Bibliographic record
Abstract
ABSTRACTThis paper uses hierarchical cluster analysis to group 35 countries listed as advanced economies by the International Monetary Fund, based upon their scores on the Index of Economic Freedom. Cluster analysis is a technique that groups together observations (cases) based on how similar they are to each other with regards to certain variables. In this paper, countries are grouped together based upon their scores on the ten components from the Index of Economic Freedom (IEF) published by the Heritage foundation. The ten component scores from the IEF that are used to cluster (or group) these nations measure the following: (1) business freedom, (2) trade freedom, (3) fiscal freedom, (4) government size, (5) monetary freedom, (6) investment freedom, (7) financial freedom, (8) property rights, (9) freedom from corruption, and (10) labor freedom. Clustering these nations by the ten components of economic freedom from the IEF should lead to nations with similar institutions and policies grouping together in the same cluster, while those with different institutions and policies should group in differing clusters.A taxonomy based upon six clusters (groups) is developed based upon the results of the cluster analysis. Some of the groupings are consistent with the historical linkages between countries as well as some of the common typologies found in the literature. The former British colonies of the United States, Canada, New Zealand and Australia tend to cluster together, indicating that these nations that share a common history with one another also share similar institutions with regards to economic freedom. Western European nations tend to cluster together as well, due to the fact that they often exhibit a combination of low corruption, strong property rights, but large governments, high taxation and fairly regulated labor markets. The Asian Tiger nations display some similarities to one another with regards to economic freedom, with Hong Kong and Singapore clustering together, as does South Korea and Taiwan.The economic freedom of countries has been used to predict a number of outcomes, including economic growth, happiness, self-employment and new firm founding rates. Using cluster membership variables to predict these outcome variables is an alternative way of examining these relationships. This can be done by simply constructing a series of dummy variables representing which cluster each country belongs to and using these dummy variables in a traditional regression analysis. This method may be particularly useful given the multicollinearity that exist between different components of economic freedom, and the complex interactions that are likely to exist among these variables.INTRODUCTIONA substantial amount of research has examined the relationship between economic freedom and a number of other variables, including economic growth (De Haan & Sturm, 2000; Dollar, 1992; Easton & Walker, 1997) personal happiness (Veenhoven, 2000) and income inequality (Ashby & Sobel, 2008). Increasingly, the relationship between economic freedom and measures of entrepreneurship such as self-employment (Nystrom, 2008), new firm creation (Campbell & Rogers, 2007), and the performance of venture capital investments (Wang & Wang, 2012) have been examined. Two measures of economic freedom at the country-level are commonly used: The Economic Freedom of the World index (Gwartney & Lawson, 2003) and the Index of Economic Freedom (Miller, Holmes, & Feulner, 2012). Both of these indices include an overall score of how economically free each country is, as well as a number of separate scores representing various components of economic freedom. These components of economic freedom include the level of taxation, the strength of property rights, the amount of business freedom, and the stability of the money supply found in each country. The development of these measures has been a tremendous aid in examining the relationship between economic freedom and a number of other variables. …
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".