Effect of Government Expenditure Components on Agricultural Productivity in Nigeria (1981-2017)

Abstract

We analyzed the relationship between government expenditure components and agricultural productivity in Nigeria by estimating the effects and their causal interaction over the period 1981-2017. The study employed the Autoregressive Distributed Lag (ARDL) technique to identify the existence of cointegration while going further by estimating the short and long run effects of government expenditure components on agricultural productivity as well as the Granger causality and Block Exogeneity tests under the Vector Autoregressive (VAR) models to analyze their causal relationship. Data were sourced from the Central Bank Statistical Bulletin, 2017 edition. The ARDL results revealed that there exists a long run relationship among the variables and showed that various components of government expenditure can positively improve agricultural productivity in Nigeria, but the current effects of these variables on agricultural productivity appears insignificant. The results revealed that the government expenditures on health and infrastructure have little effect on agricultural productivity and expenditures on education have a reduction effect on agricultural productivity in both short and long runs in Nigeria; highlighting the disconnection between agricultural knowledge acquisition and its implementation. Causality tests also showed that expenditures on education, health and infrastructure can help improve agricultural productivity in Nigeria. The study recommended effective policy implementation through a complete overhaul of the various government institutions and agencies responsible for implementation, revenue collection and monitoring of government project as it relates to agricultural development as well as encourage effective manpower development in the agricultural sector to boost productivity.

Keywords: Agricultural credit; Agriculture productivity; Autoregressive Distributed Lag, Government Expenditure, Inflation; Vector Autoregressive model.

DOI: 10.7176/JESD/10-18-01

Publication date:September 30^th 2019