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Can Intangible Investment Explain the UK Productivity Puzzle?

Published online by Cambridge University Press:  26 March 2020

Peter Goodridge*
Affiliation:
Imperial College Business School
Jonathan Haskel*
Affiliation:
Imperial College Business School and CEPR, IZA
Gavin Wallis*
Affiliation:
University College London and Bank of England

Abstract

This paper investigates whether intangibles might explain the UK productivity puzzle. We note that since the recession: (a) firms have upskilled faster than before; (b) intangible investment in R&D and software has risen whereas tangible investment has fallen; and (c) intangible and telecoms equipment investment slowed in advance of the recession. We have therefore tested to see if: (a) what looks like labour hoarding is actually firms keeping workers who are employed in creating intangible assets; and (b) the current slowdown in TFP growth is due to the spillover effects of the past slowdown in R&D and telecoms equipment investment. Our main findings are: (a) measured market sector real value added growth since the start of 2008 is understated by 1.6 per cent due to the omission of intangibles; and (b) 0.75 per cent per annum of the TFP growth slowdown can be accounted for by the slowdown in intangible and telecoms investment in the early 2000s. Taken together intangible investment can therefore account for around 5 percentage points of the 16 per cent productivity puzzle.

Type
Research Articles
Copyright
Copyright © 2013 National Institute of Economic and Social Research

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Footnotes

We are very grateful for financial support for this research from NESTA, ESRC (Grant ES/1035781/1) and UK-IRC. We also thank three anonymous referees and an editor for useful comments and suggestions. This work contains statistical data from ONS which is Crown copyright and reproduced with the permission of the controller of HMSO and Queen's Printer for Scotland. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates. The views expressed in this paper are those of the authors and do not necessarily reflect those of affiliated institutions. All errors are of course our own.

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