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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
16
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
High-value Item Low-value Item
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
No Reputation Simple Reputation
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
High-value Item Low-value Item
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
High-value Item Low-value Item
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
High-value Item Low-value Item
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
High-value Item Low-value Item
Labels are Frequency of Reneges
sequences 2-6 aggregatedPerc. Items Not Sent
No Reputation Simple Reputation
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
If Choose all Low -Value
If Choose all outside option
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
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
If Choose all Low -Value
If Choose all outside option
Periods 1-9 aggregatedEfficiency
No Reputation Simple Reputation
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
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
If Choose all Low -Value
If Choose all outside option
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
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
If Choose all Low -Value
If Choose all outside option
Periods 1-9 aggregatedEfficiency
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
No Reputation Simple Reputation
• 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
Item not Bought Sent Item
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
Item not Bought Sent Item
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
low_sent low_notsent
high_sent high_notsent
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
low_sent low_notsent
high_sent high_notsent
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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