EFFECT OF NON-OIL REVENUE ON ECONOMIC GROWTH OF NIGERIA
UDOH, Idongesit Thomas, Department of Accounting, MOUAU, Nigeria.
International Journal of Multidisciplinary Research and Bulletin | Page 01 to 21
Abstract
This study investigates the effect of non-oil revenue on economic growth in Nigeria, focusing on tax revenue, fees, fines, and government investment returns from 1990 to 2024. Recognizing the country’s heavy reliance on oil revenue and its associated vulnerability to price fluctuations, the research emphasizes the need for fiscal diversification to sustain economic growth. Using a comprehensive methodological framework, the study employs descriptive statistics to summarize trends and variability in the data, correlation analysis to examine the relationships among non-oil revenue components and real GDP, and unit root tests (ADF and Phillips–Perron) to establish stationarity properties. Cointegration analysis is conducted to determine the presence of long-run equilibrium relationships, and the Vector Error Correction Model (VECM) is applied to capture both short-term dynamics and long-term adjustments. The results indicate that tax revenue has the strongest and most consistent positive impact on economic growth, while fees and fines contribute moderately, and government investment returns exhibit delayed but significant effects over the long term. The error correction term confirms that deviations from long-run equilibrium are gradually corrected, highlighting the resilience of non-oil revenue channels in stabilizing output. The study further finds that efficient revenue administration, compliance enforcement, and prudent management of state-owned enterprises enhance the growth effects of non-oil revenue. The findings underscore the importance of broadening the non-oil revenue base, improving collection mechanisms, and reinvesting public earnings into productive sectors to reduce dependence on oil and promote sustainable economic growth. The study contributes to policy formulation by providing evidence-based insights into the role of fiscal diversification in Nigeria’s macroeconomic stability.
Keywords: Non-oil revenue, tax revenue, fees charges and fines, government investment returns, real GDP
DETERMINANTS OF MEMBERSHIP PARTICIPATION IN AGRICULTURAL COOPERATIVE SOCIETIES AMONG RURAL WOMEN IN UMUAHIA NORTH LOCAL GOVERNMENT AREA, ABIA STATE, NIGERIA
Ukoha, J. C. I. and Nwaneri, E. J, Department of Agricultural Extension and Rural Development Michael Okpara University of Agriculture, Umudike, P.M.B 7267 Umuahia, Abia State, Nigeria.
International Journal of Multidisciplinary Research and Bulletin | Page 01 to 15
Abstract
This study analysed the determinants of membership participation in Agricultural Cooperative Societies among rural women in Umuahia North Local Government Area, Abia State, Nigeria. A multi-stage sampling procedure was employed in the sampling of 80 respondents that were involved in the survey. Structured copies of questionnaire were used to get responses from the women. Both descriptive and inferential statistics analysis were used to analyse the data. Major findings showed that the rural women belonged mostly to Credit and Savings Cooperative (68.75%), Crop-based Cooperatives (57.5%) and Input Supply Cooperatives and Marketing Cooperatives (50%) among others. Also, Training programs (97.5%), Market information (96.25%) and collective marketing of farm produce (85%) came 1st, 2nd and 3 rd as the major reasons for membership participation in agricultural societies’ activities by the respondents. Further results showed that the women participated highly in the various agricultural cooperatives with a grand mean of 2.30 which is higher than the bench mark mean of 2.00. There is a significant positive relationship between respondents’ reasons for participating in agricultural cooperative societies and their level of participation in the Societies (r = 0.248, p = 0.027). The study concluded that the determinants for membership participation in Cooperatives Societies among the rural women are the greater benefits they stand to get to solve their farming needs and therefore recommends that Cooperative Societies and policymakers should prioritize understanding and responding to members’ reasons for joining while designing targeted interventions in the study area.
Keywords: Determinants, Membership participation, Rural women, Cooperative societies
Machine Learning-Driven Geomechanical Modelling Across Multiple Scales
Osaki Lawson-Jack, Department of Physics and Geology, Federal University Otuoke, Bayelsa State, Nigeria.
International Journal of Multidisciplinary Research and Bulletin | Page 01 to 15
Abstract
The conventional geomechanical modeling cannot easily integrate the heterogeneous information obtained at wellbore and seismic levels, and often leads to independent analysis that could not capture interactions across scales. This study overcomes these shortcomings by aiming to integrate machine learning in geomechanical modelling across multiple scales. This is a process of machine learning algorithms such as, neural networks, which have been trained on core samples, well logs and seismic attributes. These models are used to establish complex and non-linear relationships in order to fill in highfidelity mechanical property models at the reservoir scale, which effectively fills the gap between microscale rock physics and macro-scale deformation. The findings show that the data-driven strategy is far superior to the conventional technique and generates continuous high-resolution geomechanical profiles that better predict the stress fields and formation instability. It was found that the application of machine learning in multi-scale processes is not just an incremental change, but a paradigm shift, allowing the quantification of the uncertainty in real-time and providing a self-consistent and dynamic earth model that can be used to engineer the subsurface more safely and efficiently.
Keywords: Machine Learning, Geomechanical Modelling, Multiple Scales, Core Samples, Reservoir, Bonga Field, Seismic Attributes, Micro-scale formation, Field-scale formation.