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Optimising manufacturing growth: when to decouple from global value chains?

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  • Karishma Banga

    (King’s College London)

  • Bishwanath Goldar

    (Institute of Economic Growth)

Abstract

In this paper, we empirically examine how increasing forward and backward global value chains (GVC) linkages affect manufacturing growth in a set of low- and middle-income countries for the period 1995–2018. Using panel data methodologies of fixed-effects, quantile, and threshold regressions, we find that the relationship between these linkages and manufacturing growth is nonlinear. Our results reveal that there is a positive relationship between backward linkages (BL) and manufacturing growth, but once BL exceed the 11–20% range, the relationship becomes negative. Similarly, FL can drive manufacturing growth up to a certain threshold, beyond which it no longer boosts growth and may even start to reduce it. The negative relationship between GVC linkages and manufacturing growth is attributed to the ‘in–out–in’ theory of GVC participation. Based on these findings, we identify countries that can benefit from increasing GVC participation and those that will benefit from a decoupling from GVCs.

Suggested Citation

  • Karishma Banga & Bishwanath Goldar, 2025. "Optimising manufacturing growth: when to decouple from global value chains?," Journal of Social and Economic Development, Springer;Institute for Social and Economic Change, vol. 27(2), pages 218-242, September.
    • Handle: RePEc:spr:jsecdv:v:27:y:2025:i:2:d:10.1007_s40847-024-00399-5
    DOI: 10.1007/s40847-024-00399-5
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    JEL classification:

    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F15 - International Economics - - Trade - - - Economic Integration
    • O19 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - International Linkages to Development; Role of International Organizations
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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