This paper presents a methodologically rigorous approach to combining survey and administrative data for microsimulation purposes. The technical innovation is impressive, particularly the use of over 7,000 calibration targets. However, from a distributional analysis perspective, I have significant concerns about the poverty measurement results and the treatment of transfers and in-kind benefits.
The most troubling finding is the near-doubling of the poverty rate from 12.7% to 24.9%. This requires urgent attention:
- This discrepancy suggests fundamental issues with either the imputation methodology or the reweighting approach
- The authors acknowledge this briefly but do not adequately investigate the root causes
- A decomposition analysis showing which components (imputation vs. reweighting) drive this increase is essential
- Cross-validation with other poverty estimates (e.g., from the Supplemental Poverty Measure) is needed
The paper focuses heavily on tax variables but gives insufficient attention to benefit programs:
- How are SNAP, WIC, housing subsidies, and other in-kind benefits handled in the imputation?
- The calibration targets include program totals but not distributional aspects of benefit receipt
- For comprehensive fiscal incidence analysis, accurate modeling of both taxes AND transfers is crucial
- Consider adding validation against administrative data on program participation by income level
The poverty measurement issues may partly stem from inadequate treatment of geographic variation:
- The SPM thresholds vary geographically, but it's unclear how well the enhanced dataset captures local cost variations
- State-level calibration targets are included, but within-state variation is also important
- Consider incorporating metropolitan area identifiers and validating against regional poverty statistics
While the Gini coefficient and top income shares are reported, more comprehensive distributional validation is needed:
- Present Lorenz curves comparing the three datasets
- Include other inequality measures (Theil, Atkinson with different inequality aversion parameters)
- Validate income distributions by demographic groups (race, education, family structure)
- Compare mobility measures if panel aspects are available
- The treatment of negative incomes needs more explanation - how are business losses handled?
- Missing income components (e.g., informal income, remittances) should be discussed.
- The paper would benefit from sensitivity analysis on the dropout rate and other hyperparameters.
- Consider discussing implications for cross-country comparative analysis.
This paper makes important methodological contributions but requires major revisions to address the poverty measurement issues. The authors should either resolve the poverty rate discrepancy or provide a thorough explanation of why their estimates differ so dramatically from official statistics. Without this, the enhanced dataset's credibility for distributional analysis is questionable. EOF < /dev/null