MARKUPS IN THE DIGITAL ERA Sara Calligaris Chiara

MARK-UPS IN THE DIGITAL ERA Sara Calligaris, Chiara Criscuolo, Criscuolo Luca Marcolin OECD TPRI Competition Conference 2018 What’s the Evidence for Strengthening Competition Policy? Boston University School of Law July 23, 2018 The opinions expressed and arguments employed herein are solely those of the authors and do not necessarily reflect the official views of the OECD or of its member countries.

● ○ ○ ○ MOTIVATION AND CONTRIBUTION DATA MARK-UP ESTIMATION FACTS MARK-UPS AND THE DIGITAL TRANSFORMATION NEXT STEPS AND CONCLUSIONS

Motivation: the role of the digital transformation for competitive dynamics Digital technologies: • lower costs of entry, operation, and experimentation; • Ease sharing of ideas and innovation, network effect; • Improve real-time measurement; • Ease penetration of several markets and faster scaling up. These characteristics can potentially have different effects on the competitive environment: 1. Source of increased competition (Brynjolfsson et al. , 2005); 2. “Winner-takes-most” dynamics (Brynjolfsson et al. , 2008; Bessen, 2017). Moreover, increased importance of complementary investments in intangibles (Haskel and Westlake, 2017; Brynjolfsson and Mc. Elheran, 2016; Brynjolfsson et al. , 2017).

Motivation: Macro trends • Increase in mark-ups (De Loecker and Eeckhout, 2017; Traina, 2018; Andrews et al. , 2018); • Increase in concentration (Autor et al. , 2017; Bessen, 2017; Gutierrez and Philippon 2016, 2017 a, b; Grullon et al. , 2017; But: Shapiro, 2017, Valletti et al. , 2017); • Declining business dynamism (e. g. Haltiwanger et al. , 2017); • Decline in both labour (Autor et al. , 2017); and capital share (Barkai, 2016); • Decline in investment intensities (Gutierrez and Philippon, 2016, 2017 b). • Increase in profit dispersion (MGI, 2015; Bessen, 2017; Eggertsson et al. , 2018); • Productivity slowdown and productivity divergence (Andrews, et al. , 2017, Berlingieri et al. , 2017);

Contribution 1. Identify patterns in the evolution of mark-ups across a large set of countries since the 2000 s • On average firm-level mark-ups have increased over time; • The increase is mainly driven by in the top half of the mark-up distribution. 2. Link these changes to the digital transformation • Mark-ups are higher in digital-intensive sectors, and more so now than at the beginning of the 2000 s.

Contribution 3. Explore factors underlying the digital markup differential: • Are higher mark-ups a reflection of the regulatory environment and global markets – Product Market Regulation and international competition account only for part of these differences; • Or are they a reflection of the Production Technology in digital intensive sectors? – Intensity in intangible assets and innovation (firm level patenting) can explain almost half of the digital differential in mark-ups.

○ MOTIVATION AND CONTRIBUTION ● ○ ○ ○ DATA ○ MARK-UP ESTIMATION FACTS MARK-UPS AND THE DIGITAL TRANSFORMATION NEXT STEPS AND CONCLUSIONS

Firm Data: The OECD Matched Orbis. Pat. Stat dataset • Orbis firm-level dataset: – 25 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, USA); – Period 2001 -2014; – Manufacturing and non-financial market services sectors. Only firms with more than 20 employees; – Consolidated accounts. Final sample: approximately 2. 5 mn. obs. • Linked to all patent applications belonging to IP 5 patent families, since 1985 from Pat. Stat. – Algorithmic matching, by company name (based on Squicciarini and Dernis, 2013). • Indicators of interest: – Stock of patents: sum of the depreciated patent count since the year of first filing of the first patent family. Depreciation = 15% (Hall et al. , 2005). – Citation-weighted stock of patents. Forward citation (5 years) as a proxy for technological and economic relevance of patent.

How do we define “digital intensity”? • • e-Commerce Investments in ICT equipment Purchase of ICT intermediate goods and services Robots intensity (manufacturing only) • • • Market-related indicators Labour-input ICT specialists • Investments in software indicators Digital Capital & intermediates indicators • Computerised information (mostly) Official data: LFS, SNA, ICIO, ICT use survey; 36 sectors (ISIC 4); 2001 -15, balanced for 12 OECD countries; “Global”, cross-indicator, ranking is constructed as the weighted average of the rankings of sectors by each indicator; • For details see Calvino, Criscuolo, Marcolin and Squicciarini (2018).

A taxonomy of digital intensive sectors Sector (ISIC rev. 4) Food products, beverages and tobacco Textiles, wearing apparel, leather Wood and paper products, and printing Chemicals and chemical products Pharmaceutical products Rubber and plastics products Basic metals and fabricated metal products Computer, electronic and optical products Electrical equipment Machinery and equipment n. e. c. Transport equipment Furniture; other manufacturing; repairs of computers Wholesale and retail trade, repair Transportation and storage Accommodation and food service activities Publishing, audiovisual and broadcasting Telecommunications IT and other information services Legal and accounting activities, etc. Scientific research and development Advertising and market research; other business services Administrative and support service activities Quartile of digital intensity: 2013 -15 Low Medium-low Medium-high Medium-low Medium-high High Medium-high Low Medium-high High High

○ ○ MOTIVATION AND CONTRIBUTION ● ○ ○ MARK-UP ESTIMATION ○ DATA FACTS MARK-UPS AND THE DIGITAL TRANSFORMATION NEXT STEPS AND CONCLUSIONS

Firm-level mark-ups •

○ ○ ○ MOTIVATION AND CONTRIBUTION ● ○ FACTS ○ DATA MARK-UP ESTIMATION MARK-UPS AND THE DIGITAL TRANSFORMATION NEXT STEPS AND CONCLUSIONS

Facts 1 and 2: Rising mark-ups pushed by the top Cobb-Douglas Translog • Deciles of the mark-up distribution in the year 2 -digit sector (A 38)averaged across sectors; • Dynamics not due to a particular country.

○ ○ MOTIVATION AND CONTRIBUTION ● MARK-UPS AND THE DIGITAL TRANSFORMATION ○ NEXT STEPS AND CONCLUSIONS DATA MARK-UP ESTIMATION FACTS

Empirical framework •

Fact 3: Mark-ups in digital vs. less digital intensive sectors Average percentage differences in mark-ups (digital vs less digital sectors) • • Pooled OLS estimation; Robust to Using Translog-mark-ups and TFP; Clustering errors at industry-country; excluding particular countries (e. g. US); Only surviving firms; Fixing digital at initial period.

• Market structure – PMR: barriers to entry and barriers to investment (+); – Globalization: Differential exposure to international competition (e. g. imports of intermediates) (+/-). • Production technology – High fixed cost and low marginal costs (+); – Intensity in intangible investments (+); – Role of innovation (+).

Regulatory impact (1) 2001 -2003 Digital-Intensive Log(age)(t-1) Log(K/Sales)(t-1) Log(tfp)(t-1) Regulatory impact (2) (3) 2013 -2014 (4) 0. 119*** (0. 002) 0. 109*** (0. 002) 0. 147*** (0. 002) 0. 137*** (0. 002) -0. 156*** -0. 153*** -0. 161*** (0. 002) 0. 076*** (0. 006) 0. 141*** (0. 003) (0. 002) 0. 083*** (0. 006) 0. 138*** (0. 003) 0. 303*** (0. 002) 0. 089*** (0. 004) 0. 153*** (0. 002) 0. 090*** (0. 004) 0. 151*** (0. 002) 0. 355*** (0. 010) (0. 015) Observations 207, 958 170, 686 R-squared Fixed Effects 0. 092 0. 103 0. 159 country-year id id Cluster Note: Results of estimating OLS regressions. The “Regulatory impact” variable measures the impact of regulatory barriers to competition in non-manufacturing sectors on all industries, through intermediate inputs. Sectors are affected differently by the same regulation because they use products of the regulated sectors (intermediate inputs) to a different extent.

Intangible assets - OLS Digital-Intensive Log(age)(t-1) Log(K/Sales)(t-1) Log(tfp)(t-1) Log(ICT specialists)(t-1) (1) 0. 064*** (0. 002) -0. 117*** (0. 001) 0. 149*** (0. 003) 0. 165*** (0. 002) Log(ICT investment)(t-1) (2) 0. 045*** (0. 002) -0. 111*** (0. 001) 0. 155*** (0. 003) 0. 155*** (0. 002) 0. 034*** (0. 001) (4) 0. 034*** (0. 002) -0. 113*** (0. 001) 0. 151*** (0. 003) 0. 159*** (0. 002) 0. 050*** (0. 002) Log(Software investment)(t 1) Observations R-squared Fixed Effects Cluster (3) 0. 049*** (0. 002) -0. 112*** (0. 001) 0. 146*** (0. 003) 0. 159*** (0. 002) 0. 026*** (0. 001) 574, 437 0. 151 country-year id 574, 437 0. 169 country-year id 574, 437 0. 157 country-year id 574, 437 0. 158 country-year id Note: “ICT specialists” is the proportion of an industry’s workforce employed in ICT-specialist occupations. “ICT investment” (resp. software) is the industry’s volume of investment in ICT (resp. software) over the industry’s volume of total nonresidential investment.

Intangible assets - FE Log(age)(t-1) Log(K/Sales)(t-1) Log(tfp)(t-1) Log(ICT specialists)(t-1) Log(ICT investment)(t 1) (1) -0. 018*** (0. 001) 0. 092*** (0. 002) 0. 009*** (0. 001) (2) -0. 014*** (0. 001) 0. 089*** (0. 002) 0. 005*** (0. 001) (5) -0. 014*** (0. 001) 0. 087*** (0. 002) 0. 006*** (0. 001) (6) -0. 014*** (0. 001) 0. 089*** (0. 002) 0. 005*** (0. 001) 0. 005 (0. 004) 0. 004*** (0. 001) 0. 008*** 0. 007*** (0. 001) Log(Stock Patents/Sales)(t-1) Cluster (4) -0. 014*** (0. 001) 0. 089*** (0. 002) 0. 005*** (0. 001) 0. 030*** Log(Software investment)(t-1) Observations R-squared Fixed Effects (3) -0. 014*** (0. 001) 0. 089*** (0. 002) 0. 005*** (0. 001) 574, 437 0. 955 countryyear Firm id 574, 437 0. 954 countryyear Firm id 0. 019** 0. 018** (0. 008) 574, 437 0. 954 countryyear Firm id Note: . “Stock Patents” is the stock of patents filed by the company in one of the IP 5 Patent Offices during the considered period. “ICT specialists” is the proportion of an industry’s workforce employed in ICT-specialist occupations. “ICT investment” (resp. software) is the industry’s volume of investment in ICT (resp. software) over the industry’s volume of total non-residential investment.

NEXT STEPS AND CONCLUSIONS

○ ○ ○ MOTIVATION AND CONTRIBUTION ● NEXT STEPS AND CONCLUSIONS DATA MARK-UP ESTIMATION FACTS MARK-UPS AND THE DIGITAL TRANSFORMATION

Extensions and future work •

Preliminary conclusions • Average mark-ups are increasing over time, and driven by firms at the top of the mark-up distribution; • Mark-ups higher in digital-intensive sectors than in lessdigitally intensive sectors; • Mark-up differentials have increased significantly over time; • Market structure accounts for 8% (PMR) to 38% (import competition) of the digital dummy; • Including intangibles and patents we can account for up to ½ of the digital mark-ups differentials.

THANK YOU! sara. calligaris@oecd. org chiara. criscuolo@oecd. org luca. marcolin@oecd. org

Motivation

Digital intensive sectors • Balanced data for 36 sectors (ISIC 4) in 12 OECD countries. • Time series 2001 -15. • Multiple dimensions of the taxonomy: • ICT investment intensity: deflated ICT tangible GFCF / total GFCF; • Software investment intensity: deflated software GFCF / total GFCF; • Robot intensity: Stock of robots / employment (manufacturing); • Intermediates ICT goods and ICT services : deflated purchases of ICT intermediate goods (resp. , services) / output; • E-sales intensity: % of total sales carried out online; • ICT specialists: # of ICT specialists in all countries / total employment. • + “Global” ranking, across indicators (weighted average of the rankings of sectors by each indicator), for 2001 -03 and 2013 -15. See Calvino et al. (2018)

Supply-side approach to mark-ups •

Estimating OEs •

Mark-ups: demand- vs supply-side Advantages Disadvantages Literature Supply-side (1) Less micro data requirements AND relatively less demanding to estimate. (2) No need for information on product features. (3) No need to assume form of market conduct (FOC always valid). (3) controls for measurement error and endogeneity of inputs. … (1) Still requires data to obtain TFP (and assumptions thereof if estimated). (2) Assume cost minimisation in all firms. (3) Assume at least one input is free to adjust. … Hall (1988); Roeger (1995); Ellis and Halvorsen (2002); De. Souza (2009); De Loecker (2011); De Loecker and Warzynski (2012). Demand-side (1) No need to assume cost minimisation for all firms. (2) Estimation of demand systems, yielding direct estimate of market conduct / competition. … (1) Need detailed product-level and consumer data. (2) Assume shape of utility function. (3) Assume way firms compete and set prices (e. g. Nash Bertrand). (4) IV needed to retrieve demand elasticities. … Klette Berry (1994); BLP (1995); Goldberg (1995); Nevo (2000, 2001); Capps et al. (2003); Davis (2006); Zhelobodko et al. (2012); Berry and Haile (2015); Pakes (2015). De Loecker and Scott (2016): compare the approaches for one industry. “The results indicate fairly broad agreement between the two approaches”

Mark-ups: demand- vs supply-side Advantages Disadvantages Literature Supply-side (1) Less micro data requirements AND relatively less demanding to estimate. (2) No need for information on product features. (3) No need to assume form of market conduct (FOC always valid). (3) controls for measurement error and endogeneity of inputs. … (1) Still requires data to obtain TFP (and assumptions thereof if estimated). (2) Assume cost minimisation in all firms. (3) Assume at least one input is free to adjust. … Hall (1988); Roeger (1995); Ellis and Halvorsen (2002); De. Souza (2009); De Loecker (2011); De Loecker and Warzynski (2012). Demand-side (1) No need to assume cost minimisation for all firms. (2) Estimation of demand systems, yielding direct estimate of market conduct / competition. … (1) Need detailed product-level and consumer data. (2) Assume shape of utility function. (3) Assume way firms compete and set prices (e. g. Nash Bertrand). (4) IV needed to retrieve demand elasticities. … Klette Berry (1994); BLP (1995); Goldberg (1995); Nevo (2000, 2001); Capps et al. (2003); Davis (2006); Zhelobodko et al. (2012); Berry and Haile (2015); Pakes (2015). De Loecker and Scott (2016): compare the approaches for one industry. “The results indicate fairly broad agreement between the two approaches”

Descriptive statistics Summary statistics 2005 industry-level USD PPP, by digital intensity Variable Mean Median SD N. of obs. Real Gross Output (‘ 000) 51, 100 11, 800 401, 000 2, 285, 584 Real Value Added (‘ 000) 13, 300 2, 994 136, 000 2, 285, 584 Real Intermediates (‘ 000) 27, 200 5, 548 187, 000 2, 285, 584 177 50 1, 295 2, 285, 584 21, 500 1, 937 374, 000 2, 285, 584 Log(Mark-up): Cobb-Douglas 0. 31 0. 17 0. 38 1, 355, 201 Log(Mark-up): Translog 0. 16 0. 07 0. 25 1, 753, 176 Number of employees Real Capital Stock (‘ 000)

Descriptive statistics (2) Summary statistics 2005 industry-level USD PPP, by digital intensity Variable Real Gross Output (‘ 000) Real Value Added (‘ 000) Real Intermediates (‘ 000) Number of employees Real Capital Stock (‘ 000) Log(Mark-up): Cobb-Douglas Log(Mark-up): Translog 2001 -2003, less digital intensive Mean Median SD N. Obs. 32, 200 9, 050 250, 000 144, 169 9, 200 2, 651 98, 800 144, 169 15, 800 3, 862 125, 000 144, 169 134. 4534 47 1, 475 144, 169 13, 500 2, 252 166, 000 144, 169 0. 20 0. 14 0. 21 99, 153 0. 10 0. 06 0. 14 124, 694 2013 -2014, less digital intensive Mean Median SD N. Obs. 44, 600 8, 340 537, 000 131, 069 11, 800 2, 520 202, 000 131, 069 22, 600 4, 088 225, 000 131, 069 151. 7968 50 1, 464 131, 069 26, 700 2, 451 567, 000 131, 069 0. 25 0. 17 0. 28 88, 672 0. 12 0. 07 0. 17 108, 421 2001 -2003, digital intensive Mean Median SD N. Obs. 52, 000 12, 400 405, 000 234, 190 13, 300 2, 891 147, 000 234, 190 29, 000 6, 106 212, 000 234, 190 189. 6552 48 1, 366 234, 190 17, 300 1, 466 470, 000 234, 190 0. 32 0. 16 0. 40 131, 128 0. 18 0. 07 0. 26 172, 126 2013 -2014, digital intensive Mean Median SD N. Obs. 69, 700 14, 300 531, 000 217, 299 17, 900 3, 552 169, 000 217, 299 37, 700 7, 059 263, 000 217, 299 217. 8924 56 1, 449 217, 299 31, 000 1, 774 485, 000 217, 299 0. 41 0. 23 0. 48 118, 830 0. 23 0. 09 0. 31 156, 149 Diff. *** *** *** ***

Fact 4: rising mark-ups in digital more than less digital intensive sectors Note: The distinction between digital intensive sectors (resp. less digital intensive sectors) rank above (resp. below) the median sector by digital intensity. This graph fixes the ranking of sectors to the initial period (2001 -03), and shows only mark-ups estimated assuming a Cobb-Douglas production function.

Avg Mark-up by productivity decile

Fact 3: Mark-ups in digital vs. less digital intensive sectors (1) Digital. Intensive Top-digital intensive Log(age)(t-1) Log(K/Sales)(t 1) Log(tfp)(t-1) 0. 132** * (0. 002) 0. 049** * (0. 001) 0. 106** * (0. 006) (2) (3) 2001 -2003 (5) (6) 0. 118*** 0. 188*** 0. 151*** (0. 002) 0. 285*** (4) 0. 269*** (7) (8) 0. 475*** 0. 439*** 2013 -14 (0. 003) (0. 002) (0. 003) -0. 154*** -0. 042*** -0. 122*** -0. 058*** -0. 154*** -0. 038*** -0. 096*** (0. 002) 0. 058*** (0. 001) 0. 139*** (0. 002) 0. 102*** (0. 001) 0. 075*** (0. 002) 0. 074*** (0. 001) 0. 093*** (0. 006) 0. 138*** (0. 003) (0. 005) (0. 006) 0. 106*** (0. 003) 0. 141*** (0. 002) (0. 003) 0. 084*** (0. 002) Observations 230, 281 207, 502 R-squared 0. 090 0. 176 0. 193 0. 090 0. 147 0. 309 0. 328 • Pooled OLS 0. 059 estimation Fixed Effects country- country- country • Digital intensive sectors (above median) display higher Mark-ups. -year year • Difference between digital and less digital intensive sectors increased over time. Cluster id id

Fact 3: Mark-ups in digital vs. less digital intensive sectors (1) Digital. Intensive Top-digital intensive Log(age)(t-1) Log(K/Sales)(t 1) Log(tfp)(t-1) 0. 132** * (0. 002) 0. 049** * (0. 001) 0. 106** * (0. 006) (2) (3) 2001 -2003 (5) (6) 0. 118*** 0. 188*** 0. 151*** (0. 002) 0. 285*** (4) 0. 269*** (7) (8) 0. 475*** 0. 439*** 2013 -14 (0. 003) (0. 002) (0. 003) -0. 154*** -0. 042*** -0. 122*** -0. 058*** -0. 154*** -0. 038*** -0. 096*** (0. 002) 0. 058*** (0. 001) 0. 139*** (0. 002) 0. 102*** (0. 001) 0. 075*** (0. 002) 0. 074*** (0. 001) 0. 093*** (0. 006) 0. 138*** (0. 003) (0. 005) (0. 006) 0. 106*** (0. 003) 0. 141*** (0. 002) (0. 003) 0. 084*** (0. 002) Observations 230, 281 207, 502 R-squared 0. 090 0. 176 0. 193 0. 090 0. 147 0. 309 0. 328 • Pooled OLS 0. 059 estimation Fixed Effects country- country- country • Digital intensive sectors (above median) display higher Mark-ups. -year year • Difference between digital and less digital intensive sectors increased over time. Cluster id id

Manufacturing Vs. Services (1) Service Digital-Intensive Service Less-Digital Intensive Manufacturing Digital. Intensive Log(age)(t-1) Log(K/Sales)(t-1) Log(tfp)(t-1) Observations R-squared Fixed Effects Cluster (2) 2001 -2003 (3) 2013 -2014 (4) 0. 342*** (0. 003) 0. 155*** 0. 325*** (0. 003) 0. 154*** 0. 370*** (0. 003) 0. 123*** 0. 329*** (0. 003) 0. 121*** (0. 002) -0. 013*** (0. 002) -0. 035*** (0. 002) -0. 036*** (0. 001) -0. 028*** (0. 001) 0. 066*** (0. 005) (0. 001) -0. 102*** (0. 002) 0. 033*** (0. 006) 0. 098*** (0. 002) -0. 039*** (0. 001) 0. 051*** (0. 003) (0. 002) -0. 114*** (0. 002) 0. 050*** (0. 003) 0. 108*** (0. 002) 230, 281 0. 221 country-year id 230, 281 0. 236 country-year id 207, 502 0. 211 country-year id 207, 502 0. 243 country-year id

International competition in intermediates (1) (2) Whole sample Digital-Intensive Digital-intensive Service 0. 135*** (0. 001) 0. 086*** (0. 001) (3) (4) Manuf Services -0. 023*** (0. 001) 0. 138*** (0. 003) Less-Digital Intensive Service Log(age)(t-1) Log(K/Sales)(t-1) Log(tfp)(t-1) -0. 149*** (0. 001) 0. 063*** (0. 003) 0. 128*** (0. 001) 0. 235*** (0. 003) 0. 062*** -1. 954*** -0. 219*** -2. 577*** (0. 003) -0. 027*** (0. 001) -0. 767*** (0. 015) -0. 120*** (0. 001) 0. 016*** (0. 003) 0. 109*** (0. 001) (0. 015) -0. 043*** (0. 001) 0. 068*** (0. 004) 0. 029*** (0. 001) (0. 124) -0. 145*** (0. 002) 0. 034*** (0. 004) 0. 125*** (0. 002) (0. 022) -0. 109*** (0. 001) 0. 033*** (0. 003) 0. 096*** (0. 001) Digital-intensive Manuf Log(Intermediates Import Intens) (5) Whole sample Observations 1, 045, 551 442, 293 603, 258 1, 045, 551 R-squared 0. 134 0. 209 0. 196 0. 149 0. 235 Fixed Effects country-year country-year Note: Results of estimating OLS regressions. “Log(intermediates import intens)” is the logarithm of the ratio Cluster id id between a countryid industry’s imports of intermediates from the world and the country-industry’s output. The ratio is available from 2005 onwards only.

International competition in intermediates (1) (2) Whole sample Digital-Intensive 0. 135*** (0. 001) 0. 086*** (0. 001) Digital-intensive Service 0. 235*** (0. 003) 0. 062*** Less-Digital Intensive Service -1. 954*** (0. 003) -0. 027*** (0. 001) -0. 767*** (0. 015) -0. 120*** (0. 001) 0. 016*** (0. 003) 0. 109*** (0. 001) (0. 022) -0. 109*** (0. 001) 0. 033*** (0. 003) 0. 096*** (0. 001) Digital-intensive Manuf Log(Intermediates Import Intens) Log(age)(t-1) Log(K/Sales)(t-1) Log(tfp)(t-1) (3) Whole sample -0. 149*** (0. 001) 0. 063*** (0. 003) 0. 128*** (0. 001) Observations 1, 045, 551 R-squared 0. 134 0. 209 0. 235 Effects country-yearimport intens)” country-year Note: Results of. Fixed estimating OLS regressions. “Log(intermediates is the logarithm of the ratio between a country. Cluster id id id from 2005 onwards only. industry’s imports of intermediates from the world and the country-industry’s output. The ratio is available

Different facets of the digital transformation (1) Digital-Intensive Log(age)(t-1) Log(K/Sales)(t-1) Log(tfp)(t-1) Log(ICT specialists)(t-1) Log(ICT intermediate services)(t-1) 0. 084*** (0. 002) -0. 127*** (0. 001) 0. 107*** (0. 003) 0. 187*** (0. 002) 0. 033*** (0. 001) (2) 0. 071*** (0. 002) -0. 121*** (0. 001) 0. 107*** (0. 003) 0. 178*** (0. 002) (4) Pooled OLS 0. 134*** 0. 092*** (0. 002) -0. 128*** -0. 123*** (0. 001) 0. 098*** 0. 082*** (0. 003) 0. 191*** 0. 185*** (0. 002) Log(ICT investment) (t-1) -0. 062*** (0. 001) Log(Software investment)(t-1) R-squared Fixed Effects Cluster 0. 049*** (0. 002) -0. 112*** (0. 001) 0. 147*** (0. 003) 0. 159*** (0. 002) (6) 0. 091*** (0. 002) -0. 134*** (0. 001) 0. 103*** (0. 003) 0. 196*** (0. 002) 0. 010*** (0. 001) Log(e-sales)(t-1) Observations (5) 0. 048*** (0. 001) Log(ICT intermediate goods)(t-1) (3) 0. 028*** (0. 001) 0. 018*** (0. 001) 905, 471 840, 107 750, 006 752, 605 578, 443 737, 545 0. 191 Cou-year id 0. 199 Cou-year id 0. 193 Cou-year id 0. 194 Cou-year id 0. 157 Cou-year id 0. 184 Cou-year id

Extensions and future work • SGA expenses. • Other correlates of mark-ups/measures of competitive dynamics: – Concentration; – Other proxies of international competition. – Fixed cost intensity : • EBITDA vs Mark-ups; • Persistency of mark-ups over time; • Within-Firm vs reallocation explanation • Mark-ups at the productivity frontier and turbulence

Motivation: Market and Firm Dynamics business dynamism and digital transformation Contribution of young firms to aggregate productivity growth trends in business dynamism Productivity and wage divergence Productivity Slowdown Weight of largest firms What is happening to mark-ups? Trends in misallocation Changes in global production networks Declining labour share M&A dynamics …in the era of digital change

Contribution Are the digitalisation and competitive dynamics linked? How? 1. We identify patterns in the evolution of mark-ups in the economy across a large set of countries 2. We link these changes be linked to the digital transformation 3. Investigate some underlying mechanisms – Features of the market structure (international competition and market regulation); – Firms innovation dynamics and importance of intangible capital.

Preview of the results 1. On average firm-level mark-ups have increased over time; 2. The increase is mainly driven by in the top half of the mark-up distribution; 3. Mark-ups are higher in digital-intensive sectors, and more so now than at the beginning of the 2000 s; 4. Product market regulation and international competition account only for part of these differences; 5. A large part of the mark-up differential in digital sectors can be explained by higher intensity in intangible assets.

Fact 1: Rising mark-ups Cobb-Douglas Translog Note: Unconditional averages of firm-level log mark-ups. The figure plots log-mark-ups and indexes the 2001 level to 0, hence the vertical axes represent log-differences from the starting year which, given the magnitudes, approximates well for growth rates.