This project was completed as part of my Economic Analysis for Managers MBA class. The purpose of the project was to conduct a regression analysis for the airline industry.

1. Joseph J. Giarmo III
Economic Analysis for Managers
MBA 679
October 14, 2008

2. Develop an economic regression model for average United

States domestic passenger airfares.
Explain the price of airfares through the identification of

independent variables that have a causal relationship with the
dependent variable.

3. The airline industry (worldwide) consists of:

◦ 2,000 airlines
◦ 23,000 aircraft
◦ 3,700 airports
The U.S. accounts for 1/3rd of the world’s total air traffic

In 2006, U.S. airlines carried 754 million passengers

compared to the over 2 billion passengers that were carried
worldwide

4. World Economy

Government regulation

Global events

Fuel prices

Terrorism

Supply & Demand


5. Airlines have restructured The result:

Airlines have the capability

Increased demand for fuel-

to carry 20.4% more
efficient aircraft
passengers
Modification of existing

Aircraft use 3% fewer

aircraft
gallons of fuel than in 2000
Reduced aircraft weight
 $5 billion profit in 2007


6. In 2007, inflation adjusted (real) airfares fell 1.4%

Growth Rates (1978-present): Unadjusted terms

◦ Airfares: 53%
◦ Milk: 154%
◦ New vehicles: 345%
◦ Single-family homes: 345%
◦ Prescription drugs: 499%
◦ Public college tuition: 799%
The decrease in airfares and their low growth rate has been due to:

◦ Economic deregulation
◦ Competitive markets
◦ Advances in technology
◦ More efficient operations

7. Deregulation

◦ Open sky agreements
◦ Elimination of traffic rights restrictions
◦ Competitive air travel market
Demand for fuel-efficient planes

◦ Due to increased fuel prices
◦ Every $10 increase in a barrel of crude oil = $3.4 billion cost for the
airline industry
Mergers

◦ To generate value for the airlines, their shareholders, and their
employees
◦ Northwest Airlines and Delta Airlines

8. Dependent Variable: Average U.S. Domestic Passenger Airfares
Based on fares reported from the United States top 100 airports

o This excludes Alaska, Hawaii, and Puerto Rico
Airfares are measured per ticket and are based on domestic itinerary

fares, round-trip, or one-way for which no return is purchased
Airfares include taxes and applicable fees but do not include frequent flyer

fares and unusually high reported fares
Fares are reported on a quarterly basis by the U.S. Department of

Transportation: Bureau of Transportation Statistics (BTS)

9. Airfares ($)
100
150
200
250
300
350
400
50
0
Mar-95
Sep-95
Mar-96
Sep-96
Mar-97
Sep-97
Mar-98
Sep-98
Mar-99
Sep-99
Mar-00
Sep-00
Mar-01
Sep-01
Date
Mar-02
Sep-02
Mar-03
Sep-03
Mar-04
Sep-04
Mar-05
Sep-05
Average U.S. Domestic Passenger Airfares
Mar-06
Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation Statistics (BTS)
Sep-06
Mar-07
Sep-07
Mar-08

10. Labor Costs

Food and Beverage Costs

Fuel Costs

Other Operating Expenses

Seasonal Dummy Variables


11. 9/11

Professional Services

Landing Fees

Aircraft Insurance

Non-Aircraft Insurance

Passenger Commissions

Advertising and Promotion


12. Independent Variables Null Hypotheses (Ho) Alternative Hypotheses
(H1)
B≤0 B>0
Labor Costs
B≤0 B>0
Food/Beverage Costs
B≤0 B>0
Fuel Costs
B≤0 B>0
Other Operating Expenses
B≤0 B>0
Q1
B≤0 B>0
Q2

13. Is the model Logical?
Are the slope terms significantly positive or negative?
What is the explanatory power of the model?
Does serial correlation exist?
Does multicollinearity exist?

14. Coefficients Standard Error t Stat P-Value
Intercept 149.472 23.354 6.400 0.000
Labor 0.010 0.002 4.722 0.000
Fuel 0.004 0.001 4.021 0.000
Other Operating 0.009 0.003 2.630 0.012
Exp.
Food/Beverage 0.074 0.028 2.618 0.012
Q1 14.825 4.479 3.310 0.002
Q2 11.147 4.396 2.536 0.015
Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation Statistics (BTS), the Air
Transport Association, and Microsoft Excel

15. For labor costs, reject Ho because |4.72| > 1.684

For fuel costs, reject Ho because |4.02| > 1.684

For other operating expenses, reject Ho because |2.63| > 1.684

For food and beverage costs, reject Ho because |2.61| > 1.684

For Q1, reject Ho because |3.31| > 1.684

For Q2, reject Ho because |2.53| > 1.684


16. Multiple R .763
R Square .583
Adjusted R Square .528
Standard Error 12.896
Durbin Watson .66
Observations 53
Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation
Statistics (BTS), the Air Transport Association, and Microsoft Excel

17. Test Value of the Calculated DW Result Satisfied/Unsatisfied
1 (4-1.175) < .66 < 4 Negative serial Unsatisfied
correlation exists
2 (4-1.854) < .66 < (4-1.175) Result is Unsatisfied
indeterminate
3 2 < .66 < (4-1.854) No serial correlation Unsatisfied
exists
4 1.854 < .66 < 2 No serial correlation Unsatisfied
exists
5 1.175 < .66 < 1.854 Result is Unsatisfied
indeterminate
6 0 < .66 < 1.175 Positive serial Satisfied
correlation exists
Source: Table 4-3 and Table 4-4 from Managerial Economics: An Economic Foundation for Business Decisions

18. Labor Fuel Other Operating Food/Beverage
Exp.
Labor Costs 1
Fuel Costs 0.057 1
Other Operating - 0.106 0.154 1
Exp.
Food/Beverage 0.145 -0.618 0.048 1
Costs
Source: Data provided by the Air Transport Association and Microsoft Excel

19. Actual Airfares ($) vs. Predicted Airfares ($)
400
350
300
250
Airfares ($)
200
150 Actual Airfares ($)
100 Predicted Airfares ($)
50
0
Aug-01
Feb-98
Jun-00
Feb-05
Jun-07
Dec-96
Jul-97
Sep-98
Jan-01
Dec-03
Jul-04
Sep-05
Jan-08
Mar-95
Apr-99
Nov-99
Mar-02
Apr-06
Nov-06
Oct-95
Oct-02
May-96
May-03
Date
Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation Statistics (BTS) and
Microsoft Excel

20. The model is useful but should be used with caution

Why?
 Positive serial correlation exists
There are likely many more independent variables that

could and should be considered
The airline industry is vulnerable to many external and

internal factors making it a somewhat unpredictable
industry