International Financial Centers and the World Economy
Bibliographic record
Abstract
International financial centers (IFCs) are countries and territories with low tax rates and other features that make them attractive investment locations. These properties of IFCs occasionally raise concerns that they may erode tax collections, divert economic activity, and otherwise burden nearby high-tax countries. A large body of economic research over the last 15 years considers these issues, with findings that point sharply in the opposite direction: the evidence strongly suggests that the policies of IFCs contribute to investment, employment, and the efficient functioning of markets and government policies in other countries.\nIFCs contribute to economic activity by improving the potential profitability of business operations elsewhere. As a result, for a typical American firm, a 1 percent greater likelihood of establishing an IFC affiliate is associated with a 0.5-0.7 percent greater sales and investment growth in the same region in countries other than IFCs. Furthermore, foreign investment stimulated by IFCs also appears to encourage greater domestic investment: the American evidence is that 10 percent greater foreign capital investment triggers 2.6 percent additional domestic capital investment, and that 10 percent greater foreign employment is associated with 3.7 percent greater domestic employment. Evidence of the behavior of European, Canadian, Australian and other firms offers similar conclusions: expanded foreign economic opportunities are associated with greater domestic investment and employment.\nOther evidence indicates that the financial services offered in IFCs contribute to the competitiveness of financial markets in the regions in which they are located. Commercial banks in countries with nearby IFCs have lower interest rate spreads than do other countries, and their banking sectors are less concentrated, as reflected in lower market shares for the five largest banks. By every measure credit is more freely available in countries proximate to IFCs, reflecting the degree of banking competition and the resulting stability of their financial architectures.\nIFC economies have grown very rapidly in the period since 1980, with average per capita annual growth rates of 3.3 percent, compared to 1.4 percent for the world as a whole. This fast pace of economic growth reflects the benefits of attracting high levels of foreign investment and indirectly contributes to economic prosperity elsewhere through the usual process by which affluence spreads across countries. Among the notable features of IFCs are not only their high average incomes and small populations (many are islands), but also, according to new research findings, their very high scores on governance quality measures. Recent evidence implies that improving the quality of governance from the level of Brazil to that of Portugal raises the likelihood of a small country being an IFC from 26 percent to roughly 61 percent. This association of IFCs with governance quality carries implications for their own and other countries through the widelyobserved process by which governance influences economic outcomes, and in particular, by which bad governance retards economic performance.\nEconomic outcomes aside, are the tax policies of other countries somehow undermined by those of IFCs? IFCs are typical of small countries in imposing low income tax rates and instead relying on expenditure-type taxes. Contrary to popular impression, IFCs are not the locations of choice for anonymous accounts and other vehicles for international tax evasion, recent evidence instead indicating that large countries such as the United States and the United Kingdom instead serve this function. Modern tax competition theories indicate that the low tax rates available in IFCs contribute to a form of tax competition that is likely to contribute to the efficiency of tax policies elsewhere, by distinguishing between highly mobile international investments that are very responsive to tax rate differences, and less mobile, more commonly domestic, investments that large countries are able to tax at high rates. By fostering this type of competition, and by not taxing income that is therefore available for others to tax, IFCs very likely enhance the ability of other countries to operate their tax systems efficiently.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".