Teknoloji ve İşsizlik: Sistem GMM Tahminlerinden Ampirik Kanıtlar

Öz

Çalışmada, teknolojik ilerlemenin iş kayıplarına ve işsizliğe neden olup olmadığı sorusuna odaklanılmaktadır. Ürün ve süreç yenilikle ortaya çıkan teknolojik ilerleme, bir yandan yeni iş alanları ortaya çıkarırken diğer yandan da mevcut işlerde ihtiyaç duyulan nitelikleri değiştirmektedir. Bu durum ise makinelerin insan emeğini ikame etmesiyle sonuçlanabilmektedir. Ortaya çıkan yeni iş alanları ve verimlilik artışları önemli birer fırsat olsa da bu fırsatların kaybolan meslekleri ve açığa çıkan işgücü arzını telafi etmekte yeterli olup olmadığı konusu gerek kuramsal gerekse ampirik olarak üzerinde fikir birliğine varılmış bir konu değildir. Nihai etkinin belirsizliği, yöntemsel gelişmeler ve veri setlerinin çeşitlenmesi konunun farklı boyutlarıyla ele alınması için önemli bir motivasyon kaynağıdır. Çalışmada, 1995-2021 dönemi ve 91 ülke kapsamında, işsizlik oranının geçmiş değerinin gelecek değerini etkilemesi eğiliminden hareket edilerek dinamik bir panel model çerçevesinde analizler yapılmıştır. Modeller iki aşamalı sistem Genelleştirilmiş Momentler Yöntemi (2SGMM) kullanılarak tahmin edilmiştir. Çalışmanın bulguları, işsizliğin kalıcılığını doğrulamakta ve sabit sermaye yatırımına dahil edilen somutlaştırılmış süreç yeniliğin emek tasarruf edici niteliğine işaret etmektedir. Ürün yeniliğin bir ölçüsü olarak modele dahil edilen ekonomik karmaşıklık endeksinin ise alternatif modeller kapsamında işsizlik oranını azaltıcı etkisinin olduğu tespit edilmiştir. Bir diğer model kapsamında ürün yeniliği temsil etmek üzere analize dahil edilen patent sayısının gecikmeli olarak işsizliği etkilediği bulunmuştur. Buna göre patent sayılarındaki artış, gecikmeli olarak azalan işsizlik oranlarıyla ilişkilidir.

Anahtar Kelimeler

• İşsizlik
• Yenilik
• Teknoloji

Technology and Unemployment: Empirical Evidence from System GMM Estimates

Öz

The study concentrates on whether technological progress causes job losses and unemployment. While technological progress, embodied in product and process innovation, creates new jobs, it also changes the qualifications required by existing jobs and causes machines to substitute human labour. The emergence of new jobs and productivity gains are opportunities, but whether these opportunities are sufficient to compensate for jobs lost and the resulting labour surplus is disputable. The uncertainty of the ultimate impact, methodological developments, and diversification of data sets are essential motivations for addressing the issue with different dimensions. In this study, analyses are conducted within the framework of a dynamic panel model considering the effect of the past value of the unemployment rate on its future value for the period 1995-2021 and 91 countries. The models are estimated using the two-step system Generalised Method of Moments (2SGMM). The findings of the study confirm the persistence of unemployment. Moreover, the embodied process included in gross fixed capital formation points to the labour-saving nature of innovation. Under alternative models, the economic complexity index, a measure of product innovation, is found to reduce unemployment. The number of patents included in the analysis to represent product innovation within the scope of another model affects unemployment in lagged terms. Accordingly, the increased number of patents results in a lagged decrease in unemployment rates.

Anahtar Kelimeler

• Unemployment
• Innovation
• Technology

Kaynakça

1. Acemoglu, D. & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In Ashenfelter, O. and card, D., editors, Handbook of Labor Economics, 4, 1043–1171. North Holland, Amsterdam.
2. Aguiar, D., Costa, L., & Silva, E. (2017). An attempt to explain differences in economic growth: An stochastic frontier approach. Bulletin of Economic Research, 4, 42-65. https://doi.org/10.1111/boer.12102
3. Aguilera, A., & Barrera, M. G. R. (2016). Technological unemployment: An approximation to the Latin American case. Ad-Minister 29, 59-78. https://doi.org/10.17230/ad-minister.29.3
4. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297. https://doi.org/10.2307/2297968
5. Bennett, J. (2016). Skill-specific unemployment risks: Employment protection and technological progress - A cross-national comparison. Journal of European Social Policy, 26(5), 402-416. https://doi.org/10.1177/0958928716664294
6. Berman, E., & Machin, S. (2000). Skill-biased technology transfer around the world. Oxford review of economic policy, 16(3), 12-22. https://doi.org/10.1093/oxrep/16.3.12
7. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143. https://doi.org/10.1016/S0304-4076(98)00009-8
8. Bond, S. (2002). Dynamic panel data models: A guide to micro data methods and practice. Working Paper CWP09/02, Cemmap, Institute for Fiscal Studies. Available at http://cemmap.ifs.org.uk/wps/cwp0209.pdf.

9. Caselli, F., Esquivel, G., & Lefort, F. (1996). Reopening the convergence debate: A new look at cross-country growth empirics. J. Econ. Growth 1(3), 363–389. https://doi.org/10.1007/BF00141044
10. Chourasia, S., Tyagi, A., Pandey, S. M., Walia, R. S., & Murtaza, Q. (2022). Sustainability of Industry 6.0 in Global Perspective: Benefits and challenges. MAPAN 37, 443–452. https://doi.org/10.1007/s12647-022-00541-w
11. Cords, D., & Prettner, K. (2022). Technological unemployment revisited: Automation in a search and matching framework. Oxford Economic Papers-New Series, 74(1), 115-135. https://doi.org/10.1093/oep/gpab022
12. Crowley, P. M., & Hudgins, D. (2023). The impact of US productivity growth on unemployment in the time-frequency domain: Is AI causing a change in the relationship? Empirical Economics, 66, 2169–2190. https://doi.org/10.1007/s00181-023-02510-x
13. Du, Y., & Wei, X. (2022). Technological change and unemployment: Evidence from China. Applied Economics Letters, 29(9), 851–854. https://doi.org/10.1080/13504851.2021.1896666
14. Duggal, A.S., Malik, P.K., Gehlot, A., Singh, R., Gaba, G.S., Masud, M.& Al-Amri, J.F. (2022). A sequential roadmap to industry 6.0: Exploring future manufacturing trends. IET Commun. 16, 521–531. https://doi.org/10.1049/cmu2.12284
15. Feldmann, H. (2013). Technological unemployment in industrial countries. Journal of Evolutionary Economics, 23(5), 1099–1126. https://doi.org/10.1007/s00191-013-0308-6
16. Focacci, C. N. (2021). Technological unemployment, robotisation, and green deal: A story of unstable spillovers in China and South Korea (2008-2018). Technology in Society, 64. https://doi.org/10.1016/j.techsoc.2020.101504
17. Görkey, S. (2022). Technological change and unemployment nexus from a gender perspective: Empirical evidence from a panel cointegration approach. Gender Technology & Development, 26(2), 159-180. https://doi.org/10.1080/09718524.2022.2043986
18. Harvard University Growth Lab at. Growth Projections and Complexity Rankings, V2 [Data set]. https://doi.org/10.7910/ dvn/xtaqmc (Erişim 1/1/2024).
19. Hollander, S. (2019). Retrospectives: Ricardo on machinery. Journal of Economic Perspectives, 33 (2): 229-42. https://doi.org/10.1257/jep.33.2.229
20. International Labour Organization-ILOSTAT, Wages and Working Time Statistics Database. https://ilostat.ilo.org/data/ (Erişim 11/3/2024).
21. Irandoust, M. (2023). Employment and technology: Creative creation or creative destruction? An asymmetric analysis. Australian Economic Papers, 1–19. https://doi.org/10.1111/1467-8454.12327
22. Islam, N. (1995). Growth empirics: A panel data approach. Q. J. Econ. 110 (4), 1127–1170. https://doi.org/10.2307/2946651
23. Jones, C. (2001). İktisadi Büyümeye Giriş (Çev. Ş. Ateş ve İ. Tuncer). İstanbul: Literatür Yayıncılık.
24. Keynes, J.M. (1931). Economic possibilities for our grandchildren, in J.M. Keynes (ed.): Essays in persuasion. London, Macmillan [London, Palgrave Macmillan, 2010, pp. 321–332].
25. Konstantakopoulou, I. (2022). Does health quality affect tourism? Evidence from system GMM estimates. Economic Analysis and Policy, 73, 425-440. https://doi.org/10.1016/j.eap.2021.12.007
26. Krousie, C. (2018). Technological unemployment in the United States: A state-level analysis. Major Themes in Economics, 20, 87-101. https://scholarworks.uni.edu/mtie/vol20/iss1/6
27. Lydeka, Z., & Karaliute, A. (2021). Assessment of the effect of technological innovations on unemployment in the European Union Countries. Engineering Economics, 32(2), 130-139. http://dx.doi.org/10.5755/j01.ee.32.2.24400
28. Mankiw, N. G., Phelps, E. S., & Romer, P. M. (1995). The growth of nations. Brookings Papers on Economic Activity, 1, 275-326. https://doi.org/10.2307/2534576
29. Marx, K. (2018). Kapital Cilt I: Ekonomi Politiğin Eleştirisi. (Çev. Selik, M. ve Satlıgan, N.), İstanbul: Yordam Kitap.
30. Matuzeviciute, K., Butkus, M., & Karaliute, A. (2017). Do technological innovations affect unemployment? Some empirical evidence from European countries. Economies, 5(4), 48–19. https://doi.org/10.3390/economies5040048
31. Pini, P. (1995). Economic Growth, Technological Change and Employment: Empirical Evidence for a Cumulative Growth Model with External Causation for Nine OECD Countries: 1960–1990. Structural Change and Economic Dynamics, 6(2), 185–213. https://doi.org/10.1016/0954-349X(94)00008-W
32. Piva, M., & Vivarelli, M. (2018). Technological change and employment: Is Europe ready for the challenge?. Eurasian Bus Rev 8, 13–32. https://doi.org/10.1007/s40821-017-0100-x
33. Prat, J. (2007). The impact of disembodied technological progress on unemployment. Review of Economic Dynamics, 10(1), 106-125. https://doi.org/10.1016/j.red.2006.09.003
34. Ricardo, D. (1951). On the Principles of Political Economy and Taxation, Sraffa, P. (ed.), Cambridge: Cambridge University Press.
35. Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The stata journal, 9(1), 86-136. https://doi.org/10.1177/1536867X0900900106
36. Say, J. B. (1964). A treatise on political economy or the production, distribution and consumption of wealth. New York: M. Kelley. (first edn 1803).
37. Schumpeter, J.A. (1976). Capitalism, Socialism and Democracy (1st ed.). New York: Routledge. https://doi.org/10.4324/9780203202050
38. Shen, Y. (2024). Future jobs: analyzing the impact of artificial intelligence on employment and its mechanisms. Economic Change and Restructuring, 57(2). https://doi.org/10.1007/s10644-024-09629-6
39. Simonetti, R., Taylor, K., & Vivarelli, M. (2000). Modelling the employment impact of innovation. In M. Pianta, M. Vivarelli (Eds.), the employment impact of innovation: Evidence and policy (pp. 26–43). London and New York: Routledge. ISBN: 0-415-20433-X
40. Smith, A. (1977). An Inquiry into the Nature and Causes of the Wealth of Nations. University Of Chicago Press.
41. Solow, R. M. (1956). A contribution to the theory of economic growth. Journal of Economics, 70(1), 65-94. https://doi.org/10.2307/1884513
42. Solow, R. M. (1957). Technical change and the aggregate production function. Review of Economics and Statistics, 39(3), 312-320. https://doi.org/10.2307/1926047
43. Vivarelli, M. (2013). Technology, Employment and Skills: An Interpretative Framework. Eurasian Business Review 3 (1): 66–89. https://doi.org/10.14208/BF03353818
44. Walden, M. (2018). Occupation change and technological unemployment in North Carolina. Journal of Regional Analysis & Policy, 48(1), 12-22. https://doi.10.22004/ag.econ.339902
45. World Bank, World Development Indicators. https://databank.worldbank.org/source/world development-indicators (Erişim 1/1/2024).
46. World Intellectual Property Organization, WIPO statistics database. https://www3.wipo.int/ipstats/ips-search/patent (Erişim 12/2/2024).
47. Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems, 61, 530-535. https://doi.org/10.1016/j.jmsy.2021.10.006