Essays on SME Growth and Financing
Notice bibliographique
Résumé
The growth of small- and medium-sized enterprises (SME) accounts for a disproportionate share of employment, economic growth and prosperity (Adelino et al., 2017). However, it has been argued that SMEs suffer from severe information asymmetry and other types of market frictions and, thus, are more likely subject to credit constraints (Berger and Udell, 1998). This PhD thesis addresses this issue from three different, yet interrelated, perspectives: the relationship between SME growth and credit constraints; the impacts of firm characteristics on the full scope of the SME debt acquisition process; and the effectiveness of a credit guarantee scheme (CGS). The latter is a widely-used form of policy initiative that seeks to address SME credit constraints. Accordingly, this thesis comprises three chapters and draws on empirical analyses of unique datasets from Statistics Canada surveys, conducted from 2011 to 2017. The first chapter of this dissertation investigates the impacts of demand- and supply-side credit constraints on SME growth. It finds that evidence consistent with the premise that growth-oriented firms that apply and obtain either term loans or trade credit experience higher short-term growth than demand-constrained and supply-constrained firms. In the longer term, growth-oriented firms that apply and obtain term loans experience higher growth in revenues than supply-constrained firms. The second chapter estimates the three stages of the SME debt acquisition process (i.e., recognizing a need for capital, applying for a loan, and being approved for a loan) using a trivariate probit model that accounts for the correlation among the three stages of the SME debt acquisition process. It finds that while innovators and exporters are relatively more likely to need external financing, they do not face demand- or supply-side constraints. Conversely, firms majority-owned by women and members of visible minority groups are more likely to need credit, are more likely to be demand-constrained and are subject to statistical discrimination when seeking credit. When changing the definition of demand-constrained borrowers to a more narrow definition, innovators are relatively more likely to apply for needed financing, exporters are relatively less likely to do so, and visible minorities are relatively just as likely to apply for needed financing. The third chapter proposes a new means of assessing the economic impact of the Canadian CGS, the Canada Small Business Financing (CSBF) program. The Canadian CGS is a mechanism by which the Government of Canada guarantees a specific portion of a loan in the event of default. The new measure improves on an existing measure that seeks to evaluate the performance of CGSs: the incrementality rate (a measure that evaluates the extent to which loans advanced under the CGS program would not otherwise have been approved by lenders—the counterfactual). The new measure developed in this chapter gauges lenders’ moral hazard; that is, the extent to which lenders take excessive risks by betting on overly risky loans because they know that the losses would be covered by the program. This cannot be captured by the incrementality rate. The chapter reports on the evaluation of the CSBF performance based on both measures, the traditional incrementality rate and the new measure of lenders’ excess risk taking. It finds that the CSBF is not incremental, with a low incrementality rate of 5.3 per cent. The findings also suggest that lenders do not engage in excessive risk-taking behavior, as evidenced by an almost zero risk-taking rate. While Canadian banks do not particularly exhibit a willingness to allocate guaranteed loans to women and visible minorities (the groups of firms that are subject to statistical discrimination per Chapter II), they tend to allocate more guaranteed loans to growth-oriented borrowers, consistent with the CSBF’s objective to support the growth of small businesses (ISED Canada, 2016). Finally, the CSBF incrementality rate does not vary significantly between recession (following the 2007-2008 crisis) and post-recession time periods.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,045 | 0,000 |
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 tête enseignante, 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 ».