Journal of Economics, Innovative Management and Entrepreneurship

Analysis of Trade Balance Dynamics under the Influence of Structural Adjustment Mechanisms

Martin Okokon Ufi, Omotayo Emmanuel Olanipekun, Uzochukwudinma Awele Otakpor — Fri, 07 Mar 2025 00:00:00 -0600

Nigeria’s trade balance is influenced by key macroeconomic variables, particularly those shaped by structural adjustment policies. This study investigates the effects of GDP, inflation, and exchange rate depreciation on Nigeria’s trade balance, aiming to provide deeper insights into their long-term relationships. Specifically, the research examines how these factors contribute to trade performance and economic stability. To achieve this, the study employs econometric techniques, including the Unit Root Test (Augmented Dickey-Fuller), the Autoregressive Distributed Lag (ARDL) Bounds Co-integration test, ARDL-Error Correction Model (ECM), and the cumulative sum (CUSUM) chart for stability diagnostics. Analysis of data from 1996 to 2022 reveals a positive long-run relationship between GDP, inflation, and trade balance, while exchange rate depreciation has a significant negative effect. This suggests that while economic growth and moderate inflation may support trade, excessive currency depreciation weakens Nigeria’s trade balance. These findings underscore the need for policies that manage exchange rate volatility, promote economic diversification, and stabilize inflation to enhance trade performance. The study recommends long-term structural reforms that reduce dependence on oil exports, strengthen the non-oil sector, and ensure exchange rate stability. A well-coordinated macroeconomic framework integrating monetary and fiscal measures is essential for maintaining a stable and favorable trade balance. Ensuring sustainable economic growth while fostering an export-driven economy will be critical in improving Nigeria’s trade performance and overall economic resilience.

Impact of Capital Flight on The Growth of Nigeria’s Economy: 1980-2021

Oluwafemi Amos — Tue, 28 Jan 2025 00:00:00 -0600

This study examines the impact of capital flight on the growth of Nigeria’s Economy over the period 1980-2021 using the OLS method of estimation. Descriptive statistic, trend analysis, ADF unit root were firstly done and it was indicated that all the variables were stationary at level and first difference I(0) and I(1). The ARDL cointegration revealed that capital flight has significant relationship with economic growth and inversely related both in short-run and long-run. External debt (-2.61) and (-0.23) has negative impact on the growth of Nigeria economy both in the short-run and long-run respectively. Insecurity (1.42) and (-13.04) has negative impact of growth of Nigeria’s economy and is statistically significant both in short-run and long-run. More so, exchange rate (-0.07) was negatively related with growth and statistically significant in short-run but (0.023) positive related in the long-run. External reserves (-0.0005) and (-0.0001) also has negative impact on growth of Nigeria’s economy both in short-run and long-run respectively. ARDL model reparameterized into Error Correction Model revealed the long-run equilibrium was corrected in the current period at an adjustment speed of 79%, statistically significant and negatively signed. Based on the findings, it was recommended that federal government should include favourable economic policies, ensuring political stability and institutional developments. Also, government is expected to execute policies that will advance the level of gross domestic product growth in Nigeria.

Opportunities and Challenges in Agricultural Product Marketing: A Sociological Analysis of Bamyan Province, Afghanistan

Ramazan Ahmadi — Mon, 24 Mar 2025 00:00:00 -0500

This research aims to sociologically examine the opportunities and challenges of marketing agri-cultural products in Bamyan Province, Afghanistan, and proposes solutions to enhance local, na-tional, and international marketing systems. The study adopts a mixed-methods approach, inte-grating both quantitative and qualitative methodologies to provide a comprehensive analysis. The target population includes farmers from eight districts of Bamyan, as well as educated individuals, experts, and employees from government and non-governmental organizations. Data was collected through interviews, focus group discussions, and structured questionnaires. Quantitative data were analyzed using SPSS, while qualitative data were processed through MAXQDA, following a mixed-method analytical framework. The findings reveal that agriculture serves as the primary source of income for households in Bamyan, playing a pivotal role in sustaining livelihoods. Key crops include potatoes, wheat, and beans, with apricots being particularly significant in districts such as Kahmard. However, most farmers lack formal marketing training and rely on traditional methods and local intermediaries. This, combined with inadequate infrastructure and limited access to na-tional and international markets, significantly reduces profitability and negatively impacts living standards. Additionally, the lack of coordination among farmers underscores weaknesses in forming effective agricultural cooperatives. Social institutions and marketing support committees, particularly in the post-Republic era, have shown limited activity. Women’s participation in agri-cultural production and marketing is further constrained by social norms and a lack of training, limiting their economic contributions. Based on these findings, the study recommends several measures to address these challenges. These include developing adequate infrastructure, enhancing marketing training programs, and formulating supportive policies to improve marketing systems and increase farmers’ income. Strengthening agricultural cooperatives, promoting gender-inclusive training, and fostering better coordination among stakeholders are also critical to ensuring sus-tainable agricultural development in Bamyan Province.

Machine Learning-Driven Export Forecasting: A Comparative Analysis for MSME Growth

Shashi Kumar R, Dr Basavaraj M.S., Dr Vengalrao Pachava — Tue, 25 Feb 2025 00:00:00 -0600

Micro, small, and medium enterprises play a fundamental role in economic devel-opment by fostering employment, innovation, and international trade. However, these enterprises face substantial challenges in volatile global trade conditions, necessitating accurate forecasting methodologies for effective strategic planning. This study aims to evaluate and compare traditional time series models and advanced machine learning techniques in predicting export trends. The research employs Double Exponential Smoothing and Autoregressive Integrated Moving Average alongside Support Vector Regression, Random Forest, and Extreme Gradient Boosting to assess forecasting accuracy. Performance metrics including Root Mean Square Error, Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, and R-Square are utilized for model evaluation. Results indicate that while traditional time series models provide foundational forecasting insights, they are outperformed by machine learning techniques. Among these, Random Forest demonstrates the highest predictive accuracy and reliability. However, Extreme Gradient Boosting exhibits near-perfect met-rics, necessitating further scrutiny to address potential overfitting. The study empha-sises the necessity of integrating traditional and machine learning methodologies to enhance forecasting precision. These insights are valuable for policymakers, re-searchers, and industry practitioners seeking to strengthen export strategies and sus-tain economic growth.

Exchange Rates of Currencies, Volatility of Bitcoin Returns and Value at Risk (VaR) Analysis

Wed, 12 Mar 2025 00:00:00 -0500

There has been an increase in curiosity about the relationship between the returns on Bitcoins and returns on exchange rates in the last few years. This is especially important since Bitcoin is becoming more and more well-liked as a substitute for fiat money. This study therefore estimates the dynamic impact of exchange rates and their returns on Bitcoin return and also the value-at-risk (VaR) associated with each exchange rate and Bitcoin. The variance series derived from an estimation of the variations between the current and historical prices of Bitcoin using the exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model was used to compute data on Bitcoin volatility. Accordingly, return on Bitcoins was modelled as the natural logarithm of the difference between current day Bitcoin price and previous day price. The joint ARMA-FIGARCH models were estimated in this study to model the returns on Bitcoins transactions and currency trading rates based on time series from February 1, 2010 to August 30, 2024. The research findings underscore the presence of a significant dynamic adjustment of Bitcoin returns to exchange rate returns across all countries. This goes to indicate that there is a high possibility of incurring losses when making investments with digital currencies like Bitcoin. The originality of the paper lies on the fact the research assessed the effect of returns on currency exchange rates of rich countries and also estimated the dynamic effect of Bitcoin returns on exchange rates of the selected countries. The study establishes a substantial volatility feedback effect for returns on Bitcoins, whereas for each of the currencies, the incidence of a less significant volatility feedback effect was made evident. Investors in the foreign exchange market who chose to maximize profits at a lower risk, trading with the pound sterling/US dollar rate, the Euro/US dollar rate, the Australian dollar/US dollar rate; Canadian dollar/US dollar rate, Swiss Franc/dollar rate, New Zealand dollar/US dollar rate, and Luxembourg Franc/US dollar rate are profitable options. In policy circles, monitoring volatility dynamics is crucial for promoting forex market stability and investor confidence. This research benefits policy makers and marketers of financial assets in OECD countries.