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International, intersectoral, or unobservable? Measuring R&D spillovers under weak and strong cross-sectional dependence

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  • Mitze, Timo
  • Naveed, Amjad
  • Ahmad, Nisar

Abstract

In the theoretical and empirical growth literature, private and social returns to R&D have been identified as the key drivers of productivity gains and economic development. However, recently the debate on the relative importance of private vis-à-vis social returns has been reinforced by contributions in two emerging fields of the applied econometric literature, namely spatial panel modeling and the common factor approach, which stress the role of cross-sectional dependence as a source for a potential estimation bias linked to the measurement of returns to R&D. In this paper, we account for these methodical advances when estimating sectoral knowledge production functions for OECD countries under weak and strong cross-sectional dependence. By doing so, we are able to uncover technology- and trade-related R&D spillover channels associated with social returns to R&D, while effectively controlling for other types of productivity spillovers and latent macroeconomic shocks. Our results highlight the role played by international-intersectoral R&D spillovers for the social rate of return to R&D, while we get limited evidence for private returns to R&D once cross-sectional dependence is properly accounted for.

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  • Mitze, Timo & Naveed, Amjad & Ahmad, Nisar, 2016. "International, intersectoral, or unobservable? Measuring R&D spillovers under weak and strong cross-sectional dependence," Journal of Macroeconomics, Elsevier, vol. 50(C), pages 259-272.
  • Handle: RePEc:eee:jmacro:v:50:y:2016:i:c:p:259-272
    DOI: 10.1016/j.jmacro.2016.10.002
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    4. Anna Gloria Billé & Alessio Tomelleri & Francesco Ravazzolo, 2023. "Forecasting regional GDPs: a comparison with spatial dynamic panel data models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(4), pages 530-551, October.
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    6. Jianling Jiao & Yufei Yang & Yu Bai, 2018. "The impact of inter-industry R&D technology spillover on carbon emission in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(3), pages 913-929, April.
    7. Chen, Feiqiong & Meng, Qiaoshuang & Li, Xueying, 2018. "Cross-border post-merger integration and technology innovation: A resource-based view," Economic Modelling, Elsevier, vol. 68(C), pages 229-238.
    8. Zhu, Facang & Shi, Qiule & Balezentis, Tomas & Zhang, Chonghui, 2023. "The impact of e-commerce and R&D on firm-level production in China: Evidence from manufacturing sector," Structural Change and Economic Dynamics, Elsevier, vol. 65(C), pages 101-110.
    9. Mantas Markauskas & Asta Baliute, 2021. "Technological progress spillover effect in Lithuanian manufacturing industry," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 16(4), pages 783-806, December.

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    More about this item

    Keywords

    Sectoral productivity; R&D spillovers; Cross-sectional dependence; OECD;
    All these keywords.

    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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