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Vertical integration and the must carry rules in the cable television industry: An empirical analysis
Unformatted Document Text:  Must carry rules 18 System-level variables First, contrary to the main hypothesis, vertical integration had a significantly negative effect ( β = -.056, p < .01). That is, cable systems whose owners had more program interests in cable networks were less likely to deny carrying local stations. Specifically, a system owner that is vertically integrated with one more cable network increased the probability and odds of a station being carried by its system by 0.003 and 5.4% respectively, holding other variables constant. 26 This may be because firms with cable program interests, particularly those larger cable firms, are often partners of other media companies that have stakes in broadcast networks, TV stations and other broadcast interests. They thus have less incentive to refuse carrying broadcast stations on their systems. The national size of a system’s owner, LNSUB, had a significantly positive effect on DROP ( β = .096, p < .05). That is, larger cable MSOs were more likely to drop a broadcast station from their systems. On the average, a 1% increase in the number of a cable MSO’s subscribers increased the probability of a station being dropped by 0.006, holding other variables constant. In terms of odds ratio, that would increase the odds of a station being denied carriage by 10%. Larger cable MSOs have been shown to have lower marginal costs with respect to program supply and, as a result, tend to supply more of a unit change in an independent variable; that is, i i p x ∂∂ . For the logit model, (1 ) i i i i i p p p x β ∂ = − ∂ . In the current sample, the probability of DROP=1 is 0.065. Therefore, the marginal effect of i x is calculated as * 0.065(1 0.065) 0.061* i i i i p x β β ∂ = − = ∂ .The odds ratio estimates measure, for a unit change in i x , how the odds of the dependent variable occurring change. They are calculated as i e β . Note that odds ratio is a multiplicative measure. So an odds ratio greater than one means a positive effect, while a ratio between 0 and 1 implies a negative effect (Long, 1996, p. 73). 26 The results were similar when VI or VI_TOT were used instead of VI_N. Because VI_N and the interaction term (LNSUB*VI_N) have a nearly perfect correlation, this result implies that larger MSOs with program interest in more cable program networks were less likely to drop broadcast stations from their systems, although the impact was minuscule.

Authors: Yan, Zhaoxu.
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Must carry rules
18
System-level
variables
First, contrary to the main hypothesis, vertical
integration had a significantly negative effect (
β
= -.056, p < .01). That is, cable systems
whose owners had more program interests in cable networks were less likely to deny
carrying local stations. Specifically, a system owner that is vertically integrated with one
more cable network increased the probability and odds of a station being carried by its
system by 0.003 and 5.4% respectively, holding other variables constant.
26
This may be
because firms with cable program interests, particularly those larger cable firms, are often
partners of other media companies that have stakes in broadcast networks, TV stations
and other broadcast interests. They thus have less incentive to refuse carrying broadcast
stations on their systems.
The national size of a system’s owner, LNSUB, had a significantly positive effect
on DROP (
β
= .096, p < .05). That is, larger cable MSOs were more likely to drop a
broadcast station from their systems. On the average, a 1% increase in the number of a
cable MSO’s subscribers increased the probability of a station being dropped by 0.006,
holding other variables constant. In terms of odds ratio, that would increase the odds of a
station being denied carriage by 10%. Larger cable MSOs have been shown to have
lower marginal costs with respect to program supply and, as a result, tend to supply more
of a unit change in an independent variable; that is,
i
i
p
x

. For the logit model,
(1
)
i
i
i
i
i
p
p
p
x
β
=
. In the
current sample, the probability of DROP=1 is 0.065. Therefore, the marginal effect of
i
x
is calculated as
* 0.065(1 0.065)
0.061*
i
i
i
i
p
x
β
β
=
=
.The odds ratio estimates measure, for a unit change in
i
x
, how
the odds of the dependent variable occurring change. They are calculated as
i
e
β
. Note that odds ratio is a
multiplicative measure. So an odds ratio greater than one means a positive effect, while a ratio between 0
and 1 implies a negative effect (Long, 1996, p. 73).
26
The results were similar when VI or VI_TOT were used instead of VI_N. Because VI_N and the
interaction term (LNSUB*VI_N) have a nearly perfect correlation, this result implies that larger MSOs with
program interest in more cable program networks were less likely to drop broadcast stations from their
systems, although the impact was minuscule.


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