Potential Risk of Automation for Jobs in Slovakia: A District- and Industry-Level Analysis

ABSTRACT

The aim of this paper is to examine the impacts of automation on the Slovak labor market by applying automatability estimates to detailed employment data at the levels of districts and occupations. The district- and industry-level analysis shows that automation has the potential to significantly disrupt the Slovak labor market. Employees with lower incomes face greater risk of losing their jobs. The risk of automation is lower in districts with higher unemployment levels and vice versa, which indicates that technological progress does not have the potential to increase existing regional disparities in terms of employment opportunities. Policymakers should therefore consider differences among districts and industries when directing their job creation efforts.

KEYWORDS:

• Automation
• employment
• occupations
• regions
• Slovakia

JEL CLASSIFICATION:

• J21
• J23
• O33

Acknowledgments

This paper is a part of the VEGA research project No. 1/0781/21: Industrial Policy under the Conditions of Deindustrialisation and Automation. The opinions expressed are the authors’ personal views and not necessarily those of the institution the authors are affiliated with. The usual disclaimers apply.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1. In the following section, FO refers to Frey and Osborne (2017), DM to Dengler and Matthes (2018), and MT to Mihaylov and Tijdens (2019).

2. Unlike FO and MT, DM did not provide a forecast for the future, but estimated existing technological possibilities. Considering approximately 8,000 tasks, they assessed whether these tasks could be replaced by computers or computer-controlled machines according to programmable rules. Specifically, three coders independently researched each of these approximately 8,000 tasks to determine whether there was a computer-controlled machine or a computer algorithm that could perform this task fully automatically in 2013.

3. The relevant SK ISCO-08 codes are included in parentheses.

4. The data source for the registered unemployment rate was DATAcube. (Statistical Office of the Slovak Republic 2020).

5. However, Appendix C shows that this relationship was significantly weaker for the DM and MT estimates.

6. Based on the studies dealing with the potential impacts of automation (FO, DM, and MT), occupations at a high risk were defined as those with an estimated risk of automation of 70% or more. A probability of automation below 30% was considered low and the interval of 30% to 70% was considered medium.

7. The authors will gladly provide a complete list of industries ordered by share and absolute numbers upon request.

8. The authors will gladly provide a complete list of industries ordered by share and absolute numbers upon request.

Additional information

Funding

This work was supported by the Vedecká Grantová Agentúra MŠVVaŠ SR a SAV [1/0781/21].

Notes on contributors

Erika Majzlíková

Erika Majzlíková (née Stracová) completed her doctoral studies in Economic Policy at the Faculty of National Economy of the University of Economics in Bratislava, where she currently works as an Assistant Professor at the Department of Economic Policy. As part of her research, she focuses on structural and sectoral analyzes of industrial policy, automation, and robotization. Her main research methods include input–output analysis and applied econometrics.

Matej Vitáloš

Matej Vitáloš completed his doctoral studies in Economic Policy at the Department of Economic Policy, Faculty of National Economy, University of Economics in Bratislava. As part of his research activities, he focuses on the implications of technological progress for the labor market. His publication “Automation and Labor Demand in European Countries: A Task-Based Approach to Wage Bill Decomposition” can be found in Ekonomický časopis/Journal of Economics.