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 was 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.The resulting turnover is perhaps even more than turnover in leading U.S. firms over 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 it 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 have consistently tried to leverage internal markets for capital and talent inherent in business group structures to launch new ventures in environments where external factor markets are deficient.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 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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".