Macroeconomic Drivers of Subnational Debt: Evidence from Kenyan Counties after COVID-19
Ruth Jepchirchir Kiprop, Elijah Ng’eno, Paul Odwori
Asian Journal of Economics, Business and Accounting · pp. 467–477 · Published 22 Sep 2025
10.9734/ajeba/2025/v25i91984Abstract
This study examined how economic growth, tax revenue, government expenditure, and corruption levels affect the indebtedness of county governments in post-COVID-19 Kenya. It was based on the theory of debt accumulation and employed a fixed effects regression model. The model's results revealed that government expenditure (coefficient = 0.1724, p < 0.01) and the corruption rate (coefficient = 0.2611, p < 0.01) had significant positive effects on indebtedness. Tax revenue also had a significant positive impact (coefficient = 0.2982, p < 0.01), while economic growth was statistically insignificant (coefficient = -0.0284, p = 0.099). The study concludes that excessive government spending and corruption are the primary drivers of county indebtedness in the post-COVID-19 period. It recommends enhancing fiscal discipline, enforcing strict controls on expenditure, strengthening anti-corruption measures, and improving the mobilization of own-source revenue to reduce reliance on debt and ensure sustainable financing for counties.
Cited by 0
No indexed citations yet.
Related research
- COVID 19 Disease Caused by Coronavirus 2 (SARS-CoV-2) (Severe Acute Respiratory Syndrome) — shares topic coverage
- Assessment of Anxiety in Healthcare Providers Working in ICU during COVID-19 Pandemics — shares topic coverage
- Azithromycin and Hydroxychloroquine Accelerate Recovery of Outpatients with Mild/Moderate COVID-19 — shares topic coverage
- Elevated Levels of Lactate Dehydrogenase Predicts Poor Outcomes for Patients with COVID-19: A Review — shares topic coverage
- Addressing the Challenges of Containing Covid-19 Spread in a Rural, Poor Area in India: A Case Study — shares topic coverage
Article metrics
Real usage data collected on this platform.
22
Page views
0
PDF downloads
2
Outbound clicks
0
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.