Inventory Management, Product Quality, and Cross‑Country Income Differences
Bibliographic record
Abstract
Previous research has documented that export shipments are “lumpy”— exporters make infrequent and relatively large shipments to any given export destination. This fact has been interpreted as implying that fixed, per shipment cost and inventory management decisions play a key role in international trade. We document here that exports from poor countries are considerably more lumpy—have higher fixed per shipment cost— than those from rich countries. Using a model of trade with inventory management, we estimate that the country at the ninetieth percentile of the distribution of per shipment costs has almost three times higher costs than the one at the tenth percentile. We show that these per shipment cost differences have a reduced-form representation given by an ad valorem trade cost that varies with export country income (as in Waugh 2010 ). A calibrated version of the model that incorporates these estimates and allows for endogenous product quality reveals that cross-country differences in per shipment costs explain almost 40 percent of the observed cross-country differences in income. It also shows that policies that lower per shipment costs can lead to significant welfare gains, mainly due to induced quality upgrading. (JEL F12, F14, F43, G31, O16, O19)
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".