Entropy Based Modelling for Estimating Demographic Trends

Area of Science:

• Demography
• Computational Social Science
• Mathematical Modeling

Background:

• Accurate forecasting of demographic changes is crucial for policy and planning.
• Existing models may not fully capture the complexities of age distribution and household structures.
• The entropy of age distribution is an empirically validated indicator of demographic evolution.

Purpose of the Study:

• To propose a novel entropy-based method for forecasting country-level demographic changes.
• To frame demographic profile estimation as a constrained optimization problem.
• To develop a versatile framework applicable to various time-dependent demographic variables.

Main Methods:

• Utilizing an age-structured population model for predicting national age distributions.
• Employing an entropy-based formulation within an individual household size model for estimating household age distributions.
• Implementing a total household size model to project population age distributions onto household size distributions.

Main Results:

• The proposed three-stage method effectively forecasts demographic changes.
• Demonstrated accuracy using real-world demographic data.
• The method's generality allows for extension to other demographic variables.

Conclusions:

• The entropy-based approach provides a robust framework for demographic forecasting.
• The method integrates population age structure and household size dynamics.
• This versatile model can be adapted for predicting future demographic trends.