The Effect of Diversification under Different Ownership Structures and Economic Conditions: Evidence from the Great Recession
Bibliographic record
Abstract
The effect of corporate diversification on firm performance has been extensively documented in the literature. In the general finance literature, Kuppuswamy and Villalonga (2015) studied the diversification effect during the 2007–2009 financial crisis and found that diversification adds value in the presence of external financing constraints. Motivated by this finding, we investigate whether a similar effect applies to insurance firms and we develop hypotheses for their different ownership structures (stock vs. mutual insurers; and group vs. non-group affiliated insurers). Using a sample of property-liability insurers over a period of 2004 to 2013, we find that the effect of diversification on performance is contingent on ownership structures and economic conditions. The diversification effect for stock insurers and insurers affiliated with a group is not significantly affected by economic conditions. However, the diversification effect for mutual insurers and non-affiliated insurers is reversed during the financial crisis. More specifically, diversified firms with these kinds of ownership structures perform better than focused firms during normal economic conditions, but their performance was significantly worse during the financial crisis. Our results are robust to alternative measures of performance and diversification, and to corrections for endogeneity. Our study contributes to the diversification literature by showing how the effect of diversification varies with ownership structure under different economic conditions and the results shed light on the specific circumstances in which diversification can improve or reduce performance.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".