8
degree of competitive intensity in their strength or direc-
tion. The estimator of our interest is β3.
In this Moderator Model, the hypothesis that we pro-
pose is:
H3: Competitive intensity for coal block on offer influ-
ences the relationship between common value of coal block
on offer and bid amounts (and excess bids) that bidders
offer to acquire their mineral rights
Mediation Model
The number of bidders and the common values of assets in
auctions impact bidding behavior and outcomes in com-
mon value auctions, but the common values of assets are
observed to impact the number of bidders in several stud-
ies. These papers present mixed findings on how asset value
impacts bidding competitive intensity in common value
auctions. Two papers find that greater value uncertainty
leads to less competitive bidding -Mares &Shor (2003)
show theoretically and experimentally that when informa-
tion about an asset’s value is concentrated among fewer bid-
ders, bidding is less competitively intensive and revenue for
seller is lower. Similarly, Kremer (2002) finds that in the
limit, as the number of bidders grows large, the expected
winning bid converges to the expected value of the asset
given the information held by the “pivotal” bidder, sug-
gesting less intense competitive bidding when information
is concentrated. In contrast, other papers find that greater
value uncertainty can increase competitive intensity. Mares
&Shor (2003) shows theoretically and experimentally that
when bidders collude by pooling their information, bid-
ding becomes more intensive. Kagel &Levin (1999) finds
that inexperienced bidders in common value auctions are
prone to the “winner’s curse,” bidding too high given the
actual value of the asset, leading to negative profits. Public
information about asset value reduces the winner’s curse,
suggesting greater value uncertainty leads to more intensive
bidding.
We can argue that common value of asset on offer may
affect competitive intensity, which also affects the bidding
outcomes – thus, common value of assets impacting the
bidding outcome directly and indirectly through the com-
petitive intensity. This is the manifestation of mediating
model, wherein competitive intensity is suggested to be a
mediating variable for the relationship between common
value of assets and bidding outcomes in a common value
auction. In this research, we propose the mediator model to
examine how coal block values influence competitive inten-
sity, and thus, bid amounts and excess bids, directly and
indirectly, in common value auctions.
Based on these, we propose the following Mediator
Model presented in the Figure 3. The mathematical equa-
tions for the model are shown as Equation (3) and Equation
(4). The estimates are computed in two-stages, with the
estimator for relationship between common value of assets
and competitive intensity in first stage, followed by the
estimation for relationship of common value of asset and
competitive intensity with bidding outcomes. This model is
applied to coal block auctions in India, hence, the variables
are expressed for the same.
Ni =α1 +γ1 .Vi +ε’i (3)
Bi =α0 +β1 .Vi +β2 .Ňi +εi (4)
where, Bi is the bid amount and Excess Bid, in two ver-
sion of the relationship we intend to test, Vi is the value
for a mineral asset for the bidder and Ni is the degree of
competition intensity for the subject mineral asset. Ňi used
is Equation (4) is the estimated values of Ni from using
Equation (3) in first stage of computation. The Mediator
Model specifies that bid amounts are dependent variables
on the value of asset being auctioned, directly and indi-
rectly through degree of competitive intensity in their
strength or direction. The estimator of our interest is (β1 +
γ1 X β3) which indicated the total impact of common value
of assets on bidding outcomes – β1 being the estimator for
direct impact and (γ1 X β3)being the estimator for indirect
impact.
In this Mediator Model, the hypothesis that we pro-
pose is:
• H4: Competitive intensity for coal block on offer
mediates the relationship between common value
of coal block on offer and bid amounts (and excess
bids) that bidders offer to acquire their mineral rights
DATA AND DESCRIPTIVE STATISTICS
Government of India through its Ministry of Coal and stat-
utory agency called Nominated Authority started auction of
coal blocks from 2014. From December 2014 to February
2023, a total of 127 coal blocks have been successfully
Figure 3. Diagrammatic representation of mediator model
degree of competitive intensity in their strength or direc-
tion. The estimator of our interest is β3.
In this Moderator Model, the hypothesis that we pro-
pose is:
H3: Competitive intensity for coal block on offer influ-
ences the relationship between common value of coal block
on offer and bid amounts (and excess bids) that bidders
offer to acquire their mineral rights
Mediation Model
The number of bidders and the common values of assets in
auctions impact bidding behavior and outcomes in com-
mon value auctions, but the common values of assets are
observed to impact the number of bidders in several stud-
ies. These papers present mixed findings on how asset value
impacts bidding competitive intensity in common value
auctions. Two papers find that greater value uncertainty
leads to less competitive bidding -Mares &Shor (2003)
show theoretically and experimentally that when informa-
tion about an asset’s value is concentrated among fewer bid-
ders, bidding is less competitively intensive and revenue for
seller is lower. Similarly, Kremer (2002) finds that in the
limit, as the number of bidders grows large, the expected
winning bid converges to the expected value of the asset
given the information held by the “pivotal” bidder, sug-
gesting less intense competitive bidding when information
is concentrated. In contrast, other papers find that greater
value uncertainty can increase competitive intensity. Mares
&Shor (2003) shows theoretically and experimentally that
when bidders collude by pooling their information, bid-
ding becomes more intensive. Kagel &Levin (1999) finds
that inexperienced bidders in common value auctions are
prone to the “winner’s curse,” bidding too high given the
actual value of the asset, leading to negative profits. Public
information about asset value reduces the winner’s curse,
suggesting greater value uncertainty leads to more intensive
bidding.
We can argue that common value of asset on offer may
affect competitive intensity, which also affects the bidding
outcomes – thus, common value of assets impacting the
bidding outcome directly and indirectly through the com-
petitive intensity. This is the manifestation of mediating
model, wherein competitive intensity is suggested to be a
mediating variable for the relationship between common
value of assets and bidding outcomes in a common value
auction. In this research, we propose the mediator model to
examine how coal block values influence competitive inten-
sity, and thus, bid amounts and excess bids, directly and
indirectly, in common value auctions.
Based on these, we propose the following Mediator
Model presented in the Figure 3. The mathematical equa-
tions for the model are shown as Equation (3) and Equation
(4). The estimates are computed in two-stages, with the
estimator for relationship between common value of assets
and competitive intensity in first stage, followed by the
estimation for relationship of common value of asset and
competitive intensity with bidding outcomes. This model is
applied to coal block auctions in India, hence, the variables
are expressed for the same.
Ni =α1 +γ1 .Vi +ε’i (3)
Bi =α0 +β1 .Vi +β2 .Ňi +εi (4)
where, Bi is the bid amount and Excess Bid, in two ver-
sion of the relationship we intend to test, Vi is the value
for a mineral asset for the bidder and Ni is the degree of
competition intensity for the subject mineral asset. Ňi used
is Equation (4) is the estimated values of Ni from using
Equation (3) in first stage of computation. The Mediator
Model specifies that bid amounts are dependent variables
on the value of asset being auctioned, directly and indi-
rectly through degree of competitive intensity in their
strength or direction. The estimator of our interest is (β1 +
γ1 X β3) which indicated the total impact of common value
of assets on bidding outcomes – β1 being the estimator for
direct impact and (γ1 X β3)being the estimator for indirect
impact.
In this Mediator Model, the hypothesis that we pro-
pose is:
• H4: Competitive intensity for coal block on offer
mediates the relationship between common value
of coal block on offer and bid amounts (and excess
bids) that bidders offer to acquire their mineral rights
DATA AND DESCRIPTIVE STATISTICS
Government of India through its Ministry of Coal and stat-
utory agency called Nominated Authority started auction of
coal blocks from 2014. From December 2014 to February
2023, a total of 127 coal blocks have been successfully
Figure 3. Diagrammatic representation of mediator model