social identity and incentives in workgroups · incentives may be in uenced by workers’...
TRANSCRIPT
Social Identity and Incentives
in Workgroups ∗
Jonathan Yeo†
University of Warwick
June 30, 2019
Abstract
Incentive theory has paid relatively little attention to workers’ identities.
In this paper, I conduct the first experiment exploring the relationship be-
tween identity and optimal incentives. I construct workgroups which are either
homogeneous or heterogeneous in members’ identities and compare their pro-
ductivity at a real-effort task under tournaments or team-pay. I find that in
homogeneous workgroups, productivity is higher under team-pay. In hetero-
geneous workgroups, productivity is however similar across incentives. This
is because identities and incentives interact to influence inputs. Generally,
team-pay encourages helping — more so in homogeneous workgroups — while
tournaments encourage personal effort — less so in homogeneous workgroups.
Furthermore, I find that incentives also influence and shape workers’ identities.
Keywords: Social Identity, Minimal Groups, Incentive Structures, Cooper-
ation, Real-effort
∗I thank the College of Humanities, Arts, and Social Sciences (HASS) International PhD Schol-arship (HIPS) at Nanyang Technological University, Singapore for funding in this research.†Department of Economics, University of Warwick, Coventry, CV4 7AL, United Kingdom, e-mail:
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1 Introduction
There is a large literature in economics on optimal worker incentives. A factor which
has received relatively little attention within this literature — but seems to be im-
portant in practice — is workers’ identities. For instance, at Nucor Steel, where
employees share a strong sense of common identity, group-based incentives comprise
a large fraction of workers’ compensation (66%).1 By comparison, group-based incen-
tives comprise less than 20% of overall earnings at US Steel, where employee identity
is more fragmented (Byrnes and Arndt, 2006).2 Relatedly, unlike individualist coun-
tries (e.g. US), collectivist countries (e.g. Japan) display a strong association between
group-based incentives and organisational performance (Allen et al., 2004).
In this paper, I conduct the first experiment exploring the relationship between
optimal incentives and workers’ identities. I systematically induce identities in the
laboratory by assigning participants to one of two groups using an implementation
similar to Chen and Li (2009). They are then placed in workgroups which are either
homogeneous or heterogeneous in members’ identities. I examine productivity of
each workgroup type under two different incentive schemes: tournament or team-pay.
Participants engage in a task where one’s productivity depends on personal effort as
well as help from fellow members in their workgroup.
Results confirm that identities do influence optimal incentives. I find that in
homogeneous workgroups, productivity is higher under team-pay. In heterogeneous
workgroups, productivity is however similar across incentives. These productivity dif-
ferences can be explained by the interaction between identities and incentives in influ-
encing inputs. Generally, team-pay encourages helping, while tournaments encourage
personal effort. However, team-pay stokes greater help in homogeneous workgroups
while tournaments stoke greater effort in heterogeneous workgroups.
Results also indicate that incentives further influence and shape workers’ identi-
ties. Utilising post experiment survey data, I find that under team-pay, participants
feel greater identification with their workgroup. This hints at a feedback process
1A quote from a frontline supervisor exemplifies this: “At Nucor, we’re not ’you guys’ and’us guys’. It’s ’all of us’ guys. Wherever the bottleneck is, we go there, and everyone works onit.” (Byrnes and Arndt, 2006). Employees even refer to each other as “teammates” (B Arthur,1999). Production employees at Nucor are organised into teams including maintenence workers andsupervisors and have bonuses tied to team production which can be up to 200 percent of their basesalary. This is in addition to earnings from a profit sharing program which further increases theteam component of earnings (Sheridan, 1998; Vasanthi and Chowdary, 2009; Bakshi, 2015).
2Boyd and Gove (2000) mention the dramatic differences of culture at competitors of Nucor, withworkers having an ”us vs them” mentality, fear and distrust.
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between incentives and identities which might be important in determining optimal
incentives, especially in dynamic settings.
The structure of the paper is as follows. Section 2 summarises the literature
related to the paper. Section 3 describes the experimental design and procedures.
This is followed by a simple model to generate predictions in Section 4. Section 5
discusses the experimental results, and Section 6 concludes.
2 Related Literature
There is an existing experimental literature in economics on identity (see for example
Butler, 2014; Goette et al., 2006, 2012; Heap and Zizzo, 2009; Mcleish and Oxoby,
2007).3 A robust finding of this literature is that identity affects agent’s preferences.
For example, Chen and Li (2009) find that subjects are more charitable and reciprocal
towards members of their ingroup.
Several papers have explored the impact of identity on productivity.4 For in-
stance, Hoff and Pandey (2006) find that, when identity is made salient, individuals
of lower caste perform worse in a maze-solving task. Chen and Chen (2011) show that
individuals exert higher effort in a minimum-effort game when paired with ingroup
members. Kato and Shu (2016) find that common identity makes workers less com-
petitive, while Hamilton et al. (2012) find that common identity stokes teamwork.5
While these papers vary identities, they do not vary incentives. Hence, they do not
speak to this paper’s main question of how identities affect optimal incentives.
More closely related are papers by Bandiera et al. (2005) and Hamilton et al.
(2003) who compare workers’ productivity under a change in incentive regime.6
Bandiera et al. find a cost of relative-pay while Hamilton et al. find a benefit
of team pay when workers are “groupy”. My contribution is twofold. Firstly, I
3A psychology literature on identity starting with Tajfel et al. (1971) argues that group mem-bership can influence behaviour. More recently, there has been a growing economics literature onidentity beginning with Akerlof and Kranton (2000). See Akerlof and Kranton (2010) for a summary.
4More generally, this is related to the idea of social incentives and job-meaning. See Ashraf andBandiera (2018) and Cassar and Meier (2018) respectively for a review of the related literature.
5Under relative pay, Kato and Shu find that in Chinese textile firms with rural-urban distinctionsamong workers, productivity is higher in the presence of more able out-group workers, but not in-group ones. Under team pay, Hamilton et al. find that workgroups at a garment factory in Napa,California are more productive when composed of a single (Hispanic) ethnicity; Afridi et al. (2018)find similar results in a lab-in-field experiment in India.
6Bandiera et al. examine a switch from relative performance pay to piece rates at a UK farm.Hamilton et al. examine a switch from piece rates to team pay at a US garment factory.
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synthesise their work and show that “groupiness” — whether workers share or lack
a common identity — can affect the optimality of relative-pay versus team-pay.7 To
the best of my knowledge, my study is the first to cleanly demonstrate that optimal
incentives may be influenced by workers’ identities.8,9 Secondly, I measure two distinct
inputs: personal effort and help; this helps provide a clearer picture of the interaction
between identity and incentives in production. I find that when workers are “groupy”,
output differences between team and relative-pay arise largely due to cooperation. In
contrast, effort plays a larger role when workers are not “groupy”.
Finally, there is some theoretical work which speaks to the issues in this paper.
Most related is Huck et al. (2012) who show that a concern for others in the workgroup
can raise the effectiveness of team performance pay — and decrease that of relative
performance pay. Other work discusses how strong identification with an organisa-
tion’s mission can reduce the need for high-powered incentives (Akerlof and Kranton,
2005, 2008; Besley and Ghatak, 2005; Henderson and Van Den Steen, 2015).10
3 Experimental Design
The experiment is designed to examine the effect of identity within workgroups on op-
timal incentives. Participants perform a real-effort task under a 2×2 between-subject
design as in Table 1. On one dimension, the identity composition of workgroups is
varied: subjects are assigned either to a homogeneous workgroup or a (maximally)
heterogeneous workgroup. On the second dimension, the incentive scheme faced by
the workgroup is varied: a tournament scheme or a team-pay scheme.
7How groupiness is measured differs across their papers and mine. In Bandiera et al. (2005), itdepends on the number of co-workers in one’s workgroup. In Hamilton et al., it depends on howearly the workgroup is formed after the introduction of team-pay.
8Blazovich (2013) in the management accounting literature has a design which may in principalexamine this, but is unable to draw conclusive results due to the low sample size.
9Experiments in the management literature which examine how collectivism and individualismimpact group versus individual incentives (Naranjo-Gil et al., 2012; Papamarcos et al., 2007) arealso related to the extent that these traits affect identity in groups (Chatman et al., 2019). Moregenerally, other research has studied the impact of identity on the relative effectiveness of differentmanagement schemes like imposing control (Masella et al., 2014; Riener and Wiederhold, 2016),punishment schemes (Weng and Carlsson, 2015) and reporting structures (Towry, 2003). This paperdiffers from these studies in its focus on pecuniary performance incentives.
10These papers however differ in their modelling of how identification occurs. Akerlof and Kranton(2005, 2008) view identity as being malleable within the organisation. Besley and Ghatak (2005)discuss how assortative matching of workers and organisations naturally occurs under competitivepressure from larger surplus in such matches. Henderson and Van Den Steen (2015) instead describehow workers self select into firms with pro-social purposes due to identity and reputation benefits.
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Workgroup composition Incentive scheme Sample sizeHomogeneous Tournament 78Homogeneous Team-Pay 78Heterogeneous Tournament 72Heterogeneous Team-Pay 78
Table 1: Treatment Descriptions
A novel real effort task mimicking on-the-job-help at the workplace is utilised.
In particular, it involves both individual effort and cooperative interaction between
workgroup members. Unlike past research on team production which involve in-
duced costs, it allows for the measurement of both personal effort and help decisions
in a more realistic interaction environment. Together with the choice of incentive
treatments, this design speaks to the multi-dimensional nature of production. In par-
ticular, it seeks to address the dichotomy between motivating effort and cooperation
under two commonly debated incentives schemes in the literature.11
Each session consists of two main stages. In the first stage, near-minimal identities
are induced in subjects. In the second stage, subjects play the real-effort task in their
respective treatments. These are elaborated on in the next two subsections.
3.1 Stage 1: Identity Inducement
In the first stage of the experiment, identity is induced in subjects by adopting the
near-Minimal Group Paradigm.12 Subjects review 5 pairs of paintings sequentially,
each pair containing a painting by Paul Klee and another by Wassily Kandinsky.
They are required to select which painting they prefer without any information about
them. Subsequently, subjects are categorised into two equal sized groups: Klee and
Kandinsky according to their relative preferences within the session.13
11Tournament incentives have been described to provide superior incentives to outperform and ex-ert effort while team-pay based on aggregate team performance instead result in free riding (Lazearand Rosen, 1981; Holmstrom, 1982; Bull et al., 1987; Hannan et al., 2008). On the flip-side, tour-nament incentives have been found to encourage negative “side activities” like sabotage (Falk et al.,2008; Carpenter et al., 2010; Charness et al., 2013) while the converse occurs for team-based incen-tives (Drago and Garvey, 1998; Buser and Dreber, 2015; Friebel et al., 2017; Lazear, 2018).
12This was used in Chen and Li (2009) who adapt the procedures in Tajfel and Turner (1979).13Subjects in each session are ranked by the number of times they prefer paintings by Paul Klee.
The top(bottom) half is subsequently assigned to Group Klee(Kandinsky). This allows for a balancedassignment of groups compared to the standard procedure of using absolute preferences. Since most
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Subsequently, group members complete a joint task in order to build up their
group identity salience. They are shown a final pair of paintings and asked to guess
the artist of each individually after a 5 minute anonymous discussion with their group
members.14 If the majority of their group gets the answer correct, they are awarded
with 80 Experimental Currency Units (ECUs). Success/failure in this task is only
revealed at the end of the experiment to prevent it from influencing identity salience.
3.2 Stage 2: Production in workgroups
In the second stage of the experiment, subjects complete 4 rounds of 6 minutes of a
real-effort task.15 This is done in randomly assigned workgroups of 6 subjects with
partner matching. There are two types of workgroups: Homogeneous workgroups with
all 6 members from Klee/Kandinsky and Heterogeneous workgroups with 3 members
each from Klee and Kandinsky.16
Their individual scores as well as workgroup members’ scores in the task deter-
mine their task payment in each round. In particular, each workgroup is randomly
assigned to one of two incentive schemes (see Table 2) which determines their Task
payment. In the Tournament scheme, piece rates for the round are based on individ-
ual scores and increase with one’s score rank (ties were broken at random). In the
Team-pay scheme, piece rates are based on the average workgroup score; everyone
thus receives the same payment.17 To eliminate effects from feedback about own and
others’ past performance, relevant information about payments is only displayed at
the end of the experiment. This involves the (anonymous) breakdown of scores in the
workgroup under tournaments and the total score of the workgroup under team-pay.
subjects indicated non-familiarity with both artists (on a 7 point Likert scale, average of 1.27 forKlee and 1.45 for Kandinsky), differences between the two procedures should not be great. Balancechecks of baseline characteristics across the groups (unshown) confirm this.
14The initial five pairs of paintings are: 1A Gebirgsbildung, 1924, by Klee; 1B Subdued Glow,1928, by Kandinsky; 2A Dreamy Improvisation, 1913, by Kandinsky; 2B Warning of the Ships,1917, by Klee; 3A Dry-Cool Garden, 1921, by Klee; 3B Landscape with Red Splashes I, 1913, byKandinsky; 4A Gentle Ascent, 1934, by Kandinsky; 4B A Hoffmannesque Tale, 1921, by Klee; 5ADevelopment in Brown, 1933, by Kandinsky; 5B The Vase, 1938, by Klee. The last pair of paintingsare: 6A Monument in Fertile Country, 1929, by Klee, and 6B Start, 1928, by Kandinsky.
15Subjects are not informed of the exact number of rounds to reduce end-game effects.16Subjects only know their own workgroup composition and are not informed of other possible
workgroup compositions to reduce experimenter demand effects.17Note that the payoff structure for both incentives are designed such that for a given performance,
a participant who perceives that individual scores are drawn from the same distribution (i.e. abilityand motivation for effort are the same) has the same expected payoff in both; this allows us to makea direct comparison of productivity between the treatments.
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Incentive scheme Task Payment Piece-rate(ECU) Information provided
Tournament [1st: 13, 2nd: 11.5, 3rd: 10 Breakdown of4th: 10, 5th: 8.5, 6th: 7] individual scores
(Based on individual score)
Team-Pay [Everyone: 10] Total score(Based on average workgroup score) of workgroup
Table 2: Details of incentive treatments
3.2.1 Real Effort Task with on-the-job help
The real-effort task involves decoding sets of letters into two digit numbers from
a given letter-number grid displayed on their computer screen.18 Subjects have to
correctly complete a set of decoding questions in order to receive a new question set
(with a different letter-number grid). Their score is the total number of question sets
correctly completed during the round.
An element of cooperation is also present in the real-effort task. In particular,
choices of help by workgroup members determine the number of decoding questions
to solve in each question set. Each time a question set is generated, subjects face
either a difficult or easy question set with equal probability. Difficult question sets
contain 7 questions while easy question sets contain 3. In difficult sets, help requests
are automatically sent to a random workgroup member with known group identity.19
If the help request is accepted, the requester’s task becomes easier: the number of
questions is reduced by 3. In return, the helper has to solve 1 extra question.20
Nothing happens if the help request is rejected other than being informed.
Decisions on whether to provide help are elicited via the strategy method at
the beginning of each round. In particular, subjects have to decide for each group
(Klee/Kandinsky), whether to provide help throughout that round. For homogeneous
groups, the out-group decision is elicited hypothetically.
To increase the salience of “leisure” as an alternative to completing the task and
improve the measurement of effort, subjects are given the option of taking paid rests.
18Examples of papers which used this task are Benndorf et al. (2014) and Charness et al. (2013).19Note that they cannot discover the exact identity (within their workgroup) of whom they interact
with because there are ≥ 2 members from each group: this prevents reputation building.20This implies that help is efficient; we take this to represent a situation where workgroup members
perform related tasks where they occasionally encounter problems which would be more efficientlysolved with coordination of effort, thus raising (net) earnings.
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When submitting their answers, subjects can choose to rest for 5 seconds before the
next question set is displayed. If they do so, they are paid 2.5 ECUs.21 Their Total
payment in each round is hence the sum of their Task and Rest payments.
3.3 Procedures
At the beginning of each session, detailed printed instructions are provided to subjects
and read aloud to them.22 This is followed by a control quiz to test their understanding
of the real-effort task: all questions have to be answered correctly before they can
proceed.23 Subsequently, there are 2 non-incentivised practice rounds: a first round of
2 minutes to acquaint them with the UI, and a second round of 3 minutes to practice
the task, without the help and rest functions.24 The first and second stages of the
experiment as described then follow.
At the end of each session, subjects are asked to complete an online post-experiment
survey covering questions on demographics, academic background, behaviour and
perceptions about their groups and workgroups. A summary of their payoffs is then
shown, with subjects paid for 1 randomly chosen round. The exchange rate is set to
100 ECUs for 2.5 Sterling Pounds.
Participants were invited using the Warwick SONA system and were primarily
students at the university from a variety of majors. In total, 18 sessions were con-
ducted at the Economics Lab at the University of Warwick from Nov 2017 to Nov
2018, for a total of 306 participants (51 workgroups).25 Each session lasted 75 min-
utes on average and participants earned £10 on average including a show up fee of
£3. The study was programmed in z-Tree (Fischbacher, 2007) and preregistered at
the AEA RCT registry (AEARCTR-0002139).
21Using piece rates without an outside option in short term tasks may cause task completion to betoo salient, preventing adjustments to effort. The rate was chosen such that it would be individuallyrational to rest under team-pay, or low expected rank under tournaments. This was based on pilotswhere decoding 1 letter took 3.5 seconds on average and that there is on average 4 to 5 letters todecode in each set. In one session, the rest rate was lowered to 2 ECUs (which also satisfies theabove criteria), but no differences in behaviour were observed; The data is hence pooled for analysis.
22See Appendix B for the instructions provided to participants.23Over 90% of them stated an understanding of ≥ 5 on a scale from 1 to 7, see Figure A.1.24The second practice round also serves to provide a measure of their baseline ability.25Sessions either had 18 or 12 participants. Those with 18 had either 3 heterogeneous workgroups
or 1 heterogeneous and 2 homogeneous workgroups. Those with 12 had either 2 homogeneousworkgroups or 2 heterogeneous workgroups. The treatments conducted were pre-randomised tobalance the number of treatments across sessions. Figure A.1 in Appendix A illustrates that baselinecharacteristics of participants are roughly balanced across treatments.
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4 Model
In this section, I utilise a simple model to generate predictions about how salience of
workgroup identity salience, as influenced by the treatments, affects the optimality
of tournaments vs team-pay. In this model, individuals generate output within a
workgroup and are paid based on an exogenously chosen incentive scheme.
The production process is as follows: each member chooses 1) ei ≥ 0: effort put
into their own job, and 2) hi ≥ 0: the amount of help provided to the rest of the
workgroup. Personal output depends linearly on own effort and the help provided
by others: qi = ei + α∑
j 6=i hj where α > 0 here reflects the efficiency of help.26 To
simplify notation, I define the set of i′s chosen actions as ai = {hi, ei}. Furthermore,
I denote vectors of the workgroup’s variables in capitals (e.g. A,Q,E,H).
I examine a class of linear incentives where payment depends on own output (qi)
and others’ average output (q−i):27
Fi(Q) = wqi + (b− w)q−i, w ≥ 0, b > 0.
Here, b > 0 is treated as a fixed parameter throughout the model. w can be interpreted
as the degree of competitiveness of the incentive scheme. This covers a range of
incentives from team-pay (w < b) to piece rate (w = b) to tournament-like incentive
schemes (w > b).28 Keeping with our experimental aims, we seek to compare a
tournament (w1) and a team-pay incentive scheme (w0) with w0 < b < w1.
Individual’s choices are determined by their utility structure which comprises material
utility and social utility.29 Social utility is weighed by a factor β — a concern for
others in the workgroup — which reflects the salience of workgroup identity.30
Ui(E,H) = ui(E,H) + βvi(E,H),
β ≥ 0
26This simplifies exposition by ignoring complementarity between help received and effort whichcreates complicated interactions; results should be similar if the complementarity is small.
27In a symmetric equilibrium, average payment per unit output is independent of w.28This is not exactly the same as the experiment where piece rates in tournaments depend on a
ranking structure, but qualitatively, it should have the same implications.29This adapts the model in Huck et al. (2012) which also has material and social utility, but with
the latter depending on deviations from a social ideal. This accounts for preferences to follow socialnorms.
30In the context of our experiment, the main assumption here is that there are greater concerns forothers in a homogeneous workgroup compared to a heterogeneous workgroup. Chen and Li (2009),Chen and Chen (2011) and Mcleish and Oxoby (2007) have a similar modelling assumption. Infootnote (44), I discuss how allowing incentives to affect identity as well gives similar results.
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Material utility is linear in the material payoff from the exogenous incentive structure
Fi, with help and effort costs being quadratic:
ui(E,H) = Fi(Q)− 12e2i − 1
2h2i
Social utility is instead increasing in the “kindness” which i shows to others:
vi(E,H) = g(∑
j 6=i
[uj(ei, A\ei)− uj(eS, A\ei)
])+g(∑
j 6=i
[uj(hi, A\hi)− uj(hS, A\hi)
]),
g′ > 0, g′′ ≤ 0.
Kindness here is defined as the impact of one’s actions on others’ utility relative to
a reference point where the (unrestricted) selfish action is chosen, holding others’
actions constant.31,32 It is reflected in the bracketed terms within g( ), where eS, hS
refer to the corresponding unrestricted selfish action when β = 0. Notice that social
utility is assumed to be additively separable in kindness of chosen help and effort.
This can be justified if there is no complementarity of these actions in material utility
which is indeed true.33
It is easy to show that the unrestricted selfish amount of effort and help provided
is eS = w, hS = α(b − w), which is intuitive. Compared to individual piece rates,
team-pay (tournaments) elicit lower (higher) effort, due to the positive (negative) ex-
ternalities not being internalised. Help is higher (lower) for team-pay (tournaments)
than in a piece rate for a similar reason.
31This assumption means that for linear production and incentives, but possibly non-linear g, theactions of other members disappears in the optimisation. It helps to simplify the Nash Equilibriumand hence exposition. Removing/changing this reference point should not change theoretical resultsmuch as long as kindness is linear in it.
32The assumption about selfish actions being unrestricted is not essential for the main results,but helps to simplify presentation. It can be interpreted as structural constraints (here being thenon-negativity constraints) not being relevant on how i judges his kindness towards others.
33In the case of linear incentives and production, kindness of each action is indeed separable:uj(E,H)− uj(es, hs, e−i, h−i) = [uj(E,H)− uj(eS , e−i, H)] + [uj(E,H)− uj(hS , h−i, E)].
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Throughout the model, we also assume the following simple functional form for social
utility: 34
g(z) =
log(z), if z > 0
−∞, if z ≤ 0
Consequently, the solution for the general case (β > 0) is:
ei =
w +√β, if w < b (Team-Pay)
max(w −√β, 0), if w > b (Tournament)
hi = max(α(b− w) +√β, 0)
The following predictions can then be readily observed from comparative statics of
the relative competitiveness of the incentive scheme (w):
Prediction 1. Keeping identity salience fixed, help weakly falls as the relative com-
petitiveness of the incentive increases.
Prediction 2. Keeping identity salience fixed, effort rises as the relative competitive-
ness of the incentive increases.
The comparative statics of identity salience (β) give the following predictions:
Prediction 3. Help rises with identity salience in both team-pay and tournaments.
Prediction 4. Effort rises (falls) with workgroup identity salience in team-pay (tour-
naments). This implies that the effort difference between a fixed tournament and
team-pay incentive (eTourn− eTeamPay) should fall as team identity salience increases.
Notice that in Predictions 3 and 4, while the effects of team identity salience on
effort and help are in the same direction for team-pay, they are opposed in tour-
naments. Since output is the sum of help and effort, output rises with workgroup
identity salience in team-pay but rises at a lower rate (or even falls) in tournaments.35
34Effectively, this means that actions in social utility are not ”judged” by their prices (the incentiveparameters) which might reflect a deontological perspective of sorts.
35Computing the expression for output of each individual, we have:
qi =
w +√β + α(n− 1)[α(b− w) +
√β], if w < b
α(n− 1)[α(b− w) +√β], if w > b, β > w2
w −√
(β) + α(n− 1)[α(b− w) +√β], if w > b, [α(w − b)]2 < β < w2
w −√β, if w > b, β < [α(w − b)]2
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This gives the following prediction for output:
Prediction 5. Output rises with workgroup identity salience in team-based incentives,
but changes ambiguously in tournament incentives. The output difference between a
fixed tournament and team-based incentive (qTourn−qTeamPay) should fall as workgroup
identity salience increases.
Further, this implies a threshold β where for β > β, team incentives outperform
tournament incentives.36 This threshold increases with the relative competitiveness
of the tournament scheme (i.e. w1−w0), but decreases in the efficiency of help a. The
latter implies that as cooperation becomes more important, it is easier for team-pay
to outperform tournaments, which is intuitive.
The above predictions outline what we should observe assuming that identity
composition of the workgroup influences identity salience. However, whether we ob-
serve a (clean) reversal of the effectiveness of tournament versus team-pay in the
experiment depends on the levels of workgroup identity salience in homogeneous and
heterogeneous workgroups relative to β; in particular we need βhet << β << βhom.
5 Results
In this section, I first present a comparison of productivity under each treatment. This
is followed by an examination of its determinants: 1) help choices and 2) personal
effort. Lastly, I examine how the salience of their workgroup identity is affected by
incentives and workgroup composition. This identity salience is subsequently used in
robustness checks of the previous results.
Analysis here focuses on the balanced round level panel data set (4 Rounds × 306
Participants= 1224 Observations).37 Several common features apply throughout the
analysis. First, in all bar-plots, error bars are 95% confidence intervals. Second, all
regressions control for session fixed effects while errors are clustered at the workgroup
level to control for any dependency of decisions within workgroups. In exposition,
percentage points and standard deviations are abbreviated as PP, SD.
36β = max([w1−w0
2 (1− α2(n− 1))], [ (w1−w0)−α(n−1)(b−w0)2+α(n−1) ], 0)
This arises because the relative benefit of tournament incentives in incentivising higher effort fallsas workgroup salience rises, while help rises at an equal pace (for this functional form)
37In some rounds, there were some issues with the server which caused some disconnections midwaythrough, we however still have data for the round before the disconnections. Activity for the roundwas aggregated in these cases by adjusting for the fraction of time which the participants wereconnected. Overall, only 19 data points were affected: excluding them does not influence results.
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5.1 Productivity
In this experiment, an outcome of interest is whether optimal incentives can be influ-
enced by identity within the workgroup. Figure 1 illustrates productivity across treat-
ments as measured by the average number of question sets completed per round.38
Figure 1: Productivity over Treatments
It can be seen that in homogeneous workgroups (left), productivity is higher under
team-pay.39 In heterogeneous workgroups (right) however, productivity is indistin-
guishable under the two incentive schemes. This is consistent with Prediction 5 which
concerns how output changes with identity under each incentive scheme. Next, I per-
form random effects regressions for a more rigorous analysis.
Table 3 presents regressions with standardised productivity as the dependent vari-
able. The results in Model A are consistent with that observed in the figure. It shows
that team-pay outperforms tournaments in homogeneous workgroups (≈ 0.3 SD). The
negative interaction term of similar magnitude however indicates that the positive ef-
fect is cancelled out in heterogeneous workgroups. Models B and C show that help
choices and effort alone do not explain the productivity differences. Both factors are
required to be included for estimated effects of treatment variables to be close to 0
(Model D). These are summarised below.
Result 1. (Identity and Optimal Incentives): In homogeneous workgroups, team-pay
outperforms tournaments. In heterogeneous workgroups, there is no difference between
incentives. Both worker inputs: effort and cooperation matter for this difference.
38The values are adjusted for ability for which there was some imbalance between treatments.39This is significant under a Mann-Whitney test: p-value<0.001
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Dep var: Standardised Productivity Model A Model B Model C Model DTeam-Pay 0.306*** -0.127 0.444*** 0.014*
(0.076) (0.087) (0.036) (0.009)Heterogeneous -0.089 0.010 -0.108** -0.001
(0.079) (0.081) (0.047) (0.008)Team-Pay × Heterogeneous -0.356*** -0.178 -0.176*** -0.009
(0.105) (0.108) (0.058) (0.009)Avg questions in set -1.019*** -1.007***
(0.041) (0.017)Effort 0.904*** 1.780***
(0.024) (0.058)Avg questions in set × Effort -0.214***
(0.013)Observations 1224 1224 1224 1224
* 0.10 ** 0.05 *** 0.01, Avg questions is an inverse measure of cooperation.All regressions here control for ability, period effects and their interaction.
Table 3: Random Effects Regressions of Productivity
Above, both choices of personal effort and help are shown to matter for produc-
tivity differences across treatments. To further quantify the extent to which each
worker input matters, I first simulate counterfactual productivity for each workgroup
type, given all combinations of effort and help displayed under each incentive; this is
displayed in Table 4.40 Subsequently, I use these values to estimate the contribution
to output differences from variation in effort and help over the treatments.41 This
procedure allows for an estimate of their contribution to workgroup productivity,
accounting for non-linearity in production.
Table 5 shows the results of the above exercise. In homogeneous workgroups, co-
operation explains a majority of around 75% of output differences across incentives.
In heterogeneous workgroup, this drops to around 45%. Identity thus interacts with
incentives to influence the importance of cooperation relative to effort in the pro-
duction process. In the next 2 sub-sections, I examine in greater detail how identity
influences the inputs which consist production.
40I utilise the production function: Productivity=(360×Effort)/(Average questions per set). Effortis the rate at which a question is solved while 360 refers to the total time in seconds for each round.
41For each workgroup composition, with the lowest counterfactual output as a baseline, the ex-planatory power of each factor is calculated as how much a change in effort or help increases outputas a fraction of the difference between the largest and lowest counterfactual output. With the highestcounterfactual output as a baseline, the explanatory power of each factor is calculated as how mucha change in effort or help decreases output as a fraction of the difference between the largest andlowest counterfactual.
14
Input Team-P Help, Team-P Help, Tourn Help, Tourn Help,Workgroup Team-P Effort Tourn Effort Team-P Effort Tourn EffortHomogeneous 23.186 23.837 21.198 21.793
(0.193) (0.189) (0.199) (0.189)Heterogeneous 21.489 22.761 20.514 21.728
(0.182) (0.191) (0.176) (0.183)
Standard errors approximated by delta method.
Table 4: Simulated counterfactual productivity in each workgroup type
Homogeneous workgroups Heterogeneous workgroupsBaseline: Lowest Baseline: Highest Baseline: Lowest Baseline: Highest
Effort 0.225 0.247 0.540 0.566Help 0.753 0.775 0.434 0.460Total 0.978 1.022 0.974 1.026
Total does not sum up to 1 because of some complementarity between the two inputs.
Table 5: Proportion of counterfactual productivity differences explained
5.2 Help Choices
Here, I examine how identity and incentives influence participant’s choices to help and
hence cooperation within groups. Figure 2 illustrates average Out/In-group choices
to help and measures of workgroup cooperation over the 4 rounds.
Examining choices to help, we can see an in-group bias across all treatments:
participants on average choose to help in-group members more (top left vs top right).
Furthermore, as expected, choices to help are more prevalent in team-pay compared
to tournaments.42 Subsequently, homogeneous workgroups exhibit higher levels of
cooperation, especially under team-pay.43 Members of homogeneous workgroups are
more likely to give help (bottom left) and have less questions to solve (bottom right),
with bigger effects under team-pay. These observations are qualitatively in line with
Predictions 1 and 3. I next perform a more rigorous analysis using regressions in a
random effects/discrete choice framework.
In Table 6, I first examine choices to help by participants under several estimation
methodologies. I focus on the effects of whether the help request is from an ingroup
member, the incentive scheme and their interaction. To account for the fact that
42It is interesting to note that those in tournaments also felt that their help would be less beneficialfor the team; this may indicate some form of motivated thinking. See Figure A.1. in Appendix A.
43Except for provision of help and average questions under tournament incentives for which dif-ferences are smaller, homogeneous workgroups always have statistically significant higher levels ofcooperation (Mann-Whitney test, p-value<0.05).
15
Figure 2: Help choices and cooperation over Treatments
Dep var: Chose to help Random Effects Panel Probit Panel LogitIngroup 0.278*** 1.023*** 1.774***
(0.042) (0.130) (0.230)Team-Pay 0.213*** 0.849*** 1.473***
(0.060) (0.250) (0.440)Ingroup × Team-Pay -0.009 0.248 0.450
(0.057) (0.202) (0.363)Homogeneous -0.071 -0.228 -0.384
(0.051) (0.228) (0.401)Ingroup × Homogeneous -0.018 0.001 -0.010
(0.050) (0.172) (0.308)Team-Pay × Homogeneous -0.163** -0.665* -1.205**
(0.078) (0.349) (0.614)Ingroup × Team-Pay × Homogeneous 0.351*** 1.550*** 2.863***
(0.083) (0.450) (0.808)Constant 0.378*** -0.418 -0.729
(0.080) (0.407) (0.697)Observations 2448 2448 2448
* 0.10 ** 0.05 *** 0.01. Out-group decisions for homogenous workgroups are hypothetical.Regressions control for Ability, 1/Round and Ability × 1/Round
Table 6: Regressions of decisions to help
16
outgroup contributions are hypothetical in homogeneous workgroups, these 3 factors
are interacted with a dummy for them. In heterogeneous workgroups (first 3 coef-
ficients) team-pay and sharing common identity improve the likelihood of providing
help while there is no significant interaction between them. Comparing homogeneous
to heterogeneous workgroups (next 4 coefficients), participants are less likely to help
outgroup members, and more likely to help ingroup members under team-pay, al-
though the former is unincentivised. These results, summarised below, are robust to
different estimation methodologies.
Result 2. (Identity, incentives and choices to help) Participants are more likely to
help under Team-Pay. They also display an ingroup bias in choices to help.
Table 7, columns 1 to 2 use random effects regressions to examine the impact of
choices to help on two different measures of cooperation in the workgroup: the prob-
ability of giving help and average questions to solve. On average, team-pay raises the
probability of giving help (≈ 50 PP) and reduces the number of questions to solve (≈1 SD). However, being in a heterogeneous workgroup reduces cooperation under both
incentives, cancelling out almost half the benefits of team-pay. This confirms earlier
observations in Figure 2. Furthermore, the difference between Team-Pay and Tour-
naments in heterogeneous workgroups is significantly negative (p=0.0011), indicating
a smaller cooperation gap between incentives in heterogeneous workgroups.44 Round
effects also indicate a fall in cooperation more prevalent in tournament incentives.45
These results are summarised below:
Result 3. (Identity, incentives and cooperation in workgroups) Team-Pay improves
cooperation in workgroups. However, cooperation in workgroups is lower in heteroge-
neous workgroups under both incentives.
Table 7, columns Columns 3 to 4 instead examine participants’ internal and ex-
ternal motivations to help.46 Results are mostly qualitatively similar, though weaker.
In general, identity has an insignificant impact on internal compared to external mo-
tivation. However, while members of heterogeneous workgroups face lower external
44The model does not capture this as workgroup identity salience is not affected by incentives:amplifying it β by a factor k > 1 when incentives are team-based (w < b0) in the model wouldcapture this and keep comparative statics of workgroup identity salience the same.
45We have excluded interactions with identity as they were found to be inconsequential.46Motivation to help was obtained from a factor analysis (with 2 principal component factors) of
6 survey questions aimed at eliciting motivation to help coming from the self (internal) vs others(external), see Appendix A. Factor loadings of each question in general confirmed this distinction.
17
Dep-Var: Probability: Standardised Motivation: HelpGive Help Avg Qns Internal External
Team-Pay 0.478*** -1.114*** 0.491*** 0.663***(0.047) (0.107) (0.087) (0.119)
Tournament × Heterogeneous -0.077* 0.218** -0.045 0.195(0.044) (0.103) (0.135) (0.149)
Team-Pay × Heterogeneous -0.267*** 0.613*** -0.098 -0.290*(0.042) (0.085) (0.109) (0.170)
Tournament × 1/Round 0.201*** -0.433***(0.058) (0.134)
Team-Pay × 1/Round 0.040 -0.137*(0.031) (0.082)
Constant 0.512*** 0.259 -0.131 -0.339(0.098) (0.217) (0.154) (0.286)
Observations 1224 1224 306 306
* 0.10 ** 0.05 *** 0.01, Columns 1-2 are Random effects regressions, Columns 3-4 are OLS.Regressions control for Ability and Ability × 1/Round. Motivation is standardised.
Table 7: Regressions of measures of cooperation
pressure to help under team-pay, an opposite contradictory effect occurs under tour-
nament incentives, albeit insignificant; this may reflect an intensive margin effect of
having the ability to discriminate help between the in and out-group.
5.3 Personal Effort
In this subsection, I examine how identity and incentives influence choices to exert
personal effort. Figure 3 graphs barplots (and kernel densities) of various measures of
personal effort across the treatments. Here, effort (adjusted for ability) is measured
as the rate of solving a single decoding question within a question set.47
Consistent with Prediction 2, participants under team-pay rest more proportion-
ately (top-right) and have lower levels of overall effort compared to tournaments
(top-left/bottom).48 Also, overall effort is higher (lower) in heterogeneous work-
groups under tournament (team-pay). This is consistent with Prediction 4, although
differences are not strong. I examine this in greater detail in following regressions.
In Table 8, Columns 1 to 4, I perform random effects regressions of measures of
effort. Pooling across the identity treatments, Columns 1 and 3 show that just as in
Figure 3, effort exerted is reduced under team-pay. They solve questions at a lower
47This is calculated as the total number of decoding questions solved divided by the total timetaken to solve them, including rest.
48Those under team-pay also believe that rest would have smaller negative impacts on their ownpayment. See Figure A.1. in Appendix A.
18
Figure 3: Measures of Personal Effort over Treatments
Dep var: Standardised Proportion of Motivation: Match EffortEffort Sets Rested (Overall) (External)
Team-Pay -0.238*** -0.153 0.106*** 0.082* 0.333** 0.394***(0.067) (0.097) (0.032) (0.044) (0.125) (0.124)
Tournament × 0.021 -0.005 0.366** 0.024Heterogeneous (0.090) (0.048) (0.173) (0.137)
Team-Pay × -0.178* 0.052 -0.328* -0.232*Heterogeneous (0.101) (0.050) (0.178) (0.128)
Ability 0.755*** 0.756*** -0.043* -0.044*(0.041) (0.042) (0.022) (0.023)
1/Round -0.662*** -0.662*** 0.055*** 0.055***(0.041) (0.041) (0.017) (0.017)
1/Round × Ability -0.087** -0.087** 0.015 0.015(0.041) (0.041) (0.019) (0.019)
Constant 0.526*** 0.543*** 0.066* 0.060 -0.402** 0.066(0.108) (0.127) (0.037) (0.055) (0.166) (0.118)
Observations 1224 1224 1224 1224 306 306
* 0.10 ** 0.05 *** 0.01, Cols 1-4 are Random effects regressions while Cols 5-6 are OLS.Motivation is standardised.
Table 8: Regressions of effort variables
19
rate (≈ 0.24 SD) and rest proportionally more (≈ 10 PP). Furthermore, in Column 2,
the difference between the two interaction variables is positive and mildly statistically
significant (p=0.0978). This indicates that the negative effect of Team-Pay relative
to Tournaments is stronger in heterogeneous workgroups, which is consistent with the
second part of Prediction 4.
There is however less support for the first part of prediction 4. While the signs
of interaction coefficients in Columns 2 and 4 are as predicted, with those in hetero-
geneous workgroups putting in more effort under tournaments (≈ 0.02 SD) but less
under team-pay(≈ 0.18 SD), the effects are weak, especially in the former. I surmise
that this partially reflects a difficulty in measuring effort under piece rates.49
To address this, Table 8, Columns 5 and 6 provides supplementary evidence for
the above using survey data on motivations for matching others’ effort.50 Column
5 shows that in line with Prediction 4, members of heterogeneous workgroups face
more (less) overall pressure to match others’ effort under tournaments (team-pay),
with effect sizes being ≈ 0.3 SD. In Column 6, where pressure refers to that from
facing external disapproval, only team-pay is somewhat relevant. This likely reflects
greater concerns for the positive externalities of effort under team-pay. The results
in this section can be summarised as follows:
Result 4. (Identity, incentives and personal effort) Team pay lowers effort exerted.
Under team-pay, effort is somewhat lower in heterogeneous workgroups. Under tour-
naments, there is no significant difference in effort between workgroup types. However,
motivation to match others’ effort is lower (higher) under team-pay (tournaments) in
heterogeneous workgroups.
5.4 Salience of Workgroup Identity
In this subsection, I examine the impacts of the incentives and workgroup compo-
sition on the salience of workgroup identity. This serves as a check for the identity
manipulation and also allows for further robustness checks of results since identity
salience aligns with (β) in the model. The preferred measure for salience of work-
group identity here is obtained from a (standardised) weighted average of closeness
to members of the Klee/Kandinsky group obtained using an Inclusion of Other in
49Even with a choice to rest, the relatively short time scale of the experiment means that the taskmay feel less repetitive and thus participants do not adjust effort that much given that they knowthey are being paid per question set. This might affect the tournament treatment especially.
50See Table A.1 in Appendix A for a description of these motivation variables.
20
the Self (IOS) Scale from social psychology.51 Figure 4 plots participants feelings of
closeness towards ingroup and outgroup members and the standardised constructed
measure of identity salience (β).
Figure 4: Workgroup identity salience over Treatments
Participants can be seen to have higher feelings of closeness towards in-group
members (left) which results in workgroup identity salience being higher for homoge-
neous workgroups (right).52 The identity inducement was thus successful and in-line
with the models’ assumptions. Furthermore, there is also an added effect of incentive
schemes on identity.53 I next perform a regression analysis on these outcomes.
In Table 9, columns 1 and 2 look at how incentives and identity of the group
member influence one’s feelings of closeness. Column 1 shows that participants feel
higher levels of closeness towards ingroup members (≈ 1 SD). Team-Pay however
increases feelings of closeness to any group member (≈ 0.2 SD). Column 2 includes
interactions with the amount of message sent and received in Stage 1 as a proxy for
the amount of social interaction. Sending more messages (but not receiving messages)
51See Table A.1 in Appendix A for a description. This measure minimises the impact of experiencein the different incentive schemes. Other measures obtained from factor analysis of additional surveyquestions allude to their experiences in Stage 2 and may not reflect true ex-ante salience of workgroupidentity. Nevertheless, results do not change much depending on the measure used.
52Interestingly, they also believe that in-group members will be slightly better in performance(one sided t-test of mean>4: p<0.0001), see Figure A.1 in appendix A.
53That ex-post identity salience is influenced by the incentives is consistent with Chen et al. (2012)who find evidence that incentive structures can influence workgroup cohesion and subsequently cre-ative efforts. While our model does not account for these incentive effects on identity, comparativestatics for workgroup identity salience should not change even if team-pay were to amplify teamidentity salience. Comparative statics for incentives should also remain the same if these amplifi-cation effects are small enough. Footnote 44 discusses how a small modification to how workgroupidentity salience is determined would still be consistent with this.
21
Dep var: Feelings of closeness Workgroup Identityto group salience (βi)
Ingroup × Messages Sent 0.039***(0.012)
Ingroup × Messages Received -0.012(0.019)
Ingroup 1.047*** 0.861***(0.066) (0.133)
Team-Pay 0.201*** 0.195*** 0.320*** 0.418***(0.076) (0.074) (0.113) (0.151)
Heterogeneous -0.582*** -0.474***(0.142) (0.179)
Team-Pay × Heterogeneous -0.226(0.229)
Constant -1.223*** -1.233*** 0.047 -0.018(0.096) (0.104) (0.267) (0.275)
Observations 612 609 306 306
* 0.10 ** 0.05 *** 0.01, All measures are standardised.
Table 9: Regressions of Closeness and Salience of Workgroup Identity
raises one’s feelings of closeness. Interestingly, it suggests that there is a pure ingroup
identity effect independent of the levels of social interaction in the first stage.
Table 9, columns 3 and 4 examine how the above influences feelings of closeness to
one’s workgroup. It shows being in a heterogeneous team leads to a drop in workgroup
identity salience (≈ 0.5 SD) with Team-Pay having a comparable positive effect.
Weaker identity salience under tournament incentives may be another explanation for
the relatively weaker effects of identity on effort and cooperation within tournament
incentives. The results so far are summarised as follows:
Result 5. (Workgroup composition, Incentives and Workgroup Identity Salience)
Participants have stronger feelings of closeness towards ingroup members. Team-Pay
also strengthens feelings of closeness to any other. This results in stronger workgroup
identity salience in homogeneous workgroups and under Team-Pay.
Finally, using the constructed measure of workgroup identity salience, I check for
robustness of the previous results. This is done by replacing the identity treatment
dummy with actual elicited salience of workgroup identity and checking whether the
comparative statics for identity hold. I utilise an IV estimation procedure where
workgroup composition is used as an instrument for workgroup identity salience.
This assumes that workgroup composition only has effects on the chosen inputs and
subsequent output via workgroup identity salience.
22
Tables 10 and 11 examine whether results on main variables and motivations (re-
spectively) are robust to replacing the identity treatment with workgroup identity
salience. Table 10 (Columns 1 to 4) and Table 11 show that in general, compara-
tive statics for identity salience for help and effort are qualitatively in line with our
aforementioned findings which is reassuring. Finally, Table 10, Column 5 shows that
identity salience has a strong positive effect on productivity under Team-Pay, but
less so under Tournaments. The difference in the two interaction terms is significant
(p=0.0394) which is consistent with identity having different effects depending on the
incentive. The weak effects under Tournaments likely reflect the opposing effects of
identity salience on effort and help.
Dep var Std. Proportion: Probability: Std. Std.Effort Sets Rested Give Help Avg Qns Productivity
Team-Pay -0.287*** 0.121*** 0.299*** -0.710*** 0.001(0.071) (0.032) (0.054) (0.126) (0.073)
Tournament × β 0.001 -0.001 0.198**(0.170) (0.088) (0.085)
Team-Pay × β 0.257* -0.074 0.384***(0.134) (0.069) (0.058)
Tournament × βall -0.524*** 0.259(0.199) (0.171)
Team-Pay × βall -0.879*** 0.640***(0.152) (0.132)
Tournament × 1/Round 0.201*** -0.433***(0.058) (0.134)
Team-Pay × 1/Round 0.040 -0.137*(0.031) (0.082)
Constant 0.564*** 0.054 0.531*** 0.220 0.380***(0.123) (0.052) (0.103) (0.226) (0.106)
Observations 1224 1224 1224 1224 1224
* 0.10 ** 0.05 *** 0.01, βi, βall are based on self and workgroup respectively.Regressions are analogous to previous ones. Controls not shown here.
Table 10: Random effects IV regressions: Main variables
23
Dep var Motivation: Match Effort Motivation: Help(Overall) (External) (Internal) (External)
Team-Pay 0.036 0.237** 0.429*** 0.413***(0.125) (0.098) (0.090) (0.116)
Tournament × β -0.520* 0.089 0.103 -0.290(0.289) (0.252) (0.228) (0.289)
Team-Pay × β 0.503** 0.365** 0.140 0.420*(0.214) (0.175) (0.149) (0.226)
Constant -0.302** 0.123 -0.126 -0.288(0.138) (0.109) (0.148) (0.259)
Observations 306 306 306 306
* 0.10 ** 0.05 *** 0.01, Cluster robust standard errors used.Motivation is standardised. Controls not shown here.
Table 11: IV regressions: Motivations for Help and Effort
6 Discussion
In this paper, I studied how identity influences optimal incentives in workgroups. To
do so, I induced group identities in participants and allocated them to workgroups
with different compositions. They then performed a novel real-effort task involving
on-the-job help under tournament and team-pay incentives. Results were broadly in
line with the predictions of a simple model where individuals cared about the impacts
of one’s actions on others, as weighted by team-identity salience.
Firstly, consistent with the literature, chosen levels of help exhibited in-group bias.
This explained the higher levels of cooperation in homogeneous versus heterogeneous
workgroups under both incentives. Secondly, common identity was found to influ-
ence effort provision and motivation positively under team-pay, but negatively under
tournaments, although results were weaker. The combination of these two effects
led to team-pay being significantly more effective than tournaments in homogeneous
workgroups, but not in heterogeneous ones. Lastly, identity salience of the workgroup
is found to be dependent not only on the identity composition of the workgroup, but
also the incentives faced. Its estimated impacts on individual input choices are also
broadly consistent with the model, lending robustness to the above results.
Overall, the results show that identity can indeed influence optimal incentives. In
real life, external factors like the social dynamics of the locale and/or internal factors
like corporate culture can influence (the perceptions of) common identity amongst
workers. This can then influence the optimality of incentive schemes — even across
firms with similar production processes. This may be of future empirical interest
both independently and in relation to the literature on management and productivity
24
differences across firms.54
Practically, the results also have implications for organisational design. They sug-
gest that effective management involves a joint choice of incentives and identity, albeit
with possible constraints on the latter.55 More thought thus needs to be put into pur-
posefully designing performance incentives to optimise productivity, accounting for
identity factors. Organisations can engineer identity salience through team alloca-
tion, hiring decisions and/or training procedures while choosing compatible incentive
schemes.56 Nucor is one company which has used this to great effect: workers share
a strong common identity and high levels of team-pay are used, perhaps resulting in
one of the best labour productivity levels in the steel industry.57
Nevertheless, it should be emphasised that there is no “one size fits all” recom-
mendation of this joint choice. Our results hint at this: tournaments are optimal in
both workgroup types considering effort alone (less so in homogeneous groups), but
not with cooperation. It is thus important to consider all elements of the production
process as these may influence the relationship between identity and optimal incen-
tives in more nuanced ways. A useful thought process would be to consider for each
incentive, the externalities imposed on others by each worker input, as these would
determine workers’ choices in interaction with identity.58
That incentives here are found to influence perceptions of shared identity and that
optimal incentives themselves depend on it also implies a feedback mechanism which
has several consequences. On one hand, it suggests that initial exogenous differences
in social identities may be amplified, giving rise to a wider spread of incentives.
For example, an initially homogeneous organisation might prefer team-pay — this
amplifies perceived homogeneity and subsequently the preference for team-pay. On
the other hand, it also suggests that early mistakes in incentive choices might “lock”
organisations into a wrong identity-incentive equilibrium, resulting in possibly large
54Bloom and Reenen (2007, 2010) examine management practices across firms and countries. Des-sein and Prat (2018) review the literature on productivity differences between firms. In particular,the factors examined here would fall under their classification of organizational capital.
55Possible (external) constraints on engineering identity salience include external social dynamics,high turnover, anti-discrimination laws etc.
56The possibility of influencing workers’ identification is discussed in Akerlof and Kranton (2008)who focus on how monitoring decisions, by affecting perceived organisational identity, can influencethe amount of individual compensation needed.
57This may an (intentional) result of selective hiring, retainment of workers with particular at-tributes and the relatively flat organisational structure (Collins, 2001).
58This is considered in Ashraf and Bandiera (2018) where they review the literature on horizontalrelationships as a source of social incentives in firms.
25
opportunity costs. Persistent differences between Nucor and other steel companies are
consistent with this (Ghemawat, 1995). Paying more attention to not only incentives,
but also (long run) identity salience would be important in helping organisations avoid
these costly adjustments.
A Survey variables
Figure A.1: Treatment comparison of select survey variables
26
Understand TaskHow well did you understand the instructions and the decoding task in Part 2?[1: Did not understand at all - 7: Understood it very well]
Task InterestPlease tell us how you felt about the decoding task in Part 2? (Groups here refer toKLEE/KANDINSKY)[1: Very boring - 7: Very interesting]
Help benefits the teamWith respect to the decoding task, how do you think YOUR provision of help to otherswill affect the TOTAL score of the team? (4 = no effect on total score)[1: Total score decreases - 7: Total score increases]
Rest benefits myselfWith respect to the decoding task, how do you think taking breaks will affect your totalpayment? (4 = no effect on earnings)[1: Total payment decreases - 7: Total payment increases]
In-Group is betterIn solving a single set of 3 decoding questions, how do you think your own group will per-form relative to the other group on average? (Groups here refer to KLEE/KANDINSKY)[1: Much worse (slower) on average - 7: Much better (faster) on average]
Motivation: Match Effort (Overall)I felt worried about taking breaks during each round because others on my team mightbe putting a lot of effort.I felt that I had to put in my best effort into the decoding task to match the effort ofothers on the team.
Motivation: Match Effort (External)I felt that others on my team would be upset if I did not put in effort into the decodingtask.
Motivation: HelpI felt pressured by *MYSELF* to provide help.I valued being able to help others during the decoding taskI felt that helping others during the task is the right thing to do.I felt pressured by my *TEAMMATES* to provide help.I felt that others on my team valued being able to help others during the decoding task.I felt that others on my team believed that helping others is the right thing to do.
Closeness to each group (IOS scale)Please select the option which best describes your feeling toward the KLEE (KANDIN-SKY) group after Stage 1 of the experiment.
Motivation questions were on a likert scale from 1 (strongly disagree) to 7 (strongly agree).
In motivation to help, the first 3 statements loaded more positively on the first factor while thenext 3 statements loaded more positively on the second factor. These two factors are referred tointernal and external motivation respectively. In motivation to match effort, the two statementsloaded positively on a single factor.
Table A.1: Description of select survey variables
27
Instructions
Welcome to the experiment.
Please remain silent throughout the course of the experiment and refrain from using any
communication devices, otherwise we may be forced to stop the experiment. If you have any
questions at any point, please raise your hand and an experimenter will come over to see you.
In this experiment, there will be 2 stages and you will earn money based on your performance
in each stage. Please read the instructions below carefully.
The experiment will be conducted in an anonymous fashion. You will not be able to discover
others’ exact identities, neither will others be able to discover your exact identity. Rest assured
that your anonymity will be strictly preserved.
In the experiment, your payoffs will be in Experimental Currency Units (ECUs). At the end of the
experiment, your earnings will be converted into Pounds according to the rate: 100ECU: £2.5.
This will be added to your show up fee of £3. Information about your earnings in each stage will
only be provided at the end of the experiment. You will be paid your earnings privately and
confidentially at the end of the experiment after completing a questionnaire.
If you need to write down anything, please use the paper and pen provided. Please do not write
anything on this instruction sheet.
Stage 1
In the first stage, you will be shown five pairs of paintings sequentially. Each pair contains a
painting by Paul Klee and another by Wassily Kandinsky. They are abstract artists from the last
century. You will not be informed of the artist of each painting. For each pair, you will be asked
which painting you prefer. Your preferences relative to others in the session will then be used
to classify you into one of two groups: i.e. Group Klee and Group Kandinsky. This means that
the more times that you have indicated preference for paintings by Klee (Kandinsky) relative to
others, the more likely you will be assigned to Group Klee (Kandinsky).
For easier identification, Group Klee will be represented by the colour blue while Group
Kandinsky will be represented by the colour red throughout the experiment.
Subsequently, you will be shown a final pair of paintings, one by Klee and the other by Kandinsky
for which you have to guess the artist of each painting. To help in answering the question, you
will have a 5 minutes discussion (subject to restrictions) with members of your group (Klee or
Kandinsky); you will be able to refer to the past pairs of paintings during the discussion.
Subsequently, you will be asked to answer individually. Your answer as well as the answers of
others in your group (Klee or Kandinsky) will determine your payment in this stage:
If the majority of your group members get the answer right, then you will obtain a payment
of 80 ECUs.
You will then move on to Stage 2 of the experiment.
B Instructions
28
Stage 2
Team Assignment
In the second stage, you will be randomly allocated to a work-team consisting of 6 members and
play several rounds of a decoding task. Given the random allocation, your team members may
or may not belong to the same group (Klee or Kandinsky) as you. You will be notified of your
work-team’s composition at the beginning of the second stage. Note that your team members
will always be the same in every round.
The Decoding Task
In this task, you will have to solve sets of decoding questions in each round of 6 minutes. There
will be opportunities to help others on your work-team, as well as to take (paid) breaks during
the round. This will be described in detail below.
1) Solving question sets
Each question set involves converting several letters into numbers using a provided table. See
Figure 1.
There will be two different kinds of question sets in the task which occur with equal chance:
1. An easy question set with 3 decoding questions (represented by ~).
2. A difficult question set with 7 decoding questions (represented by !).
To complete a question set, use the provided table (1) to convert the letters into numbers, filling
the answers in the corresponding boxes (2). Then, submit your answers by clicking either of the
submission buttons (3).
Correct answers are denoted by an O while incorrect or incomplete answers are denoted by an
X. If all submitted answers are correct, you will earn 1 point and the next question set (and a
new table) will automatically appear. Your score (4) at the end of the round will thus be the
total number of question sets you have completed correctly. Your time left in the round is
shown on the top right (5).
2) Taking breaks
Depending on which button is used to submit your answers in (3), you can choose to take a break
for 5 seconds before receiving the next set of questions.
If you click the “Submit and Rest” button, and provided your answers are all correct, then you
will receive the next question set after a 5 second rest period where the Task screen will be
temporarily blanked out; this is shown in Figure 2.
For each break taken, you will be paid 2.5 ECUs.
(Note that this rate is equivalent to 1.75 ECUs for 3.5 seconds: the average time taken to convert 1 letter
into a number using the table in past experiments.)
If you click the “Submit” button, and provided your answers are all correct, then you will receive
the next question immediately.
The total number of times rested during the round is shown under your score (6).
(1)
(2)
(3)
(4)
(5)
Figure 2: After clicking the submit and rest button
(6)
Figure 1: The decoding task
3) Help Requests to Teammates
If you receive a difficult question set of 7 questions, you will automatically send a help request
after 3 seconds to a random teammate whose group (Klee or Kandinsky) you will be informed.
See the left side of Figures 3a/b.
Note that you can continue solving the question set before the help request is sent out.
If the teammate accepts your help request, you will be notified and the number of questions
you have to solve will be reduced by 3. Accepting your help request means that your teammate
will have to solve 1 extra question. See the top right of Figures 3a/b.
If the teammate rejects your help request, you will be notified as well, but the number of
questions will not be reduced. Rejecting your help request means that your teammate will not
have any extra questions. See the bottom right of Figures 3a/b.
Figure 3a: Help request to KLEE group
Figure 3b: Help request to Kandinsky group
7 Questions reduced by 3
7 Questions not reduced
by 3
7 Questions reduced by 3
7 Questions not reduced
by 3
4) Providing Help
At the beginning of each round, you will be asked to decide whether you want to provide help
to your teammates during the round (7).
In particular, you will be asked for your helping decision in two cases: 1) when the requester
belongs to the Klee group and 2) when the requester belongs to the Kandinsky group.
See Figure 4.
This decision will then apply to all help requests sent to you during that round. As mentioned,
each help request accepted during a question set means that you have to solve 1 extra question;
this will appear automatically during the question set. See Figure 5.
Figure 4: Deciding on how much help to give
Figure 5: What happens when help requests are accepted
(7)
5) Information about Help requests
For your interest, a provided sidebar on the left (8) will show you the number of times you have
accepted or rejected help requests from your teammates (in each group) during the ongoing
question set. Relevant icons and numbers will pop up once you have received a help request
during the ongoing question set: these are described in Figure 6 below. If help requests are
received during a rest period, you will see these icons immediately after your rest period ends.
Information on help requests to teammates (i.e. the displays in Figure 3) are also located here.
Figure 6: Information on help requests
Help request from
KLEE accepted
Help request from
KANDINSKY accepted
Help request from
KLEE rejected
Help request from
KANDINSKY rejected
(8)
6) Earnings
Your task payment in each round will depend on your performance as well as your teammates’
performance during the round. You will be notified of the exact payment scheme at the
beginning of the second stage.
Note that members of your work-team will receive the exact same payment scheme as you.
Your total payment in each round will be calculated as the sum of your task payment and the
“rest” payment (number of breaks taken × 2.5 ECU).
Out of the several rounds in the second stage, only 1 will be randomly chosen to make up your
final payment.
_____________________________________________________________________________
We have now come to the end of the instructions. There is a hard copy of the instruction sheet
in case you need to refer to it again. If you have any questions please raise your hand and we
will attend to you privately.
If not, we will now have a short quiz to test your understanding of the Stage 2 instructions. You
will have to answer all questions correctly to proceed.
Following the quiz, we will have 2 practice rounds for the Stage 2 decoding task:
In the first practice round, you will have 2 minutes to experience the user interface of the game;
This will be done with simulated partners, so there will not be any real interactions.
In the second practice round, you will have 3 minutes to practice completing the decoding task.
Note that the helping and resting mechanisms will be unavailable during the second practice
round.
After the 2 practice rounds, we will then begin Stage 1 of the experiment proper.
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