Looking under the carpet: A granular approach to the unusual productivity growth in Ireland
Bibliographic record
Abstract
This paper is aimed to empirically test for an open and small economy like Ireland the “granular hypothesis” (Gabaix 2011), originally proved for the US, which posits that firm-level productivity shocks can explain a sizable portion of aggregate productivity fluctuations. Additionally, the author tries and tests a second hypotheses suggesting the, given the small size, less diversification of the Irish economy, compared to that of the US, the granular effect is likely to be stronger and more important. The Irish case is particularly relevant as Ireland has been experiencing increasing economic concentration in recent years, to the point that micro shocks to a few selected firms in 2015 led to significant level shifts in aggregate variables like GDP (+34 per cent) and, particularly, labour productivity (+23 per cent) and total factor productivity (-12 per cent). Making use of an original combination of macro data from the Ireland’s Central Statistics Office (CSO) and the OECD with micro data from the Annual Business Survey of Economic Impact (ABSEI), both granular hypotheses are tested in Ireland for the period 2000-2016. Research findings confirm that productivity shocks to the 5 largest firms (in terms of value added) in Ireland account for a large fraction (about one third) of aggregate productivity growth, which is much larger than that found of the US economy. These empirical results shed light on the origins of Irish productivity fluctuations, the consequences of economic concentration on resilience and the importance of diversification policies aimed at broadening Ireland’s enterprise base of productive firms.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".