Bibliographic record
Abstract
The authors study the empirical, cross-country relationship between macroeconomic volatility and long-run economic growth. They address four central questions: 1) Does the volatility-growth link depend on country and policy characteristics, such as the level of development or trade openness? 2) Does this link reflect a statistically and economically significant causal effect from volatility to growth? 3) Has this relationship been stable over time and has it become stronger in recent decades? 4) Does the volatility-growth connection actually reveal the impact of crises rather than the overall effect of cyclical fluctuations? The authors find that macroeconomic volatility, and long-run economic growth are indeed negatively related. This negative link is exacerbated in countries that are poor, institutionallyunderdeveloped, undergoing intermediate stages of financial development, or unable to conduct counter-cyclical fiscal policies. They find evidence that this negative relationship actually reflects the harmful effect from volatility to growth. Furthermore, the authors find that the negative effect of volatility on growth has become considerably larger in the past two decades, and that it is mostly due to large recessions rather than normal cyclical fluctuations.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".