EFFECT OF MACROECONOMIC DETERMINANTS ON COUNTY GOVERNMENTS’ INDEBTEDNESS POST-COVID-19 PERIOD IN KENYA

Abstract

The indebtedness of county governments in Kenya played a critical role in shaping post- pandemic economic stability and development, influenced by several macroeconomic factors. While borrowing helped sustain county operations and resilience during the COVID-19 period, it also amplified fiscal vulnerabilities, including declining tax revenue, rising corruption levels, and increased reliance on credit. Many counties entered a debt cycle exacerbated by high-interest loan borrowing. Between 2020 and 2023, county government expenditures surged to 28% of GDP, rising further to approximately 32.1% by the 2023/2024 financial year. Simultaneously, corruption levels, measured by the average size of bribes paid to access public services, increased from Ksh 4,600 in 2019 to Ksh 7,800 in 2021 and remained high at Ksh 7,300 through 2023. This persistent rise in corruption undermined public trust, inflated the cost-of-service delivery, and likely escalated borrowing as counties struggled to fill widening fiscal gaps. In the same period, county own-source revenues, measured as a percentage of total revenue, declined, with many counties reporting shortfalls. As of the first half of the 2023/2024 fiscal year, county governments had accumulated pending bills totalling Ksh 156.34 billion. The study’s overall objective is to examine the effect of macroeconomic determinants on county governments’ indebtedness in post-COVID-19 Kenya, focusing on economic growth, tax revenue, government expenditure, and corruption levels. Anchored on the Keynesian Consumption Theory and Debt Accumulation Theory, the study employed a descriptive and explanatory research design using panel secondary data from 47 counties for 2020/2021 to 2023/2024. Analysis involved descriptive statistics, Pearson correlation, and panel regression .The descriptive statistics indicated that, on average, GDP growth stood at 3.4%, tax revenue at 17.2%, county governments’ expenditure at Ksh 53.9 billion, corruption levels at Ksh 23,500, and county governments’ indebtedness at Ksh 2.18 billion between 2020 and 2024. Pearson correlation analysis revealed that county debt was positively associated with GDP growth (r = 0.492), tax revenue (r = 0.427), government expenditure (r = 0.668), and corruption (r = 0.354). To determine the appropriate model, the Breusch–Pagan Lagrange Multiplier (LM) test was conducted, which rejected the null hypothesis that Pooled OLS is sufficient (LM = 27.386, p < 0.001), suggesting that panel data models RE was more suitable. The F-test for Fixed Effects also rejected the null hypothesis that all county-specific intercepts are equal (F = 12.45, df = 46,138, p < 0.05), confirming the presence of significant county-specific effects and the inadequacy of the Pooled OLS model. The Hausman Specification Test (H = 11.752, df = 4, p = 0.05) rejected the null hypothesis, indicating that county-specific effects were correlated with the regressors. This led to the selection of the Fixed Effects (FE) model as the preferred specification. The Fixed Effects model revealed that government expenditure (β = 0.1724, p < 0.01) and corruption (β = 0.2611, p < 0.05) had positive significant effects, tax revenue had a positive significant effect (β = 0.2982, p < 0.05), and GDP growth was statistically insignificant (β = -0.0284, p = 0.05). The study concludes that excessive spending and corruption are the main drivers of post-COVID-19 county indebtedness. It recommends enhancing fiscal discipline, enforcing strict expenditure controls, strengthening anti-corruption measures, and improving own-source revenue mobilization to reduce debt reliance and ensure sustainable county financing.