A Vision for an Integrated Data Infrastructure Using Artificial Intelligence to Analyze Household Income, Consumption, and Wealth in US Federal Law

Abstract

 

This article presents a vision for an integrated data infrastructure powered by artificial intelligence (AI) in the United States, enabling precise analysis of household income, consumption, and wealth. Faced with declining survey response rates, rising costs, and the limitations of a fragmented federal statistical system, the analysis encompasses three reports from the National Academies of Sciences, Engineering, and Medicine (2023–2024) and current federal law (including the Evidence Act and the Federal Data Strategy). The main hypothesis is that integrating administrative, survey, and potentially private data sources using AI will enable more accurate, timely, and consistent well-being measurements and ongoing assessments of the impact of economic policies on citizens’ living standards. The study is based on a content analysis of the reports and a holistic review of legal regulations concerning data management and AI in the federal government. The study determined that the proposed infrastructure could overcome existing problems (underestimation, inconsistent definitions, and poor timeliness) by creating uniform measures based on national accounts, enabling precise determination of income sources and wealth structure, and assessing the effects of redistribution, pension provision, and intergenerational transfers. The conclusions point to the groundbreaking potential of the implemented neural data management model using AI, which is expected to increase the accuracy, timeliness, and usefulness of statistics while maintaining privacy protection. The originality of the article lies in its synthesis of recent academic reports with legal analysis and the introduction of the concept of household “budget identity”.