The application of CIS to Portugal:

1. The application of CIS to Portugal: Survey Implementation and Results Analysis - Innovation vs. Productivity Manuel João Bóia mjboia@dem.ist.utl.pt Pedro Faria pedro.faria@dem.ist.utl.pt Science and Technology Policy Program MSc Engineering Policy and Management of Technology 5th November 2004

2. Outline • Part 1 – Innovation Indicators • Innovation Indicators • The Community Innovation Survey • Students Presentation • Results (CIS 3), • Innovative Enterprises by Sector and CIS Trajectories in the European Context • Input vs. Output of Innovation in Europe • Some Innovation Characteristics • Other Strategic and Organizational Important Changes • Innovation Sources • Innovation Barriers • Lessons Learned and Conclusions

3. Outline • Part 2 - Innovation and Productivity: What can we learn from the CIS 3 Results for Portugal? • Innovation and Productivity Theory • A model for the analysis of innovation and productivity in the short run • Results with CIS 3 data • Lessons Learned and Conclusions

4. Innovation Indicators 1.1

5. Services CIS 3 2.1 Portugal • What Survey Target Population? • All Manufacturing and Service firms with more than 10 employees • How to establish a Survey Sample? • Initial Sample: 4727 firms stratified by firm size and sector (INE–1999 Data) prepared by “Instituto Nacional de Estatística” • Corrected sample: 4127 firms; prepared by the Support Team (OCES and outsourced survey enterprise) • What Sectors were surveyed? • Mining and Quarrying (NACE 10-14) • all Manufacturing (NACE 15-37) • Utilities (NACE 40-41) • Wholesale Trade (NACE 51) • Transport, Storage and Communication (NACE 60-64) • Financial Intermediation (NACE 65-67) • Computer and Related Activities (NACE 72) • Research and Development (NACE 73) • Architectural and Engineering Activities (NACE 74.2) • Technical Testing and Analysis (NACE 74.3)

6. CIS 3 2.2 Portugal • How was the Survey implemented? • Institutions involved: • - Observatório da Ciência e Ensino Superior (funding and support team), • - IN+ (Scientific and operational coordination; data treatment and analysis; reporting); • - Instituto Nacional de Estatística (sample preparation); • - outsourced survey enterprise (infrastructure, logistics, communications, support team Management, databases); • Data acquisition Phases: • From 1st October 2001 to 15th April 2002 • Sample verifying and validation (Name and Address) and identification of a contact person • Mailing of Questionnaire with innovations examples and a postage free envelope for replying (fax reply also accepted) • Systematic phone reminders plus two fax reminders and an additional questionnaire re-mailing • Support provided in working days by phone, fax or e-mail by a multidisciplinary team of 6 trained staff people (3 Engineers, 1 Economist and 2 Sociologists)

7. CIS 3 2.3 Portugal • Innovation Definition Used: • Market introduction of a product (Good or Service) new or significantly improved, or the introduction of new or significantly improved processes, based on new technological developments, new combinations of existing technologies or on the use of other type of knowledge acquired. • The innovation should be new to the company and not necessarily to the market.

8. Companies Characteristics Innovation Extension Companies Options Systemic Characteristics CIS 3 2.4 Portugal • Questionnaire • Harmonized questionnaire (the same for Services and Manufacturing and other industries) • Questions regarding: • General Information • Basic Economic Information • Product and Process Innovation • Patents and Other Protection Methods • Innovation Activities and Expenditure • Intramural R & D • Other Strategic and Organizational Important Changes • Effects of Innovation • Public Funding • Innovation Co-operation • Sources of Information for Innovation • Hampered Innovation Activity

9. CIS 3 2.5 Portugal • Survey Data Processing: • Unit Non-respondents analysis • Non-respondents survey for results calibration (only if Resp. Rate < 70%) • Respondents and Non-respondents distribution of responses analysis • Statistical software SAS routines testing and implementation • Data consistency checks and first data processing • Data imputation of missing variables (Item Non-respondents) • Final data processing and tabulations • Data validation (Eurostat) • Final Database and Codebook

10. CIS 3 2.6 Portugal • Response Rates • Small – 10 to 49 Employees • Medium – 50 to 249 Employees • Large - over 250 Employees

11. 5.1 • Lessons Learned from the CIS III Implementation: • Unreliable Initial Sample (1999 Data) • Non-Enforcement of the Policy regarding Mandatory Surveys • Biased General perception of Innovation Definition (“Radical” Innovation) • Services misperception of Innovation Definition (Product = Service or Goods) • Non-Disclosure Policy of Financial Data • Lack of Qualifications of the Questionnaire Filling Contact Person (“Cultural” bias towards Non Response or Non Innovation) • Lack of correspondence between the surveyed data/indicators and Companies data/indicators gathering. • Mergers and Acquisitions (Availability of Contact Person and Data) • Huge paperwork! • In Data Processing, • High values of “Item Non-response” in some strata (CAE 2 Digits*Dimension) of the realized sample for some variables,”Exports Sales”, “Innovation Expenditure”, “Level of importance in Cooperation”, “Innovation Hampering Factors (partially)” and Patents • Unreliable missing values imputation methodology and routines provided by Eurostat, surpassed in cooperation with other member states.

12. Students Presentations

13. Results - Innovative Enterprises by Sector and CIS Trajectories in the European Context CIS II CIS III Upward Trajectory Upward and Downward Trajectory Downward Trajectory 4.1 80% Ireland 60% Austria Luxemburg Germany Proportion of Service Innovating Enterprises UK 40% The Netherlands Greece France Sweden Portugal Italy Denmark Spain Norway Finland 20% Belgium 0% 20% 40% 60% 80% Proportion of Manufacturing Innovating Enterprises Note: The CIS 3 data is not directly comparable to CIS 2 data due to the enlargement of the CIS sample. Enterprises in between 10 and 19 employees in Manufacturing and selected sectors (NACE 63, 73, 74.3 and all the 64 in addition to 64.2) in Services were included in the exercise.

14. Manufacturing Sector 80% IRL DE AT NL 60% UK SE DK LU NO Porportion of Innovative Enterprises CIS II FR 40% CIS III ES FI IT GR BE PT 20% 0% 0.0% 2.0% 4.0% 6.0% 8.0% Expenditure in Innovating Activities as Share of Turnover 4.2 Results – Input vs. Output of Innovation in Europe

15. 4.3 Results – Some Innovation Characteristics • Innovation is Firm Size dependent (larger firms innovate more) • Innovation has sector specificities • The integration of the firm in a network (e.g., integration into a group) increases the probability to innovate • The level of competition in a market influences a firm’s probability to innovate (Highly competitive markets provide more innovative firms)

16. 4.4 • Results - Other Strategic and Organizational Changes

17. 4.5 • Results - Innovation Sources of Highly Importance for Manufacturing

18. 4.6 • Results - Innovation Barriers of Highly Importance

19. 5.2 • Lessons Learned and Conclusions: • The CIS is a good evolving instrument for benchmarking and follow up of the best practices, although incomplete in what concerns the systemic characteristics of innovation. • A significant increase in the innovation extension and in the firms innovation expenditure was achieved for Portugal in CIS III compared to CIS II. • In the innovation process, both sources and barriers to innovation profiles remain consistent with the CIS II data, where the most relevant are respectively “Within the Enterprise” and financial constraints. • Innovation expenditure has reached a milestone above which innovation effectiveness appears to be more correlated with factors of systemic nature. • Technological innovation appears to be strongly correlated with Organizational Innovation and Change.

20. Outline • Part 2 - Innovation and Productivity: What can we learn from the CIS 3 Results for Portugal? • Innovation and Productivity Theory • A model for the analysis of innovation and productivity in the short run • Results with CIS 3 data • Lessons Learned and Conclusions

21. Innovation vs. Productivity - + Technological Innovation Productivity Long Run Short Run Three theories explain the short relationship between Innovation and Productivity: - Learning - Technology and Organizational Rigidities - Adjustments Costs

22. Theories Arguments Main References Innovation - new skills - productivity decrease New skills necessary to adopt correctly new technologies Jovanovic and Nyarko (1996) Time and costs of the adoption process not Learning Ahn (1999, 2001) neglegetable - learning cost Innovation implies the execution of non-productivity activities - drop in productivity in the short run More productive firms have difficulties to change technology negative relationship between innovation and levels of productivity When technologies appear perform less effectively than the technologies already diffused Technology transfer imply a change on management techniques in order to synchronize the firm Leonard-Barton (1988, 1992) characteristics with the innovation Utterback (1994) Christensen and Bower (1996) More productive firms may be reluctant to switch to Christensen (1997) new technologies that would imply significant Technology and Organizational Rigidities Young (1991, 1993) productivity losses Benner and Tushman (2002) More productive firms are those that stick more Tripsas and Gavetti (2002) closely to existing routines Decision not to innovate - level of productivity and level of organizational rigidity Periods of adoption of new technologies - adjustment costs and decrease of levels of output May be a lag between the growth in investment and its benefits Bessen (2001) Adjustment costs - Bernstein et al. (1999) costs related to setting up new equipment, training of and innovation Adjustment Costs positive relationship between levels of productivity Hall (2002) employees (resources used to fully utilize the capital) Leung (2004) During the introduction of the innovation stage, innovative firms will have a lower rate of productivity growth than non-inovative firms More productive firms are those that are more capable to deal with adjustments costs and liquidity constrains Theoretical arguments that explain the negativerelationship between innovation and productivity

23. Econometric Model (1) 1) Endogeneity: Hausman Test OLS – inconsistent 2) Equation System: 3) Covariance Correction: Murphy-TopelMethod - two step estimation method for mixed models that include limited dependent variables

24. Econometric Model (2) Where: Prdg –Productivity Measure – log (Turnover / nº Workers) Inov –Innovation Dummy Variable Exp –Exports / Turnover NF –Dummy Variable that indicates if the firm was created in 1998-2000 GP –Dummy Variable that indicates if the firm is part of a group ED –Share of the Workforce engaged in specialized tasks CS –Gross Investments in Capital Goods S –Sector Dummy Variables Log_Turn_Inic –Critical Identification Variable - log (Turnover 1998)

25. The CIS 3 Data Advantages of the survey data: 1) Data on innovation and productivity for a two year period (1998-2000); 2) Separation between firms that do not innovate, those that have attempted to innovate and innovative firms; 3) Gathering information, not only about radical innovations linked to patents applications, but also about not radical innovations in the context of the market but new to the firm; 4) Inquiring firms, not only from the manufacturing sector, but also from the service sector, making possible a more complete analysis from the Portuguese economic reality; 5) Existence of information that allows the creation of instruments to correct endogeneity; 6) Differentiation between product and process innovation

26. Results Note: * Significant at 10%; ** 5%; *** 1%; Sector Dummies Variables included but not reported

27. In the universe of Portuguese firms enquired by the CIS III, innovative firms have a lower degree of productivity growth when compared with non-innovative firms The more productive firms are more innovative – result coherent with the Adjustment Costs theory The inclusion of the variable Gross Investment in Capital Goods gives robustness to the model Conclusions

28. Additional Slides

29. Results - Innovation Extension

30. Results – Product and Process Innovation in Manufacturing

31. Results - Innovation by Firm Size

32. CIS 3 Portugal

33. Results – Innovation by Technological Intensity (Manufacturing)

34. Results – Education and Innovation by Sector

35. Results – Qualifications and Innovation by Sector

36. Results - Innovation Sources of Highly Importance for Services

37. Results - Patenting Clear characteristic: the Portuguese companies ignore or do not choose to use patenting as a protection tool

38. Results – Other Protection Methods Used