Nonparametric measures of scale economies and capacity utilization: An application to U.S. manufacturing

Abstract

a b s t r a c tAn economic measure of scale efficiency is the ratio of the minimum average cost to the average cost at the actual output level of a firm. It is easily measured by the ratio of the total cost of this output under the constant and variable returns to scale assumptions. This procedure does not identify the output level where the average cost reaches a minimum. This paper proposes a nonparametric method of measuring this output level using DEA. The relation between this efficient production scale, the short run physical capacity output, and the most productive scale size (MPSS) is also discussed. An empirical application using state level data from U.S. manufacturing is used to illustrate the procedure. The DEA findings are further analyzed using a smoothed bootstrap procedure.

Coal overcapacity in China: Multiscale analysis and prediction

“…Given the serious impacts of overcapacity on sustainable economic development, many scholars have attempted to understand the causes and mechanisms that lead to overcapacity (Wang et al, 2014;Dagdeviren, 2016) and develop measurement methods (Klein and Preston, 1967;Ray, 2015;Arfa et al, 2017) and governance policies (Wu and Li, 2015;. Quantitative studies on the causes and prediction of coal overcapacity are limited.…”

“…Most studies have used capacity utilization as an indicator of overcapacity. Other measurement methods include the peak value method (Klein and Preston, 1967), the production function method , the cost function method, the cointegration analysis method (Ray, 2015), data envelopment analysis (Karagiannis, 2015), and stochastic frontier analysis (Arfa et al, 2017). The basic procedure of these methods is to first estimate the output of production capacity and then use actual output divided by production capacity to calculate capacity utilization.…”

Russian manufacturing production capacity: Primary trends and structural characteristics

“…The simplest methodologies for this approach include linear interpolation of values between local maximums and interpretation of those values as a potential output, the Wharton Business School Index (Klein and Summers, 1966), a comparison between current output and the output calculated as a ratio of capital and minimum capital-output ratio within a local neighborhood, 3 and a comparison between the average and maximum hours worked and capital used (Taubman and Gottschalk, 1971;Beaulieu and Mattey, 1998). In the framework of this approach, more complex methodologies should be mentioned: the optimal utilization level would occur if either short-term costs areminimized (Klein,1960;Friedman,1963;Hickman,1964;Morrison,1985;Prior and Filimon, 2002;Ray, 2015), short-term output is maximized (Johansen, 1968;Fareetal.,1989;Rayetal.,2006),orprofitismaximized (Coellietal., 2002). Recently, the structural and cyclical components of economic trends have beenisolatedusingfiltersorstructuralvectorautoregressionmethods (Andrle, 2013;Haviketal.,2014;Apokinetal.,2014;BankofRussia,2014;Sinelnikov-Murylev et al, 2014;IMF, 2014), along with econometric analyses of the correlationbetweenchangesinthecoreinflationrateandcapacityutilizationrates (McElhattan,1978;OomesandDynnikova,2006;MironovandKanofiev,2014).…”

Overcapacity Risk of China’s Coal Power Industry: A Comprehensive Assessment and Driving Factors

“…Many scholars have researched on the coal power overcapacity, including its causes and formation mechanisms [4][5][6], measurement methods [7,8] and governance strategies [9,10]. However, there are still some gaps in the research on the risk assessment system of coal power overcapacity.…”