Firm-Embedded Productivity and Cross-Country Income Differences
Bibliographic record
Abstract
We measure the contribution of firm-embedded productivity to cross-country income differences. By firm-embedded productivity we refer to the components of productivity that differ across firms and that can be transferred internationally, such as blueprints, management practices, and intangible capital. Our approach relies on micro-level data on the cross-border operations of multinational enterprises (MNEs). We compare the market shares of the exact same MNE in different countries and document that they are about four times larger in developing than in high-income coun-tries. This finding indicates that MNEs face less competition in less-developed coun-tries, suggesting that firm-embedded productivity in those countries is scarce. We propose and implement a new measure of firm-embedded productivity based on this observation. We find a strong positive correlation between our measure and output per worker across countries. In our sample, differences in firm-embedded productivity account for roughly a third of the cross-country variance in output per worker.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".