Rammer - Measuring output of process innovation at the firm level
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This document discusses measuring the output of process innovation at the firm level using data from German panel surveys. It presents two measures of process innovation output: (1) the share of unit cost reduction from process innovation and (2) the share of sales growth resulting from quality improvements. The document finds that both measures work reasonably well, though responses to the quantitative parts are categorical. Process innovation output is positively associated with firm performance indicators like export share and profit margins. While determinants of undertaking process innovation can be identified, the level of output is more difficult to explain.
Rammer - Measuring output of process innovation at the firm level
- 1. Measuring Output of Process Innovation at the Firm Level: Results from German Panel Data Christian Rammer Centre for European Economic Research (ZEW) Mannheim, Germany OECD Blue Sky Forum III 19-21 September 19-21, 2016, Ghent
- 2. Background (1) Process innovation is a main part of firms’ innovation activities (204,000 product innovators vs. 187,400 process innovators) (2) No established measure for measuring the output of process innovation (in contrast to product innovation) (3) Theoretical models of R&D/innovation often use cost reduction as result variable (4) Oslo Manual mentions cost reduction and quality improvement as potential output measures (5) Some national innovation surveys did implement cost reduction and other output measures (BE, CA, CH, DE, NO)
- 3. Objectives - Present empirical evidence on the relevance and reliability of quantitative measures of process innovation output - Analyse data on process innovation output collected over the past 20 years as part of the German innovation survey: - share of unit cost reduction owing to process innovation - share of sales growth resulting from quality improvements - Evaluate the usefulness of these measures for better understanding the innovation process in firms and its impacts Structure: 1) measurement issues (item non-response, consistency) 2) determinants of process innovation output 3) performance impacts of process innovation output
- 4. Process Innovation Output: Conceptual Issues - Process innovation often associated with changing the cost function (but leaving products unchanged) - But process innovation often targets quality aspects: increasing flexibility, ensuring constant quality - In services, product and process innovation often go hand in hand, altering both product quality and process efficiency - Lean management and total quality management have developed a large set of process output indicators (lead time, processing time, on- time delivery, customer satisfaction, defect rate, accuracy rate, reworking rate, scrap rate, number of steps needed)
- 5. Process Innovation Output Measures in the German CIS - Main challenge: using simple measures that can be applied to all types of firms and sectors - Cognitive testing resulted in the following design: annually since 1994 annually since 2002
- 6. Item Non-Response by no. of Responses 0 2 4 6 8 10 12 14 16 1 2 3 4 5 6 7 8 9 10 11 12* 13 14 15 16 17 18+ Number of survey responses w ith process innovation "yes" Shareofitemnonresponse(%) cost reduction (y/n) quality improvement (y/n) Yes/no part * For quality improvement: 12 or more responses
- 7. Item Non-Response by no. of Responses Quantitative part * For quality improvement: 11 or more responses 0 5 10 15 20 25 30 35 40 1 2 3 4 5 6 7 8 9 10 11* 12 13+ Number of survey responses w ith cost reduction / quality improvement "yes" Shareofitemnonresponse(%) share of cost reduction (%) increase in sales due to quality improvement (%) sales share of product innovations (%)
- 8. Item Non-response by Size/Sector Yes/no part Quantitative part 0 2 4 6 8 10 12 14 NACE 5 to 24 NACE 25 to 33 NACE 35 to 53 NACE 55 to 93 5 to 9 employees 10 to 19 employees 20 to 49 employees 50 to 99 employees 100 to 249 employees 250 to 499 employees 500 to 999 employees 1,000+ employees Share of item non response (%) cost reduction (y/n) quality improvement (y/n) 0 5 10 15 20 25 30 35 40 45 50 55 Share of item non response (%) cost reduction (%) quality improvement (% sales increase) sales share product innovations (%)
- 9. Variety of Responses by no. of Responses 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14+ Number of responses to share of cost reduction/increase in sales due to quality improvement/sales share of product innovations Numberofdifferentvaluesperrespondingfirm share of cost reduction change in sales due to quality improvement sales share of product innovations
- 10. Determi- nants 1: yes/no (probit) 1) These models include all variables of part 1. All models include 12 year dummies and 45 sector dummies. * / **: significant at the 0.05 / 0.01 level
- 11. Determi- nants 2: quanti- tative if yes (OLS) 1) These models include all variables of part 1. All models include 12 year dummies and 45 sector dummies. * / **: significant at the 0.05 / 0.01 level
- 12. Impacts 1: Export Share (OLS) 1) These models include all variables of part 1. All models include 12 year dummies and 45 sector dummies. * / **: significant at the 0.05 / 0.01 level
- 13. Impacts 2: Profit Margin (Int- reg) 1) These models include all variables of part 1. All models include 12 year dummies and 45 sector dummies. * / **: significant at the 0.05 / 0.01 level
- 14. Conclusions (1) Both process innovation output measures work quite well (2) Share of item non-response for quantitative part high and not falling with response frequency (3) Responses to the quantitative part are categorical in nature (4) Weighted data of the two indicators for the German enterprise sector provides meaningful results (5) Determinants of process innovation output (y/n) largely the same as for product innovation output, but level of output difficult to explain (6) Process innovation output positively associated with performance it is possible and useful to collect process innovation output data Using measures that can be applied across industries limits explanatory power of data, but a differentiated approach would restrict comparability
- 15. Thank you for your attention! [email protected]
- 16. Appendix
- 17. 0 2 4 6 8 10 12 14 16 18 20 22 0 >0, <1 1 >1, <2 2 >2, <3 3 >3, <4 4 >4, <5 5 >5, <6 6 >6, <7 7 >7, <8 8 >8, <9 9 >9, <10 10 >10, <15 15 >15, <20 20 >20, <25 25 >25, <30 30 >30, <50 40 >40, <50 50 >50, <60 60 >60, <70 70 >70, <80 80 >80, <90 90 >90, <100 100 >100 Responsevalue(%) Share in total responses (%) cost reduction change in sales due to quality improvement sales share of product innovations Response Values on Quantitative Measures
- 19. Process Innovation Output by Size (2014) 0 10 20 30 40 50 60 70 5-9 10-19 20-49 50-99 100-249 250-499 500-999 1,000+ Sizeclass(no.ofemployees) Share of firms (%) Firms w ith cost reduction Firms w ith quality improvements 0 1 2 3 4 5 6 Share of cost reduction/increase in sales (%) Share of cost reduction Increase in sales due to quality improvements Weighted results.
- 20. Process Innovation Output 1993-2014 0 1 2 3 4 5 6 7 8 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06* '07 '08 '09 '10 '11 '12 '13 '14 Shareinallfirms(%) Share of cost reduction - manufacturing Share of cost reduction - services Increase in sales due to quality improvements - manufacturing Increase in sales due to quality improvements - services Weighted results. - Manufacturing: divisions 5 to 33 (NACE 2), divisions 10 to 37 (NACE 1); Services: divisions 35-39, 46, 49-53, 58-66, 69-74, 78-82 (NACE 2), divisions 40-41, 51, 60-67, 72-74, 90 and groups 92.1, 92.2 (NACE 1). * Break in series due to change in economic classification systems (from NACE 1 to NACE 2) and change in the statistical source for total firm population figures (introduction of the official business register in 2006).
- 21. Determi- nants 3: quanti- tative mea- sures (tobit) 1) These models include all variables of part 1. All models include 12 year dummies and 45 sector dummies. * / **: significant at the 0.05 / 0.01 level