R&D and performance in modern economies: survey R&D versus broader R&D measures Prof. Hannu Piekkola University of Vaasa

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1 R&D and performance in modern economies: survey R&D versus broader R&D measures Prof. Hannu Piekkola University of Vaasa Challenges in measuring productivity, growth and intangibles EUROSTAT BRYSSELS

3 Intangible capital IC in Innodrive Missing input in production function Tacit knowledge (not public), balance sheets not enough Linked employer-employee data LEED Intangibles as input in production function: constant returns to scale recovered? Expenditure EXP vs. performance-based PER measures EXP production of intangible capital: IC related work, use of intermediates and capital from input-output data PER: productivity of IC work and its value added share using output elasticities from production function estimation Tobin s q well above 1 Intangible capital explains the gap?

Firm-level innovation and competitiveness INNODRIVE 2008-2011 MICRO APPROACH Linked employer-employee data

4 Firm-level innovation and competitiveness INNODRIVE MICRO APPROACH Linked employer-employee data Finland, Norway, the UK, Germany,m Czech Republic, Slovenia Figure 1. Occupational shares of IC work

Functional occupational codes in Finnish data + Skill level expert or above required except computer 071 +Education field codes such as engineers in services as

5 Functional occupational codes in Finnish data + Skill level expert or above required except computer 071 +Education field codes such as engineers in services as proxy for R&D work in services, see Piekkola, Hannu (2016), 'Intangible Investment and Market Valuation', 5 Review of Income and Wealth, 62 (1), 28-51, 9% of all work

6 ISCO08 R&D work 8% of all, also evaluated with Danish data Technical and mathematical work professional R&D managers 1223 (1237) Science and Engineering Professionals 21 (excluding telecommunication engineering 2153) Physical and earth science professionals 211 (211), Engineering Professionals 212 (212) Mathematicians, Statisticians, Lifescience professionals 213 (212), 214 (212), Electrical, Electronics Engineering 2151, 2152 (212), Architects, Planner 216 (212) Health professionals 22 Medical doctors 221 (222), Nursing and Midwifery Professionals 222 (223), Other Health Professionals 226 (223), 22 (isco3 not available) Science and Engineering Associate Professionals 31 Physical and Engineering Science Technicians 311 (311), Life Science Technicians and Related Associate Professionals 314 (321) Organizational Capital (OC management and marketing) work and education field not in social sciences and education field technical education 1,12,13,23,24,

7 Register-based occupational R&D Finland Statistics Finland data Labour producing intangible intangibles IC=OC, R&D, ICT, OC: Organisational workers such as management (incl. owners) and marketing employees R&D: personnel is defined by technicians, engineers, and similar ICT: personnel is defined by information and communication experts Table 1. Multiplier for related intermediates and capital services for one unit of labor in intangible investment and depreciation in INNODRIVE Multiplier for IC labor costs= use of intermediates and capital services X share of investment-type IC work (0.2 in OC, 0.35 in R&D and 0.5 in ICT work) ICT R&D OC Depreciation

8 Figure 2. Survey R&D versus broader R&D and OC, ICT investment per employment in production and market services Finland in thousand 2010 Note: All firms with on average workers, broad R&D 64% (GDP share 6.4%), survey R&D 10% of firms (GDP share 5.5%) Source: Piekkola 2017 R&D and performance in modern economies: broad versus survey R&D (mimeo)

9 Broad and survey R&D, Finland Broad/total R&D register-based from occupations following Innodrive Statistics Finland: Occupational data to asses intangible workers (ISCO-08) and educational sectors computing, social sciences Innovative work: Information and communication (ICT), Research and development (R&D), Organisational (OC management of marketing). A multiplier to account for fixed capital and intermediate inputs External R&D from R&D survey 2. R&D survey every year, every second-year innovation survey , Internal and external R&D

10 Productivity using CDM-model A CDM model (Crépon et al. 1998) free steps: (i) Heckman method for missing R&D, book value and firm-size identifying positive R&D, intensity depends on sector, market share, tangibles, sector, firm size and age (ii) predicted R&D explains probability of process, product innovations and OC innovations (iii) predicted process, product and organizational innovations explain productivity

11 CDM-model Expected results: Product innovations improve both productivity and profitability, whereas process innovations promote productivity but lower profitability. The latter negative effects explained by using processes already adopted by competing firms and therefore for being a follower rather than a leader in adoption of more innovative methods. Griffith et al. (2006 Oxford Review EP) : process and product innovations increase productivity France not in Germany Spain and UK: only product innovations Italy: only in SMEs (Hall et al. 2009)

12 CDM-model (i) Broad R&D from full sample (using occupational data) and survey R&D are comparable in product innovation analysis driven by similar relation to explaining factors (instruments) (ii) Process innovation results are unclear and profitability effects are likely to be negative to both process and product innovations, correlated with organizational innovations (iii) Instruments: type of sectors, size, and R&D, can be too general

13 Table 7. Panel instrumenting* Labor productivity Knowledge firms in innovation survey Broad R&D Survey R&D Broad R&D (1.05) Survey R&D (1.44) Broad R&D, Process innovation (1.51) Survey R&D, Process innovation (1.5) Broad R&D, Product innovation *** - - (3.33) Survey R&D, Product innovation ** (2.69) Tangible capital per empl *** ** *** *** (4.66) (2.79) (4.67) (3.45) Lagged tangible capital per empl ** ** (3.02) (1.92) (2.71) (1.85) Inverse Mill's ratio *** *** *** (8.63) (1.3) (7.28) (5.89) N R Squared Non-centered R Squared Centered Kleibergen-Paap Wald p-value Hansen J statistic P value * The generalized method of moments (GMM) and 2SLS with the Wooldridge (2009) modification, two-period lagged tangible investment as instrument 15

14 Table 8. Panel instrument Return on assets Knowledge firms in innovation survey Total R&D Survey R&D Broad R&D 0.369*** (3.66) Survey R&D (0.46) Broad R&D, Process innovation (1) Survey R&D, Process innovation (0.42) Broad R&D, Product innovation * - - (2.51) Survey R&D, Product innovation (0.32) Tangible capital per empl * (2.47) (0.21) (1.06) (0.41) Lagged tangible capital per empl (1.08) (0.9) (0.32) (0.66) Book value *** *** *** *** (13.77) (4.73) (7.45) (3.78) Firm age > *** ** *** *** (9.52) (2.99) (8.93) (3.33) N R Squared Non-centered R Squared Centered Kleibergen-Paap Wald p-value Hansen J statistic P value

15 Results and future work IC jobs using ISCO three-digit and without any qualifications OC work 4% of all work, R&D work 8% of all work CDM analysis Simple instrumenting by market share, firm-size, sectors etc. gives less robust results Broad and survey R&D: product innovations increase productivity and profitability (return on asset) Panel data 2SLS Instruments: two-period lagged tangible investment, lagged values of IC, same results with broad and survey R&D for product innovations Broad R&D to evaluate productivity and profitability Preferred proxy for R&D for SMEs and services where surveys may not be representative Survey R&D more sensitive to the choice of instruments (a lot of missing observations) More refined methods require ISC 4-digit / functional occupations Overlap of R&D and ICT avoided with using innovative work estimates Future work with remote access to Statistics office data Productivity analysis using register data in Finland and Denmark, co-operation with University of Århus, Denmark Environmental and intangible capital driven innovations and productivity, cooperation with University of Umeå, Sweden 17

Innovation Capability from Intangible Assets

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