MARKET STRUCTURE AND ENTRY: WHERE'S THE BEEF? - CEPR

etween rival outlets and the random effect ( μ iRIVAL)
is positive and significant. Note that
the introduction of unobserved heterogeneity drives the coefficient of rival outlets in S(.),
the market size function, into insignificance (p-value 0.165). As we cannot reject the Null
of no unobserved heterogeneity, we conclude that BK does indeed use rival outlets to
update its beliefs of market size.
We then estimate the model assuming that unobserved heterogeneity enters in a
linearly separable way (i.e., as part of the fixed costs of entry) and present the results in
column (3). The statistical significance of coefficients is almost uniformly weaker than
in columns (1) and (2). Estimated fixed costs of entry are notably higher (4.17) than
before. The coefficient of the random effect (s.e.) is 0.907 (0.937), and the coefficient of
existing rival outlets in S(.) 0.069 (0.227). These results, together with our belief that
unobserved heterogeneity is important, if at all, on the demand side rather than on the
cost of entry side lead us to reject this specification in favor the alternatives. Indeed, we
cannot reject the simplest formulation of column (1).
[TABLE VI HERE]
Finally, we explore the learning effect in more detail by allowing the coefficient of
rival outlets in S(.) take different values in markets with own, and no own existing
outlets. The results, reported in Table VI, are mostly in line with those in Table V; the
coefficient on pensioners now becomes significant in column 1. Importantly, they clearly
show that BK learns from McD only if it has no own existing outlets in the market.
5.4. Leader (McD) Results
All McD results are produced using MSL. As with BK, we estimate the model under
standard assumptions about the error term, and also assuming an equi-correlated error
structure. Under the standard assumptions, the follower response is simulated; with an
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