Technological progress spillover effect in Lithuanian manufacturing industry
DOI:
https://doi.org/10.24136/eq.2021.029Keywords:
technological progress, agent-based modelling, intersectoral technological progress spillover, manufacturing industry, economic developmentAbstract
Research background: Various methods for technological progress assessment and evaluation exist in the context of economic development. Each of the methods possesses distinct advantages and disadvantages in analysis of technological progress fluctuations. For most neoclassical growth theories, technological progress measures are included as exogenous variables, thus excluding evaluation of factors influencing technological progress variation throughout time.
Purpose of the article: The aim of this article is to offer improvements on classical technological progress evaluation methodologies for manufacturing industries, separating effect of intersectoral technological progress spillover effect from internal factors influencing technological progress growth and perform analysis in the case of Lithuanian manufacturing industry.
Methods: Earlier research papers used linear time series regression and vector autoregression methods to assess technological progress values and define equations explaining effect of different manufacturing level indicators on technological progress measure growth. This research paper uses results of previously mentioned methods and performs simulation analysis applying agent-based modelling framework.
Findings & value added: The conducted vector autoregression analysis has showed that two variables which influence technological progress most significantly are labor productivity measure and gross profit value. Sensitivity analysis emphasizes that effect of these two variables on technological progress growth is substantially different. Increase in gross profit value affects technological progress growth for wider range of sectors from Lithuanian manufacturing industry (15 out of 18 analyzed sectors? technological progress measure values are affected by changes in gross profit, while changes in labor productivity influence technological progress values in the case of 9 sectors). Rising gross profit values also produce intersectoral technological progress spillover effect more significantly, while growth in labor productivity measure has stronger effect on technological progress fluctuations for sectors which are able to exploit this effect. Presented research suggests improved methodology for intersectoral technological progress spillover effect assessment in the context of manufacturing industries.
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References
Aghion, P., & Howitt, P. (2009). The economics of growth. Cambridge, London: The MIT Press.
View in Google Scholar
Alcouffe, A., & Kuhn, T. (2004). Schumpeterian endogenous growth theory and evolutionary economics. Journal of Evolutionary Economics, 14(2), 223?236. doi: 10.1007/s00191-004-0205-0.
DOI: https://doi.org/10.1007/s00191-004-0205-0
View in Google Scholar
Apa, R., De Noni, I., Orsi, L., & Sedita, S. R. (2018). Knowledge space oddity: how to increase the intensity and relevance of the technological progress of European regions. Research Policy. 47, 1700?1712. doi: 10.1016/j.respol.2018.06 .002.
DOI: https://doi.org/10.1016/j.respol.2018.06.002
View in Google Scholar
Awokuse, T. O. (2006). Export-led growth and the Japanese economy: evidence from VAR and directed acyclic graphs. Applied Economics, 38(5), 593?602. doi: 10.1080/00036840600619594.
DOI: https://doi.org/10.1080/00036840600619594
View in Google Scholar
Benos, N., Karagiannis, S., & Karkalakos, S. (2015). Proximity and growth spillovers in European regions: the role of geographical, economic and technological linkages. Journal of Macroeconomics, 43, 124?139. doi: 10.1016/j.jmacro.2014 .10.003.
DOI: https://doi.org/10.1016/j.jmacro.2014.10.003
View in Google Scholar
Bloom, N., Schankerman, M., & Van Reenen, J. (2013). Identifying technology spillovers and product market rivalry. Econometrica, 81(4), 1347?1393. doi: 10.3982/ECTA9466.
DOI: https://doi.org/10.3982/ECTA9466
View in Google Scholar
Bharadwaj, S., Clark, T., & Kulviwat, S. (2005). Marketing, market growth, an endogenous growth theory: inquiry into the causes of market growth. Journal of the Academy of Marketing Science, 33(3), 347?359. doi: 10.1177/009207030 5276324.
DOI: https://doi.org/10.1177/0092070305276324
View in Google Scholar
Chu, A. C., Cozzi, G., Furukawa, Y., & Liao, C.-H. (2017). Inflation and economic growth in a Schumpeterian model with endogenous entry of heterogeneous firms. European Economic Review, 98, 392?409. doi: 10.1016/j.euroecorev.201 7.07.006.
DOI: https://doi.org/10.1016/j.euroecorev.2017.07.006
View in Google Scholar
Comin, D. (2010). Total factor productivity. In S. N. Durlauf & L. E. Blume (Eds.). Economic growth. London: Palgrave Macmillan. doi: 10.1057/978023 0280823_32.
DOI: https://doi.org/10.1057/9780230280823_32
View in Google Scholar
Crespi, G., & Zuniga, P. (2012). Innovation and productivity: evidence from six Latin American countries. World Development, 40(2), 273?290. doi: 10.1016/j .worlddev.2011.07.010.
DOI: https://doi.org/10.1016/j.worlddev.2011.07.010
View in Google Scholar
Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modelling. In A. J. Heppenstall, A. T. Crooks, L. M. See & M. Batty (Eds.). Agent-based models of geographical systems. Dordrecht: Springer, 85?105.
DOI: https://doi.org/10.1007/978-90-481-8927-4_5
View in Google Scholar
Dieppe, A., & Mutl, J. (2013). International R&D spillovers: technology transfer vs. R&D synergies. European Central Bank Working Paper Series, 1504.
DOI: https://doi.org/10.2139/ssrn.2194327
View in Google Scholar
Englmann, F. C. (1994). A Schumpeterian model of endogenous innovation and growth. Journal of Evolutionary Economics, 4, 227?241. doi: 10.1007/BF0123 6370.
DOI: https://doi.org/10.1007/BF01236370
View in Google Scholar
Fagerberg, J. (2000). Technological progress, structural change and productivity growth: a comparative study. Structural Change and Economic Dynamics, 11(4), 393?411. doi: 10.1016/S0954-349X(00)00025-4.
DOI: https://doi.org/10.1016/S0954-349X(00)00025-4
View in Google Scholar
Galor, O., & Tsiddon, D. (1997). Technological progress, mobility, and economic growth. American Economic Review, 87(3), 363?382.
View in Google Scholar
Greenwood, J., & Seshadri, A. (2004). Technological progress and economic transformation. NBER Working Paper, 10765. doi: 10.3386/w10765.
DOI: https://doi.org/10.3386/w10765
View in Google Scholar
Hu, J., Wang, Z., Lian, Y., Huang, Q. (2018). Environmental regulation, foreign direct investment and green technological progress - evidence from Chinese manufacturing industries. International Journal of Environmental Research and Public Health, 15(2), 221. doi: 10.3390/ijerph15020221.
DOI: https://doi.org/10.3390/ijerph15020221
View in Google Scholar
Hulten, C. R. (2001). Total factor productivity: a short biography. In Ch. R. Hulten, E. R. Dean & M. J. Harper (Eds.). New developments in productivity analysis. University of Chicago Press, 1?54.
DOI: https://doi.org/10.7208/chicago/9780226360645.003.0001
View in Google Scholar
Iwasaki, I., & Tokunaga, M. (2016). Technology transfer and spillovers from FDI in transition economies: a meta-analysis. Journal of Comparative Economics, 44(4), 1086?1114. doi: 10.1016/j.jce.2016.10.005.
DOI: https://doi.org/10.1016/j.jce.2016.10.005
View in Google Scholar
Janssen, M. A. (2005). Agent-based modelling. In J. Proops & P. Safonov (Eds.). Modelling in ecological economics. Cheltenham: Edward Elgar Publising, 155?172.
DOI: https://doi.org/10.4337/9781781958667.00015
View in Google Scholar
Lafi, M. (2018). Foreign affiliates and technology spillovers in the French manufacturing sector: an analysis using panel data. International Journal of Economics and Financial Issues, 8(5), 229?242.
View in Google Scholar
Lopez-Pueyo, C., Barcenilla, S. & Sanau, J. (2008). International technological spillovers and manufacturing productivity: a panel data analysis. Structural Change and Economic Dynamics, 19(2), 152?172. doi: 10.1016/j.strueco.2007 .12.005.
DOI: https://doi.org/10.1016/j.strueco.2007.12.005
View in Google Scholar
Lin, J., Yu, Z., Wei, Y. D., & Wang, M. (2017). Internet access, spillover and regional development in China. Sustainability, 9(6), 1?18. doi: 10.3390/su9060 946.
DOI: https://doi.org/10.3390/su9060946
View in Google Scholar
Markauskas, M., & Baliute, A. (2020). Modelling technological progress evaluation: case of Lithuanian manufacturing industry. Mediterranean Journal of Social Sciences, 11(6), 1?11. doi: 10.36941/mjss-2020-0058.
DOI: https://doi.org/10.36941/mjss-2020-0058
View in Google Scholar
Markauskas, M., & Saboniene, A. (2019). Evaluation of technological progress measures: case of Lithuanian manufacturing industry. In Proceedings of IAC 2019 in Budapest. Budapest: Czech Institute of Academic Education, 104?111.
View in Google Scholar
Mitze, T., Naveed, A., & Ahmad, N. (2016). International, intersectoral, or unobservable? Measuring R&D spillovers under weak and strong cross-sectional dependence. Journal of Macroeconomics, 50, 259?272. doi: 10.1016/j.jmacro .2016.10.002.
DOI: https://doi.org/10.1016/j.jmacro.2016.10.002
View in Google Scholar
Naveed, A., & Ahmad, N. (2016) Technology spillovers and international borders: a spatial econometric analysis. Journal of Borderlands Studies, 31(4), 441?461. doi: 10.1080/08865655.2016.1188669.
DOI: https://doi.org/10.1080/08865655.2016.1188669
View in Google Scholar
Shan, J. (2005). Does financial development ?lead' economic growth? A vector auto-regression appraisal. Applied Economics, 37(12), 1353?1367. doi: 10.1080 /00036840500118762.
DOI: https://doi.org/10.1080/00036840500118762
View in Google Scholar
Solow, R. M. (1999). Neoclassical growth theory. In J. B. Taylor & M. Woodford (Eds.). Handbook of macroeconomics, 1, Elsevier, 637?667. doi: 10.1016/S15 74-0048(99)01012-5.
DOI: https://doi.org/10.1016/S1574-0048(99)01012-5
View in Google Scholar
Sredojevic, D., Cvetanovic, S., & Boskovic, G. (2016). Technological changes in economic growth theory: neoclassical, endogenous, and evolutionary-institutional approach. Economic Themes, 54(2), 177?194. doi: 10.1515/etheme s-2016-0009.
DOI: https://doi.org/10.1515/ethemes-2016-0009
View in Google Scholar
Van den Berg, H. (2012). Explaining neoclassical economists? pro-growth agenda: does the popular Solow growth model bias economic analysis? International Journal of Pluralism and Economics Education, 3(1), 40?62. doi: 10.1504/IJPE E.2012.047472.
DOI: https://doi.org/10.1504/IJPEE.2012.047472
View in Google Scholar
Verspagen, B., & Loo, I. D. (1999). Technology spillovers between sectors and over time. Technological Forecasting and Social Change, 60(3), 215?235. doi: 10.1016/S0040-1625(98)00046-8.
DOI: https://doi.org/10.1016/S0040-1625(98)00046-8
View in Google Scholar
Wang, M., & Wong, M. C. S. (2016). Effects of foreign direct investment on firm-level technical efficiency: stochastic frontier model evidence from Chinese manufacturing firms. Atlantic Economic Journal, 44(3), 335?361. doi: 10.1007 /s11293-016-9509-3.
DOI: https://doi.org/10.1007/s11293-016-9509-3
View in Google Scholar
Wei, Y., & Liu, X. (2006). Productivity spillovers from R&D, exports and FDI in China?s manufacturing sector. Journal of International Business Studies, 37(4), 544?557. doi: 10.1057/palgrave.jibs.8400209.
DOI: https://doi.org/10.1057/palgrave.jibs.8400209
View in Google Scholar
Worthington, A. C., & Lee, B. L. (2008). Efficiency, technology and productivity change in Australian universities, 1998?2003. Economics of Education Review, 27(3), 285?298. doi: 10.1016/j.econedurev.2006.09.012.
DOI: https://doi.org/10.1016/j.econedurev.2006.09.012
View in Google Scholar
Yang, Z., Shao, S., Yang, L., & Liu, J. (2017). Differentiated effects of diversified technological sources on energy-saving technological progress: empirical evidence from China's industrial sectors. Renewable and Sustainable Energy Reviews, 72, 1379?1388. doi: 10.1016/j.rser.2016.11.072.
DOI: https://doi.org/10.1016/j.rser.2016.11.072
View in Google Scholar
Zhao, X., Lin, D., & Hao, T. (2019). A new discussion on the relationship between M&A and innovation in an emerging market: the moderating effect of post-acquisition R&D investment. Technology Analysis & Strategic Management, 31(12), 1447?1461. doi: 10.1080/09537325.2019.1627310.
DOI: https://doi.org/10.1080/09537325.2019.1627310
View in Google Scholar
Zhou, G., & Luo, S. (2018). Higher education input, technological innovation, and economic growth in China. Sustainability, 10(8), 2615. doi: 10.3390/su1008 2615.
DOI: https://doi.org/10.3390/su10082615
View in Google Scholar
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