Regional and spatial dependence of poverty factors in Thailand, and its use into Bayesian hierarchical regression analysis
<|reference_start|>Regional and spatial dependence of poverty factors in Thailand, and its use into Bayesian hierarchical regression analysis: Poverty is a serious issue that harms humanity progression. The simplest solution is to use one-shirt-size policy to alleviate it. Nevertheless, each region has its unique issues, which require a unique solution to solve them. In the aspect of spatial analysis, neighbor regions can provide useful information to analyze issues of a given region. In this work, we proposed inferred boundaries of regions of Thailand that can explain better the poverty dynamics, instead of the usual government administrative regions. The proposed regions maximize a trade-off between poverty-related features and geographical coherence. We use a spatial analysis together with Moran's cluster algorithms and Bayesian hierarchical regression models, with the potential of assist the implementation of the right policy to alleviate the poverty phenomenon. We found that all variables considered show a positive spatial autocorrelation. The results of analysis illustrate that 1) Northern, Northeastern Thailand, and in less extend Northcentral Thailand are the regions that require more attention in the aspect of poverty issues, 2) Northcentral, Northeastern, Northern and Southern Thailand present dramatically low levels of education, income and amount of savings contrasted with large cities such as Bangkok-Pattaya and Central Thailand, and 3) Bangkok-Pattaya is the only region whose average years of education is above 12 years, which corresponds (approx.) with a complete senior high school.<|reference_end|>
@article{gómez-méndez2024regional,
    title={Regional and spatial dependence of poverty factors in Thailand, and its
  use into Bayesian hierarchical regression analysis},
    author={Irving G'omez-M'endez, Chainarong Amornbunchornvej},
    journal={arXiv preprint arXiv:2408.09760},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09760},
    primaryClass={stat.ME cs.SI econ.GN q-fin.EC stat.AP}
}