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

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. 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. 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. ... 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. …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. The best are thriving

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. 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. 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. 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. 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. 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. 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. 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. • 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. 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. THANK YOU!
sara.calligaris@oecd.org
chiara.criscuolo@oecd.org
multiprod@oecd.org

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