A Novel Framework for Analyzing Structural Transformation in Data-Constrained Economies Using Bayesian Modeling and Machine Learning

TL;DR

This paper introduces a Bayesian and machine learning-based framework for analyzing structural economic transformation in data-scarce environments, enabling robust insights into sectoral shifts in low- and middle-income countries.

Contribution

The paper develops a novel statistical framework combining Bayesian modeling, machine learning data imputation, and factor analysis tailored for data-sparse economic analysis.

Findings

Framework accurately predicts structural changes with up to 60% missing data.

Effective management of data uncertainty through Bayesian models.

Robust sectoral insights derived despite data limitations.

Abstract

Structural transformation, the shift from agrarian economies to more diversified industrial and service-based systems, is a key driver of economic development. However, in low- and middle-income countries (LMICs), data scarcity and unreliability hinder accurate assessments of this process. This paper presents a novel statistical framework designed to address these challenges by integrating Bayesian hierarchical modeling, machine learning-based data imputation, and factor analysis. The framework is specifically tailored for conditions of data sparsity and is capable of providing robust insights into sectoral productivity and employment shifts across diverse economies. By utilizing Bayesian models, uncertainties in data are effectively managed, while machine learning techniques impute missing data points, ensuring the integrity of the analysis. Factor analysis reduces the dimensionality of complex datasets, distilling them into core economic structures. The proposed framework has been validated through extensive simulations, demonstrating its ability to predict structural changes even when up to 60\% of data is missing. This approach offers policymakers and researchers a valuable tool for making informed decisions in environments where data quality is limited, contributing to the broader understanding of economic development in LMICs.