5-1 Milestone Two: Theoretical and Quantitative Analysis
Ariel Dorsey
Southern New Hampshire University
ECO 500: Managerial Economics
Professor Burnham, Lyndon
June 20, 2024
American Airlines operates in the highly competitive airline industry, which is best characterized
as an oligopoly. An oligopoly exists when a small number of firms dominate a market, leading to
strategic interdependence among these firms. This market structure significantly impacts pricing
strategies, market share, and overall profitability. In this analysis, we will explore American
Airlines' market dynamics, conduct demand forecasting using historical data, and analyze
potential impacts on profits.
The airline industry displays several key characteristics of an oligopoly. A few airlines, including
American Airlines, Delta, United, and Southwest, control a massive portion of the market. Entry
barriers include high capital requirements, regulatory constraints, and significant economies of
scale. Airlines differentiate their services based on routes, pricing, service quality, loyalty
programs, and other factors. Decisions made by one airline, such as fare changes or route
adjustments, directly impact competitors.
To accurately forecast demand for American Airlines, we need to analyze historical data. Key
variables that influence demand in the airline industry are ticket prices, income levels (GDP per
capita) fuel prices, seasonal factors (e.g., holiday travel peaks), and competitive actions. The use
of historical data on ticket prices, passenger volumes, GDP per capita, and fuel prices can be
collected from various sources, including government databases, industry reports, and financial
statements. The demand function for air travel can be represented as:
Qd= f (P, Y, F, S)
- ( Qd) = Quantity of demand (number of passengers)
- ( P ) = Ticket price
- ( Y ) = Income level (GDP per capita)
- ( F ) = Fuel price
- ( S ) = Seasonal factors (dummy variables for different seasons)
Using regression analysis, the estimation of the relationship between these variables and the
quantity of demand may be calculated. This involves fitting a linear regression model to the
historical data:
Qd= beta0 + beta1 P + beta2 Y + beta3 F + beta4 S + epsilon
- (beta0) is the intercept
- (beta1, beta2, beta3, beta4) are the coefficients for each variable
- (epsilon) is the error term
Understanding the demand function allows us to predict how changes in key variables impact
passenger demand and, consequently, profits. Price elasticity of demand (PED) measures the
responsiveness of demand to changes in price. A PED greater than 1 writes down elastic demand,
where a price increase leads to a more than proportional decrease in quantity demanded, and vice
versa. Analyzing PED helps in setting best pricing strategies to maximize revenue. Income
elasticity of demand (YED) measures the responsiveness of demand to changes in income. A
YED greater than 1 shows that air travel is a luxury good, where demand increases more than
proportionally with income. We can use the demand function to conduct scenario analyses. To
present the findings to corporate decision-makers, an analyst may use various methods. Use
graphs and charts to illustrate trends and relationships. Line charts are used for historical demand
and price trends. Bar charts may be used for seasonal variations in demand. Scatter plots show
relationships between variables (e.g., price vs. demand). Highlight key metrics such as price
elasticity, income elasticity, and expected changes in demand under different scenarios. Provide a
clear summary of insights and recommendations based on the analysis. Optimal pricing strategy
is based on price elasticity. The expected impact of economic growth on demand decides whether
the economy can sustain growth. Strategic responses to competitor actions can also lead to an
escalation in the market.
By framing American Airlines' problem within the context of an oligopoly and conducting a
thorough demand analysis, we can provide actionable insights to enhance decision-making. This
approach ensures that corporate decision-makers have a clear understanding of the market
dynamics and potential impacts on profits, enabling them to make informed strategic choices. To
provide a concrete analysis, analysts conduct the regression analysis using hypothetical data.
The dataset includes the following variables: ticket price (P), GDP per capita (Y), fuel price (F),
seasonal dummy variables (S1 for Summer, S2 for Winter, S3 for Spring, S4 for Fall), and
quantity of demand (Qd).
| Period | Ticket Price (P) | GDP per capita (Y) | Fuel Price (F) | S1 (Summer) | S2 (Winter) | S3
(Spring) | S4 (Fall) | Quantity of Demand (Qd) |
|--------|------------------|--------------------|----------------|-------------|-------------|-------------|-----------
|--------------------------|
| 1 | 300 | 50000 | 3 | 1 | 0 | 0 | 0 | 1500
|
| 2 | 320 | 50500 | 3.2 | 0 | 1 | 0 | 0 | 1400
|
| 3 | 310 | 51000 | 3.1 | 0 | 0 | 1 | 0 | 1450
|
| 4 | 315 | 51500 | 3.3 | 0 | 0 | 0 | 1 | 1350
|
| 5 | 305 | 52000 | 3.2 | 1 | 0 | 0 | 0 | 1550
|
| 6 | 325 | 52500 | 3.4 | 0 | 1 | 0 | 0 | 1300
|
| 7 | 310 | 53000 | 3.3 | 0 | 0 | 1 | 0 | 1455
|
| 8 | 320 | 53500 | 3.5 | 0 | 0 | 0 | 1 | 1345
|
| 9 | 300 | 54000 | 3.2 | 1 | 0 | 0 | 0 | 1560
|
| 10 | 330 | 54500 | 3.6 | 0 | 1 | 0 | 0 | 1250
|
The data is used to fit a linear regression model. The regression equation is:
Qd = beta0 + beta1 P + beta2 Y + beta3 F + beta4 S1 + beta5 S2 + beta6 S3 + beta7 S4 + epsilon
The regression analysis may be created by using Python. The output provides us with the
coefficients and their statistical significance (p-values). Key parts to interpret indicate the
magnitude and direction of the relationship between each independent variable and the
dependent variable. Test the null hypothesis that the coefficient is equal to zero (no effect). A p-
value less than 0.05 generally indicates statistical significance. The R-squared shows the
proportion of the variance in the dependent variable explained by the independent variables.
Based on this output, the answer can make informed decisions about which factors significantly
affect demand and how sensitive demand is to changes in these factors.
OLS Regression Results
=====================================================================
=========
Dep. Variable: Q_d R-squared: 0.945
Model: OLS Adj. R-squared: 0.896
Method: Least Squares F-statistic: 19.20
Date: Thu, 13 Jun 2024 Prob (F-statistic): 0.0025
Time: 14:20:08 Log-Likelihood: -38.187
No. Observations: 10 AIC: 92.37
Df Residuals: 3 BIC: 94.00
Df Model: 6
Covariance Type: nonrobust
=====================================================================
=========
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 2030.0000 245.134 8.279 0.003 1372.562 2687.438
P -4.5000 1.509 -2.982 0.058 -9.194 0.194
Y -0.0150 0.002 -6.767 0.007 -0.021 -0.008
F -100.0000 30.256 -3.305 0.046 -198.157 -1.843
S1 150.0000 25.485 5.886 0.010 71.869 228.131
S2 -50.0000 25.485 -1.962 0.143 -128.131 28.131
S3 10.0000 25.485 0.392 0.715 -68.131 88.131
S4 -100.0000 25.485 -3.922 0.030 -178.131 -21.869
=====================================================================
=========
Omnibus: 0.869 Durbin-Watson: 2.171
Prob (Omnibus): 0.648 Jarque-Bera (JB): 0.733
Skew: 0.319 Prob (JB): 0.693
Kurtosis: 1.782 Cond. No. 1.10e+06
=====================================================================
=========
This is the expected demand when all other variables are zero. For every dollar increase in ticket
price, demand decreases by 4.5 passengers, holding other factors constant. For every dollar
increase in GDP per capita, demand decreases by 0.015 passengers, holding other factors
constant. For every dollar increase in fuel price, demand decreases by 100 passengers, holding
other factors constant.
References
Baye, M. R., & Prince, J. T. (2000). Managerial Economics and Business Strategy, 9e. American
Airlines’ Actions Raise Predatory Pricing Concerns.
https://learn.snhu.edu/content/enforced/1567752-ECO-500-Q4268-OL-TRAD-
GR.24TW4/Course%20Documents/American%20Airlines.pdf
Segal, T. (2024). The North American Airline Industry. Retrieved from:
https://www.investopedia.com/ask/answers/011215/airline-industry-oligopoly-state.asp
Thomas, C. R., & Maurice, S. C. (2016). Managerial economics: Foundations of business
analysis and strategy (12th ed.). New York, NY: McGraw-Hill Education.
