Confidence sets for dynamic poverty indexes

Area of Science:

• Economics
• Statistics
• Econometrics

Background:

• Poverty indexes are crucial for economic analysis but often lack dynamic considerations.
• Modeling individual income fluctuations is essential for accurate poverty assessment.

Purpose of the Study:

• To develop and validate a dynamic framework for poverty measurement.
• To assess the accuracy of approximating poverty indexes using large numbers in economic systems.
• To establish methods for confidence sets in dynamic poverty measures.

Main Methods:

• Application of the strong law of large numbers to an infinite agent economic system.
• Utilizing the theory of U-statistics to derive a multivariate central limit theorem.
• Developing confidence sets for dynamic poverty indexes.

Main Results:

• A multivariate central limit theorem for dynamic poverty measures was established.
• The study demonstrates how to construct confidence sets, validating the model's appropriateness.
• The approach effectively models the evolution of poverty and inequality.

Conclusions:

• The dynamic framework provides a robust method for analyzing poverty and inequality.
• The model's effectiveness is confirmed by application to Italian income data.
• This approach enables the determination of poverty and inequality trends in real economies.