Must carry rules

17

relatively prosperous areas. *RETAIL*, the amount of retail sales per capita in a system’s

county, is included to test this assumption.

22

A positive effect of *RETAIL* on the

dependent variable is more consistent with the anti-competitive argument.

**Estimation **

This study used binary regression models due to the binary nature of the

dependent variable, *DROP*.

23

Specifically, the logit model was chosen because its

coefficients allow for a simpler interpretation in terms of odds ratio.

24

A variety of model specifications were considered. The models and their

Maximum Likelihood Estimation (MLE) results are summarized in Table 3. As shown,

coefficient estimates of these models were largely consistent with each other. The three

statistics presented in the last three rows of the table measure the goodness-of-fit of each

model and do not show clear superiority of one model over another. The following

section focuses on the results of the full model, Model IV.

**Results**

Table 4 lists again the logit coefficients from Model IV and offers two

interpretations of the coefficients. The first one represents the marginal effects of the

independent variables on *DROP* while the second one represents their effects on the

change in the dependent variable’s odds.

25

22

To test the theory that cable systems deny carrying local stations to monopolize local advertising

revenues, it would be more appropriate to include variables that measure the amount of local advertising

sales each cable system had. However, not only was local advertising unpopular in the late 1980s,

consistent data for those systems that did sell local ads were unavailable.

23

It is well-known that it is problematic to use linear regression models when the dependent variable is

dichotomous (see Long, 1996, pp. 39-41).

24

Probit is the other popular model for binary regression analysis. The regression results from both probit

and logit are qualitatively similar even though the coefficients may be different (Long, 1996, p. 84).

25

The marginal effect represents the partial change in the probability of the dependent variable as a result