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Sticks and Carrots in Procurement: An Experimental Exploration

  • Maria Bigoni EMAIL logo , Giancarlo Spagnolo und Paola Valbonesi
Veröffentlicht/Copyright: 5. Juni 2014

Abstract

We test the robustness of recent findings on the benefits of penalty contracts to the environments typical of B2B (and B2G) procurement, where buyers and sellers interact repeatedly, matching is endogenous and competitive, there are contractible and non-contractible tasks, and reputation-based relationships can emerge. Both bonuses and penalties boost efficiency, strongly increasing effort in the contractible task while only mildly crowding it out in the non-contractible one. However, sellers grab a higher fraction of surplus with bonuses, as buyers’ offers become more generous. Consequently, buyers prefer penalties, which may explain why they are so widespread in procurement.

JEL Classifications: H57; C92; L14; M52

Acknowledgments

We would like to thank Ken Binmore, Matteo Colombo, Luca Corazzini, Dirk Engelmann, Arsen Palestini, Patrick Rey, Stefane Saussier, Fabio Tufano, Tommaso Valletti and seminar participants at Royal Holloway – University of London, the 2009 Italian Young Economists’ Meeting in Forlì, the 2009 Behavioral and Experimental Economics Workshop in Florence, the 2009 European ESA Meeting in Innsbruck, the May 2010 PRIN Workshop – University of Padova, the 2010 “Contract, Procurement, Public–Private Arrangements” Conference in Paris-Sorbonne, and at the IMEBE 2010 Conference in Bilbao, for helpful comments and suggestions. We gratefully acknowledge the financial support of the Italian Ministry of Education, University and Research (grant PRIN2006-2006130472_003), the University of Padova (grant N. CPDA084881/08) and the Swedish Academy of Science (Vetenskapradet). All remaining errors are ours.

Appendix A

Instructions for Treatment I

(originally in Italian)

Introduction

Welcome to this experiment. These instructions contain all the information you need to participate, read them carefully. If something remains unclear, please raise your hand and we will answer your questions individually. From now on, we ask you not to communicate with other participants in any way, until the end of the experiment.

During the experiment your earnings will be calculated in points. At the beginning of the experiment you will receive an endowment of 100 points. Over the course of the experiment you can gain or lose points. The number of points you will earn depends on your decisions, and on the decisions of other participants.

At the end of the experiment your points will be converted to Euros, according to the following rate:

1 point=0.02€

If at the end of the experiment the sum of the points you have was negative, your earnings will be equal to 0 Euros. In addition to the sum that corresponds to the points you accumulated, in any event you will receive 4€ for your participation. Your earnings, plus the 4€ attendance payment will be paid to you privately and in cash at the end of the session.

In this experiment, we reproduce a situation involving two agents: a buyer and a seller. All participants in the experiment are divided into two groups: the group of buyers and the group of sellers. At the beginning of the experiment you will be told whether you are a buyer or a seller, and the role you are assigned will remain the same throughout the experiment.

In every period, each buyer can buy a good from a seller. The seller profits from this transaction if he sells the good at a price that is higher than its production cost. The buyer gets a profit from the transaction if he buys the good at a price below the value he attributes to it.

The cost of production for the seller and the value of the good for the buyer both depend on the quality of the good and on the delivery time of the good itself.

All the choices you make during this experiment are anonymous. You will not know the identity of the other participants with whom you will interact in the course of the experiment; similarly, your identity will remain hidden.

Every participant, however, will receive an identification number (ID) which will remain the same throughout the whole experiment. You will be told your ID at the beginning of the experiment, and it will always be visible in the upper part of your screen.

General elements of the experiment

  1. Every period starts with a contracting phase. In this phase, buyers can make contractual offers, which can be accepted or rejected by sellers. Each contractual offer proposed by a buyer should specify:

    1. the base compensation offered to the seller

    2. the delivery time requested

    3. the quality requested

    4. the addressee of the offer. Buyers can make two types of offers: public offers and private offers. Public offers are posted to every seller, and any seller can accept them. Private offers are addressed to a specific seller, and can be accepted only by that seller.

The buyer can also introduce in the offered contract an incentive mechanism. The incentive can be in the form of a penalty or a bonus. The activation of the incentive depends on the delivery time for the good. For example, the buyer may decide that the seller will receive a bonus if he delivers the good by a given deadline, or that the seller will have to pay a penalty in the event he fails to deliver the good by a given deadline.

There is no limit to the number of offers a buyer can make in each period. The offers can be accepted by sellers at any moment.

Each seller, however, cannot accept more than one contractual offer in each period. Similarly, in each period each buyer can sign only one contract.

In this experiment, the number of sellers is two more than the number of buyers. For this reason, in every period at least two sellers will not sign any contracts.

  1. After the contracting phase, all sellers who have signed a contract will have to choose the quality of the good they actually produce for the buyer, and the actual delivery time. The seller is not compelled to provide the quality requested by the buyer, nor to deliver the good by the deadline requested.

  2. When every seller has made his decisions, the profits earned by each participant will be calculated. Every participant will be able to read on his screen the profits they recorded in that period, together with the profits of the other player with whom he signed a contract in that period, if any. Once the profits have been displayed, a new period begins.

The experiment in details

The contracting phase

Every period begins with a contracting phase. This phase lasts no more than 150 seconds. At the end of this time, it will not be possible for buyers to make offers, or for sellers to accept them.

During this phase, every buyer will be able to make both public offers and private offers. To address a private offer to a specific seller, the buyer will have to indicate the ID of the seller in the contractual offer. It will not be possible to address private offers to sellers who have already accepted an offer in that period. Buyers will be shown the IDs of sellers that are still available in a box in the lower-right corner of the screen.

Every offer should indicate:

  1. the base compensation offered to the seller. This compensation should be between 0 and 130.

  2. the delivery time requested, which can be equal to 1, 2, 3, 4, or 5 weeks.

  3. the quality requested, that can be equal to 0 (minimum), 1, 2, 3, or 4 (maximum)

  4. the addressee of the offer

    1. all sellers (public offer)

    2. a specific seller, identified by his ID (private offer)

  5. the type of incentive, which can be

    1. bonus

    2. penalty

    3. no incentives

In the event the buyer chooses to introduce a bonus or a penalty, he will also have to specify in the offer the deadline by which the good should be delivered by the seller to secure the bonus or to avoid the penalty.

This deadline can be equal to 1, 2, 3, 4, or 5 weeks.

The value of the bonus or of the penalty is equal to 20 points.

Every buyer can make as many offers as they wish, within the time limit (150 seconds). As soon as one of these offers is accepted, however, all other offers made by the same buyer will be closed, as every buyer can sign only one contract per period.

Every buyer will see on his screen a list of all the public and private offers he has made in that period. In a second table, he will also be shown a list of the public offers – but not of the private offers – made by other buyers.

Every seller will be shown an on-screen list of all the public offers input by every buyer, and all the private offers addressed to him. To accept an offer, the seller will have to select it with the mouse, and then click “Accept”.

Once he has accepted an offer, the seller will not be able to receive other offers, as no sellers can sign more than one contract per period.

In the contracting phase, when a seller receives a contractual offer – public or private – he is also informed of the ID of the buyer who made it. When an offer is accepted by a seller, the buyer is also informed of the ID of the seller who accepted it.

The contracting phase ends when all the buyers have signed a contract, or at the end of the 150-seconds window.

The decision of the sellers

After the contracting phase, all sellers who have signed a contract have to determine the quality of the good they provide to the buyer and the actual delivery time. The delivery time and the quality requested in the contract are not binding for the seller.

Calculating profits

When the seller has made his decisions, the computer evaluates the profits of the buyer and of the seller, given the terms of the contract, the value of the good for the buyer and the cost of production for the seller.

Value of the good for the buyer

The value of the good for the buyer depends on the actual delivery time and on the quality of the good provided. The value of the good is defined depending on the possible levels of quality and on the possible delivery times and is listed in Table 11:

Table 11

Value of the good for the buyer

Quality
01234
Delivery time1 week7288104120136
2 weeks567288104120
3 weeks40567288104
4 weeks2440567288
5 weeks824405672

For example, from Table 11 we can see that, if the quality is equal to 3 and the good is delivered in 2 weeks, the value of the good for the buyer is equal to 104.

Table 12

Cost of the good for the seller

Quality
01234
Delivery time1 week1625364964
2 weeks916253649
3 weeks49162536
4 weeks1491625
5 weeks014916

Production cost

The cost of production for the seller also depends on the actual delivery time and on the quality of the good provided. The cost of production corresponding to the various possible levels of quality and the possible delivery times is listed in Table 12.

For example, from Table 12 we can see that, to produce a good with quality equal to 1 and to deliver it within 4 weeks, the seller bears a cost equal to 4.

Profits

The buyer’s and seller’s profits depend on the type of contract they have signed and on the decisions made by the seller. Table 13 lists all the possible cases.

Table 13

Profits

Profit of the sellerProfit of the buyer
1. If he does not sign any contract
40
2. Contract without any incentive
Base compensation – production costValue of the good – base compensation
3. Contract with bonus, if the seller delivers the good by the deadline
Base compensation – production cost +20 (bonus)Value of the good – base compensation – 20 (bonus)
4. Contract with bonus, if the seller does not deliver the good by the deadline
Base compensation – production costValue of the good – base compensation
5. Contract with penalty, if the seller delivers the good by the deadline
Base compensation – production costValue of the good – base compensation
6. Contract with penalty, if the seller does not deliver the good by the deadline
Base compensation – production cost –20 (penalty)Value of the good – base compensation +20 (penalty)

Number of periods and end of the experiment

The total duration of the experiment is random. The experiment will last at least 15 periods and not more than 30. From the fifteenth until the twenty-ninth period, the computer will draw a number between 1 and 100. If this number is higher than 33, the experiment continues for an additional period; if instead it is less than or equal to 33 the experiment ends. This means that from the fifteenth to the twenty-ninth period, the experiment continues for an additional period with a probability of 67%, while it ends with a probability of 33%.

Available information

Profit calculator

Every participant will have access to a “profit calculator”, which can be activated by pressing the “profit calculator” button on the computer screen.

The profit calculator evaluates profits for the buyer and for the seller, corresponding to each possible level of quality and to each delivery time, given the level of the base compensation and the type of incentive adopted (as presented in Tables 11 and 12). The results generated by the profit calculator are displayed on the screen in two tables, called “your profits” and “profits of the seller” (in the event the participant plays the role of a buyer) or “profits of the buyer” (in the event the participant plays the role of a seller).

Before the experiment, you will be allowed to practice using this calculator in order to understand its operation.

Information about the game and history of play

At the end of each period, all participants will be informed of their profit in that period. Participants who have signed a contract in that period will also be able to read on their screen:

  1. the ID of their counterpart

  2. the base compensation fixed by the contract

  3. the delivery time requested in the contract

  4. the quality requested in the contract

  5. the type of incentive possibly used and its deadline

  6. the actual delivery time

  7. the quality actually provided by the seller

  8. the profit recorded by the counterpart

All these data, relative to each of the previous periods, are also collected in a table called “history table”, to which every participant can refer throughout the experiment, starting from the second period of play, by clicking the “history of play” button.

In the top right corner of the screen, each participant will also see his “total score”, that is, the number of points accumulated since the beginning of the experiment.

Trial periods and control questions

You will be able to operate the computer with your keyboard or with the mouse. Before the experiment starts, you will have a chance to familiarize yourself with the program over the course of three trial periods. During these periods, the ID (identifying number) assigned to you and to each of the other participants will be different from the one you will be assigned for the real experiment.

Profits earned during the trial periods do not count toward your earnings during the experiment.

Before starting the trial periods, you will be asked to answer some control questions to verify your complete understanding of the instructions. The trial periods will start as soon as all participants have correctly answered all the control questions.

We remind you once more that you are not allowed to talk during the experiment. If you have questions or concerns, please raise your hand and we will come to your desk.

Appendix B

Additional regressions

Table 14

Treatment effect on the average share of private offers – GLS panel regression

Dependent variable: average share of private offers
CoefficientS.e.
Treatment TBaseline
Treatment B–0.1510.052***
Treatment P–0.2420.075***
Treatment I–0.2150.051***
Period0.0060.003**
Constant0.3650.042***
Tests on coefficientsp-value
Treatment B vs. Treatment I0.0481**
Treatment P vs. Treatment I0.6660
Treatment B vs. Treatment P0.1541
R2-between0.662
R2-within0.035
R2-overall0.292
Number of Observations180
Table 15

Treatment and incentive effect on the average cost of voluntary effort – GLS panel regression

Dependent variable: average cost of voluntary effort
CoefficientS.e.
Treatment TBaseline
Treatment B1.1881.934
Treatment P1.2861.889
Treatment I3.3131.000***
Treatment P – Penalty–4.0461.729**
Treatment I – Penalty–6.6431.131***
Treatment B – Bonus–3.6902.452
Treatment I – Bonus–6.2781.579***
Period–0.4810.102***
Constant7.4141.286***
R2-between0.560
R2-within0.124
R2-overall0.223
Number of observations327
Table 16

Treatment and incentive effect on the average buyers’ profits – GLS panel regression

Dependent variable: average buyers’ profit
All contractsExcluding unacceptable contracts
CoefficientS.e.CoefficientS.e.
Treatment I – BonusBaseline
Treatment B14.1176.978**14.0786.975**
Treatment P19.8427.122***14.4377.545*
Treatment I – Penalty17.7937.827**14.3697.794*
Period0.4380.211**0.3410.204*
Constant15.3357.169**16.1497.150**
R2-between0.5630.434
R2-within0.0240.012
R2-overall0.2220.131
Number of observations175175
Table 17

Treatment and incentive effect on the average profit offered to buyers – GLS panel regression

Dependent variable: offered profit
All contractsExcluding unacceptable contracts
CoefficientS.e.CoefficientS.e.
Treatment I – BonusBaseline
Treatment B–11.0395.992*‒10.9975.991*
Treatment P–20.9593.197***‒13.5004.079***
Treatment I – Penalty–17.3924.098***‒12.4723.799***
Period–1.5780.197***‒1.3900.200***
Constant43.4763.166***41.9303.157***
R2-between0.6810.470
R2-within0.4070.330
R2-overall0.5140.364
Number of observations175175

Appendix C

Loss-averse buyers

We now present a simple application of prospect theory (Tversky and Kahneman 1992) showing that the expected utility for a loss-averse buyer tends to be higher with bonus contracts than with payoff-equivalent penalty contracts.

Consider a simple static setting in which a buyer offers a contract with a penalty to a seller. If after signing the contract the seller complies with it, the buyer earns

νwpe,

where ν is the buyer’s gross profit and wpe is the wage paid to the seller. If the seller shirks instead, the buyer’s gross profit is 0 and he up ends with

wpe+20

where 20 is the penalty the buyer will receive from the seller for her poor performance.

When the buyer offers a contract with a bonus – where wbo is the wage paid to the seller – the buyer gets

νwbo20,

in the event the seller complies, and he gets

wbo

if the seller shirks.

The buyer’s payoffs in adopting a bonus or penalty are equivalent for

[3]wpe=wbo+20.

Assume now the buyer believes that the seller may make a mistake and shirk with some small probability p. When choosing between bonus and penalty contracts, the buyer needs to compare two prospects, Pbo and Ppe, where:

Pbo=(νwbo20,(1p);wbo,p)

and

Ppe=(νwpe,(1p);wpe+20,p)

Let us assume the buyer’s decision process is consistent with Cumulative Prospect Theory (Tversky and Kahneman 1992): they code outcomes as gains and losses, and simplify prospects by combining the probability associated with identical outcomes. The two prospects are then perceived as follows:

Pbo=wbo+ν20,(1p);0,p

and

Ppe=wpe+ν,(1p);20,p

In addition, we assume that buyers’ preferences can be represented by a function V() For simplicity, we assume that the probability weighting functions ω+ and ω for positive and negative outcomes are identical and given by

ω+(q)=ω(q)=q

To simplify the problem even further, we consider a linear value function v():

v(x)={xifx0λxifx<0

where λ>1 is the measure of loss-aversion.

We now compute the value of the two prospects Pbo and Ppe for the buyer:

V(Ppe)=vwpe+pv20+1pvν=λwpe+p(20)+1pν
V(Pbo)=vwbo+1pvν20=λwbo+1pν20.

If the penalty and the bonus contracts are payoff-equivalent, as in eq. [3], it is easy to show that the value of prospect Pbo for the buyer is higher than the value of prospect Ppe for every λ>1:

λwpe20+1p(ν20)>λwpe+1pν+p20
λ201p20>p20
20(λ1)p>0.

This shows that – to be equivalent from the point of view of a loss-averse buyer – a penalty and a bonus contract should offer the seller a different profit levels, i.e., the penalty contract should feature a lower profit than the corresponding bonus contract.

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  1. 1

    See e.g. Laffont and Tirole (1993), Bajari and Tadelis (2001), Albano et al. (2006). The USA government procurement uses both fixed-price and cost-plus contracts. Recently, President Obama has signed a memorandum addressed to Federal Executive Departments and Agencies where he explicitly declares that “there shall be a preference for fixed-price type contracts” to eliminate wasteful and inefficient government contracts (Obama 2009).

  2. 2

    In Anglo-Saxon countries liquidated damages are only enforceable up to the courts’ estimates of the harm produced by the contractual violation; and in most other countries, contractual penalties are only enforceable up to a fraction of the value of the exchange.

  3. 3

    Kahneman, Knetsch, and Thaler (1986) pointed out that, in violation of normative standards, people tend to be more sensitive to losses than to foregone gains, which renders their preferences vulnerable to framing effects (see also Tversky and Kahneman 1986).

  4. 4

    Hannan, Hoffman, and Moser (2005) confirm Luft (1994)’s result that agents prefer bonus contracts and perceive them to be fairer than penalty contracts. They also find that incentives framed as bonuses foster more trust and reciprocity, although this effect is dominated by a substantially larger increase in effort induced by penalties because of loss aversion. In our set-up, bonus and penalty contracts are such to induce even fully rational agents to exert the maximum level of effort in the verifiable task, hence in principle loss aversion should not induce differences in agents’ behavior.

  5. 5

    In the words of Holmstrom and Milgrom (1991), “Incentive pay serves not only to allocate risks and motivate hard work, it also serves to direct the allocation of the agents’ attention among their various duties.”

  6. 6

    See MacLeod (2007) for an excellent recent survey.

  7. 7

    Bonuses are typically seen as “positive” incentives, favoring trust among the parties and possibly facilitating cooperation (Luft 1994; Hallman, Hartzell, and Parsons 2005). Fehr and Gächter (2002) observe that if, for a given incentive contract, multiple effort levels are consistent with equilibrium behavior, then the framing may affect the selection of equilibria, thus leading to a different outcome for the game. They also point out that, in cases where concerns of fairness and reciprocity are important, the framing of incentives may affect perceptions of the kindness or hostility of the principals’ actions, which are crucial for reciprocal responses.

  8. 8

    Hossain and List (2012) also mention that one of the reasons why their framing manipulation is much more powerful among groups of workers than among individuals, is that in the former case workers face a riskier setting because of team production externalities.

  9. 9

    If our result is confirmed in future studies, it may imply that social preferences are not only complementary to reputation, as suggested by Brown, Falk, and Fehr (2004), but may actually be essential to its effectiveness.

  10. 10

    In our setting, explicit incentives are deterministic, and the punishment in the event of a seller’s default is automatic and cost-free for the buyer. This is consistent with procurement practice but contrasts with the typical set-up adopted in previous experiments (Andreoni, Harbaugh, and Vesterlund 2003; Fehr and Schmidt 2007; Sutter, Haigner, and Kocher 2010).

  11. 11

    We opted for an uncertain time horizon to minimize backward induction reasoning, but also to mimic real-life procurement interactions more closely. This marks a difference from previous experiments where subjects knew they were playing for a fixed number of rounds: in Brown, Falk, and Fehr (2004) the number of rounds was 15, in Falk, Huffman, and Macleod (2008), in Fehr and Gächter (2002) and in Gächter, Kessler, and Königstein (2010) it was 18, 12 and 10, respectively.

  12. 12

    In line with the instructions of Brown, Falk, and Fehr (2004) – our instructions explicitly state that “The seller is not compelled to provide the quality requested by the buyer, nor to deliver the good by the deadline requested.”

  13. 13

    Detailed analytical solutions are presented in Bigoni, Spagnolo, and Valbonesi (2009).

  14. 14

    Our theoretical predictions and experimental results are specific to this production function. Whether a higher degree of complementarity between the two efforts would have generated different results is an interesting research question, which is outside the scope of this study.

  15. 15

    Even when all traders are rational, Kreps et al. (1982) show that cooperation can be sustained in a finite horizon game by players who are aware of its finite duration as long as they believe that, with some positive probability, they are facing a certain “commitment type”.

  16. 16

    As Brown, Falk, and Fehr (2004) write “In the presence of fair-minded workers, who respond to generous offers with a generous effort level, employers have an incentive to offer a rent in the final period. This rent can be used to discipline the selfish workers in the non-final periods by the threat of termination of the relationship.”

  17. 17

    We had one session with 14 subjects, and one with 12 because some subjects failed to show up.

  18. 18

    The instructions explicitly informed the subjects that, in the event their total number of points at the end of the experiment was lower than zero, they would not have to pay any money to the experimenters, and that they would receive the show-up fee regardless. This circumstance, however, never took place.

  19. 19

    In all the analyses contained in this section, we consider only the first 15 periods of each session, so to even out the end game effect. If not otherwise specified, the unit of observation is the average, per session, per period; this allows us to equalize to 45 the number of observations per treatment. In all regressions, we obtain qualitatively similar results by using individual-level data, and adopting a model with random effects at the subject level and standard errors robust for clustering at the session level. Regression results are available from the authors upon request.

  20. 20

    Frank, Gilovich, and Regan (1993) report several empirical results suggesting that economics and business students appear less cooperative than others. Yezer, Goldfarb, and Poppen (1996) provide further evidence that undergraduate students of economics display uncooperative behavior in specialized games or surveys, although they disagree with Frank, Gilovich, and Regan (1993) on the additional conclusion that economists’ “real-world” behavior is also less cooperative. More recently, Gaechter et al. (2012) find that economists cooperate less – i.e. contribute less – than non-economists in a leader–follower experimental set-up.

  21. 21

    Evidence of the framing effects of instruction in this and similar settings is also somewhat mixed. Charness, Frechette, and Kagel (2004) found that providing complete and detailed information about the players’ feasible payoffs – in their case by means of a profit table – significantly reduces the impact of intrinsic motivation and fairness in gift exchange laboratory experiments. By contrast, Gürerk and Selten (2012) find that granting subjects access to a payoff table induces a more cooperative behavior in oligopoly experiments. Engelmann and Ortmann (2009) recently found that the framing of instructions exerts only limited effects on subjects’ behavior in gift-exchange games. Hoffman et al. (1994) did find that ultimatum game offers are lower in a buyer–seller frame than in a standard bargaining frame, but in a following study, Hoffman, McCabe, and Smith (2000) report treatments where offers in a buyer–seller frame were similar to those from bargaining frames.

  22. 22

    This difference does not affect our main results, which stem from the comparison across treatments, because this feature of our design equally affects all treatments.

  23. 23

    We drop from the following analyses 3 out of 1,290 observations, in which e1<e1 and e2>0.

  24. 24

    We set c(e1)=0 when no explicit incentives are present.

  25. 25

    The difference is not significant when we compare Treatment T with either B or P.

  26. 26

    A bonus contract with wage w is equivalent to a penalty contract with wage w+20. Hence, to capture only the framing difference between bonuses and penalties, when the contract includes a bonus the fixed payment is increased by 20 points.

  27. 27

    In Treatment T, e1=0 and the cost of voluntary effort coincides with the total cost of effort exerted.

  28. 28

    These additional controls include: the fixed compensation offered to the seller, a dummy indicating whether the offer is public or private, the number of private offers received by a seller in the period, and the desired levels of non-verifiable and verifiable effort.

  29. 29

    We thank Dirk Engelmann for pointing this out.

  30. 30

    To avoid such an asymmetry across treatments, we should have set different restrictions on the set of possible fixed wages w, depending on the type of incentive adopted. More specifically, we should have allowed buyers to choose w from the set [16,146] in contracts with a penalty, and from the set [4,126] in contracts with a bonus, so that the minimum possible earning for the seller is zero, both in trust contracts and in contracts with explicit incentives. Yet, we believe this would have seriously distorted subjects’ perception of the alternatives.

  31. 31

    25.9% of the total offers are unacceptable, and 11.2% of the contracts originate from unacceptable offers.

  32. 32

    21.4% vs 7.3%. The Wilcoxon signed-rank test with N = 8 confirms that the difference is significant at the 5% level. In 4 out of 12 sessions, all buyers made at least two mistakes, so no matched observations are available.

  33. 33

    11.2% vs 6.4%. The difference is significant at the 5% level, according to a Wilcoxon signed-rank test with N=11. In one out of 12 sessions, all sellers made at least two mistakes, so no matched observations are available.

  34. 34

    28.0 vs. 20.5. The difference is significant at the 5% level, according to a Wilcoxon signed-rank test with N=9 (the comparison is not possible for 3 sessions).

  35. 35

    11.2 vs. 14.8. The difference is significant at the 5% level according to a Wilcoxon signed-rank test with N=10 (the comparison is not possible for 2 sessions).

  36. 36

    Filiz-Ozbay and Ozbay (2007) also report the presence of overbidding in experimental auctions and explain it by arguing that bidders may overbid if they anticipate regret for losing the auction.

  37. 37

    According to a GLS panel regression, however, the difference is significant only when we compare contracts with bonuses and with penalties in Treatment I, while the estimated coefficients from Treatments B and P are not significantly different at the 10% level according to a Wald test.

  38. 38

    In our game, this is always equal to w(e)2.

  39. 39

    Table 17 in Appendix B.

Published Online: 2014-6-5
Published in Print: 2014-7-1

©2014 by Walter de Gruyter Berlin / Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Advances
  3. Preferential Admission and MBA Outcomes: Mismatch Effects by Race and Gender
  4. Quantity Uncertainty and Demand: The Case of Water Smart Reader Ownership
  5. Contributions
  6. Employment Effects of the 2009 Minimum Wage Increase: New Evidence from State-Based Comparisons of Workers by Skill Level
  7. Introducing Carbon Taxes in Russia: The Relevance of Tax-Interaction Effects
  8. Estimating Parents’ Valuations of Class Size Reductions Using Attrition in the Tennessee STAR Experiment
  9. Local Option, Alcohol and Crime
  10. To Work or Not to Work? The Effect of Childcare Subsidies on the Labour Supply of Parents
  11. Understanding Ransom Kidnappings and Their Duration
  12. Screening Stringency in the Disability Insurance Program
  13. Sticks and Carrots in Procurement: An Experimental Exploration
  14. Peer Effects and Policy: The Relationship between Classroom Gender Composition and Student Achievement in Early Elementary School
  15. Topics
  16. Competition and Innovation in Product Quality: Theory and Evidence from Eastern Europe and Central Asia
  17. Trading the Television for a Textbook?: High School Exit Exams and Student Behavior
  18. The Effect of Parental Migration on the Educational Attainment of Their Left-Behind Children in Rural China
  19. Do Parents’ Social Skills Influence Their Children’s Sociability?
  20. The Role of Infrastructure in Mitigating Poverty Dynamics: The Case of an Irrigation Project in Sri Lanka
  21. Congestion of Academic Journals Under Papers’ Imperfect Selection
  22. Endogenous Merger with Learning
  23. Do Low-Skilled Migrants Contribute More to Home Country Income? Evidence from South Asia
  24. The Minimum Wage and Crime
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