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Deindustrialization and the incidence of poverty: Empirical evidence from developing countries

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  • Liu, Laihui
  • An, Suxia

Abstract

Poverty has been an important impediment to development in developing countries, and its eradication is a common human mission. Although the literature has extensively analyzed the determinants of poverty in developing countries, little attention has been paid to the impact of deindustrialization. Based on this, this paper collects cross-country panel data for 44 developing countries from 1990 to 2018 and systematically examines the effects and mechanisms of deindustrialization on the incidence of poverty based on a fixed-effects model, and then identifies the hindering factors and causes of deindustrialization in the process of poverty reduction in developing countries. The empirical results show that deindustrialization increases poverty in developing countries, and a 1 % increase in the share of services relative to manufacturing in developing countries is associated with a 0.417 % increase in the incidence of poverty, all else being equal, and its effect is stable, even after accounting for endogeneity. Mechanistic analysis suggests that deindustrialization exacerbates poverty through three channels: depressed economic growth, lower quality of employment, and loss of traditional manufacturing jobs. Further research finds that the effect of deindustrialization on poverty is more pronounced in countries that are likely to have premature deindustrialization, and that lower agricultural productivity and labor migration across gradients are important causes of premature deindustrialization in developing countries. Therefore, low middle income countries, especially in sub-Saharan Africa, need to focus on guarding against a possible increase in the proportion of the population living in poverty due to shrinking manufacturing.

Suggested Citation

  • Liu, Laihui & An, Suxia, 2023. "Deindustrialization and the incidence of poverty: Empirical evidence from developing countries," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
  • Handle: RePEc:eee:tefoso:v:187:y:2023:i:c:s0040162522007235
    DOI: 10.1016/j.techfore.2022.122202
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    2. Mengdi Wang & Xiaobing Peng, 2023. "How to Develop Sustainably after Poverty Alleviation in Poverty-Stricken Areas under Paired Assistance: A Quantitative Assessment Framework Based on System Dynamics Model," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
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