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- 6.5.1. Average Price Elasticities for Corrected and Uncorrected Models
- 6.5.2. Price Elasticities as a Function of Advance Booking
6.5. Methodology and ResultsRegression models were run on daily bookings at the flight-level, across 21 departure dates (3,952 observations). In order to correct for price endogeneity, 2SLS was used with a set of valid instruments. Table 6.6 below provides variable definitions for all variables, and the last three rows of the table are the instrumental variables. The price variable is the one-way price captured from JetBlue’s website. The set of instruments includes three variables. The main instrument is based on Hausman-type price instruments, which uses a firm’s own prices in other markets as instruments for a market of interest (Hausman, 1996; Hausman, Leonard and Zona, 1994). We build these instruments by using JetBlue’s equivalent one-way price from the OTA website (round-trip prices divided by two). The second instrument is based on Stern (1996), which introduces measures of the level of market power by multiproduct firms and measures of the level of competition as instruments. Based on Stern’s approach we use the number of daily flights in a market as a proxy for multiproduct firms. The third instrument is the square of the number of days from departure that a flight is booked. In order to compare OLS to 2SLS coefficient estimates, all observations missing an instrumental variable were dropped. This decreased the total number of bookings by 2.3%, for a total number of bookings of 7,352. Table 6.7 below shows the results of the OLS and 2SLS regressions; both use robust standard errors clustered by market. Notice that the price coefficient for the 2SLS regression becomes more negative, as expected. Another point of interest is that many of the coefficient estimates in the OLS regression are insignificant. However, after correcting for endogeneity, most of the coefficient estimates become significant. The set of instruments used were tested against the three tests discussed in Chapter 5, Section 4, and requirements of all tests were satisfied. The test for weak instruments, rejected the null hypothesis that instruments are weak, with a p-value of 0.04. The adjusted R-square of the first stage regression on price is 0.49. The null hypothesis that price is actually an exogenous regressor was rejected, with a p-value of 0.006, and the test for instrument validity did not reject the null hypothesis that the instruments are not valid, with a p-value of 0.10. Table 6.6: Variables and Descriptions
Table 6.7: OLS and 2SLS Regression Results
Note: Both models use robust standard errors, clustered by market. 6.5.1. Average Price Elasticities for Corrected and Uncorrected ModelsTable 6.8 shows the comparison between the price elasticities of demand estimated by the OLS and 2SLS regression models. For the OLS regression model, the estimated price elasticity of demand evaluated at the mean price is 0.64, which represents inelastic demand. After correcting for endogeneity using 2SLS, the estimated price elasticity of demand is 1.84, which represents elastic demand. This difference is important, as pricing recommendations differ for inelastic and elastic models. Specifically, inelastic models suggest that prices should be raised whereas elastic models suggest prices should be lowered. Evaluating the price elasticities at the median price gives similar results, as shown in Table 6.9. Table 6.8: OLS and 2SLS Price Elasticity Results (At the Mean of Price)
Note: Price elasticities are calculated over the means of all variables. Table 6.9: OLS and 2SLS Price Elasticity Results (At the Median of Price)
Note: Price elasticities are calculated over the means of all non-price variables. 6.5.2. Price Elasticities as a Function of Advance BookingPrice elasticities were calculated from the 2SLS model as a function of number of days from flight departure. Table 6.10 provides the price elasticities of demand at both the mean of price and also the median of price. The table shows that JetBlue’s customers are less price sensitive closer to flight departure. This is intuitive, as leisure passengers generally book further in advance of departure and business passengers often book closer to departure. Table 6.10: 2SLS Price Elasticity Results as a Function of Days from Departure
Note: DFD=Days from Flight Departure Directory: bitstream -> handle -> 1853 handle -> The Economics of Technology Sharing: Open Source and Beyond Josh Lerner and Jean Tirole handle -> The Rise of Digital Curation and Cyberinfrastructure: From Experimentation to Implementation, and Maybe Integration handle -> Mobile augmented reality applications for library services handle -> Libraries, Archives, and Museums as Epistemic Infrastructure1 Download 1.47 Mb. Share with your friends:
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