深度报告-2025-09-01-兰德-联邦预算绘图工具（英）页_44页_567kb

Federal Budget Mapping Tool Summary

This working paper introduces a set of tools for mapping major U.S. federal spending to specific populations, businesses, and geographic areas. Developed by RAND Education and Labor, the tools are part of the von Furstenberg Family Foundation RAND Budget Model, which aims to provide more detailed insights into the distributional effects of federal policies.

Approach

The tools process federal spending data from USAspending.gov, a transparent source required under the Digital Accountability and Transparency Act. USAspending provides detailed information but faces issues like incompleteness and quality concerns, especially with subaward data. The tools address these issues by:

• Using parent award records exclusively.
• Constructing synthetic population datasets by statistically matching nationally representative surveys (Current Population Survey, American Community Survey, Survey of Consumer Finances).
• Linking spending data to the synthetic dataset using demographic, geographic, and economic attributes.

Key Features

• Spending Data Processing: The tools include methods for cleaning and categorizing federal spending data, extracting attributes like recipient type, NAICS codes, and budget functions to match spending to population segments.
• Synthetic Population Dataset: A statistical matching approach merges data from multiple surveys to create detailed demographic, geographic, and economic profiles.
• Integration: The tools map federal spending to recipients and beneficiaries by matching funding records with the synthetic population dataset, enabling analysis of geographic and sectoral distributions.

Limitations

• The U.S. Aspending dataset lacks coverage for some smaller agencies and has inconsistent detail.
• Benefit assignments for certain programs, like Medicaid, can be indirect and complex to map.
• Subaward data quality is poor, limiting geographic and beneficiary mapping.
• Mismatches may occur due to differences in demographic distributions across surveys.

Applications

• Displaying geographic distributions of federal spending.
• Analyzing spending by industry and population groups.
• Evaluating the effects of federal policies by mapping expenditures to beneficiary demographics.

Future Improvements

• Incorporating additional data sources like the Survey of Income and Program Participation.
• Using natural language processing to extract eligibility criteria from program descriptions.
• Expanding beneficiary mapping to non-universal programs.

Concluding Thoughts

This analysis highlights the importance of integrating diverse data sources for policy research. While the tools are still in their first iteration, they provide granular insights into how federal spending impacts different segments of the population. These tools will be updated to improve accuracy and expand their applicability.

This summary captures the essence of the working paper, focusing on its methodology, key findings, and future directions within a concise and structured format.