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American Airlines Case

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1. ¿Cuáles son los determinantes de la demanda de boletos de avión? Señale el o los principales y cómo se interrelacionan entre ellos.
“La cantidad demandada de un bien o servicio se expresa como la cantidad de un bien o servicio que los consumidores están dispuestos a pagar durante un periodo de tiempo (semana, mes, año, etc.)” (Maurice, 2005)

De acuerdo a Thomas, C. R., & Maurice, S. C. (2011), página 37, la demanda para un bien o servicio está determinada por la función general de demanda basada en seis variables principales (se listan también sus identificadores):

Cantidad demandada del bien o servicio (Qd). Precio del bien o servicio (P). Ingreso de los consumidores (M). Precio de los bienes relacionados (PR). Gustos o preferencias de los consumidores (T). Precio esperado del bien o servicio en el futuro (Pe). Número de consumidores en el mercado (N).

En Estados Unidos hasta antes de la desregulación de la industria aeronáutica el precio era el mayor determinante de la demanda de boletos de avión (Michael, S. C. & Silk, A.J. 1993, pág 4); esto se confirma porque en los 10 años siguientes a la desregulación las tarifas aéreas decayeron casi un 15% y el volumen de pasajeros creció casi en 80% en el mismo periodo.

De acuerdo a lo anterior, se considera al precio, el mayor determinante de la demanda de boletos de avión y la cantidad de opciones de horarios y horas de salida de vuelos (American Airlines’ Value Pricing, Harvard Business School, pag. 4), es otra variable importante.

Para este caso, los principales determinante que se consideran son:

Precio del bien o servicio Ingreso de los consumidores (porcentaje del ingreso que el precio representa para el consumidor) Precio de los bienes sustitutos (autobús, tren) Precio de los bienes complementarios (hoteles, comida)

Todos los determinantes de la demanda están interrelacionados, ya que un incremento o una disminución de algún determinante, es factor para que la demanda suba o baje. Es decir, si el precio de un bien o servicio sube, la cantidad demanda bajará, y viceversa; al igual pasa con el ingreso de los consumidores, al tener más entrada de dinero, se tiene la posibilidad de comprar un boleto de avión, en caso contrario se busca otra opción, aquí es donde los consumidores buscan un bien o servicio substituto (puede ser tren, autobús, automóvil, etc.); con respecto a los gustos y preferencias, también son determinante para la demanda, por ejemplo, hay consumidores que temen volar y buscan otro medio de transporte, así mismo el número de consumidores en el mercado es un factor muy importante, a mayor mercado, mayor demanda, a menor mercado, menos demanda.

2. Especifique la función de demanda para transporte aéreo de pasajeros que sería adecuada para la industria aérea.

Para el caso de los boletos de avión se usará de la siguiente función de demanda:

Q = a - bPx + cM + dPs + ePr

Donde:

Px = Precio de los boletos de avión de la compañía M = ingreso de los consumidores Ps = Precio de bienes sustitutos Pr = precio de bienes complementarios

Donde a es la constante de la industria de pasajeros de aviones comerciales.
Los signos de cada factor de la función están definidos por la relación de la cantidad demandada y el determinante (Thomas, C. R., & Maurice, S. C. (2011), página 41). Ver detalles abajo.

b = ∆Qd / ∆P (negativo porque la cantidad demandada es inversamente proporcional al precio) c = ∆Qd / ∆M (positivo porque se trata de un bien normal) dc = ∆Qd / ∆Pc (negativo porque son bienes complementarios) ds = ∆Qd / ∆Ps (positivo porque son bienes sustitutos)

3. Con base en los determinantes de la demanda de transporte aéreo estime la función de demanda para una aerolínea de su interés.

De acuerdo a los siguientes datos:
Año Cantidad demanda en base pasajeros transportados (miles) Precio promedio de los boletos de Avión / hora Precio promedio de los boletos de camión/hr. Precio promedio de transporte sustituto Precio promedio de hotel y complementarios Ingreso per capita promedio/mes/miles de pesos mensuales Y X1 X2 X3 X4 X5
1981 10,032.0 3,759.4 2,956.8 897.6 2,112.0 12,672.0
1982 7,392.0 4,245.1 2,851.2 1,003.2 2,323.2 11,616.0
1983 7,392.0 4,245.1 2,745.6 1,003.2 2,323.2 11,616.0
1984 11,616.0 3,759.4 2,956.8 1,056.0 2,112.0 12,672.0
1985 7,180.8 4,245.1 2,851.2 1,003.2 2,323.2 11,616.0
1986 7,392.0 4,245.1 2,851.2 1,003.2 2,323.2 11,616.0
1987 10,560.0 3,759.4 2,956.8 1,056.0 2,112.0 12,672.0
1988 11,088.0 3,759.4 2,956.8 1,056.0 2,112.0 12,672.0
1989 9,187.2 4,245.1 2,851.2 844.8 2,112.0 12,144.0
1990 6,864.0 4,245.1 2,851.2 792.0 2,323.2 11,616.0
1991 7,286.4 4,245.1 2,851.2 792.0 2,323.2 11,616.0
1992 12,144.0 3,759.4 2,956.8 1,161.6 2,640.0 13,200.0
1993 10,560.0 3,854.4 3,062.4 950.4 2,112.0 13,200.0
1994 6,336.0 4,350.7 2,745.6 844.8 2,323.2 11,616.0
1995 6,864.0 4,350.7 2,745.6 844.8 2,323.2 11,616.0
1996 7,392.0 4,350.7 2,745.6 844.8 2,323.2 12,144.0
1997 7,180.8 3,822.7 2,745.6 792.0 2,323.2 11,616.0
1998 7,603.2 3,854.4 2,745.6 844.8 2,323.2 12,144.0
1999 7,920.0 3,854.4 2,745.6 844.8 2,270.4 12,672.0
2000 7,392.0 3,854.4 2,745.6 739.2 2,270.4 12,144.0
2001 7,286.4 3,854.4 2,745.6 739.2 2,270.4 12,144.0
2002 7,286.4 3,854.4 2,745.6 739.2 2,323.2 11,616.0
2003 7,392.0 3,854.4 2,745.6 844.8 2,323.2 11,616.0
2004 10,032.0 4,350.7 3,062.4 1,056.0 2,640.0 13,200.0

Se realizará un análisis de regresión múltiple el cual estudia la relación de una variable dependiente con dos o más variables independientes (Anderson, Sweeney y Williams, Estadística para la Administración y Economía, 10ª Edición, Cengage Learning) En donde podemos observar como las variables independientes que en este caso son los determinantes de la demanda afectan a la Demanda (Qd).
Por lo tanto, los resultados de la regresión son:
Estadísticas de la regresión
Coeficiente de correlación múltiple 0,958996006
Coeficiente de determinación R^2 0,919673339
R^2 ajustado 0,897360377
Error típico 556,890481
Observaciones 24

ANÁLISIS DE VARIANZA Grados de libertad Suma de cuadrados Promedio de los cuadrados F Valor crítico de F
Regresión 5 63912524,74 12782504,95 41,21700021 3,06895E-09
Residuos 18 5582286,141 310127,0078
Total 23 69494810,88

Coeficientes Error típico Estadístico t Probabilidad Inferior 95% Superior 95% Inferior 95,0% Superior 95,0%
Intercepción -12441,64934 5013,102689 -2,481826149 0,023165906 -22973,78725 -1909,511428 -22973,78725 -1909,511428
Precio Promedio de los Boletos de avión -1,712329326 0,62008467 -2,761444376 0,012854443 -3,015078874 -0,409579777 -3,015078874 -0,409579777
Precio promedio de los boletos de camión 4,193590335 2,03271513 2,063048714 0,05383793 -0,076985676 8,464166346 -0,076985676 8,464166346
Precio promedio de transporte sustituto 4,573844409 1,362786854 3,356243419 0,003516044 1,710735476 7,436953341 1,710735476 7,436953341
Precio promedio de hotel y complementarios -1,169462034 0,948589314 -1,232843356 0,233493788 -3,162374228 0,823450161 -3,162374228 0,823450161
Ingreso per cápita promedio/mes/miles de pesos mensuales 1,181305685 0,355629323 3,321733078 0,00379475 0,434156204 1,928455166 0,434156204 1,928455166

Donde se puede establecer con base a la tabla 2.1 página 39 del libro Thomas – Maurice, Mangerial Economics, octava edición, y a la relación arriba descrita lo siguiente:

Variable Signo del parámetro
Precio promedio de los boletos de avión (Px) b = ∆Qd / ∆P es negativo, por lo que podemos ver que la relación es correcta, ya que el resultado de esta variable en la regresión es negativa (-1,712329326)
Precio Promedio de los boleto de camión y/o precio promedio de transporte sustituto (Ps) d = ∆Qd / ∆P es positivo, por lo que podemos ver que la relación es correcta, ya que en la regresión el signo de ambas variables es positivo (4,193590335 y 4,573844409 respectivamente)
Precio promedio de hotel y complementarios (Pr) e = ∆Qd / ∆P es negativo, por lo que podemos ver que la relación es correcta, ya que el resultado de esta variable en la regresión es negativa (-1,169462034)
Ingreso per cápita promedio/mes/miles de pesos mensuales c = ∆Qd / ∆P es positivo, por lo que la relación es correcta, ya que el signo de la variable en la regresión es positivo (1,181305685)

Por lo que, basándonos en los resultados de la regresión, podemos concluir lo siguiente:

Los signos de los coeficientes están de acuerdo a lo establecido en la teoría del libro.

Analizando los datos podemos ver que R^2 nos entrega un resultado de 89.7%, lo que quiere decir que en esa proporción los datos se ajustan a la ecuación derivada de la regresión, en donde para cada variable determinante de la demanda tenemos los siguientes coeficientes:

Coeficientes
Intercepción -12441.57008
Precio promedio de los boletos de Avión / hora -1.712375386
Precio promedio de los boletos de camión/hr. 4.193522826
Precio promedio de transporte sustituto 4.573961766
Precio promedio de hotel y complementarios -1.169545582
Ingreso per cápita promedio/mes/miles de pesos mensuales 1.181337007

Los coeficientes representan la estimación del cambio en Q_d debido a un cambio en una unidad en alguna de las variables independientes (determinantes de la demanda) mientras todas las demas variables permanecen constantes. (Anderson, Sweeney, 2010)

Con el objetivo de realizar un análisis de regresión completo en necesario realizar algunas pruebas de significancia (F,t)

En la regresión múltiple estas pruebas tienen propósitos diferentes:

La prueba F se usa para determinar si existe una relación de significancia entre la variable dependiente y el conjunto de variables independientes. Se usa la prueba t para ver si cada una de las variables es significativa. (Anderson, Seeney,2010)

Si observamos los datos del análisis de varianza tenemos varias conclusiones.

ANÁLISIS DE VARIANZA Grados de libertad Suma de cuadrados Promedio de los cuadrados F Valor crítico de F
Regresión 5 63912524,74 12782504,95 41,21700021 3,06895E-09
Residuos 18 5582286,141 310127,0078
Total 23 69494810,88

Empleando un nivel significancia α=0.05, el valor-p correspondientes F= 41.21 es .000000003 al ser este valor menor a 0.05 podemos concluir que la relación de las variables es significante. En otras palabras este estadístico nos da la seguridad de que la Cantidad Demanda (Qd) está influenciada de manera determinante por el conjunto de variables independientes que definimos en la ecuación en un grado de confianza de 95%.

A continuación realizamos una prueba t, para cada uno de los determinantes de la demanda con la intención de evaluar su significancia en la ecuación de función de la demanda, para esto buscaremos que los valores sean representativos por lo menos en un 80% es decir utilizaremos α=0.2.

Observando los resultados de la regresión:

Coeficientes Error típico Estadístico t Probabilidad Inferior 95% Superior 95% Inferior 95,0% Superior 95,0%
Intercepción -12441,64934 5013,102689 -2,481826149 0,023165906 -22973,78725 -1909,511428 -22973,78725 -1909,511428
Precio Promedio de los Boletos de avión -1,712329326 0,62008467 -2,761444376 0,012854443 -3,015078874 -0,409579777 -3,015078874 -0,409579777
Precio promedio de los boletos de camión 4,193590335 2,03271513 2,063048714 0,05383793 -0,076985676 8,464166346 -0,076985676 8,464166346
Precio promedio de transporte sustituto 4,573844409 1,362786854 3,356243419 0,003516044 1,710735476 7,436953341 1,710735476 7,436953341
Precio promedio de hotel y complementarios -1,169462034 0,948589314 -1,232843356 0,233493788 -3,162374228 0,823450161 -3,162374228 0,823450161
Ingreso per cápita promedio/mes/miles de pesos mensuales 1,181305685 0,355629323 3,321733078 0,00379475 0,434156204 1,928455166 0,434156204 1,928455166

Como sabemos el estadístico t se obtiene de la división del coeficiente entre la desviación estándar (error típico), de donde obtenemos el valor-p (Probabilidad), si los valores de esta columna los comparamos con un nivel de confianza α que definimos con anterioridad podremos evaluar la significancia de cada variable.

Determinante Probabilidad α Conclusión
Precio promedio de los boletos de Avión / hora 0.01 < 0.2 Significante

Precio promedio de los boletos de camión/hr. 0.05 < 0.2 Significante

Precio promedio de transporte sustituto 0.00 < 0.2 Significante

Precio promedio de hotel y complementarios 0.23 > 0.2 No Significante
Ingreso per capita promedio/mes/miles de pesos mensuales 0.00 < 0.2

Significante

En conclusión podemos decir que las variables independientes son significantes en un 80% a excepción del Precio Promedio de Hotel y Complementarios en donde la significancia es menor.

Las variables que en mayor grado afectan la demanda serán el Precio Promedio de Transporte Sustituto y el Ingreso Per Cápita.

Bibliografía Thomas-Maurice, Managerial Economics: Foundations of Bussiness Analisys and Strategy, 10 ed, McGraw Hill “American Airlines’ Value Pricing”, Harvard Business School Anderson, Sweeney y Williams, Estadistica para la Administracion y Economia, 10ª Edicion, Cengage Learning, USA 2010

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...MBA 610 Case Study: Southwest Airlines Corporation 1. Southwest Airlines Corporation is an extremely popular and profitable company due to their strategy of being the nations’ low-fare, high customer satisfaction airline. Southwest is able to offer the nation’s lowest fares due in part to their low operating-cost structure, the lowest in the domestic airline industry. This low operating-cost structure (refer to Exhibit 1 for 5 year financial highlights and 2004 data) is the basis on which Southwest builds its competitive advantage, as it allows Southwest to sell low fare tickets while still enjoying a gross margin percentage of sales (29.2% of sales in 2004) much higher than United Airlines (22.7% of sales), American Airlines (1.9% of sales), and Delta Airlines (18.9% of sales). Southwest achieved this low cost operating structure through eschewing the traditional “hub-and-spoke” approach used by their competitors, and instead flying short haul, medium haul, and point-to-point flights, allowing for more frequent flights. As a result, about 80% of Southwest’s passengers fly non-stop and the overall passenger length is approximately 758 miles. Additionally, Southwest consistently seeks out ways to improve its efficiencies and low cost structure. For an example, at Southwest, turnaround time (from the time a plane lands until it is ready for takeoff) takes approximately 20-25 minutes and requires a ground crew of four plus two people at the gate. By comparison, turnaround times...

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