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Investigating employment patterns and determinants in the European Union through panel data insights Cover

Investigating employment patterns and determinants in the European Union through panel data insights

Open Access
|Mar 2025

Figures & Tables

Table 1

Indicator description.

IndicatorDescriptionPeriod
Employment rateThe percentage of employed persons, 15–64 years (% of total population)2011–2023
Gini CoefficientGini Coefficient for disposable income before social transfers (pensions included in social transfers) (takes values between 0 and 100)2011–2023
Women managersShare of women holding senior management positions (%)2012–2023
Tertiary educationShare of the population aged 25–34 who have completed tertiary education, ISCED level 5–8 (%)2011–20223
Social protection expensesSocial protection expenditure (% of GDP)2011–2022
R&D expendituresGross domestic expenditure on research and experimental development – GERD (% of GDP)2011–2022
Trade opennessThe sum of imports and exports (% of GDP)2011–2023
RemittancesPersonal remittances received (% of GDP)2011–2022

Source: Author’s contribution.

Figure 1

Employment rate (%), EU member states in 2023.

Source: Author’s estimation, based on the Eurostat Database.

Table 2

Causality testing and model identification for panel data analysis.

Explanatory variableGranger causalityPooled OLSFERE
Gini coefficient11.76*1 −0.129*−0.210**−0.201*
Women managers5.91*1 0.039*0.050*0.049*
Trade openness7.64*1 0.059*0.118*0.075*
Research and Development expenditures5.23*1 0.137*0.0300.064*
Tertiary education23.08*1 0.064*0.201*0.189*
Remittances14.05*1 0.0060.0080.004
Social protection expenses9.31*2 −0.045−0.028−0.036
Constant 4.348* 3.657* 3.883*

Source: Author’s estimations in STATA16, based on the Eurostat and UNCTAD data.

Note: *statistical significance at 5%, **statistical significance at 10%, 1first lag, 2second lag.

Figure 2

The heterogeneous slopes for the three clusters.

Source: Author’s estimation, using Stata 16.

Figure 3

Country clusters, based on employment rate and its influencing factors.

Source: Author’s estimation, based on the Eurostat Database.

Table 3

Estimation results.

Explanatory variableCluster 1Cluster 2Cluster 3
Gini coefficient−0.181*0.180*−0.366*
Women managers−0.00030.036*0.086*
Trade openness0.203*0.0450.006
Research and Development expenditures0.054*−0.005−0.003
Tertiary education0.157*0.413*−0.036
Remittances0.043*0.015−0.075*
Social protection expenses−0.045−0.140*−0.079**
Constant 3.169* 2.104* 5.797*
No. of countries1287
No of observations1328877
R 2 within0.790.850.84

Source: Author’s estimations in STATA16, based on the Eurostat and UNCTAD data.

Note: *statistical significance at 5%, **statistical significance at 10%.

DOI: https://doi.org/10.2478/mmcks-2025-0005 | Journal eISSN: 2069-8887 (formerly 1842-0206) | Journal ISSN: 1842-0206
Language: English
Page range: 1 - 14
Submitted on: Sep 12, 2024
Accepted on: Apr 3, 2025
Published on: Mar 30, 2025
Published by: Society for Business Excellence
In partnership with: Paradigm Publishing Services

© 2025 Maria Denisa Vasilescu, Larisa Stănilă, Silvana Crivoi, Maria Berta Belu, published by Society for Business Excellence
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.