Investigating the effects of public expenditure structure and fiscal discipline on SDGs in EU countries: An empirical analysis

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Type of the article: Research Article

Abstract
Public finance management has become increasingly important for achieving Sustainable Development Goals in European Union countries, where fiscal constraints, debt sustainability pressures, and development-oriented investment needs coexist. This study examines the effects of public expenditure structure and fiscal discipline on Sustainable Development Goals performance in EU countries. The analysis uses annual panel data for EU countries over the period 2007–2023. The SDG performance index is constructed from SDG 8, SDG 9, SDG 10, SDG 11, SDG 16, and SDG 17 indicators, while the public expenditure structure index is constructed from indicators reflecting collective government spending, total government expenditure, and the government investment share. Difference GMM and System GMM estimations are applied, and Driscoll–Kraay and KRLS estimators are used for robustness checks. The results show that public expenditure structure has a negative and statistically significant effect on SDG performance, with coefficients of –0.284 in the Difference GMM model and –0.048 in the System GMM model. Fiscal discipline has a positive and statistically significant effect, with coefficients of 0.353 and 0.315, respectively. Robustness estimations also support the negative effect of public expenditure structure and the positive effect of fiscal discipline. However, the Driscoll–Kraay coefficient of fiscal discipline is statistically significant only at the 10% level. These findings suggest that fiscal discipline may support SDG performance through macro-fiscal stability, whereas the scale and composition of public expenditure alone may be insufficient unless they are aligned with SDG-oriented priorities.

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    • Figure 1. Research model of the study
    • Table 1. Variables used in the study
    • Table 2. PCA data
    • Table 3. Statistical values of variables
    • Table 4. Correlation analysis results
    • Table 5. GMM analysis results
    • Table 6. Diagnostic test results
    • Table 7. Robust estimator results
    • Conceptualization
      Mosab I. Tabash, Özge Özkan, Ahmet Şit, Nazan Güngör Karyağdi, Zokir Mamadiyarov
    • Data curation
      Mosab I. Tabash, Özge Özkan, Ahmet Şit, Nazan Güngör Karyağdi, Zokir Mamadiyarov
    • Formal Analysis
      Mosab I. Tabash, Özge Özkan, Ahmet Şit, Nazan Güngör Karyağdi, Zokir Mamadiyarov
    • Writing – original draft
      Mosab I. Tabash, Özge Özkan, Ahmet Şit, Nazan Güngör Karyağdi, Zokir Mamadiyarov