Do Incentives Destroy Voluntary Cooperation?

Do Incentives Destroy Voluntary Cooperation?†

Simon Gächter*, Esther Kessler** and Manfred Königstein***

7 November 2010

PRELIMINARY VERSION, COMMENTS WELCOME!

Abstract

We investigate experimentally how explicit performance incentives in incomplete employment contracts interact with agents’ voluntary cooperation in one-shot and repeated gift-exchange experiments. If contracts are incentive compatible, agents choose their best-reply effort and there is no voluntary cooperation. By contrast, there is substantial voluntary cooperation if the contract is not incentive compatible. Experiencing incentive contracts reduces voluntary cooperation even after incentives are abolished. We then examine the robustness of these findings with regard to two variables: Experience with voluntary cooperation and the possibility of implicit incentives in repeated interactions. We find that incentive contracts have no lasting negative effects on voluntary cooperation if agents experience voluntary cooperation before being exposed to incentive contracts or if there are implicit incentives in repeated interactions. Implicit incentives increase voluntary cooperation strongly and make explicit incentives unnecessary or even counterproductive.

Key words: principal-agent games; gift-exchange experiments; incomplete contracts, explicit incentives; implicit incentives; repeated games; robustness.

JEL-Codes: M5, C7, C9

† This paper is part of the MacArthur Foundation Network on Economic Environments and the Evolution of Individual Preferences and Social Norms. Support from the EU-TMR Research Network ENDEAR (FMRX-CT98-0238) and from the Grundlagenforschungsfonds at the University of St. Gallen is gratefully acknowledged. We benefited from excellent research assistance by Christian Thöni and Eva Poen and from helpful comments in numerous workshops and seminars. Simon Gächter gratefully acknowledges the hospitality of the Institute for Advanced Studies at Hebrew University in Jerusalem while working on this paper. * University of Nottingham, School of Economics, Sir Clive Granger Building, University Park, Nottingham NG7 2RD, United Kingdom. Simon Gächter is also affiliated with CESifo and IZA. E-mail: [email protected]. **University College London, Institute of Child Health, Behavioural & Brain Sciences Unit, 30 Guilford Street, London WC1N 1EH, United Kingdom. E-mail: [email protected]. *** Universität Erfurt and IZA. Professur für Angewandte Mikroökonomie, Nordhäuser Str. 63, D-99089 Erfurt. E-mail: [email protected].

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1. INTRODUCTION

Understanding the behavioral consequences of incentives is a fundamental goal in

economics. In this paper we contribute to this goal by investigating experimentally how

explicit and implicit incentives that give agents a selfish incentive to perform interact with

many people’s propensity to act against their self-interest. Numerous experiments and other

evidence in economics and the behavioral sciences demonstrates that many people are willing

to sacrifice their self-regarding motivations to benefit collective welfare (Fehr and

Fischbacher (2002); Camerer (2003); Gintis, et al. (2005); Bewley (2007); Henrich and

Henrich (2007)). Yet, in reality, many (potential) acts of voluntary cooperation do not take

place in isolation from situations that give people self-interested motivations to cooperate:

explicit and implicit incentives to cooperate are often present as well. The question we ask in

this paper is “What do explicit and implicit incentives imply for voluntary cooperation?”

There are at least two reasons why an answer to this question is important. First, on a

practical note, many contracts in reality are incomplete, which leaves important aspects

unregulated and therefore non-enforceable by third parties. Contractual incompleteness gives

agents an incentive to act opportunistically which may impair efficiency. Thus, many scholars

have argued that voluntary cooperation is necessary to ensure efficiency (Akerlof (1982);

Williamson (1985); Simon (1997); Bewley (1999); Bowles (2003); MacLeod (2007)).

Understanding interaction effects helps outlining the conditions under which voluntary

cooperation is likely to mitigate contractual incompleteness. Second, on a more fundamental

level, our question is on whether material interests and the ‘moral sentiments’ as expressed in

voluntary cooperation are separable, that is, whether incentives and voluntary cooperation are

independent of the levels of the other. If separability fails, incentives may be overused or

underused, and this has important implications for mechanism design (Bowles (2008);

Bowles and Hwang (2008); Bowles and Polanía Reyes (2010)).

We approach the separability issue on how incentives and voluntary cooperation

interact by means of laboratory experiments in the framework of gift-exchange games.1 The

gift-exchange game is a two-player game in which a principal (‘an employer’) offers a fixed

wage to an agent (‘an employee’). The agent can accept or reject the fixed wage offer. If the

agent accepts, he or she chooses an effort (or, equivalently, an output) level. Effort (output) is

costly for the agent and beneficial for the principal. Parameters are such that efficiency calls

1 We chose laboratory experiments for two reasons: (i) only the laboratory allows for the comprehensive investigation of all interaction effects we are interested in (Falk and Heckman (2009); Croson and Gächter (2010)) and (ii) controlling for selfish incentives, which will be crucial for our approach, is hardly feasible in the field.

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for the maximal effort whereas selfish incentives induce the agent to provide the minimal

effort irrespective of the wage he or she has accepted (that is, there is no voluntary

cooperation). The results of numerous experiments refute this prediction and demonstrate the

relevance of voluntary cooperation – wages and effort are positively correlated even in one-

shot games.2 We will replicate this finding in a version of the gift-exchange game we call the

‘Trust game’. The results from the Trust games will provide the necessary benchmark for the

comparisons we are mainly interested in.

The explicit incentives take a simple form in our experiments: The principal offers a

contract that specifies a desired effort (= output) level and two compensation levels, one for

the case in which the agent delivers at least the contractually pre-specified output, and a

second (lower) compensation level if the agent’s output falls short of the desired output. We

design the set of feasible contracts such that the maximally enforceable output (by means of

incentive compatible contracts) is substantially less than the efficient output level. Thus, there

is room for efficiency-enhancing voluntary cooperation beyond the maximally enforceable

level. In general, we define voluntary cooperation as any effort the agent puts forward that

exceeds his or her selfishly-rational best-reply effort level. Our design also allows for an easy

distinction of incentive- and non-incentive compatible contracts; the latter are directly

comparable to Trust contracts which are non-incentive compatible by design.

One fundamental reason why separability may fail in this environment is the following:

Voluntary cooperation and incentive contracts operate on different psychological mechanisms

and contracts send messages that appeal to these mechanisms in a more or less coherent way.

The psychological sources of voluntary cooperation are motives like fairness and equity

concerns (Adams (1965); Akerlof (1982); Fehr and Schmidt (1999); Bolton and Ockenfels

(2000)), reciprocity (Rabin (1993); Levine (1998); Dufwenberg and Kirchsteiger (2004); Falk

and Fischbacher (2006)); let-down aversion (Dufwenberg and Gneezy (2000); Charness and

Dufwenberg (2006)); loyalty and goodwill (Simon (1991), Bewley (1999)); or social esteem

(Bénabou and Tirole (2006); Ellingsen and Johannesson (2008)). By contrast, explicit

incentives give the agent a self-regarding motive to perform. The general point is that

contracts convey messages about what the principal thinks the agent should do and what the

principal’s intentions are (Bénabou and Tirole (2003); Fehr and Rockenbach (2003); Falk and

Kosfeld (2006); Sliwka (2007); Bowles and Polanía Reyes (2010)). Trust contracts appeal to

the psychological sources of voluntary cooperation, whereas incentive-compatibly designed

2 For a recent overview of laboratory gift-exchange experiments see Fehr, et al. (2009). Evidence on gift exchange is not confined to the laboratory. See Gneezy and List (2006) and Falk (2007) for recent field experiments on gift exchange.

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incentive contracts appeal to an agent’s rational self interest. Explicit incentive contracts that

are not designed incentive compatibly send an ambiguous message to the agent. Thus, the

predictions are that, on average, the amount of voluntary cooperation under Trust contracts

depends on the generosity of the contract: higher wages will lead to higher effort. By contrast,

incentive-compatible contracts will lead to incentive-compatible effort choices and little

voluntary cooperation beyond that, even if people would in principle be willing to provide

more effort. If this happens, we speak of a ‘short-run crowding out effect’. If the incentive

contract is not incentive compatible, we might observe ambiguous reactions: agents might

focus on the fact that the contract is an inconsistent appeal to the agent’s self-interest and

either shirk or respond to the perceived generosity of the contract.

A second reason why separability might fail is that explicit incentives change how

people interpret the nature of the relationship they are in (Bowles (1998); Gneezy and

Rustichini (2000); Bohnet, et al. (2001)). Since incentive contracts can be perceived as

appeals to self-interest, people might become more selfish. We test this argument by running

our experiments in two phases: in a first phase subjects act in an environment where incentive

contracts are possible and in a second phase the possibility of explicit incentives is removed:

only Trust contracts are feasible. If a spillover effect from the first phase to the second phase

exists and agents indeed shirk more after having been exposed to incentives, as compared to

having been exposed to Trust games in a control experiment, we speak of a ‘long-run

crowding out effect’.

A third reason for failure of separability might be related to how the explicit incentives

frame the decision for the agent (Liberman, et al. (2004); Salant and Rubinstein (2008);

Bowles and Polanía Reyes (2010); Dufwenberg, et al. (2010)). To study this aspect, we use

two incentive-equivalent forms of explicit incentives (from a selfishly-rational point of view):

in one set of games (called the ‘Bonus game’) principals can pay a bonus (‘a wage increase’)

if actual effort is at least as high as the desired effort, whereas in another set of games (the

‘Fine game’) principals can ask for a fine (a ‘wage deduction’) if the actual effort falls short

of the desired effort. A bonus might be perceived as a promise, and a fine as a threat, and

these perceptions might have different impacts on both short-run and long-run crowding out.3

Consistent with existing evidence (as surveyed in Bowles and Polanía Reyes (2010)),

our first set of experiments, reported in Section 4, provides evidence for all three sources of

3 There are theoretical arguments that frames in the context of social preferences might matter: Frames influence beliefs and beliefs influence motivations (see Dufwenberg, Gächter and Hennig-Schmidt (2010)). Empirically, the type of frames we study here may matter as well. In a natural field experiment using junior and senior academic economists as subjects, Gächter, et al. (2009) showed that labels akin to fine and bonus can have different behavioral consequences despite being the same in terms of the incentive structure.

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failures of separability. Incentive-compatible contracts crowd out voluntary cooperation

almost entirely. If confronted with an incentive-compatible contract, agents choose their exact

best-reply effort in the majority of cases, under both Bonus and Fine contracts. By contrast,

both in the benchmark Trust games with no incentives and under non incentive-compatible

incentive contracts, we observe substantial voluntary cooperation. However, unlike in the

Trust games, the voluntary cooperation we observe under non-incentive compatible contracts

is unrelated to the offered wages; framing is unimportant for this result as well. We also

observe clear evidence for long-run crowding out: agents who have been exposed to incentive

contracts are significantly more likely to shirk in the subsequent Trust games.

After having established the importance of crowding out of voluntary cooperation in

our environment we investigate the robustness of our findings in two further sets of

experiments. Our second set of experiments, documented in Section 5, investigates the role of

experience with Trust contracts before being exposed to incentives. Thus, the first phase

consists of Trust games, the second phase of Bonus games or Fine games and the third phase

consists again of Trust games. We find again that incentive-compatible contracts lead to best-

reply effort choices in the majority of cases and hence very little voluntary cooperation. By

contrast to the first set of experiments, we find that (i) effort choices under non incentive

compatible contracts are now significantly positively correlated with the offered

compensation and (ii) the long-run crowding out effect has largely vanished. This result

demonstrates clearly that the perception of the contract is crucial for agents’ behavior: if

agents are experienced with Trust contracts, they can interpret the generosity of a non-

incentive compatible contract much better than when they lack this experience.

Our final set of experiments, which we report in Section 6, allows for both explicit and

implicit incentives. While in the first two sets of experiments we randomly changed pairings

between principal and agent to avoid confounds of crowding effects with strategic incentives,

we kept the pairs fixed in these experiments. This allows for implicit incentives coming from

repeated interactions with the same partner. Implicit incentives are arguably a very important

feature of many real life contractual relationships (e.g., employment relations). Our results

show a very strong positive effect of implicit incentives on voluntary cooperation. The

implicit incentives add to the voluntary cooperation we see in the one-shot experiment. There

is no long-run crowding out. In the repeated games, most contracts are designed in a non

incentive-compatible way. However, like in the first two sets of experiment, in those

contracts which are incentive compatible we find full crowding out of voluntary cooperation:

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agents play their stage-game best replies in almost all instances. Thus, implicit incentives

cannot be added on top of explicit incentives.

While incentive-compatible contracts lead to incentive-compatible behavior we also

observe that non-incentive compatible contracts can be quite successful in inducing high

effort levels, in particular, when implicit incentives are present. We discuss this phenomenon

in detail in Section 6. Section 7 provides a concluding discussion.

2. DESCRIPTION OF GAMES AND BENCHMARK SOLUTIONS

2.1 The Games

We investigate three versions of principal-agent games based on the gift-exchange game

(Fehr, et al. (1993)). We summarize the games and parameters in Table 1. Each game consists

of three stages. The principal first offers a contract to the agent. In the Trust game the contract

comprises two components, a fixed wage w and a desired effort ed (effort can also be

interpreted as output in our games). The contract offer has to obey the restrictions

, in integers. 1 20 and -700 700de≤ ≤ ≤ ≤w

0

Second, the agent can accept or reject the offered contract. If he or she rejects the

contract, the game ends and both players earn nothing. If the agent accepts, he or she chooses

effort e in integers (where 1 ). The agent is not restricted by ed in his or her choice of

e. This reflects contractual incompleteness because ed is not enforceable. The stage game ends

after effort has been chosen.

2e≤ ≤

TABLE 1 GAMES AND PARAMETERS

Trust game Fine game Bonus game Wage Desired effort (=output) Performance incentive

w ∈ [-700, 700] ed ∈ [1,20]

w ∈ [-700, 700] ed ∈ [1,20]

f ∈ {0, 24, 52,80}

w ∈ [-700, 700] ed ∈ [1,20]

b ∈ {0, 24, 52,80} Agent’s payoff w – c(e) w – c(e) if e ≥ ed

w – c(e) – f if e < ed w – c(e) + b if e ≥ ed

w – c(e) if e < ed Principal’s payoff 35e – w 35e – w if e ≥ ed

35e – w + f if e < ed 35e – w – b if e ≥ ed

35e – w if e < ed Effort cost: c(e) = 7e – 7 Payoff if contract rejected: 0 for both

In the Trust game the principal’s profit is 35e – w and the agent’s profit is w – c(e) in

case of acceptance. Thus, effort induces a return that accrues to the principal and causes a

cost (disutility of effort) to be borne by the agent. The cost function is increasing and linear in

effort: c(e) = 7e – 7. Because w cannot be conditioned on effort, we refer to this game as the

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‘Trust game’. It is a game of complete information. Each player knows the rules, including

the payoff functions for both players, and is informed about all choices made in the game.

In the Fine game the principal may punish the agent if effort falls short of ed. The

offered contract in the Fine game consists of w, ed and f, where f represents a fine (it can be

interpreted as a wage reduction). The principal can choose one of four fine levels: f ∈ {0, 24,

52, 80}. If e < ed, f is subtracted from the agent’s payoff and the principal’s wage bill is

reduced to w – f. If e ≥ ed, the fine is not imposed.

In the Bonus game the principal has the opportunity to reward the agent if the chosen

effort e is equal to or larger than the desired effort ed. Thus, the offered contract contains w, ed

and b, where b is a bonus (a wage increase) with b ∈ {0, 24, 52, 80}4. If e ≥ ed, the bonus is

added to the agent’s payoff and subtracted from the principal’s payoff. If e < ed, the bonus is

not due.

2.2 Benchmark Solutions for Rational Money Maximizers

Trust game: Since providing effort is costly and payment does not depend on effort, the

agent will choose e = emin = 1 irrespective of the offered wage. Anticipating minimal effort,

the principal offers the wage that just ensures the agent’s acceptance, namely w = 1 (or w = 0

if one assumes acceptance in case the agent is indifferent between acceptance and rejection).

These decisions result in payoffs of 34 money units for the principal and 1 money unit for the

agent. This solution is inefficient, since the efficient surplus is 567 at e = emax = 20.

Fine game and Bonus game: In choosing effort the agent has to consider two

alternatives, e = ed or e = 1. A higher effort than ed is suboptimal since it causes higher cost

without increasing payment. Conditional on e < ed, minimal effort e = 1 is best because fine

and bonus payments are independent of e. Hence, the optimal effort level is:

* if ( ) (1) ( ) or ( ) (1) ( ); 1 otherwise.

d d d de w c e w f c f c e w b c e w c b c ee

⎧ − ≥ − − ⇔ ≥ + − ≥ − ⇔ ≥= ⎨⎩

d

(1)

Notice that the best-reply efforts are the same in the Fine game and the Bonus game; any

behavioral difference for a given contract is therefore due to a framing effect.

The agent’s best reply function (1) is the incentive-compatibility constraint for the

principal’s contract design problem. For each level of f or b there exists a maximal level of

desired effort that satisfies f, b ≥ c(ed). The maximally enforceable effort is 12. Before

4 In our setup the bonus (as well as the fine) is enforceable. This is in contrast to Fehr, et al. (2007), where paying the bonus is at the discretion of the principal after the agent has chosen the effort.

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choosing effort the agent has to accept an offered contract. This requires that the agent has

non-negative earnings (because the outside option is 0). With the parameters from Table 1 it

is optimal for the principal to set w such that the agent is just compensated for his or her effort

cost c(e*); furthermore, the solution to the principal’s problem is f, b = 80, ed = 12 and wf =

c(12) = 77 or wb = b – c(12)= –3 (where wf (wb) denotes the wage in the Fine (Bonus) game).

Accordingly, the agent will accept the contract and choose e = 12. The solution is more

efficient than the solution without incentives but it does not generate the maximal surplus. It

induces a rather asymmetric distribution of payoffs: 343 money units for the principal and 0

money units for the agent.

As we have just shown, the maximally enforceable effort under incentive-compatible

contracts is 12 in both the Fine game and the Bonus game. This feature is crucial for our

purposes because it allows for voluntary cooperation beyond what incentives can achieve.

Moreover, it reflects contractual incompleteness that characterizes many real life contracts,

even if some aspects of the contract can be contractually regulated. By allowing for different

fine and bonus levels (including zero) we give the principal the possibility to set the strength

of the incentives he or she wants to apply to the agent.5 Moreover, different combinations of

f, b, and ed that satisfy the incentive-compatibility constraint can induce different best-reply

efforts and this variation allows for a sharper test of whether agents choose best-reply efforts

than a more restricted (e.g., binary) set would have allowed for.

3. RESEARCH QUESTIONS, EXPERIMENTAL DESIGN AND PROCEDURES

3.1 Research Questions and Experimental Design

In the experiment the participants played two, or respectively three, blocks (‘phases’) of

ten games. Table 2 lists our research questions and all respective treatments. Table 2 also

indicates the respective matching procedure. In the first two sets of experiments subjects were

matched randomly in each round. In the third set of experiments subjects were matched with

the same player during the whole experiment (‘Partner’ matching). Within a treatment (the

rows) our experiments applied a within-subject design because subjects participated in

multiple phases. Across the eight different treatments we employed a between-subject design.

Subjects kept their role as principal or agent throughout the experiment.

The first treatment is TTT, which serves as our main benchmark. In these experiments

subjects play three phases where each phase comprises ten one-shot Trust games played in

5 We included zero because there is evidence that deliberately abstaining from using incentives when incentives could have been used induces more cooperation (Fehr and Rockenbach (2003)).

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randomly matched pairs. If effort is higher than predicted according to the benchmark

solution (i.e., e > e*), we refer to this as ‘voluntary cooperation’. Based on previous

experiments on the gift-exchange game we predict that the fixed wage will be positively

correlated with effort.

Against this benchmark we conduct three sets of experiments that correspond to the

three main purposes of our experiments. The first set of experiments (panel A in Table 2)

aims at measuring (i) the impact of explicit incentives on effort choices; (ii) the role of

framing (fines vs. bonuses); and (iii) the short- and long-run crowding effects of voluntary

cooperation that are neither confounded with strategic incentives, nor with prior experience of

Trust contracts before being exposed to incentive contracts. Therefore, subjects play one-shot

experiments in two phases of ten periods each. In Phase 1 principals can design either Fine or

Bonus contracts, whereas in Phase 2 only Trust contracts are feasible. We refer to these

treatments as the FT or BT treatment.

TABLE 2

MAIN RESEARCH QUESTIONS, EXPERIMENTAL DESIGN, NUMBER OF SUBJECTS AND NUMBER OF MATCHING GROUPS

Treatment label Phase 1 (Period 1-10)

Phase 2 (Period 11-20)

Phase 3 (Period 21-30)

# Subjects # independent matching groups

0. Establishing a benchmark of voluntary cooperation.

TTT Trust Trust Trust 78 6 A. Establishing the effects of explicit incentives: Are there short-run and long-run crowding effects? Does the framing of incentives matter?

FT Fine Trust - 80 6 BT Bonus Trust - 78 6

B. Introducing incentives after experiencing Trust contracts: What is the impact of explicit incentives on short-run and long-run crowding effects? Does the framing of incentives matter?

TFT Trust Fine Trust 86 6 TBT Trust Bonus Trust 84 6

C. How do implicit incentives available in repeated interactions influence voluntary cooperation, the use of explicit incentives and short-run and long-run crowding effects? Does the framing of incentives matter?

TTT-R Trust Trust Trust 24 12 TFT-R Trust Fine Trust 36 18 TBT-R Trust Bonus Trust 34 17

The second set of experiments (panel B in Table 2) investigates the relevance of prior

experience of Trust contracts before being exposed to Incentive contracts. All subjects play

three phases of ten one-shot games with random matching. Phase 1 and Phase 3 consist of

Trust contracts, whereas Phase 2 allows for either Fine or Bonus contracts. We refer to these

experiments are TFT or TBT treatments. These experiments are interesting for two reasons.

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First, we can investigate the impact of experience of Trust contracts on the robustness of

short- and long-run crowding. Second, it is also of practical relevance to understand what

happens to voluntary cooperation if incentive contracts are introduced into an existing Trust

environment.

The final set of experiments (panel C in Table 2) allows for both explicit and implicit

incentives. Participants play either the TTT, the TFT or the TBT experiments in fixed pairs

(‘Partner’ matching) for three phases of ten periods each. We refer to these experiments as

TTT-R, TFT-R and TBT-R, where the suffix ‘R’ stands for repeated interaction. Although

subjects are aware that they play finitely repeated games there are theoretical and empirical

reasons why there are implicit (that is strategic) incentives to cooperate: First, if selfishness

and rationality are not common knowledge cooperation can be sequentially rational (Kreps, et

al. (1982)). Second, boundedly rational play can also lead to cooperation (Selten and Stoecker

(1986)). Third, previous experimental evidence suggests that cooperation in the finitely

repeated gift-exchange game is higher than in one-shot games (Falk, et al. (1999)).

We are now in a position to give a precise definition of crowding effects. Because

contracts can obey or violate incentive compatibility, we also have to distinguish these cases

in our definition of crowding effects.

Definition 1 (short-run crowding):

(a) Contracts are incentive-compatible (“IC”): A short-run crowding effect occurs in

Phase 1 of treatment FT, BT if * ,

, ,( 1) ( )d d

Trust Fine BonusICw e w e

e e e− ≠ − where e is the

average effort (over all ten periods) in Phase 1 of the Trust game and the Fine or

Bonus game, respectively.

(b) Contracts are not incentive-compatible (“NIC”): A short-run crowding effect occurs

in Phase 1 of treatment FT if , ,

( ) | 0dw e fTrust Fine

NICe e− ≠ , and in BT if

, ,( ) | 0dw e b

. Trust BonusNICe e− ≠

Thus, for establishing short-run crowding effects in our first set of experiments we

compare Phase 1 of FT, BT with Phase 1 of TTT, holding the offered contracts constant.

Analogously, measuring short-run crowding effects in TFT and TBT (or TFT-R and TBT-R)

requires comparing Phase 2 of TFT and TBT (or TFT-R and TBT-R) with Phase 2 of TTT (or

TTT-R), holding the offered contracts constant.

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Establishing a long-run crowding effect requires measuring how under Trust contracts

the prior experience of Incentive contracts influences effort, compared to a prior experience

of Trust contracts:

Definition 2 (long-run crowding): Long-run crowding occurs if AFTER AFTER /

,( ) ≠ 0d

Trust Trust Trust Fine Bonus

w ee e− , where e indicates the average effort (across all

periods of the respective phase).

Thus, measuring long-run crowding effects requires comparing Phase 2 of TTT with

Phase 2 of FT, BT, and Phase 3 of TTT with Phase 3 of TFT, TBT, and TFT-R and TBT-R,

holding the offered contract constant.

3.2 Procedures

We conducted 20 sessions at the University of St. Gallen with a total of 500 participants

(first-year undergraduates of business, economics or law). We recruited our subjects from a

large data base of people who volunteered to participate in experiments. We drew a random

selection from the data base and invited them by email. In a typical session 28 participants

were present at the same time.

After arrival at the lab, participants first had to read instructions (see Appendix A). The

instructions were the same for principal and agent. Subjects also had to answer a set of

control questions to test their understanding of payoff calculations. The experiment did not

start before all participants had answered all questions correctly. All participants could ask

questions privately. Role assignment was random and fixed throughout the session. We

explained that all decisions would be anonymous during the whole experiment. A short

summary of the instructions was read aloud by the experimenter. At the beginning of each

session we told subjects that there would be different parts and that they would learn about

them one after the other.

The experiments were computerized and conducted with the software ‘z-Tree’

(Fischbacher (2007)). Participants were separated from each other by partitions and matched

anonymously via a computer network. In sessions with random matching (all treatments

except TFT-R and TBT-R and TTT-R) we formed two independent matching groups of 14

subjects each. Subjects were not informed about the matching groups but only that they

would be randomly matched with another player present in the room. Subjects also never

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learned the identity of their opponent players. Each session lasted about two hours and the

subject's average earnings were about CHF 45 (about €30).

4. RESULTS I: GAUGING CROWDING EFFECTS

4.1 The TTT Benchmark: Voluntary Cooperation under Trust Contracts

In our benchmark we are in particular interested in how actual effort depends on the

offered wage and how this relationship changes over the up to thirty periods we have planned

for our main experiments. Based on previous evidence on closely related variants of our Trust

game (e.g., Fehr, Kirchsteiger and Riedl 1993; Fehr, Gächter and Kirchsteiger 1997; as well

as the psychological sources of voluntary cooperation discussed above) we predict that wage

and effort will be positively correlated. We expect these effects at least for the first ten

periods (Phase 1), since this is about the duration of most previous gift-exchange

experiments. The fact that we observe behavior in three phases allows us to investigate the

role of experience for the stability of voluntary cooperation. Figure 1 is a scatter plot of fixed

wage and effort choices in each of the three phases of treatment TTT; each dot is a single

observation.6

05

1015

20A

ctua

l effo

rt

0 100 200 300 400Fixed wage

Trust

05

1015

20A

ctua

l effo

rt

0 100 200 300 400Fixed wage

Trust

05

1015

20A

ctua

l effo

rt

0 100 200 300 400Fixed wage

Trust

FIGURE 1. Relation between offered wage and actual effort in each of the three Phases of TTT.

6 Figure 1 shows the relation between actual effort and fixed wage for wages between 0 and 450. We had a few wages above 450 (up to wages of 700), but they comprised less than 2 percent of cases. For expositional clarity we therefore restrict our attention in this (and subsequent figures) to wages up to 450. In the econometric analyses we include all accepted contracts, not just those with wages up to 450. The lines in Figure 1 (as well as in all subsequent scatter plots) are locally weighted regressions of actual effort e on wage for e > 1.

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In line with previous evidence Figure 1 shows extensive voluntary cooperation (e > e*)

and a clearly positive correlation between fixed wage and actual effort in a large number of

choices. The wage-effort relationship appears to be stable across all three phases.

While effort is increasing in fixed wages for a large fraction of the observations, there is

another fraction of observations with e = 1 independent of the wage. It seems that the

population of players can be partitioned into two subpopulations: a subpopulation that is

responsive to different levels of fixed wage and a subpopulation that does not respond to it.

This data pattern appears for other treatments of our experiment as well (see below). It

motivates our econometric model: instead of applying a simple tobit regression model we rely

on hurdle models (see Wooldridge (2002), p. 536). This allows for the possibility that the

decision for e > 1 versus e = 1 (the decision to pass the hurdle) follows different rules than the

choice of e conditional on e > 1. Thus, in a first step we estimate a probit regression for the

choice of e > 1 (value = 1) or e = 1 (value = 0), and in a second step we estimate a tobit

regression with an upper bound of 20 for e | e > 1. This is similar to the Heckman two-step

estimation procedure, just that in step two we apply a tobit regression rather than OLS to

account for censoring at e = 20. To account for conditioning on e > 1 choices we include the

inverse Mills ratio into the tobit regressions. We will use a hurdle approach for all estimations

reported below.

Table 3 shows the regression results for the three phases of TTT. The two explanatory

variables in Table 3 are the two elements of a contract offer in a Trust contract – the fixed

wage and the desired effort.

For Phase 1 Table 3 reports estimated coefficients for the influence of fixed wage of

0.003 in the probit regression and 0.086 in the tobit regression, with both being highly

statistically significant. This means that the probability of choosing above-minimal effort

increases in fixed wage and that effort conditional on e > 1 increases in effort as well.

Evaluating the two estimated models (probit and tobit) at mean values for all explanatory

variables predicts that minimal effort (e = 1) is chosen with a probability of 0.415. With

residual probability 0.585 players choose a higher effort, and this higher effort conditional on

e > 1 is predicted at 8.7. The two parts of the regression analysis can be put together to

provide a predicted value of effort E(e) with7

( ) ( ) ( ) ( )1 1 (1– 1 ) | 1 5.5E e prob e prob e E e e= = ⋅ + = ⋅ > = .

7 One might argue that the calculation of a predicted value of effort could have been done more simply by running a tobit regression with bounds at 1 and 20. However, given the distributions shown in Figure 1 our approach seems more adequate.

12

We refer to this as the total effect. Similarly, in all regression analyses below we may

consider the partial effects of an explanatory variable on prob(e=1) and e|e>1 separately or

combined (total effect).

TABLE 3 THE WAGE-EFFORT RELATIONSHIP IN THE THREE PHASES OF THE

BENCHMARK TTT EXPERIMENTS

Phase 1 Phase 2 Phase 3 probit

(e>1) tobit

(e|e>1) probit (e>1)

tobit (e|e>1)

probit (e>1)

tobit (e|e>1)

Fixed wage 0.003*** 0.086*** 0.006*** 0.054*** 0.005*** 0.050*** (0.000) (0.027) (0.002) (0.009) (0.001) (0.005) Desired effort 0.038*** 1.236*** -0.004 0.140 0.006 0.181 (0.013) (0.401) (0.020) (0.089) (0.027) (0.116) Inverse Mills ratio 39.156** 5.457*** 8.509** (17.006) (1.721) (3.546) Constant -1.015*** -56.877** -1.105*** -8.717*** -0.888*** -11.399** (0.208) (24.151) (0.175) (2.445) (0.147) (4.571) No. of obs. 316 142 284 116 281 123 LR chi2 57.42*** 111.83*** 31.42*** 313.33*** 88.29*** 1544.94*** probit indicates a probit model whether e>1 or e=1. tobit indicates a tobit regression on e>1 choices only (censored at 20). Robust standard errors in parentheses; * p< 10%; ** p< 5%; *** p< 1%.

Table 3 reports that fixed wages have a significantly positive effect on effort (partially

and combined) for phases 2 and 3 as well. Overall, the regression model supports our

impression from Figure 1 that (1) there is voluntary cooperation, (2) voluntary cooperation is

reciprocity based, and (3) voluntary cooperation is stable over time. These behavioral patterns

under Trust contracts will serve as basis for comparison in other treatments.8

4.2 Monetary Incentives and Short-run Crowding Effects

We now look at the Phase 1 data of treatments FT, BT to investigate the influence of

Fine contracts and Bonus contracts (see panel A in Table 2). Since subjects decide under

monetary incentives without having experienced Trust contracts before we refer to them as

inexperienced subjects.

Figure 2 is a scatter plot of actual effort choices against effort predicted by the

theoretical solution e* for FT (left panel) and BT (right panel). The size of dots in Figure 2 is

proportional to the number of underlying observations (a “bubble plot”). Figure 2 shows a

highly structured pattern which is very similar in both panels. Many observations cluster 8 Since desired effort is part of an offered contract our regression model also controls for desired effort. Table 3 reports a significant influence of desired effort in Phase 1, but not in later phases. Although we find that fixed wage and desired effort are positively correlated in all phases (all correlations exceed 0.70) we conclude that it is mainly the fixed wage level that drives behavior under Trust contracts. Desired effort plays a minor role.

13

exactly on the 45°-line (where e = e*). Provided the principal offers an incentive-compatible

contract (henceforth IC-contract; offered in 69.4 percent of all cases in FT, and in 67.6

percent of cases in BT), the agent chooses best-reply effort in 69.7 (71.7) percent of the cases

in FT and BT respectively.9 Given an IC-contract (e* > 1), if agents deviate from best-reply

effort, they tend to choose minimal effort (e =1). Effort levels at e = e* or e = 1 explain 93.1

percent of all effort choices. There is a crowding-out of voluntary cooperation, since only

very few effort choices exceed e* and, if contracts are not incentive-compatible (henceforth

NIC-contract; which implies e* = 1, like under Trust contracts), many agents choose a

substantially higher than minimal effort level (36.0 percent of the choices).

14

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1 4 8 12Optimal effort (best reply)

Phase 1 FT

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Phase 1 BT

FIGURE 2. Relation between actual effort and best-reply effort in Phase 1 of FT (left panel)

and Phase 1 of BT (right panel). The size of dots is proportional to the number of underlying observations. The horizontal line at 12 indicates the maximally enforceable effort level under incentive-compatible contracts.

Although only positive deviations are feasible, it is nevertheless interesting to note that

effort above 12 – which according to equation (1) is the maximal level that can be enforced

via incentives – is relatively more frequent for e* = 1 than for e* > 1. Thus, NIC-contracts

may allow for effort levels that are largely infeasible with IC-contracts.

Figure 3 looks closer at NIC-contracts by showing a scatter plot of chosen effort against

offered compensation, with offered compensation defined as w – f in Fine games and w in

Bonus games.10 Unlike Figure 1 we do not observe a clear positive correlation between effort

9 The variation in best-reply efforts visible in Figure 2 is mainly due to desired effort levels rather than fines and bonuses. In FT the percentages of cases of fines of 80, 52, 24, 0 are 85.5, 8.9, 4.0, and 1.6 percent, respectively. In BT percentages for the respective bonus levels are 73.4, 9.0, 12.8, and 4.8, respectively. These distributions are significantly different from each other (χ2-test, 26.250, p=0.000). 10 We look at offered compensations rather than the fixed wage only because NIC-contracts give agents a selfishly rational incentive to shirk in which case they have to pay the fine or they forego the bonus.

14

and offered compensation here. It appears that the presence of an NIC Bonus or Fine contract

destroys reciprocity, but it does not entirely crowd out voluntary cooperation because a

substantial number of effort choices are non-minimal.

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0 100 200 300 400Offered compensation (w-f)

Phase 1 FT-NIC

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0 100 200 300 400Offered compensation (w-f)

Phase 1 BT-NIC

FIGURE 3. Offered compensation and effort under NIC-contracts in Phase 1 of FT and BT.

The impressions we get from these figures are supported by the econometric model

reported in Table 4. It is a hurdle model that uses the Phase 1 data of FT, BT and TTT and

estimates the influences of fixed wage, monetary incentives and other variables for different

contract types (Trust contracts, IC-contracts and NIC-contracts) within a single regression

model. In a first step we estimate a probit regression for the choice of e > 1 (value = 1) or e =

1 (value = 0). The left column of Table 4 shows the estimation results. In a second step we

estimate a probit regression for the choice of e = e* (value = 1) or e ≠ e* (value = 0)

conditional on e > 1. And in a third step we estimate a tobit regression (with an upper bound

of 20) for effort conditional on e > 1 and e ≠ e*. In steps 2 and 3 we include the inverse Mills

ratio as regressor.11 The middle and the right column of Table 4 report the results.

11 Note that under incentive compatible contracts there are a few observations that choose e > 1 and e ≠ e*. For these observations step 3 should be conditioned on both, steps 1 and step 2. Since these are very few observations in the step 3 estimation, we think this is negligible and for simplicity we condition only on step 1.

15

TABLE 4

EXPLAINING PHASE 1 EFFORT CHOICES IN TREATMENTS TTT, FT AND BT

probit (e>1) probit (e=e*|e>1)

tobit (e|e>1, e≠e*)

Fixed wage (TTT) 0.003*** 0.022*** (0.001) (0.006) Desired effort (TTT) 0.038* 0.248*** (0.020) (0.084) Fixed wage (BT, IC) 0.007*** 0.019 (0.002) (0.015) Bonus b (BT, IC) 0.021*** 0.040*** (0.007) (0.008) Fixed wage (FT, IC) 0.005*** 0.016 (0.002) (0.011) Fine f (FT, IC) 0.016** 0.039*** (0.007) (0.009) Desired effort (BT/FT, IC) -0.143*** 0.019 (0.035) (0.062) Dummy IC 1.280*** 0.745 (0.440) (3.692) Fixed wage (BT, NIC) 0.002 0.010** (0.001) (0.005) Bonus b (BT, NIC) 0.011** 0.055*** (0.004) (0.013) Fixed wage (FT, NIC) 0.003** 0.007 (0.001) (0.007) Fine f (FT, NIC) -0.000 0.051* (0.005) (0.026) Desired effort (BT/FT, NIC) -0.082*** -0.110 (0.031) (0.266) Dummy NIC 0.982*** 7.064** (0.365) (2.938) Inverse Mills ratio -0.029*** -3.273 (0.354) (2.286) Constant -1.015*** -1.947*** 8.043 (0.148) (0.444) (5.471) No. of obs. 988 566 253 χ2=222.29*** χ2=233.44*** F=22.89*** Pseudo R2 0.190 0.645 0.066 probit (e>1)indicates a probit model whether e>1 or e=1. probit (e=e*|e>1) indicates a probit model whether e=e* (best-reply effort) or e≠e*. tobit (e|e>1, e≠e*) indicates a tobit regression with an upper bound of e=20. IC (NIC) indicates (non)incentive-compatible contracts. Robust standard errors in parentheses; * p< 10%; ** p< 5%; *** p< 1%.

The upper part of Table 4 shows the estimated effects of the variables fixed wage (TTT)

and desired effort (TTT) on behavior in Trust games.12 This reproduces the findings from

Table 3. The middle panel shows the estimated influences of IC Bonus and Fine contracts. For

12 Most variables in this table are defined as treatment-specific variables. For example, Fixed wage (Trust games) has value = fixed wage when treatment = TTT and value = 0 when treatment = BT or FT. Similarly for desired effort (Trust games). Fixed wage (BT, IC) has value = fixed wage when treatment = BT and if the contract is incentive compatible and value = 0 otherwise. And so on.

16

example, the significant coefficients 0.021 (Bonus) and 0.016 (Fine) reported in the left

column imply that prob(e > 1) is increasing in the size of the bonus (fine) offered in the

contract. The fixed-wage influence upon prob(e > 1) is positive as well and is estimated as

0.007 for fixed wage (BT, IC) and 0.005 for fixed wage (FT, IC). The influence of desired

effort (BT/FT, IC) is estimated as -0.143, meaning that asking for higher effort decreases

prob(e>1), ceteris paribus. All of these effects are plausible in sign and statistically

significant. In the middle column we find that the probability of choosing a best-reply effort

(provided e > 1) is significantly increasing in the bonus (0.040) and the fine (0.039) while

desired effort is insignificant. Furthermore, the third column reports results on effort

conditional on e > 1 and e ≠ e*. We find insignificant effects of fixed wages and an

insignificant coefficient of dummy IC (a variable that has value 1 for IC-contracts and 0

otherwise).

To understand the total effect of IC Bonus or Fine contracts on effort one has to consider

the partial effects shown in the middle panel together. This is illustrated in the left panel of

Figure 4 which is a predicted value graph based on the regression model. The predicted values

E(e) are calculated as

( ) ( ) ( ) )( ) ( )

1 1– * 1 ( | 1 and *

with 1 and * | 1 .

E e p p q e q E e e e e

p prob e q prob e e e

= ⋅ + ⋅ ⋅ + − ⋅ > ≠⎡⎣= = = = >

]

For calculating the predicted efforts we fix the desired effort at 12 and both the bonus

and fine at 80. Figure 4 shows predicted values for different contract types and for varying

compensation levels holding other influences constant.13

The right panel of Figure 4 shows that effort levels up to 12 can be implemented at

much lower compensation levels with incentive-compatible bonus or fine contracts than with

trust contracts. However, effort above 12 seems infeasible with IC-contracts. Increasing

compensation beyond 300 does not lead to substantially higher effort. This is different with

trust contracts were the regression model predicts further increases in effort even beyond 12.

Bonus contracts perform somewhat better than fine contracts, but the difference (framing) is

relatively minor.

The bottom panel of Table 4 reports the regression results for non-incentive compatible

bonus and fine contracts. The left panel of Figure 4 shows predicted value graphs analogous

to the right panel of Figure 4. While non-incentive compatible contracts by and large perform

worse than IC-contracts, there is substantial voluntary cooperation since predicted effort is

13 For Trust contracts and IC Fine contracts, compensation is simply equal to fixed wage. For an IC Bonus contract, compensation is equal to fixed wage plus bonus.

17

above the minimal level. Furthermore, effort is increasing in compensation (reciprocity-based

voluntary cooperation), but the influence of compensation is not as strong as within trust

contracts. We will further investigate the performance of NIC-contracts below.

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50 150 250 350 450 550 650Offered compensation

TTT FT-ICBT-IC

Phase 1 TTT, FT-IC, BT-IC

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TTT FT-NICBT-NIC

Phase 1 TTT, FT-NIC, BT-NIC

FIGURE 4. Predicted effort as a function of offered compensation (based on estimations in

Table 4, for ed = 12, and b, f = 80.

We summarize our findings in our first main result.

Result 1 (a) If it is possible to design a bonus or fine contract, most subjects in the role

of the principal design a contract that exhibits a non-zero bonus or fine. Most of the bonus

and fine contracts are incentive compatible (67.4 percent,) and IC-contracts induce exact

best-reply effort in the majority of cases (73 percent). Given an IC-contract we find no

voluntary cooperation. (b) IC-contracts can induce effort levels up to 12 quite effectively and

at lower compensation levels than Trust contracts and NIC-contracts. Yet, effort above 12 is

infeasible with IC-contracts while it is feasible with Trust contracts. (c) NIC-contracts are

relatively infrequent and perform worse than IC-contracts at low compensation levels, and

they are worse than Trust contracts at high compensation levels. Nevertheless there is some

voluntary cooperation. (d) The framing of incentives (bonus versus fine) is relatively

unimportant in particular when contracts are incentive compatible.

18

4.3 Long-Run Crowding Effects

Figures 5a and 5b now turn attention to the long-run crowding effect. Specifically we

investigate behavior under the condition of a Trust contract in Phase 2 after experiencing

Bonus or Fine contracts in Phase 1. Figure 5a shows the development of efforts over time and

Figure 5b provides a scatter plot of effort and fixed wages. The left-hand panel of Figure 5a

depicts the average effort observed in Phase 1, separately for TTT, BT and FT, and without

distinguishing whether the contract is incentive compatible or not. On average Bonus and

Fine contracts clearly lead to higher effort levels than Trust contracts. The right panel of

Figure 5a shows the development of effort in Phase 2, when there is no Bonus (Fine) contract

any longer. For BT and FT the average effort level drops from more than 6 to about 2. By

contrast, for TTT the average effort in Phase 2 is around 4.

Figure 5b shows scatter plots of effort (in Phase 2) against fixed wage for FT (left panel)

and BT (right panel). Visual comparison with Phase 2 of Figure 1 suggests that voluntary

cooperation is lower after the experience of Bonus (Fine) contracts. This is confirmed by the

regression model shown in Table 5 and the predicted value graphs in Figure 6 (based on

desired effort of 12). The regression model uses the Phase 2 data of BT, FT and TTT. It is

similar to the model shown in Table 3 except for introducing dummies for BT and FT.

Accordingly, having experienced Fine contracts in Phase 1 has a significantly negative effect

on prob(e >1) in Phase 2. Having experienced Bonus contracts also has a significantly

negative effect on e|e>1. Thus, voluntary cooperation in Phase 2 is lower after experiencing

Bonus (Fine) contracts rather than Trust contracts in Phase 1. The differences between FT and

BT are insignificant.14

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1 2 3 4 5 6 7 8 9 10Period

TT FT BT

Phase 1 (period 1-10)

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11 12 13 14 15 16 17 18 19 20Period

TT FT BT


FIGURE 5A. Mean effort levels over time in Phase 1 and 2 of TTT, FT and BT.

14 A test that compares the coefficients of ‘Dummy BT treatment’ with ‘Dummy FT treatment’ shows that they are not significantly different from each other (χ2-test, 0.00, p=0.964).

19

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0 100 200 300 400Fixed wage

Phase 2 FT

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0 100 200 300 400Fixed wage

Phase 2 BT

FIGURE 5B. Wages and effort in Phase 2 of FT and BT.

TABLE 5

LONG-RUN CROWDING OUT (COMPARING PHASE 2 DATA IN FT, BT, AND TTT)

probit (e>1)

tobit (e|e>1)

Fixed wage 0.006*** 0.038*** (0.001) (0.012) Desired effort -0.008 0.097** (0.012) (0.047) Dummy BT treatment -0.295 -1.195* (0.210) (0.696) Dummy FT treatment -0.616*** -1.150 (0.135) (1.589) Inverse Mills ratio 2.346 (3.156) Constant -0.980*** -2.478 (0.135) (4.83) No. of obs. 876 215 LR chi2 123.57*** 159.89*** Pseudo R2 0.246 0.287

probit indicates a probit model whether e>1 or e=1. tobit indicates a tobit regression on e>1 choices only (censored at 20). Robust standard errors in parentheses; * p< 10%; ** p< 5%; *** p< 1%.

20

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TTT FTBT

Phase 2 TTT, FT, BT

FIGURE 6. Predicted effort in Phase 2 (based on estimates of Table 5 and ed = 12) as a function of wage after the

experience of trust or incentive contracts.

We summarize our findings on long-run crowding out in our second main result.

Result 2 (long-run crowding effect): The experience of Bonus or Fine contracts

reduces the extent of voluntary cooperation under subsequent Trust contracts. Framing of

incentives (bonus versus fine) is relatively unimportant for long-run crowding out.

Result 1 and 2 document short- and long-run crowding (out) of voluntary cooperation in

situations that are neither confounded with prior experiences of Trust contracts, nor with

strategic incentives in repeated interactions. Yet, these are precisely two interesting and

practically relevant situations, which we investigate in the next two sections. The goal is to

understand how Result 1 and 2 change if people are experienced with Trust contracts before

they are exposed to explicit performance incentives (Section 5), and if contractual relations

are embedded in an ongoing employment relationship which permits implicit incentives due

to the repeated nature of interactions (Section 6).

5. RESULTS II: THE ROLE OF EXPERIENCE WITH TRUST CONTRACTS FOR CROWDING EFFECTS

In this section we investigate the data of treatments TBT, TFT and TTT (see Table 2,

panel B) to address the question how the experience of Trust contracts before being exposed

to incentive contracts influences voluntary cooperation and the effectiveness of different

contract types. This is important because if subjects learn voluntary cooperation under Trust

21

contracts first, they might provide voluntary cooperation thereafter even under incentive

contracts. In this case short-run crowding out might be avoided. Similarly, long-run crowding

out when returning to Trust contracts might be avoided as well.

Furthermore, it is important to know how NIC-contracts perform with prior experience

of Trust contracts. NIC-contracts are psychologically quite interesting since the principal

neither sends a clear trust signal (as with a Trust contract), nor a clear signal that he relies on

straight pay for performance (as with an IC-contract). He uses a “stick or carrot” and at the

same time asks for more effort than the agent should provide if she were primarily interested

in “stick or carrot”. Thus a NIC-contract is neither a clear appeal to self-interest, nor an

unambiguous signal of the principal’s generosity and thereby an appeal to norms of

reciprocity. This ambiguity may be the reason for low effort by inexperienced subjects under

NIC-contracts. However, in principle a NIC-contract that offers a generous compensation

could be understood as an appeal to cooperation as well. Agents might learn to interpret NIC-

contracts this way, especially after experiencing Trust contracts.

The analysis in this section parallels the one in the previous section. Figure 7 illustrates

behavior in Phase 2 of TBT and TFT, that is, when monetary incentives are present

(analogous to Figure 2). It shows a bubble plot of actual effort against best-reply effort.15 The

results appear very similar to Figure 2. If the contract is incentive compatible and

consequently best-reply effort is above minimal effort (e* > 1), agents either choose e*

exactly or they deviate to e = 1. Compared to Figure 2 this pattern is even more pronounced

and clustering at e = e* for high levels of e* (levels 10, 11, 12) is particularly strong.

Together, e = e* choices and e = 1 choices comprise 98.6 percent of the decisions in

incentive-compatible contracts. Thus, given an IC-contract there is no voluntary cooperation.

If the contract is not incentive compatible and consequently best-reply effort is minimal

(e* = 1), effort is dispersed with e = 1 being the most frequent choice. Compared to Figure 2

we see more clustering at levels above 12 indicating that NIC-contracts perform better when

subjects are experienced with Trust contract.

15 In Phase 2 of TFT the percentages of cases of fines of 80, 52, 24, 0 are 78.8, 11.9, 5.8, and 3.5 percent, respectively. In Phase 2 of TBT percentages for the respective bonus levels are 83.1, 9.0, 5.6, and 2.3, respectively. These distributions are significantly different from each other (χ2-test, 12.392, p=0.006).

22

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Phase 2 TFT

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Phase 2 TBT

FIGURE 7. Relation between actual effort and best-reply effort in Phase 2 of TFT (left panel)

and Phase 2 of TBT (right panel). The size of dots is proportional to the number of underlying observations.

Table B1 in the Appendix reports an econometric model that is equivalent to the one

reported in Table 4 except that it relies on the Phase 2 data of TTT, TFT and TBT. Figure 8

shows predicted values of effort based on the regression model and for varying compensation

levels. Qualitatively we find about the same effects as outlined in Result 1: IC-contracts are

quite effective in implementing high effort for low compensation levels, but effort above 12 is

infeasible. Trust contracts can implement higher effort than IC-contracts at high

compensation levels. Yet, comparing Figures 8 and 4 we also see an important difference:

With experience Trust contracts are superior to IC-contracts for a much wider range of

compensation levels. Figure 8 displays that Trust contracts perform better than IC-contracts

for all compensation levels of about 270 and above. In Figure 4 Trust contracts predicted

higher effort than IC-contracts only for compensation levels of about 500 and above. While

experiencing Trust contracts in Phase 1 does not increase voluntary cooperation under IC-

contracts in Phase 2, it does increase voluntary cooperation under Trust contracts in Phase 2.

For NIC-contracts predicted value graphs based on Table B1 are displayed in Figure 8.

Similar to the analysis in Section 4.2 NIC-contracts are worse than Trust contracts at high

compensation levels. But NIC-contracts can induce a substantially higher effort than 12.

Moreover, at high compensation levels NIC-contracts induce higher effort than IC-contracts.

Thus, experiencing Trust contracts in Phase 1 increases reciprocity-based voluntary

cooperation and strengthens the performance of NIC-contracts.

23

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Phase 2 TTT, TFT-IC, TBT-IC

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TTT TBT-NICTFT-NIC

Phase 2 TTT, TFT-NIC, TBT-NIC

FIGURE 8. Predicted effort as a function of offered compensation (based on estimations in Table B1, for ed = 12,

and b, f = 80.

To investigate how experience with Trust contracts affects long-run crowding out we

use the Phase 3 data of TTT, TBT and TFT to estimate a regression model reported in Table

B2 in the Appendix and illustrated in Figure 9. The model structure is equivalent to the one

reported in Table 5. Figure 9 looks strikingly similar to Figure 6 except that the differences

between the three shown curves have become smaller: the long-run crowding out effect has

become smaller.

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TTT TFTTBT

Phase 3 TTT, TFT, TBT

FIGURE 9. Predicted effort in Phase 3 (based on estimates of Table B2 and ed = 12) as a function of wage after

the experience of trust or incentive contracts.

24

We summarize the findings of our second set of experiments on the role of experience

with Trust contracts before being exposed to incentives in Result 3:

Result 3 (The role of experience of Trust contracts for crowding effects): (a) The

experience of Trust contracts in Phase 1 has no impact on behavior under IC-contracts in

Phase 2. Under IC-contracts agents choose their best-reply effort in the majority of cases and

there is no voluntary cooperation. (b) Yet, the experience of Trust contracts in Phase 1 does

increase the effectiveness of NIC-contracts in Phase 2. Under NIC-contracts reciprocity-

based voluntary cooperation is stronger than without prior experience of Trust contracts. (c)

The experience of Trust contracts in Phase 1 improves also the effectiveness of Trust

contracts in Phase 2. Trust contracts perform better than IC-contracts (NIC-contracts) at

compensation levels larger than about 270 (250), that is, Trust contracts are optimal for a

wide range of compensation levels. (d) The experience of Trust contracts in Phase 1 mitigates

long-run crowding out effects of monetary incentives.

So far we analyzed the data of random matching sessions. The main reason for this

design feature was to minimize strategic effects that might confound the crowding effects we

are interested in. However, in reality most employment relations are long-term relations

where strategic effects might influence the amount of voluntary cooperation we see. Long-

term relations permit implicit incentives and it is important to understand how implicit and

explicit incentives interact and influence crowding effects. The next section is devoted to this

analysis.

6. RESULTS III: THE BEHAVIORAL CONSEQUENCES OF IMPLICIT INCENTIVES

6.1 Short- and Long-Run Crowding Effects in the Presence of Implicit Incentives

To study the role of implicit incentives we compare the repeated game treatments

(partner-matching treatments) TFT-R, TBT-R and TTT-R with the respective random

matching treatments TFT, TBT and TTT. See Table 2C for further details. Figures 10a-c

display effort across periods for all treatments and all phases. The figures show that the

implicit incentives inherent in repeated games are a very strong motivator for high effort.

Every single repeated game curve is clearly above the respective random matching curve.

Under TTT-R (Figure 10a) average effort approaches almost the maximal (and efficient)

25

effort level. All time paths for repeated games show an endgame effect, that is, a decline of

effort in the last few periods of a phase. And finally, while the time paths for TFT and TBT

jump upwards in Phase 2 (the incentive phase), the corresponding time paths for TFT-R and

TBT-R show a less strong reaction upon the introduction of explicit incentives. This indicates

that explicit incentives are less important in repeated games. We will address these points

more formally in the statistical analyses below. 0

24

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TTT TTT-R


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TTT TTT-R


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TTT TTT-R


FIGURE 10A. Mean effort over time in the one-shot games TTT and in the repeated games TTT-R.

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1 2 3 4 5 6 7 8 9 10Period

TFT TFT-R


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TFT TFT-R


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TFT TFT-R


FIGURE 10B. Mean effort over time in the one-shot games TFT and in the repeated games TFT-R.

26

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Act

ual e

ffort

1 2 3 4 5 6 7 8 9 10Period

TBT TBT-R


02

46

810

1214

1618

20

11 12 13 14 15 16 17 18 19 20Period

TBT TBT-R


02

46

810

1214

1618

20

21 22 23 24 25 26 27 28 29 30Period

TBT TBT-R


FIGURE 10C. Mean effort over time in the one-shot games TBT and in the repeated games TBT-R.

Figure 11 shows a bubble plot of effort against best-reply effort for the Phase 2 data of

TFT-R and TBT-R. The data pattern looks the same as in Figures 2 and 7, but the relative

sizes of bubbles have changed indicating that certain choices have become more frequent

while other have become less frequent. Specifically, the most frequent choice is maximal

effort under NIC-contracts (e* = 1). Furthermore, in the repeated games only 25.2 percent of

contracts are incentive compatible whereas with random matching (TBT, TFT) 74.5 percent

of contracts are incentive compatible.16 These changes may be understood by noting that in

repeated games, the implicit incentives of an ongoing relationship can substitute for explicit

incentives. Yet, while the implicit incentives lead to very high effort levels under NIC-

contracts, they do not increase effort under IC-contracts. As Figure 11 reveals, when the

contract is incentive compatible, agents choose their stage game best-reply effort in almost all

cases. Agents choose e = e* despite the fact that, as the NIC-contracts show, people in the

large majority of cases are willing to provide effort even beyond the maximally enforceable

effort level e = 12.

16 The reason for this striking difference is not in the design of fines and bonuses but in the desired effort levels. In the TFT and TBT experiments the average desired effort level is 10.8 (11.3) in Phase 2 of TFT (TBT), whereas it is 16.3 (14.9) in Phase 2 of TFT-R (TBT-R). The distribution of fines and bonuses is as follows: In TFT-R the percentages of cases of fines of 80, 52, 24, 0 are 58.9, 15.4, 13.7, and 12.0 percent, respectively. In TBT-R percentages for the respective bonus levels are 70.5, 15.1, 11.4, and 3.0, respectively. These distributions are significantly different from each other (χ2-test, 11.166, p=0.011).

27

14

812

20A

ctua

l effo

rt


Phase 2 TFT-R

14

812

20A

ctua

l effo

rt


Phase 2 TBT-R

FIGURE 11: Relation between actual effort and best-reply effort in Phase 2 of TFT-R and TBT-R.

Based on the repeated game data, Tables B3 and B4 in Appendix B report regression

models analogous to those reported in Tables B1 and B2 for random matching data.17 Figures

12a and 12b provide predicted value graphs for varying compensation levels (and assuming b,

f = 80, ed = 12).

The left panel of Figure 12a highlights that in repeated games Trust contracts perform

better than IC-contracts at most compensation levels. Only for very low compensation levels

IC-contracts perform somewhat better. The right panel of Figure 12a shows that at low

compensation levels (up to about 270) trust contracts perform substantially better than NIC-

contracts. However, at larger compensation levels NIC-contracts outperform Trust contracts

(and IC-contracts). Thus, NIC-contracts – i.e., using “stick or carrot” and asking for an effort

that is higher than e* – is a good strategy if compensation overall is sufficiently high. If

compensation is too low, this strategy causes a substantial loss in effort.

Figure 12b illustrates long-run crowding effects. Differences between the three curves

can hardly be detected. Consequently if subjects experience Trust contracts and implicit

incentives there is no long-run crowding out of voluntary cooperation anymore.

17 In the regressions for repeated games we excluded the data of the final three periods of each phase to downplay influences of endgame effects.

28

02

46

810

1214

1618

20Ex

pect

ed e

ffort


TTT-R TFT-R-ICTBT-R-IC

Phase 2 TTT-R, TFT-R-IC, TBT-R-IC

02

46

810

1214

1618

20Ex

pect

ed e

ffort


TTT-R TFT-R-NICTBT-R-NIC

Phase 2 TTT-R, TFT-R-NIC, TBT-R-NIC

FIGURE 12A: Predicted effort as a function of offered compensation (based on estimations in Table B3, for ed = 12, and b, f = 80.

02

46

810

1214

1618

20E

xpec

ted

effo

rt


TTT-R TFT-RTBT-R

Phase 3 TTT-R, TFT-R, TBT-R

FIGURE 12B: Predicted effort in Phase 3 (based on estimates of Table B4 and ed = 12) as a function of wage after

the experience of trust or incentive contracts.

We collect the findings on the repeated games in our final main result.

Result 4 (The role of implicit incentives): (a) Implicit incentives inherent in repeated

interactions have a very strong behavioral impact on voluntary cooperation under Trust

contracts and under NIC-contracts. (b) Repeated interactions hardly influence behavior

under IC-contracts. IC-contracts induce stage game best-reply effort. This is effective at low

compensation levels, but it forgoes efficiency increases at higher compensation levels which

are feasible with Trust contracts and NIC-contracts. (c) NIC-contracts induce very low efforts

at low compensation levels but very high effort levels at high compensation levels. (d) There

is no long-run crowding out effect any longer.

NIC-contracts perform worse than IC-contracts and Trust contracts under conditions of

inexperienced subjects and random matching. But NIC-contracts perform best with

29

experience and partner matching and if compensation is sufficiently high. While it is not that

surprising that experience and partner matching increase effort, the question is why these

factors have relatively more impact on behavior under NIC-contracts than under IC-contracts

and Trust contracts. We address this question in the following subsection.

6.2 The Surprising Effectiveness of NIC-Contracts

We believe that behavior in our experimental games is driven by two mechanisms – the

pay for performance mechanism and the trust and reciprocity mechanism. Above we provided

clear evidence that both mechanisms exist. It is quite plausible that individuals consider both

aspects at the same time.

If inexperienced agents face a Bonus or Fine contract, they focus on the pay for

performance character of the interaction. The trust and reciprocity mechanism (a positive

wage-effort correlation) – which characterizes behavior under Trust contracts, is hampered as

Figure 3 illustrates. With experience of Trust contracts and in repeated games trust and

reciprocity become more and more important even under Bonus and Fine contracts. Figure 13

supports this claim. This figure displays scatter plots of effort against offered compensation

for NIC-contracts in Phase 2 of treatments TFT, TBT, TFT-R and TBT-R.

05

1015

20A

ctua

l effo

rt

0 200 400Offered compensation

TFT-NIC

05

1015

20A

ctua

l effo

rt


TBT-NIC

05

1015

20A

ctua

l effo

rt


TFT-R-NIC

05

1015

20A

ctua

l effo

rt


TBT-R-NIC

FIGURES 13: Offered compensation and effort under non-incentive-compatible contracts in Phase 2 of TFT,

TBT, TFT-R and TBT-R.

30

In the repeated game trust and reciprocity, strengthened by implicit incentives, are more

important than explicit incentives. From this viewpoint high offered compensation might

induce reciprocally high effort for NIC-contracts as well as for IC-contracts. But if one notes

in addition that voluntary cooperation usually does not mean that effort is chosen above

desired effort but is chosen equal to desired effort or below (see Table 6), one understands

why NIC-contracts outperform IC-contracts at high compensation levels. Ceteris paribus,

NIC-contracts ask for higher effort than IC-contracts.

TABLE 6 ACTUAL AND DESIRED EFFORT IN TTT-R, TFT-R AND TBT-R

IC-contracts (TFT-R, TBT-R)

NIC-contracts (TFT-R, TBT-R)

Trust contracts (TTT-R)

cases # percent # percent # percent

e > ed 1 1.89% 87 10.13% 8 2.40%

e = ed 44 83.02% 355 41.33% 133 39.94%

1 < e < ed - - 296 34.46% 174 52.25%

e = 1 (ed > 1) 8 15.09% 121 14.09% 18 5.41%

Sum 53 100% 859 100% 333 100%

A remaining question is why NIC-contracts outperform Trust contracts (for high

compensation levels in repeated games) even though the latter seem an even stronger,

unambiguous appeal to trust and reciprocity than the former. A reason may be seen in the

higher frequency of choices e with 1 < e < ed under Trust contracts than under NIC-contracts

(see Table 6). By choosing effort between minimal and desired effort the agent may fine-tune

the distribution of payoffs between principal and agent. This is more difficult in NIC-

contracts than in Trust contracts, since in NIC-contracts undercutting desired effort implies

that the entire fine is to be paid or that the entire bonus is lost. Consequently, under NIC-

contracts, if the agent has decided to provide higher than minimal effort at all, it makes little

sense to marginally undercut desired effort. In turn, choosing effort at the desired level or

above is a stronger cooperative signal than choosing effort above the minimum but below

desired effort. The higher relative frequency of choices e ≥ ed under NIC-contracts might

therefore facilitate and improve cooperation relative to trust contracts.

Our view is supported by Figure 14 which displays prob(e > 1) in the left panel and

e|e>1 in the right panel according to the regression model (see Table B3). For high

31

compensation levels we see that prob(e > 1) is about equal but e|e>1 is higher for NIC-

contracts. 0

.1.2

.3.4

.5.6

.7.8

.91

Pro

babi

lity

e>1




0

24

68

1012

1416

1820

e|e>

1




FIGURE 14: Behavior in NIC-contracts in the presence of implicit incentives. LEFT PANEL: Probability to choose

non-minimal effort; RIGHT PANEL: predicted effort conditional on non-minimal effort.

Overall we conclude that NIC-contracts perform better than IC-contracts in repeated

games and for high compensation levels since they give more leverage to trust and

reciprocity. And NIC-contracts perform better than Trust contracts because Bonus and Fine

contracts reduce the frequency of undercutting desired effort. While NIC-contracts have been

shown as quite effective for high compensation levels they are risky since effort is rather low

and much lower than under IC-contracts and Trust contracts when compensation is small.

7. CONCLUDING DISCUSSION

Within the experimental study presented here we used a comprehensive experimental

design to investigate whether and under what conditions incentives and social preferences are

separable ({Bowles, 2008 #3734}; {Bowles, 2008 #3419}; {Bowles, 2010 #5503}). One part

of our results confirms and strengthens previous findings of other studies, and another part of

the results refines or extends the research on principal-agent experiments.

We confirm that both Trust contracts and explicit incentive contracts are highly

effective in inducing high effort. Under Trust contracts agents either choose minimal effort or

respond with reciprocally higher effort the higher the offered compensation is. This is

evidence for reciprocity-based voluntary cooperation, and this sort of behavior is clearly

visible under conditions of random matching as well as partner matching. Under incentive

compatible Bonus or Fine contracts agents either choose minimal effort or best-reply effort.

32

There is a strong crowding-out effect in the sense that under IC-contracts agents

almost never exceed their best-reply effort level. Surprisingly this is the case even with

partner matching where one could have expected that reciprocity induces higher effort

choices. While implicit incentives in repeated games do not improve cooperation under IC-

contracts, they do so under Trust contracts. Remember that we deliberately designed the stage

games such that best-reply behavior predicts a maximally implementable effort level of 12

leaving room for efficiency-enhancing increases in effort. Such increases occur systematically

under Trust contracts – especially in repeated games – but not at all under IC-contracts. IC-

contracts perform well in implementing best-reply effort at relatively low compensation

levels. But they cannot induce higher than best-reply effort even at large compensation levels.

Whether incentives are provided in the form of a bonus or a fine (framing of incentives) was

shown to have a relatively minor influence.

The effectiveness of Trust contracts in inducing reciprocally high effort is only to a

small degree weakened when the participants have experienced a phase of incentive

contracting before. We called this the “long-run crowding-out effect” and showed that it

unambiguously exists for subjects who are inexperienced with Trust contracts but long-run

crowding out is substantially reduced by experience with Trust contracts.

We were surprised by the good performance of NIC-contracts under partner matching.

At high compensation levels these contracts led to rather high effort levels. There are two

things we found surprising: first, that NIC-contracts may perform better than IC-contracts,

and second, that NIC-contracts may perform better than Trust contracts. We provide some

speculative explanations for these effects.

The first effect, that NIC-contracts induce higher effort than IC-contracts at large

compensation levels, may be caused by agents choosing usually no larger than the desired

effort and by principals being too cautious. Principals ask for best-reply effort instead of

higher than best-reply effort, that is, they do not rely enough on reciprocity.

The second effect, that NIC-contracts induce higher effort than Trust contracts at large

compensation levels, is surprising since we view e.g. a fine contract (that asks for a higher

than best-reply effort and threatens a fine) as a less cooperative signal than a Trust contract

that offers equal compensation and asks for equal effort. While we view a Trust contract a

stronger cooperative signal being sent from the principal to the agent, many agents send a

weaker signal of cooperation to the principal under Trust contracts than under NIC-contracts.

Choosing lower than the desired effort is more frequent under Trust contracts and this may be

interpreted by the principal as a weaker signal of cooperation.

33

We are not the first to investigate issues of separability.18 However, to our knowledge

no study has investigated several failures of separability of implicit and explicit incentives in

one comparable design. This approach allows us to gauge which effects are of first-order

importance, and which ones are of second-order importance. Our experiment shows that best-

reply effort behavior and reciprocity-based voluntary cooperation are both first-order effects

in the sense that they are sizeable and important across all three sets of experiments. Framing

effects and long-run crowding out are much less robust and may hence be considered of

second-order importance.

In conclusion, given that both voluntary cooperation and incentives are of first-order

importance, it is not always the best idea to rely on an IC-contract in principal-agent

relationships. Such contracts guide behavior very strongly (“you get what you pay for”), but

they also unambiguously crowd out any (reciprocity-based) voluntary cooperation, even for

very high compensation levels and even in repeated games with otherwise strong implicit

incentives.

18 For overviews of important findings see Frey and Jegen (2001); Fehr and Falk (2002); Bowles (2008);Bowles and Polanía Reyes (2010).

34

Appendix A: Instructions Here we document the instructions of the Trust game and the Fine game used in our TFT experiment. The instructions in the other treatments were adapted accordingly. The instructions were originally written in German.

The experiment in which you participate today is joint research with the Humboldt-University Berlin. It is financed by several science foundations. During the experiment your income will be calculated in points. In the beginning you get a lump-sum of 1500 points. It is possible that some of your decisions lead to losses. You will have to finance them out of the gains from your other decisions, or, if necessary out of your lump-sum. However, you can always make decisions that avoid any losses. The exchange rate of points into Swiss Francs is:

1 Point = 0.6 Rappen.

At the end of the experiment all points which you have earned will be summed up, exchanged into Swiss Francs and paid out to you in cash. Please note that during the experiment communication is not allowed. If you have any questions, please raise your hand. We will answer your questions individually.

Instructions 1. Introduction In this experiment you will learn about a decision problem that involves two persons. The persons will be called participant X and participant Y. All participants in this experiment are allocated into two groups: the group of the participants X and the group of the participants Y. After the experiment has started you can see on your computer's display whether you are participant X or participant Y.

At the beginning you will be randomly matched with a participant of the other group. You will make your decisions at the computer. Your decisions will be transmitted via computer to the participant of the other group. This participant will only get informed about your decision. He will never learn about your name or your participant - number, i.e. your decisions remain anonymous.

2. An overview of the experiment It may help you to understand the decision situation if you think about the following scenario. Participant X decides in the role of a "firm". The firm engages an employee (participant Y), who's work effort produces some period return. Y can choose his work effort freely in each period. Below we will explain what work effort means and how the period return comes about. A higher effort leads to a higher period return, but it also causes costs that Y has to bear. Y's payment is determined in an employment contract. The employment contract consists of a fixed wage defined by X and a "desired effort". The fixed salary has to be paid by participant X to participant Y regardless of the period return. Thus, each period consists of three stages:

1. Participant X proposes in accordance with the rules a employment contract including the fixed salary and the

"desired effort".

2. Participant Y decides to accept or reject the contract.

3. Y chooses his effective effort. The desired effort of X is not binding for Y. Afterwards X and Y will be paid according to the rules. There are 10 periods. You will be matched in each period randomly with another person.

35

3. The experimental details 3.1 Employment contract: The proposal of participant X

At the beginning of each period a employment contract will be determined. For the employment contract the following rules hold: The proposed employment contract consists of two components: a fixed wage and a desired effort. Participant X can design the contract according to the below-mentioned rules.

• The contract can contain a positive or a negative fixed salary. If the fixed salary is positive, this means that participant Y gets the wage form participant X, regardless of the period return. A negative fixed wage means that Y has to pay that amount to X, regardless of the period return.

• The proposed employment contract is only valid if participant Y accepts the labour contract. If Y accepts the contract, then Y decides about his effective work effort. X's desired work effort is not binding for Y. The participant Y can choose an effective work effort, which is higher, equal or lower than the desired effort.

• For the contract design the following rules hold: −700 ≤ fixed wage ≤ 700

1 ≤ desired work effort ≤ 20

All numbers must be integer.

In designing the contracts all combinations that are compatible with these rules are possible! To make the rules clear to you, we depict the screen that will be shown to X at the beginning of period 1:

On this screen (as well as in all other screens in which you have to make a decision) you see the current period number on top left and the remaining time on top right. Participant X makes up his proposed employment contract on this screen. 3.2 Employment contract: Acceptance of the contract

After participant Y has received the proposal of the contract, he decides whether he accepts or rejects the contact.

36

3.3 Work effort of participant Y

After the acceptance of the contract Y determines his work effort. The desired work effort stated by participant X in the contract is not binding for participant Y. The effort is expressed as a number. In the enclosed table all possible work effort (all integer numbers between 1 and 20) as well as the produced returns are given. The table also contains the costs of the work that Y has to bear. The higher the work effort, the higher is the return, but also the costs of the work effort.

The screen of participant Y is shown below.

3.4 Period payoffs and end of period After participant Y has entered his work effort into the computer, the period gains will be calculated and shown on the display. The following cases result for the calculation of the profits:

Profit of X: Profit of Y:

Y rejects the contract: Zero Zero

Y accepts the contract: Period return of the effective work effort

– fixed wage Fixed wage – cost of the effective work effort

Please note: For the profit only the effective work effort is relevant. After this-screen the period is finished and the next one starts. On the whole there are 10 periods.

37

Effort, period return of the work effort and costs of the work effort for Y:

Work effort : Period return of the work effort costs of the work effort for Y

1 35 0 2 70 7 3 105 14 4 140 21 5 175 28 6 210 35 7 245 42 8 280 49 9 315 56

10 350 63 11 385 70 12 420 77 13 455 84 14 490 91 15 525 98 16 560 105 17 595 112 18 630 119 19 665 126 20 700 133

Profit of Y: Fixed wage − costs of the effective work effort

Profit of X: Period return of the effective work effort – fixed wage

Profit of Y and X by rejection of the contract of Y: Zero

Only the effective work effort is relevant for the calculation of the profits! __________________________________________________________________________________________

Information about the new experiment

The new experiment also consists of 10 periods. In this experiment, too, you are matched randomly with another person in each period. Again you don’t get to know the others person's identity. As before all decisions are anonymous. The only change compared to the first experiment is given by an additional parameter that X can offer in the proposed employment contract. In addition to the fixed salary and the desired effort participant X determines a potential deduction from wages. This is the only difference to the previous experiment. The potential deduction from wages is enforced if Y chooses a work effort that is lower than the desired effort of X. If Y choose a work effort which is higher or equal than the desired effort than the deduction from wages isn’t enforced. There are four possible levels of potential deduction from wages. The deduction from wages can be either 0 or 24 or 52 or 80. The deduction from wages will only be enforced if the effective effort is lower than the desired effort!

For the contract design the following rules hold:

−700 ≤ fixed wage ≤ 700

Potential deduction from wages: either 0 or 24 or 52 or 80

1 ≤ desired work effort ≤ 20

All numbers must be integers. In designing the contract all combinations that are compatible with these rules are possible!

To make the rules clear to you, we depict the screen that will be shown to X at the beginning of the first period.

38

The profits are calculated as follows: Profit of X: Profit of Y:

Y rejects the contract:

Zero Zero

The effective work effort is higher or equal than the desired work effort.

Period return of the effective work effort – fixed wage Fixed wage – costs of the effective work effort

The effective work effort is lower than the desired work effort:

Period return of the effective work effort – fixed wage

+ deduction from wages

Fixed wage –deduction from wage – costs of the

effective work effort

The process of this experiment is identical with the previous experiment.

39

APPENDIX B: SUPPORTING ANALYSES

Measuring incentive effects and short-run crowding out effects in TFT and TBT The regression model reported in Table B1 is very similar to the one reported in Table 4 in the paper. The

only difference is that we include a variable “Individual cooperation level” to measure the average effort chosen by the individual agent in Phase 1. We included this to control for an individual (fixed effect) inclination to provide high effort.

TABLE B1 EXPLAINING PHASE 2 EFFORT CHOICES IN TREATMENTS TTT, TFT AND TBT

Probit (e>1)

Probit (e=e*|e>1)

Tobit (e|e>1, e≠e*)

Fixed wage (Trust games) 0.006*** 0.040*** (0.001) (0.005) Desired effort (Trust games) -0.011 0.086 (0.014) (0.110) Individual cooperation level 0.080*** -0.171*** -0.411** (0.015) (0.027) (0.172) Fixed wage (Bonus games, IC) 0.000 0.003 (0.002) (0.003) Bonus b (Bonus games, IC) 0.049*** 0.060*** (0.009) (0.017) Fixed wage (Fine games, IC) 0.006*** -0.000 (0.002) (0.003) Fine f (Fine games, IC) 0.045*** 0.070*** (0.009) (0.016) Desired effort (Bonus/Fine games, IC) -0.276*** -0.056 (0.053) (0.094) Dummy IC 0.695* 19.974*** (0.416) (3.704) Fixed wage (Bonus games, NIC) 0.006*** 0.024*** (0.002) (0.009) Bonus b (Bonus games, NIC) 0.002 0.010 (0.008) (0.029) Fixed wage (Fine games, NIC) 0.005*** 0.017*** (0.002) (0.006) Fine f (Fine games, NIC) -0.002 0.054 (0.008) (0.042) Desired effort (Bonus/Fine games, NIC) -0.098** 0.525 (0.042) (0.354) Dummy NIC 1.099*** -0.472 (0.386) (1.349) Inverse Mills ratio -0.34 3.449 (0.342) (1.165)*** Constant -1.392*** -1.153 -8.257 (0.134) (0.274)*** (2.760)*** No. of obs. 981 619 231 χ2=291.68*** χ2=327.38*** F=295.28*** Pseudo R2 0.285 0.900 0.149 Phase 2 data of TTT, TFT and TBT. Probit (e>1) indicates a probit model whether e>1 or e=1. Probit (e=e*|e>1) indicates a probit model whether e=e* (best-reply effort) or e≠e*. Tobit (e|e>1, e≠e*) indicates a Tobit regression with an upper bound of e=20. Robust standard errors in parentheses; * p< 10%; ** p< 5%; *** p< 1%.

40

Measuring long-run crowding out in TFT and TBT The regression model reported in Table B2 is the same as the one reported in Table 5 in the paper except

for adding the variable “Individual cooperation level” (see Table B1).

TABLE B2

LONG-RUN CROWDING OUT (COMPARING PHASE 3 DATA IN TFT AND TBT WITH TTT)

Probit (e>1)

Tobit (e|e>1)

Fixed wage 0.006*** 0.041*** (0.001) (0.009) Desired effort -0.014 0.117 (0.017) (0.073) Dummy TBT treatment -0.363 0.224 (0.246) (0.890) Dummy TFT treatment -0.598*** 0.905 (0.213) (0.822) Individual cooperation level 0.166*** 0.351 (0.018) (0.260) Inverse Mills ratio 2.781 (2.451) Constant -1.689*** -6.292 (0.247) (4.747) No. of obs. 929 300 LR chi2 279.34*** 125.54*** Probit (e>1) indicates a probit model whether e>1 or e=1. Tobit (e|e>1) indicates a Tobit regression on e>1 choices only (censored at 20). Robust standard errors in parentheses; * p< 10%; ** p< 5%; *** p< 1%.

41

Measuring incentive effects and short-run crowding out in TFT-R and TBT-R The regression model reported in Table B3 is very similar to the one reported in Table 4 in the paper and

in Table B1. In contrast to Tables 4 and B1 we did not run the Probit regression to estimate prob(e = e*|e > 1). Since there were almost no deviations (see Figure 11 in the paper) this probability is virtually equal to 1.

TABLE B3 EXPLAINING PHASE 2 EFFORT CHOICES IN TREATMENTS TTT-R, TFT-R AND TBT-R

Probit (e>1)

Tobit (e|e>1)

Fixed wage (Trust games) 0.006 0.020 (0.005) (0.015) Desired effort (Trust games) -0.016 0.685** (0.077) (0.323) Individual cooperation level 0.079** 0.425*** (0.039) (0.151) Fixed wage (Bonus games, IC) 0.010*** (0.002) Bonus b (Bonus games, IC) 0.008 (0.007) Fixed wage (Fine games, IC) 0.011*** (0.003) Fine f (Fine games, IC) -0.010 (0.007) Desired effort (all games, IC) -0.159** (0.069) Dummy IC 1.672** 12.535*** (0.779) (1.735) Fixed wage (Bonus games, NIC) 0.071*** (0.016) Bonus b (Bonus games, NIC) 0.110*** (0.028) Fixed wage (Fine games, NIC) 0.080*** (0.016) Fine f (Fine games, NIC) -0.018 (0.028) Desired effort (all games, NIC) -0.438 (0.317) Dummy NIC 0.366 -1.851 (0.945) (2.639) Inverse Mills ratio 6.725** (3.147) Constant -0.176 -7.125*** (0.621) (2.480) No. of obs. 289 265 χ2 55.23*** 31.91*** Pseudo R2 0.333 0.216

Phase 2 data of TTT-R, TFT-R and TBT-R. Probit (e>1) indicates a probit model whether e>1 or e=1. Tobit (e|e>1) indicates a Tobit regression on e>1 choices only (censored at 20). Robust standard errors in parentheses; * p< 10%; ** p< 5%; *** p< 1%.

42

Measuring long-run crowding out in TFT-R and TBT-R The regression model reported in Table B4 is analogous to the ones reported in Tables 5 and B2.

TABLE B4 LONG-RUN CROWDING OUT (COMPARING PHASE 3 DATA IN TFT-R AND TBT-R WITH TTT-R)

Probit (e>1)

Tobit (e=e*|e>1)

Fixed wage 0.009*** 0.042*** (0.002) (0.010) Desired effort 0.002 0.570*** (0.034) (0.219) Dummy TBT-R treatment -0.572 -0.964 (0.541) (0.89) Dummy TFT-R treatment -0.615 0.153 (0.527) (0.971) Individual cooperation level -0.046 0.103 (0.03) (0.09) Inverse Mills ratio 10.965*** (3.995) Constant 0.313 -9.265*** (0.614) (2.922) No. of obs. 304 270 LR chi2 57.24*** 77.39*** Pseudo R2 0.510 0.287

Probit indicates a probit model whether e>1 or e=1. Tobit indicates a Tobit regression on e>1 choices only (censored at 20). Robust standard errors in parentheses; * p< 10%; ** p< 5%; *** p< 1%.

43

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Do Incentives Destroy Voluntary Cooperation?

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