Consumer Surplus From Suppliers: How Big Is It and Does It Matter for Growth?
Bibliographic record
Abstract
Consumer surplus, the area between the demand curve and the price, plays a key role in many models of trade and growth. Quantifying it typically requires estimating and extrapolating demand curves. This paper provides an alternative approach to measuring consumer surplus by focusing on firms as consumers of inputs. We show that the elasticity of a downstream firm's marginal cost to supplier additions and separations measures the downstream firm's consumer surplus relative to its input costs. Using Belgian data and instrumenting for changes in supplier access, we find that for every 1% of suppliers gained or lost, the marginal cost of downstream firms falls or rises by roughly 0.3%. Our estimates are directly informative about the strength of love‐of‐variety effects and the gains from movements along quality ladders. We use our microeconomic estimates of consumer surplus to assess the macroeconomic importance of supplier additions and separations in a growth accounting framework. We find that supplier churn plausibly accounts for about half of aggregate productivity growth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".