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

doi:10.1002/app5.70016

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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Abstract

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.

Original language	English
Article number	e70016
Pages (from-to)	1-17
Number of pages	17
Journal	Asia and the Pacific Policy Studies
Volume	12
Issue number	2
DOIs	https://doi.org/10.1002/app5.70016
Publication status	Published - May 2025

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Pakistan Institute of Development Economics (2025). 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. Asia and the Pacific Policy Studies, 12(2), 1-17. Article e70016. https://doi.org/10.1002/app5.70016

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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",

abstract = "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.",

keywords = "Huang segmentation, machine learning, new structural economics, rural development, rural transformation",

author = "Dong Wang and Chunlai Chen and Christopher Findlay and Jikun Huang and Lin, {Justin Yifu} and Alam, {Mohammad Jahangir} and Abid Hussain and Nunung Nuryartono and Tahlim Sudaryanto and David Shearer",

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year = "2025",

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language = "English",

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Pakistan Institute of Development Economics 2025, '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', Asia and the Pacific Policy Studies, vol. 12, no. 2, e70016, pp. 1-17. https://doi.org/10.1002/app5.70016

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. / Pakistan Institute of Development Economics.
In: Asia and the Pacific Policy Studies, Vol. 12, No. 2, e70016, 05.2025, p. 1-17.

Research output: Contribution to journal › Article › peer-review

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T2 - Combining Machine Learning and New Structural Economics to Facilitate International Agricultural Development and Policy Design

AU - Wang, Dong

AU - Chen, Chunlai

AU - Findlay, Christopher

AU - Huang, Jikun

AU - Lin, Justin Yifu

AU - Alam, Mohammad Jahangir

AU - Hussain, Abid

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AU - Sudaryanto, Tahlim

AU - Shearer, David

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AB - 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.

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KW - machine learning

KW - new structural economics

KW - rural development

KW - rural transformation

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Pakistan Institute of Development Economics. 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. Asia and the Pacific Policy Studies. 2025 May;12(2):1-17. e70016. doi: 10.1002/app5.70016

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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