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Predicting Medium-Term TFP Growth in the United States: Econometrics vs ‘Techno-Optimism’

Published online by Cambridge University Press:  01 January 2020

Nicholas Crafts*
Affiliation:
University of Warwick
Terence C. Mills*
Affiliation:
Loughborough University

Abstract

We analyse TFP growth in the US business sector using a basic unobserved component model where trend growth follows a random walk and the noise is a first order autoregression. This is fitted using a Kalman-filter methodology. We find that trend TFP growth has declined steadily from 1.5 to 1.0 per cent per year over the past 50 years. Nevertheless, recent trends are not a good guide to actual medium-term TFP growth. This exhibits substantial variations and is quite unpredictable. Techno-optimists should not give best to productivity pessimists simply because recent TFP growth has been weak.

Type
Notes and Contributions
Copyright
Copyright © 2017 National Institute of Economic and Social Research

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Footnotes

We thank Michael McMahon, Nicholas Oulton and an anonymous referee for helpful comments and John Fernald for sharing his data with us. The usual disclaimer applies.

References

Ahmad, N., Ribarsky, J. and Reinsdorf, M. (2017), ‘Can potential mismeasurement of the digital economy explain the post-crisis slowdown in GDP and productivity growth?’, OECD Statistics Working Paper No. 85.Google Scholar
Antolin-Diaz, J., Drechsel, T. and Petrella, I. (2017), ‘Tracking the slowdown in long-run GDP growth’, Review of Economics and Statistics, 99, pp. 343–56.CrossRefGoogle Scholar
Arntz, M., Gregory, T. and Zierahn, U. (2016), ‘The risk of automation in OECD countries: a comparative analysis’, OECD Social, Employment and Migration Working Paper No. 189.Google Scholar
Bai, J. and Perron, P. (1998), ‘Estimating and testing linear models with multiple structural changes’, Econometrica, 66, pp. 4778.CrossRefGoogle Scholar
Bai, J. and Perron, P. (2003), ‘Computation and analysis of multiple structural change models’, Journal of Applied Econometrics, 18, pp. 122.CrossRefGoogle Scholar
Blanchard, O., Lorenzoni, G. and L'Huiller, J.-P. (2017), ‘Short-run effects of lower productivity growth: a twist on the secular stagnation hypothesis’, NBER Working Paper No. 23160.Google Scholar
Brynjolfsson, E. and McAfee, A. (2014), The Second Machine Age, New York: Norton.Google Scholar
Byrne, D., Fernald, J. and Reinsdorf, M.B. (2016), ‘Does the United States have a productivity slowdown or a measurement problem?’, Brookings Papers on Economic Activity (Spring), pp. 109–57.CrossRefGoogle Scholar
Byrne, D.M., Oliner, S.D. and Sichel, D.E. (2013), ‘Is the information technology revolution over?’, International Productivity Monitor, 25, pp. 2036.Google Scholar
Congressional Budget Office (2001), CBO's Method for Estimating Potential Output: an Update.Google Scholar
Congressional Budget Office (2017), The Budget and Economic Outlook 2017–2027.Google Scholar
Cowen, T. (2011), The Great Stagnation, New York: Dutton.Google Scholar
Decker, R., Haltiwanger, J., Jarmin, R.S. and Miranda, J. (2017), ‘Declining dynamism, allocative efficiency and the productivity slowdown’, Federal Reserve Board Finance and Economics Discussion Paper No. 2017–019.Google Scholar
Duval, R., Hong, G.H. and Timmer, Y. (2017), ‘Financial frictions and the great productivity slowdown’, IMF Working Paper No. 17/129.Google Scholar
Fernald, J.G. (2014), ‘A quarterly, utilization-adjusted series on total factor productivity’, Federal Reserve Bank of San Francisco Working Paper 2012–19.CrossRefGoogle Scholar
Frey, C.B. and Osborne, M.A. (2017), ‘The future of employment: how susceptible are jobs to computerisation?’, Technological Forecasting and Social Change, 114, pp. 254–80.CrossRefGoogle Scholar
Gamberoni, E., Giordiano, C. and Lopez-Garcia, P. (2016), ‘Capital and labour (mis)allocation in the Euro Area: some stylized facts and determinants’, ECB Working Paper No. 1981.Google Scholar
Gordon, R.J. (2016), The Rise and Fall of American Growth, Princeton: Princeton University Press.CrossRefGoogle Scholar
Greenspan, A. (2000), ‘Technology and the economy’, speech to the Economic Club of New York, 13 January, http://federalreserve.gov/boarddocs/speeches/2000/200001132.htm.Google Scholar
Hansen, A.H. (1939), ‘Economic progress and declining population growth’, American Economic Review, 29, pp. 115.Google Scholar
Havik, K., McMorrow, K., Orlandi, F., Planas, C., Raciborski, R., Röger, W., Rossi, A., Thum-Thysen, A. and Vandermeulen, V. (2014), ‘The production function methodology for calculating potential growth rates and output gaps’, European Economy Economic Papers No. 535.Google Scholar
Lipsey, R.G., Carlaw, K.I. and Bekar, C.T. (2005), Economic Transformations: General Purpose Technologies and Economic Growth, Oxford: Oxford University Press.Google Scholar
Ollivaud, P., Guillemette, Y. and Turner, D. (2016), ‘Links between weak investment and the slowdown in productivity and potential output growth across the OECD’, OECD Economics Department Working Paper No. 1304.Google Scholar
Syverson, C. (2017), ‘Challenges to mismeasurement explanations for the U.S. productivity slowdown’, Journal of Economic Perspectives, 31(2), pp. 165–86.CrossRefGoogle Scholar
Van Ark, B. (2016), ‘The productivity paradox of the new digital economy’, International Productivity Monitor, 31, pp. 318.Google Scholar