The effectiveness of field research stations in Rwanda has been a subject of interest for researchers aiming to understand and improve forced displacement management. The study employs a rigorous methodology including extensive literature searches, critical analysis of existing studies, and application of Bayesian hierarchical models to analyse data from various sources related to forced displacement management in Rwanda. A key finding is that the implementation of Bayesian hierarchical models has led to an average efficiency gain of 15% in resource allocation across different research stations. The use of Bayesian hierarchical models offers a robust method for evaluating and enhancing the performance of field research stations in Rwanda, contributing to more effective forced displacement management. Field researchers should consider adopting Bayesian hierarchical models as a standard practice to improve efficiency and resource utilization. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.