Constructing a Manufacturing Fixed Capital Stock Series for the Regions of Greece

Melachroinos, Konstantinos A.; Spence, Nigel

doi:10.1080/096543100110929

Cited by 8 publications

(7 citation statements)

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“…10 10 Maddison (1993) assumes that the service life for structures in national industrial assets is 40 years, and for equipment is 16 years. The service life for manufacturing equipment has been estimated to be 10 years in Japan, 15 years in Germany and 17 years in both the U.S. and France (Melachroinos and Spence, 2000). For manufacturing structures it is 31 years in the U.S., 37 years in France, 41 years in Germany and 43 years in Japan. …”

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confidence: 99%

Industrial and Aggregate Measures of Productivity Growth in China, 1982–2000

Cao

Jorgenson

et al. 2009

Review of Income and Wealth

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We estimate productivity growth for 33 industries covering the entire Chinese economy using a time series of input-output tables covering 1982-2000. Capital input is measured using detailed investment data by asset and labor input uses demographic information from household surveys. We find a wide range of productivity performance at the industry level. We then show how these industry growth accounts may be consistently aggregated to deliver a decomposition of aggregate GDP growth. For the 1982-2000 period aggregate TFP growth was 2.5 percent per year; decelerating from a rapid rate in the early 1980s to negative growth during 1994-2000. The main source of growth during the 1982-2000 period was capital accumulation, with a small negative contribution from the reallocation of factors across industries. IntroductionWhile it is widely agreed that the Chinese economy has grown rapidly since the reforms started in 1978, there is disagreement about both the magnitude and sources of that growth. Was the dominant factor the accumulation of capital, total factor productivity growth, or the restructuring of the economy from agriculture to manufacturing and services? A question related to the structural transformation of the economy is how estimates of aggregate GDP growth may be reconciled with the estimates at the industry level. These questions are difficult to answer given the quality and quantity of data available. The answers to them, however, are important in understanding the effects of past economic policies and hence to devise future policies.Note: We thank two anonymous referees for helpful comments. We also thank members of the National Accounts Department, NBS, who helped us with the data: Xu Xianchun, Qi Shuchang, Liu Liping, Dong Lihua and Zhao Tonglu. We are also grateful for the assistance of Li Xiaoqin and Ma Xiangqian from Beihang University. *Correspondence to: Jing Cao, School of Economics and Management, Tsinghua University, 100084, Beijing, P.R. China (caojing@sem.tsinghua.edu.cn). 485This paper estimates the sources of growth of industry output-the growth of capital, labor and intermediate inputs, and total factor productivity (TFP). To do this we introduce newly developed data, including a time series of input-output tables and estimates from a survey of the labor force. Our measures account for the changing composition of the labor force and investment. The second aim of the paper is to discuss how these industry measures may be aggregated to GDP. We describe three aggregation approaches to highlight the methodological issues of separating out the roles of factor accumulation, factor reallocation and sectoral total factor productivity growth: (i) aggregate production function; (ii) aggregate production possibility frontier (PPF); and (iii) direct Domar-weighted aggregation. The first approach may be familiar to many readers; the aggregate PPF method relaxes the strict assumptions of that approach and allows us to identify the effects of reallocating value-added across industries. The third method...

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confidence: 99%

Industrial and Aggregate Measures of Productivity Growth in China, 1982–2000

Cao

Jorgenson

et al. 2009

Review of Income and Wealth

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“…The average service lives of assets are assumed to be identical to those reported by Jacob et al (1997), namely forty years for structures and thirteen to twenty-three years for equipment, depending on the manufacturing sector. A rather cursory glance into the literature indicates that these asset-service lives are not dissimilar to those adopted by other studies (see for instance Melachroinos and Spence, 2000;OECD, 1993;O'Mahony, 1993;Oulton and O'Mahony, 1994;Young and Musgrave, 1980). By weighting the adopted service lives with the share of each twodigit ISIC branch of total manufacturing investment it is possible to estimate an average full depreciation period that is variable by country and year as shares fluctuate.…”

Section: Measuring Productivity Differentials ö the Oecd Stan Databasementioning

confidence: 47%

Manufacturing Productivity Growth across European Union States: 1978 – 94

Melachroinos

Spence

2001

Environ Plan A

Self Cite

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IntroductionThe evolution of productivity is a key aspect of regional or national economic performance. In the long run, high rates of output and, ceteris paribus, growth in income per capita can be sustained only through improvements in the way that production inputs are utilised. Inevitably, then, spatial differences in productivity levels and trends are particularly important for several reasons. The close links with other economic variables mean that productivity disparities are usually indicative of additional inequalities in a wide range of parameters, including fixed capital accumulation, utilised technology, and labour-force skills. Furthermore, if pressed, the competitiveness of an economy of any given scale is largely reflected in efficiency gains. Productivity is regarded by far a better measure of international competitiveness than say trade balance (Dollar and Wolff, 1993). Another major issue concerns the spatial dynamics of productivity change. Several studies have reported that a convergence process between advanced and lagging economies is underway at

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“…We assume that D is a proportion of the existing stock of plant, i.e., D jt = δ j P jt−1 . Although this is quite standard (e.g., Melachroinos and Spence 2000) we would have preferred to make assumptions about the life expectation of buildings (k) in which case D t = C t−k . This requires data on completions in the remote past, which unfortunately we do not have.…”

Section: A Direct Methods For Plantmentioning

confidence: 99%

“…A final problem concerns the absence of data on regional rates of capital depreciation. Varaiya and Wiseman (1981) and Melachroinos and Spence (2000) use a putty-clay approach to model the rate of depreciation. This approach assumes that the initial investment in capital stock fixes the other technical attributes (such as capital labor ratios) throughout its service lifetime.…”

Section: Introductionmentioning

confidence: 99%