The impact of growth and technology on employment the case of Türkiye

In this study, the effects of growth and technology on employment were examined. GDP was used for growth data, R&D expenditures were used for technology data, and employment rates were used for employment data. Then, employment was grouped according to education levels and three models were obtained.Since technology has existed in human history, its effects on human life have been visibly large. Technological developments from past to present have increased their effectiveness while affecting all units. Especially after the Industrial Revolution, the use of machinery in production has brought a different dimension to the field of employment. Technological innovations and mechanization, which directly affect production, caused unemployment as well as created new employment lines. In order for the employment-creating effect of technology to be greater than the employment-constricting effect, human capital must be strengthened. Trainings should be increased for employment lines created by innovation. On the other hand growth is a new concept in the economics literature. First of all, the model in which employment is the dependent variable and R&D and growth is the independent variable is examined. Then, 3 more models were established in which undergraduate employment (LA), high school and vocational high school employment (LM), and employment with higher education (YO) were dependent variables. When we look at the Turkish economy, it can be said that there is a growth towards the present. It has observed that unemployment rates increased as growth increased. It can be said that the Okun's law is valid for Turkey. The aim of the study is to analyze the concepts of technology, growth and employment in Turkey and to examine the effect of technology and growth on employment. First of all, the model in which employment is the dependent variable and R&D and growth is the independent variable is examined. Then, 3 more models were established in which employment below high school (LA), employment in high school and vocational high school (LM), employment with higher education (YO) is the dependent variable.In this context, ARDL Bounds Test, Unit Root Test with Structural Break, KPSS Test Statistics, Tado Yamamoto Causality Test methods were used. In the research model, the dependent variable is employment, and the independent variables are growth and R&D. In the study, data related to the period of 2000-2020 were used. The data of the research were obtained from TUIK and the World Bank. As a result of the study, there were breaks in the model in which employment was the dependent variable in 2004 and 2014. There is no long-term relationship between the variables. There is also unidirectional causality from employment to R&D and from employment to growth. For the additional 3 models where employment is heterogeneous, long-term relationships; While there is a cointegrated relationship between the variables in the long run for LA, a long-term relationship could not be established in the model where LM and YO employment is the dependent variable. In addition, while unidirectional causality was found from LM to growth, from YO to R&D, a bidirectional relationship was found between LM and R&D.