regulation and are thus more likely to advocate policies that are favorable to incumbents.
The lack of effect on platform-based entry may reflect the low fixed costs that platform-
based entry requires, and the resulting ability of an entrant to exit a market relatively
quickly.
Estimates with Population Interactions
Coefficient estimates
As described above, the estimates in Table 3-3 and Table 3-4 and estimates in prior
studies assume that the effects of the explanatory variables do not vary across states.
However, the effects of market and political factors may well differ by the size of the
state. To account for such potential variation, the model is also estimated such that each
explanatory variable is interacted with the state population.
Table 3-5 details the coefficient estimates and t-statistics that result from including
population interactions. For each explanatory variable, two coefficients are reported: the
coefficient on that variable and the coefficient of the variable multiplied by the state
population. As such, one can interpret the second coefficient as the additional effect of
the explanatory variable as the population is increased.
For the variables that measure revenue potential, the strong effect of the average
revenue on the platform share persists when population interactions are included.
However, whereas previously average revenue did not have a statistically significant
effect in the loop share equation, the average revenue interacted with the population does.
Further, the coefficient on the uninteracted average revenue variable is larger than the
estimate in Table 3-3 and falls just short of a statistical significance level of 90%.
Market size effects also appear to be important in measuring the effects of costs on
entry share. For the loop connection charge in the loop share equation, the negative