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ON THE MEASUREMENT OF TOTAL FACTOR PRODUCTIVITY: A LATENT VARIABLE APPROACH

Published online by Cambridge University Press:  22 March 2010

J. Rodrigo Fuentes  and
Marco Morales
J. Rodrigo Fuentes*
Affiliation:
Pontificia Universidad Católica de Chile
Marco Morales
Affiliation:
Universidad Diego Portales
*
Address correspondence to: J. Rodrigo Fuentes, Instituto de Economía, Pontificia Universidad Católica de Chile Avda. Vicuña Mackenna 4860, Macul, Santiago, Chile; e-mail: rfuentes@faceapuc.cl.
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Abstract

Despite the important role that total factor productivity (TFP) has played in the growth literature, few attempts have been made to change the methodology to estimate it. This paper proposes a methodology based on a state-space model to estimate TFP and its determinants. With this methodology, it is possible to reduce the measurement of our ignorance. As a by-product, this estimate yields the capital share in output and the long-term growth rate. When applied to Chile, the estimation shows a capital share around 0.5 and long-term growth of TFP around 1%. Capital accumulation tends to explain the growth rate in the fast growth periods under the econometric estimation more than the traditional growth accounting methodology.

Keywords

Productivity MeasurementLatent Variables
Type
Articles
Information
Macroeconomic Dynamics , Volume 15 , Issue 2 , April 2011 , pp. 145 - 159
Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

Aghion, P. and Howitt, P. (1998) Endogenous Growth Theory. Cambridge, MA: MIT Press.Google Scholar
Bosworth, B. and Collins, S.M. (2003) The empirics of growth: An update. Brookings Papers on Economic Activity 2, 103–206.Google Scholar
Chen, B. and Zadrozny, P.A. (2009) Estimated US manufacturing capital and technology based on an estimated dynamic economic model. Journal of Economic Dynamics and Control 33, 1398–1418.Google Scholar
Chumacero, R. and Fuentes, J.R. (2006) Chilean growth dynamics. Economic Modelling 23, 197–214.CrossRefGoogle Scholar
Costello, D.M. (1993) A cross-country, cross-industry comparison of productivity growth. Journal of Political Economy 101 (2), 207–222CrossRefGoogle Scholar
Durbin, J. and Koopman, S.J. (2001) Time Series Analysis by State Space Methods. New York: Oxford University Press.Google Scholar
Easterly, W. and Levine, R. (2001) It's not factor accumulation: Stylized facts and growth models. World Bank Economic Review 15 (2), 177–219.CrossRefGoogle Scholar
Elias, V.J. (1990) Sources of Growth: A Study of Seven Latin American Economies. San Francisco: International Center for Economic Growth.Google Scholar
Esposti, R. and Pierani, P. (2000) Modeling technical change in Italian agriculture: A latent variable approach. Agricultural Economics 22, 261–270.CrossRefGoogle Scholar
French, M. (2001) Estimating Changes in Trend Growth of Total Factor Productivity: Kalman and H-P Filters versus a Markov-Switching Approach. Federal Reserve Board Finance and Economics Discussion Series 44.Google Scholar
Fuentes, J.R., Larraín, M., and Schmidt-Hebbel, K. (2006) Measuring and explaining total factor productivity in Chile. Cuadernos de Economía 43 (May), 113–142.Google Scholar
Gollin, D. (2002) Getting income shares right. Journal of Political Economy 110, 458–474.CrossRefGoogle Scholar
Greenwood, J. and Jovanovic, B. (2000) Accounting for Growth. Rochester Center for Economic Research Working paper 475, University of Rochester.Google Scholar
Hall, R.E. and Jones, C. (1999) Why do some countries produce so much more output per worker than others? Quarterly Journal of Economics 114 (1), 83–116.Google Scholar
Hamilton, J.D. (1994) State-space models. In Engle, R.F. and McFadden, D.L. (eds.), Handbook of Econometrics, Volume IV, pp. 3039–3080.Amsterdam: Elsevier Science.Google Scholar
Harberger, A.C. (1998) A vision of the growth process. American Economic Review 88 (1), 1–32.Google Scholar
Harvey, A.C., Henry, S.G.B., Peters, S., and Wren-Lewis, S. (1986) Stochastic trends in dynamic regression models: An application to the employment-output equation. Economic Journal 96, 975–985.CrossRefGoogle Scholar
Jorgenson, D.W. and Griliches, Z. (1967) The explanation of productivity change. Review of Economics Studies 34 (2),: 249–80.CrossRefGoogle Scholar
Klenow, P. and Rodríguez-Clare, A. (1997) The neoclassical revival in growth economics: Has it gone too far? In Bernanke, B. and Rotemberg, J. (eds.), NBER Macroeconomics Annual 1997, pp. 73–103. Cambridge, MA: MIT Press.Google Scholar
Lucas, R.E. Jr. (1988) On the mechanics of economic development. Journal of Monetary Economics 22 (1): 3–42.Google Scholar
Mankiw, N.G., Romer, D., and Weil, D.N. (1992) Contribution to the empirics of economic growth. Quarterly Journal of Economics 107 (2): 407–437.CrossRefGoogle Scholar
Parente, S.L. and Prescott, E.C. (1994) Barriers to technology adoption and development. Journal of Political Economy 102 (2), 298–320.CrossRefGoogle Scholar
Prescott, E.C. (1997) Needed: A Theory of Total Factor Productivity. Research Department Staff Report 242, Federal Reserve Bank of Minneapolis.Google Scholar
Romer, P. (1986) Increasing returns and long run growth. Journal of Political Economy 94 (5), 1002–1037.CrossRefGoogle Scholar
Romer, P. (1990) Endogenous technological change. Journal of Political Economy 98 (5, Part 2), S71–102.Google Scholar
Slade, M.E. (1989) Modeling stochastic and cyclical components of technical change: An application of the Kalman filter. Journal of Econometrics 41, 363–383.Google Scholar
Solow, R. (1957) Technical change and the aggregate production function. Review of Economics and Statistics 39, 312–320.Google Scholar