Country Clustering in Comparative Political Economy
Bibliographic record
Abstract
In the comparative political economy of rich democracies there is a long tradition of classifying countries into one of a small number of categories based on their economic institutions and policies. The most recent of these is the Varieties of Capitalism project, which posits two major clusters of nations: coordinated and liberal market economies. This classification has generated controversy. We leverage recent advances in mixture model-based clustering to see what the data say on the matter. We find that there is considerable uncertainty around the number of clusters and, barring a few cases, which country should be placed in which cluster. Moreover, when viewed over time, both the number of clusters and country membership change considerably. As a result, arguments about who has the “right” typology are misplaced. We urge caution in using these country classifications in structuring qualitative inquiry and discourage their usage as indicator variables in quantitative analysis, especially in the context of time-series cross-section data. We argue that the real value of both Esping-Andersen’s work and the Varieties of Capitalism project consists of their theoretical contributions and heuristic classification of ideal types.\n\nIn der vergleichenden Politischen Ökonomie reicher Demokratien gibt es eine lange Tradition, Länder aufgrund ihrer unterschiedlichen wirtschaftlichen Institutionen und Policies zu typologisieren. Die jüngste dieser Typologien – das "Varieties-of-Capitalism"-Konzept – erfasst zwei Gruppen von Ländern: koordinierte und liberale Marktwirtschaften. Da diese Klassifizierung einige Kontroversen hervorgerufen hat, nutzen die Autoren neueste Fortschritte im "mixture model-based clustering", um zu prüfen, welche Erkenntnisse die Daten zu diesem Problem liefern. Die Ergebnisse weisen eine beträchtliche Unsicherheit hinsichtlich der Anzahl der Cluster und, mit wenigen Ausnahmen, der Zuordnung der Länder zu Clustern auf. Betrachtet man größere Zeiträume, variieren darüber hinaus die Anzahl der Cluster und Ländermitgliedschaften erheblich. Als Folge dieser Befunde halten die Autoren Argumentationen über die "richtige" Typologisierung für unangebracht und raten davon ab, diese Länderklassifizierungen zur Strukturierung qualitativer Studien heranzuziehen oder als Indikatorvariablen in quantitativen Analysen zu nutzen. Dies gilt insbesondere im Kontext von gepoolten Zeitreihen- und Querschnittsdaten. Sie argumentieren, dass der substanzielle Wert sowohl der Forschung von Esping-Andersen als auch des "Varieties-of-Capitalism"-Ansatzes in den Beiträgen zur Theorie und den heuristischen Klassifizierungen von Idealtypen besteht.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.040 | 0.025 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.015 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".