The Quality-Complementarity Hypothesis: Theory and Evidence from Colombia
Bibliographic record
Abstract
This paper presents a tractable formalization and an empirical investigation of the quality-complementarity hypothesis, the hypothesis that input quality and plant productivity are complementary in generating output quality. We embed this complementarity in a general-equilibrium trade model with heterogeneous, monopolistically competitive firms, extending Using uniquely rich and representative data on the unit values of outputs and inputs of Colombian manufacturing plants, we then document three facts: (1) output prices are positively correlated with plant size within industries on average; (2) input prices are positively correlated with plant size within industries on average; and (3) both correlations are more positive in industries with more scope for quality differentiation, as measured by the advertising and R&D intensity of U.S. industries. The predicted and observed correlations between export status and input and output prices are similar to those for plant size. We present additional evidence that market power of either final-good producers or input suppliers does not fully explain the empirical patterns we observe. These findings are consistent with the predictions of our model and difficult to reconcile with alternative models that impose symmetry or homogeneity of either inputs or outputs. We interpret the results as broadly supportive of the quality-complementarity hypothesis.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".