AN EMPIRICAL-STUDY OF COST DRIVERS IN THE UNITED-STATES AIRLINE INDUSTRY

• 成果类型: Note
• 署名作者: BANKER, RD; JOHNSTON, HH
• 署名单位: Rice University
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• 发表日期: 1993
• 页码: 576-601
• 关键词: seemingly unrelated regressions; AUTOREGRESSIVE DISTURBANCES; scale

摘要

Recent research on cost driver analysis by Miller and Vollman (1985) and Cooper and Kaplan (1987) suggests that transactions deriving from the diversity of a firm's product line and the complexity of its production process, in addition to output volume, drive overhead costs. As a consequence, it is argued, conventional cost accounting systems based only on volume-related measures, such as units of output, direct labor hours, or machine hours, produce biased and materially misleading cost estimates for managerial decisions on price and product line (whether to continue or discontinue products, or to offer additional products). Systematic biases in cost estimates may also lead to distortions in flexible budgeting systems, variance analyses, and responsibility-accounting systems. Perhaps more important in the long run, omission of operations-based cost drivers may distort the investigation of the likely effects on costs of changes in operating strategies. Many firms have moved ahead on the basis of this perceived need for more accurate cost estimates and have designed and implemented activity-based costing systems (Schiff 1991). From an academic perspective, however, there is a need for further formal empirical research in this field. Cooper and Kaplan's (1987) evidence is based on field-study discussions with managers in a variety of manufacturing settings and experimentation with cost allocation and product-costing systems based on transactions. Foster and Gupta (1990) provide some of the first empirical evidence on the correlation of manufacturing overhead with output volume and operations-based measures that reflect characteristics of the manufacturing process. Using data obtained from 37 plants of a single manufacturing firm, Foster and Gupta found that most of the volume-related measures of output were highly correlated with manufacturing overhead (MOH), but because only a few measures of manufacturing complexity and efficiency were highly correlated with MOH, their findings leave the impression that systems based on just volume may not significantly distort information generated for managerial decision making.1 In contrast, we find empirical evidence in favor of incorporating operations-based cost drivers along with measures of volume in cost driver models. We draw upon previous work in cost accounting and economics to develop analogs in the airline industry for product diversity, production run volumes, and process complexity, and propose a framework for cost driver analysis in the U.S. airline industry. Using a panel of quarterly data for 1981-1985 compiled primarily from traffic and financial statistics submitted by carriers to the Civil Aeronautics Board (CAB) and Department of Transportation (DOT), we specify and estimate a multivariate system of cost functions with multiple cost drivers for the industry during the transition following deregulation. We find both volume- and operations-based cost drivers to be statistically significant. We also demonstrate the potential managerial importance of the operations-based drivers by explaining variations in marginal costs across airlines in terms of operating strategies reflected in the cost driver values. Empirical cost driver analysis is managerially significant for the industry and period that we examine. The proportion of indirect costs is large, and identification of input consumption for specific services is difficult. During the transition following deregulation, carriers adopted a rich variety of strategies to improve productivity, reduce costs, and increase market share. These strategies directly involved both volume- and operations-based cost drivers. The analytical framework and model that we have developed on the basis of prior literature concerned with the airline industry enable us to examine the differential cost effects of some of the most important strategies adopted.