Impact of public debt profile on economic growth: Evidence from Nigeria

  • Received December 12, 2021;
    Accepted February 11, 2022;
    Published February 18, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/pmf.11(1).2022.02
  • Article Info
    Volume 11 2022, Issue #1, pp. 10-19
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This work is licensed under a Creative Commons Attribution 4.0 International License

An excessive increase in public debt characterizes the contemporary development of the global economic and financial system. The paper aims to examine the short- and long-run impact of state debt on economic growth in Nigeria. The model was estimated using an autoregressive distributed lag (ARDL) bounds testing method to co-integration for the long-run investigation. At the same time, the contemporaneous dynamics were explored using an unrestricted error correction model. The data were collected from the Central Bank of Nigeria’s statistical bulletins and annual reports, and it spanned the years from 1990 to 2020. The study uncovers evidence of a long-term link between the study variables. In addition, the study finds that all the explanatory is statistically significant. Specifically, economic growth is significant and negatively responsive to changes in external debt by 0.19% and debt servicing by 0.07%, contrary to its positive response to changes in domestic debt and exchange rate by 0.27% and 0.18%, respectively. The paper, therefore, recommends that government may consider more domestic borrowings to foreign borrowings that should only be resorted to when it is indispensable. Moreover, the government should also strive to balance loan servicing and the economic sustainability.

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    • Figure 1. Trend of economic growth and government debt in Nigeria
    • Figure 2. CUSUM test
    • Table 1. Structure of economic growth and government debt in Nigeria, 1990–2020 (%)
    • Table 2. Stationarity results
    • Table 3. ARDL model estimation (dependent variable: DLGDP)
    • Table 4. ARDL bound test for co-integration
    • Table 5. Estimated ECM (dependent variable: DLGDP)
    • Conceptualization
      John O. Aiyedogbon, Fedir Zhuravka, Olena Banchuk-Petrosova
    • Data curation
      John O. Aiyedogbon, Olena Banchuk-Petrosova
    • Formal Analysis
      John O. Aiyedogbon, Maxim Korneyev, Olena Kravchenko
    • Investigation
      John O. Aiyedogbon, Fedir Zhuravka, Maxim Korneyev
    • Validation
      John O. Aiyedogbon, Fedir Zhuravka
    • Writing – original draft
      John O. Aiyedogbon, Olena Banchuk-Petrosova
    • Supervision
      Fedir Zhuravka
    • Writing – review & editing
      Fedir Zhuravka
    • Resources
      Maxim Korneyev, Olena Banchuk-Petrosova, Olena Kravchenko
    • Software
      Maxim Korneyev, Olena Banchuk-Petrosova
    • Visualization
      Maxim Korneyev, Olena Banchuk-Petrosova, Olena Kravchenko
    • Methodology
      Olena Banchuk-Petrosova, Olena Kravchenko