Short and long-run cost efficiency in Indian public bus companies using Data Envelopment Analysis

State Transport Undertakings (STUs) are key players in providing mass road transport in India. Given that they operate under high levels of government imposed regulatory constraints, it is imperative to study their efficiency levels. Given that capital is a relatively scarce resource in developing countries like India, it is important to obtain efficiency in the short-run where some inputs are fixed as well as over the long run, where all inputs are variable. The technique used for capturing efficiency is Data Envelopment Analysis (DEA). A key possible limitation of DEA models based on physical inputs and outputs is that for an inefficient firm, reduction in some or all inputs may be recommended. It may often be desirable for an inefficient firm to increase some less expensive inputs while reducing the use of relatively expensive ones. Hence, when market price data is available, it is advisable to use the cost variant of DEA. Also, it is possible to determine variable cost efficiency in the short run when some inputs cannot be varied. Such inputs are referred to as “quasi-fixed” inputs. In this paper, we examine short and long term efficiencies of select bus companies in India known as State Transport Undertakings (STUs) over a period of 10 years. Fleet strength has been used as the quasi-fixed input. It is possible to ascertain, through a comparison of shadow price of the quasi-fixed input, vis-à-vis its market price, as to whether the quantity of this input is sub-optimally small or large. It is found that by adopting efficiency enhancing practices, STUs can cumulatively reduce their operating costs to the extent of 9123.35 million dollars. Also the tendency to minimize costs is found to be declining over time. In the short run some STUs are found to operate with a sub optimally low fleet size.     

Short-run labor productivity in a dynamic model

A classic empirical finding is that the short-run output elasticity of demand is smaller than unity and is less than in the long run. This phenomenon is called ‘short-run increasing returns to labor’ (SRIRL). In this paper we analyze SRIRL using a dynamic factor demand model for variable and quasi-fixed inputs, where the latter incur increasing marginal internal adjustment costs. Speeds of adjustment of quasi-fixed inputs are endogenous and variable, not constant parameters. Labor hoarding is shown to be neither necessary nor sufficient for SRIRL. These results are illustrated empirically using annual U.S. manufacturing data, 1952-71.     

Measuring Hospital Performance in the Presence of Quasi-Fixed Inputs: An Analysis of Québec Hospitals

This research proposes an approach to measure hospital performance based on a generalization of Banker and Morey (1986) and Førsund (1996). This approach considers quasi-fixed inputs explicitly, calculates their implicit cost, and quantifies returns to scale. The performance measure is decomposed into allocative and technical inefficiencies. Based on a very complete data set of Québec hospitals, we find that significant inefficiencies of up to 17% ($700 CAN million) could have been saved through improved performance. Postestimation analyses that include qualitative measures of care suggest that differences in performance are attributable to differences in management or unobservable quality of care rather than patient case mix.     

Infrastructure-production system dynamics in national and regional systems: An econometric study of the Indian economy

Treating infrastructure inputs as quasi-fixed in the short run, a multi-equation econometric model of production-infrastructure (social overhead capital) interlinkages and adjustments is developed based on a flexible functional form. Adjustment dynamics are endogenized and costs of adjustments are explicitly incorporated. The model is estimated with regional and national data from India; results include optimal paths and speeds of adjustments for infrastructure inputs market inputs' own and cross-price elasticities and demand elasticities with respect to the level of output, infrastructure stocks and associated user costs; and production cost elasticities with respect to output and infrastructure stocks.     

Estimating capacity utilization of Chinese manufacturing industries

This study investigates the capacity utilization (CU) of Chinese manufacturing industries, using a CU indicator based on data envelopment analysis and directional distance functions (DDFs). The inputs are separated into variable inputs and a quasi-fixed input to measure the gap of DDFs, which indicated either under-utilization of inputs or overcapacity. Moreover, we define an indicator for CU change over time and introduced the corresponding decomposition. We note that, during the study time period (2007–2010), the CU of Chinese manufacturing industries improved, which implies that Chinese manufacturing industries expanded their production and got closer to their capacity during the examined period. The driving force of this improvement is technical changes. The higher average CU values of light manufacturing industries than that of the heavy industries and the extremely high CU values of two light industries reveal a severe overcapacity problem in the light industries. We also provided the methods and conduct analysis on determining optimal variable inputs and the type of the overcapacity on specific DMUs. The bootstrap regression procedures are employed to test the influence of environmental variables on CU values. Finally, we provide policy implications and suggestions for policymakers who oversee the development of Chinese manufacturing industries.     

A new methodology to measure efficiencies of inputs (outputs) of decision making units in Data Envelopment Analysis with application to agriculture

This paper aims at developing a new methodology to measure and decompose global DMU efficiency into efficiency of inputs (or outputs). The basic idea rests on the fact that global DMU's efficiency score might be misleading when managers proceed to reallocate their inputs or redefine their outputs. Literature provides a basic measure for global DMU's efficiency score. A revised model was developed for measuring efficiencies of global DMUs and their inputs (or outputs) efficiency components, based on a hypothesis of virtual DMUs. The present paper suggests a method for measuring global DMU efficiency simultaneously with its efficiencies of inputs components, that we call Input decomposition DEA model (ID-DEA), and its efficiencies of outputs components, that we call output decomposition DEA model (OD-DEA). These twin models differ from Supper efficiency model (SE-DEA) and Common Set Weights model (CSW-DEA). The twin models (ID-DEA, OD-DEA) were applied to agricultural farms, and the results gave different efficiency scores of inputs (or outputs), and at the same time, global DMU's efficiency score was given by the Charnes, Cooper and Rhodes (Charnes et al., 1978) [1], CCR78 model. The rationale of our new hypothesis and model is the fact that managers don't have the same information level about all inputs and outputs that constraint them to manage resources by the (global) efficiency scores. Then each input/output has a different reality depending on the manager's decision in relationship to information available at the time of decision. This paper decomposes global DMU's efficiency into input (or output) components' efficiencies. Each component will have its score instead of a global DMU score. These findings would improve management decision making about reallocating inputs and redefining outputs. Concerning policy implications of the DEA twin models, they help policy makers to assess, ameliorate and reorient their strategies and execute programs towards enhancing the best practices and minimising losses.     

The specification and estimation of interrelated factor demands under uncertainty

This paper studies a model of the firm's interrelated demands for the factors of production incorporating exogenous variables with uncertain future values, multiple quasi-fixed factors, depreciation in use, and non-symmetric internal costs of adjustment. The inputs considered are production labor, non-production workers, capital, inventories of non-finished goods, and capacity and non-production worker utilization rates. A simultaneous system of factor demand equations with constraints is estimated and using U.S. manufacturing data. The model is shown to be tractable under certain assumptions. The results are encouraging given the complexity and the nature of the model.     

Decomposing the operational efficiency of major cruise lines: A network data envelopment analysis approach in the presence of shared input and quasi-fixed input

To examine the top cruise lines' operational efficiency in three dimensions, namely, production, marketing, and profitability, this study adopts a network data envelopment analysis approach considering shared and quasi-fixed input factors. The empirical results reveal the following findings. First, the production process tends to outperform marketing and profitability processes. Second, inefficiency of a cruise line mostly stems from overconsumption of the exogenous inputs in each process and the shared input. Third, the relationships between operational efficiency of cruise lines and some categorical factors are examined. Finally, some managerial implications derived from the empirical results are formulated for cruise lines.     

Multiple capital inputs, Q, and investment spending

Despite their explicit treatment of dynamics and solid theoretical basis, investment models based on the Brainard-Tobin Q have recorded a generally disappointing empirical performance. When the Q model is expanded to recognize the possibility that the value of the firm depends on two or more capital inputs with differing adjustment cost technologies, the econometric equation following from optimizing behavior includes Q as well as a set of additional explanatory variables. The importance of these omitted variables is assessed, and the capital homogeneity assumption for equipment and structures implicit in Conventional Q models is rejected. The Multi-Capital Q model is then extended in two ways: (i) adding inventory, research and development, and labor as quasi-fixed factors and (ii) exploring the sensitivity of the instrumental variables estimates to normalization. We conclude that the Multi-Capital Q model is a useful extension that overcomes an important omitted variables problem in the Conventional Q framework.     

Tests on the validity of static equilibrium models

This paper provides a framework to test the validity of static cost minimizing equilibrium assumptions that form the basis for much of the empirical literature on industrial production. The point of departure in our model is to allow the observed technology to be at a short-run equilibrium where firms minimize variable costs while being constrained by the utilization levels of quasi-fixed factors. The long-run equilibrium is then inferred by minimizing total costs with the optimal adjustment of quasi-fixed factor levels.We use results from the optimization problem to form tests with respect to quantities and prices. In the quantity space version, departures between the actual and the optimal long-run levels of quasi-fixed factors are tested for statistical significance. A significant non-zero departure implies the rejection of a static equilibrium specification. In the price space version, the test is cast as a comparison of the market price and the long-run shadow value of a quasi-fixed factor. Although the two versions would give identical results in the non-stochastic case, the rejection powers of these two tests are found to depend on the particular functional form chosen to represent the production process (i.e. cost function).In an application based on aggregate U.S. manufacturing where capital is taken to be quasi-fixed, we were able to reject the static equilibrium specification. These results cast doubt on the validity of a number of previous empirical studies.     

Reviewing the literature on non-parametric dynamic efficiency measurement: state-of-the-art

Much of the literature on static efficiency measurement models assumes that the inputs are fully used for producing outputs in the same period, with the result that no time interdependence exists between the input utilization and output realizations for a production unit in consecutive periods. A review of the literature on non-parametric dynamic efficiency models identifies five key factors of the inter-temporal dependence between input and output levels over different periods: (i) production delays; (ii) inventories (inventories of exogenous inputs or inventories of intermediate and final products); (iii) capital or generally quasi-fixed factors (and associated embodied technological change, vintage specific capital); (iv) adjustment costs; and (v) incremental improvement and learning models (disembodied technological change). This paper reviews the literature and finds that the dynamic issues associated with adjustment costs and capital have received considerable attention in the literature, whereas the dynamic issues associated with inventories have received less attention. The paper concludes with suggestions for future research such as relaxing the perfect anticipation/knowledge assumption for future variables, prices, and states. Moreover, Dynamic Network Data Envelopment Analysis has provided a unifying framework for some dynamic factors, but further development of these models is necessary including meaningful applications.     

Material And Value-Adding Inputs In Manufacturing Enterprises

Because of the nature of assembly-line operations, one would expect technologies in manufacturing firms to differ in systematic ways from those in many other types of firms. Identifying such differences can therefore be useful in the formulation and testing of restrictions to flexible functional forms. In the present paper, I characterize a multiproduct manufacturing firm and use the characterization to formulate testable hypotheses about the firm's technology, cost function, and profit function. I argue that manufacturing enterprises exhibit a form of almost nonjointness in input quantities and prices in which the individual sub-technologies have quasi-fixed proportions. Substitution across product lines is responsible for the substitutability between material and value-adding inputs that we often observe in aggregate data. Neglecting the special structure of manufacturing technology can seriously bias estimates of aggregate productivity growth rates.     

Data Envelopment Analysis with Reverse Inputs and Outputs

Data envelopment analysis (DEA) assumes that inputs and outputs are measured on scales in which larger numerical values correspond to greater consumption of inputs and greater production of outputs. We present a class of DEA problems in which one or more of the inputs or outputs are naturally measured on scales in which higher numerical values represent lower input consumption or lower output production. We refer to such quantities as reverse inputs and reverse outputs. We propose to incorporate reverse inputs and outputs into a DEA model by returning to the basic principles that lead to the DEA model formulation. We compare our method to reverse scoring, the most commonly used approach, and demonstrate the relative advantages of our proposed technique. We use this concept to analyze all 30 Major League Baseball (MLB) organizations during the 1999 regular season to determine their on-field and front office relative efficiencies. Our on-field DEA model employs one output and two symmetrically defined inputs, one to measure offense and one to measure defense. The defensive measure is such that larger values correspond to worse defensive performance, rather than better, and hence is a reverse input. The front office model uses one input. Its outputs, one of which is a reverse output, are the inputs to the on-field model. We discuss the organizational implications of our results.     

Efficiency evaluation of China's high-tech industry with a multi-activity network data envelopment analysis approach

This paper employs a Russell multi-activity network DEA model and divides the overall innovation process into the upstream Research and Development (R&D) process and the downstream commercialization process to appraise the innovation performance of China's high-tech industries from 2009 to 2013. This model can deal with the problems of intermediates, shared inputs and slack-based measure in a unified framework, and the result can provide policy makers with process-specific information on how to improve the innovation performance of China's high-tech industries. The main findings are presented as follows. First, the overall efficiency of China's high-tech industries still remains at a low level, which has its roots mainly in commercialization inefficiencies other than R&D inefficiencies. Second, for most provinces, their R&D efficiencies do not match up with their commercialization efficiencies. Finally, the innovative activities of China's high-tech industries should be driven by the market demand -oriented for the improvement of innovation efficiency.     

THE INTERTEMPORAL DIMENSION OF NEOCLASSICAL PRODUCTION THEORY

Abstract. The aim of this survey paper is to provide a guide to the literature on optimal dynamic factor demands to the non-specialist reader interested in applied work. We start with the distinction between variable and quasi-fixed factors of production and use these to characterize the firm's temporary equilibrium. We then review the optimal intertemporal behaviour of the firm, using the notion of adjustment costs as a means to solve the firm's optimization problem. This process gives rise to a system of interrelated dynamic factor demands in a flexible accelerator format. Theoretical difficulties and empirical limitations of this model are discussed. This fact leads us to review the theory of intertemporal duality. We next analyze the issue of expectations in this class of dynamic models. A section reviewing the empirical work on dynamic factor demands follows, after which we offer some concluding remarks.     

Multi-directional program efficiency: the case of Lithuanian family farms

The present paper analyses both managerial and program efficiencies of Lithuanian family farms, in the tradition of Charnes et al. (Manag Sci 27(6):668–697, 1981) but with the important difference that multi-directional efficiency analysis rather than the traditional data envelopment analysis approach is used to estimate efficiency. This enables a consideration of input-specific efficiencies. The study shows clear differences between the efficiency scores on the different inputs as well as between the farm types of crop, livestock and mixed farms respectively. We furthermore find that crop farms have the highest program efficiency, but the lowest managerial efficiency and that the mixed farms have the lowest program efficiency (yet not the highest managerial efficiency).     

Effective Rates of Sectoral Productivity Change

In effective rates of sectoral productivity change, some of the inputs are treated as produced. Here, this is extended to cover all the inputs. All the sectoral rates of productivity growth based on a static input–output (IO) framework are shown to be equal to the corresponding rates of decrease in the production price. For the direct rate, all the input prices are treated as exogenous constants. For the effective rates, prices of the inputs, which are treated as produced, are determined by production technology. The fully effective rate is derived from the price equations of the closed dynamic IO model. It is equal to the rate of decrease in the production price when the prices of all inputs, human capital and human time included, depend on production technology. The overall rate, obtained as a weighted sum of the fully effective sectoral rates, is equal to the rate of growth in the growth potential of the economy.     

Analysis of regional productivity with capital as a quasi-fixed factor

Most existing studies of regional productivity growth do not incorporate the effect of variations in capacity utilization on changes in output. By failing to do so, their factor productivity estimates are biased. To overcome this shortcoming, we adjust multifactor productivity growth measure for changes in capacity utilization. Our technique recognizes that capital is a quasi-fixed factor which implies that capital in the short run can be either under- or over-utilized by a firm. Our results from 1974 to 1978 show that capacity adjusted multifactor productivity growth measure exceeds capacity unadjusted multifactor productivity growth measure for the nine census divisions. The bias in the capacity unadjusted measure of multifactor productivity growth is approximately 8 percent in East North Central and over 33 percent in Mountain. We find that the aggregate factor productivity growth is slowest in the traditional manufacturing belt (Middle Atlantic and East North Central divisions). The level of aggregate factor productivity in the manufacturing belt, however, is almost 33 percent higher than in regions in the south.     

Reprint of "Performance evaluation of China's high-tech innovation process :Analysis based on the innovation value chain"

The Chinese high-tech industry has developed greatly since the beginning of China's “National High-tech R&D (863) Program” and “China Torch Program”. This paper introduces a conceptual model extended from the innovation value chain model to simultaneously estimate the R&D and commercialization efficiencies for the high-tech industries of 29 provincial-level regions in China. To match reality, a network DEA incorporating both shared inputs and additional intermediate inputs is constructed to open the “black box” view of decision making units used in single-stage DEA. This study is the first attempt to link the R&D and commercialization with a solid theoretical foundation and feasible mathematical methods. The empirical findings show that most of the 29 regions have low efficiency in the commercialization sub-process compared to the R&D sub-process, although there are regional differences in China's high-tech industry. Pearson correlation shows that the R&D sub-process is not closely correlated to the commercialization sub-process in terms of efficiency. Our analysis can provide information for the formulation of policies to achieve high innovation efficiency.     

The Dynamic Production Axioms and System Dynamics Behaviors: The Foundation for Future Integration

Realistically, organizational and/or system performance is dynamic and non-linear. However, in the efficiency literature, system performance is frequently evaluated considering linear combinations of the input/output variables and without explicitly taking into account the causes of efficiency behavior nor the dynamic behavior of systems. Policy decisions based on these results may be sub-optimized because the non-linear relationships among variables, causal relationships, and feedback mechanisms are ignored.This research takes the initial step of evaluating system performance in a dynamic environment, by relating the factors that effect system performance to the policies that govern it. To accomplish this, this paper extends the concepts of the static production axioms into a dynamic realm, where inputs are not instantaneously converted into outputs. The relationships of these new dynamic production axioms to the basic behaviors associated with system dynamics structures are explored.