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
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