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When Do Firms Offer Higher Product Quality? Evidence from the Allocation of Inflight Amenities

“The availability of Wi-Fi influenced the flight selection process for a majority (66 percent) of those surveyed...and 17 percent have switched from their “preferred" airline to another airline where the odds of a Wi-Fi enabled flight were higher."

Honeywell Aerospace survey (Rabinowitz, 2014)


We examine how competition impacts the provision of product quality. Using a unique data set of inflight amenities provided by U.S. airlines, we find that the composition of competition matters. There is significantly higher product quality - Wi-Fi, entertainment, and at-the-seat electrical power outlets - on on more competitive routes (with lower HHI). The presence of Southwest Airlines on the route, however, is shown to reduce product quality offerings. We also find significantly higher product quality on routes with more passengers, tourist destinations, and red-eye flights. We find lower posted base ticket prices on routes with Wi-Fi and entertainment amenities.

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  1. For example, Mazzeo (2003), Rupp (2009) and Gil and Kim (2021) show that increased competition leads to better on-time performance. On the other hand, Prince and Simon (2015) document worse on-time performance by the network carrier on routes with LCC competition.

  2. See accessed on 21 October 2022.

  3. Seat pitch is the measurement of space between one point on an aircraft passenger seat and the same point on the seat in front of it. In coach class, the industry standard is a seat pitch of 31 inches.

  4. The decision of what to include in the base product and what to charge for extra extends beyond the airline industry. For example, hotels need to decide whether to provide Wi-Fi for free or charge for extra [e.g., Lin (2017)]. Rental car companies also charge unlimited miles and drop-off at different locations etc. Also, see Ellison (2005), Santana et al. (2020) and the papers cited there for literature on add-on pricing and drip pricing.

  5. See, for example, Mazzeo (2003), Rupp (2009), Prince and Simon (2015) and Gil and Kim (2021).

  6. The historical on-time performance of the flight is observed but not the realized performance at the time of booking. See Forbes et al. 2019 for a discussion of schedule padding.

  7. See Rupp and Tan (2019), Gayle and Yigma (2018), Bishop et al. (2011), and Mazzeo (2003).

  8. We exclude smaller LCCs since they had no variation in the Wi-Fi amenity as neither Spirit nor Frontier offered Wi-Fi aircraft in our sample, while every Virgin America flight in our sample had Wi-Fi.

  9. The exclusion of Southwest Airline fares from online travel agency data and limiting the sample to direct non-stop flights cause some of the top 500 routes to be dropped from the sample.

  10. The variable WN does not include the presence of Southwest at adjacent or nearby airports. In a different specification we also control for route fixed effects.

  11. During our sample period, the Federal Aviation Administration (FAA) limits scheduled air traffic at four airports– Chicago O’Hara (ORD), Newark (EWR), LaGuardia (LGA), and John F. Kennedy (JFK). The FAA requires each airline to have a departure and arrival slot at these airports.

  12. We define our airline markets as airport-pairs rather than city-pairs since this allows a cleaner comparison across carriers which serve identical airports. In particular, the airport-pair analysis is less of measurement concerns in dealing with the On-Time-Performance variables. For example, the quality of a DFW to DCA (Ronald Reagan Washington National) flight might be perceived differently than a DFW to IAD (Washington Dulles) flight. See Brueckner et al. (2014) for a thorough discussion of the differences between airport-pairs and city-pairs.

  13. As a result each flight appears multiple times, differing by the data collection time.

  14. For simplicity we refer to t as day even though it is a day and flight combination. Since our data are at the flight level, we can distinguish different flights on the same carrier-route-day.

  15. Both HHI and WN are likely endogenous. In subsequent estimations we use SSR to address endogeneity concerns.

  16. We also conduct a test of the equivalence of the four amenity regressions. The results overwhelmingly reject that the regression coefficients are equivalent across models (1)-(4).

  17. As a robustness check we also considered flight duration (instead of distance) and found very similar results.

  18. While we could have chosen the presence of an airport hub at either endpoint as a measure of competition on the route, we believe that such a measure would not be informative given the overwhelming proportion of network carrier flights are either to/from a hub. In contrast, HHI provides needed variation to identify the competitive environment. Another common competition measure is the number of carriers in a market. We find that HHI and number of carriers are highly correlated.

  19. The geometric means of the metropolitan (MSA) populations of the endpoint cities have been widely used as instruments for HHI since Borenstein and Rose (1994). Larger populations at route endpoints have higher demands and hence strengthen related market competition. Our second instrument is the enplanement ratio, which is the observed carrier’s geometric mean of enplanements at the route endpoints divided by the sum across all carriers of the geometric mean of each carrier’s enplanements at the endpoint airports. The likelihood of entry by a carrier benefits from the synergy of large-scale operations at both airports, which in turn affects the observed carriers’ route shares and HHI. Following Goolsbee and Syverson (2008) or Kim et al. (2021), we employ the following proxy for an airport operating cost by using the mean ticket fares from this airport to all other destinations except the focal destination. Airports with lower operating costs are more likely to attract entry and hence are considered more competitive.

  20. We do not control for aircraft type in the estimations because when travelers search for flights, information about aircraft type is displayed less prominently than amenities such as Wi-Fi. In addition, consumers may not understand the differences among aircraft type or model, while the meaning of a Wi-Fi icon is better understood by most travelers. Also note that when firms make supply-side decisions, they are likely to take into account potential demand side factors. Therefore, our amenity regressions are the results of the interaction of supply-side costs/technologies and firms’ anticipation of consumer demands/preferences.

  21. We are not aware of any quality differences among the free entertainment and paid entertainment offerings by the carriers. All of the major airlines (except JetBlue) charge a fee for Wi-Fi access (as of 27 July 2021).

  22. We also conduct a likelihood ratio (LR) test of the equivalence of estimates across the three HHI percentiles. The results strongly suggest that the percentile groupings are significantly different.

  23. We also conduct a Likelihood Ratio test to see whether the six carriers have the same estimates, which are rejected by the test results.

  24. Variables such as HHI, Tourist, and Passengers do not vary along the route, and are thus absorbed by route fixed effects. Similarly, bankruptcy status and airline financial variables - as well as the price of \(Wi-Fi\) - are fixed at the airline level and are absorbed by airline fixed effects.

  25. The relationship between lower posted price and days in advance of departure is not monotonic: Posted prices rise rapidly immediately prior to departure. For a thorough look at pricing patterns prior to departure see Escobari et al. (2019).

  26. The preferred specification of the Lewbel generalized IV employs at least one external IV. In our estimation of the Lewbel, however, we are unable to locate an external IV; hence these Lewbel estimations should be considered as an exploratory robustness check.

  27. Condition (3) – the scale heteroscedasticity related to X – is confirmed by the Breusch-Pagan (BP) test.

  28. We use the whole set of exogenous variables: \({\tilde{X}}=X\). We also tried different subsets of the exogenous variables in constructing the generated IVs and ran the same regressions as robustness checks. The results are qualitatively the same.

  29. We perform an under-identification test (KP LM test), an over-identification test (Hansen J test), and weak IV tests (Cragg-Donald Wald F test and Stock-Yogo test) for each of the four sets of instruments. The Lewbel instruments pass most but not all of these tests.

  30. The use of lagged values requires a panel data set. In contrast, the Lewbel et al. (2012) approach works well for cross-sectional data. The tradeoff is that the Lewbel approach requires the heteroscedasticity of the idiosyncratic errors.


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Correspondence to Nicholas G. Rupp.

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We thank the Editor Larry White, Jan Brueckner, Arthur Lewbell, Claudio Piga, Jeffrey Prince, Kerry Tan, and an anonymous reviewer for comments and suggestions which helped us improve the paper substantially. We also thank seminar and conference participants at University of Nebraska Omaha, participants at the 2016 Southern Economic Association meeting, 2017 Chinese Economists Society North American meeting and the 2017 International Industrial Organization Conference for helpful comments and suggestions.



1.1 Price regression with IV

The price regressions in the main text do not take into account that carriers choose both inflight amenities and ticket prices. There may also be an omitted variables problem. For example, there may be confounding factors beyond our explanatory variables, and these factors may also vary within a carrier or a route and thus are uncontrolled for by the carrier and route fixed effects. As a result, the possibility exists that the amenity variables may be correlated with the error terms, making inflight amenity variables in the price regressions potentially endogenous. We perform Durbin-Wu-Hausman test to check for such a possibility, with the null hypothesis being that the amenity variable is exogenous. Our tests reject that null hypothesis for Seat and Entertainment, but fail to reject the null for Wifi and Power. To deal with the potential endogeneity issue between these two variables, we implement an instrumental variable (IV) estimation method.

Construction of the IVs

Finding a proper external instrument set is often hard. Commonly employed IVs in the IO literature are cost shifters and average characteristics of products in other markets. Unfortunately, these IVs will likely violate the condition that the IV must have an indirect impact on the dependent variable instead of a direct impact. In the absence of external IVs, we construct Lewbel (2012) instruments by exploiting the higher moments of the data.Footnote 26

The identification strategy of the Lewbel method heavily relies on several assumptions: Suppose that the endogeneity is caused by an omitted variable u so that the error term has the form \(e=\alpha \cdot u +v\). The first assumption involves the exogeneity of the independent variables vector X (which does not include the amenity variable): \(E(X\cdot e)=0\). This implies that X is uncorrelated with both the omitted variable u and the idiosyncratic error v. Next, let \({\tilde{X}}\) be a sub-vector of X. Assume the \({\tilde{X}}\) is uncorrelated with \(u^2\), \(v_iv_j\) and \(uv_i\), \(i\ne j\); and \(Cov({\tilde{X}},v^2)\ne 0\). Combined, these assumptions allow the following conditions to be satisfied: (1) \(E(X\cdot e)=0\); (2) \(cov( {\tilde{X}},e_i\cdot e_j)=0\), \(i\ne j\) and (3) \(\sigma ^2_i \ne \sigma ^2\): The error terms e are heteroskedastic.Footnote 27 These conditions allow us to construct the vector of instruments described next.

The Lewbel instruments are constructed as follows: For each inflight amenity variable – Wifi– in the first stage of 2SLS we regress the amenity variable on the exogenous subvector \({\tilde{X}}\).Footnote 28 Let \({\hat{\varepsilon }}\) denote the residuals. We then use the residuals to interact with the mean-centered exogenous variables – \({\tilde{X}}-E({\tilde{X}})\) – to obtain our instruments Z. Suppose that there are K variables in \({\tilde{X}}\): \({\tilde{X}}=({\tilde{x}}_{1},\cdots ,{\tilde{x}}_{k},\cdots ,{\tilde{x}}_{K})\). For each exogenous variable \({\tilde{x}}_{k}\), we create an instrument \(z_{k}\) as follows:

$$\begin{aligned} z_{k}=\left[ {\tilde{x}}_{ik}-E({\tilde{x}}_{k})\right] \cdot {\hat{\varepsilon }} _{i},\quad k=1,\cdots K. \end{aligned}$$

Note that we construct a total of four sets of instruments: one for each of the four amenity variables.Footnote 29 Although we find that the DWH test suggests that Wifi and Power are less of a concern, nonetheless we use an IV estimation for these two variables as robustness checks. In the second stage, we use \(Z=(z_1,\cdots ,z_k,\cdots z_K)\) as instruments to regress the dependent variable (price, or average one-way posted domestic fare) on the exogenous variables X and the amenity variable.

This generated Lewbel instrumental variable technique can be applied to other settings where external instruments are unavailable. This is in the same spirit as Arellano and Bond (1991) which uses lagged endogenous variables as IVs.Footnote 30

1.2 IV results

The IV results are presented in Table A1. Once again, we find significantly lower baseline posted prices for flights that provide Wi-Fi and Entertainment. The magnitude of these coefficient estimates has increased substantially as we find the presence of Wifi significantly reduces the base posted price of the airline ticket by \(\$28\), while Entertain reduces ticket prices by \(\$31\). This ticket price reduction clearly exceeds the daily potential revenue from Wifi since carriers charge just \(\$16\) for a one-day Wi-Fi access pass. Even if every passenger pays for Wi-Fi, this extra revenue won’t be enough to compensate the loss of base ticket prices.

We believe the following thought experiment may provide some insights on this puzzle: Recall that the U.S. airline industry is replete with price discrimination. Before the airlines began offering Wi-Fi and entertainment inflight amenities, to segment travelers airlines relied on restrictions such as advance purchase, Saturday night stay over, ticket refundability and so on. Whether a passenger pays for inflight amenities provides a much more direct measure to infer consumer type and willingness to pay. So the baseline ticket price is for a more refined group of passengers with the lowest willingness to pay. Since the lowest consumer group is more refined, one would expect that ticket price to drop further. Given that our pricing data selects the lowest posted coach fare, we don’t observe higher quality-offerings such as United’s Economy Plus. Our conjecture is that better refinement of travelers, although reducing the fare for the lowest group, also raises the fare for higher consumer groups: One would expect more expensive upgrades from standard United Economy to, say, Economy Plus. Also, a Honeywell Aerospace survey reveals a growing demand for Wi-Fi by travelers: 17% indicated that they have switched from a “preferred" airline to another carrier that offered greater likelihood of having Wi-Fi on the flight (Rabinowitz, 2014). Accordingly, carriers may be willing to lose a few dollars on Wifi in order to retain (or attract) passengers.

For the other two flight amenity variables, we find that neither Power nor Seat provide any significant explanatory power with respect to the base airline ticket price. This is a change from the OLS estimates as Power was previously found to be associated with slightly higher ($0.81) posted ticket prices.

Table 11 IV estimates of inflight amenities and U.S. domestic airfares

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Kim, M., Liu, Q. & Rupp, N.G. When Do Firms Offer Higher Product Quality? Evidence from the Allocation of Inflight Amenities. Rev Ind Organ 62, 149–177 (2023).

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