We examine the strengths, limitations, and potential applications of the Enhanced CPS dataset, along with directions for future development.
Strengths¶
Comprehensive Coverage¶
The Enhanced CPS uniquely combines:
- Demographic detail from the CPS including state identifiers
- Tax precision from IRS administrative data
- Calibration to contemporary official statistics
- Open-source availability for research use
This combination enables analyses that would be difficult or impossible with existing public datasets alone.
Methodological Contributions¶
The use of Quantile Regression Forests for imputation represents an advance over traditional matching methods:
- Preserves full conditional distributions
- Captures non-linear relationships
- Maintains realistic variable correlations
- Allows uncertainty quantification
The large-scale calibration to 7,000+ targets ensures consistency with administrative benchmarks across multiple dimensions simultaneously.
Practical Advantages¶
For policy analysis, the dataset offers state-level geographic detail enabling subnational analysis, household structure for distributional studies, tax detail for revenue estimation, program participation for benefit analysis, and recent data calibrated to current totals.
Limitations¶
Temporal Inconsistency¶
The temporal gap between data sources presents a limitation, with 2015 PUF data imputed onto 2024 CPS creating a nine-year gap in underlying populations. This gap means demographic shifts are not fully captured, and tax law changes since 2015 are not reflected in the imputed variables.
While we uprate dollar amounts and calibration partially addresses this, we may not reflect fundamental demographic changes.
Imputation Assumptions¶
The QRF imputation assumes that relationships between demographics and tax variables remain stable, seven predictors sufficiently capture variation, the PUF represents the tax-filing population well, and missing data patterns are ignorable.
These assumptions may not hold perfectly, particularly for subpopulations that the PUF underrepresents.
Calibration Trade-offs¶
With 7,000+ targets, perfect fit to all benchmarks is impossible. The optimization must balance competing objectives across target types, the relative importance of different statistics, stability of resulting weights, and preservation of household relationships.
Users should consult validation metrics for targets most relevant to their analysis.
Applications¶
Tax Policy Analysis¶
The dataset excels at analyzing federal tax reforms through accurate income distribution at high incomes, detailed deduction and credit information, state identifiers for SALT analysis, and household structure for family-based policies.
State and Local Analysis¶
Unlike the PUF, the Enhanced CPS enables state-level studies including state income tax modeling, geographic variation in federal policies, state-specific program interactions, and regional economic impacts.
Integrated Policy Analysis¶
The combination of tax and transfer data supports analysis of universal basic income proposals, earned income tax credit expansions, childcare and family benefit reforms, and healthcare subsidy design.
Microsimulation Model Development¶
As the foundation for PolicyEngine US, the dataset demonstrates how enhanced microdata improve model capabilities through more accurate baseline distributions, better behavioral response modeling, improved validation against benchmarks, and enhanced credibility of results.
Comparison with Alternatives¶
Versus Synthetic Data¶
Unlike fully synthetic datasets, our approach preserves actual survey responses where possible, imputes only missing tax variables, maintains household relationships, and provides transparent methodology.
Versus Administrative Data¶
While not replacing restricted administrative data, the Enhanced CPS offers public availability, household structure, geographic detail, integration with survey content, and no access restrictions.
Versus Other Matching Approaches¶
Compared to traditional statistical matching, QRF better preserves distributions, large-scale calibration ensures consistency, open-source implementation enables replication, and modular design allows improvements.
Future Directions¶
Methodological Enhancements¶
Potential improvements include incorporating additional predictors for imputation, using more recent administrative data when available, developing time-series consistency methods, and adding uncertainty quantification.
Additional Data Integration¶
We could incorporate in future versions state tax return data, program administrative records, consumer expenditure information, and health insurance claims data.
Model Development¶
We could extend the framework to dynamic microsimulation over time, behavioral response estimation, geographic mobility modeling, and life-cycle analysis.
International Applications¶
Researchers could adapt the methodology for other countries facing similar data availability challenges, need for tax-benefit integration, open-source implementation requirements, and cross-national comparison needs.
Conclusion for Researchers¶
The Enhanced CPS provides a valuable resource for policy analysis, though users should understand the limitations (particularly temporal inconsistency), validate results against external benchmarks, consider sensitivity to methodological choices, and contribute improvements to the open-source project.
The dataset represents a pragmatic solution to data limitations. It enables analyses that advance our understanding of tax and transfer policy impacts while we await improved data access.