Total Debt Servicing And Macroeconomic Performance In Kenya

ABSTRACT

Kenya seeks to meet the Millennium Development Goals, under the guidance of The Kenya Vision 2030. The leading challenge to this course remains the soaring debt servicing obligations, capturing a significant portion of the national budget. Kenya has been borrowing externally at higher rates and continually expanding the debt ceiling. The government will therefore in future spend a significant portion of its revenue repaying the debts at the cost of important local investment. The government is therefore limited to fully fund critical sectors of the economy that will spur sustainable growth and increased investment opportunities; key to widening the tax base. This has an overall implication on the country‟s revenue, income, employment and poverty level.

This study aimed at determining the long-run relationship between total debt service and selected macroeconomic variables and to analyze the dynamic response of the variables following innovations in total debt service. Long run equations expressing the relationship between total debt service and real Gross Domestic Product, Real effective Exchange Rate and private investment were estimated. The results showed evidence of crowding out effect but no existence of debt overhang. Kenya was also found to have weak policy institutions thus remained vulnerable to adverse global exogenous shocks. In analyzing the dynamic impact of innovations in debt servicing on selected macroeconomic variables, a Vector Autoregressive (VAR) model was estimated with subsequent derivation of the Impulse Response Functions (IRF) and Variance Decompositions that explained the dynamics of the model. Based on the results, the impact of an unexpected shock in total debt service in the economy lasts for more than ten years to fully decay. The study tested time series behaviours like presence of unit root and serial correlation in the data to guard against spurious results. A string of diagnostic tests were also undertaken to establish the predictive power of the models. The Akaike Information Criterion (AIC) was used to determine the optimal lag length of the variables included in the VAR model.