Income Verification On The Exchanges: The Broader Policy Picture
Bibliographic record
Abstract
The Affordable Care Act scandal de jour (or at least one of them) is the difficulty the exchanges have faced in verifying the eligibility of many premium tax credit applicants. Two Department of Health and Human Services Office of Inspector General Reports in early July documented the existence of these problems. One reported that as of the first quarter of 2014, the federal exchange alone had been unable to resolve 2.6 or 2.9 million data inconsistencies. Another reported that internal controls at the federal and two state exchanges were not fully effective in ensuring that individuals enrolled in exchanges were in fact eligible. House Republicans claim that in fact there are 4 million data inconsistencies affecting half of all enrollments. In House Energy and Commerce hearings on June 10, 2014, Republican Representative Charles Bustany Jr. claimed that $44 billion in improper payments would be made over the next 10 years. Douglas Holtz-Eakin, a former Bush Administration official, who testified at the hearings claims that improper payments may equal $152 billion. The House Energy and Commerce Health Subcommittee is holding further hearings on data inconsistencies on July 16. The seriousness of verification issues should not be overestimated. The administration has been put in place procedures to verify carefully premium tax credit applications. Many of the discrepancies CMS is attempting to resolve do not relate to income eligibility, and those that do may result ultimately in a finding of eligibility for increased, rather than decreased, premium tax credits. A discrepancy that could result in the need for additional documentation may be as trivial as a hyphen left out of a name or a digit transposed on a Social Security number. Unfortunately, programs proposed by Republicans and other ACA opponents that in fact make a serious attempt to cover the uninsured will require income reporting and face similar difficulties. Current reform proposals that avoid coverage eligibility determinations will not in fact cover the uninsured. While the administration could have perhaps done a better job in making eligibility determinations, any means-tested program faces a similar challenge. It is possible to design a system that does not rely on means testing and could cover low-income and high-cost uninsured Americans, as I describe below. But it would be a very different system than the ACA or alternatives currently being proposed.
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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 | 0.036 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.045 | 0.036 |
| Insufficient payload (model declined to judge) | 0.044 | 0.003 |
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".