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Science, Technology, and Innovation - Topics



In devising science, technology and innovation policies, Bank staff and national policy makers will inevitably confront a number of common policy dilemmas. Specifically:


 

Basic Research vs. Innovation and Technology Upgrading
Basic research and innovation are not synonymous concepts, especially in countries where most enterprises operate far below the technological frontier. Generally, very few domestic enterprises innovate and most of these firms innovate by importing capital equipment rather than by either conducting basic research themselves or purchasing research services from local or foreign research institutes. In other words, innovation and basic research are often separate, distinct, and discrete activities. Policy makers may be missing an important opportunity to increase employment, wages, and overall standards of living if they focus on basic research to the exclusion of the more “mundane” tasks of technology upgrading -- design and engineering, the ability to acquire technology developed outside the country, and the managerial, organizational and technical capacity simply to utilize more advanced technology – in those core industries which operate far below the technology frontier. It is also important to note that adopting, adapting, and applying the results of basic research requires advanced managerial and organizational capacities. When firms do not have these capacities, it will be futile for governments to finance large amounts of basic research in the hope that this will generate increased levels of innovation and enterprise productivity.

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High Tech Sectors vs. High Value Added 
Contrary to popular opinion, high tech is not always synonymous with high value added, high wages and rapid growth. On the contrary, developing and transition economies may get more development “bang for the buck” by helping such “low tech” sectors as forestry and food processing increase value added than by trying to develop a few high tech niche products and industries. Policy makers, however, tend to view high tech as the surest route to competitiveness and prosperity. They mistakenly devote considerable resources to building up a small high tech sector while ignoring the competitiveness enhancing opportunities available from the much larger non-high tech part of the economy. For example, computers are generally regarded as high-tech activities. However, assembling computers is not a high wage, high tech activity, even though computers are classified as a high tech export in international trade statistics. Similarly, forestry sector exports are classified as a low tech export, although forestry activities can be either high tech or low tech depending on how much skill, knowledge, and research is applied. At a minimum, some balance needs to be restored to the high tech/non-high tech equation. An imbalance could be especially damaging to long run growth and economic stability if government support of high tech sectors creates a dual economy: on the one hand a low wage, low productivity traditional sector responsible for the bulk of employment, GDP and exports and, on the other hand, a small high-tech sector that is more or less disconnected from the rest of the economy.

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Production and Sale of Knowledge Produced Locally vs. the Import, Absorption, and Diffusion of Knowledge Produced Elsewhere
Policy makers should not focus solely on the commercialization of knowledge produced inside the country at the expense of helping firms import innovative technology produced elsewhere and adapting it for local use. Total annual R&D spending in most developing countries from all public, private and foreign sources is about equal to a few weeks of R&D spending by one large US or Japanese corporation. Therefore, most of the economically relevant knowledge that firms from developing countries will need to boost productivity and compete internationally will have already been produced elsewhere. Policy makers and business executives, therefore, need to devote more attention to enhancing their country’s ability to scour the world for knowledge, import it into the country, adapt it for local use, and integrate it into local production processes.

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SMEs vs. Large Enterprises
Policy recommendations to improve the functioning of the R&D and innovation systems typically focus on the promotion of high tech SMEs. This is prompted by a desire to replicate the success of Silicon Valley. But it is also based on a misunderstanding of the Silicon Valley phenomenon. True, Silicon Valley is a hotbed of small, high tech startups. But these SMEs did not arise in a vacuum or in isolation from large dynamic enterprises. On the contrary, SMEs which operate without a dense network of linkages to dynamic larger (foreign or domestic) enterprises will most likely not become a source of well paying jobs, economic competitiveness and rapid growth. Instead, they are likely to become little more than low productivity, subsistence operations. Put differently, links to dynamic large enterprises may be a critical pre-requisite for the emergence of dynamic SMEs. If so, policy makers may be making a serious blunder if their SME policies do not pay sufficient attention to helping large enterprises become more dynamic and competitive and helping SMEs become qualified suppliers to dynamic international large enterprises. Developing these supplier relationships through well targeted training policies, supplier development programs, and entrepreneurship education should become a more prominent feature of SME policy, innovation policy, and competitiveness strategy.

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Innovation vs. Everything Else
Innovation policy covers many issues that at first glance would appear to have little to do with innovation. For example, one influential analysis of factors that influence the “national environment for innovation” refers to such items as “sophisticated and demanding local customers,” “home customer needs that anticipate those elsewhere,” the “presence of capable local suppliers and related companies,” “vigorous competition among locally based rivals,” and the “presence of clusters instead of isolated industries” (Michael E. Porter and Scott Stern, “The Determinants of National Innovative Capacity” New York, Oxford University Press, 2002). These business environmental factors help to establish a strong demand for innovation. They give local enterprises the incentive to innovate, the knowledge about what innovation could be most profitable, the capacity to assess technology options. In this respect, they are a critical complement to local R&D capacity. Unfortunately, many developing and transition economies rank rather well on indices of scientists and engineers and perform rather poorly on indices of clusters and linkages. Their major weakness, in other words, is their relative inability to utilize knowledge and human capital effectively and efficiently. This suggests that policy makers will maximize the effectiveness of education, training, and R&D initiatives if they embed them in a broader policy of competitiveness, linkages, cluster formation, and entrepreneurship.

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Scientists vs. Entrepreneurs
It is generally accepted that entrepreneurs cannot use their entrepreneurial skills to become good scientists. But the converse is also true. Most good scientists cannot use their scientific skills to become good entrepreneurs. Unfortunately, this truism is often overlooked when policy makers attempt to promote technology commercialization. Policy makers establish incubators and technoparks to nurture new businesses started and operated by scientist-entrepreneurs. These commercialization institutions frequently fail to live up to their founders’ expectations, in part because they tacitly assume that top notch scientists can handle the marketing, sales, financial, legal and overall managerial tasks performed by a top notch entrepreneurs. This is rarely the case. Therefore, if policy makers want to promote technology commercialization, they will need to establish linkages between top notch scientists on the one hand and top notch entrepreneurs on the other hand.

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Numerical R&D Targets vs. Structural Reforms
The Lisbon Strategy calls on EU members to increase average R&D expenditures to 3% of GDP by 2010. Achieving this numerical target would entail a major increase in most of the developing countries annual R&D expenditures. An increase of this magnitude over the next six to seven years is clearly unfeasible and, more importantly, without significant reforms in the structure of R&D spending, would be tantamount to throwing good money after bad. Countries with higher per capita GDP do indeed spend more on R&D (relative to GDP) and there is no doubt that increased R&D spending contributes to higher per capita GDP. But it would be wrong to assume that there is a straight-forward, mechanistic relationship between increased R&D spending and higher per capita GDP. Simply increasing R&D spending will not lead to higher per capita GDP. On the contrary, as per capita incomes increased in Korea, Ireland and Finland, both the volume and composition of R&D changed significantly. For example, the source of R&D financing shifted gradually from the public to the private sector. Perhaps even more importantly, the performance of R&D shifted from public research laboratories to private enterprises. In other words, increased R&D spending and increased per capita GDP went hand in hand with increased private sector R&D. And this in turn entailed a parallel increase in the sophistication of private sector enterprises so that they had the capacity and interest in financing and conducting R&D. All this is currently missing in most developing countries. Therefore, merely increasing the volume of R&D spending will do little to remedy their problems unless this increase is preceded by significant institutional reforms.

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Separation of Education Policy from Economic Growth and Competitiveness Issues
As the example of the transitional economies demonstrates, a highly educated workforce is not synonymous to prosperity. Despite the abundance of human capital and legacy of science-intensive production, traditional economies’ principal attraction today for foreign investors and its principal comparative advantage is its supply of low wage labor performing relatively unskilled tasks while working in comparatively low productivity, low technology enterprises. Producing knowledge intensive, technologically sophisticated, higher value goods and services is not possible without a labor force with a particular mix of technical, managerial and vocational skills. Vocational, secondary and tertiary education must all contribute to turning out graduates with the necessary skills. Moreover, since the skills required by today’s labor market may not be the same as those that will be required in the future, a process of life long learning must be built into the education system. And at all levels and life-cycle stages, the education system must work with the private sector to understand and respond to its needs.

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