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An Experimental Study of Trust and Reputation with Differently-Valued

Goods

Anya SavikhinPurdue University

I thank my advisor Tim Cason for his guidance and support on this project.

Introduction

• Reputation mechanisms are necessary because they facilitate transactions when there is an opportunity to cheat.

• Trust among strangers is strengthened through the use of a reputation system, which tracks seller’s history of actions– which reveals seller types– helps reduce asymmetry – increasing efficiency in the market

2

Related Literature

• Homogeneous goods & Reputation Systems– Helping/trust game

(Bolton et al, 2000; Engelmann and Fischbacher, 2004; Seinen and Schram, 2004)

– Labor Market(Healy, 2007; Holstrom, 1981; Shapiro and Stiglitz, 1982)

– Prisoner’s dilemma(Kreps et al, 1982)

– Firm Behaviors (Fudenberg and Tirole, 1985, Kreps and Wilson, 1982; Milgrom and Roberts, 1982)

3

Motivation 1

• Previous studies look at homogeneously-valued goods• In practice, we have heterogeneously-valued goods:

– On eBay, can buy a house or a toaster

• Does it matter?– We think so – empirical work has shown that sellers on

eBay strategize with a “feedback market” (false reputation)

(Bhattacharjee & Goel, 2005; Brown and Morgan, 2006)

– Impact of reputation is higher for more expensive products (Dell, 2005; Resnick et al., 2006)

4

Motivation 2

• With homogeneously valued goods, buyers have full information about transaction history

• With heterogeneously valued goods, information is decreased under the current reputation system, we don’t know whether the transaction was high or low value

• Does it matter?– We use a new treatment to restore information to the

previous level– Turns out that it doesn’t matter

5

Contributions

• How does introduction of heterogeneously valued goods change behavior and efficiency, with and without reputation?

• Does the restoration of complete information have an effect?

• We use a trust framework with a high value good and a low value good

• Research has broad implications for reputation systems on online exchanges (e.g., eBay, Amazon Marketplace)

6

Treatments

• No Reputation (3 sessions)– No information about seller history

• Simple Reputation (3 sessions)– Information about seller history, value of

transactions is unknown

• Separate Reputation (3 sessions)– Restores information about seller history, know

also the value of each transaction– 2 reputation numbers, one for each type of item

7

Experimental Environment

• ZTree (Fischbacher, 2007) • 99 Purdue undergraduate students

– 7 sellers, 4 buyers – randomly assigned, stay in same designation throughout session

– 2 types of items– high value, low value– Average earnings $18 for experiment lasting 90

minutes• 50 experimental dollars = 1 US dollar• Includes $5 show-up fee, $1/each correct answer on quiz (for

total of 4 questions)

• Risk elicitation, quiz, demographic questionnaire

8

Sequences

– Finite number of periods (9, with 6 sequences) (C&W, 88)

– 3 random periods paid from each sequence – 18 total

– Reputation number automatically updated, % items sent and number of items sent, all future buyers see the reputation numbers

1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9

Reputation

Seller Chooses an Item

10

Buyers Enter one by one to buy

11

Seller Chooses to send/not send

12

Decision TreeBuyer

Buy high value item

Buy item from the computer

(N/A, +20)

Don’t sendhigh value item

Low value market

(+75,-40)

Don’t sendlow value item

Seller

(+60,+35)

Sendlow

value item

(seller, buyer)(seller , buyer) ( / , buyer)

High value market

(+150,-250)

Seller

(seller , buyer)

Sendhigh value

item

(+70,+40)

(seller , buyer)

Buy low value item

13

General Intuition

• Multiple equilibria exist– Kreps et al. (1982) – mixed strategy

• Camerer and Weigelt (1988)

– Healy (2007) – pure strategy “full reputation equilibrium”

• Heterogeneity of subjects’ social preferences– Standard Preference (SP)

– Medium Preference (MP)

– High Preference (HP)

14

No Reputation

• Prediction 1: Greater seller reneging in high than low– in high SP, MP

– in low SP

• Prediction 2: Buyers may not buy many high value goods

15

Reputation Strategic Behavior

• Prediction 3: “false reputation building” SP types may act like MP/HP types in order to attract buyers

• Prediction 4: Buyers may buy more high value goods, Sellers may offer more high value goods

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Integrated System – High and Low

• Seller who reneges on high may continue by selling low (he could be MP type!)

• Simple Reputation– Renege on either good (SP or MP) future low

buyers

• Separate Reputation– Renege on high (SP or MP) future low buyers

more likely

– Renege on low (SP) no future buyers17

Result Overview

1. Simple reputation is effective at increasing efficiency (as compared to no reputation)

Increased offering/buying high

Decreased reneging

2. Not much difference between Simple and Separate the additional information is not necessary for an effective reputation system in a heterogeneous good setting

18

Result 1: Offers/Buys

• Result 1: Reputation increases proportion of offers/buys in high value good.

No Reputation Simple Reputation

02

04

06

08

01

00

Fre

que

ncy

of

Cho

ices

1 2 3 4 5 6 7 8 9

Period

High-value Item Low-value Item

sequences 2-6 aggregatedSeller's Choice of Item

02

04

06

08

01

00

Fre

que

ncy

of

Cho

ices

1 2 3 4 5 6 7 8 9

Period


sequences 2-6 aggregatedSeller's Choice of Item

Seller’s Offer of Good

19

02

04

06

0

Fre

que

ncy

of

Buy

s

1 2 3 4 5 6 7 8 9

Period

High-Value Item Low-Value Item Outside Option

sequences 2-6 aggregatedWhich item is bought over periods 1-9

02

04

06

0

Fre

que

ncy

of

Buy

s

1 2 3 4 5 6 7 8 9

Period

High-Value Item Low-Value Item Outside Option

sequences 2-6 aggregatedWhich item is bought over periods 1-9


Buyer’s Choice of Good

5/31

2/26 2/24 3/272/27

1/34

4/32 3/28 2/25

27/74 30/7928/81 29/78 27/78 26/71 28/73 29/77

23/80

020

4060

8010

0

Per

cent

age

1 2 3 4 5 6 7 8 9

Period


Labels are frequency of buys over offers

sequences 2-6 aggregatedPercentage of Items Bought of Offered

43/7348/76 44/73 45/76 45/79 42/77

36/73

17/63

3/58

9/32

11/29

16/32

10/2910/26

12/2816/32

16/42

2/47

020

4060

8010

0

Per

cent

age

1 2 3 4 5 6 7 8 9

Period


Labels are frequency of buys over offers

sequences 2-6 aggregatedPercentage of Items Bought of Offered

Proportion of Goods Bought

20

Results 2 & 3 - Reneging• Result 2: Greater reneging in high and low when

there is no reputation– No Reputation treatment reneging in high is higher than in

low

8/10 4/5

2/4

3/5

5/5 2/2

1/4

2/3

4/4

8/327/35

9/348/35 8/32

3/29 4/336/33

10/26

02

04

06

08

01

00

Don

't S

end

Per

cen

tag

e

1 2 3 4 5 6 7 8 9

Period


Labels are Frequency of Reneges

sequences 2-6 aggregatedPerc. Items Not Sent

1/473/55 2/52 1/52 0/51

3/48

8/436/24

5/7

1/130/13

1/170/12

1/140/15 0/19

3/17

2/4

02

04

06

08

01

00

Don

't S

end

Per

cen

tag

e

1 2 3 4 5 6 7 8 9

Period


Labels are Frequency of Reneges

sequences 2-6 aggregatedPerc. Items Not Sent


21

Result 4 - Efficiency• Efficiency:

Actual Earnings of All Sellers

Earnings if all Offer High, All buy High, All Send High

• Result 4: Significantly greater efficiency with reputation versus without.

01

02

03

04

05

06

07

08

09

01

00

Per

cent

ag

e

1 2 3 4 5 6 7 8 9

Period

Actual Ef f iciency

If Choose all Low -Value

If Choose all outside option

Sequences 2-6 aggregatedEfficiency

01

02

03

04

05

06

07

08

09

01

00

Per

cent

ag

e

1 2 3 4 5 6

Sequence

Actual Ef f iciency



Periods 1-9 aggregatedEfficiency

01

02

03

04

05

06

07

08

09

01

00

Per

cent

ag

e

1 2 3 4 5 6 7 8 9

Period

Actual Ef f iciency




01

02

03

04

05

06

07

08

09

01

00

Per

cent

ag

e

1 2 3 4 5 6

Sequence

Actual Ef f iciency





22

x 100

Result 5 – Value of Information

• Result 5: The additional information provided did not have a significant effect on efficiency.

Separate Reputation Simple Reputation

01

02

03

04

05

06

07

08

09

01

00

Per

cent

ag

e

1 2 3 4 5 6 7 8 9

Period

Actual Ef f iciency




01

02

03

04

05

06

07

08

09

01

00

Per

cent

ag

e

1 2 3 4 5 6

Sequence

Actual Ef f iciency




01

02

03

04

05

06

07

08

09

01

00

Per

cent

ag

e

1 2 3 4 5 6 7 8 9

Period

Actual Ef f iciency




01

02

03

04

05

06

07

08

09

01

00

Per

cent

ag

e

1 2 3 4 5 6

Sequence

Actual Ef f iciency




23

Conclusions

• Market failure occurs when there is no reputation system, as subjects do not trade sufficient quantities of the high value good. – Efficiency is increased with a reputation system. – Reputation is especially effective for increasing trade

in high value goods.

• Efficiency is unchanged with restored information– The information provided by systems used in practice

is sufficient and additional information is not necessary for a successful reputation system.

24

Future Work

• Voluntary feedback for buyer (costly)– More likely to post feedback in high versus low?

• Cost to Buyer to obtain extra information– More likely to pay for extra information for high

value items?

25

Instances with No Availability0

20

40

60

80

100

Per

cen

tag

e

1 2 3 4 5 6 7 8 9

Period

High Value Item Low Value Item

sequences 2-6 aggregatedPercentage of times none of the item was available

02

04

06

08

01

00

Per

cen

tag

e

1 2 3 4 5 6 7 8 9

Period

High Value Item Low Value Item

sequences 2-6 aggregatedPercentage of times none of the item was available


• Efficiency may be understated for reputation

Result 2 – Seller Types

• Result 2: A positive number of each of HP, MP, and SP seller types exist in the market.

Separate and Simple No Reputation

SP (standard social preference)19% (12/42)

range: 12-36 (19%-86%)

62% (13/21)

accurate: 13 (62%)

MP (medium social preference)57% (24/42)

range: 2-27 (5%-64%)

9% (2/21)

range: 2-5 (9%-24%)

HP (high social preference)14% (6/42)

range: 3-6 (7%-14%)

29% (6/21)

range: 3-6 (14%-28%)

Sellers Separate

Hig

hLo

wH

igh

Low

Hig

hLo

wH

igh

Low

Hig

hLo

w

0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54

0 9 18 27 36 45 54

1 2 3 4 5

6 7 12 13 14

15 16 17 18 23

24 25 26 27 28

29

Item not Bought Sent Item

Did Not Send

dec

isio

n

period

Graphs by Subject

Sellers SimpleH

igh

Low

Hig

hLo

wH

igh

Low

Hig

hLo

wH

igh

Low

0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54

0 9 18 27 36 45 54

34 35 36 37 38

39 40 45 46 47

48 49 50 51 56

57 58 59 60 61

62


Did Not Send

dec

isio

n

period

Graphs by Subject

Sellers No ReputationH

igh

Low

Hig

hLo

wH

igh

Low

Hig

hLo

wH

igh

Low

0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54

0 9 18 27 36 45 54

67 68 69 70 71

72 73 78 79 80

81 82 83 84 89

90 91 92 93 94

95


Did Not Send

dec

isio

n

period

Graphs by Subject

Buyers SeparateH

igh

Lo

wH

igh

Lo

wH

igh

Lo

w

0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54

8 9 10 11

19 20 21 22

30 31 32 33

low_sent low_notsent

high_sent high_notsent

dec

isio

n

period

Graphs by Subject

Buyers Simple

Hig

hL

ow

Hig

hL

ow

Hig

hL

ow

0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54

41 42 43 44

52 53 54 55

63 64 65 66



dec

isio

n

period

Graphs by Subject

Buyers No Reputation

Hig

hL

ow

Hig

hL

ow

Hig

hL

ow

0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54 0 9 18 27 36 45 54

74 75 76 77

85 86 87 88

96 97 98 99



dec

isio

n

period

Graphs by Subject

Predictions

• Prediction 7– Buying a low value good from a seller who has

reneged in the high value market is more likely in Separate versus Simple.

– Buying a low value good from a seller who has reneged in the low value market never happens in Separate but may happen in Simple.

• Prediction 8– Sellers are more likely to renege on the low value

good in Simple as compared to Separate.

What happens after reneging?• Result 6: In Separate, reneging in the low

value market never occurred. In Simple, reneging in low continued to attract a few future buyers.

Reneging Behavior and Frequency of Attracting a Future Buyer

Separate Reputation Simple ReputationBuyer for high only

Buyer for low only

Buyer for neither

Buyer for high only

Buyer for low only

Buyer for neither

Renege High Only

1 (5%) 2 (10%) 15 (75%) 0 3 (10.3%) 23 (79.3%)

Renege Low Only

0 0 0 1 (3.5%) 2 (6.9%) 0

36

Probit – Seller Offer Decision

TREATMENT Separate Simple No ReputationDependent Variable, Seller’s Offer Decision [1 if high value decision]period 8 -0.209 -0.421** -0.08[1 if t=8] (0.16) (0.16) (0.17)Period 9 -0.28 -0.416** -0.128[1 if t=9] (0.17) (0.16) (0.17)1/Sequence 0.14 -0.22 0.269[inverse of sequence order] (0.18) (0.18) (0.19)Decision_lag 1.004** 0.864** 0.991**[1 if decision was high value in t-1] (0.13) (0.11) (0.12)Hasbuyer_lag 0.310* 0.342** -0.224[1 if had buyer in t-1] (0.13) (0.11) (0.14)Reputation 100_dummy 0.495** 0.127[1 if reputation is 100% in high (all) goods] (0.14) (0.17)Lowered_reputation_dummy -0.566 -0.019[1 if reputation <100% in high (all) goods] (0.36) (0.25)Reputation100_dummy_low -0.594**[1 if reputation is 100% in low goods] (0.15)Lowered_reputation_dummy_low (dropped)[1 if reputation<100% in low goods] (no

observations)# of safe options 0.01 0.015 -0.148**[degree of risk aversion] (0.02) (0.06) (0.04)Constant -0.452 -0.575 0.54

(0.25) (0.72) (0.58)Observations 840 840 840

Standard errors in parentheses.Asterisks indicate ** p<0.01, * p<0.05

Probit – Buyer Buy Decision

TREATMENT Separate Simple No Rep. Separate Simple No Rep.Dependent Variable, Buyer’s Buy Decision [1 if __ value]

High Value Good

High Value Good

High Value Good

Low Value Good

Low Value Good

Low Value Good

period 8 -1.214** -1.379** 0.187 0.23[1 if t=8] (0.22) (0.22) (0.23) (0.22)Period 9 -1.695** -2.170** -0.626* -1.492**[1 if t=9] (0.29) (0.32) (0.29) (0.39)1/Sequence -0.753** -0.441 0.974** 1.200** 0.700** -0.329[inverse of sequence order] (0.25) (0.25) (0.37) (0.27) (0.26) (0.24)Partnercoop_lag_dummy 1.159** 1.230** 0.381 0.580* -0.278 1.046**[1 if received good in t-1] (0.22) (0.24) (0.25) (0.26) (0.27) (0.15)# of safe options 0.012 0.194* 0.105 -0.15 -0.252** -0.009[degree of risk aversion] (0.07) (0.08) (0.09) (0.10) (0.10) (0.13)Low_availability_dummy -0.863** -0.608** 0.002[1 if low value good available] (0.29) (0.23) (0.38)High_availability_dummy -2.350** -0.402 -0.261[1 if high value good available] (0.48) (0.56) (0.24)Constant 0.359 -1.383 -3.634** 1.654 1.422 -0.139

(0.85) (0.81) (0.98) (1.15) (1.05) (1.31)Observations 480 480 480 480 480 480

Note: All results are from probit models with random effects. Standard errors in parentheses.Asterisks indicate ** p<0.01, * p<0.05

Probit – Seller’s Decision to Send Good

TREATMENT Separate/Simple No ReputationDependent VariableCooperate Decision[1 if sent good]period 8 -0.664** 0.059[1 if t=8] (0.10) (0.16)Period 9 -1.911** -0.453*[1 if t=9] (0.21) (0.18)1/Sequence 0.059 -0.285[inverse of sequence order] (0.12) (0.19)Decision 0.048 -1.235**[1 if decision is high value in t] (0.08) (0.21)Decision_lag 0.097 -0.134[1 if decision was high value in t-1] (0.08) (0.17)Hasbuyer_lag -0.715 -0.464[1 if had buyer in t-1] (0.40) (0.30)Cooperate_lag 1.424** 0.379[1 if sent good in t-1] (0.40) (0.32)# of safe options 0.02 0.051[degree of risk aversion] (0.01) (0.03)Constant -0.660** -1.275**

(0.16) (0.47)Observations

• Result 3: Stronger end-period effect with reputation (“false reputation building”)

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