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Record W2249040193

Les Incitatifs Fiscaux Pour Les Anges Investisseurs (Tax Incentives for Business Angels)

2013· article· fr· W2249040193 on OpenAlexaffabout
Cécile Carpentier, Jean‐Marc Suret

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsUniversité Laval
Fundersnot available
KeywordsIncentiveTax incentiveBusinessVenture capitalFinanceAccountingPublic economicsEconomicsMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Recently, public policy makers have devoted increasing attention to business angels as a means of increasing the supply of early stage venture capital. Business angels are valuable smart money investors, who are qualified to provide advice about management and contacts, along with hands-on assistance. To promote investing by angels, several governments have implemented or are studying tax incentives. We have detected such programs in more than 60 jurisdictions. In Canada, five provinces and one territory offer business angels tax incentives. However, the design and implementation of tax incentives in the area of small business finance remains a challenging task, and several programs miss their target.This article surveys and provides a critical analysis of all the programs we have detected worldwide. Our objective is to provide an analysis framework and suggestions for policy makers and various groups that are clamouring for tax benefits. After clarifying the definition of business angels and surveying the motives behind the implementation of tax incentives focused on this category of investors, we develop a twofold analytical framework to study various types of incentives. The first framework is devoted to the advantages and drawbacks of the main types of incentives: upfront credit, capital gains deduction, loss deductions. The second framework relies on best practice rules in the area of small business finance incentives, as proposed by researchers and international organizations like the OECD. In the following sections of the article, we use this framework as a tool to analyze the main programs devoted to business angels in the United States, Europe, Asia, and Canada.As the OECD asserts, granting high net worth individuals greater incentives may increase the number of financial investors but not investors -- that is, the ones who are presumably providing expertise and contacts in addition to money. Our review indicates that policy makers around the world have not considered this risk. We observe a double paradox. First, despite the benefits of ex-post advantages over upfront credits, the bulk of the programs use upfront credits to promote angel investing. Second, even if the programs are generally presented and described as targeting business angels, most are open to all informal investors, without consideration of their capacity to provide advice and guidance to startups. Most often, the programs are not focused on good-quality high-growth companies, which provide most of the job creation and economic growth effects. Those weaknesses are significant. As designed, the programs should attract numerous unsophisticated investors looking for tax relief. In turn, those investors will increase the stock's valuation and reduce the expected returns of business angels. We provide our recommendations in the last part of this article. We contend that this analysis can provide helpful insight and serve as a reference for Canadian policy makers. The appendix contains a short description of each of the 60 programs we analyzed and the links thereto.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.249
Teacher spread0.224 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations2
Published2013
Admission routes2
Has abstractyes

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