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
I first thank the organizer and the OECD to give me a great opportunity to read and discuss three
very interesting papers on knowledge and competitiveness.
I was asked by the organizer to first summarize three presentations, then discuss their strength
and weakness, and go on to adress elements that are missing in their presentations. I am a
Unviersity economist recently joined the Government, and most University economists usually
do ignore the instruction of the organizer of conferences and speak only their views about the
issue, paying little attention to what are said by fellow presenters. I am afraid I might make the
same original sin, but I try to follow the organizer’s instruction as much as possible.
Mrs. Burgi-Schmelz’s paper identifies “Success Factors” in competitiveness and indicators
associated with them. The first success factor is “Impact of Science and Technology” and
corresponding indicators are the number of patents and R&D share in GDP. The second factor is
Impact of Human Capital Measurement, and the associated indicator is “Rate of Return on
education (private/social).” The author suggests a positive correlation between these success
factors and competitiveness.
The author goes on to emphasize importance of the above measured indicators and others (CO2,
health care, higher education) in guiding policies in “Knowledge-based economies.
In Mr. Rosted’s presentation, the author sees Total Factor Productivity (TFP) or Multi-Factor
Productivity (MFP) as one of the most important determinants of competitiveness. He then
identifies factors influencing TFP/MFP and search for indicators representing them in a broad
spectrum of economic data. They include: “Human resources”, “Knowledge accumulation and
networking”, “ICT capital stocks”, and “Entrepreneurship”. He then goes on to identify the
most important policy areas based on the results.
In his presentation, Mr. Murray identified “adult skills” a major determinant of competitiveness.
He proposed several key indicators of these crucial “adult skills” based on educational
assessment and household survey methods. He shows some success of these indicators in
explaining employability and wage differences. He then discusses remaining data problems as
well as policy-implementation ones based on these indicators.
These three presentations are a wealth of information. They together show that useful
information can be “data-mined” in aggregate indicators of probable determinants of
competitiveness.
Also, Mrs. Burgi-Schmelz’s paper and Mr. Rostad’s paper share the same focus on cross-country
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performance differences and their determinants, which are remarkable.
crucial role on indicators (statistics) to guide public policy.
Three papers also put a
Then, as a discussant, I ask myself “What can be added to these impressive presentations?” Here,
as an economist engaging in research on information and communication technology for past
several years, I confidently put disaggregate and/or micro-micro analysis as a necessary
extension to their analyses in the context of the knowledge-based economy, if we want to know
more than a superficial relationship between aggregates.
Here we should take a proper account of the complexity of the issue. Simple positive correlation
on an aggregate of some indicators of “competitiveness” or “achievement” on the one side, and
that of indicators of “determinants” on the other, is suggestive but not entirely convincing, and
surely not a sufficient guidance for public policy.
To entangle complexity, disaggregate analysis are needed and clear awareness of heterogeneity is
in need. In this respect, industry-level analysis and period-wise analysis might be more helpful
than cross-country, cross-cultural comparison.
Vast difference in rules, organization, and culture is existent among countries and this makes
very hard to interpret “difference” found in many indicators. Economists are usually more
cautious than, say, Government statisticians in interpreting these differences. However, it is
relatively easy to interpret difference among industries and between periods in the same country.
Even further disaggregation may be helpful: disaggregation to the firm-level and the
consumer-level.
Moreover, let me remind you that possible determinants of competitiveness are both in the
supply side and the demand side.
On the supply-side, we have “Push factors”: We all agree with three presenters that the following
three factors are among the most important.
Total Factor Productivity (TFP) (level/growth)
Improved labor inputs
Innovation
The demand side is as important as the supply side and there are “Pull factors” in
competitiveness. However, they are not touched upon by three presentations. They include
among others
Consumer attitude (adjustability)
Changing needs of population (aging, etc.)
Since I do not have enough time, I just touch these issues by referring my own work in this field.
Firstly, disaggregate analysis is really needed, since heterogeneity is a key piece of information
to understand real determinants in competitiveness.
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There in fact is wide difference among industries and between periods of the same country.
Here I show a Japanese example.
In
Nishimura and Shirai, “Can Information and Communication Technology
Solve Japan’s Productivity-Slowdown Problem?” Asian Economic Papers 2
(1) (2003), 85-139.
TFP Growth is calculated among industries and between periods. You see a lot of heterogeneity
among industries. It seems almost naïve to discuss about the Japanese TFP growth without
knowing such a tremendous difference among industries and between periods. And I would like
to point out that these differences are explicable and such explanation is much more informative
than that of based on aggregate TFP.
Secondly, firm-level competitiveness research is capable of sharpening our understanding of real
determinants of competitiveness. Here government statistics play a crucial role.
In
Nishimura and Kurokawa “Total Factor Productivity in Japanese Information
Service Industries: Firm-Level Analysis” 2004, available at ESRI website.
I use Census-like Government Statistical data surveying all firms engaging information services
including software. Using this statistics, I am able to make activity-level calculation of Total
Factor Productivity of information service firms. Then, heterogeneity is properly accounted for
by a panel analysis with firm-specific effects.
In this study, I find organizational structure matters a great deal to determine TFP of information
service industries. To achieve high productivity, firm’s organizational structure should be
changed accordingly to changes in information technology. In particular, seemingly
productivity-enhancing outsourcing (see US examples in popular presses) has in fact negative
effects on productivity. It is a clear indication of a possible pitfall in “best practice”
methodology/policy, which I suspect is behind indicator approaches a la OECD.
The study also indicates large adjustment costs: Employment adjustment costs on productivity,
and possibly more important, organizational adjustment costs on productivity.
Finally, let me touch on the consumer side briefly. This is admittedly unknown field in the
so-called Knowledge-Based Economy.
In
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
my colleague and I conduct interview-based sample surveys about Automobile Drivers in 1999
and 2001. Thus, this is not based on Government Statistics. Incidentally, this shows we need to
expand our statistical data sources in understanding the knowledge-based economy. Respondents
roughly represent the whole Japanese automobile drivers.
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In this study, I find ailenation in the Internet society. Surprisingly, a major change has occurred
among non-Internet users, not in Internet users. New, Internet-driven strategies of manufacturers
and dealers ignore non-Internet users, and non-Internet users are increasingly disappointed and
alienated. Some are not so happy in the Internet society.
Let me conclude. Three papers in this session are a good start in a right direction. I learned a lot
from them. But we must be cautious to derive policy conclusions from their results …
Competitiveness in a knowledge-based economy is a very complex animal to investigate. I urge
more emphasis on disaggregate and micro-micro-level research to enrich our understanding of
competitiveness in knowledge-based economies.
Thank you for your attention.
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