Introduction
Theory
Experimental Design
Results
E¢ ciency in Multi-Object Auctions
with (Failed) Resale
Marco Pagnozzi
Krista Jabs Saral
June 2014
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Auctions are often followed by a resale market,
where winners can resell the objects acquired
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Auctions are often followed by a resale market,
where winners can resell the objects acquired
– Spectrum licenses
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Auctions are often followed by a resale market,
where winners can resell the objects acquired
– Spectrum licenses
– Treasury bills
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Auctions are often followed by a resale market,
where winners can resell the objects acquired
– Spectrum licenses
– Treasury bills
– Emission permits ...
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Auctions are often followed by a resale market,
where winners can resell the objects acquired
– Spectrum licenses
– Treasury bills
– Emission permits ...
How does resale a¤ect bidders’strategies,
e¢ ciency and the seller’s revenue?
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Auctions are often followed by a resale market,
where winners can resell the objects acquired
– Spectrum licenses
– Treasury bills
– Emission permits ...
How does resale a¤ect bidders’strategies,
e¢ ciency and the seller’s revenue?
Should resale be allowed?
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Auctions are often followed by a resale market,
where winners can resell the objects acquired
– Spectrum licenses
– Treasury bills
– Emission permits ...
How does resale a¤ect bidders’strategies,
e¢ ciency and the seller’s revenue?
Should resale be allowed?
What if resale is uncertain?
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Why does resale happen?
Bidders do not participate in the auction
(Milgrom, 1987; Bikhchandani & Huang, 1989)
Bidders’valuations change after the auction
(Haile, 2000, 2003)
Value uncertainty (in 1st - price auctions)
(Gupta & Lebrun, 1999; Hafalir & Krishna, 2007)
Auction price a¤ects bargaining in resale market
(Pagnozzi, 2007)
Strategic behavior: demand reduction and speculation
(Garratt & Tröger 2006; Pagnozzi, 2009, 2010)
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
In multi-object auctions, bidders often bid less than value for
marginal units to keep the auction price low
(Demand Reduction – Wilson, 1979; Ausubel & Cramton, 98)
(e.g., FCC auctions, German GSM auction, electricity)
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
In multi-object auctions, bidders often bid less than value for
marginal units to keep the auction price low
(Demand Reduction – Wilson, 1979; Ausubel & Cramton, 98)
(e.g., FCC auctions, German GSM auction, electricity)
Demand reduction reduces the seller’s revenue and yields an
ine¢ cient allocation, making bidders willing to trade
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
In multi-object auctions, bidders often bid less than value for
marginal units to keep the auction price low
(Demand Reduction – Wilson, 1979; Ausubel & Cramton, 98)
(e.g., FCC auctions, German GSM auction, electricity)
Demand reduction reduces the seller’s revenue and yields an
ine¢ cient allocation, making bidders willing to trade
Resale induces weak (low-value) bidders to speculate:
bid aggressively to win and sell to strong (high-value) bidders
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
In multi-object auctions, bidders often bid less than value for
marginal units to keep the auction price low
(Demand Reduction – Wilson, 1979; Ausubel & Cramton, 98)
(e.g., FCC auctions, German GSM auction, electricity)
Demand reduction reduces the seller’s revenue and yields an
ine¢ cient allocation, making bidders willing to trade
Resale induces weak (low-value) bidders to speculate:
bid aggressively to win and sell to strong (high-value) bidders
Resale increases strong bidders’incentive to reduce demand,
because they can purchase after the auction the units lost
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
UK 3.4GHz Auction (June 2003)
Simultaneous Ascending Auction for 15 licenses for
broadband wireless services
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
UK 3.4GHz Auction (June 2003)
Simultaneous Ascending Auction for 15 licenses for
broadband wireless services
PCCW (a Hong-Kong telecom company) was the
highest-value bidder and was expected to win 15 licenses
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
UK 3.4GHz Auction (June 2003)
Simultaneous Ascending Auction for 15 licenses for
broadband wireless services
PCCW (a Hong-Kong telecom company) was the
highest-value bidder and was expected to win 15 licenses
Red Spectrum and Public Hub were companies created for
the auction and chose to be eligible for only 1 license
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
UK 3.4GHz Auction (June 2003)
Simultaneous Ascending Auction for 15 licenses for
broadband wireless services
PCCW (a Hong-Kong telecom company) was the
highest-value bidder and was expected to win 15 licenses
Red Spectrum and Public Hub were companies created for
the auction and chose to be eligible for only 1 license
As soon as PCCW, RS and PH were the only bidders left,
PCCW reduced demand to 13 licenses to end the auction
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
UK 3.4GHz Auction (June 2003)
Simultaneous Ascending Auction for 15 licenses for
broadband wireless services
PCCW (a Hong-Kong telecom company) was the
highest-value bidder and was expected to win 15 licenses
Red Spectrum and Public Hub were companies created for
the auction and chose to be eligible for only 1 license
As soon as PCCW, RS and PH were the only bidders left,
PCCW reduced demand to 13 licenses to end the auction
PCCW’s failure to win all licenses was described as
“a surprise, ... a ga¤e,” “a costly mistake that may cost the
chance of o¤ering a nationwide service”
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
UK 3.4GHz Auction (June 2003)
Simultaneous Ascending Auction for 15 licenses for
broadband wireless services
PCCW (a Hong-Kong telecom company) was the
highest-value bidder and was expected to win 15 licenses
Red Spectrum and Public Hub were companies created for
the auction and chose to be eligible for only 1 license
As soon as PCCW, RS and PH were the only bidders left,
PCCW reduced demand to 13 licenses to end the auction
PCCW’s failure to win all licenses was described as
“a surprise, ... a ga¤e,” “a costly mistake that may cost the
chance of o¤ering a nationwide service”
... but PCCW took over RS and PH, obtaining all licenses
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Experimental literature on auctions with resale
Lange et al. (2004), Georganas (2007), Saral (2008),
Georganas & Kagel (2011), Jog & Kosmopoulou (2014)
consider single-unit auctions with resale
either "automatic" or through auction
Pagnozzi & Saral (2013), Filiz-Ozbay et al. (2013)
consider multi-object auctions with di¤erent resale forms
and valuation structures
First study of auctions when resale may exogenously fail ...
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Overview
In multi-object uniform-price auctions with asymmetric bidders
and resale through bargaining:
1
Demand reduction and speculation emerge
when the auction winner can resell
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Overview
In multi-object uniform-price auctions with asymmetric bidders
and resale through bargaining:
1
Demand reduction and speculation emerge
when the auction winner can resell
2
E¤ects of resale on e¢ ciency and seller’s revenue
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Overview
In multi-object uniform-price auctions with asymmetric bidders
and resale through bargaining:
1
Demand reduction and speculation emerge
when the auction winner can resell
2
E¤ects of resale on e¢ ciency and seller’s revenue
3
E¤ects of changing the probability of resale
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Overview
In multi-object uniform-price auctions with asymmetric bidders
and resale through bargaining:
1
Demand reduction and speculation emerge
when the auction winner can resell
2
E¤ects of resale on e¢ ciency and seller’s revenue
3
E¤ects of changing the probability of resale
4
Bargaining in resale market
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
THEORETICAL BACKGROUND
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
Model
2 units of an identical good for sale
Uniform-price auction: the 2 highest bids win, and
winner(s) pay the 3rd -highest bid for each unit
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
Model
2 units of an identical good for sale
Uniform-price auction: the 2 highest bids win, and
winner(s) pay the 3rd -highest bid for each unit
2 asymmetric (risk-neutral) bidders:
– S (strong) demands 2 units and has high value vs
– W (weak) demands 1 unit and has low value vw
Marco Pagnozzi, Krista Jabs Saral
U [30; 50]
U [10; 30]
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
Model
2 units of an identical good for sale
Uniform-price auction: the 2 highest bids win, and
winner(s) pay the 3rd -highest bid for each unit
2 asymmetric (risk-neutral) bidders:
– S (strong) demands 2 units and has high value vs
– W (weak) demands 1 unit and has low value vw
U [30; 50]
U [10; 30]
! Either S wins both units or S and W win one unit each
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
Resale Market
After the auction, if W wins a unit, he may resell it to S
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
Resale Market
After the auction, if W wins a unit, he may resell it to S
Resale takes place through bargaining
Gains from trade are vS
vW
S obtains a share α of the gains from trade
W obtains a share (1 α) of the gains from trade
(results are robust to many alternative models of resale market)
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
Resale Market
After the auction, if W wins a unit, he may resell it to S
Resale takes place through bargaining
Gains from trade are vS
vW
S obtains a share α of the gains from trade
W obtains a share (1 α) of the gains from trade
(results are robust to many alternative models of resale market)
q = exogenous probability of a resale market
(trading friction or measure of market e¢ ciency)
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
W’s Bidding Strategy
W bids up to
bW
vW + q (1
|
Marco Pagnozzi, Krista Jabs Saral
α) (E [vS j W wins]
{z
expected resale pro…t
vW )
}
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
W’s Bidding Strategy
W bids up to
bW
vW + q (1
|
α) (E [vS j W wins]
{z
expected resale pro…t
vW )
}
q = 0 (no resale): W bids vW (dominant strategy)
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
W’s Bidding Strategy
W bids up to
bW
vW + q (1
|
α) (E [vS j W wins]
{z
expected resale pro…t
vW )
}
q = 0 (no resale): W bids vW (dominant strategy)
q > 0: W speculates because of the option to resell
and bids higher than vW
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
S’s Bidding Strategy
Since W bids bW , in the auction S can
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
S’s Bidding Strategy
Since W bids bW , in the auction S can
outbid W and win 2 units, obtaining 2 (vS
Marco Pagnozzi, Krista Jabs Saral
E [bW ])
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
S’s Bidding Strategy
Since W bids bW , in the auction S can
outbid W and win 2 units, obtaining 2 (vS
E [bW ])
2nd
unit (reduce demand), win 1 unit
bid 0 for the
and then try to buy from W , obtaining
vs 0
| {z }
auction pro…t
Marco Pagnozzi, Krista Jabs Saral
+ qα (vS E [vW ])
{z
}
|
expected resale pro…t
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
S’s Bidding Strategy
Since W bids bW , in the auction S can
outbid W and win 2 units, obtaining 2 (vS
E [bW ])
2nd
unit (reduce demand), win 1 unit
bid 0 for the
and then try to buy from W , obtaining
vs 0
| {z }
+ qα (vS E [vW ])
{z
}
|
vS (1 + qα)
qαE [vW ] =
auction pro…t
expected resale pro…t
Indi¤erent type vS is such that:
2 (vS
E [vW ]
q (1
Marco Pagnozzi, Krista Jabs Saral
α) (E [vS j vS < vS ]
E [vW ]))
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
S’s Bidding Strategy
Since W bids bW , in the auction S can
outbid W and win 2 units, obtaining 2 (vS
E [bW ])
2nd
unit (reduce demand), win 1 unit
bid 0 for the
and then try to buy from W , obtaining
vs 0
| {z }
+ qα (vS E [vW ])
{z
}
|
vS (1 + qα)
qαE [vW ] =
auction pro…t
expected resale pro…t
Indi¤erent type vS is such that:
2 (vS
E [vW ]
,
q (1
vS
Marco Pagnozzi, Krista Jabs Saral
α) (E [vS j vS < vS ]
40
E [vW ]))
10q (1 + α)
1 q
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
S’s Bidding Strategy
) S reduces demand i¤ vS < vS =
Marco Pagnozzi, Krista Jabs Saral
40
10q (1 + α)
1 q
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
S’s Bidding Strategy
) S reduces demand i¤ vS < vS =
40
10q (1 + α)
1 q
When q = 0, S reduces demand i¤ vS < 40
S ’s incentive to reduce demand giving up 1 unit
is lower when he has a higher value
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
S’s Bidding Strategy
) S reduces demand i¤ vS < vS =
40
10q (1 + α)
1 q
When q = 0, S reduces demand i¤ vS < 40
S ’s incentive to reduce demand giving up 1 unit
is lower when he has a higher value
q"
) less costly to lose auction )
Marco Pagnozzi, Krista Jabs Saral
vS "
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
S’s Bidding Strategy
) S reduces demand i¤ vS < vS =
40
10q (1 + α)
1 q
When q = 0, S reduces demand i¤ vS < 40
S ’s incentive to reduce demand giving up 1 unit
is lower when he has a higher value
q"
) less costly to lose auction )
α"
)
bW #
vS "
) cheaper to outbid W
Marco Pagnozzi, Krista Jabs Saral
)
vS #
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
S’s Bidding Strategy
) S reduces demand i¤ vS < vS =
40
10q (1 + α)
1 q
When q = 0, S reduces demand i¤ vS < 40
S ’s incentive to reduce demand giving up 1 unit
is lower when he has a higher value
q"
) less costly to lose auction )
α"
)
bW #
vS "
) cheaper to outbid W
)
vS #
When α = 12 , S always reduce demand if q > 0.3
S wins 1 unit at price 0 and then buys from W
with high probability (rather than pay both units)
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
Summing up:
1
Without resale (q = 0), W bids vW and
S reduces demand if and only if vS < 40
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
Summing up:
1
Without resale (q = 0), W bids vW and
S reduces demand if and only if vS < 40
2
With resale (q
0), W bids above vW and
S always reduces demand
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Model
Bidding Strategies
Summing up:
1
Without resale (q = 0), W bids vW and
S reduces demand if and only if vS < 40
2
With resale (q
0), W bids above vW and
S always reduces demand
Due to uncertainty of resale, risk-averse bidders
speculate and reduce demand less (with resale)
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
EXPERIMENTAL DESIGN
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
vW
U [10; 30]; vS
U [30; 50]
(same values for all treatments)
Subjects are randomly assigned to S or W
and do not change role
Each subject participates in only 1 treatment
Bidders are randomly rematched each period
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Uniform-Price Ascending Clock Auction
Bidders choose when to drop out of the auction
as the price increases
When one bidder drops out, the auction is over
(# of units on sale = # of units demanded)
Winner(s) pay the dropout price for each unit
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Uniform-Price Ascending Clock Auction
Bidders choose when to drop out of the auction
as the price increases
When one bidder drops out, the auction is over
(# of units on sale = # of units demanded)
Winner(s) pay the dropout price for each unit
Resale Market
Unstructured bargaining: multiple o¤ers, chat and exit option
Exogenous probability determines if bidders enter resale
when W wins a unit
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Treatments - between subjects design
1. No Resale: after the auction, there is no resale market
2. 30% Resale: if W wins the auction,
bidders participate in resale with 30% probability
3. 50% Resale: if W wins the auction,
bidders participate in resale with 50% probability
4. 100% Resale: if W wins the auction,
bidders participate in resale with certainty
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Session Information
3 sessions of 16 subjects per treatment
(48 subjects per treatment)
All sessions had at least 20 periods, maximum 30
(# periods di¤er because of bargaining & 2 hour limit)
All 12 sessions were run in the xs/fs laboratory at FSU
Spring 2011-Fall 2012; Fall 2013-Spring 2014
Mostly undergraduate subjects
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
RESULTS
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
Weak Bidding Hypotheses
No Resale: W bids value
Resale (q > 0): W bids above value
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Without resale, W tends to bid value
0
10
20
30
40
No Resale
10
15
20
25
30
Does W speculate more with resale?*
40
30
20
10
0
0
10
20
30
40
50
30% Resale
50
No Resale
bid
10
15
20
25
30
10
20
25
30
25
30
40
30
20
10
0
0
10
20
30
40
50
100% Resale
50
50% Resale
15
10
15
20
25
30
10
v alue
* Fewer observations with resale because of W winning more and fewer periods
15
20
Bidding by W – Random E¤ects Tobit
(unobserved bids censored at the auction price)
W’s Bid
Constant
vw
30% Resale
50% Resale
100% Resale
vw 30% Resale
vw 50% Resale
vw 100% Resale
Period
Coe¢ cient
1.20
0.99***
3.89**
4.77**
6.20**
–0.07
–0.16**
–0.23**
–0.03
*** and ** indicate statistical signi…cance at 1% and 5%, bootstrapped standard errors
Bidding by W – Random E¤ects Tobit
(unobserved bids censored at the auction price)
W’s Bid
Constant
vw
30% Resale
50% Resale
100% Resale
vw 30% Resale
vw 50% Resale
vw 100% Resale
Period
Coe¢ cient
1.20
0.99***
3.89**
4.77**
6.20**
- Bids are higher with resale
- E¤ect weaker with more uncertainty
–0.07
–0.16**
–0.23**
–0.03
*** and ** indicate statistical signi…cance at 1% and 5%, bootstrapped standard errors
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
Strong Bidding Hypotheses
No resale: S reduces demand (bidding zero) if vS < 40,
S does not reduce demand if vS > 40
Resale (q
0): S always reduces demand
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
Demand Reduction by S:
% S’s bids 2
v s < 40 v s > 40
No Resale
30% Resale
50% Resale
100% Resale
29.9
43.2
44.8
48.1
12.4
34.8
37.5
49.5
% W Wins
v s < 40 v s > 40
51.9
74.2
73.7
74.0
25.1
55.7
61.8
70.5
% of all auctions where vs < 40 or vs > 40 for both bids and wins
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Without resale, S reduces demand more when v s < 40
0
10
20
30
40
50
No Resale
30
35
40
45
50
With resale, S reduces demand more, for all values
but uncertainty reduces demand reduction
40
30
20
10
0
0
10
20
30
40
50
30% Resale
50
No Resale
bid
30
35
40
45
50
30
40
45
50
45
50
40
30
20
10
0
0
10
20
30
40
50
100% Resale
50
50% Resale
35
30
35
40
45
50
v alue
30
35
40
Bidding by S – Random E¤ects Tobit
(unobserved bids censored at the auction price)
Strong Bid
Constant
vs
vs >40
30% Resale
50% Resale
100% Resale
vs >40 30% Resale
vs >40 50% Resale
vs >40 100% Resale
Period
Coe¢ cient
5.26
0.58***
4.99*
–6.71**
–7.31**
–10.34***
–4.59
–5.75*
–9.71***
–0.41***
*** and ** indicate statistical signi…cance at 1% and 5%
Bidding by S – Random E¤ects Tobit
(unobserved bids censored at the auction price)
Strong Bid
Constant
vs
vs >40
30% Resale
50% Resale
100% Resale
vs >40 30% Resale
vs >40 50% Resale
vs >40 100% Resale
Period
Coe¢ cient
5.26
0.58***
4.99*
–6.71**
–7.31**
–10.34***
–4.59
–5.75*
–9.71***
–0.41***
*** and ** indicate statistical signi…cance at 1% and 5%
- Without resale, S bids higher
when he has a high value
Bidding by S – Random E¤ects Tobit
(unobserved bids censored at the auction price)
Strong Bid
Constant
vs
vs >40
30% Resale
50% Resale
100% Resale
vs >40 30% Resale
vs >40 50% Resale
vs >40 100% Resale
Period
Coe¢ cient
5.26
0.58***
4.99*
–6.71**
–7.31**
–10.34***
–4.59
–5.75*
–9.71***
–0.41***
*** and ** indicate statistical signi…cance at 1% and 5%
- Bids are lower with resale
especially with less uncertainty
Bidding by S – Random E¤ects Tobit
(unobserved bids censored at the auction price)
Strong Bid
Constant
vs
vs >40
30% Resale
50% Resale
100% Resale
vs >40 30% Resale
vs >40 50% Resale
vs >40 100% Resale
Period
Coe¢ cient
5.26
0.58***
4.99*
–6.71**
–7.31**
–10.34***
–4.59
–5.75*
–9.71***
–0.41***
*** and ** indicate statistical signi…cance at 1% and 5%
- Lower bids at high values
when resale is less uncertain
Bidding by S – Random E¤ects Tobit
(unobserved bids censored at the auction price)
Strong Bid
Constant
vs
vs >40
30% Resale
50% Resale
100% Resale
vs >40 30% Resale
vs >40 50% Resale
vs >40 100% Resale
Period
Coe¢ cient
5.26
0.58***
4.99*
–6.71**
–7.31**
–10.34***
–4.59
–5.75*
–9.71***
–0.41***
*** and ** indicate statistical signi…cance at 1% and 5%
- Signi…cant learning by S
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
E¢ ciency and Revenue Hypotheses
Auction e¢ ciency is lower with resale than without
(because of demand reduction)
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
E¢ ciency and Revenue Hypotheses
Auction e¢ ciency is lower with resale than without
(because of demand reduction)
Final e¢ ciency depends on the amount of demand reduction
and the probability of resale
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
E¢ ciency and Revenue Hypotheses
Auction e¢ ciency is lower with resale than without
(because of demand reduction)
Final e¢ ciency depends on the amount of demand reduction
and the probability of resale
Seller’s revenue is lower with resale than without
(because of demand reduction)
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Auction E¢ ciency: Frequency of S winning 2 units
! resale reduces e¢ ciency because of demand reduction and speculation
30 35 40 45 50
30 35 40 45 50
v alue
.8
1
100% Resale
0
.2
.4
.6
.8
.6
.2
0
0
.2
.4
.6
.8
1
50% Resale
1
30% Resale
.4
.6
.4
.2
0
relative frequency
.8
1
No Resale
30 35 40 45 50
30 35 40 45 50
Final E¢ ciency: Resale increases e¢ ciency after the auction
! ambiguous e¤ect on …nal e¢ ciency
30 35 40 45 50
30 35 40 45 50
v alue
.8
1
100% Resale
0
.2
.4
.6
.8
.6
.2
0
0
.2
.4
.6
.8
1
50% Resale
1
30% Resale
.4
.6
.4
.2
0
relative frequency
.8
1
No Resale
30 35 40 45 50
30 35 40 45 50
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
Auction E¢ ciency: winner’s value/S’s value
Resale Prob.
0%
30%
50%
100%
Auction E¢ ciency
.82
.69
.68
.65
.82
.75
.82
.95
.75
.75
.75
.75
(average)
Final E¢ ciency
(average)
Random E¢ ciency
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
FInal E¢ ciency: …nal owner’s value/S’s value
Resale Prob.
0%
30%
50%
100%
Auction E¢ ciency
.82
.69
.68
.65
.82
.75
.82
.95
.75
.75
.75
.75
(average)
Final E¢ ciency
(average)
Random E¢ ciency
100% Resale
0% Resale
Marco Pagnozzi, Krista Jabs Saral
50% Resale
30%Resale
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
Seller’s Revenue:
– Resale reduces revenue because it induces S to reduce demand
(when S wins revenue is higher because of speculation by W )
Resale Prob.
0%
30%
50%
100%
Average Revenue
Revenue (W wins)
Revenue (S wins)
14.6
8.0
18.8
11.2
6.6
17.2
10.0
6.0
18.6
8.4
5.2
17.2
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
E¤ects of changing the probability of resale on success of resale:
30% Resale
50% Resale
100% Resale
Resale Possible
(W won)
Successful
Resale
Exogenous
Failure
Endogenous
Failure
65%
68%
73%
19%
42%
79%
75%
47%
0%
6%
11%
21%
Subjects agree to trade more often when resale is less likely
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
Average O¤er, Resale Price, Resale Earnings
First O¤er
Last O¤er
Weak / Strong
Weak / Strong
30% Resale
34 / 21
28 / 25
50% Resale
34 / 20
28 / 25
100% Resale
34 / 19
29 / 25
Resale Price
Earnings
(Auction Price)
Weak / Strong
26
7 / 12
27
7 / 12
27
8 / 12
(11)
(10)
(8)
No signi…cant di¤erences across treatments
S earns more than W in resale
Both S and W improve o¤ers through bargaining
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
Total Earnings: Auction + Resale Pro…ts
Average
Total Earnings
No Resale
30% Resale
50% Resale
100% Resale
15.83
Weak Bidder
4.95
9.86
12.02
(std. dev)
(8.838)
(11.171)
(12.134)
(12.620)
Strong Bidder
38.64
37.73
40.45
44.62
(std. dev)
(16.911)
(16.122)
(16.260)
(17.147)
W always obtains higher pro…ts with resale
S obtains higher pro…ts with resale when it’s more certain
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale
Introduction
Theory
Experimental Design
Results
Weak Bidding
Strong Bidding
E¢ ciency and Revenue
Resale Market
Conclusions
Experiments of multi-object auction with resale and
asymmetric bidders
uncertain resale through bargaining
Without resale, strong bidders with low values reduce demand less
With resale (even when uncertain), weak bidders speculate and
strong bidders reduce demand more frequently
Resale reduces auction e¢ ciency and seller’s revenue
Very uncertain resale decreases …nal e¢ ciency
(compared to an auction without resale)
Strong bidders earn more than bidders in resale
Marco Pagnozzi, Krista Jabs Saral
E¢ ciency in Multi-Object Auctions with (Failed) Resale