133 results found
Goodridge P, Haskel J, Edquist H, 2021, We see data everywhere except in the productivity statistics, Review of Income and Wealth, ISSN: 0034-6586
This paper uses Labor Force Survey datafor European countries to estimate nationa linvestment in data assets, where the asset boundary is extended beyond that for software and databases as currently defined in theSystem of National Accounts. We find that: a) in 2011-18, 1.4% of EU-28 employment was engaged in the formation of (softwareand) data assets, with a mean growth rate of 5% pa; b) on average in 2011–16, expanding the asset boundary raises the level of own-account GFCF in software and databases in the EU-16 by 61%, and mean growth in realinvestment in own-account software and data assets to 6.9% pa, compared to 2.7% pa in national accounts; c) in 2011-2016, expansion of the asset boundary raises labour productivity growth in the EU-13 from 0.79% to 0.83% pa, and the contribution of software and data capital deepening over three-fold, from 0.03% to 0.10% pa.
Corrado C, Haskel J, Jona-Lasinio C, 2021, Artificial intelligence and productivity: an intangible assets approach, Oxford Review of Economic Policy, Pages: 440-440, ISSN: 0266-903X
Can artificial intelligence (AI) raise productivity? If we regard AI as a com-bination of software, hardware and database use, then it can be modelled as acombination of the deployment of intangible and tangible assets. Since some aremeasured and some are not, then conventional productivity analysis might miss thecontribution of AI. We set out whether there is any evidence to support this view.
Haskel J, Corrado C, Jona-Lasinio C, 2021, Arti cial Intelligence and Productivity: An Intangible Assets Approach
Edquist H, Goodridge P, Haskel J, 2021, The internet of things and economic growth in a panel of countries, Economics of Innovation and New Technology, Vol: 30, Pages: 262-283, ISSN: 1043-8599
Is the world on the cusp of a fourth industrial revolution driven by technological developments in ICT including artificial intelligence and the Internet of Things (IoT)?Thispaper focuses on IoT and how it might affect economic growth. We attempt to gauge the potential impact of IoT using: 1) regressions based on current IoT data; and 2) longer run estimates of growth accounting parameters based on those observed in a previous wave of the ICT-revolution. We find that: a) according to definitions in the literature IoT an innovational complementarity to ICT; b) early data already suggest an economically and statistically significant correlation between IoT connections and TFP growth, implying that an increase of 10 percentage points in the growth of IoT connections per inhabitant is associated with a 0.23 percentage points increase in TFP growth; c) longer run predictions of the IoT contribution based on a growth-accounting framework suggest a potential global annual average contribution to growth of 0.99% per annum (pa) in 2018-2030, approximately $849 billion pa of world GDP in 2018 prices.
Edquist H, Goodridge P, Haskel J, 2021, The economic impact of streaming beyond GDP, Applied Economics Letters, Pages: 1-6, ISSN: 1350-4851
This article finds that the shift from buying music as a physical product towards subscribing to music services implies an unmeasured decrease of 85% in the price paid per song. Traditionally, price indexes have focused on tangible prices on CDs. These price indexes have partly become obsolete as more of the consumption of music is streamed. However, the price indexes for streamed music still do not capture the price paid per song. We estimate that the shift from buying CDs to streaming music online generates a price decrease of 85% per song. This implies that in 2019 the global quality-adjusted value from streamed music was $76 billion compared to current revenues of $11 billion. Thus, the shift from consuming music in physical form towards subscribing to music services creates an enormous consumer surplus that is not recorded in GDP.
Haskel J, Westlake S, 2021, Capitalism without Capital: The Rise of the Intangible Economy (an excerpt), Publisher: NATL RESEARCH UNIV HIGHER SCH ECONOMICS
Haskel J, Corrado C, Jona-Lasinio C, 2021, Artificial intelligence and productivity: an intangible assets approach
Can arti cial intelligence (AI) raise productivity? If we regard AI as a com-bination of software, hardware and database use, then it can be modelled as acombination of the deployment of intangible and tangible assets. Since some aremeasured and some are not, then conventional productivity analysis might miss thecontribution of AI. We set out whether there is any evidence to support this view.
Goodridge P, Haskel J, 2020, We see data everywhere except in the productivity statistics
This paper sets out a framework for estimating capital formation in data assets in the context ofthe System of National Accounts (SNA) and the methods of national statistics agencies. Using European Union (EU) Labour Force Survey (LFS) data, we apply that framework to estimate investmentin data assets in European countries and its economic impact. We nd that: a) in 2011-18, 1.5% ofEU-28 employment was engaged in the formation of (software and) data assets, ranging from 3.5% inLuxembourg to 0.6% in Greece; b) mean (2011-18) growth in employment engaged in (software and) datacapital formation in the EU-28 was 5.2% per annum (pa), ranging from 13.2% pa in Portugal to -2.1%pa in Latvia; c) on average in 2011-16, incorporating a fuller de nition of data investment into nationalaccounts measures of GFCF raises own-account GFCF in software and databases in the EU-16 by 85%;d) mean (2011-16) growth in real own-account investment in software and data assets in the EU-16 was6.7% pa compared to 2.7% pa in national accounts; and e) incorporating uncapitalised investment indata into the national accounts (2011-16) raises: i) labour productivity growth in the EU-13 from 0.8%pa to 0.88% pa, which translates to ¿10.9bn pa of additional output growth in 2016 if applied to theEU-28 aggregate; and ii) the contribution of software and data capital deepening almost four-fold from0.03% pa to 0.11% pa, which translates to ¿11.8bn pa in 2016 if applied to the EU-28 aggregate.
Goodridge P, Haskel J, Edquist H, 2019, The economic contribution of the "C" in ICT: evidence from OECD countries, Journal of Comparative Economics, Vol: 47, Pages: 867-880, ISSN: 0147-5967
Numerous studies have documented the contribution of ICT to growth. Less has been done on the contribution of communications technology, the “C” in ICT. We construct an international dataset of fourteen OECD countries and present contributions to growth for each ICT asset (IT hardware, CT equipment and software) using alternative ICT deflators. Using each country’s deflator we find that the contribution of CT capital deepening to productivity growth is lower in the EU than the US. Thus we ask: is that lower contribution due to a lower rate of CT investment or differing sources and methods for measurement of price change? We find that: (a) there are still considerable disparities in measures of ICT price change across countries; (b) in terms of growth-accounting, price harmonisation has a greater impact on the measured contributions of IT hardware and software in the EU relative to the US, than that of CT equipment; over 1996–2013, harmonising investment prices explains just 15% of the gap in the EU CT contribution relative to the US, compared to 25% for IT hardware; (c) over 1996–2013, CT capital deepening accounted for 0.11% pa (6% as a share) of labour productivity growth (LPG) in the US, compared to 0.03% pa (2.5% of LPG) in the EU-13 when using national accounts deflators; and (d) using OECD harmonised deflators, the figure for the EU-13 is raised to 0.04% pa (4% of LPG).
Corrado C, Haskel J, Iommi M, et al., 2019, Intangible capital, innovation, and productivity à la Jorgenson evidence from Europe and the United States, Measuring Economic Growth and Productivity: Foundations, KLEMS Production Models, and Extensions, Pages: 363-385, ISBN: 9780128175965
This chapter focuses on intangible capital as source of economic growth in Europe and the United States. It (1) sets out a Jorgenson-like model for thinking about the role of knowledge investments and innovation in fostering economic growth; (2) uses standard growth accounting decompositions to illustrate the empirical relevance of intangible capital deepening as a source of growth, and (3) considers how knowledge spillovers from investments in both R&D and non-R&D intangible assets might contribute to productivity.
Corrado C, Haskel J, Jona-Lasinio C, 2019, Productivity growth, capital reallocation and the financial crisis: Evidence from Europe and the US, Publisher: Elsevier BV
Goodridge P, Haskel J, Edquist HO, 2019, Productivity, network effects and telecommunications capital: Evidence from the US and Europe, Publisher: Centre for Economic Policy Research (C.E.P.R Discussion papers)
Did the huge investment in telecommunications networks in the 1990s affect subsequent total factor productivity? Using data from 13 European countries and the US, 1995-2013, we document the substan- tial growth and then slowdown in "telecommunications" capital and ask if this is related to the growth and slowdown in TFP. We explore this by disaggregating ICT equipment investment into "IT" and "CT" equipment investment. We test for distinct effects from each using a simple framework where CT cap- ital has network externalities and so potentially impacts TFP, with the marginal impact of CT capital growth being higher in countries spending more on renting CT capital. We find: a) evidence of a robust correlation between (lagged) growth in (rental share-weighted) CT capital services and TFP growth; b) the estimated externality from CT capital potentially explains around 30-40% of TFP growth in North European countries, 60% in Scandinavia and around 90% in the US; c) CT capital has a social return around five times its private return; and d) a slowdown in the accumulation of CT capital accounts for just over half of the post-2003 TFP slowdown in the US but only one-tenth of the TFP slowdown in the EU
Haskel J, Corrado C, Jona-Lasinio C, et al., 2018, Intangible investment in the EU and US before and since the Great Recession and its contribution to productivity growth, Journal of Infrastructure, Policy and Development, Vol: 2, Pages: 11-36, ISSN: 2572-7923
This paper uses a new cross-country cross-industry dataset on investment in tangible and intangible assets for 18 European countries and the US. We set out a framework for measuring intangible investment and capital stocks and their effect on output, inputs and total factor productivity. The analysis provides evidence on the diffusion of intangible investment across Europe and the US over the years 2000-2013 and offers growth accounting evidence before and after the Great Recession in 2008-2009. Our major findings are the following. First, tangible investment fell massively during the Great Recession and has hardly recovered, whereas intangible investment has been relatively resilient and recovered fast in the US but lagged behind in the EU. Second, the sources of growth analysis including only national account intangibles (software, R&D, mineral exploration and artistic originals), suggest that capital deepening is the main driver of growth, with tangibles and intangibles accounting for 80% and 20% in the EU while both account for 50% in the US, over 2000-2013. Extending the asset boundary to the intangible assets not included in the national accounts (Corrado, Hulten and Sichel (2005)) makes capital deepening increase. The contribution of tangibles is reduced both in the EU and the US (60% and 40% respectively) while intangibles account for a larger share (40% in EU and 60% in the US). Then, our analysis shows that since the Great Recession, the slowdown in labour productivity growth has been driven by a decline in TFP growth with relatively a minor role for tangible and intangible capital. Finally, we document a significant correlation between stricter employment protection rules and less government investment in R&D, and a lower ratio of intangible to tangible investment.
Corrado C, Haskel J, Jona-Lasinio C, 2017, Public intangibles: the public sector and economic growth in the SNA, Review of Income and Wealth, Vol: 63, Pages: s355-s380, ISSN: 1475-4991
This paper sets out a framework for analyzing the impact of public investments on industry-level productivity and economic growth. The concept of capital in the public sector is broad-ened from that which is mostly tangible (e.g., physical infrastructure) to that which alsoincludes intangibles and long-lasting societal assets. For the analysis of public investments,we find that in addition to expanding the asset boundary, national accounts also need to: (a)impute a net return to government and other nonmarket capital—or provide industry-leveldata by institutional sector of origin, allowing researchers to do so; (b) include all publicpayments to industry in industry-level gross operating surplus (i.e., all subsidies, productionand product, and the annuity value of investment grants); and (c) provide crosswalks forkey components of government expenditure by function of government (e.g., public funds forextramural R&D or worker training) to kind-of-activity classifications used for industries.
Goodridge PR, Haskel J, Edquist H, 2017, Network effects and productive externalities from ICT and knowledge capital
Haskel J, 2017, Knowledge spillovers, ICT and productivity growth, Oxford Bulletin of Economics and Statistics, Vol: 79, Pages: 592-618, ISSN: 1468-0084
This paper looks at the channels through which intangible assets affect productivity.The econometric analysis exploits a new dataset on intangible investment (INTAN-Invest)in conjunction with EUKLEMS productivity estimates for 10 EU member states from 1998to 2007. We find that (a) the output elasticity of intangible capital depends upon ICT inten-sity, consistent with complementarities between ICT and intangible capital; (b) non-R&Dintangible capital has higher estimated output elasticities than its factor shares, as does(c) labour quality. The last two findings are consistent with spillovers from investments inknowledge-based/intangible capital and skills.
Edquist H, Goodridge PR, Haskel J, et al., 2017, How important are mobile broadband networks for global economic development?, Publisher: Imperial College Business School
Since the beginning of the 21st century mobile broadband has diffused very rapidly in many countries around the world. This paper investigates to what extent the diffusion of mobile broadband has impacted economic development in terms of GDP. The results show that there isa significanteffect from mobile broadband on GDP both when mobile broadband is first introduced and gradually as mobile broadband diffuses throughout different economies. Based on a two stage model we are able to conclude that on average a 10 percent increase of mobile broadband adoption causes a 0.6–2.8 percent increase in economic growth depending on the model specifications.
Goodridge PR, Haskel J, Wallis G, 2017, Spillovers from R&D and other intangible investment: evidence from UK industries, Review of Income and Wealth, Vol: 63, Pages: S22-S48, ISSN: 1475-4991
Many agree that evidence exists consistent with spillovers from R&D. But is there any evidence of spillovers from a broader range of intangibles, such as software, design or training? We collect investment data for these wider intangibles for a panel of seven UK industries 1992–2007. Using the industry-level method in the R&D literature, e.g. Griliches (1973), we regress industry TFP growth on lagged external knowledge stock growth, where the latter are outside industry measures weighted by matrices based on (a) flows of intermediate consumption or (b) workers. Our main new result is that we find (controlling for time and industry effects) statistically significant correlations between TFP growth and knowledge stock growth in (a) external R&D and (b) total intangibles (excluding R&D). We show our results are robust to controls for imperfect competition and non-constant returns; likewise they are robust to including foreign R&D, and other controls, and various lags.
Smith PA, Self A, Michaelson J, et al., 2017, Discussion on the paper by Allin and Hand, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 180, Pages: 24-43, ISSN: 0964-1998
Haskel J, Corrado C, Jona-Lasinio C, 2017, Intangibles, ICT and industry productivity growth: Evidence from the EU, The World Economy: Growth or Stagnation?, Editors: Jorgenson, Fukao, Timmer, Publisher: Cambridge University Press, Pages: 281-318, ISBN: 9781316534502
Goodridge PR, Haskel J, Wallis G, 2016, UK intangible investment and growth: new measures of UK investment in knowledge assets and intellectual property rights
Haskel J, Goodridge P, Wallis G, 2016, Accounting for the UK productivity puzzle: a decomposition and predictions, Economia, ISSN: 1744-6783
Haskel J, 2016, Intangibles, mismeasurement and the international productivity slowdown, Publisher: Imperial College Business School
Goodridge PR, Haskel J, 2016, Big Data in UK industries: an intangible investment approach, Publisher: Imperial College Business School
In Goodridge and Haskel (2015b) we present an economic framework for measuring the contribution of Big Data to UK growth, building on measures of investment and employment presented in Goodridge and Haskel (2015a) and Chebli, Goodridge et al. (2015) respectively. In this paper we extend that framework to present industry estimates of UK investment and employment in producing data-based information and knowledge assets, and their contribution to industry-level growth. In doing so, we focus on industries that are considered to be intensive users of knowledge or intangible capital, including for instance Financial services and Manufacturing. We find that the four industries: Information and Communication, Professional and Administrative Services, Financial Services and Manufacturing account for 88% of big data employment and 89% of investments in data-based assets in the UK market sector. Similarly, of the total contribution of data-based assets to UK growth, we find that it is fully accounted for by these four industries
Haskel J, 2016, Do poor countries catch up to rich countries? Review article on productivity convergence: theory and evidence by Edward Wolff, International Productivity Monitor, Vol: 30, Pages: 111-117, ISSN: 1492-9759
This article provides a detailed review of Edward Wolff’s Productivity Convergence: Theoryand Evidence. Wolff examines the long run productivity growth and convergence experienceof a variety of countries from across the world. Wolff’s main contribution is the definition oftwo general classes of forces of convergence. He delineates “strong” forces of productivityconvergence, such as the catch-up effect, capital formation, and education, from “weak”forces contributing to convergence like international trade, economic geography, andregulation. While some of the individual forces of convergence may switch categories asnew research emerges, the categorization remains highly relevant. The focus onconvergence suggests that non-frontier countries may not yet be in dire straits as a resultof the purported recent productivity slowdown, as productivity growth may well still comefrom forces of convergence for years to come.
Corrado C, Haskel J, Jona-Lasinio C, 2016, Intangibles, ICT and industry productivity growth: Evidence from the EU, The World Economy: Growth or Stagnation?, Pages: 319-346, ISBN: 9781107143340
Using intangible investment data by industry for fourteen EU countries in 1995-2010 we investigate industry growth accounting and complementarities between intangibles and ICT. We find: (a) intangible investment has grown in manufacturing and services, but most strongly in services; (b) the contribution of intangibles to labor productivity growth is similar in both manufacturing and services and in the high growth economies (Austria, Germany, Finland, France, Netherlands, UK) exceeds the contribution of labor quality; (c) the very large size of the service sector means that countries with good manufacturing but poor service productivity growth (Germany and France) have done relatively badly overall and those with good service sector growth (UK, Netherlands) have performed well; (d) Spain and Italy have very low labor productivity growth due to very low total factor productivity (TFP) growth; and (e) we find evidence of complementarities between intangible capital and information and communications technology (ICT). Empirical evidence shows that once intangible capital is included in a sources-of-growth analysis it accounts for 20-33 percent of labor productivity growth in the market sector of the United States and European Union economies. As a consequence, the measurement of intangible investment is a potentially important addition to both sources-of-growth analysis and national accounting practice. Following the work of Corrado et al. (2005, 2009) and Nakamura (1999), building in turn on the work of Machlup (1962), among others, major research efforts were undertaken to measure intangible investment and intangible capital for the business sector of European countries (INNODRIVE and COINVEST FP7 European funded projects). This led to the development of the INTAN-Invest harmonized framework for measuring intangible investment in these countries. At the same time, estimates for many other countries (not necessarily harmonized) have emerged, e.g., for Japan (Fukao et
Goodridge PR, Chebli O, Haskel J, 2015, Measuring activity in big data: New estimates of big data employment in the UK market sector, Publisher: Imperial College Business School
Goodridge PR, Haskel J, 2015, How does big data affect GDP? Theory and evidence for the UK, Publisher: Imperial College Business School
We present an economic approach to measuring the impact of Big Data on GDP and GDP growth. Wedefine data, information, ideas and knowledge. We present a conceptual framework to understandand measure the production of “Big Data”, which we classify as transformed data and data-basedknowledge. We use this framework to understand how current official datasets and concepts used byStatistics Offices might already measure Big Data in GDP, or might miss it. We also set out howunofficial data sources might be used to measure the contribution of data to GDP and presentestimates on its contributions to growth. Using new estimates of employment and investment in BigData as set out in Chebli, Goodridge et al. (2015) and Goodridge and Haskel (2015a) and treatingtransformed data and data-based knowledge as capital assets, we estimate that for the UK: (a) in 2012,“Big Data” assets add £1.6bn to market sector GVA; (b) in 2005-2012, account for 0.02% of growthin market sector value-added; (c) much Big Data activity is already captured in the official data onsoftware – 76% of investment in Big Data is already included in official software investment, and76% of the contribution of Big Data to GDP growth is also already in the software contribution; and(d) in the coming decade, data-based assets may contribute around 0.07% to 0.23% pa of annualgrowth on average.
Goodridge PR, Haskel J, 2015, How much is UK business investing in big data?, Publisher: Imperial College Business School
Haskel J, Goodridge P, Hughes A, et al., 2015, The contribution of public and private R&D to UK productivity growth, Publisher: Imperial College Business School
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