D
David T. Coe
Researcher at International Monetary Fund
Publications - 50
Citations - 12251
David T. Coe is an academic researcher from International Monetary Fund. The author has contributed to research in topics: Total factor productivity & Productivity. The author has an hindex of 25, co-authored 50 publications receiving 11714 citations. Previous affiliations of David T. Coe include National Bureau of Economic Research & Organisation for Economic Co-operation and Development.
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International R&D Spillovers
TL;DR: In this paper, the effects of both domestic and foreign R&D capital stocks on total factor productivity were investigated and it was shown that the foreign stocks had large effects on the smaller countries in the sample.
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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.
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North-South R & D Spillovers
TL;DR: This article examined the extent to which developing countries that do little, if any, research and development themselves benefit from R&D that is performed in the industrial countries and found that R&DI spillovers from twenty-two industrial countries over 1971-90 are substantial.
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North-South R&D Spillovers
David T. Coe,David T. Coe,Elhanan Helpman,Elhanan Helpman,Elhanan Helpman,Alexander W. Hoffmaister +5 more
TL;DR: This paper examined the extent to which developing countries that do little, if any research and development themselves benefit from R&D that is performed in the industrial countries, and found that the spillover from the industrial country in the North to the developing countries in the South is substantial.
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International R&D Spillovers and Institutions
TL;DR: In this paper, the authors revisited the empirical analysis in "International R&D Spillovers" (Coe and Helpman, 1995) by applying modern panel cointegration estimation techniques to an expanded data set that was constructed for the purpose of this study.