This paper analyses theoretical and empirical scientific literature about the impact of technological innovations on unemployment, considering the former as a key driver of long-term productivity and economic growth. Using panel data from 25 European countries for the period of 2000-2012, we aim to examine whether technological innovations affect unemployment. We used triadic patent families per million inhabitants as our main proxy for technological innovations, as well as other unemployment controls, in our model, which were estimated using System Generalized Method of Moments (SGMM). Finding no significant relationship between technological innovations and unemployment in our base estimation, we re-estimated it testing the impact with a time lag as well as using alternative proxies for technological innovations. Overall, the research estimations do not suggest that technological innovations have an effect on unemployment.
Innovation and unemployment are two economic elements related to each other that have been constantly analyzed in the economic debates from the beginning of the 21st century. A classical question is whether innovation creates or destroys jobs. The conventional approach contemplates innovation as a transformation instrument of an economy, resulting in economic growth and jobs creation. Another approach points out to various mechanisms which can compensate the primary effect of innovations and cause an ultimate effect of innovations on labour demand to be unclear. In view of the fact that there are many different explanations about the impact of innovations on labour demand, this paper, after the analysis of theoretical and empirical scientific literature in this field, provides an empirical analysis with unemployment as the dependent variable. The authors use data from 28 European Union countries for the period of 1992–2016 and pursue to research how technological innovations affect unemployment rate. There are two core independent variables – expenditure on R&D (research and development) and number of patent applications – as the main proxies for technological innovations. Control variables that affect unemployment are included to the model as well. The model was estimated using a dynamic two-step System Generalized Method of Moments (GMM-SYS) of a panel data system. After the composition of 12 different estimations of the model, the results suggest that, in some cases, technological innovations affect unemployment.
Straipsnyje analizuojama psichologinių ir ekonominių veiksnių įtaka asmenų taupymo lygiui. Psichologinių ir ekonominių veiksnių poveikio taupymui vertinti Lietuvoje pasirinkta naudoti empirinio tyrimo metodą apklausiant Lietuvos gyventojus ir gautus duomenis įvertinant panaudojus kiekybinę duomenų analizę. Surinkti duomenys apdoroti taikant mažiausių kvadratų metodą, sudarant daugialypės regresijos modelį. Atliktas tarpgrupinių duomenų tyrimas, tiriamasis laikotarpis -2019 m., naudojami rodikliai -vidutinės bendros analizuojamų metų respondentų charakteristikos. Veiksniai, kurie buvo išskirti taupymo lygio įtakai apskaičiuoti, tokie: finansinis raštingumas, taupymo motyvų skaičius, aplinkos įtaka, tikimybė elgtis iracionaliai, asmenybės tipas. Duomenys apdoroti Gretl programa. Reikšminiai žodžiai: vartotojų elgsena, psichologiniai veiksniai, ekonominiai veiksniai, taupymas, Gretl.Article focuses on the effects of psychological and economic factors on the rate of savings of individuals. To assess the effects of psychological and economic factors on savings in Lithuania, it was chosen to use the method of empirical analysis by interviewing the Lithuanian population and evaluating the obtained data using quantitative data analysis. The collected data were processed using the least squares method to construct a multiple regression model. Analysis was prepared using cross-sectional data, the analysis period -year of 2019, and the indicators used in the analysis were the average general characteristics of the respondents in analyzed years. Factors that were chosen to calculate the effects of the rate of savings: financial literacy, number of motives for saving, environmental influence, probability of behaving irrationally, personality type. Data were processed using Gretl program.
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