TEKNOLOJİK İLERLEMENİN İSTİHDAM YARATMADAKİ ROLÜ VE ÖNEMİ: TÜRKİYE ÖRNEĞİ

Teknolojik ilerlemenin ve yeniliğin işsizliğe yol açacağı beklentisi Klasik İktisadi Okula kadar uzanmaktadır. Bununla birlikte Klasik İktisat Okulu temsilcileri teknolojik işsizliğin uzun süreli olamayacağını ve telafi edici mekanizmalar yoluyla iş kaybının dengeleneceğini de vurgulamışlardır. Çalışmada teknolojik ilerlemenin istihdam üzerine etkileri Türkiye ekonomisi için 1990-2018 yılları için analiz edilmekte ve bu gelişmelerin işgücü tasarruf ederek işsizliği mi artıracağı yoksa telafi mekanizmasının devreye girerek işsizliği azaltacağı mı Quantile Regresyon yöntemleriyle tespit edilmeye çalışılmıştır. İşsizlik oranı bağımlı değişken olarak alınmış, AR-GE harcamaları ve ekonomik büyüme bağımlı değişken olarak alınmıştır. İşsizlik ve AR-GE harcamaları arasında negatif yönlü bir gelişme bulunmuştur. AR-GE harcamalarındaki %1’lik bir artışın, işsizliği %5,73 oranında azalttığı görülmüştür. Buna karşın, ekonomik büyüme ile işsizlik arasında anlamlı ilişki bulunmamıştır ve istihdam yaratmayan büyümeye işaret edebilir. Bu duruma neden olanın ekonomik büyümeyle birlikte işgücü tasarruf eden üretim yöntemlerinin kullanılması olduğu düşünülmektedir. Dolayısıyla Türkiye ekonomisinde, AR-GE harcamaları beklenildiği gibi işsizliği azaltırken, ekonomik büyümenin işsizlik üzerinde herhangi bir etkisinin olmadığı görülmektedir.

IMPORTANCE AND ROLE OF TECHNOLOGICAL PROGRESS ON JOB CREATION: AN EVIDENCE FOR TURKEY

It can be seen since the Industrial Revolution that the advancing technology has caused big changes in the society and economics. Following the Industrial Revolution, it is always debated among scholars whether the mechanization in production increases the unemployment or not which is a socio-economic problem. Today robots, artificial intelligence show themselves in the society more and unemployment increases in the worldwide deepen these debates. Classical Economics School representatives emphasized that the technological unemployment would not be long-termed and job losses would be balanced by compensatory mechanisms. In most of the studies, the effects of technological progress and innovation on employment is found to be negative but in the long-term because of the increasing quality of the goods and services and decreasing costs their effects turn to be positive. In this study, the effects of technological progress on the employment for Turkey during the period 1990-2018 is analyzed using Quantile Regression method and it is tried to be determined whether it will increase the unemployment rate through labour saving or decrease the unemployment through compensatory mechanisms. Unemployment rate is taken as a dependent variable and change in the Research-Development expenditures and economic growth are taken as independent variables. There is found to be a negative relationship between unemployment and Research-Development expenditures. 1% increase in the Research and Development expenditures decrease the unemployment rate by 5.73%. However, there is no significant relationship between economic growth and unemployment and may point out a jobless growth case. This may be because of the economic growth with labour saving production methods. Consequently, in the Turkish economy, Research and Development expenditures as expected increases labour productivity but the unemployment problem following labour production methods would be balanced by structural measures. 

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  • Alagidede, p. ve Panagiotidis, T. (2012). Stock returns and ınflation: evidence from quantile regressions. Economics Letters, 117, 283-286.
  • Andres, A., Gabriela, M. ve Barrera, R. (2016). Technological unemployment: an approximation to the latin american case. Administer, Vol. 29: 59-78.
  • Batjargal, B. ve Şahin, A. (2019). Takipteki kredilerin makroekonomik belirleyicileri: moğolistan bankacılık sektörü üzerine bir uygulama. Uluslararası Sosyal, Beşeri ve İdari Bilimler Sempozyumu, 18-20 Nisan 2019, Alanya, Türkiye.
  • Campa, R. (2018). Technological employment, a brief history of an idea, https:// www.researchgate.net/publication/314187966_Technological_Unemployment_A_Brief_History_of_an_Idea, 03.10.2018.
  • Feldmann, H. (2013). Technological unemployment in ındustrial countries, Journal of Evolutionary Economics, 23(5), 1099-1126.
  • Ferrando, L., Ferrer, R. ve Jareno, F. (2017). Interest Rate Sensitivity of Spanish Industries: A Quantile Regression Approach. The Manchaster School, Vol. 85, No. 2: 212-242.
  • Gujarati, D. N., Porter, D.C. (2012). Temel ekonometri, Çev. Ümit Şensen, Gülay Günlük Şensesen, Literatür Yayınları, İstanbul.
  • Kapeliushnikov, R. (2019). The phantom of technological unemployment, Russian Journal of Economics, 5 (2019), 8-116.
  • Koenker, R. ve Bassett, G. (1982). Robust tests for heteroscedasticity based on regression quantiles, Econometrica, Vol. 50, No. 1: 43-61.
  • Lee, H. ve Cho, M. S. (2017). What drives dynamic comovements of stock markets in the pacific basin region? A Quantile Regression Approach. International Review of Economics and Finance, Vol. 51, 314-327.
  • Newey, W. ve Powell J. L. (1987). Asymmetric Least Squares Estimation, Econometrica, 55, 819-847.
  • Nusair, S. A. ve Al-khassawneh, J. A. (2018). Oil price shocks and stock market returns of the GCC countries: empirical evidence from quantile regression, Econ Change Restrict, Vol. 51, 339-372.
  • Peters, B. (2004). Employment effects diffrent ınnovation activities: new microeconometric evidence, ZEW Discussion Papers no. 0473.
  • Pettinger, T. (2017) Investment and economic growth. economics Help, 6 May 2017, www.economicshelp.org/blog/495/economics/investment-and-economic-growth/.
  • Piva, M. ve Vivarelli, M. (2004). Technological change and employment: Some micro evidence from Italy. Applied Economics Letters, 11, 373–376.
  • Piva, M. ve Vivarelli, M. (2017), Technological change and employment: were ricardo and marx right?, IZA Institute of Labor Economics, January 2017, pp.1-36.
  • Piva, M. ve Vivarelli, M. (2018). Technological change and employment: ıs europe ready for the challenge?, Eurasian Business Review, Vol. 8, No. 1: 13-32.
  • Swamy, V. Dharant, M. ve Takeda, F. (2019). Insvestor attention ve google search volume ındex: evidence from and emerging market using quantile regression analysis. Research in International Bussines and Finance, Vol: 50, 1-17. Şahin, A. (2014). Stock market returns and oil prices relationship revisited. Quo Vadis Social Sciences: Artvin Coruh University International Congress on Social Sciences, Ekim, 15-17, 2014, Artvin, Türkiye.
  • Vivarelli, M., Evangelista, R.ve Pianta, M. (1996). Innovation and employment in Italian manufacturing industry. Research Policy, 25(7), 1013–1026.
  • Wang, J. Song, X. ve Chen, K. (2020). Which influencing factor cause CO_2 emissions differences in china’s provincial construction industry: emprical analysis from a quantile regression model”, Pol. J. Enviromental Studies, Vol. 29. No: 1. pp.331-347.
  • Wicksell, K. (1961). Lectures on political economy. London: Routledge & Kegan.