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Beyond Aggregates : A Sector-Specific Framework for Long-Term Growth Modeling

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  • Jooste, Charl
  • Tercioglu, Remzi Baris

Abstract

This paper develops a bottom-up, sector-specific approach to modeling potential output that overcomes limitations of traditional top-down estimates for long-term projections and policy analysis. The model disaggregates total-factor productivity (TFP) growth into within-sector productivity effects and between-sector reallocations. Such endogenous between effects capture structural transformation, notably the shift from low-productivity sectors like agriculture to higher-productivity industrial and service sectors—a key driver of growth in developing countries. At the heart of the framework, wedges in sectoral factor prices, substitution elasticities, and productivity differentials describe the contribution of between-effects to aggregate productivity. Although the approach here can be applied to any macro-structural model, its benefits are illustrated by introducing it into the World Bank’s semi-structural models for Ghana and the Kyrgyz Republic to showcase its potential to enhance the analysis of long-run growth dynamics through structural change.

Suggested Citation

  • Jooste, Charl & Tercioglu, Remzi Baris, 2025. "Beyond Aggregates : A Sector-Specific Framework for Long-Term Growth Modeling," Policy Research Working Paper Series 11127, The World Bank.
  • Handle: RePEc:wbk:wbrwps:11127
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    References listed on IDEAS

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    1. Timo Boppart & Patrick Kiernan & Per Krusell & Hannes Malmberg, 2023. "The Macroeconomics of Intensive Agriculture," NBER Working Papers 31101, National Bureau of Economic Research, Inc.
    2. Robert E. Hall, 2018. "New Evidence on the Markup of Prices over Marginal Costs and the Role of Mega-Firms in the US Economy," NBER Working Papers 24574, National Bureau of Economic Research, Inc.
    3. Robinson, Sherman, 1991. "Macroeconomics, financial variables, and computable general equilibrium models," World Development, Elsevier, vol. 19(11), pages 1509-1525, November.
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