Perceived barriers and the price inflating effects of informal payments in fresh food retailing in urban Bangladesh
Bibliographic record
Abstract
Abstract Insufficient fruit and vegetable consumption in Bangladesh, currently 25% below the WHO-recommended level remains a major public health concern. One overlooked factor contributing to high consumer prices and limited access is the presence of informal payments within urban fresh food supply chains. This study investigates the extent, nature, and economic implications of informal payments among fruit and vegetable retailers in Bangladesh’s urban markets. A mixed-method approach was employed, combining face-to-face surveys with retailers in two purposively selected areas, Dhaka City Corporation and Manikganj district and in-depth interviews with key supply chain stakeholders. Findings reveal that 36% of retailers reported making informal payments, with a markedly higher incidence in Dhaka (42%) than Manikganj (2%). Mobile (64%) and street vendors (44%) were disproportionately affected, while wet market retailers experienced fewer cases (14%). On average, informal payments amounted to BDT 2720 (US$ 26.93) per month, equivalent to 9.6% of monthly profit—rising to 10.97% among street vendors and 7.09% among wet market sellers. Thematic analysis of qualitative data highlighted how such payments constrain business profitability, discourage formalization, and act as an indirect tax on consumers, thereby inflating food prices and reducing affordability. The study concludes that addressing informal payments is critical to improving supply chain efficiency and food accessibility. Policy interventions should prioritize the formalization and integration of informal retailers, alongside targeted investments in transport logistics, cold storage, marketing infrastructure, and financial inclusion through government support or public–private partnerships.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".