Dynamic microsimulation

Research articles · Aug 31, 2020

Modelling foreign labour inflows using a dynamic microsimulation model of an ageing country - Slovakia

Miroslav Štefánik, Tomáš Miklošovič

Institute of Economic Research Slovak Academy of Sciences, Bratislava, Slovakia

Volume 13(2) Summer 2020

Cite as: M. Štefánik, T. Miklošovič; 2020; Modelling foreign labour inflows using a dynamic microsimulation model of an ageing country - Slovakia; International Journal of Microsimulation; 13(2); 102–113. doi: 10.34196/ijm.00220

Abstract

In this paper, we introduce our approach adopted to project the development of available labour supply. We focus on the situation at the Slovak labour market, where the increased inflow of foreign labour between 2012 and 2019 was driven by the demand for labour arising because of a need for replacing workers leaving the labour market. Using data from the Labour Force Survey, SLAMM simulates the development of domestic labour supply by reproducing the demographic processes, educational attainment, together with decisions on economic activity and employment. The discrepancy between the domestic labour supply and structure of employment expected by a macroeconomic forecast drives the projected inflow of foreign workers. The deficit of labour, driven mainly by the demographic change, is expected to be balanced by labour immigration. Besides its potential in predicting future labour supply, we also show some of the policy-relevant scenarios simulated by SLAMM.

This article contains 6 figures and 1 table.

Figures

Scheme 1. Structure of SLAMM in modules
Figure 1. Expected annual change in total employment.
Figure 2. Predicted development of the dependency ratio, in scenarios.
Figure 3. Predicted annual inflow of employed persons born out of the country, in scenarios.
Figure 4. The annual number of workers in need of replacement in scenarios.
Figure 5. Stock of employed persons born out of the country, by educational level.

Tables

Table 1. Variables used in the SLAMM model.
Variable Values
Gender MaleFemale
Age In years
Highest level of education achieved Primary
Lower secondary
Upper secondary
Post-secondary
Tertiary
Field of highest education achieved 10 fields (based on ISCED-F 2013)
Economic status in the current period Employed Unemployed
Student
Retired
Disabled
Inactive
Economic status one year ago
Economic sector of employment in the current period 18 sectors (based on NACE 2.0)
Economic sector of employment one year ago
Occupational group 3 groups* (based on ISCO 08 skill levels)
(ISCO1-3; ISCO 4-8; ISCO 9)
Occupational group one year ago
Country of birth Native
EU
Non-EU

Source: Model SLAMM.

Corresponding to the skill levels of the ISCO classification allows constructing indicators of skills mismatch (ILO, 2012, p. 12).