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Stage Segmentation of Rural Transformation and Comparisons Among Bangladesh, China, Indonesia, and Pakistan: Combining Machine Learning and New Structural Economics to Facilitate International Agricultural Development and Policy Design

Dong Wang et al.

Asia and the Pacific Policy Studies2025https://doi.org/10.1002/app5.70016article
AJG 1
Weight
0.52

What the paper says

This paper contributes a new paradigm for international agricultural development research. It uses machine learning techniques to aid expert diagnosis of development problems in conjunction with New Structural Economics (NSE) to analyse and design policies to enable effective rural transformation. It conducts a multi‐country, multi‐regional, multi‐level and multi‐dimensional analysis in Bangladesh, China, Indonesia, and Pakistan to identify stage segmentations of rural transformation and examine stagewise associate policies and applicable learnings across each dimension. By presenting structured stages of rural transformation, we provide guidance on designing dynamic comparative‐advantage‐adapting policies that are able to adapt at each stage. This analytical procedure can serve other relevant agricultural development studies.

7 citations

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https://doi.org/https://doi.org/10.1002/app5.70016

Or copy a formatted citation

@article{dong2025,
  title        = {{Stage Segmentation of Rural Transformation and Comparisons Among Bangladesh, China, Indonesia, and Pakistan: Combining Machine Learning and New Structural Economics to Facilitate International Agricultural Development and Policy Design}},
  author       = {Dong Wang et al.},
  journal      = {Asia and the Pacific Policy Studies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1002/app5.70016},
}

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Stage Segmentation of Rural Transformation and Comparisons Among Bangladesh, China, Indonesia, and Pakistan: Combining Machine Learning and New Structural Economics to Facilitate International Agricultural Development and Policy Design

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Evidence weight

0.52

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.47 × 0.4 = 0.19
M · momentum0.68 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.