Price discrimination in obstetric services – a case study in Bangladesh
Bibliographic record
Abstract
This article examines the existence of price discrimination for obstetric services in two private hospitals in Bangladesh, and considers the welfare consequences of such discrimination, i.e. whether or not price discrimination benefited the poorer users. Data on 1212 normal and caesarean section patients discharged from the two hospitals were obtained. Obstetric services were chosen because they are relatively standardised and the patient population is relatively homogeneous, so minimising the scope and scale of product differentiation due to procedure and case-mix differences. The differences between the hospital list price for delivery and actual prices paid by patients were calculated to determine the average rate of discount. The welfare consequences of price discrimination were assessed by testing the differences in mean prices paid by patients from three income groups: low, middle and high. The results suggest that two different forms of price discrimination for obstetric services occurred in both these hospitals. First, there was price discrimination according to income, with the poorer users benefiting from a higher discount rate than richer ones; and second, there was price discrimination according to social status, with three high status occupational groups (doctors, senior government officials, and large businessmen) having the highest probability of receiving some level of discount.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".