Corporate Financial Constraints and Internal Capital Markets: Evidence from Emerging Countries
Bibliographic record
Abstract
This paper investigates whether the internal capital markets of business groups mitigate the financial constraints of affiliated firms,and affect their financing policies.It aims to extend the evidence on internal capital markets to emerging countries where financing constraints are prevalent, and adds to the literature on trade credit by revealing that the distressed group-affiliated firms rely less on trade credit than their non-affiliated counterparts despite the positive relation between trade credit and distress. Group firms that have high investments in prior periods use less trade credit in the subsequent periods than non-affiliated firms. The study rests on panel data regressions covering 3906 firms from six emerging countries for the 2006-2012 period. The findings indicate that the Q-sensitivity of the investments of affiliated firms is lower than that of their unaffiliated counterparts in all countries and that the investment cash flow sensitivity of affiliates is lower in five countries, strongly indicating that group-affiliated firms are financially less constrained. The distressed group firms use significantly lower leverage than distressed unaffiliated firms despite the positive relation between distress and leverage. Group firms in high–Q industries invest less than unaffiliated firms. This paper contributes to the existing literature on internal capital markets by expanding the scope to emerging countries where market imperfections and financing constraints are more pronounced, and provides strong evidence for the role of business groups, prevalent in most emerging countries, in mitigating the constraints on the investments and financing choices of the group-affiliated firms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".