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Capital formation is widely regarded as a key driver of economic growth, yet empirical evidence for Nigeria remains mixed. This study offers valuable insights into the growth implications of capital formation in Nigeria by disaggregating it into domestic savings, public investment, and private investment. Anchored in the Harrod–Domar growth theory, the study uses annual time-series data covering 1990 to 2023 from the Central Bank of Nigeria Statistical Bulletin, the National Bureau of Statistics, and the World Bank World Development Indicators. The analysis includes descriptive statistics, Augmented Dickey–Fuller unit root tests, and Autoregressive Distributed Lag (ARDL) model estimation, along with post-estimation tests. The ADF unit root test showed a mix of I(0) and I(1) series, necessitating the ARDL bounds testing approach. The bounds test confirmed a long-run relationship among the variables. The ARDL estimates showed that domestic savings had a positive and significant effect on real GDP growth in both the long run and the short run. This suggests that the composition and productivity of private capital in Nigeria, rather than its volume, limit its contribution to growth. Private investment, by contrast, had a significant negative long-run effect and an insignificant short-run effect. This suggests that the composition and productivity of private capital in Nigeria, rather than its volume, limit its contribution to growth. Public investment also has a negative long-run relationship with growth, though it is not statistically significant. This suggests persistent weaknesses in the planning, execution, and efficiency of public capital spending. The error correction term (−0.829) was negative and significant, indicating that about 83% of any disequilibrium is corrected within a year. Thus, we recommend that policymakers, especially the Central Bank of Nigeria, strengthen domestic savings mobilisation by deepening financial inclusion, especially in rural and informal sectors, through digital banking, microfinance, and agent networks.
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Ozigbu et al. (2026) studied this question.
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