Job acquisition processes and job searches in the Hungarian labour market : Examining regional and county differences

Mészáros, Réka (2025) Job acquisition processes and job searches in the Hungarian labour market : Examining regional and county differences. ECONOMY AND FINANCE: ENGLISH-LANGUAGE EDITION OF GAZDASÁG ÉS PÉNZÜGY, 12 (1). pp. 3-32. ISSN 2415-9379

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Abstract

This article focuses on estimating the matching function that models job-matching processes in the Hungarian labour market. This matching function, whichcharacterises the costly process of matching labour market demand and supply, isestimated using both aggregated data and regional and county-level data sources.Based on the estimated parameters of the function, we draw conclusions abouthow sensitive the finding rate is to changes in labour market tightness. Furthermore, we assess how changes in the number of job vacancies and unemployedindividuals affect the outcome of successful matches in the Hungarian labourmarket. The results indicate that labour market tightness has a positive effect onthe finding rate. On the aggregated labour market, labour supply plays a moresignificant role in determining successful job matches than labour demand.Due to the geographical immobility of the Hungarian labour force – characterised by a low willingness to relocate or commute – the domestic labour market isdivided into regional and county-level sub-markets, each represented by matching functions with different parameters. Using regional and county-level data,we highlight the differences in job-matching processes across territorial units,as well as the potential factors and causes underlying these disparities. Initially,we hypothesise that the variation arises from differences in the technological parameter of the matching function. Subsequently, separate matching functions areestimated for each region and county. These estimations are based on datasetsfrom Eurostat, the Hungarian Central Statistical Office (KSH), and the NationalEmployment Service (NFSZ), covering time series data from the second quarterof 2010 to the third quarter of 2023.

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