Have a personal or library account? Click to login

Classification of District Employment Agencies in Terms of Employment and Cost-Effectiveness Using Regression Trees

Open Access
|Jan 2021

Abstract

Research background: The efficiency of the functioning of District Employment Agencies is often assessed on the basis of the level of employment and cost-effectiveness indices. The values of these indices are influenced by various socio-economic factors, which were grouped into five areas in the paper: unemployment, demography, environment, entities and the human potential of District Employment Agencies (PUPs). The research was conducted in 340 District Employment Agencies in 2017.

Purpose: The purpose of the study is to separate groups of District Employment Agencies with similar values of employment and cost-effectiveness indices, with the simultaneous identification of the level of factors that characterize the socio-economic situation and staff potential in each of the separated groups.

Research methodology: One of the methods of a multidimensional statistical analysis – the regression trees method was used in the work.

Results: The use of regression trees allowed the separation of groups of District Employment Agencies, which differed in terms of the level of employment and cost-effectiveness indices, and characterized these groups due to socio-economic factors and staffing potential.

Novelty: The survey covers all District Employment Agencies in Poland and the obtained research results can be useful for labor market institutions to assess the efficiency of PUPs.

DOI: https://doi.org/10.2478/foli-2020-0033 | Journal eISSN: 1898-0198 | Journal ISSN: 1730-4237
Language: English
Page range: 20 - 35
Submitted on: Oct 23, 2019
Accepted on: Sep 9, 2020
Published on: Jan 29, 2021
Published by: University of Szczecin
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2021 Iwona Bąk, Katarzyna Wawrzyniak, Antoni Sobolewski, published by University of Szczecin
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.