Introduction to innovation surveys and some very basic econometrics. Pierre Mohnen August 2016

Transcription

1 Introduction to innovation surveys and some very basic econometrics Pierre Mohnen August 2016

2 Main references Mairesse, Jacques and Pierre Mohnen, «Using innovation surveys for econometric analysis», in the Handbook of the Economics of Innovation, B. H. Hall and N. Rosenberg (editors), Elsevier, Amsterdam, 2010,

3 Innovation and indicators R&D Patents + patent citations Bibliometrics Innovation surveys: inputs and outputs

4 Definition of R&D Frascati Manual

5 Contents of patents Title and abstract Technical class(es) Patent and non-patent references (citations) Inventors and applicants (assignees) Date of filing, priority date, date of publication, date of granting

6 Example (taken from OECD Patent Statistics Manual, 2009)

7 Innovation Surveys Late 80s, early 90s: pilot innovation surveys in Canada, France, Germany, Italy, Luxembourg, the Netherlands, Norway, Sweden, Uruguay and the United States Oslo Manual (1992, 1996, 2005) Community Innovation Surveys (CIS) CIS1 (90-92), CIS2 (94-96), CIS3 (98-00), CIS4 (02-04), CIS2006 (04-06), CIS2008 (06-08), Yearly data from Mannheim innovation panel (Germany) since 1992, ESSE (Spain) since 1990 World Bank and EBRD, Innovation Follow-up, BEEPS

8 Purpose of innovation surveys Measure of innovation performance Input side Output side Comparison of innovation performance across sectors, countries Link of innovation performance with productivity, employment, competition,

9 History Carter and Williams, UK, 1950s, for the Science and Industry Committee of the British Association for the Advancement of Science Arthur D. Little, E. Mansfield and S. Myers, U.S., 1960s for the National Science Foundation K. Pavitt, 1970s, Science and Policy Research Unit (SPRU) in Brighton

10 History (2) Late 80s, early 90s: pilot innovation surveys in Canada, France, Germany, Italy, Luxembourg, the Netherlands, Norway, Sweden, Uruguay and the United States Oslo Manual (1992, 1996, 2005) Community Innovation Surveys (CIS) CIS1 (90-92), CIS2 (94-96), CIS3 (98-00), CIS4 (02-04), CIS2006 (04-06), CIS2008 (06-08)

11 History (3) Annual surveys in Germany since 1993, bi-annual surveys in the Netherlands since 1999 Surveys on particular sectors (e.g. Canadian survey on construction industry, Dutch LEI survey on agriculture) Surveys with questions on innovation: ESEE in Spain: 10 years of data World Bank administered Investment Climate Survey Chinese National Bureau of Statistics annual survey on large and medium size enterprises

12 Related surveys Literature-based counts of innovation like the one commissioned in 1982 by the U.S. Small Business Administration Statistics Canada Survey on the Commercialization of Innovation, 2007 and repeated Surveys of Advanced Technologies in Manufacturing French surveys of organizational innovation and on intellectual property rights Yale Survey on Industrial Research and Development Carnegie Mellon University R&D Survey Patval Survey on inventors

13 Countries with innovation surveys EU countries (CIS surveys) Canada Norway, Switzerland, Russia, Turkey Australia, New Zealand Argentina, Brazil, Chile, Colombia, Mexico, Peru, Uruguay, Venezuela South Korea, Taiwan, Singapore, Malaysia, Thailand, Japan, China, Vietnam, Indonesia South Africa, Tunisia, NEPAD

14 2012 innovation survey questionnaire I. General information Independent or part of a group? Domestic or foreign group? Any change in enterprise (closure, creation, merger, acquisition, ) Main geographic market Country of location Main industry affiliation Number of employees (level and growth) Turnover (level and growth) % of employees with a tertiary degree II. Are you an innovator? Product innovation (new to firm, new to market) Process innovation Ongoing or abandoned innovation activities

15 2012 innovation survey questionnaire Questions conditional on being a product innovator Questions conditional on being a process innovator Questions conditional on being a product innovator, a process innovator, or with ongoing or abandoned innovation activities Occurrence and extent of innovation activities Financial support Co-operation Competitiveness Information sources

16 2012 innovation survey questionnaire Questions to all: Organizational innovation Marketing innovation Public procurement Innovation strategies Innovation obstacles

17 Summary of content Innovation outputs Commercialization as opposed to invention (to compare with patents and bibliometrics) Different types (product, process, organis., marketing) Levels of novelty Innovation inputs Besides R&D, other innovation expenditures Innovation modalities Cooperation, sources of information, objectives of innovation, public support, obstacles, innovation strategies

18 Characteristics of the data 1. Subject approach ( object approach) Enterprise = unit of analysis and not the innovation Advantage of subject approach: Merge with other available data More homogeneity Disadvantage of the subject approach: We cannot identify projects

19 Characteristics of the data (2) 2. Qualitative data Dichotomous, ordered categorical, unordered categorical Latent variables and distributional assumptions Less informative than continuous data

20 Econometrics of qualitative data Observed values of a variable related to values of an index function If ε follows a normal distribution Probit model

21 Maximum likelihood estimation By numerical methods search for β that maximize the likelihood function.

22 Characteristics of the data (3) 3. Censored variables Potential sample selection Few explanatory variables to discriminate between innovators and non-innovators

23 Characteristics of the data (4) 4. Subjective data + errors in variables What is new? New to the market? Share of innovative sales Innovation expenditures

24 Characteristics of the data (5) 5) Simultaneity Most of the variables in the survey are simultaneously determined, feeding on each other Solution: IV estimation Problem: Cross-sectional data Few variables to instrument If panel data, Few years 4 years apart Random sampling timing

25 IV estimator Solution: Find instrumental variables Instead of exploiting X ε=0=x (Y-Xβ)=0 hence we exploit the no-correlation between Z and ε: Z ε=0=z (Y-Xβ)=0 hence ˆ OLS 1 ( X ' X ) ˆ IV 1 ( Z' X ) X ' Y Z' Y

26 Interest of panel data Panel data have two dimensions of variation, across individuals (index i) and over time (index t) Advantages: more observations Dynamic models Control for unobserved heterogeneity

27 Use of innovation surveys Monitoring and benchmarking Understanding innovation Policy guidance

28 Monitoring and benchmarking Cross-tabulations Innovation-size Innovation-government support Innovation-cooperation, Interest: Do firms with government support innovate more or do more R&D? Follow the evolution of a country s innovation performance and compare it to other countries Interest: Are some countries lagging behind or catching? Caution: correlation does not mean causality

29 Constructing indexes Aggregate in one index many indicators Examples European innovation scoreboard Global summary innovation index Difficulties: Which indicators to include in the index? Weights? Intertemporal comparison when components change? International comparisons when questions differ? How to aggregate qualitative variables? Importance of complementarities

30 Global Innovation Index (WIPO, Cornell Univ., INDEAD)

31 Understanding innovation Determinants Effects Productivity Employment Survival Persistence Complementarities

32 Determinants of innovation Cohen, W. M. (2010), Fifty years of empirical studies of innovative activity and performance, in the Handbook of the Economics of Innovation, B. H. Hall and N. Rosenberg (editors), Elsevier, Amsterdam, 2010, Size Market structure (competition) Industry characteristics: demand, technological opportunity, appropriability Firm characteristics

33 How little we can explain Innovation accounting framework (Mairesse-Mohnen, 2001, 2003) A E C E A E Z E A E C E A e f Z Z f w w f y y 2) / ( ) ( ) ( B E C E B E Z E B E C E B e f Z Z f w w f y y 2) / ( ) ( ) (

34 Tobit type II model (A1) * y1 i x b 1i 1 u1 i (A2) * z2i x b 2i 2 u2i (A3) z2 ln( y /(1 y )) z i and y 1 if 2i 2i * 2i 1i * y 1i >0 z 2 unobserved and y 0 if i 1i * y1i 0 u 1 i u 2 i ~ N(0, ) where 2 2 [ ], 1 =

35 Three kinds of expectations E( y1 i x1 i, x2i ) E( y1 i x1 i) ( x1 ib1 ) (4) (5) E( y x, x ) y f ( u, u ) du du y f ( u, u ) du du 2i 1i 2i 2i 1i 2i 1i 2i x b 2i 1i 2i 1i 2i 1i 1 E( y x, x, y 1) y f ( u, u u x b ) du du or 2i 1i 2i 1i x b 2i 1i 2i 1i 1i 1 1i 2i 1i 1 E( y x, x, y 1) E( y x, x ) / ( x b ) 2i 1i 2i 1i 2i 1i 2i 1i 1 (6)

36 Intensity of Innovation Unconditionally on Being Innovative Size Sum of Source: European Industry and Group Environ- structural Expected Observed intensity effects effects R&D effects ment effects effects intensity Innovativity intensity High-tech Industries Belgium Denmark Germany Ireland Italy Netherlands Norway Average

37 References Mairesse, J. and P. Mohnen (2001), To Be or Not to Be Innovative: An Exercise in Measurement, STI Review. Special Issue on New Science and Technology Indicators, OECD, 27, Mairesse, J. and P. Mohnen (2002), Accounting for Innovation and Measuring Innovativeness: An Illustrative Framework and an Application, American Economic Review, Papers and Proceedings, 92(2), Mairesse, J. and P. Mohnen (2005), The Importance of R&D for Innovation: A Reassessment Using French Survey Data, The Journal of Technology Transfer, special issue in memory of Edwin Mansfield, 30(1-2),

38 How does innovation affect Product innovation Cannibalization Complementarities productivity? Scale economies, learning by doing Latent demand for the new product Process innovation Cost reduction, which can lower price, increase demand, lead to scale economies

39 How does innovation affect productivity (2)? Part of the productivity effect may reflect monopoly power, quality improvements, i.e. price effects that do not correspond to quantity effects There could be complementarities between different types of innovation Process and organizational innovations Product and marketing innovations

40 The «CDM» model Diversification Market share Demand Pull Technology Push R&D Size Industry Knowledge Capital Innovation Physical Capital Skills Productivity

41 Some references Crépon, B., E. Duguet and J. Mairesse (1998), Research and Development, Innovation and Productivity: An Econometric Analysis at the Firm Level, Economics of Innovation and New Technology, 7(2), Mairesse Jacques, Pierre Mohnen and Élisabeth Kremp, «The Importance of R&D and Innovation for Productivity: A Reexamination in Light of the 2000 French Innovation Survey», Annales d Économie et de Statistique, 79/80, , Hall, B.H., Innovation and productivity. Nordic Economic Policy Review, 2, pp Mohnen, P. and B. Hall, Innovation and productivity: An update, Eurasian Business Review, 3(1), 47-65, 2013.

42 One Possible Structure of CDM model Variables: Endogenous in blue Exogenous in black R&D selection R&D intensity Process innovation Product new to market innovation Labor productivity Industry x x x x x Size x x x x x Group x x x x x Government support x x x x x Cost push x x x Demand pull x x x International x x Cooperation x x Diversification x x Market share x x Cooperation x x Sources of information: Internal (firm, group) x x Market (suppliers) x x Basic (univers., public labs) x x Tacit (exhibits, conferences) x x Materials/employee x Capital/employee x Process innovation x Product innovation x R&D/ employee x x ((x))

43 Different direct and indirect estimates of productivity elasticities (in %) with respect to R&D High-Tech Industries Estimates of productivity elasticities with respect to R&D*: R&D per employee Through Products new to the firm Through Products new to the market Through Patent holdings Correcting only for selectivity (complete model - full sample) 3.40 (0.52) 0.02 (0.56) 0.00 (0.31) 0.04 (0.36) Correcting only for endogeneity (complete model sub-samples) 4.50 (1.44) 3.80 (1.57) 3.73 (1.39) 4.44 (1.59) Correcting for selectivity and endogeneity (complete model - full sample) 4.28 (0.93) 4.55 (1.13) 4.37 (0.99) 4.88 (1.14) Mairesse, Mohnen, Kremp, AES, 2005 report importance of correcting for endogeneity and selectivity

44

45

46

47

48 Stylized results using continuous measures of innovation Elasticity of productivity w/t sales of new products/employee: positive, significant, around 0.25 Elasticity lower and more volatile if w/t share of sales due to new products Lower elasticity if dependent variable = growth instead of level of productivity Lower elasticity if human capital or physical capital is controlled for. In general, lower elasticities for manufacturing than for services In general, elasticities are similar with products new to the firm and products new to the market.

49 Stylized results using dichotomous measures of innovation Individual indicators of innovation yield positive and significant coefficients Simultaneous introduction of various innovation indicators often yields non-significant coefficients, and if one is significant it is more often product than process Process innovation has often a negative coefficient if together with product innovation intensity Less significant semi-elasticities if growth regression New-to-market stronger than new-to-firm innovations

50 Difficulties Identification problems The stronger measure (intensity) dominates Product and process often occur jointly Effect of process innovation more difficult to identify because less of a revenue effect Results less informative because: Timing is unclear (3-year period) Magnitude of success is unknown No correction for size

51 Persistence If a firm has innovated in the past, will it keep on innovating in the future? Difference between true persistence (having innovated in the past) and spurious persistence (unobserved individual effect) True persistence of innovation found by Peters (2007, 2009), Duguet and Monjon (2001), Cefis (1996), Raymond et al (2007) More in innovation input than in innovation output More in high-tech than in low-tech

52 Complementarities Edgeworth complementarity doing more of one thing increases the return of doing more of another thing Scattered evidence Complementarity product-process innovations: Miravete and Pernias (2006), Martinez-Ros & Labeaga (2009), Ballot, Fakhfakh, Galia, Salter (2012), Polder, van Leeuwen, Mohnen, Raymond (2010) Schmidt and Rammer (2007): Product and process innovations lead to higher cost reductions or new-to-market product innovation when combined with both organizational and marketing innovations Productivity gains from innovation often involves organizational innovation (alone, with process, with product and process) (Polder et al, 2010) Contingencies (Ballot et al, 2012): complementarity may depend on size, knowledge intensity, may show up in some countries, not in others. Importance of correcting for unobserved heterogeneity (Miravete and Pernias, 2006)

53 Miravete and Pernias (2006) MIRAVETE, E. and J. PERNÍAS (2006), Innovation complementarity and scale of production, Journal of Industrial Economics, 54, Output, product and process innovations: complementarity? Spanish firm data, 1988 and Panel data allow the control for unobserved heterogeneity Objective function is unobserved, hence CORR approach Unconditional correlation points to complementarity. Conditional correlations, i.e. on the (generalized) residuals of reduced form regressions, also point to complementarity. Differences in firms characteristics suffice to account for previously observed differences in magnitudes of pairwise correlations. Correlations among firm strategies disappear when unobservable heterogeneity is controlled for.

54 Cassiman and Veugelers (2006) CASSIMAN, B. and R. VEUGELERS (2006), In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition, Management Science, 52(1), 68-82). Belgian firms, CIS1 Make (own R&D) and buy (license, R&D contracting, take over, hire away): binary variables Positive effect of make and buy in regression explaining share of sales due to new products Positive correlation of the error term in a bivariate probit after controlling for industry, size and innovation intensity, but no more once basic R&D reliance and effectiveness of strategic protection are included (source of complementarity) Two-step estimation where the NoMakeNoBuy, MakeOnly, BuyOnly and MakeAndBuy binary observations are instrumented by predicted probabilities from a binary probit or a multinomial logit: interaction term no longer significant (probably because of poor instruments)

55 Suggestions of improvement Harmonization of surveys across countries Stability of questionnaire over time, at least for core questions More information about non-innovators Follow a core of firms survey after survey: longitudinal data Examine the sensitivity of the results to respondent, to order of questions Merge innovation survey data with other data Ease access to data Collect data on groups and especially on multinationals Collaboration between researchers and statisticians

56 What have we learned? Using innovation surveys, confirmation of magnitude and significance of rates of return to R&D Complementarity between different types of innovation: mixed results Importance of non-technological innovation Importance of spillovers, hence level of aggregation of the analysis matters Importance of dynamics: length in time dimension of the analysis matters; persistence Less evidence of feedback from productivity to innovation than of innovation causing productivity Causality?