Mobile money has expanded financial access in Somalia, where over 75% of adults rely on telecom-led services for daily payments and remittances. Transactions exceed 60% of GDP, with monthly volumes surpassing USD 2 billion. However, weak consumer protection and fragmented oversight have contributed to unauthorized reversals, SIM swapping, and agent collusion, while under-reporting obscures the true scale of fraud. This study examined whether operator fraud detection mechanisms, from manual reconciliation to real-time machine learning analytics, are associated with lower actual fraud incidence in Somalia's telecom sector. Using a comparative quasi-experimental design across five operators over 12 months, the study triangulated a survey of 353 technical staff, transaction logs, and complaint records. Forensic sampling of 60,000 transactions estimated detection rates and adjusted for reported fraud. Negative binomial models with operator and month fixed effects showed that medium and high sophistication were associated with markedly lower fraud incidence. Fraud increased during Ramadan and with conflict intensity, whereas cross-border partnerships were associated with lower incidence independently of sophistication. Advanced detection is associated with substantially lower fraud in low-sanction environments, though weaker in conflict-affected regions. These findings support tiered minimum detection standards, compulsory incident reporting, and measures targeting collusion and informal governance constraints.
The valuation of critical mineral projects under uncertainty remains anchored to deterministic Discounted Cash Flow and Geometric Brownian Motion frameworks, which impose constant volatility and log-normal price assumptions poorly suited to regime-dependent commodity markets. This study specifies a Critical Mineral Systemic Risk Index Threshold Autoregressive (CMSRI-TAR) model and compares it with GBM inside a common Monte Carlo project engine, applied to the Dolphin Tungsten Project on King Island, Tasmania, over a 14-year mine life. Using 10,000 paired paths under Common Random Numbers, CMSRI-TAR yields a narrower and less adverse NPV distribution than GBM, with CVaR 95% estimates separated by approximately A$39.5 million. None of the three directional hypotheses is supported. The divergence reflects two properties of the estimated specification rather than any demonstrated forecasting advantage: the high-CMSRI regime carries a positive intercept and covers four-fifths of the sample, and negative autoregressive coefficients in both regimes slow the growth of log-price variance relative to a random walk. Because no out-of-sample evaluation is reported, the analysis establishes that the choice of price process is consequential for project-level tail risk, but not which process better represents tungsten prices.
This study examines South Africa’s prolonged economic growth slowdown and its implications for the secular stagnation hypothesis. Using quarterly data spanning 1960Q1–2025Q3, the analysis combines Bayesian Structural Time Series (BSTS), Bai–Perron multiple structural break analysis, regime comparisons, and Bayesian Model Averaging (BMA). The structural break analysis identifies four changes in the growth process, occurring in 1968Q2, 1982Q1, 1993Q2, and 2008Q3. BSTS estimates reveal a persistent decline in underlying trend growth, averaging 3.07% before the Global Financial Crisis, 1.53% during the post-crisis/pre-COVID period, and 0.97% after COVID-19. BMA results identify investment growth as the most consistently supported contemporaneous predictor of economic growth, while oil-price changes exhibit strong posterior support in the expanded specification. The importance of exchange-rate movements varies across model specifications and lag structures. Robustness exercises involving alternative trend extraction, growth measures, and distributed-lag investment specifications broadly support the findings. Overall, the evidence establishes persistent structural growth deceleration in South Africa, exhibiting characteristics consistent with secular stagnation. However, the analysis does not conclusively identify a demand-driven secular stagnation mechanism, highlighting the need to distinguish prolonged growth weakness from its underlying causes.
The rising trend of sustainable investing and the increasing incorporation of Environmental, Social and Governance (ESG) criteria into financial decisions have necessitated an assessment of ESG mutual funds’ performance in India. There is limited empirical evidence on whether ESG funds deliver better risk-adjusted returns and the impact of ESG rankings and expense ratios on fund performance. The study employs a quantitative research method to analyze the monthly return data of the ESG mutual funds in India from 2021 to 2025. Risk-adjusted performance measures like the Sharpe, Treynor, Jensen’s alpha and Sortino ratios are used to rank the funds. The funds are compared with the Nifty 50 index and the Fama-French 3-factor model is used to analyze the impact of market, size and value factors on returns. Furthermore, a cross-country Granger Causality test examines the direction of causality between ESG and traditional market indexes in emerging markets. The findings state that the returns of ESG funds are positively correlated with the returns of the benchmark indices and the higher the ESG ranking, the better the fund’s performance, with least effect from expense ratios. The Fama-French analysis indicates that ESG funds did not generate positive abnormal returns over this period. Remarkably, the Granger causality is one-way from ESG to market indices in India, which is attributed to the regulatory framework in the country.
Ethiopia’s tax-to-GDP ratio has remained persistently low, averaging only 6.5% over the past three decades. This paper investigates short-run and long-run associations (not structural causal effects) of tax revenue from 1990 to 2023, incorporating a comprehensive set of economic, structural, and institutional variables. Using the ARDL bounds testing approach with a parsimoniously specified model to avoid overfitting, given the sample size (N = 34 observations), a stable long-run cointegrating relationship is found. In the core model, GDP growth, trade openness, VAT, and digital tax administration (ITAS) show significant positive associations, while inflation, agriculture share, the shadow economy, and conflict intensity show significant negative associations. Robustness specifications that add manufacturing share, FDI, and corruption perception yield results consistent with these core associations. The error correction term (−0.431) indicates a relatively rapid adjustment speed. A novel conflict intensity index is constructed that captures the full spectrum of Ethiopia’s conflicts, including the Eritrean-Ethiopian war (1998–2000), the Ogaden insurgency (1992–2018), the Tigray conflict (2020–2022), and ongoing instability, providing robust evidence that conflict is negatively associated with tax revenue. Causal claims are avoided; results are conditional correlations. The findings underscore the need for structural transformation, formalization of the informal economy, prudent FDI incentive design, and peace-building to strengthen domestic revenue mobilization.
