Variations In Capacity Utilization Research Articles

Impact of Plant Utilization on Irreversible Investment Under Uncertainty with Application to Refinery Investment

Why are some regions preferred when investors consider irreversible investment? This study offers an explanation to this question and suggests improvements that will assist disadvantaged regions improve on their bid for funds. The paper considers irreversible investment under uncertainty when installed capacity utilization is incorporated. We develop a normative model for irreversible investment problem under uncertainty using real options approach. Capacity utilization was not a major consideration by previous authors who assumed that installed capacity would be fully utilized. Variations in capacity utilization may be attributed to disruption in input supply or infrastructural bottlenecks that limit firms to get their products to customers. This study modifies the geometric Brownian motion for the value of a project to account for capacity utilization in the derivation of irreversible investment decision rule. The proposed model provides a theoretical explanation of how utilization affects irreversible investment decisions. Data on petroleum refinery margins is used to illustrate application of the proposed model to refinery investment. The study reveals that capacity utilization has an inverse effect on the investment trigger, and so, links irreversible investment decisions to plant utilization. We recommend optimal utilization of installed plant capacity for regions seeking funds for irreversible investment.

Fluctuation in operational energy efficiency of ships and its implications for performance appraisal

This paper develops a dynamic regression model to quantify the contribution of key external factors to operational energy efficiency of ships. On this basis, kernel density estimation is applied to explore distribution patterns of fluctuations in operational performance. An empirical analysis based on these methods show that distribution of fluctuations in Energy Efficiency Operational Indicator (EEOI) is leptokurtic and fat tailed, rather than a normal one. Around 85% of fluctuations in EEOI can be jointly explained by capacity utilization and sailing speed, while the rest depend on other external factors largely beyond control. The variations in capacity utilization and sailing speed cannot be fully passed on to the energy efficiency performance of ships, due to complex interactions between various external factors. The application of the methods is demonstrated, showing a potential approach to develop a rating mechanism for use in the legally binding framework on operational energy efficiency of ships.

Tolerable ranges of variation in the rate of capacity utilisation and corridor instability: a reply to Florian Botte

AbstractThis reply to Botte (2019, Estimating normal rates of capacity utilization and their tolerable ranges: a comment on Mark Setterfield, Cambridge Journal of Economics, forthcoming) responds to criticisms of the methods used to estimate the normal rate of capacity utilisation and a tolerable interval of variation in the actual rate of capacity utilisation around the normal rate in Setterfield (2019a, Long-run variation in capacity utilization in the presence of a fixed normal rate, Cambridge Journal of Economics, vol. 43, no. 2, 443–63). It concludes with some further reflections on the concept of corridor instability.

Long-run variation in capacity utilization in the presence of a fixed normal rate

A generic Kalecki-Robinson model of growth is developed that, subject to different closures, illustrates the different channels through which the economy can adjust to a change in demand conditions in the long run. The closures are shown to have different implications for the behaviour of the rate of capacity utilization and hence whether and how the economy achieves a ‘fully-adjusted position’ (equalization of the actual and normal rates of capacity utilization). Assuming that the normal rate of capacity utilization is exogenously fixed, it is then shown that variation in the actual capacity utilization rate can nevertheless occur—at least within limits—without triggering ‘Harrodian instability’. This result emanates from a discontinuity in the investment function that is grounded in Harrod’s own macrodynamics, so that it is ultimately the combination of Harrodian and Kaleckian dynamics that gives rise to long-run variations in the actual rate of capacity utilization in the presence of a fixed normal rate. Aggregate and industry-level US capacity utilization data are then used to calculate possible bands within which the rate of capacity utilization may vary without triggering Harrodian instability. A key finding is that the conditions necessary for the latter appear to be relatively rare.

Long-Run Variation in Capacity Utilization in the Presence of a Fixed Normal Rate

We develop a generic Kalecki-Robinson model of growth that, subject to different closures, illustrates the different channels through which the economy can adjust to a change in demand conditions in the long run. The closures are shown to have different implications for the behaviour of the rate of capacity utilization and hence whether and how the economy achieves a “fully-adjusted position” (equalization of the actual and normal rates of capacity utilization). Assuming that the normal rate of capacity utilization is exogenously fixed, it is then shown that variation in the actual capacity utilization rate can nevertheless occur – at least within limits – without triggering “Harrodian instability”. This result emanates from a discontinuity in the investment function that is grounded in Harrod’s own macrodynamics, so that it is ultimately the combination of Harrodian and Kaleckian dynamics that gives rise to long-run variations in the actual rate of capacity utilization in the presence of a fixed normal rate. Aggregate and industry-level US capacity utilization data are then used to calculate possible bands within which the rate of capacity utilization may vary without triggering Harrodian instability. A key finding is that the conditions necessary for the latter appear to be relatively rare.

Correction for variations in capacity utilization in the measurement of productivity growth: A non-parametric approach

The multifactor productivity growth estimate published by statistical agencies should be corrected for the effect of the short run variations in capacity utilization for such estimate to be a measure of technological progress. But such correction is not normally made as the rate of capacity utilization is often not observed. This paper develops a nonparametric approach for adjusting multifactor productive growth measure for variation in capacity utilization over time. In the approach developed here, the capital utilization measure is derived from the economic theory of production and is estimated by comparing the ex-post return with the ex-ante expected return on capital. The approach offers a practical solution that can be used by statistical agencies to adjust for capacity utilization in their multifactor productivity growth measure. The nonparametric approach is implemented using the data for the manufacturing sector from the Canadian Productivity Program of Statistics Canada, and is found to correct for the bias from the variation in capacity utilization in that sector.

Factors Affecting Capacity Utilization Decisions in Nigeria: A Time Series Analysis

This study examined the impact of some macroeconomic variables and power supply on the performance of the Nigerian manufacturing sector, using ex-post facto research design. Secondary data were sourced from Central Bank of Nigeria (CBN) statistical bulletin (2009) and other publications. The main findings of the study were that power supply had positive and significant impact on capacity utilization while inflation rate and interest rate had negative impact on capacity utilization. However, the impact of interest rate was significant at 5% level while lending rate was insignificant. Time series data were analysed with the aid of e-views 5.0 econometric computer package using least square multiple regression technique. The regression model explained 88.54% of the variation in capacity utilization, after correcting for linearity, normality, auto-correlation and heteroscedascity. The study recommended that the ongoing privatisation of Power Holding Company of Nigeria should be pursued with vigour and that the policy thrust of single digit inflation and lending rates by CBN should be sustained. The government should also put in place monetary and fiscal policies to create an enabling environment for the manufacturing sector, thereby giving a boost to the economy as a whole.

Trade Reforms and Total Factor Productivity Growth in India’s Aluminium Industry with Adjustment for Capacity Utilization

This paper examines productivity performance of India’s aluminium industry in the pre and post-economic reforms and tries to relate economic capacity utilization with productivity growth. The results on partial productivity of factors show improvement in productivity of material and capital but not of labour. The total factor productivity growth in the post-reform period, however, declined. Total output growth in Indian aluminium industry is found to be mainly input-driven rather than productivity-driven. With adjustment for variations in capacity utilization, trend in total factor productivity growth remains declining. The paper also reconfirms the important links that exist between manufacturing productivity, trade orientation, industry specific characteristics and some macro economic variables. The liberalization process is found to have adverse impact on total factor productivity growth.

Chrysler and J. D. Power: Pioneering Scientific Price Customization in the Automobile Industry

Pricing is a critical component in the marketing-mix plans of automobile manufacturers. Because they tend to keep their manufacturer's suggested retail prices (MSRPs) and wholesale prices fixed throughout the model year, they customize pricing to reflect supply and demand by using incentives; in the US market, they represent approximately $45 billion per year. In addition, variations in capacity utilization have immediate and substantial effects on profitability. This, together with legacy costs and inflexible labor contracts, makes the effectiveness and efficiency of price-customization decisions particularly vital for the industry. Chrysler, a pioneer in using science in its pricing decisions, engaged J. D. Power and Associates (JDPA) to implement an incentive planning model. The approach used is based on a random-effects multinomial nested logit model of product (vehicle model), acquisition (cash, finance, lease), and program-type (e.g., consumer cash rebates, reduced interest-rate financing, cash/reduced interest-rate combinations, lease-support) selection. The model uses sales transaction data that are collected daily from approximately 10,000 dealerships. It uses a hierarchical Bayes modeling structure to capture response heterogeneity at the local market level. This specification allows users to apply the model to pricing decisions at the local, regional, and national market levels. Based on implementing this model, Chrysler learned that, for any given price level, the pricing structure (e.g., a combination of retail price, interest rates, or rebates) is important. The set of the most efficient pricing structures for each price level constitutes an efficient frontier; efficient pricing structures vary across products, price levels, and markets. The system provides three alternative approaches to identify efficient (and effective) pricing programs: (a) what-if-scenario simulations, (b) a batch scenario generator that allows users to identify and examine the profit-share/volume efficient frontier, and (c) an optimizer that, given an objective and a set of constraints, allows users to search for incentive programs rapidly. The Chrysler Corporate Economics Office estimates that Chrysler's annual savings from implementing the model are approximately $500 million.

Measures of capacity utilization and its determinants: a study of Indian manufacturing

We estimate Capacity Utilization (CU) rates in for selected industries in Indian manufacturing for the 20-year period 1976–1996. We estimate a generalized Leontief variable cost function, with capital as a quasi fixed input, to derive our CU measures, using error-component techniques. We note substantial variations in CU both across industries and over time. In general, we find that CU rates were higher in the earlier time-period, dropped in the mid-80s and started rising again in the early 90s. CU rates in our analysis are sensitive to input prices with the sole exception of the price of labor. We also confirm the standard result that variations in demand are a significant driving force for variations in CU. We find that CU is positively related to the magnitude of labor intensity in production. This holds for both between-industries and within-industries. Empirical results also indicate that traditional measures of CU such as minimum capital output ratio and peak-to-peak are not appropriate proxies for the short-run decision making of the firm regarding CU. As compared to the estimates derived from the choice-theoretic framework, we find that the traditional measures exhibit substantial bias.

Utilization of optimal capacity in Indian manufacturing, 1974–1996

This paper attempts to estimate a consistent series for the economic capacity utilization of the Indian non-electrical machinery manufacturing sector. The economic or optimum capacity is defined as the output where the short-run average total cost is minimized. Using an iterative version of Zellner's seemingly unrelated regression estimation technique, a translog short-run cost function is estimated, imposing the conditions for economic optimizing behaviour of firms to calculate the optimal output. Such a measure assumes that variations in capacity utilization (CU), defined as the ratio of actual to optimal output, are the systematic results of rational optimization procedures, depending on price and cost conditions of firms. A comparison of the new series with the conventional engineering measures of CU shows that the widely used ‘installed CU’ figures clearly underestimate the true economic utilization levels, mainly due to definitional problems.

A Dynamic Model of Industrial Energy Demand in Kenya

This paper analyses the effects of input price movements, technology changes, capacity utilization and dynamic mechanisms on energy demand structures in the Kenyan industry. This is done with the help of a variant of the second generation dynamic factor demand (econometric) model. This interrelated disequilibrium dynamic input demand econometric model is based on a long-term costfunction representing productionfunction possibilities and takes into account the asymmetry between variable inputs (electricity, other-fuels and labour) and quasi-fixed input (capital) by imposing restrictions on the adjustment process. Variations in capacity utilization and slow substitution process invoked by the relative input price movement justifies the nature of input demand disequilibrium. The model is estimated on two IS1C digit Kenyan industry time series data (1961 - 1988) using the Iterative Zellner generalized least square method.

Morrison's measure of capacity utilization: A critique

Morrison (1986) has developed and used new capacity utilization measures based on an optimization framework to adjust productivity growth for changes in capacity utilization. She argues that we can adjust productivity growth for variations in capacity utilization by dividing a conventional productivity growth rate by a capacity utilization measure. However, Morrison's analysis is based on a conceptual error, which she recognizes in later research. This error can have considerable empirical significance.

Productivity Measurement in Common Property Resource Industries: An Application to the Pacific Coast Trawl Fishery

This article measures total factor productivity in industries that exploit resources held in common. Particular attention is paid to the valuation and specification of in situ commonproperty resources in a neoclassical production technology, catchability of the resource, and variations in economic capacity utilization. An empirical analysis of the open-access Pacific coast trawl fishing industry demonstrates that disentangling the productivity residual from changes in resource abundance, its catchability, and variations in capacity utilization hones the productivity residual to finer precision, lowering mean productivity growth by about haK Removing biological noise from highly variable resources is also important. The results are related to aprogram limiting the number of vessels and can contribute to sustainable resource management whenever resources are held in common.

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.

Productivity measurement with adjustments for variations in capacity utilization and other forms of temporary equilibrium

One of the most persistent puzzles addressed by recent productivity research is the substantial decline in measured productivity growth during the 1970's. In this paper we trace a substantial part of the measured decline to the fact that traditional methods of productivity measurement assume that producers are in long-run equilibrium when in fact they may be in short-run or temporary equilibrium. We utilize the Marshallian framework of a short-run production or cost function with certain inputs quasi-fixed to provide a theoretical basis for accounting for temporary equilibrium. Within this theoretical framework it is the value of services from stocks of quasi-fixed inputs which should be altered rather than the quantity. The empirical application to U.S. manufacturing data 1958–81 shows that, depending on the measurement procedure, one can attribute somewhere between 18% and 65% of the traditionally measured decline in TFP growth between 1965–73 and 1973–81 to the effects of temporary equilibrium.

Industrial energy efficiency: A look at the data

It is extremely difficult to make a precise, quantitative assessment of the impact of the myriad of factors affecting the improvement in industrial energy efficiency. It is certainly not correct to conclude that housekeeping measures alone have led to the observed improvement. Changing product mix among four digit SIC industries within the same two digit classification, variations in capacity utilization (returns to scale) and energy price increases as well as technological innovations have all contributed to part of the realized reduction in energy use per dollar value added over the period of investigation. Unfortunately, data limitations as well as modeling weaknesses prohibit an exact delineation of the impact of each of the factors on the increase in energy efficiency. The best that can be done—and quite convincingly so—is to qualitatively show that unequivocally these factors had an impact on the efficiency with which energy was used in the manufacturing process for the ten most energy intensive industries in the period 1971–1976.

A Variable-Parameter Model of Exporting Behaviour

This paper has two purposes. The first is to introduce an econometric model of export pricing and sales behaviour, the parameters of which vary with some indicator of excess productive capacity. For the individual firm this is achieved simply by proposing that it follows a demand-constrained regime when levels of working are low, and a supplyconstrained regime when full capacity is approached. Application of this sort of model to the whole economy would lead to implausible discontinuities; as overall capacity utilization rose a switching point would be reached where factors influencing demand-world trade, competitor prices-became suddenly unimportant and those influencing supply-investment, profitability-became suddenly all-important. The second purpose of this paper is to offer a resolution of this conceptual inelegance by postulating that at any one time firms experience a variety of capacity utilization conditions distributed around means which are generally high at peaks of the domestic business cycle and low in troughs. The resulting aggregate equations exhibit no unwonted discontinuities. The potential area of application of this device is large, as all multi-regime models involve some abrupt changes in the behaviour patterns of the agents they describe. It is, however, circumscribed by the technical problems of combining the aggregation procedure with constraints on the agents' behaviour which are essentially stochastic, and by the need to fabricate data on the way the switching variable (capacity utilization) is distributed across individual agents (firms). We shall look at two versions of the export model, the first resting on the assumption that the constraints on firm behaviour share a common stochastic term, thus effectively reducing these technical problems to those of pure aggregation, the second on the more general premise that such random elements are independent. The first suffices to illustrate the important points that weighting or sample-splitting schemes are inappropriate to its estimation, and that there is a real danger of incorrectly rejecting the hypothesis that two regimes are at work if firms differ in their experience of the switching variable. The second model is developed partly to reveal a further source of bias in this direction, but mainly to catalogue the practical problems of obtaining a manageable estimating equation for a model in which independent stochastic constraints coexist with an aggregation problem. This is in fact achieved only by confining all random variables to a non-normal class of distribution functions which is tailored to produce relatively simple expressions for total exports while making full use of the meagre data available on intraindustry variations in capacity utilization. In the first section below, the basic model of the exporting firm is formalized and set in the context of current research on models with varying parameters. In the second section, aggregate equations for the economy's exports are derived and the problems of estimation and measurement posed by them are discussed. Finally, estimated equations for the volume and price of UK exports of manufactured goods are presented with special emphasis on the distinct effects of cyclical variations in the degree of capacity utilization on export demand and supply.

Die verschiedenen Konzepte des Akzelerators

In theoretical analysis the accelerator concept is attractive because it can explain changes in investment which do not run parallel to demand. Empirical tests of the accelerator usually show unsatisfactory results. This is partly due to the specification used for the estimation: the relation between capital stock and production (the first level of estimation) is not popular because of the time trend in the variables; instead frequently a regression of investment expenditure on the change in production (second level of estimation) is applied. But because of the cyclical variations in the capital-output ratio this specification is not a good substitute for the first one. This paper shows that with the help of the relation between capital stock and output (“naive” accelerator) the highly different phases of investment activity can be explained, but that the individual annual observations follow another set of rules. Within the “flexible” accelerator the attemps that use only flow magnitudes do not represent a further development, but rather a counter-hypothesis of the original accelerator concept: flow magnitudes can explain medium-term variations only to a very small extent. This is contrary to the accelerator concept which explains considerable investment variations with the help of relatively small output variations. Attempts which explain investment variations through a connection between output and capital stock, expand the accelerator theory in so far as they take account of disequilibrium situations. The econometrically estimated equations give an excellent picture of short-term and medium-term investment activity. The economic interpretation of this equation shows, however, that in addition to the explicitly used variables (capital stock and output) other magnitudes contribute implicity to these excellent results. The merit of the accelerator theory then is not to be seen in a complete “explanation” of investment behavior, but rather in the fact that it shows highly variable investment demand for capacity purposes which again is dampened by other factors in such a way as to exhibit medium-term variations in capacity utilization.