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

Maria Denisa Vasilescu

Maria Denisa Vasilescu

  • Department of Statistics and Econometrics, Bucharest University of Economic StudiesBucharest, Romania
  • Wages, Income and Taxation Department, National Scientific Research Institute for Labour and Social ProtectionBucharest, Romania
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Vasilescu, Maria Denisa
, 
Larisa Stănilă

Larisa Stănilă

Wages, Income and Taxation Department, National Scientific Research Institute for Labour and Social ProtectionBucharest, Romania
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Stănilă, Larisa
, 
Silvana Crivoi

Silvana Crivoi

  • Wages, Income and Taxation Department, National Scientific Research Institute for Labour and Social ProtectionBucharest, Romania
  • Social Assistance Department, Faculty of Sociology and Social Work, University of BucharestBucharest, Romania
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Crivoi, Silvana
 and 
Maria Berta Belu

Maria Berta Belu

  • Wages, Income and Taxation Department, National Scientific Research Institute for Labour and Social ProtectionBucharest, Romania
  • Department of Informatics and Economic Cybernetics, Bucharest University of Economic StudiesBucharest, Romania
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Belu, Maria Berta
  
Mar 30, 2025
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Article Category: Research Article
Published Online: Mar 30, 2025
Page range: 1 - 14
Received: Sep 12, 2024
Accepted: Apr 03, 2025
DOI: https://doi.org/10.2478/mmcks-2025-0005
Keywords
© 2025 Maria Denisa Vasilescu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

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

Figure 2

The heterogeneous slopes for the three clusters.
The heterogeneous slopes for the three clusters.

Figure 3

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

Causality testing and model identification for panel data analysis_

Explanatory variable Granger causality Pooled OLS FE RE
Gini coefficient 11.76*1 −0.129* −0.210** −0.201*
Women managers 5.91*1 0.039* 0.050* 0.049*
Trade openness 7.64*1 0.059* 0.118* 0.075*
Research and Development expenditures 5.23*1 0.137* 0.030 0.064*
Tertiary education 23.08*1 0.064* 0.201* 0.189*
Remittances 14.05*1 0.006 0.008 0.004
Social protection expenses 9.31*2 −0.045 −0.028 −0.036
Constant − 4.348* 3.657* 3.883*

Estimation results_

Explanatory variable Cluster 1 Cluster 2 Cluster 3
Gini coefficient −0.181* 0.180* −0.366*
Women managers −0.0003 0.036* 0.086*
Trade openness 0.203* 0.045 0.006
Research and Development expenditures 0.054* −0.005 −0.003
Tertiary education 0.157* 0.413* −0.036
Remittances 0.043* 0.015 −0.075*
Social protection expenses −0.045 −0.140* −0.079**
Constant 3.169* 2.104* 5.797*
No. of countries 12 8 7
No of observations 132 88 77
R 2 within 0.79 0.85 0.84

Indicator description_

Indicator Description Period
Employment rate The percentage of employed persons, 15–64 years (% of total population) 2011–2023
Gini Coefficient Gini Coefficient for disposable income before social transfers (pensions included in social transfers) (takes values between 0 and 100) 2011–2023
Women managers Share of women holding senior management positions (%) 2012–2023
Tertiary education Share of the population aged 25–34 who have completed tertiary education, ISCED level 5–8 (%) 2011–20223
Social protection expenses Social protection expenditure (% of GDP) 2011–2022
R&D expenditures Gross domestic expenditure on research and experimental development – GERD (% of GDP) 2011–2022
Trade openness The sum of imports and exports (% of GDP) 2011–2023
Remittances Personal remittances received (% of GDP) 2011–2022
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Business and Economics, Business Management, Marketing, Sales, Customer Relations, Management, Organization, Corporate Governance, Business Development, Change Management, Cooperation, Entrepreneurship
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