Monetary incentives in the loss domain
An experimental comparison of three rewarding schemes
including real losses

Nathalie Etchart-Vincent (CNRS-CIRED)&
Olivier L’Haridon(GREGHEC)

The status of monetary incentives in economics
 A drawing line between experimental economists and decision theorists
 Some flexibility among the latter Some intransigency among the former
 A symptomatic feature: publications! The lack of monetary incentives seems to bea sufficient reason to motivate a rejection in most economic journals

 The usual argument among economists
 The decision maker is an homo economicus who does not think/work
hard unless she is paid and sufficiently paid for that
Davis and Holt (1993); Gibbons (1997); Harrison (1994); Lazear(2000); Smith (1976); Smith and Levin (1996)• Without any performance-based procedure, subjects will not make any effort
when answering the questions• Without any (cognitive) effort, poor performance and unreliable data Empirically, monetary incentives seem to reduce the variance of thedata and favour convergence towards optimality
Hertwig and Ortmann (2001); Burke (1996); Harrison and
Rutström (2004); van Wallendael and Guignard (1992)

Some counterarguments (1) The link between monetary incentives, effort andperformance is not so tight (Extrinsic) monetary incentives do not (always) affecteffort, while intrinsic motivation does
Bonner and Sprinkle (2002); Camerer (1995);
Henrich (2001); Lee, Locke and Phan
(1997); Ryan and Deci (2000)
 Effort does not (always) improve performance (due tocognitive limitations, for instance)
Camerer (1992); Tversky and Kahneman (1992)

Some counterarguments (2) Monetary incentives may be detrimental to intrinsic
motivation and reduce performance Monetary incentives are likely to harm intrinsic motivation andbe detrimental to effort, thus to performance
Baron (2000); Deci, Koestner and Ryan (1999); Frey
(1997); Frey and Oberholzer-Gee (1997); Gneezy
and Rustichini (2000); Kreps (1997)
 Monetary incentives may result in greater effort … but lower
performance, be it:• because capital is more important than labor for the task underconsideration
Camerer and Hogarth (1999)• because incentives induce some distorsive rationalization of an
intuition-based decision
Haberstroh, Betsch and Aarts (2000); Slovic et al.
(2004); Wilson and Schooler (1991)

A question that is still in debate Somewhat contradictory meta-analytic studies or surveys:
Bonner and Sprinkle (2002), Camerer and Hogarth (1999),
Hertwig and Ortmann (2001), Jenkins et al. (1998), Read(2005) …
 Holt and Laury (2002)’s well-known recent study: Directly confronts Kahneman and Tversky (1979)’s claim that hypotheticalchoices are a good proxy of real choices.
 Show that real incentives change behavior (towards more RA).
 But also a much criticized study (Heinemann, 2003; Harrison, Johnson,
McInnes & Rutström, 2005)

 In question: opportunistic samples, the selection of the subjects may
sharply influence the results
Hertwig and Ortmann (2007)
 Moderate incentives seem to improve performance, but high incentives
appear to decrease it
Pokorny (2007)

The positioning of our study Evaluate the impact of incentives on a kind of behaviour forwhich no performance criterium exists: behaviour towards risk
 Evaluate the impact of incentives in a specific domain: the loss
domain (delicate implementation of incentives) Compare some simple procedures that can be easily
reproduced in any experiment
Æ We are not testing any sophisticated procedureÆ Focus on a very specific pointÆ No claim to provide a definitive answer to thecomplex incentive question in generalA specific case: experimenting over losses (1) An ethical difficulty: Making subjects lose their own money is ethicallyquestionable A first practical difficulty: Most subjects may not accept to take part in anexperiment that may make them lose their own money
 A second practical difficulty: Hypothetical choices are often considered as …
non realistic, thus leading to meaningless and unreliable data.
e.g.: Cox and Grether (1996), Harrison (1994), Kühberger et al.
(2002)

A specific case: experimenting over losses (2) A third practical difficulty: The most natural procedure (an initialendowment from which subjects can lose without losing their own money)
can be subject to some biases, and especially a « house money » effect(Thaler and Johnson, 1990)
Typically, the ‘house money’ effect consists in more risk seeking (RS)after a prior gain
Æ Arkes and Blumer (1985), Gärling and Romanus (1997), Romanus et al.
(1996), Thaler and Johnson (1990) actually find more RS when a prior gain
is introduced
Æ Arkes et al. (1988) and Isen and Patrick (1985) find less RS whenlosses are realÆ Clark (2002) does not find any ‘house money’ effect either (but:Harrison, 2006 criticizes the statistical analysis of the data)

So: What to do in the loss domain? Which payment scheme to adopt?

Our basic assumption
The random-lottery procedure with real losses is a procedure thatallows to capture the subject’s genuine preferencesÆ we use it as a benchmark allowing to evaluate the accuracy ofother payment procedures

 Some other studies consider that the genuine real procedure =a procedure involving a unique choice
Beattie and Loomes (1997)
Cubitt, Starmer and Sugden (1991, 1998)
 But: cannot be usually considered as an appropriate
experimental payment procedure (because experiments neverintroduce a unique choice).

Qu.1: Are monetary incentives necessary in the loss domain?
OR
Do hypothetical choices differ from real ones?
 Difficult to introduce real losses in an experiment. So:
Can hypothetical losses be considered as an acceptable proxy?

 The potential bias: If subjects do not consider hypothetical losses
seriously, they may be tempted to take more risks than when they arelikely to lose their own money
 Tested assumption
A1: Subjects exhibit more RS when choices are hypothetical than real
Æ Comparison between hypothetical losses and real losses

(Subsidiary) Qu.2:
Are monetary incentives necessary in the gain domain?
OR
Do hypothetical choices differ from real ones?
 The potential bias: If subjects do not consider hypothetical gains
seriously, they may be tempted to take more risks (or be less risk
averse) than when they are really likely to win

Tested assumption

A2: Subjects exhibit more RS when choices are hypothetical than real
Æ Comparison between hypothetical gains and real gains

Qu.3: Is the usual loss-with-initial-endowment
procedure appropriate? (1) Difficult to introduce real losses in an experiment. So:
Can ‘covered’ losses be considered as an acceptable proxy?
 Two potential and opposite biases (Thaler and Johnson, 1990) :3.1: the « prospect theory with memory » effect Subjects may translate losses into gains (by substracting each loss
from the initial endowment) Insofar as people do not treat losses as gains (convex utility vs. concave
utility; RS vs. RA), the procedure is biased towards RA

3.2: the « house money » effect Subjects may not consider losses as real, and be playing with housemoney The procedure is biased towards RS

Qu.3: Is the usual loss-with-initial-endowment
procedure appropriate? (2) Tested assumptions
A3.1: When losses are ‘covered’, subjects
 behave as if they were facing the corresponding gains and exhibit less risk seeking than when losses are real

Æ Comparison between 'covered' losses recoded as gains
and the corresponding real gains

A3.2: When losses are ‘covered’, subjects
 exhibit more risk seeking than when losses are real

Æ Comparison between 'covered losses' and real losses

How to test the four assumptions (andanswer the research questions)Hypo.
lossesReallosses
Real
gains

A1

Real
lossesCov.
losses

Hypo.
gains

A3.2

A3.1
(recoded)

A2

Real
gains

The experimental design (1) A within-subject design More reliable than a between-subject design But a potential bias: memory effects across sessions

 A paper-and-pencil series of questionnaires allowing toinvestigate:
 each subject’s risk attitude through the determination ofcertainty equivalents
 the subjective components of risk attitude (utility, probability
weighting)The experimental design (2) A questionnaire with both loss and gain choice situations
 Hypothetical, real and 'covered' losses, only the incentive
procedure differs (A1, A3.2)
 Real gains:Æ to allow the comparison with 'covered' losses (to see whether
people consider ‘covered’ losses as if they were gains andrecode them as such) (A3.1)
ANDÆ to make the experiment more attractive!
 Hypothetical gains (as a bonus) :Æ to allow the comparison between real gains and hypotheticalones (A2)
 To make comparisons possible, gain choice situations are built fromloss ones by a simple translation (+20 euros, being the initialendowment in the 'covered' treatment)
Ex: (-5, 0.75; -15, 0.25) is translated into (15, 0.75; 5, 0.25)

The experimental design (3)A gain and a loss part in each questionnaireHalf of the subjects first answer loss (resp. gain) questions
To allow the detection of any order effectIn each part, 2 sub-parts

1st one: « Outcome part » Æ Consequences vary but notprobabilities.
Three different orders to avoid any order effect
NB: Gains: 75% chances (high p) of winning the highest
amountLosses: 25% chances (low p) of losing the largest
amount

2nd one: « Probability part » Æ Probabilities vary but notconsequences.
Same order for every subject (from the lowest to the highest
probability)

The experimental design (4) For each subject, the sessions are strictly identical(same questionnaire, same order), except the payment
scheme Session 1: hypothetical gains and losses
 Session 2: real gains and losses
 Session 3: hypothetical gains (only for the sake of control)and ‘covered’ real losses (with an initial endowment) The variables we aim at comparing
 Primarily: Certainty equivalents
 Secondarily:– Utility and probability weighting parameters– Probability weights

The experimental procedure (1) 46 voluntary subjects
 Possible selection bias towards risk seeking (only risk seekers may be interested
taking part in such an experiment) But: the typical findings seem to have the same flavour as in previous studies(with even more risk aversion …)
 Significant gains and losses
 Highest gain on the whole experiment: 80 euros
 Largest loss: 20 euros
 The asymmetry is meant to counter loss aversion and make the experimentattractive The pretty high level of losses is meant to make them matter for the subject
 12 loss situations and the corresponding 12 gain situations
 [-10, -5] and [10, 15], [-20, 0] and [0, 20], etc.
 + a special gain situation [0, 60] to make the experiment more attractive
 3 widely time-spaced sessions (15 days at least between each session, andusually 3 weeks including a vacation)
 Small groups from 1 to 7 subjects

The experimental procedure (2) Two parts (gains/losses) and two sub-parts
 Outcome part:
9 Only consequences vary, with p=0.75 for the best consequence in each task)
9 Allows the estimation of utility function u (using an expo-power parametric form); Probability part:
9 Consequences are kept constant (0/(-)20) while probabilities vary (5 probas from0.05 to 0.95)
9 Allows the estimation of probability weighting function w (using Goldstein andEinhorn, 1987 and Prelec, 2000, two-parameter parametric specifications).
 Controlling for order effects
 18 gain/loss and 18 loss/gain questionnaires to detect a potential order effectbetween the domains of consequences
 3 different questionnaires A, B and C to avoid any order effects across lotteries in the« outcome part » of the questionnaire Because abrupt changes in probability are cognitively complicated to deal with andtend to result in inconsistencies, the same increasing (from 0.05 to 0.95) order waschosen for everyone in the « probability part » of the questionnaires
 Checking consistency
 Loss part: Lottery (0, 0.75; -20) is given in both the outcome and probability parts
 Gain part: Lottery (0, 0.25; 20) is given in both the outcome and probability parts

A typical choice situation
Situation A

Situation B(aléatoire)(certaine)
Vous
Vous
Choisissez Choisissez
A
B
X- 5.00 €- 5.50 €- 6.00 €- 6.50 €- 7.00 €- 7.50 €- 8.00 €- 15 €- 8.50 €25%- 9.00 €-5 €- 9.50 €75%- 10.00 €- 10.50 €- 11.00 €- 11.50 €- 12.00 €- 12.50 €- 13.00 €- 13.50 €- 14.00 €- 14.50 €X- 15.00 €Instructions to the subjects (1) The recruitment campaign Æ only little information about the expe.
 Subjects aware they can lose or win
 They know the probability of winning at the end of the experiment ishigher, with a potential gain of 80 euros and a potential loss of 20 euros
 No information as regards the precise probability of gain and loss No information as regards the content of each session
 Students who accept to participate must (morally) commit themselves totake part in the whole experiment (no defection actually)
 Session 1: Hypothetical gains and losses At the beginning of the session, subjects are informed they will receive a
3 euros flat payment at the end of the session.
 They are also told they should put themselves in each choice situationand think seriously as if they could lose or win for realInstructions to the subjects (2) Session 2: Real gains and losses At the beginning of the session, subjects are informed that their final payment will
be determined as follows. They will be invited to pick up:• a situation among the 25 choice situations (which can be either a gain or loss
situation), and then
• a specific line in the selected choice situation, that will be played for real. Theirearnings or losses will depend on the choice they made when fulling in thequestionnaire.
 They are told that their interest is to answer sincerely and to carefully think about
their choices
 Session 3: Hypothetical gains and ‘covered’ losses At the beginning of the session, subjects are given a 20 euros endowment and they
are informed that their final payment will be determined as follows:• Gain situations are only hypothetical• A loss situation, and then a question, will be picked up and played for real todetermine their final payment as in the second session• To help them consider the losses as real losses, they are presented thelotteries as inducing them to potentially lose an amount comprised between
nothing and 20 euros.
NB: A more sophisticated and theoretically efficient procedure would have been to
supply the subjects with the initial endowment a few days, weeks or even months before
the experiment (as in Thaler and Johnson, 1990, Laury, 2006 or BoschDomenech and Silvestre, 2006 for instance) to minimize integration. But ourintention was to use a procedure that could be easily reproduced.

Some general results (1) Subjects’ consistency:– Rather good
• Losses: p = 0.95 in hypo, p = 0.1 in real, p = 0.73 in ‘covered’.
Consistency is even better in hypothetical choices!!
• Gains: p = 0.34 in hypo, p = 0.26 in real
– No difference in consistency between the 3 treatments (Anova andFriedman tests)
 Order effects:– See whether subjects who answered the loss (resp. gain) part before theloss (resp. gain) part behave differently– No significant effect Gain/loss effects:– See whether subjects who lost (their own) money in the second session
behave differently in the 3rd session as those who have won– Mann-Whitney tests on each pair of ECs: No significant effectSome general results (2) Gender effects:– See whether males behave differently as females– No significant effectÆ Pooling of the data An opportunistic sample? Æ No apparent selection bias
– Subjects’ behavior appears to be consistent with the usual fourfold
pattern:
Small probabilities

High probabilities

Gains

Risk seeking (proba. part)

Risk aversion (outcome
part)LossesRisk aversion (outcome part)

Risk seeking (proba. part)– Utility is convex over losses and concave over gains– Underweighting of small probabilities and Overweighting of large probabilities

LOSSES, outcome part: the CE distributions-10€; 0€-20€; 0€-10€; -5€-15€; -5€-20€; -15€

Covered Losses

Real Losses

Hypothetical Losses-5€; 0€

-5

-2.5

0 -10

-5

0 -20

-10

0 -10

-7.5

-5 -15-10-5 -20-17.5-15 EC < EV: risk aversion
 Consistent with the 4-fold pattern: RA in losses with low probability (0.25)

Some statistics
 EC by EC
 Very similar distributions between the sessions (no significant difference using
Kolmogorov-Smirnov tests)
 Very similar CE between sessions (Friedman tests all p-values >0.26)
 Less dispersion in the real treatment than in the hypothetical one (as measured
by the IQR). But no significant difference in variance (Levene’s test)
 Only 1 significant difference ((-15; -10), hypo vs covered) (Wilcoxon tests onpairs of ECs)

 Factorial Anovas
 For each lottery, the risk premium RP=EV-CE is computed
 Anova repeated measures on RP
9 Repeated: Lottery, session
9 Between: Gain/Loss order, Lottery order, gender Significant effect on RP:
9 Lottery (p = 0.0001)
9 Interaction lottery x session (p = 0.001): Sessions only have an indirect
effect9 No order effect, no gender effectPooled risk premia
Risk premia, Hypothetical Losses

100
50
0
-5

-4

-3

-2

-1

0

1

2

3

4

5

2

3

4

5

2

3

4

5

Risk premia, Real Losses

100
50
0
-5

-4

-3

-2

-1

0

1

Risk premia, Covered Losses

100
50
0
-5

-4

-3

-2

-1

0

1

Higly similar distributions !!!

LOSSES, probability part: the CE distributions
p = 0.25

p = 0.5

p = 0.75

p = 0.95

CoveredReal

Hypotheticalp = 0.05

 EC vs. EV depends on the probability Æ RS increases with p Consistent with the 4-fold pattern: RA in losses with low probability, RS inlosses with moderate and high probability

Some statistics

 Very similar distributions (no difference using KS or Friedman,
except for p = 75%)
 EC by EC
 No significant difference hypo/real, hypo/couv
 2 significant differences real/covered (50% and 75%)

GAINS, outcome part: the CE distributions
5€; 15€10€; 15€0€; 20€10€; 20€15€; 20€

Real Gains

Hypothetical Gains0€; 5€

0




2.5

5

5

10

15

10

12.5

15

0

10

20

10

15

20

15

17.5

20

EC < EV: RA
Consistent with the 4-fold pattern: RA in gains with high probability (0.75)

Some statistics
 EC by EC
 Very similar distributions between sessions (no significant difference using KS tests)
 Only 1 significant difference hypo/real (0; 20) (using Wilcoxon tests on pairs of ECs) BUT: Only 1 NON significant difference real/recoded ‘covered’ losses (0; 5)(Wilcoxon)
 AND: 2 NON significant differences hypo/recoded ‘covered’ losses (0; 20); (10,20) (Wilcoxon)

 Factorial Anovas
 For each lottery, the risk premium RP=EV-CE is computed
 Anova repeated measures on RP
9 Repeated: Lottery, session
9 Between: Gain/Loss order, Lottery order, gender Significant effect on RP:
9 Lottery (p = 0.0001)
9 Session (p = 0.03)
9 Interaction lottery x session (p = 0.004): Sessions also have an indirect effect
9 No order effect, no gender effectPooled risk premia
Risk premia, Hypothetical Gains

100
50
0
-5

-4

-3

-2

-1

0

1

2

3

4

5

2

3

4

5

Risk premia, Real Gains

100
50
0
-5

-4

-3

-2

-1

0

1

Higly similar distributions !!!

Gains, probability part: the CE distributions
p = 0.25

p = 0.5

p = 0.75

p = 0.95

RealHypotheticalp = 0.05

0

10
EV:1€

20

0

10
EV:5€

20

0

10
EV:10€

20

0

10
EV:15€

20

0

10

20

EV:19€

 EC vs. EV depends on the probability Æ RA increases with p Consistent with the 4-fold pattern: RS in gains with low probability, RA ingains with moderate and high probability

Some statistics
 Very similar distributions (no difference using KS or Friedman) EC by EC
 No significant difference real/recoded ‘covered’ losses andhypo/recoded ‘covered’ losses
 3 significant differences hypo/real (intermediate probabilities: 25%,
50%, 75%)
Æ Similar extreme CEs (cf. possibility and certainty effects), but different
intermediary CEs between sessions (Wilcoxon, p < 0.05)

The utility functions under PT(with expo-power specification)Median data

Expo-power
LossesGains

Hypothetical

1.31

1.44

Real

1.21

1.17

Covered1.27

 No difference in losses
Some difference in gains

The pwf in the 3 loss situations
1

Hypothetical
Real
Covered

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

The pwf in the 2 gain situations
1

0.9

Hypothetical
Real

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

The probability weighting functions(with GE specification & expo-power utility)
Median data

Goldstein and Einhorn (1987)
LossesGains

Curvature

Elevation

Curvature

Elevation

Hypothetical

0.61

0.90

0.63

0.82

Real

0.53

1.15

0.56

0.89

Covered

0.64

1.08

 No difference in losses
No difference in gains

Some implications of the results
 No significant difference between Real, Covered andHypothetical losses as regards behaviour (A1, A3.2)
Æ One should be allowed to choose his/her preferred payment
procedure in the loss domain (when investigating risk attitude) Significant difference between (recoded) Covered losses andReal gains (A3.1)
Æ No house money effect.
Æ Instead of using hypothetical losses, one may prefer tointroduce a loss-with-initial-endowment procedure (which is a
performance-based procedure) Difference between Hypothetical gains and Real gains: real
gains seem to generate more risk aversion (A2)
Æ A performance-based procedure should be used in the gain
domain