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The digital transformation: Measurement and implications for competition and growth

OECD Global Forum on Productivity Workshop - Berlin - 15 September 2017,Sara Calligaris (OECD, STI/PBD)

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The digital transformation: Measurement and implications for competition and growth

  1. 1. THE DIGITAL TRANSFORMATION: MEASUREMENT AND IMPLICATIONS FOR COMPETITION AND GROWTH Sara Calligaris (OECD, STI/PBD), sara.calligaris@oecd.org Based on work by Chiara Criscuolo, Matej Bajgar, Giuseppe Berlingieri (OECD and ESSEC), Patrick Blanchenay (University of Toronto), Sara Calligaris, Flavio Calvino , Luca Marcolin, and Jonathan Timmis; Dan Andrews, and Peter Gal. Global Forum of Productivity Workshop, Berlin, 15 September 2017
  2. 2. Productivity growth has slowed across much of the OECD Decomposition of labour productivity growth Percentage point contribution to labour productivity growth, annual Source: OECD, Productivity Statistics (database), July 2017. -1 0 1 2 3 4 5 6 7 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 1990-2000 2000-07 2007-10 2010-15 BEL CAN DNK FIN FRA DEU IRL ITA JPN NLD ESP GBR USA % Multifactor productivity ICT capital deepening Non-ICT capital deepening
  3. 3. At the same time rising productivity gap between global frontier firms and laggards… Widening multifactor productivity gap between global frontier firms and other firms Source: Andrews, D., C. Criscuolo and P. Gal (2016). -0.1 0.0 0.1 0.2 0.3 0.4 0.5 2001 2003 2005 2007 2009 2011 2013 Manufacturing Frontier firms (top 5%) Laggards -0.1 0.0 0.1 0.2 0.3 0.4 0.5 2001 2003 2005 2007 2009 2011 2013 Non-financial business services Frontier firms (top 5%) Laggards
  4. 4. ... and mainly seems due to a divergence in technology MFP in ICT vs. non-ICT services sector Source: Andrews, D., C. Criscuolo and P. Gal (2016).  Potential role of digital technologies to create winner-takes-the- most dynamics. -0.2 0.0 0.2 0.4 0.6 0.8 1.0 2001 2003 2005 2007 2009 2011 2013 ICT services Frontier firms Laggards Top 2% Top 10% -0.2 0.0 0.2 0.4 0.6 0.8 1.0 2001 2003 2005 2007 2009 2011 2013 Non-ICT services Frontier firms Laggards Top 2% Top 10%
  5. 5. …which is also found in wages Wage dispersion between more ICT-intensive vs less ICT-intensive sectors 90-10 difference in log Wages Source: Berlingieri, G., P. Blanchenay and C. Criscuolo (2017) and MultiProd (2017). Note: we define “ICT” sectors above the median ICT level and non-ICT those below. The figure plots the year dummy estimates t of a regression of log-wage dispersion (measured as the difference between the 90th and 10th percentiles of log-wages) within country-sector pairs, using data from the following countries: AUS, AUT, BEL, CHL, DNK, FIN, FRA, HUN, ITA, JPN, NLD, NOR, NZL, SWE. -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 ICT vs Non-ICT wage dispersion Non-ICT intensive ICT-intensive -0.02 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Overall between-firms wage dispersion
  6. 6. The best are thriving
  7. 7. What is a digital business? Which sectors can be defined “digital”?  The OECD is working on the definition of different indicators of ICTs at country-sectoral level to characterize the digital intensity: How to measure digitalization? Then, a “global”, cross-indicator, ranking is constructed as the weighted average of the previous ones (2001-2003 and 2013-2015). • ICT specialists • [ICT skills] • e-Commerce • [Platforms] • Investments in software • [Use of computers and more advanced digital technologies (CC, CRM)] • Investments in ICT equipment • Purchase of ICT intermediate goods and services • robots intensity Market-related indicators Labour-input indicators Capital & intermediates indicators Organisational indicators Digital Source: Calvino, F. and C. Criscuolo (2017).
  8. 8. Which are the implications of the digital transformation for competition and firms’ market power? • Scale up of few winners, with consequent exclusion of others from the market/entry barriers; – But also… Lower marginal costs with consequent increase in competition and lowering of prices; • Negative effects on innovation and technology diffusion; can we still talk about creative destruction? – But also… lower costs of experimentation can spur innovation; • Better price discrimination; – But also… Larger market available, e.g. through e-commerce. • … Market power in the digital era - Motivation
  9. 9. Data: • Bureau van Dijk Orbis firm-level dataset: – 26 countries (AUS, AUT, BEL, BGR, DEU, DNK, ESP, EST, FIN, FRA, GBR, HUN, IDN, IND, IRL, ITA, JPN, KOR, LUX, NLD, PRT, ROU, SVN, SWE, TUR, USA); – Period 2001-2014; – Only manufacturing and non-financial market services sectors; – Only firms with more than 20 employees; – Final sample: approximately 2.3 mn. observations; – Information on GO, VA, L, K, M. • OECD global, cross-indicator, ranking at 2-digit sectoral level: – value 0 for the least digitalized sector, 32 for the most digitalized one; – 2 different rankings for the initial period (2001-2003) and for the final one (2013-2014) Market power in the digital era - Data
  10. 10. Markups: 𝜇𝑖𝑡 = 𝑝𝑖𝑡 𝑐𝑖𝑡 estimated relying on the De Loecker and Warzynski (2012) framework. Two different production functions: • industry-specific Cobb Douglas with 3 inputs (K, L, M); • industry-specific translog with 3 inputs (K, L, M); Digital measures: • top ¼ digitalized sectors vs. bottom ¾ of the “global” ranking; • top half digitalized sectors vs. bottom half of the “global” ranking. Market power in the digital era - measurement
  11. 11. The evolution of markups CD Translog Variable mean SD p10 p50 p90 Markup (CD) 1.45 1.74 1.05 1.24 1.94 Markups (translog) 2.8 1.61 2.08 2.44 3.7 Top decile Top decile CD Translog Source: Calligaris, S., C. Criscuolo and L. Marcolin (2017).
  12. 12. Preliminary results 2001-2003 2013-2014 CD Translog CD Translog top 1/2 digital 0.019*** 0.013*** 0.029*** 0.033*** (0.001) (0.001) (0.001) (0.001) Top 1/4 digital 0.215*** 0.237*** 0.355*** 0.354*** (0.002) (0.002) (0.002) (0.002) Controls Size, age Size, age Size, age Size, age Size, age Size, age Size, age Size, age Country-year FE Yes Yes Yes Yes Yes Yes Yes Yes Observations 308,157 308,157 363,027 363,027 266,624 222,302 319,685 279,491 R-squared 0.060 0.184 0.094 0.254 0.088 0.473 0.106 0.441 Note: Standard errors clustered at firm level. Source: Calligaris, S., C. Criscuolo and L. Marcolin (2017). Dependent variable: (ln) markups
  13. 13. To sum up: • Average markups are increasing over time; • This result seems to be driven by the top decile of the markups distribution; • Firms belonging to top digitalized sectors have on average higher markups; • The difference in average firms’ markups between digitalized and non-digitalized sectors is stronger in 2013-2014 than in 2001-2003. Next steps: • Consider 4 categories for the digital measure; • Explore the difference between manufacturing and services, as well different patterns across 2-digit sectors; • More measures of markups; • Use of the sub-indicators; • Control for productivity; • Evolution over time of the correlation between markups and digital measures; • Role of policies and policy implications. Main findings and next steps
  14. 14. Limits of Orbis and comparison with MultiProd • Orbis is limited to a sample of bigger firms, and the coverage varies from country to country; • Big issue? Firms with higher markups are those driving the increase in average markups. • However… it is important to ascertain what happens to the whole population of firms, including the small ones The MultiProd dataset might be the natural candidate. % of obs. w.r.t. STAN aggregates Share of employment by size class Source: Bajgar, M., G. Berlingieri, S. Calligaris, C. Criscuolo and J. Timmis (2017).
  15. 15. • Distributed microdata project aimed at building a representative picture of differences in productivity patterns across countries, sectors and periods. • Harmonized Stata routine sent to researchers in NSOs with access to confidential firm-level longitudinal data. • Coverage: • 24 countries (and expanding) [AUS, AUT, BEL, BRA, CAN, CHE, CHL, CHN, CRI, DEU, DNK, FIN, FRA, HUN, IDN, ITA, JPN, LUX, NLD, NOR, NZL, PRT, SWE, VNM]; • Period: 1995-2012; • Whole economy, detailed at 2-digit level; • Some statistics further refined by: i) age or/and size classes, ii) ownership, iii) quantiles of the productivity distribution or quantiles of the size distribution. • Aggregates and distributions over time. Productivity within and across Countries: The OECD MultiProd project
  16. 16. Representativeness: • Usually population of firms; • For countries with partial data (production survey): o Reweight using Business Register population weights (if available) o Compute nb. of firms by year / sector / size class (with thresholds at 5, 10, 20, 50, 100 and 250). Policy questions that can be answered using MultiProd: • Static and dynamic allocative efficiency; • Distributional changes (productivity, wages, size, etc.) over time and potential consequences; • Role of business dynamics and link with job creation/destruction; • …. MultiProd – Representativeness and policy questions
  17. 17. THANK YOU! sara.calligaris@oecd.org chiara.criscuolo@oecd.org multiprod@oecd.org
  18. 18. • Andrews, D., C. Criscuolo and P. Gal (2016), "The Best versus the Rest: The Global Productivity Slowdown, Divergence across Firms and the Role of Public Policy", OECD Productivity Working Papers, No. 5, OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/63629cc9-en • Bajgar, M., G. Berlingieri, S. Calligaris, c. Criscuolo (2017), "Can Business Micro Data Match Macro Trends? Comparing MultiProd data with STAN", forthcoming. • Bajgar, M., G. Berlingieri, S. Calligaris, C. Criscuolo, J. Timmis (2017), "To Use or Not to Use (and How to Use): Coverage and Performance of Orbis Data", forthcoming. • Berlingieri, G., P. Blanchenay , S. Calligaris and C. Criscuolo (2017) "The MultiProd project: A comprehensive overview", OECD Science, Technology and Industry Working Papers, No. 2017/04, OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/2069b6a3-en • Berlingieri, G., P. Blanchenay and C. Criscuolo (2017), "The great divergence(s)", OECD Science, Technology and Industry Policy Papers, No. 39, OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/953f3853-en • Calligaris, S., C. Criscuolo and L. Marcolin (2017), "Market power in the digital era", forthcoming. • Calvino, F. and C. Criscuolo (2017), "Business dynamics and digitalization", forthcoming. References

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