Economics of the U.S. Commercial Airline Industry: Productivity, Technology and Deregulation pp 139-164 | Cite as

# Empirical Estimation and Quantitative Analysis

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## Abstract

The translog variable cost function (equation 6.2) in our study is specified with one output (Y) revenue passenger miles, one quasi-fixed factor, the capital stock (K) of flight equipment that has been quality adjusted with technological parameters — payload, SFC, range, thrust and passenger capacity — and five variable inputs, labor (L), energy (E), materials (M), business services (S) and other expenses (O). A full description of the model can be found in Chapter 6. Efficient estimates of The Restricted Variable Cost Function (RVCF) over the period 1970-1992 (161 observations) for the seven major carriers were obtained using the Generalized Method of Moments (GMM)^{1} estimation algorithm in the TSP (Time Series Processor) econometric software program.^{2} All of the parameters in the RVCF model are identified by estimating a pooled time series cross-sectional translog variable cost function jointly with a revenue equation, and five input demand equations, instead of the value shares of inputs. The input demand equations are subject to the same linear homogeneity and symmetry restrictions as the share equations. Our regression coefficients are therefore in quantity terms rather than input value shares. The techniques of estimating translog cost functions with input demand quantities are described by McElroy (1987)^{3} and Norsworthy and Jang (1992)^{4}. The fitted variable cost function satisfies at every sample point the regularity conditions that it be non-decreasing and concave in input prices.

## Keywords

Total Factor Productivity Capacity Utilization Airline Industry Cross Price Elasticity Shadow Cost## Preview

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## Endnotes

- 1.Hansen, L., (1982) “Large Sample Properties of Generalized Method of Moments Estimators,”
*Econometrica*, Vol. 50, pp. 1029–1054; Hansen, L. and Singleton, K. (1982) “Generalized Instrumental Variables Estimation of Non-Linear Rational Expectations Models,”*Econometrica*, Vol. 50, pp. 1269-1286.CrossRefGoogle Scholar - 2.TSP International Software, P.O. Box 61015, Station A, Palo Alto, CA. 94306.Google Scholar
- 3.McElroy, M. (1987) “Additive General Error Models for Production Cost, and Derived Demand or Shared Systems,”
*Journal of Political Economy*, Vol. 95(4), pp. 737–757.CrossRefGoogle Scholar - 4.Norsworthy, J.R. and Jang, S.L. (1992)
*Empirical Analysis of Technology and Productivity in High Technology and Service Industries*, North-Holland Press.Google Scholar - 5.The Restricted Variable Cost Function specification is Equation (6.2).Google Scholar
- 6.Input Demand Equations are specified by Equation (6.6).Google Scholar
- 7.See Norsworthy and Jang (1992), Chapter 12, pp.277-298 for a detailed discussion on how to specify a revenue equation that is to be jointly estimated with a restricted variable cost function.Google Scholar
- 8.Judge, G. Griffiths, W., Hill, C, Lutkepohl, H., and Lee, T. (1985)
*The Theory and Practice of Econometrics*, John Wiley and Sons, New York, Chapter 13, pp. 515–560.Google Scholar - 9.Boeing Commercial Airplane Group
*Current Market Outlook (1993)*, Seattle, Section 2.8.Google Scholar - 10.ibid., Section 2.5Google Scholar
- 11.Economies of scale is specified in equation (5.70).Google Scholar
- 12.The term “equilibrium” means little in this context.Google Scholar
- 13.See the discussion in Norsworthy and Jang (1992), Chapter 4, pp. 83-104.Google Scholar
- 14.Brown, R. and Christensen, L. (1981) “Estimating Elasticities of Substitution in a Model of Partial Static Equilibrium: An Application to U.S. Agriculture, 1947-1974,” in
*Modeling and Measuring Natural Resource Substitution*, edited by E. Berndt and B. Field, The MIT Press, Mass.Google Scholar - 15.Berndt E. (1991)
*The Practice of Econometrics*, Addison-Wesley, Mass., page 484. See also Berndt, E. R. and M. Fuss (1986) for a detailed exposition of the relationship between shadow cost and capacity utilization.Google Scholar - 16.Our model produced shadow cost of capital estimates in which 98% of the observations were negative The shadow cost of capital estimates were positive in the years 1987, 1991 and 1992 for Continental Airlines and in 1992 for TWA. These two carriers were among the weakest carriers in industry at the time. See Equation (5.72) for the specification of the shadow cost of capital.Google Scholar
- 17.The Boeing Company
*Boeing World Jet Airplane Inventory Year End 1990*Seattle Washington p. 30Google Scholar - 18.Loftin, L. (1985)
*Quest for Performance*—*The Evolution of Modern Aircraft*, NASA, Washington, D.C. pp. 227–228Google Scholar - 19.ibid., pp. 439-440.Google Scholar
- 20.Brown I. (1981) “The Sources of Airline Productivity-Technical Report on Total Factor Productivity and Specific Factor Contributions,”
*Air Transport Association of American*, Washington, D.C, Appendix B, pp. 79–99.Google Scholar - 21.Norsworthy and Jang (1992).Google Scholar
- 22.Berndt(1991).Google Scholar
- 23.Loftin (1985), p. 407Google Scholar
- 24.See Equation (6.3) for our method of incorporating the quality variables into the model.Google Scholar
- 25.Own and Cross Price Elasticities of Demand are specified by Equation (5.54).Google Scholar
- 26.Gordon, R. (1991) “Productivity in the Transportation Sector,”
*Working Paper*, National Bureau of Economic Research, Cambridge, MA.Google Scholar - 27.Jorgenson(1987).Google Scholar
- 28.See the TFP discussion in Chapter 5, section (5.7).Google Scholar
- 29.One may reasonably ask, however, why such an industry would have been regulated in the first place.Google Scholar
- 30.It may be argued that US Air underwent a transition from a regional to a national airline during the observation period, and is therefore atypical.Google Scholar