Bibliographic record
Abstract
The thesis profiles SME access to finance for more than 31,000 firms in high-income and emerging markets, with financial accounting data for more than 15,000 emerging markets firms.SMEs account for nearly 40% of the total sample.The author finds (1) bank credit allocation favoring large-scale businesses versus SMEs is rational due to large firms' positive financial performance indicators, fees paid, disclosure practices and market power (demand side) as well as creditor operational efficiency and cost effectiveness from lending economies of scale (supply side); (2) despite this, large-scale lenders have an interest in financing SMEs for portfolio diversification and management of concentration risk, while mid-sized lenders often provide credit to SMEs due to the lender's more limited capital and/or business model orientation as a "stakeholder" institution; (3) multivariate statistical tests reveal less consistently positive correlation between firm size and credit access than expected, although this partly reflects testing issues and narrow bounds of SME classifications; by contrast, (4) univariate indicators show a very high level of credit access by large-scale firms that dwarf credit access of SMEs, and support arguments of firm size bias in favor of large-scale firms in credit access and "low leverage puzzle" theory.Weakness in moveable property registries makes it harder for firms to value and pledge machinery and equipment as assets for secured transactions.Correspondingly, SMEs are constrained in their access to credit, particularly LTD, because the legal and institutional environment works against them due to their dependence on machinery and equipment for operations and because these are the predominant fixed assets they have to pledge as collateral.Despite this, positive correlation of markets with high credit access and strong legal and institutional variables shows firms in stronger environments for credit information, minority shareholder protection, regulatory effectiveness, property registration, contract enforcement and insolvency resolution have better chances of accessing credit (consistent with institutional theory).More generally, legal and institutional variables are weak descriptors in relation to dependent variables, whereas financial indicators are stronger.Category of interest dummy variables for income levels, listed status and sector also showed good results, whereas regional indicators were less reliable.similar to separate research that reports lines of credit account for about 15% of total corporate assets (Lins, Servaes, & Tufano, 2010).This research claims lines of credit are used not only for working capital purposes but also to (a) exploit business opportunities as they emerge (e.g., capital investment needs, undervalued properties or securities purchases) and (b) hedge liquidity and market risks, particularly when external credit markets are poorly developed.Therefore, depending on levels of external credit market development and the specific access and preferences of firms, there are overlapping uses of lines of credit in both working capital and fixed asset investment.As for long-term debt, the sample of firms in the research reporting relevant financial accounting information shows LTD (including CMLTD) to approximate 90% of total debt when all figures are summed.For emerging markets (EM), the figures are lower at 78% of total EM debt, but higher than the 43% average long-term debt maturity profile of 24 publicly-listed
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".