The Evolution of Concentrated Ownership in India Broad patterns and a History of the Indian Software Industry
Bibliographic record
Abstract
As in many countries (Canada, France, Germany, Japan, Italy, Sweden), concentrated ownership is a ubiquitous feature of the Indian private sector over the past seven decades. Yet, unlike in most countries, the identity of the primary families responsible for the concentrated ownership changes dramatically over time, perhaps even more than it does in the U.S. during the same time period. It does not appear that concentrated ownership in India is entirely associated with the ills that the literature has recently ascribed to concentrated ownership in emerging markets. If the concentrated owners are not exclusively, or even primarily, engaged in rent-seeking and entry-deterring behavior, concentrated ownership may not be inimical to competition. Indeed, as a response to competition, we argue that at least some Indian families the concentrated owners in question have consistently tried to use their business group structures to launch new ventures. In the process they have either failed hence the turnover in identity or reinvented themselves. Thus concentrated ownership is a result, rather than a cause, of inefficiencies in capital markets. Even in the low capital-intensity, relatively unregulated setting of the Indian software industry, we find that concentrated ownership persists in a privately successful and socially useful way. Since this setting is the least hospitable to the existence of concentrated ownership, we interpret our findings as a lower bound on the persistence of concentrated ownership in the economy at large.
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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 | 0.001 |
| 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".