Labour Share Fluctuations in Emerging Markets: The Role of the Cost of Borrowing
Bibliographic record
Abstract
This paper contributes to the literature by documenting labour income share fluctuations in emerging-market economies and proposing an explanation for them. Time-series data indicate that emerging markets differ from developed markets in terms of changes in the labour share over the business cycle. Labour share is more volatile in emerging markets and is procyclical, especially in countries facing countercyclical interest rates. In contrast, labour share in developed markets is more stable and slightly countercyclical. A frictionless small open-economy real business cycle model cannot account for these facts. I introduce working capital into this model, which generates liquidity need for labour payments. The main result is that the behaviour of the cost of borrowing can predict the right sign of the co-movement between labour share and output in both country groups, and can partly be responsible for the volatility of labour share. I also show that imperfect financial markets in the form of credit restrictions not only amplify the results for the variability of labour share but also help better explain some of the striking business cycle regularities in emerging markets, such as highly volatile consumption, strongly procyclical investment and countercyclical net exports.
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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".