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Demand uncertainty and capacity utilization in airlines

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Abstract

This paper studies the relationship between demand uncertainty—the key source of excess capacity—and capacity utilization in the US airline industry. We present a simple theoretical model that predicts that lower demand realizations are associated with higher demand volatility. This prediction is strongly supported by the results of estimating a panel GARCH framework that pools unique data on capacity utilization across different flights and over various departure dates. A one unit increase in the standard deviation of unexpected demand decreases capacity utilization by 21 percentage points. The estimation controls for unobserved time-invariant specific characteristics as well as for systematic demand fluctuations.

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Notes

  1. For hospitals, Gaynor and Anderson (1995) estimated that increasing the occupancy rate from 65 to 76 % reduced costs by 9.5 %.

  2. Capacity utilization is important for other industries as well. Kim (1999) argues that it is an important issue in economic analysis, while Schultze (1963) explains that it serves as a productivity measurement and can be used as an indicator of the strength of aggregate demand.

  3. Nelson (1989) discusses practical problems in measuring capacity utilization and offers suggestions for estimating theoretical measures, while Shapiro (1993) describes how to estimate the capital utilization of an industry as a whole using the survey data of individual plants. Kim (1999) argues that conventional capacity utilization measures (e.g., Nelson 1989) appear to be biased and proposes a measure that incorporates information about production and demand.

  4. The GARCH modeling approach is widely used in the financial economic literature to measure market uncertainty with conditional volatility over time.

  5. The dynamic interpretation is in line with Hazledine (2010) and Kutlu (2012), although these papers work under demand certainty. Deneckere and Peck (2012) present a generalization of Prescott’s (1975) one-period model to allow sellers to change prices over different periods.

  6. Notice that we keep track of two distributions that capture demand uncertainty. The first is the distribution of demand states \(h\) and the second is the distribution of demand realizations, \(DEMAND_{h}\).

  7. At the highest demand state when \(h=\{2,\ldots 18\}\), the last batch of consumers faces a price larger than \(\theta \) and so does not buy any tickets. This explains why the highest demand realization of \({\textit{DEMAND}}_{h} = 13.59\) is twice as likely—during the two highest demand states of \(h=17\) and \(h= 18\).

  8. We thank an anonymous referee for raising this point. In the empirical work below, the effects of truncated conditional means will be taken into consideration.

  9. Seats protected for later purchases (usually labeled as preferred or prime seats) are counted as available seats. This is consistent with serial nesting of booking classes. In this case, for booking classes within the same cabin, seats from a higher booking class (e.g., prime seats) are ready to be released into a lower booking class if needed (e.g., in an expected off-peak fight), see Escobari (2012, p. 719).

  10. Bilotkach et al. (2011) use similar information on seat capacity availability to see how yield management affects a flight’s load factors.

  11. Escobari (2012) empirically studies the dynamics of prices and inventories as the departure date nears.

  12. An alternative specification that included contemporaneous posted prices showed that estimates for the key variable \(\sigma _{it}\) remain close to those reported in Table 8. Because of the potential endogeneity of posted prices we have included the ticket price variable in an IV model for the conditional mean equation using a sequential procedure (rather than the simultaneous estimation), in which the GARCH-in-mean term is included along with the ticket price variable in the second step. The instruments include the lagged values of the explanatory variables. We do not report those results partly due to a lack of theoretical motivation for such a specification. In addition, Deneckere and Peck (2012) suggest that airlines post prices based on beginning-of-period cumulative bookings and not really cumulative bookings as a function of posted prices.

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Acknowledgments

We thank comments by Marco Alderighi, Volodymyr Bilotkach, Damian Damianov, Vivek Pai, and our session participants at the 2012 International Industrial Organization Conference. We also thank two anonymous referees, whose comments helped improve the paper. Stephanie C. Reynolds provided excellent assistance with the data.

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  1. The University of Texas–Pan American, Edinburg, TX, USA

    Diego Escobari

  2. Texas A&M University–Corpus Christi, Corpus Christi, TX, USA

    Jim Lee

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  1. Diego Escobari
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Correspondence to Diego Escobari.

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Escobari, D., Lee, J. Demand uncertainty and capacity utilization in airlines. Empir Econ 47, 1–19 (2014). https://doi.org/10.1007/s00181-013-0725-2

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