Adaptation of the Foster‐Greer‐Thorbecke poverty measures for the measurement of catastrophic health expenditures
Bibliographic record
Abstract
In this paper we provide an adaptation of the Foster-Greer-Thorbecke (FGT) family of poverty measures for the measurement and analysis of catastrophic health expenditure (CHE). The adaptation entails introducing the FGT-type family of CHE measures with a single CHE aversion parameter whose value can be increased to put greater emphasis on the health expenditure proportions that overshoot the prescribed threshold proportions for CHE characterization by the greatest margins. The subgroup decomposition property of the FGT-type family of CHE measures (i.e., the ability to isolate the contributions of the various mutually exclusive population subgroups to the overall FGT-type CHE measure) is discussed along with other normative properties. We also show how the estimation and subgroup decomposition of the FGT-type family of CHE measures can be conveniently accomplished using ordinary least squares regression. An illustrative example is also provided to show how the FGT approach can provide valuable insights into the distribution of CHE among the healthcare spending units that incur CHE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".