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Technology Diffusion: Any Further Evidence for Computer Industry?


This paper explores the technology diffusion specifically in computer industry for a panel of 18 countries between the period 1999 and 2011. Strong evidence is found for the existence of diffusion of computer technology through bilateral capital good flows. This study shows that knowledge dissemination arises as an important determinant of output in computer sector. Still, domestic research and development efforts of countries are found to be the leading contributing factors in all model specifications. Besides, among two subgroups, in diffusion of computer technology, it is observed that USA contributes more to the knowledge dissemination in the related industry relative to three Asian countries i.e., Chinese Taipei, Korea and Singapore.

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  1. They report that in terms of “R&D flow intensity,” i.e., the world R&D flow into an equipment type divided by total sales made by R&D-performing countries, computer sector comes the second.

  2. R&D expenditure is expressed in the form of stocks constructing from flows using the perpetual inventory method.

  3. Sector code for capital good imports, final good imports, and R&D expenditure is D26. It is C30T33X for output and TTL_C30T33X for gross-fixed capital formation.

  4. Due to data unavailability, Hong Kong is not included in our estimation.

  5. Bilateral final good imports with sector code D26 is constructed for the countries of the sample. This variable represents the total final good imports of economy i from other countries in the sample.

  6. Patent variable is chosen in accordance with the inventor’s country of residence and priority date in order to be accurate about the location and the date of the invention. Patent applications to USPTO are chosen; hence, the organization received the largest number of applications in ICT technology.

  7. As stated byHoechle (2007), by applying a Newey-West-type correction, Driscoll-Kraay estimator guarantees that the covariance matrix estimator is consistent, independently of the cross-sectional dimension.

  8. The large sample is estimated with the R&D capital stocks calculated for 5, 10, and 15% depreciation rates, separately. It is obtained that the significance of variables does not change in accordance with different depreciation rates. The results reported in the paper belong to the estimates with 10% depreciation rate as in Henry et al. (2009). It still used different rates in the literature (Bloom and Van Reenen 2007).


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Correspondence to Cemil Faruk Durmaz.



Domestic R&D capital stocks of the countries are calculated using the perpetual inventory method. Capital stock for the initial year is calculated as in Eq. (1). The term E0 denotes the R&D expenditure in the first year of the data, i.e., year 1999, and g stands for the average annual logarithmic growth of R&D expenditures in all years and δ for the depreciation rate taken as 10%.Footnote 8

$$ {R}_0={E}_0/\left(g+\delta \right) $$

In Eq. (2), Rt − 1 stands for the capital stock in year t − 1 and Et denotes the expenditure on R&D in year t. (1 − δ) represents the remaining capital stock after the depreciation.

$$ {R}_t=\left(1\hbox{--} \delta \right){R}_{t-1}+{E}_t $$

It should be noted that Rt for each year and country is shown as \( {RD}_{it}^D \) for the domestic R&D capital stock of the receiver and \( {RD}_{jt}^D \) for the domestic knowledge capital of the exporter country.

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Durmaz, C.F., Polat, U. Technology Diffusion: Any Further Evidence for Computer Industry?. J Knowl Econ 11, 356–372 (2020).

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  • R&D spillover
  • Technology diffusion
  • Computer sector

JEL Classification

  • L6
  • O3
  • O4