# Conclusion

We present a methodology for creating enhanced microsimulation datasets that combine the strengths
of survey and administrative data sources. The Enhanced CPS dataset demonstrates that careful
application of modern statistical methods can substantially improve the data available for policy
analysis.

## Summary of Contributions

Our work makes several key contributions:

**Methodological Innovation**: The use of Quantile Regression Forests for imputation preserves
distributional characteristics while maintaining computational efficiency. The large-scale
calibration to 2,813 targets pushes the boundaries of survey data enhancement.

**Practical Tools**: We provide open-source implementations that enable researchers to apply,
modify, and extend these methods. The modular design facilitates experimentation with alternative
approaches.

**Validated Dataset**: The Enhanced CPS itself serves as a public good for the research community,
enabling studies that would otherwise require restricted data access.

**Reproducible Research**: All code, data, and documentation are publicly available, supporting
reproducibility and collaborative improvement.

## Key Findings

The validation results demonstrate that combining survey and administrative data through principled
statistical methods can achieve:

- Improved income distribution representation
- Better alignment with program participation totals
- Maintained demographic and geographic detail
- Suitable accuracy for policy simulation

While no dataset perfectly represents the full population, the Enhanced CPS provides a pragmatic
balance of accuracy, detail, and accessibility.

## Implications for Policy Analysis

Enhanced microdata availability creates immediate implications for policy analysis. More accurate
representation of high incomes enables better analysis of progressive tax reforms and revenue
estimates. Researchers can now analyze tax and transfer policies jointly rather than in isolation.
Geographic identifiers enable subnational policy analysis not possible with administrative tax data
alone. Finally, household structure allows examination of policy impacts across family types and
income levels.

## Broader Implications

Beyond the specific dataset, this work demonstrates several broader principles. Combining multiple
data sources can overcome individual limitations, showing the value of data integration. Making
methods and data publicly available accelerates research progress and demonstrates open science
benefits. While perfect data may never exist, pragmatic enhancements can substantially improve
analysis capabilities. Furthermore, open-source approaches enable community contributions and
continuous improvement.

## Limitations and Future Work

We acknowledge important limitations including temporal inconsistency between data sources,
imputation model assumptions, calibration trade-offs, and validation challenges. Future work should
address these through more recent administrative data, enhanced imputation methods, additional
validation exercises, and uncertainty quantification.

## Call to Action

We encourage researchers to apply the Enhanced CPS to policy questions where combined demographic
and tax detail adds value, compare findings with other data sources and contribute validation
results, leverage the open-source nature to make methodological enhancements, and document use
cases, limitations discovered, and suggested improvements.

## Final Thoughts

The Enhanced CPS represents one approach to a fundamental challenge in microsimulation: the need for
comprehensive, accurate microdata. While not perfect, it demonstrates that substantial improvements
are possible through careful methodology and open collaboration.

As data availability evolves and methods advance, this work contributes to a future where policy
analysis rests on increasingly solid empirical foundations. Our ultimate goal remains better
informed policy decisions that improve social welfare.

The enhanced dataset, complete documentation, and all source code are available at
[https://github.com/PolicyEngine/policyengine-us-data](https://github.com/PolicyEngine/policyengine-us-data).
