Research Article
Socio-Economic and Demographic Drivers of Irregular Migration to South Africa: A Bayesian and Logistic Regression Analysis
Shambel Selman Abdo*
,
Tariku Abate Erigicho
Issue:
Volume 14, Issue 3, June 2025
Pages:
99-108
Received:
10 May 2025
Accepted:
29 May 2025
Published:
23 June 2025
DOI:
10.11648/j.ajtas.20251403.11
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Abstract: Human trafficking negatively impacts individuals and national development, yet its root causes are poorly understood. This study aimed to investigate the socioeconomic and demographic factors influencing irregular migration from Shashogo Woreda, Hadiyya Zone, Central Ethiopia to South Africa. Data from 346 respondents across eight Kebeles were analyzed using bivariate and Bayesian logistic regression models. The findings revealed that about 50L. 57% of household heads plan to send a family member abroad, while 49.42% do not. Female-headed households are significantly less likely to plan irregular migration than male-headed ones (Coeff = -1.527, OR = 0.217, P = 0.001). The odds of planning migration rise by 45.7% per additional household member (Coeff = 0.784, OR = 1.457, P = 0.000) and by 21.2% for each year increase in the household head’s age (Coeff = 0.193, OR = 1.212, P = 0.000). Education negatively correlates with migration plans, as those with primary education (Coeff = -2.652, OR = 0.816, P = 0.001) or a diploma and above (Coeff = -3.228, OR = 0.040, P = 0.001) are less likely to plan migration compared to those with secondary education, while uneducated respondents show no significant difference. Non-agricultural employment such as trade (Coeff = -2.781, OR = 0.062, P = 0.001), formal jobs (Coeff = -1.549, OR = 0.212, P = 0.020), or other work (Coeff = -2.453, OR = 0.086, P = 0.002) also lowers migration plans compared to agricultural work. Urban residents are more likely to plan migration than rural ones (Coeff = 1.309, OR = 3.704, P = 0.001), and those unaware of migration risks are significantly more likely to plan migration than those who are aware (Coeff = 1.623, OR = 5.066, P = 0.001). In conclusion, irregular migration from Shashogo Woreda is driven by structural socio-economic challenges and the allure of better opportunities abroad. Key predictors include age, sex, family size, education, employment type, residence, and risk awareness. Despite awareness of migration risks, economic hardships remain dominant drivers. Effective policy responses should focus on rural development, youth employment, education access, and safe migration alternatives to address the root causes.
Abstract: Human trafficking negatively impacts individuals and national development, yet its root causes are poorly understood. This study aimed to investigate the socioeconomic and demographic factors influencing irregular migration from Shashogo Woreda, Hadiyya Zone, Central Ethiopia to South Africa. Data from 346 respondents across eight Kebeles were anal...
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Research Article
Stationarity in Prophet Model Forecast: Performance Evaluation Approach
Evelyn Adebola Omotoye,
Bunmi Segun Rotimi*
Issue:
Volume 14, Issue 3, June 2025
Pages:
109-117
Received:
29 April 2025
Accepted:
12 May 2025
Published:
30 June 2025
DOI:
10.11648/j.ajtas.20251403.12
Downloads:
Views:
Abstract: Stationarity plays a crucial role in time series analysis, significantly influencing model performance and the reliability of forecasts. Despite its importance, many real-world datasets exhibit non-stationary behaviour, which can lead to misleading or spurious forecasting outcomes. This study explores the impact of stationarity on the performance of the Prophet Model, a scalable time series forecasting tool developed by Facebook, by comparing forecasts from both stationary and non-stationary versions of the same dataset. The monthly international airline passenger data was downloaded from the Kaggle website. We applied the Prophet to generate forecasts from raw (non-stationary) data and its transformed (stationary) version, obtained through first differencing. Three metrics, including the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coverage, were used to evaluate both versions of the forecasts. The findings reveal that forecasted values from stationary and non-stationary data exhibit strong correlations with actual values, as confirmed by T-tests and Pearson correlation coefficients. However, the Prophet model demonstrated notably better performance on stationary data compared to the non-stationary version, with the forecast for stationary data showing lower RMSE and MAE values and higher coverage percentages. The study shows the importance of ensuring stationarity before forecasting, even when using advanced models like the Prophet Model. We suggest that integrating stationarity considerations into future iterations of the Prophet algorithm could further enhance its predictive capabilities.
Abstract: Stationarity plays a crucial role in time series analysis, significantly influencing model performance and the reliability of forecasts. Despite its importance, many real-world datasets exhibit non-stationary behaviour, which can lead to misleading or spurious forecasting outcomes. This study explores the impact of stationarity on the performance o...
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