Open AccessSECURITY EXPENDITURE ON ECONOMIC GROWTH IN NIGERIA
Author(s) -
Asen Ayange,
Udo Emmanuel Samuel,
Abner Ishaku Prince,
Victor Ndubuaku
Publication year - 2020
Publication title -
humanities and social sciences reviews
Language(s) - English
Resource type - Journals
ISSN - 2395-6518
DOI - 10.18510/hssr.2020.8360
Subject(s) - economics , human capital , error correction model , capital expenditure , government expenditure , short run , government (linguistics) , convergence (economics) , time series , cointegration , macroeconomics , econometrics , development economics , economic growth , finance , statistics , public finance , linguistics , philosophy , mathematics
Purpose of study: This study examines security expenditure as an economically contributive or a non-contributive expenditure on human capital development and economic growth in Nigeria.
Methodology: Adopting the ARDL bounds test and Error Correction Model (ECM) on quarterly time-series data from January 2010-December 2018.
Result: The findings and results indicate that security expenditure is economically a contributive expenditure. In the long-run a positive and significant impact on economic growth and human capital development, in the shot-run a negative relationship. The ECM model conveyed the speed of convergence from disequilibrium in the short-run back to long-run equilibrium by 86% quarterly.
Implication/Application: The finding and results have critical implications for the government and policymakers, protection of life, properties, economic, and business assets positively stimulate economic growth. A unit increase in government expenditure on human capital development decreases insecurity and increase economic growth.
Novelty/Originality of this study: Previous studies conducted globally and in Nigeria reported diverse results on the co-integrating relationship between security expenditure and economic growth, using diverse variables and annualized time series data predominantly. This study differs from the previous studies to adopt quarterly time-series data, the ARDL, and the ECM models as the major techniques of analysis along with a battery of pre-test and diagnostic tests.