# In A Study Of The Determinants Of Direct Airfares To

In a study of the determinants of direct airfares to Cleveland, Paul W. Bauer and Thomas J. Zlatoper obtained the following regression results (in tabular form) to explain one-way airfare for first class, coach, and discount airfares. (The dependent variable is one-way airfare in dollars). The explanatory variables are defined as follows:
Carriers = the number of carriers
Pass = the total number of passengers flown on route (all carriers)
Miles = the mileage from the origin city to Cleveland
Pop = the population of the origin city
Inc = per capita income of the origin city
Corp = the proxy for potential business traffic from the origin city
Slot = the dummy variable equaling 1 if the origin city has a slot-restricted airport
= 0 if otherwise
Stop = the number of on-flight stops
Meal = the dummy variable equaling 1 if a meal is served
= 0 if otherwise
Hub = the dummy variable equaling 1 if the origin city has a hub airline
= 0 if otherwise
EA = the dummy variable equaling 1 if the carrier is Eastern Airlines
= 0 if otherwise
CO = the dummy variable equaling 1 if the carrier is Continental Airlines
= 0 if otherwise
The results are given in Table 6-11.
a. What is the rationale for introducing both carriers and squared carriers as explanatory variables in the model? What does the negative sign for carriers and the positive sign for carriers squared suggest?
b. As in part (a), what is the rationale for the introduction of miles and squared miles as explanatory variables? Do the observed signs of these variables make economic sense?
DETERMINANTS OF DIRECT AIR FARES TO c. The population variable is observed to have a negative sign. What is the implication here?
d. Why is the coefficient of the per capita income variable negative in all the regressions?
e. Why does the stop variable have a positive sign for first-class and coach fares but a negative sign for discount fares? Which makes economic sense?
f. The dummy for Continental Airlines consistently has a negative sign. What does this suggest?
g. Assess the statistical significance of each estimated coefficient. Since the number of observations is sufficiently large, use the normal approximation to the t distribution at the 5% level of significance. Justify your use of one-tailed or two-tailed tests.
h. Why is the slot dummy significant only for discount fares?
i. Since the number of observations for coach and discount fare regressions is the same, 323 each, would you pull all 646 observations and run a regression similar to the ones shown in the preceding table? If you do that, how would you distinguish between coach and discount fare observations?
j. Comment on the overall quality of the regression results given in the preceding table.

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