<![CDATA[Purpose: This study aims to examine the relationship between government spending and agricultural output in Zimbabwe, while also evaluating the effects of macroeconomic variables such as inflation, carbon emissions, rainfall, population growth, and temperature on agricultural output. Methodology: The Autoregressive Distributed Lag (ARDL) model was employed using time-series data from 1980 to 2022. Data were sourced from the World Bank and the IMF. Diagnostic tests, including stationarity checks, cointegration analysis, and stability assessments (CUSUM and CUSUMSQ), were conducted to ensure the robustness of the model. Results: The findings reveal significant short- and long-run relationships between government spending and agricultural output. Government expenditure, rainfall, and population positively influenced agricultural productivity, whereas inflation and carbon emissions had a negative effect. The ARDL model explains 95% of the variation in agricultural output, indicating a strong model fit and predictive power. Conclusions: The Autoregressive Distributed Lag (ARDL) model demonstrated a positive relationship between government spending and agricultural output in both the short and long terms. Based on the results, the study concluded that sustained government support through subsidies, grants, and other resources has the potential to enhance agricultural productivity in Zimbabwe over time. Limitations: The study is limited by the availability and quality of historical data, which may constrain the precision of certain estimates. Contributions: This study extends Keynesian fiscal theory and Musgrave’s public finance theory by integrating recent perspectives on sustainable agricultural development and climate resilience, providing new insights into the relationship between government spending and agricultural productivity in developing economies.]]>
