6
Sahuguet (2006) finds capping bids in asymmetric all-pay
auctions with incomplete information increases revenue,
confirming Che and Gale (1996) and completing Gavious
et al. (2003).
There is mixed evidence on how competition affects
auctions in the oil and gas industry. Some papers find that
certain factors reduce competition, while others find the
opposite. Joint bidding, where companies bid together,
seems to increase competition according to some studies.
DeBrock &Smith (1983) found that joint bidding leads to
more aggressive bidding and higher total social value cap-
tured. Hoffman et al (1991) also found evidence that joint
bidding was consistent with competitive behavior. However,
Matoso &Rezende (2014) found that the Brazilian oil
company Petrobras had an informational advantage over
other bidders when bidding jointly, reducing competition.
The number of bidders also impacts competition, but the
findings are mixed. Hernando-Veciana (2005) showed that
with many bidders and auctioneers, optimal reserve prices
approach cost, indicating high competition. However,
Smith (1982) found that fewer bidders leads to the cap-
ture of more economic rent. Reece (1978) also suggested
that small numbers of bidders in offshore lease sales may
reduce competition. Uncertainty in the value of leases or
tracts offered also influences competition. Smith (1982)
found that higher uncertainty is related to higher competi-
tion for the most valuable tracts. However, Reece (1978)
suggested that high uncertainty in offshore lease sales could
allow bidders to capture more rent. Government policy and
regulation play a role in competition according to some
studies. Matoso &Rezende (2014) suggested that policy
changes could increase competition in Brazilian oil auc-
tions. Rodriguez &Suslick (2009) found that Brazil’s shift
to competitive auctions in 1997 increased competition.
Hoffman &Marsden (1986) found no evidence that regu-
lation of joint bidding in the U.S. increased competition.
There are many factors that determine the level of
competition in auctions for mineral rights. According to
Cramton (2009), the structure of bidder preferences and
degree of competition are key. With simple bidder values
and weak competition, a standard simultaneous first-price
sealed-bid auction can work, but more complex bidder val-
ues require dynamic auctions with package bids to promote
efficiency and revenue. Rothkopf &Engelbrecht-Wiggans
(1992) suggest novel auction designs and terms can increase
competition.
The number of bidders and auctioneers also impacts
competition. Hernando-Veciana (2005) found that with
many bidders and auctioneers, auctioneers will set low
reserve prices equal to production costs to attract bidders.
Athey &Cramton (2005) also notes that the more complex
the bidder values, the more dynamic the auction needs to
be to generate competition.
The distribution of bids impacts competition. Pelto
(1971) found that bids on a tract follow a lognormal dis-
tribution, and the ratio of the winning bid to the average
bid times the square root of bidders is also lognormal. The
consistency of the lognormal distribution’s standard devia-
tion enables evaluating bidding policies. Ming-jun (2008)
argues that because mineral rights are special, they need
special pricing mechanisms, like auctions, to determine
their price. Auctions can optimize allocation and pre-
vent loss of state-owned resources and maximize revenue.
However, Jehiel &Moldovanu (2003) warns that auctions
of complementary goods that will then compete, like min-
eral rights, can enable bidders to gain market power and
harm consumers. More flexible auctions may seem good
but can be manipulated.
Smith (1982) develops an equilibrium model showing
competition fluctuates based on the quality of prospects
offered. Competition is higher for higher-quality tracts.
The equilibrium bid configuration also depends on struc-
tural factors like capital constraints that limit competition.
Evidence supports the theory but contradicts the idea that
capital constraints reduce competition in offshore lease
sales.
The papers analyze existence and properties of equilib-
rium in Bayesian static games, provide models of compe-
tition, apply game theory to methods to analyze strategic
behavior. The results provide a theoretical foundation for
studying competition and strategic interaction under
imperfect information. Several factors determine the level
of competition in auctions for mineral rights, including
the auction design, number of bidders, distribution of
bids, and structural characteristics of the market. Overall,
competition seems highest when the mineral rights are of
high quality, bidders are numerous, and auction designs are
based on dynamic game models.
THEORY-BUILDING, MODELS AND
HYPOTHESES
For Bayesian Nash Equilibrium in static games with incom-
plete information, common value auctions of mineral
rights being their specific example, the strategies are defined
as bi(Vi), where bi is bid amounts offered by player i who
has a value function Vi, which for n-player games is derived
as bi(Vi) =(n–1)/n*Vi (Gibbons, 1997). This provides a
framework to analyze various models for impact of compe-
tition on outcomes of bidding as described below.
Sahuguet (2006) finds capping bids in asymmetric all-pay
auctions with incomplete information increases revenue,
confirming Che and Gale (1996) and completing Gavious
et al. (2003).
There is mixed evidence on how competition affects
auctions in the oil and gas industry. Some papers find that
certain factors reduce competition, while others find the
opposite. Joint bidding, where companies bid together,
seems to increase competition according to some studies.
DeBrock &Smith (1983) found that joint bidding leads to
more aggressive bidding and higher total social value cap-
tured. Hoffman et al (1991) also found evidence that joint
bidding was consistent with competitive behavior. However,
Matoso &Rezende (2014) found that the Brazilian oil
company Petrobras had an informational advantage over
other bidders when bidding jointly, reducing competition.
The number of bidders also impacts competition, but the
findings are mixed. Hernando-Veciana (2005) showed that
with many bidders and auctioneers, optimal reserve prices
approach cost, indicating high competition. However,
Smith (1982) found that fewer bidders leads to the cap-
ture of more economic rent. Reece (1978) also suggested
that small numbers of bidders in offshore lease sales may
reduce competition. Uncertainty in the value of leases or
tracts offered also influences competition. Smith (1982)
found that higher uncertainty is related to higher competi-
tion for the most valuable tracts. However, Reece (1978)
suggested that high uncertainty in offshore lease sales could
allow bidders to capture more rent. Government policy and
regulation play a role in competition according to some
studies. Matoso &Rezende (2014) suggested that policy
changes could increase competition in Brazilian oil auc-
tions. Rodriguez &Suslick (2009) found that Brazil’s shift
to competitive auctions in 1997 increased competition.
Hoffman &Marsden (1986) found no evidence that regu-
lation of joint bidding in the U.S. increased competition.
There are many factors that determine the level of
competition in auctions for mineral rights. According to
Cramton (2009), the structure of bidder preferences and
degree of competition are key. With simple bidder values
and weak competition, a standard simultaneous first-price
sealed-bid auction can work, but more complex bidder val-
ues require dynamic auctions with package bids to promote
efficiency and revenue. Rothkopf &Engelbrecht-Wiggans
(1992) suggest novel auction designs and terms can increase
competition.
The number of bidders and auctioneers also impacts
competition. Hernando-Veciana (2005) found that with
many bidders and auctioneers, auctioneers will set low
reserve prices equal to production costs to attract bidders.
Athey &Cramton (2005) also notes that the more complex
the bidder values, the more dynamic the auction needs to
be to generate competition.
The distribution of bids impacts competition. Pelto
(1971) found that bids on a tract follow a lognormal dis-
tribution, and the ratio of the winning bid to the average
bid times the square root of bidders is also lognormal. The
consistency of the lognormal distribution’s standard devia-
tion enables evaluating bidding policies. Ming-jun (2008)
argues that because mineral rights are special, they need
special pricing mechanisms, like auctions, to determine
their price. Auctions can optimize allocation and pre-
vent loss of state-owned resources and maximize revenue.
However, Jehiel &Moldovanu (2003) warns that auctions
of complementary goods that will then compete, like min-
eral rights, can enable bidders to gain market power and
harm consumers. More flexible auctions may seem good
but can be manipulated.
Smith (1982) develops an equilibrium model showing
competition fluctuates based on the quality of prospects
offered. Competition is higher for higher-quality tracts.
The equilibrium bid configuration also depends on struc-
tural factors like capital constraints that limit competition.
Evidence supports the theory but contradicts the idea that
capital constraints reduce competition in offshore lease
sales.
The papers analyze existence and properties of equilib-
rium in Bayesian static games, provide models of compe-
tition, apply game theory to methods to analyze strategic
behavior. The results provide a theoretical foundation for
studying competition and strategic interaction under
imperfect information. Several factors determine the level
of competition in auctions for mineral rights, including
the auction design, number of bidders, distribution of
bids, and structural characteristics of the market. Overall,
competition seems highest when the mineral rights are of
high quality, bidders are numerous, and auction designs are
based on dynamic game models.
THEORY-BUILDING, MODELS AND
HYPOTHESES
For Bayesian Nash Equilibrium in static games with incom-
plete information, common value auctions of mineral
rights being their specific example, the strategies are defined
as bi(Vi), where bi is bid amounts offered by player i who
has a value function Vi, which for n-player games is derived
as bi(Vi) =(n–1)/n*Vi (Gibbons, 1997). This provides a
framework to analyze various models for impact of compe-
tition on outcomes of bidding as described below.