Role of Bank Credit and External Commercial Borrowings in Working Capital Financing: Evidence from Indian Manufacturing Firms
Bibliographic record
Abstract
Determinants and levels of working capital financing (WCF) in the manufacturing sector have been empirically proven to impact firm profitability across emerging as well as developed nations. With time, firms adjust toward financing their working capital requirement (WCR), although the speed of adjustment, financing constraints, and bargaining power are subject to variations. In this study, we estimate the effect of bank credit and firm foreign currency borrowing on working capital financing with three distinct models for manufacturing firms in India. We examine the relationship between short-term foreign currency borrowings and WCF. Further, we investigate if the internal capital market affects WCF in the form of business group affiliation; lastly, we assess the impact of bank dependency and financial distress on WCF. We conclude that the debt–equity ratio becomes relevant, whereas firm characteristics such as age, size, and asset tangibility become irrelevant. Our original contribution to the literature is the finding that even smaller emerging market firms with well-managed, low debt exposure have improved access to WCF. Our results support that financial distress negatively impacts WCF but deviates from macroeconomic fundamentals, such as the GDP growth rate. This indicates deterioration in the health of Indian manufacturing, as a capital-intensive sector. Bank dependency remains significant, wherein smaller firms and those without a dividend pay-out continue to have longer cash conversion cycles and less efficient WCR. As a unique finding, we note foreign currency borrowings significantly contribute to WCF in the case of less developed credit markets in emerging economies such as India.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".