Impacts of capital, education, and income inequality on economic growth: Evidence from Germany

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Type of the article: Research Article

Abstract
Against the backdrop of economic stagnation, weak external demand, and rising social disparities, this study examines the determinants of economic growth in Germany. Specifically, it investigates the long- and short-run effects of gross capital formation (GCF), government expenditure on education (GEE), and income inequality (GINI) on GDP per capita (GDPPC) over the period 1996–2022. The empirical analysis applies the Autoregressive Distributed Lag (ARDL) bounds testing approach, supported by unit root tests, an error correction model (ECM), and Fully Modified Ordinary Least Squares (FMOLS) estimation. The results confirm the existence of a long-run cointegration relationship among the variables, as the F-statistic of 5.67 surpasses the upper bound critical value at the 1% significance level. The ECM results show that approximately 51.8% of short-run deviations are corrected within one year, indicating a moderate speed of adjustment toward long-run equilibrium. FMOLS estimates suggest that gross capital formation and government expenditure on education are positively associated with GDP per capita in the long run, with coefficients of 0.475 and 0.088, respectively. Income inequality also exhibits a statistically significant but relatively small positive association with economic growth (0.024). In addition, external shocks show asymmetric effects, where financial crises are associated with a 0.055 increase in GDP per capita, while pandemic-related shocks are associated with a 0.089 decrease. Overall, the findings indicate that capital formation and public education expenditure are key long-run correlates of economic growth in Germany, while inequality and external shocks play supplementary but comparatively smaller roles in shaping growth dynamics.

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    • Figure 1. Trends in GDP per capita, capital formation, education expenditure, and income inequality (1996–2022)
    • Figure 2. Crisis–pandemic dummy variables
    • Figure 3. Correlation matrix of the variables
    • Figure 4. CUSUM test results
    • Table 1. Descriptive statistics of data variables
    • Table 2. Unit root test
    • Table 3. F-bound test results
    • Table 4. ECM results
    • Table 5. Model diagnostic test results
    • Table 6. Regression results: FMOLS estimation
    • Conceptualization
      Zeynab Giyasova, Jeyhun Hajiyev
    • Data curation
      Zeynab Giyasova, Gunay Panahova, Asli Kazimova, Mustafa Kemal Oktem
    • Formal Analysis
      Zeynab Giyasova, Jeyhun Hajiyev, Gunay Panahova, Asli Kazimova, Mustafa Kemal Oktem
    • Methodology
      Zeynab Giyasova, Jeyhun Hajiyev
    • Project administration
      Zeynab Giyasova, Jeyhun Hajiyev
    • Resources
      Zeynab Giyasova, Gunay Panahova
    • Software
      Zeynab Giyasova
    • Supervision
      Zeynab Giyasova, Jeyhun Hajiyev, Gunay Panahova, Asli Kazimova, Mustafa Kemal Oktem
    • Visualization
      Zeynab Giyasova
    • Writing – original draft
      Zeynab Giyasova, Jeyhun Hajiyev
    • Investigation
      Jeyhun Hajiyev, Gunay Panahova, Asli Kazimova, Mustafa Kemal Oktem
    • Validation
      Gunay Panahova, Asli Kazimova, Mustafa Kemal Oktem
    • Writing – review & editing
      Gunay Panahova, Asli Kazimova, Mustafa Kemal Oktem