Disruptive load shedding and its dynamic impact on municipal financial performance in KwaZulu-Natal, South Africa
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Author(s)Khulani Mzimela ,Jean Damascene MvunabandiLink to ORCID Index: https://orcid.org/0000-0002-3356-9493
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Bomi Cyril NomlalaLink to ORCID Index: https://orcid.org/0000-0001-5471-1172
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DOIhttp://dx.doi.org/10.21511/pmf.14(3).2025.10
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Article InfoVolume 14 2025, Issue #3, pp. 130-144
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Type of the article: Research Article
Abstract
Electricity energy consumption plays a significant role in both local and international financial development. However, an imbalance between demand and supply of energy, especially electricity, impedes financial performance on both national and local levels. The purpose of this study is to investigate the dynamic impact of load shedding on financial performance in KwaZulu-Natal. A panel Autoregressive Distributed Lag (ARDL) model, the Toda-Yamamoto Granger causality test, and Error Correction Model (ECM) approaches were applied to a data sample from seven district municipalities for a period from 2016 to 2022. The results reveal an inverse long-term relationship between load shedding and municipal financial performance. Additionally, the Toda-Yamamoto causality analysis indicates a short-run bidirectional causality between load shedding and financial performance. This implies that a high level of electricity cuts leads to poor financial performance. Based on these findings, the study recommends that the government and policymakers implement strategies to improve electricity generation and distribution, and foster a more competitive energy market by allowing the entry of multiple electricity producers beyond Eskom. Furthermore, it advocates for increased investment in alternative energy sources such as solar, wind, and biogas as a means to mitigate load shedding and its adverse effects.
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JEL Classification (Paper profile tab)D58, M41, L94, R51
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References69
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Tables8
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Figures0
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- Table 1. Description of variables
- Table 2. Summary statistics
- Table 3. Panel unit root results
- Table 4. Estimated long-run results
- Table 5. The error correction model (ECM) and short-run dynamics
- Table 6. Toda-Yamamoto Granger causality results
- Table 7. Diagnostic test results
- Table 8. Cross-section dependence test
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