Les Incitatifs Fiscaux Pour Les Anges Investisseurs (Tax Incentives for Business Angels)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».