Question

At the end of 2019 the Metropolitan Transportation Authority (MTA) a public agency responsible for serving the commuter rail transportation needs of Boston was faced with rising operating deficits. Also, because of a fiscal austerity program at both either federal or state levels, the hope of receiving additional subsidy support was very unlikely.

The Board of Directors of MTA asked the system manager to explore alternatives to alleviate the financial plight of the system. The first suggestion made by the general manager was to institute a major cutback in service. This cutback would eliminate service after 7PM, cancel service on weekends and operate on a reduced schedule during the midday period Monday through Friday. The Board of MTA indicated that these service cutbacks were not acceptable to the Mayor and could only be considered as a last resort.

The MTA Board suggested that because it had been over five years since the last basic fare increase, an increase from the current 2019 level of $1.35 to a new level of $2.00 a ride in 2020 should be considered. Accordingly, the Board ordered the manager of research a recent graduate of the Navitas program at UMD, to conduct a study of the likely impact of this proposed fare increase.

The system manager collected data on important variables thought to have a significant impact on the demand for rides on MTA. This data has been collected over the past 33 years and includes the following variables:

1. Fare per trip (cents) symbol “FARE”.

2. Population (thousands) in the metropolitan area served by the MTA symbol “POP”.

3. Disposable income per capita ($) - symbol “INC”

4. Price per gallon of gas (cents) - symbol “PGAS”

5. Number of commuter riders per week (thousands) --symbol “RIDERS”

The transit manager having recalled a similar case in FIN 500 decided to perform a multiple regression analysis with EXCEL using the data to determine the impact of the proposed fare increase.

1. What is the dependent variable in this demand study?

2. What are the independent variables?

3. What are the expected signs of the coefficients of the independent variables in forecasting demand?

4. Using the regression analysis program in EXCEL estimate the coefficients of the independent variables using the data in Table 1.

5. Provide an economic interpretation for the value of each of the estimated coefficients.

6. What is the coefficient of determination (R2) and interpret the value? Are any coefficients statistically significant?

7. Calculate the price elasticity of demand using 2019 data. What does it mean?

8. Calculate the income elasticity of demand in 2019. Please interpret.

9. If the MTA increases the fare per trip from $1.35 to $2.00 in 2020 what is the expected impact on the number of trips per week and on weekly revenue. Assume all other values in 2020 remain at their 2019 levels.

10. Would you recommend that the fare be increased from $1.35 to $2.00 in 2020 or cut service ? Why? Explain thoroughly.

Answer 1

Part (1) FARE is the dependent variable. The dependent variable is the one that gets derived from independent variables. If y = f (a, b, c,..) then y is the dependent variable.

Part (2) Independent variables are:

POP, INC, PGAS, RIDERS

Part (3)

Dependent variable should behave positively and directly with respect to disposable income and fuel gas price. If these variables are higher, the FARE should be higher. With the other two variables, the dependence should be the other way round. If the population in the target market is higher and number of trips per commuter is higher, fare per trip per per person should be lower. Hence

• INC and PGAS are expected to have positive signs of the coefficients in forecasting demand.
• POP and RIDERS are expected to have negative signs of the coefficients in forecasting demand.

For the balance question, we need the data in Table 1. Your question doesn't contain any data or table 1. Please post the balance questions along with the data and table 1.