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The Rise of Technology for the Future Labor Force: The Nexus between Technology and Unemployment in OECD Countries

Year 2022, Volume: 5 Issue: 75, 2775 - 2794, 24.10.2022

İbrahim Dağlı

https://doi.org/10.54752/ct.1191460

Abstract

This paper studies the impact of technology on unemployment, focusing on OECD countries. Obviously, there is no consensus in the literature about the future impacts of technological breakthroughs on employment. The clear point is that the current skills will not match the occupations of the future and the companies will need many new skills. Technological advances will create millions of jobs but the other millions of jobs will disappear in this process. The purpose of this paper is to point out the ultimate impact of technology on unemployment at the macro level, which is quite insufficient quantitatively, related to the impact of technology on employment. In this paper, the nexus between technology and unemployment has been analyzed with S-GMM estimator in 33 OECD member countries for the years 2005-2018. According to panel data analysis, it is seen that all the control variables but GDP are statistically significant. The independent variable, IP5 patents representing technology is statistically highly significant and has a negative correlation with the dependent variable. The empirical results show that a 1% increase in technology reduces unemployment by 0.07%.

Keywords

Unemployment Technological Change Automation Innovation Regional Studies

References

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  • Adanu, K. (2005). A cross-province comparison of Okun's coefficient for Canada. Applied Economics, 37(5), 561-570.
  • Anderson, T. W., & Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76, 598-606.
  • Anderson, T. W., & Hsiao, C. (1982). Formulation and estimation of dynamic models u-using panel data. Journal of Econometrics, 18, 47-82.
  • Arellano, M. (2003). Panel data econometrics. Oxford: Oxford University Press. 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.
  • Arellano, M., & Bover, O. (1995). Another look at the instrumental variables estimation of error component models. Journal of Econometrics, 68, 29-51.
  • Aubert-Tarby, C., Escobar, O. R., & Rayna, T. (2017). The impact of technological change on employment: the case of press digitisation. Technological Forecasting & Social Change, 128, 36-45.
  • Avom, D., Dadegnon, A. K., & Igue, C. B. (2021). Does digitalization promote net job creation? Empirical evidence from WAEMU countries. Telecommunications Policy, 45(8), 102215.
  • Baltagi, B. H. (2005). Econometric analysis of panel data. New York, NY: John Wiley & Sons Inc.
  • Başol, O., & Yalçın, E. C. (2021). How does the digital economy and society index (DESI) affect labor market indicators in EU countries? Human Systems Management, 40(4), 503-512.
  • Bimber, B. (1990). Karl Marx and the three faces of technological determinism, Social Studies of Science, 20(2), 333-351.
  • Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115-143.
  • Bogliacino, F., & Pianta, M. (2010). Innovation and employment: A reinvestigation using revised Pavitt classes. Research Policy, 39, 799-809.
  • Bordot, F. (2022). Artificial intelligence, robots and unemployment: Evidence from OECD countries. Journal of Innovation Economics Management, 37(1), 117-138.
  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, NY: W.W. Norton.
  • Chang, S. C. (2007). The interactions among foreign direct investment, economic growth, degree of openness and unemployment in Taiwan. Applied Economics, 39(13), 1647-1661.
  • Cirillo, V., Pianta, M., & Nascia, L. (2018). Technology and occupations in business cycles. Sustainability, 10(463), 1-25. doi.org/10.3390/su10020463
  • Dachs, B., & Peters, B. (2014). Innovation, employment growth, and foreign ownership of firms. Research Policy, 43(1), 214-232.
  • Dağlı, İ., & Kösekahyaoğlu, L. (2021a). Bilim ve teknoloji politikalari bağlaminda teknoloji-işsizlik ilişkisi: Ampirik bir inceleme. Yaşar Üniversitesi E-Dergisi, 16(63), 1237-1255. doi: 10.19168/jyasar.911828
  • Dağlı, İ., & Kösekahyaoğlu, L. (2021b). Will destructive destruction beat creative destruction? Does the rising of technology favor the future of humanity? In B. Selçuk, S. Ünal, Y. L. Mert (Ed.), Academic Studies in Social Sciences (pp. 231-253), İzmir: Duvar Yayınevi.
  • Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11(1), 1-25.
  • Dauth, W., Findeisen, S., Suedekum, J., & Woessner, N. (2021). The adjustment of labor markets to robots. Journal of the European Economic Association, 19(6), 3104-3153.
  • Domini, G., Grazzi, M., Moschella, D., & Treibich, T. (2021). Threats and opportunities in the digital era: automation spikes and employment dynamics. Research Policy, 50(7), 104137.
  • Dottori, D. (2021). Robots and employment: evidence from Italy. Economia Politica, 38(2), 739-795.
  • Du, Y., & Wei, X. (2021). Technological change and unemployment: evidence from China. Applied Economics Letters, DOI: 10.1080/13504851.2021.1896666.
  • Evangelista, R., & Vezzani, A. (2011). The impact of technological and organizational innovations on employment in European firms. Industrial and Corporate Change, 21(4), 871-899.
  • Evangelista, R., Guerrieri, P., & Meliciani, V. (2014). The economic impact of digital technologies in Europe. Economics of Innovation and New Technology, 23(8), 802–824.
  • Falk, M. (2015). Employment effects of technological and organizational innovations: evidence based on linked firm-level data for Austria. Jahrbücher Für Nationalökonomie Und Statistik, 235(3), 268-285
  • Feldmann, H. (2013). Technological unemployment in industrial countries. Journal of Evolutionary Economics, 23, 1099-1126.
  • Felice, G., Lamperti, F., & Piscitello, L. (2021). The employment implications of additive manufacturing. Industry and Innovation, https://doi.org/10.1080/13662716.2021.1967730
  • Foronda, C., & Beverinotti, J. (2021). Effects of innovation on employment: An analysis at the firm level in Bolivia (No. 11626). Inter-American Development Bank.
  • Fraile, M., & Ferrer, M. (2005). Explaining the determinants of public support for cuts in unemployment benefits spending across OECD countries. International Sociology, 20(4), 459-481.
  • Freeman, C. & Soete, L. (1997). The economics of industrial revolution, MIT Press. Greenan, N., & Guellec, D. (2000). Technological Innovation and Employment Reallocation. Labor, 14(4), 547-590.
  • Hall, B. H., Lotti, F., & Mairesse, J. (2008). Employment, innovation, and productivity: evidence from Italian microdata. Industrial and Corporate Change, 17(4), 813-839.
  • Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50, 1029-1054.
  • Hsiao, C. (2003). Analysis of panel data, Cambridge: Cambridge University Press. Huo, J., & Feng, H. (2010). The political economy of technological innovation and employment. Comparative Political Studies, 43(3), 329-352.
  • Jongwanich, J., Kohpaiboon, A., & Obashi, A. (2022). Technological advancement, import penetration and labor markets: Evidence from Thailand. World Development, 151, 105746.
  • Kangasharju, A., Tavera, C., & Nijkamp, P. (2012). Regional growth and unemployment: The validity of Okun’s law for the Finnish regions. Spatial Economic Analysis, 7(3), 381-395.
  • Katz, R., Callorda, F., & Jung, J. (2021). The impact of automation on employment and its social implications: evidence from Chile. Economics of Innovation and New Technology, DOI: 10.1080/10438599.2021.1991798.
  • Keane, M. P., & Runkle, D. E. (1992). On the estimation of panel-data models with serial correlation when instruments are not strictly exogenous. Journal of Business and Economic Statistics, 10, 1-9.
  • Kwon, S. J., Park, E., Ohm, J. Y., & Yoo, K. (2015). Innovation activities and the creation of new employment: An empirical assessment of South Korea’s manufacturing industry. Social Science Information, 54(3), 354-368.
  • Lachenmaier, S., & Rottmann H. (2011). Effects of innovation on employment: A dynamic panel analysis. International Journal of Industrial Organization, 29, 210–220. Lee, J. (2000). The robustness of Okun’s law: Evidence from OECD countries. Journal of Macroeconomics, 22(2), 331-356.
  • Leontief, W. (1979). Is technological unemployment inevitable?, Challenge, 22(4), 48-50. Matuzeviciute, K., Butkus, M., & Karaliute, A. (2017). Do technological innovations affect unemployment? Some empirical evidence from European countries. Economies, 5(48), 1-19.
  • Medase, S. K., & Wyrwich, M. (2021). The role of innovation for employment growth among firms in developing countries: Evidence from Nigeria. African Journal of Science, Technology, Innovation and Development, 1-10.
  • Meriküll, J. (2008). The impact of innovation on employment: Firm- and industry-level evidence from Estonia. Eesti Pank Bank of Estonia, Working Paper Series, 1/2008.
  • Mileva, E. (2007). Using Arellano-Bond dynamic panel GMM estimators in Stata, New York: Fordham University.
  • Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49, 1417-1426.
  • Nickell, S., Nunziata, L., & Ochel, W. (2005). Unemployment in the OECD since the 1960s. What do we know? The Economic Journal, 115 (500), 1-27.
  • OECD (2022). Patents by technology. https://stats.oecd.org/Index.aspx?DataSetCode =PATS_IPC (retrieved November 11, 2021).
  • Okun, A. (1962). Potential GNP: Its measurement and significance. Proceedings of the Business and Economic Statistics Section of the American Statistical Association, 7(1), 89-104.
  • Peters, B. (2005). Employment effects of different innovation activities: Microeconometric evidence. ZEW-Centre for European Economic Research, Discussion Paper 04-073.
  • Phillips, A. W. (1958). The relationship between unemployment and the rate of change of money wage rates in the United Kingdom 1861–1957. Economica, 25, 283–299.
  • Pierdzioch, C., Rülke, J.C., & Stadtmann, G. (2011). Do professional economists’ forecasts reflect Okun’s law? Some evidence for the G7 countries. Applied Economics, 43(11), 1365-1373.
  • Piva, M., & Vivarelli, M. (2018). Is innovation destroying jobs? Firm-level evidence from the EU. Sustainability, 10(1279), 1-16.
  • Ricardo, D. (1817). The principles of political economy & taxation, Kitchener, 3rd.Edition, 1821, Canada: Batoche Books
  • Roodman, D. M. (2009a). A note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics, 71, 135–158.
  • Roodman, D. M. (2009b). How to do xtabond2: An introduction to “Difference” and “System” GMM in Stata. The Stata Journal, 9(1), 86-136.
  • Samuelson, P. A., & Solow R. M. (1960). Analytical aspects of anti-inflation policy. American Economic Review, 50, 177–94.
  • Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, 26, 393-415.
  • Schumpeter, J. A. (1943). Capitalism, socialism and democracy, 2003, New York: Harper Collins. ISBN 0-203-26611-0
  • Schumpeter, J.A. (1939). Business cycles: A theoretical, historical, and statistical analysis of the capitalist process, (Ed.Rendigs Fels) ,New York and London : McGraw-Hill, 1964.
  • Schwab, K. (2016). The fourth industrial revolution. Currency. Switzerland: World Economic Forum.
  • Schwab, K. (2018). Shaping the future of the fourth industrial revolution. New York: World Economic Forum.
  • Sharma, A., & Cardenas, O. (2019). The labor market effects of FDI: A panel data evidence from Mexico. International Economic Journal, 1–17.
  • Simonetti, R., Taylor, K., & Vivarelli, M. (2000). Modelling the employment impact of innovation. In M. Pianta and M. Vivarelli (Eds.), The employment impact of innovation: evidence and policy (pp. 26-46), Routledge.
  • Sinclair, P. J. N. (1981). When will technical progress destroy jobs? Oxf. Econ. Pap., 31, 1–18.
  • Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations, (Edited with an Introduction, Notes, Marginal Summary and an Enlarged Index by Edwin Cannan), London: Methuen. 1904.
  • Sögner, L. (2001). Okun's law does the Austrian unemployment–GDP relationship exhibit structural breaks? Empirical Economics, 26, 553-564.
  • Sögner, L., & Stiassny, A. (2002). An analysis on the structural stability of Okun’s law-a cross-country study. Applied Economics, 34(14), 1775-1787.
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Geleceğin İşgücü İçin Teknolojinin Yükselişi: OECD Ülkelerinde Teknoloji ve İşsizlik Arasındaki Bağlantı

Year 2022, Volume: 5 Issue: 75, 2775 - 2794, 24.10.2022

İbrahim Dağlı

https://doi.org/10.54752/ct.1191460

Abstract

Bu makale, OECD ülkelerine odaklanarak teknolojinin işsizlik üzerindeki etkisini incelemektedir. Literatürde teknolojik atılımların istihdam üzerindeki gelecekteki etkileri hakkında bir fikir birliği olmadığı açıktır. Açık olan nokta, mevcut becerilerin geleceğin meslekleriyle örtüşmeyeceği ve şirketlerin birçok yeni beceriye ihtiyaç duyacağıdır. Teknolojik gelişmeler milyonlarca iş yaratacak ancak yine milyonlarca iş bu süreçte yok olacaktır. Bu çalışmanın amacı, teknolojinin istihdam üzerindeki etkisi ile ilgili olarak, literatürde nicel olarak oldukça yetersiz olan makro düzeyde bir çalışma ile teknolojinin işsizlik üzerindeki nihai etkisine işaret etmektir. Bu çalışmada, 2005-2018 yılları için 33 OECD üyesi ülkede teknoloji ve işsizlik arasındaki ilişki S-GMM tahmincisi ile analiz edilmiştir. Panel veri analizine göre GSYİH dışındaki tüm kontrol değişkenlerinin istatistiksel olarak anlamlı olduğu görülmektedir. Bağımsız değişken, teknolojiyi temsil eden IP5 patentleri istatistiksel olarak oldukça anlamlıdır ve bağımlı değişkenle negatif bir korelasyona sahiptir. Ampirik sonuçlar, teknolojideki %1'lik bir artışın işsizliği %0,07 oranında azalttığını göstermektedir

Keywords

İşsizlik Teknolojik Değişim Otomasyon İnovasyon Bölgesel Çalışmalar.

References

  • Abor, J., & Harvey, S. K. (2008). Foreign direct investment and employment: Host country experience. Macroeconomics and Finance in Emerging Market Economies, 1(2), 213–225.
  • Adanu, K. (2005). A cross-province comparison of Okun's coefficient for Canada. Applied Economics, 37(5), 561-570.
  • Anderson, T. W., & Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76, 598-606.
  • Anderson, T. W., & Hsiao, C. (1982). Formulation and estimation of dynamic models u-using panel data. Journal of Econometrics, 18, 47-82.
  • Arellano, M. (2003). Panel data econometrics. Oxford: Oxford University Press. 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.
  • Arellano, M., & Bover, O. (1995). Another look at the instrumental variables estimation of error component models. Journal of Econometrics, 68, 29-51.
  • Aubert-Tarby, C., Escobar, O. R., & Rayna, T. (2017). The impact of technological change on employment: the case of press digitisation. Technological Forecasting & Social Change, 128, 36-45.
  • Avom, D., Dadegnon, A. K., & Igue, C. B. (2021). Does digitalization promote net job creation? Empirical evidence from WAEMU countries. Telecommunications Policy, 45(8), 102215.
  • Baltagi, B. H. (2005). Econometric analysis of panel data. New York, NY: John Wiley & Sons Inc.
  • Başol, O., & Yalçın, E. C. (2021). How does the digital economy and society index (DESI) affect labor market indicators in EU countries? Human Systems Management, 40(4), 503-512.
  • Bimber, B. (1990). Karl Marx and the three faces of technological determinism, Social Studies of Science, 20(2), 333-351.
  • Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115-143.
  • Bogliacino, F., & Pianta, M. (2010). Innovation and employment: A reinvestigation using revised Pavitt classes. Research Policy, 39, 799-809.
  • Bordot, F. (2022). Artificial intelligence, robots and unemployment: Evidence from OECD countries. Journal of Innovation Economics Management, 37(1), 117-138.
  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, NY: W.W. Norton.
  • Chang, S. C. (2007). The interactions among foreign direct investment, economic growth, degree of openness and unemployment in Taiwan. Applied Economics, 39(13), 1647-1661.
  • Cirillo, V., Pianta, M., & Nascia, L. (2018). Technology and occupations in business cycles. Sustainability, 10(463), 1-25. doi.org/10.3390/su10020463
  • Dachs, B., & Peters, B. (2014). Innovation, employment growth, and foreign ownership of firms. Research Policy, 43(1), 214-232.
  • Dağlı, İ., & Kösekahyaoğlu, L. (2021a). Bilim ve teknoloji politikalari bağlaminda teknoloji-işsizlik ilişkisi: Ampirik bir inceleme. Yaşar Üniversitesi E-Dergisi, 16(63), 1237-1255. doi: 10.19168/jyasar.911828
  • Dağlı, İ., & Kösekahyaoğlu, L. (2021b). Will destructive destruction beat creative destruction? Does the rising of technology favor the future of humanity? In B. Selçuk, S. Ünal, Y. L. Mert (Ed.), Academic Studies in Social Sciences (pp. 231-253), İzmir: Duvar Yayınevi.
  • Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11(1), 1-25.
  • Dauth, W., Findeisen, S., Suedekum, J., & Woessner, N. (2021). The adjustment of labor markets to robots. Journal of the European Economic Association, 19(6), 3104-3153.
  • Domini, G., Grazzi, M., Moschella, D., & Treibich, T. (2021). Threats and opportunities in the digital era: automation spikes and employment dynamics. Research Policy, 50(7), 104137.
  • Dottori, D. (2021). Robots and employment: evidence from Italy. Economia Politica, 38(2), 739-795.
  • Du, Y., & Wei, X. (2021). Technological change and unemployment: evidence from China. Applied Economics Letters, DOI: 10.1080/13504851.2021.1896666.
  • Evangelista, R., & Vezzani, A. (2011). The impact of technological and organizational innovations on employment in European firms. Industrial and Corporate Change, 21(4), 871-899.
  • Evangelista, R., Guerrieri, P., & Meliciani, V. (2014). The economic impact of digital technologies in Europe. Economics of Innovation and New Technology, 23(8), 802–824.
  • Falk, M. (2015). Employment effects of technological and organizational innovations: evidence based on linked firm-level data for Austria. Jahrbücher Für Nationalökonomie Und Statistik, 235(3), 268-285
  • Feldmann, H. (2013). Technological unemployment in industrial countries. Journal of Evolutionary Economics, 23, 1099-1126.
  • Felice, G., Lamperti, F., & Piscitello, L. (2021). The employment implications of additive manufacturing. Industry and Innovation, https://doi.org/10.1080/13662716.2021.1967730
  • Foronda, C., & Beverinotti, J. (2021). Effects of innovation on employment: An analysis at the firm level in Bolivia (No. 11626). Inter-American Development Bank.
  • Fraile, M., & Ferrer, M. (2005). Explaining the determinants of public support for cuts in unemployment benefits spending across OECD countries. International Sociology, 20(4), 459-481.
  • Freeman, C. & Soete, L. (1997). The economics of industrial revolution, MIT Press. Greenan, N., & Guellec, D. (2000). Technological Innovation and Employment Reallocation. Labor, 14(4), 547-590.
  • Hall, B. H., Lotti, F., & Mairesse, J. (2008). Employment, innovation, and productivity: evidence from Italian microdata. Industrial and Corporate Change, 17(4), 813-839.
  • Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50, 1029-1054.
  • Hsiao, C. (2003). Analysis of panel data, Cambridge: Cambridge University Press. Huo, J., & Feng, H. (2010). The political economy of technological innovation and employment. Comparative Political Studies, 43(3), 329-352.
  • Jongwanich, J., Kohpaiboon, A., & Obashi, A. (2022). Technological advancement, import penetration and labor markets: Evidence from Thailand. World Development, 151, 105746.
  • Kangasharju, A., Tavera, C., & Nijkamp, P. (2012). Regional growth and unemployment: The validity of Okun’s law for the Finnish regions. Spatial Economic Analysis, 7(3), 381-395.
  • Katz, R., Callorda, F., & Jung, J. (2021). The impact of automation on employment and its social implications: evidence from Chile. Economics of Innovation and New Technology, DOI: 10.1080/10438599.2021.1991798.
  • Keane, M. P., & Runkle, D. E. (1992). On the estimation of panel-data models with serial correlation when instruments are not strictly exogenous. Journal of Business and Economic Statistics, 10, 1-9.
  • Kwon, S. J., Park, E., Ohm, J. Y., & Yoo, K. (2015). Innovation activities and the creation of new employment: An empirical assessment of South Korea’s manufacturing industry. Social Science Information, 54(3), 354-368.
  • Lachenmaier, S., & Rottmann H. (2011). Effects of innovation on employment: A dynamic panel analysis. International Journal of Industrial Organization, 29, 210–220. Lee, J. (2000). The robustness of Okun’s law: Evidence from OECD countries. Journal of Macroeconomics, 22(2), 331-356.
  • Leontief, W. (1979). Is technological unemployment inevitable?, Challenge, 22(4), 48-50. Matuzeviciute, K., Butkus, M., & Karaliute, A. (2017). Do technological innovations affect unemployment? Some empirical evidence from European countries. Economies, 5(48), 1-19.
  • Medase, S. K., & Wyrwich, M. (2021). The role of innovation for employment growth among firms in developing countries: Evidence from Nigeria. African Journal of Science, Technology, Innovation and Development, 1-10.
  • Meriküll, J. (2008). The impact of innovation on employment: Firm- and industry-level evidence from Estonia. Eesti Pank Bank of Estonia, Working Paper Series, 1/2008.
  • Mileva, E. (2007). Using Arellano-Bond dynamic panel GMM estimators in Stata, New York: Fordham University.
  • Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49, 1417-1426.
  • Nickell, S., Nunziata, L., & Ochel, W. (2005). Unemployment in the OECD since the 1960s. What do we know? The Economic Journal, 115 (500), 1-27.
  • OECD (2022). Patents by technology. https://stats.oecd.org/Index.aspx?DataSetCode =PATS_IPC (retrieved November 11, 2021).
  • Okun, A. (1962). Potential GNP: Its measurement and significance. Proceedings of the Business and Economic Statistics Section of the American Statistical Association, 7(1), 89-104.
  • Peters, B. (2005). Employment effects of different innovation activities: Microeconometric evidence. ZEW-Centre for European Economic Research, Discussion Paper 04-073.
  • Phillips, A. W. (1958). The relationship between unemployment and the rate of change of money wage rates in the United Kingdom 1861–1957. Economica, 25, 283–299.
  • Pierdzioch, C., Rülke, J.C., & Stadtmann, G. (2011). Do professional economists’ forecasts reflect Okun’s law? Some evidence for the G7 countries. Applied Economics, 43(11), 1365-1373.
  • Piva, M., & Vivarelli, M. (2018). Is innovation destroying jobs? Firm-level evidence from the EU. Sustainability, 10(1279), 1-16.
  • Ricardo, D. (1817). The principles of political economy & taxation, Kitchener, 3rd.Edition, 1821, Canada: Batoche Books
  • Roodman, D. M. (2009a). A note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics, 71, 135–158.
  • Roodman, D. M. (2009b). How to do xtabond2: An introduction to “Difference” and “System” GMM in Stata. The Stata Journal, 9(1), 86-136.
  • Samuelson, P. A., & Solow R. M. (1960). Analytical aspects of anti-inflation policy. American Economic Review, 50, 177–94.
  • Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, 26, 393-415.
  • Schumpeter, J. A. (1943). Capitalism, socialism and democracy, 2003, New York: Harper Collins. ISBN 0-203-26611-0
  • Schumpeter, J.A. (1939). Business cycles: A theoretical, historical, and statistical analysis of the capitalist process, (Ed.Rendigs Fels) ,New York and London : McGraw-Hill, 1964.
  • Schwab, K. (2016). The fourth industrial revolution. Currency. Switzerland: World Economic Forum.
  • Schwab, K. (2018). Shaping the future of the fourth industrial revolution. New York: World Economic Forum.
  • Sharma, A., & Cardenas, O. (2019). The labor market effects of FDI: A panel data evidence from Mexico. International Economic Journal, 1–17.
  • Simonetti, R., Taylor, K., & Vivarelli, M. (2000). Modelling the employment impact of innovation. In M. Pianta and M. Vivarelli (Eds.), The employment impact of innovation: evidence and policy (pp. 26-46), Routledge.
  • Sinclair, P. J. N. (1981). When will technical progress destroy jobs? Oxf. Econ. Pap., 31, 1–18.
  • Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations, (Edited with an Introduction, Notes, Marginal Summary and an Enlarged Index by Edwin Cannan), London: Methuen. 1904.
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Details

Primary Language English
Subjects Labor Economics
Journal Section Research Article
Authors

İbrahim Dağlı 0000-0001-8199-821X

Publication Date October 24, 2022
Published in Issue Year 2022 Volume: 5 Issue: 75

Cite

APA Dağlı, İ. (2022). The Rise of Technology for the Future Labor Force: The Nexus between Technology and Unemployment in OECD Countries. Çalışma Ve Toplum, 5(75), 2775-2794. https://doi.org/10.54752/ct.1191460
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