Motivation and problem statement

The 1996 new legislation concerning the partial deregulation of bus industry led to a major structural change in the whole industry in Taiwan and provide a new framework for all bus operation (as will be seen below in Chapter 2). This dissertation intends to study the impacts of privatization and regulatory changes in the public transport industry, with special reference to efficiency and/or effectiveness measurement. On one hand, the TMTC’s privatization programme offers a unique opportunity to analyze the effects on the efficiency change of its kind. On the other hand, except a few cases, most of long established operators, so-called multimode transit firms, seem to have worked effectively and have still survived following deregulation. Therefore there is a requirement to examine carefully transit performance based on the concepts of efficiency and/or effectiveness.

The study of TMTC's privatization is of particular interest in several respects. First, it is especially unfortunate that few frontier studies have focused on the effects of privatization and regulatory changes in the public transport industry (De Borger et al., 2002). Second, it has been the first privatization case through employee buy-out (EBO) since the beginning of Taiwanese government's privatization programs in 1989. The combination of the direct employee shareholding in buy-out and a sector where individual skills are important may be

expected to generate significant effects on performance (Wright et al., 1992). Third, TMTC has been facing long-term financial difficulties due mainly to its inefficiency since 1988. The most notable have focused on the after-effects of transferring to the private sector, that is, whether the newly privatized firm Kuo Kuang Motor Transport Company (KKTC) is capable of improving this situation or is quickly driven out of market. Fourth, the economic literature that deals with the existence of employee-owned firms has paid little attention to EBOs (Bonnie and Putterman, 1987; Bonnie et al., 1993). And lastly, as an important case study, the comparison of TMTC's performance before and after privatization provides new empirical evidence and theoretical extension on the property right theory, focusing upon the privatization of the Taiwanese bus industry. Therefore, the TMTC’s privatization program offers a unique opportunity to analyze the effects on the performance of its kind.

On the other hand, despite the transit sector has been experiencing declining ridership in the early 1990s, bus transit remains an important mode in Taiwan. Bus transit systems are, therefore, increasingly under pressure to improve their performance, both from the point of view of technical and allocative efficiencies as they yield complementary information about the management effectiveness of an individual bus firm. Technical efficiency has a diagnostic purpose as it yields comparative information about the effectiveness with which individual units convert their input resource into outputs. On the other hand, allocative efficiency has a planning orientation since the objective of assessment is to gauge efficiency improvements by means of resource reallocation. However, most of the extant literature on performance measurement for transit firms restrict their analyses to the use of technical efficiency (see e.g., Gathon 1989; Chang and Kao 1992; Fazioli et al. 1993; Obeng, 1994; Bhattacharyya et al.

1995; Sakano et al. 1997; Costa 1998; Lijesen 1998; Cowie and Asenove 1999; Kerstens 1999; Nolan et al. 2002; Odeck 2003; Karlaftis 2004).

The reasons for studying technical efficiency stem from several factors. Allocative efficiency calculation requires input prices (Lovell, 1993). The data needed for this

calculation were not readily available. Allocative efficiency assumes that firms are cost minimizing (Viton, 1995). This assumption may not be valid for the urban transit industry. As indicated by some literature, transit firms have a variety of goals, including but not limited to cost minimization. The second reason for choosing technical efficiency is that it provides some insight into underlying research issues such as how economies of scale and density of the urban center relate to transit efficiency. From efficiency scores one can judge whether a firm is using its inputs in the most productive way relative to the sampled firms (Labrecque, 1996).

However, the lack of published research on combining technical efficiency and allocative efficiency measures of performance and thereby measuring further price distortions in the bus transit market places a limit on our understanding of production processes, or even market mechanism. In light of this, a novel approach leads to a derivation of an allocative efficiency index, which measures price distortions using data on observed costs and revenues without requiring explicit information on prices is clearly needed to deal with this problem, under the assumption of cost minimization.

As indicated by Tomazinis (1975), one of the major problem in productivity studies of a social system (such as a transportation system) is based on the handling of undesirable (bad) outputs of the process. All desirable (good) inputs can be added of course, either as physical units or on the basis of their market prices. Undesirable outputs, however, are negative by-products with no market value. If such undesirable by-products are left alone (not sold and not requiring any cost for their disposal), as has been the case for air pollution for many years, the undesirable outputs do not enter any productivity analysis of the production process. In case special costs are required for the treatment or disposal of such undesirable by-products, their cost should enter somewhere in the productivity analysis.

From many points of view the most effective treatment of this issue would be to include the additional costs as part of the production process of the desirable output itself. In other

words, when evaluating the performance of producers it makes sense to credit them for their provision of desirable outputs and penalize them for their provision of undesirable outputs.

That is to say, “goods” and “bads” should be treated asymmetrically in gauging producer performance. In fact, most currently available performance measures do treat the two asymmetrically, by valuing goods and ignoring bads (Fare et al. 1989).

On the other hand, due to the development of frontier methods for the study of efficiency there is a large strain of literature on the efficiency of bus transportation. Moreover, a comprehensive survey of frontier methodologies and empirical results for public transit has been presented by De Borger et al. (2002). Relevant performance indicators and the methods to measure them have been reviewed. The existing frontier studies measuring transit performance have also been systematically summarized and critically assessed (see e.g., Chang and Kao, 1992; Chu et al., 1992; Bhattacharyya et al., 1995; Viton, 1997; Cowie and Asenova, 1999; Nolan et al. 2002).

Most of these previous research studies on measuring firms’ efficiency and productivity are typically conducted without taking into account undesirable outputs which may not be freely or costlessly disposable.

Accidents of all kinds are an inescapable part of bus operations, however much one seeks to avoid them. Inevitably, they involve insurance procedures and very often the intervention of police; perhaps even court proceedings of one kind or another. The manager of a bus firm will have clear duties in the case of an accident within his area of responsibility (Hibbs, 1985).

In other words, transportation safety has bee paramount issue, due mainly to producers (operators), consumers, and policy makers have paid increasing attention to the safety performance of bus transport. Operators no longer consider the transport risks as a secondary concern of the service produced.

One component of the public debate on the competitiveness of transport services has focused on the role of transportation safety. In fact, the reputation for safety has been one of

the key qualities of transportation that contribute to market segment. Such opportunities are regarded as a “win-win” situation, because business and social goals are both met. Many public policy efforts seek to identify and eliminate the production inefficiency that prevents simultaneous improvements in both efficiency and transportation safety. Whether these types of public policy initiatives are successful depends on the extent to which such inefficiencies are widespread in transport services, especially in intercity bus services. There is a requirement to measure the magnitude of these “double wins” opportunities where transport risks can be reduced with efficiency improved concurrently among a set of DMUs producing bus services. This may help both operators and policy makers to set up their targets to reduce the inefficiencies.

Improved efficiency will, ceteris paribus, reduce cost, boost transit ridership, as well as reduce the need to subsidize the transit systems, and hence it has been widely held to be one of the principal objectives in most transportation organizations. In light of this, it is an appropriate way to measure and compare performance with peer groups, in particular reference to the efficient use of resources.

Some transportation organizations engage in various activities (services) simultaneously;

for example, an airline, railway, or marine company may simultaneously provide passenger, freight, and other services respectively. Another famous example could be a public transit company, which involves various transportation modes simultaneously. On the other hand, for a variety of applications to which DEA could be applied, there is often a shared resource (or cost) which is imposed on some (or all) decision making units (DMUs, refer to transit firms here).

A problem then arises with respect to how this resource (or cost) can be assigned in an equitable or optimal way to the various DMUs. Few DEA studies relating to multimode transit agencies deal with the shared input problem in a proper way. For example, Viton (1997, 1998) analyzed the efficiency of U.S. multimode bus transit systems operating conventional

motor-bus (MB) and demand-responsive (DR) services using DEA. However, the allocation problems of the system costs data appear to have been ignored.

Clearly, the allocation problem of shared inputs need to be considered and dealt with properly, and thereby estimating the efficiency or effectiveness of transportation organizations that engage in several services simultaneously. DMUs in this situation may have some inputs and outputs among all the services, and in doing so, estimate the efficiency or effectiveness with a given organization carries out each activity.

A wide variety of methods can be derived for measuring performance based on the concepts of efficiency and effectiveness. However, while evaluating transit performance it is worth noting that, unlike the production and consumption processes of the manufacturing sectors a transit service cannot be stored, and therefore the output consumed or the final output produced, such as passenger-kms may vary considerably from the output produced or the intermediate output, such as vehicle-kms, in a transit system. Specifically, the consumed services occur concurrently with the produced services, If the final output is not consumed simultaneously with the intermediate output, it is lost (Tomazinis, 1975). This perishability of the commodity produced, and the fact that only a proportion of the services produced are actually consumed is often neglected in transit performance measures (see for example, De Borger et al., 2002). If these unique unstorable characteristics of transit services are justified, then it is vitally important to obtain valid estimates of transit performance. These estimates must be obtained by combining the cost efficiency measure, service effectiveness measure and cost effectiveness measure into a single model, taking into account explicit modeling of produced services and consumed services inside the technology.

In addition, as indicated by Beasley (2003), organizations of any complexity typically consist of a number of individually identifiable units. For example, within a transit firm these units may correspond to different transit services. Such units are linked at the company level in the way of allocating resources (such as management and sales staff) to individual units.

The total amount of resources that the firm can allocate will be limited. This problem is plainly important in a number of transit firms. It is currently, for the most part, dealt with through a mixture of standard accounting approaches and negotiations between individual services and the organization, or even ignored (see Vition 1997,1998). To estimate the efficiency and effectiveness achieved by multimode transit firms with the two production functions using shared inputs, a specific model needs to be developed and incorporated into aforementioned single model, so as to solve these problems mentioned above.