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Using Knowledge to Boost Competitiveness: Comments on Three Presentations

Kiyohiko G. Nishimura Professor of Economics, University of Tokyo Executive Research Fellow, ESRI, Cabinet Office Member, Statistical Council. Using Knowledge to Boost Competitiveness: Comments on Three Presentations. Identify “Success Factors” in competitiveness

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Using Knowledge to Boost Competitiveness: Comments on Three Presentations

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  1. Kiyohiko G. Nishimura Professor of Economics, University of TokyoExecutive Research Fellow, ESRI, Cabinet Office Member, Statistical Council Using Knowledge to Boost Competitiveness: Comments on Three Presentations

  2. Identify “Success Factors” in competitiveness Impact of Science and Technology Measurement: # of patents and R&D share in GDP Impact of Human Capital Measurement: Rate of Return on education (private/social) Suggest positive correlation between success factors and competitiveness Emphasize importance of the above measured indicators and others (CO2, health care, higher education) in guiding policies in “Knowledge-based economies Burgi-Schmelz’s Paper

  3. Identify Total Factor Productivity (TFP) or Multi-Factor P (MFP) as one of the most important determinants of competitiveness Identify factors influencing TFP/MFP and search indicators representing them Human resources Knowledge accumulation and networking ICT capital stocks Entrepreneurship Identify the most important policy areas based on the results Rosted’s Presentation

  4. Identify “adult skills” a major determinant of competitiveness. Propose indicators of these crucial “adult skills” based on educational assessment and household survey methods Show some success of these indicators in explaining employability and wage differences Discuss remaining data problems as well as policy-implementation ones based on these indicators Murray’s Presentation

  5. A wealth of information is found about aggregate indicators of probable determinants of competitiveness A focus on cross-country performance differences and their determinants Crucial role of indicators (statistics) to guide public policy Three presentations

  6. Complexity Results based on simple correlation on an aggregate of some indicators of “competitiveness” or “achievement” and that of indicators of “determinants” are suggestive but not entirely convincing To entangle complexity, disaggregated analysis are needed: industry-level and period-wise Even further disaggregation may be helpful: firm-level and consumer-level analyses What can be added to them?Disaggregated/Micro-micro Analysis

  7. Supply-side – Push factors Total Factor Productivity (TFP) (level/growth) Improved labor inputs Innovation Demand-side – Pull factors Consumer attitude (adjustability) Changing needs (aging, etc.) Determinants of Competitiveness

  8. Wide difference among industries and periods Nishimura and Shirai, “Can Information and Communication Technology Solve Japan’s Productivity-Slowdown Problem?” Asian Economic Papers 2 (1) (2003), 85-139. TFP Growth Heterogeneity is a key factor

  9. Nishimura and Kurokawa “Total Factor Productivity in Japanese Information Service Industries: Firm-Level Analysis” 2004, available at ESRI website. Census-like Government Statistics (all firms engaging information services) Activity-level calculation of Total Factor Productivity of firms Heterogeneity is properly accounted for (Panel Analysis with Firm-Specific Effect) Firm-Level Competitiveness Research

  10. To achieve high productivity, firm’s organizational structure should be changed Seemingly productivity-enhancing outsourcing has in fact negative effects on productivity Clear indication of a pitfall in “best practice” methodology/policy Large adjustment costs Employment adjustment costs on productivity Organizational adjustment costs on productivity Organizational structure matters

  11. Nishimura and Morita “Alienation in the Internet Society: Changes in Car Buyer Attitudes in the Japanese Automobile Industry,” International Journal of Automobile Management and Technology 2 (2) (2002) 190-205 Conduct Interview-based Sample Surveys about Automobile Drivers in 1999 and 2001 Respondents roughly represent the whole Japanese automobile drivers. The ratio of Internet users to all car owners was doubled between 1999 and 2001, from 16% to 32% Examine the magnitude of Internet’s influence on car purchasing behavior Examine changes between 1999 and 2001 Investigate not only Internet users but also non-Internet users Consumer and Knowledge-Based Economy

  12. Surprisingly, the major change has occurred among non-Internet users, not in Internet users New, Internet-driven strategies of manufacturers and dealers ignore non-Internet users non-Internet users were increasingly disappointed and alienated losing interests in cars in general and human relations with salespersons Internet society is likely to leave consumers segmented and diversified. Some are not so happy. Ailenation in Internet Society

  13. Three papers: a good start in a right direction Must be cautious to derive policy conclusions from their results … Competitiveness in a knowledge-based economy is a very complex animal to investigate. More emphasis on micro-level research to enrich our understanding of competitiveness in knowledge-based economies Conclusions

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