Inequality and Macroeconomic Factors: A Time-Series Analysis for a Set of OECD Countries
Bibliographic record
Abstract
In this work, we study the short- and long-run properties of different inequality series vis-à-vis the most important macroeconomic series for a set of OECD countries. We employ standard tools of time series macro-econometrics (e.g. stationarity tests, detrending, comovements analysis, Granger-causality tests, etc.) in order to possible uncover some fresh stylized facts about inequality. The broad picture emerging from our empirical analysis is one where some common patterns coexist together with several country specificities. More specifically, most of inequality series are not stationary; long-run equilibrium relationships between share prices and inequality emerge in Canada, the U.S., and the U.K.; at the business cycle frequencies, most inequality series are counter-cyclical (with the exception of Germany), negatively correlated with inflation and positively correlated with unemployment; consumption inequality is pro-cyclical in English-speaking countries; the comovements between inequality series and government consumption appear to be heavily dependent on the institutions of the countries under analysis; Granger-causality tests suggest that in some cases inequality Granger-causes output.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".