Sectoral Shifts and Growth: A World Bank Framework for Better Economic Projections

In a significant departure from traditional economic modeling, a new working paper from the World Bank’s Economic Policy Global Practice introduces a sector-specific framework for long-term growth projections. Authored by Charl Jooste and Remzi Baris Tercioglu and supported by the Climate Support Facility, the paper draws from global research institutions such as the European Central Bank, the Reserve Bank of Australia, Denmark’s Ministry of Finance, and the National Bureau of Economic Research. The research challenges the dominant top-down modeling approaches by constructing a granular, bottom-up model that better reflects how real economies transform through the movement of labor and capital between sectors. This shift has enormous implications for development policy and economic resilience in the face of shocks like climate change.

Disaggregating Productivity to Capture Structural Change

The core innovation of the model lies in disaggregating Total Factor Productivity (TFP) into two components: improvements within sectors and reallocation effects between them. Instead of treating TFP growth as an abstract residual, the model traces how labor and capital shift from low-productivity sectors like agriculture into more productive sectors such as industry and services. These between-sector reallocations, termed “structural transformation,” are at the heart of long-term development trajectories, particularly in emerging markets. The framework replaces a singular production function with sector-specific Cobb-Douglas functions. It also uses constant elasticity of transformation (CET) and substitution (CES) formulas to allocate labor and capital, respectively, thereby ensuring more realistic and dynamic resource flows that respond to economic signals like wage differentials and capital costs.

Case Studies: Ghana and Kyrgyz Republic in Contrast

To illustrate its practical utility, the paper applies the framework to Ghana and the Kyrgyz Republic, two countries with contrasting data richness and economic profiles. Despite more limited data in Ghana, the model effectively captured the positive productivity effects of labor shifting into services. This shift cushioned the impact of shocks, enhancing potential output and national productivity. In Kyrgyzstan, the model revealed that since 2010, declines in aggregate TFP were largely due to between-sector reallocation effects, particularly labor moving out of agriculture. The model showed how these shifts, driven by changes in sectoral employment and capital stock, can significantly alter TFP outcomes over time. Unlike standard macro models, the sectoral framework uncovers how such reallocation patterns drive national productivity, offering a sharper and more policy-relevant analysis.

When Shocks Hit: Sectoral Insights Matter

One of the model’s most compelling strengths is its ability to simulate sector-specific shocks and reveal their macroeconomic ripple effects. When the authors simulated a 10% permanent decline in agricultural productivity due to drought, the differences between the standard and sectoral models became clear. In Kyrgyzstan, if agricultural demand was elastic, labor flowed out of the sector, amplifying the negative effect on GDP and TFP. But when demand was modeled as inelastic, reflecting real-world consumption patterns, employment in agriculture increased, moderating the impact. In Ghana, the shock led to labor movement into services, offsetting much of the productivity loss in agriculture. These findings illustrate how structural transformation can either cushion or intensify economic shocks, depending on how mobile labor and capital are, and the productivity landscape of different sectors.

Elasticities and Initial Shares Shape the Future

The sensitivity analyses conducted further underscore the model’s sophistication. By altering the elasticity of labor transformation and capital substitution, the authors showed how the economy’s response to shocks depends critically on how easily resources can move. With higher elasticities, labor and capital reallocate more freely, magnifying or mitigating the effects of sectoral disruptions depending on initial conditions. The paper also demonstrates that the historical labor and capital shares in sectors influence model outcomes. For example, if agriculture’s employment share is low and mobility is limited, a negative shock to the sector reverberates more intensely throughout the economy. These findings suggest that policymakers need to pay close attention not just to headline GDP or TFP figures, but to the underlying sectoral dynamics and factor mobility that shape them.

Looking Ahead: Toward More Resilient Forecasting

Jooste and Tercioglu conclude with a call for richer, more realistic macroeconomic models that capture the complexities of structural change. Their sectoral framework positions itself as a powerful alternative to traditional approaches, offering not only better diagnostics but also enhanced policy foresight. By capturing the within and between components of TFP and showing how labor and capital allocations evolve in response to economic shocks, the model opens new pathways for understanding development. Future extensions could integrate endogenous within-sector productivity dynamics, such as learning by doing, technological diffusion, or research and development, and map firm-level behavior onto the macro model. These advancements could further strengthen economic forecasting and make it more responsive to the realities of transformation and inequality. For developing economies, especially, this could prove pivotal in navigating a rapidly shifting global landscape.