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Record W3125903811

How tax policy and incentives affect foreign direct investment - a review

2000· review· en· W3125903811 on OpenAlexaboutno aff
Jacques Morrisset, Neda Pirnia

Bibliographic record

VenueRePEc: Research Papers in Economics · 2000
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveMultinational corporationBusinessTax creditForeign direct investmentTax incentiveValue-added taxTax reformInternational economicsAd valorem taxIndirect taxPublic economicsTax policyEconomic policyEconomicsMarket economyFinanceMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

With an increasing number of governments
\n competing to attract multinational companies, fiscal
\n incentives have become a global trend that has grown
\n considerably in the 1990s. Poor African countries rely on
\n tax holidays, and import duty exemptions, while industrial
\n Western European countries allow investment allowances, or
\n accelerated depreciation. Have governments offered
\n unreasonably large incentives to entice firms to invest in
\n their countries? The authors review the literature on tax
\n policy, and foreign direct investment, and explore
\n possibilities for research. They observe that tax incentives
\n neither make up for serious deficiencies in a country's
\n investment environment, nor generate the desired
\n externalities. Long-term strategies to improve human, and
\n physical infrastructure - and, where necessary, to
\n streamline government policies and procedures - are more
\n likely than incentives to attract genuine long-term
\n investment. But more recent evidence has shown that when
\n other factors - such as infrastructure, transport costs, and
\n political and economic stability - are more or less equal,
\n the taxes in one location may have a significant effect on
\n investors' choices. This effect is not straightforward,
\n however. It may depend on the tax instrument used by the
\n authorities, the characteristics of the multinational
\n company, and the relationships between the tax systems in
\n the home country, and recipient countries. For example, tax
\n rebates are more important for mobile firms, for firms that
\n operate in multiple markets, and for firms whose home
\n country exempts any profit earned abroad (Canada, France)
\n rather than using tax credit systems (Japan, the United
\n Kingdom, the United States). Even if tax incentives were
\n quite effective in increasing investment flows, the costs
\n might well outweigh the benefits. Tax incentives are not
\n only likely to have a negative direct effect on fiscal
\n revenues, but also frequently create significant
\n opportunities for illicit behavior by tax administrators,
\n and companies. This issue has become crucial in emerging
\n economies, which face more severe budgetary constraints, and
\n corruption than industrial countries do. The authors suggest
\n research in five areas: 1) The eventual non-linear impact of
\n tax rates on the investment decisions of multinational
\n companies. 2) the effect of tax policy on the composition of
\n foreign direct investment (for example, green-field,
\n reinvested earnings, and mergers and acquisitions). 3) The
\n development of new technologies, and global companies that
\n are likely to be more sensitive to, and able to exploit
\n incentives. 4) The need for a global approach to the
\n taxation of multinational companies. 5) The question of
\n whether tax incentives should be directed only at (foreign)
\n investors that make the "right things" (such as
\n environmentally safe products) or more broadly at those that
\n bring jobs, technology transfers, and marketing skills.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.057
GPT teacher head0.326
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations95
Published2000
Admission routes1
Has abstractyes

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