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Relating the Knowledge Production Function to total Factor Productivity

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  • Frederick L. Joutz
  • Yasser Abdih

Abstract

The knowledge production function is central to R&D-based growth models. This paper empirically investigates the knowledge production function and intertemporal spillover effects using cointegration techniques. Time-series evidence suggests there are two long-run cointegrating relationships. The first captures a long-run knowledge production function; the second captures a long-run positive relationship between TFP and the knowledge stock. The results indicate the presence of strong intertemporal knowledge spillovers and that the long-run impact of the knowledge stock on TFP is small. This evidence is interpreted in light of existing theoretical and empirical evidence on endogenous growth.

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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 05/74.

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Length: 39
Date of creation: 01 Apr 2005
Date of revision:
Handle: RePEc:imf:imfwpa:05/74

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Keywords: Production; Productivity; Economic growth; Economic models; r & d; patents; patent applications; equation; cointegration; statistic; statistics; time series; standard errors; total patent applications; number of patent applications; dummy variables; number of researchers; patent office; total patents; patent filings; prediction; empirical model; significance levels; r & d expenditures; standard error; total number of patent applications; number ? of ? patent ? applications; correlations; basic research; dummy variable; equations; outliers; research and development; r & d activities; logarithms; survey; econometrics; functional form; patent ? applications; probabilities; research programs; significance level; constant mean; total ? number ? of ? patent ? applications; research policy; samples; calculus; empirical validity; r & d spillovers; correlation; predictions; random walks; sample size; hypothesis testing; constant term; scientific research personnel; autocorrelation; scientific research; covariance; statistical methodology; patent protection; probability; linear trend; diffusion process; logarithm; r & d resources; surveys;

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References

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  1. Peter Thompson & Melanie Fox Kean, 2004. "Patent Citations and the Geography of Knowledge Spillovers: A Reassessment," Working Papers 0401, Florida International University, Department of Economics.
  2. Zvi Griliches, 1989. "Patents: Recent Trends and Puzzles," NBER Working Papers 2922, National Bureau of Economic Research, Inc.
  3. Philippe Aghion & Peter Howitt, 1990. "A Model of Growth Through Creative Destruction," NBER Working Papers 3223, National Bureau of Economic Research, Inc.
  4. Jaffe, Adam B & Trajtenberg, Manuel & Henderson, Rebecca, 1993. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," The Quarterly Journal of Economics, MIT Press, vol. 108(3), pages 577-98, August.
  5. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
  6. Jerry A. Hausman & Bronwyn H. Hall & Zvi Griliches, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," NBER Technical Working Papers 0017, National Bureau of Economic Research, Inc.
  7. Manuel Trajtenberg & Adam B. Jaffe & Michael S. Fogarty, 2000. "Knowledge Spillovers and Patent Citations: Evidence from a Survey of Inventors," American Economic Review, American Economic Association, vol. 90(2), pages 215-218, May.
  8. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth Through Creative Destruction," Scholarly Articles 12490578, Harvard University Department of Economics.
  9. Paul M Romer, 1999. "Endogenous Technological Change," Levine's Working Paper Archive 2135, David K. Levine.
  10. Arora, Ashish & Gambardella, Alfonso, 1994. "The changing technology of technological change: general and abstract knowledge and the division of innovative labour," Research Policy, Elsevier, vol. 23(5), pages 523-532, September.
  11. Hendry, David F, 1986. "Econometric Modelling with Cointegrated Variables: An Overview," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(3), pages 201-12, August.
  12. Elhanan Helpman & David T. Coe, 1993. "International RandD Spillovers," IMF Working Papers 93/84, International Monetary Fund.
  13. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  14. Kortum, Samuel, 1993. "Equilibrium R&D and the Patent-R&D Ratio: U.S. Evidence," American Economic Review, American Economic Association, vol. 83(2), pages 450-57, May.
  15. Jones, Charles I, 1995. "Time Series Tests of Endogenous Growth Models," The Quarterly Journal of Economics, MIT Press, vol. 110(2), pages 495-525, May.
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Citations

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Cited by:
  1. L. Rachel Ngai & Roberto M. Samaniego, 2006. "An R&D-Based Model of Multi-Sector Growth," CEP Discussion Papers dp0762, Centre for Economic Performance, LSE.
  2. Sarath Delpachitra & Pham Van Dai, 2012. "The Determinants of TFP Growth in Middle Income Economies in ASEAN: Implication of Financial Crises," International Journal of Business and Economics, College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 11(1), pages 63-88, June.
  3. Roberto M Samaniego, 2005. "Investment-Specific Technical Change and the Production of Ideas," Computing in Economics and Finance 2005 291, Society for Computational Economics.

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