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Research Monograph Long-run Economic Growth and Technological Progress 2005.12.30

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Series No. 2005-01

Research Monograph Long-run Economic Growth and Technological Progress #경제성장 #시장규제: 진입, 가격, 품질규제 #기술혁신 및 창업

2005.12.30

  • KDI
    박준경
영문요약
Income Growth, Productivity Gap and Convergence

US per capita GDP grew at an annual average rate of 1.8% between 1870 and 1998. The major acceleration above the long-run trend was in the post-war golden age, 1950-73. Starting from the same level of productivity and per capita income as the US in the mid-19th century, Western Europe fell behind steadily to a level of barely half in 1950, and then began a rapid catch-up. Western Europe, Japan and the US were approaching equality of income by the later 1980s.

Since the early 1970s, growth has been slower: average growth rates of GDP per capita for much of the OECD countries were only half of the preceding period. This triggered widespread concern over the possibility of continued slow growth or even retardation in coming decades. There was a growing sense of insecurity and instability, alongside rising indicators of malaise such as unemployment. Since the 1980s, there has been a new wave of interest in economic growth, catch-up and convergence. In the 1990s, a few OECD countries had seen acceleration in income growth, while other major economies have lagged. This divergence has caused renewed interests in the main factors driving economic growth and policies that might influence it.

The suspicion that persistent differences in economic growth across countries may have something to do with technology has been around for a long time. In spite of the massive and systematic exploitation of scientific discoveries and technological innovations, economists were unable to understand . or possibly just uninterested in . the sources of innovation. Since the mid-1980s, economists have, to some extent, addressed this gap. In the 1980s, it became obvious that the neoclassical growth theory had little to offer in terms of policy advice. Even the new growth model has, due to its high level of abstraction, short-comings for managers and policymakers confronting concrete problems: its assumption suppresses the rich complexity of real-world technological innovations.

The technology-gap theory recognize technological differences as the prime cause for differences in GDP per capita across countries, and argued that technology is embedded in organizational structures (firms, networks, institutions, etc.), and is difficult and costly to transfer from one setting to another. Technical change is analyzed as the outcome of innovation and learning activities in organizations, and interaction between these and their environment. The path-dependency of this process is often emphasized: country-specific factors influence the process of technological change, and thus give the technologies of different countries a distinct national flavor. Thus, the concept of national innovation systems . each with its own specific dynamics . is used as an analytical device.

Empirical studies on technology-gap suggest that catch-up is very difficult and only countries with appropriate economic and institutional characteristics will succeed. Countries characterized by a large technological gap and a low social capability run the risk of being caught in a low-growth trap. As a country moves closer towards the technological frontier, indigenous technological capabilities become more and more important.

The catch-up literature is mostly descriptive, with emphases on historical analysis. However, it has not been very successful in explaining why some societies are technologically more creative than others. The diversity of technological history is such that picking up regularities in this massive amount of qualitative and often uncertain and incomplete information is hazardous. Yet without it, the role of technology in history of economies will remain incomprehensible.

Economic growth can occur as the result of four distinct processes: increase in the capital-labor ratio, increases in trade, increases in the stock of human capital, and scale and size effects. These four forms of economic growth reinforce each other in many complex ways. Studies on technological change inevitably must move between an aggregate and the individual level of analysis. The economic historian is directed to the macro foundations of technological creativity, that is, what kind of social environment makes individuals innovative; what kind of institutions create an economy that encourages technological creativity?

For a society to be technologically creative, many diverse conditions have to be satisfied simultaneously. There must be a cadre of ingenuous and resourceful innovators. Socioeconomic institutions have to encourage potential innovators. Innovation requires diversity and tolerance: in every society there are stabilizing forces that protect the status quo. Some of these forces protect entrenched vested interests that might incur losses if innovations were introduced.

The technology stalemate perspective accord with the long-wave hypothesis . the exhaustion of technological styles in the later phases of long-waves: the diffusion of ICT remained limited across sectors of the economy; their full impact will come when they became pervasive in their adoption across a wide range of user industries. It seems likely that there will be high R&D costs and only limited economic payoffs in such an area for a considerable time to come, though the long-term payoffs are prospectively massive.

Technology, Competition and Industrial Dynamics

In place of the static competition based on prices and costs, characteristics of much of the relatively stable postwar boom era, competition in the ensuing years has become more dynamic, based on product differentiation, with quality (relative to price) as the prime issue. This dynamic competition is manifested in expanding product ranges and shortening product lifecycles. Organizational change permit-ting success in dynamic competition (flexible automation, flexible specialization, and dynamic networks) becomes major ingredients of competitive advantage, but dynamic management isnecessary to bring them about.

There has been growing diversity of products and technologies. Applicable science becomes far more interdisciplinary. The scientific and technological complexity of each product has been rising. Pervasive technologies were being installed in an everwidening range of products. Even older products have been de-maturing and drawing upon this broadening range of technologies. Production of top-end, high quality items necessitated commitment to technology as well as design. Many more technologies are required to produce a single product, and many more products are produced from a given technology. The managerial implications were intensely complicated. The mapping of relationships between technologies and products was becoming hugely complicated and traditional firm and even industry boundaries were loosing their rationale. The obvious need and most difficult accomplishment was to develop heuristics for ‘systemic coordination’ to realize economies of scale, in the context of an ever-moving target. The rapid diversification of products was further accentuated by the globalization of product competition.

There is emerging an intellectual paradigm of management of technology (MOT), as studies about scientific and technological progress have been accumulating and as techniques for technological innovation have been developed. The issues central to MOT include:
  • Understanding ling-term economic development;
  • Understanding how national S&T infrastructures contribute to competitiveness;
  • Forecasting changes in technologies;
  • Effectively managing the engineering and research functions in business systems;
  • Integrating technology strategy and business strategy.

Market failures and Policy Responses

The strategies and policies that affect the development and use of technology must be conceptualized and analyzed in the context of the broader economic growth process. More comprehensive policy analysis involves the assessment of technology, business strategy, and economic trend.

Industrial technologies are mixed assets in that they have both private and public elements. The R&D process that produce public elements should be financed to varying degrees by sources beyond a single firm. Market failures that appear either at specific phases of R&D process or are associated with specific types of R&D that focus on public elements vary significantly in severity across technologies and the associated industry structures thus require targeted policy responses.

Policy analysis can be grouped into 3 categories. 1) Market failures are identified and characterized, which lead to rationales for R&D support programs. 2) R&D programs approved should be implemented through strategic planning. 3) Economic impact assessment studies should be regularly conducted to determine the effectiveness of various projects within the program.

Over the last two decades, considerable efforts have been made to improve the design and conduct of effective policies. Increasing attention is paid to the way in which evaluation can inform strategy. Past behavior is analyzed (evaluation), technological options for the future are reviewed (technology foresight), and the implications of adopting particular options were assessed (technology assessment). The results of such strategic intelligence tools are exploited in the formulation of new policies. It has become obvious that there is a need to use such tools in more flexibly and intelligently combined ways, thereby exploiting potential synergies of the variety of strategic intelligence pursued at different places and levels across countries.

The concept of ‘distributed strategic intelligence’ starts from the observation that policy makers only use or have access to a small share of the strategic intelligence of potential relevance to their needs, or the tools and resources necessary to provide relevant strategic intelligence. Such assets exist in a wide variety of institutional settings and at many organizational levels. Consequently, they are difficult to find, access, and use. Hence rectifying this situation will require efforts to develop inter-faces enhancing transparency and accessibility of already existing information, and convince potential users of the need to adopt a broader perspective in their search for relevant intelligence expertise and output.
목차
1. Introduction

2. Income Growth, Productivity Gap and Convergence
 2.1 Long-Run Economic Growth Since 1820
 2.2 Theoretical Perspectives on Economic Growth
 2.3 Technology Gap and Productivity Convergence
 2.4 Long-Waves of Socioeconomic Development

3. Technological Advances and Industrial Progress
 3.1 Introduction
 3.2 Industrialization in Europe Before 1820
 3.3 Industrialization in Continental Europe, 1830-1914
 3.4 Industrialization in the US, 1870-1930
 3.5 Industrialization in the West, 1930-1970
 3.6 Industrialization since 1970

4. Technology, Competition and Industrial Dynamics
 4.1 Industrial Dynamics and Innovation Process
 4.2 Management of Technology
 4.3 Integrating Technology and Business Strategy

5. Market Failures and Policy Responses
 5.1 R&D Policy as a Critical Element of Growth Policy
 5.2 Technology-Based Market Failures
 5.3 Funding Generic Technology Research
 5.4 Evaluation as a Source of Strategic Intelligence

References
관련 자료 ( 9 )
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