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TRANSCRIPT
Rank as an Inherent incentive: Experimental Evidence from a Cognitively Challenging Task
Tony So
University of Auckland
Abstract
We study the impact of relative performance feedback as an inherent incentive mechanism to enhance productivity in a cognitively challenging task. In each of multiple rounds subjects are shown two cue values, Cue A and Cue B, and asked to predict the value of a third variable X, which is a function of the two cue values. We use forecast errors, the absolute difference between the predicted value of X and the actual value of X, as the metric for performance. Our treatments include: (1) piece rates, where subjects are paid on the basis of only their own absolute errors; (2) piece-rate-win-lose, where subjects are paired and paid a piece rate that depends on their own absolute errors only, but informed about whether they did better or worse than their partners; (3) a two-person winner-take-all-tournament where subjects are paired and the one with the smaller error earns a positive payoff while the other earns nothing. We find that average forecast errors are smaller in the piece-rate-win-lose treatment, compared to the piece-rate and the winner-take-all-tournament treatments, with no difference between the last two.
JEL Codes: C91; J24, J33, J39
Keywords: Experiments; Payment schemes; Tournaments; Piece-rates; Productivity; Learning
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Preface
This paper is based upon my recently submitted PhD thesis at the University of
Auckland titled: Essays in Behavioural Labour Economics, supervised by Professor Ananish
Chaudhuri and Dr Ryan Greenaway-McGrevy. The thesis analyses how different pay
schemes and the provision of relative performance feedback affects worker performance.
This paper focuses on the notion of tournament decomposition – separating the effects
of competition for rank and competition for payoffs, both of which may play a part in
motivating performance under tournaments. As will be discussed in greater detail in this
paper, competition for rank refers to people’s desire to compete in order to achieve
favourable comparisons with others, while competition for payoffs refers to the desire to
compete for the monetary prizes which are associated with how people rank within the
tournament.
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1. Introduction
A fundamental question in labour economics relates to how workers are paid and how
productivity is affected by the choice of payment schemes. There is a vast literature looking
at the motivational aspects of various pay schemes. See Prendergast (1999) for a review.
Conventional wisdom in economics suggests that incentive schemes that pay on the basis of
performance, such as output-dependent piece rates, are effective in inducing better
performance (for example, see Lazear (2000)).
Piece rates, however, are inapplicable in situations where output cannot be easily
observed or measured. In these instances, an employer could implement tournament pay
schemes as they do not rely on absolute performance. Rank-order tournaments pay workers a
fixed amount depending on how their performance ranks amongst others, with the ‘prize’
monotonically increasing with rank such that those with a higher rank receive higher pay.
Theoretical analyses of tournaments (Lazear & Rosen, 1981; Green & Stokey, 1983;
Nalebuff & Stiglitz, 1983) show that they are effective in eliciting output at a level analogous
to piece rates. This result is borne out by results in a laboratory experiment by Bull, Schotter,
and Weigelt (1987), where they show that, on average, numerical effort choices made under
tournaments are statistically not different than those under piece rates, though variance of
effort choices under tournaments are larger. There is empirical evidence suggesting that the
rank-dependent incentives of tournaments positively influence behaviour in various field
settings such as corporate promotions and sporting competitions (for example see Eriksson,
1999; Ehrenberg & Bognanno, 1990; Taylor & Trogdon, 2002).
However, while prior work in the area has compared tournaments with piece rates,
one issue has not received enough attention. In moving from piece-rates to tournaments, the
nature of the competition and the inherent incentives change with two distinct components to
that change. The first component – and the more obvious one – arises from the fact that in a
tournament players are competing for a higher payoff, which we will refer to as competing
for payoffs; in a piece rate, payoffs depend on only on one’s own performance while in a
rank-order tournament it depends on one’s rank. If the tournament happens to be of a winner-
take-all type, then coming second implies zero monetary payoff.
But there is a second component to this change, since, in a tournament, agents must
outperform their peers in order to attain a higher rank. While a higher rank may correspond to
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a higher tangible reward (such as promotion tournaments), agents may simply be motivated
by the higher rank itself, in the sense that they derive pleasure or pain from the act of winning
or losing respectively (as in a friendly game of tennis, squash or chess).1 We will refer to this
loosely as competing for rank. There is ample evidence that information about one’s relative
rank, vis-à-vis one’s peers, has a positive impact on performance, even when that higher rank
does not translate into higher monetary payoffs. We provide a brief overview of this literature
in Section 2 below.
In this paper we study the distinction between the notions of competing for rank and
competing for payoffs and their impact on performance in a multiple cue probabilistic
learning (MCPL) task introduced by Brown (1995, 1998). This is a cognitively challenging
task where in each of multiple rounds subjects are shown two cue values and asked to predict
the value of an unknown variable, which is a function of those two cue values. The cue
values shown to subjects change from one round to the next but the underlying function does
not. Subjects do not know what the underlying functional form is but they do know that this
function remains unchanged in every round. The goal for the subjects is to make accurate
predictions on the basis of the cue values shown to them in each round, where accuracy is
measured by the forecast error – the absolute deviation of their predicted value from the
actual value of the variable in that round. Henceforth, we will refer to forecast errors, rather
loosely, as “productivity”, in the sense that smaller errors represent higher productivity and
vice versa.
We implement three different treatments: (1) piece rates, where subjects are paid on
the basis of their own forecast errors; (2) piece-rate-win-lose, where subjects are paired and
paid a piece rate that depends on their own absolute errors only, but informed about whether
they did better or worse than their partners; (3) a two-person winner-take-all-tournament
where subjects are paired and the one with the smaller error earns a positive payoff while the
other earns nothing. Our primary aim is to understand which one of these treatments leads to
the lowest forecast errors.
We explain our design in greater detail in Section 3 below. In summary, we will show
the following. Average productivity is highest (and forecast errors smallest) under the “piece-
rate win-lose” treatment where subjects get paid a piece rate but are also provided with rank
information even though the latter does not impact their earnings. Average productivity in
1 The idea behind rank competition in our study is similar to what Kräkel (2008) describes as “emotions”, where positive and negative emotions are derived from winning and losing respectively.
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this treatment is higher (and average errors smaller) compared to that under a piece rate and a
winner take-all tournament. Echoing the results of Bull et al. (1987), average productivity is
not different under piece rates or tournaments. Our results suggest that, in the MCPL task that
we implement, competing for rank provides better incentives than competing for payoffs.
We proceed as follows. In Section 2 we provide a brief overview of the related
literature that looks at the impact of rank information on productivity. In Section 3 we explain
our experimental design and present several hypotheses. In Section 4 we discuss our findings
and finally in Section 5 we make some concluding comments.
2. Impact of Rank Information
As noted above, one thing that changes in going from a piece rate scheme to a
tournament is that subjects now get to see how they are performing vis-à-vis their peers.
There is now a large literature which suggests that information about rank – even if that rank
does not affect monetary payoffs – has implications for worker productivity. Mas and Moretti
(2009) show that supermarket checkout operators who are paid fixed wages became more
productive when, with a change in shifts, the average productivity of their peers increase.
This effect was found to be attributable to workers who were directly in the line of sight of
co-workers, implying that agents’ productivity improved when they were being observed by
others. In other words, peer pressure is found to have a motivating effect.
Productivity spill-overs and the incentive to free-ride off the effort of fellow workers
are key features of the Mas and Moretti (2009) study. Falk and Ichino (2006) look into peer
effects in the absence of productivity spill-overs. They analyse levels and variance of
performance when students were paid a flat wage to stuff envelopes. In the control treatment,
subjects work alone while in an experimental treatment they work on their own but in the
presence of a second subject located in the same room. In the latter setup, subjects are able to
observe each other’s output. The authors find that performance (in terms of number of
enveloped stuffed) was higher in the experimental treatment where subjects work in pairs.
Furthermore, variation of output within pairs is lower than between pairs, indicating that peer
effects mutually motivated each individual to perform at a level similar to her partner.
Blanes i Vidal and Nossol (2011) assess the effect of relative performance feedback
on the performance of German warehouse workers, who are paid a piece rate on top of a base
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salary.2 Here the workers were notified two months in advance that they would be receiving
additional rank information in their payslips. The revelation of rank information was found to
have a positive effect on productivity. Moreover, a distinct positive effect on productivity
exists even prior to the relative rank information being revealed as long as subjects are aware
that it is impending. In other words, both the anticipation and revelation of relative feedback
has a motivating effect on performance.
In Kuhnen and Tymula (2012) participants solve multiplication problems over a
number of timed rounds and are paid a fixed salary for their participation. In one treatment
participants receive relative performance feedback while in a second they receive such
feedback with 0.5 probability while in a third treatment no feedback is provided. Players in
both certain feedback and probabilistic feedback performed better than those who did not
receive feedback while there are no differences in performance in the first two treatments. It
appears that while feedback matters even the likelihood of receiving feedback can serve as a
motivating force.
Cadsby, Engle-Warnick, Fang and Song (2010) run experiments with Chinese
students and Chinese factory workers, where participants are asked to add five 2-digit
numbers over multiple rounds. The study has a 2 x 2 design which varies payment type –
rank-order tournament or fixed salary – and the nature of feedback after each round – public
or private. Students paid under a tournament scheme perform better when the rank
information is public while the nature of feedback had no effect for factory workers.
Two papers that fail to find a positive impact of peer pressure are Eriksson et al.
(2009) and Bellamare et al. (2010). Eriksson et al.’s subjects undertake a number adding task
like Cadsby et al. while the feedback is either continuous (i.e., up to date feedback is
available at every point in time) or discrete (provided once at the half way mark). There is a
third treatment with no feedback. Eriksson et al. (2009) found no difference in the number of
correct answers between participants who were provided either discrete or continuous
feedback compared to participants who do not receive relative feedback at all. Eriksson et al.
suggest that performance was high in the no feedback treatment and hence there was not
much scope for upward movement in the feedback treatments which may explain the lack of
an effect.
2 In the literature it is usually assumed that payment schemes that combine fixed salaries with piece rates have the same incentive properties as that of the latter.
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In Bellemare et al. (2010) subjects undertake a data-entry task and are paid either a
fixed wage or a piece rate. Bellemare et al. (2010) report an inverse U-shaped impact of peer
pressure for men under fixed wages in that extremely low and extremely high levels of peer
pressure affected performance adversely. But, by and large, the provision of relative
performance feedback had little impact on performance. However, as opposed to other
studies where relative information is provided in real time with all subjects performing the
task concurrently, Bellemare et al. provide relative feedback by comparing the behaviour of a
current participant with that of someone who took part in the experiment on a previous
occasion. It is possible that this design feature made the relative performance feedback less
salient for subjects.
Two further papers find mixed evidence regarding the effect of providing relative
feedback. Both Hannan, Krishnan and Newman (2008) and Azmat and Iriberri (2016) find
that the provision of relative performance feedback has different effects depending on how
people are paid. In Hannan et al. (2008) subjects participate in a stylised profit maximisation
problem where they took the role of a manager and selected the output level in order to
maximise profit under uncertainty. The manager was paid according to either a tournament
bonus or a piece rate commission on profit earned. For each of these two pay schemes,
subjects in different treatments either received feedback about their rank or not. The
feedback, when provided, could be either coarse or fine. The former notifies the subject
whether the profit points earned is above or below the median, while the latter is more precise
indicating their performance decile. Hannan et al. find that the firm’s profits are higher under
piece rates when relative feedback is provided than when it is not. On the other hand, the
provision of feedback has no effect in tournaments when relative feedback is coarse and
actually worsens earnings when fine feedback is provided.
In an arithmetic task, Azmat and Iriberri (2016) look at the effect of relative feedback
on piece rates and flat salaries. Relative feedback improves performance only under piece
rates, while having no effect whatsoever under salaries. Finally, Azmat and Iriberri (2010)
and Tran and Zeckhauser (2012) use natural data to examine the effect of relative
performance feedback. Azmat and Iriberri (2010) use data from 1986-94 for Spanish high
school students to understand whether providing relative rank information leads to improved
student achievement. In the academic year 1990-91, due to exogenous changes, student report
cards provided information about the average class grade alongside their own grade. This
resulted in students attaining higher grades that year compared to previous and subsequent
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years where no such relative feedback was provided. Similarly, Tran and Zeckhauser (2012)
found that Vietnamese English-language students who were notified of how they were ranked
within their class, performed better than the control group who were not provided such
information. It made no difference whether feedback was made publicly to all members of
the class or privately to individual students.
3. Experimental Design
3.1. Task
Our experiment is based on a multiple cue probabilistic learning (MCPL) task, where
in each one of 20 rounds (t) participants are required to predict the value of a variable Xt
based on the observation of two numerical “cues” provided to them3. This task is
representative of real life tasks. For example, the variable Xt can be thought of as the
underlying price of a stock; the cues as variables that affect the value of a stock; and the task
at hand as one of forecasting stock prices. The actual stock value is determined by the
underlying equation:
where Xt is the actual stock value participants are required to predict, Cue At and Cue
Bt are the values of the two numerical cues provided to the participant, and εt is a random
variable with uniform distribution drawn from the set [-5, 5] in each round t. Participants do
not know about the error term, the exact relationship between the cue values and the value of
X, or even whether the relationship is linear or non-linear. They do know, however, that
while the cue values change from one round to the next, the underlying relationship does not
change.
We implement two variants of the task. In the “Single Cue” task, Cue A is fixed at
the value of 150 for each of the 20 rounds, while Cue B changes each round. This is designed
to be less difficult than the “Dual Cue” task, where both cue values change in each round.
For both tasks, the sequence in which the cue values appear from one round to the next, is
identical across treatments. Table 1 shows the cue values and corresponding stock prices for
each round.
3 MCPL tasks are commonly used in psychology to study learning (see Balzer, Doherty & O’Connor, 1989 for a review). In economics, besides Brown (1995, 1998), this task has been used by Vandegrift and Brown (2003), Vandegrift, Yavas, and Brown (2007) as well.
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Our metric of performance is the absolute forecast error, the absolute distance
between the predicted value (XtP) and the actual value Xt
*, (|XtP- Xt
*|). Forecast errors measure
the accuracy of the predicted value, so smaller errors imply more accurate forecasts and
therefore better performance (and higher productivity). Absolute errors are an appropriate
measure of performance as they reflect the cognitively challenging nature of the MCPL task.4
Table 1: Actual Cues and Stock Prices
RoundSingle Cue Task Dual Cue Task
Cue A Cue B Stock Price Cue A Cue B Stock
Price1 150 201 192 105 37 692 150 263 243 242 96 1513 150 88 117 443 159 2564 150 248 232 1 339 2455 150 201 200 41 146 1246 150 196 194 155 32 807 150 353 305 20 288 2238 150 173 173 104 422 3359 150 270 248 102 107 11210 150 243 222 296 188 23111 150 60 102 413 266 32112 150 320 274 165 412 35313 150 340 289 172 167 17414 150 361 311 359 262 29815 150 321 285 271 418 38516 150 361 309 227 31 9817 150 148 155 381 435 42618 150 309 275 262 339 32319 150 135 145 316 92 16420 150 142 156 196 285 269
3.2. Treatments
We report on data from three treatments for the purposes of this paper, which is part
of a larger study involving other treatments and analyses. These three treatments are: (1)
Piece Rate (PR), (2) Piece Rate Win-Lose (PRWL); and (3) two-person Winner-Takes-All
Tournament (WTAT). The treatment intervention comes into play from round 6.
4 As will be clear from Table 1, the MCPL task is difficult enough even when a single cue is changing. When both cues change the degree of difficulty increases. Subjects figure out relatively quickly that the actual stock price is a convex combination of the two cue values and therefore get reasonably close to the actual value when the cue values are close but the errors get large when the cue values are far apart.
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In the first five rounds of each of these treatments, subjects are paid piece rates
according to their individual performance. At this point, subjects do not know which
treatment they are assigned to, but do know that treatment interventions might or might not
take place prior to the sixth round. Since these rounds are identical in each of the treatments,
we are able to use performance across these rounds to proxy for subjects’ underlying ability
pertaining to the task. It is important to control for ability given that our MCPL task is
relatively difficult and given the possibility of significant heterogeneity in ability levels
across subjects.
The piece rate that takes place across rounds 1 to 5 in each of the treatments pays
participants NZ $1 minus their forecast error for the round. For example, if the absolute error
is 20 then the payment for that round is NZ $0.80. If the forecast error is greater than 100,
then earnings for that round is set to zero. Here subjects aim to minimise their forecast error
in each round, which in turn will lead to higher payoff.
From round 6 onwards, treatment interventions take place. In the PR treatment,
subjects continue to earn piece rates on their individual performance across rounds 6 to 20,
and subjects are instructed accordingly. Figure 1A presents a screenshot to show what the
subjects get to see at the end of a round. This is the information that a subject will be looking
at prior to the beginning of round 10.
The PRWL treatment adds rank competition on top of the PR treatment. Here, starting
with round 6, subjects are paired, with random re-matching of pairs between rounds. They are
paid according to their own forecast errors in each round, i.e. the payment scheme is the same
piece rate. Furthermore, from round 6 onwards, in each round the subjects are also told
whether they have “Won” or “Lost” depending on whether a particular subject’s error was
smaller than or larger than her pair member respectively. However, whether a subject won or
lost a particular round has no bearing on her earnings for that round since each subject
continues to get paid on the basis of her own forecast errors. The rank information is simply
designed to capture positive or negative emotions from winning or losing respectively. Figure
1B shows a screenshot of this treatment.
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Figure 1A: Screenshot for PR treatment
10 Tso1
1 296 188
Round
1
Cue A
105
Cue B
37
Your Forecast
75
Actual Price
69
Forecasting Error
6
Earnings this round
$0.94
Round number Player name
Player ID Cue A
2 242 96 201 151 50 $0.50 Cue B 3 443 159 174 256 82 $0.18 4 1 339 268 245 23 $0.77 Enter Forecast 5 41 146 113 124 11 $0.89 6 155 32 116 80 36 $0.64 SUBMIT RESET 7 20 288 241 223 18 $0.82 8 104 422 315 335 20 $0.80 9 102 107 108 112 4 $0.96
Figure 1B: Screenshot for PRWL treatment
10 Tso1
1 296 188
Round Cue A Cue B Your Actual Forecasting Earnings WIN Round number
1
105
37
Forecast
75
Price
69
Error
6
this round
$0.94
or LOSE
---
Player name Player ID
Cue A 2 242 96 201 151 50 $0.50 --- Cue B 3 443 159 174 256 82 $0.18 --- 4 1 339 268 245 23 $0.77 --- Enter Forecast 5 41 146 113 124 11 $0.89 --- 6 155 32 116 80 36 $0.64 WIN SUBMIT RESET 7 20 288 241 223 18 $0.82 WIN 8 104 422 315 335 20 $0.80 LOSE 9 102 107 108 112 4 $0.96 WIN
Figure 1C: Screenshot for WTAT treatment
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10 Tso1
1 296 188
Round Cue A Cue B Your Actual Forecasting Earnings WIN Round number
1
105
37
Forecast
75
Price
69
Error
6
this round
0.94
or LOSE
---
Player name Player ID
Cue A 2 242 96 201 151 50 0.5 --- Cue B 3 443 159 174 256 82 0.18 --- 4 1 339 268 245 23 0.77 --- Enter Forecast 5 41 146 113 124 11 0.89 --- 6 155 32 116 80 36 $1 WIN SUBMIT RESET 7 20 288 241 223 18 $1 WIN 8 104 422 315 335 20 $0 LOSE 9 102 107 108 112 4 $1 WIN
The WTAT treatment also starts in round 6, following five rounds of piece rate
payments. As in the PRWL treatment, from round 6 onwards, we form subjects into pairs
(with random re-matching from one round to the next), except here we implement a winner-
takes-all tournament scheme, where in each round the subject with the smaller absolute error
wins NZ $1 while the subject with the larger error gets zero.5 Figure 1C presents a screen-
shot. Compared to the PRWL treatment, from round 6 onwards, the WTAT treatment not
only provides the win/lose information but changes the payoffs as well. So the WTAT
treatment incorporates payoff competition on top of the rank competition in the PRWL
treatment.
3.3. Research Questions and Hypotheses
In this section we formulate some hypotheses that will guide our analysis later in the
paper. We start by comparing our two pay schemes: piece rates and tournaments. Theory
predicts that the level of effort exerted under tournaments are identical to those under piece
rates, a finding confirmed empirically by Bull et al. (1987). We shall refer to this as Piece
Rate Equivalence. As such, we would expect the PR and WTAT treatments to perform
similarly and have similar forecast errors. This leads to our first hypothesis.
H1.
Piece Rate Equivalence holds, where the productivity of tournaments
are similar to that of piece rates. This implies: PR(Errors)
WTAT(Errors).
5 If the forecast errors of a particular pair are equal in particular round, then the tie is broken by randomisation.
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Next we look at how rank competition affects productivity. In order to address this we
compare the PR and PRWL treatments. Both treatments are identical, except that the latter
provides additional information at the end of each round about whether subjects performed
better or worse than their partners. Subjects are paid piece rates in both treatments; the rank
information does not impact earnings, allowing us to isolate the effect of rank competition
from payoff competition.
If people are motivated solely by earnings, then we would not expect rank
competition to have any effect on productivity.6 On the other hand if people derive utility
(disutility) from winning (losing) a particular round, then we would expect them to work
harder to improve (reduce) their chances of winning (losing) – improving their forecast
errors. This effect may consist of an ex-ante anticipation effect (Blanes i Vidal & Nossol,
2011) that may be associated with preferences for status and respect (see Ellingsen &
Johannesson, 2007 for a review), or an ex-post revelation effect when people react to the
feedback received. Either of these effects or both, would lead better performance in the
PRWL treatment compared to the PR treatment. This leads to our second hypothesis.
H2.The provision of relative performance feedback improves productivity.
This implies: PRWL (Errors) < PR (Errors).
We now turn to the issue of competing for payoffs by comparing the PRWL and
WTAT treatments. Both treatments feature rank feedback, but differ in terms of incentives:
piece rates and rank-dependent prizes respectively. From Hypothesis H1, we expect
tournaments to perform similarly to piece rates according to Piece Rate Equivalence. Does
this equivalence still hold with our rank-augmented piece rates?
If piece rates perform no differently to tournaments (H1), and rank competition
induces higher performance from players (H2), then we would accordingly expect the
feedback-augmented piece rates (PRWL) to perform better than tournaments (WTAT).
Eriksson et al. (2009) provide evidence indicative of this; piece rates with relative feedback
perform better than tournaments featuring identical feedback. This suggests that the rank-
dependent payoffs inherent in tournaments do not perform as well as piece rates do once rank
6 Under a principal-agent framework, Aoyagi (2010) studies the effect of the principal providing feedback about the intermediate performance gap to agents in a dynamic tournament. He finds that the effect this feedback has on the effort exerted by agents depends on the nature of their cost of effort functions. If agents’ marginal cost of effort are concave (convex), then no (full) feedback induces low effort from agents. If marginal cost is linear, then effort is not affected by feedback policy. We caution applying the insights of Aoyagi to our study of rank competition, since the dynamics will be different according to the different setup (multi-stage tournaments in Aoyagi; finitely repeated single stage tournament here) and pay schemes (tournaments in Aoyagi; piece rates in our study).
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competition has been controlled for. It is the motivating effect of rank competition that
underpins Piece Rate Equivalence. This leads us to our third hypothesis.
H3.
When relative feedback is added to piece rates, this will lead to better
performance than in tournaments. This implies PRWL (Errors) < WTAT
(Errors).
There are at least two reasons why tournament performance may be worse than that
under piece rates with rank feedback. First, there is an element of uncertainty in rank payoffs.
In order to get a payoff, one has to perform better than one’s peers while not knowing how
others will perform – due to random rematching of partners and since players are only
informed of whether they have won or lost, rather than on the margin of winning or losing.
Under winner-takes-all tournaments, the possibility of losing means that the exertion of
costly effort might not yield any return. For a given degree of risk aversion a person would
likely exert less effort under a tournament than a piece rate, for which payoffs are
deterministic.
The second reason why tournament performance may suffer relates to the concept of
shying away from competition (Niederle & Vesterlund, 2007). Those averse to competition
may find the tournament set-up challenging. While there is an element of competition in both
PRWL and WTAT, since relative performance feedback is provided in both, competition is
more salient in WTAT since payoffs are directly linked to rank. Competition avoidance could
further explain why productivity may be lower in WTAT than in PRWL. While we cannot
explicitly observe instances of this, we control for competition-aversion through trait anxiety
scores, which are elicited before participants learn of the task. See Segal and Weinberg
(1984) for a discussion on how trait anxiety can serve as a proxy for competition avoidance.
To summarize, our three hypotheses jointly predict forecast errors would be such that
PRWL (Errors) < PR (Errors) WTAT (Errors). Taken together this suggests that with the
provision of rank feedback, piece rates would perform better than tournaments, meaning that
competing for rank provides a stronger incentive than competing for payoffs.
3.4. Experimental Procedure
Sessions were conducted at a computer lab in our University using primarily first year
students in commerce. We report data obtained from 236 participants in the three treatments
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described above. Participants are seated at computer cubicles with privacy partitions and are
cautioned about not communicating with any other subject. To start with, participants are
asked to fill out a questionnaire which elicits participants’ trait anxiety level. See Spielberger,
Gorsuch, Lushene, Vagg and Jacobs (1983). This is shown in the appendix. The
questionnaire consists of 20 questions that are answered on a 1 to 4 scale. Questions 1, 6, 7,
10, 13, 16 and 19 are reverse scored. The questionnaire is designed to measure a subject’s
general tendency to feel anxious rather than their current level of anxiety (McNaughton,
2011). A higher score generated from the pre-task questionnaire indicates a higher level of
trait anxiety associated with the individual. We use trait anxiety as a proxy of each subject’s
competitive preferences, so we can account for potential dropouts as a result of shying away
from rank or feedback competition (Niederle and Vesterlund, 2007). Segal and Weinberg
(1984) find that trait anxiety is higher among women than men which is attributed to
differences in competitive preferences.
Following this we hand out the instructions for the forecasting task. The instructions
are read out loud after subjects have had a chance to read them privately on their own. The
appendix contains a copy of the instructions.
These instructions describe the task, inform players that they will be earning piece
rates in the first five rounds, and that there might be a change in the way the game is played
in rounds 6 to 20. The instructions are identical for people in different treatments. They are
told that they will be provided further information prior to the start of round 6. Subjects are
also provided with ten examples for Cue A, Cue B and X. This is shown in Table 2.
Table 2: Cue Values given to subjects as practice examples
Single Cue Task Dual Cue Task
Cue A Cue B Actual Price Cue A Cue B Actual
Price150 92 117 12 64 54150 143 157 372 63 162150 379 321 179 109 137150 373 313 415 146 240150 240 220 116 186 175150 285 256 355 223 275150 187 188 145 286 255150 143 153 199 356 317150 191 185 439 354 372
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150 361 311 73 442 345
In the PR treatment, following round 5, they are told that there are no further
instructions and they should continue as before. In the PRWL treatment, after round 5, they
are told that in going forward they will be paired with another player in each round, and
informed of whether they won or lost a round. They are also told that this rank information
has no bearing on their earnings, which still depend only on their absolute errors in any given
round. In the WTAT treatment they are told both about the pairing and that from round 6
onwards they will earn either $1 or nothing in each round. In both the PRWL and WTAT
treatments subjects are told that they will be randomly re-matched from one round to the
next.
At the conclusion of the task, participants are asked to fill out a post-task
questionnaire, which elicits information about participants’ intrinsic motivation, including
self-reports of how competent they felt at the task, how motivated they were, how interesting
they found the task, how much effort they exerted and how close to they felt to other
participants in the room. We also collected basic demographic information including gender,
age and ethnicity. We do not elaborate on the psychological questionnaires since we do not
exploit data from them for the purposes of this study.
After filling out the questionnaire, participants are privately paid their earnings from
the 20 rounds of the forecasting task in cash, plus a $5 show-up fee. On average, participants
earned $20 in each treatment.7
4. Results
Table 3 provides a broad overview of the forecast errors in different treatments along
with the number of subjects and sessions in each treatment.8 Not surprisingly the errors are
much smaller in the single cue task than the dual cue one. What is noticeable is that in both
the single and dual cue tasks, average forecast errors are highest in the WTAT treatment,
7 Under tournaments, the expected earnings in a round is $0.50 (50% chance of winning the $1 prize), which is much lower than the ex-post average earnings under piece rates in the PR and PRWL treatments. In the WTAT treatment, we paid participants an extra $4 to calibrate their average earnings with other treatments.
8 We had 36 subjects in the PRWL treatment but due to reasons beyond our control, one subject left early. Since we needed to put subjects in pairs from round 6 onwards, we discreetly replaced the departing subject with one of our experimental assistants (who had no prior experience with the game). We have excluded the choices made by this subject (and the replacement) from our analysis. However, given the random re-matching of subjects and the fact that subjects never get to see the ID numbers of their partners, we have retained the data for the remaining 35 subjects.
16
followed by the PR treatment. Errors are smallest in the PRWL treatment. To explore these
issues more rigorously we turn to regression analysis next.
As noted before forecast errors serve as our metric for performance (= |Predicted
stock value – Actual stock value|). Smaller forecast errors indicate higher productivity. In the
first three columns of Table 4 we present random effects regressions with forecast errors as
the dependent variable, with robust standard errors clustered by individual subjects. In
running these regressions, we pool the data from the single cue and dual cue tasks. We use a
random effects specification because we have both time-varying and time-invariant variables
among our regressors, where the time-invariant regressors are inestimable under fixed effects
regressions. In Column 4 of Table 4, we also present results of a quantile regression as a
robustness check.
17
Table 3: Average errors across treatments
TreatmentsSingle Cue Dual Cue Pooled Data
Session # N Average
ErrorSessio
n # N Average Error N Average
Error
Piece Rate (PR)
1 1610.2
1 2026.6 81 18.1
2 26 2 19
Piece Rate Win-Lose (PRWL)
1 229.6
1 2024.0 77 16.2
2 20 2 15
Winner Take All
Tournament (WTAT)
1 1610.0
1 1830.7 78 20.1
2 24 2 20
Total 124 112 236
Recall that in all treatments subjects play the PR treatment for the first 5 rounds and
the treatment (if any) is implemented only at the start of Round 6. Hence in running these
regressions we use data for rounds 6 through 20. In our simplest regression model, we regress
forecast errors against two dummy variables which represent the PRWL and WTAT
treatments (with the PR treatment serving as the reference category), as well as a linear time
trend denoted by Round which captures learning over time. Our second regression model
includes additional controls of each subject’s gender (= 1 for women and 0 for men) and trait
anxiety score. Our third regression model builds on the second, but better controls for
learning by allowing time trends to differ across treatments. Our fourth regression model
shares the same specification as Model 2, but instead of being estimated with random effects,
is estimated with a quantile regression. The bottom of Table 4 presents a pairwise Wald test
for each regression model comparing the performance of the PRWL and WTAT treatments.
18
Table 4: Pooled regressions for forecast errors; columns (1) to (3) present results for random effects regressions while column (4) presents results for a quantile regression
Dependent variable = forecast errors = |Predicted value – Actual Value|
Independent variables
Model 1 Model 2 Model 3 Model 4Pooled Pooled Pooled Pooled
Random Effects
Random Effects
Random Effects
Quantile Regression
PRWL-1.897(2.186)[0.385]
-4.388(2.425)[0.070]
-6.267(3.200)[0.050]
-1.318(0.743)[0.076]
WTAT2.052
(2.740)[0.454]
0.503(2.958)[0.865]
3.181(3.390)[0.358]
-0.442(0.750)[0.556]
Female ---8.317
(1.928)[0.000]
8.317(1.929)[0.000]
3.496(0.610)[0.000]
Trait anxiety ---0.162
(0.150)[0.281]
0.162(0.150)[0.281]
0.085(0.044)[0.052]
Round-0.136(0.075)[0.070]
-0.161(0.082)[0.049]
----0.093(0.070)[0.182]
PR X Round --- ----0.142(0.146)[0.331]
---
PRWL X Round --- ---0.003
(0.153)[0.984]
---
WTAT X Round --- ----0.348(0.119)[0.004]
---
Constant19.83
(1.878)[0.000]
10.90(6.164)[0.077]
10.65(6.630)[0.108]
5.915(2.052)[0.004]
R2 0.004 0.033 0.034 0.007(Pseudo R2)
Wald χ2 6.27 29.93 38.92 ---p > χ2 0.099 0.00 0.00 ---
No. of observations 3540 3180* 3180 3180No. of participants 236 212 212 212
PRWL = WTAT χ2 = 2.71p = 0.100
χ2 = 4.06p = 0.044
χ2 = 7.70p = 0.006
F = 1.43p = 0.231
19
* 15 subjects did not provide gender information, 7 subjects did not complete the trait
anxiety questionnaire properly, and 2 providing neither, thereby leading to a loss of 360
observations over 15 rounds. Standard errors in parentheses; p-values in square brackets.
In Model 1 of Table 4, we see that the PRWL treatment dummy is negative while that
for the WTAT treatment is positive. Neither of the coefficients are, however, significant at
conventional levels. When we compare the PRWL and WTAT treatments, the Wald test
shows that the PRWL treatment performs significantly better. From Model 1, we also see that
learning occurs, with forecast errors improving over time.
When we control for gender and trait anxiety in Model 2, we see that the average
forecast errors are significantly lower in the PRWL treatment compared to the reference PR
treatment. A Wald test also show that forecast errors are also smaller in the PRWL treatment
compared to the WTAT treatment. What is also noticeable is the large positive coefficient for
the gender dummy showing that, on average, women performed much worse than men across
the board.
In Model 3, when we allow for treatment-interacted time trends, we continue to see
similar results. The coefficient on the PRWL dummy continues to be negative and the WTAT
dummy remains insignificant, showing that performance in these treatments are, respectively,
better than and no different to the PR treatment. Comparing the PRWL and WTAT treatment
coefficients, the forecast errors continue to be smaller in the PRWL treatment. While the
WTAT treatment does not appear to perform well compared to the other treatments, it is
made up for by better learning. Of the treatment-interacted time trends, only the WTAT trend
is significant and in the direction that indicates improved forecasts over time. We will come
back to this shortly.
As a robustness check, in Model 4 we run a quantile regression, which is robust to
outlier values. However, quantile regressions do not accommodate the panel structure of our
data, nor do they allow us to estimate time trends. The quantile regression in Model 4
corroborates the previous findings.
On the basis of these results, we conclude that forecast errors in the PRWL treatment
are smaller than in the PR and WTAT treatments, with no difference in performance between
the latter two treatments. This provides support for each of our three hypotheses, where
PRWL (Errors) < PR (Errors) WTAT (Errors). We find support for Piece Rate
Equivalence, and we can infer from our results that it is driven by the motivating effects
20
associated with rank competition. When rank competition is controlled for in piece rates,
tournaments do not perform as well.
The regressions in Table 4 had pooled the single and dual cue treatments. We now
repeat our regressions, but separating single and dual cue treatments. Table 5 consists of two
random effects regressions for each of the single and dual cue tasks across rounds 6 to 20.
Similar to Model 3 of Table 4, each of the regression models in Table 5 regress forecast
errors against the treatment dummies (with PR as the reference treatment), gender (with
women = 1), trait anxiety, as well as time trends for each treatment. In disaggregating the
treatments by task difficulty, we are spreading the number of observations thin, effectively
using half the number of observations compared to the previous pooled regressions in Table
4.
In the single cue regression in Model 1 of Table 5, we see that although the PRWL
treatment dummy is insignificant, it carries a negative sign similar to what we observed in the
pooled regressions. The PRWL coefficient in the dual cue regression in Model 2 is also
negative and insignificant, but is larger in magnitude compared to the corresponding single
cue coefficient. While the signs on the PRWL coefficients for both single and dual cue
regressions are consistent with that from the pooled regressions, the fact that neither are
statistically significant suggests that the smaller number of observations plays a role.
Consistent with what we found earlier for the pooled regressions, the WTAT
treatment for both single and dual cue regressions remain insignificant. The Wald test shows
that the PRWL treatment performs no differently from the WTAT treatment in the single cue
regression in Model 1 of Table 5, while it performs better than the WTAT treatment in the
dual cue regression of Model 2.
The patterns of learning are also similar when we disaggregate our results by task
difficulty. In both single and dual cue regressions in Table 5, the time trends estimated for
each treatment show that it is negative and significant only in the WTAT treatment.
21
Table 5: Random effects regressions for forecast errors separated by Single and Dual Cue treatments
Dependent variable = Forecast errors = |Predicted value – Actual Value|
Independent variables Model 1 Model 2Single Cue Dual cue
PRWL-2.519(3.329)[0.449]
-7.224(4.935)[0.145]
WTAT0.243
(3.187)[0.939]
6.282(4.961)[0.205]
Female1.313
(1.432)[0.359]
11.86(2.811)[0.000]
Trait anxiety-0.128(0.116)[0.270]
0.252(0.249)[0.312]
PR X Round-0.158(0.140)[0.260]
-0.127(0.246)[0.606]
PRWL X Round-0.038(0.182)[0.836]
0.055(0.262)[0.833]
WTAT X Round-0.197(0.100)[0.050]
-0.494(0.212)[0.020]
Constant17.34
(5.728)[0.002]
12.34(11.05)[0.264]
R2 0.007 0.044Wald χ2 6.57 31.81p > χ2 0.475 0.00
No. of observations 1620 1560No. of participants 108 104
PRWL = WTAT χ2 = 0.92p = 0.338
χ2 = 6.01p = 0.014
Standard errors in parentheses; p-values in square brackets.
22
4.1. Heterogeneous Ability
In this section we disaggregate analyses by subjects’ ability. Since our MCPL task is
difficult, there could be a large degree of heterogeneity in players’ ability, which would in
turn adversely affect our statistical analyses. Furthermore, it is possible that rank and payoff
competition have different effects for subjects of different ability, which are not reflected by
our aggregated results.
Recall that participants in each treatment play under piece rates in the first five
rounds. Performance in those five rounds then provides us with a benchmark of participants’
ability. We use the median error in the first five rounds to categorise our participants into
“high” or “low” performers according to how they performed during the first five rounds. We
compute the median forecast error for each subject and those subjects whose median error
exceeded the aggregate median during the first five rounds are treated as “low” performers
while those whose median falls below the aggregate median are “high” performers.9 In Table
6 we report results of random effects regressions similar to the ones we presented earlier,
except we break this up by high and low performers as we have previously defined. As in
Table 4, we pool data from the two tasks (single cue and dual cue) for the regressions. We
report two regression specifications for each category of high and low performers. In the first
specification, the regressors include two treatment dummies PRWL and WTAT (with PR as
the reference category), controls for gender (Female = 1 for women, 0 for men) and trait
anxiety, as well as a linear time trend denoted by Round. In the second specification, we
include treatment interacted time trends instead of the aggregate trend.
9 The median error for all subjects across the first five rounds for the dual cue task is 21 while that for the first five rounds of the single cue task is 8. These serve as the ability thresholds which we use to categorise high and low performers. In the dual cue task a subject is classified as a high (low) performer if the median error for this subject during the first five rounds is less than or equal to (greater than) 21. In the single cue task a subject is classified as high (low) performer if the median error for this subject during the first five rounds is less than or equal to (greater than) 8.
23
Table 6: Pooled random effects regression for forecast errors broken up by high and low performers; columns (1) and 2 present results for high performers while columns (3) (4) presents results for low performers.
Dependent variable = Forecast errors = |Predicted value – Actual Value|
Independent variables
High performers Low performersModel 1 Model 2 Model 3 Model 4
PRWL-1.091(2.114)[0.606]
-3.528(3.423)[0.303]
-7.732(4.242)[0.068]
-9.312(5.743)[0.105]
WTAT1.250
(2.831)[0.659]
1.333(4.107)[0.746]
-0.308(4.993)[0.951]
5.292(5.336)[0.321]
Female5.429
(2.110)[0.010]
5.429(2.111)[0.010]
8.754(3.181)[0.006]
8.754(3.183)[0.006]
Trait anxiety-0.028(0.130)[0.830]
-0.028(0.130)[0.830]
0.275(0.215)[0.202]
0.275(0.216)[0.203]
Round-0.280(0.072)[0.000]
----0.022(0.155)[0.889]
---
PR X Round ----0.346(0.124)[0.005]
---0.088
(0.272)[0.746]
PRWL X Round ----0.158(0.108)[0.141]
---0.210
(0.319)[0.511]
WTAT X Round ----0.352(0.138)[0.011]
----0.343(0.200)[0.087]
Constant16.00
(5.416)[0.003]
16.85(5.810)[0.004]
10.41(9.304)[0.263]
8.981(10.06)[0.372]
R2 0.025 0.025 0.031 0.032Wald χ2 22.66 23.58 11.27 16.35p > χ2 0.000 0.001 0.046 0.022
No. of observations 1710 1710 1470 1470No. of participants 114 114 98 98
PRWL = WTAT χ2 = 0.79p = 0.373
χ2 = 1.89p = 0.169
χ2 = 3.48p = 0.062
χ2 = 5.71p = 0.017
24
What stands out is that the performance of the high performers does not change across
treatments. However, low performers perform better in the PRWL treatment than in the PR
and WTAT treatments. In Model 3, we note that the dummy for the PRWL treatment is
negative and significant (at 7%) indicating that the low performers committed smaller errors
compared to the PR treatment. Pairwise Wald tests for the equality of coefficients suggest
that the coefficient for the PRWL treatment is significantly smaller than that of the WTAT
treatment. In Model 4, the coefficient of PRWL narrowly misses conventional levels of
significance (p = 0.105). Once again, the pairwise Wald test suggests that compared to the
WTAT treatment, performance is better in the PRWL treatment.
In terms of learning, in Model 2 we observe significant learning for high performing
PR and WTAT subjects. For low performers, from Model 4 we observe learning only in the
WTAT treatment.
To summarise: high performing subjects performed equally well across the different
treatments, while low performing subjects performed particularly well in the PRWL
treatment compared to the PR and WTAT treatments. It appears that our results are mainly
borne out by low performers.
As a robustness check, we can instead separate players by their ex-post record of
winning. Since competition does not occur in the PR treatment, we will focus only on the
PRWL and WTAT treatments. We define subjects to be ‘winners’ if they have won 8 or more
of the 15 post-intervention rounds which feature an element of rank or payoff competition,
while ‘losers’ have won 7 or fewer rounds. Table 7 present similar regressions to the ones
before, where we regress forecast errors for winners and losers separately. In Models 1 and 3,
we regress forecast errors against the WTAT treatment dummy (with PRWL as the reference
category), the gender and trait anxiety of players as controls, as well as a time trend. In
Models 2 and 4, we replace the trend with time trends specific to the PRWL and WTAT
treatments.
25
Table 7: Pooled random effects regression for forecast errors broken up by winners and losers; columns (1) and 2 present results for winners while columns (3) (4) presents results for losers.
Dependent variable = Forecast errors = |Predicted value – Actual Value|
Independent variables
Winners LosersModel 1 Model 2 Model 3 Model 4
WTAT2.278
(2.202)[0.301]
2.145(3.362)[0.523]
9.843(4.560)[0.031]
16.17(6.262)[0.010]
Female4.217
(2.327)[0.070]
4.217(2.328)[0.070]
9.160(3.443)[0.008]
9.160(3.445)[0.008]
Trait anxiety0.074
(0.151)[0.624]
0.074(0.151)[0.624]
0.202(0.296)[0.494]
0.202(0.296)[0.494]
Round-0.267(0.091)[0.003]
----0.201(0.179)[0.261]
---
PRWL X Round ----0.273(0.118)[0.021]
---0.056
(0.304)[0.853]
WTAT X Round ----0.262(0.136)[0.054]
----0.430(0.195)[0.028]
Constant10.20
(6.503)[0.117]
10.27(6.527)[0.116]
5.985(13.66)[0.661]
2.635(13.74)[0.848]
R2 0.024 0.024 0.042 0.043Wald χ2 15.94 17.07 13.66 21.20p > χ2 0.003 0.004 0.009 0.000No. of
observations 990 990 1020 1020
No. of participants 66 66 68 68Standard errors in parentheses; p-values in square brackets.
26
From Models 1 and 2 of Table 7, we observe that winners in the PRWL and WTAT
treatments perform similarly to one another. Learning is observed for winners in both
treatments. Similar to what we have found with low performers, losers in the WTAT
treatment do not perform as well as losers in the PRWL treatment. For losers, we observe
learning for subjects in the WTAT treatment but not for the PRWL treatment.
5. Concluding Remarks
In this paper, we have distinguished the effects of rank competition and payoff
competition, each of which could motivate performance under tournaments. Rank
competition refers to the act of competing that is independent from monetary payoffs, while
payoff competition refers to competing for the tournament prizes. In a cognitively
challenging task, we find that rank competition plays a central role in tournaments, to the
extent that once its effect has been controlled for, tournaments actually perform worse than
rank-augmented piece rates (PRWL). We find that this effect is concentrated amongst low
performing participants, with high performers unaffected by the provision of relative
performance feedback.
Whether our findings generalize beyond the lab depends on the extent to which our
MCPL task mimics real-life work and which types of jobs are represented well by this task. It
appears to us that most jobs in real life require some cognitive effort and in that regard a task
like ours probably provides a better approximation of many work-places rather than the more
mechanical number adding type tasks used in many prior studies.
27
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29
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30
Appendix: Instructions
The University of XXXXInstructions for the Experiment
WELCOME.
PLEASE TURN YOUR CELL PHONES OFF NOW.
This is a study examining the manner in which people make decisions. The University of XXXX has provided the funds to conduct this research. If you follow the instructions and make good decisions you might earn a considerable amount of money.
At the beginning of the session each person will be given an Earnings Account with $5.00 in it. You will participate in a decision making task for each of 20 rounds. You will have the chance to earn money each round, with your earnings for each round being added to your Earnings Account. At the end of the experiment, the balance of your Earnings Account will be paid to you in cash.
___________________________________________________________________________DESCRIPTION OF THE TASK:
In each round you will be asked to predict the future value of a fictitious ‘stock’. The value of this stock is unknown to all participants, but you will be able to observe two CUES that can help you form your forecast. These cues can be used to predict the stock’s value much the same way that the amount of rainfall and the average temperature can be used to predict the quality of a corn crop, the number of unoccupied apartments and student enrolment this year can be used to predict next year’s rent increases, or the demand for sports cars can be used to predict their future price.___________________________________________________________________________In each round you will be shown the values for the two CUES.
NOTE: One of the CUE values will always be fixed at 150 for each of the 20 rounds.The other cue value will change each round. But the relation of the cue values to the stock’s price will remain the same.
Example:For example let the value of Cue A be fixed at 150. Suppose the values for the cues in a round were given as:
CUE A = 150CUEB = 100
You will be asked to predict the price of the stock given these two cue values. The next round one of the cues will take on a different value, such as:
CUE A = 150CUEB = 450
31
You will then predict that round’s price using these new cue values. Remember that even though the values of the cues change, the underlying relation between the cue values and the stock’s price remains the same. Thus, in order to make accurate forecasts you will need to determine the relation between the cues and the price of the stock.
___________________________________________________________________________
YOUR FORECASTING ERROR
After making your forecast, the computer will calculate the distance between your forecast and that round’s actual price (your absolute forecasting error). This amount will be your forecasting error.
Example:Suppose your forecast was 230. If the actual price of the stock was 200 then your forecasting error would be 30:
Your forecasting error = 230 – 200 = 30
Suppose your forecast was 148. If the actual price of the stock was 200 then your forecasting error would be 52:
Your forecasting error = 200 – 148 = 52___________________________________________________________________________
YOUR EARNINGS IN EACH ROUND:
In each round, your earnings will depend on your forecasting error. Your earnings in each round will equal $1 less your forecasting error for that round.
That is, your earnings (E) in each round will be given by E = $1.00 – (forecast error).
Example:Suppose your forecast error in a particular round is 30. Then you will earn $0.70 in that round. This is because:
$1.00 – $0.30 = $0.70
Suppose in another round your forecast error is 8. Then you will earn $0.92 in that round. This is because:
$1.00 - $0.08 = $0.92
Note that if your error is 100 or over, then you will earn nothing in that round. The minimum amount you can earn in a round is $0.00.
Suppose in another round your forecast error is 102. Then you will earn $0.00 because:
$1.00 - $1.00 = $0.00
32
SPECIFIC INSTRUCTIONS:
1. Before Round 1:You will be shown 10 examples of cues and stock prices. You will have 5 minutes in which to examine these examples.
2. Round 1:At the end of the 5-minute example round you will be shown the first two cue values and asked to forecast the price of the stock in Round 1. You will have 90 seconds to make your forecast.
3. End of Round 1:At the end of the 90 seconds all participants will have entered their forecasts. After all earnings have been calculated you will be shown your results for Round 1. The computer will then show you your earnings for the round, including:
Cue A Cue B Your Forecast
Actual Price
Forecasting Error
Earning this
Round
Total Earnings
Please record this information on the RECORD SHEET provided to you.
4. Beginning of Round 2:After examining and recording the earnings from round 1, you will be shown the values of CUE A and CUE B in Round 2. You will have 90 seconds to form your forecast.
5. Subsequent Rounds:
Each subsequent round proceeds in the same way and will be repeated for each of the 20 rounds. In each round, you will make a forecast based on two new cue values. At the end of round 20, you will receive a cash payment in the amount indicated by the earnings account.
However, after you have finished the first five rounds of play, we will have a pause. It is possible that there will be a change in the way in which you earn money for the subsequent rounds 6 through 20. If there is no change then we will tell you so and ask you to simply continue playing the game in the same manner as in the first five rounds. However, if there is a change in payment, then we will provide you with further instructions at that point and explain these changes and also answer any questions you may have.
Do not use calculators. Your forecasts and your earnings are your private information. It is important that you do not talk or in any way try to communicate with other people during the experiment. If you violate the rules, you will be asked to leave the experiment.
GOOD LUCK!!
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PRWL Specific Instructions
Rounds 6 to 20
Rounds 6 to 20 are played exactly as rounds 1 to 5 but with the following exceptions:
Each period you will be paired with another participant in the session today. Your partner will change each round, so you will never be paired with the same partner for more than one consecutive rounds;
After you have made your forecast, the computer will compare your forecasting error to your partner’s forecasting error in that round;
Your results will show whether your forecasting error was greater or less than your partner’s for that round;
If your error is less than your partner’s, then you will be told you WIN that round. If your error is more than your partner’s, you will be told you LOST that round. If your error is equal to your partner’s, then the computer will randomly decide the winner and loser.
Your payment will remain unchanged. That is, each round you will continue to be paid:
Earnings = $1.00 – Forecasting Error
You will also be shown your partner’s forecast and forecasting error at the end of the round. That is, at the end of each round you will observe:
Cue A
Cue B
Your Forecast
Actual Price
Forecasting Error
Earnings this round
WIN or LOSE
Example: Suppose the actual price was 210, your forecast was 168, and your partner’s forecast was 163. Your forecasting error would be 42 and your partner’s forecasting error would be 47. You would see the following results for that round:
Cue A
Cue B
Your Forecast
Actual Price
Forecasting Error
Earnings this round
WIN or LOSE
168 210 42 $0.58 WIN
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Example: Suppose the actual price was 210, your forecast was 168, and your partner’s forecast was 173. Your forecasting error would be 42 and your partner’s forecasting error would be 37. You would see the following results for that round:
Cue A
Cue B
Your Forecast
Actual Price
Forecasting Error
Earnings this round
WIN or LOSE
168 210 42 $0.58 LOSE
Do you have any questions?
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WTAT Specific Instructions
Rounds 6 to 20
Rounds 6 to 20 are played exactly as rounds 1 to 5 but with the following exceptions: You will have an additional $4.00 added to your Earnings Account;
Each period you will be paired with another participant in the session today. Your partner will change each round, so you will never be paired with the same partner for more than one consecutive rounds;
After you have made your forecast, the computer will compare your forecasting error to your partner’s forecasting error in that round;
Your results will show whether your forecasting error was greater or less than your partner’s for that round;
If your error is less than your partner’s, then you will be told you WIN that round. If your error is more than your partner’s, you will be told you LOST that round. IF your error is equal to your partner’s, then the computer will randomly decide the winner and loser;
Your payment will depend upon whether your forecasting error is greater or less than your partners. That is, each round you will earn either $1.00 or $0.00. You will be paid either:
Earnings = $1.00 if you WIN
Or
Earnings = $0.00 if you LOSE
Example: Suppose your forecasting error was 42 and your partner’s forecasting error was 47. You would see the following results for that round:
Cue A
Cue B
Your Forecast
Actual Price
Forecasting Error
Earnings this round
WIN or LOSE
42 $1.00 Win
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Example: Suppose your forecasting error was 42 and your partner’s forecasting error was 37. You would see the following results for that round:
Cue A
Cue B
Your Forecast
Actual Price
Forecasting Error
Earnings this round
WIN or LOSE
42 $0.00 LOSE
Do you have any questions?
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Pre-Task Questionnaire
Player ID ____________
PLEASE ANSWER ALL OF THE FOLLOWING QUESTIONSA number of statements which people have used to describe themselves are given below. Read each statement and, using the scale below, tick the appropriate number indicating how you generally feel. There are no right or wrong answers. Do not spend too much time on any one statement but give the answer which seems to describe how you generally feel.
1 2 3 4 Almost Sometimes Often Almost never always
Alm
ost N
ever So
met
imes
Ofte
n
Alm
ost a
lway
s
1 2 3 41. I feel pleasant2. I tire quickly3. I feel like crying4. I wish I could be as happy as others seem to be5. I am losing out on things because I can’t make up my mind soon enough6. I feel rested 7. I am “calm, cool and collected”8. I feel that difficulties are piling up so that I cannot overcome them 9. I worry too much over something that doesn’t really matter 10. I am happy11. I am inclined to take things hard 12. I lack self-confidence 13. I feel secure14. I try to avoid facing a crisis or difficulty15. I feel blue16. I am content17. Some unimportant thoughts run through my mind and bother me18. I take disappointments so keenly that I can’t put them out of my mind 19. I am a steady person20. I get in a state of tension or turmoil as I think over my recent concerns and interests
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Post-Task Questionnaire
Player ID ____________
PLEASE ANSWER ALL OF THE FOLLOWING QUESTIONS
A) For each of the following statements, please indicate how true the statement is for you using the following scale:
1 2 3 4 5 6 7 Not at all Somewhat Very
true true true
Not
at
al
l tru
e
Som
ewha
t tru
e
Ver
y tru
e
1 2 3 4 5 6 71. I enjoyed this activity very much2. I think I am pretty good at this activity3. I put a lot of effort into this activity4. I did not feel nervous at all which doing this activity5. This activity was fun to do6. I think I did pretty well at this activity, compared to other participants7. I did not try very hard to do well at this activity8. I felt very tense while doing this activity9. I thought this activity was boring10. After working at this activity for a while, I felt pretty competent11. I tried very hard on this activity12. I was very relaxed doing this activity13. This activity did not hold my attention14. I am satisfied with my performance at this task15. It was important to me to do well at this task16. I was anxious while working on this task17. I would describe this activity as very interesting18. I was pretty skilled at this activity19. I did not put much energy into this20. I felt pressured while doing this activity.21. I thought this activity was quite enjoyable.22. This was an activity that I could not do very well.23. While I was doing this activity, I was thinking about how much I enjoyed it.
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B) The following items ask about how you felt about the other participants during the session.
Not
at
al
l tru
e
Som
ewha
t tru
e
Ver
y tru
e
1 2 3 4 5 6 71. I felt really distant to them2. I really doubt they and I would ever be friends3. I felt I could really trust them4. I’d really like the chance to interact with them more often5. I’d really prefer not to interact with them in the future6. I don’t feel like I could really trust them7. It is likely that they and I could become friends if we interacted a lot8. I felt close to them
C) How many of the people in this session did you know before the experiment? ____________
D) Basic information about you:
Your Gender (Male/ Female) ________
Age
Major: ______________________________________________
Year in School (e.g., Stage 2) _______________________________
Ethnicity (Please circle one): Maori Pacific Island NZ European
Asian Other _______________
Country where you were born? __________________
If you were born outside of New Zealand, at what age did you move here? _________
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