FAMILY FIRMS, BANK RELATIONSHIPS, AND FINANCIAL CONSTRAINTS: A COMPREHENSIVE SCORE CARD
Bibliographic record
Abstract
We examine the effect of financial constraints on firm investment and cash flow. We combine data from the Spanish Mercantile Registry and the Bank of Spain Credit Registry to classify firms according to whether they are family-owned, not family-owned, or belong to a family-linked network of firms and according to their number of banking relations (with none, one, or several banks). Our empirical strategy is structural, based on a dynamic model solved numerically to generate the joint distribution of firm capital (size), investment and cash flow, both in cross-sections and in panel data. We consider three alternative financial settings: saving only, borrowing and lending, and moral hazard constrained state-contingent credit. We estimate each setting via maximum likelihood and compare across these financial regimes. Based on the estimated financial regime, we show that family firms, especially those belonging to networks based on ownership, are associated with a more flexible market or contract environment and are less financially constrained than non-family firms. This result survives stratifications of family and non-family firms by bank status, region, industry and time period. Family firms are better able to allocate funds and smooth investment across states of the world and over time, arguably done informally or using the cash flow generated at the level of the network. We also validate our structural approach by demonstrating that it performs well in traditional categories, by stratifying firms by size and age and find that smaller and younger firms are more constrained than larger and older 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".