In the quest to understand and foster economic growth in low- and middle-income countries (LMICs), researchers have long grappled with a persistent challenge: incomplete and inconsistent data. This data scarcity has made it difficult to measure structural transformation—the shift of labor and resources from agriculture to industry and services—a critical driver of economic development. However, a groundbreaking study published in *Scientific Reports* (known in English as *Nature Scientific Reports*) offers a new framework to tackle this issue, with significant implications for policymakers and the energy sector.
Led by Ronald Katende of the Department of Mathematics at Kabale University, the research integrates Bayesian hierarchical modeling, machine learning-based imputation, and factor analysis to create a unified approach for analyzing structural transformation. The study simulates data sparsity using World Bank data from Kenya, Nigeria, and Ghana between 2000 and 2020, evaluating three imputation techniques: SoftImpute, k-Nearest Neighbors, and a traditional method. The results reveal that SoftImpute achieves the lowest root mean square error (RMSE) for sectoral indicators, while k-Nearest Neighbors excels in reconstructing GDP.
“This framework allows us to distill the latent drivers of productivity change and incorporate sectoral and temporal heterogeneity under uncertainty,” Katende explains. “It provides a more accurate and interpretable tool for structural diagnostics, even when data is missing.”
The study’s findings highlight distinct national trajectories: Kenya’s service-led growth, Nigeria’s oil-linked industrial volatility, and Ghana’s balanced expansion. These insights could be transformative for the energy sector, particularly in countries like Nigeria, where industrial volatility is closely tied to oil production. By better understanding these dynamics, energy policymakers can make more informed decisions about investment, infrastructure, and resource allocation.
The framework’s ability to handle missing data with greater accuracy than traditional models offers a scalable tool for data-informed policymaking. “This is not just about filling in the gaps; it’s about understanding the underlying patterns and drivers of economic change,” Katende adds. “It’s a tool that can help us navigate the complexities of economic development in LMICs.”
As the world grapples with the impacts of climate change and the need for sustainable energy solutions, this research could shape future developments in the energy sector. By providing a clearer picture of structural transformation, it enables policymakers to design strategies that align economic growth with environmental sustainability. The framework’s adaptability and accuracy make it a valuable asset for researchers, policymakers, and industry leaders alike, offering a path forward in the pursuit of sustainable and inclusive economic development.