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Yasser Abdih is an Economist in the Middle East and Central Asia Department. Frederick Joutz is a Professor of Economics at the George Washington University. The authors would like to thank Tim Fuerst, Arthur Goldsmith, Jim Hirabayashi, Costas Mastrogianis, Stephanie Shipp, and Holger Wolf for providing valuable comments. The first author would also like to thank John Kendrick. A preliminary version of this research was awarded the John Kendrick Prize, which supports research in the areas of productivity and growth.
In this paper, knowledge, technology, and ideas are used interchangeably.
The case where ϕ=0 allows the “fishing out” effect to completely offset the “standing on shoulders” effect. That is, current research productivity is independent of the stock of knowledge.
Note that we measure knowledge/technology using patent applications rather than patent grants. The lag between application and grants could be quite long and it varies over time partly due to changes in the availability of resources to the U.S. Patent Office. This notion is best articulated by Griliches (1990): “A change in the resources of the patent office or in its efficiency will introduce changes in the lag structure of grants behind applications, and may produce a rather misleading picture of the underlying trends. In particular, the decline in the number of patents granted in the 1970s is almost entirely an artifact, induced by fluctuations in the U.S. Patent Office, culminating in the sharp dip in 1979 due to the absence of budget for printing the approved patents.” This paper views patent applications as a much better measure of knowledge/technology than patent grants. Also, It is widely believed that patent application data is a better measure of new knowledge produced in an economy than R&D expenditures [see, e.g., Joutz and Gardner (1996)]. The reason is that R&D expenditures are more properly thought of as inputs to technological change while patents are an output. Hence patent applications more closely approximate the output of the knowledge production function in R&D-based growth models than R&D expenditures.
By definition, the stock of total patent applications includes (1) the cumulated numbers of domestic patent applications (i.e., patent applications by U.S. inventors at the U.S. Patent Office), and (2) the cumulated numbers of foreign patent applications (i.e., patent applications by foreign inventors at the U.S. Patent Office). We examined the data on the number of foreign patent applications, and found that it is characterized by a sustained smooth rise over the period 1948–97. There is no evidence of a more rapid increase in the series beginning in the mid 1980s. As such, the more rapid increase in the stock of total patent applications since the mid 1980s indeed captures the more rapid increase in the numbers of domestic patent applications.
Below, we also use the stock of domestic patents as an alternative measure of the stock of knowledge. We compare the results from using such a measure with the results where the stock of total (domestic and foreign) patents is used.
Recall that the R&D-based growth models of Romer (1990) and Jones (1995b) assume a Cobb-Douglas specification for the knowledge production function expressed in terms of the levels of the variables. Since the function F(.) in the text is expressed in terms of the log-levels of the variables, it is therefore assumed to be linear.
Table 1 also includes a fifth variable, sdp. sdp is the stock of domestic patents constructed from the number of domestic patent applications using the perpetual inventory method. This variable will be discussed at the appropriate time below. For now, it suffices to say that the statistical arguments mentioned in the text for stp also apply to sdp.
Elliott (1998) points out the pitfalls in using cointegration methods when the data are stationary. However, this is a limitation of any research using this methodology.
Stpl1 is simply stp lagged one period. Since stp is calculated as end of period stocks, we enter it with a lag in the VAR and cointegration analysis.
The diagnostic tests mentioned in the text are vector or system tests. Diagnostic tests performed on each equation of the VAR separately yield the same results as those for the entire system.
In fact the likelihood ratio statistic rejects the null hypothesis that that coefficient is unity: χ2 (1)= 29.12 [0.00].