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THE DİGİTAL ECONOMY AND STRUCTURAL UNEMPLOYMENT: EMPİRİCAL EVİDENCE FROM OECD COUNTRİES

Yıl 2024, Cilt: 22 Sayı: Özel Sayı: Endüstri 4.0 ve Dijitalleşmenin Sosyal Bilimlerde Yansımaları, 1299 - 1323, 30.09.2024
https://doi.org/10.35408/comuybd.1468996

Öz

The digital economy has emerged as an important contribution to economies worldwide. The digital economy has come to the forefront as a field that examines the effects of technological advances and digitalization on economic activity. However, identifying and quantifying the impact of the digital economy on national economies remains a complex endeavor. This study investigates the effects of the digital economy on unemployment in selected OECD countries over the period 2000-2022. In the study, a model is constructed with unemployment rate as the dependent variable and digital economy, economic growth and inflation rate as explanatory variables. First, the model was analyzed for cointegration using the Westerlund (2008) test. A cointegration relationship was found for the model. Then, Panel ARDL method is used for short and long run coefficient estimates. According to the Panel ARDL results, there is no effect of the digital economy on the unemployment rate in the short run across the panel. However, as a result of the short-run analysis conducted separately for each country, it was found that the digital economy increases and decreases the unemployment rate. Economic growth and inflation rate, included as explanatory variables in the model, were found to decrease the unemployment rate in the short run. Long-run Panel ARDL results show that the digital economy decreases the unemployment rate. In addition, according to the Panel ARDL long-run results, economic growth decreased the unemployment rate, while the inflation rate increased the unemployment rate. These findings suggest that some of the selected OECD countries experience structural unemployment in the short run due to the digital economy. However, the disruptive effect of the digital economy in the short run is transformed into a positive economic situation in the long run in the form of increased employment. The study makes an important contribution to the literature on the relationship between the digital economy and unemployment. It provides results on the positive and negative effects of the expansion of the digital economy on unemployment and employment.

Kaynakça

  • Abbasabadi, H. M., ve Soleimani, M. (2021). Examining the effects of digital technology expansion on Unemployment: A cross-sectional investigation. Technology in Society, 64, 101495.
  • Acemoglu, D., ve Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244.
  • Avom, D., Dadegnon, A. K., ve Igue, C. B. (2021). Does digitalization promote net job creation? Empirical evidence from WAEMU countries. Telecommunications Policy, 45(8), 102215.
  • Azmuk, N. (2016). Unemployment and digital employment opportunities for reducing unemployment. Economics of Development, 79(3), 13-19.
  • Balcılar, M., Gupta, R., Lee, C. C., ve Olasehinde-Williams, G. (2020). Insurance and economic policy uncertainty. Research in International Business and Finance, 54, 101253.
  • Blanchflower, D. G., ve Burgess, S. M. (1998). New technology and jobs: comparative evidence from a two country study. Economics of Innovation and New Technology, 5(2-4), 109-138.
  • Bogliacino, F., Piva, M., ve Vivarelli, M. (2012). R&D and employment: An application of the LSDVC estimator using European microdata. Economics Letters, 116(1), 56-59. Borys, P., Doligalski, P., ve Kopiec, P. (2021). The quantitative importance of technology and demand shocks for unemployment fluctuations in a shopping economy. Economic Modelling, 101, 105527.
  • Brice, M. G. (2024). Gender disparity and enterprise expansion in the impact and transmission channels of ICT on unemployment in developing countries. Technology in Society,77, 102515.
  • Brouwer, E., Kleinknecht, A., ve Reijnen, J. O. (1993). Employment growth and innovation at the firm level: an empirical study. Journal of Evolutionary Economics, 3, 153-159.
  • Bukht, R., ve Heeks, R. (2017). Defining, conceptualising and measuring the digital economy. Development Informatics working paper. Manchester: Centre for Development Informatics, Global Development Institute, SEED.
  • Coad, A., ve Rao, R. (2011). The firm-level employment effects of innovations in high-tech US manufacturing industries. Journal of Evolutionary Economics, 21, 255-283.
  • Cong, L. W., Xie, D., ve Zhang, L. (2021). Knowledge accumulation, privacy, and growth in a data economy. Management Science, 67(10), 6480-6492.
  • Cushman, D. O., ve Michael, N. (2011). Nonlinear trends in real exchange rates: A panel unit root test approach. Journal of International Money and Finance, 30(8), 1619-1637.
  • Duc, D. T. V., Dat, T. T., Linh, D. H., ve Phong, B. X. (2023). Measuring the digital economy in Vietnam. Telecommunications Policy, 102683.
  • Esily, R. R., Yuanying, C., Ibrahiem, D. M., Houssam, N., Makled, R. A., ve Chen, Y. (2023). Environmental benefits of energy poverty alleviation, renewable resources, and urbanization in North Africa. Utilities Policy, 82, 101561.
  • Frey, C. B., ve Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological Forecasting and Social Change, 114, 254-280.
  • Gao, F., ve He, Z. (2024). Digital economy, land resource misallocation and urban carbon emissions in Chinese resource-based cities. Resources Policy, 91, 104914.
  • Graetz, G., ve Michaels, G. (2018). Robots at work. Review of Economics and Statistics, 100(5), 753-768. Guliyev, H., ve Tatoğlu, F. Y. (2023). The relationship between renewable energy and economic growth in European countries: Evidence from panel data model with sharp and smooth changes. Renewable Energy Focus, 46, 185-196.
  • Guo, C., Song, Q., Yu, M. M., ve Zhang, J. (2024). A digital economy development index based on an improved hierarchical data envelopment analysis approach. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2024.02.023
  • Hall, B. H., Lotti, F., ve Mairesse, J. (2008). Employment, innovation, and productivity: evidence from Italian microdata. Industrial and Corporate Change, 17(4), 813-839.
  • Hausman, J.A., 1978. Specification tests in econometrics. Econometrica, 46, 983–990. Isiksal, A. Z., ve Assi, A. F. (2022). Determinants of sustainable energy demand in the European economic area: Evidence from the PMG-ARDL model. Technological Forecasting and Social Change, 183, 121901.
  • Jahan, N., ve Zhou, Y. (2023). Covid-19 and digital inclusion: Impact on employment. Journal of Digital Economy, 2, 190-203.
  • Javaid, M., Haleem, A., Singh, R. P., ve Sinha, A. K. (2024). Digital economy to improve the culture of industry 4.0: A study on features, implementation and challenges. Green Technologies and Sustainability, 100083.
  • Jung, S., Lee, J. D., Hwang, W. S., ve Yeo, Y. (2017). Growth versus equity: A CGE analysis for effects of factor-biased technical progress on economic growth and employment. Economic Modelling, 60, 424-438.
  • Khan, M. A., Rehan, R., Chhapra, I. U., ve Bai, A. (2022). Inspecting energy consumption, capital formation and economic growth nexus in Pakistan. Sustainable Energy Technologies and Assessments, 50, 101845.
  • Kılıçaslan, Y., ve Töngür, Ü. (2019). ICT and employment generation: evidence from Turkish manufacturing. Applied Economics Letters, 26(13), 1053-1057.
  • Keynes, J. M. (1931). Economic possibilities for our grandchildren. D. Moogridge (Ed.), In Essays in Persuasion içinde, (s. 321-332). London: Palgrave Macmillan UK.
  • Kolokytha, E., Kolokythas, G., Perdiki, F., ve Valsamidis, S. (2018). Labour job digitalization: Myths and realities. Scientific Bulletin-Economic Sciences/Buletin Stiintific-Seria Stiinte Economice, 17(2), 3-18.
  • Lachenmaier, S., ve Rottmann, H. (2011). Effects of innovation on employment: A dynamic panel analysis. International Journal of Industrial Organization, 29(2), 210-220.
  • Laudien, S. M., ve Pesch, R. (2019). Understanding the influence of digitalization on service firm business model design: a qualitative-empirical analysis. Review of Managerial Science, 13, 575-587.
  • Li, K., Kim, D. J., Lang, K. R., Kauffman, R. J., ve Naldi, M. (2020). How should we understand the digital economy in Asia? Critical assessment and research agenda. Electronic Commerce Research and Applications, 44, 101004.
  • Li, C., Razzaq, A., Ozturk, I., ve Sharif, A. (2023). Natural resources, financial technologies, and digitalization: the role of institutional quality and human capital in selected OECD economies. Resources Policy, 81, 103362.
  • Lu, J., Xiao, Q., ve Wang, T. (2023). Does the digital economy generate a gender dividend for female employment? Evidence from China. Telecommunications Policy, 47(6), 102545.
  • Manavalan, E., ve Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers and Industrial Engineering, 127, 925-953.
  • Mensah, I. A., Sun, M., Gao, C., Omari-Sasu, A. Y., Zhu, D., Ampimah, B. C., ve Quarcoo, A. (2019). Analysis on the nexus of economic growth, fossil fuel energy consumption, CO2 emissions and oil price in Africa based on a PMG panel ARDL approach. Journal of Cleaner Production, 228, 161-174.
  • Novakova, L. (2020). The impact of technology development on the future of the labour market in the Slovak Republic. Technology in Society, 62, 101256.
  • Oye, N. D., Inuwa, I., ve Shakil, A. M. (2011). Role of information communication technology (ICT): implications on unemployment and Nigerian GDP. Journal of International Academic Research, 11(1), 9-17.
  • Pesaran, M. H., ve Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79-113.
  • Pesaran, M. H., Shin, Y., ve Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621-634.
  • Pesaran, M. H. (2004). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13-50. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross‐section dependence. Journal of Applied Econometrics, 22(2), 265-312.
  • Pesaran, M. H., Ullah, A., ve Yamagata, T. (2008). A bias‐adjusted LM test of error cross‐section independence. The Econometrics Journal, 11(1), 105-127.
  • Piva, M., Santarelli, E., ve Vivarelli, M. (2005). The skill bias effect of technological and organisational change: Evidence and policy implications. Research Policy, 34(2), 141-157.
  • Sikder, M., Wang, C., Yao, X., Huai, X., Wu, L., KwameYeboah, F., Wood, J., Zhao, Y., ve Dou, X. (2022). The integrated impact of GDP growth, industrialization, energy use, and urbanization on CO2 emissions in developing countries: evidence from the panel ARDL approach. Science of the Total Environment, 837, 155795.
  • Tapscott, D. (1996). The digital economy: Promise and peril in the age of networked intelligence. New York: Mc Graw-Hill.
  • Vernardakis, N. (2016). Innovation and Technology: Business and Economics Approaches. London: Routledge.
  • Westerlund, J. (2008). Panel cointegration tests of the Fisher effect. Journal of Applied Econometrics, 23(2), 193-233.
  • Wu, B., ve Yang, W. (2022). Empirical test of the impact of the digital economy on China's employment structure. Finance Research Letters, 49, 103047.
  • Yadav, A., ve Mahalik, M. K. (2024). Does renewable energy development reduce energy import dependency in emerging economies? Evidence from CS-ARDL and panel causality approach. Energy Economics, 131, 107356.
  • Xiufan, Z., Xiaomin, W., Wenhai, Z., ve Ningning, F. (2024). Research on the green innovation effect of digital economy network-Empirical evidence from the manufacturing industry in the Yangtze River Delta. Environmental Technology and Innovation, 103595.
  • Zemtsov, S. (2020). New technologies, potential unemployment and ‘nescience economy’during and after the 2020 economic crisis. Regional Science Policy and Practice, 12(4), 723-744.
  • Zhang, Z. (2023). The impact of the artificial intelligence industry on the number and structure of employments in the digital economy environment. Technological Forecasting and Social Change, 197, 122881.
  • Zheng, Y., ve Gong, B. (2024). Nexus between natural resources and digital economy: The role of geopolitical risk. Resources Policy, 89, 104600.
  • Zhou, H., Li, D., Mustafa, F., ve Altuntaş, M. (2022). Natural resources volatility and South Asian economies: Evaluating the role of COVID-19. Resources Policy, 75, 102524.
  • Zimmermann, K. F. (1991). The employment consequences of technological advance, demand and labor costs in 16 German industries. Empirical Economics, 16(2), 253-266.
  • Zuniga, P., ve Crespi, G. (2013). Innovation strategies and employment in Latin American firms. Structural Change and Economic Dynamics, 24, 1-17.
  • UNCTAD (2024). Datacentre Erişim: 15 Mart 2024,https://unctadstat.unctad.org/datacentre/dataviewer/US.IctGoodsShare
  • World Bank (2024). World Development Indicators Erişim: 15 Mart 2024, https://databank.worldbank.org/source/world-development-indicators

DİJİTAL EKONOMİ VE YAPISAL İŞSİZLİK: OECD ÜLKELERİNDEN AMPİRİK KANITLAR

Yıl 2024, Cilt: 22 Sayı: Özel Sayı: Endüstri 4.0 ve Dijitalleşmenin Sosyal Bilimlerde Yansımaları, 1299 - 1323, 30.09.2024
https://doi.org/10.35408/comuybd.1468996

Öz

Dijital ekonomi, dünya çapında ekonomilere önemli bir katkı olarak ortaya çıkmıştır. Dijital ekonomi, teknolojik ilerlemelerin ve dijitalleşmenin ekonomik faaliyetler üzerindeki etkilerini inceleyen bir alan olarak ön plana çıkmaya başlamıştır. Bununla birlikte, dijital ekonominin ulusal ekonomiler üzerindeki etkisini tanımlayabilmek ve tespit edebilmek karmaşık bir çaba olmaya devam etmektedir. Bu çalışma, 2000-2022 dönemi kapsamında seçilmiş OECD ülkelerinde dijital ekonominin işsizlik üzerindeki etkilerini araştırmaktadır. Çalışmada işsizlik oranı bağımlı değişken, dijital ekonomi, GSYİH ve enflasyon oranı açıklayıcı değişkenler olacak şekilde bir model oluşturulmuştur. Öncelikle söz konusu model, Westerlund (2008) testi ile eşbütünleşme analizi yapılmıştır. Model için bir eşbütünleşme ilişkisi tespit edilmiştir. Daha sonra kısa ve uzun dönem katsayı tahminleri için Panel ARDL yöntemi kullanılmıştır. Panel ARDL sonuçlarına göre panel genelinde kısa dönemde dijital ekonominin işsizlik oranı üzerinde bir etkisine rastlanamamıştır. Ancak her bir ülke için ayrı ayrı yapılan kısa dönem analizi sonucunda, dijital ekonominin işsizlik oranını arttırdığı ve azalttığına yönelik sonuçlar tespit edilmiştir. Modelde açıklayıcı değişken olarak yer alan GSYİH ve enflasyon oranı ise kısa dönemde işsizlik oranını azalttığı görülmüştür. Uzun dönem Panel ARDL sonuçları ise dijital ekonominin işsizlik oranını azalttığını göstermiştir. Bunun yanında Panel ARDL uzun dönem sonuçlarına göre GSYİH işsizlik oranını azaltırken, enflasyon oranı işsizlik oranını arttırmıştır. Bu bulgular, seçilmiş OECD ülkelerinin bazılarında dijital ekonomi nedeniyle kısa dönemde yapısal işsizlik yaşandığını göstermektedir. Ancak kısa dönemde dijital ekonominin yıkıcı etkisi uzun dönemde istihdamın artması şeklinde olumlu bir ekonomik duruma evirilmiştir. Çalışma, dijital ekonomi ve işsizlik ilişkini ele alan literatüre önemli bir katkı sağlarken, dijital ekonomin yaygınlaşmasının işsizlik ve istihdam üzerinde meydana getirebileceği olumlu ve olumsuz etkiler hakkında sonuçlar sunmaktadır.

Kaynakça

  • Abbasabadi, H. M., ve Soleimani, M. (2021). Examining the effects of digital technology expansion on Unemployment: A cross-sectional investigation. Technology in Society, 64, 101495.
  • Acemoglu, D., ve Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244.
  • Avom, D., Dadegnon, A. K., ve Igue, C. B. (2021). Does digitalization promote net job creation? Empirical evidence from WAEMU countries. Telecommunications Policy, 45(8), 102215.
  • Azmuk, N. (2016). Unemployment and digital employment opportunities for reducing unemployment. Economics of Development, 79(3), 13-19.
  • Balcılar, M., Gupta, R., Lee, C. C., ve Olasehinde-Williams, G. (2020). Insurance and economic policy uncertainty. Research in International Business and Finance, 54, 101253.
  • Blanchflower, D. G., ve Burgess, S. M. (1998). New technology and jobs: comparative evidence from a two country study. Economics of Innovation and New Technology, 5(2-4), 109-138.
  • Bogliacino, F., Piva, M., ve Vivarelli, M. (2012). R&D and employment: An application of the LSDVC estimator using European microdata. Economics Letters, 116(1), 56-59. Borys, P., Doligalski, P., ve Kopiec, P. (2021). The quantitative importance of technology and demand shocks for unemployment fluctuations in a shopping economy. Economic Modelling, 101, 105527.
  • Brice, M. G. (2024). Gender disparity and enterprise expansion in the impact and transmission channels of ICT on unemployment in developing countries. Technology in Society,77, 102515.
  • Brouwer, E., Kleinknecht, A., ve Reijnen, J. O. (1993). Employment growth and innovation at the firm level: an empirical study. Journal of Evolutionary Economics, 3, 153-159.
  • Bukht, R., ve Heeks, R. (2017). Defining, conceptualising and measuring the digital economy. Development Informatics working paper. Manchester: Centre for Development Informatics, Global Development Institute, SEED.
  • Coad, A., ve Rao, R. (2011). The firm-level employment effects of innovations in high-tech US manufacturing industries. Journal of Evolutionary Economics, 21, 255-283.
  • Cong, L. W., Xie, D., ve Zhang, L. (2021). Knowledge accumulation, privacy, and growth in a data economy. Management Science, 67(10), 6480-6492.
  • Cushman, D. O., ve Michael, N. (2011). Nonlinear trends in real exchange rates: A panel unit root test approach. Journal of International Money and Finance, 30(8), 1619-1637.
  • Duc, D. T. V., Dat, T. T., Linh, D. H., ve Phong, B. X. (2023). Measuring the digital economy in Vietnam. Telecommunications Policy, 102683.
  • Esily, R. R., Yuanying, C., Ibrahiem, D. M., Houssam, N., Makled, R. A., ve Chen, Y. (2023). Environmental benefits of energy poverty alleviation, renewable resources, and urbanization in North Africa. Utilities Policy, 82, 101561.
  • Frey, C. B., ve Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological Forecasting and Social Change, 114, 254-280.
  • Gao, F., ve He, Z. (2024). Digital economy, land resource misallocation and urban carbon emissions in Chinese resource-based cities. Resources Policy, 91, 104914.
  • Graetz, G., ve Michaels, G. (2018). Robots at work. Review of Economics and Statistics, 100(5), 753-768. Guliyev, H., ve Tatoğlu, F. Y. (2023). The relationship between renewable energy and economic growth in European countries: Evidence from panel data model with sharp and smooth changes. Renewable Energy Focus, 46, 185-196.
  • Guo, C., Song, Q., Yu, M. M., ve Zhang, J. (2024). A digital economy development index based on an improved hierarchical data envelopment analysis approach. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2024.02.023
  • Hall, B. H., Lotti, F., ve Mairesse, J. (2008). Employment, innovation, and productivity: evidence from Italian microdata. Industrial and Corporate Change, 17(4), 813-839.
  • Hausman, J.A., 1978. Specification tests in econometrics. Econometrica, 46, 983–990. Isiksal, A. Z., ve Assi, A. F. (2022). Determinants of sustainable energy demand in the European economic area: Evidence from the PMG-ARDL model. Technological Forecasting and Social Change, 183, 121901.
  • Jahan, N., ve Zhou, Y. (2023). Covid-19 and digital inclusion: Impact on employment. Journal of Digital Economy, 2, 190-203.
  • Javaid, M., Haleem, A., Singh, R. P., ve Sinha, A. K. (2024). Digital economy to improve the culture of industry 4.0: A study on features, implementation and challenges. Green Technologies and Sustainability, 100083.
  • Jung, S., Lee, J. D., Hwang, W. S., ve Yeo, Y. (2017). Growth versus equity: A CGE analysis for effects of factor-biased technical progress on economic growth and employment. Economic Modelling, 60, 424-438.
  • Khan, M. A., Rehan, R., Chhapra, I. U., ve Bai, A. (2022). Inspecting energy consumption, capital formation and economic growth nexus in Pakistan. Sustainable Energy Technologies and Assessments, 50, 101845.
  • Kılıçaslan, Y., ve Töngür, Ü. (2019). ICT and employment generation: evidence from Turkish manufacturing. Applied Economics Letters, 26(13), 1053-1057.
  • Keynes, J. M. (1931). Economic possibilities for our grandchildren. D. Moogridge (Ed.), In Essays in Persuasion içinde, (s. 321-332). London: Palgrave Macmillan UK.
  • Kolokytha, E., Kolokythas, G., Perdiki, F., ve Valsamidis, S. (2018). Labour job digitalization: Myths and realities. Scientific Bulletin-Economic Sciences/Buletin Stiintific-Seria Stiinte Economice, 17(2), 3-18.
  • Lachenmaier, S., ve Rottmann, H. (2011). Effects of innovation on employment: A dynamic panel analysis. International Journal of Industrial Organization, 29(2), 210-220.
  • Laudien, S. M., ve Pesch, R. (2019). Understanding the influence of digitalization on service firm business model design: a qualitative-empirical analysis. Review of Managerial Science, 13, 575-587.
  • Li, K., Kim, D. J., Lang, K. R., Kauffman, R. J., ve Naldi, M. (2020). How should we understand the digital economy in Asia? Critical assessment and research agenda. Electronic Commerce Research and Applications, 44, 101004.
  • Li, C., Razzaq, A., Ozturk, I., ve Sharif, A. (2023). Natural resources, financial technologies, and digitalization: the role of institutional quality and human capital in selected OECD economies. Resources Policy, 81, 103362.
  • Lu, J., Xiao, Q., ve Wang, T. (2023). Does the digital economy generate a gender dividend for female employment? Evidence from China. Telecommunications Policy, 47(6), 102545.
  • Manavalan, E., ve Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers and Industrial Engineering, 127, 925-953.
  • Mensah, I. A., Sun, M., Gao, C., Omari-Sasu, A. Y., Zhu, D., Ampimah, B. C., ve Quarcoo, A. (2019). Analysis on the nexus of economic growth, fossil fuel energy consumption, CO2 emissions and oil price in Africa based on a PMG panel ARDL approach. Journal of Cleaner Production, 228, 161-174.
  • Novakova, L. (2020). The impact of technology development on the future of the labour market in the Slovak Republic. Technology in Society, 62, 101256.
  • Oye, N. D., Inuwa, I., ve Shakil, A. M. (2011). Role of information communication technology (ICT): implications on unemployment and Nigerian GDP. Journal of International Academic Research, 11(1), 9-17.
  • Pesaran, M. H., ve Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79-113.
  • Pesaran, M. H., Shin, Y., ve Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621-634.
  • Pesaran, M. H. (2004). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13-50. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross‐section dependence. Journal of Applied Econometrics, 22(2), 265-312.
  • Pesaran, M. H., Ullah, A., ve Yamagata, T. (2008). A bias‐adjusted LM test of error cross‐section independence. The Econometrics Journal, 11(1), 105-127.
  • Piva, M., Santarelli, E., ve Vivarelli, M. (2005). The skill bias effect of technological and organisational change: Evidence and policy implications. Research Policy, 34(2), 141-157.
  • Sikder, M., Wang, C., Yao, X., Huai, X., Wu, L., KwameYeboah, F., Wood, J., Zhao, Y., ve Dou, X. (2022). The integrated impact of GDP growth, industrialization, energy use, and urbanization on CO2 emissions in developing countries: evidence from the panel ARDL approach. Science of the Total Environment, 837, 155795.
  • Tapscott, D. (1996). The digital economy: Promise and peril in the age of networked intelligence. New York: Mc Graw-Hill.
  • Vernardakis, N. (2016). Innovation and Technology: Business and Economics Approaches. London: Routledge.
  • Westerlund, J. (2008). Panel cointegration tests of the Fisher effect. Journal of Applied Econometrics, 23(2), 193-233.
  • Wu, B., ve Yang, W. (2022). Empirical test of the impact of the digital economy on China's employment structure. Finance Research Letters, 49, 103047.
  • Yadav, A., ve Mahalik, M. K. (2024). Does renewable energy development reduce energy import dependency in emerging economies? Evidence from CS-ARDL and panel causality approach. Energy Economics, 131, 107356.
  • Xiufan, Z., Xiaomin, W., Wenhai, Z., ve Ningning, F. (2024). Research on the green innovation effect of digital economy network-Empirical evidence from the manufacturing industry in the Yangtze River Delta. Environmental Technology and Innovation, 103595.
  • Zemtsov, S. (2020). New technologies, potential unemployment and ‘nescience economy’during and after the 2020 economic crisis. Regional Science Policy and Practice, 12(4), 723-744.
  • Zhang, Z. (2023). The impact of the artificial intelligence industry on the number and structure of employments in the digital economy environment. Technological Forecasting and Social Change, 197, 122881.
  • Zheng, Y., ve Gong, B. (2024). Nexus between natural resources and digital economy: The role of geopolitical risk. Resources Policy, 89, 104600.
  • Zhou, H., Li, D., Mustafa, F., ve Altuntaş, M. (2022). Natural resources volatility and South Asian economies: Evaluating the role of COVID-19. Resources Policy, 75, 102524.
  • Zimmermann, K. F. (1991). The employment consequences of technological advance, demand and labor costs in 16 German industries. Empirical Economics, 16(2), 253-266.
  • Zuniga, P., ve Crespi, G. (2013). Innovation strategies and employment in Latin American firms. Structural Change and Economic Dynamics, 24, 1-17.
  • UNCTAD (2024). Datacentre Erişim: 15 Mart 2024,https://unctadstat.unctad.org/datacentre/dataviewer/US.IctGoodsShare
  • World Bank (2024). World Development Indicators Erişim: 15 Mart 2024, https://databank.worldbank.org/source/world-development-indicators
Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ekonomik Modeller ve Öngörü, Büyüme, Enflasyon, Uygulamalı Ekonomi (Diğer)
Bölüm Makaleler
Yazarlar

Yusuf Ünsal 0000-0002-7856-5402

Erken Görünüm Tarihi 30 Eylül 2024
Yayımlanma Tarihi 30 Eylül 2024
Gönderilme Tarihi 16 Nisan 2024
Kabul Tarihi 5 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 22 Sayı: Özel Sayı: Endüstri 4.0 ve Dijitalleşmenin Sosyal Bilimlerde Yansımaları

Kaynak Göster

APA Ünsal, Y. (2024). DİJİTAL EKONOMİ VE YAPISAL İŞSİZLİK: OECD ÜLKELERİNDEN AMPİRİK KANITLAR. Yönetim Bilimleri Dergisi, 22(Özel Sayı: Endüstri 4.0 ve Dijitalleşmenin Sosyal Bilimlerde Yansımaları), 1299-1323. https://doi.org/10.35408/comuybd.1468996

Sayın Araştırmacı;

Dergimize gelen yoğun talep nedeniyle Ekim 2024 sayısı için öngörülen kontenjan dolmuştur, gönderilen makaleler ilerleyen sayılarda değerlendirilebilecektir. Bu hususa dikkat ederek yeni makale gönderimi yapmanızı rica ederiz.

Yönetim Bilimler Dergisi Özel Sayı Çağrısı
Yönetim Bilimleri Dergisi 2024 yılının Eylül ayında “Endüstri 4.0 ve Dijitalleşmenin Sosyal Bilimlerde Yansımaları” başlıklı bir özel sayı yayınlayacaktır.
Çanakkale Onsekiz Mart Üniversitesi Biga İktisadi ve İdari Bilimler Fakültesi tarafından 5-6 Temmuz 2024 tarihlerinde çevrimiçi olarak düzenlenecek olan 4. Uluslararası Sosyal Bilimler Konferansı’nda sunum gerçekleştiren yazarların dergi için ücret yatırmasına gerek olmayıp, dekont yerine Konferans Katılım Belgesini sisteme yüklemeleri yeterli olacaktır.
Gönderilen makalelerin derginin yazım kurallarına uygun olması ve DergiPark sistemi üzerinden sisteme yüklenmesi gerekmektedir. Özel sayı ana başlığı ile ilgisiz makaleler değerlendirmeye alınmayacaktır. Özel sayı için gönderilen makalelerin "Makalemi özel sayıya göndermek istiyorum" kutucuğu işaretlenerek sisteme yüklenmesi gerekmektedir. Özel sayı için gönderilmemiş makalelerin bu sayıya eklenmesi mümkün olmayacaktır.
Özel Sayı Çalışma Takvimi
Gönderim Başlangıcı: 15 Nisan 2024
Son Gönderim Tarihi: 15 Temmuz 2024
Özel Sayı Yayınlanma Tarihi: Eylül 2024

Dergimize göndereceğiniz çalışmalar linkte yer alan taslak dikkate alınarak hazırlanmalıdır. Çalışmanızı aktaracağınız taslak dergi yazım kurallarına göre düzenlenmiştir. Bu yüzden biçimlendirmeyi ve ana başlıkları değiştirmeden çalışmanızı bu taslağa aktarmanız gerekmektedir.
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