
This would be binding, and then afterwards you would kind of simulate the game with different
probabilities and see what the payoff then would be for the person.
This is very nice because you have lots of data for all nodes, but there is also one
big problem, which is called hot versus cold emotions.
If you're sitting in front of a piece of paper and you're not actually acting, but just deciding
ex ante in a very strategic, very cold, rational setting, then your decisions might be different
than if you sequentially draw and see what the other does and then see what you do.
This might temper the results, if you want.
Also, if you have a large strategy space, this is very difficult to explain method,
and extremely boring if you have more than 100 or 150 decisions.
It may happen that experimental subjects walk out on you because they are bored by your
approach.
There is an alternative to the strategy method, and I'm just going to mention this briefly
because we're going to see that later on.
You can also put them in a hypothetical decision situation, and we're going to see that in
the rebates experiment.
People act, they play the game as they go, and there is one critical moment where we're
interested in the decision independent of what chance move happens.
We ask them before the chance move happens, we ask them how they would decide if A turned
true, and how they would decide if B turned true.
Then the chance move happens and the game goes on from there on.
Belief elicitation.
One very important issue is that it's absolutely an absolute no-go to cheat on your subjects.
I will show you later on why it is tempting to cheat on your subjects.
We have had that, I think even in both our studies, it would have been tempting, we would
have liked to cheat on them, but you really can't do it.
First of all, of course, because it's immoral, I want to make that point very clear.
But then also, even for a long-term perspective, if you weren't so moral as I am, the thing
is that you lose your reputation towards the subjects.
If you deceive them once, chances are that you'll deceive them again, and they won't
take for granted what you tell them in the instructions.
This blurs the incentive structure you've built up, and then also the behavior of the
subjects.
You can't be sure what's going on in their minds when they play.
You can only be sure that they are sure that what you say is true.
In a sense, it's a prisoners dilemma between researchers.
If you were the only one who would deceive the subjects, then that would be fine maybe,
but as we want to keep the subject pool clean, we have to be very clear on that.
And then, of course, you'll be criticized, and your research will never ever, ever, ever
going to be published.
One of the big issues that you have with any empirical data is the validity of the results
that you can conclude.
What does validity mean?
Validity is the limits to the conclusions you can draw from your data.
What we do from the data, the idea behind it is that the data that we collect gives
us an idea about irregularity in the world.
The question is whether this generalization is okay.
We differentiate three types of validity, internal, external, and statistical validity.
Your point is external validity, I'll be right on that.
What's internal validity?
Internal validity refers to the coherence of your empirical result.
It's easiest to explain that by examples.
Results are lacking internal validity if there is noise that is correlated with both the
dependent and the independent variable.
This noise has not been in any way controlled for.
That's why internal validity is one of the strongest reasons for conducting experiments,
because you can create such a clean environment.
One of the classical problems would be that, for instance, subjects were not randomly assigned
to the treatment group.
That would be a reason against internal validity.
External validity refers more generally to the generalizability of the results.
That's what you're alluding to.
The collection of our empirical data is usually limited to special contexts, to special situations,
to special groups, etc., etc.
If you have an experiment with male German workers, then you can only generalize this
to the group of male German workers if your sample was drawn from this population randomly.
Then you could generalize it to this, but you could not generalize your results to female
workers or to, as I said, Chinese entrepreneurs, for instance.
This, of course, is a big question that is always asked when conducting experiments.
Usually in the laboratory experimental labs, you have students who go through the experiments,
and is that a problem?
We've seen that for most of the strong behavioral effects, it's not an issue.
They have been replicated with other groups, and you can usually see that they occur in
other groups too.
The only real way to make sure that this is the case is to replicate the result with different
groups or with larger groups or so on.
When internal validity is the strongest reason for conducting experiments, then external
validity is the strongest reason against conducting experiments.
You always strip the situation of context, and you have to make sure that still the remaining
situation has still all characteristics of the situation that you want to generalize
to.
This is especially a point with legal experiments.
Statistical validity, in a sense, is basically the idea that your results should not be due
to chance.
What's the probability of your results being based on chance?
This is where the statistical tests come in, and where we can help with the help of
the law of large number actually get around these problems easiest.
As a rule of thumb, maybe that's important.
In many experimental settings, 30 to 50 participants will be sufficient to show a strong experimental
effect or a strong effect.
Just to give you a little idea why this is the case, a quick example, imagine you have
earned with 6,000 balls, of which 75% are blue and the rest is gray.
If you stimulate the data, you would need something like 250 draws for a correct prediction
of the distribution, but you would only need about 20 draws to show that there are significantly
more blue than gray balls in the Earth.
This is actually how experiments work and why, with the help of the law of large numbers,
you can get through with a rather small number of experimental observations.
That would be in a nutshell, which has not been quite the nutshell I had expected it
to be, what I wanted to tell you generally about experimental setup.
Any questions regarding that?
If that's not the case, then let's go on with the rebates experiment.
I'll try to be very quick on that because I really love the gambling experiment and
I really want to go through that, working through that.
The sticky rebates is a classically aligned economics theory-driven experiment.
An experiment which is very strongly related to a legal question.
That's why I want to walk you through that.
What's it all about?
We're talking about targeted rollback rebates.
What's a targeted rollback rebate?
You all know, maybe you have a Miles and More card from Lufthansa or you have a coffee shop
card where you get a stamp each time you buy an espresso.
If you have, I don't know, ten espressos, then one is on the house.
Or here, if you have a certain amount of Miles in a certain period of time, then you get
a free flight or rebate on all subsequent flights.
There's a whole bunch of real-world examples of rollback rebates, both in retail markets
and in consumer markets.
They have been a very controversial issue in antitrust regulation and they've been
dealt with differently in the US and in the European Union.
In the US, the issue is discussed under Section 2 of the Sherman Act, under the issue of
monopolization and in the EU, it's about the abuse of market dominance according to Article
82EC.
And we have different cases in both legal systems.
In the US, and this makes the whole issue really interesting, in the US, rebates are
close to per se legal, whereas in the EU, rebates are close to per se illegal.
I'm simplifying things, so if you're very much into it, please bear with me if I, for
the sake of time, rush through this and make it a bit simplified a little bit.
The underlying assumption in the US is that buyers are rational, they behave according
to rational choice, whereas the DG Comp, or yeah, in the Mission 2 case, referred to the
weak psychological position of dealers in a retail market, so they allude to some behavioral
effects that might be non-rational.
We know that from rational choice theory, that targeted rebates can have different effects,
so that we have ambiguous predictions.
We know that targeted rebates may lead to market foreclosure, we know that targeted
rebates may lead to higher prices, this is established in the literature, and it's also
established that targeted rebates may reduce inefficiencies in the market.
So we don't really know what targeted rebates may do.
One thing that I want to quickly explain is the suction effect, because to make sure you
understand what we're not after.
The suction effect is an effect that can foreclose a market even if the buyers behave rationally.
How does that work?
What's the suction effect?
Assume you have, I don't know, maybe a dozen of lectures, Hamburg lectures on law and economics,
and for each of the speakers, you buy a bottle of water, and the incumbent on the market
knows that your demand of water is 10 bottles in each series, so he gives you a certain price
for the bottle.
An entrant wants to enter the market, and the entrant only has the possibility to produce
two bottles of the water, but his production costs are much lower than that of the incumbent.
Now what's the incumbent going to do if he can imply a targeted rebate scheme?
He's going to sell you eight to a huge price, whatever he wants to take, because you have
to buy the eight from him, and he's going to say, if you have reached the target of
eight bottles, then two are going to be free, and these are exactly the two that the entrant
would be able to sell to you.
If he was really nasty, you could even say, for the last two, I give you a euro if you
buy them with me, because he can refinance this with the eight bottles he sold to you,
and you have to buy with him.
So this is what we call suction effect, this is rational, there's no behavioral effect
in that, and that's not what we're after.
What we're after is a behavioral question, in a sense on top of this rational considerations,
what we want to see is whether the uncertainty that is created by this rollback rebates,
the uncertainty whether you're going to reach the threshold, whether that actually changes
your behavior, and whether that makes you stick with the choice with the rebate, rather
than move to a cheaper or outside option.
Our hypotheses are derived from prospect theory, Kahneman and Tversky.
The idea they have is that people have a reference point in terms of consumer rent, and if the
payoff is lower than the reference point, then people are in the loss frame, they code
this as a loss, and if the payoff is larger than the reference point, then they consider
this a gain.
What Kahneman and Tversky say and have shown empirically is that when people are in the
loss frame, they tend to be risk seeking.
If we derive our hypothesis from this, the null hypothesis would be that people behave
rationally according to rational choice.
Our first alternative hypothesis would be that this rebates induce an irrational continued
buying for the initially selected rebated option, the intuition being that the reference
point is shifted to the goal.
The price you consider is not the price that you actually pay for the good, but the price
that you can achieve if you achieve the rebate.
That's your reference point.
Anything that's going to be more expensive than the rebated price is going to be coded
as a loss.
It's going to be considered as a loss, and therefore people are going to be risk seeking
because all other prices will be a loss, and select the risky rebate scheme, continue
to select that, instead of the cheaper outside option, that is the outside option with higher
expected utility, so they're not going to switch away from the rebate scheme.
Just going to show you the experimental setup.
People could buy tokens in our very context-stripped environment in our experiment.
They could buy tokens, either rebated tokens in a rebate scheme, or they could buy an outside
option token.
Initially, the expected value was higher if you bought the rebated token.
Then you would buy that for a while.
We had different lengths of periods.
You could buy that for a while, and then there was a chance move in the critical round.
If the critical round did not take place, the idea is you got the rebate if you had
more than nine tokens in the rebate scheme.
You did not get the rebate if you had bought less than nine tokens in the rebate scheme.
In the critical round, the critical round could either take place or be skipped, and
the same was true for the last round.
There was a certain probability that you would not reach your target.
When the critical round did not take place, and this was with probability of 17%, then
it would have been rational to exit to the outside option, and buy the remaining tokens
from the outside option.
That would have been the rational choice with a higher expected value.
If people, however, still continued to buy this, then this is what we would call stickiness.
They would stick to the rebate scheme.
As one of the control treatments that we implemented, if the critical round took place, then they
should stay within the rebate scheme.
That was our experimental setup.
They had screens where they could buy a token, either token A for the outside option or token
B for the rebate scheme option.
To introduce the uncertainty, or in this sense, we even only introduced risk for them not
to reach the rebate threshold.
Two rounds, namely the critical round and the last round, could be blocked.
They could be barred from playing.
They would only see a red screen saying, this round was skipped, you can't play.
I don't know, if you have a flight, you want to have, for Lufthansa to get status, you
have to reach 30 flights this year.
If you get sick on your 28th and 29th, yeah, exactly, that's the idea, volcanic ashes.
This is the uncertainty that comes in there.
The way we designed the experiment is that the critical round is decisive because when
the critical round is not played, the expected value of staying in the rebate scheme is significantly
lower when exiting and going to the outside option, independent of the last round.
We followed expected values, that's the external norm against which we measured the stickiness,
and moreover, we had to make sure that our results were not based on strange risk behavior
or loss behavior, we also checked risk aversion and loss aversion.
We had four treatments, which had to do with our second and third hypothesis, which also
depended on the variance, low and high variance of, I'll skip that, what were the results?
The results were that clearly in the experiment, the rebates were sticky.
What do we see here?
We see that whenever people should buy rebates, they all bought rebates, they did buy rebates,
but if they should not buy it, so whenever they should exit the rebate scheme, they would
not.
If you apply a simple chi-square test, then you can see this is an extremely significant
effect.
We also double checked that with a second control treatment, which was just a lottery
which had the same expected values than the game that we had them play, and we then checked
the results from the lottery against those of the rebate, and even there, although we
see that even in the lottery, people tend to be risk seeking and therefore not maximize
over expected values, still the difference is highly significant.
I just want to quickly go through a logistic regression we've run on the irrational buying.
What we see is that the sunk cost effect, this has to do with the second and third hypothesis
that I skipped now, so I'm not going to tell you these, but what I'm going to tell you
is this effect, I like that, which shows that females stick stronger.
Really significantly stronger, so the suggestion would be to all women to throw away their
rebate cards because it's going to make them stick.
Quickly to the discussion, we can reject our null hypothesis that people actually behave
rationally.
We have the sickness effect, so targeted rollback rebates, I showed that, are indeed sticky.
I've told you about the gender effect, and we also have, I didn't show that in the regression,
this is very interesting, we have an interaction effect of payoff variance and sunk cost.
So if both are high, if we have massive high risk and high stakes, then people are alert,
and this in a sense is good news.
As a rebate designer, you have to be very careful not to overdo it.
Our suggestion is to do, in fact, model buyers as susceptible to stickiness.
Prospect theory explains most of our effects.
I've told you about the good news, and we have two strands of further research.
One of them is that we have to adapt the standard models and to adapt our research
to other rebate models and check the robustness, that is the external validity of the results,
and we have to see how do buyers actually decide whether they enter or don't enter a
rebate scheme, because we still don't know much about whether people are reluctant to
enter rebates or not.
We've seen in our experiment, we've had of, I think, 98 participants, we have had four
who were consequent rebate evaders.
They didn't go into a rebate scheme right from the beginning, and they stayed out of
it consequently.
So there seem to be people who are reluctant to go into rebates at all.
Policy implications, we do find stickiness effect on top of the suction effect, so the
market foreclosure effect of rebates might be underestimated, and this actually does
give support to the European perspective on rebates rather than to the American.
That's in a nutshell.
Well, not in a nutshell, not so much in a nutshell.
What I had to tell you about rebates, and I'm sorry, I kind of lost track of time, so
for the, I guess, for the skillful gambling, you have to come and visit me in Bonn.
I'm going to tell you there about that.
Thank you very much.