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Секция «Экономика предпринимательства: концептуальный и региональный аспекты»
УДК 331.56
ВЛИЯНИЕ ДИДЖИТАЛИЗАЦИИ НА УРОВЕНЬ БЕЗРАБОТИЦЫ В НИДЕРЛАНДАХ
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 Нидерландов.
Ключевые слова: Нидерланды, диджитализация, уровень безработицы, рынок труда, экономический рост.
THE IMPACT OF DIGITALIZATION ON UNEMPLOYMENT RATE IN THE
NETHERLANDS
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 unemployment in the case of the Netherlands.
Keywords: The Netherlands, 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. According to some authors, the investment in data and digital infrastructure is essential to support innovation, growth and jobs in the digital economy [1]. Whereas other authors claim that it increases unemployment. 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 [3].
In this article, we are going to estimate the impact of digitalization on unemployment in the case of the Netherlands. According to a report of European Commission about the Digital Economy and Society Index 2020 (DESI), the Netherlands ranks 4th out of 28 EU countries. It therefore remains one of the top performers across Europe, with solid and steady digital growth. DESI index includes an analysis of connectivity, human capital, use of internet services, integration of digital technology
Актуальные проблемы авиации и космонавтики - 2021. Том 3
and digital public services. According to the report, the Netherlands increased its score in human capital, use of internet services, integration of digital technology and digital public services, in line with a comparable average increase across the EU. It is among the top performers in connectivity, with near-complete fast broadband coverage and 4G coverage [4].
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. Moreover the Netherlands is one of the leading countries in terms of digitalization and the estimation of an impact of digitalization on unemployment has a high relevance for this country. In this article, we are going to estimate the impact of digitalization on the unemployment rate in case of the Netherlands 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 is 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.
Descriptive Statistics
Table 1
Variable Obs Mean Std. Dev. Min Max
year 27 2004 7.937 1991 2017
unempl 27 5.193 1.537 2.12 7.42
ICT 27 7.082e+09 5.536e+09 4.053e+08 1.650e+10
All the yearly values of dependent and independent variables were taken from The World Bank official site [5].
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 0,14, which means that the distribution of the residuals is close to normal. 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.
Time-series regression wit
i trend
Table 2
lunempl Coef. St.Err. t-value p-value [95% Conf Interval] Sig
lICT -.392 .168 -2.33 .028 -.739 -.045 **
t .06 .026 2.28 .032 .006 .114 **
Constant 9.449 3.375 2.80 .01 2.482 16.415 * * *
Mean dependent var 1.598 SD dependent var 0.337
R-squared 0.186 Number of obs 27.000
F-test 2.738 Prob > F 0.085
Akaike crit. (AIC) 17.323 Bayesian crit. (BIC) 21.211
*** p<.01, ** p<.05, *p<.1
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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.
Table 3
Time-series regression for detrended data
dlunempl Coef. St.Err. t-value p-value [95% Conf Interval] Sig
dlICT -.392 .165 -2.38 .025 -.732 -.053 **
Constant 0 .06 0.00 1 -.123 .123
Mean dependent var 0.000 SD dependent var 0.337
R-squared 0.185 Number of obs 27.000
F-test 5.671 Prob > F 0.025
Akaike crit. (AIC) 15.323 Bayesian crit. (BIC) 17.915
*** 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 0,025 which is less than 5%), and negative association with unemployment. These results allow us to make a conclusion that the increase of variable ICT by 1% leads to decrease of unemployment rate by 0,392% in case of the Netherlands.
Conclusion
The empirical results show that nowadays the process of digitalization in the Netherlands stimulates the creation of new jobs in the country. An increase of information and communication technology service exports by 1% leads to decrease of unemployment by 0,392%.
References
1. OECD, The Digital Economy: Innovation, Growth and Social Prosperity [Electronic resource]. URL: http://www.oecd.org/digital/ministerial/meeting/New-Markets-and-New-Jobs-discussion-paper.pdf (appeal date 10.03.2021).
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. European Commission, Digital Economy and Society Index (DESI) 2020, The Netherlands [Electronic resource]. URL: https://ec.europa.eu/digital-single-market/en/scoreboard/netherlands (appeal date: 10.03.2021).
5. World Bank data [Electronic resource]. URL: https://data.worldbank.org/indicator/ (appeal date: 01.02.2021).
© Ivanitskaia V. V., Tsvettsykh A. V., 2021
