The Effects of Express Lane Eligibility on Medicaid and <scp>CHIP</scp> Enrollment among Children
Bibliographic record
Abstract
OBJECTIVE: To estimate the impact of Express Lane Eligible (ELE) implementation on Medicaid/CHIP enrollment in eight states. DATA SOURCES/STUDY SETTING: 2007 to 2011 data from the Statistical Enrollment Data System (SEDS) on Medicaid/CHIP enrollment. STUDY DESIGN: We estimate difference-in-difference equations, with quarter and state fixed effects. The key independent variable is an indicator for whether the state had ELE in place in the given quarter, allowing the experience of statistically matched non-ELE states to serve as a formal counterfactual against which to assess the changes in the eight ELE states. The model also controls for time-varying economic and policy factors within each state. DATA COLLECTION/EXTRACTION METHODS: We obtained SEDS enrollment data from CMS. PRINCIPAL FINDINGS: Across model specifications, the ELE effects on Medicaid enrollment among children were consistently positive, ranging between 4.0 and 7.3 percent, with most estimates statistically significant at the 5 percent level. We also find that ELE increased combined Medicaid/CHIP enrollment. CONCLUSIONS: Our results imply that ELE has been an effective way for states to increase enrollment and retention among children eligible for Medicaid/CHIP. These results also imply that ELE-like policies could improve take-up of subsidized coverage under the ACA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".