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Секция «Экономика предпринимательства: концептуальный и региональный аспекты»
УДК 331.56
ВЛИЯНИЕ ДИДЖИТАЛИЗАЦИИ НА УРОВЕНЬ БЕЗРАБОТИЦЫ В ГЕРМАНИИ
1 2 В. В. Иваницкая *, А. В. Цветцых
1 Будапештский университет имени Корвина Венгрия, 1093, г. Будапешт, ул. Fovam ter 8 2Сибирский государственный университет науки и технологий имени академика М. Ф. Решетнева Российская Федерация, 660037, г. Красноярск, просп. им. газ. «Красноярский рабочий», 31
*E-mail: violetta.ivanitskaya@yandex.ru
В статье рассматривается влияние диджитализации на уровень безработицы на примере Германии.
Ключевые слова: Германия, диджитализация, уровень безработицы, рынок труда, экономический рост.
THE IMPACT OF DIGITALIZATION ON UNEMPLOYMENT RATE IN GERMANY
V. V. Ivanitskaia1*, A. V. Tsvettsykh2
:Corvinus University of Budapest 8, Fovam ter, Budapest, 1093, Hungary 2Reshetnev Siberian State University of Science and Technology 31, Krasnoyarskii rabochii prospekt, Krasnoyarsk, 660037, Russian Federation *E-mail: violetta.ivanitskaya@yandex.ru
The article considers the impact of digitalization on the unemployment rate in the case of Germany.
Key words: Germany, digitalization, unemployment rate, labour market, economic growth.
Introduction
Nowadays the topic of digitalization has a high relevance in the literature, many researchers try to figure out the impact of digitalization on unemployment in the short and long run. The process of digitalization leads to the structural changes in the labour market, but there is still no consensus in the literature regarding the impact of digitalization on unemployment. Some authors argue that the process of digitalization will create new jobs, whereas other authors claim that it increases unemployment. According to Eichhorst and Spermann (2016), the existence of different internet platforms creates new services and jobs and stimulates a demand on the market [1]. At the same time, according to Brynjolfsson, McAfee (2011), technologies are able to replace not only jobs with routine tasks, but also non-routine which require high skills [2]. Rifkin (2014) argues that the long run digital revolution will reduce employment. According to him, even a low-paid worker will be more expensive than the additional cost of using a machine. As a result, there will be a growth in jobs for innovative products and decline in jobs for standard products [3].
In this article, we are going to estimate the impact of digitalization on unemployment in the case of Germany. Nowadays Germany has the largest national economy in Europe and the fourth-largest by nominal GDP in the world. In 2014 the country spent more than EUR 80 billion on R&D, what is 66 percent more than in 2000 [4]. This can be evidence that Germany is experiencing the process of digitalization already. Frey and Osborne (2015) tried to forecast the future of employment and
Актуальные проблемы авиации и космонавтики - 2021. Том 3
the impact of digitalization and pointed out that around 51% of the jobs in Germany will be under the threat of replacement by machines, robots or computer programs [5]. At the same time, Pfeiffer (2016) suggests that this pessimistic prediction is not going to happen [6].
As we can see, there is still no consensus in the literature regarding the impact of digitalization on unemployment and this topic requires further research. In this article, we are going to estimate the impact of digitalization on unemployment in case of Germany with help of time-series ordinary least squares (OLS) regression in STATA. Based on data availability, the chosen period for regression is 1991-2017.
Description of data and empirical results
The descriptive statistics of the data is presented in Table 1. The dependent variable in the regression is unemployment rate, which measured in percentages of the total labour force. The explanatory variable is the variable which is the best approximation to variable of digitalization, in this case we have chosen the variable of information and communication technology service exports (ICT), which includes computer and communications services (telecommunications and postal and courier services) and information services (computer data and news-related service transactions). Data are in current U.S. dollars.
Table 1
Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max
year 27 2004 7.937 1991 2017
unempl 27 7.572 2.099 3.75 11.17
ICT 27 1.365e+10 1.124e+10 9.916e+08 3.740e+10
All the yearly values of dependent and independent variables were taken from The World Bank official site [7].
As the first step, we have generated the logarithm of dependent and independent variables to be able to receive the more precise results. As a result, we are going to estimate a log-log regression model. As the second step we checked the normality assumption with the help of Jarque-Bera test for normality. According to the Jarque-Bera test we received a p-value of 0,669, which means that the residuals are normally distributed. To be able to see the trend existence in our time-series data we have run the regression with variable of trend for dependent variable of unemployment. The results are presented in Table 2.
Table 2
Time-series regression wit i trend
lunempl Coef. St.Err. t-value p-value [95% Conf Interval] Sig
lICT .49 .192 2.55 .018 .094 .886 **
t -.086 .026 -3.30 .003 -.14 -.032 * * *
Constant -8.016 4.037 -1.99 .059 -16.347 .315 *
Mean dependent var 1.983 SD dependent var 0.304
R-squared 0.453 Number of obs 27.000
F-test 9.952 Prob > F 0.001
Akaike crit. (AIC) 0.947 Bayesian crit. (BIC) 4.835
*** p<.01, ** p<.05, *p<.1
According to Table 2, we can see that trend has a high significance for the regression. Because of that we have detrended our dependent and explanatory variables and repeated the regression. The results are shown in Table 3.
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Table 3
Time-series regression for detrended data
dlunempl Coef. St.Err. t-value p-value [95% Conf Interval] Sig
dlICT .49 .188 2.60 .015 .102 .877 **
Constant 0 .044 0.00 1 -.091 .091
Mean dependent var 0.000 SD dependent var 0.253
R-squared 0.213 Number of obs 27.000
F-test 6.779 Prob > F 0.015
Akaike crit. (AIC) -1.053 Bayesian crit. (BIC) 1.539
*** p<.01, ** p<.05, *p<.1
According to Table 3, we can see that the variable of ICT has a high significance (p-value is 0,015 which is less than 5%), and positive association with unemployment. These results allow us to make a conclusion that the increase of the variable of ICT by 1% leads to an increase of unemployment rate by 0,49% in case of Germany.
Conclusion
The empirical results show that nowadays the process of digitalization in Germany leads to an increase of unemployment. An increase of information and communication technology service exports by 1% leads to an increase of unemployment by 0,49%.
References
1. Spermann A., Eichhorst W. Sharing Economy: Mehr Chancen als Risiken?: Wirtschaftsdienst, 96(6), pp. 433-439. 2016.
2. Brynjolfsson E., McAfee A. Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy: Lexington, MA, Digital Frontier Press. 2011.
3. Rifkin, J. The Zero Marginal Cost Society: The internet of things, the collaborative commons, and the eclipse of capitalism: Basingstoke, Palgrave Macmillan. 2014.
4. Naude W., Nagler P. Technological Innovation and Inclusive Growth in Germany.: IZA DP No. 11194. 2017.
5. Frey C., Osborne M. Technology at Work: The Future of Innovation and Employment.: Citi GPS Global Perspectives and Solutions. 2015.
6. Pfeiffer, S. Robots, Industry 4.0 and Humans, or Why Assembly Work is More than Routine: Societies, 6(2):16. 2016.
7. World Bank data [Electronic resource]. URL: https://data.worldbank.org/indicator/ (appeal date: 01.02.2021).
© Ivanitskaia V. V., Tsvettsykh A. V., 2021
