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The purpose of this research is to develop a predictive model that can estimate the volume of remittances channeled toward Yemen’s economic reconstruction efforts.

This study utilized a time-series dataset encompassing remittance inflows into Yemen’s economy from 1990 to 2022. The Box-Jenkins autoregressive integrated moving average (ARIMA) methodology was employed to forecast remittance inflows for the period 2023 to 2030.

The study’s findings indicate a downward trajectory in remittance inflows over the next eight years, with projections suggesting a potential decline to 4.122% of Yemen’s gross domestic product by the end of 2030. This significant decrease in remittance inflows highlights the immediate need for concrete steps from economic policymakers to curb the potential decline in remittance inflows and its impact on Yemen’s economic recovery efforts.

The impact of global remittance inflows on various macroeconomic and microeconomic factors has long been of interest to researchers, policymakers, and academics. Yemen has been embroiled in violent clashes over a decade, leading to a fragmentation of central authority and the formation of distinct local alliances. In such prolonged turmoil, foreign aid often falls short, providing only temporary relief for basic needs. Consequently, the importance of migrant remittances in sustaining communities affected by conflict and disasters has increased. Remittances have played a crucial role in fostering economic progress and improving social services for families transitioning from conflict to peace. Therefore, this study aims to estimate and forecast the volume of remittances flowing into Yemen, to assist in the nation’s economic reconstruction.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

This paper presents the findings of assessing the strategies required for improved work-life balance (WLB) of construction workers in Eswatini. This was done to improve the work-life relationship of construction workers and, in turn, improve the service delivery of the construction industry in the country.

The study adopted a quantitative research approach using a questionnaire administered to construction professionals in the country. The data gathered were analysed using frequency, percentage, Mann–Whitney U test, exploratory and confirmatory factor analysis (CFA).

The findings revealed that the level of implementation of WLB initiatives in the Eswatini construction industry is still low. Following the attaining of several model fitness, the study found that the key strategies needed for effective WLB can be classified into four significant components, namely: (1) leave, (2) health and wellness, (3) work flexibility, and; (4) days off/shared work.

The findings offer valuable benefits to construction participants as the adoption of the identified critical strategies can lead to the fulfilment of WLB of the construction workforce and by extension, the construction industry can benefit from better job performance.

This study is the first to assess the strategies needed for improved WLB of construction workers in Eswatini. Furthermore, the study offers a theoretical platform for future discourse on WLB in Eswatini, a country that has not gained significant attention in past WLB literature.