Hostname: page-component-6d856f89d9-xkcpr Total loading time: 0 Render date: 2024-07-16T03:27:54.822Z Has data issue: false hasContentIssue false
  • English
  • Français

ON THE RELATION BETWEEN GROSS OUTPUT– AND VALUE ADDED–BASED PRODUCTIVITY MEASURES: THE IMPORTANCE OF THE DOMAR FACTOR

Published online by Cambridge University Press:  15 September 2009

Bert M. Balk
Bert M. Balk*
Affiliation:
Statistics Netherlands and Erasmus University
*
Address correspondence to: Bert M. Balk, Rotterdam School of Management, Erasmus University, P. O. Box 1738, 3000 DR Rotterdam, the Netherlands, e-mail [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper I consider the relation between gross output– and value added–based total factor productivity (TFP) measures. It appears that, without any (micro-)economic theory being required, a conditional relationship between TFP indices can be derived, in which the Domar factor plays an important role. At the same time it turns out that gross output– and value added–based TFP indicators (difference-type measures) always coincide. In the Divisia index framework and maintaining the classical assumptions (profit maximization and a production technology that exhibits globally constant returns to scale), it appears that both TFP indices measure technological change, albeit in a dual way. In establishing this result, no separability assumptions are involved. Both indices are in general path-dependent. Path independence of the gross output–based TFP index requires that the technology exhibit Hicks input neutrality, whereas path independence of the value added–based TFP index requires Hicks value-added neutrality. These two concepts of neutrality are, however, not dual.

Keywords

Total Factor ProductivityDomar FactorGross OutputValue AddedTechnological ChangeHicks NeutralityPath (In)dependence
Type
Articles
Information
Macroeconomic Dynamics , Volume 13 , Supplement S2: Measurement with Theory , September 2009 , pp. 241 - 267
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aulin-Ahmavaara, P. (2003) The SNA93 values as a consistent framework for productivity measurement: Unsolved issues. Review of Income and Wealth 49, 117–133.CrossRefGoogle Scholar
Balk, B.M. (1998) Industrial Price, Quantity, and Productivity Indices: The Micro-Economic Theory and an Application. Boston: Kluwer Academic.Google Scholar
Balk, B.M. (2003) The residual: On monitoring and benchmarking firms, industries, and economies with respect to productivity. Journal of Productivity Analysis 20, 5–47.Google Scholar
Balk, B.M. (2008) Price and Quantity Index Numbers: Models for Measuring Aggregate Change and Difference. New York: Cambridge University Press.CrossRefGoogle Scholar
Bruno, M. (1978) Duality, intermediate inputs and value-added. In Fuss, M. and McFadden, D. (eds.), Production Economics: A Dual Approach to Theory and Applications. Amsterdam: North-Holland.Google Scholar
Denny, M., Fuss, M., and Waverman, L. (1981) The measurement and interpretation of total factor productivity in regulated industries, with an application to Canadian telecommunications. In Cowing, T. G. and Stevenson, R. E. (eds.), Productivity Measurement in Regulated Industries. New York: Academic Press.Google Scholar
Diewert, W.E. (1978a) Hicks' aggregation theorem and the existence of a real value added function. In Fuss, M. and McFadden, D. (eds.), Production Economics: A Dual Approach to Theory and Applications. Amsterdam: North-Holland.Google Scholar
Diewert, W.E. (1978b) Superlative index numbers and consistency in aggregation. Econometrica 46, 883–900.Google Scholar
Diewert, W.E. (2005) Index number theory using differences rather than ratios. American Journal of Economics and Sociology 64, 311–360.Google Scholar
Diewert, W.E. and Nakamura, A.O. (2003) Index number concepts, measures and decompositions of productivity growth. Journal of Productivity Analysis 19, 127–159.Google Scholar
Domar, E.D. (1961) On the measurement of technological change. Economic Journal 71, 709–729.Google Scholar
Färe, R. and Primont, D. (1995) Multi-output Production and Duality: Theory and Applications. Boston: Kluwer Academic.CrossRefGoogle Scholar
Gollop, F.M. (1987) Modelling aggregate productivity growth: The importance of intersectoral transfer prices and international trade. Review of Income and Wealth 33, 211–226.Google Scholar
Hulten, C.R. (1973) Divisia index numbers. Econometrica 41, 1017–1025.Google Scholar
Jorgenson, D.W. and Griliches, Z. (1967) The explanation of productivity change. Review of Economic Studies 34, 249–283.CrossRefGoogle Scholar
OECD (2001) Measuring Productivity: Measurement of Aggregate and Industry-Level Productivity Growth. Paris: Organisation for Economic Co-operation and Development.Google Scholar
Sato, K. (1976) The meaning and measurement of the real value added index. Review of Economics and Statistics 58, 434–442.Google Scholar
Schreyer, P. (2000) Separability, Path-Dependence and Value-Added Based Productivity Measures: An Attempt of Clarification. Mimeo, National Accounts Division, OECD.Google Scholar
Slade, M.E. (1988) Value-added total-factor-productivity measurement: A Monte-Carlo assessment. In Eichhorn, W. (ed.), Measurement in Economics. Theory and Applications of Economic Indices. Heidelberg, Germany: Physica-Verlag.Google Scholar
Solow, R.M. (1957) Technical change and the aggregate production function. Review of Economics and Statistics 39, 312–320.Google Scholar