United States Domestic AirfaresJoseph J. Giarmo III

Economic Analysis for Managers

MBA 679

October 14, 2008

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.

Project Purpose

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

Literature Review

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

Factors Affecting the Airline Industry

The Effects of High Fuel Prices Airlines have restructured

Increased demand for fuel-efficient aircraft

Modification of existing aircraft

Reduced aircraft weight

The result:

Airlines have the capability to carry 20.4% more passengers

Aircraft use 3% fewer gallons of fuel than in 2000

$5 billion profit in 2007

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

Airfares

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

Airline Industry Trends

Dependent Variable: Average U.S. Domestic Passenger Airfares

Based on fares reported from the United States top 100 airportso 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)

Overview of the Data

Average U.S. Domestic Passenger Airfares

Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation Statistics (BTS)

Mar-9

5

Aug-9

5

Jan-

96

Jun-

96

Nov-9

6

Apr-9

7

Sep-

97

Feb-

98

Jul-9

8

Dec-9

8

May-9

9

Oct-9

9

Mar-0

0

Aug-0

0

Jan-

01

Jun-

01

Nov-0

1

Apr-0

2

Sep-

02

Feb-

03

Jul-0

3

Dec-0

3

May-0

4

Oct-0

4

Mar-0

5

Aug-0

5

Jan-

06

Jun-

06

Nov-0

6

Apr-0

7

Sep-

07

Feb-

080

50

100

150

200

250

300

350

400

Average U.S. Domestic Passenger Airfares

Date

Air

fare

s ($

)

Labor Costs

Food and Beverage Costs

Fuel Costs

Other Operating Expenses

Seasonal Dummy Variables

Independent Variables

9/11

Professional Services

Landing Fees

Aircraft Insurance

Non-Aircraft Insurance

Passenger Commissions

Advertising and Promotion

Other Independent Variables Considered

Independent Variables Null Hypotheses (Ho) Alternative Hypotheses (H1)

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 B ≤ 0 B > 0

Hypotheses to be Tested

Evaluating a Regression Model

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?

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 Exp.

0.009 0.003 2.630 0.012

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

Regression Statistics

Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation Statistics (BTS), the Air Transport Association, and Microsoft Excel

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

Test of the Null Hypotheses

Other Regression Statistics

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

Test Value of the Calculated DW Result Satisfied/Unsatisfied

1 (4-1.175) < .66 < 4 Negative serial correlation exists

Unsatisfied

2 (4-1.854) < .66 < (4-1.175) Result is indeterminate

Unsatisfied

3 2 < .66 < (4-1.854) No serial correlation exists

Unsatisfied

4 1.854 < .66 < 2 No serial correlation exists

Unsatisfied

5 1.175 < .66 < 1.854 Result is indeterminate

Unsatisfied

6 0 < .66 < 1.175 Positive serial correlation exists

Satisfied

Evaluation of the Durbin Watson Statistic (DW)

Source: Table 4-3 and Table 4-4 from Managerial Economics: An Economic Foundation for Business Decisions

Labor Fuel Other Operating Exp.

Food/Beverage

Labor Costs 1

Fuel Costs 0.057 1

Other Operating Exp.

- 0.106 0.154 1

Food/Beverage Costs

0.145 -0.618 0.048 1

Correlation Matrix

Source: Data provided by the Air Transport Association and Microsoft Excel

Actual Airfares ($) vs. Predicted Airfares ($)

Mar-9

5

Oct-9

5

May-9

6

Dec-9

6

Jul-9

7

Feb-

98

Sep-

98

Apr-9

9

Nov-9

9

Jun-

00

Jan-

01

Aug-0

1

Mar-0

2

Oct-0

2

May-0

3

Dec-0

3

Jul-0

4

Feb-

05

Sep-

05

Apr-0

6

Nov-0

6

Jun-

07

Jan-

080

50

100

150

200

250

300

350

400

Actual Airfares ($) vs. Predicted Airfares ($)

Actual Airfares ($)Predicted Airfares ($)

Date

Air

fare

s ($

)

Source: Data provided by the U.S. Department of Transportation: Bureau of Transportation Statistics (BTS) and Microsoft Excel

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

Summary and Conclusions