The Ties That Bind: The Relationship Between Law Firm Growth and Law Firm Survival
Bibliographic record
Abstract
For the better part of the twentieth century, law firms hired, trained, and grew through a stable and predictable pattern: hire new law school graduates, monitor and evaluate their work, and pick promising attorneys from among their ranks and elevate them to partner. Rinse, lather, repeat. A combination of professional norms and organizational inertia made this approach the dominant method of growth among large corporate law firms until changes in legal market broke down these customary practices, ushering in a new era of lawyer mobility. Now, it has become commonplace for lawyers to leave for greener pastures as more law firms seek to grow their practices through lateral hiring. The question that this Article seeks to answer is: what (if any) effect has this change had on the stability of these law firms? Conventional wisdom holds that law firms that grow through entry-level hiring and training young attorneys (a practice long associated with the most prestigious “white shoe” firms) are more stable in the long run than law firms that poach attorneys from other firms via lateral acquisition. But why should hiring inexperienced and untested lawyers result in greater success for the firm than hiring lawyers that are proven to be competent and successful? This Article presents a comprehensive analysis of the relationship between law firm profits, firm growth strategy, and the life course of large American corporate law firms. I draw on an original longitudinal dataset to provide new insights on the determinants and effects of firm growth over a quarter of a century, from 1985 to 2011. I hypothesize that (1) “organic” growth, which relies on entry-level hiring and internal promotion, helps successful firms protect their positions by creating dense firm networks that allow the firm to survive threats to the organization, while (2) “mimetic” growth, which relies on firm merger or mass lateral hiring fails to create these dense networks and thus fails to provide long-term benefit to these firms. Ultimately, my findings both corroborate and complicate the conventional wisdom, with special resonances for what predicts the longevity of corporate law firms. I find that less profitable firms pursued mimetic growth in response to the organic growth of their more successful peers. In addition, controlling for observed potential confounders, those firms that grew organically in response to organizational need were at lower risk for dissolution than firms that intentionally pursued a growth strategy involving mergers and acquisitions. Furthermore, the increase in risk associated with this mimetic growth strategy hits low-status law firms the hardest. I conclude that mimetic growth has the potential to damage firm cohesion and upset the unique internal dynamics of law firms, thus fraying the professional ties that bind clients and lawyers alike to the firm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".