Medicaid, Family Spending, and the Financial Implications of Crowd-Out
Bibliographic record
Abstract
A primary purpose of health insurance is to protect families from medical expenditure risk. Despite this goal and despite the fact that research has found that Medicaid can crowd out private coverage, little is known about the effect of Medicaid on families' spending patterns. This paper implements a simulated instrumental variables strategy with data from the Consumer Expenditure Survey to estimate the effect of an additional family member becoming eligible from Medicaid on family-level health insurance coverage and spending. The results indicate that an additional family member becoming eligible for Medicaid increases the number of people in the family with Medicaid coverage by about 0.135 to 0.142 and decreases the likelihood that a family has any medical spending in a quarter by 2.7 percentage points. As previous research often finds with different data sets, I find evidence that Medicaid expansions crowd out some private coverage. Unlike most other data sets, the Consumer Expenditure Survey allows for considering the financial implications of crowd-out. The results indicate that families that transition from private coverage to Medicaid are able to spend significantly less on health insurance expenses, meaning Medicaid expansions can be welfare improving for families even when crowd-out occurs.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".