Government Interventions, Market Imperfections, and Technical Inefficiency in a Mixed Economy: A Case Study of Indian Agriculture

This paper uses an output-maximizing framework in the presence of expenditure constraint to measure output loss and input misallocation resulting from market distortions and technical inefficiency. A generalized indirect production function accommodating allocative distortions and technical inefficiency is used. Allocative distortions are captured in terms of effective (shadow) prices in which distortion parameters are both farm- and input-specific. The stochastic frontier approach is used to model technical inefficiency. Using farm-level data on 105 jute growers from West Bengal, India, we find that average output losses due to allocative distortions and technical inefficiency are 6.3% and 14%, respectively.     

TIMING OF LUMPY INVESTMENT,PRICING AND TECHNICAL PROGRESS

In equipment‐intensive sectors – such as water utilities, power generation, and gas – billions of dollars are spent in capital equipment. The nature of the investment is often lumpy: at some point a plant has to be replaced and a large investment is required. We characterize the dynamic optimal investment policy of profit‐maximizing and welfare‐maximizing firms. We first show that, when there is no technical progress, the duration of the plant is longer for a profit‐maximizing firm. We then consider technical progress leading to either capacity expansion or to operating costs reduction. We show that duration tends to increase when the installed capacity increases over time, while it tends to decrease when technical progress reduces operating costs, both for profit‐maximizing and welfare‐maximizing firms. Under some conditions, when capacity expands over time the duration of the plant is longer for a profit‐maximizing firm than for a welfare‐maximizing firm.     

PERFORMANCE MEASUREMENT IN SOLIDARITY ECONOMY ORGANIZATIONS: THE CASE OF FAIR TRADE SHOPS IN ITALY

ABSTRACT: The aim of this paper is to introduce a multidimensional assessment model for organizations that have multiple goals and are not driven exclusively by profit. Therefore, it is an assessment method particularly suitable for social enterprises. In order to measure the efficiency of production units in each dimension, the Data Envelopment Analysis non‐parametric method is applied. Our case study is concerned with Fair Trade shops and members of the consortium Altromercato for which we collected variables that could be associated with input and output for the economic, socio‐cultural and organizational dimensions. The results of the analysis confirm the presence of economies of scale in the economic dimension but not in the socio‐cultural dimension. Moreover, our organizational analysis confirmed a low general professionalization. Our results also confirm, on the one hand, the more pronounced capacity of cooperatives to sell Fair Trade products compared to associations, which, on the other hand, have the tendency to achieve proportionately better results in the socio‐cultural dimension.     

Economic inefficiency in a revenue setting: the Norwegian whitefish fishery

The main focus in the inefficiency literature is on suboptimal input use and how this causes increased costs, due to technical and allocative inefficiency. Production or cost functions are then typically used to describe the underlying technology of the firm. The possible revenue loss, due to lower than maximum production levels and suboptimal output mix, has received substantially less attention. By using a revenue function to measure inefficiency, the focus, model and estimation technique presented in this article differ from those of previous studies. A shadow revenue model is used to decompose revenue inefficiency into its technical and allocative components, in which the allocative inefficiency is due to a suboptimal output mix. The approach is illustrated using panel data of Norwegian whitefish trawlers. The results reveal large inefficiencies, with respect to output levels as well as output mix, indicating that this can be an important part of the picture when investigating economic inefficiency. To identify the determinants of revenue inefficiency, we conduct a second-step regression, in which technical and allocative inefficiency is regressed upon a set of explanatory variables. The inefficiencies are partly explained by the management system and fleet structure.     

Technical efficiency effects of input controls: evidence from Australia's banana prawn fishery

This paper provides the first ex post estimates of the effects of input controls on technical efficiency in a fishery. Using individual vessel data from the northern prawn fishery of Australia for the years 1990–1996 and 1994–2000, stochastic production frontiers are estimated to analyse the efficiency impacts of input controls on engine and vessel size. The results indicate that technical efficiency is increasing in a measure of vessel size and engine capacity that was controlled by the regulator from 1985 to 2001, and decreasing in an unregulated input, gear headrope length. The study shows that fishers have substituted from regulated to unregulated inputs over the period 1990–2000 and technical efficiency has declined coincident with increasing restrictions on vessel size and engine capacity. The decline in technical efficiency indicates that the goal of the regulator to increase economic efficiency has not been realized.     

Biased Technical Change and Parallel Neutrality

The Luenberger productivity indicator has many attractive features to evaluate productivity, technical and efficiency changes. Paralleling Färe et al. (1997), this paper shows that the technical change indicator can be expressed as the sum of a magnitude indicator and a bias indicator that is the sum of two bias indicators input and output oriented respectively. Using a recent concept of ``parallel neutrality'' introduced by Briec et al. (2006), some conditions under which each bias indicator makes no contribution to productivity change are established. Among the key contributions of this paper is a new linear programming model involving a graph translation homotheticity property.     

Real Exchange Rate Volatility and Imports of Intermediate Inputs: A Microeconometric Analysis of Manufacturing Plants

This paper uses plant‐level data from the manufacturing sector of Chile for the period 1995–2007 to examine the effect of real exchange rate (RER) volatility on imports of intermediate inputs at the micro level. Using input–output tables, we construct sector‐level RERs relevant for input import decisions and find that increases in the RER and its volatility reduce the fraction of imported intermediate inputs used by plants, while plants' probability of importing is not affected. Thus, fluctuations in the RER affect the intensive margin of imports (the amount of inputs imported) but not the extensive margin (the decision to import).     

THE MICRO‐DYNAMICS OF CATCH‐UP IN INDONESIAN PAPER MANUFACTURING

In this study we analyze the micro‐dynamics of catch‐up in Indonesian paper manufacturing, using a two‐country plant‐level dataset for the period 1975–97. We apply data envelopment analysis (DEA) to measure to what extent Indonesian paper mills are catching up with Finnish mills in terms of technical efficiency. Three questions are addressed: What is the distribution of Indonesian plant technical efficiency vis‐à‐vis the technological frontier? What is the role of entry, exit, and survival in Indonesia for catch‐up in the paper industry as a whole? In what ways do catching‐up plants in Indonesia differ from non‐catching‐up plants? We find that on average the Indonesian paper industry moved closer to the technological frontier during the 1990s. However, catch‐up has been a highly localized process in which only a few large establishments have achieved near best‐practice performance, while most other plants have stayed behind.     

Keynes,Sraffa and the labour market ∗

The paper argues that input–output analysis existed long before it received its name and Wassily Leontief made it popular as a tool of empirical analysis and a foundation of economic policy. It grew out of an attempt to ascertain the capacity of an economic system to reproduce itself and generate a surplus that can be used for various purposes. Primitive pronouncements are encountered in early civilizations, for example Mesopotamia, in terms of the ratio of the amount of grain produced and the amount of it used up, directly and indirectly. These ideas reappeared in a more sophisticated form at the time of the inception of systematic economic analysis in the 17th and 18th centuries in Europe and found a two-sector expression in François Quesnay's Tableau économique. The material input–output structure was then considered the core of the economic system that contained one of the keys to basically all other important economic phenomena and magnitudes. The way in which the potentialities embodied in the input–output structure, conceived as a system of production, have, or have not, been exploited over time define both the problems and perspectives of contemporary input–output analysis. Three aspects will be scrutinized more closely: the problem of value added, the treatment of fixed capital and the problem of technical change. Happily enough, while the problems are huge, the prospects are encouraging. There is no fear that input–output analysts will soon have to look for new fields of research because the old ones have been exhausted.     

Biases in technical efficiency scores caused by intra-input aggregation: mathematical analysis and a DEA application using simulated data

In Data Envelopment Analysis (DEA) applications involving multiple inputs and outputs, inputs are aggregated into the total amounts of each type of input. For example, if input types ‘labour’ and ‘capital’ are used to produce multiple outputs, the total amount of labour used to produce all outputs is treated as one aggregated input and the total amount of capital as another. Resources are not disaggregated into input variables measuring the amount of labour used to produce the first output, the amount of labour used to produce the second output, the amount of labour used to produce the third output and so on, for both labour and capital. It is shown that such intra-input aggregation causes downward bias in reported technical efficiency scores, with variations in bias unrelated to true technical efficiency. Therefore, with few exceptions, any technical efficiency comparisons among DMUs are invalid. The presence of intra-input aggregation bias is demonstrated mathematically, simulation is used to exhibit its severity, and the exceptions that permit intra-input aggregation without causing bias are identified. It is concluded that, for multiple-input, multiple-output DEA applications, inputs must be disaggregated into the amounts used to produce each output in order to validly report technical efficiency, unless one of the exceptions is present.     

Are overtime plants more efficient than standard-time plants? A stochastic production frontier analysis using the IAB Establishment Panel

Using data from the IAB Establishment Panel, 1995–2000, we investigate between-plant variations in productivity. The study compares technical efficiencies between plants which work overtime (overtime plants) and plants which do not work overtime (standard-time plants), where technical efficiency measures the gap between feasible output and actual output for a given input combination. A fixed effects approach is applied, which does neither rely on any distributional assumptions of the inefficiencies, nor on the independence between the regressors and inefficiencies. For both, models with time-invariant and time-varying inefficiencies, we do not find evidence that the two plant-types differ in their technical efficiency.First version received: May 2002 / Final version received: August 2004The comments by Martyn Andrews and Bob Hart are gratefully acknowledged. The article has also benefited from the comments of two anonymous referees.     

Ecological pricing and economic efficiency

There is a need to accurately account for the contributions of environmental assets to the overall economy. Such accounting would permit policies that allow protection of important natural resources and aid the analytic process to determine an accurate basis for a sustainable economy. The aim is to develop an accounting framework for ecology that is sufficiently consistent with the economic framework that the two can be fruitfully combined. With appropriate definitions of the flows, the two systems can be connected into a common framework. No single measure of the system productivity and efficiency can be given for the combined system, however, until the ecosystem metabolism can be converted into economic terms. This could be done with a series of economic valuation techniques. Ecological prices could then be estimated and a single measure of ecological economic output could be given. With the net combined system input and output now in common terms, a technical system efficiency measure can logically be proposed. Because human activity inevitably involves dissipation, such emissions would now have a monetary price. Because such emissions are irrecoverable, the total output of the combined system is greater than it is under the current definition, giving rise to a technical system-wide efficiency measure.     

MEASUREMENT OF TOTAL FACTOR INPUT, TECHNICAL CHANGE AND OUTPUT BY INDUSTRY IN THE FEDERAL REPUBLIC OF GERMANY 1958–1968

This paper deals with some results of an extended investigation which was carried out by the German Institute of Economic Research, Berlin, and the Ifo-Institute, Munich, and financed by the Stiftung Volkswagenwerk. For 29 sectors of manufacturing Cobb-Douglas production functions have been calculated, based on quarterly figures 1958–1968 of value added, input of hours worked, input of utilized capital stock (net of scrappage), and of potential value added, potential labor input and total capital stock. The income distribution is used as production elasticities. For each of the 29 sectors 12 time series of quarterly indices of total factor input and technical change have been computed, using utilized data (variation 1-6) and capacity data (variation 7-12). Two different time series of α are used, taking quarterly interpolated data (variation 1, 3, 5, 7, 9, 11) and the geometric mean 1958–1968 (variation 2, 4, 6, 8, 10, 12). Moreover three different parameters of homogeneity are introduced, taking r= 1 (variation 1, 2, 7, 8), r= 1.1 (variation 3, 4, 9, 10) and r= 1.25 (variation 5, 6, 11, 12). Seven of the 29 sectors show a very high sensitivity of the rate of technical change due to the assumed r, six sectors a rather high sensitivity. Ten of the 29 sectors show a rather small sensitivity of technical change due to the assumed r, six sectors a very small or even negative sensitivity, i.e. an increasing r creates an increasing technical change. These results can be explained by taking account of the fact that total factor input in many branches increased very slowly or even decreased (labor input alone decreased in nearly all branches). A hierarchy of technical change has been calculated; this hierarchy is difficult to explain, because fast growing industries as well as industries with a small or a negative growth rate of output rank in both the leading and the last group of technical change. Very high rates of output result in high rates of technical change (chemicals, mineral oil refining, plastics manufactures), but some industries with a rather small growth of output (shipbuilding, fine ceramics, steel drawing, and cold rolling mills) show a high rate of technical change too.     

Financial liberalization and bank efficiency: a comparative analysis of India and Pakistan

This paper provides a comparative analysis of the evolution of the technical efficiency of commercial banks in India and Pakistan during 1988–1998, a period characterized by far-reaching changes in the banking industry brought about by financial liberalization. Data Envelopment Analysis is applied to two alternative input–output specifications to measure technical efficiency, and to decompose technical efficiency into its two components, pure technical efficiency and scale efficiency. The consistency of the estimated efficiency scores are checked by examining their relationship with three traditional non-frontier measures of bank performance. In addition, the relationship between bank size and technical efficiency is examined. It is found that the overall technical efficiency of the banking industry of both countries improved gradually over the years, especially after 1995. Unlike public sector banks in India, public sector banks in Pakistan witnessed improvement in scale efficiency only. It is also found that banks are relatively more efficient in generating earning assets than in generating income. This is attributed to the presence of high non-performing loans. In addition, it is found that the gap between the pure technical efficiency of different size groups has declined over the years.     

Dynamic and endogenous change of input–output structure with specific layers of technology

There are obvious gaps between long-term change in economic structure and its principal driving force—technological progress. History has shown the influence of technological progress on the economy and current insights in technological development can almost predict the technological waves of the next 50 years, but their potential impact on the economy has not yet been assessed. In this paper, we aim to simulate the evolution of economic structure as represented by input–output structure under specific technological change. A new version of a dynamic input–output model is developed in which both technological progress and deployment are endogenous. Investment in R&D drives the development of new technologies, installation of capital stock brings new technical processes into sector production, new and old technical processes within a sector exchange their relative weights in production as they are phased in or out, and sectors evolve or transform over time. Scenario analysis using this model applied to the Chinese electric power industry shows that the phasing-in of non-fossil energy technology will greatly change the structure of both the sector and the economy over the next 100 years.     

创新型城市创新效率评价与投入冗余分析

推动创新型城市建设,是我国实施创新驱动发展战略的重要抓手。运用数据包络分析方法（DEA）,综合考虑创新的经济产出、科技产出和社会环境效益,构建CCR和BCC模型,测算了2013- 2014年53个国家创新型试点城市的综合效率及其分解的纯技术效率和规模效益,对非DEA有效城市的投入冗余进行了对比和原因分析,并根据创新型试点城市创新效率测算结果,提出了推进创新型城市建设的政策建议。     

The Innovation‐R&D Nexus in an Emerging Economy: Evidence from the Indian Manufacturing Sector

While R&D is considered an important input for innovation, its relationship with the output of the innovation process continues to remain enigmatic, especially in developing countries with low levels of domestic research. Using patents and knowledge capital stock as proxies for the output and input of the innovation process, we study their nexus in the emerging economy of India using a large sample of manufacturing firms over the period 2001–2010. Using a specification that addresses the problem of ‘excess zeroes’ in count data, the firm's non‐technical knowledge stock as an additional factor of the success with which R&D transforms into patents, and a linear feedback mechanism that yields consistent estimators, we find that a one unit increase in knowledge capital likely raises expected patent count by only about 4.3% at most. Further, although patenting experience, resource access and knowledge spillovers are strongly significant in explaining patent counts, the magnitude of these effects is marginal at best. In brief, evidence for the knowledge production function is rather weak even in the context of a dynamic developing economy.     

Evaluating the productive performance of UK universities as cost-constrained revenue maximizers: an empirical analysis

The study presents an empirical analysis of the relative revenue efficiency of UK universities in providing teaching and research. With government policies pressurizing the largely public-funded universities to secure efficiency in both input usage and output revenue, university producers are modelled as cost-constrained revenue maximizers. Taking explicit account of the quality of research output, the methodology uses linear programming techniques to construct nonparametric cost indirect production frontiers and to compute revenue efficiency relative to these frontiers. Revenue efficiency is then decomposed into its (output) allocative and technical components. Further analysis investigates the sources of allocative and technical inefficiencies.     

Notes on some inequalities in economics

Summary. Inequalities created by relating gauge (distance) functions and their dual support functions have been used in economics and operations research/management science to measure efficiency and productivity. The most familiar example is the Farrell (1957) measure of cost efficiency and its decomposition into technical and allocative components, which can be shown to be an application of the Mahler (1939) inequalities, although Farrell was probably unaware of the connection. In this short paper we add to this literature by providing explicit relationships between distance functions and support functions to form five different inequalities. These inequalities are derived from three support functions: the cost function, the revenue function and the profit function, and three gauge (distance) functions: the input distance function, the output distance function and the directional (technology) distance function.     

技术需求与智能制造技术创新——数字经济的导向性赋能机制

智能制造技术创新是持续推动制造业高质量发展的关键动力。为探究制造业技术需求、数字经济赋能对非市场与市场导向下智能制造技术创新的作用机制，基于技术创新需求拉动理论，引入数字经济赋能构建投入-产出两阶段模型。分析发现，当政府研发补贴较高时，劳动替代需求与智能制造技术创新投入呈倒U型相关，对智能制造研发投入产生挤出效应；以效率提升为主的技术市场需求通过影响企业智能制造技术创新投入，间接推动技术创新产出。有调节的中介模型检验表明，数字经济赋能正向调节市场导向的智能制造技术创新产出，但对非市场导向的技术创新不具赋能作用。在充分利用数字经济发展优势的同时，应依托需求拉动机制开发更多需求侧政策工具，进一步激发智能制造技术创新。