One-shirt-size policy cannot handle poverty issues well since each area has its unique challenges, while having a custom-made policy for each area separately is unrealistic due to limitation of resources as well as having issues of ignoring dependencies of characteristics between different areas. In this work, we propose to use Bayesian hierarchical models which can potentially explain the data regarding income and other poverty-related variables in the multi-resolution governing structural data of Thailand. We discuss the journey of how we design each model from simple to more complex ones, estimate their performance in terms of variable explanation and complexity, discuss models' drawbacks, as well as propose the solutions to fix issues in the lens of Bayesian hierarchical models in order to get insight from data. We found that Bayesian hierarchical models performed better than both complete pooling (single policy) and no pooling models (custom-made policy). Additionally, by adding the year-of-education variable, the hierarchical model enriches its performance of variable explanation. We found that having a higher education level increases significantly the households' income for all the regions in Thailand. The impact of the region in the households' income is almost vanished when education level or years of education are considered. Therefore, education might have a mediation role between regions and the income. Our work can serve as a guideline for other countries that require the Bayesian hierarchical approach to model their variables and get insight from data.

翻译：“一刀切”的政策无法妥善处理贫困问题，因为每个地区都有其独特的挑战，而由于资源限制以及忽视不同地区间特征依赖性问题，为每个地区单独制定定制政策也不现实。本研究提出使用贝叶斯分层模型，该模型能够解释泰国多层级治理结构数据中与收入及其他贫困相关变量相关的信息。我们探讨了从简单到复杂模型的设计过程，评估各模型在变量解释能力与复杂度方面的表现，讨论模型存在的缺陷，并提出基于贝叶斯分层模型视角的解决方案以从数据中获取洞见。研究发现，贝叶斯分层模型的表现优于完全合并模型（单一政策）和完全独立模型（定制政策）。此外，通过加入教育年限变量，分层模型在变量解释能力上得到增强。结果表明，对于泰国所有地区而言，更高教育水平会显著提升家庭收入。当考虑教育水平或教育年限时，地区对家庭收入的影响几乎消失。因此，教育可能在地区与收入之间起中介作用。本研究可为其他需要采用贝叶斯分层方法建模变量并从数据中获取洞见的国家提供参考。