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Determinants of Demand 79

cities was brisk during years in which the Texas and Florida economies enjoyed

rapid expansion. But, during the slowdown of 2008, air travel fell between the

two cities.

Your immediate goal is to analyze demand for coach-class travel between

the cities. (The small aircraft used on this route does not accommodate first-

class seating.) You begin by writing down the following demand function:

[3.1]

This expression reads, “The number of your airline’s coach seats sold per flight

(Q) depends on (is a function of) your airline’s coach fare (P), your competi-

tor’s fare (P), and income in the region (Y).” In short, the demand function

shows, in equation form, the relationship between the quantity sold of a good

or service and one or more variables.

The demand function is useful shorthand, but does not indicate the exact

quantitative relationship between Q and P, P, and Y. For this we need to write

the demand function in a particular form. Suppose the economic forecasting

unit of your airline has supplied you with the following equation, which best

describes demand:

[3.2]

Like the demand equations in Chapter 2, Equation 3.2 predicts sales quantity

once one has specified values of the explanatory variables appearing on the

right-hand side.^3 What does the equation say about the present state of

demand? Currently your airline and your competitor are charging the same

one-way fare, $240. The current level of income in the region is 105.^4 Putting

these values into Equation 3.2, we find that

A comparison of this prediction with your airline’s recent experience shows

this equation to be quite accurate. In the past three months, the average num-

ber of coach seats sold per flight (week by week) consistently fell in the 90- to

105-seat range. Since 180 coach seats are available on the flight, the airline’s

load factor is 100/180 55.5 percent.

 100 seats.

Q 25 3(105)1(240)2(240)

Q 25 3YP°2P.

Qf 1 P, P°, Y 2.

(^3) Methods of estimating and forecasting demand are presented in Chapter 4.
(^4) This value is an indexof aggregate income—business profits and personal income—in Texas and
Florida. The index is set such that realincome (i.e., after accounting for inflation) in 2005 (the so-
called base year) equals 100. Thus, a current value of 105 means that regional income has increased
5 percent in real terms since then. In the depth of the Texas recession, the index stood at 87, a 13
percent reduction in real income relative to the base year.
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