International R&D spillovers
TL;DR: In this paper, a model is presented based on recent theories of economic growth that treat commercially oriented innovation efforts as a major engine of technological progress, and the authors study the extent to which a country's total factor productivity depends not only on domestic R&D capital but also on foreign capital.
About: This article is published in European Economic Review.The article was published on 1995-05-01 and is currently open access. It has received 3397 citations till now. The article focuses on the topics: Total factor productivity & Productivity.
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TL;DR: This article developed a Ricardian trade model that incorporates realistic geographic features into general equilibrium and delivered simple structural equations for bilateral trade with parameters relating to absolute advantage, comparative advantage, and geographic barriers.
Abstract: We develop a Ricardian trade model that incorporates realistic geographic features into general equilibrium It delivers simple structural equations for bilateral trade with parameters relating to absolute advantage, to comparative advantage (promoting trade), and to geographic barriers (resisting it) We estimate the parameters with data on bilateral trade in manufactures, prices, and geography from 19 OECD countries in 1990 We use the model to explore various issues such as the gains from trade, the role of trade in spreading the benefits of new technology, and the effects of tariff reduction
3,782 citations
TL;DR: In this article, a systematic review of literature published over the past 27 years, synthesize various research perspectives into a comprehensive multi-dimensional framework of organizational innovation - linking leadership, innovation as a process, and innovation as an outcome.
Abstract: This paper consolidates the state of academic research on innovation. Based on a systematic review of literature published over the past 27 years, we synthesize various research perspectives into a comprehensive multi-dimensional framework of organizational innovation - linking leadership, innovation as a process, and innovation as an outcome. We also suggest measures of determinants of organizational innovation and present implications for both research and managerial practice.
2,414 citations
TL;DR: The authors surveys the recent empirical literature on economic growth, starting with a discussion of stylized facts, data problems, and statistical methods and concludes that efficiency has grown at different rates across countries, casting doubt on neoclassical models in which technology is a public good.
Abstract: Why do growth rates differ? This paper surveys the recent empirical literature on economic growth, starting with a discussion of stylized facts, data problems, and statistical methods. Six research questions are emphasized, drawing on growth and convergence research. In answering these questions, the paper argues that efficiency has grown at different rates across countries, casting doubt on neoclassical models in which technology is a public good. The latter half of the paper rounds up a variety of findings before providing answers to all six questions, including a short summary of how differences in growth rates arise.
2,396 citations
Posted Content•
TL;DR: In this article, the authors present an empirical examination of the determinants of country-level production of international patents and introduce a novel framework based on the concept of national innovative capacity, the ability of a country to produce and commercialize a flow of innovative technology over the long term.
Abstract: Motivated by differences in R&D productivity across advanced economies, this paper presents an empirical examination of the determinants of country-level production of international patents. We introduce a novel framework based on the concept of national innovative capacity. National innovative capacity is the ability of a country to produce and commercialize a flow of innovative technology over the long term. National innovative capacity depends on the strength of a nation's common innovation infrastructure (cross-cutting factors which contribute broadly to innovativeness throughout the economy), the environment for innovation in its leading industrial clusters, and the strength of linkages between these two areas. We use this framework to guide our empirical exploration into the determinants of country-level R&D productivity, specifically examining the relationship between international patenting (patenting by foreign countries in the United States) and variables associated with the national innovative capacity framework. While acknowledging important measurement issues arising from the use of patent data, we provide evidence for several findings. First, the production function for international patents is surprisingly well-characterized by a small but relatively nuanced set of observable factors, including R&D manpower and spending, aggregate policy choices such as the extent of IP protection and openness to international trade, and the share of research performed by the academic sector and funded by the private sector. As well, international patenting productivity depends on each individual country's knowledge stock.' Further, the predicted level of national innovative capacity has an important impact on more downstream commercialization and diffusion activities (such as achieving a high market share of high-technology export markets). Finally, there has been convergence among OECD countries in terms of the estimated level of innovative capacity over the past quarter century.
2,006 citations
References
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TL;DR: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples.
Abstract: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples. If each element of a vector of time series x first achieves stationarity after differencing, but a linear combination a'x is already stationary, the time series x are said to be co-integrated with co-integrating vector a. There may be several such co-integrating vectors so that a becomes a matrix. Interpreting a'x,= 0 as a long run equilibrium, co-integration implies that deviations from equilibrium are stationary, with finite variance, even though the series themselves are nonstationary and have infinite variance. The paper presents a representation theorem based on Granger (1983), which connects the moving average, autoregressive, and error correction representations for co-integrated systems. A vector autoregression in differenced variables is incompatible with these representations. Estimation of these models is discussed and a simple but asymptotically efficient two-step estimator is proposed. Testing for co-integration combines the problems of unit root tests and tests with parameters unidentified under the null. Seven statistics are formulated and analyzed. The critical values of these statistics are calculated based on a Monte Carlo simulation. Using these critical values, the power properties of the tests are examined and one test procedure is recommended for application. In a series of examples it is found that consumption and income are co-integrated, wages and prices are not, short and long interest rates are, and nominal GNP is co-integrated with M2, but not M1, M3, or aggregate liquid assets.
27,170 citations
Book•
01 Jan 1991
TL;DR: Grossman and Helpman as discussed by the authors developed a unique approach in which innovation is viewed as a deliberate outgrowth of investments in industrial research by forward-looking, profit-seeking agents.
Abstract: Traditional growth theory emphasizes the incentives for capital accumulation rather than technological progress. Innovation is treated as an exogenous process or a by-product of investment in machinery and equipment. Grossman and Helpman develop a unique approach in which innovation is viewed as a deliberate outgrowth of investments in industrial research by forward-looking, profit-seeking agents.
6,911 citations
TL;DR: In this paper, it is pointed out that it is very common to see reported in applied econometric literature time series regression equations with an apparently high degree of fit, as measured by the coefficient of multiple correlation R2 or the corrected coefficient R2, but with an extremely low value for the Durbin-Watson statistic.
5,922 citations
Posted Content•
TL;DR: In this paper, the authors present a survey on the use of patent data in economic analysis, focusing on the patent data as an indicator of technological change and concluding that patent data remain a unique resource for the study of technical change.
Abstract: This survey reviews the growing use of patent data in economic analysis. After describing some of the main characteristics of patents and patent data, it focuses on the use of patents as an indicator of technological change. Cross-sectional and time-series studies of the relationship of patents to R&D expenditures are reviewed, as well as scattered estimates of the distribution of patent values and the value of patent rights, the latter being based on recent analyses of European patent renewal data. Time-series trends of patents granted in the U.S. are examined and their decline in the 1970s is found to be an artifact of the budget stringencies at the Patent Office. The longer run downward trend in patents per R&D dollar is interpreted not as an indication of diminishing returns but rather as a reflection of the changing meaning of such data over time. The conclusion is reached that, in spite of many difficulties and reservations, patent data remain a unique resource for the study of technical change.
4,845 citations
TL;DR: In this article, the authors outline the production function approach to the estimation of the returns to R&D and then discuss in turn two very difficult problems: the measurement of output in R&DI intensive industries and the definition and measurement of the stock of R&DC 'capital'.
Abstract: The article outlines the production function approach to the estimation of the returns to R&D and then proceeds to discuss in turn two very difficult problems: the measurement of output in R&D intensive industries and the definition and measurement of the stock of R&D 'capital'. Multicollinearity and simultaneity are taken up in the next section and another section is devoted to estimation and inference problems arising more specifically in the R&D context. Several recent studies of returns to R&D are then surveyed, and the paper concludes with suggestions for ways of expanding the current data base in this field.
4,003 citations