DURATION OF THE PROJECT:

LEAD PARTNER:

UP Faculty of Management

PROJECT PARTNERS:

UP Faculty of Health Sciences

TEAM AT UP FACULTY OF HEALTH SCIENCES:

Assoc. Prof. Klemen Širok  (SICRIS, ResearchGate)

Summary:

Background and motivation
Alongside population aging and globalization, technological change, particularly digitalization, is one of the main transformative drivers of the future of work. The pace at which digital technologies develop is tremendous – over the last two decades the ICT usage levels have increased by more than twofold, the global installations of industrial robots fivefold, and the platform economy by more than sixfold. Although digitalization is one of the main strategic priorities, at the same time brings disruptions to the labor market and raises several questions on its implications on, both, the quantity and quality of jobs, the development of new forms of work, and its impact on occupations and skills. Despite the unavoidable penetration of digitalization in the sphere of work, in Slovenia, there is no empirical study that would provide an in-depth and comprehensive assessment of the impact of digitalization on labor market outcomes. There are some aggregate estimates of the future risk of automation in Slovenia, but they do not give detailed insights and support policymaking. No research for Slovenia systematically evaluates the job quality of platform work, paying also attention to algorithmic management, or provides an in-depth assessment of the job quality of remote work. There are also no systematic studies, nor mechanisms that would seriously consider the types of skills needed in the long and medium-term for specific occupations that would be at most affected by digitalization.

Objectives

 The proposed project builds around four main objectives:
1. Study the effects of automation on the Slovene labor market by empirically estimating the impact of automation on labor market outcomes, assessing the risk of automation for jobs and examining implications of automation on job quality.
2. Study the challenges of platform work in Slovenia by estimating the job quality of platform work (across several dimension of job quality, including physical environment, work intensity, working time quality, social environment, skills and discretion, prospects and earnings), studying the role and effects of algorithmic management on the job quality and analyzing legal aspects of platform work.
3. Study the implication of remote work by estimating the remote work prevalence in Slovenia, analyzing job quality of remote workers (across dimension listed above) and drawing comparisons with platform work, and studying legal aspect of remote work.
4. Map the effects of digitalization on future skills needed.

Methodology
To estimate the effects of automation, measured with the use of robots, we will apply a difference-in-differences approach (i.e., “treatment-and-control” comparison) and propensity score matching. The risk of automation will be estimated using both occupation-based (Frey and Osborne, 2017) and task-based approaches (used by the OECD). The novelty of our approach is that will be based on individual- and firm-level data. The analysis of job quality of platform work and remote work will be based on qualitative research using semi-structured interviews. Findings on the risk of automation will be used to identify 10 – 15 occupations that are at the highest risk of automation. For these occupations, a detailed overview of labor market trends and estimation of potential future changes in skills and work tasks will be implemented using a systematic review of the literature and available data.

Data
The proposed project will draw on several sources of data, including individual-level data on workers’ labor market events, accounting data on firms, and data on the use and purchases of robots. To study the implications of the platform and remote work data from semi-structured interviews will be used; the analysis will be supplemented with data from the EWCS, LFS, and other relevant data. The analysis of skills will also rely on available datasets and forecasts of the OECD and the CEDEFOP Skills Panorama.