Acharya, R. C. and Keller, W. (2009). Technology transfer through imports. Canadian Journal of Economics/Revue canadienne dconomique, 42:1411–48.
Aghion, P., Bloom, N., Blundell, R., Griffith, R., and Howitt, P. (2005). Competition and innovation: An inverted-U relationship. Quarterly Journal of Economics, 120:701–28.
Aghion, P., Howitt, P., and Prantl, S. (2015). Patent rights, product market reforms, and innovation. Journal of Economic Growth, 20:223–62.
Autor, D., Dorn, D., Hanson, G. H., Pisano, G., and Shu, P. (2016). Foreign competition and domestic innovation: Evidence from U.S. patents. NBER Working Paper, National Bureau of Economic Research, Cambridge, MA, 22879.
Autor, D., Dorn, D., Hanson, G. H., and Song, J. (2014). Trade adjustment: Worker level evidence. Quarterly Journal of Economics, 129:1799–1860.
Autor, D., Dorn, D., Katz, L. F., Patterson, C., and Reenen, J. V. (2017). The fall of the labor share and the rise of superstar firms. NBER Working Paper, National Bureau of Economic Research, Cambridge, MA, 22879.
Aw, B. Y., Roberts, M. J., , and Xu, D. Y. (2011). R&D investment, exporting, and productivity dynamics. Review of Economic Studies, 101:1312–44.
Bilir, L. K. (2014). Patent laws, product life-cycle lengths, and multination activity. The American Economic Review, 104:1979–2013.
Bloom, N., Draca, M., and Reenen, J. V. (2016). Trade induced technical change? the impact of chinese imports on innovation, IT and productivity. Review of Economic Studies, 83:87–117.
Bloom, N., Romer, P. M., Terry, S. J., and Reenen, J. V. (2013). A trapped-factors model of innovation. American Economic Review, 103:208–213.
Coe, D. T., Helpman, E., and Hoffmaister, A. W. (2009). International R&D spillovers and institutions. European Economic Review, 53:723–41.
Coelli, F., Moxnes, A., and Ulltveit-Moe, K. H. (2016). Better, faster, stronger: Global innovation and trade liberalization. NBER Working Paper, 22647.
Gilbert, R. (2006). Looking for mr. schumpeter: Where are we in the competition innovation debate? In Innovation Policy and the Economy, volume 6. The MIT Press.
Gutiérrez, G. and Philippon, T. (2017). Declining competition and investment in the U.S. NBER Working Paper, National Bureau of Economic Research, Cambridge, MA., 23583.
Kao, C. and Chiang, M.-H. (2001). On the estimation and inference of a cointegrated regression in panel data. In Nonstationary Panels, Panel Cointegration, and Dynamic Panels (Advances in Econometrics), volume 15. Elsevier.
Keller, W. (2010). International trade, foreign direct investment, and technology spillovers. In Handbook of the Economics of Innovation, volume 2, pages 793–829. Elsevier.
Long, C. X. and Wang, J. (2016). Global declining competition. Evaluating Patent Promotion Policies in China: Consequences for Patent Quantity and Quality.
Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61:653–670.
Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20:597–625.
Peri, G. (2005). Determinants of knowledge flows and their effect on innovation. Review of Economics and Statistics, 87 (2):308–22.
Santos Silva, J. and Tenreyro, S. (2011). Further simulation evidence on the performance of the poisson pseudo-maximum likelihood estimator. Economics Letters, 112:220–22.
Williams, H. L. (2013). Intellectual property rights and innovation: Evidence from the human genome. Journal of Political Economy, 121:1–27.
This paper is dedicated to the memory of our friend and colleague Giang Ho. Her wit and smile will be sorely missed. We thank Helge Berger, Gian Maria Milesi-Ferretti and Maurice Obstfeld for insightful comments. Pankhuri Dutt, Chanpheng Fizzarotti and Menexenia Tsaroucha provided excellent research assistance.
Most of the analysis is carried out on international patent families. For a definition, see Section 3.
In this way, it is possible to nest several relations between knowledge and long-run productivity growth. For instance, if ρ = 1 and the R&D flow is calculated in terms of the number of employed research scientists, then exponential growth in productivity can be achieved even with a constant amount of knowledge. On the other hand, if ρ = 0 then exponential growth is possible only if the stock of knowledge grows exponentially which, by equation (1), would require exponential growth in at least one of the two R&D stocks. For an in-depth discussion, see Kortum (1997) and Bloom et al. (2017).
See Appendix A.2 for the inter-sectoral estimation of knowledge flows. Note that in our dataset sectors are already defined in broad way, and hence production activities are very different across sectors. For this reason, it should not be surprising that inter-sectoral knowledge flows are found to be small.
Our use of international patent families as a quality-adjusted patent measure is different from Peri (2005), who weights the simple patent count by citation in the first 4 years after the patent was granted. While citation weights are a standard quality adjustment, they assume comparability of the underlying patenting and citation behavior. In our sample, which includes vastly different patenting cultures (e.g., Japan and China), a patent count based on the higher-level international patent families seems more appropriate, as it reduces the influence of country specificity.
We attribute a patent to the publication year for the patent count, but to the application year for the citation count. Citations are counted only if they occur within four years of the publication year (see Table 8 in Appendix A.1 for discussion). Self-citations, which are defined as citations between patents with identical inventor, are excluded.
The difference in technological specialization is based on compositional differences in patent application. Similar to Peri (2005), for each country-sector a vector is produced where the cells are the proportions of all patent applications that relate to each of the 23 IPC subsections. The variable is then defined as 1 minus the uncentered correlation between the two country-sectors’ proportion vectors. Both technological variables use absolute values in order to capture the technological proximity, independently of the sign.
The empirical equation is based on the assumption that the probability that an idea created in country-industry (l, i) is cited by (c, i) during an interval τ, is ϕcli = ef(c,l,i)(1-e-bτ).
The removal of the fixed effects from the predicted values allow us to exclude the country-time drivers of patent citations that, by themselves, do not affect the intensity of bilateral international knowledge flows.
For instance, changing legal and cultural factors may affect the level of patent applications and citations at the country-level, without implying any variation in international knowledge flows.
Results not reported, but available from the authors on request.
The 95 percent confidence band for the period 1995–199 is [8.4, 12.0] and for 2010–2014 is [11.5, 22.4]
Results available upon request.
Peri (2005) estimates an elasticity to foreign R&D of 0.4–0.47, and elasticity to domestic R&D of 0.74–0.81. As already mentioned, the many differences in the estimation sample and in the level of analysis (ours is at the industry level), make our results not easily comparable with his.
These results are available upon request.
Data on bilateral goods imports are from the UN COMTRADE database (we aggregate the six-digit product level data into the two-digit ISIC industry level), and data on sectoral gross output are from the World Input-Output Table (WIOT).
The inclusion of both country-year and sector fixed effects removes most of the variation in the data, and thus the results are not discussed here.
The correlation between sector-level R&D stock and this interacted variable is about 0.49 (calculated over country-sectors for which both are available).
For example, Coe et al. (2009) use aggregate data at the country level and estimate a panel cointegrating equation. Acharya and Keller (2009) use industry-level data as in our study, but estimate R&D spillovers from G6 countries (United States, Japan, Germany, France, United Kingdom, Canada) separately and either do not weight the foreign R&D variables or weight by import shares.
In the international context, the rent and escape competition effects have a wider interpretation (Akcigit et al. (2017).
To complicate matters further, product market competition appears to interact in important ways with the degree of intellectual property rights protection – another determinant of innovators’ rents. For instance, some evidence suggests that stronger product market competition is associated with more innovation only when intellectual property rights protection is strong (Aghion et al. (2015)). However, while strong protection motivates multinational companies to transfer technology across countries, it reduces innovation in other contexts (Williams (2013); Bilir (2014)). See Also, Boldrin and Levine (2008).
This measure is available for the period 1998–2014. Alternative measures using final instead of total goods trade yield similar results.
The calculation of market shares uses the largest 1000 firms in each four-digit NACE sector from 28 countries. The shares of the largest four firms are then aggregated to the two-digit ISIC industry level using sectoral revenues as weights. This measure is available for the period 2000–15. An alternative measure using the Herfindahl index produces similar results. We thank Federico Diez for providing us with these data.
Autor et al. (2014) did the opposite in their U.S.-focused study, instrumenting import penetration in the United States with that in other advanced economies.
2SLS coefficients can also be larger than the ones from OLS in the presence of weak instruments. However, our confidence in the instrumentation is supported by first-stage test statistics, which strongly reject the hypotheses of under- or weak identification, suggesting that the instruments are relevant. In particular, the Kleibergen-Paap rk LM statistic (under-identification test) is 22.4, with a p-value of 0. The Kleibergen-Paap rk Wald F statistic (weak identification test) is 23.702, also comfortably larger than any Stock-Yogo weak ID test critical values.
Detailed results available upon request.
Detailed results are available upon request.
The increase in Chinese patenting would be much more dramatic if simple patent applications would be used. Their representativeness is however questionable, as the Chinese Patent Promoting Policies (PPP) have contributed not only to an explosion in patent applications, but also to a deterioration in the average patent quality (e.g. Long and Wang (2016)).
The Im-Pesaran-Shin test which generally works with unbalanced panels had insufficient observations even when using interpolation to fill in missing data in the series.
In order to satisfy this condition for the three variables, we linearly interpolate missing years in the data and drop any country-sector with less than 5 observations.