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The role of EU cohesion funds in Romanian labour productivity: Insights from machine learning and econometric modelling Cover

The role of EU cohesion funds in Romanian labour productivity: Insights from machine learning and econometric modelling

Management & Marketing
Volume 20 (2025): Issue 2 (June 2025)
By: ,  ,   and    
Open Access
|Jun 2025
Figures & tables

Figures & Tables

Figure 1

Research methodology.

Source: Author’s own creation.

Table 1

LASSO’s results regarding the most important determinants.

VariableLASSOPost-estimation OLS
log (EAFRDEMFFpc)10.034810.3199**
log (ERDFpc)2.98313.0211**
log (GFCF)29.289629.4165**
log (GERD)4.91124.9793**
EQI−15.7593−16.1696**
EduLow−0.5370−0.5364**
PrimGVA−0.8517−0.8623**
CONS0.43880061.846910**

Source: Authors’ own research.

Note: *, **, and *** represent 10, 5, and 1% significance level.

Table 2

Estimation of the impact of ESIF on labour productivity.

VariableCoefficients
log (EAFRDEMFFpc)7.3573***
log (ERDFpc)3.2260***
log (GFCF)9.5602**
EduLow−1.0459***
PrimGVA1.4592*
CONS49.6282**
N of observations104
N of groups8
F(5,91)25.68
Prob > F 0.0000
F (FE are jointly zero)38.71
Prob > F 0.0000

Source: Authors’ own research.

Note: *, **, and *** represent 10, 5, and 1% significance level.

Table 3

Empirical results of dynamic Dif-GMM estimation.

VariableCoefficients
LP(−1)0.5863***
log (EAFRDEMFFpc)3.1287***
log (GFCF)7.6210**
EduLow−0.0400**
PrimGVA1.1370**
CONS13.3223*
N of observations96
N of groups8
Wald χ 2 (5)5725.69
Prob > χ 2 0.0000

Source: Authors’ own research.

Note: *, **, and *** represent 10, 5, and 1% significance level.

Table 4

Estimated variable coefficients for modelling labour productivity.

RegionInterceptlog (EAFRDpc)EduLowlog (GFCF)PrimGVALP(−1)
RO11−9.6143.826−0.21512.6900.4840.657
RO12−15.3459.5330.337−6.4081.1640.644
RO21−18.865−0.7230.337−6.4081.1640.826
RO22−20.1628.6770.619−5.4580.2120.660
RO3155.8562.2990.0671.9760.0090.272
RO32−26.2142.6470.57833.3404.7310.386
RO4177.1215.999−1.50011.051−2.6040.446
RO4245.8842.392−0.9256.6613.8070.143

Source: Authors’ own research.

Table A1

Data sources.

IndicatorSource
Labour productivity Eurostat
EAFRD per capita Directorate-General for Regional and Urban Policy, EU Payments History – Regionalized and Modeled dataset
EMFF per capita Directorate-General for Regional and Urban Policy, EU Payments History – Regionalized and Modeled dataset
ERDF per capita Directorate-General for Regional and Urban Policy, EU Payments History – Regionalized and Modeled dataset
GFCF Eurostat
Gross Domestic Expenditure on R&D (GERD) Eurostat
European Quality of Government Index (EQI) European Quality of Government Index (EQI)
Proportion of Population with Low Education (EduLow) Eurostat
Share of Primary Sector in Gross Value Added (PrimGVA) Eurostat
Initial GDP per capita (2007) Eurostat
Population size Eurostat
Road accessibility DG Regio Regional Competitiveness Index – 2022 edition
Air accessibility DG Regio Regional Competitiveness Index – 2022 edition
Regions analysed: 8 NUTS2 regions in Romania
Period covered: 2007–2020
Total observations: 112 observations (8 regions × 14 years)

Source: Data from Eurostat; DG REGIO (EU Payments History – Regionalized & Modeled dataset; Regional Competitiveness Index 2022); European Quality of Government Index.

Table A2

Empirical results of the LASSO method for the labour productivity indicator.

LambdaL1-NormEBIC R-square
5241.224050.00000705.665560.0000
4775.608153.48932696.134880.1274
2268.8007721.77004616.516060.6119
1883.5982623.47559607.445920.6599
1563.7963724.66088601.043080.6941
742.9299727.86920582.666850.7549
561.9992829.55684582.160150.7667
425.13188433.10793580.577580.7803

Source: Authors’ own research.

Table A3

Testing the hypothesis on random effects in modelling labour productivity.

FE test
Statistic p-valueAlternative
2.95080.0078Significant effects
Hausman endogeneity test
Statistic p-valueAlternative
24.71770.0001One model is inconsistent

Source: Authors’ own research.

DOI: https://doi.org/10.2478/mmcks-2025-0007 | Journal eISSN: 2069-8887 (formerly 1842-0206) | Journal ISSN: 1842-0206
Journal RSS Feed
Language: English
Page range: 11 - 22
Submitted on: Nov 20, 2024
Accepted on: Mar 12, 2025
Published on: Jun 26, 2025
Published by: Society for Business Excellence
In partnership with: Paradigm Publishing Services
Keywords:
Regional labour productivity,
European structural and investment funds,
Cohesion policy impact,
Econometrics,
Machine learning,
EU funds efficiency
Related subjects:
Business and economics,
Business management,
Marketing, sales, customer relations,
Management, organization, corporate governance,
Business development, change management, cooperation,
Entrepreneurship

© 2025 Adriana AnaMaria Davidescu, Monica Mihaela Maer Matei, Marina-Diana Agafiței, Maria Bianca Bolboașă, published by Society for Business Excellence
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 20 (2025): Issue 2 (June 2025)
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